sample size in a case study

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• v.35(2); Apr-Jun 2013

How to Calculate Sample Size for Different Study Designs in Medical Research?

Jaykaran charan.

Department of Pharmacology, Govt. Medical College, Surat, Gujarat, India

Tamoghna Biswas

1 Independent Researcher, Kolkata, West Bengal, India

Calculation of exact sample size is an important part of research design. It is very important to understand that different study design need different method of sample size calculation and one formula cannot be used in all designs. In this short review we tried to educate researcher regarding various method of sample size calculation available for different study designs. In this review sample size calculation for most frequently used study designs are mentioned. For genetic and microbiological studies readers are requested to read other sources.

INTRODUCTION

In the recent era of evidence-based medicine, biomedical statistics has come under increased scrutiny. Evidence is as good as the research it is based on, which in turn depends on the statistical soundness of the claims it make. One of the important issues faced by a biomedical researcher during the design phase of the study is sample size calculation. Various studies published in Indian and International journals revealed that sample size calculations are not reported properly in the published articles. Many of the studies published in these journals have less than required sample size and hence less power.[ 1 , 2 , 3 ] Many articles have been published in existing literature explaining the methods of calculation of sample size but still a lot of confusion exists.[ 4 , 5 , 6 ] It is very important to understand that method of sample size calculation is different for different study designs and one blanket formula for sample size calculation cannot be used for all study designs. In this article different formulae of sample size calculations are explained based on study designs. Readers are advised to understand the basics of prerequisites needed for calculation of sample size calculation through this article and from other sources also and once they have understood the basics they can use different paid/freely available software available for sample size calculations. For simple study designs formulae given in this article can be used for sample size calculation.

Sample size calculation for cross sectional studies/surveys

Cross sectional studies or cross sectional survey are done to estimate a population parameter like prevalence of some disease in a community or finding the average value of some quantitative variable in a population. Sample size formula for qualitative variable and quantities variable are different.

For qualitative variable

Suppose an epidemiologist want to know proportion of children who are hypertensive in a population then this formula should be used as proportion is a qualitative variable.

So if the researcher is interested in knowing the average systolic blood pressure in pediatric age group of that city at 5% of type of 1 error and precision of 5 mmHg of either side (more or less than mean systolic BP) and standard deviation, based on previously done studies, is 25 mmHg then formula for sample size calculation will be

So if the researcher wants to calculate sample size for the above-mentioned case control study to know link between childhood sexual abuse with psychiatric disorder in adulthood and he wants to fix power of study at 80% and assuming expected proportions in case group and control group are 0.35 and 0.20 respectively, and he wants to have equal number cases and control; then the sample size per group will be

So, the researcher has to take 59 samples in each group.

It is worthy of mention here that these formulas for case control and cohort study are for independent design studies. They are not for matched case control and cohort studies. These formulae can be modified or corrected depending on population size or ratio between sample size and population size. Detailed text should be read to know more about technical aspects of sample size calculation.[ 7 , 8 ] Readers are advised to use various freely available epidemiological calculators like openEpi given in appendix to calculate sample size formula.

Sample size calculation for testing a hypothesis (Clinical trials or clinical interventional studies)

In this kind of research design researcher wants to see the effect of an intervention. Suppose a researcher want to see the effect of an antihypertensive drug so he will select two groups, one group will be given antihypertensive drug and another group will be give placebo. After giving these drug s for a fixed time period blood pressure of both groups will be measured and mean blood pressure of both groups will be compared to see if difference is significant or not. Complex formulae are used for this type of studies and we want to advise readers to use statistical software for calculation of exact sample size. The procedure for calculation of samle size in clinical trials/intervention studies involving two groups is mentioned here. In the case of only two groups method of calculation is mentioned here but if design involves more than two groups then statistical software like G Power should be used for sample size calculation. But understanding of various prerequisites which are needed for sample size calculation is very important.

Formula for sample size calculation for comparison between two groups when endpoint is quantitative data

When the variable is quantitative data like blood pressure, weight, height, etc., then the followingformula can be used for calculation of sample size for comparison between two groups.

So researcher needs 294 subjects per group.

So simple calculation for sample size when comparison is for two independent groups can be done manually by given formulae but for more than two groups or for matched data and for other complex calculations software should be used [ appendix 1 ].

Sample size formula for animal studies

For animal studies there are two method of calculation of sample size. The most preferred method is the same method which has been mentioned in sample size calculation for testing the hypothesis. While all efforts should be done to calculate the sample size by that method, sometimes it is not possible to get information related to the prerequisites needed for sample size calculation by power analysis like standard deviation, effect size etc. In that condition a second method can be used this is called as “resource equation method”.[ 9 ] In this method a value E is calculated based on decided sample size. The value if E should lies within 10 to 20 for optimum sample size. If a value of E is less than 10 then more animal should be included and if it is more than 20 then sample size should be decreased.

E = Total number of animals - Total number of groups

Suppose in an animal study a researcher formed 4 groups of animal having 8 animals each for different interventions then total animals will be 32 (4 × 8). Hence E will be

E = 32 – 4 = 28

This is more than 20 hence animals should be decreased in each group. So if researcher takes 5 rats in each group then E will be

E = 20 – 4 = 16

E is 16 which lies within 10-20 hence five rats per group for four groups can be considered as appropriate sample size. This is a crude method and should be used only if sample size calculation cannot be done by power analysis method explained in above section for testing the hypothesis.

APPENDIX 1: – FREE SOFTWARE AND CALCULATORS AVAILABLE ONLINE FOR SAMPLE SIZE CALCULATION

Source of Support: Nil

Conflict of Interest: None.

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Sample Size Determination: Definition, Formula, and Example

Are you ready to survey your research target? Research surveys help you gain insights from your target audience. The data you collect gives you insights to meet customer needs, leading to increased sales and customer loyalty. Sample size calculation and determination are imperative to the researcher to determine the right number of respondents, keeping in mind the research study’s quality.

So, how should you do the sample size determination? How do you know who should get your survey? How do you decide on the number of the target audience?

Sending out too many surveys can be expensive without giving you a definitive advantage over a smaller sample. But if you send out too few, you won’t have enough data to draw accurate conclusions. 

Knowing how to calculate and determine the appropriate sample size accurately can give you an edge over your competitors. Let’s take a look at what a good sample includes. Also, let’s look at the sample size calculation formula so you can determine the perfect sample size for your next survey.

What is Sample Size?

‘Sample size’ is a market research term used for defining the number of individuals included in conducting research. Researchers choose their sample based on demographics, such as age, gender questions , or physical location. It can be vague or specific. 

For example, you may want to know what people within the 18-25 age range think of your product. Or, you may only require your sample to live in the United States, giving you a wide population range. The total number of individuals in a particular sample is the sample size.

What is sample size determination?

Sample size determination is the process of choosing the right number of observations or people from a larger group to use in a sample. The goal of figuring out the sample size is to ensure that the sample is big enough to give statistically valid results and accurate estimates of population parameters but small enough to be manageable and cost-effective.

In many research studies, getting information from every member of the population of interest is not possible or useful. Instead, researchers choose a sample of people or events that is representative of the whole to study. How accurate and precise the results are can depend a lot on the size of the sample.

Choosing the statistically significant sample size depends on a number of things, such as the size of the population, how precise you want your estimates to be, how confident you want to be in the results, how different the population is likely to be, and how much money and time you have for the study. Statistics are often used to figure out how big a sample should be for a certain type of study and research question.

Figuring out the sample size is important in ensuring that research findings and conclusions are valid and reliable.

Why do you need to determine the sample size?

Let’s say you are a market researcher in the US and want to send out a survey or questionnaire . The survey aims to understand your audience’s feelings toward a new cell phone you are about to launch. You want to know what people in the US think about the new product to predict the phone’s success or failure before launch.

Hypothetically, you choose the population of New York, which is 8.49 million. You use a sample size determination formula to select a sample of 500 individuals that fit into the consumer panel requirement. You can use the responses to help you determine how your audience will react to the new product.

However, determining a sample size requires more than just throwing your survey at as many people as possible. If your estimated sample sizes are too big, it could waste resources, time, and money. A sample size that’s too small doesn’t allow you to gain maximum insights, leading to inconclusive results.

LEARN ABOUT: Survey Sample Sizes

What are the terms used around the sample size?

Before we jump into sample size determination, let’s take a look at the terms you should know:

1. Population size: 

Population size is how many people fit your demographic. For example, you want to get information on doctors residing in North America. Your population size is the total number of doctors in North America. 

Don’t worry! Your population size doesn’t always have to be that big. Smaller population sizes can still give you accurate results as long as you know who you’re trying to represent.

2. Confidence level: 

The confidence level tells you how sure you can be that your data is accurate. It is expressed as a percentage and aligned to the confidence interval. For example, if your confidence level is 90%, your results will most likely be 90% accurate.

3. The margin of error (confidence interval): 

There’s no way to be 100% accurate when it comes to surveys. Confidence intervals tell you how far off from the population means you’re willing to allow your data to fall. 

A margin of error describes how close you can reasonably expect a survey result to fall relative to the real population value. Remember, if you need help with this information, use our margin of error calculator .

4. Standard deviation: 

Standard deviation is the measure of the dispersion of a data set from its mean. It measures the absolute variability of a distribution. The higher the dispersion or variability, the greater the standard deviation and the greater the magnitude of the deviation. 

For example, you have already sent out your survey. How much variance do you expect in your responses? That variation in response is the standard deviation.

Sample size calculation formula – sample size determination

With all the necessary terms defined, it’s time to learn how to determine sample size using a sample calculation formula.

Your confidence level corresponds to a Z-score. This is a constant value needed for this equation. Here are the z-scores for the most common confidence levels:

90% – Z Score = 1.645

95% – Z Score = 1.96

99% – Z Score = 2.576

If you choose a different confidence level, various online tools can help you find your score.

Necessary Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Here is an example of how the math works, assuming you chose a 90% confidence level, .6 standard deviation, and a margin of error (confidence interval) of +/- 4%.

((1.64)2 x .6(.6)) / (.04)2

( 2.68x .0.36) / .0016

.9648 / .0016

603 respondents are needed, and that becomes your sample size.

Free Sample Size Calculator

How is a sample size determined?

Determining the right sample size for your survey is one of the most common questions researchers ask when they begin a market research study. Luckily, sample size determination isn’t as hard to calculate as you might remember from an old high school statistics class.

Before calculating your sample size, ensure you have these things in place:

Goals and objectives: 

What do you hope to do with the survey? Are you planning on projecting the results onto a whole demographic or population? Do you want to see what a specific group thinks? Are you trying to make a big decision or just setting a direction? 

Calculating sample size is critical if you’re projecting your survey results on a larger population. You’ll want to make sure that it’s balanced and reflects the community as a whole. The sample size isn’t as critical if you’re trying to get a feel for preferences. 

For example, you’re surveying homeowners across the US on the cost of cooling their homes in the summer. A homeowner in the South probably spends much more money cooling their home in the humid heat than someone in Denver, where the climate is dry and cool. 

For the most accurate results, you’ll need to get responses from people in all US areas and environments. If you only collect responses from one extreme, such as the warm South, your results will be skewed.

Precision level: 

How close do you want the survey results to mimic the true value if everyone responded? Again, if this survey determines how you’re going to spend millions of dollars, then your sample size determination should be exact. 

The more accurate you need to be, the larger the sample you want to have, and the more your sample will have to represent the overall population. If your population is small, say, 200 people, you may want to survey the entire population rather than cut it down with a sample.

Confidence level: 

Think of confidence from the perspective of risk. How much risk are you willing to take on? This is where your Confidence Interval numbers become important. How confident do you want to be — 98% confident, 95% confident? 

Understand that the confidence percentage you choose greatly impacts the number of completions you’ll need for accuracy. This can increase the survey’s length and how many responses you need, which means increased costs for your survey. 

Knowing the actual numbers and amounts behind percentages can help make more sense of your correct sample size needs vs. survey costs. 

For example, you want to be 99% confident. After using the sample size determination formula, you find you need to collect an additional 1000 respondents. 

This, in turn, means you’ll be paying for samples or keeping your survey running for an extra week or two. You have to determine if the increased accuracy is more important than the cost.

Population variability: 

What variability exists in your population? In other words, how similar or different is the population?

If you are surveying consumers on a broad topic, you may have lots of variations. You’ll need a larger sample size to get the most accurate picture of the population. 

However, if you’re surveying a population with similar characteristics, your variability will be less, and you can sample fewer people. More variability equals more samples, and less variability equals fewer samples. If you’re not sure, you can start with 50% variability.

Response rate: 

You want everyone to respond to your survey. Unfortunately, every survey comes with targeted respondents who either never open the study or drop out halfway. Your response rate will depend on your population’s engagement with your product, service organization, or brand. 

The higher the response rate, the higher your population’s engagement level. Your base sample size is the number of responses you must get for a successful survey.

Consider your audience: 

Besides the variability within your population, you need to ensure your sample doesn’t include people who won’t benefit from the results. One of the biggest mistakes you can make in sample size determination is forgetting to consider your actual audience. 

For example, you don’t want to send a survey asking about the quality of local apartment amenities to a group of homeowners.

Select your respondents

Focus on your survey’s objectives: 

You may start with general demographics and characteristics, but can you narrow those characteristics down even more? Narrowing down your audience makes getting a more accurate result from a small sample size easier. 

For example, you want to know how people will react to new automobile technology. Your current population includes anyone who owns a car in a particular market. 

However, you know your target audience is people who drive cars that are less than five years old. You can remove anyone with an older vehicle from your sample because they’re unlikely to purchase your product.

Once you know what you hope to gain from your survey and what variables exist within your population, you can decide how to calculate sample size. Using the formula for determining sample size is a great starting point to get accurate results. 

After calculating the sample size, you’ll want to find reliable customer survey software to help you accurately collect survey responses and turn them into analyzed reports.

LEARN MORE: Population vs Sample

In sample size determination, statistical analysis plan needs careful consideration of the level of significance, effect size, and sample size. 

Researchers must reconcile statistical significance with practical and ethical factors like practicality and cost. A well-designed study with a sufficient sample size can improve the odds of obtaining statistically significant results.

To meet the goal of your survey, you may have to try a few methods to increase the response rate, such as:

• Increase the list of people who receive the survey.
• To reach a wider audience, use multiple distribution channels, such as SMS, website, and email surveys.
• Send reminders to survey participants to complete the survey.
• Offer incentives for completing the survey, such as an entry into a prize drawing or a discount on the respondent’s next order.
• Consider your survey structure and find ways to simplify your questions. The less work someone has to do to complete the survey, the more likely they will finish it. 
• Longer surveys tend to have lower response rates due to the length of time it takes to complete the survey. In this case, you can reduce the number of questions in your survey to increase responses.  

QuestionPro’s sample size calculator makes it easy to find the right sample size for your research based on your desired level of confidence, your margin of error, and the size of the population.

LEARN MORE         FREE TRIAL

Frequently Asked Questions (FAQ)

The four ways to determine sample size are: 1. Power analysis 2. Convenience sampling, 3. Random sampling , 4. Stratified sampling

The three factors that determine sample size are: 1. Effect size, 2. Level of significance 3. Power

Using statistical techniques like power analysis, the minimal detectable effect size, or the sample size formula while taking into account the study’s goals and practical limitations is the best way to calculate the sample size.

The sample size is important because it affects how precise and accurate the results of a study are and how well researchers can spot real effects or relationships between variables.

The sample size is the number of observations or study participants chosen to be representative of a larger group

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Why is Sample Size Important? (Explanation & Examples)

Sample size refers to the total number of individuals involved in an experiment or study.

Sample size is important because it directly affects how precisely we can estimate population parameters.

To understand why this is the case, it helps to have a basic understanding of confidence intervals.

A Brief Explanation of Confidence Intervals

In statistics, we’re often interested in measuring population parameters – numbers that describe some characteristic of an entire population.

For example, we might be interested in measuring the mean height of all individuals in a certain city.

However, it’s often too costly and time-consuming to go around and collect data on every individual in a population so we typically take a random sample from the population instead and use data from the sample to estimate the population parameter.

For example, we might collect data on the height of 100 random individuals in the city. We can then calculate the mean height of the individuals in the sample. However, we can’t be certain that the sample mean exactly matches the population mean.

To account for this uncertainty, we can create a confidence interval . A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence.

The formula to calculate a confidence interval for a population mean is:

Confidence Interval =  x   +/-  z*(s/√ n )

• x : sample mean
• z:  the chosen z-value
• s:  sample standard deviation
• n:  sample size

The z-value that you will use is dependent on the confidence level that you choose. The following table shows the z-value that corresponds to popular confidence level choices:

The Relationship Between Sample Size & Confidence Intervals

S uppose we want to estimate the mean weight of a population of turtles. We collect a random sample of turtles with the following information:

• Sample size  n = 25
• Sample mean weight  x = 300
• Sample standard deviation  s = 18.5

Here is how to find calculate the 90% confidence interval for the true population mean weight:

90% Confidence Interval:  300 +/-  1.645*(18.5/√ 25 ) =  [293.91, 306.09]

We are 90% confident that the true mean weight of the turtles in the population is between 293.91 and 306.09 pounds.

Now suppose instead of 25 turtles, we actually collect data for 50 turtles. 

90% Confidence Interval:  300 +/-  1.645*(18.5/√ 50 ) =  [295.79, 304.30]

Notice that this confidence interval is narrower than the previous confidence interval. This means our estimate of the true population mean weight of turtles is more precise.

Now suppose we instead collected data for 100 turtles. 

90% Confidence Interval:  300 +/-  1.645*(18.5/√ 100 ) =  [296.96, 303.04]

Notice that this confidence interval is even narrower than the previous confidence interval.

The following table summarizes each of the confidence interval widths:

Here’s the takeaway: The larger the sample size, the more precisely we can estimate a population parameter .

Additional Resources

The following tutorials provide other helpful explanations of confidence intervals and sample size.

An Introduction to Confidence Intervals 4 Examples of Confidence Intervals in Real Life Population vs. Sample: What’s the Difference?

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Please note you do not have access to teaching notes, sample size for qualitative research.

Qualitative Market Research

ISSN : 1352-2752

Article publication date: 12 September 2016

Qualitative researchers have been criticised for not justifying sample size decisions in their research. This short paper addresses the issue of which sample sizes are appropriate and valid within different approaches to qualitative research.

Design/methodology/approach

The sparse literature on sample sizes in qualitative research is reviewed and discussed. This examination is informed by the personal experience of the author in terms of assessing, as an editor, reviewer comments as they relate to sample size in qualitative research. Also, the discussion is informed by the author’s own experience of undertaking commercial and academic qualitative research over the last 31 years.

In qualitative research, the determination of sample size is contextual and partially dependent upon the scientific paradigm under which investigation is taking place. For example, qualitative research which is oriented towards positivism, will require larger samples than in-depth qualitative research does, so that a representative picture of the whole population under review can be gained. Nonetheless, the paper also concludes that sample sizes involving one single case can be highly informative and meaningful as demonstrated in examples from management and medical research. Unique examples of research using a single sample or case but involving new areas or findings that are potentially highly relevant, can be worthy of publication. Theoretical saturation can also be useful as a guide in designing qualitative research, with practical research illustrating that samples of 12 may be cases where data saturation occurs among a relatively homogeneous population.

Practical implications

Sample sizes as low as one can be justified. Researchers and reviewers may find the discussion in this paper to be a useful guide to determining and critiquing sample size in qualitative research.

Originality/value

Sample size in qualitative research is always mentioned by reviewers of qualitative papers but discussion tends to be simplistic and relatively uninformed. The current paper draws attention to how sample sizes, at both ends of the size continuum, can be justified by researchers. This will also aid reviewers in their making of comments about the appropriateness of sample sizes in qualitative research.

• Qualitative research
• Qualitative methodology
• Case studies
• Sample size

Boddy, C.R. (2016), "Sample size for qualitative research", Qualitative Market Research , Vol. 19 No. 4, pp. 426-432. https://doi.org/10.1108/QMR-06-2016-0053

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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Five Is the Maximum Sample Size for Case Reports: Statistical Justification, Epidemiologic Rationale, and Clinical Importance

2014, World Neurosurgery

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Vladimir Cortez

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ORIGINAL RESEARCH article

Do homegardens act as agent of agrobiodiversity conservation: a case study of homegardens of diverse socio-ecological zones in the brahmaputra valley, assam.

• 1 School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India
• 2 Department of Environmental Sciences, Tezpur University, Tezpur, Assam, India
• 3 Arunachal University of Studies, Namsai, Arunachal Pradesh, India

Introduction: Homegardens are one of the oldest agroforestry systems reported around the world. These agroforestry systems are often reported as harbingers of plant biodiversity conservation. However, a comprehensive understanding of these systems from the perspective of species level agrobiodiversity conservation is often missing.

Methodology: This study first visualizes the comprehensive role of homegardens in species level agrobiodiversity conservation and then assesses any variation in agrobiodiversity along diverse Socio-ecological Zones (SEZs) in the study site. The prominent SEZs identified in the study site were Protected Area (PA), Riverine (RI), Rural Market (RM), and Tea Estate (TE). Eight ethnic/linguistic groups were also identified at the study site. Agrobiodiversity inventorying of 192 homegardens from 16 villages was done.

Results: The results of the study highlight that homegardens in the study site have high species level agrobiodiversity concentration (101 total tree species reported, 39.58% of homegardens (HGs) had more than 10 varieties of vegetables, 68% had atleast one variety of bamboo, 76% had atleast one banana variety, 20.83% had pond). A total of 64% of HGs had livestock and around 85% had poultry. Moreover, this agrobiodiversity distribution also varied along different SEZs. The livestock diversity indices ranged from 0.49 (TE) to 1.04 (PA). The average plant diversity among homegarden was found to be in the range of 1.09 (PA) to 1.48 (TE) for Shannon, 0.45 (PA) to 0.66 (TE) for Simpson, 0.31 (PA) to 0.71 (TE) for Pileou evenness and 2.39 (PA) to 2.76 (RM) for Margalef. The plant composition reflected the dominance of the food species i.e. an average of 37% in each SEZ. Sorenson similarity index among different SEZs for plant and livestock was found to be highest between the HGs of the PA and RM (0.82). Among the ethnic/linguistic groups, the highest mean number of plant species (51) was found among the Mishing tribe. Also, high similarity index (0.78) was found in plant and livestock composition among the Mishing and the Bodo tribes.

Discussion: The findings imply that HGs exemplify diversified and integrated systems, showcasing their potential to play a crucial role in the development of sustainable food systems.

1 Introduction

Agrobiodiversity, or Biodiversity for Food and Agriculture (BFA), is defined as a subset of biodiversity that relates to agriculture and food production ( FAO, 2019 ). Agrobiodiversity can be broadly defined at three levels, i.e., genetic, species, and ecosystem levels ( FAO, 2004 ). At a time when agrobiodiversity loss from the agricultural landscape is a major concern ( Pilling, 2019 ), an integrated food system that can also help in agrobiodiversity conservation is of interest to one and all ( IFPRI, 2021 ). Homegarden agroforestry, which is one of the earliest systems of food production both in tropical and temperate countries ( Kumar and Nair, 2006 ; EURAF, 2021 ; Sharma et al., 2022 ), is also often referred to as the system that can also play an important role in agrobiodiversity conservation ( Galluzzi et al., 2010 ; Galhena et al., 2013 ). An attempt to link homegardens for agrobiodiversity conservation has been reported from around the world ( Wiehle et al., 2014 ). However, a comprehensive description of all species-level agrobiodiversity and management practices in homegardens (HG) has been scarcely reported. For example, though HG is often reported as a very important type of agrosilvopastoral system ( FAO, 2015 ; Nair et al., 2021 ), the literature mentioning characteristic livestock species in HGs is limited ( Soler et al., 2018 ). This study, by analyzing the species-level agrobiodiversity of all components of HG, including livestock and diverse management practices, along with major challenges faced in homegardening, tries to fill in this gap. Furthermore, taking on the framework of socio-ecological systems, which, as defined by Ostrom (2009) and Berkes et al. (2000) consist of social and ecological systems, this study tries to draw inferences on how HG structure and agrobiodiversity distribution are influenced by them.

HGs of Brahmaputra Valley, situated in the north-eastern state of Assam in India, were analyzed in this study. This part of the Brahmaputra Valley lying in the north-eastern state of India, i.e., Assam, was selected as the study site as it reports among the highest number of HGs ( Sharma et al., 2022 ). In addition, according to an ICAR report, there are about 6.4 million HGs in Assam, which is about 85% of the total households in the state ( Barua et al., 2019 ), making it a crucial land management practice in the region. In addition, specifically, Sonitpur district in the Brahmaputra Valley of Assam was chosen for this study because of its location in the foothills of the Eastern Himalayas and the occurrence of diverse types of habitations with different social and ecological structures juxtaposed to each other ( Srivastava et al., 2002 ), which makes it an ideal location to perform a comparative study based on the SES framework. Although the National Bureau of Soil Survey and Land Use Planning, Nagpur, based on soil, bioclimatic, and physiographic features ( Sehgal et al., 1992 ), classified Assam and the adjoining areas into warm humid to per humid (Assam and North Bengal Plains) and warm per humid (North Eastern Hills, Purvanchal) agro-ecological regions, detailed socio-ecological classification schemes are not available for this region. With the help of suggested literature, satellite data, and land holding patterns, we classified the study site into four major socio-ecological zones (SEZs). These SEZs were protected areas (PA), riverine (RI), rural market (RM), and tea estate (TE). Moreover, though the major objective of the study was to analyze the role of homegarden in agrobiodiversity conservation, it also tries to compare and contrast the agrobiodiversity pattern in HG situated in diverse SEZs and also among the major ethnic/linguistic groups in the study. The main hypothesis of the study is that the PA HG would have a large size and high plant and livestock diversity as they are situated away from the major commercial areas and close to the forest. HG in RI zones would be more disaster-prone as these areas are periodically flooded; HG in RM would represent more modern trends in homegardening and TE HG represents the very small HGs with a restricted and limited scope of expansion. HGs are often the personal space maintained by individuals based on their cultural beliefs, needs, and traditions ( Mazumdar and Mazumdar, 2012 ).

Therefore, we also looked into the comparative assessment of HGs belonging to different ethnic/cultural groups inhabiting our study area. And hypothesize that tribal HGs would have more agrobiodiversity concentration as compared to the non-tribal ones. This study has two major contributions: first, it will enable policymakers to make decisions regarding the importance of HGs as diversified, integrated, and conservation-based agriculture systems for all. Secondly, it would help in better policy formulation for all major types of HGs lying in varying SEZs and ethnic/linguistic groups. Understanding the comprehensive agrobiodiversity composition of homegardens would not only help in their better conservation but could also support efforts in the direction of developing sustainable food systems.

2 Methodology

2.1 study area.

The location of the study is the Brahmaputra Valley in Assam. The Brahmaputra valley has a total drainage area of 580,000 km 2 ( Debnath et al., 2023 ) and encompasses an area of 70,634 km 2 in Assam ( GOA, 2023 ). For this study, we specifically focused on the Sonitpur district of Assam, which is part of the northern bank plains of the Brahmaputra ( Chaturvedi et al., 2021 ). Apart from its location in the foothills of the Eastern Himalayas and the occurrence of diverse types of habitations ( Srivastava et al., 2002 ), this district of Sonitpur is categorized as highly vulnerable to climate change ( Ravindranath et al., 2011 ), which was one of the design criteria. The erstwhile Sonitpur district, with an area of 5,105 km 2 ( Srivastava et al., 2002 ), was, however, bifurcated around the same time when this study was planned (between May 2021 and April 2022). Hence, the new Sonitpur district with an area of 2,109 km 2 ( Assam, 2022 ) was selected for this study.

For demarcating different SEZs, we first performed a Land Use and Land Cover (LULC) classification using the satellite images Sentinel 2 (10 m resolution) of 19 November 2020 (less cloud cover) using the supervised classification ( Campbell and Wynne, 2011 ) in ArcGIS version 10.8. The major land use types identified were river, protected/plantation area, builtup/settlement area, and arable land. Based on field verification (December–February 2020) and further literature review ( Srivastava et al., 2002 ; NRSC, 2019 ; Chaturvedi et al., 2021 ; Mahato et al., 2021 ), four prominent SEZs, i.e., PA, RI, RM, and TE, were demarcated. The PA in the study site refers to three major classes, viz. , the national parks (IUCN Category II), the wildlife sanctuary (IUCN Category IV), and the reserve forest (forests accorded a certain degree of protection according to the Indian Forest Act 1927) and comprises 45% of the study area ( NRSC, 2019 ; Assam, 2022 ). The RI SEZs form another major dominant feature of the district, with three major rivers, i.e., Brahmaputra, Kameng, and Gabharu, and about 18% of the area of the district ( NRSC, 2019 ). The TE SEZs are the areas under a larger tea estate plantation in the district. Tea cultivation started during the British colonial period in Assam, and at present, there are 799 TE in Assam and about 59 in Sonitpur district ( DOTTAA, 2023 ). The current tea labor force in Assam is primarily composed of the descendants of people who were brought over from the areas that now constitute the tribal dominant states of Jharkhand, Orissa, and Chhattisgarh to work there during the colonial era. These individuals are now collectively referred to as tea tribes or Adivasis ( Mahanta et al., 2015 ; DOTTAA, 2023 ). The cities (major built-in areas) were excluded as the study focused on HGs in rural areas. To commensurate with this, the RM zone was considered an important feature; the villages that had at least one major market a week, had facilities for banks or post offices, and were in close proximity to the National Highway (2 km) were considered as RM.

The SEZs PA, RI, and TE comprise 45, 18, and 12% of the total area of Sonitpur district ( Census, 2011 ; Assam, 2022 ). A total of four replicate villages were then selected from each SEZ, i.e., 16 villages in total. The villages for PA were selected from the vicinity of Nameri National Park (Sonai Miri, Bhalukmari Pathar) and Sonai Rupai Wildlife Sanctuary (Naharani Basti Gaon, Urohiloga). These villages lie on the fringes of PAs (up to 5 km from the PA).

For RI, the villages on the bank or 2 km from the bank of the Brahmaputra River (Sithalmari, Bhomoraguri, Siddeswari) and Kameng or Jia Bharali River (Tow Bhanga) were selected. The villages were selected based on the number of flooding days that they experienced, i.e., a minimum of 60 days. The RM villages were selected based on the classification scheme mentioned earlier, and care was taken to select them from distinct blocks of the district (i.e., Pitha Khowa: Block—Tezpur; Thelamara Ghat: Block—Dhekiajuli; Jamugurihat: Block—Naduar; and Goraimari: Block—Balipara).

The TE-based (inside and around TEs) villages representing the distinctness of the eastern (i.e., Dhekialuji and Singri) and western parts (Phulbarie and Addabarie) of the district were selected for the study. This LULC classification scheme and the villages selected for the HG study are represented in Figure 1 . While selecting the villages, it was made sure that they represented distinctive features of the district, i.e., they were selected from different blocks and along different directions. All the villages except the TE villages were revenue villages; the TE villages are generally the inhabitations inside the TE owned by the plantation companies, where respective company rules are followed ( Mahanta et al., 2015 ). The permission to do the agrobiodiversity survey in all these villages was obtained from the District Collector of Sonitpur District. Permission was also obtained from the individual homegardeners in a consent form before participating in the study.

Figure 1 . Description of the study site.

2.2 Determining the sample size

The sample size of households in each village was determined using the following formula ( Corvar, 1974 ; Abdoellah et al., 2020 ):

where n is the total number of samples, N is the population size, z y is the normal distribution in y quantile, π is the proportion of sub-population, and δ is the margin of error. According to the 2011 Census, there are 352,647 rural households in the erstwhile Sonitpur district. Assuming a 7.1% margin of error and a 95% confidence level, 191 households in 16 villages—approximately 192 were surveyed to ensure that the households were distributed equitably among the villages. With 48 villages per SEZ, 12 households per village were selected using the probability sampling technique of simple random sampling. At first, the map of the village with the major feature was obtained from the Gaon Bura (village head). Then the households were randomly selected, starting from one cardinal direction and entering the village.

2.3 Data collection methods

Agrobiodiversity inventorying was performed by field visits ( Avilez-López et al., 2020 ) between March 2022 and April 2023. For agrobiodiversity inventorying, the questionnaire was administered to the landowners of the HGs, and where they were not present, the questions were asked of the person responsible for most managerial decision-making in the HG (43% of the respondents were women). In addition, a questionnaire method was used to understand the general economic status, management practices followed, challenges in homegardening, and future plans for HGs. Apart from HGs attached to the residential plots, some respondents maintained land parcels away from the households too. However, considering the standard definition of the term ( Kumar and Nair, 2004 ; Galhena et al., 2013 ), we considered only the cultivation practices within the fenced area of the households or very near the dwelling units. Das and Das (2005) reported that the average size of HGs in Assam ranged from 0.02 ha to 1.20 ha. However, in TE, where the average size of each worker’s quarter is 0.04 ha ( Kar, 1984 ; Ahmmed and Hossain, 2016 ), we considered anything larger than 0.01 ha as an HG. Since the homegardeners in Sonitpur district generally used separately designated spaces within the HGs for the cultivation of vegetables, cereals, ornamentals, medicinal bamboo, boundary plants, plantation crops (e.g., Betel Nut [ Areca catechu ], Teak [ Tectona grandis ], Tea plant [ Camellia sinensis ], Rubber tree [ Hevea brasiliensis ], etc.), and livestock, we adopted whole-plot sampling and counted all plant and livestock species and their relative distribution in the HGs ( Poot-Pool et al., 2012 ). All tree and shrub species having diameter at breast height (DBH) of 10 cm or more were enumerated. Since deliberate cultivation of vegetable crops and other herb species was done in land parcels assigned to these crops, their area was measured, and the name and type were noted. The names of various ornamental plants, climbers, and medicinal plants were recorded along with their frequency of occurrence. Homegarden age was recorded as stated by the respondent, which was cross-checked with the village head. The ethnicity/linguistic group to which particular homegardeners belonged was noted based on how they identified themselves and also as per the ethnicity classification given in the Census (2011) .

2.4 Data analysis

Plant species diversity for each HG was computed using the Shannon diversity index.

where pi is the proportion of individuals belonging to a specific species determined by dividing the count of individuals from that species (n) by the total number of individuals observed ( N ). In this context, ln represents the natural logarithm, Σ denotes the summation of these calculations, and S represents the total number of species ( Shannon, 1963 ).

Dominance index (Cd)

where pi is the proportion of individuals belonging to a specific species determined by dividing the count of individuals from that species ( n ) by the total number of individuals observed ( N ) following Simpson (1949) .

The species evenness was calculated using the Pielou evenness index using.

D p = H / ln s

where H is the Shannon index, and S is the total number of species ( Pielou, 1969 ).

Species richness was calculated using the Margalef Index.

where S is the total number of species, N is the total number of individuals, and ln is the natural logarithm ( Margalef, 1958 ). The data for herb, shrub, and tree components were pooled to perform the above computations. Apart from the plant diversity estimate, the Margalef Index was also used to estimate the livestock diversification index ( Mekuria and Mekonnen, 2018 ). Whether the difference between variables of HGs of different socio-ecological types was statistically significant was tested by ANOVA followed by the Duncan multiple range test ( George and Christopher, 2020 ). The Sorenson test of similarity (S.I.) using.

S . I . = 2 l × 100 / 2 a + b

where L is the number of species two samples have in common, a is the number of species in the first sample, and b is the number of species in the second sample was used to find the similarity between HG of different ethnicities and different SEZs. Furthermore, cluster analysis was performed on the variables Shannon Index and Livestock Diversification Index. In addition, regression and correlation coefficients were derived to understand the relationship between different variables. All the data analysis is performed in R, Excel, OriginPro2023, and PAST software.

3.1 Structural characteristics of HGs located in different SEZs

There was a preponderance of HGs in all the SEZs evaluated ( Table 1 ). However, a large variation in size and main purpose was noticed. The preference for different crop types based on the dominant features of the SEZs and the cultural and economic background of the respondents led to four major types of HGs in the study area. The PA-type HG, which were mostly documented from the PA, were HGs with usually large sizes (>0.5 ha) often incorporating plantation species. Here, the size ranged from 0.07 ha to 1.806 ha. These HGs also had the highest number of stems of plants greater than 10 cm DBH. In addition, they had the maximum land, i.e., 53% devoted to plantation crops like Areca catechu , Hevea brasiliensis , and Camellia sinensis , and 47.73% of HG in the PA also had ponds with as many as five varieties of fish. The majority of HGs reported here are old HGs (age > 40 years) and had designated spaces allotted to vegetables, ornamentals, plantation species, and ponds. Intercropping species like pineapple ( Ananas comosus ) with lemon ( Citrus limon ) and Areca catechu was fairly common. Seventy-nine percent of HGs had bamboo species, with almost 50% of HGs having more than one species of bamboo. Often, bamboo was planted at the end of HGs, connecting them to the field. These HGs also had a sizeable number of livestock (and a high livestock diversification index) and were also more commercialized. The major characteristics and composition of these HGs are shown in Figures 2A , 3A,B .

Table 1 . General characteristics of villages in the study site.

Figure 2 . Diagrammatic visualization of homegardens of different socio-ecological zones: (A) homegarden of protected area socio-ecological zone; (B) homegarden of rural market socio-ecological zone; (C) homegarden of tea estate socio-ecological zone; (D) homegarden of riverine socio-ecological zone.

Figure 3 . Representation of general characteristics of homegardens: (A,B) large plantation spaces and multiple livestock shed in HG of PA; (C,D) vegetable species cultivation as dominant feature of riverine HG; (E,F) cultural species, i.e., orchid and GI-tagged litchi in RM HG; (G,H) TE HG being small in size support small livestock species and fencing plants.

The RI type HG majorly documented from the RI SEZs were the ones that were more prone to floods (almost 60 days), had large variations in size (ranging from 0.013 ha to 0.67 ha), often had less livestock reared, and had more preference for vegetable cultivation. Although the RI zones were vulnerable and prone to floods, homegardening was enthusiastically pursued by the farmers in this zone. In RI HG, the maximum land is for vegetable cultivation (51%). The major vegetables cultivated were okra ( Abelmoschus esculentus ), brinjal ( Solanum melongena ), potato ( Solanum tuberosum ), radish ( Raphanus sativus ), cabbage ( Brassica oleracea ), and cauliflower ( Brassica oleracea ) as shown in Figures 2D , 3C,D . In the RI zones, apart from the HGs attached to the household, the cultivation was also carried out in the Char (floodplain sediment island) areas. The most widely cultivated plants on the Char lands were jute ( Corchorus olitorius ), vegetables, and black lentil ( Vigna mungo ).

The RM type HG had a relatively average size ranging between 0.013 ha and 0.47 ha, and since RM SEZs are multigenerational old (as they are among the oldest habituated areas in the study site), even though they are reduced in size, they represent high cultural values, and they seem to have the highest concentration of culturally important species like Orchid Rhynchostylis retusa (Kopu Ful; Figure 3A ); GI-tagged Citrus limon (Kaji nemu); and Litchi chinensis Sonn (Lichu; Figure 3F ). In addition, almost 56% of the HGs had more than three varieties of banana. Most common among them were Musa chinensis (Jahaji Kol), Musa champa (Cheni-champa Kol), Musa assamica (Malbhog Kol), Musa paradisiaca (Kach Kol), and Musa gigantea (Bhim Kol). These all play important roles in Assamese cuisine. Vegetable cultivation and plantation were allotted approximately the same extent of land in rural market-based HG, i.e., 45 and 47%, respectively, as shown in Figure 2B . Ponds are present in 21% of RM HGs. In addition to being most proximate to the market, the species diversity was also very dominated by ornamental species.

The TE type HG was the HGs mostly documented among the TE workers were smaller (size <0.03 ha), ranging from 0.013 to 0.134 ha, had few tree species, mostly ornamental, fruit, and vegetable species, and had small places for worship and poultry or small avian species (67% of HGs) reared for meat and eggs; however, the number of large cattle reared were few (15%). In TE-based HGs, the maximum area was used for vegetable and ornamental plant cultivation, as shown in Figure 2C . Ponds were present in less than 5% of HGs in TEs. Since the homegardening area was small, it was therefore judiciously used for ornamental and vegetable cultivation, along with scattered tree species for cultural values and subsistence ( Figures 3G , H ).

In addition, the mean HG age is found to be 59 years in PA HG, 45 in RI zones, 47.4 in HGs adjacent to RMs, and 34.5 in the TEs. In addition, though the average age of HGs does not seem to be significantly different ( p  > 0.005) in each SEZ, the oldest HG of 110 years was reported from the RM locations, and the youngest of 5 years was observed in the RI as well as RM types.

3.2 Plant species composition and diversity in HGs of diverse SEZs

The plant composition in the HGs of the PA was found to be very diverse, with an average number of plant species of 43 (range 26–75). The number of plant species was found in the order PA > RM > RI > TE. In addition, the total stems of plants greater than 10 cm DBH were highest in PA and were observed in the same order. Overall, 101 tree species were identified from HGs in the study area. Areca Catechu (betel nut) was the most frequently observed tree species in all SEZs. In addition, three major species of bamboo were observed in our study area, viz. , Bambusa balcooa (Bhaluka Bah), Bambusa pallida Munro (Bijuli Bah), and Bambusa nutans (Mokal Banh). Sixty-four percent of HGs surveyed had one or more species of bamboo. Moreover, four varieties of bananas were found to be cultivated, and 76% of HGs were found to have at least one variety of banana. Although diverse functional groups of plants were observed, most of the plant species obtained in the HGs of each SEZ were food plants (36.6, 45.7, 35.8, and 36.5% in the HGs of PA, RI, RM, and TE, respectively). The species classified for food include vegetables, fruits, and other edible species. Figure 4 shows the distribution of plant species in different use categories and their distribution in corresponding SEZs.

Figure 4 . Use category of different plant species in homegarden of different socio-ecological zones.

The statistical distribution of plant species diversity observations in the HG of different villages in diverse SEZs is depicted in Table 2 . The Shannon diversity index was estimated to be highest at 1.48 in TE HGs and 1.44, 1.39, and 1.09 in HGs of RI, RM, and PA HG, respectively. The Simpson diversity index was also estimated at the highest values of 0.66 in TEs HG and 0.62, 0.51, and 0.45 in RI, RM, and PA HGs, respectively. The Margalef index was highest at 2.76 in RM and 2.58, 2.57, and 2.39 in TEs, RI, and PA HG, respectively. The Evenness index was highest at 0.70 in TE and 0.61, 0.40, and 0.31 in RI, RM, and PA HG, respectively. Table 2 shows that diversity indices, i.e., Shannon, Simpson, Margalef, or Evenness, were significantly different in each SEZ. However, there were no significant differences among the villages in the same SEZs except for those villages in the TE or RM.

Table 2 . Socio-ecological zone-wise average diversity indices in homegardens.

3.3 Livestock composition and diversity index

Livestock formed the most important part of 68% of all HGs surveyed; if we also take into consideration poultry farming and rearing avian birds for eggs and meat, this number becomes 85%. The common livestock observed in the field were Bos taurus (cattle), Bubalus bubalis (buffalo), Capra aegagrus hircus (goat), Sus scrofa domesticus (pig), and Ovis aries (sheep). In birds rearing for meat and eggs, the common birds reared were Gallus gallus domesticus (chicken), Anas platyrhyncos (ducks), and Columba livia (domestic pigeon). In addition, 20% of the homegardeners had more than one large animal. The highest average livestock variety and numbers were found in the HGs of PA (range 3–16) and least in the HGs of TE (range 1–3), shown in Table 2 . A total of 21.87% of HG had a pond with an average of three fish varieties and a maximum of six varieties. The most common of them are Rohu ( Labeo rohita ), Catla ( Catla catla ), Mrigal carp ( Cirrhinus cirrhosis ), and Bariala ( Aspidoparia morar ). The SEZ with the highest number of ponds attached to HG is the PA (47.73%). The HG in the study area also reportedly had quite a high average livestock diversity index (0.67), and the PA had the highest (1.04) as shown in Table 3 . Often, fodder tree species like Gmelina arborea Roxb were found to be cultivated in the HG to meet the requirements of this livestock. In addition, a small, outside, separated kitchen-like space was found to be present in almost all HGs where the food for livestock was prepared. For preparing the fodder, the fuelwood tree species cultivated in the HGs were used. Table 3 gives the agrobiodiversity distribution in different villages of diverse SEZs. The Sorenson similarity index among different SEZs for plant and livestock diversity was found to be highest between the HGs of the PA and RM (0.82) and least between the HGs of the TE and RI areas (0.58).

Table 3 . Socio-ecological zone-wise average agrobiodiversity distribution in the study site.

3.4 HG composition and type among different ethnic and linguistic groups

In total, we encountered HG of eight linguistic and ethnic groups in the study region: the Assamese, Bodo, Bengali, Hindi Speaking, Mishing, the Gorkhalis, the Tea Tribes or Adivasis (staying outside the TEs), and the Tea Garden Workers (staying inside the TEs). Except for two SEZs, which were dominated by one ethnic group, others were more heterogeneous. The RM area is dominated by Assamese, the TE area is found to be dominated by the tea tribe, and the PA and RI are found to be dominated by a mixture of tribal and non-tribal groups. The largest size of HG was found to be in the Mishing tribe-managed HGs (0.268 ha) and the smallest size was found in the HGs of tea tribes (0.013 ha). The oldest unaltered HG was found in the RM (110 years) in the Assamese household, and the youngest HG was also found in the same zone among the Assamese household (5 years).

The average diversity indices among different ethnic and linguistic groups are shown in Table 4 . The Shannon diversity index was found to be highest at 1.56 for the tea tribe. The average Simpson diversity index was also 0.66, with the highest for the tea tribe and the lowest 0.48 for Bengali. The Margalef Index was found to be highest at 3.01 for Adivasi and lowest at 2.43 for the Bengali linguistic group. This tendency to have high diversity indices in TE HG could be because there is no scope for planting plantation species because of their small size. This could be further verified from the observation that though diversity indices are higher for HGs of the tea tribe, the number of plant species observed is the least there (28).

Table 4 . Ethnicity-wise average agrobiodiversity distribution in the study site.

The highest mean number of plant species was found in the HGs maintained by Mishing Tribe (51) followed by Bodo Tribe (42), Assamese (41), Gorkhali (40), and Adivasi (33). The highest average variety (up to 4) and several livestock (up to 8) were found to be reared in Mishing HGs. Bos taurus and Capra aegagrus hircus were found to be raised in HGs of all communities, whereas Sus scrofa domesticus was mostly reared in the tribal communities, i.e., Bodo and Mishing HGs. For poultry and bird rearing, it was noticed that Anas platyrhyncos and Columba livia were predominantly reared in the Assamese HGs, and another form of small poultry was predominantly observed in the TE HGs. Though big livestock was comparatively fewer in the TE HGs, however, they had small birds and poultry more frequently. HG age is often reported as an important characteristic that tells about diversity, usage trends, and carbon stock. The highest Sorenson similarity index among plant species of HGs of an ethnic group is between Mishing and Bodo (0.78) and the lowest between Tea Tribe and Mishing (0.41). The comparative difference in these parameters among diverse ethnic groups is shown in Table 4 .

3.5 HGs as the site of conservation of endangered as well as cultural species

HGs as an important site for the conservation of both floral and faunal diversity have been mentioned in the literature ( Sharma et al., 2022 ). Similar to the HG study ( Das and Das, 2015 ; Barbhuiya et al., 2016 ), we found the critically endangered species Agarwood ( Aquilaria malaccensis ) to be frequently cultivated in the HGs in all three SEZs except TEs. In addition, the endangered tree species Livistona jenkinsiana Griff (Fan palm) and Mesua ferrea L. (Cobra saffron) were observed. The IUCN-vulnerable species like Canarium strictum Roxb (Black Dhup) was also observed.

Apart from these tree species, many culturally important species like the Orchid Rhynchostylis retusa (Kopo ful; Figure 3E ), a very commonly used plant for the Bihu festival (majorly by the Assamese family), Euphorbia splendens (Bathou), a very auspicious tree for the Batho religion (majorly of Bodo people), and Citrus limon (frequently used in Assamese cuisine) were frequently observed. This is similar to literature that mentions HGs as the site for the conservation of culturally important species ( Galluzzi et al., 2010 ).

In addition, unique management practices were observed in the HGs, some of them being the use of homemade biopesticide and fertilizer, mixed cropping, and the use of indigenous seeds. Moreover, the majority of knowledge exchanges for plant species selection and techniques of homegardening were family- or community-based. Only 7% of homegardeners reported having received any formal training.

4 Discussion

The findings that HGs were found to be unanimously distributed along the SEZs identified in the study site are consistent with the findings of Barua et al. (2019) . They reported that a large majority of households in rural Assam (≈85%) have HGs. The results of the study reflect that the HGs of Brahmaputra Valley are important reserves of plant and livestock agrobiodiversity conservation. This further strengthens the conjecture that HGs worldwide are indeed an important reserve of plant and agrobiodiversity ( Galluzzi et al., 2010 ; Tynsong and Tiwari, 2010 ). Moreover, the number of standing stock observed in the HG of the study site was quite larger than the HGs of Barak Valley in Assam ( Das and Das, 2005 ) but was lesser than the HGs of Kerala ( Kumar, 2023 ) and Brahmaputra Valley, Assam ( Dutta et al., 2023 ). The lower number could be because of the smaller size classes of trees and shrubs sampled; e.g., in this study, trees and shrubs greater than 10 cm DBH were sampled, while Kumar (2023) sampled trees and shrubs above 5 cm DBH. In addition, the values of diversity indices reflect low diversity (average Shannon index = 1.35) compared to HG plant diversity being mentioned in other parts of the world and even in Brahmaputra Valley, Assam (average Shannon index = 3.48; Dutta et al., 2023 ). This could be because there is a trend of commercialization incorporating plantation species, especially in the HGs of PA and RM. Moreover, if we compare the species composition, the food plants consisting of vegetables, pulses, cereals, and fruiting trees were most cultivated; this is similar to HGs in different parts of the world where the maximum number of cultivated plants were for food ( Vlkova et al., 2011 ; Panyadee et al., 2018 ; Whitney et al., 2018 ). Even in the HGs. of Assam, the food species are most commonly cultivated ( Das and Das, 2005 ). Livestock formed an important part of all HG studies. HGs in this study also had a high livestock diversity index as well as a high livestock number. Livestock forms a very important food source for millions of people around the world, and a large amount of land and resources are often required to manage them; hence, the promotion of natural agrosilvopastoral systems like HG could help in achieving sustainability ( Leroy et al., 2022 ).

Table 5 gives a comparative analysis of HGs at our sites with those reported in the literature. From this table, we can conclude that the features of HGs observed at the study site were similar to those reported in the literature.

Table 5 . Comparative assessment of homegardens observed in the field to that reported in the literature.

In addition, aligned with our hypothesis, we found that PA HG is larger with high plant and livestock species. The largest size of HGs reported from the PA is also similar to that in the literature that reports the largest size of HGs from the indigenous communities, with sizes ranging from 0.045 ha to 3.517 ha ( Pinho et al., 2011 ; Barbhuiya et al., 2016 ), whereas the size of HGs reported from other SEZs is not that large ( Murrieta and WinklerPrins, 2009 ; Panyadee et al., 2016 ). As expected, in the RI HG, more focus was on seasonal crops and the cultivation of species that are more flood-resilient. The RI HGs observed in the field are similar to those mentioned earlier in the literature of Assam, where the maximum proportion is of vegetable species ( Boruah, 2007 ); also, they are similar to the HGs of the Amazonian Caboclo Community, which, though smaller in size, had a significant contribution to the cultural and food needs of the community. Though these HGs are periodically washed away by floods, the community again builds them back ( Murrieta and WinklerPrins, 2009 ).

The RM HG, as expected, is on the pedestal of both being ancient and modern and is the traditional HG that needs maximum conservation. These HGs were similar to peri-urban HGs reported from Beijing, which reported the highest number of ornamental and culturally important species ( Clarke et al., 2014 ). The characteristics of the TE HG observed highlight that, despite being smaller in size, the HG could act as an important locus for the cultivation of chosen food and fruit species, highlighting its importance in both food security and food sovereignty. Though these TE workers’ HGs are rarely mentioned in the literature, some studies mention the plant composition (fencing plants) used in the HGs of TEs in Assam ( Borkataki et al., 2008 ). The size and number of plant species present in the HG varied significantly ( p <0.05) with both ethnicity and among diverse SEZs ( Figures 5A , B ).

All HG ages observed in the field except for those in the TE were found to be in the category of old HGs ( Pinho et al., 2011 ). Often, the older HGs are described as the heralds of biodiversity conservation and carbon stocks ( Kassa et al., 2022 ). Even from the field, we found that HG age was directly correlated with HG plant diversity (r 2  = 0.73; Figure 5C ). In addition, the finding that both the youngest and oldest HG are present in the RM could be because the RM is among the oldest habituated areas in the study site, hence the probability of having older gardens, but also since the commercialization and urbanization-based land fragmentation are very fast in these regions, the land holding size is decreasing. Though HG in the PA area demonstrated high livestock numbers and variety, other SEZs also demonstrated a fair share of livestock numbers and diversity.

Figure 5 . (A) Plot of HG age with total plant species in HG along different socio-ecological zones; (B) plot of HG age with total plant species among different cultural and ethnic groups; (C) correlation plot between HG variables among different socio-ecological zones.

In addition, the HG structure and diversity of plants and livestock varied among different ethnic groups. The highest number of plant species reported as 51 among the Mishing tribe is higher in number than 31.58 among the Sonowal Kachari tribe in Brahmaputra Valley ( Dutta et al., 2023 ). The higher number of plant species in the HG of tribal communities as compared to non-tribal communities reflects the fact that tribal communities are still acting as the custodians of the conservation of agrobiodiversity ( George and Christopher, 2020 ). However, there is an exception for tea tribe workers living inside tea estates because they have a very small area for homegardening. This suggests that HGs vary not only according to linguistic or ethnic groups, but that the total SEZ features influence them more.

5 Challenges and future trends in homegardening

Though the HG in each SEZ and ethnicity was different, in each scenario they are playing a significant role in agrobiodiversity conservation. However, the structure and pattern of agrobiodiversity in HGs of all SEZs and ethnicity are undergoing major changes, with the focus being on commercialization. Almost 70% of HG surveyed had Areca Catechu plantation objectives, and 5% of HG have been converted into small-scale tea plantation units. This trend of commercialization observed was similar to those reported in Indonesian homegardens ( Abdoellah et al., 2020 ), where cultivation of commercial crops is becoming more common. Moreover, all homegardeners in the study site seek government and institutional support for better management of their HGs. The major challenges faced by homegardeners in the study site are represented in Figure 6 . We can see that almost all SEZs have the major challenge of human–wildlife conflict. This is similar to a study by Yashmita-Ulman et al. (2020) , which reported that traditional HGs in the Sonitpur district are at the receiving end of the negative impacts of the human–wildlife conflict. It was also reported that though in other agroforestry systems, the wild animals were killed for meat, in HGs they are mostly chased away (82%; Yashmita-Ulman et al., 2020 ). Hence, homegardeners can further be given incentives to promote coexistence with wild animals. In this way, HGs could also act as a conservation hotspot for wildlife. In addition, the development of market access to the products of these homegardeners and value addition to these products can strengthen the livelihood opportunities of homegardeners ( Sharma et al., 2022 ). The result highlighting characteristic differences observed in HG among different SEZs and ethnicities in this study suggests HG could be an important contributor toward food sovereignty.

Figure 6 . Major challenges in homegardening.

6 Conclusion

The result of this study highlights that HG in the study site is indeed acting as a high agrobiodiversity hotspot. The study also further strengthens the premise that HGs are a classic example of a diversified and integrated agricultural system. Moreover, though plant and livestock diversity was found to be characteristically different in HGs of different SEZs and ethnicities, the common component among all of them was the high emphasis given on the food species. These results are crucial at a time when the search for sustainable food systems is being given high priority. Promoting an integrated and conservation agricultural system like HG can be a win–win situation for all. However, more studies are required to understand how these differences in agrobiodiversity and management practices in different SEZs and ethnicities can influence the potential of HGs to enhance food security in the region. Furthermore, incentives should be provided for the conservation of traditional HGs. In addition, institutional support is crucial for mitigating the challenges observed by the homegardeners. In addition, a major trend of commercialization was observed in the homegardens; the impact of this commercialization on agrobiodiversity and food security can be the subject of further study.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval were obtained for the study on human participants in accordance with institutional ethics review board of Jawaharlal Nehru University (Reference No: 2022/Ph.D Student/329) and also District Collector of Sonitpur district (Order No: SMJ.29/Misc./2020/244). Furthermore, the consent was also taken from the individual respondent participating in the study.

Author contributions

RS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing, Project administration. UM: Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing – review & editing, Resources, Validation. AD: Investigation, Supervision, Validation, Writing – review & editing, Resources. BK: Formal analysis, Supervision, Validation, Writing – review & editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. RS would like to express her gratitude to the Ministry of Human Resource Development, Government of India, for providing her with a Prime Minister Research Fellowship (PMRF ID: 3400685) for her PhD work.

Acknowledgments

The author is also grateful to all the homegardeners and gaon buras who allowed us to study their homegardens. The author is also very thankful to the District Commissioner, Sonitpur District, who allowed her to conduct this study smoothly in all socio-ecological zones of the district. The author would also like to especially thank Ramesh Sharma, Research Associate, ISB, Hyderabad (currently affiliated), and Anurag Verma, TERI, Delhi, for valuable insights for statistical analysis and constructive feedback.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

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Keywords: agroforestry, agrobiodiversity, livestock diversity index, indigenous communities, commercialization

Citation: Sharma R, Mina U, Devi A and Kumar BM (2024) Do homegardens act as agent of agrobiodiversity conservation: a case study of homegardens of diverse socio-ecological zones in the Brahmaputra Valley, Assam. Front. Sustain. Food Syst . 8:1366499. doi: 10.3389/fsufs.2024.1366499

Received: 06 January 2024; Accepted: 24 June 2024; Published: 18 July 2024.

Reviewed by:

Copyright © 2024 Sharma, Mina, Devi and Kumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Usha Mina, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Indwelling-tunneled-central-venous-catheter-related early bacteremia and preoperative prophylaxis: a case and control study

Affiliation.

• 1 Pediatric Surgery Department. Hospital Universitario Virgen del Rocío. Sevilla (Spain).
• PMID: 39034873
• DOI: 10.54847/cp.2024.03.10

Abstract in English, Spanish

Introduction: The indication of preoperative prophylaxis in the insertion of indwelling tunneled central venous catheters (ITCVC) has a low level of evidence. Our objective was to assess risk factors of ITCVC-related early bacteremia in oncological pediatric patients and to determine the need for preoperative prophylaxis.

Materials and methods: A univariate and multivariate retrospective analysis of patients in whom an ITCVC was placed from January 2020 to July 2023, according to whether they had ITCVC-related early bacteremia (EB) in the first 30 postoperative days, was carried out. Demographic variables, leukopenia, neutropenia, use of preoperative antibiotic prophylaxis, and history of central venous catheter (CVC) or bacteremia were collected. Calculations were carried out using the IBM SSPS29® software.

Results: 176 patients with a mean age of 7.6 years (SD: 4.82) were analyzed. 7 EB cases were identified, with a greater frequency of neutropenia (p= 0.2), history of CVC in the 48 hours before insertion (p= 0.08), and intraoperative CVC (p= 0.04). The presence of intraoperative CVC increased the risk of EB 9-fold [OR: 9.4 (95%CI: 1.288-69.712) (p= 0.027)]. The lack of preoperative prophylaxis did not increase the risk of EB [OR: 2.2 (CI: 0.383-12.669) (p= 0.3)]. The association with other variables was not significant.

Conclusions: The intraoperative presence of CVC was a risk factor of EB in our patients. Preoperative prophylaxis had no impact on the risk of EB, which in our view does not support its use. However, further studies with a larger sample size are required. Leukopenia or neutropenia at diagnosis were not associated with a greater prevalence of infection.

Introduccion: La indicación de profilaxis preoperatoria en la colocación de catéteres venosos centrales tunelizados permanentes (CVCTP) tiene bajo nivel de evidencia. Nuestro objetivo fue evaluar factores de riesgo de bacteriemia precoz asociada a CVCTP en pacientes pediátricos oncológicos y determinar la necesidad de profilaxis preoperatoria.

Material y metodos: Realizamos un análisis retrospectivo univariante y multivariante de los pacientes con colocación de CVCTP entre enero 2020 y julio 2023, en función de si presentaron bacteriemia precoz (BP) relacionada con CVCTP en los primeros 30 días postoperatorios. Recogimos variables demográficas y otras como: leucopenia, neutropenia, uso de profilaxis antibiótica preoperatoria y antecedente de catéter venoso central (CVC) o bacteriemia. Los cálculos se realizaron mediante el software IBM SSPS29®.

Resultados: Analizamos 176 pacientes, con edad media de 7,6 años (SD 4,82). Identificamos 7 casos de BP, que presentaron mayor frecuencia de neutropenia (p= 0,2) y antecedente de CVC las 48h previas a la colocación (p= 0,08) y CVC intraoperatorio (p= 0,04). La presencia de CVC intraoperatorio aumentó 9 veces el riesgo de BP [OR 9,4 (IC 95% de 1,288-69,712) (p= 0,027)]. La falta de profilaxis prequirúrgica no aumentó el riesgo de BP [OR 2,2 (IC 0,383-12,669) (p= 0,3)]. La relación con otras variables no fue significativa.

Conclusiones: La presencia intraoperatoria de CVC fue factor de riesgo de BP en nuestros pacientes. La profilaxis preoperatoria no influyó sobre el riesgo de BP, por lo que creemos que su empleo no está justificado, aunque se necesitarían más estudios con mayor tamaño muestral. La leucopenia o neutropenia al momento diagnóstico no se relacionaron con mayor prevalencia de infección.

Keywords: Antibiotic prophylaxis; Bacteremia; Central venous catheters; Pediatrics; Surgical oncology.

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