A Bayesian analysis of longitudinal farm surveys in Central Malawi reveals yield determinants and site-specific management strategies

Abstract

Understanding the challenges to increasing maize productivity in sub-Saharan Africa, especially agronomic factors that reduce on-farm crop yield, has important implications for policies to reduce national and global food insecurity. Previous research on the maize yield gap has tended to emphasize the size of the gap (theoretical vs. achievable yields), rather than what determines maize yield in specific contexts. As a result, there is insufficient evidence on the key agronomic and environmental factors that influence maize yield in a smallholder farm environment. In this study, we implemented a Bayesian analysis with plot-level longitudinal household survey data covering 1,197 plots and 320 farms in Central Malawi. Households were interviewed and monitored three times per year, in 2015 and 2016, to document farmer management practices and seasonal rainfall, and direct measurements were taken of plant and soil characteristics to quantify impact on plot-level maize yield stability. The results revealed a high positive association between a leaf chlorophyll indicator and maize yield, with significance levels exceeding 95% Bayesian credibility at all sites and a regression coefficient posterior mean from 28% to 42% on a relative scale. A parasitic weed, Striga asiatica, was the variable most consistently negatively associated with maize yield, exceeding 95% credibility in most cases, of high intensity, with regression means ranging from 23% to 38% on a relative scale. The influence of rainfall, either directly or indirectly, varied by site and season. We conclude that the factors preventing Striga infestation and enhancing nitrogen fertility will lead to higher maize yield in Malawi. To improve plant nitrogen status, fertilizer was effective at higher productivity sites, whereas soil carbon and organic inputs were important at marginal sites. Uniquely, a Bayesian approach allowed differentiation of response by site for a relatively modest sample size study (given the complexity of farm environments and management practices). Considering the biophysical constraints, our findings highlight management strategies for crop yields, and point towards area-specific recommendations for nitrogen management and crop yield.

Fig 1. Bayesian regression model results for 95% credible intervals associated with drivers of maize yield for four EPA locations in Central Malawi.

Fig 2. Bayesian regression model results for 95% credible intervals associated with drivers of SPAD for four EPA locations in Central Malawi.

Fig 3. Logistic regression results for 95% credible intervals associated with drivers of striga prevention for four EPA locations in Central Malawi.

Fig 4. Bayesian regression model results for 95% credible intervals for the factors associated with striga control for four EPA locations in Central Malawi.