Application of a bias-corrected meta-frontier approach and an endogenous switching regression to analyze the technical efficiency of conservation tillage for wheat in South Asia

Abstract

Conservation tillage (CT) options are among the most rapidly spreading land preparation and crop establishment techniques globally. In South Asia, CT has spread dramatically over the last decade, a result of strong policy support and increasing availability of appropriate machinery. Although many studies have analyzed the yield and profitability of CT systems, the technical efficiency impacts accrued by farmers utilizing CT have received considerably less attention. Employing a DEA framework, we isolated bias-corrected meta-frontier technical efficiencies and meta-technology ratios of three CT options adopted by wheat farmers in Bangladesh, including bed planting (BP), power tiller operated seeding (PTOS), and strip tillage (ST), compared to a control group of farmers practicing traditional tillage (TT). Endogenous switching regression was subsequently employed to overcome potential self-selection bias in the choice of CT, in order to robustly estimate efficiency factors. Among the tillage options studied, PTOS was the most technically efficient, with an average meta-technology ratio of 0.90, followed by BP (0.88), ST (0.83), and TT (0.67). The average predicted meta-frontier technical efficiency for the CT non-adopters under a counterfactual scenario (0.80) was significantly greater (P = 0.00) than current TE scores (0.65), indicating the potential for sizeable profitability increases with CT adoption. Conversely, the counterfactual TE of nonadopters was 23% greater than their DEA efficiency, also indicating efficiency gains from CT adoption. Our results provide backing for agricultural development programs in South Asia that aim to increase smallholder farmers' income through the application of CT as a pathway towards poverty reduction.

DEA Performance Measurements in Cotton Production of Harran Plain, Turkey: A Single and Double Bootstrap Truncated Regression Approaches

“…Thus, the bias-corrected technical efficiency scores under the assumed VRS technology increase as the household size increases. This finding is consistent with research reported elsewhere [27]. The increase in household size can pose a challenge, especially if children are present because the adults may not allocate time to cotton production when needed.…”

Impact of Credit Demand on the Productivity of Rice Farmers in South West Nigeria

“…Therefore, this study precisely corrects for any possible sample selection bias that may arise from other interventions that provide multiple services to farmers in addition to credit with the aid of an endogenous switching regression model (Lee, 1978 andMaddala, 1983). Following Lokshin & Sajaia (2004), Nyangena & Köhlin (2008), Asfaw, Mithöfer, & Waibel (2010), and Aravindakshan et al (2018), this study estimated a endogenous switching regression (ESR) model (Maddala & Nelson 1975;Maddala 1983) to deal with the problems presented by both sample selection bias and endogeneity (Heckman 1979;Hausman 1978), allowing for interactions between credit market participation and other covariates (Alene & Manyong 2007). This model is divided into two parts: the first correct for endogeneity due to self-selection using a probit selection model in which farmers are sorted into participants and non-participants, while the second part of the model addresses the outcome equations on factors influencing productivity.…”

Impact of Recapitalisation and Development Programme on Performance of Land Reform Beneficiary Farmers in KwaZulu-Natal, South Africa

“…Following Lokshin and Sajaia (2004), Khanal et al (2018), and Aravindakshan et al (2018), an endogenous switching regression model (ESRM) was employed for this study. This approach, however, estimated the impact of RADP participation on the net farm income of farmers using RADP participation as a dummy variable, which might yield biased and inconsistent estimates because participation is potentially endogenous (Ojo & Baiyegunhi, 2020a).…”