What Motivates Farm-Level Adaptation in India? A Systematic Review

Abstract

Indian farmers are adopting several farm-level adaptation options to mitigate potential impacts from climate changeand extreme events. Although different studies have identified determinants, there is limited literature on systematic review of factors that drive farmer’s adaptive behaviour, particularly in India. This chapter aims to address this gap, and in doing so, it furnishes three major findings. First, non-climatic factors like demographic characteristics, asset and income, education, dependency on agriculture, farm characteristics and access to financial institutions are observed as major motivating factors. Second, this chapter advocates future studies to establish the causal relationship of farm-level options with climate changeand extreme events. Third, there is a paucity of studies with reference to perception, risk attitude behaviour, behavioural anomalies, climate information and government policies related to generic and specific disaster. These remarks render important avenues for scholarly communities and policy makers for expanding the domains of future research while realigning existing plans to address the observed gaps and develop evidence-based policies for scaling up farm-level adaptation mechanisms.

Fertilizer Use, Value, and Knowledge Capital: A Case of Indian Farming

“…[1]). Although agricultural growth is impossible without mechanization, low adoption has been reported in India [1,[11][12][13]. Numerous studies, therefore, identified the confounding factors of adoption, and those are related to economics, farm organization, demographics, extension agents, risk aversion behavior, social learning, and environmental conditions [11,12,14].…”

“…Although agricultural growth is impossible without mechanization, low adoption has been reported in India [1,[11][12][13]. Numerous studies, therefore, identified the confounding factors of adoption, and those are related to economics, farm organization, demographics, extension agents, risk aversion behavior, social learning, and environmental conditions [11,12,14]. On the other hand, several impact evaluation studies have estimated the impact of these technologies on reducing poverty and enhancing yield across African and Asian countries [10,15].…”

Do risk management strategies prevent economic and non‐economic loss and damages? Empirical evidence from drought affected households in western India

“…Therefore, there are a growing number of studies with reference to two aspects: identifying determinants and evaluating effectiveness across geographical locations as well as disasters. In regard to the former, studies identify the major influential factors under socioeconomic characteristics, farm characteristics, agro-climatic and meteorological conditions, and risk attitude behavior of the households (see Bahinipati & Patnaik, 2021). Several studies, on the other hand, also look into the extent of loss and damage (L&D) or impact that could be mitigated by enhancing resilience capacity of society through various generic and disaster-specific adaptation measures (Bahinipati & Patnaik, 2015, 2020Patnaik et al, 2017Patnaik et al, , 2019.…”