An empirical, Bayesian approach to modelling crop yield: Maize in USA

Abstract

We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18°C-19°C, corresponding to a daily maximum temperature of 24°C-25°C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of 2°C, relative to 1981-2014, results in the mean yield decreasing by 8%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate.

Climate Science, Solutions and Services for Net Zero, Climate-Resilient Food Systems

“…Improvements in seasonal forecasting to support agricultural decision making (e.g., Falloon et al, 2018) would help farmers choose appropriate varieties for upcoming seasons and prepare adaptations to farming practices in advance; there is also potential for long-range forecasts to support decision-making across the broader food chain (Falloon et al, 2022). Further developments to process-based models and statistical/machine learning models of wheat yield (e.g., Shirley et al, 2020) will be valuable in identifying indirect impacts on yields (e.g., farming practices and pest and disease pressures). to evaluate the feasibility of early transplanting of rice in the month of June with the aim of achieving higher system productivity by early harvesting of rice and subsequent timely sowing of wheat before 15 November with the provision of assured irrigation.…”

Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2

“…Recently, Ovalle-Rivera et al [ 137 ] was able to improve model accuracy by making use of a hierarchical Bayesian model calibration for a parameter-rich dynamic model for coffee agroforestry. Other applications Bayesian calibration, for example, go from maize yield predictions from empirical non-liner growth response functions [ 9 ], over accounting for temporal variability in growth models in the field of aquatic sciences [ 7 ] to considering uncertainties related even in astronomical estimations [ 138 ]. Moreover, improvements in modeling phenological development of maize were achieved with Bayesian calibration [ 79 ].…”

“…The benefits of Bayesian hierarchical models include capturing realistic confidence intervals allowing for valuable predictions resembling natural variability [ 7 , 8 ]. While, for instance, crop growth modelers frequently utilize Bayesian models [ 9 , 10 ], they are still rare in specialty crops research, e.g., in grapevine research. Recently, Ellis et al [ 11 ] developed a model to predict grape yield with a double-sigmoid growth model, and Schmidt et al [ 12 ] incorporated Bayesian predictive uncertainties in a functional-structural plant (FSP) model called Virtual Riesling, but mainly limited to modeling the phenological aspect of budburst depending on growing-degree-days (GDD).…”

Bayesian inference of spatially correlated random parameters for on-farm experiment

“…In particular, localised p-values are required to be adjusted to avoid a large number of false positives in the spatial map of treatment effects; see Rakshit et al (2020) for the details of computing adjusted p-values in GWR. Due to the availability of adequate computing resources and due to the fact that both model fitting and statistical inference under Bayesian framework are extremely intuitive, Bayesian modelling has become popular for analysing agricultural field trials in the last few years (Besag and Higdon 1999;Theobald et al 2002;Che and Xu 2010;Donald et al 2011;Montesinos-López et al 2018;Selle et al 2019;Shirley et al 2020). Montesinos-López et al (2018) proposed a multivariate Bayesian analysis to estimate multiple-trait and multiple-environment on-farm data.…”