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1. How much control do smallholder maize farmers have over yield?

   Cecil, M; Gatti, N; Krell, N; Vergopolan, N; Chilenga, A; Baylis, K; Caylor, K; Evans, T; Konar, M; Sheffield, J; et al (October 2023, Field crops research)

   Smallholder agriculture is critical for current and future food security, yet quantifying the sources of smallholder yield variance remains a major challenge. Attributing yield variance to farmer management, as opposed to soil and weather constraints, is an important step to understanding the impact of farmer decision-making, in a context where smallholder farmers use a wide range of management practices and may have limited access to fertilizer. This study used a process-based crop model to simulate smallholder maize (Zea mays) yield at the district-level in Zambia and quantify the percent of yield variance (effect size) attributed to soil, weather, and three management inputs (cultivar, fertilizer, planting date). Effect sizes were calculated via an ANOVA variance decomposition. Further, to better understand the treatment effects of management practices, effect sizes were calculated both for all years combined and for individual years. We found that farmer management decisions explained 27–82 % of total yield variance for different agro-ecological regions in Zambia, primarily due to fertilizer impact. Fertilizer explained 45 % of yield variance for the average district, although its effect was much larger in northern districts of Zambia that typically have higher precipitation, where it explained 72 % of yield variance on average. When fixing a specific fertilizer amount, the “low-cost” management options of varying planting dates and cultivars explained 20–28 % of yield variance, with some regional variation. To better understand why management practices impact yield more in particular years, we performed a correlation analysis comparing yearly management effect sizes with four meteorologically based variables: total growing season precipitation, rainy season onset, extreme heat degree days, and longest dry spell. Results showed that fertilizer’s impact generally increased under favorable weather conditions, and planting date’s impact increased under adverse weather conditions. This study demonstrates how a national yield variance decomposition can be used to understand where specific management interventions would have a greater impact and can provide policymakers with quantification of soil, weather, and management effects. In addition, the variance composition can easily be adapted to a different range of management inputs, such as other cultivars or fertilizer quantities, and can also be used to assess the effect size of management adaptations under climate change. 

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2. Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields

   https://doi.org/10.1088/1748-9326/ac6083  

   Kluger, Dan_M; Owen, Art_B; Lobell, David_B (April 2022, Environmental Research Letters)

   Abstract With climate change threatening agricultural productivity and global food demand increasing, it is important to better understand which farm management practices will maximize crop yields in various climatic conditions. To assess the effectiveness of agricultural practices, researchers often turn to randomized field experiments, which are reliable for identifying causal effects but are often limited in scope and therefore lack external validity. Recently, researchers have also leveraged large observational datasets from satellites and other sources, which can lead to conclusions biased by confounding variables or systematic measurement errors. Because experimental and observational datasets have complementary strengths, in this paper we propose a method that uses a combination of experimental and observational data in the same analysis. As a case study, we focus on the causal effect of crop rotation on corn (maize) and soybean yields in the Midwestern United States. We find that, in terms of root mean squared error, our hybrid method performs 13% better than using experimental data alone and 26% better than using the observational data alone in the task of predicting the effect of rotation on corn yield at held-out experimental sites. Further, the causal estimates based on our method suggest that benefits of crop rotations on corn yield are lower in years and locations with high temperatures whereas the benefits of crop rotations on soybean yield are higher in years and locations with high temperatures. In particular, we estimated that the benefit of rotation on corn yields (and soybean yields) was 0.85 t ha⁻¹(0.24 t ha⁻¹) on average for the top quintile of temperatures, 1.03 t ha⁻¹(0.21 t ha⁻¹) on average for the whole dataset, and 1.19 t ha⁻¹(0.16 t ha⁻¹) on average for the bottom quintile of temperatures. This association between temperatures and rotation benefits is consistent with the hypothesis that the benefit of the corn-soybean rotation on soybean yield is largely driven by pest pressure reductions while the benefit of the corn-soybean rotation on corn yields is largely driven by nitrogen availability. 

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3. An integrated approach of field, weather, and satellite data for monitoring maize phenology

   https://doi.org/10.1038/s41598-021-95253-7  

   Nieto, Luciana; Schwalbert, Raí; Prasad, P. V.; Olson, Bradley J.; Ciampitti, Ignacio A. (December 2021, Scientific Reports)

   Abstract Efficient, more accurate reporting of maize ( Zea mays L.) phenology, crop condition, and progress is crucial for agronomists and policy makers. Integration of satellite imagery with machine learning models has shown great potential to improve crop classification and facilitate in-season phenological reports. However, crop phenology classification precision must be substantially improved to transform data into actionable management decisions for farmers and agronomists. An integrated approach utilizing ground truth field data for maize crop phenology (2013–2018 seasons), satellite imagery (Landsat 8), and weather data was explored with the following objectives: (i) model training and validation—identify the best combination of spectral bands, vegetation indices (VIs), weather parameters, geolocation, and ground truth data, resulting in a model with the highest accuracy across years at each season segment (step one) and (ii) model testing—post-selection model performance evaluation for each phenology class with unseen data (hold-out cross-validation) (step two). The best model performance for classifying maize phenology was documented when VIs (NDVI, EVI, GCVI, NDWI, GVMI) and vapor pressure deficit (VPD) were used as input variables. This study supports the integration of field ground truth, satellite imagery, and weather data to classify maize crop phenology, thereby facilitating foundational decision making and agricultural interventions for the different members of the agricultural chain. 

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4. Volatility and the Gains From Trade

   https://doi.org/10.3982/ECTA14411  

   Allen, Treb; Atkin, David (January 2022, Econometrica)

   Trade liberalization changes the volatility of returns by reducing the negative correlation between local prices and productivity shocks. In this paper, we explore these second‐moment effects of trade. Using forty years of agricultural micro‐data from India, we show that falling trade costs due to expansions of the Indian highway network reduced the responsiveness of local prices to local yields but increased the responsiveness of local prices to yields elsewhere. In response, farmers shifted their production toward crops with less volatile yields, especially so for those with poor access to risk mitigating technologies such as banks. We then characterize how volatility affects farmers' crop allocation using a portfolio choice framework where returns are determined in general equilibrium by a many‐location, many‐good Ricardian trade model with flexible trade costs. Finally, we structurally estimate the model—recovering farmers' risk‐return preferences from the gradient of the mean‐variance frontier at their observed crop choices—to quantify the second‐moment effects of trade. The simultaneous expansion of both the highway and rural bank networks increased the mean and the variance of farmer real income, with the first‐moment effect dominating such that expected welfare rose 4.4%. But had rural bank access remained unchanged, welfare gains would have been only half as great, as risk mitigating technologies allowed farmers to take advantage of higher‐risk higher‐return allocations. 

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5. Understanding farmer views of precision agriculture profitability in the U.S. Midwest

   https://doi.org/10.1016/j.ecolecon.2023.107950  

   Wang, Tong; Jin, Hailong; Sieverding, Heidi; Kumar, Sandeep; Miao, Yuxin; Rao, Xudong; Obembe, Oladipo; Mirzakhani Nafchi, Ali; Redfearn, Daren; Cheye, Stephen (November 2023, Ecological Economics)

   Precision Agriculture (PA) technologies are well known to be useful in addressing field heterogeneities and enabling informed site-specific management decisions. While profitability is the foremost factor considered by farmers when making PA adoption decisions, information in this regard is lacking from the farmers' perspective. This paper analyzed 1119 farmer responses from a 2021 survey conducted in four states along the western margins of the U.S. Midwest. Our findings show that while most (around 60%) non-adopters indicate that they are unaware of PA profit change, adopters are likely to rate a major profit increase. About two thirds of adopters rated at least a 5% increase in profitability towards variable rate (VR) fertilizer application (72%), VR seed application (68%), and automatic section control (66%). We modeled farmers' profit change subsequent to PA adoptions. Our regression results demonstrate that the profits from PA usage increase over time and that use of conservation practices likely influences PA profitability in a positive way. As soil quality and weather factors also affect profit ratings, it would be beneficial to compare and demonstrate profitability potential of various PA technologies on a regional basis and tailor the promotion efforts to farmers most likely to benefit from them. 

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