
==== Front
Data Brief
Data Brief
Data in Brief
2352-3409
Elsevier

S2352-3409(24)00688-7
10.1016/j.dib.2024.110721
110721
Data Article
Dataset on the status of crop diversification in the Eastern Indo Gangetic Plains of South Asia
Nandi Ravi r.nandi@cgiar.org
a⁎
Krupnik Timothy J. t.krupnik@cgiar.org
a
Jackson Tamara tamara.jackson@adelaide.edu.au
b
a International Maize and Wheat Improvement Centre (CIMMYT), Bangladesh
b The University of Adelaide, Australia
⁎ Corresponding author. r.nandi@cgiar.org
14 7 2024
10 2024
14 7 2024
56 11072128 5 2024
1 7 2024
2 7 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
South Asiaʼs Eastern Indo-Gangetic Plains (EIGP) is home to approximately 450 million people. This region is characterized by the highest global concentration of rural poverty and a predominant reliance on agriculture for nutritional sustenance and economic livelihoods. Agriculture in the EIGP is highly cereal-centric, making crop diversification indispensable for its development. This data article is part of the research conducted by an interdisciplinary team of researchers analysing the status and determinants of crop diversification in South Asiaʼs EIGP. The data presented here were collected from 1,400 farm households across 72 communities in eight locations within the EIGP of India, Nepal, and Bangladesh during the year 2023. The research employed a simple random sampling method for empirical data collection. The primary agricultural decision-makers were given a tailored questionnaire comprising seven modules. These modules sought comprehensive data on livelihood practices, changes in agriculture, aspirations, diet, food security, mechanization, demographics, and asset ownership. The questionnaire was translated from English into Nepali and Bangla to facilitate a thorough understanding of the farmers' livelihoods in the study areas. The survey successfully ended with 1400 properly filled and captured questionnaires, which was quite representative. The cross-sectional data presented here describe location-specific farm-level crop distribution, enabling the analysis of geographic variations in crop diversification. The generation of this dataset addresses a significant gap in the availability of information on the current state of crop diversification in the EIGP, offering a foundational baseline for future research and interventions by regional governments and development partners. We employed the Herfindahl–Hirschman Index (HHI) to calculate crop diversification and a Tobit Regression Model to identify the region-specific determinants of crop diversification. The dataset is hereby made available as it is considered vital for regional policy and practical recommendations.

Keywords

Crop diversification
Herfindahl–Hirschman index
Eastern gangetic plains
South Asia
==== Body
pmcSpecifications TableSubject	Agricultural Sciences, Socioeconomics	
Specific subject area	Livelihood and crop diversification, Agricultural Economics,	
Type of data	Table, chart, figure, and Excel data file	
How data were acquired	Farm household survey with a well-structured questionnaire (Submitted with the article)	
Data format	Raw and analyzed	
Parameters for data collection	Face-to-face interviews	
Description of data collection	The primary dataset was derived from a survey conducted using structured questionnaires administered across 72 villages from eight locations selected through random sampling. This process resulted in the interview of 1400 farm households.	
Data source location	Eastern Gangetic Plains of India (Cooch Behar), Nepal (Koshi province), and Bangladesh (Rangpur division).	
Data accessibility	Repository name: CIMMYT dataverse:
https://data.cimmyt.org/dataverse/cimmytdatadvn
Provisional access before publication:
https://data.cimmyt.org/privateurl.xhtml?token=ebfa01cc-7894–41dc-a4f0-c453b53cd27b
Access to data after publication:
https://hdl.handle.net/11529/10549093	
Related research articles	Authors: Ravi, Tamara, Arifa, Biplab, Arunava, Kali, Pragya, Anjana, Wakilur, Emma, Gunjan, Pashupati, Timothy J. Krupnik
Title: Status and drivers of crop diversification in the Eastern Gangetic Plains of South Asia: Across borders and communities. Under review.	

1 Value of the Data

• These data provide important information on the farm household's socioeconomic, demographic, livelihood options, and farm characteristics within the Eastern Gangetic Plains (EIGP), including country-specific crop diversification determinants.

• Country-specific data about determining factors for crop diversification will help tailor approaches to facilitate crop diversification in the EIGP.

• These data can serve as a baseline for crop diversification promotion interventions by local governments, development partners, and researchers.

• These data presented here can be used by food system actors and researchers interested in understanding crop diversification and farm households’ livelihood opportunities in the Eastern Gangetic Plains of South Asia.

2 Background

Data presented in this data article came out of the Rupantar project, led by the University of Adelaide, which has a primary objective of catalyzing the transformation and diversification of smallholder farmers' livelihoods across the Eastern Indo Gangetic Plains (EIGP) in Bangladesh, India, and Nepal. This project is an offshoot of the Sustainable and Resilient Farming System Intensification (SRFSI) project, which focuses primarily on Conservation Agriculture based Sustainable Intensification (CASI) [1] and other ACIAR-funded projects and aims to understand the best mechanism for scaling diversification options among smallholder farmers in the EIGP. As part of this initiative, a comprehensive baseline study was conducted and led by the International Maize and Wheat Improvement Centre (CIMMYT) to assess the status of diversification and livelihood practices in the project sites across these three countries.

2.1 Data description

The data includes results from the farm household's survey with a sample of 1,397 located in the Eastern Indo Gangetic Plains (EIGP) of India, Nepal, and Bangladesh. The dataset includes information on the farm household's characteristics, land resources, asset and animal ownership, livelihood options, crop species richness, and crop diversity index at regional and seasonal levels within EIGP. Besides, region-specific determinants of crop diversification in the EIGP. The first three tables (Table 1, Table 2, Table 3) contain descriptive statistics of farm household characteristics, primary and secondary sources of livelihood among the survey households, crop species richness, and various crops cultivated by households across regions, seasons, and geographies. Furthermore, Table 4 contains descriptive statistics of the crop diversity index calculated using HH at the community level. Finally, Table 5 contains Tobit regression analysis results explaining regional-specific determinants of crop diversification in the EIGP of India, Nepal, and Bangladesh. The variable specifications are presented in Annex I, and the details of variable measurements are included in the supplementary file as a code sheet that describes each variable.Table 1 Characteristics of survey households.

Table 1:Characteristic	All	India	Nepal	Bangladesh	
Tufanganj 2	Mathabhanga 2	CB *1	CB 2	Sunsari	Morang	Jhapa	Rangpur	
Survey responses	1397**	79	44	182	143	159	159	154	477	
Household (HH) members	
 Average number of household members	5.03	4.65	5.10	4.65	4.96	5.17	5.71	4.96	4.91	
 Average number of agricultural members	3.16	2.97	2.99	2.97	2.97	3.61	3.62	3.32	3.0	
 Male	1.62	1.54	1.55	1.55	1.55	1.83	1.82	1.65	1.56	
 Female	1.53	1.42	1.44	1.42	1.42	1.78	1.80	1.67	1.46	
HH with off-farm earners (%)	62.00	73.8	48.9	54.1	64.2	67.5	76.3	61.9	57.3	
Land Resources	
 Average land ownership (ha)	0.92	0.93	0.91	0.93	0.92	0.89	0.91	0.95	0.62	
 Average land cultivated (ha)	0.98	0.95	0.93	0.95	0.94	0.98	1.03	1.21	0.66	
 Average number of plots (Rabi)	2.35	2.63	2.68	2.62	2.62	1.74	1.75	1.33	3.00	
 Average number of plots Kharif)	2.86	3.02	3.07	3.00	3.01	2.47	2.52	2.80	3.36	
 Average number of plots (Pre Kharif)	2.35	2.55	2.56	2.55	2.55	1.65	1.67	2.17	2.64	
Household Assets:% Households who own	
 Television	60.42	67.50	55.32	44.33	66.04	72.50	78.13	77.50	48.75	
 Electric Fan (Ceiling+ Stand)	98.06	97.50	100.00	98.45	96.86	97.50	97.50	98.13	98.54	
 Almirah/Wardrobe	57.29	28.75	38.30	51.55	31.45	80.63	71.88	72.50	57.08	
 Refrigerator	22.71	5.00	8.51	4.12	8.18	32.50	23.75	38.13	30.63	
 Landline	0.69	2.50	0.00	1.03	0.63	1.25	1.25	0.00	0.21	
 Mobiles (Touch screen)	75.49	80.00	91.49	74.23	77.99	91.88	91.88	91.25	56.67	
 Scooter/Bike	29.72	37.50	36.17	41.75	27.67	43.75	56.25	48.13	3.96	
 Three-wheel tempo/E-Rick/Tuk-Tuk	3.82	3.75	2.13	2.58	1.89	2.50	6.25	2.50	5.21	
 Car/ Tractor/ Truck	2.50	2.50	4.26	1.55	0.63	0.63	6.88	5.00	1.67	
 Animal drawn Cart	1.39	0.00	0.00	0.00	0.00	2.5	6.25	3.125	0.00	
 Irrigation pump (%)	44.10	65.00	74.47	78.35	69.81	11.88	35.00	46.88	28.13	
Animal Ownership:% Households who own	
 Indigenous cow	71.53	67.50	85.11	80.41	83.02	53.75	69.38	52.50	76.46	
 Hybrid cow	6.04	2.50	4.26	1.03	1.89	10.00	4.38	11.88	7.50	
 Bullocks	4.03	0.00	2.13	3.61	2.52	4.38	13.13	10.00	0.42	
 Buffalo	5.07	0.00	0.00	0.00	0.00	13.75	15.63	16.25	0.00	
 Goat	58.89	20.00	55.32	45.88	56.60	72.50	78.13	60.00	60.42	
 Sheep	0.56	0.00	2.13	2.06	0.00	0.00	0.00	0.00	0.63	
 Pig	2.57	0.00	0.00	0.52	2.52	5.00	6.25	8.75	0.00	
 Chicken	39.03	11.25	36.17	23.20	25.16	28.13	22.50	28.75	67.50	
 Duck	16.74	17.50	8.51	10.31	13.84	8.75	19.38	5.63	26.46	
 Pigeon	7.85	11.25	4.26	5.67	4.40	8.13	20.63	3.75	6.67	
 Fishpond	13.54	23.75	23.40	19.07	10.69	1.25	8.13	2.50	19.17	
 Orchard	11.11	16.25	21.28	13.40	23.27	3.75	3.13	5.63	11.25	
*CB=Cooch Behar; **Regional= aggregate of all eight communities.

Table 2 Major sources of livelihood among the survey households (colour code: Dark shades=High dependency; medium shades= medium dependency; and light shades=low dependency).

Table 2:Image, table 2	
P=Primary; S=Secondary.

Table 3 Prevalence of crops grown by a household (regionally, seasonally, and by geography). Colour coding applies as follows: green (<5 %), Yellow (5 %–50 %) and red (50–100 %). The color intensity increases with the response rate within each specific color category.

Table 3:Image, table 3	

Table 4 Descriptive statistics of crop diversity index among communities (HHI).

Table 4:Season	No of villages	HHI	
Mean HHI	Std. Dev.	Min	Max	
Kharif	50	0.98	0.06	0.50	1	
Pre-Kharif	65	0.95	0.07	0.62	1	
Rabi	72	0.10	0.06	0.01	0.31	

Table 5 Tobit regression result on household diversification by location.

Table 5:Image, table 5	
Note: ***, **, and * show the value statistically significant at 1 %, 5 %, and 10 % level, respectively.

3 Experimental Design, Materials, and Methods

3.1 Location selection

A comprehensive baseline study was conducted across Cooch Behar District in India, Rangpur Division in Bangladesh, and Koshi Province in Nepal (Fig. 1) to assess the spatiotemporal patterns of crop diversification and livelihood practices. This study involved a survey of 72 communities, with 24 villages from each country selected based on their similar agroecological and socioeconomic characteristics. This strategic selection process aims to ensure that the project's findings and strategies could be broadly applicable across similar regions, marking a significant step towards achieving sustainable livelihood transformations for smallholder farmers in the EGP.Fig. 1 Survey location representing 72 communities (villages) across eight locations in three countries.

Fig. 1:

3.2 Questionnaire development

The questionnaire for the study was structured using the Decision-making Dartboard (DmD) framework [2]. With its proven efficacy in diverse regions, as seen in the LPA framework and corroborated by subsequent studies, this framework breaks down decision-making into six distinct levels across four asset categories [3]. Furthermore, the Rupantar livelihood framework [4] aided in classifying livelihood options, ensuring the questionnaire captured pertinent information on livelihood categories. The primary agricultural decision-makers were given a tailored questionnaire comprising seven modules. These modules sought comprehensive data on livelihood practices, changes in agriculture, aspirations, diet, food security, mechanization, demographics, and asset ownership. The questionnaire was translated from English into Nepali and Bangla, thus facilitating a thorough understanding of the farmers' livelihoods in the study areas (Fig. 1).

3.3 Participant selection

The research included both male and female farmers, focusing on those who make the primary agricultural decisions in the household. While interviews were primarily conducted with the main decision-makers of the household, to address the underrepresentation of women decision-makers, interviews were also extended to the spouses of 25 % of these main decision-makers, resulting in a total of 500 interviews per location. A simple random sampling technique was employed, using a skip pattern determined by each village's population, which ranged from 250 to 300 households. By applying a skip number of 10, the study ensured that at least 10 % of each village's households were interviewed, equating to every tenth house.

3.4 Survey implementation

In each country location, a team of 15 experienced enumerators was selected to collect the data in local languages, followed by a comprehensive four-day training program, which included pilot interview sessions held in different communities. The timing was a critical consideration, and the interviews were conducted after the Rabi planting season during February through April 2023. This timing was chosen to ensure that the respondents had relatively more time availability and were not overwhelmed by their agricultural activities. The duration of each interview ranged from 60 to 90 min. The interviews were carried out using the KoboCollect app. Prior to commencing each interview, the enumerators initiated a brief introduction of themselves and the project to the respondents. Subsequently, they read the detailed consent form from the app. Interviews were conducted only after obtaining the respondents' consent.

3.5 Measurement

3.5.1 Crop diversity index (CDI)

Crop diversity refers to the cultivation several crop species in a year in a given landholding. There are different methods to measure crop diversification, and the relevant one for our study is the HHI (Fig. 2). HHI is calculated by taking the sum of squares of acreage proportion of each to the total cropped area (Kumar [5,6]). HHI is defined as:(1) HHI=∑jNPj2.

where, Pj is the proportion of area under jth season i.e., Kharif, Rabi and Pre-Kharif for each crop. N is the number of crops.(2) Pj=Ai∑iNAi

Fig. 2 Crop Diversity Index (HHI), disaggregated by region (country), season, and location (country, district, and community level) in EGP.

Fig. 2:

Eq. (2)Ai is the total actual area for ith crops and ∑iNAi denotes the total cropped area. N is the number of crops. The value of HHI range between 0 and 1, where ‘0ʼ indicates ‘perfect diversification’ and 1 indicates ‘perfect specialization.’ The value of HHI approaches zero as ‘N’ becomes large and takes the value ‘1ʼ when only one crop is cultivated (Kumar [5,7]). The HHI is categorized into three levels: below 0.1 is ‘high diversification,’ 0.1–0.18 is ‘Medium Diversification’, and HHI above 0.18 is ‘Low diversification.’

3.6 Modelling factors influencing agricultural diversification

Numerous studies have investigated the factors influencing the adoption of modern technology and/or crop selection and diversity. These studies often employ probit, Tobit, or logit regressions to analyse the relationship between individual crops and the socioeconomic conditions of farmers [[8], [9], [10]]. The underlying theoretical framework in all these modelling endeavours rests on the assumption of crop diversification, which serves as the foundational basis for our study. We calculated the HHI for the locations and at the household level to understand the spatial and temporal diversification level. We used this to further understand which factors influence agricultural diversification in the EGP. Our estimated HHI score stands between 0.16 and 1 while doing crop diversification analysis at the household level. In this scenario, a censored regression model becomes necessary due to the nature of the data. A Tobit model is preferable because it incorporates all observations, including those at the limits (like zero, representing those who have diversified) and those above the limit (representing those who have not diversified), to create a more comprehensive regression line. The Tobit model is preferred over OLS because using OLS can lead to biased and unreliable parameter estimates. This bias grows with more observations at the value of 0. In Tobit, the coefficients don't directly indicate the impact of explanatory variables on the dependent variable, but their signs indicate the direction of the associations [11].

3.7 Model specification

A multivariate Tobit model was developed to investigate the factors influencing diversification empirically. The dependent variable is the HH Index, which varies between ‘0ʼ and ‘1ʼ, where the respondents are specialized in one crop (near to value 1) and more diversified (near to value 0). However, before running the Tobit model, we estimated the multi-collinearity using the variance inflation factors and identified no multi-collinearity [12]. The parameters of the Tobit model were also estimated using the maximum likelihood method in Stata version 14 software. The model specification on the diversification can thus be estimated as follows:(3) HHI=β0+β1Familysize+B2MaleInvolvementB3Jointdecisionmaking+B4Interactionwithextension+B5Non−farmincome+B6Accesstosubsidyforseeds

The variable specifications from Eq. (3) are given in Annexure 1.

Ethics Statement

All research protocols and questionnaires were developed in collaboration with scientists from all project partner organizations. Furthermore, ethical clearance was obtained from the CIMMYT Internal Research Ethics Committee (IREC) registered under IRB 00012744 (Reference number IREC.2023.019) and the University of Adelaide's Human Research Ethics Committee (Number H-2022-062).

CRediT Author Statement

Ravi: Conceptualization, Methodology, Data curation, Writing Original draft preparation, Visualization, Investigation. Timothy J. Krupnik and Tamara Jackson: Reviewing and Editing.

Annexure 1: The variables specification from the Eq. (1)

Variables	Description	Variables	Description	
Dependent variable: HHI	Categorical	
Independent Variables	
Gender	Yes/No	Irrigation pump	Yes/No	
Education	Yes/No	Land owned (ha)	Decimal	
Age	Numerical	Land cultivated (ha)	Decimal	
Family size	Numerical	Total number plots	Numerical	
Labor hire Kharif	Yes/No	Mechanization	Yes/No	
Labor hire Rabi	Yes/No	FPO	Yes/No	
Labor hire Pre-kharif	Yes/No	SHG	Yes/No	
Male involved	Numerical	Access to subsidy for seed	Yes/No	
Female involved	Numerical	Access to subsidy for fertilizer	Yes/No	
Rabi irrigation source	Categorical	Access to subsidy for tools	Yes/No	
Non-farm activity	Yes/No	Interaction to extension	Yes/No	
Access to electricity	Yes/No	Market mechanism	Categorical	
Decision making	Categorical	Orchard	Yes/No	
Own ponds	Yes/No	Non-farm income	Yes/No	
Livestock	Yes/No	Eggs	Yes/No	
Horticulture	Yes/No			

Data Availability

Household Survey “Baseline of Crop Diversification in Eastern Gangetic Plains (EGP) (Original data) (CIMMYT dataverse).

Acknowledgments

This research was supported through the RUPANTAR project supported by ACIAR. In addition, the write-up of this work was supported in part by the CGIAR Regional Integrated Initiative Transforming Agrifood Systems in South Asia (TAFSSA). We thank all funders who supported this research through their contributions to the CGIAR Trust Fund: https://www.cgiar.org/funders.

We especially thank all researchers and field enumerators from RUPANTAR project partner organizations in India, Nepal, and Bangladesh for their active support in data collection and all the farmers for their time spent with us. These data sets are a subset of the wider baseline survey conducted in the EIGP.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have, influenced the work reported in this article.
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