Oil palm cultivation is a primary income source for millions of rural farm and non-farm households in the tropics but management systems of this tropical crop often vary in space. Understanding this spatial variation and driving factors is crucial in order to design effective and geographically targeted, and optimized interventions that support local farm productivity and sustainability. However, this has been hampered partly due to a shortage of data and methods to examine spatial heterogeneity in smallholder-dominated farming systems systematically. Here, this issue is addressed using primary household data and a structured additive regression model including nonlinear spatial effects—so-called geosplines—to analyze micro-level spatial variation in smallholder oil palm yield, input use, and output prices in Jambi Province, Indonesia. We add several standard covariates in our estimation to help investigate the causes of the spatial variation. We identify distinct spatial variation in oil palm production activities within the different parts of the farm households’ settlements. Our results show that farm characteristics indicating stability (e.g., land titles) and specialization in oil palm production are associated with significantly higher oil palm yields, input use, and output prices. Further, proximity to a market center significantly increases input use and realized output prices. Finally, the estimated geosplines reveal that standard covariates explain only 50-60 percent of the spatial heterogeneity in our dependent variables. Controls for unexplained variation at smaller scales (e.g., village) can help, yet significant spatial patterns remain for input use and output prices. To explain these remaining patterns, a purely quantitative approach might not be sufficient. Thus, a combination of quantitative and qualitative information might be needed to target and optimize agricultural productivity and sustainability interventions geographically.
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