Ecophysiological Crop Modelling Combined with Genetic Analysis Is a Powerful Tool for Ideotype Design

Improving the grain yield of crops in both favourable and stressful environments is the main breeding objective required to ensure food security. In this review, I outline a genotype-to-phenotype approach that exploits the potential values of quantitative genetics and process-based crop modelling in developing new plant types with high yields. The effects of quantitative trait locus (QTL), for traits typically at the single-organ level over a short time scale, were projected for their impact on crop growth during the whole growing season in the field. This approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for ideotyping under contrasting conditions, i.e., use of single-environment information for predicting phenotypes under different environments.

Planning maize hybrids adaptation to future climate change by integrating crop modelling with machine learning

Crop hybrid improvement is an efficient and environmental-friendly option to adapt to climate change and increase grain production. However, the adaptability of existing hybrids to a changing climate has not been systematically investigated. Therefore, little is known about the appropriate timing of hybrid adaptation. Here, using a novel hybrid model which coupled CERES-Maize with machine learning, we critically investigated the impacts of climate change on maize productivity with an ensemble of hybrid-specific estimations in China. We determined when and where current hybrids would become unviable and hybrid adaptation need be implemented, as well as which hybrid traits would be desirable. Climate change would have mostly negative impacts on maize productivity, and the magnitudes of yield reductions would highly depend on the growth cycle of the hybrids. Hybrid replacement could partially, but not completely, offset the yield loss caused by projected climate change. Without adaptation, approximately 53% of the cultivation areas would require hybrid renewal before 2050 under the RCP 4.5 and RCP 8.5 emission scenarios. The medium-maturing hybrids with a long grain-filling duration and a high light use efficiency would be promising, although the ideotypic traits could be different for a specific environment. The findings highlight the necessity and urgency of breeding climate resilient hybrids, providing policy-makers and crop breeders with the early signals of when, where and what hybrids will be required, which stimulate proactive investment to facilitate breeding. The proposed crop modelling approach is scalable, largely data-driven and can be used to tackle the longstanding problem of predicting hybrids’ future performance to accelerate development of new crop hybrids.

Study of the effect of climate variation on irrigated and rainfed rice productivity based on aquacrop crop modelling simulation (Case Study of Java Island)

Aquacrop is free-licensed Food and Agricultural Organization’s crop modelling that requires minimum inputs of climate variables namely rainfall, maximum temperature, minimum temperature variables and geographic information of the area to be simulated (longitude, latitude, altitude). This study aims to measure the difference in irrigated and rainfed rice productivity from the Aquacrop crop modelling simulation to the influence of climate pattern variations in Java Island, Indonesia. The k-means clustering method applied to the rainfall, maximum, and minimum temperature variables from the bias-corrected MERRA2 data resulted in two climate regions. The principal component analysis result showed that the maximum and minimum temperature variables are the variables that most contribute to the determination of the clustering area using the k-means method compared to the rainfall variable. This study has calculated the probability of the irrigated and rainfed rice productivity resulting from the Aquacrop simulation in those climate regions during La Nina [El Nino] years that will be higher [smaller] than the mean value of rice productivity during neutral years. However, the validation between the actual irrigated and rainfed rice productivity with the Aquacrop simulation results from 2001-2014 showed low correlation values that vary between negative and positive values in all climate regions. Meanwhile, the validation on the El Nino composite years generally showed positive correlation values. In addition, the neutral and La Nina composite years resulted in varying correlation values between negative and positive correlation.

Grand challenges for the 21st century: what crop models can and can't (yet) do

Crop production is at the core of a ‘perfect storm’ encompassing the grand challenges of achieving food and nutrition security for all, in the face of climate change, while avoiding further conversion of natural habitats for agriculture and loss of biodiversity. Here, we explore current trends in crop modelling related to these grand challenges by reflecting on research presented at the Second International Crop Modelling Symposium (iCropM2020). A keyword search in the book of abstracts of the symposium revealed a strong focus on ‘climate change’, ‘adaptation’ and ‘impact assessment’ and much less on ‘food security’ or ‘policy’. Most research focused on field-level investigations and far fewer on farm(ing) systems levels – the levels at which management decisions are made by farmers. Experimentation is key to development and testing of crop models, yet the term ‘simulation’ outweighed by far the terms ‘experiments’ and ‘trials’, and few contributions dealt with model improvement. Cereals are intensively researched, whereas roots, tubers and tropical perennials are under-researched. Little attention is paid to nutrient limitations apart from nitrogen or to pests and diseases. The aforementioned aspects represent opportunities for future research where crop models can help in devising hypotheses and driving new experimentation. We must also ensure that crop models are fit for their intended purposes, especially if they are to provide advice to policymakers. The latter, together with cross-scale and interdisciplinary efforts with direct engagement of stakeholders are needed to address the grand challenges faced by food and agricultural systems in the next century.