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

Abstract

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.

Model-based design of crop diversification through new field arrangements in spatially heterogeneous landscapes. A review

“…In general, the number of available crops for simulation is higher for temperate and sub-tropical regions than those for tropical regions. There are fewer options for simulating less studied orphan crops, tuber and root crops, fodder crops, or newly introduced catch or cover crops as was also mentioned earlier (Kollas et al 2015;Luedeling et al 2016;Malezieux et al 2009;Silva and Giller 2021), stressing the need to extend models for such crops.…”

Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need

“…In the past, different methods and approaches of decision support systems were used to answer why and how questions that include probabilistic and optimization models (Hindsborg & Kristensen, 2019), supervised learning models (Witten & Frank, 2002), Bayesian models (Wang et al, 2012), time series analysis (Michel & Makowski, 2013) and genetic programming (Samadianfard et al, 2022). Silva and Giller, (2021) reported about what crop models can and can't (yet) do. They explored current trends in crop modeling after reviewing research presented in the 2nd International Crop Modeling Symposium (iCropM2020).…”

Changes in Climate Extremes and Their Effect on Maize (Zea mays L.) Suitability Over Southern Africa

“…Statistical models fitting local, regional or national yields to weather parameters and other variables have also been used to elucidate crop-climate responses over time and area [for example, Lobell et al (2011), Laudien et al (2020]. While both experimental studies and statistical models provide valuable information on crop-climate relationships and climate change impacts, they are difficult to use for adaptation planning over large heterogeneous countries or regions such as Southern Africa (Silva and Giller, 2020).…”