Site-specific nutrient management advice and agricultural intensification in maize-based systems in Nigeria - Oyinbo, O. (Katholieke Universiteit Leuven, 2019)
Despite the potentially large gains from intensification and agricultural productivity growth in Sub-Saharan Africa (SSA), yields of staple crops, such as maize are far below attainable yields. Depletion of soil fertility associated with low and inappropriate use of nutrients play a crucial role in this. Yet, fertilizer use is low in SSA, which partly relates to information constraints. Relaxing such constraints via agricultural extension interventions is expected to produce positive outcomes but do not always result in the intended effects, which may be connected with the highly heterogeneous smallholder farming systems. Yet, traditional extension systems in SSA countries, including Nigeria, provide general fertilizer use recommendations, which do not account for the substantial variation in production conditions. A potential intervention in this regard is site-specific nutrient management (SSNM) paradigm. In light of the rapid digital transformation, digital decision support tools (DSTs) can be leveraged to allow provision of SSNM extension advice. There are research gaps in the theoretical and empirical literature on design, adoption and impact of DST-enabled site-specific extension services, and in the broader literature related to fertilizer use in maize. This PhD thesis focuses on a nutrient management DST for maize ‘Nutrient Expert’ in northern Nigeria, and addresses some of the gaps. In chapter 2, I analyze farmers’ preferences for intensification of maize production supported by DST-enabled SSNM recommendations in the maize belt of Nigeria. I use data from a choice experiment (CE) among farmers, and estimate different econometric models to control for attribute non-attendance and account for preference as well as scale heterogeneity. The findings show that overall, farmers have strong preferences to switch from general to DST-enabled SSNM recommendations, which lend credence to the inclusion of digital tools in agricultural extension. Also the findings show two latent classes or preference groups of farmers, early and late adopters of intensified maize production, and the heterogeneous preferences can be related to the farmers’ resource endowment, sensitivity to risk and access to services and institutions. The findings imply that improving the design of DSTs to enable provision of information on the riskiness of expected investment returns and flexibility in switching between low- and high-risk recommendations will help farmers to make better informed farm decisions. In chapter 3, I analyze preferences of extension agents for the design of a nutrient management DST for extension, and their willingness to use such tool. I use data from a CE among extension agents, and estimate different models to capture preference heterogeneity and account for attribute non-attendance. The findings show that the extension agents in general have a high willingness to use DSTs for SSNM extension advice, which supports the emerging policy interest in design of such DSTs for maize. They prefer a DST with a more user-friendly interface that requires less time to generate an output but have substantial heterogeneous preferences for other design features. The findings also show two preference groups of extension agents, the more committed agents – who prioritize the effectiveness-related features of DSTs, and the more pragmatic agents – who care more about practical features of DSTs. The differences in observed characteristics between the two groups are very small, which suggests that unobservable characteristics likely play a role in explaining preference heterogeneity. The findings imply that accommodating preference differences may facilitate the adoption of DSTs by extension agents and thus enhance the scope for such tools to impact the production decisions of farmers. In chapter 4, I analyze the impact of farmers’ access to SSNM recommendations for maize enabled by a DST on fertilizer use rates, fertilizer management practices, maize yield and revenue. I implement a randomized controlled trial with two treatment groups, T1 without and T2 with additional information on variability of expected returns and a control group. I use three-period panel data to estimate the impact. The findings show that SSNM recommendations bring about improvements in fertilizer management practices, yield and gross revenue after one-year treatment but not fertilizer use for T1. This suggests that optimal management practices can improve yield and revenue by reducing technical inefficiencies. The findings also show that yield and revenue gains are quite similar for the two treatment groups despite considerable increase in fertilizer by T2 over T1. This suggests that the increase in fertilizer does not result in substantial revenue gains, which may be connected to low yield responses to higher fertilizer levels. The findings also show that SSNM recommendations, combined with additional information on the distribution of expected returns, appears to induce more fertilizer use after one year and foster continued fertilizer investment after two years. In addition, the findings show that there are only gradual increases in investment, maize yield and especially net revenue after two years. Overall, this dissertation shows that there is high adoption potential of nutrient management DSTs for maize by extension agents, and of extension advice from such DSTs by farmers, which aligns with the widespread interest and investments in site-specific and digital tools for agricultural applications in developing countries. Yet, the findings show economically small but significant effects of DST-enabled SSNM recommendations on intensification of maize production. This underscores the need for more research with longer periods and with complementary interventions to allow better understanding of the impact of DST-enabled site-specific recommendations in the long run while accounting for other shortcomings.