Rethinking Farmers' Self-Reliance: A regional exploration of cultivation in India

 Corresponding author: Ashok Nayak. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Rethinking Farmers' Self-Reliance: A regional exploration of cultivation in India Ashok Nayak * and Kshamanidhi Adabar Department of Studies in Economics and Planning (DSEP), Central University of Gujarat, India. World Journal of Advanced Research and Reviews, 2025, 27(02), 454-460 Publication history: Received on 18 June 2025; revised on 24 July 2025; accepted on 26 July 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.2.2785 Abstract This study analyses the income from cultivation, consumption expenditures, and saving habits of farmers and uses econometric estimation with a Probit regression model to understand the self-reliance of Indian farmers across regions. The study finds significant regional differences in farmers' cultivation income and an alarming trend of negative savings, which implies a heavy reliance on non-farm income for sustenance. The estimated econometric results validate that marginal and small farmers, as well as farmers in Eastern and Northern regions, are significantly less likely to be selfsufficient in cultivation. Keywords: Self -Reliance of Farmers; Regional Disparity; Income of Cultivate; Probit Regression Model 1. Introduction There is a large variation in self-sufficiency in Indian agriculture in terms of space, structure, and policy. It is determined by various factors, such as farmers' landholding size, the crops they cultivate, their input usage habits, and their policies concerning agriculture. India's agriculture dynamics are heterogeneous due to its unique topography and agricultural techniques. Marginal and small farmers constitute a large part of the Indian agricultural population and are mostly constrained by their lack of access to institutional credit, farm inputs, technology, and market infrastructure (Deshpande, 2002; Deshpande and Prabhu, 2005; Gill and Singh, 2006; Suri, 2006). These challenges often result in a reliance on traditional practices, which can hinder productivity and sustainability. To address these issues, there is a pressing need for policies that enhance access to resources and promote technological advancements tailored to the specific needs of these farmers. Instead, these farmers are often reliant on external sources, like government subsidies, public distribution systems, and other social safety nets, for their survival (Agarwal and Agrawal, 2017). They have limited control over their own financial independence, and ever-present environmental shocks create greater difficulty for farmers to negotiate their way towards self-reliance. However, limited access to credit, tardy adoption of modern farming technologies, and weak market linkages are impediments to lifting agricultural productivity and income (Jeromi, 2007; Vadivelu and Kiran, 2013). Additionally, environmental uncertainties, such as irregularities caused by climate change, have emerged as major risks to the stability of agriculture; for example, erratic rainfall, extended dry spells, and flash floods are making agriculture unsustainable (Aggarwal, 2008). This further reinforces vulnerabilities and becomes dependent on institutional support. Due to these systemic and context-specific factors, most farmers are unable to earn enough income from farming to cover their minimum consumption expenditure. Especially marginal and small farmers earn little from agriculture, and the earnings are volatile with little scope for savings (Indo-Global, 2017; Singh et al., 2017). Thus, against this backdrop, the current study investigates the degree of self-sufficiency in the cultivation of Indian farmers. The self-reliance on cultivation is determined by the savings from cultivation as the only source of income. If the savings of a farmer after accounting for consumption are equal to or greater than zero, they are self-reliant on cultivation. If the savings are negative, this indicates that agricultural proceeds are insufficient to meet basic needs, and farmers are not self-reliant on cultivation. World Journal of Advanced Research and Reviews, 2025, 27(02), 454-460 455 Therefore, this study is divided into four sections. Section 1 gives an introduction to the study. Section 2 presents the data sources and the analysis methodology. Section 3 discusses the estimation results and analysis. Lastly, Section 4 concludes and suggests policy measures that would improve Indian farmers’ autonomy over finances. 2. Data and Methodology This study is based on secondary data from the National Sample Survey Office's (NSSO) Situation Assessment Survey of Agricultural Households, performed during the 70th round. The sample size is 34,054 farm households from various parts of the country. All Indian states have been divided into six main regions based on their administrative areas: north, east, west, south, northeast, and center. The purpose of this study is to determine if a farmer is self-sufficient in agriculture, which is stated in equation 1 below. The self-reliance indicator is denoted by Φ, whereas λ represents total yearly income from cultivation and μ indicates total annual consumption expenditure. If ϕ > 0, the farmer is selfsufficient in cultivation, but if ϕ < 0, the farmer is not. A dummy variable Y is constructed based on the farmer's savings status; for example, Y=1. If the farmer is self-sufficient in cultivation, then Y=0; otherwise, it is not. Because the dependent variable is qualitative and binary in form, the Probit regression model is used to assess the significance of factors influencing a farmer's self-reliance in agriculture. Assuming that a farmer is completely dependent on agriculture and ignores other sources of income that may impact consumer spending, equation 2 provides a symbolic depiction of the idea of self-sufficiency in farming. ϕ= λ−μ ------------- (1) P(Y=1∣X) = G (β0 + β1 x1+β2 x2+β3 x3+ β4 x4+ β5 x5+ β6 x6+ β7 x7+ β8 x8+ β9 x9) ------- (2) In equation 2, G(.) represents the cumulative distribution function (CDF) of a conventional normal distribution, whereas X denotes a vector of independent variables reflecting various farmer types and geographical factors. The model's goal is to investigate the impact of landholding size and geographic location on farmers' chances of being self-sufficient in farming. The explanatory variables are specified as follows: x1 = marginal farmer, x2 = small farmer, x3 = semi-medium farmer, x4 = medium farmer, x5 = Northern, x6 = Central, x7 = Eastern, x8 = Western, and x9 = Southern region. 3. Analysis This study's analysis is organised into three main sections, focusing on the assessment of farmers' financial independence across various geographical regions of India. Section 3.1 addresses the percentage distribution of farmers' savings status across various regions. Section 3.2 analyses average annual income from cultivation, consumption expenditure, and saving patterns across various regions. Section 3.3 of the study presents the results of the Probit regression model, identifying the factors that promote self-reliance in farmer cultivation. This structured approach facilitates a systematic assessment of the economic issues and factors affecting Indian farmers. 3.1 Percentage distribution of farmers’ saving across different regions The farmers' savings in various parts of the country reveal (Table 1) some unexpected differences, even within regions. In the Northern Region, Punjab leads with 25% of farmers reporting positive savings (the most in the region), while Jammu & Kashmir lags at 7% (the lowest), suggesting an 18 percentage point regional difference, while the overall positive savings gap is 13%. The situation in the Northeastern Region shows significant variation, with Meghalaya having a high of 28% of farmers reporting positive savings and Sikkim having a low of 3%, resulting in a 25-point difference; the regional average is 19%, which is much higher than the national average. In the Central Region, both the states of Chhattisgarh and Madhya Pradesh exhibit a similar situation, with positive savers accounting for 23% of all farmers in the state, whereas Uttar Pradesh's ratio is 10%. The intra-regional variance is substantial, although the regional average is only about 14% (Table 1). How do we know that the Eastern region is the poorest-performing area in the country? India has a national average of 29% positive saving rate, with the Western Region performing best with around 37% and the Eastern Region performing worst with around 4% average positive savings. The Western Region of Maharashtra (18%) outperforms Gujarat (11%) by 7 percentage points, with the regional average at 16%. The Southern Region has the most intra-region variability, with Telangana leading both the area and the country at 31% of farmers reporting positive savings, while Kerala lags the lowest in the region at 8%, resulting in a remarkable 23 percentage point disparity and a regional average of 17%. According to the all-India average, just 13% of farmers have surplus funds, while 87% are in deficit on a national basis. On the state level, Telangana has the largest share of positive savers (31%), while West Bengal has the lowest (2%), resulting in a national differential of 29 percentage points. These findings illustrate the regional and state-level variation in India's farmer financial situation, with some regions demonstrating resilience and others trapped in a cycle of widespread negative saving. World Journal of Advanced Research and Reviews, 2025, 27(02), 454-460 456 Table 1 The distribution of farmers' savings status as a percentage across regions Status of saving Negative Positive Total Northern Region Jammu and Kashmir 93.00 7.00 100 Himachal Pradesh 92.00 8.00 100 Punjab 75.00 25.00 100 Haryana 80.00 20.00 100 Rajasthan 89.00 11.00 100 Total 87.00 13.00 100 Northeastern Region Sikkim 97.00 3.00 100 Arunachal Pradesh 76.00 24.00 100 Nagaland 86.00 14.00 100 Manipur 90.00 10.00 100 Mizoram 88.00 12.00 100 Tripura 94.00 6.00 100 Meghalaya 72.00 28.00 100 Assam 79.00 21.00 100 Total 81.00 19.00 100 Central Region Uttar Pradesh 90.00 10.00 100 Chhattisgarh 78.00 23.00 100 Madhya Pradesh 77.00 23.00 100 Total 86.00 14.00 100 Eastern Region Bihar 95.00 5.00 100 West Bengal 98.00 2.00 100 Jharkhand 93.00 7.00 100 Orissa 95.00 6.00 100 Total 96.00 4.00 100 Western Region Gujarat 89.00 11.00 100 Maharashtra 82.00 18.00 100 Total 84.00 16.00 100 Southern Region Andhra Pradesh 89.00 11.00 100 Karnataka 79.00 21.00 100 Kerala 92.00 8.00 100 Tamil Nadu 90.00 10.00 100 Telangana 69.00 31.00 100 Total 83.00 17.00 100 All India 87.00 13.00 100 Source: Author’s calculation from NSSO Unit level Data, 70th round, schedule 33, 2013 World Journal of Advanced Research and Reviews, 2025, 27(02), 454-460 457 3.1 Analyses average annual income from cultivation, consumption expenditure, and saving patterns across various regions The statistics on average annual cultivation income, consumption expenditure, and total savings among farmers (Table 2) reveal a similar pattern of negative savings across all regions, although there is large interand intra-regional variability. Punjab has the highest income (₹130,331) and lowest negative saving (₹-29,428 in the Northern Region), while Jammu and Kashmir has the lowest income (₹36,715) and the highest negative saving (₹-71,405) in the region and country. The regional average income is ₹56,219, with consumption of ₹104,859 and a savings difference of ₹- 48,639. Arunachal Pradesh (₹-5,308) and Meghalaya (₹-5,580) have the lowest negative savings in the Northeastern Region, indicating a close match between spending and income. Tripura (₹-49,806) and Nagaland (₹-48,872) have significant negative savings. The region's average income (₹50,753) and spending (₹73,063) contribute to a modest deficit of ₹-22,310, making it the best-performing region in terms of pecuniary net balance. In the Central Region, Madhya Pradesh (₹-12,241) and Chhattisgarh (₹-13,666) have lesser deficits than Uttar Pradesh (₹-40,645), resulting in a regional average negative saving (₹-31,597) (Table 8b). In fact, the Eastern Region continues to be the lowest in terms of income (only ₹11,754 in West Bengal) and the highest deficit state (₹-58,895), together with two other socalled impoverished states. The region has the lowest mean income (₹16,560), high spending (₹63,122), and a significant negative saving value (₹-46,562). Maharashtra has a lesser deficit (₹-22,905) despite a middling revenue (₹46,297) compared to Gujarat, which has a significantly bigger deficit (₹-56,827). The national average is ₹42,232 income, ₹77,212 consumption, and −₹34,979 deficit, whereas the regional average is ₹42,232 income, ₹77,212 consumption, and −₹34,979. Kerala has the highest individual deficit in the country, with individuals earning ₹42,536 and spending ₹131,666 while saving ₹-89,129. Telangana (₹-9,842) and Karnataka (₹-11,550) have the lowest deficits in absolute terms compared to others. It is also the lowest in the region, with a saving of ₹-34,523.Nationally, the average income in India is ₹36,965, whereas the average spending is ₹74,574, resulting in an average negative saving of ₹- 37,609. Kerala and Arunachal Pradesh had deficits of ₹-89,129 and ₹-5,308, respectively, resulting in an ₹83,821 disparity. Similarly, the Northeastern Region has the lowest average deficit, while the Eastern Region has the greatest, highlighting the huge disparities in financial sustainability among Indian farmers across states and regions. This research shows that farmers with continually negative savings are not totally self-sufficient in terms of agricultural sustainability. Agriculture income offers insufficient purchasing power for consumption, and many households require nonfarm income streams, borrowings, subsidies, or remittances to provide a sustainable living. The income-toexpenditure mismatch indicates structural instability in the agricultural economy and has an impact on the long-term viability of farming as a source of income, which is especially concerning for small and marginal farmers. Table 2 Average annual cultivation income, consumption expenditure and saving of all farmer ⟨₹) Income, Consumption Expenditure and Savings Income from Cultivation Consumption Expenditure Total Saving Northern Region Jammu and Kashmir 36,715 108,119 -71,405 Himachal Pradesh 34,989 85,739 -50,750 Punjab 130,331 159,759 -29,428 Haryana 94,494 127,677 -33,183 Rajasthan 36,628 89,036 -52,408 Total 56,219 104,859 -48,639 Northeastern Region Sikkim 20,350 68,045 -47,695 Arunachal Pradesh 80,227 85,535 -5,308 Nagaland 38,545 87,417 -48,872 Manipur 35,075 77,874 -42,799 Mizoram 54,736 95,219 -40,483 Tripura 33,257 83,063 -49,806 Meghalaya 77,662 83,242 -5,580 Assam 50,546 69,165 -18,619 World Journal of Advanced Research and Reviews, 2025, 27(02), 454-460 458 Total 50,753 73,063 -22,310 Central Region Uttar Pradesh 34,266 74,911 -40,645 Chhattisgarh 40,303 53,969 -13,666 Madhya Pradesh 48,065 60,306 -12,241 Total 37,977 69,574 -31,597 Eastern Region Bihar 20,401 65,814 -45,413 West Bengal 11,754 70,648 -58,895 Jharkhand 17,389 56,181 -38,792 Orissa 16,893 51,657 -34,764 Total 16,560 63,122 -46,562 Western Region Gujarat 34,877 91,703 -56,827 Maharashtra 46,297 69,202 -22,905 Total 42,232 77,212 -34,979 Southern Region Andhra Pradesh 24,146 71,112 -46,966 Karnataka 59,000 70,550 -11,550 Kerala 42,536 131,666 -89,129 Tamil Nadu 23,079 69,557 -46,478 Telengana 50,994 60,837 -9,842 Total 39,976 74,499 -34,523 All India 36,965 74,574 -37,609 Source: Author’s calculation from NSSO Unit level Data, 70th round, schedule 33, 2013 3.3 Factor influencing farmers’ self-reliance on cultivation The descriptive statistics of the independent variables employed in the Probit model demonstrate that farmers vary in terms of structure and geography. The majority of the split sample is made up of marginal farmers (69.5%), followed by small farmers (16.9%), semi-medium farmers (9.3%), and medium farmers (3.6%). The signals are projected to be negative for marginal, small, and semi-medium farmers, supporting the idea that lower classes' landholdings would be excessive if they achieved acceptable levels of savings or economic self-sufficiency. On the other hand, the positive expected coefficient for medium farms suggests that bigger landowners are more likely to acquire financial capacity through economies of scale. Geographically, the sample is mostly composed of Central (29.7%) and Eastern (22.8%) regions (both with negative predicted signs), indicating that farmers in these areas experience structural disadvantages. The Southern (16.9%), Northern (12.7%), and Western (12.3%) areas also show negative expected signs, indicating that farmers in these regions may be limited, although to varying degrees. The columnar distribution of the variables appears to indicate that the Probit model was designed to investigate the effect of farm size and region on the likelihood that a farmer will be financially sustainable, with the majority of the indicators pointing to structural vulnerability, ranging from being smallholders to being situated in economically weaker regions (Table 3). Table 3 Descriptive statistics of independent variable and their expected sign of Probit Model Description of variable Min Max Mean Std. Dev Std. Err Exp. Sign Economic factor Marginal farmer (D.V) 0 1 0.695 0.460 0.000049 - Small farmer (D.V) 0 1 0.169 0.375 0.0000399 - Semi-medium farmer (D.V) 0 1 0.093 0.291 0.000031 - World Journal of Advanced Research and Reviews, 2025, 27(02), 454-460 459 Medium farmer (D.V) 0 1 0.036 0.188 0.0000201 + Geographical factor Northern (D.V) 0 1 0.127 0.333 0.0000355 - Central (D.V) 0 1 0.297 0.457 0.0000487 - Eastern (D.V) 0 1 0.228 0.419 0.0000447 - Western (D.V) 0 1 0.123 0.329 0.000035 - Southern (D.V) 0 1 0.169 0.374 0.0000399 - Source: Author’s calculation from NSSO Unit level Data, 70th round, schedule 33, 2013; Note- [D.VDummy variable] Table 4 Regression results of Probit model Results Nature of Variable Description of variable Coef. dy/dx=Margina l Effect Delta-method Std. Err. z P>│z│ Dependent (Y) Farmer is Self-reliant on Cultivation (Y=1) Independent (Xi) Economic variable Marginal farmer (D.V) -1.868* -0.30769 0.0003 -885.9 0.000 Small farmer (D.V) -0.881* -0.14516 0.0003 -414.05 0.000 Semi-medium farmer (D.V) -0.485* -0.07998 0.0003 -226.12 0.000 Medium farmer (D.V) -0.024* -0.00402 0.0003 -11.03 0.000 Geographical variable Northern (D.V) -0.545* -0.08976 0.0001 -602.54 0.000 Central (D.V) -0.221* -0.03652 0.0001 -281.59 0.000 Eastern (D.V) -0.694* -0.1143 0.0001 -775.11 0.000 Western (D.V) -0.504* -0.08304 0.0001 -570.09 0.000 Southern (D.V) -0.279* -0.04603 0.0001 -336.61 0.000 Source: Author’s calculation from NSSO Unit level Data, 70th round, schedule 33, 2013; Number of obs = 34907, Pseudo R2 = 0.2107, Prob > chi2 =0.000; Note-[D.VDummy variable, *1% level of significance] With 34,907 data and a pseudo-R² of 0.2107, the model is statistically significant and offers helpful information regarding the roots of farmers' self-reliance on farming in India. The Probit regression results reveal that economic and geographical factors are equally important. As an economic determinant, landholding size is again adversely related with the self-reliance effect, with marginal farmers having the lowest chance of being self-sufficient (effect: -0.3077), followed by small (effect: -0.1452), semi-medium (effect: -0.0800), and medium farmers (effect: -0.0040). Simply said, the larger the farm, the more likely one is to be self-sufficient. There is also a geographical element; each regional dummy (Northern, Central, Eastern, Western, and Southern) has considerably negative coefficients. Farmers in the Eastern (- 0.1143) and Northern (-0.0898) areas are particularly hard hit in comparison to other groups (Table.4). Overall, the research demonstrates that small and marginal landholdings and other regional features are substantial impediments to farmers generating a living purely from agriculture, and it implies that measures must be targeted to farmers' best interests to close these structural gaps. World Journal of Advanced Research and Reviews, 2025, 27(02), 454-460 460 4. Conclusion Economic and geographical vulnerabilities differentiate the discrepancies, revealing deep structural inequities in the self-reliance of Indian farmers in cultivation. For the great majority of regions, gross agriculture income is insufficient to meet consumption requirements, resulting in negative savings and economic distress. This tendency is much worse for marginal and small farmers, who have been found to have significantly reduced self-sufficiency, as examined by a Probit regression that reveals broadly negative marginal effects. The other cause is geographical disadvantages, which are mostly concentrated in eastern and northern India, exacerbating the problem and indicating spatial inequities in agricultural expansion and income creation. The current research concludes with two essential policy proposals to improve farmers' financial capability and autonomy. First and foremost, marginal and small farmers require targeted assistance in obtaining institutional financing, interest subsidy schemes, and adequate crop insurance coverage. They can reduce reliance on informal loans, lower the risk of income shocks, and provide a financial buffer in the event of below-average agricultural harvests. Second, prioritising region-specific agricultural development methods is critical. Targeted infrastructure and technology expenditures are required in the Eastern and Northern areas, which have selfreliance levels significantly lower than national averages. 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