Can Crop Models Identify Critical Gaps in Genetics, Environment, and Management Interactions?

摘要: Increasing food demand under climate change constraints may challenge and strain agricultural systems. The use of crop models to assess genotypes performance across diverse target environments management practices, i.e., the genetic × environment interaction (GEMI), can help understand suitability genotype agronomic possibly accelerate turnaround in plant breeding programs. However, readiness support these tasks be debated. In this article, we point out modeling data limitations argue need for evaluation improvement relevant process algorithms as well model convergence. Under conditions suitable growth, without meteorological extremes or soil limitation root exploration, simulate resource capture, yield with relative ease. As stresses accumulate, species- genotype-specific attributes their interactions atmospheric generate a large range responses, including where resources become so limiting make yields very low. space between high low is most rainfed production occurs, current user skill at representing GEMI varies. We also review studies comparing number lessons learned. overall message that appears necessary condition progress, perhaps relevancy. Model ensembles mitigate input, model, user-driven uncertainty some but not all applications, sometimes cost. Successful model-based assessment only requires better knowledgeable users, realistic representation environmental landscape crops are grown, which trivial given 3D nature water nutrient transport. Models remain best quantitative repository our knowledge on functioning; they contain narrative plant, soil, functioning computer language train mind couple processes. But quest tame GEMI, will lead way just ride along history?