Assessing farmers’ perceived constraints to climate adaptation using Garrett ranking and non-parametric analysis

Introduction

Climate change is widely recognized as one of the most critical global challenges of the 21st century. Rising greenhouse gas concentrations due to anthropogenic activities have increased global mean surface temperatures by approximately 1.1-1.2 °C above pre-industrial levels, with 2023 recorded as the warmest year on record (Anonymous, 2023; WMO, 2024) [2] . This warming has intensified extreme weather events, altered precipitation regimes, and increased the frequency of droughts, floods, and heatwaves. Such climatic disruptions pose severe risks to agriculture, which remains highly sensitive to temperature variability and rainfall instability. Globally, climate change is already constraining agricultural productivity and threatening food security (Ortiz-Bobea et al., 2021) [13] . Developing countries are particularly vulnerable due to their dependence on climate-sensitive sectors and limited adaptive capacity. India, classified as a high-risk country under the INFORM Risk Index (2023) [6] , faces increasing climate-related disasters, including erratic monsoons, extreme rainfall events, and prolonged heatwaves (Anonymous, 2023a) [3] . With nearly half of its workforce engaged in agriculture and a large proportion of land being rain-fed (World Bank, 2023) [19] , climatic variability directly affects rural livelihoods and economic stability. Madhya Pradesh, a major agricultural state in central India, is experiencing rising temperatures and increasing rainfall variability. Projections indicate further warming and heightened drought frequency, which could significantly reduce crop productivity (Mishra et al., 2016) [9] . Given the state's reliance on monsoon-dependent agriculture, climateinduced disruptions threaten farmers' livelihood security. Although climate-resilient agricultural practices have been promoted to enhance adaptation, adoption remains uneven. Farmers face multiple constraints, including financial limitations, inadequate institutional support, limited access to information, technological barriers, and sociopsychological factors influencing risk perception and decision making. Identifying and prioritizing these constraints is essential for designing targeted policy interventions. Therefore, the present study examines the major constraints perceived by farmers in adopting climate-resilient www.extensionjournal.com agricultural practices in Madhya Pradesh. Using Garrett ranking along with inferential statistical techniques such as the Friedman test and Kendall's coefficient of concordance, the study provides statistically validated prioritization of adaptation barriers, contributing evidence for strengthening climate resilience at the grassroots level.

Materials and Methods

Study Area

The investigation was conducted in the Central Narmada Valley agro climatic zone of Madhya Pradesh, India. The zone was selected purposively due to its agricultural prominence, monsoon-dependent production systems, and increasing exposure to climatic variability such as irregular rainfall, rising temperatures, and extreme weather events. Three districts within this zone, namely Narmadapuram, Narsinghpur, and Harda, were selected to represent varying farming conditions, irrigation systems, and levels of technological adoption.

Sampling Procedure

A multistage sampling procedure was adopted for selecting the respondents. In the first stage, three districts, namely Narmadapuram, Narsinghpur, and Harda, were purposively selected from the Central Narmada Valley agro-climatic zone of Madhya Pradesh, considering their agricultural importance and vulnerability to climatic variability. In the second stage, two blocks from each selected district were chosen using random sampling. Thereafter, villages were randomly selected from each block to ensure adequate representation of different farming situations within the study area. The sample size was determined scientifically using the finite population correction formula suggested by Singh and Masuku (2014) [7] .

Where; n = required sample size Z = Z-value corresponding to the desired confidence level p = estimated population proportion e = margin of error N = total population size A confidence level of 95% was considered, with a margin of error of 5%. Since there was no prior estimate of the population proportion, a value of 0.5 was assumed to obtain a sufficiently large and statistically reliable sample. Based on the total number of farming households in the selected villages, the required sample size was calculated to be 353 respondents. After determining the overall sample size, the respondents were distributed proportionately across the three districts based on the number of farming households in each district. This ensured that districts with larger farming populations contributed proportionately more respondents to the study. Within each selected village, individual farmers were then chosen through simple random sampling from the prepared list of agricultural households.

Where, Ni = respondents to be selected from village i Ni= total farmers (population) in village i N = total farmers across all selected villages Total sample size = 353

Data Collection

Primary data were collected during the agricultural year 2024-25 using a structured and pre-tested interview schedule. The schedule was developed based on literature review, expert consultation, and field interactions. Informal discussions were conducted with farmers to understand the practical difficulties they faced in adopting climate-resilient agricultural practices. Data were collected through personal interviews to ensure completeness and reliability.

Identification and categorization of constraints

Based on insights obtained from literature and field discussions, a list of constraints was compiled and classified into major categories such as economic, technical, institutional, social and psychological, and resource and infrastructure constraints. These constraint statements were included in the interview schedule. Respondents were asked to rank the listed constraints according to the severity they experienced, assigning higher ranks to more serious constraints. The ranking data were analysed using Garrett's Ranking Technique to determine the relative importance of each constraint.

Garrett's Ranking Technique

To determine the relative importance of the constraints, Henry Garrett's Ranking Technique was applied. The ranks assigned by each respondent were first converted into percent positions using the following formula:

Where, Rij denotes the rank given to the ith constraint by the jth respondent, Nj represents the total number of constraints ranked by that respondent.

The calculated percent positions were then transformed into Garrett scores with the help of the conversion table developed by Garrett and Woodworth (1969) [5] . For each constraint, the individual scores were summed and divided by the total number of respondents to obtain the mean Garrett score.

Statistical Analysis

To statistically examine differences among the ranked constraints, the Friedman non-parametric test was applied. This test assessed whether the observed differences in rankings were statistically significant. www.extensionjournal.com

Where, Rj = Sum of ranks for each statement n = Total number of respondents k = Number of constraints in that category Further, Kendall's coefficient of concordance (W) was computed to measure the degree of agreement among respondents regarding the ranking of constraints.

The value of W ranges between 0 and 1, with higher values indicating stronger consensus among respondents.

Results and Discussion

The findings of the study are presented in this section to examine the nature and relative severity of constraints perceived by farmers in adopting climate resilient agricultural practices. Category wise prioritization was carried out using Garrett's ranking technique to determine the relative importance of constraints. Subsequently, inferential analysis was performed to assess whether statistically significant differences existed in the ranking patterns within each category.

Economic Constraints in the Adoption of Climate-Resilient Practices

As presented in Table 1, rising prices of seeds, fertilizers, and pesticides (Mean Score = 59.16) emerged as the most severe economic constraint (Rank I). The high cost involved in adopting climate-resilient practices (57.57) was ranked second, followed closely by limited farm income (56.97). Limited access to credit facilities (53.35) occupied the fourth position, whereas delayed or insufficient crop insurance compensation (50.94) was perceived as relatively less severe. The findings indicate that escalating input costs and limited financial capacity constitute the primary economic impediments to climate resilient adoption. Cost intensive adaptation measures appear to be beyond the immediate affordability threshold of many farmers, thereby constraining investment decisions.

Technical Constraints in the Adoption of Climate-Resilient Practices

Institutional Constraints in the Adoption of Climate-Resilient Practices

The ranking results in Table 3 indicate that unreliable weather forecasts for farm planning (Mean Score = 56.21) was perceived as the most severe institutional constraint. Complicated procedures for availing benefits under climate related schemes (55.43) ranked second, followed by limited accessibility of extension services (55.08). Delayed scheme implementation and inadequate information dissemination were ranked moderately, while limited interaction with agricultural officers (53.71) was perceived as the least severe institutional constraint.

Social & Psychological Constraints in the Adoption of Climate-Resilient Practices

As indicated in Table 4, fear of social criticism in the event of failure (Mean Score = 51.42) emerged as the most severe social and psychological constraint. Fear of crop failure (50.21) ranked second, while generational gaps affecting adoption decisions (48.37) was perceived as the least severe constraint within this category. The results demonstrate that social risk perception and reputational concerns influence adoption behaviour. Although these constraints recorded relatively lower mean scores compared to economic and technical barriers, psychological hesitation remains a non-negligible factor in adaptation decisions.

Resource & Infrastructure Constraints in the Adoption of Climate-Resilient Practices

As presented in Table 5, lack of access to appropriate farm machinery (Mean Score = 59.10) was identified as the most severe resource related constraint. Poor storage and market facilities (58.24) ranked second, followed by limited irrigation availability (57.59). Labour shortages (52.82) and small landholding size (50.25) were perceived as relatively less severe.

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