Perceived feasibility of soil health card recommendations: A multidimensional analysis of technical, economic and social aspects

Introduction

Sustainable agricultural production is increasingly contingent on the judicious management of soil nutrients, which forms the cornerstone of both food security and environmental stewardship. Soil degradation, driven by overuse of chemical inputs, imbalanced fertilization and neglect of organic matter replenishment, has emerged as a critical challenge for Indian agriculture, particularly in intensively farmed states such as Telangana (FAO, 2015; Singh et al., 2023) [8,25] . The National Academy of Agricultural Sciences has highlighted that nearly 60% of Indian soils are deficient in at least two essential macronutrients and over 48% show multi-nutrient deficiencies compounded by secondary and micronutrient limitations. Against this backdrop, the Government of India introduced the Soil Health Card (SHC) scheme in February 2015 under the National Mission for Sustainable Agriculture (NMSA), with the ambitious objective of providing every farmer household with a card containing information on 12 soil parameters pH, electrical conductivity, organic carbon, nitrogen, phosphorus, potassium, sulfur, zinc, boron, iron, manganese and copper along with cropspecific fertilizer recommendations (GoI, 2015; Reddy, 2017) [12, 21] . The SHC scheme has been described as the world's largest precision agriculture initiative in terms of geographic and beneficiary coverage, with more than 220 million cards distributed across two cycles by 2021 (Timilsina et al., 2025; Ohlan et al., 2025) [26,18] . Despite this massive institutional investment, field-level evidence suggests a considerable gap between card distribution and actual adoption of SHC-based recommendations (Chouhan et al., 2017; Ghaswa et al., 2019) [5,10] . Farmers' decisions to adopt or reject SHC recommendations are influenced by a complex interplay of technical comprehensibility, economic affordability and social acceptability dimensions that constitute the construct of 'perceived feasibility' (Rogers, 2003; Saravanan, 2010) [22,24] . The feasibility of implementing soil test-based nutrient management is not merely a question of technical correctness but is embedded in the farmer's resource base, access to inputs, extension contacts and peer norms. www.extensionjournal.com Several studies have reported that while farmers acknowledge the informational value of SHCs, barriers such as the high cost of recommended inputs, non-availability of site-specific fertilizers, low comprehension of card content and inadequate post-distribution extension follow-up significantly constrain recommendation compliance (Jayalakshmi et al., 2021; Sahay et al., 2019; Ankhila et al., 2023) [14,23,2] . Moreover, the psychological attributes of farmers scientific orientation, innovativeness and economic motivation have been shown to mediate the relationship between information exposure and actual behavioral change (Bandura, 1977; Feder et al., 1985) [3,9] . Studies on technology adoption in Indian agriculture have consistently identified education, farm size, extension contact and access to credit as determinants of farmer responsiveness to new recommendations. Specifically in Telangana, the erstwhile Karimnagar district presents an agronomically significant study locale, characterized by diverse soil types (red sandy loams, black cotton soils and mixed red and black soils), a range of cropping systems including paddy, maize and cotton and an active network of soil testing laboratories established under the SHC program (Telangana State Government, 2020) [27] . Despite the existence of individual studies on SHC awareness, knowledge and adoption, there is a relative paucity of research that simultaneously examines the technical, economic and social dimensions of feasibility and specifically measures these across multiple crops and farmer profiles in a single integrated framework. The present investigation addresses this gap by offering a multidimensional feasibility analysis of SHC-based recommendations, with implications for policy, extension programming and input delivery systems. The study was undertaken with the following specific objectives • To assess the personal, socio-economic and psychological profile of respondent farmers in erstwhile Karimnagar district, Telangana. • To examine the technical, economic and social feasibility of Soil Health Card-based recommendations across rice, maize and cotton crops.

Materials and Methods Study Area and Research Design

The study was conducted in the erstwhile Karimnagar district of Telangana State, India. This district was purposively selected owing to its agronomic significance (a major producer of rice, maize and cotton), its varied soil types (ranging from red sandy loams in the upland areas to black cotton soils in the alluvial plains) and the presence of an active soil testing laboratory network under the SHC scheme. The district spans an area of approximately 11,823 km² and recorded a gross cultivated area of 4.26 lakh hectares in 2019-20 (Telangana State Government, 2020) [27] . An ex-post facto research design was adopted, as the study aimed to investigate an existing phenomenon farmers' perceived feasibility of soil health management without any experimental manipulation or intervention (Kerlinger, 1986) [15] . This design is appropriate for social science investigations where the researcher examines variations in an outcome variable (feasibility) in relation to antecedent conditions (farmer profile characteristics) that have already occurred in the natural setting.

Sample Size and Sampling Procedure

A multi-stage random sampling technique was employed for the selection of respondents. In the first stage, six mandals were randomly selected from the erstwhile Karimnagar district using simple random sampling without replacement. In the second stage, four villages were randomly chosen from each selected mandal, yielding a total of 24 villages. In the final stage, from each village, five farmers who had received SHCs in the most recent distribution cycle were randomly selected from the beneficiary lists maintained at the village-level Rythu Seva Kendra. This yielded a total sample size of 120 respondents (6 mandals × 4 villages × 5 farmers). The sample size satisfies the minimum requirement for reliable Pearson correlation analysis at the 5% level of significance (Cochran, 1977; Krejcie and Morgan, 1970) [7,16] .

Data Collection

Primary data were collected through personal interview method using a pre-tested, structured interview schedule. The interview schedule was developed in Telugu (the local language) and back-translated to English to ensure conceptual equivalence. A pilot study was conducted with 15 farmers (not included in the final sample) to assess clarity, comprehensiveness and appropriateness of questions. The schedule was refined based on pilot feedback before the main survey. Psychological variables scientific orientation, economic motivation, achievement motivation, and innovativeness were measured using standardized scales adapted from Saravanan (2010) [24] , with all scales demonstrating acceptable internal consistency (Cronbach's alpha > 0.70). Perceived feasibility of SHC-based recommendations was operationalized as the degree to which farmers perceived specific SHC recommendations to be achievable given their existing resources, knowledge and social context. The feasibility scale comprised items related to: (i) soil sample collection, (ii) application of FYM, (iii) application of nitrogen fertilizers, (iv) application of phosphorus fertilizers, (v) application of potassium fertilizers and (vi) application of micronutrients. Each dimension was scored separately for rice, maize and cotton.

Measurement of Variables

Respondents rated feasibility on a three-point Likert-type scale (1=Low, 2=Medium, 3=High). Composite overall feasibility scores were computed by summing item scores and categorized by mean ± standard deviation into low, medium and high feasibility groups.

Statistical Analysis

Descriptive statistics (frequencies, percentages, means and standard deviations) were used to characterize the profile of respondents and the distribution of feasibility scores. Pearson product-moment correlation coefficients were computed to examine the association between farmer profile variables and overall feasibility scores. Chi-square tests were applied to assess the independence of categorical variables. All statistical analyses were performed using SPSS Version 25.0 (IBM Corp., 2017) [13] . Statistical significance was determined at p ≤ 0.05 and p ≤ 0.01 levels. [20] and Patel et al. (2019) [19] , who attributed this trend to the sustained migration of younger rural populations toward non-farm employment in periurban and urban areas, a demographic shift documented across peninsular India. Regarding educational status, the majority had studied up to high school level (35.00%), followed by intermediate level (26.67%), reflecting a gradual improvement in rural literacy rates in Telangana over the past two decades (Census of India, 2011) [4] . Only 1.67% were illiterate and 13.33% were graduates, indicating a reasonably literate farming population capable of engaging with text-based SHC content. This educational profile carries positive implications for SHC comprehension, corroborating Ghaswa et al. (2019) [10] who demonstrated a significant positive association between educational level and SHC literacy scores. Farm size distribution revealed that small farmers ( [3] and empirically supported by Singh et al. (2023) [25] . [5] who noted that while farmers can physically collect samples, systemic delays in result communication undermine the perceived utility and therefore the feasibility of the entire process. [14] and Ghate and Kamble (2020) [11] , who identified declining organic manure use as a key driver of soil organic carbon (SOC) deterioration in intensively farmed districts. Policy frameworks that incentivize organic matter recycling and FYM distribution infrastructure could address this constraint. Nitrogen application exhibited the highest feasibility perceptions across all three crops, with 66.67% (rice), 65.00% (maize) and 60.00% (cotton) of respondents rating it as highly feasible. This result is not surprising, given that nitrogenous fertilizers particularly urea are the most widely used, heavily subsidized, and readily available inputs in the Indian fertilizer market. The price support and ubiquitous distribution network for urea ensure that nitrogen application remains the most economically accessible SHC recommendation for most farmers. However, SHC www.extensionjournal.com recommendations often advocate for split application of nitrogen in multiple doses, which requires additional labour and timing discipline that some farmers find challenging (Patel et al., 2019) [19] . The slightly lower feasibility for cotton (60%) compared to rice and maize may reflect the more complex and extended crop cycle of cotton, which demands more precise nitrogen scheduling. [23] . SHC recommendations for phosphorus often suggest quantities that deviate considerably from farmer practice (either recommending reductions where over-application is common, or increases where soils are deficient), which adds an economic risk perception dimension to feasibility. These findings align with Timilsina et al. (2025) [26] who identified phosphorus compliance as one of the weakest dimensions of SHC adoption nationally. Potassium application followed a pattern broadly similar to phosphorus, with medium feasibility predominating. The low feasibility proportion was highest for rice (35.83%) and lower for maize (27.50%) and cotton (32.50%). Potassium fertilizers predominantly muriate of potash (MOP) are not subsidized under India's nutrient-based subsidy (NBS) scheme and have historically been underutilized in Telangana's rice systems, where farmers often substitute potassium with excess nitrogen. Ankhila et al. (2023) [2] noted that potassium deficiency correction, while economically beneficial in the long run, requires an upfront investment that many smallholders are unwilling or unable to make in a single cropping season. The relatively higher feasibility perceptions for cotton and maize may reflect these crops' established potassium demand in farmer experience. [14, ] . The particularly low feasibility for maize and cotton may also reflect the fact that these crops are grown predominantly in upland areas with lighter soils where micronutrient deficiencies may be more severe and require larger corrective doses, amplifying the cost dimension of perceived feasibility. (2025) [26] who reported national-level SHC compliance at around 43% in the medium-to-high range, and with Chouhan et al. (2017) [5] who documented similarly stratified feasibility perceptions in a multi-district study in Madhya Pradesh. The present study's contribution is the granular disaggregation by crop and nutrient dimension, which provides more actionable diagnostic information for extension planners and input delivery system managers.

Results and Discussion 1. Profile Characteristics of Respondent Farmers

Feasibility of

Feasibility of FYM Application

Feasibility of Nitrogen Application

Feasibility of Phosphorus Application:

Feasibility of Potassium Application:

Feasibility of Micronutrient Application:

Overall Feasibility of SHC-Based Recommendations

Conclusions and Recommendations

The farmer profile is characterized by a predominance of middle-aged, high-school-educated, small-farm holders with medium levels of farming experience, extension contact, and psychological attributes, reflecting the typical demographic structure of Telangana's agrarian economy. Nitrogen application commands the highest feasibility across all three crops, driven by the wide availability and subsidized price of urea -but the uniformity of nitrogen favorability masks the need for split-application compliance that many farmers still do not follow. FYM application and phosphorus application represent moderate constraints, with over 30% of farmers rating each as having low feasibility due to declining livestock ownership, input cost, and complex soil-specific prescription variability.

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