literature review on agricultural insurance

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• > Journals
• > Environment and Development Economics
• > Volume 26 Special Issue 5-6: Weather and Climate I...
• > Determinants of uptake and strategies to improve agricultural...

Article contents

• Introduction
• Agricultural insurance in Africa: an overview
• Literature search methodology

Determinants of uptake and strategies to improve agricultural insurance in Africa: a review

Published online by Cambridge University Press:  12 April 2021

• Supplementary materials

Weather shocks affect smallholder farmers and pastoralists in Sub-Saharan Africa unequally. Agricultural insurance has emerged as a safety net option to protect farmers’ welfare. However, in comparison to other regions, fewer African farmers and pastoralists have adopted agricultural insurance. This review synthesises broad recent literature on why insurance take-up has remained low and highlights six key themes, including: (1) product quality, (2) product design, (3) affordability, (4) information and education, (5) behavioural and sociocultural factors, and (6) the role of government in enabling markets. We shed light on how insurance uptake can be encouraged.

JEL classification

1. introduction.

Smallholder farmers face myriads of climate hazards and agricultural insurance has increasingly been promoted to provide protection (Hellmuth et al ., Reference Hellmuth, Osgood, Hess, Moorhead and Bhojwani 2009 ). However, take-up of agricultural insurance in Sub-Saharan Africa remains the lowest (Hess and Hazell, Reference Hess and Hazell 2016 ). Instead, smallholder farmers continue to rely on less effective mechanisms such as asset depletion (Börner et al ., Reference Börner, Shively, Wunder and Wyman 2015 ; Yilma et al ., Reference Yilma, Mebratie, Sparrow, Abebaw, Alemu and Bedi 2017 ) or dependency on livestock (McPeak and Doss, Reference McPeak and Doss 2006 ; Ng'ang’a et al ., Reference Ng'ang’a, Bulte, Giller, Ndiwa, Kifugo and McIntire 2016 ) and savings even when insurance options are available (Delavallade et al ., Reference Delavallade, Dizon, Hill and Petraud 2015 ). Agricultural insurance remains unpopular, unattractive and poorly demanded by a majority of farmers in low- and middle-income countries (Binswanger-Mkhize, Reference Binswanger-Mkhize 2012 ). This is despite the evidence of its potential in improving farmers’ and pastoralists’ livelihoods, unlocking investments in production and eventual poverty reduction. In this review paper, we explore why take-up has remained low and what strategies might be employed to spur its take-up among farmers and pastoralists in Africa.

This review supplements other recent reviews (Marr et al ., Reference Marr, Winkel, van Asseldonk, Lensink and Bulte 2016 ; Smith, Reference Smith 2016 ; Carter et al ., Reference Carter, de Janvry, Sadoulet and Sarris 2017 ; Jensen and Barrett, Reference Jensen and Barrett 2017 ; Platteau et al ., Reference Platteau, De Bock and Gelade 2017 ; Yuzva et al ., Reference Yuzva, Wouter Botzen, Brouwer and Aerts 2018 ; Ali et al ., Reference Ali, Abdulai and Mishra 2020 b ) but also makes important contributions that other reviews have not addressed. First, the review exclusively focuses on Sub-Saharan Africa (SSA) due to its higher vulnerability to weather shocks than other regions (Coe and Stern, Reference Coe and Stern 2011 ). In the region, droughts between 1980 and 2013 are said to have affected more than 360 million people and caused more than US$31 billion in losses (FAO, 2015 ). The 2008–2011 drought in Kenya alone led to US$11 billion in losses (FAO, 2015 ) and the 2016 drought in Malawi dented the country's economy by US$400 million (Reeves, Reference Reeves 2017 ). The trend of losses is not likely to decrease (Haile et al ., Reference Haile, Tang, Hosseini-Moghari, Liu, Gebremicael and Leng 2020 a ; Spinoni et al ., Reference Spinoni, Barbosa, Bucchignani, Cassano, Cavazos and Christensen 2020 ). Despite these losses, insurance take-up remains the lowest in the world. Accordingly, of the 51 million smallholder farmers in Africa (Lowder et al ., Reference Lowder, Skoet and Raney 2016 ), Footnote 1 only about 1.3 per cent have agricultural insurance (Hess and Hazell, Reference Hess and Hazell 2016 ). Our more updated estimate suggests current take-up around 3.5 per cent but this remains far below rates in Asia and Latin America ( Table 1 ). The low insurance coverage situation in SSA therefore raises questions possibly specific to the region and requires closer assessment.

Table 1. Agricultural insurance coverage in smallholder farmers across developing and middle-income countries

Sources : Number of farms (Lowder et al ., Reference Lowder, Skoet and Raney 2016 ) – data does not include Somalia, Sudan, Eritrea, Mauritius and Burundi, Number of insurance policies (Hess and Hazell, Reference Hess and Hazell 2016 ), Current coverage – authors’ estimates from several sources (see online appendix table A1).

This review further broadens the focus on various issues that previous reviews did not cover. For instance, Platteau et al . ( Reference Platteau, De Bock and Gelade 2017 ) assessed demand for micro-insurance, which is technically different from agricultural insurance. Marr et al . ( Reference Marr, Winkel, van Asseldonk, Lensink and Bulte 2016 ), Carter et al . ( Reference Carter, de Janvry, Sadoulet and Sarris 2017 ) and Jensen and Barrett ( Reference Jensen and Barrett 2017 ) specifically focused on index insurance only, which is only one of the several agricultural insurance types (Iturrioz, Reference Iturrioz 2009 ). Yuzva et al . ( Reference Yuzva, Wouter Botzen, Brouwer and Aerts 2018 ) assessed the effect of basis risk and did not refer to many other challenges farmers might face in their take-up decisions. The reviews by Smith ( Reference Smith 2016 ) and Ali et al . ( Reference Ali, Abdulai and Mishra 2020 b ) are the closest to our study in consideration of a wide range of issues covered. A critical addition to these is the diversion from the ‘expert review’ methodology to one that provides more details on the process of literature search as well as inclusion/exclusion decisions. None of the existing reviews precisely covers Africa.

By condensing broad qualitative and quantitative literature, and taking an integrative-style review (Pautasso, Reference Pautasso 2013 ), we expound on issues hindering the take-up of agricultural insurance, clustered in six key themes. These are: (1) product quality, (2) product and contract design, (3) income and affordability, (4) information, knowledge and education reasons, (5) behavioural and socio-cultural factors, and finally (6) the role of governments in policy setting, regulation and market stabilisation. These six themes all cut across demand and supply. From the demand side, there is an insufficient purchase of agricultural insurance products by farmers – partly because of budget limitations and because of the low knowledge of and about insurance. We elaborate on how demand can be spurred by providing demand subsidies, which relax farmer budgets and increase affordability. From the supply side, farmers require the products not only to be available but also to be of good quality since poor quality products are likely to leave farmers in worse off situations (Clarke, Reference Clarke 2016 ). We not only explore how suppliers can be enabled to provide more insurance but also how to increase the quality of existing products. Moreover, while insurance remains voluntary, governments and related institutions can spur markets not only through infrastructure and meso-level services such as reinsurance, but also by setting up enabling policies and regulatory institutions that build confidence. Through enabling innovation and setting quality standards, governments can avoid low product equilibria (Clarke and Wren-Lewis, Reference Clarke and Wren-Lewis 2013 ). These factors are not singular recommendations of dos and don'ts. Indeed, as figure A1 (in the online appendix) shows, they are interconnected and should be adjudged in the broader ecosystem.

The rest of this paper is organised as follows. In section 2 , we provide a brief historical and current status of agricultural insurance in Africa. Section 3 gives our review methodology and literature search strategy elaborating on the inclusion and exclusion strategy. In section 4 , we detail the results of this review, and make a short conclusion in section  5 .

2. Agricultural insurance in Africa: an overview

Agricultural insurance has been present in some African countries since the early 20th century (Burger, Reference Burger 1939 ; Adesimi and Alli, Reference Adesimi and Alli 1980 ; Alli, Reference Alli 1980 ; Atlas Magazine, 2017 ), however, the market remains very small. As of 2008, four out of 47 countries in the region had a functioning agricultural insurance program and an additional six were implementing pilot projects (Mahul and Stutley, Reference Mahul and Stutley 2010 ). The last decade has observed gradual improvement ranging from agriculture micro-insurance (Di Marcantonio and Kayitakire, Reference Di Marcantonio, Kayitakire, Tiepolo, Pezzoli and Tarchiani 2017 ) with several countries piloting index insurance (Sandmark et al ., Reference Sandmark, Debar and Tatin-Jaleran 2013 ). Hess and Hazell ( Reference Hess and Hazell 2016 ) found that about 653,000 farmers had some form of insurance coverage and our updated program coverage suggests over 2 million smallholder farmers have insurance in Africa (see table A1, online appendix). At the continental level, the African Risk Capacity (ARC), set up in 2012, has facilitated the entry of countries into regional risk pools (Vincent et al ., Reference Vincent, Besson, Cull and Menzel 2018 ). Currently, the ARC comprises 34 member states of which 11 took part in the 2019–2020 risk pool (see table A2, online appendix). While countries’ participation has increased, there is a need for more political support. In 2016, both Kenya and Malawi dropped out of the risk pool due to internal politics and have not been able to re-join ever since (Hohl, Reference Hohl 2019 ). Moreover, for the risk pool to be more effective, more countries need to enrol. In general, while agricultural insurance in Africa has grown, coverage is still very small especially in comparison to other regions and the number of smallholder farmers and pastoralists in the region. While several countries have started pilot programmes and a few like Zambia are scaling up, climate shocks tend to be cross-country and covariate in nature; hence the necessity for regional and continental risk pools remains critical.

3. Literature search methodology

We searched literature from Scopus and Web of Science covering the period up to September 2020. In the online appendix, we provide a detailed search process showing all variations of search terms used. In addition, we included grey literature from organisations known to work on agricultural insurance. These were the World Bank, the International Fund for Agriculture and Development, the International Food Policy Research Institute, the United Nations Institute for Environment and Human Security, and the Feed the Future Innovation Lab for Markets, Risk and Resilience of the University of California, Davis. All our literature is in English. We included both qualitative and quantitative studies.

Starting with 687 documents, we selected 120 documents for final full-text review. The literature selected included qualitative, quantitative empirical studies as well as simulation studies. The majority of the literature (80/120 papers) was from the period 2016–2020, further underlining our effort to cover the most recent evidence. The studies reviewed were from 23 countries across the continent with 58 per cent from Ethiopia and Kenya, mainly from the evaluations of the Index-Based Livestock Insurance programmes. Whereas Zambia had the highest insurance coverage, only three studies were from the country. Figure 1 shows the distribution of the studies reviewed by country. Details of the inclusion criteria, studies reviewed by their research methodology, as well as the summary of their time coverage can be found in online appendix figures A1–A3, respectively.

Figure 1. Coverage of studies reviewed by country.

Source : Authors.

4.1 Product quality

Agricultural insurance has come under scrutiny on how valuable it is to smallholder farmers in developing countries (Binswanger-Mkhize, Reference Binswanger-Mkhize 2012 ). A key concern is that insurance products are often of poor quality and their acquisition can lead to worse outcomes than without them (Clarke, Reference Clarke 2016 ). One aspect of poor product quality is the level of basis risk, which refers to the probability that insurance does not cover an insurance-holding farmer when they experience the insured shock because the level of the insurance threshold (often an index) is imperfectly correlated with losses incurred. Both simulations (Elabed et al ., Reference Elabed, Bellemare, Carter and Guirkinger 2013 ) and empirical studies (Hill et al ., Reference Hill, Hoddinott and Kumar 2013 ; Jensen et al ., Reference Jensen, Mude and Barrett 2018 , Reference Jensen, Stoeffler, Fava, Vrieling, Atzberger and Meroni 2019 ) have shown that when basis risk is higher, farmers are less likely to purchase insurance. There are three categories of basis risk. The first is the geographical/spatial basis risk (Jensen et al ., Reference Jensen, Mude and Barrett 2018 ), which measures the distance from a farmer's plot to the measurement point. The second is design basis risk which emanates from the models and variables used to construct an index (Elabed et al ., Reference Elabed, Bellemare, Carter and Guirkinger 2013 ; Jensen et al ., Reference Jensen, Stoeffler, Fava, Vrieling, Atzberger and Meroni 2019 ). The third is temporal basis risk which is related to the timeframe in which the index is measured (Díaz Nieto et al ., Reference Díaz Nieto, Cook, Läderach, Myles and Jones 2010 ). Simulation studies in Cameroon and Niger have indicated that basis risk might be as high as 50 per cent in most indices (Leblois et al., Reference Leblois, Quirion, Alhassane and Traoré 2014 a , Reference Leblois, Quirion and Sultan 2014 b ), implying that there is a 50 per cent chance that an insured farmer's risk might not be covered by their insurance due to such poor correlation. To this farmer, purchasing insurance with substantial basis risk might not only introduce a loss of income (in paid premiums) but also leave her in a worse off situation since it also limits her alternative options (Barré et al ., Reference Barré, Stoeffler and Carter 2016 ; Clarke, Reference Clarke 2016 ; Jensen et al ., Reference Jensen, Barrett and Mude 2016 ). To providers, there are reputation risks to contend with (Morsink et al ., Reference Morsink, Clarke and Mapfumo 2016 ; Reeves, Reference Reeves 2017 ) further limiting their future market. Spatial basis risk is the most pronounced of the three categories. For instance, in some studies, increasing distance from the farmer's plot to the weather station reduced demand by up to 20 per cent (Hill et al ., Reference Hill, Hoddinott and Kumar 2013 ; Amare et al ., Reference Amare, Simane, Nyangaga, De, Hamza and Gurmessa 2019 ). Spatial basis risk is also linked to adverse selection, where some farmers might know local agro-climatic and agro-ecological conditions likely to affect the cropping or livestock outcomes, which may be unknown to the insurance provider. Jensen et al . ( Reference Jensen, Mude and Barrett 2018 ) found that households in locations that had higher average losses and those in locations that had less basis risk had higher insurance demand than the rest. The reverse can also hold, that households not enrolling in insurance are also aware of their disadvantageous locations. Moreover, it is not just the presence of basis risk but also the perception of its presence that influences farmers (Castellani, Reference Castellani 2015 ; Chantarat et al ., Reference Chantarat, Mude, Barrett and Turvey 2017 ). These perceptions might be shaped by local geographies, which might not be considered during the construction of the index insurance product. Basis risk might also arise from random idiosyncratic shocks due to random variation in, for instance, rainfall. Evidence from northern Kenya showed the existence of these idiosyncratic shocks in villages covered by insurance which, even after insurance coverage, left insurance holders with 69 per cent of their original risk exposure due to basis risk (Jensen et al ., Reference Jensen, Barrett and Mude 2016 ).

Basis risk can be reduced through several strategies. First, Morsink et al . ( Reference Morsink, Clarke and Mapfumo 2016 ) suggest that products can cover all possible losses related to the peril which the insurance product covers as opposed to a single dimension of losses. They call this strategy insured peril basis risk. Secondly, insurance might cover losses from agricultural production that might not be caused by the insured peril (production smoothing basis risk). To capture the entirety of insured peril basis risk, insurance product development might consider using multiple sources and types of data to better explain both the production risk and multiple dimensions of possible losses (Wang et al ., Reference Wang, Karuaihe, Young and Zhang 2013 ). While indices have always been constructed by single variables such as remotely sensed vegetation or rainfall or temperature data (Hochrainer-Stigler et al ., Reference Hochrainer-Stigler, Van Der Velde, Fritz and Pflug 2014 ; Vrieling et al ., Reference Vrieling, Meroni, Shee, Mude, Woodard, de Bie and Rembold 2014 ; De Oto et al ., Reference De Oto, Vrieling, Fava and De Bie 2019 ), more recently, new variables such as soil moisture have shown promise in indices (Enenkel et al ., Reference Enenkel, Osgood, Powell, Petropoulos and Islam 2017 , Reference Enenkel, Osgood, Anderson, Powell, McCarty, Neigh, Carroll, Wooten, Husak, Hain and Brown 2019 ; Von Negenborn et al ., Reference Von Negenborn, Weber and Musshoff 2018 ). Comparing index insurance based on a rainfall index and an evapotranspiration index, Von Negenborn et al . ( Reference Von Negenborn, Weber and Musshoff 2018 ) found that the rainfall-based index underestimates the effect of weather on the risk of repaying agricultural credit, especially during harvest months. The evapotranspiration-based index was, on the other, hand more precise in accounting for the spikes in weather on credit risk. Rainfall-based weather insurance had more basis risk than the evapotranspiration-based index. Enenkel et al . ( Reference Enenkel, Osgood, Powell, Petropoulos and Islam 2017 ) assessed the benefit of using soil moisture data and found that it had a higher agreement with vegetation anomalies than conventional rainfall data, implying that soil moisture data has the potential of reducing basis risk. However, it is important for insurance providers to consider both precision and time lags. Depending on the indices used, some products are likely to be more asset replacing while others are more asset protecting (Jensen et al ., Reference Jensen, Stoeffler, Fava, Vrieling, Atzberger and Meroni 2019 ). Some suitable products might be more costly while more precise (low basis risk) products might have more time lags and delays to trigger. Timeliness of products might depend on the amount of monitoring for which costs can be prohibitive to providers. More recently, picture-based crop monitoring offers promise. First tested and implemented in India (Ceballos et al ., Reference Ceballos, Kramer and Robles 2019 ; Hufkens et al ., Reference Hufkens, Melaas, Mann, Foster, Ceballos, Robles and Kramer 2019 ), it is currently being tested in Ethiopia (Porter et al ., Reference Porter, Kramer, Assefa and Abzhamilova 2020 ) and Kenya (ACRE Africa, 2020 ). Picture-based monitoring reduces the costs of monitoring while leveraging farmer participation and increasing their motivation and still providing real-time evidence of crop health to assess losses and triggering pay-outs.

These innovations, while promising, have drawbacks and should be carefully considered. For instance, remotely sensed data collection for variables such as evapotranspiration or soil moisture, is expensive in acquisition and processing (Coleman et al ., Reference Coleman, Dick, Gilliams, Piccard, Rispoli and Stoppa 2017 ). Secondly, the success of these innovations depends on the level of technology diffusion in rural SSA. For instance, while picture-based insurance contracts are based on farmers’ access to smartphones and the internet, overall internet adoption in Ethiopia is only per cent, according to the World Bank's World Development Indicators, and most likely much lower in rural communities. Moreover, low internet adoption and high internet costs introduce additional costs to farmers, which might wade off prospective demand. Technologies based on such platforms might therefore struggle to be adopted.

4.2 Product and contract design

A second major reason hampering agricultural insurance take-up in Africa is product design. By product design, we imply four main issues: (1) the spatial or geographic scale of coverage, (2) product item coverage, (3) timing of index triggers, and (4) insurance provided alongside other services (bundling).

4.2.1 Spatial coverage of insurance policies

Conventional insurance products are single-scale products with one trigger set at a certain spatial scale (Elabed et al ., Reference Elabed, Bellemare, Carter and Guirkinger 2013 ). For these, basis risk arises from both the random or systematic idiosyncratic risk as well as the design risk at the spatial scale of coverage. Because of higher basis risk from such contracts, farmers are less likely to purchase such insurance, especially those with more local agro-ecological information than insurance providers (Jensen et al ., Reference Jensen, Mude and Barrett 2018 ). Accordingly, design risk can be reduced by implementing multiple-trigger contracts where the index is not assessed on one scale but rather on more than one scale (Elabed et al ., Reference Elabed, Bellemare, Carter and Guirkinger 2013 ). In an experiment with Malian cotton farmers, Elabed et al . ( Reference Elabed, Bellemare, Carter and Guirkinger 2013 ) found that demand for a two-trigger or two-scale insurance contract was about 40 per cent higher than the conventional single trigger contract. They found that the multi-scale insurance contract reduced both false negatives, where an individual whose yield was below the average and he/she did not receive insurance payout (basis risk), and false positives, where an individual whose yield was above the average still received an insurance payout. One example of a multi-scale insurance product is an area-yield insurance product in Tanzania that introduces a conditional audit (Flatnes et al ., Reference Flatnes, Carter and Mercovich 2018 ). The authors compared a satellite-based index insurance contract and another contract that incorporated an audit requested by farmers if basis risk reached a certain threshold. Flatnes et al . ( Reference Flatnes, Carter and Mercovich 2018 ) found that willingness to pay an audit-incorporated contract was 64 per cent higher than the non-audit contract. This implies that a reduction in basis risk from better measurement and allowing farmer grievance management increases trust in products and demand.

4.2.2 Product item coverage profile

The product item coverage profile is another dimension of product design. Here, we imply a basic assessment of how many perils and how many crops the insurance product covers. Conventional insurance products cover a specific crop that is faced with one particular peril. However, farmers are not only faced with various covariate and idiosyncratic shocks but also grow multiple crops in a single growing season. Crop specific insurance products might, therefore, not be appropriate for such farmers. For instance, Berg et al . ( Reference Berg, Quirion and Sultan 2009 ), using simulated insurance contracts based on 20 years of production across five crops, found that farmers cropping maize and groundnuts were more protected compared to those growing millet, sorghum or cotton. Siebert ( Reference Siebert 2016 ) showed the necessity of using two different indices to cover millet and rice across similar climatic regions because of the negative correlation in the shocks affecting both crops. Therefore, in such cases where the incentives for the provision of single index-based insurance products are not conducive, providers might devise multi-crop and multi-peril insurance products with one or two ‘leading’ crops and additional ‘secondary crops’. Recent evidence shows farmers value multi-crop, multi-peril insurance products highly (Bulte et al ., Reference Bulte, Cecchi, Lensink, Marr and van Asseldonk 2019 ) though there is need for more research to assess the willingness to pay for such products versus the common single crop, single peril insurance products.

4.2.3 Trigger period

The other dimension of product design is the time of the trigger. For any kind of insurance, coverage happens at the onset of a shock, i.e., when an individual falls sick – for health insurance; when an automotive accident happens – for motor insurance; or when one losses employment – for employment insurance; and so forth. Basic agricultural insurance operates along similar lines: payoff occurs after the shock has been experienced. However, the difference between agricultural insurance is that shocks can be predicted with more confidence than with other kinds of insurance. When the possibility of shocks is known, what matters more is if the insurance can prevent the effects of the shock and if it can provide the insured with other options of production and consumption smoothing. Farmers and livestock owners are, therefore, likely to demand insurance based on how much it provides this protection.

Recent evidence suggests that insurance participation reduced herd offtake behaviour (selling of livestock) as pastoralists were less fearful and more confident that losses would be covered (Gebrekidan et al ., Reference Gebrekidan, Guo, Bi, Wang, Zhang, Wang and Lyu 2019 ). However, the prevention of losses is more important than the losses being covered. This is, therefore, a matter of how much loss insurance covers and how frequent the payments can be made to allow farmers to exercise protective options. Karlan et al . ( Reference Karlan, Osei, Osei-Akoto and Udry 2014 ) suggested that experimenting with small losses and higher frequency payouts could improve demand. Norton et al . ( Reference Norton, Osgood, Madajewicz, Holthaus, Peterson and Diro 2014 ) showed that farmers preferred high-frequency insurance when such products were on offer. By pointing at the benefits such as insuring high probability-small loss events (which are usually self-insured), farmers might find such products attractive (Norton et al ., Reference Norton, Osgood, Madajewicz, Holthaus, Peterson and Diro 2014 ). In addition to the frequency of payouts, their timing also matters. Optimising remote sensing data for instance and providing earlier payouts could allow vulnerable farmers to utilise mitigation strategies such as alternative forage sources for livestock (Vrieling et al ., Reference Vrieling, Meroni, Mude, Chantarat, Ummenhofer and de Bie 2016 ) or access to food to prevent farmers from falling below the minimum food requirements (Hochrainer-Stigler et al ., Reference Hochrainer-Stigler, Van Der Velde, Fritz and Pflug 2014 ). Vrieling et al . ( Reference Vrieling, Meroni, Mude, Chantarat, Ummenhofer and de Bie 2016 ) showed that payouts made between one to three months before the onset of the drought would give farmers more time to optimise protective alternatives.

Jensen et al . ( Reference Jensen, Stoeffler, Fava, Vrieling, Atzberger and Meroni 2019 ) also showed that the insurance product with a one-month early payment supported 91 per cent of the pastoralists compared to one with a one-month late payment, which was helpful to only 68 per cent of the households. However, such early-payment policies might have higher unaffordable premiums as Jensen et al . ( Reference Jensen, Stoeffler, Fava, Vrieling, Atzberger and Meroni 2019 ) further showed. In their study, the early-payment contract product would cover only 46 per cent of households if the premium loading were 50 per cent and almost none of the households if the loading were 100 per cent. Moreover, insurance products with high-frequency payments also increase provider transaction costs. Therefore, it is important to consider the desire for more attractive products alongside their related costs.

4.2.4 Bundling insurance with other services

Agricultural insurance can be combined with other products or services. Carter et al . ( Reference Carter, Cheng and Sarris 2016 ) made a theoretical case for combining insurance with other services such as credit (often referred to as bundling), suggesting it as one of the ways to make insurance popular. Insurance can be combined with credit services (Giné and Yang, Reference Giné and Yang 2009 ; Meyer et al ., Reference Meyer, Hazell and Varangis 2018 ; Ahmed et al ., Reference Ahmed, McIntosh and Sarris 2020 ) or inputs such as drought-tolerant seeds, high yielding seeds or fertilisers (Leblois et al ., Reference Leblois, Quirion and Sultan 2014 b ; Lybbert and Carter, Reference Lybbert, Carter, Balisacan, Chakravorty and Ravago 2015 ; Awondo et al ., Reference Awondo, Kostandini and Erenstein 2020 ; Visser et al ., Reference Visser, Jumare and Brick 2020 ). The basic idea is that farmers might be more enticed to purchase insurance if they get more services through one contract. It is also easier for providers to deliver multiple services without increasing their administrative and transaction costs, thereby lowering the unit costs for a single product. Another attraction to bundling insurance with credit is that farmers would generally pay insurance premiums off their credit and therefore do not have to pay cash up front, hence relaxing their budget stress. Karlan et al . ( Reference Karlan, Osei, Osei-Akoto and Udry 2014 ) showed that after removing credit constraints by providing a cash grant, insurance demand increased by 40–50 per cent in Ghana. Programmes delivering insurance by bundling it with other services seem to be the ones able to achieve some scale. The Zambia Farmer Input Support Programme offers an example of bundling insurance with inputs. Under this programme, farmers pay premiums when receiving inputs from a government programme. In case of triggers, the insurance companies (through the Ministry of Agriculture) pay farmers through e-vouchers to secure inputs for a new cropping season. The programme covered more than 900,000 farmers in the 2017/18 Zambian financial year (World Bank, 2019 ). Similarly, the second largest insurance programme in the region, ACRE Africa, currently providing coverage to over 313,000 farmers, works with input service providers under One Acre Fund, a farm inputs and credit providing organisation (Hess and Hazell, Reference Hess and Hazell 2016 ).

There are, however, certain caveats to bundling insurance with other services. The first is that farmers generally prefer to have freedom in deciding which products and services to purchase and not to be forced to take products that they do not want, and products that might be inadequate for them later. In one of the major studies on agricultural insurance in SSA, Giné and Yang ( Reference Giné and Yang 2009 ) found that farmers who were offered loans with insurance for high yielding groundnuts had a 13 percentage point lower insurance demand compared to those with simple credit without insurance. Unlocking credit constraints alone increased demand for insurance while packaging credit with insurance might have given farmers fewer choices. Gallenstein et al . ( Reference Gallenstein, Mishra, Sam and Miranda 2019 ) arrived at a closely similar conclusion through a willingness to pay experiment in Ghana. They found that compelling farmers to purchase index insurance as they took out an agricultural loan generally lowered loan demand because it generally increased the cost of the loan when the insurance premium was included. Loan demand was 75 per cent compared to 54 per cent for a loan with insurance. Finally, regarding bundling with other technologies such as high yielding or drought-tolerant seeds, farmers need to have a good understanding of these technologies available as they might have different levels of benefits. Testing bundling across 19 improved maize varieties with insurance in 49 locations in Eastern and Southern Africa, Awondo et al . ( Reference Awondo, Kostandini and Erenstein 2020 ) found a high variation in the performance of different combinations such that farmers were highly likely to select a sub-optimal combination. Awondo et al . ( Reference Awondo, Kostandini and Erenstein 2020 )'s simulations indicate that bundling has to be a very precise activity across regions with both providers and farmers choosing the best combination of systematic bundling, especially where more than one product is available. It is, therefore, important that bundling be considered carefully, as it might not solve all challenges.

4.3 Income and affordability

A major challenge with bolstering demand for agricultural insurance is farmers’ budget constraints. The prospective market for insurance is therefore divided into those with higher incomes demanding more insurance and those with lower incomes who cannot afford it (Hill et al ., Reference Hill, Hoddinott and Kumar 2013 ; Karlan et al ., Reference Karlan, Osei, Osei-Akoto and Udry 2014 ; Bogale, Reference Bogale 2015 ; Takahashi et al ., Reference Takahashi, Ikegami, Sheahan and Barrett 2016 ; Tadesse et al ., Reference Tadesse, Alfnes, Erenstein and Holden 2017 ; Bishu et al ., Reference Bishu, Lahiff, O'Reilly and Gebregziabher 2018 ; Fonta et al ., Reference Fonta, Sanfo, Kedir and Thiam 2018 ; Janzen and Carter, Reference Janzen and Carter 2019 ). Moreover, weather shocks in previous periods reduce farmers’ future income and demand. Conventionally, as farmers’ incomes improve, so does their demand for insurance. Agriculture income is, in particular, predictive of insurance demand (Takahashi et al ., Reference Takahashi, Ikegami, Sheahan and Barrett 2016 ; Abugri et al ., Reference Abugri, Amikuzuno and Daadi 2017 ; Bageant and Barrett, Reference Bageant and Barrett 2017 ). However, as households’ incomes improve, so does the likelihood to move out of agriculture and therefore income diversification tends to dampen demand (Bogale, Reference Bogale 2015 ). To increase demand, we highlight two avenues below that might increase demand through income-based interventions.

4.3.1 Demand subsidies

Demand can be induced through discounts and demand subsidies (Mcintosh et al ., Reference McIntosh, Sarris and Papadopoulos 2013 ; Giné et al ., Reference Giné, Karlan, Ngatia, Lundberg and Mulaj 2014 ; Karlan et al ., Reference Karlan, Osei, Osei-Akoto and Udry 2014 ; Tadesse et al ., Reference Tadesse, Alfnes, Erenstein and Holden 2017 ; Bulte et al ., Reference Bulte, Cecchi, Lensink, Marr and van Asseldonk 2019 ; Janzen and Carter, Reference Janzen and Carter 2019 ; Matsuda et al ., Reference Matsuda, Takahashi and Ikegami 2019 ; Ahmed et al ., Reference Ahmed, McIntosh and Sarris 2020 ; Stoeffler et al ., Reference Stoeffler, Carter, Gelade and Guirkinger 2020 ). A third party such as the government would then pay the remainder of the premium. Moreover, discounts and subsidies might work in favour of politicians who make policy (Hazell et al ., Reference Hazell, Sberro-Kessler and Varangis 2017 ). Also, subsidies might lower the costs of insurance and overall costs of social protection as households participate in its financing (Janzen et al ., Reference Janzen, Carter and Ikegami 2020 ). Recipient households contribute to social protection financing but also reduce their future social protection needs if they are more protected.

However, two crucial issues remain of concern regarding demand subsidies. The first concerns the sustainability of subsidies and the eventual demand when subsidies end. There is limited evidence on this but one study in Ethiopia provides some useful information. Takahashi et al . ( Reference Takahashi, Ikegami, Sheahan and Barrett 2016 ) found that demand was not affected when subsidies ended. They found that households that purchased insurance after receiving a discount voucher in the first year did not change their demand behaviour in the subsequent year when discounts were lifted, as learning effects after purchasing insurance dominated price anchoring effects. The second issue is whether subsidies are the best use of public resources in comparison to alternative social protection mechanisms such as cash transfers or input support. With limited resources, policymakers have to choose the most effective instruments. Evidence on which instruments have better returns for farmers is mixed. In a simulation study, Ricome et al . ( Reference Ricome, Affholder, Gérard, Muller, Poeydebat, Quirion and Sall 2017 ) compared insurance subsidies with reducing the cost of credit, subsidising fertilisers or offering cash transfers. They found that insurance subsidies brought the least utility and lowest certainty equivalent income in smallholder groundnut farmers in Senegal in comparison to any kind of credit, fertilisers or cash transfers. In an assessment of farmer preferences, Marenya et al . ( Reference Marenya, Smith and Nkonya 2014 ) found that Malawian smallholder farmers reduced demand for crop index insurance when they were offered a slightly higher cash benefit, showing a preference for cash transfers. Mahul and Stutley ( Reference Mahul and Stutley 2010 ) also warned that premium subsidies might be inefficient and increasingly expensive for governments, especially given that once started, it is not easy to roll them back.

However, simply choosing between cash and insurance might reveal immediacy biases rather than effective long-term protection and poverty reduction potential (Delavallade et al ., Reference Delavallade, Dizon, Hill and Petraud 2015 ). The evidence indicates that while farmers might prefer cash, agricultural insurance provides better opportunities for long-term poverty reduction and disaster management. In a simulation study, Carter and Janzen ( Reference Carter and Janzen 2018 ) showed that enabling farmers to be insured dominated other social protection instruments such as cash transfers. By testing a multi-generational household model of consumption, accumulation and risk management, they found that a 50 per cent increase in premium subsidies propped up insurance and slowed both the poverty rate and poverty depth, leading to a 20 per cent increase in GDP growth. Jensen et al. ( Reference Jensen, Barrett and Mude 2017 a , Reference Jensen, Ikegami and Mude 2007 b ) also found that subsidy-supported insurance was overall more poverty averting compared to cash transfers. While cash transfers improved short-term health measures, insurance increased investments in productive assets, reduced livestock sales and increased adult-equivalent income (Jensen et al ., Reference Jensen, Barrett and Mude 2017 a , Reference Jensen, Ikegami and Mude 2017 b ). Finally, Janzen et al . ( Reference Janzen, Carter and Ikegami 2020 ), in another simulation study spanning over 50 years of data in Kenya and Ethiopia, showed that insurance not only had higher vulnerability reduction potential but also brought additional investment incentive potential. They found that with insurance, farmers could contribute to their social protection, hence reducing the overall costs of protection and reaching wider coverage. The study further revealed that without insurance, asset poverty increased by 50 per cent while with insurance it decreased by 42 per cent. A further 10 per cent of poverty reduction came from unlocking resources for farm investments. These studies show that higher adoption of agricultural insurance can help economies move from reactive social protection policies to more proactive policies and hence achieve more poverty reduction.

However, some insurance types, such as livestock insurance, seem to protect a section of households that are better off (Chantarat et al ., Reference Chantarat, Mude, Barrett and Turvey 2017 ; Ricome et al ., Reference Ricome, Affholder, Gérard, Muller, Poeydebat, Quirion and Sall 2017 ; Janzen and Carter, Reference Janzen and Carter 2019 ). Chantarat et al . ( Reference Chantarat, Mude, Barrett and Turvey 2017 ) and Janzen and Carter ( Reference Janzen and Carter 2019 ) showed that effective coverage for livestock index insurance was between 10 and 15 total livestock units. The majority of rural households do not have as much livestock as these thresholds suggest. There is, therefore, room for other social protection instruments for various sub-groups. As Janzen et al . ( Reference Janzen, Jensen and Mude 2016 ) showed, poorer households receiving cash transfers retained and accumulated assets faster, while non-poor households with insurance protected and invested in their livestock more. Governments and insurance providers, therefore, need to think more about equity (Fisher et al ., Reference Fisher, Hellin, Greatrex and Jensen 2019 ) and consider insurance alongside other instruments so that insurance does not breed inequality.

4.3.2 Flexible payment mechanisms

Alternatively, insurance providers can offer flexible premium payment mechanisms, which relax budget limitations without affecting premiums. We highlight two of these, namely: (1) flexible time of payment, and (2) labour-based payments. In conventional insurance of all kinds, premiums are paid before coverage begins. For rural farming households, agricultural insurance premiums would have to be paid before the cropping season. However, at such times, smallholder farmers’ budgets are limited due to other necessary expenditures such as farm inputs and extension services. Insurance ends up being at the bottom of their priorities list (Binswanger-Mkhize, Reference Binswanger-Mkhize 2012 ). One avenue of easing farmer budgets is by making flexible the time of payment. Casaburi and Willis ( Reference Casaburi and Willis 2018 ) tested a pay-at-harvest insurance product with smallholder sugarcane contract growers in Kenya. In their experiment, they found first that farmers demanded less insurance at the beginning of the season, not due to liquidity challenges but because paying upfront was not marginal appropriate for their expected return. Secondly, when farmers had the option of paying premiums after harvest, demand increased from 5 per cent in the pay-up-front option to 72 per cent in pay-at-harvest option. In a related study in Ethiopia, Belissa et al . ( Reference Belissa, Bulte, Cecchi, Gangopadhyay and Lensink 2019 ) found that when farmers had a pay-at-harvest option, demand increased from 8 per cent to 24 per cent. However, the main challenge with pay-at-harvest insurance products might be contract enforcement as default rates might be high. Contract enforcement might not have been an issue in the Casaburi and Willis ( Reference Casaburi and Willis 2018 ) study because they worked with contract farmers. These do not represent the average rural farmer. It is therefore essential for providers to consider default and contract enforcement for average smallholders.

Another budget relaxing payment mechanism is labour-based payments linked with other social protection programmes (Tadesse et al ., Reference Tadesse, Alfnes, Erenstein and Holden 2017 ; Vasilaky et al ., Reference Vasilaky, Diro, Norton, McCarney and Osgood 2019 ). An integral component of such social protection programmes is public works, where individuals are paid for a specific number of public works. Two studies evaluated the possibility of farmers paying premiums with their labour. Tadesse et al . ( Reference Tadesse, Alfnes, Erenstein and Holden 2017 ) conducted a willingness to pay for agricultural insurance in Ethiopia and found that individuals were more willing to work in return for insurance, even at lower daily wage equivalents than the conventional cash-for-work programmes. They find that 60 per cent of the farmers would pay insurance using their labour. In another study, Vasilaky et al . ( Reference Vasilaky, Diro, Norton, McCarney and Osgood 2019 ) used experimental games and offered real commercial insurance after the game. First, they find that participating in the Productive Safety-Net Programme (PSNP) increased insurance purchase by up to 19 percentage points. They then compared between cash-paying and labour-paying sub-samples. They found that while the effect of experimental games on purchasing insurance with cash increased insurance purchases by 10 per cent, it increased purchases by 17 per cent in households that paid insurance with labour. They further found that total insurance purchased was higher for labour-paying households compared to cash-paying households. From these two studies in Ethiopia, it seems convincing that linking agricultural insurance with other social protection programmes, in particular public works programmes, could achieve higher gains in encouraging more rural households to purchase insurance. However, the complementarity of insurance and other social protection mechanisms is not always guaranteed. Households enrolled in other social protection programmes might demand insurance less if they consider that current programmes provide sufficient insurance (Duru, Reference Duru 2016 ).

Finally, there is the role of Information and Communications Technologies (ICTs) in enabling farmers to utilise payment platforms. Insurance companies are often thin on the ground and not able to reach most locations where farmers are located. Moreover, agricultural factor markets in SSA are grossly lacking in that most services are urban biased (Dillon and Barrett, Reference Dillon and Barrett 2017 ; Allen IV, Reference Allen 2018 ). Lack of well-functioning agricultural factor markets makes distribution expensive and prohibitive. However, ICTs help to bridge the gap of financial intermediation between suppliers and consumers by providing the last mile payment service. In other types of insurance such as health insurance, utilising the opportunities of mobile payment systems has enabled massive enrolment through mobile-based insurance payments (Wasunna and Frydrych, Reference Wasunna and Frydrych 2017 ). However, agricultural insurance is still lagging. Of the 31 million mobile-based insurance policies worldwide that were issued by June 2015, only 7 per cent were for agricultural insurance (GSMA, 2015 ). However, some opportunities will require full leveraging. For instance, all insurance policies by ACRE Africa in Kenya, Tanzania and Rwanda are provided over mobile-based payment services. Other smaller initiatives have been registered in several countries, and mobile payment could provide options to suppliers. However, the cost of internet services and mobile-based payment service taxes and costs remain high in many countries. Removing or decreasing some of the costs of using these platforms might make it simpler for rural farmers to adopt such technologies.

4.4 Education, knowledge and information

Most rural farmers are not only illiterate but also unaware of new technologies such as insurance. An assessment in Ethiopia found that 49 per cent had never heard of insurance, 41 per cent did not know how it worked, and 25 per cent did not know where to find it (World Bank, 2018 ). More educated farmers and pastoralists portray higher demand and the less educated portray lower demand (Giné and Yang, Reference Giné and Yang 2009 ; Patt et al ., Reference Patt, Peterson, Carter, Velez, Hess and Suarez 2009 ; Hill et al ., Reference Hill, Hoddinott and Kumar 2013 ; Bogale, Reference Bogale 2015 ; Okoffo et al ., Reference Okoffo, Denkyirah, Adu and Fosu-Mensah 2016 ; Takahashi et al ., Reference Takahashi, Ikegami, Sheahan and Barrett 2016 ; Abugri et al ., Reference Abugri, Amikuzuno and Daadi 2017 ; Bishu et al ., Reference Bishu, Lahiff, O'Reilly and Gebregziabher 2018 ; Fonta et al ., Reference Fonta, Sanfo, Kedir and Thiam 2018 ; Amare et al ., Reference Amare, Simane, Nyangaga, De, Hamza and Gurmessa 2019 ; Janzen and Carter, Reference Janzen and Carter 2019 ; Vasilaky et al ., Reference Vasilaky, Diro, Norton, McCarney and Osgood 2019 ). Literacy is not only important in order to know about insurance but also to correctly understand insurance contracts. When farmers are not able to understand concepts like basis risk, demand remains low (Stoeffler and Opuz, Reference Stoeffler and Opuz 2020 ). Education and information tend to run concurrently. While insurance providers might not change the literacy skills of the farmers, they can provide more information. The evidence shows that where information has been provided, farmers and pastoralists increase their understanding of insurance as well as demand (Patt et al ., Reference Patt, Peterson, Carter, Velez, Hess and Suarez 2009 ; Lybbert et al ., Reference Lybbert, Galarza, McPeak, Barrett, Boucher and Carter 2010 ; McPeak et al ., Reference McPeak, Chantarat and Mude 2010 ; Takahashi et al ., Reference Takahashi, Ikegami, Sheahan and Barrett 2016 ; Belissa et al ., Reference Belissa, Bulte, Cecchi, Gangopadhyay and Lensink 2019 ; Vasilaky et al ., Reference Vasilaky, Diro, Norton, McCarney and Osgood 2019 ; Ali et al ., Reference Ali, Egbendewe, Abdoulaye and Sarpong 2020 a ). Information might be provided through games (McPeak et al ., Reference McPeak, Chantarat and Mude 2010 ; Vasilaky et al ., Reference Vasilaky, Diro, Norton, McCarney and Osgood 2019 ), information brochures (Takahashi et al ., Reference Takahashi, Ikegami, Sheahan and Barrett 2016 ), or training sessions (Dercon et al ., Reference Dercon, Hill, Clarke, Outes-Leon and Seyoum Taffesse 2014 ). However, it is not merely information or literacy, but a better understanding of insurance concepts and underlying mechanisms that is is crucial. While farmers might know more about insurance, demand does not seem to improve with knowledge automatically (Takahashi et al ., Reference Takahashi, Ikegami, Sheahan and Barrett 2016 ). Exposure needs to be consistent to nudge demand. Previous experience also matters in that farmers who have previously insured are more informed and hence more likely to purchase insurance again (Karlan et al ., Reference Karlan, Osei, Osei-Akoto and Udry 2014 ; Castellani and Viganò, Reference Castellani and Viganò 2017 ; Belissa et al ., Reference Belissa, Bulte, Cecchi, Gangopadhyay and Lensink 2019 ). Insurance providers could therefore invest in increasing insurance awareness through more marketing campaigns and training.

4.5 Behavioural and socio-cultural factors

Farmers and pastoralists are also influenced by behavioural, social and cultural factors in their decisions to purchase insurance. These factors are portrayed through risk perceptions, trust and, in some communities, cultural and religious beliefs play a role. We elaborate on these below.

4.5.1 Risk perceptions and attitudes

Generally, risk perception can be in three dimensions. (1) Risk aversion – where individuals have a concave utility function in that as risk increases, they are more likely to adopt risk mitigation mechanisms. (2) Risk neutrality – where individuals are indifferent to risk. (3) Risk loving – where individuals portray a convex utility function such that they increase risky ventures even when the possibility of loss is high. Risk-averse farmers are more likely to demand insurance more than risk-loving and risk-neutral farmers. Risk aversion in agricultural insurance can be categorised in two aspects. The first is risk aversion towards the probability of a weather shock happening and the farmers having losses. For this kind of risk aversion, insurance is always attractive (Outreville, Reference Outreville 1998 ). An increase in risk aversion is associated with more insurance take-up (Belissa et al ., Reference Belissa, Lensink and van Asseldonk 2020 ; Haile et al ., Reference Haile, Nillesen and Tirivayi 2020 b ). Risk aversion is further informed by how individuals assess the probability of a shock happening. Previous shocks provide reference points on which to assess possible losses or losses averted (gains) from a future shock and therefore influence risk aversion (Lybbert et al ., Reference Lybbert, Galarza, McPeak, Barrett, Boucher and Carter 2010 ; Hill et al ., Reference Hill, Hoddinott and Kumar 2013 ; Karlan et al ., Reference Karlan, Osei, Osei-Akoto and Udry 2014 ; Bogale, Reference Bogale 2015 ; Fonta et al ., Reference Fonta, Sanfo, Kedir and Thiam 2018 ; Janzen and Carter, Reference Janzen and Carter 2019 ). For instance, previous shocks expressed in the units of tropical livestock units lost in the previous year was associated with an increase in demand for insurance (Janzen and Carter, Reference Janzen and Carter 2019 ). Previous shocks can also alert individuals to undertake protective and precautionary actions including risk-averse behaviour and hence increase the likelihood of purchasing insurance (Clarke, Reference Clarke 2016 ).

However, farmers might underestimate the probability of weather shocks and therefore demand less insurance (Abugri et al ., Reference Abugri, Amikuzuno and Daadi 2017 ; Dougherty et al ., Reference Dougherty, Flatnes, Gallenstein, Miranda and Sam 2019 ). When farmers underestimate the probability of several shocks and insurance failures such as basis risk, they develop compound risk aversion, which greatly affects demand as experimental evidence from Mali shows (Elabed et al ., Reference Elabed, Bellemare, Carter and Guirkinger 2013 ; Elabed and Carter, Reference Elabed and Carter 2015 ). Compound risk aversion is related to ambiguity aversion. Ambiguity aversion occurs when farmers are not able to correctly interpret the value of new technologies such as agricultural insurance (Bryan, Reference Bryan 2019 ). Evaluating experiments in Kenya and Malawi, Bryan ( Reference Bryan 2019 ) found that ambiguity averse farmers insured less. However, what seems to be important is not just risk aversion but how the insurance provider frames the narrative. While risk-averse farmers might demand less insurance, when the narrative is framed from a loss dimension, loss-averse farmers are more likely to purchase insurance (Visser et al ., Reference Visser, Jumare and Brick 2020 ) because they prefer the certainty of non-decreasing welfare with insurance (Serfilippi et al ., Reference Serfilippi, Carter and Guirkinger 2020 ). In an experiment in Burkina Faso, Serfilippi et al . ( Reference Serfilippi, Carter and Guirkinger 2020 ) found that when farmers were offered a premium rebate contract, where they received the equivalent of their premiums in a bad year, they demanded higher insurance than when the contract did not specify a rebate but rather conventional coverage. An additional 41 per cent willingness to pay emanated simply from this framing as farmers’ welfare would decrease less with premium rebate than with no rebates. An appropriate framing could overcome some component of risk aversion and attract demand.

4.5.2 Low trust

Distrust in insurance products and insurance providers reduces agricultural insurance demand among farmers and pastoralists pastoralists (Patt et al ., Reference Patt, Peterson, Carter, Velez, Hess and Suarez 2009 ; Suarez and Linnerooth-Bayer, Reference Suarez and Linnerooth-Bayer 2010 ; Karlan et al ., Reference Karlan, Osei, Osei-Akoto and Udry 2014 ; Tadesse et al ., Reference Tadesse, Alfnes, Erenstein and Holden 2017 ). Low trust is partly related to education and inadequate knowledge and information about formal insurance. Farmers are therefore not able to understand how new technologies such as insurance work (McPeak et al ., Reference McPeak, Chantarat and Mude 2010 ; Bryan, Reference Bryan 2019 ). Farmers reveal distrust in: (1) the insurance product, (2) the insurance providers, (3) the technology on which insurance is based, and (4) interpersonal trust among individuals (Platteau et al ., Reference Platteau, De Bock and Gelade 2017 ). Lack of trust in the product can be improved if farmers receive better information about insurance (see section 4.4 ). Distrust in insurers might be related to three issues. First, insurers generally have a low presence in rural areas in SSA. Agricultural insurance is generally new and has not proliferated in rural areas. Rural farmers are less likely to trust institutions that they do not have a previous relationship with and do not know well. With this bottleneck, insurance providers could use channels of higher trust such as community-based groups. We expand on this issue in sub-section 4.5.5 . Other channels might include well-known financial institutions, such as banks and microfinance organisations, and input retailers (World Bank, 2018 ) or farmer organisations (Patt et al ., Reference Patt, Peterson, Carter, Velez, Hess and Suarez 2009 ). In some instances, farmers trust governments over commercial insurance companies (Tadesse et al ., Reference Tadesse, Alfnes, Erenstein and Holden 2017 ). In general, it can be very useful to leverage existing trusted institutions rather than starting new operations. It might also be useful if prospective providers conduct sufficient market research before they launch operations. Strategies to reduce basis risk can increase trust in indices.

4.5.3 Farmer participation

To increase insurance acceptability, farmer-driven product design should be fostered and prioritised, especially at early design stages (Patt et al ., Reference Patt, Peterson, Carter, Velez, Hess and Suarez 2009 ; Greatrex et al ., Reference Greatrex, Hansen, Garvin, Diro, Blakeley and Le Guen 2015 ). Patt et al . ( Reference Patt, Peterson, Carter, Velez, Hess and Suarez 2009 ) provided two examples of participation that increased trust. In Ethiopia, farmers and experts worked together using local materials to assemble historical rainfall distribution data of the area. Farmer provided information was found to highly correlate with historical meteorological data, and insurance experts, therefore, used it to calculate the monthly weights for rainfall in these areas. The second example was in Malawi where through farmer workshops, farmers participated in calculating the payout levels under different rainfall regimes, increasing both their understanding and building trust in the products. Leblois et al . ( Reference Leblois, Quirion and Sultan 2014 b ) gave another dimension of farmer participation. They compared an index constructed with a simulated cotton sowing date with that provided by the farmers, collected through a farmer association. They found that the index that used farmer-provided sowing date data was preferred and also reduced basis risk more than the simulation-based product. Indeed, in their example, it was easy to access this farmer provided data. However, it might be at a cost to insurance providers in the absence of such a farmer organisation that records this kind of data. The evolving picture-based insurance in Ethiopia (Porter et al ., Reference Porter, Kramer, Assefa and Abzhamilova 2020 ) and Kenya (ACRE Africa, 2020 ) improves farmer participation in monitoring and loss verification. It is, therefore, able to contribute to reducing basis risk as well as increasing farmer trust through their participation.

4.5.4 Cultural perceptions

We discuss culture in two dimensions: first, the general position of women in society and second, the influence of religion. While women comprise a very large demographic within farming households, their roles regarding decision making in agriculture investments are largely limited by restrictive cultural norms (Fisher and Carr, Reference Fisher and Carr 2015 ; Perez et al ., Reference Perez, Jones, Kristjanson, Cramer, Thornton, Förch and Barahona 2015 ). Such barriers also permeate insurance adoption (Delavallade et al ., Reference Delavallade, Dizon, Hill and Petraud 2015 ; Abugri et al ., Reference Abugri, Amikuzuno and Daadi 2017 ; Born et al ., Reference Born, Spillane and Murray 2019 ) among others. Since women farmers are likely to be poorer than men farmers, their involvement in insurance is limited (Delavallade et al ., Reference Delavallade, Dizon, Hill and Petraud 2015 ; Abugri et al ., Reference Abugri, Amikuzuno and Daadi 2017 ; Fonta et al ., Reference Fonta, Sanfo, Kedir and Thiam 2018 ). Though in some cases there are no significant differences between women and men farmers regarding insurance adoption (Bageant and Barrett, Reference Bageant and Barrett 2017 ), gendered data on the adoption of insurance is not broadly available (Born et al ., Reference Born, Spillane and Murray 2019 ), and this limits analysis of how women farmers are affected in agricultural insurance provision. Such data would be essential in tailoring insurance products to cater to any gender-disaggregated needs (Fletschner and Kenney, Reference Fletschner, Kenney, Quisumbing, Meinzen-Dick, Raney, Croppenstedt, Behrman and Peterman 2014 ; Born et al ., Reference Born, Spillane and Murray 2019 ).

Regarding the influence of religion, individual beliefs might conflict with market-oriented technologies such as insurance. This issue has been observed in northern Kenya (Johnson et al ., Reference Johnson, Wandera, Jensen and Banerjee 2019 ) and Niger (Fava et al ., Reference Fava, Upton, Banerjee, Taye and Mude 2018 ), both predominantly Muslim regions. In Kenya, Johnson et al . ( Reference Johnson, Wandera, Jensen and Banerjee 2019 ) qualitatively detail the case of index-based livestock insurance in northern Kenya regarding expectations, aspirations and the challenges experienced. In their narrative, they show that some of the difficulties related to low sales emanated from the way predominantly Muslim communities viewed profit-making insurance products as not culturally and religiously permissible under the Sharia Law. To build more trust in insurance, a new provider that adhered to the cultural and religious preferences of the communities was introduced in the market (Banerjee et al ., Reference Banerjee, Khalai, Galgallo and Mude 2017 ; Johnson et al ., Reference Johnson, Wandera, Jensen and Banerjee 2019 ). The result was an increase in trust, retention of a higher number of local agents (Banerjee et al ., Reference Banerjee, Khalai, Galgallo and Mude 2017 ), expansion into other areas and insuring of greater numbers of livestock units (Johnson et al ., Reference Johnson, Wandera, Jensen and Banerjee 2019 ). Though the costs of operations remained high, and the model was costly (Banerjee et al ., Reference Banerjee, Khalai, Galgallo and Mude 2017 ), the positive effects on trust-building and expansion were clear. With this experience, the inception of index insurance in Niger was purposefully made sharia-compliant (Fava et al ., Reference Fava, Upton, Banerjee, Taye and Mude 2018 ). This suggests that insurance providers can, after learning, leverage such normatively hidden preferences such as religion and make their products popular and attractive. Moreover, insurance, being a financial product, would need to heed different norms that govern financial products across different regions.

4.5.5 Offering insurance to groups

On a theoretical level, De Janvry et al . ( Reference De Janvry, Dequiedt and Sadoulet 2014 ) offered several reasons why insurance provided through groups should be considered more. First, community-based informal social support and risk management organisations are very important in building trust. Social support groups are formed for social support in all kinds of idiosyncratic shocks to households and have gained prominence mainly for easing targeting difficulties due to their near-universal coverage community (Bold and Dercon, Reference Bold and Dercon 2014 ). They, therefore, act as effective points of information dissemination and reduce the costs of reaching clients (Bhattamishra and Barrett, Reference Bhattamishra and Barrett 2010 ).

Secondly, these institutions already have a good understanding of insurance since they already provide informal insurance under the basis of risk-sharing. Thirdly, the nature of their reciprocal relationships (Fafchamps, Reference Fafchamps, Benhabib, Bisin and Jackson 2011 ) implies that trust levels in these organisations are generally high. Providers of agricultural insurance might find them appropriate platforms for introducing and distributing insurance (Trærup, Reference Trærup 2012 ; Dercon et al ., Reference Dercon, Hill, Clarke, Outes-Leon and Seyoum Taffesse 2014 ; Belissa et al ., Reference Belissa, Bulte, Cecchi, Gangopadhyay and Lensink 2019 ). Because individuals trust groups in which they already have informal membership, they prefer group-based contracts to individual contracts (Hill et al ., Reference Hill, Hoddinott and Kumar 2013 ; Dercon et al ., Reference Dercon, Hill, Clarke, Outes-Leon and Seyoum Taffesse 2014 ; Sibiko et al ., Reference Sibiko, Veettil and Qaim 2018 ; Belissa et al ., Reference Belissa, Bulte, Cecchi, Gangopadhyay and Lensink 2019 ). Belissa et al . ( Reference Belissa, Bulte, Cecchi, Gangopadhyay and Lensink 2019 ) observed that in contrast to individual index insurance take-up of only 8 per cent, when farmers had the offer of insurance through their informal groups, take-up rates increased to 43 per cent. Some of the benefits of group-based insurance include cost-effectiveness in information transmission and the pre-existing experience of risk-sharing (Dercon et al ., Reference Dercon, Hill, Clarke, Outes-Leon and Seyoum Taffesse 2014 ). Finally, groups enhance community social capital that enables the flow of information. In Ghana, Karlan et al . ( Reference Karlan, Osei, Osei-Akoto and Udry 2014 ) found that farmers who knew a farmer who had been insured and received a payout were more likely to purchase insurance in forthcoming years. Therefore, farmers improve their trust in insurance by observing the experiences of other farmers in their networks.

Nonetheless, there are three important caveats to make in the encouragement of group-based contracts. The first is the complexity of making legally-binding agreements with informal groups. Informal groups do not have a legal framework in which they operate beyond the informal trust and norms of group members. In such cases, even when contracts with informal groups are preferred, providers cannot enter into contracts with them (Dercon et al ., Reference Dercon, Hill, Clarke, Outes-Leon and Seyoum Taffesse 2014 ). Providers offering group contracts would, therefore, have to make prudent decisions, including investing in contract monitoring at the individual level for each individual in a group.

Secondly, there might be fears that group contracts might promote moral hazard if farmers change their behaviour on issues such as farm investment. There is a need for more research to test these fears. One such research study is Bulte et al . ( Reference Bulte, Cecchi, Lensink, Marr and van Asseldonk 2019 ) who tested whether taking insurance made farmers invest less in their farms in Kenya. They did not find supporting evidence; instead they found that insured farmers invested more in their farms. Stoeffler et al . ( Reference Stoeffler, Carter, Gelade and Guirkinger 2020 ) arrived at a similar result in Burkina Faso, where they observed that enrolment in insurance encouraged intensive farm investments.

The third caveat, related to moral hazard, is the concerns of free riding and crowding out informal social insurance systems. The argument is that formal insurance, promoted and provided through informal groups, weakens informal mechanisms and hence crowds out existing informal social protection mechanisms. Though the evidence is mixed, free riding might not be ruled out (De Janvry et al ., Reference De Janvry, Dequiedt and Sadoulet 2014 ), and it has been observed in other insurance types (such as health insurance). Two recent studies both assessing crowding out due to index-based insurance in Ethiopia do not find confirmatory evidence (Matsuda et al ., Reference Matsuda, Takahashi and Ikegami 2019 ; Takahashi et al ., Reference Takahashi, Barrett and Ikegami 2019 ). While evidence of crowding out is still scarce, suppliers would need to closely monitor the behaviour of the insured and check that such innovations do not disrupt existing systems.

4.6 The role of governments

So far, this review has explored demand from farmers and supply from insurance providers. A key connection to complete the circle is the role of governments in both demand and supply dimensions. Meso- to macro-level factors might pose a challenge for a single insurance provider. Many providers require market regulation and policy oversight and governments can induce demand through various support strategies to farmers. In this section, we consider the role of governments in: (1) reducing the costs of delivering insurance through better market coordination, (2) providing both consumer and provider subsidies, and (3) developing and updating policies to suit an increasingly dynamic market.

4.6.1 Reducing the costs of delivering insurance

Whereas other kinds of insurance (such as health or motor insurance) have a concept of self-protection such as better use of preventive health services or more disciplined driving (which in turn might lower premiums), farmers do not have much leverage over the weather. Moreover, climate change increases the frequency and intensity of weather-related risks, and most shocks are covariate in nature. Agricultural insurance, therefore, becomes a high-frequency, high loss insurance type making it more complicated than other kinds of insurance.

The cost of commercially viable agricultural insurance is therefore high and prohibitive to both the provider and farmers. The costs of providing agricultural insurance can be subdivided into start-up costs, operational costs and transaction costs. Insurance companies are required to have sufficient reserve capital to ascertain meeting insurees’ claims when they arise. Providers also have to pay reinsurance costs to insure their losses too. For high loss events such as weather shocks, thresholds for reserve capital are high. The predictable losses increase the cost of reinsurance which is in turn transmitted to the consumers through increased premiums (Miranda and Mulangu, Reference Miranda and Mulangu 2016 ). The second type of costs is related to operational costs associated with development, maintenance and monitoring of indices and insurance payoff thresholds. The infrastructure to acquire and process data needed for the indices can be expensive for single and small providers. For more precise insurance products, providers require more information, even up to plot-level data. The third dimension of costs is transaction costs. These include administrative costs incurred to reach the farmers and pastoralists, especially in remote areas. Where there is low demand, the costs of maintaining insurance company staff in remote areas are high (Johnson et al ., Reference Johnson, Wandera, Jensen and Banerjee 2019 ). A combination of all these costs puts insurance providers at risk (Meze-Hausken et al ., Reference Meze-Hausken, Patt and Fritz 2009 ). Insurance providers can ease some of the costs through better coordination mechanisms. For instance, many smaller micro-insurance providers can work in association and thus pool both human and capital resources together to afford some of the costs of market entry (Meze-Hausken et al ., Reference Meze-Hausken, Patt and Fritz 2009 ).

Governments can reduce costs through the provision of infrastructure. Infrastructure such as weather stations and their maintenance can be provided as public services. While these exist in many countries, their sparse distribution (Webster, Reference Webster 2013 ; Parker, Reference Parker 2015 ) suggests that governments need to invest more in this infrastructure to make weather services more affordable and available even for commercial use (Georgeson et al ., Reference Georgeson, Maslin and Poessinouw 2017 ). Governments can also provide risk layering and aggregation which is a process in which overall risk is classified across levels of severity to provide insurance and reinsurance services. Governments can then provide insurance and reinsurance services to providers involved in highest risk and high severity events across geographical spaces. There is no published example of these services in Africa to include in this review, however, the Mongolian Index-Based Livestock Insurance Programme (Rao et al ., Reference Rao, Davi, D'Arrigo, Skees, Nachin and Leland 2015 ) and the crop insurance programme in Tajikistan (Weber et al ., Reference Weber, Fecke, Moeller and Musshoff 2015 ) might provide some insights for Africa. Footnote 2 In Tajikistan, Weber et al . ( Reference Weber, Fecke, Moeller and Musshoff 2015 ) discuss insurance products designed with inter-regional and intra-regional risk aggregation and risk coverage scenarios to achieve risk reduction. In Mongolia, livestock losses are insured at three levels (Rao et al ., Reference Rao, Davi, D'Arrigo, Skees, Nachin and Leland 2015 ; Hohl, Reference Hohl 2019 : 299). At 6 per cent mortality, pastoralists are required to self-insure for this low risk. Between 6 and 30 per cent of mortality, insurance providers cover losses. At mortalities of more than 30 per cent, insurance providers are also adversely affected and unable to provide effective coverage. In turn, the Agricultural Reinsurance Company covers the losses of both the insurance provider and the pastoralists. In both cases, the government had a dominant role in providing meso-level insurance thus enabling insurance providers to function effectively. A comparable example from Africa is the ARC that provides drought risk pooling reinsurance to countries through its annual risk pools (Awondo, Reference Awondo 2019 ). Similar to sub-regions in Tajikistan, Awondo ( Reference Awondo 2019 ) found that as more member countries join the ARC risk pool, the risk was better covered. Moreover, the buffer fund per country, required to cover extreme drought events, decreases as more countries participate, indicating strong benefits for risk pooling.

4.6.2 Subsidies and other public goods

Governments can also provide subsidies to make insurance affordable. Almost all agricultural insurance programmes in both high and low-income countries already have some form of subsidisation. The level of subsidies determines how much insurance is made affordable. Moreover, providing demand (premium) subsidies increases take-up rates and take-up does not decrease when subsidies end (see section 4.3.1 ). Mahul and Stutley ( Reference Mahul and Stutley 2010 ) suggested eight reasons why governments should support agricultural insurance programmes. These included systemic risk associated with agriculture risks; information asymmetries between providers and insurance seekers; weaknesses and insufficiency of post disasters programmes (especially with building sustainable resilience); limited international reinsurance markets; limited and expensive agriculture risk market infrastructure (such as weather stations); low-risk awareness, lack of (formal) insurance culture; and finally, regulatory impediments.

Furthermore, governments can support providers through provider subsidies (Mahul and Stutley, Reference Mahul and Stutley 2010 ; Hazell et al ., Reference Hazell, Sberro-Kessler and Varangis 2017 ). Provider market enhancing subsidies aim at developing strong risk market infrastructure and might include subsidies for infrastructure improvements, provider start-up costs, market stabilisation subsidies and reinsurance subsidies (Miranda and Farrin, Reference Miranda and Farrin 2012 ). One example of such subsidies is the joint World Bank – Government of Kenya initiative that supports public-private partnerships to provide premium subsidies ranging from 50 to 100 per cent of premiums for the most vulnerable pastoralists (Hazell et al ., Reference Hazell, Sberro-Kessler and Varangis 2017 ). Moreover, with more purpose and coordination, these subsidies can be provided at cross-country and regional levels. For instance, countries joining the Africa Disaster Risk Financing Facility receive a 50 per cent subsidy upon entry (ARC, 2018 ). The success of broad subsidy programmes needs both fiscal discipline and a view beyond political motivations. Political electoral motivations led to the withdrawal of Kenya and Malawi from the 2016/17 ARC risk pool (Hohl, Reference Hohl 2019 : 288) and such happenings could jeopardise the success of the industry.

4.6.3 Policy, regulation and legal environment

Finally, the key role of government support in developing policies, regulating the markets and creating an enabling environment for insurance providers, cannot be overstated. There are two dimensions of how governments might act. First, governments as the main market player through the provision of other agricultural services such as extension and farm inputs can include agricultural insurance in its core services provided such as in Zambia (World Bank, 2019 ). Other relatively larger programmes such as ACRE Africa in East Africa also provide insurance alongside farm inputs. This combination of services with inputs is often referred to as bundling. Although evidence on bundling insurance with other services is clear (see section 4.2.4 ) we do not have strong views regarding making programmes compulsory as is the case in Zambia. However, national governments can take decisive and cost-effective actions especially by leveraging an already existing infrastructure of delivering other services, and mandatory insurance might be one of the options.

Governments can also provide regulation and market coordination functions. The principal role of the government should be to address market and regulatory imperfections so that private insurance and reinsurance providers can participate (Mahul and Stutley, Reference Mahul and Stutley 2010 ). Market regulation is therefore necessary to both farmers and pastoralists on the one hand and insurance providers on the other. It protects farmers from underhanded and predatory behaviour from providers and creates a competitive environment for providers. From a demand perspective, without government regulation, there is a high likelihood of a low product quality equilibrium (Clarke and Wren-Lewis, Reference Clarke and Wren-Lewis 2013 ; Carter and Chiu, Reference Carter and Chiu 2018 ), which in turn hurts farmers through poor, unhelpful products. From a supply-side perspective, regulation keeps prices in check to avoid high premiums and low demand and finally exiting by providers due to poor markets.

Effective regulation ought to be embedded in the law and regulatory organisations have to be legally empowered to act to balance markets. One major challenge is that agricultural insurance is relatively new in many SSA countries and laws and regulations on it are nonexistent or still in the early stages of development (Jegede et al ., Reference Jegede, Addaney, Mokoena, Filho and Jacob 2020 ; Onyiriuba et al ., Reference Onyiriuba, Okoro and Ibe 2020 ). Countries, therefore, need to re-evaluate their policies and laws, given recent innovations. There is some progress but it could be made faster with better in-country and across countries’ coordination. Two examples of progress on this front are the West African Inter-African Conference of the Insurance Markets (CIMA) countries and in Kenya. Pre-2012, the CIMA countries operating a regional regulatory body on insurance and reinsurance had only one article regarding agricultural insurance (Mahul and Stutley, Reference Mahul and Stutley 2010 ). In 2012, 14 CIMA countries adopted a new law that allowed regulation and oversight of agricultural insurance (World Bank, 2015 a ). In Kenya, the government has updated the Insurance Bill and expanded the jurisdiction of the Insurance Regulatory Authority to agricultural insurance (World Bank, Reference World Bank 2015 b ). These two examples provide opportunities on how countries and inter-country bodies can update their laws, regulations and policies to provide more space for insurance providers while setting standards with emerging technologies.

5. Conclusion

In this comprehensive review, we assessed a wide range of qualitative and quantitative peer-reviewed and grey literature to build on existing knowledge on the factors that influence the take-up of agricultural insurance and how take-up might be encouraged. The review sheds light on six main themes. These include: (1) product quality; (2) product design; (3) household incomes and investments under limited budgets; (4) education, information and knowledge of insurance; (5) behavioural preferences and cultural barriers; and (6) the role of governments in providing an enabling, stable and efficient market.

It is worth noting that agricultural insurance is not and should not be viewed as a singular magic answer to all weather-related problems that farmers face. It is rather part of a range of risk management options, alongside other instruments such as cash transfers and informal risk-sharing and risk management strategies. Comparing it with cash transfers, the review invites policymakers to consider the costs and opportunities between what Janzen et al . ( Reference Janzen, Carter and Ikegami 2020 ) refer to as ‘reactive’ social protection versus ‘proactive’ social protection with a long term view. Moreover, the trend of adoption of cash transfers might also provide a policy-learning path for agricultural insurance. Based on the Zambian example, governments might have to integrate insurance into existing extension and farm input programmes as evidence shows that combining these can provide larger coverage at lower costs. In regions where insurance is relatively new, providers might need to invest in information services, promoting awareness and building trust. Participation is crucial as farmers are not only consumers but also hold important information that might make insurance provision more efficient. Finally, the role of governments cannot be overstated. Governments can respond not only through the provision of demand and supply subsidies but also by designing policies and laws that enable the growth of agricultural insurance markets. Strategically, governments can join regional risk pools, which further attracts private providers and strengthens reinsurance services. Ultimately, SSA will benefit from increased adoption of well-designed insurance products and services that take into consideration the local context for all players in the agricultural insurance markets, with the support of local and regional governments – particularly in the face of climate change, and its current and projected adverse effects in the region.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S1355770X21000085

1 The data does not include Somalia, Sudan, Eritrea, Mauritius and Burundi.

2 These two papers were not part of the literature search process and have been referenced only to expound on the understanding of meso and macro insurance risk layering and reinsurance.

Figure 1. Coverage of studies reviewed by country. Source : Authors.

Nshakira-Rukundo et al. supplementary material

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• Volume 26, Special Issue 5-6
• Emmanuel Nshakira-Rukundo (a1) (a2) (a3) , Juliet Wanjiku Kamau (a4) and Heike Baumüller (a2)
• DOI: https://doi.org/10.1017/S1355770X21000085

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Research on the relationship between agricultural insurance participation and chemical input in grain production.

1. Introduction

2. literature review, 3. theoretical basis, 3.1. as consumers, analysis of the mechanism that affects farmers’ insurance participation, 3.2. as producers, analysis of the mechanism that affects farmers’ production input behavior, 4. model and data description, 4.1. model setting and research methods, 4.2. data source, 4.3. descriptive analysis, 5. results and discussion, 5.1. endogeneity test of equations, 5.2. effects of farmers’ chemical input behavior on insurance participation decisions, 5.3. effects of farmers’ insurance participation decisions on their chemical input behavior, 5.4. analysis of the relationship between farmers’ decision to participate in agricultural insurance and input of food production chemicals, 5.5. robustness test, 6. conclusions and recommendations.

• Attention should be paid to the increasing role of agricultural insurance in agricultural sustainable development, and the agricultural insurance policy system should be improved.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Mao, H.; Hu, R.; Zhou, L.; Sun, J. Crop insurance and the farmers’ adoption of green technology: Empirical analysis based on cotton farmers. J. Agrotech. Econ. 2022 , 11 , 95–111. [ Google Scholar ]
• Ahmed, N.; Hamid, Z.; Mahboob, F.; Rehman, K.U.; Ali, M.S.e.; Senkus, P.; Wysokińska-Senkus, A.; Siemiński, P.; Skrzypek, A. Causal Linkage among Agricultural Insurance, Air Pollution, and Agricultural Green Total Factor Productivity in United States: Pairwise Granger Causality Approach. Agriculture 2022 , 12 , 1320. [ Google Scholar ] [ CrossRef ]
• Nie, W.; Zuo, T.D.; Chen, J. Analysis of influencing factors of farmers’ cognition of green agricultural development and adopting green production behavior. J. Northeast. Agric. Univ. Soc. Sci. Edition 2020 , 18 , 1–9. [ Google Scholar ]
• Zhang, Q.; Wang, K.; Li, Y.; Wei, T.D. Effectiveness and Problems of Risk Coverage of Agricultural Insurance in China and Suggestions. Agric. Outlook 2022 , 18 , 40–47. [ Google Scholar ]
• Ramaswami, B. Supply Response to agricultural Insurance: Risk Reduction and Moral Hazard Effects. Am. J. Agric. Econ. 1993 , 75 , 914–925. [ Google Scholar ] [ CrossRef ]
• Smith, V.H.; Goodwin, B.K. Crop Insurance, Moral Hazard, and Agricultural Chemical Use. Am. J. Agric. Econ. 1996 , 78 , 428–438. [ Google Scholar ] [ CrossRef ]
• He, J.; Zheng, X.Y.; Rejesus, R.; Yorobe, J. Input use under cost-of-production crop insurance: Theory and evidence. Agric. Econ. 2020 , 51 , 343–357. [ Google Scholar ] [ CrossRef ]
• Tang, L.; Luo, X.F. Can agricultural insurance encourage farmers to apply biological pesticides? Evidence from rural China. Food Policy 2021 , 105 , 102174. [ Google Scholar ] [ CrossRef ]
• Niu, Z.H.; Yi, F.; Chen, C. Agricultural Insurance and Agricultural Fertilizer Non-Point Source Pollution: Evidence from China’s Policy-Based Agricultural Insurance Pilot. Sustainability 2022 , 14 , 2800. [ Google Scholar ] [ CrossRef ]
• Quiggin, J. Testing between Alternative Models of Choice under Uncertainty-Comment. J. Risk Uncertain. 1993 , 6 , 161–164. [ Google Scholar ] [ CrossRef ]
• Horowitz, J.K.; Lichtenberg, E. Risk-Reducing and Risk-Increasing Effects of Pesticides. J. Agric. Econ. 1994 , 45 , 82–89. [ Google Scholar ] [ CrossRef ]
• Zhong, F.N.; Ning, M.X.; Xing, L.; Miao, Q. A Study on the Relationship between Crop Insurance and Agrochemical Uses—An Empirical Analysis of the Manas Watershed, Xinjiang, China. China Econ. Q. 2007 , 01 , 291–308. [ Google Scholar ]
• Chen, J.C.; Wang, H.M. Agricultural Insurance and Agricultural Non-point Source Pollution: Influencing Factors and Their Measurement—Scenario Simulation Based on Simultaneous Equations Model. J. Shanghai Univ. Financ. Econ. 2015 , 17 , 34–43+56. [ Google Scholar ]
• Zhang, W.; Guo, S.P.; Luo, X.M. Environmental Effects Review of Policy Agricultural Insurance. Insur. Stud. 2012 , 12 , 52–60. [ Google Scholar ]
• Ning, M.X. Environmental and Economic Effects of Agricultural Insurance System: An Analysis Framework Based on Farmers’ Production Behavior. J. Agrotech. Econ. 2007 , 03 , 28–32. [ Google Scholar ]
• Ma, G.Y.; Lai, Y.B. Study on the Ecological Environment Effects of Policy Crop Insurance. For. Econ. 2021 , 43 , 22–38. [ Google Scholar ]
• Tuo, G.Z.; Zhang, Q. On the Policy Objectives of Agricultural Insurance in China. Insur. Stud. 2018 , 07 , 7–15. [ Google Scholar ]
• Zhang, Z.X.; Mu, Y.Y.; Hou, L.L. Does Participation in Agricultural Insurance Optimize Factor Allocation? An Analysis of Endogenous Farmers’ Insurance Decision-making and Its Effect on Production. Chin. Rural. Econ. 2018 , 10 , 53–70. [ Google Scholar ]
• Shi, Z.H.; Mu, H.J.; Sun, Y. A Study on the Influence of Agricultural Subsidy on Farmer Participation in Green Development of Agricultural. Ind. Econ. Rev. 2019 , 10 , 144–154. [ Google Scholar ]
• Liu, Y. Influence of Agricultural Insurance on Adoption of Environment-friendly Agricultural Technology. Master’s Thesis, Chinese Academy of Agricultural Sciences, Beijing, China, 2021. [ Google Scholar ]
• Li, T.; Chen, L.; Li, X.; Li, S.; Chen, H.; Ji, H. The Impact of Cost-of-Production Insurance on Input Expense of Fruit Growing in Ecologically Vulnerable Areas: Evidence from Shaanxi Province of China. Sustainability 2021 , 13 , 12083. [ Google Scholar ] [ CrossRef ]
• Chambers, R.G. Insurability and Moral Hazard in Agricultural Insurance Markets. Am. J. Agric. Econ. 1989 , 71 , 604–616. [ Google Scholar ] [ CrossRef ]
• Zheng, C.J.; Yu, G.X.; Zhao, X.H. Difference in the Endowment, Risk Preference and Insurance Decision-Making Behavior of Farmers—Field Survey Based on Farmers in Xinjiang Cotton Area. Rural. Econ. 2017 , 10 , 104–111. [ Google Scholar ]
• Xu, B.; Sun, R. Effects of Agricultural Insurance on Farmers’. TIF, + Production Behavior under the Background of Grain Security: Evidence from Grain Production Areas. Financ. Econ. 2016 , 06 , 97–111. [ Google Scholar ]
• Chen, Q. Advanced Econometrics and Stata Applications ; Higher Education Press: Beijing, China, 2010. [ Google Scholar ]
• Dai, S.R. Econometrics , 2nd ed.; China Agriculture Press: Beijing, China, 2009. [ Google Scholar ]
• Li, H.J.; Yuan, K.H.; Cao, A.; Zhao, X.M.; Guo, L.L. The role of crop insurance in reducing pesticide use: Evidence from rice farmers in China. J. Environ. Manag. 2022 , 306 , 114456. [ Google Scholar ] [ CrossRef ]
• Li, Q.; Lin, G.H.; He, J. A Correlation Study on Farmer′s Concurrent Business Behavior and Changes in Factors of Production: Analysis Based on a Survey of Farmers from Rural Fixed Obsewation Points. J. Nanjing Agric. Univ. Soc. Sci. Ed. 2013 , 13 , 27–32. [ Google Scholar ]
• Ning, M.X.; Xing, L.; Zhong, F.N. Factors Influencing Farmers’ Decisions to Purchase Crop Insurance: A Case Study on Manas Valley, Xinjiang. Probl. Agric. Econ. 2005 , 06 , 38–44. [ Google Scholar ]
• Hou, D.A.; Wang, X. Inhibition or Promotion?—The Effect of Agricultural Insurance on Agricultural Green Development. Front. Public Health 2022 , 10 , 910534. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ma, J.J.; Yang, C.; Cui, H.Y.; Wang, X. The environmental effect and formation mechanisms of the promotion of agricultural insurance—From the perspective of non-point source pollution of chemical fertilizers in China. Insur. Stud. 2021 , 09 , 46–61. [ Google Scholar ]

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Zhang, L.; Yang, Y.; Li, X. Research on the Relationship between Agricultural Insurance Participation and Chemical Input in Grain Production. Sustainability 2023 , 15 , 3045. https://doi.org/10.3390/su15043045

Zhang L, Yang Y, Li X. Research on the Relationship between Agricultural Insurance Participation and Chemical Input in Grain Production. Sustainability . 2023; 15(4):3045. https://doi.org/10.3390/su15043045

Zhang, Lu, Yuxin Yang, and Xiaofeng Li. 2023. "Research on the Relationship between Agricultural Insurance Participation and Chemical Input in Grain Production" Sustainability 15, no. 4: 3045. https://doi.org/10.3390/su15043045

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Annual Review of Economics

Volume 9, 2017, review article, agricultural insurance and economic development.

• Shawn A. Cole 1 , and Wentao Xiong 2
• View Affiliations Hide Affiliations Affiliations: 1 Finance Unit, Harvard Business School, Boston, Massachusetts 02163; email: [email protected] 2 Department of Economics, Harvard University, Cambridge, Massachusetts 02138; email: [email protected]
• Vol. 9:235-262 (Volume publication date August 2017) https://doi.org/10.1146/annurev-economics-080315-015225
• First published as a Review in Advance on May 01, 2017
• © Annual Reviews

This article provides a review of recent research on agricultural insurance (AI) in developing countries. Agricultural producers face a variety of significant risks; historically, only government-subsidized products have achieved widespread adoption. A recent contractual innovation, which links insurance payouts to realized weather rather than farmer indemnity, has spurred substantial research in the past decade. This review begins by describing the experience in developed economies and then turns to developing countries, covering the following topics: farmers' adoption of AI, how AI affects their decision to invest in risky assets, and the extent to which AI helps farmers smooth income and consumption. We conclude with suggestions for future research and practice related to AI in developing countries.

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Literature Cited

• Allen T , Atkin D . 2016 . Volatility, insurance, and gains from trade Unpublished manuscript, MIT Cambridge, MA: [Google Scholar]
• Angeletos G-M , Laibson D , Repetto A , Tobacman J , Weinberg S . 2001 . The hyperbolic consumption model: calibration, simulation, and empirical evaluation. J. Econ. Perspect. 15 : 3 47– 68 [Google Scholar]
• Atwood JA , Watts MJ , Baquet AE . 1996 . An examination of the effects of price supports and federal crop insurance upon the economic growth, capital structure, and financial survival of wheat growers in the northern High Plains. Am. J. Agric. Econ. 78 : 1 212– 24 [Google Scholar]
• Babcock BA , Hennessy DA . 1996 . Input demand under yield and revenue insurance. Am. J. Agric. Econ. 78 : 2 416– 27 [Google Scholar]
• Barnett BJ . 2004 . Agricultural index insurance products: strengths and limitations Presented at Agric. Outlook Forum, Feb. 19–20 Arlington, VA: [Google Scholar]
• Barnett BJ , Mahul O . 2007 . Weather index insurance for agriculture and rural areas in lower-income countries. Am. J. Agric. Econ. 89 : 5 1241– 47 [Google Scholar]
• Barseghyan L , Molinari F , O'Donoghue T , Teitelbaum JC . 2013 . The nature of risk preferences: evidence from insurance choices. Am. Econ. Rev. 103 : 6 2499– 529 [Google Scholar]
• Bekkerman A , Smith VH , Watts MJ . 2012 . The SURE program and incentives for crop insurance participation: a theoretical and empirical analysis. Agric. Finance Rev. 72 : 3 381– 401 [Google Scholar]
• Binswanger-Mkhize HP . 2012 . Is there too much hype about index-based agricultural insurance?. J. Dev. Stud. 48 : 2 187– 200 [Google Scholar]
• Bourgeon J-M , Chambers RG . 2003 . Optimal area yield insurance reconsidered. Am. J. Agric. Econ. 85 : 3 590– 604 [Google Scholar]
• Bozic M , Newton J , Thraen CS , Gould BW . 2014 . Tails curtailed: accounting for nonlinear dependence in pricing margin insurance for dairy farmers. Am. J. Agric. Econ. 96 : 4 1117– 35 [Google Scholar]
• Breustedt G , Bokusheva R , Heidelbach O . 2008 . Evaluating the potential of index insurance schemes to reduce crop yield risk in an arid region. J. Agric. Econ. 59 : 2 312– 28 [Google Scholar]
• Bryan G . 2014 . Ambiguity aversion decreases demand for partial insurance: evidence from African farmers Work. Pap., London School Econ London: [Google Scholar]
• Cai H , Chen Y , Fang H , Zhou L-A . 2015 . The effect of microinsurance on economic activities: evidence from a randomized field experiment. Rev. Econ. Stat. 97 : 2 287– 300 [Google Scholar]
• Cai J . 2015 . The impact of insurance provision on household production and financial decisions. Am. Econ. J. Econ. Policy 8 : 44– 88 [Google Scholar]
• Cai J , de Janvry A , Sadoulet E . 2015 . Social networks and the decision to insure. Am. Econ. J. Appl. Econ. 7 : 2 81– 108 [Google Scholar]
• Cai J , Song C . 2017 . Do disaster experience and knowledge affect insurance take-up decisions?. J. Dev. Econ. 124 : 83– 94 [Google Scholar]
• Carter M , Barrett C . 2006 . The economics of poverty traps and persistent poverty: an asset-based approach. J. Dev. Stud. 42 : 2 178– 99 [Google Scholar]
• Carter MR , de Janvry A , Sadoulet E , Sarris A . 2014 . Index-based weather insurance for developing countries: a review of evidence and a set of propositions for up-scaling Work. Pap., Fond. Études Rech. Dév. Int Clermont-Ferrand, Fr.: [Google Scholar]
• Casaburi L , Willis J . 2016 . Interlinking product and insurance markets: experimental evidence from contract farming in Kenya Work. Pap., Harvard Univ. Cambridge, MA: [Google Scholar]
• Chambers RG . 1989 . Insurability and moral hazard in agricultural insurance markets. Am. J. Agric. Econ. 71 : 3 604– 16 [Google Scholar]
• Clarke DJ , Mahul O , Rao KN , Verma N . 2012 . Weather based crop insurance in India Policy Res. Work. Pap. World Bank Washington, DC: [Google Scholar]
• Coate S , Ravallion M . 1993 . Reciprocity without commitment: characterization and performance of informal insurance arrangements. J. Dev. Econ. 40 : 1 1– 24 [Google Scholar]
• Coble KH , Barnett BJ . 2013 . Why do we subsidize crop insurance?. Am. J. Agric. Econ. 95 : 2 498– 504 [Google Scholar]
• Coble KH , Knight TO , Pope RD , Williams JR . 1997 . An expected indemnity approach to the measurement of moral hazard in crop insurance. Am. J. Agric. Econ. 79 : 1 216– 26 [Google Scholar]
• Cole S . 2015 . Overcoming barriers to microinsurance adoption: evidence from the field. Geneva Pap 2015 : 1– 21 [Google Scholar]
• Cole S , Giné X , Tobacman J , Topalova P , Townsend R , Vickery J . 2013 . Barriers to household risk management: evidence from India. Am. Econ. J. Appl. Econ. 5 : 1 104– 35 [Google Scholar]
• Cole S , Giné X , Vickery J . 2016 . How does risk management influence production decisions? Evidence from a field experiment. Rev. Financ. Stud. https://doi.org/10.1093/rfs/hhw080 [Crossref] [Google Scholar]
• Cole S , Healy A , Werker E . 2012 . Do voters demand responsive governments? Evidence from Indian disaster relief. J. Dev. Econ. 97 : 2 167– 81 [Google Scholar]
• Cole S , Kirwan B . 2009 . Between the corporation and the household: commodity prices, risk management, and agricultural production in the United States. Am. J. Agric. Econ. 91 : 5 1243– 49 [Google Scholar]
• Cole S , Stein D , Tobacman J . 2014 . Dynamics of demand for index insurance: evidence from a long-run field experiment. Am. Econ. Rev. 104 : 5 284– 90 [Google Scholar]
• Cole S , Tufano P . 2007 . BASIX Harvard Bus. School Case 207–099, Harvard Univ. Cambridge, MA: [Google Scholar]
• Cole S , Xu L . 2013 . China Life Harvard Bus. School Case 212–030, Harvard Univ. Cambridge, MA: [Google Scholar]
• Collins KJ . 2011 . The state of US livestock insurance NCIS Spec. Rep. 160538, Natl. Crop Ins. Serv. Overland Park, KS: [Google Scholar]
• Cui R . 2015 . A detailed introduction to “Swine Price Index Insurance.”. China Swine Information Feb. 6 [Google Scholar]
• Dercon S , Vargas Hill R , Clarke D , Outes-Leon I , Seyoum TA . 2014 . Offering rainfall insurance to informal insurance groups: evidence from a field experiment in Ethiopia. J. Dev. Econ. 106 : 132– 43 [Google Scholar]
• Dubois P , Jullien B , Magnac T . 2008 . Formal and informal risk sharing in LDCs: theory and empirical evidence. Econometrica 76 : 4 679– 725 [Google Scholar]
• Emerick K , de Janvry A , Sadoulet E , Dar MH . 2016 . Technological innovations, downside risk, and the modernization of agriculture. Am. Econ. Rev. 106 : 6 1537– 61 [Google Scholar]
• Garrido A , Zilberman D . 2008 . Revisiting the demand for agricultural insurance: the case of Spain. Agric. Finance Rev. 68 : 1 43– 66 [Google Scholar]
• Gaurav S , Cole S , Tobacman J . 2011 . Marketing complex financial products in emerging markets: evidence from rainfall insurance in India. J. Mark. Res. 48 : 150– 62 [Google Scholar]
• Gennaioli N , Shleifer A , Vishny R . 2012 . Neglected risks, financial innovation, and financial fragility. J. Financ. Econ. 104 : 3 452– 68 [Google Scholar]
• Giné X , Yang D . 2009 . Insurance, credit, and technology adoption: field experimental evidence from Malawi. J. Dev. Econ. 89 : 1 1– 11 [Google Scholar]
• Glauber JW . 2013 . The growth of the federal crop insurance program, 1990–2011. Am. J. Agric. Econ. 95 : 2 482– 88 [Google Scholar]
• Goodwin BK , Smith VH . 2003 . An ex post evaluation of the Conservation Reserve, federal crop insurance, and other government programs: program participation and soil erosion. J. Agric. Res. Econ. 28 : 2 201– 16 [Google Scholar]
• Goodwin BK , Smith VH . 2010 . Private and public roles in providing agricultural insurance in the United States. Public and Private Roles in Insurance JR Brown 173– 209 Washington, DC: AEI Press [Google Scholar]
• Goodwin BK , Smith VH . 2013 . What harm is done by subsidizing crop insurance?. Am. J. Agric. Econ. 95 : 2 489– 97 [Google Scholar]
• Goodwin BK , Vandeveer ML , Deal JL . 2004 . An empirical analysis of acreage effects of participation in the Federal Crop Insurance Program. Am. J. Agric. Econ. 86 : 4 1058– 77 [Google Scholar]
• Gramig BM , Horan RD , Wolf CA . 2009 . Livestock disease indemnity design when moral hazard is followed by adverse selection. Am. J. Agric. Econ. 91 : 3 627– 41 [Google Scholar]
• Hanouch M , Parker SR . 2013 . Microfinance and mobile banking: blurring the lines? Work. Pap., Consult. Group Assist Poor (CGAP) Washington, DC: [Google Scholar]
• Hazell PBR , Pomareda C , Valdés A . 1986 . Crop Insurance for Agricultural Development: Issues and Experience Baltimore, MD: Johns Hopkins Univ. Press [Google Scholar]
• Hess U . 2003 . Innovative financial services for India: monsoon-indexed lending and insurance for smallholders Agric. Rural Dev. Work. Pap. 9 World Bank Washington, DC: [Google Scholar]
• Holt MT , Aradhyula SV . 1998 . Endogenous risk in rational-expectations commodity models: a multivariate generalized ARCH-M approach. J. Empir. Finance 5 : 2 99– 129 [Google Scholar]
• Holt MT , Moschini G . 1992 . Alternative measures of risk in commodity supply models: an analysis of sow farrowing decisions in the United States. J. Agric. Resour. Econ. 17 : 1 1– 12 [Google Scholar]
• Horowitz JK , Lichtenberg E . 1993 . Insurance, moral hazard, and chemical use in agriculture. Am. J. Agric. Econ. 75 : 4 926– 35 [Google Scholar]
• Jack W , Suri T . 2014 . Risk sharing and transactions costs: evidence from Kenya's mobile money revolution. Am. Econ. Rev. 104 : 1 183– 223 [Google Scholar]
• Kahneman D , Tversky A . 1979 . Prospect theory: an analysis of decision under risk. Econometrica 47 : 2 263– 92 [Google Scholar]
• Karlan D , Osei R , Osei-Akoto I , Udry C . 2014 . Agricultural decisions after relaxing credit and risk constraints. Q. J. Econ. 129 : 2 597– 652 [Google Scholar]
• Karlan D , Zinman J . 2009 . Observing unobservables: identifying information asymmetries with a consumer credit field experiment. Econometrica 77 : 6 1993– 2008 [Google Scholar]
• Kerer J . 2013 . Background paper on the situation of agricultural insurance in Kenya with reference to international best practices Adapt. Clim. Change Insur. (ACCI) Rep Nairobi, Kenya: [Google Scholar]
• Knight TO , Coble KH . 1997 . Survey of US multiple peril crop insurance literature since 1980. Appl. Econ. Perspect. Policy 19 : 1 128– 56 [Google Scholar]
• Kramer RA . 1983 . Federal crop insurance 1938–1982. Agric. Hist. 57 : 2 181– 200 [Google Scholar]
• Lence SH . 1996 . Relaxing the assumptions of minimum-variance hedging. J. Agric. Resour. Econ. 21 : 1 39– 55 [Google Scholar]
• Li X . 2014 . The development of agricultural insurance in China Presented at CAS Reinsur. Semin., May 21–22 New York: [Google Scholar]
• Ligon E , Thomas JP , Worrall T . 2002 . Informal insurance arrangements with limited commitment: theory and evidence from village economies. Rev. Econ. Stud. 69 : 1 209– 44 [Google Scholar]
• Mahlase A . 2013 . Crop insurance in South Africa Presented at South Afr. Insur. Assoc. Conf., Feb. 22 [Google Scholar]
• Mahul O , Skees JR . 2007 . Managing agricultural risk at the country level: the case of index-based livestock insurance in Mongolia Policy Res. Work. Pap. 4325 World Bank Washington, DC: [Google Scholar]
• Mahul O , Stutley CJ . 2010 . Government Support to Agricultural Insurance: Challenges and Options for Developing Countries Washington, DC: World Bank [Google Scholar]
• Maynard N , Mehta P , Parker J , Steinberg J . 2012 . Can games build financial capability? Financial entertainment: a research overview Work. Pap. WR-963-SSA, Financ. Lit. Cent RAND Corp Santa Monica, CA: [Google Scholar]
• Miranda MJ . 1991 . Area-yield crop insurance reconsidered. Am. J. Agric. Econ. 73 : 2 233– 42 [Google Scholar]
• Miranda MJ , Farrin K . 2012 . Index insurance for developing countries. Appl. Econ. Perspect. Policy 34 : 3 391– 427 [Google Scholar]
• Miranda MJ , Glauber JW . 1993 . Estimation of dynamic nonlinear rational expectations models of primary commodity markets with private and government stockholding. Rev. Econ. Stat. 75 : 3 463– 70 [Google Scholar]
• Miranda MJ , Glauber JW . 1997 . Systemic risk, reinsurance, and the failure of crop insurance markets. Am. J. Agric. Econ. 79 : 1 206– 15 [Google Scholar]
• Miranda MJ , Gonzalez-Vega C . 2011 . Systemic risk, index insurance, and optimal management of agricultural loan portfolios in developing countries. Am. J. Agric. Econ. 93 : 2 399– 406 [Google Scholar]
• Mobarak AM , Rosenzweig MR . 2013a . Informal risk sharing, index insurance, and risk taking in developing countries. Am. Econ. Rev. 103 : 3 375– 80 [Google Scholar]
• Mobarak AM , Rosenzweig MR . 2013b . Selling formal insurance to the informally insured Work. Pap., Stanford Univ Stanford, CA: [Google Scholar]
• Mobarak AM , Rosenzweig MR . 2014 . Risk, insurance and wages in general equilibrium NBER Work. Pap. 19811 [Google Scholar]
• Moschini G , Hennessy DA . 2001 . Uncertainty, risk aversion, and risk management for agricultural producers. Handbook of Agricultural Economics 1 BL Gardner, GC Rausser 87– 153 Amsterdam: Elsevier [Google Scholar]
• OECD (Organ. Econ. Coop. Dev.). 2011 . Managing Risk in Agriculture: Policy Assessment and Design Paris: OECD [Google Scholar]
• Padmavathi V . 2013 . The lapsation of life insurance policies in India: causes and costs Ind. Rep., Chart. Insur. Inst. London: [Google Scholar]
• Quiggin JC , Karagiannis G , Stanton J . 1993 . Crop insurance and crop production: an empirical study of moral hazard and adverse selection. Aust. J. Agric. Resour. Econ. 37 : 2 95– 113 [Google Scholar]
• Raju SS , Chand R . 2008 . Agricultural insurance in India: problems and prospects Work. Pap. 8, Natl. Cent. Agric. Econ. Policy Res., Indian Counc. Agric. Res. New Delhi: [Google Scholar]
• Rosenzweig MR , Binswanger HP . 1993 . Wealth, weather risk and the composition and profitability of agricultural investments. Econ. J. 103 : 416 56– 78 [Google Scholar]
• Rosenzweig MR , Wolpin KI . 1993 . Credit market constraints, consumption smoothing, and the accumulation of durable production assets in low-income countries: investments in bullocks in India. J. Polit. Econ. 101 : 2 223– 44 [Google Scholar]
• Schoengold K , Ding Y , Headlee R . 2015 . The impact of ad hoc disaster and crop insurance programs on the use of risk-reducing conservation tillage practices. Am. J. Agric. Econ. 97 : 3 897– 919 [Google Scholar]
• Shields DA . 2013 . Federal crop insurance: background Congr. Res. Serv. Rep. R40532 Washington, DC: [Google Scholar]
• Sigurdson D , Sin R . 1994 . An aggregate analysis of Canadian crop insurance policy. Economics of Agricultural Crop Insurance: Theory and Evidence DL Hueth, WH Furtan 45– 72 Boston: Kluwer Acad. [Google Scholar]
• Singh R . 2013 . Agricultural livelihoods and crop insurance in India: situation analysis and assessment Dtsch. Ges. Int. Zsarb. (GIZ) Rep New Delhi: [Google Scholar]
• Smith VH . 2011 . Premium payments: why crop insurance costs too much Policy Pap., Am. Enterp. Inst Washington, DC: [Google Scholar]
• Smith VH , Glauber JW . 2012 . Agricultural insurance in developed countries: Where have we been and where are we going?. App. Econ. Perspect. Policy 34 : 3 363– 90 [Google Scholar]
• Smith VH , Goodwin BK . 1996 . Crop insurance, moral hazard, and agricultural chemical use. Am. J. Agric. Econ. 78 : 2 428– 38 [Google Scholar]
• Smith VH , Watts MA . 2009 . Index based agricultural insurance in developing countries: feasibility, scalability and sustainability Bill Melinda Gates Found. Rep. Seattle, WA: [Google Scholar]
• Thaler RH , Sunstein CR . 2008 . Nudge New Haven, CT: Yale Univ. Press [Google Scholar]
• Townsend RM . 1994 . Risk and insurance in village India. Econometrica 62 : 3 539– 91 [Google Scholar]
• UNCTAD (UN Conf. Trade Dev.). 1994 . Agricultural insurance in developing countries UNCTAD Secr. Rep. UNCTAD/SDD/INS/1/Rev 1, Geneva Switz.: [Google Scholar]
• USGAO (US Gov. Account. Off.). 1999 . Agriculture in transition: farmers’ use of risk management strategies USGAO Rep. RCED-99-90 Washington, DC: [Google Scholar]
• USGAO (US Gov. Account. Off.). 2015 . Crop insurance: In areas with higher crop production risks, costs are greater, and premiums may not cover expected losses USGAO Rep. GAO-15–215 Washington, DC: [Google Scholar]
• Vercammen J , Van Kooten GC . 1994 . Moral hazard cycles in individual coverage crop insurance. Am. J. Agric. Econ. 41 : 2 250– 61 [Google Scholar]
• Wang J . 2015 . Shanghai meteorological index insurance won “Financial Innovation” award. China Meteorological Administration Feb. 15 [Google Scholar]
• World Bank. 2007 . China—Innovations in Agricultural Insurance Washington, DC: World Bank [Google Scholar]
• World Bank. 2010 . Agricultural Insurance in Latin America: Developing the Market Washington, DC: World Bank [Google Scholar]
• World Bank. 2011 . Enhancing Crop Insurance in India Washington, DC: World Bank [Google Scholar]
• Wright BD , Hewitt JD . 1994 . All risk crop insurance: lessons from theory and experience. Economics of Agricultural Crop Insurance: Theory and Evidence DL Hueth, WH Furtan 73– 112 Boston: Kluwer Acad. [Google Scholar]
• Xue B , Qiao X . 2014 . China has become the world's second largest agricultural insurance market. People's News March 11 [Google Scholar]
• Yamauchi T . 1986 . Evolution of the crop insurance program in Japan. Crop Insurance for Agricultural Development: Issues and Experience PBR Hazell, C Pomareda, A Valdés 223– 39 Baltimore, MD: Johns Hopkins Univ. Press [Google Scholar]
• Yao H . 2015 . China's first milk price index insurance launched in Tai'an. Sino Insurance June 5 [Google Scholar]
• Ye Q , Jiang X . 2014 . Agro-meteorological index insurance pilot programs in Anhui Province. People's Daily March 19 [Google Scholar]
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Gender-Inclusive, -Responsive and -Transformative Agricultural Insurance: A Literature Review

• Women's empowerment
• Eastern Africa
• Sub-Saharan Africa

Timu A, Kramer B. 2021. Gender-Inclusive, -Responsive and -Transformative Agricultural Insurance: A Literature Review. CCAFS Working paper no.417. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).

This literature review uses a gender analysis framework proposed by Johnson et al. (2018) to explore the extent to which agricultural insurance reaches, benefits and empowers women and men. We find that most studies on gender and agricultural insurance focus on gender inclusivity by analyzing gender gaps in insurance reach and studying how to increase take-up among women. By contrast, limited attention has been paid to understanding gender equity in the distribution of insurance outcomes, that is, the extent to which insurance benefits and empowers women as much as men. We show that insurance programs can promote gender equity in benefits by providing quality insurance products that are beneficial to both men and women, and through long-term monitoring of individual outcomes measured within households using gender-disaggregated data. Insurance programs can support gender empowerment by ensuring that contracts purchased by women are registered under their names and payouts are subsequently paid to their accounts, by bundling insurance with empowerment programs, and by preserving and promoting informal mutual assistance group activities and membership. We then draw on a case study in Kenya to illustrate how this framework can be applied to design more gender-inclusive, -responsive and -transformative insurance schemes.

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The impact of agricultural insurance on farmers’ income: Guangdong Province (China) as an example

Miraj ahmed bhuiyan.

1 School of Economics, Guangdong University of Finance & Economics, Guangzhou, China

Mkhitaryan Davit

2 School of Business, Fuzhou Melbourne Polytechnic, MinJiang University, Shangjie District, Fuzhou, Fujian Province, China

Zeng XinBin

Zhang zurong, associated data.

All relevant data are within the paper.

This paper aims to test whether agricultural insurance significantly impacts farmers’ income increase or not. We have used the ordinary least squares method (OLS), panel fixed effects, and system generalized moment estimation (GMM) for the test. The results show that the increase in agricultural insurance density and the increase in agricultural insurance per capita compensation positively impact farmers’ income growth significantly. Agricultural insurance density and per capita compensation are the indicators used in this article to measure agricultural insurance development. Therefore, it can be considered that the development of agricultural insurance in Guangdong Province (China) can effectively increase the income level of farmers. Based on the results of theoretical and empirical analysis, combined with the current situation of agricultural insurance development in Guangdong Province, this paper finally puts forward relevant countermeasures and suggestions. It provides some ideas for giving full play to the role of agricultural insurance in promoting farmers’ income from the perspectives of pertinent system design, subsidy methods, insurance innovation, service level, and publicity.

1. Introduction

Agriculture is the basic industry of China’s national economy, and it plays an irreplaceable strategic role in the process of China’s take-off from a small agricultural country to economic power. Guangdong Province is at the forefront of reform and opening up in the country. The province’s economic development is among the best in the country, with developed industries and high levels of urbanization, but there is also the problem of uneven development within the region. As far as the province is concerned, the three agricultural issues have not been fully resolved. As agriculture plays a fundamental role in developing secondary and tertiary industries in the fast-growing Guangdong Province, the issue of agricultural development cannot be ignored.

Guangdong Province is located in the southeast coastal area. Bai [ 1 ] pointed out that Guangdong is a typical "climate fragile area" with solid convective weather such as typhoons and heavy rains in summer, freezing disasters and droughts in winter mountains, and the frequency of natural disasters. And the intensity is at the forefront of the country, causing a severe impact on agricultural production. Agricultural insurance is an effective risk transfer tool, which is significant for improving the resilience of the farming output, reducing the losses caused by natural disasters to agrarian production, and protecting farmers’ overall income. It is also significant in transferring agricultural risks, economic compensation, and financing.

Several impacts have been taken into measure, the most critical of which is reducing farmers’ income volatility. Although agricultural insurance plays a certain role in promoting agricultural development and stabilizing farmers’ income, what impact will it have on farmers’ income growth? What is the mechanism and extent of the impact? How the government and insurance companies should play the role of agricultural insurance is still unclear. More scholars are paying attention to the research on economic development in Guangdong Province. Research on the relationship between farmers’ income is even rarer. This article considers the contemporary theme of agricultural development and the importance of agricultural insurance in solving the three rural issues, combined with the current academic research results and gaps, and determines the research theme.

Agricultural insurance in Guangdong Province has been trial-run since 1985 and has undergone a very long development process. From 1985 to 1989, Guangdong Province began to explore the development of agricultural insurance, which was initially held in the form of Chinese private insurance companies. The average annual premium of agricultural insurance in Guangdong Province during the five-year period was about 4.2 million yuan, and the comprehensive loss rate reached 95%, which was challenging to operate. As of 2019, Guangdong Agricultural Insurance has provided 78.2 billion yuan of risk protection for agricultural production, and the number of provincial-level insured varieties has reached 23. with the scale of agricultural insurance premiums reaching 6.222 billion yuan, the depth of agricultural insurance reaching more than 1.2%, and the density of agricultural insurance reaching 500 yuan per person. The income of agricultural insurance premiums has increased exponentially, from 53 million in 2007 to 1.882 billion yuan in 2019, indicating that the development of agricultural insurance under financial subsidies has achieved excellent results. It can be found that agricultural insurance premium income varies greatly from municipality to municipality.

In the Pearl River Delta region, except For Guangzhou, agricultural insurance premium income is low. In Shaoguan, Meizhou, Qingyuan, Zhanjiang, Maoming, and other cities, agricultural insurance premium income is higher, reaching 100 million to 300 million yuan. These five cities are rich in agricultural resources, creating space for the development of agricultural insurance. The agricultural insurance premium income of Jiangmen, Yangjiang, Zhaoqing, Heyuan, and Yunfu is in the second echelon, and the premium scale is about 60 million to 8000 yuan. In addition, the development of agricultural insurance in the remaining Foshan, Zhongshan, Dongguan, Chaozhou, Jieyang, and other areas is relatively backward.

At present, many scholars have paid attention to the problems of agricultural insurance and agricultural production and have obtained specific research results, which provide some ideas and enlightenment for the research of this article. At the same time, the research of this article also has specific innovations. From the perspective of research methods, most of the research results of predecessors used time-series data for research and mainly used static panel model analysis due to the lack of consideration of autocorrelation issues. This paper selects the panel data of 20 cities in Guangdong Province from 2010 to 2019, constructs a panel model of fixed effects and system generalized moments, and corrects the estimation bias caused by the endogenous information problem of the lag term of the explained variable. In this paper, the threshold effect model used by Shi [ 2 ] is adjusted in combination with Zhou’s [ 3 ] method, which enriches the threshold to a certain extent. The research idea of the effect also supplements the research on agricultural insurance at the provincial level in Guangdong Province. From the perspective of research, most of the previous studies are based on the national or regional level, but few studies consider this issue from the standpoint of Guangdong Province. The research in this article has achieved high quality for Guangdong agricultural insurance to a certain extent.

This article provides a theoretical basis for effectively playing the role of agricultural insurance. Many scholars in China and abroad have paid attention to agricultural insurance issues, mainly focusing on the nature of agrarian insurance, influencing demand factors, impact on farm output and income of farmers, etc. But, research on the impact of Guangdong agricultural insurance on farmers’ income is temporarily limited. Based on previous studies, this paper uses the agricultural insurance data of 20 cities in Guangdong Province in the past ten years as the research basis and uses empirical research methods to test the impact of agricultural insurance on farmers’ income. It enriches the research ideas of agricultural insurance issues at the provincial level to a certain extent.

This paper is constructed as follows: the first section is the introduction, which includes the research gap, research significance, and key research questions. The next part is a literature review where we have added related previous studies from Chinese and international scholars. Chapter three discusses the research methodology. Chapter four presents the empirical analysis, and the next chapter explains the results of the investigation. In the last chapter in the conclusion, we have added the study’s summary, limitations, and future scope.

2. Literature review

2.1. research on the influencing factors of agricultural insurance demand.

Scholars have researched the influencing factors of farmers’ demand for agricultural insurance. They mainly believe that the demand for agricultural insurance is not only affected by farmers’ income. Abraham et al. [ 4 ] used a three-stage sampling procedure to select 120 rural households in their research. A questionnaire survey concluded that age, education level, and agricultural income could influence farmers’ willingness to participate in agricultural insurance. Moschini and Hennessy [ 5 ] believe that farmers’ risk preferences will affect whether they participate in agricultural insurance, and farmers with high-risk tolerance tend to bear themselves, but risk-averse people may not use agricultural insurance to transfer risks. King & Singh [ 6 ] found that insurance demand is replaced by access to private transfers. However, participation in a farmer’s union contributes to understanding why farmers value index insurance. Coble et al. [ 7 ] proposed that it is usually a single economic factor that affects farmers’ participation in agricultural insurance and includes farmers’ risk awareness and crop risk status. The study by Sujarwo et al. [ 8 ] proposed that the scale of agriculture, the experience of purchasing agricultural insurance, and even the willingness of farmers’ group meetings will impact farmers’ willingness to accept agrarian insurance. In addition, Age, female gender, and prior insurance experiences all appear to favor participation in the insurance policy [ 9 ].

2.2. Research on the impact of agricultural insurance on farmers’ income

Scholars’ views are divided into two major sides in studying the impact of agricultural insurance on farmers’ income. Some believe that agricultural insurance positively affects agricultural output and farmers’ income, and others hold the opposite view. As early as the 1980s, Yamauchi [ 10 ] used farmers who purchased rice insurance in Aomori Prefecture, Japan, as the research object. He found that compulsory agricultural insurance helped stabilize farmers’ income, especially in severe disasters. Xavier et al. [ 11 ] studied farmers who purchased storm insurance in southern India and found that agricultural insurance effectively increased the income of the local farmers. Hosseini & Gholizadeh [ 12 ] and Enjolras [ 13 ] found that agrarian insurance can positively reduce farmers’ income volatility and increase farmers’ income. Scholars such as Leatham [ 14 ] conducted field investigations on the development of agricultural insurance in North Dakota, the United States, and concluded that for every dollar of agrarian insurance compensation farmers receive, their final income would increase by $1.03. Barry [ 15 ] concluded through statistics that farmers’ income in the years exposed to agricultural risks exceeds more than half of the normal production years, which illustrates the positive impact of agricultural insurance on farmers’ income. Babcock and Hart [ 16 ], Glauber et al. [ 17 ], in their research results, all believe that although agricultural insurance increases agricultural output, it will shift the supply curve to the right, and thus the price of agricultural products will fall, but it will not necessarily increase farmers’ income in the end. Through statistical data testing, Robert et al. [ 18 ] found that the impact of agricultural insurance on farmers’ income is not necessarily significant, and even in some years, the two have a reverse relationship.

2.3. Research on the nature of agricultural insurance

For the research on the nature of agricultural insurance, many scholars believe that agricultural insurance has the attribute of public goods. For example, in Tuo and Wang’s [ 19 ] study, agricultural insurance has both the attributes of private goods and public products, a quasi-public product. Feng & Su [ 20 ] also believe that agricultural insurance is not a personal good; it has apparent externalities. Zhang [ 21 ] proposed that the failure of the agricultural insurance market is precisely due to its positive externalities. Zhang and Chen [ 22 ] proposed that agricultural insurance should be carried out as a government’s beneficial agricultural project rather than a purely commercial operation. Zhang [ 23 ] further proposed that the government should adopt diversified subsidy methods to support the healthy development of agricultural insurance. Liu and Sun [ 24 ] also believe that implementing premium subsidies can further promote farmers’ willingness to participate in agricultural insurance.

2.4. Research on the impact of agricultural insurance on agricultural output

Many scholars have researched agricultural insurance and agricultural output. Most scholars believe there is a significant positive correlation between agricultural insurance and agricultural output. Akinrinola & Okunola [ 25 ] evaluated the success of the Nigerian Agricultural Insurance Scheme’s goals in Ondo State. The study demonstrates that the farmers’ participation in the insurance program was solely motivated by their ability to get financing. On the other hand, the farmers claimed that more investments had led to higher gains in output. Scholars such as Feng [ 26 ] and Fei [ 27 ] believe that agricultural insurance can promote agrarian output to a certain extent. Zhou & Zhao [ 28 ] and Wang [ 29 ] used a dynamic panel model to conduct empirical analysis and concluded that agricultural insurance has largely promoted agricultural production. Scholars such as Huang & Pu [ 30 ], Cheng et al. [ 31 ], and Jiang & Zhang [ 32 ] also believe that agricultural insurance can increase agricultural output.

In contrast, some scholars do not believe there is a strong relationship between these two. For example, Zhang et al. [ 33 ] assume that under the condition that the level and proportion of agricultural insurance subsidies are low, the total production of agricultural products will not significantly change. Hu [ 34 ] analyzed the impact of agricultural insurance on agricultural production capacity by hypothesis testing, and the results showed that the impact is almost non-existent, and there is no significant correlation between agricultural insurance and food production.

2.5. Research on the direction and path of agricultural insurance’s impact on farmers’ income

Some scholars have researched the issue of agricultural insurance on farmers’ income. Jiang [ 35 ] believes that agricultural insurance under financial subsidies significantly affects farmers’ income. Yuan et al. [ 36 ], Sun & Chen [ 37 ] analyzed based on the data of Jilin Province and found that agricultural insurance also promoted the income growth of local farmers to a certain extent. Zhang & Sun [ 38 ] used panel data from 31 provinces across the country to perform a cluster analysis and found that agricultural insurance played a certain role in promoting the growth of farmers’ income from a national perspective. However, other scholars believe that the impact of agricultural insurance on farmers’ income is not necessarily noticeable. For example, through cluster analysis, Yang and Shi [ 39 ] found that china’s agricultural insurance did not significantly increase farmers’ income. Hou et al. [ 40 ] also pointed out that agricultural insurance plays a small role in promoting farmers’ income growth. Zhu & Tao [ 41 ] tested the impact of agricultural insurance on farmers’ income through panel data and found that agricultural insurance not only does not Promote the increase of farmers’ income but also has a significant negative effect.

Scholars have different opinions regarding agricultural insurance’s impact on farmers’ income. Zhou et al. [ 42 ] believe that agricultural insurance can protect farmers’ income, but this protective effect only appears in post-disaster compensation. Zhang & Sun [ 38 ] used cluster analysis to divide 31 provinces into six regions and used the Hausman test method and generalized least squares (GLS) estimation method to conduct empirical research and found that agricultural insurance can significantly increase farmers’ operating income. According to them, the effect of agricultural insurance on financial subsidies is more prominent. Fei et al. [ 43 ] believe that agricultural insurance reduces the fluctuation of farmers’ income through the payment of indemnities and the promotion of agricultural technology by insurance companies. Lu et al. [ 44 ] stated that agricultural insurance is carried out through financial subsidies in the form of transfer payments to increase farmers’ income, and there are obvious differences in the internal mechanisms of farmers’ income increase in eastern and western China.

3. Research methodology

This article analyzes many Chinese and foreign agricultural insurance documents on agricultural production and farmers’ income and documents on the development of agricultural insurance in Guangdong Province. It sorts out the mechanism and path of agricultural insurance’s impact on farmers’ income and further analyzes the impact of agricultural insurance on farmers’ income. At the same time, it also analyzes other related factors affecting farmers’ income, which provides a certain basis for the selection of control variables in the empirical analysis of this article. In addition, we have considered related theories, such as expected utility theory, welfare economics affect approach, and non-Walrasian equilibrium theory, to explore their application in agricultural insurance and provide a foundation for a thorough understanding of the nature of agricultural insurance. That helped us for improving the theoretical level of this article.

Considering the availability and completeness of the data, the per capita disposable income of farmers reflecting the income level of farmers is selected as the explanatory variable. The relevant indicators of the development level of agricultural insurance are used as the explanatory variables. The urbanization rate, mechanization level, industrial structure, and agricultural investment level are added as control variables. The data studied in this paper are all from the China Insurance Yearbook from 2011 to 2020, the Guangdong Statistical Yearbook from 2009 to 2020, the Guangdong Rural Statistics Yearbook, the China Rural Research Database, and the Chinese Rural Research Database.

The empirical analysis is an important research method for this article. After referring to the practice of Zhou (2018) and other scholars, this article uses ordinary least squares, fixed effects, and system generalized moment estimation methods to analyze whether agricultural insurance impacts farmers’ income. On this basis, referring to Shi [ 2 ] and Li [ 45 ], a panel threshold model was established to test the characteristics of the impact of agricultural insurance on farmers’ income. First, by collecting and sorting out the relevant data of 20 cities in Guangdong Province (except Shenzhen) from 2009 to 2019, establish a static panel model, use Stata 15 software to operate, and compare the results obtained with the estimated results of the dynamic panel model. Next stage, we analyzed the test results of the system GMM that considers the endogenous problem. Subsequently, a panel threshold model was established to test whether there is a threshold value for agricultural insurance density and per capita compensation. Finally, an objective, standardized, and rigorous empirical analysis conclusion can be drawn to test whether the hypothesis in this article is correct, and this article is summarized research conclusions accordingly.

A statistical income probability distribution method is adopted to explore further the role of agricultural policy insurance in guaranteeing farmers’ income. After analyzing, we have made the following four hypotheses:

(1) The risk hazards faced in the agricultural production process are lucid; the hazards either occur or do not occur. The probability of occurrence is set to P, and the likelihood of non-occurrence is 1-P. And 0<P≤1.

(2) The income of farmers in production and operation obeys the binomial distribution: either no loss occurs, and the income is Y at this time, or there is a loss, and the loss causes the current production and operation income to be 0.

(3) Assuming that farmers’ proficiency in production technology, crop quality, and other factors are consistent, there are two ways for farmers to avoid production risks: participating in agricultural insurance (M) and not participating in agricultural insurance (N).

(4) assuming that the premium of agricultural insurance is B. The government subsidy ratio for agricultural insurance is L. When the loss does not occur, the farmer’s income is Y. Otherwise, it is 0, but at this time, the actual income obtained by the farmer who purchases agricultural insurance is A, 0<A≤Y.

Therefore, the income probability distributions of farmers who purchase policy-based agricultural insurance and those who do not purchase policy-based agricultural insurance are obtained when risks occur and when risks do not occur, as follows ( Table 1 ):

From Table 1 , it can be concluded that the expected benefits of farmers who purchase agricultural insurance and those who do not purchase agricultural insurance are:

Let ϕ = PA−B, where PA is the insurance compensation farmers who purchase agricultural insurance expect to receive. If the amount is equal to the premium B paid when buying agricultural insurance, the farmers believe that there is no need to participate in the insurance, so the enthusiasm for buying agricultural insurance is not high. However, since most of the existing agricultural insurance in Guangdong Province is policy-based, the government subsidizes farmers’ premiums relatively. Therefore, the premiums paid by farmers themselves must be lower than the expected indemnity PA, ϕ = PA− The existence of B = PA−(1−L)B>0 means that farmers’ participation in agricultural insurance can increase their expected income. Therefore, theoretically, agricultural insurance can increase farmers’ expected income.

To study the impact of agricultural insurance on farmers’ income, we must first sort out the mechanism of agricultural insurance’s effect on farmers’ income. The impact of agricultural insurance on farmers’ income is complex to a certain extent. After sorting out and thinking about the previous research results, this paper believes that the effect of agricultural insurance on farmers’ income is mainly transmitted through direct and indirect paths. For reference, Zhou [ 46 ], Wang [ 47 ], and Li [ 48 ] put forward the idea which summarizes the impact of agricultural insurance on farmers’ income into direct and indirect mechanisms, as shown in Fig 1 .

Source: authors’ elaboration.

This paper studies the impact of agricultural insurance in Guangdong Province on farmers’ income. Considering the availability and completeness of the data, the per capita disposable income of farmers, which reflects the income level of farmers, is selected as the explanatory variable, and indicators related to the level of agricultural insurance development are used as the explanatory variable. Then add urbanization rate, mechanization level, industrial structure, agricultural investment level, etc., as control variables. The data studied in this article are from the "China Insurance Yearbook" from 2011 to 2020, the "Guangdong Statistical Yearbook" and "Guangdong Rural Statistical Yearbook" from 2009 to 2020, the China Rural Research Database and AREMOS China Agricultural Statistics Database collects and sorts out the required data indicators.

4. An empirical analysis of the effect of Guangdong agricultural insurance on farmers’ income increase

This article uses agricultural insurance density (ind), Per capita income of farmers (y), and per capita compensation expenditure (ex) to express the development level of agricultural insurance, which are measured by agricultural insurance premium income/rural population and agricultural insurance indemnity expenditure/rural population, respectively. Agricultural insurance density refers to farmers’ expenditure in a certain area to transfer risks during the production process, that is, the average insurance premium paid by farmers, which can reflect the level of agricultural insurance development in a region. The greater the agricultural insurance density, the greater the level of agricultural insurance development in the region. The higher the value, the more obvious the role of agricultural insurance in protecting farmers’ income. Per capita indemnity expenditure refers to the insurance indemnity compensation received by farmers due to disasters—the post-disaster effect of agricultural insurance to help farmers resume reproduction and stabilize farmers’ income. Generally speaking, the larger the value, the higher the development of agricultural insurance. However, when the insurance compensation expenditure is large, it also means that there are many risk accidents and the farmers suffer a lot. Therefore, from the perspective of theoretical analysis, the direction of this indicator’s impact on farmers’ income levels cannot be determined.

To study the impact of agricultural insurance on farmers’ income and consider the core variables, to be rigorous in the empirical analysis, it is also necessary to consider other factors affecting farmers’ income. Based on the existing research results of the predecessors and considering the actual situation that affects farmers’ income, this paper selects the other five control variables, which are as follows: (1) The level of urbanization (urb) . Based on the particularity of China’s urban-rural dual structure, although Guangdong Province has a highly developed economy, there is still a gap between urban and rural areas. Urbanization is an inevitable process of local social development. Wang [ 49 ] found that the level of urbanization is related to farmers’ income. Wang’s [ 50 ] research directly proposed that the urbanization rate can effectively increase farmers’ income. (2) The level of agricultural mechanization (mec) . The level of agricultural mechanization refers to the proportion of machinery and equipment used in agricultural production in the total workload. Traditional production methods require substantial labor costs, while advanced production technology can save agricultural production costs, improve agricultural production efficiency, and increase farmers’ income to a certain extent. (3) Industrial structure (ins) . In economic accounting, the gross product value of a country or a region is mainly composed of the output value of the primary, secondary, and tertiary industries, and the industrial structure refers to the proportion of each industry’s three major industries. (4) Agricultural investment level (inv) . The level of agricultural investment represents the degree of importance the government and social capital attach to agricultural production. The more significant the value, the more fixed assets are used in agricultural production, including modern machinery and equipment, high-quality seeds, fertilizers, etc., which can positively promote agricultural output. (5) The per capita planting area of crops (area) . Taking this indicator as one of the control variables, it is mainly considered that the planting area of crops is one of the important factors affecting agricultural output. Zhou [ 46 ] believes that under certain production technologies, the larger the per capita planting area of crops, the greater the value of agricultural output.

4.1. Model construction

The time selected for the empirical analysis data in this paper is the relevant data of each city (except Shenzhen) in Guangdong Province from 2010 to 2019. The specific representation methods and data characteristics are shown in Table 2 , and descriptive statistics for each variable are in Table 3 .

Source: authors’ elaboration

Source: authors’ calculation

This paper refers to the modified C-D production function of Zhou et al. [ 42 ] and Clarke et al. [ 51 ]. The following static panel measurement model is established, initially using ordinary least square & Two methods of multiplication and panel fixed effects are used for estimation.

Among them, C is a constant term, and the value of i is 1–20, which means that in the 20 prefectures and cities in Guangdong Province except for Shenzhen, the value of t is 1–10, which means 2010–2019. ind i t , ex i t , ins i t , area i t , mec i t , urb i t , inv i t respectively refer to the agricultural insurance density, agricultural insurance per capita planting area of agricultural insurance, percentage of mechanized agricultural investment per capita, percentage of agricultural investment in the first industry, the proportion of agricultural investment per capita, and the proportion of agricultural investment per capita. Data in year t in the i-th city. Considering that the static panel model ignores the endogenous problem of the lag term of the explained variable, the estimation result may be biased.

Therefore, the dynamic panel model is further established, and the specific expression form is as follows:

Among them, L.Y refers to the per capita income of farmers in the ith city in year t-1, that is, the per capita income of farmers in year t

The above model tests the linear relationship between agricultural insurance and farmers’ income, but it fails to consider the non-linear relationship. To further explore the impact of agricultural insurance on farmers’ income, this article adds a threshold effect model to examine whether the impact of agricultural insurance’s pre-disaster and post-disaster effects on farmers’ income has a threshold effect and refers to the practice of Lin [ 52 ]. Set as a single threshold effect, as follows:

α1 and α2 are coefficients, and γ1 is the threshold value. The above two models respectively study whether the impact of agricultural insurance density and agricultural insurance per capita compensation on the per capita income of rural residents shows significant differences within different threshold intervals to study further the characteristics of agricultural insurance’s impact on farmers’ income.

4.2. Panel data unit root test

Before processing panel data containing time series, to avoid using non-stationary series data for regression, the phenomenon of "pseudo-regression" usually appears the stationarity of the data must be tested first. This article’s type of panel data is similar to that of Zhou et al. [ 42 ]. Therefore, this article refers to the practice of previous scholars and uses the LLC unit root test method to verify whether the panel data is stable. The null hypothesis of the LLC test is that there is a common unit root. The test results are shown in Table 4 . The P-value of each variable test is less than 0.01. The null hypothesis is rejected at the 1% significance level, indicating that all variables do not contain unit roots. Therefore, the next step of regression analysis can be performed on this variable data set, and no "pseudo-regression" problem indicates that the null hypothesis is rejected at the 1% significance level.

*** indicates rejection of the null hypothesis p<0.01. source: authors’ calculation

5. Empirical test results

5.1. direct mechanism.

(1) and (2) in Table 5 are the estimation results using the ordinary least squares method and panel fixed effect. The former ignores the individual differences among the 20 cities in Guangdong Province, while the panel fixed effect improves this problem. The result is better than the former. However, neither of the above two static panel models can account for the lagging items of farmers’ income, which will cause a large gap between the regression results and reality. Therefore, this paper also uses the System GMM (System GMM) method to estimate. This method considers the individual differences between each city’s samples and avoids the endogenous problem caused by the autocorrelation of the farmers’ income lag. From the P value of AR(2) and the P value of Sargan’s test, we can see that the system GMM model does not have second-order autocorrelation, nor does it have the problem of over-recognition and the estimated result is relatively reliable.

Note: (1) The t statistic is reported in parentheses

*** p<0.01

** p<0.05

* p<0.1

(2) AR(2) means that the residual after the first-order difference is doubled. The P-value obtained by the first-order serial correlation test, when P>0.05, indicates that there is no second-order serial correlation problem; (3) Sargan’s P-value is used to test whether there is an over-identification problem. When P>0.05, it indicates that there is no over-identification problem. Source: authors elaboration

Judging from the test results of the three models, the agricultural insurance density is positively correlated with farmers’ income to different significant degrees. According to the above analysis, the system generalized moment estimation results are considered to be better than the other two methods. Therefore, the following analysis will be based on the test results of this method. From the results in Table 5 , it can be seen that the lag term (LY) of farmers’ income is significantly positively correlated with farmers’ per capita income (Y) at the level of 1%, indicating that the previous period’s per capita income of farmers will positively affect the current period’s income. Per capita income of farmers. At the same time, the current agricultural insurance density also has a significant positive impact on farmers’ income. The estimated coefficient is 0.017, which means that when the agricultural insurance density increases by 1 unit, farmers’ income can increase by 0.017 units.

Although farmers may experience a decrease in income in the short term after paying premiums, in the long run, this conclusion is consistent with the operating conditions of agricultural insurance. The density of agricultural insurance represents agricultural insurance coverage in a certain area, and an increase in the density of agricultural insurance represents more farmers in the area. Under normal circumstances, insurance companies will provide professional disaster prevention and loss prevention services for participating farmers, including training, donations of materials, etc., to improve farmers’ ability to prevent risks, thereby reducing the probability of risks caused by human factors. When risks occur, the degree of loss of farmers’ income can also be reduced through mitigation work, and this positive effect will be reflected as the insurance company’s underwriting experience and service level improve. Therefore, the increase in agricultural insurance density will positively impact farmers’ income in the long run. Secondly, after participating in agricultural insurance, farmers can be more daring to try new technologies in the production process, thereby increasing the efficiency of agricultural output and helping farmers increase their income. Schultz [ 53 ] proposed that the popularization of agricultural insurance can change the risk appetite of farmers to a certain extent, is conducive to the promotion of advanced agricultural technology, and helps to promote farmers’ income.

Furthermore, using agricultural insurance can encourage farmers to expand their production scale. Cai et al. [ 54 ] took a live pig and reproductive sow insurance as examples. Both found that farmers who purchased live pigs and reproductive sow insurance will further expand the production scale, thereby significantly increasing the value of output and boosting income. Finally, most of the agricultural insurance currently on the market enjoys financial subsidies from the central or local governments. After the effect of financial support for agriculture and farmers is reflected through the role of agricultural insurance, it can promote the development of rural finance and economy in the region, and increasing Farmers’ income also has a beneficial effect.

Therefore, it is reasonable to believe that increasing agricultural insurance density can significantly increase farmers’ income, and the previous hypothesis is valid.

5.2. Indirect mechanism

To test the impact of the indirect mechanism of agricultural insurance on farmers’ income, we continue to use farmers’ income (Y) as the explained variable and agricultural per capita compensation as the core explanatory variable. Other control variables remain unchanged. The test results are listed in Table 6 . The test result also considers the impact of the farmers’ income lag. At the same time, the P values of AR(2) and Sargan tests are greater than 0.05, indicating that there are no second-order series correlation and over-identification problems.

Note: Ex in the table represents the per capita indemnity of agricultural insurance, and the meanings of other items are the same as those in Table 5 . Source: authors’ calculation

The results show that agricultural insurance’s per capita compensation expenditure (ex) positively correlates with farmers’ income at a significant level of 1%. The estimated coefficient is 0.035, which means that when the per capita compensation for agricultural insurance increases by one unit, the per capita income of farmers can increase by 0.035 units. First, when a disaster occurs in agricultural production and operation and causes losses, insurance companies can reduce the loss of farmers by paying insurance indemnities and allowing farmers to have the funds to continue production and quickly resume reproduction. Secondly, due to the different subsidy ratios and protection levels of different agricultural insurance types, farmers may adjust the agricultural production structure based on the previous agricultural losses and are more inclined to choose crops with high-risk protection and large subsidy ratios to optimize production structure, helping stabilize income. Furthermore, for areas where risks frequently occur, on the one hand, insurance companies will adjust premiums and underwriting conditions accordingly to improve risk management; on the other hand, farmers will not only increase risk prevention awareness after receiving compensation from risks, Can also further realize the vital role of agricultural insurance, thereby increasing the insurance rate to ensure the stability of agricultural production and operation. Finally, because of the economic compensation function of agricultural insurance, it can guarantee crops and provide a suitable environment for promoting the development of rural finance. At present, emerging financial business models such as policy credit enhancement and policy mortgage loans are being promoted, which supports the economic development of rural areas and thereby encourages the increase of farmers’ income.

In addition, the test results found that the industrial structure (ins), urbanization rate (urb), and per capita planting area of crops significantly impact farmers’ income. The proportion of the agricultural industry structure is positively correlated with farmers’ income. Even though the proportion of the primary industry in the three industries in Guangdong Province is gradually decreasing, and farmers’ income is gradually increasing, the proportion of the industrial structure will change in the long run, Which represents the continuous adjustment and optimization of the industrial structure, and the continuous improvement of social resource utilization efficiency. From the perspective of economics, improving resource utilization efficiency promotes the overall economic level of society, thereby increasing farmers’ income. With the acceleration of urbanization, more laborers will be transferred to cities and towns, and the number of farmers engaged in agricultural production will decrease. Those who stay in the countryside will have the opportunity to obtain more means of production and land for operation, which will help increase farmers’ income.

5.3. Threshold effect on farmers’ income

To further study whether there are threshold characteristics for the impact of agricultural insurance on farmers’ income, this paper continues to use the threshold effect model to conduct empirical testing and put the agricultural insurance density and compensation into the inspection model.

The threshold effect test is first performed on the model to determine the number of thresholds. The test results are shown in Tables ​ Tables7 7 and ​ and8. 8 . At a significance level of 1%, for formula (5–5) under the null hypothesis with 1 threshold effect, the statistic of F is 20.15, and the P-value is 0.0933. The test result shows that it cannot be rejected. The original hypothesis indicates that agricultural insurance density’s impact on farmers’ income has a single threshold effect, and the threshold value is 28.861. The test results of (formula 8) have the same characteristics, the P-value is 0.07, and the threshold value is 14.892. In the double-threshold test, both p-values are greater than 0.1, accepting the null hypothesis that "there is no double-threshold," indicating no threshold effects of 2 or more.

*** means significant at p<0.01

*** means significant at p<0.01. source: authors’ calculation

Table 9 shows the estimation results of the model parameters of agricultural insurance density and farmers’ income sheet threshold. The threshold estimation results show that the threshold value of agricultural insurance density is ind = 28.681. Regardless of whether the threshold is crossed or not, agricultural insurance density positively correlates with farmers’ income at a significant level of 1%. At the same time, when agricultural insurance density ind <28.681, the positive correlation coefficient of agricultural insurance density on farmers’ income is greater. This shows that compared with areas with higher agricultural insurance density, each increase in insurance density has a greater impact on farmers’ income in areas with lower agricultural insurance density.

Table 10 shows the estimation results of the model parameters of agricultural insurance per capita compensation and farmer’s income statement threshold. The threshold estimation results show that the threshold value of agricultural insurance per capita compensation is ex = 14.892. Similar to the agricultural insurance density threshold estimation result, whether above or below the threshold, the per capita compensation of agricultural insurance can always positively affect farmers’ income at a significant level of 10%. At the same time, when the per capita compensation ex of agricultural insurance is less than 14.892, the positive correlation coefficient of the per capita compensation of agricultural insurance to farmers’ income is greater. Increasing a unit’s insurance compensation has a greater impact on farmers’ income.

*** means significant at the p<0.01. source: authors’ calculation

Generally speaking, agricultural insurance has less impact on farmers’ income in areas with higher agricultural insurance density and higher per-capita insurance indemnities. In areas with lower agricultural insurance density and lower per capita insurance indemnities, agricultural insurance significantly impacts farmers’ income bigger. This phenomenon shows that although agricultural insurance has a significant positive impact on farmers’ income, the effect of agricultural insurance on the increase of farmers’ income is not infinite. As a risk management tool, agricultural insurance can increase farmers’ income to a certain extent from the perspective of transferring risks and guaranteeing production, but it cannot be used as the fundamental driving force to increase farmers’ income. In addition, participating in agricultural insurance requires a certain cost, and holding agricultural insurance activities requires realistic risk requirements. Otherwise, it will increase the burden of farmers’ insurance premiums; farmers may be interested in agriculture in areas with high agricultural insurance density. Higher insurance dependence slack in daily operation and management is more likely to occur moral hazard, which is detrimental to farmers’ income growth. In areas with low agricultural insurance density, farmers pay more attention to production management and take more proactive measures to prevent them. Risk, at this time, every increase in the density of agricultural insurance by one unit will bring more obvious effects on farmers’ income.

The high per capita compensation for agricultural insurance does not necessarily mean that agricultural insurance development is higher. It may be due to improper operation and management of agricultural insurance and immature mechanisms that have led to increased compensation due to the serious damage to local agriculture. Insurance companies have increased compensation expenditures. However, even with insurance protection, it may not cover farmers’ income fully. It is not surprising that all losses have a small impact on farmers’ income when per capita compensation is large. In addition, from an economic point of view, when farmers receive less indemnity, each additional unit of indemnity can bring A greater marginal effect; at this time, agricultural insurance significantly impacts farmers’ income.

6. Conclusion

This paper uses static and dynamic panel models to test whether agricultural insurance significantly impacts farmers’ income increases. This study uses the ordinary least squares method, panel fixed effects, and system generalized moment estimation test. This article analyzes the system’s generalized moment estimation results considering the endogenous problem by selecting test results. The test results show that the increase in agricultural insurance density and the increase in agricultural insurance per capita compensation positively impact farmers’ income growth significantly. Agricultural insurance density and agricultural insurance per capita compensation are the indicators used in this article to measure the level of agricultural insurance development. Therefore, it can be considered that the development of agricultural insurance in Guangdong Province can effectively increase the income level of farmers.

The threshold model test found that in different insurance density ranges and insurance compensation areas, the effect of agricultural insurance on farmers’ income is significantly different. In areas with low agricultural insurance density, the impact of agricultural insurance on farmers’ income is more significant than in High-density areas. We believe farmers will pay more attention to daily production and operation management in areas with low agricultural insurance density and low per capita compensation to prevent future risk losses. The participation of agricultural insurance will not make them slack in management; on the contrary, It will increase their confidence in the current output and psychological expectations and make them more actively carry out production activities to increase their income levels. In areas where agricultural insurance density is high and per capita compensation is high, farmers may become dependent on insurance to a certain extent, and even moral hazards may occur, and management slack may occur. Although the economic compensation function of agricultural insurance can stabilize their income, it is not the Source of motivation for increasing farmers’ income. It should also be based on the scale of production and the level of agricultural modernization to improve farmers’ income.

From the above conclusion, we would like to suggest improving system design and vigorously promoting the development of agricultural insurance in Guangdong Province. Insurance subsidies must be carefully planned to be "smart," in the sense that they are efficient in accomplishing their fundamental goals, reduce difficulties with disincentives, and do not add to the government’s mounting financial burden. Governments should also ensure that the fundamental public goods required to establish an environment conducive to insurance are in place before subsidizing insurance since, without them, neither insurance markets nor subsidies can be expected to function as intended [ 55 ]. Although policy-oriented agricultural insurance boosts farmers’ incomes overall, it has a considerable variability on farmers in various income brackets, and this effect is stronger as farmers’ incomes rise [ 56 ]. In the future, China should place a high priority on the design of a differentiated subsidy system and adhere to the principle of demand orientation to prevent agricultural insurance from becoming the catalyst for a widening income gap in rural areas as a result of its aggressive development of policy-based agricultural insurance over time.

Thus, related government departments and insurance companies must do their respective jobs efficiently. New policies should strengthen disaster prevention and loss prevention and improve post-disaster compensation levels. It is also suggested to divide risk areas and scientifically determine insurance rates. Furthermore, the government should Increase publicity in relatively backward areas and increase farmers’ willingness to apply for insurance.

This paper mainly studies the impact of agricultural insurance in Guangdong Province on farmers’ income. The research method specifically compares the empirical results of the static panel model and the dynamic panel model and uses the threshold effect model to explore the characteristics of the impact of agricultural insurance on farmers’ income. Although there are certain innovations in research perspectives and ideas, this article has some shortcomings due to limited research capabilities. On the one hand, due to the difficulty of data collection, this article uses annual panel data from 20 cities in Guangdong Province. The results of the empirical regression may deviate slightly from reality. In the selection of control variables, we mainly refer to previous studies. The selection of indicators may not be typical factors affecting farmers’ income in Guangdong Province. We have not screened and analyzed the indicators that may affect the explained variables one by one. In addition, this article’s research angle and thinking direction may have certain limitations, and it is impossible to consider all the influence mechanisms, which may also impact the research conclusions.

Acknowledgments

We thank all the co-authors and our Master student Zeng Xinbin for supporting and collecting the data.

Abbreviations

Funding Statement

The authors received no specific funding for this work.

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• Published: 18 September 2024

Towards a healthier future for the achievement of SDGs: unveiling the effects of agricultural financing, energy poverty, human capital, and corruption on malnutrition

• Cuicui Ding 1 ,
• Khatib Ahmad Khan 2 , 3 ,
• Hauwah K. K. AbdulKareem 4 ,
• Siddharth Kumar 5 ,
• Leon Moise Minani   ORCID: orcid.org/0009-0000-8458-2148 6 &
• Shujaat Abbas 7  

Humanities and Social Sciences Communications volume  11 , Article number:  1241 ( 2024 ) Cite this article

Metrics details

• Development studies
• Environmental studies

The objective of the present study is to address child and maternal malnutrition in nine African countries located in the Western sub-region of Sub-Saharan Africa (SSA) by incorporating three types of agricultural financing (domestic and external) along with energy poverty, human capital and corruption on malnutrition for the 1990–2019 period and present implications for Sustainable development goals (SDGs). This objective is realized by employing recently advanced panel techniques such as the second-generation panel econometrics techniques and the method of moments quantile regression (MMQR) approach. The estimated results reveal that agricultural credit and foreign aid in the agriculture sector significantly and negatively affect the malnutrition of children and mothers, while research spending in agriculture positively influences malnutrition. Energy poverty and human capital exert a negative and significant influence on child and maternal malnutrition, while corruption induces it. The study finally recommends several policy insights for the governments across the SSA region for tackling child and maternal malnutrition and advancing towards the achievement of SDG 3 through investment in SDG 4, SDG 7 and SDG 17.

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Introduction.

According to the Global Hunger Index report, globally, it is estimated that 735 million people were undernourished in 2022, which indicates an increase of 122 million when compared with the 2019 statistics (von Grebmer et al., 2023 ; FAO, IFAD, UNICEF, WFP and WHO, 2023 ). With the rapid increase in population growth rate, the figures are soaring higher, especially in Sub-Saharan Africa (SSA) region, which has the fastest-growing number of malnourished and accounts for about a third of the malnourished children (Akombi et al., 2017 ; FAO, IFAD, UNICEF, WFP and WHO, 2023 ). The rate of malnutrition in the region in 2022 is reported to be 22.5% and it is not only the highest of all regions in the world but also represents more than double the global average of 9.2% (FAO, IFAD, UNICEF, WFP and WHO, 2023 ). Consequently, SSA is home to the highest number of people who cannot afford a healthy diet, which stood at 83.4% in 2021 (FAO, IFAD, UNICEF, WFP and WHO, 2023 ). The emergence of COVID-19 at the end of 2019 disrupted the food supply chain in SSA with severe consequences for the production of crops as well as food prices. As a result, the International Food Security Assessment report indicated that food insecure people in SSA region increased by more than 9% between 2019 and 2021 compared to only 4% increase between 2017 and 2019 (Dabou et al., 2024 ).

The challenge gets further compounded as child and maternal malnutrition embodies a triple liability of undernutrition, deficiency in micronutrients as well as obesity (Giller, 2020 ). Given the fast-rising population growth rate and stagnant agricultural productivity, a huge threat looms regarding child and maternal health (Benzekri et al., 2015 ). Even though progress is being made globally on stunting and wasting (key indicators of malnutrition), SSA still houses the highest number of malnourished children in the globe, with about one-third of children in the region estimated to be malnourished (FAO, IFAD, UNICEF, WFP and WHO, 2023 ; Njatang et al., 2023 ).

The high prevalence of malnutrition has induced major health causes and challenges. In the realm of public health, malnutrition contributes significantly to the high incidence of diseases and infections since the immune system is lowered as a result of deficiencies in macronutrients such as protein and carbohydrates and micronutrients like vitamins and minerals (Müller and Krawinkel, 2005 ). In SSA, an increase in malnutrition from 5.5 million in 2009 to 30 million in 2019 contributed to child mortality of over 3.5 million (Drammeh et al., 2019 ). Christian and Dake ( 2022 ) noted that SSA had experienced a significant increase in obesity or overweight, and in approximately all sub-regions, undernutrition has been on the rise. The region is vulnerable to the simultaneous coexistence of all forms of malnutrition including undernutrition, obesity as well as micronutrient deficiency. FAO reported that malnutrition in children in 2020 for SSA consisted of 37% stunting, 6.7% wasting, and 5.7% overweight (Njatang et al., 2023 ), all of which have a direct association with death occurrence as discovered by Müller and Krawinkel ( 2005 ). Furthermore, malnutrition has been linked with several health issues responsible for premature deaths, such as acute respiratory infections, malaria, diarrhea, and perinatal deaths, among others (Müller and Krawinkel, 2005 ).

Figure 1 depicts the overall scenario of child and maternal malnutrition among different regions of the world. Before the millennium, South Asia was the region with the highest child and maternal malnutrition, but SSA overtook South Asia after 2000.

Percentage of child and maternal malnutrition in terms of 7 regions between the years 1990, 2000, 2010 and 2019.

In western and central Africa, stunting in children rose by 6.5 million between 2000 and 2018. This is one of the factors influencing the continued bane of child and maternal deaths in SSA, even though the rates are declining. Child mortality amounted to 6.2 million in 2018, with almost 300,000 maternal deaths in 2017 (Schlein, 2019 ). When compared to the global statistics, half of the child mortality and two-thirds of maternal mortality occur in SSA. This is detrimental to the much-coveted sustainable development goals (SDGs), particularly SDGs 2, 3, and 8, which are on zero hunger/improving nutrition, ensuring healthy lives, and sustaining economic growth, respectively. Regarding economic growth, it is important to note that adequate nutrition helps to accumulate human capital and increase productivity. Child malnutrition is associated with poor labor market and educational outcomes (Haile et al., 2021 ). According to Horton and Steckel ( 2013 ), ~12% of GDP is lost due to lower productivity and high healthcare costs in poorer countries that face high rates of malnutrition.

Agriculture is the mainstay of livelihoods and food security for a large proportion of the SSA population, contributing to 20–50% of GDP, and is a major employer of labor (Giller, 2020 ). According to Tesfaye et al. ( 2021 ), low agricultural productivity contributes to the region’s high poverty rate, food insecurity, malnutrition, and their ripple effects. Another major cause of malnutrition in the SSA region has been the lack of electricity or energy poverty. Studies such as Kose ( 2019 ) and Thomson et al. ( 2017 ) show that energy poverty is associated significantly with the different measures of health conditions where lack of energy or inadequate access causes poor health conditions due to poor housing conditions and temperatures. Another potentially influencing factor of malnutrition is human capital since superior knowledge and nutrition education can reduce the prevalence of malnutrition (El Mouzan et al., 2010 ). Furthermore, institutional quality has also been found to be an important factor in affecting malnutrition (Cassimon et al., 2022 ).

Owing to the above discussion, this study attempts to identify the potential factors influencing child and maternal malnutrition in the western sub-region of SSA with a specific emphasis on agricultural financing and energy poverty. As such, there are several significant ways this study contributes to the empirical works of literature. First, we focus on the western part of SSA because this sub-region experiences the highest child and maternal malnutrition (SDG 3) burden in the entire SSA. Figure 2 shows that within the SSA region, the burden of child and maternal malnutrition as measured by Disability-adjusted life years (DALY) has consistently rested heavily on the western part of SSA through the years from 1990. This classification is according to the global burden of disease (GBD) classification criteria, which classifies the world regions into seven superregions with 21 sub-regions (Vos et al., 2020 ). The SSA region, as a super region, contains Eastern, Central, Western and Southern SSA.

Number of child and maternal malnutrition in terms of 4 SSA subregions between the years 1990, 2000, 2010 and 2019.

Second, this study considers agricultural credit and agricultural research spending as the domestic government investment and development aid in the total agricultural sector is considered as foreign aid. Most previous studies have only considered these interventions in isolation. For example, Mcdermott et al. ( 2015 ), Iftikhar and Mahmood ( 2017 ), and Fleuret and Fleuret ( 1980 ) focused on agricultural research, agricultural credit, and agricultural flows, respectively. We firmly believe that charting this usually unexplored method of analyzing the agriculture-malnutrition relationship will allow for a more accurate, effective, and precise policy targeting in combating the double-edged sword of child and maternal malnutrition in SSA. Foreign aid is advocated by SDG 17.2, which aims to enhance development assistance.

Third, most of the studies only focus on child malnutrition (see Debela et al., 2021 ; Shafiq et al., 2019 ; Carletto et al., 2017 ; Gillespie and van den Bold, 2017 ; Akombi et al., 2017 ), with only Kabir et al. ( 2020 ) examining maternal malnutrition. This study is one of the few attempts that incorporate both child and maternal malnutrition in the same study framework, as they both constitute a nutrition bane in SSA. Fourth, our paper investigates the impacts of energy poverty, corruption and human capital development on malnutrition, which, although critical, have been mainly neglected in the literature for child and maternal malnutrition. To the best of our knowledge, this is the first study to take account of energy poverty, corruption and human capital development simultaneously in affecting child and maternal malnutrition, and therefore the contribution of this study is large.

Lastly, the study applies a novel methodological approach to the analysis of malnutrition by using superior econometric techniques such as the second-generation approach and the method of moments quantile regression (MMQR) (Machado and Silva, 2019 ). MMQR regression allows us to ascertain the effects of agricultural financing, energy poverty, corruption, and human capital across different quantiles. It is advantageous over other conventional models like NARDL as it produces robust results in nonlinear models with varied conditions and also allows for location-specific asymmetries in the impact analysis (An et al., 2021 ). This analytical technique is, therefore, suitable for the impending analysis as it handles asymmetry and nonlinearity, thereby circumventing the problem of heterogeneity and endogeneity concurrently. For robustness analysis, we estimate bootstrap quantile regression.

The rest of the study is organized as follows: the second part of the study consists of previous literature summary, the third part contains data and methodology, the fourth part is regarding the result and analysis, and the last part provides a summary of the whole study and recommendations.

Literature review

Agricultural financing and malnutrition.

Amao et al. ( 2023 ) assessed the role of credit finance and agricultural revenue in their study to investigate the factors that influence food security in Nigerian households using dietary diversity as a proxy for the latter. Data was gathered from the Living Standards Measurement Study—Integrated Surveys on Agriculture (LSMS-ISA). Employing IV-Poisson, they found that agricultural revenue has a negative relationship with nutrition diversity in urban households, while it has a positive relationship for rural households. Overall, agricultural revenue was found to be positively affecting nutrition diversity. On the other hand, credit access has a positive relationship for all households and rural areas, while no significant relationship was discovered between credit access and nutrition for urban households.

Similarly, Kihiu and Amuakwa-mensah ( 2020 ) assessed the effect of gendered agricultural market access in terms of market infrastructure, credit facilities, and marketing channels on dietary diversity in Kenyan homes. The study used an inverse probability-weighted treatment-effect estimator and found that improving agricultural market access for both genders exerts a positive influence on a household’s dietary diversity.

Focusing on the effect of foreign capital proxied by foreign direct investment (FDI) and foreign aid food security, Dhahri and Omri ( 2020 ) employed four measures of foreign aid, which include agriculture–forestry–fishing-aid (AFFA). Employing Tobit analysis, the findings reveal that both FDI and AFFA positively impact food security, while the effect becomes stronger when FDI and aid work together.

A similar study explored the relationship between credit access, household income, and diet variety in Ghana using the Food Diversity Index and Food Consumption Score. Annim and Frempong ( 2018 ) employed household samples from the Ghana Living Standards Survey while employing instrumental variable analysis to analyze the data gathered. Results revealed income and credit have an incremental effect on diet variety. This establishes a negative effect of credit on malnutrition as diet diversification will allow the incorporation of various classes of food for the supply of the required dietary needs.

These findings, however, contradict the studies of Iftikhar and Mahmood ( 2017 ) and Islam et al. ( 2016 ). Iftikhar and Mahmood ( 2017 ) document the effect of agricultural credit to be mixed when they found institutional credit to be instrumental in improving food security, while non-institutional credit was found to have a contrary effect. The work of Haque et al. ( 2013 ) supports the former assertion. In addition, Islam et al. ( 2016 ) investigated the effect of microcredit on various dimensions of food security—calorie availability, dietary diversity, and anthropometric measures in reproductive women and children under 5—in Bangladesh. The results of the regression-adjusted propensity score matching (PSM) indicated that credit has a positive, negative, and mixed effect on calorie availability, dietary diversity, and anthropometric measures, respectively. Despite the diverse results obtained in the review, the conclusion of the majority of literature tends towards the fact that agricultural credit has a huge potential to curb malnutrition, mostly through boosting agricultural production, enhancing food security, and promoting diet diversification.

Focusing on the second aspect of internal investment, which is agricultural research spending, Adjaye-Gbewonyo et al. ( 2019 ) examined the effect of government assistance to agricultural trade on child nutrition in a cross-country analysis. Results from the fixed effect regression analysis indicated positive associations as increases in the 5-year average assistance rate improved children’s nutritional status. However, the conclusions of Mcdermott et al. ( 2015 ) differ as they report that agricultural research efforts have not yielded the required and desired nutritional outcomes. A similar view is expressed in a dated study by Harriss ( 1987 ), who implied the impact of agricultural research spending on nutrition is inconclusive, majorly due to factors bordering on identification, categorization, and location of the malnourished, among others. In terms of external development investment in agriculture, Fleuret and Fleuret ( 1980 ) classified this type of intervention as the indirect approach to tackling malnutrition which has been found to have a positive influence on nutritional status. However, other studies such as Berg and Muscat ( 1973 ) and Reutlinger and Selowsky ( 1976 ) are of diverse opinions noting that external development assistance may not be influential in combating malnutrition. This is attributed to the uneven, inefficient, and/or unfair distribution of external investments.

Human capital and malnutrition

Osei and Lambon-Quayefio ( 2022 ) investigated how malnutrition in children affects educational outcomes using panel data random-effects and Poisson estimations. The findings indicate that although malnutrition hampers educational outcomes, the effect is temporary as it is discovered to disappear in the future. With a focus on adults, Eglseer et al. ( 2019 ) examined the effect of nutrition education on malnutrition in medical schools in Europe. Being a cross-sectional study, data was gathered from 31 European countries from an online Web-Survey. The study concluded that 50% of the curricula covered malnutrition as a topic. This agrees with Fadare et al. ( 2019a ), who examined the effect of nutrition-related knowledge of the mother on the child’s nutritional status using DHS data for Nigeria. The authors concluded that a mother’s nutrition knowledge has a significant and positive effect on a child’s nutrition, thereby significantly reducing malnutrition among children. Benson et al. ( 2018 ), on the other hand, showed that parent’s educational status does not have any effect on the child being stunted in northern Nigeria.

Fadare et al. ( 2019b ) adopted a cross-sectional survey method to examine factors that influence micronutrient-rich food and investigate the effect of the same on child stunting. Gathering data from 419 children and 413 households in Kwara State, Nigeria, logistic regression and descriptive analysis were applied to the data collected. Results show that higher levels of education among parents and superior knowledge of micronutrients have a high likelihood of improving consumption of micronutrient food. The study, therefore, concludes that human capital reduces the prevalence of malnutrition. The study outcome agrees with Amare et al. ( 2021 ), who demonstrated that a mother’s educational attainment, along with that of a spouse, has a negative impact on children’s stunting. Adesugba et al. ( 2018 ) also found that uneducated households are more likely to have underweight children.

A similar study on the relationship between maternal education and malnutrition in children was conducted by Hasan et al. ( 2015 ) in Bangladesh using data from 1996 to 2011 with log-binomial as the analytical technique. Results show malnutrition was constantly high in children with mothers who have low educational qualifications. Smith and Haddad ( 2015 ) found education among the influencing factors of child undernutrition in their study cross-country study using data from 1970 to 2012. El Mouzan et al. ( 2010 ) explored a different direction of the education-malnutrition nexus by examining the effect of educational attainment of the household head on children’s malnutrition in Saudi Arabia. Gathering data from a stratified multistage sampling, the prevalence of malnutrition was calculated using weight for age, height for age, and weight for height for children under 5 as indicators. The likelihood of malnutrition was revealed to increase from 7.4% for tertiary education to 15.2% for illiterate heads of house.

Energy poverty and malnutrition

Dake and Christian ( 2023 ) examined the effect of energy poverty on malnutrition in 18 SSA countries using the DHS data. Energy poverty was measured in terms of energy used for lighting, cooking, entertainment, and information access, while malnutrition was proxied by indicators such as undernutrition, overnutrition, and anemia among children under 5 years and women aged 15–49 years. Findings reveal energy poverty to be associated with a higher likelihood of undernutrition but a lower probability of overnutrition. This aligns with the study of Kose ( 2019 ) which shows that energy poverty is associated significantly with the different measures of health. By studying Turkish household surveys, they provided multi-level model evidence of the negative relation between energy poverty and the health of the persons. Some of the several situations related to energy poverty include poor housing conditions, health as well as indoor temperature (Thomson et al., 2017 ).

The nutritional status of children was explored from the perspective of rural electrification in rural areas of Bangladesh by Fujii et al. ( 2018 ). The authors found that electricity access improves children’s nutritional status specifically through fertility and wealth channels. Lewis ( 2018 ) found that electrification in the rural US has contributed to a 15–19% decrease in infant mortality.

Corruption and malnutrition

Investigating the impact of corruption on food security at the macro level, Onder ( 2021 ) utilized panel data from 75 countries for the period between 2012 and 2016. With Driscoll and Kraay’s method of analysis, his findings indicated that corruption negatively influences food security. Also, Qingshi et al. ( 2020 ) found that corruption, among other factors, worsens food security in 124 countries comprising both developing and developed countries. This aligns with the study of Anik et al. ( 2013 ), whose study suggests a reduction in food security is occasioned by farm-level corruption.

The effects of different governance quality indicators on food security were examined by Cassimon et al. ( 2021 ). It was revealed that regulatory quality and rule of law have positive effects, while government effectiveness and corruption control have negative effects on food security. On the other hand, the overall governance index had no significant effect on food security from the pooled OLS. The random effect result demonstrated a positive effect of controlling corruption on food security. On the other hand, control of corruption was positively associated with nutrition security from pooled OLS.

In another study, Cassimon et al. ( 2022 ) have also suggested that undernourishment is negatively affected by corruption control mechanisms and the stability of the political system. It was also found that child undernutrition is negatively influenced by good governance quality. In another seminal work, Ogunniyi et al. ( 2020 ) demonstrated from their system GMM analysis that food and nutrition security in SSA are positively affected by corruption control score, governance effectiveness, rule of law, and stability of the political system. However, controlling corruption had the largest effect. Another work on SSA by Cassimon et al. ( 2023 ) revealed that stability of the political system and control of corruption have significant effects on nutrition and food security and thereby reduce undernourishment.

Data and methodology

Model specification.

The model used to explore the behavior of child and maternal malnutrition in selected Western Sub-Saharan countries is reported as follows:

where MALNUT refers to child and maternal malnutrition, AGRCREDIT refers to the credit in the agricultural sector, RAGR is research spending in agriculture, FAID is foreign aid, intercepts \({{X}}_{{it}}\) is a composite term that incorporates the effect of other control variables used in the model, such as energy poverty, corruption, and human capital index. The dependent variable (MALNUT) is disability-adjusted life in years due to child and maternal malnutrition, and the data for this variable is collected from the Institute for Health Metrics and Evaluation (IHME), which coordinates the Global Burden of Disease database. The GBD provides global health metrics data for 204 nations and sub-nations and classifies world regions into 7 super regions and 21 sub-regions, with SSA being one of the super regions and the Western part of SSA being one of these sub-regions (Vos et al., 2020 ; GBD, 2021 ).

The data of agricultural interventions such as agricultural credit and foreign aid are collected from FAOSTAT whereas research spending in agriculture by the government comes from Agricultural Science and Technology Indicators. The reason for selecting foreign aid instead of foreign direct investment into agriculture is because of the data limitation associated with the agricultural foreign direct investment in the western part of the SSA region. The data on human capital is derived from the Penn World table. Energy poverty is collected from WDI measured by access to electricity. Finally, Bayesian corruption index data is collected from Standaert ( 2015 ). This corruption index is selected instead of other corruption measures because this index can represent the underlying data in a true manner, and it is not biased by the composer’s modeling choices (Teorell et al., 2021 ). Table 1 reports a detailed description, source, and reference of data used in this study.

The analysis is carried out for 9 countries that are located in the western part of Sub-Saharan African countries and the data period is from 1990 to 2019. The list of the countries included in this study is presented in Supplementary Table S1 online . The selected variables with different units are transformed by taking the natural logarithm. The logarithmically transformed version of Eq. ( 1 ), along with the control variables, is presented as follows:

where ln represents the natural logarithm, EPOV is Energy Poverty, HCI is the human capital index and CORRUPTION is the Bayesian corruption index.

Estimation strategy

Cross-sectional dependence.

The selected panel of variables is first subject to a cross-sectional dependence test for exploring the effect of shock in one country on others as all countries belong to the same region (Western Sub Sharan Africa). There are various variants of cross-sectional dependence tests to realize this objective, such as the Lagrange multiplier (LM) test proposed by Breusch and Pagan ( 1980 ), scaled LM test and CD test introduced by Pesaran ( 2004 ), and recently developed bias-corrected scaled Lagrange multiplier test proposed by the Baltagi et al. ( 2012 ). The cross-section dependence test proposed by Breusch and Pagan ( 1980 ) would be appropriate when N is fixed and \(T\to \infty\) . The drawback of this test is that it cannot be applicable in the case that N tends to be infinite. Therefore, Pesaran ( 2004 ) has introduced a cross-sectional dependence test that is applicable with finite N and T . But in the case when N  >  T , Pesaran ( 2004 ) has proposed a different test. A more recent test to explore cross-section dependency by addressing potential issues associated with the above-discussed test is that of Baltagi et al. ( 2012 ), who proposed a bias-corrected scaled LM test. The test statistic is assumed to be distributed asymptotically under the weak cross-sectional dependence null hypothesis.

CIPS unit root test

The existence of cross-sectional dependence implies that the residuals of panel cross-sections (i.e. countries or industries in the panel) are significantly correlated with each other, which implies that shock to one of the cross-sectional entities has an impact on one or more other cross-sectional entities. Therefore, a traditional first-generation panel unit root test can result in a biased conclusion as they do not allow cross-sections to be dependent. In contrast, the recently advanced second generation of panel unit root tests augments traditional tests to address cross-sectional dependency. Pesaran ( 2007 ) augmented the traditional Dickey–Fuller unit root test to address cross-sectional dependency. The null distribution of this test is that of homogenous nonstationary, and the rejection of which proves that the series is stationary.

Second-generation cointegration test

After the CD and stationary test, investigation of the long-run relationships of the studied variables is essential, which will be carried out using panel cointegration techniques. Specifically, in the presence of CD, robust panel cointegration methods developed by Westerlund ( 2007 ) are employed. The rationale behind this is that the techniques provide statistical values that ascertain whether the data series have a long-run relationship. There are four test statistics and the null hypothesis of the methods suggested that there is no evidence of cointegration. Hence, if the statistic value of each test is greater than the critical value, the hull hypothesis will be rejected in favor of the alternative hypothesis, which denotes the presence of a long-run association.

Method of moments-quantile regression (MMQR)

After establishing the presence of cointegration, the next stage is to assess the long-run relationship. However, estimation techniques such as FMOLS OR DOLS provide bias estimates in the presence of outliers; therefore, Koenker and Bassett ( 1978 ) introduced quantile regression in their seminal paper. Generally, in the presence of outliers, the simple quantile regression provides more robust estimates and also gives more pertinent results when the relationship between the conditional mean values of variables is weak or nonexistent. The drawback of simple quantile regression is that it is unable to address unobserved heterogeneity across cross-sections along with the endogeneity issues. This problem is addressed by Machado and Silva ( 2019 ) by introducing the method of moments quantile regression (MMQR) that authorizes the individual effects and addresses the conditional heterogeneous covariance effect. The MMQR approach is the most appropriate panel estimation technique that can incorporate both asymmetric and nonlinear linkages and also deals with both endogeneity and heterogeneity issues simultaneously. This study, therefore, employed the recently advanced MMQR technique to address the behavior of child and maternal malnutrition in selected Western Sub-Saharan Africa. For robustness purposes, bootstrap quantile regression is employed (Koenker, 2005 ).

Panel Dumitrescu and Hurlin's causality estimates

The estimation of the long-run association between explained and explanatory variables cannot explore the flow of the Granger causality trend, and in the empirical analysis, investigation of the causality flow is crucial owing to its expediency efficient policy designing (Usman and Hammar, 2021 ). In this regard, Dumitrescu and Hurlin ( 2012 ) developed a panel non-causality test named the panel Dumitrescu and Hurlin (D–H) test that illustrated the casual association between variables. The D–H test offers produce more efficient and robust outcomes as compared to other traditional causality tests (Intisar et al., 2020 ). Moreover, this panel causality test is further suitable for evaluating the balanced and unbalanced time series and cross-correlations across each unit (countries).

Results and discussion

Table 2 provides the descriptive statistics of the variables under study with logarithmic presentation. Before the estimation of the long-run coefficients, cross-sectional dependence (CSD) needs to be tested to avoid erroneous analysis and conclusions from the models. Table 3 shows the results of the four types of CSD tests. For all the variables, the majority of tests demonstrate that there is cross-sectional dependence. Table 4 presents the result of the second-generation CIPS unit root test, which reveals that variables have a mixed integration order with lnMalnut, lnHCI, and lnCORRUPTION being integrated at order 1.

For conducting a meaningful long-run analysis, the variables should also be cointegrated. The four test results (Gt, Ga, Pt, Pa) of Westerlund cointegration are presented in Table 5 . All the tests have a null hypothesis of no cointegration. Two tests (Pt and Pa) have a robust probability of more than 99% to reject the null hypothesis, which strongly indicates the presence of cointegration or a long-term relationship amongst the variables.

With the confirmation of the long-term cointegrating relationship amongst the variables, we can move toward estimating the models for quantifying the long-term relationship.

Our main estimation technique is MMQR, the result of which is presented in Table 6 . There are three facets of investment in the agricultural sector for improving the child and maternal nutritional status. The first one is the credit provided in the agricultural sector. The second facet is the investment in research and development in the agricultural sector to improve productivity and thereby enhance nutritional status. The third and last facet is the external assistance or the foreign aid received for the betterment of the state of the agriculture sector, which should lead to better nutritional status. In the results of the MMQR estimation, the credit provided to the agriculture, forestry, and fishing sectors is reducing child and maternal malnutrition across the quantiles. This finding is in line with the literature of Kiresur et al. ( 2010 ), who argue that increasing access to agricultural credit in rural areas is a significant way to increase the purchasing power of rural households, which in turn contributes to enhancing the status of the child and maternal nutrition. Amao et al. ( 2023 ) also found that credit access increases nutrition security for all households.

To our surprise, the investment in research and development in the agricultural sector is increasing the malnutrition in the countries selected for the study. When we refer to the literature to find out the reasons for such unexpected results, Kadiyala et al. ( 2014 ) argue that agricultural research and development projects may increase the demand for household labor. It increases the women’s time burden, and they are left with less time to take care of their children, which ultimately has a negative influence on the children’s nutrition. Herforth et al. ( 2012 ) also blamed the investment in agricultural research and developmental projects for the decline in women’s own and their children’s nutritional status due to the physically demanding nature of such projects. Due to these negative effects, McDermott et al. ( 2015 ) have recommended that investment in research and development projects in agriculture must take into account their impact on the women included in such projects. People managing such projects should make sure that in order to improve the nutritional status, these projects should not inadvertently harm the existing nutritional balance of the women and their children.

After the R&D investments in agriculture, we see the impact of external assistance on the nutritional status of mothers and their children. External assistance, as expected, qualifies to mitigate malnutrition and improve maternal and child nutritional status. The values keep on rising from the lowest to the highest quantile, which means that the higher the external assistance, the more the reduction in maternal and child malnutrition. This finding corroborates with Fleuret and Fleuret ( 1980 ) where they mention that external assistance acts as a development fund for developing or underdeveloped nations and enables them to meet their needs which improves the child and maternal nutritional status in these countries.

Human capital has a negative and significant impact on malnutrition, and a higher human capital value leads to less malnutrition in mothers and their children. All the values of the quantiles are negative and significant. The result agrees with Victora et al. ( 2008 ) who observed that a better human capital value signifies higher economic productivity, which would help in combating the malnutrition problem. This finding is also in line with several studies conducted in countries of SSA, such as Fadare et al. ( 2019a ), Adesugba et al. ( 2018 ), and Amare et al. ( 2021 ).

The result of energy poverty shows that it has a significant and negative influence on child and maternal malnutrition. The values keep on rising from lowest to highest quantile, again proving that as access to electricity increases, child and maternal malnutrition decreases. In other words, as energy poverty gets reduced, so does malnutrition. This is in line with Churchill and Smyth ( 2021 ) who found that if there is a standard deviation rise in energy poverty, it will reduce health by standard deviations of 0.099 and 0.296. Kose ( 2019 ) also proved that individuals’ health status is negatively affected by energy poverty.

Finally, the effect of corruption is reported, and it shows that it has a positive and significant effect on child and maternal malnutrition. This indicates that if there is widespread corruption in a SSA country, this will lead to increased malnutrition among the children and mothers. This is confirmed by Cassimon et al. ( 2021 ), who found that nutrition security is improved when corruption is controlled. In another study, Cassimon et al. ( 2022 ) again confirmed that child undernutrition can be negatively affected by the quality of good governance. This result can be explained by the statement of Bain et al. ( 2013 ) where they mentioned that misappropriation of state funds, poor governance, nepotism, tribalism, and corruption of public funds have resulted in income inequalities, and people in the lower segment are facing acute food insecurity and malnutrition.

The result of the bootstrap quantile regression result is presented in Table 7 for robustness analysis. For most of the quantiles, our result from MMQREG agrees with bootstrap quantile regression result.

Table 8 shows the findings of the pairwise Dumitrescu–Hurlin panel Granger causality test. There is one-way causality running from human capital to malnutrition, malnutrition to corruption, human capital to agricultural credit, energy poverty to agricultural credit, human capital to foreign aid, human capital to energy poverty, foreign aid to research spending in agriculture. Bidirectional causality can be observed between human capital and corruption, research spending in agriculture and corruption, research spending in agriculture and human capital, human capital and foreign aid, and energy poverty and foreign aid.

Conclusion and policy implications

Child and maternal malnutrition creates various challenges for society as well as for the economy as a whole. Women and children are considered to be the most vulnerable in terms of nutrition as they have higher nutrient requirements, but these are not often met (Lartey, 2008 ). Globally, one of the highest burdens of child malnutrition is in the Sub-Saharan Africa region, and it is a major public health burden that requires urgent actions (Akombi et al., 2017 ). The prevailing economic and environmental conditions in Sub-Saharan Africa make it more challenging for women and children to meet their nutrition requirements in this region. This study presents the scope and opportunities of agricultural financing to tackle child and maternal malnutrition in the Western part of Sub-Saharan Africa, which bears the highest burden of this type of malnutrition in this region. Additionally, the roles of energy poverty, human capital, and corruption in malnutrition are also analyzed. To achieve the above-mentioned objectives, several econometric techniques, including the second-generation unit root test, Westerlund Cointegration test, and the novel Method of Moments Quantile regression, are implemented.

The results of the study can be summarized as follows: (1) Agricultural credit, external investment in agriculture and human capital have significant negative impact on child and maternal malnutrition; (2) Access to electricity has negative and significant impact on malnutrition, implying that tackling energy poverty tackles malnutrition as well; (3) Research spending in agriculture in the economy induces malnutrition in child and mother; (4) Corruption has positive and significant effect on child and maternal malnutrition; (5) Bidirectional causality can be observed between human capital and corruption, research spending in agriculture and corruption, research spending in agriculture and human capital, human capital and foreign aid and energy poverty and foreign aid; (6) There is one way causality running from human capital to malnutrition, malnutrition to corruption, human capital to agricultural credit, energy poverty to agricultural credit, human capital to foreign aid, human capital to energy poverty, foreign aid to research spending in agriculture.

Based on the findings, several policy implications can be derived for the Western sub-region of Sub-Saharan African countries in order to achieve SDG 3, which mentions health and well-being. Although achieving food and nutrition security is not the burden of the agricultural sector only, it does play a significant role in enhancing nutrition security. Therefore, access to credit and external investment in agriculture (SDG 17.2) should be increased in order to tackle the malnutrition problem among the children and mothers in this sub-region. Especially, sustainable nutrition-focused interventions should be promoted by both private and public sectors and by the domestic government as well as by international donors. Different Incentives to promote nutrition-friendly agricultural development are also crucial for achieving nutrition and food security. Urgent investment and action are needed to prevent and treat malnutrition so that this problem does not become severe. Effective implementation of nutrition programs is also necessary to tackle this phenomenon. The result highlighted that research spending in agriculture has a negative effect on nutritional outcomes for the child and mother. This could be due to the fact that researchers are mostly concerned with the short-term profit generation of the agriculture sector rather than the long-term. Therefore, more research expenditure should be driven towards developing sustainable agricultural policies that will improve the quality of the food and ultimately help reduce malnutrition in this region. Especially, research and development in the smallholder farming system should be promoted, which has very little environmental concerns. Research spending in agriculture also needs to be aligned with nutrition strategies and the commitments of the governments. More coordinated and combined efforts of the agricultural and nutrition sectors can overcome hindrances of nutrition governance. Furthermore, the establishment of indicators to track national commitments and coordinating mechanisms is inevitable to plan for, advocate for and promote better nutrition. With regards to energy poverty, it is necessary to tackle energy poverty which can be achieved by investment in renewable and sustainable energy (SDG 7). It is also important to tackle corruption in these countries and ensure a just governance system that can support the government initiatives taken to minimize child and maternal malnutrition. Governance policies must be well aligned with the agricultural sector such that research and development are utilized properly to increase nutrition security for the mother and the children. However, not just the government initiatives, to tackle malnutrition, it is especially important to increase awareness among the households, especially among the women. In this regard, women’s empowerment through education can help to tackle their own malnutrition as well as that of the child. Therefore, in addition to increasing expenditure in the agricultural sector, it is also important to increase expenditure in women’s education.

Due to the limitations of data, many important variables (e.g. climate change) have not been incorporated in this study. Future studies can incorporate them and provide robust findings. Also, the study can be extended to other SSA regions as well to provide a comparative analysis.

Data availability

The datasets analyzed during the current study are shared. In addition, the raw data are open-access and publicly available on the websites of IHME, FAOSTAT, WDI, Agricultural Science and Technology Indicators, PENN World Table, and Standaert ( 2015 ). Specifically, the data on Child and maternal malnutrition comes from the IHME database ( https://vizhub.healthdata.org/gbd-results/ ), the data for agricultural credit and external aid can be found at FAOSTAT ( https://www.fao.org/faostat/en/#data/IC , https://www.fao.org/faostat/en/#data/EA ), the data for agricultural research spending comes from Agricultural Science and Technology Indicators ( https://www.asti.cgiar.org/data-graphics ). Energy poverty, measured by access to electricity, comes from WDI ( https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS ), Human capital index is from Penn World Table ( https://www.rug.nl/ggdc/productivity/pwt/?lang=en ) and Bayesian corruption index is sourced from Standaert ( 2015 ) ( https://users.ugent.be/~sastanda/BCI/BCI.html ).

Adesugba M, Edeh H, Mavrotas G (2018) Child nutritional status, welfare and health in Nigerian households. Discussion paper no. 1113. International Food Policy Research Institute, Washington, DC, USA

Adjaye-Gbewonyo K, Vollmer S, Avendano M, Harttgen K (2019) Agricultural trade policies and child nutrition in low-and middle-income countries: a cross-national analysis. Glob Health 15(1):1–17

Google Scholar  

Akombi BJ, Agho KE, Merom D, Renzaho AM, Hall JJ (2017) Child malnutrition in sub-Saharan Africa: a meta-analysis of demographic and health surveys (2006–2016). PLoS ONE 12(5):e0177338. https://doi.org/10.1371/journal.pone.0177338

Article   CAS   Google Scholar  

Amao IO, Ogunniyi AI, Mavrotas G, Omotayo AO (2023) Factors affecting food security among households in Nigeria: the role of crop diversity. Sustainability 15(11):8534

Article   Google Scholar  

Amare M, Arndt C, Mahrt K, Mavrotas G (2021) Polygynous family structure and child undernutrition in Nigeria. J Dev Stud 57(10):1640–1661

Amna Intisar R, Yaseen MR, Kousar R, Usman M, Makhdum MSA (2020) Impact of trade openness and human capital on economic growth: a comparative investigation of Asian countries. Sustainability 12(7):2930. https://doi.org/10.3390/su12072930

An H, Razzaq A, Haseeb M, Mihardjo LW (2021) The role of technology innovation and people’s connectivity in testing environmental Kuznets curve and pollution heaven hypotheses across the Belt and Road host countries: new evidence from Method of Moments Quantile Regression. Environ Sci Pollut Res 28:5254–5270

Anik AR, Manjunatha AV, Bauer S (2013) Impact of farm level corruption on the food security of households in Bangladesh. Food Secur 5:565–574. https://doi.org/10.1007/s12571-013-0282-8

Annim SK, Frempong RB (2018) Effects of access to credit and income on dietary diversity in Ghana. Food Secur 10(6):1649–1663

Bain LE, Awah PK, Geraldine N, Kindong NP, Siga Y, Bernard N, Tanjeko AT (2013) Malnutrition in Sub-Saharan Africa: burden, causes and prospects. Pan Afr Med J 15(1):120. https://doi.org/10.11604/pamj.2013.15.120.2535

Baltagi BH, Feng Q, Kao C (2012) A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. J Econ 170(1):164–177. https://doi.org/10.1016/j.jeconom.2012.04.004

Article   MathSciNet   Google Scholar  

Benson T, Amare M, Oyeyemi M, Fadare O (2018) Study of the determinants of chronic malnutrition in northern Nigeria: qualitative evidence from Kebbi and Bauchi states. Food Nutr Bull 39:296–314

Benzekri NA, Sambou J, Diaw B, Sall EHI, Sall F, Niang A, Gottlieb GS (2015) High prevalence of severe food insecurity and malnutrition among HIV-infected adults in Senegal, West Africa. PLoS ONE 10(11):e0141819. https://doi.org/10.1371/journal.pone.0141819

Berg A, Muscat RJ (1973) The nutrition factor: Its role in national development. Brookings Institution

Breusch TS, Pagan AR (1980) The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47:239. https://doi.org/10.2307/2297111

Carletto C, Corral P, Guelfi A (2017) Agricultural commercialization and nutrition revisited: Empirical evidence from three African countries. Food Policy 67, 106–118

Cassimon D, Fadare O, Mavrotas G (2021) Development finance, governance quality and their impact on food and nutrition security in Sub-Saharan Africa. Rev Dev Financ 11(2):1–17. https://doi.org/10.1016/j.foodpol.2016.09.020

Cassimon D, Fadare O, Mavrotas G (2022) The combined effect of institutional quality and capital flows on food and nutrition security and undernourishment in Sub-Saharan Africa. PLoS ONE 17(10):e0275345

Cassimon D, Fadare O, Mavrotas G (2023) The impact of food aid and governance on food and nutrition security in Sub-saharan Africa. Sustainability 15(2):1417

Christian AK, Dake FA (2022) Profiling household double and triple burden of malnutrition in sub-Saharan Africa: prevalence and influencing household factors. Public Health Nutr 25(6):1563–1576

Churchill SA, Smyth R (2021) Energy poverty and health: panel data evidence from Australia. Energy Econ 97:105219

Dabou S, Ngo DV, Sinsai R, Dongho BD, Kepgang E, Solange Dabou DVN (2024) Tackling the double burden of malnutrition in sub-Saharan Africa: a post covid-19 perspective. On Policy Africa. https://onpolicy.org/tackling-the-double-burden-of-malnutrition-in-sub-saharan-africa-a-post-covid-19-perspective/

Dake FAA, Christian AK (2023) Cold, dark and malnourished: a cross-sectional analysis of the relationship between energy poverty and household burden of malnutrition in sub-Saharan Africa. BMJ Open 13:e074601. https://doi.org/10.1136/bmjopen-2023-074601

Debela BL, Gehrke E, Qaim M (2021) Links between maternal employment and child nutrition in rural Tanzania. Am J Agric Econ 103(3):812–830. https://doi.org/10.1111/ajae.12113

Dhahri S, Omri A (2020) Are international capital flows really matter for achieving SDGs 1 and 2: ending poverty and hunger? Rev World Econ 156:731–767

Drammeh W, Hamid NA, Rohana AJ (2019) Determinants of household food insecurity and its association with child malnutrition in Sub-Saharan Africa: a review of the literature. Curr Res Nutr Food Sci 7:610–623. https://doi.org/10.12944/CRNFSJ.7.3.02

Dridi M (2013) Corruption and economic growth: the transmission channels. J Bus Stud Quar 4(4):121–152

Drimie S, Mini S (2003) Food security and sustainable development in Southern Africa (Vol. 6). HSRC Press

Dumitrescu EI, Hurlin C (2012) Testing for Granger non-causality in heterogeneous panels. Econ Model 29(4):1450–1460. https://doi.org/10.1016/j.econmod.2012.02.014

Eglseer D, Visser M, Volkert D, Lohrmann C (2019) Nutrition education on malnutrition in older adults in European medical schools: need for improvement? Eur Geriatr Med 10:313–318

El Mouzan MI, Foster PJ, Al Herbish AS, Al Salloum AA, Al Omar AA, Qurachi MM (2010) Prevalence of malnutrition in Saudi children: a community-based study. Ann Saudi Med 30(5):381–385

Fadare O, Amare M, Mavrotas G, Akerele D, Ogunniyi A (2019a) Mother’s nutrition-related knowledge and child nutrition outcomes: empirical evidence from Nigeria. PLoS ONE 14(2):e0212775. https://doi.org/10.1371/journal.pone.0212775

Fadare O, Mavrotas G, Akerele D, Oyeyemi M (2019b) Micronutrient-rich food consumption, intra-household food allocation and child stunting in rural Nigeria. Public Health Nutr 22:444–454

FAO, IFAD, UNICEF, WFP and WHO (2023) The State of Food Security and Nutrition in the World 2023. Urbanization, agrifood systems transformation and healthy diets across the rural–urban continuum. FAO, Rome

Fleuret P, Fleuret A (1980) Nutrition, consumption, and agricultural change. Hum Organ 39(3):250–260

Fujii T, Shonchoy AS, Xu S (2018) Impact of electrification on children’s nutritional status in rural Bangladesh. World Dev 102:315–330

Giller KE (2020) The food security conundrum of sub-Saharan Africa. Glob Food Secur 26:100431. https://doi.org/10.1016/j.gfs.2020.100431

Gillespie S, van den Bold M (2017) Agriculture, food systems, and nutrition: meeting the challenge. Glob Chall 1(3):1600002. https://doi.org/10.1002/gch2.201600002

Global Burden of Disease Study 2021 (GBD 2021) Global Burden of Disease Collaborative Network. Air pollution exposure estimates 1990–2021. Institute for Health Metrics and Evaluation (IHME), Seattle, USA, 2024. https://ghdx.healthdata.org/record/ihme-data/gbd-2021-air-pollution-exposure-estimates-1990-2021

Haile B, Azzarri C, Ahn HE (2021) Literature review on linkages between child nutrition and economic growth. IFPRI–MCC Technical Paper Series: Prioritizing Agricultural Investments for Income, Poverty Reduction, and Nutrition (No. 6). International Food Policy Research Institute (IFPRI), Millennium Challenge Corporation (MCC). https://doi.org/10.2499/p15738coll2.134795

Haque KB, Bidisha SH, Suhrawardy GM (2013) The role of credit in food production and food security. In: Ba Khondker BH, Bidisha SH, Suhrawardy GM (eds) The role of credit in food production and food security in Bangladesh. A published study for the National Food Policy Capacity Strengthening Programme, Bureau of Economic Research, University of Dhaka

Harriss B (1987) Nutrition and international agricultural research. Food Policy 12(1):29–34

Hasan T, Magalhaes RJS, Williams GM, Mamun AA (2015) Original Article: the role of maternal education in the 15-year trajectory of malnutrition in children under 5 years of age in Bangladesh 1–11. https://doi.org/10.1111/mcn.12178

Herforth A, Jones A, Pinstrup-Andersen P (2012) Prioritizing nutrition in agriculture and rural development: guiding principles for operational investments. Health, Nutrition, and Population (HNP) discussion paper. The World Bank, Washington

Horton S, Steckel RH (2013) Malnutrition: global economic losses attributable to malnutrition 1900–2000 and projections to 2050. How much have global problems cost the earth? A scorecard from 1900 to 2050. Cambridge University Press. 2013:247–272

Iftikhar S, Mahmood HZ (2017) Ranking and relationship of agricultural credit with food security: a district level analysis. Cogent Food Agric 3(1):1333242

Islam A, Chandana M, Debayan P, Smyth R (2016) Microcredit programme participation and household food security in rural Bangladesh. J Agric Econ https://doi.org/10.1111/1477-9552.12151

Kabir A, Rashid M, Hossain K, Khan A, Sikder SS (2020) Women’s empowerment is associated with maternal nutrition and low birth weight: evidence from Bangladesh. Demographic and Health Surveys, pp. 1–12

Kadiyala S, Harris J, Headey D, Yosef S, Gillespie S (2014) Agriculture and nutrition in India: mapping evidence to pathways. Ann NY Acad Sci 1331(1):43–56

Article   ADS   Google Scholar  

Kihiu EN, Amuakwa-mensah F (2020) Agricultural market access and dietary diversity in Kenya: gender considerations towards improved household nutritional outcomes. Food Policy. https://doi.org/10.1016/j.foodpol.2020.102004

Kiresur VR, Melinamani VP, Kulkarni VS, Bharati P, Yadav VS (2010) Agricultural productivity, rural poverty and nutritional security: a microevidence of inter-linkages from Karnataka State. Agric Econ Rev 23:29–40

Koenker R (2005) Quantile regression. Cambridge University Press

Koenker R, Bassett Jr G (1978) Regression quantiles. Econometrica 46(1):33–50

Kose T (2019) Energy poverty and health: the Turkish case. Energy Sources Part B: Econ Plan Policy 14(5):201–213

Lartey A (2008) Maternal and child nutrition in Sub-Saharan Africa: challenges and interventions. Proc Nutr Soc 67:105–108. https://doi.org/10.1017/S0029665108006083

Lewis J (2018) Infant health, women’s fertility, and rural electrification in the United States, 1930–1960. J Econ Hist 78(1):118–154

Machado JAF, Silva JMCS (2019) Quantiles via moments. J Econ 213:145–173. https://doi.org/10.1016/j.jeconom.2019.04.009

McDermott J, Johnson N, Kadiyala S et al. (2015) Agricultural research for nutrition outcomes—rethinking the agenda. Food Secur 7:593–607. https://doi.org/10.1007/s12571-015-0462-9

Müller O, Krawinkel M (2005) Malnutrition and health in developing countries. CMAJ 173(3):279–286

Njatang DK, Djourdebbé FB, Wadou NDA (2023) Climate variability, armed conflicts and child malnutrition in sub-saharan Africa: A spatial analysis in Ethiopia, Kenya and Nigeria. Heliyon 9(11)

Ogunniyi AI, Mavrotas G, Olagunju KO, Fadare O, Adedoyin R (2020) Governance quality, remittances and their implications for food and nutrition security in Sub-Saharan Africa. World Dev 127:104752

Onder H (2021) The impact of corruption on food security from a macro perspective. Future Food: J Food Agric Soc 9(1):1–11. https://doi.org/10.17170/kobra-202011192215

Osei RD, Lambon-Quayefio MP (2022) Effects of long-term malnutrition on education outcomes in Ghana: evidence from a panel study. Eur J Dev Res 34(1):1–21

Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. Cambridge Working Papers. Economics 1240(1):1

MathSciNet   Google Scholar  

Pesaran MH (2007) A simple panel unit root test in the presence of cross-section dependence. J Appl Econ 22:265–312. https://doi.org/10.1002/jae

Qingshi W, Awan MA, Ashraf J (2020) Current research in nutrition and food science. The Impact of Political Risk and Institutions on Food Security 08. https://doi.org/10.12944/CRNFSJ.8.3.21

Reutlinger S, Selowsky M (1976) Malnutrition and poverty: magnitude and policy options. World Bank Staff Occasional Papers No. OCP 23. World Bank Group. http://documents.worldbank.org/curated/en/660451468739511445/Malnutrition-and-poverty-magnitude-and-policy-options

Schlein L (2019) UN: Most Child, Maternal Deaths Occur in Sub-Saharan Africa. VOA News. https://www.voanews.com/a/africa_un-most-child-maternal-deaths-occur-sub-saharan-africa/6176106.html

Shafiq A, Hussain A, Asif M, Hwang J, Jameel A, Kanwel S (2019) The effect of “women’s empowerment” on child nutritional status in Pakistan. Int J Environ Res Public Health 16(22):4499

Smith LC, Haddad L (2015) Reducing child undernutrition: past drivers and priorities for the post-MDG era. World Dev 68:180–204. https://doi.org/10.1016/j.worlddev.2014.11.014

Standaert S (2015) Divining the level of corruption: a bayesian state-space approach. J Comp Econ 43(3), 782–803

Teorell J, Sundström A, Holmberg S, Rothstein B, Alvarado Pachon N, Mert Dalli C (2021) The quality of government standard dataset, version January 21. University of Gothenburg, The Quality of Government Institute. http://www.qog.pol.gu.se https://doi.org/10.18157/qogstdjan21

Tesfaye W, Blalock G, Tirivayi N (2021) Climate‐smart innovations and rural poverty in Ethiopia: exploring impacts and pathways. Am J Agric Econ 103(3):878–899. https://doi.org/10.1111/ajae.12161

Thomson H, Snell C, Bouzarovski S (2017) Health, well-being and energy poverty in Europe: a comparative study of 32 European countries. Int J Environ Res Public Health 14(6):584

Usman M, Hammar N (2021) Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: fresh insights based on the STIRPAT model for Asia Pacific Economic Cooperation countries. Environ Sci Pollut Res 28(12), 15519–15536

Victora CG, Adair L, Fall C, Hallal PC, Martorell R, Richter L, Sachdev HS (2008) Maternal and child undernutrition: consequences for adult health and human capital. Lancet 371(9609):340–357

von Grebmer K, Bernstein J, Geza W, Ndlovu M, Wiemers M, Reiner L, Fritschel H (2023) Global Hunger Index: the power of youth in shaping food systems. Welthungerhilfe (WHH)

Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Bhutta ZA (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396(10258):1204–1222

Westerlund J (2007) Testing for error correction in panel data. Oxf Bull Econ Stat 69(6):709–748

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Acknowledgements

Cuicui Ding: This work is supported by the Research Project of basic research business expenses in colleges and universities of Xinjiang Uygur Autonomous Region “Study on the Coupling and Coordination of New Urbanization and Green Development in Xinjiang Bingtuan County in the New Era”(XJEDU2024J015).

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Leon Moise Minani

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Cuicui Ding: conceptualized the idea, wrote the original draft, provided supervision throughout the research process, provided supervision, conducted the formal analysis, contributed to review and editing; Khatib Ahmad Khan: conceptualized the idea, wrote the original draft, performed data collection, analyzed data, provided supervision, and contributed to review and editing; Hauwah K. K. Abdul Kareem: wrote the original draft, contributed to review and editing; Siddharth Kumar: wrote the original draft, contributed to review and editing; Leon Moise Minani: wrote the original draft, contributed to review and editing; Shujaat Abbas: wrote the original draft, contributed to review and editing, provided supervision, conducted visualization using software.

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Midwestern specialty crop impacts on the environment and health: a scoping review

• Laura E. Balis   ORCID: orcid.org/0000-0002-8981-1621 1 ,
• Emily Shaw 1 ,
• Whitney Fung Uy 1 ,
• Katie Nelson 1 ,
• Maryan Isack 1 ,
• Laura Flournoy 1 , 2 ,
• Daniele Vest 1 ,
• Jessie Deelo 2 &
• Amy L. Yaroch 1  

Agriculture & Food Security volume  13 , Article number:  38 ( 2024 ) Cite this article

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Introduction

The United States food system mainly relies on monoculture farming, leading to negative impacts on human and environmental health. Transitioning to specialty crop production (fruits, vegetables, and tree nuts) could alleviate challenges. The goal of this scoping review was to understand environmental and health impacts of locally distributed specialty crops in the Midwest.

Researchers searched databases for peer-reviewed literature and agricultural websites for grey literature. Inclusion criteria were specialty crop production; environmental, economic, or health outcomes; Midwest location; and local distribution. Researchers charted data based on the reach, effectiveness, adoption, implementation, and maintenance framework.

Grey (n = 9) and peer-reviewed (n = 19) sources met inclusion criteria. Sources reported specialty crops reached diverse populations through community gardens and farmers’ markets with positive impacts on nutritional intake. Effectiveness of production practices on soil and plant quality and greenhouse gas emissions was mixed.

Conclusions

Local specialty crop production shows promise, but more rigorous study designs with long-term follow-up are needed.

The United States (U.S.) food system, with its reliance on large-scale monoculture, leads to negative impacts on both human health and the environment [ 1 , 2 ]. Monocropping (i.e., single crops grown continuously, such as corn and soybeans) was initiated to feed the growing U.S. population during the 20th century and resulted in increased yield and reduced costs [ 1 , 2 , 3 ]. However, these advances led to long-term negative impacts on both human and environmental health [ 1 , 4 ].

First, related to human health, monocropping has been shown to decrease dietary diversity and contribute to the overconsumption of nutrient-deficient staple crops [ 5 ]. The reduced availability of diverse, nutrient-rich foods contributes to increased intake of nutrient-poor, high-calorie foods, which increases risk of chronic disease [ 6 ]. As well, monocropping depletes soil nutrients over time, leading to reduced nutrient availability in the food supply [ 7 ].

Second, as for environmental health, monocropping systems can cause significant erosion and alter the microbial landscape of the soil [ 8 ]. To counteract soil nutrient depletion, synthetic fertilizers are often added to monocrops to encourage plant growth [ 9 ]. Production of these fertilizers relies on fossil fuels, which contribute to greenhouse gas (GHG) emissions and can leave harmful residues that accumulate in the soil and leech into water systems [ 10 , 11 ]. In addition, farming practices commonly associated with monocropping such as mechanical tillage and use of heavy equipment can cause soil compaction and contribute to erosion, eventually resulting in a loss of soil fertility and reduced carbon sequestration [ 10 ]. Furthermore, compaction can reduce water absorption and increase runoff, which leaves soils prone to drought [ 8 ].

Transitioning to specialty crop production and away from monocropping has the potential to help alleviate these challenges and mitigate the impacts of climate change. Specialty crops are defined as fruits and vegetables, tree nuts, dried fruits, and horticulture and nursery crops that are produced for human use (as compared to monocrops, which are produced primarily for animal feed and biofuels, as well as highly processed foods for human consumption) [ 12 ]. Producing specialty crops diversifies agricultural production systems and could enhance impacts on both human health and the environment.

In addition, there is growing interest in local food systems as a method of distributing specialty crops [ 13 , 14 ]. While there is no universal definition of “local food systems”, the 2008 Food, Conservation, and Energy Act considers foods that were produced within 400 miles of where they are marketed to be “local” [ 14 ]. Local food systems have the potential to increase access to nutritious foods and improve environmental and health outcomes, but these benefits depend on the supply chain, product type, and local context [ 14 ]. Distributing specialty crops locally could potentially benefit human health and the environment through decreasing transportation outputs [ 13 , 14 ]. However, little is known about the environmental and health impacts of specialty crops, including those that are distributed locally [ 13 , 14 ].

To begin answering these questions, focusing on the Midwest region of the United States is key. The Midwest (Michigan, Ohio, Indiana, Illinois, Wisconsin, Minnesota, Iowa, Missouri, Kansas, Nebraska, South Dakota, and North Dakota) is the primary agriculture-producing region in the U.S., with the highest number of acres operated and highest gross output compared to other regions [ 15 ]. However, the Midwest is especially suited for corn and soy production, and there is a disproportionate share of acreage between monocrops and specialty crops [ 16 , 17 ]. Seventy-five percent of the 127 million acres of agricultural land in the Midwest is used to produce monocrops, such as corn primarily for animal feed and ethanol feedstock, while the other 25% is used to produce specialty crops including apples, asparagus, grapes, cherries, cranberries, blueberries, and pumpkins, along with multiple other types of fruits and vegetables [ 18 ].

New practices are needed to respond to agricultural and practical challenges. For example, as electric vehicles become more common, the demand for corn ethanol will decrease alongside a decreased demand for gasoline [ 19 ]. Given the unpredictability of the future of this market, farmers would benefit from diversifying with higher value crops to remain viable [ 20 ]. However, major changes to agricultural systems typically occur first on small scales before diffusing across the country [ 4 ]. Thus, beginning with a focus on the Midwest, with its reliance on monocrops and related challenges to specialty crop production, could lead to implications for other regions of the U.S. and expanded specialty crop production across the country.

Taken together, a deeper understanding of the potential environmental and health impacts of locally distributed specialty crops in the Midwest is necessary to inform next steps for expanding specialty crop production. Thus, the goal of this study was to understand environmental and health impacts of locally distributed specialty crops in the Midwest.

Scoping review methodology was used to examine links between diverse fields of study (agriculture, environment, human health and nutrition) and provide flexibility in investigating complex relationships between factors across disciplines [ 21 , 22 , 23 ]. PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines were followed (see Supplementary File) [ 24 ]. Data are available upon reasonable request to the authors.

Data sources

The scoping review of peer-reviewed and grey literature was conducted from March to April 2023. The search for peer-reviewed literature was conducted through searching the databases Cab Direct, PubMed, Environment Complete, and Academic Search Complete for search terms developed in consultation with a research librarian. Key search terms focused on the agricultural production practices (e.g., local food systems, short food supply chains, specialty crops, and alternative food networks), location (Michigan, Ohio, Indiana, Illinois, Wisconsin, Minnesota, Iowa, Missouri, Kansas, Nebraska, South Dakota, and North Dakota), and impacts (e.g., nutrition, health, chronic disease, economic benefit, rural development, environmental impact, and climate change), see the Appendix for the complete search strategy.

The search for grey literature was conducted through a customized internet and database search [ 25 , 26 , 27 , 28 ]. Search terms were modified from the peer-reviewed literature search, as the website and database search engines do not have the ability for complex search syntax. Thus, we searched for “specialty” or “local” or “supply chain” in the Land-Grant Impact Statements database, North Central Sustainable Agriculture Research and Education (SARE), USDA Agricultural Marketing Service, Center for Rural Affairs, and Specialty Growers’ Associations and Cooperative Extension System websites for each of the Midwest states. Searches were adapted based on each website’s area of focus and search function capabilities; for example, the Land Grant Impact Statements database allowed filtering by region, and the SARE website included search parameters for specific commodities and year of publication, see Table 1 for details. Events, staff member biographies, and marketing posts were removed from initial search results.

Study selection

Peer-reviewed literature and grey literature sources were included if they (1) focused on specialty crop production (i.e., fruits and vegetables, tree nuts, dried fruits, horticulture, and/or nursery crops including floriculture) [ 29 ], (2) included environmental (biodiversity, climate change, soil health, water quality, tillage practices, and soil fertility) or health outcomes (fruit and vegetable intake, food security, and chronic disease), (3) took place in the Midwest (MI, OH, IN, IL, WI, MN, IA, MO, KS, NE, SD, and ND) [ 30 ], (4) included local product distribution (as defined by the authors), (5) were written in English, and (6) were published between 2004 and 2023 (to align with the initiation of the Specialty Crops Competitiveness Act of 2004) [ 29 ].

Two authors independently reviewed each peer-reviewed publication’s title for inclusion or exclusion. Authors met to resolve discrepancies and used a senior researcher to assist with resolving, if necessary. Next, for the included articles, two authors reviewed each publication’s abstract, coded for inclusion/exclusion, and resolved using the same process. Finally, for the included articles, two authors reviewed the full text, determined inclusion/exclusion, and resolved. As grey literature typically does not contain a descriptive abstract, we used a simplified approach. Two authors independently reviewed the title of each grey literature publication, coded for inclusion or exclusion, and met to resolve discrepancies. For included grey literature, two authors reviewed the full text, determined inclusion/exclusion, and resolved.

Data charting

Data were extracted using a coding guide based on the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) Framework, which was developed to speed the translation of research to practice by considering both the individual and organizational factors that determine overall impact of interventions in real world settings [ 31 ]. RE-AIM dimensions assess intervention reach (number, proportion, and representativeness of individuals who participate or are influenced), effectiveness (impacts on primary outcomes), adoption (number, proportion, and representativeness of staff or settings willing to initiate), implementation (cost and consistency of delivery), and maintenance (long-term impacts on primary outcomes and long-term institutionalization within organizations) [ 31 ]. Operationalization of each dimension for this study is detailed in Table 2.

Critical appraisal of the evidence was not included due to the pragmatic nature of the research, inclusion of grey literature, and broad nature of the research topic [ 22 , 32 ]. Two authors independently coded two sources and met to discuss and resolve discrepancies. The data charting form was then refined based on items that were deemed unclear. Next, two authors independently coded and met to reconcile the remaining sources, consulting with a senior researcher as a “critical friend” [ 33 ] as needed. Finally, to synthesize results, peer-reviewed and grey literature sources were organized by their primary outcomes (environment or health and nutrition) and summarized by RE-AIM dimension.

Selection of sources

The initial search for grey literature sources yielded 1184 articles. Article titles were screened and 461 were excluded, because they did not focus on specialty crop production (n = 283), were duplicates (n = 153), were not reported in English (n = 10), took place outside the Midwest (n = 9), did not report environmental or health outcomes (n = 4), did not focus on products that were locally distributed, or were inaccessible (n = 1). This left 723 articles that underwent full text screening, and 714 were excluded, because they did not report environmental or health outcomes (n = 545), were not focused on specialty crops (n = 132), did not focus on products that were locally distributed (n = 12), were duplicates (n = 10), were inaccessible (e.g., behind a paywall, n = 8), or took place outside of the Midwest (n = 7). The screening process resulted in nine articles that were eligible and included in this review (see Fig.  1 ).

Eligibility and inclusion of grey literature in scoping review

The initial search for peer-reviewed literature yielded 3189 articles. Study titles were screened, and 2550 articles were excluded, because they were not focused on specialty crop production (n = 2201), they took place outside the Midwest (n = 179), they did not report environmental or health outcomes (n = 137), or were duplicates (n = 33). This left 639 article abstracts that underwent screening, and 578 were excluded, because there were no environmental and health outcomes reported (n = 260), they were not focused on specialty crop production (n = 194), they took place outside of the Midwest (n = 68), were inaccessible (n = 37), were systematic reviews (n = 14), were duplicates (n = 3), or did not focus on products that were distributed locally (n = 2). Abstract screening left 61 articles that underwent full text screening, and 42 were excluded, because they did not report environmental or health outcomes (n = 16), were not focused on specialty crop production (n = 13), took place outside of the Midwest (n = 6), did not focus on products that were locally distributed (n = 4), were systematic reviews (n = 2), or were inaccessible (n = 1). The screening process resulted in 19 articles that were eligible and included in this review (see Fig.  2 ).

Eligibility and inclusion of peer-reviewed literature in scoping review

Finally, during the data charting phase, the included articles and reports (herein, “reports” is used to refer to both) were classified into either environmental impacts or health and nutrition impacts based on the study outcomes. Of the grey literature reports, eight included environmental impacts and one included health and nutrition impacts. Of the peer-reviewed articles, five included environmental impacts and 14 included health and nutrition impacts. Thus, for data charting and analysis, reports were organized by environmental impacts (n = 13) and health and nutrition impacts (n = 15). Peer-reviewed and grey literature are presented together to consolidate all available evidence [ 34 ]. Scoping review data is organized by RE-AIM dimension for reports detailing environmental impacts, followed by reports detailing health and nutrition impacts. Results by RE-AIM dimension for comprehensive data (i.e., reported in most sources) are detailed in Tables 3–6.

Characteristics of sources

Environmental impacts.

Reach. Studies took place across the Midwestern states: Minnesota [ 35 ], Indiana [ 36 , 37 ], Wisconsin [ 38 ], Michigan [ 39 , 40 ], Ohio [ 41 , 42 , 43 ], Missouri [ 44 , 45 ], and Illinois [ 46 , 47 ]. Consumer distribution channels varied across studies. Most commonly, channels included gardens (private, community, and institutional) and farmers’ markets [ 35 , 42 , 43 , 47 ]. Less commonly, studies described other methods of distribution, including food system venues [ 37 ], urban farms [ 35 , 47 ], Community Supported Agriculture (CSA) [ 36 ], you-pick operations [ 44 , 45 ], restaurants [ 44 ], food pantries [ 46 ], cooperatives [ 38 , 46 ], and campus dining halls [ 40 ]. Overall, there was much variation in the channels used to distribute specialty crops to consumers.

Effectiveness. The 13 included studies varied in their aims and research designs. Study aims included assessing home and community gardens [ 35 , 42 , 43 , 47 ], specific production practices (e.g., hoop houses or high tunnels) [ 37 , 40 ], amendments (biochar or other organic amendments and decomposition specialty fungi) [ 36 , 38 , 44 , 45 , 46 ], pest control (e.g., copper fungicides) [ 39 ], and food waste reduction practices [ 41 ]. No studies compared environmental outcomes of specialty crops compared to monoculture/commodity crop systems. Research designs were observational and experimental, with approaches including paired comparison [ 35 , 36 , 38 , 39 , 42 , 43 , 44 , 45 , 46 ] and pragmatic pre–post or single timepoint assessment designs [ 37 , 40 , 41 , 47 ].

The studies focused on diverse environmental outcomes, including soil health and quality, GHG impact, and plant quality. Soil health and quality was assessed through measures including chemical, biological, and physical properties including texture [ 35 , 42 , 43 ], density [ 35 ], aggregate stability [ 35 ], nutrients [ 35 , 38 , 39 , 43 , 45 , 47 ], pH [ 35 , 38 , 43 , 45 , 47 ], organic matter [ 35 , 38 , 42 , 43 , 47 ], heavy metals [ 43 ], water infiltration rate [ 35 , 43 ], hydraulic conductivity [ 35 ], microbial activity [ 38 , 42 ], nematode trophic composition [ 42 , 47 ], and insect biodiversity [ 35 ]. GHG impact was primarily assessed indirectly through these soil measures (i.e., soil’s ability to store GHG that would otherwise be released into the atmosphere) as well as by measuring reduction of food waste and comparison of carbon dioxide emitted through production in different climatic zones [ 40 , 41 ]. Plant quality was assessed through plant size and productivity, disease presence and management, and pest issues [ 36 , 37 , 38 , 39 , 42 , 44 , 46 ]. Finally, two studies assessed crop diversity, one among community gardens and one among high tunnel users [ 37 , 43 ]. Overall, there are a variety of research questions asked related to the effectiveness of specialty crops, and multiple methods of answering these questions.

As for the study results, soil health and quality outcomes were mixed, depending on the production practices used. Improvements to soil pH were found through the use of biochar and copper-resistant soil bacteria [ 39 , 45 , 46 ], while other studies found no difference in soil health using practices including biochar, wine cap mushrooms as a soil amendment, or hyperaccumulating plants (alfalfa) [ 36 , 38 ]. As for other observations of home and community gardens, two studies found mixed properties of soil health in urban home food gardens [ 43 ], while another found that assessing existing soil quality when establishing new food production sites in urban areas is more important than applying specific amendments [ 35 ]. One study found a reduction in food waste by working with farmers to use seconds and between-market produce to create value-added products [ 41 ].

Regarding plant quality, primarily positive outcomes were found through the use of specific production practices, including worm casting; azomite; and soil, copper, and biochar amendments [ 36 , 38 , 39 , 46 ]; combined amendments (e.g., pine bark and coffee grounds) produced mixed results [ 44 ]. Yields were improved through [ 37 ] worm casting and azomite in greenhouses and gardens as well as use of high tunnels [ 36 , 37 ]. Crop diversity was high among urban home food gardeners and farmers with high tunnels [ 37 , 43 ].

Adoption . Several reports examined multiple farm plots, gardens, or hoop houses [ 37 , 41 , 42 , 43 , 47 ], whereas others focused on a single operation [ 36 , 38 , 44 , 45 , 46 ]. The amount of land dedicated to specialty crop production ranged from small urban gardens to a 40-acre commercial farm [ 36 , 38 , 42 , 43 , 47 ]. The number and characteristics of producers were not typically specified; production teams included small teams of two, family-run farms, and operations including multiple owners and full-time workers [ 36 , 44 , 45 , 46 ]. Overall, characteristics of farms and gardens varied widely across reports.

Implementation . Production practices varied broadly between reports, with the most common practice being the use of cover crops [ 35 , 38 , 45 , 46 ]. Both tillage and no till approaches were described. No till or minimal till practices were highlighted in three reports [ 38 , 46 , 47 ], while tillage strategies were discussed in two [ 40 , 45 ]. General strategies around increasing soil nutrients were also reported, including soil enrichment [ 36 , 45 ]; nutrient cycling, nutrient management, and use of organic fertilizers [ 38 , 43 ]; soil amendments such as biochar and basalt [ 36 , 45 ]; and compost practices [ 40 , 47 ]. Few reports provided information about whether their production was organic or not; two mentioned using both organic and conventional production [ 39 , 40 ], and one mentioned growing organic fruits and vegetables [ 36 ].

To reduce climate change challenges, multiple reports detailed efforts to find innovative and sustainable ways to continue to grow Midwest specialty crops, such as blueberries [ 41 , 44 , 45 ], cabbage [ 35 , 36 , 41 , 43 ], and strawberries [ 36 , 41 ]. As for cost, many of the projects were funded by SARE grants through the USDA [ 35 , 36 , 38 , 39 , 41 , 44 , 45 , 46 ]. While the grant amount was reported for each SARE-funded project (and ranged from $7,496 to $198,529, depending on scope), the full costs of the studies or new production practices were not provided.

Maintenance . Maintenance was underreported across the included reports, with one detailing actual long-term results [ 39 ]. Two reports shared plans to continue data collection and develop additional goals following their initial findings [ 41 , 44 ]. In addition to extracting data on maintenance of long-term production practices, data were also captured on producers’ efforts to disseminate their work to scale practices to their peers. Multiple reports described sharing the concepts and results of research through multiple means, including lectures, workshops, media, research presentations, and organization- and community-wide educational outreach [ 38 , 39 , 44 , 45 , 46 ].

Health and nutrition impacts

Reach . Reports focused on health and nutrition impacts also took place across the Midwestern states: Missouri [ 48 , 49 , 50 ], Illinois [ 51 ], Ohio [ 41 , 42 ], Minnesota [ 52 , 53 ], Wisconsin [ 54 ], and Michigan [ 55 ]. One report detailed a study that took place in a small city in the Midwest with no mention of the city or state name [ 56 ]. Food access points and interventions detailed in the reports primarily reached consumers through community gardens [ 48 , 50 , 52 , 56 , 57 , 58 , 59 ] and farmers’ markets [ 51 , 53 , 54 , 55 , 57 , 60 ]. Other distribution channels included a CSA [ 57 ], a local food hub (including a local produce market and healthy food café) [ 61 ], Fresh Stops (farmers' markets organized by community-based organizations) [ 62 ], and a sliding scale cooperative grocery store [ 49 ].

Reports indicated that specialty crops reached diverse populations through food access points, including community members from racial and ethnic minority groups and those experiencing food insecurity. Of those that reported demographics, four studies primarily served African American residents [ 50 , 55 , 60 , 61 ], one was implemented in a Marshallese community [ 56 ], and one surveyed a community of refugee and immigrant families (including Karen, Bhutanese, Hmong, and Lisu) [ 52 ]. The number of participants reached ranged from 120 community gardeners [ 58 ] to 1320 community members receiving SNAP (Supplemental Nutrition Assistance Program) benefits who participated in a farmers’ market Electronic Benefits Transfer (EBT) program [ 54 ].

Effectiveness . The majority of the 15 included reports aimed to understand the association between a specific food access point (e.g., a community garden) or intervention (e.g., financial incentives to use a farmers’ market) and fruit and vegetable consumption [ 48 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. Less commonly, studies aimed to test innovative local food distribution models (e.g., turning a large neighborhood lot into a market garden to supply food to a cooperative grocery store) [ 49 , 50 , 61 , 62 ]. Research designs were observational and experimental, including quasi-experimental [ 59 , 61 ], parallel [ 56 ], pre–post [ 52 , 60 ], and cross-sectional or post-test only designs [ 48 , 51 , 53 , 54 , 55 , 58 ]. Mixed methods studies were also conducted, although the outcomes of interest (i.e., impacts on community members’ health or nutrition status) were typically captured through quantitative (pre–post or cross-sectional surveys) rather than qualitative methods [ 50 , 57 , 62 ].

As for study outcomes, 11 assessed fruit and vegetable consumption and five examined food security [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 57 , 58 , 60 , 61 , 62 ]. Outcomes were typically assessed through self-report survey items, with nine studies using validated measures (e.g., the Healthy Eating Index Score, USDA Household Food Security Module, or Behavioral Risk Factor Surveillance Survey) [ 48 , 50 , 51 , 52 , 54 , 55 , 57 , 58 , 60 , 61 ]. Of studies that assessed participants’ fruit and vegetable intake [ 48 , 50 , 51 , 52 , 53 , 54 , 58 , 60 , 62 ] or food security [ 49 , 50 , 58 , 61 ], most found positive results. This included both studies that aimed to understand impacts of food distribution points and studies examining impacts of specific interventions.

Implementation . Reports detailing health and nutrition impacts included limited implementation data. Little information on study funding was provided, with three projects receiving funds through the USDA (e.g., the Healthy Food Financing Initiative [ 54 , 62 ]) and others from SARE [ 49 , 61 ]. Only two reports used a theory, framework, or model to guide intervention development or evaluation [ 56 , 59 ]. Finally, as for the specialty crops available to study participants, most sources mentioned fruits and vegetables without details on specific types [ 49 , 53 , 55 , 56 , 57 , 58 , 59 , 61 ].

Maintenance . Most reports provided little or no detail on long-term individual-level maintenance or program sustainability, with only one reporting that most intervention components were not maintained [ 61 ]. Others reported sustainability challenges, including a need for funding and more supportive policies [ 54 , 57 , 60 ].

This scoping review used the RE-AIM framework to synthesize both peer-reviewed and grey literature on the environmental and health impacts of locally distributed specialty crops in the Midwest. Overall, the available evidence was primarily from peer-reviewed (vs. grey) literature and related to environmental (vs. health) impacts. Here, we highlight key findings and future directions related to achieving beneficial impacts on both the environment and human health and nutrition, including improved synergy between producers and food access practitioners to achieve these outcomes.

First, the review aimed to understand specialty crops’ environmental impacts. Results indicate that specialty crops reach consumers through a variety of distribution channels; have varying effects on soil health and quality, GHG impact, and plant quality; are adopted by farms of diverse size and structure; and are implemented through practices designed to improve soil health and alleviate climate challenges. Little is known about long-term maintenance plans. As the RE-AIM framework defines the overall impacts of an intervention as the combination of each dimension, more information on effectiveness and maintenance is needed to determine the extent to which specialty crops improve the environment, and whether improvements are maintained over time.

Effectiveness was difficult to determine because of variation in research designs, aims, outcomes, and results. For example, the effectiveness of amendments on soil health and plant quality were mixed. While some studies found that plant quality was improved (e.g., through biochar or a combination of pine bark and coffee grounds), overall environmental benefits varied. In addition, multiple studies examined effectiveness of production practices on soil health and found mixed results. Soil health was measured through various methods including chemical, biological, and physical properties. Because of these mixed findings, tools and technology that allow producers to measure soil health at scale and in real time are recommended for future studies. These tools should be affordable and adaptable to differing types of specialty crop production in diverse geographical regions.

In addition, more direct measures of GHG emissions are needed to better understand effectiveness. Specifically, there is a gap in practical use among specialty crop producers to measure environmental impact due to specialized food production and varying inputs that are different from traditional agricultural methods. Life cycle assessments (LCAs) can be useful to model the processes from farm to fork and food waste management and identify areas that impact the environment [ 63 ]. Yet, there is a lack of specialized LCAs for specialty crop production, and no reports included in this review used LCAs. To enhance existing measures and increase practical use for formal measures of GHG emissions, literature suggests the need to “identify food-tailored methods in LCA” and a combination of LCA methods to be applied to individual farm and larger scale food productions [ 63 , 64 ]. As well, there is a need to identify standardized measures with strong correlation to GHG emissions that are feasible and acceptable for producers (e.g., GHG calculators that have been developed through LCAs tailored to specialty crop production). Researchers, funders, and policymakers could support producers by increasing research and funding (e.g., through the Environmental Protection Agency (EPA) or National Institute of Environmental Health Sciences) to conduct context-specific LCAs and identifying practical ways to measure environmental impact and GHG emissions [ 63 , 64 ]. This could also increase the prevalence of longitudinal study designs assessing emissions, which can be challenging, expensive, and hard to control without appropriate measures [ 65 ].

Finally, more information is needed on adoption and organizational-level maintenance. While much has been studied about the dissemination of agricultural interventions [ 66 ], data detailing specific reasons for adopting specialty crop production was limited in the scoping review, and could aid future efforts in expanding production. More investigations are needed on which dissemination sources and channels are most effective for producers to share and receive reliable information on production practices (e.g., how to manage specific insect and disease outbreaks or develop fruit and vegetable varieties suitable for local conditions) [ 67 , 68 ]; this could also lead to improved implementation and maintenance of specialty crop production.

Second, as for health and nutrition impacts, findings indicate that specialty crops reached diverse populations through multiple food access points and were effective in increasing fruit and vegetable consumption and improving food security, while little is known about implementation or maintenance . Again, more complete information is needed to fully assess the public health impacts of specialty crops. First, reporting implementation data (fidelity, adaptations, and cost) is important to understand whether interventions are implemented as intended, what adaptations could improve fit and delivery, and whether costs are reasonable for long-term integration in systems [ 31 ].

Next, related to maintenance, this review also uncovered several barriers to the sustainability of food access interventions across the Midwest, with limited funding being the most occurrent. Moreover, many reports suggested that innovative approaches to dedicated funding and buy-in from the community members were needed for long-term program sustainability and expansion. According to Kim, 2016, little public funding is available for the research and development of specialty crops [ 67 ]. And most recently, on August 23, 2023, USDA announced funding for the 2023 Specialty Crop Block Grant Program (SCBGP), which provides grants to state departments of agriculture to fund programs that enhance the competitiveness of specialty crops [ 69 ]. With this grant, funding will be distributed to state programs across the U.S. to address the needs of specialty crop producers [ 69 ]. In addition to funding, building evidence to show successful outcomes and developing policy-level support have been identified as facilitators to sustaining food access interventions [ 70 ].

Finally, while effectiveness data was present, use of validated nutrition security measures [ 71 ] are needed, as nutrition insecurity remains a challenge for community members facing health disparities. The food access interventions found in this review reached individuals who received federal food assistance from programs such as SNAP or WIC (Special Supplemental Nutrition Program for Women, Infants, and Children), suggesting that specialty crops are reaching community members facing health disparities; however, the extent to which nutrition security was improved is unknown. More robust study designs (beyond cross-sectional or post-test only) are also needed. Ideally, these designs assess maintenance of primary outcomes 6 months or more after completion of the intervention to gauge long-term benefits. However, individual-level, long-term maintenance is rarely reported for food access interventions, due in part to additional resources (i.e., staffing and funds) needed for maintenance-related efforts and loss of attrition, especially when working with limited resource audiences [ 72 , 73 ].

Synergy between agriculture and health sectors

More synergy between the agriculture and health sectors is also warranted to enhance the impacts of specialty crop production on human health and nutrition. Programs that connect food system actors from food production to food waste management can optimize the potential to positively impact both the farming and greater community by increasing access to locally grown fruits and vegetables. For example, this review found that food access practitioners reached community members through community gardens and farmers’ markets, but there was little mention of alternative distribution methods, such as food hubs or other innovative local distribution models [ 74 ].

One example is the USDA National Institute of Food and Agriculture’s (NIFA) investment in nutrition incentive (NI) programs that increase access to fresh fruits and vegetables and boost economic support for local farmers. Since the Food, Nutrition, and Conservation Act of 2008 [ 75 ], the USDA NIFA has piloted and continued to support NI projects that connect populations with diet-related health conditions (e.g., heart disease) or receiving SNAP benefits with various incentives to purchase fresh fruits and vegetables through the Healthy Incentive Pilot (HIP), Food Insecurity Nutrition Incentive (FINI) grant program, and most recently, the Gus Schumacher Nutrition Incentive Program (GusNIP) [ 76 ]. In general, NI projects offer incentives for program participants to purchase more fruits and vegetables, e.g., a SNAP household that purchases $10 of produce using their SNAP benefits will receive an additional $10 to spend on produce at a participating location like a farmers’ market or grocery store.

Further, invested parties involved in food production can “close the loop” by increasing food recovery in various ways. Reducing food waste is a promising practice to reduce GHG emissions and a priority area for the USDA and EPA [ 77 ]. However, food waste was only examined in one study in this review [ 41 ]. Given that food loss and waste is a major contributor to GHG emissions (primarily the generation of methane, a more harmful GHG than carbon dioxide) when food waste ends up in landfills [ 78 ], future research and practice could engage supply chain companies and food brands in identifying opportunities to reduce food loss and waste by diverting food waste that can be used in other ways. The EPA provides guidance for sustainable food waste management and prioritizes a hierarchy of the most to least preferred recovery methods: reduce food loss and waste at the source (e.g., during food production); if food is still edible, use the food to feed people instead of throwing it away; if inedible, divert food to feed animals; create other methods or reusing food waste, such as through industrial uses (e.g., anaerobic digestion) or composting; and finally, discard food at the landfill [ 79 ].

Reducing food waste and adopting food recovery practices may also have positive impacts on human health outcomes, such as diverting still-edible food to populations in need and increasing access to food that might have been thrown away instead. For example, organizations such as Upcycled Foods and Imperfect Foods work with producers across the country to develop processes to divert potential food waste (e.g., spent grains) into new products, which becomes an added distribution channel for growers to gain additional income [ 80 , 81 , 82 ]. If food recovery methods are adopted or desired, communities could gauge the demand for “ugly produce” and upcycled products that are still perfectly edible. This may involve conducting targeted marketing approaches or social marketing campaigns to increase awareness and demand for produce or products that are recovered. Based on communications research, this could include the environmental, resource-saving, and nutritional value of upcycled foods to boost consumers’ interest in their consumption of upcycled foods [ 83 ].

Since distribution channels vary by market and producer needs, communities should identify opportunities to recover food through local avenues. This would require funding to support research and development of secondary markets and opportunities for local collaboration. These partnerships could involve local food producers, food access practitioners, food businesses interested in purchasing locally grown produce, and municipal entities that oversee regulations related to business or organic waste management. As an example, a farm in Orlando, Florida receives local food scraps from a composting company and wood chips from local arborists to create their own compost [ 84 ].

In addition, a potential area of improvement is the idea of refining the efficiency of the food supply chain by connecting actors across the food system [ 85 ]. Often, small producers lack processing or cold storage facilities to take advantage of broader market opportunities [ 86 ]. Supply chains can be strengthened with the integration of food hubs that connect rural, mid-sized, and large farmers to increase market opportunities for local farmers [ 87 ]. While producers can identify secondary markets by selling to restaurants or other institutions [ 88 ], they can also connect with community organizations that might have funding to purchase local produce or are able to receive donations that are unsalable but still edible through “Farm to Food Bank” programs [ 89 ]. These examples can support producers and practitioners who distribute emergency food simultaneously. Overall, more ways to use imperfect or blemished foods at the packer and processor level are needed.

Future directions

Further investigation of agricultural practices with high potential to contribute to environmental health and adapt to climate change is critical, ideally through large-scale studies with robust designs and evaluation methods. Standardized measures and tools assessing environmental and health outcomes (i.e., soil health, GHG emissions, and food security) are necessary to monitor the success of specialty crop production and food access programs. Multisector efforts such as food waste recovery should be explored for their potential to impact both human health and the environment. This could include research on supply chain infrastructure to assess the limiting factors for harvesting, processing, storing, and distributing specialty goods; and the role of food retailers to address food access while maximizing supply chain effectiveness. Future research could also explore consumer interest in regional food sources; for example, the “Buy Fresh, Buy Local” movement could potentially become “Buy Regenerative, Buy Regional.”

Limitations

As with any scoping review, results are limited by the databases and search terms used, which may not have captured all relevant peer-reviewed and grey literature. In addition, limiting the area of focus to the Midwest may have excluded important insights into environmental and health impacts of specialty crops from other areas of the country. For example, only one study included in the scoping review examined local versus distant specialty crop production, but no health or nutrition outcomes were included [ 40 ], making it difficult to understand whether local produce is better for health. Studies directly comparing the impacts of local versus non-local foods or specialty crops versus conventional row crops could provide valuable insight into ways to improve environmental and health outcomes. Related, the methodological limitations of the included reports contributed to this difficulty, as many were cross-sectional, single-timepoint assessments or pragmatic pre–post designs. As such, observer bias, self-selection bias, or recall bias could have occurred [ 90 ]. Finally, the scoping review methodology aimed for breadth of findings, and as such it was difficult to compare results across studies. For example, we could not quantify the diversity of soil types, textures, or production methods in the included reports . Overall, it is difficult to assess direct impacts of specialty crop production because of complexities of the food system, and more research is needed to answer specific questions.

This scoping review aimed to understand environmental and health impacts of locally distributed specialty crops in the Midwest. Related to environmental impacts, results indicate that specialty crops reach consumers through a variety of distribution channels; have varying effects on soil health and quality, GHG impact, and plant quality; are adopted by farms of diverse size and structure; and are implemented through practices designed to improve soil health and alleviate climate challenges. As for health and nutrition impacts, findings indicate that specialty crops reached diverse populations through multiple food access points and were effective in increasing fruit and vegetable consumption and improving food security, while little is known about implementation or maintenance. More synergy between the agriculture and health sectors is warranted to enhance the impacts of specialty crop production on human health and nutrition. Taken together, specialty crop production shows promise for positively impacting environmental and health outcomes, but more research is needed.

Availability of data and materials

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Bourke PM, Evers JB, Bijma P, van Apeldoorn DF, Smulders MJM, Kuyper TW, et al. Breeding beyond monoculture: putting the “intercrop” into crops. Front Plant Sci. 2021. https://doi.org/10.3389/fpls.2021.734167 .

Article   PubMed   PubMed Central   Google Scholar  

Horrigan L, Lawrence RS, Walker P. How sustainable agriculture can address the environmental and human health harms of industrial agriculture. Environ Health Perspect. 2002;110:445–56.

Jacques PJ, Jacques JR. Monocropping cultures into ruin: the loss of food varieties and cultural diversity. Sustainability. 2012;4:2970–97.

Article   Google Scholar  

Spangler K, Burchfield EK, Schumacher B. Past and current dynamics of U.S. agricultural land use and policy. Front Sustain Food Syst. 2020. https://doi.org/10.3389/fsufs.2020.00098 .

Nkonde C, Audain K, Kiwanuka-Lubinda RN, Marinda P. Effect of agricultural diversification on dietary diversity in rural households with children under 5 years of age in zambia. Food Sci Nutr. 2021;9:6274–85.

Ziso D, Chun OK, Puglisi MJ. Increasing access to healthy foods through improving food environment: a review of mixed methods intervention studies with residents of low-income communities. Nutrients. 2022.14. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182982/ . 30 Aug 2023

Owino V, Kumwenda C, Ekesa B, Parker ME, Ewoldt L, Roos N, et al. The impact of climate change on food systems, diet quality, nutrition, and health outcomes: a narrative review. Front Clim. 2022. https://doi.org/10.3389/fclim.2022.941842 .

Magdoff F. Building soils for better crops: organic matter management. Soil Sci. 1993;156:371.

Machado S. Does intercropping have a role in modern agriculture? Journal of Soil and Water Conservation. 2009;64(2):55A. https://doi.org/10.2489/jswc.64.2.55A

Status of the World’s Soil Resources. ReliefWeb. 2015. https://reliefweb.int/report/world/status-worlds-soil-resources . 31 Aug 2023

The Roots of America’s Broken Food System. Healthy places by design. https://healthyplacesbydesign.org/the-roots-of-americas-broken-food-system/ . 31 Aug 2023

What is a Specialty Crop? Agricultural Marketing Service. https://www.ams.usda.gov/services/grants/scbgp/specialty-crop . 2 Aug 2023

Stein AJ, Santini F. The sustainability of “local” food: a review for policy-makers. Rev Agric Food Environ Stud. 2022;103:77–89.

Article   PubMed   Google Scholar  

Enthoven L, Van den Broeck G. Local food systems: reviewing two decades of research. Agric Syst. 2021;193: 103226.

Agricultural Production and Prices. U.S. Department of agriculture economic research service. https://www.ers.usda.gov/data-products/ag-and-food-statistics-charting-the-essentials/agricultural-production-and-prices/ . 31 Aug 2023

Midwest Climate and Specialty Crops. Iowa state university extension and outreach extension store. https://store.extension.iastate.edu/product/Midwest-Climate-and-Specialty-Crops . 7 Aug 2023

Burchfield EK, Nelson KS. Agricultural yield geographies in the United States. Environ Res Lett. 2021;16: 054051.

Agriculture in the Midwest. U.S. Department of Agriculture Climate Hubs. https://www.climatehubs.usda.gov/hubs/midwest/topic/agriculture-midwest . 2 Aug 2023

Janzen SH and J. The impact of long-run declines in gasoline use on the US corn market. Farmdoc daily. 2022. 12. https://farmdocdaily.illinois.edu/2022/12/the-impact-of-long-run-declines-in-gasoline-use-on-the-us-corn-market.html . 10 May 2024

Ethanol’s future unclear as electric vehicles grow in popularity. South dakota public broadcasting. 2022. https://listen.sdpb.org/business-economics/2022-09-19/ethanols-future-unclear-as-electric-vehicles-grow-in-popularity . 10 May 2024

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19–32.

Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5:69.

Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18:143.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467–73.

Godin K, Stapleton J, Kirkpatrick S, Hanning R, Leatherdale S. Applying systematic review search methods to the grey literature: a case study examining. Syst Rev. 2015. https://doi.org/10.1186/s13643-015-0125-0 .

Mahood Q, Van Eerd D, Irvin E. Searching for grey literature for systematic reviews: challenges and benefits. Res Synth Method. 2014;5:221–34.

Balis L, Strayer T, Ramalingam N, Wilson M, Harden S. Open-access physical activity programs for older adults: a pragmatic and systematic review. Gerontologist. 2019;59:e268–78.

Adams RJ, Smart P, Huff AS. Shades of grey: guidelines for working with the grey literature in systematic reviews for management and organizational studies: shades of grey. Int J Manag Rev. 2017;19:432–54.

Johnson R. Defining “specialty crops”: a fact sheet. Congressional research service. Report No.: R44719. 2017. https://crsreports.congress.gov/product/pdf/R/R44719/5#:~:text=USDA's%20list%20clarifies%20that%20%E2%80%9Cspecialty,%2C%20turfgrass%2C%20and%20hops5 ).

U.S. Bureau of Labor Statistics. Midwest census region: midwest information office. https://www.bls.gov/regions/midwest/midwest.htm . 29 Mar 2023

Glasgow R, Harden S, Gaglio B, Rabin B, Smith M, Porter G, et al. RE-AIM planning and evaluation framework: adapting to new science and practice with a 20 year review. Front Public Health. 2019. https://doi.org/10.3389/fpubh.2019.00064 .

O’Brien KK, Colquhoun H, Levac D, Baxter L, Tricco AC, Straus S, et al. Advancing scoping study methodology: a web-based survey and consultation of perceptions on terminology, definition and methodological steps. BMC Health Serv Res. 2016;16:305.

Sparkes AC, Smith B. Qualitative research methods in sport, exercise and health. London: Routledge; 2013.

Book   Google Scholar  

Paez A. Gray literature: an important resource in systematic reviews. J Evid-Based Med. 2017;10:233–40.

Jelinski N. Collaborative evaluation of ecosystem services provided by urban agricultural best management practices in the twin cities metropolitan area. SARE grant management system. https://projects.sare.org/sare_project/LNC17-392/ . 7 Jul 2023

Randolph J. Creative solutions to reverse indianapolis’ healthy food accessibility trends. SARE grant management system. https://projects.sare.org/sare_project/FNC20-1245/ . 7 Jul 2023

Bruce AB, Farmer JR, Maynard ET, Valliant JCD. Assessing the impact of the EQIP high tunnel initiative. J Agric Food Syst Commun Dev. 2017;7:159–80.

Google Scholar  

Bender L. Cultivating the wine cap mushroom while building soil health and suppressing plant disease—an innovative and economical approach to two common agricultural problems. SARE grant management system. https://projects.sare.org/sare_project/fnc17-1070/ . 8 Jul 2023

Sundin G. Examining the sustainability of copper use for disease management and horticultural benefit in tart cherry systems. SARE grant management system. https://projects.sare.org/sare_project/LNC06-268/ . 8 Jul 2023

Plawecki R, Pirog R, Montri A, Hamm MW. Comparative carbon footprint assessment of winter lettuce production in two climatic zones for Midwestern market. Renew Agric Food Syst. 2014;29:310–8.

Seabrook J. Formalizing partnerships to scale-up value-added local food in rural Ohio. SARE grant management system. https://projects.sare.org/sare_project/FNC16-1056/ . 8 Jul 2023

Reeves J, Cheng Z, Kovach J, Kleinhenz MD, Grewal PS. Quantifying soil health and tomato crop productivity in urban community and market gardens. Urban Ecosyst. 2014;17:221–38.

Taylor JR, Lovell ST. Urban home gardens in the global north: a mixed methods study of ethnic and migrant home gardens in Chicago. IL Renew Agric Food Syst. 2015;30:22–32.

Bohner E. Blueberry field renovation. SARE grant management system. https://projects.sare.org/sare_project/FNC15-985/ . 7 Jul 2023

Mareske R. Growing organic blueberries using biochar. SARE grant management system. https://projects.sare.org/sare_project/FNC17-1087/ . 8 Jul 2023

Welbel G. Investigating the ecological impact of pairing agroforestry establishment with biochar production. SARE grant management system. https://projects.sare.org/sare_project/FNC21-1315/ . 8 Jul 2023

Ugarte CM, Taylor JR. Chemical and biological indicators of soil health in Chicago urban gardens and farms. Urban Agric Reg Food Syst. 2020;5: e20004.

Barnidge EK, Hipp PR, Estlund A, Duggan K, Barnhart KJ, Brownson RC. Association between community garden participation and fruit and vegetable consumption in rural Missouri. Int J Behav Nutr Phys Act. 2013;10:128.

Neighborhood-Based Cooperative Market Gardening in Carondelet, St. Louis, MO. SARE grant management system. https://projects.sare.org/sare_project/FNC21-1293/ . 11 Jul 2023

Baker EA, Motton F, Seiler R, Duggan K, Brownson RC. Creating community gardens to improve access among african americans: a partnership approach. J Hunger Environ Nutr. 2013;8:516–32.

Singleton CR, Fouché S, Deshpande R, Odoms-Young A, Chatman C, Spreen C. Barriers to fruit and vegetable consumption among farmers’ market incentive programme users in Illinois, USA. Publ Health Nutr. 2018;21:1345–9.

Hartwig KA, Mason M. Community gardens for refugee and immigrant communities as a means of health promotion. J Commun Health. 2016;41:1153–9.

Norman J, Moore A, Beman M, Kelly P, Barlow P. Power of produce: farmers’ market incentive program targeting eating behaviors of children. J Ext. 2018;56(2):6.

Krokowski K. Evaluating the economic and nutrition benefits and program challenges of EBT programs at farmers’ markets. J Agric Food Syst Commun Dev. 2014;4:37–44.

Saxe-Custack A, Sadler R, LaChance J, Hanna-Attisha M, Ceja T. Participation in a fruit and vegetable prescription program for pediatric patients is positively associated with farmers’ market shopping. Int J Environ Res Publ Health. 2020;17:4202.

Weltin AM, Lavin RP. The effect of a community garden on HgA1c in diabetics of marshallese descent. J Commun Health Nurs. 2012;29:12–24.

Castellanos DC, Keller J, Majchrzak E. Exploring the connection between community food security initiatives and social-cognitive factors on dietary intake. J Agric Food Syst Commun Dev. 2016;7:21–31.

Hopkins LC, Holben DH. Food insecure community gardeners in rural Appalachian Ohio more strongly agree that their produce intake improved and food spending decreased as a result of community gardening compared to food secure community gardeners. J Hunger Environ Nutr. 2018;13:540–52.

Barnidge EK, Baker EA, Schootman M, Motton F, Sawicki M, Rose F. The effect of education plus access on perceived fruit and vegetable consumption in a rural African American community intervention. Health Educ Res. 2015;30:773–85.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Trapl ES, Smith S, Joshi K, Osborne A, Benko M, Matos AT, et al. Dietary impact of produce prescriptions for patients with hypertension. Prev Chronic Dis. 2018;15:E138.

Freedman DA, Bell BA, Clark J, Ngendahimana D, Borawski E, Trapl E, et al. Small improvements in an urban food environment resulted in no changes in diet among residents. J Commun Health. 2021;46:1–12.

Ohri-Vachaspati P, Masi B, Taggart M, Konen J, Kerrigan J. City fresh: a local collaboration for food equity. J Ext. 2009;47(6):1.

Notarnicola B, Sala S, Anton A, McLaren SJ, Saouter E, Sonesson U. The role of life cycle assessment in supporting sustainable agri-food systems: a review of the challenges. J Clean Prod. 2017;140:399–409.

Fan J, Liu C, Xie J, Han L, Zhang C, Guo D, et al. Life cycle assessment on agricultural production: a mini review on methodology, application, and challenges. Int J Environ Res Publ Health. 2022;19:9817.

Robertson GP, Allen VG, Boody G, Boose ER, Creamer NG, Drinkwater LE, et al. Long-term agricultural research: a research, education, and extension imperative. Bioscience. 2008;58:640–5.

Rogers E. Diffusion of innovations. 4th ed. New York: Free Press; 2010.

Kim H-J. Opportunities and challenges of alternative specialty crops: the global picture. HortScience. 2016;51:1316–9.

Larson KL, Duram LA. Information dissemination in alternative agriculture research: an analysis of researchers in the north central region. Am J Altern Agric. 2000;15:171–80.

Specialty Crop Block Grant Program. Agricultural marketing service . https://www.ams.usda.gov/services/grants/scbgp . 24 Aug 2023

Balis L, Yaroch A, Palmer S, Shaw E, Lima Dos Santos P, Byker SC. Implementation and impact of perinatal food is medicine: a qualitative research study. J Acad Nutr Diet. 2024. https://doi.org/10.1016/j.jand.2024.02.007 .

Calloway EE, Carpenter LR, Gargano T, Sharp JL, Yaroch AL. Development of new measures to assess household nutrition security, and choice in dietary characteristics. Appetite. 2022;179: 106288.

Harden S, Gaglio B, Shoup J, Kinney K, Johnson S, Brito F, et al. Fidelity to and comparative results across behavioral interventions evaluated through the RE-AIM framework: a systematic review. Syst Rev. 2015;4:1–3.

Oronce CIA, Miake-Lye IM, Begashaw MM, Booth M, Shrank WH, Shekelle PG. Interventions to address food insecurity among adults in Canada and the US. JAMA Health Forum. 2021;2: e212001.

O’Hara S. The urban food hubs solution: building capacity in urban communities. Metrop Univ. 2017;28:69–93.

Browning M. A look back: Food insecurity nutrition incentive program increases fruit and vegetable purchases, leads to launch of GusNIP. National institute of food and agriculture. https://www.nifa.usda.gov/about-nifa/impacts/look-back-food-insecurity-nutrition-incentive-program-increases-fruit-vegetable . 1 May 2024

Gus Schumacher Nutrition Incentive Program (GusNIP). National institute of food and agriculture. https://www.nifa.usda.gov/grants/programs/hunger-food-security-programs/gus-schumacher-nutrition-incentive-program . 1 May 2024

Draft National Strategy for Reducing Food Loss and Waste and Recycling Organics. U.S. department of agriculture. 2023. https://www.usda.gov/foodlossandwaste/national-strategy . 1 May 2024

From Farm to Kitchen: The environmental impacts of U.S. Food waste. U.S. environmental protection agency. 2024. https://www.epa.gov/land-research/farm-kitchen-environmental-impacts-us-food-waste . 1 May 2024

Sustainable Management of Food: Wasted Food Scale. U.S. environmental protection agency. 2015. https://www.epa.gov/sustainable-management-food/food-recovery-hierarchy . 2 Sep 2023

Moshtaghian H, Bolton K, Rousta K. Challenges for upcycled foods: definition, inclusion in the food waste management hierarchy and public acceptability. Foods. 2021;10:2874.

Imperfect Foods. Impact report. 2020. https://res.cloudinary.com/relay-foods/image/upload/v1617218157/Impact%20Reports/Imperfect_Foods_-_2020_Impact_Report.pdf . 21 Nov 2023

Upcycled Food Insights. Upcycled foods inc. https://upcycledfoods.com/insights/ . 21 Nov 2023

Aschemann-Witzel J, Asioli D, Banovic M, Perito MA, Peschel AO. Communicating upcycled foods: frugality framing supports acceptance of sustainable product innovations. Food Qual Prefer. 2022;100: 104596.

Anderson K, Jenkins S. Everoak farm and O-town compos: growing, eating, and wasting local. O-town compost. 2024. https://o-towncompost.com/otcblog . 1 May 2024

Berti G, Mulligan C. Competitiveness of small farms and innovative food supply chains: the role of food hubs in creating sustainable regional and local food systems. Sustainability. 2016;8:616.

Chen H, Ellett JK, Phillips R, Feng Y. Small-scale produce growers’ barriers and motivators to value-added business: food safety and beyond. Food Control. 2021;130: 108192.

Matson J, Sullins M, Cook C. The role of food hubs in local food marketing. Service report 73. U.S. department of agriculture rural development. https://www.rd.usda.gov/files/sr73.pdf . 22 Nov 2023

Berkenkamp J, Nennich T. Beyond beauty: the opportunities and challenges of cosmetically imperfect produce. 2015. Report no.: 1. https://misadocuments.info/Beyond_Beauty_Grower_Survey_Results_052615.pdf

Feeding the Carolinas. Farm to food bank. https://feedingthecarolinas.org/farm-to-food-bank/ . 1 May 2024

Popovic A, Huecker MR. Study bias. Treasure island: StatPearls Publishing; 2024.

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Balis, L.E., Shaw, E., Fung Uy, W. et al. Midwestern specialty crop impacts on the environment and health: a scoping review. Agric & Food Secur 13 , 38 (2024). https://doi.org/10.1186/s40066-024-00490-4

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Assessing effects of agriculture and industry on CO 2 emissions in Bangladesh

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Statistics, Begum Rokeya University, Rangpur, Rangpur, Bangladesh

• Md. Mushaddiqul Islam Amin, 
• Md. Mahafuzur Rahman

• Published: September 19, 2024
• https://doi.org/10.1371/journal.pclm.0000408
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Climate change is a critical global issue, driven primarily by the continuous rise in carbon dioxide (CO 2 ) levels. Addressing this challenge requires innovative solutions and proactive measures to mitigate its impact. This study investigates the impact of Bangladesh’s industrialization, agriculture, and imports on CO 2 emissions, exploring both linear and asymmetric relationships to inform sustainable development strategies. Advanced modeling techniques, namely autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) models are used to evaluate the impact of Bangladesh’s agricultural and industrial sectors on CO 2 emissions. Time-series data ranging from 1990 to 2022 are analyzed to ensure data stationarity, employing the augmented Dickey-Fuller (ADF) test. Subsequently, the existence of non-linear associations is validated using the Brock-Dechert-Scheinkman (BDS) test, with further confirmation through bounds testing to establish both symmetric and asymmetric long-run cointegrating relationships. Long and short-run coefficients are assessed using linear and asymmetry ARDL models, revealing that industrialization contributes to increased carbon emissions in Bangladesh. While the ARDL model reports that the effect of agriculturalization on CO 2 emissions is insignificant in the long-run, the asymmetry ARDL model suggests a rapid reduction in carbon emissions due to agriculturalization, observed both in the long and short-run. Additionally, imports have considerable impact on carbon emissions. Diagnostic tests have confirmed the adequacy of the model, while stability tests have validated the estimated parameters’ stability. Finally, the direction of association between variables is determined by applying linear and nonlinear Granger causality tests. This study underscores the importance of promoting sustainable industrial practices, enhancing agricultural efficiency, and regulating imports as pivotal strategies for mitigating CO 2 emissions and achieving enduring environmental sustainability in Bangladesh.

Citation: Amin MMI, Rahman MM (2024) Assessing effects of agriculture and industry on CO 2 emissions in Bangladesh. PLOS Clim 3(9): e0000408. https://doi.org/10.1371/journal.pclm.0000408

Editor: Abdul Rehman, Henan Agricultural University, CHINA

Received: March 26, 2024; Accepted: September 2, 2024; Published: September 19, 2024

Copyright: © 2024 Amin, Rahman. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The dataset is collected and analyzed during the current study are available in the World Development Indicators of the World Bank repository, accessible via https://databank.worldbank.org/bd_carbon_emissions/id/42abcb7e .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Environmental degradation due to CO 2 emissions has become a global challenge. Over the past few decades, we have been witnessing the effects of climate change, which is primarily due to excessive carbon dioxide emissions. In March 2024, global atmospheric CO 2 levels reached 423.16 ppm, up from 420.02 ppm in March 2023, indicating a continuing rise in greenhouse gas concentrations [ 1 ]. According to numerous research, the majority of CO 2 emissions are attributed to non-renewable energy sources. The utilization of non-renewable energy sources is steadily rising, both in developing and developed nations. According to the [ 2 ], “Developing countries account for 63% of the annual global emissions of carbon dioxide”. In contrast, developed countries, while contributing a smaller proportion of current annual emissions, have historically been the largest emitters. These nations have higher per capita emissions and have significantly contributed to the accumulation of greenhouse gases over the past century. As of 2023, the cumulative emissions from developed countries remain a major driver of global warming [ 3 ]. In response to this crisis, the United Nations initiated the Sustainable Development Goals (SDGs) in 2015, aiming to eradicate poverty, protect the environment, and promote global prosperity and peace by 2030. [ 4 ]. Nations must take proactive steps in formulating updated nationally determined contributions (NDCs) to meet these goals. Achieving a 45% reduction in carbon dioxide (CO 2 ) emissions by 2030 from 2010 levels and transitioning to net-zero emissions by 2050 is imperative [ 5 ]. CO 2 emissions should peak as early as possible to limit global warming to 1.5°C approximately before declining rapidly [ 6 ]. That is why controlling carbon dioxide emissions has become a major challenge and goal to ensure the sustainable development of low-income countries like Bangladesh. Worryingly, the country’s carbon dioxide emissions are rapidly increasing day by day.

Achieving higher economic growth has long been a cornerstone of Bangladesh’s macroeconomic policies. Over the past five decades, the country has made significant strides in economic development. However, this progress has come at a considerable environmental cost, marked by escalating carbon emissions, severe pollution, land degradation, deforestation, and resource depletion. These environmental challenges threaten Bangladesh’s sustainable development [ 7 ]. Despite contributing just 0.4% to global GHG (Greenhouse Gas) emissions, Bangladesh’s emissions could surge with continued economic growth and a large population. Air pollution already costs the country 9% of GDP annually. Improved air quality standards can enhance health and climate resilience. Bangladesh’s 2021 NDCs target a 21.8% emissions reduction by 2030, with the potential to exceed this through strong implementation, technological advancements, and regional cooperation [ 8 ]. Bangladesh, in alignment with its Nationally Determined Contributions (NDC), has embarked on a pathway of low-carbon development to address the pressing issue of climate change. Central to this commitment is the objective to reduce greenhouse gas emissions by 2030. Specifically, Bangladesh aims to cut 12 Mt CO 2 equivalent in the power, transport, and industry sectors, representing a 5% reduction below business-as-usual (BAU) emissions for these sectors. Furthermore, with the aid of international support, the country targets an additional reduction of 24 Mt CO 2 equivalent, which would achieve a total reduction of 10% below BAU emissions by 2030 [ 9 ].

In the 21st century, the rapid expansion of the petrochemical industry has significantly increased the demand for oil and energy production. Since 1985, Bangladesh has witnessed a notable surge in carbon pollution across the nation [ 10 ]. The contribution of Bangladesh’s industrial sector to GDP grew from 20% in 1990 to 30% in 2022 (see Fig 1 ). Fig 1 illustrates the average carbon emissions in Bangladesh from 1990 to 2022, highlighting the upward trend. These concerning trends highlight the critical need to examine how the industrial sector impacts carbon emissions. Notably, Bangladesh’s agriculture industry alone emits approximately 50 metric tons of CO 2 annually, largely due to practices like rice farming, field residue burning, and livestock management [ 11 ]. These emissions highlight the significant environmental footprint of agricultural practices in Bangladesh, which is crucial to examine in contrast to our paper’s focus on assessing the contributions of agriculture value added, industry value added, and imports on CO 2 emissions. By understanding these contributions, our study aims to provide insights into how policy interventions can effectively mitigate environmental impacts while promoting economic growth and sustainability in Bangladesh. “Bangladesh is currently the world’s third-largest importer”, according to the FAO [ 12 ]. Food grains including rice and wheat, edible oil, oilseeds, raw cotton, milk and milk products, spices, sugar, and coconut oil are some of the main agricultural imports into the nation. From these, cotton, sugar, and oil are on the list of top 10 import commodities of the country in 2021, according to the Bangladesh Import Statistics. Moreover, this list also includes several essential industrial resources, such as garbage, scrap, bituminous minerals, petroleum, medium oils, and mineral fuels. There is a chance that these import trends will have an effect on Bangladesh’s CO 2 emissions, both directly and indirectly. The first figure depicts the evolving contributions of agriculture, industry, and imports to GDP from 1990 to 2022 (see Fig 1 ). It reveals a declining trend in agricultural production, contrasting with a rising trajectory in industrial value-added, while imports display a more erratic pattern over the years. These trends highlight the imperative for a thorough exploration of how these sectors influence carbon emissions, emphasizing the necessity for targeted policies to manage environmental impacts alongside economic growth.

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However, controlling CO 2 emissions is crucial for the nation’s sustainable development. No one study evaluates the combined effects of the industrial and agricultural sectors on carbon emission in Bangladesh with particular importance, even though few scholars have examined the agricultural and industrial sectors separately in their research. To bridge this critical research gap, our study aims to thoroughly investigate the combined impacts of the agricultural and industrial sectors on carbon emissions in Bangladesh. Additionally, we decided to conduct this research to determine whether or not the impact of the country’s industrialization and agricultural sector on carbon emissions is linear or non-linear and to what degree this impacts carbon emissions. It will also check the direct impacts of imports on carbon emissions, as well as indirect impacts of imports by influencing industrialization and reducing agriculturalization of the country. Consequently, in order to better comprehend the role of imports, our research includes an examination of them. Moreover, A new combination of variables are used in this study.

The research is comprised of six sections. We addressed the context and rationale for the subsequent investigation in the initial section. Previous research on this subject was reviewed in the second section. We elaborated on data curation and the statistical tests utilized in the analysis in the following section. Empirical findings and Discussions are contained in the fourth and fifth sections respectively. In the final section, we provide recommendations for policies that reduce CO 2 emissions and draw conclusions regarding the study’s limitations.

2. Literature review

The study assesses the short- and long-run impacts of Bangladesh’s agricultural and industrial sectors on carbon emissions. A substantial amount of research has been conducted on subject of interest. Additionally, numerous investigations have been conducted in Bangladesh. Certain research investigations are carried out using time series data for a single country, whereas others utilize panel data to examine a group of countries. An element that unifies all the studies is the utilization of annual data obtained from the World Bank database. However, for this study, we considered the value added to GDP by agriculture and industry, the percentage of GDP attributed to imports of products and services, and per capita CO 2 emissions in metric tons. We will now proceed to discuss the studies that are pertinent to our variables and the objectives of our research.

It has been demonstrated on a global scale that agricultural production and CO 2 emissions are interconnected. [ 13 ] used the FMOLS approach to examine how agriculture affects CO 2 emissions in industrialized and developing nations. Their findings indicate an inverted U-shaped association of CO 2 emissions and agriculture. [ 14 ] investigated the long-run association between China’s agricultural output and carbon emission using the ARDL, FMOLS, CCR, and DOLS techniques. He demonstrates that China’s agricultural sector is a significant determinant of CO 2 emissions. [ 15 ] used DOLS, FMOLS, and ARDL to investigate the relationship between CO 2 emissions and Indonesian agriculture. The analyses revealed the existence of statistically significant and positive long-run association of agricultural value added and carbon emissions. [ 16 ] propose that agriculture and CO 2 emissions have a positive relation in the short run. Carbon emissions in Brazil are hypothesized to decrease as agriculture value added rises, according to [ 17 ]. Additionally, the agricultural sector of Saudi Arabia decreases CO 2 emissions, according to [ 18 ]. A further study conducted in Saudi Arabia by [ 19 ] provides support for the hypothesis that agricultural sector expansion can result in a decrease in CO 2 emissions. By employing ARDL and NARDL, [ 20 ] determine that the contribution of agriculture value added to GDP has an adverse impact on carbon emissions in Pakistan. According to a study by [ 21 ], the agricultural sector in Pakistan is a significant contributor to CO 2 emissions. To determine the impact of Vietnam’s agricultural sector on carbon emissions, [ 22 ] utilized a variety of models such as ARDL, VECM, FMOLS, DOLS, and CCR. He found that increasing agriculture value added decreases CO 2 emissions. [ 23 ] conducted a study in Bangladesh using ARDL approach to check the effects of agricultural sector on carbon emissions. They found that agricultural sector of Bangladesh positively affects CO 2 emissions. The study from [ 24 ] also supports the result of [ 23 ] using ARDL and ECM that agricultural sector of Bangladesh is responsible for carbon emissions. Granger causality test results suggest that value added to GDP from agriculture (AVA) doesn’t Granger cause CO 2 emissions, but carbon emissions granger cause agricultural production. [ 25 ] analyzed the nexus between agricultural ecology and carbon emissions using FMOLS, DOLS and CCR. They found that agricultural sector of Bangladesh has positive significant impacts on CO 2 emissions. The Granger causality test result supports the result of [ 24 ].

[ 26 ] evaluated the environmental Kuznets curve of the influence of industrialization on CO 2 emissions in Bangladesh using the ARDL approach. The researchers’ findings indicate the presence of an environmental Kuznets curve that connects industrialization with CO 2 emissions. They indicate that the industrial sector of Bangladesh has a long-run impact on carbon emissions. [ 27 ] used the CCR, FMOLS, ARDL, and DOLS methodologies to analyze the association between industry value added and carbon emissions in India. The long run relationship between the industrial sector and CO 2 emissions is negative but statistically negligible, according to each model. Additionally, by using the ARDL model, [ 28 ] contend that industrialization does not yield substantial consequences in the short or long-run. [ 29 ] examine the association between industrial growth and emissions of carbon in Bangladesh by employing the ARDL and Granger causality tests. Both in the short- and long-run, industrial expansion has a significant impact on CO 2 emissions, according to the study. The Granger test determined that industrial expansion is the sole cause of carbon emissions. [ 30 ] examined the impacts of industrial expansion on carbon emissions in India utilizing the NARDL model. It was discovered that industrial expansion has a short-run adverse impact on carbon emission, but there exists a long-run positive effect on carbon emission. Increasing industry value degrades environmental quality in Europe and Central Asia by increasing carbon emissions, according to [ 31 ]. [ 32 ] analyzed data from 1971 to 2019 to explore the relationship between the industrial sector and carbon emissions in Pakistan. Utilizing advanced techniques such as ARDL, DOLS, and FMOLS, they discovered that CO 2 emissions from industrialization and the manufacturing sector negatively impact economic efficiency in Pakistan.

[ 33 ] utilized the VAR and Granger causality tests to examine the impact of imports on CO 2 emissions in Bangladesh. No causal relationship was identified between imports and CO 2 emissions. The outcome of restricted VAR indicates that carbon emissions and imports are related in the long term. In six regions, [ 34 ] examined the relationship between trade, imports, exports, and CO 2 emissions. Most countries’ imports have a positive effect on carbon emissions, whereas certain nations have a negative impact. They discovered that carbon emissions only occur when trade exceeds 40% of total GDP. [ 35 ] revealed a positive and meaningful relationship between trade openness and carbon emissions in fourteen MENA countries. Imports have long-run significant positive impacts on CO 2 emission in Algeria, according to [ 36 ]. A spatial analysis conducted by [ 37 ] in North Africa indicates that imports have a positive impact on CO 2 emissions.

Our research introduces a fresh perspective to the existing body of literature on carbon emissions in Bangladesh. We have employed both linear and nonlinear autoregressive distributed lag models to investigate the impact of both linear and nonlinear changes in the agricultural and industrial sectors, and linear changes of import on carbon emissions. In Bangladesh, various studies have delved into the separate impacts of agriculture, industry, and import on CO 2 emissions. Yet, a significant void remains understanding how these factors interact together. Our research seeks to fill this gap by exploring the combined influence of agriculture, industry, and imports on CO 2 emissions. By focusing sharply on Bangladesh, our study aims to unravel the complex web of connections among these variables. This distinctive focus aims to provide a more comprehensive understanding of the factors influencing carbon emissions in the country, thereby contributing to the development of effective policy interventions for sustainable development.

3. Data and methodology

In the context of Bangladesh, the research utilizes the yearly time series dataset from 1990 to 2022 to assess the asymmetric influence that socioeconomic variables have on CO 2 emissions. Per person CO 2 emission in metric tons, the value added from industry and agriculture, and import percentages to GDP are the variables used for the study. The data were gathered from the World Development Indicators (WDI) [ 38 ]. The fill-forward technique was implemented to handle missing values. The variable’s name, data sources, and units of measurement are detailed in Table 1 .

https://doi.org/10.1371/journal.pclm.0000408.t001

3.2 Methodology

The present research examines the correlation between CO 2 emissions and several socio-economic indices to assess the specific model. A unit root test was conducted to assess stationarity and ascertain the level of integration of the variables. Additionally, the variables will be evaluated for a cointegrating connection using the ARDL Bounds test [ 39 ] and the NARDL Bounds test [ 40 ]. Autoregressive distributed lag (ARDL) model [ 41 ] and nonlinear autoregressive distributed lag model [ 42 ] are used to quantify the effects of socio-economic variables on CO 2 emissions. These models are particularly advantageous due to their ability to handle variables with different integration orders and to capture both short-term dynamics and long-term equilibrium relationships. An additional benefit of the ARDL framework is its direct link to the Error Correction Model (ECM), derived through a linear transformation. This ECM integrates short-term adjustments towards long-run equilibrium without loss of long-run information, thereby enhancing the model’s forecasting and policy implications [ 39 ]. Model performance was assessed by diagnostic tests and stability testing. In addition, we conducted a linear Granger causality test [ 43 ] and a non-linear Granger causality test [ 44 ] to evaluate the bi-directional relationships. Next figure (see Fig 2 ) clearly illustrates all the processes. All analyses are performed using the EViews software.

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3.3 Model specification

3.3.1 ARDL model specification.

3.3.2 NARDL model specification.

4.1 Descriptive analysis

A succinct summary of the descriptive statistics corresponding to each variable is presented in Table 2 . The mean carbon dioxide (CO 2 ) emissions per person is 0.29 metric tons, with a slightly lower median of 0.25. The range of emissions spans from 0.099 to 0.586. The standard deviation of CO 2 emissions suggests a reduced level of variability, while other variables exhibit a moderate level of variability around the mean. The positive skewness value of all the variables indicates that the distributions have rightward tails, while kurtosis values suggest that the distribution has heavier tails, reflecting a propensity for more extreme values. The Jarque-Bera statistics and their associated probability values suggest that the distributions closely resemble a normal distribution.

https://doi.org/10.1371/journal.pclm.0000408.t002

4.2 Unit root test

Before preceding ARDL and NARDL models, it is essential to check the stationarity of time series data. Every variable must be stationary at first difference or at level before applying ARDL or NARDL models. Here the order of integration is not important, it can be implemented with all variables having the same order (all I(1) or all I(0)) or a mixed order of integration (combination of I(1) and I(0)) [ 45 ]. In this study, The Augmented Dicky-Fuller test, one of the most powerful unit root tests was employed to verify stationarity. The ADF unit root test suggests that imports (IMP) and agricultural value added (AVA) are both are stationary at the level, according to the data shown in Table 3 . However, after the first difference, industry value added (IVA) and CO 2 emissions (CE) exhibit stationarity.

https://doi.org/10.1371/journal.pclm.0000408.t003

4.3 Non-linearity test

To explore non-linearity within macroeconomic variables, the study employs the Brock-Dechert-Scheinkman (BDS) testing technique [ 46 ]. Table 4 presents the results of the BDS test for non-linearity conducted on the variables AVA (Agriculture Value Added) and IVA (Industry Value Added), with CO 2 emissions serving as the response variable. The analysis reveals that the BDS statistics for both AVA and IVA are significant at the 1% level. This indicates the presence of non-linearity within these macroeconomic variables, suggesting that the relationship between these sectors and CO 2 emissions is not simply linear, but involves more complex dynamics.

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4.4 Lag length selection

The findings from the Vector Autoregression (VAR) lag order selection criterion is shown in Table 5 . The determination of the appropriate lag length is necessary for conducting the ARDL bounds test for cointegration, as the F-statistic’s sensitivity is linked to this parameter. In this research, a lag length of three was selected to validate cointegration, guided by the Akaike information criterion (AIC). This decision aids in ensuring the robustness of our findings and the validity of our cointegration analysis.

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4.5 ARDL estimates

4.5.1 cointegration test..

It is crucial to confirm that a cointegration relationship exists before doing an ARDL analysis. In this research we utilized the Bounds test to verify cointegration over other approaches. Table 6 displays the outcomes of the Bounds test for ARDL, unveiling an F-statistic value of 7.1019, surpassing the upper limit of 4.66 at the 1% significance level. This observation signifies that there exists a long-run cointegrating relation.

https://doi.org/10.1371/journal.pclm.0000408.t006

4.5.2 ARDL model selection.

Once the long-run cointegrating relation has been established, selecting the suitable lag length for each of the underlying variables becomes crucial for employing ARDL. We prefer error terms that adhere to the standard normal distribution and are devoid of non-normality, autocorrelation, heteroscedasticity, and other such issues. Therefore, determining the right lag length is crucial [ 45 ]. The figure ( Fig 3 ) displays the top 20 model selection findings based on Log-likelihood, AIC, BIC, HQ, and adjusted R-squared. The outcome shows that the chosen ARDL model uses up to 2, 0, 3, and 3 lags of the variable CO 2 emissions (CE), value added from agriculture (AVA), industry (IVA), and imports (IMP).

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4.5.3 Long-run and short-run estimates.

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4.5.4 Model diagnostics.

The diagnostic tests result for the ARDL model are summarizes in the last part of Table 8 . These tests include the Lagrange Multiplier (LM) test for Serial Correlation, the Breusch-Pagan-Godfrey test and ARCH test for heteroscedasticity, the Jarque-Bera test for normality, and the Ramsey RESET test for model specification. The results indicate that the ARDL model successfully passes all diagnostic tests, indicating the absence of serial correlation and heteroscedasticity. Furthermore, the model is deemed well-specified, and the distribution of residuals conforms to normality.

https://doi.org/10.1371/journal.pclm.0000408.t008

4.5.5 Stability diagnostics.

The application of CUSUM and CUSUMQ tests is a crucial step in evaluating the stability of long-run parameters within the context of a linear autoregressive distributed lag (ARDL) model. Upon conducting these tests with a predetermined significance threshold of 5%, the CUSUM test graph demonstrates a reassuring outcome, suggesting that the long-run parameters exhibit stability over the observed period (see Fig 4 ). However, the corresponding CUSUMQ test graph unveils a nuanced picture, revealing a subtle but discernible instability around the year 2018. The discrepancy observed between the outcomes of the two tests prompts a further examination into the temporal dynamics of the model. While the CUSUM test suggests stability overall, the identified instability in CUSUMQ specifically draws attention to potential variations in the squared residuals, indicating the presence of underlying structural shifts or unaddressed factors within the specified time frame ( Fig 4 ). To confirm the parameters stability, we also performed chow breakpoint test which results are displayed in Table 8 . F-statistic and p-value of chow breakpoint test indicate that there is no structural break. As the chow breakpoint test is more powerful than the CUSUMQ test, we may conclude that the long run parameters of ARDL model are stable.

https://doi.org/10.1371/journal.pclm.0000408.g004

4.6 NARDL estimates

4.6.1 cointegration test..

The findings obtained from the NARDL Bounds test, as presented in Table 9 , are compelling. The computed F-statistic value of 58.575 surpasses the upper limit threshold of 4.15 at the 1% significance level. This outcome strongly indicates the presence of a long-run cointegrating relationship among the variables under consideration.

https://doi.org/10.1371/journal.pclm.0000408.t009

4.6.2 NARDL model selection.

Top 20 results of non-linear ARDL model selection criteria are shown in the next figure ( Fig 5 ). The lag length of each variable is selected based on Log-Likelihood, AIC, BIC, HQ, and adjusted R-squared. The result indicates that up to 3, 3, 3, 2, 3, 3 lags of the predefined variables CE , AVA + , AVA − , IVA + , IVA − , IMP are used in selected asymmetry ARDL model.

https://doi.org/10.1371/journal.pclm.0000408.g005

4.6.3 Long-run and short-run estimates.

https://doi.org/10.1371/journal.pclm.0000408.t010

4.6.4 Model diagnostics.

Diagnostics tests results are attached in Table 11 which are performed to investigate the autocorrelation, heteroscedasticity, normality and specification of the asymmetry ARDL model. Based on F-statistics and their respective probability values, the findings suggest that the NARDL model successfully passed all diagnostic tests.

https://doi.org/10.1371/journal.pclm.0000408.t011

4.6.5 Stability diagnostics.

We again utilized the CUSUM and CUSUMQ tests to assess the stability of the asymmetry ARDL model. Both plots ( Fig 6 ) fall within the 5 percent critical bounds, indicating the stability of the model’s parameters. Additionally, the findings suggest that the long-run parameters of the asymmetry ARDL model exhibit greater stability compared to those of the linear ARDL model.

https://doi.org/10.1371/journal.pclm.0000408.g006

4.6.6 Asymmetric dynamic multipliers.

The dynamic multiplier graph presented in two graphs ( Fig 7 ) offers insights into the dynamic adjustments of agriculture and industry value added to GDP subsequent to a new long-run equilibrium after a positive and negative shocks. Analysis of the graph provides a notable asymmetrical association between these variables, evident from the zero line not falling within the critical bounds at the 5% level of significance. This asymmetry underscores the differential impact of changes in agriculture and industry value added on the equilibrium. Specifically, it is observed that a positive change in AVA provides a more substantial impact compared to a negative change, while conversely, a negative change in IVA has a greater effect than a positive change over the long run.

https://doi.org/10.1371/journal.pclm.0000408.g007

4.7 Granger causality test

Finally, the investigation into causal relationships among the variables employed both linear and nonlinear Granger causality tests. Table 12 presents the results of the pairwise linear Granger causality test, revealing significant insights. Specifically, the findings indicate a unidirectional causal relationship, with CO 2 emissions Granger causing agricultural value added. Moreover, a bidirectional causality is observed between industry value added and carbon emissions, while the causality from imports to CO 2 emissions is unidirectional. Furthermore, no discernible causal relationships were found between agricultural and industrial sectors, value added from agriculture and imports, or industry and imports.

https://doi.org/10.1371/journal.pclm.0000408.t012

Table 13 presents a summary of the results obtained from the nonlinear Granger causality test, revealing significant insights. Specifically, the analysis indicates a unidirectional causality from carbon emission to negative shocks of agricultural value added, while the causality of carbon emission to positive shocks of industry value added is bidirectional. Moreover, the causality of imports to CO 2 emissions and positive shocks of agriculture added to negative shocks of industry value added is unidirectional, while the causality between negative AVA to positive AVA and negative AVA to negative IVA is bidirectional. There exists no significant causal relationship between the other pairs of variables.

https://doi.org/10.1371/journal.pclm.0000408.t013

5. Discussion

Using ARDL cointegration approach, our study examines the long-run relations as well as their short-run interactions of agriculture value added (AVA), industry value added (IVA) and imports of goods and services (IMP) on CO 2 emissions (CE), they presume symmetric relations. Accordingly, they are not adequate to capture potential asymmetries in the agriculture and industrial value added on carbon emissions. That’s why we used non-linear ARDL cointegration approach (NARDL) as an asymmetric extension to the well-known ARDL model to capture both long run and short run asymmetries in the variables of interest.

The ARDL model ( Table 7 ) reveals important insights into the relationship between economic activities and CO 2 emissions. The long run ARDL model suggest that the relationship between agriculture value added (AVA) and CO 2 emissions is negative but not significant. Moreover, no significant short-run relationship is observed. This suggests that agricultural activities in Bangladesh do not substantially impact carbon emissions. These findings align with previous research, including studies on Indonesia [ 47 ], North African countries [ 48 ], a panel of 53 countries consisting of high- and low/medium-income countries [ 49 ], global analyses [ 50 ], and European regions [ 51 ]. All these studies consistently indicate that agriculture contributes minimally to CO 2 emissions. Since last 2 decades Bangladesh has been a fast-growing emerging economy, with an average growth rate of 6.05% per annum (From 2000 to 2023). Besides, its growth mainly depends on industrial sector and our findings suggest that industrial sector of Bangladesh is responsible for carbon emissions both in the long-run and short-run. This finding is consistent with prior research which has shown that industrial sector is positively influencing the level of carbon emissions in Bangladesh ([ 10 , 26 , 29 , 52 – 54 ]) and other economics ([ 19 ] for Saudi Arabia; [ 31 ] for Europe and Central Asia; [ 55 – 59 ] for China). However, this finding is not consistent with prior few studies ([ 60 ] for Chinese economy; [ 27 ] for India; [ 28 ] for Pakistan). Moreover, the long run ARDL estimates reveal a positive and significant relationship between imports and CO 2 emissions, with a coefficient of 0.015 (p < 0.01). This indicates that in the long run, an increase in imports is associated with an increase in carbon emissions. This finding suggests that the imported goods and services in Bangladesh may be carbon-intensive or that the increase in imports leads to higher economic activity and, consequently, higher emissions. This result is in line with previous studies (on Algeria [ 36 ] and North Africa [ 37 ]) indicating that imports have positive impacts on carbon emissions. In the short run, the relationship between imports and CO 2 emissions is more nuanced. The short-run ARDL results show that the immediate impact of changes in imports on CO 2 emissions is not significant (Δ IMP t ) with a coefficient of 0.001430, p = 0.1264). However, the lagged effects of imports are significant and negative. This suggests that while the immediate impact of imports on emissions is negligible, over time, imports may contribute to a reduction in CO 2 emissions.

The subsequent analysis explores the question: "How do the results change when applying the nonlinear ARDL model?" To assess whether the effects of agriculturalization, industrialization, and imports on CO 2 emissions are asymmetric, Table 10 presents the short-term and long-term estimates of the nonlinear ARDL model.

Based on the findings from our study using both ARDL and NARDL approaches, several crucial policy implications emerge for Bangladesh. Firstly, our research underscores the limited impact of agricultural activities on CO 2 emissions, suggesting that promoting sustainable agricultural practices could further mitigate environmental impacts without significantly affecting emissions. Secondly, the significant positive relationship between industrialization and CO 2 emissions highlights the urgent need for stringent regulatory measures and technological advancements to curb industrial carbon footprints. Thirdly, while imports show mixed impacts, with short-term reductions in CO 2 emissions and inconclusive long-term effects, policies should focus on encouraging low-carbon import practices and fostering domestic industries that align with sustainable development goals. These insights call for targeted policies that balance economic growth with environmental sustainability, ensuring Bangladesh navigates towards a greener and more resilient future.

6. Conclusion and policy recommendations

6.1 conclusion.

This research paper delves into the intricate nexus of CO 2 emissions within Bangladesh’s agricultural and industrial sectors, as well as its import dynamics. Through the application of NARDL modeling techniques, the study uncovers compelling insights, demonstrating the adequacy of the NARDL model in comparison to its linear counterpart. The findings of the NARDL model unveil a noteworthy relationship, indicating that agricultural production exerts a negative significant long-run effect on carbon emission. The findings highlight that agricultural production has a significant long-run negative effect on carbon emissions, illustrating the role of agriculture in mitigating CO 2 levels. Moreover, the research underscores the existence of a unidirectional causal relation, with CO 2 emissions exerting a substantial influence on agricultural production. This elucidates the intricate interplay between environmental considerations and agricultural productivity within the Bangladeshi context. Furthermore, the analysis indicates that the industrial sector positively affects carbon emissions over different time horizons. Both linear and nonlinear models show that increases in industrial activity leads to higher carbon emissions, with the nonlinear model indicating a more pronounced effect. Notably, the causal relation between the industrial sector and carbon emission is bidirectional, reflecting the intricate feedback mechanisms at play. Additionally, this research underscores the positive effect of imports on CO 2 emission within the linear model framework, further emphasizing the multifaceted nature of factors influencing CO 2 dynamics within the Bangladeshi context. In sum, these findings offer valuable insights into the complex interrelationships between socioeconomic sectors and CO 2 emission in Bangladesh, providing a nuanced understanding essential for informed policy formulation and sustainable development initiatives.

6.2 Policy suggestions

Bangladesh’s economy is largely dependent on agriculture, although this dependence has gradually decreased in recent years. The good news is that in the developed countries of the world, the tendency of environmental degradation from the agricultural sector is high, but the tendency in Bangladesh is extremely low. In contrast, the findings of this research highlight the agricultural sector’s capacity to make substantial contributions to long-term carbon emission reductions. With a focus on sustainable development, government officials and policymakers must prioritize initiatives aimed at bolstering Bangladesh’s agricultural sector. Hence, policymakers should promote sustainable agricultural practices such as organic farming, climate-smart agriculture, and the use of solar-powered irrigation systems, which could further enhance the sector’s environmental performance. Given that the industrial sector is a major contributor to carbon emissions, the government should enforce stricter regulations on industrial emissions and incentivize the adoption of cleaner technologies. Implementing carbon and green taxes can help mitigate the environmental impact without hindering industrial productivity. Furthermore, the import sector’s positive correlation with carbon emissions necessitates the promotion of green logistics and the importation of environmentally friendly goods. Additionally, investment in research, public awareness campaigns, and stakeholder engagement is crucial to foster a comprehensive understanding and support for these initiatives. These integrated efforts are vital for Bangladesh to achieve its ambitious targets under the updated Nationally Determined Contributions (NDCs), which aim to reduce greenhouse gas emissions by 21.85% by 2030 [ 65 ].

6.3 Limitations

Despite some meaningful insights, there are some limitations also. A primary constraint pertains to the temporal scope, as the analysis exclusively encompasses data spanning the period from 1990 to 2022. This temporal constraint may restrict the comprehensive understanding of long-term trends and patterns in the variables under consideration. Another limitation is that it only deals with four variables which are CO 2 emissions, value addition to GDP of agriculture, industry and imports, although several other socioeconomic variables can be observed and proven to influence carbon emissions. Moreover, the study has only worked with the data of Bangladesh, other countries of the world are not included. To enhance the robustness and comprehensiveness of future research endeavors, it is recommended to address these limitations by extending the temporal range, incorporating a broader array of relevant variables, and encompassing data from a more diverse set of countries. By doing so, future investigations can offer a more nuanced and exhaustive comprehension of the multifaceted interplay between socioeconomic factors and carbon emission on a global level.

Acknowledgments

The authors thank Charls Darwin, Associate Professor, Department of Statistics, Begum Rokeya University, Rangpur for his valuable feedback and input during the research process.

• 1. CO2 Earth. Monthly CO2. In:NOAA-ESRL GMD [Internet]. 2024. Available: https://www.co2.earth/monthly-co2
• 2. Center For Global Development. Available: https://www.cgdev.org/media/developing-countries-are-responsible-63-percent-current-carbon-emissions
• View Article
• Google Scholar
• 8. World Bank Group. Urgent Climate Action Crucial for Bangladesh to Sustain Strong Growth. In: The World Bank [Internet]. 2022. Available: https://www.worldbank.org/en/news/press-release/2022/10/31/urgent-climate-action-crucial-for-bangladesh-to-sustain-strong-growth
• PubMed/NCBI