Abstract
Increased fertilizer use will likely be crucial for raising and sustaining farm productivity in Africa, but adoption may be limited by ineffectiveness under certain conditions. This article quantifies the impacts of soil characteristics on maize response to fertilizer in Zambia using a nationally representative sample of 1453 fields, combining economic, farm management and soil analysis data. Depending on soil regimes, average maize yield response estimates range from insignificant (0) to 7 maize kg per fertilizer kg. For the majority of farmers, the estimated average value cost ratio is between 1 and 2, meaning fertilizer use would be fiscally rational, barring uncertainty and transfer costs. Since transfer costs exist and outcomes are uncertain, however, many farmers may sensibly pause before deciding whether to adopt fertilizer. This suggests shifting the emphasis of chronically low fertilizer use in Africa away from explanations of “market failure” toward greater emphasis onimproving fertilizer efficacy.
Highlights
Fertilizer use will be crucial for raising and sustaining farm productivity in Africa (Jayne and Rashid, 2013)
We report the results one model specification at a time as described in the sub-headings according to which soil characteristic is used as each threshold type
12 We control for omitted farmer ability with our education proxy, though admittedly the correlation between this proxy and the omitted variable it represents is probably less strong than that between soil characteristics and otherwise ambiguously defined “soil quality”
Summary
Fertilizer use will be crucial for raising and sustaining farm productivity in Africa (Jayne and Rashid, 2013). The typical argument states if fertilizer is more likely to be used on land where yields are more responsive, the estimated effect will be upwardly biased if soil quality is unobserved Farmer ability is another commonly cited source of bias – the argument being positive correlation between skill and fertilizer application could make fertilizers seem more productive. This analysis is far less vulnerable to such omitted variable bias because we include specific soil quality indicators in the model, controlling for their effects (both direct and interacted with fertilizer application), thereby removing the omitted variable issues that plague other studies. We control for farm management (timing of planting, timing of fertilizer application, seed and fertilizer application rates, seed type, and tillage methods) explicitly, and use proxies to further control for unobservable farmer characteristics
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