An Integrated Approach to Unravelling Smallholder Yield Levels: The Case of Small Family Farms, Eastern Region, Ghana
Date
2020-06-06
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agriculture
Abstract
Yield levels and the factors determining crop yields is an important strand of research on
rainfed family farms. This is particularly true for Sub-Saharan Africa (SSA), which reports some of
the lowest crop yields. This also holds for Ghana, where actual yields of maize, the most important
staple crop, are currently about only a third of achievable yields. Developing a comprehensive
understanding of the factors underpinning these yield levels is key to improving them. Previous
research endeavours on this frontier have been incumbered by the mono-disciplinary focus and/or
limitations relating to spatial scales, which do not allow the actual interactions at the farm level to be
explored. Using the sustainable livelihoods framework and, to a lesser extent, the induced innovation
theory as inspiring theoretical frames, the present study employs an integrated approach of multiple
data sources and methods to unravel the sources of current maize yield levels on smallholder farms
in two farming villages in the Eastern region of Ghana. The study relies on farm and household
survey data, remotely-sensed aerial photographs of maize fields and photo-elicitation interviews
(PEIs) with farmers. These data cover the 2016 major farming season that spanned the period
March–August. We found that the factors that contributed to current yield levels are not consistent
across yield measures and farming villages. From principal component analysis (PCA) and multiple
linear regression (MLR), the timing of maize planting is the most important determinant of yield
levels, explaining 25% of the variance in crop cut yields in Akatawia, and together with household
income level, explaining 32% of the variance. Other statistically significant yield determinants include
level of inorganic fertiliser applied, soil penetrability and phosphorus content, weed control and
labour availability. However, this model only explains a third of the yields, which implies that
two-thirds are explained by other factors. Our integrated approach was crucial in further shedding
light on the sources of the poor yields currently achieved. The aerial photographs enabled us to
demonstrate the dominance of poor crop patches on the edges and borders of maize fields, while the
PEIs further improved our understanding of not just the causes of these poor patches but also the
factors underpinning delayed planting despite farmers’ awareness of the ideal planting window.
The present study shows that socioeconomic factors that are often not considered in crop yield
analyses—land tenure and labour availability—often underpin poor crop yields in such smallholder
rainfed family farms. Labour limitations, which show up strongly in both in the MLR and qualitative
data analyses, for example, induces certain labour-saving technologies such as multiple uses of
herbicides. Excessive herbicide use has been shown to have negative e ects on maize yields.
Description
Research Article
Keywords
smallholder agriculture, yield determinants, socioeconomic factors, maize, principal component analysis, photo-elicitation interviews, Ghana