Characterization and impact of organic vis-à-vis conventional farming systems in Jammu District

Introduction

Organic farming has received increasing attention because of its potential to address several environmental and resource-related challenges confronting agriculture. The approach may contribute to environmental protection, conservation of non-renewable resources, and improvement in food quality (Charyulu and Biswas, 2010) [1] . As a holistic productionmanagement system, organic agriculture seeks to support agroecosystem functioning, biodiversity, and soil health. Organic production generally relies on organic nutrient sources and minimizes dependence on synthetic chemicals for crop protection. Conventional production, by contrast, commonly uses chemical fertilizers and pesticides to raise yields, sometimes with insufficient attention to biodiversity and long-term soil health (Ramesh et al., 2010) [5] . Organic agriculture has expanded globally and has also gained policy attention in India. The source manuscript reports that approximately 3.2 million ha in India are managed under organic practices, with substantial areas located in Madhya Pradesh, Rajasthan, and Karnataka. Despite this growing interest, accessible empirical evidence comparing the livelihood effects of organic and conventional farming systems in India remains limited. Such comparisons should consider not only crop production but also the integration of complementary farm enterprises, particularly on smallholder farms where resources are shared across activities. Examining enterprise combinations under the two production approaches is therefore necessary to understand their diversity and operational complexity (Raghavendra et al., 2022) [4] . Against this background, the present study characterized organic and conventional farming systems in Jammu district and evaluated their effects on employment and farm income.

Materials and Methods

A primary farm survey was conducted in Jammu district during 2022 to document and compare organic and conventional farming systems.

A multistage sampling design was adopted. Three blocks were selected from the district, followed by two villages from each block, giving a total of six villages. The reported sample design comprised 10 organic and 10 conventional farmers from each village, resulting in 120 respondents: 60 organic farmers and 60 conventional farmers. Information was collected on household socioeconomic characteristics, farm organization and production practices, consumption patterns, and constraints encountered by farmers. The analysis first described the social characteristics and enterprise combinations of the sampled households.

Costs, gross returns, net returns, and benefit-cost (B:C) ratios were then compiled for the five major farming systems identified in the district. The effects of organic farming on employment generation and net farm income were estimated using propensity score matching (PSM). Kernel and Radius matching methods were employed. The covariates reported for the matching procedure were respondent age, landholding, family size, seed price, and use of extension services.

Kernel matching

) -[∑(j=1 to Nᶜᵢ) Y(j,0)K((P(X(j,0)) -P(X(i,1)))/bᵥ)] / [∑(j=1 to Nᶜᵢ) K((P(X(j,0)) -P(X(i,1)))/bᵥ)]} Here, T denotes the set of treated observations and N is the number of treated cases. Y(i,1) and X(i,1) are the outcome and covariates for treated case i. Y(j,0) and X(j,0) represent the corresponding values for comparison case j within the neighborhood of treated case i, where |P(X(i,1)) -P(X(j,0))| < bᵥ/2. Nᶜᵢ is the number of comparison cases in the neighborhood of i, K is the kernel function, and bᵥ is the bandwidth.

Radius matching

In this expression, Yᵀ is the outcome for a treated individual, Yᶜ is the outcome for a control individual, Nᵀ is the number of treated observations, and wⱼ denotes the matching weight assigned to control observation j.

Constraint-facing index

Farmer constraints were ranked using a constraint-facing index (CFI), following the severity categories described by Chunera (2020) [3] . Respondents classified each constraint as high, medium, low, or absent. Scores of 3, 2, 1, and 0, respectively, were assigned to these four response categories. The index for each constraint was calculated as:

where Cₕ, Cₘ, Cₗ, and Cₙ are the numbers of respondents reporting high, medium, low, and no constraint, respectively.

Results

The results are presented in five parts: household characteristics, distribution of farming systems, costs and returns, estimated impacts of organic farming, and production and marketing constraints.

Socioeconomic characteristics of sampled households

Table 1 summarizes the social characteristics of the surveyed households by farm-size category. Farmers were classified as marginal (<1 ha), small (1-2 ha), or medium and above (>2 ha). Among the 60 organic farmers, 22 were marginal, 28 were small, and 10 were in the medium-and-above category. Among the 60 conventional farmers, the corresponding numbers were 28, 17, and 15. The source narrative reports mean ages of 47.5 years for organic farmers and 45 years for conventional farmers. Across the farm-size categories shown in Table 1, mean age ranged from 45.2 to 49.2 years for organic farmers and from 44.7 to 45.4 years for conventional farmers. Conventional farmers had larger average holdings in the marginal and small categories, whereas organic farmers had larger holdings in the medium-and-above category. The reported average family sizes were 5.7 persons for organic households and 5.4 persons for conventional households. Most respondents in both groups had completed primary or secondary education, while comparatively fewer had attained intermediate or higher education.

Distribution of major farming systems

Five major enterprise combinations were identified (Table 2). The Crop+Horticulture+Dairy (C-H-D) system was the most common overall, accounting for 30 households, followed closely by Crop+Dairy (C-D), which was reported by 29 households. Dairy was an important component of organic farms because it supplied organic manure for crop production.

Within the organic group, C-D was the most frequently observed system, adopted by 17 households (28.3%), followed by Horticulture+Dairy (H-D), adopted by 13 households (21.6%), and C-H-D, adopted by 12 households (20%). Among conventional farmers, C-H-D was the dominant system, with 18 households (30.0%).

Estimated effects of organic farming

The PSM estimates are reported in Table 4. Kernel matching estimated an average treatment effect on the treated (ATT) of 42 additional employment man-days, with a standard error of 20.17. Radius matching produced a comparable estimate of 43 man-days, with a standard error of 19.31. Both employment estimates were significant at the 5% level.

For net farm income, the estimated ATT was Rs. 12,678 ha⁻¹ under Kernel matching and Rs. 12,069 ha⁻¹ under Radius matching. The corresponding standard errors were 6,367.22 and 6,942.86. The Kernel estimate was significant at the 1% level, whereas the Radius estimate was significant at the 5% level. Within the reported matching specifications, organic farming was therefore associated with higher employment and net farm income than conventional farming.

Constraints faced by farmers

The CFI rankings for eight constraints are shown in Table 5. Low market prices for farm outputs ranked first for both organic and conventional farmers, with CFI values of 111 and 98, respectively. For organic farmers, non-availability of quality inputs such as biofertilizers and biopesticides ranked second (CFI 95), followed by inadequate storage and processing facilities (CFI 94). For conventional farmers, the high price of manure or fertilizer ranked second (CFI 85), while high labour prices ranked third (CFI 78). Lack of adequate extension services received the lowest rank in both groups, with CFI values of 69 for organic farmers and 68 for conventional farmers.

Discussion

The findings show that farming in Jammu district is organized around diversified combinations of crops, horticulture, dairy, and poultry rather than around single enterprises. The predominance of the C-H-D and C-D systems illustrates the importance of crop-livestock integration. This integration is particularly relevant to organic production because dairy enterprises can supply manure and thereby support internal nutrient cycling. The cost-return results indicate that the C-D-P system performed strongly under both production approaches. Among organic farms, it generated the highest net return and the highest B:C ratio, whereas C-H also achieved a comparatively high B:C ratio. These results suggest that enterprise diversification may contribute to farm profitability by combining several income sources and enabling complementary use of farm resources. However, the study reports accounting results for the sampled systems and does not provide measures of year-to-year variability or production risk. The PSM estimates further suggest that organic farming was associated with additional employment and higher net farm income. The similarity of the Kernel and Radius estimates supports the internal consistency of the reported direction of effect. Nevertheless, the interpretation should remain confined to the observed covariates and matching procedures described in the study, because the manuscript does not report balance diagnostics, common-support statistics, sensitivity analysis, or tests for hidden bias. Marketing and input-related constraints may limit the practical gains from organic production. Low output prices ranked as the most serious constraint in both groups, indicating that production-system improvements alone may not translate into stronger livelihoods unless farmers have access to remunerative markets. Organic farmers additionally emphasized the limited availability of quality bioinputs, while conventional farmers highlighted fertilizer costs. The importance of institutional and market constraints is consistent with the constraint-oriented observations reported by Choudhary et al. (2007) [2] . The study is limited by its cross-sectional design, its focus on 120 farmers from six villages in one district, and the restricted methodological details reported for the PSM analysis. The manuscript also does not present seasonal or multi-year evidence, environmental indicators, or independent verification of organic management practices. Future research based on the same research problem should include a larger geographic area, repeated observations over time, clearer documentation of certification or practice status, and complete matching diagnostics. These additions would strengthen the assessment of livelihood effects without changing the questions addressed in the present study.

Conclusion

This study compared the characteristics and livelihood implications of organic and conventional farming systems in Jammu district. The sampled farms were dominated by integrated enterprise combinations, particularly C-H-D and C-D. The reported PSM estimates showed positive and statistically significant associations between organic farming and both employment generation and net farm income. At the same time, low output prices emerged as the leading constraint under both systems; organic farmers also faced shortages of quality inputs, whereas conventional farmers were affected by high fertilizer costs. These findings support the development of stronger market channels, access to remunerative prices, reliable supplies of quality organic inputs, and targeted farmer training. State agencies and relevant institutions should strengthen awareness, production, harvesting, processing, storage, and transportation support, particularly for small and marginal farmers, to facilitate informed adoption of organic practices and improve farm livelihoods in the Jammu region.

Acknowledgement

AcknowledgementsThe authors gratefully acknowledge the Chief Agronomist, Senior Scientist, Principal Scientist, technical staff, Research Assistants (RAs), Senior Research Fellows (SRFs), and other personnel of the IFS Centre, SKUAST, Jammu, for their continuous support during the conduct of this study.

References

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