Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model

Machine learning versus crop growth models: an ally, not a rival

The rapid increases of the global population and climate change pose major challenges to a sustainable production of food to meet consumer demands. Process-based models (PBMs) have long been used in agricultural crop production for predicting yield and understanding the environmental regulation of plant physiological processes and its consequences for crop growth and development. In recent years, with the increasing use of sensor and communication technologies for data acquisition in agriculture, machine learning (ML) has become a popular tool in yield prediction (especially on a large scale) and phenotyping. Both PBMs and ML are frequently used in studies on major challenges in crop production and each has its own advantages and drawbacks. We propose to combine PBMs and ML given their intrinsic complementarity, to develop knowledge- and data-driven modelling (KDDM) with high prediction accuracy as well as good interpretability. Parallel, serial and modular structures are three main modes can be adopted to develop KDDM for agricultural applications. The KDDM approach helps to simplify model parameterization by making use of sensor data and improves the accuracy of yield prediction. Furthermore, the KDDM approach has great potential to expand the boundary of current crop models to allow upscaling towards a farm, regional or global level and downscaling to the gene-to-cell level. The KDDM approach is a promising way of combining simulation models in agriculture with the fast developments in data science while mechanisms of many genetic and physiological processes are still under investigation, especially at the nexus of increasing food production, mitigating climate change and achieving sustainability.

Integration of Crop Growth Models and Genomic Prediction

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.

A Mechanistic Framework for Understanding the Effects of Climate Change on the Link Between Flowering and Fruiting Phenology

Phenological shifts are a widely studied consequence of climate change. Little is known, however, about certain critical phenological events, nor about mechanistic links between shifts in different life-history stages of the same organism. Among angiosperms, flowering times have been observed to advance with climate change, but, whether fruiting times shift as a direct consequence of shifting flowering times, or respond differently or not at all to climate change, is poorly understood. Yet, shifts in fruiting could alter species interactions, including by disrupting seed dispersal mutualisms. In the absence of long-term data on fruiting phenology, but given extensive data on flowering, we argue that an understanding of whether flowering and fruiting are tightly linked or respond independently to environmental change can significantly advance our understanding of how fruiting phenologies will respond to warming climates. Through a case study of biotically and abiotically dispersed plants, we present evidence for a potential functional link between the timing of flowering and fruiting. We then propose general mechanisms for how flowering and fruiting life history stages could be functionally linked or independently driven by external factors, and we use our case study species and phenological responses to distinguish among proposed mechanisms in a real-world framework. Finally, we identify research directions that could elucidate which of these mechanisms drive the timing between subsequent life stages. Understanding how fruiting phenology is altered by climate change is essential for all plant species but is particularly critical to sustaining the large numbers of plant species that rely on animal-mediated dispersal, as well as the animals that rely on fruit for sustenance.