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Abstract. Soil moisture is highly variable in space and time, and deficits (i.e., droughts) play an important role in modulating crop yields. Limited hydroclimate and yield data, however, hamper drought impact monitoring and assessment at the farm field scale. This study demonstrates the potential of using field-scale soil moisture simulations to support high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field scale. We present a multiscale modeling approach that combines HydroBlocks – a physically based hyper-resolution land surface model (LSM) – with machine learning. We used HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3 h 30 m resolution. These simulations, along with remotely sensed vegetation indices, meteorological data, and descriptors of the physical landscape (related to topography, land cover, and soils) were combined with district-level maize data to train a random forest (RF) model to predict maize yields at district and field scales (250 m). Our model predicted yields with an average testing coefficient of determination (R2) of 0.57 and mean absolute error (MAE) of 310 kg ha−1 using year-based cross-validation. Our predicted maize losses due to the 2015–2016 El Niño drought agreed well with losses reported by the Food and Agriculture Organization (FAO). Our results reveal that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Soil moisture was also a more effective indicator of drought impacts on crops than precipitation, soil and air temperatures, and remotely sensed normalized difference vegetation index (NDVI)-based drought indices. This study demonstrates how field-scale modeling can help bridge the spatial-scale gap between drought monitoring and agricultural impacts.

Soybean production has been widely promoted in sub-Saharan Africa as a means of improving rural household income. Numerous studies point to poor adoption levels, low yield levels, and limited profitability among smallholder farmers. Poor performance of soybean among smallholders generates numerous hypotheses as to the root causes. One logical cause is low prices, which result from anecdotes from the field, especially among producers and policymakers. In this study, the first of its kind that we are aware of, analyzes regional soybean prices over time across six key growing and commercial regions of Ghana. We employ cointegration and multivariate vector error correction model to measure the level of international and inter-market integration and performance. The results show regional and international integration as well as Granger Causality results consistent with the local supply-demand context. Specifically, the international market Granger causes Kumasi, Bolgatanga, and Wa markets, while the Tamale and Kumasi, serve as the leading production and demand markets, respectively. The results of the study provide evidence that prices do perform well in Ghana and are not a major source of weak adoption and low levels of profitability among smallholder soybean farmers.

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