Seasonal crop yield forecast: Methods, applications, and accuracies

By Bruno Basso, Lin Liu. Advances in Agronomy, 2019

258 citations recorded by the source corpus.

Abstract The perfect knowledge of yield before harvest has been a wish puzzling human being since the beginning of agriculture because seasonal forecast of crop yield plays a critical role in decision making for different stakeholders—from farmers to policy makers to governments for food security, to commodities traders. Different methods have been used to forecast yield with different levels of granularity, accuracy and timing. This chapter presents a critical review of the current seasonal crop yield forecasting methods found in the scientific literature. Extensive research has been conducted on crop yield forecast, particularly for wheat, maize, rice, barley, and soybean. Yield forecast are mainly based on field surveys, statistical regressions between historical yield and in-season variables (agrometeorological, or remotely sensed data), crop simulation models, or on integration between statistical modeling with dynamic process-based crop simulation models. A low number of studies rely on field surveys as a means to forecast yield, but they remain the main methods of yield forecast and estimation in several countries (i.e., USA). This chapter aims to report results found in peer-review journals for different crops, methods, geographies, and accuracies, and to end with a critical perspective on the advantage and disadvantage of the different methods currently employed by researchers and stakeholders.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Plant Science

View Paper: https://www.semanticscholar.org/paper/2735ed06f77b270bd2db8410eb7400fc129be1cc

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Citations in the corpus

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt (2020, background): “There are trade-offs among approaches in terms of accuracy, explanatory power, and desired scale and resolution (Basso and Liu, 2019).”
Development of a nitrogen recommendation tool for corn considering static and dynamic variables (2019, methodology): “The developed models are field specific and fill the gap between very simple EONR prediction tools (one input) and more complex N tools such as simulations models that require several input parameters (Basso and Liu, 2018).”
Machine learning for large-scale crop yield forecasting (2020, background): “Crop yield forecasts are valuable to many stakeholders in the agri-food chain, including farmers, agronomists, commodity traders and policymakers (Basso and Liu 2019; Chipanshi et al. 2015).”