Constraint analysis in adoption of soil health card recommendations among farmers in Karimnagar district of Telangana state

Introduction

Agricultural sustainability and food security fundamentally depend on maintaining optimal soil health status. However, Indian agriculture faces a paradoxical situation where decades of input-intensive farming have simultaneously increased production while systematically depleting soil quality. The persistent imbalance in fertilizer application patterns, characterized by disproportionately high nitrogen use relative to phosphorus and potassium, has created widespread multi-nutrient deficiencies across agroecological zones (Ohlan et al. 2025) [7] . This nutrient mining, coupled with declining organic carbon content and micronutrient depletion, poses serious threats to long-term productivity and environmental sustainability (Singh et al. 2023) [1] . Recognizing these challenges, the Government of India introduced the Soil Health Card scheme as a transformative policy intervention on February 19, 2015. The scheme's conceptual foundation rests on providing farmers with individualized, soil test-based nutrient management recommendations to replace the prevailing blanket fertilizer prescriptions. Through systematic soil sampling, laboratory analysis of 12 critical parameters (NPK macronutrients, sulfur as secondary nutrient, five micronutrients including Zn, Fe, Cu, Mn, B, and physicochemical properties pH, EC, OC) and generation of crop-specific fertilizer advisories, the SHC mechanism aims to optimize input efficiency while enhancing soil fertility (PIB 2025). The scheme envisions complete coverage of India's 14 crore farm holdings through triennial soil testing cycles, with technological enhancements including GIS-integrated portal systems and mobile applications facilitating sample collection and card distribution. The scheme's implementation has achieved substantial quantitative milestones, with cumulative distribution exceeding 270 million soil health cards across multiple cycles. Recent technological interventions in 2023 revamped the SHC portal with Geographic Information System integration, while the establishment of 665 Village-Level Soil Testing Laboratories across 17 states by February 2025 has decentralized testing infrastructure (PIB 2025) [9] . Additionally, the innovative School Soil Health Programme has engaged 125,972 students in 1,020 schools, creating awareness among future farming generations. These achievements notwithstanding, empirical evidence increasingly reveals significant disconnects between card distribution and actual adoption of recommendations by farming communities. Recent research across diverse agroecological contexts consistently documents this awareness-adoption gap. Madhuri et al. (2024) [6] reported that while 82% of paddy farmers in North Coastal Andhra Pradesh demonstrated awareness of the SHC scheme, only 48% actually implemented the recommended fertilizer applications. Similarly, Kumar (2025) [4] identified that 57% of farmers in Madhya Pradesh reported minimal usage of SHC recommendations despite receiving cards. This implementation deficit stems from multifaceted constraints operating across technical, operational, socio-economic and psychological dimensions. Technical barriers include delayed report generation, inadequate information on emerging nutrient deficiencies and sampling methodology errors. Operational constraints encompass input availability challenges, price disincentives and weak market linkages. Socio-personal factors involve educational limitations affecting comprehension of technical advisories, while psychological barriers reflect motivational deficits and skepticism regarding perceived benefits. Garrett ranking methodology provides a robust analytical framework for hierarchically prioritizing these constraints based on farmers' perceptions, enabling evidence-based policy formulation. Previous applications of this technique in agricultural technology adoption studies have successfully identified critical intervention points. However, constraint analysis specific to SHC adoption in Telangana State, particularly in districts with intensive implementation like Karimnagar, remains inadequately documented. Erstwhile Karimnagar district represents an appropriate research locale given its agricultural predominance, diverse cropping systems encompassing paddy, cotton, maize and pulses and active engagement of Krishi Vignana Kendras and District Agricultural Advisory and Transfer of Technology Centres (DAATTCs) in extension service delivery. Understanding constraint hierarchies from farmers' perspectives is imperative for designing targeted interventions that enhance scheme effectiveness. Whereas earlier studies have examined adoption patterns and impacts, systematic analysis of implementation barriers using standardized ranking procedures remains sparse. This knowledge gap undermines evidence-based policy refinement and extension strategy formulation. Therefore, the present investigation was conceived with specific objectives: (i) to profile the socio-economic and psychological characteristics of SHC beneficiary farmers; (ii) to identify and prioritize technical constraints limiting SHC recommendation adoption using Garrett ranking technique; (iii) to analyze operational barriers affecting implementation; (iv) to examine socio-personal and psychological factors hindering utilization; and (v) to formulate evidence-based suggestions for strengthening scheme implementation and enhancing adoption levels among farming communities.

Materials and Methods

Research design and rationale of the Study

An ex-post facto research design was employed for this investigation, given that the Soil Health Card scheme had already been operationalized and the phenomenon under study constraints in adoption occurred naturally without researcher manipulation. This non-experimental design enabled systematic examination of existing relationships between farmer characteristics and constraint perceptions in authentic field settings, thereby enhancing external validity and policy relevance of findings. The retrospective nature of constraint assessment allowed farmers to reflect on their cumulative experiences across multiple cropping seasons since receiving SHC, providing comprehensive insights into persistent barriers rather than isolated incidents.

Research locale and sampling procedure

The study was conducted in Erstwhile Karimnagar district of Telangana State, which was purposively selected based on three criteria: (i) substantial coverage of farm holdings under SHC scheme implementation; (ii) active engagement of Krishi Vignana Kendras in facilitating scheme operations; and (iii) representative agrarian characteristics including diverse cropping patterns and predominance of small and marginal landholdings typical of the region. A multi-stage stratified random sampling procedure was adopted to ensure adequate representation. In the first stage, six mandals were randomly selected from erstwhile Karimnagar district. In the second stage, four villages were randomly chosen from each selected mandal, resulting in a total of 24 villages. In the final stage, from each village, 05 farmers who had received Soil Health Cards were randomly selected, making a total sample size of 120 respondents. This sample size aligns with statistical adequacy norms for constraint analysis studies in agricultural extension research (Patel et al. 2023) [8] . Primary data were collected through face-to-face personal interviews conducted, coinciding with the post-Rabi harvest period when farmers had adequate time for interaction. A pre-tested structured interview schedule was developed following established psychometric procedures. The instrument comprised three sections: (i) socio-economic profile characteristics including age, education, farm size, farming experience, training exposure and extension contact; (ii) adoption-related variables including awareness, understanding and implementation of SHC recommendations; and (iii) constraint statements across four dimensions-technical (8 items), operational (7 items), sociopersonal (7 items) and psychological (7 items). The constraint statements were developed through extensive literature review, consultation with soil scientists, extension specialists and KrishiVignana Kendra officials, followed by pre-testing with 15 non-sample farmers to ensure content validity, comprehension clarity and response variability. Each constraint item was evaluated by respondents on its severity using Garrett ranking continuum. Supplementary qualitative information was gathered through informal discussions and field observations to contextualize quantitative findings.

Operationalization and measurement of variables

Profile variables were operationalized using established scales. Age was recorded in completed years and categorized as young (up to 35 years), middle (35-50 years) and old (above 50 years). Education was measured as years of formal schooling completed, classified into six ordinal categories from illiterate to post-graduation. Farm size was assessed in hectares and grouped according to official landholding classifications. Farming experience was computed as years of active agricultural engagement. Extension contact was measured using a composite index incorporating frequency of interaction with various extension agencies, participation in training programs, and attendance in farmers' meetings, following Singh and Sharma (2004) [14] . Psychological constructs including scientific orientation, achievement motivation, economic motivation, and innovativeness were assessed using standardized scales with demonstrated reliability coefficients exceeding 0.75.

Garrett ranking technique for constraint prioritization

Constraints were prioritized using Garrett ranking technique, which converts ordinal ranks assigned by respondents into numerical scores amenable to statistical comparison. Respondents ranked each constraint based on perceived severity from most serious to least serious. These ranks were converted to percent positions using the formula: Percent Position = 100(Rij-0.5)/Nj, where Rij represents the rank assigned by the ith respondent to jth constraint, and Nj denotes total number of constraints in that category. The percent positions were then converted to Garrett scores using Garrett table of score conversion. For each constraint, the Garrett Mean Score (GMS) was calculated by summing individual Garrett scores and dividing by number of respondents. Constraints were hierarchically ranked based on descending Garrett Mean Scores, with higher scores indicating greater severity. This technique offers superior analytical rigor compared to simple frequency distributions as it accounts for complete ranking patterns and provides interval-level measurement enabling meaningful score comparisons.

Results and Discussion

Profile characteristics of respondents

The socio-economic and psychological profile of respondents (Table 1) reveals important demographic patterns. Age distribution showed predominance of middleaged farmers (51.67%), followed by elderly (25.00%) and young farmers (23.33%). This pattern reflects typical farming demographics in Telangana where agriculture remains primarily a middle-aged occupation, with limited youth engagement a concerning trend for long-term sustainability (Ankhila et al. 2023) [1] . The lower representation of young farmers (less than one-fourth) indicates potential labor migration and limited intergenerational succession in agriculture. Educational attainment showed encouraging distribution with only 1.67% illiteracy, while majority possessed high school (35.00%) and intermediate (26.67%) education. This relatively favorable literacy profile suggests potential for understanding technical information when presented appropriately. However, post-graduation representation remained marginal (3.33%), indicating limited penetration of advanced agricultural education. Similar educational patterns among SHC beneficiaries were documented by Madhuri et al. (2024) [6] in Andhra Pradesh. Landholding distribution reflected typical agrarian structure with small farmers (36.67%) and semi-medium farmers (26.67%) constituting majority, while marginal farmers represented 20.00%. This predominance of smaller operational holdings has critical implications for SHC adoption, as resource constraints often limit implementation capacity despite awareness. Large farmers with holdings exceeding 10 hectares comprised only 1.66%, indicating minimal presence of commercial farming enterprises in the study area. Farming experience displayed normal distribution with 46.66% having medium experience, 30.00% high experience, and 23.34% low experience. Extension contact and training exposure both showed medium levels for approximately half the sample (52.50% and 48.34% respectively), suggesting moderate but improvable engagement with agricultural knowledge systems. Psychological variables-scientific orientation, economic motivation, achievement motivation, and innovativenesspredominantly exhibited medium categories (43.33% to 48.33%), indicating that while farmers possess baseline receptivity to scientific practices, intensified capacity building could elevate these attributes to support enhanced adoption.

Technical constraints in SHC adoption

Technical constraints represent systemic deficiencies in scheme design and implementation processes that impede effective utilization of SHC recommendations. Garrett ranking analysis (Table 2) identified critical technical barriers that farmers encounter.

Operational constraints limiting implementation

Operational constraints encompass practical barriers related to input availability, affordability, and market access that prevent farmers from implementing SHC recommendations even when technically understood and accepted. Garrett ranking analysis (Table 3) revealed the severity hierarchy of operational impediments. [4] in Madhya Pradesh and Ohlan et al. (2025) [7] in Haryana, suggesting nationwide supply chain inadequacies. High cost and unaffordability of fertilizers ranked second (GMS 64.21, Rank II), reflecting economic barriers to adoption. The removal of fertilizer subsidies for certain products and price volatility in international markets have substantially increased input costs, constraining purchasing power of small and marginal farmers who constitute the majority. Micronutrient fertilizers particularly carry premium prices compared to conventional NPK products.

Socio-personal constraints affecting utilization

Socio-personal constraints reflect individual and socialstructural factors influencing farmers' capacity and willingness to engage with SHC recommendations. Garrett ranking (Table 4) identified critical socio-personal barriers. Lack of motivation to adopt soil health practices constituted the foremost psychological constraint (Garrett Mean Score 66.78, Rank I). This motivational deficit reflects farmers' failure to perceive tangible benefits or immediate returns from implementing SHC recommendations. Unlike yieldenhancing technologies with visible, short-term impacts, soil health improvements manifest gradually over multiple cropping cycles, creating perception that benefits are distant or uncertain. Additionally, when micronutrient applications do not produce dramatic yield increases in nutrientsufficient soils, farmers may conclude recommendations are ineffective, reinforcing apathy. Motivation enhancement requires result demonstrations, success story documentation, and economic impact quantification to make benefits tangible and proximate rather than abstract and distant.

Fear of yield reduction or lack of faith in scientific results ranked second (GMS 63.45, Rank II). Some farmers expressed apprehension that reducing fertilizer doses as recommended for high-nutrient soils might compromise yields, particularly when recommendations deviate substantially from traditional practices. This risk aversion stems from livelihood dependency on agricultural income where yield losses translate directly into economic hardship. Past experiences with failed recommendations from extension agencies compound this skepticism. Building confidence requires participatory on-farm trials where farmers observe results on their own fields with scientific support, reducing perceived risk. Other psychological barriers included preference for traditional practices over scientific recommendations (GMS 59.87, Rank III), skepticism about accuracy of soil testing (GMS 56.23, Rank IV), perception that benefits are too distant or uncertain (GMS 53.45, Rank V), lack of confidence in implementing complex recommendations (GMS 50.78, Rank VI), and fatalistic attitude attributing outcomes to fate rather than management (GMS 48.12, Rank VII). These findings emphasize need for cognitivebehavioral interventions through experiential learning, social proof via farmer-to-farmer diffusion, and participatory approaches that build self-efficacy and shift locus of control toward internal attribution.

Integrated constraint analysis and theoretical perspectives

The multidimensional constraint analysis reveals that SHC adoption is impeded by complex, interacting barriers operating across technical, operational, socio-personal, and psychological domains. This finding aligns with technology adoption theories that conceptualize adoption as a multistage process influenced by innovation attributes, adopter characteristics, social systems, and organizational infrastructure (Rogers 2003) [11] . The relatively higher severity of technical and operational constraints compared to socio-personal and psychological barriers suggests that systemic implementation failures constitute more critical impediments than individual farmer limitations-an important insight contradicting deficit models that blame non-adoption on farmer ignorance or backwardness.

Conclusion

This investigation systematically examined the multidimensional constraints limiting adoption of Soil Health Card recommendations among farming communities in Karimnagar district. The constraint hierarchy identified through Garrett ranking technique reveals that implementation barriers predominantly stem from systemic service delivery failures rather than farmer inadequacies. Technical constraints-particularly inadequate micronutrient information and delayed card issuanceconstitute primary impediments. Operational barriers centered on input non-availability and unaffordability further compound adoption challenges. Socio-personal constraints reflect communication gaps necessitating information redesign, while psychological factors emphasize need for experiential learning and motivation enhancement.

The findings underscore that the Soil Health Card scheme, despite its conceptual soundness and policy significance, suffers from critical implementation deficits that prevent realization of intended benefits. Bridging the awarenessadoption gap requires systemic improvements spanning laboratory infrastructure strengthening for timely reporting, farmer-friendly communication redesign for enhanced comprehension, intensive pre-seasonal capacity building for skill development, robust extension follow-up mechanisms for implementation support, input supply chain development ensuring availability and affordability, result demonstrations for motivation enhancement, and programmatic convergence for holistic impact. Future research should examine differential constraints faced by distinct farmer categories (small versus large, educated versus less educated, progressive versus traditional) to enable targeted interventions. Longitudinal studies tracking constraint evolution across multiple SHC cycles would reveal whether implementation improves with program maturity. Comparative analyses across states with varying implementation models could identify best practices for replication. Impact assessments quantifying agronomic, economic, and environmental outcomes of sustained SHC adoption would strengthen evidence base for continued policy support. Ultimately, transforming the SHC scheme from a card distribution exercise into a genuine soil health improvement program requires addressing these multifaceted constraints through coordinated institutional action and sustained political commitment.

References

1. Ankhila RH, Singh A, Kumar P, Kumar S, Meena MC, Singh R, et al. Socio-economic impact of Soil Health Card scheme in the state of Andhra Pradesh. Indian Journal of Agricultural Sciences. 2023;93(6):683-686. doi: 10.56093/ijas.v93i6.134765.
2. Charel JM, Vagepare VP, Parmar VS, Baria NL. Perception about Soil Health Card scheme among farmers. International Journal of Current Microbiology and Applied Sciences. 2018;7(2):3233-3236. doi: 10.20546/ijcmas.2018.702.388.
3. Chowdary KR, Theodore RK. Soil health card adoption behaviour among beneficiaries of Bhoochetana project in Andhra Pradesh. Journal of Extension Education. 2016;28(1):5588-5597. doi: 10.26725/JEE.2016.1.28.5588-5597.
4. Kumar S. Analyzing adoption impediments in soil health card-based fertilizer application by farmers. Indian Journal of Extension Education. 2025;61(2):114117. doi: 10.48165/IJEE.2025.612RN04.
5. Lade AH, Ingle SN, Gawali K, Gadge P. Analysis of Soil Health Card awareness and constraints among farmers in Vidarbha Region. Asian Journal of Agricultural Extension, Economics & Sociology. 2024;42(2):100-106. doi: 10.9734/ajaees/2024/v42i22367.
6. Madhuri CR, Naik A, Kumar A, Padhy C, Ray S. Perception and adoption of Soil Health Card by Paddy growers of North Coastal Andhra Pradesh. Indian Journal of Extension Education. 2024;60(4):24-29. doi: 10.48165/IJEE.2024.60405.
7. Ohlan R, Ohlan A, Singh R. Adoption of Soil Health Card by farmers in Haryana: Perceptions, challenges and way forward. Indian Journal of Extension Education. 2025;61(1):15-20.
8. Patel DK, Kumar A, Dwivedi S. Farmers' challenges in adopting soil health card recommendations in Saharsa, Bihar. Indian Journal of Extension Education. 2023;59(4):154-156. doi: 10.48165/IJEE.2023.59431.
9. PIB (Press Information Bureau). Celebrating a Decade of Soil Health Cards. New Delhi: Ministry of Agriculture and Farmers Welfare, Government of India; 2025 [cited 2026 Feb 19]. Available from: https://www.pib.gov.in/PressReleasePage.aspx?PRID = 2104403.
10. Reddy AA. Impact Study of Soil Health Card Scheme. Hyderabad: National Institute of Agricultural Extension Management (MANAGE); 2017. p 210. Available from: https://www.manage.gov.in/publications/reports/shc.pdf
11. Rogers EM. Diffusion of Innovations. 5th ed. New York: Free Press; 2003.
12. Sahay R, Singh AK, Singh A, Maurya RC, Tiwari DK, Singh S. Impact of Soil Health Card in Unnao District of Uttar Pradesh. Indian Journal of Extension Education. 2019;55(3):101-103.
13. Singh BP, Kumar V, Chander M, Reddy MB, Singh M, Suman RS, et al. Impact of Soil Health Card Scheme on soil fertility and crop production among the adopted farmers. Indian Journal of Extension Education. 2023;59(1):122-126. doi: 10.48165/IJEE.2023.59125.
14. Singh N, Sharma RK. A scale to measure extension contact of dairy farmers. Indian Journal of Dairy Science. 2004;57(4):276-280.
15. Subhash R, Monika J, Himanshu T, Vani DK, Gupta MK. Study on knowledge and adoption of soil health card-based fertilizer application in Khandwa district (M.P.). International Journal of Chemical Studies. 2019;7(3):3152-3155.