The Genome-to-Phenome (G2P) problem is one of the highest-priority challenges in applied biology. Ecophysiological crop models (ECM) and genomic prediction (GP) models are quantitative algorithms, which, when given information on a genotype and environment, can produce an accurate estimate of a phenotype of interest. In this article, we discuss how the GP algorithms can be used to estimate genotype-specific parameters (GSPs) in ECMs to develop robust prediction methods. In this approach, the numerical constants (GSPs) that ECMs use to distinguish and characterize crop cultivars/varieties are treated as quantitative traits to be predicted by genomic prediction models from underlying genetic information. In this article we provide information on which GP methods appear favorable for predicting different types of GSPs, such as vernalization sensitivity or potential radiation use efficiency. For each example GSP, we assess a number of GP methods in terms of their suitability using a set of three criteria grounded in genetic architecture, computational requirements, and the use of prior information. In general, we conclude that the most useful algorithms were dependent on both the nature of the particular GSP and the GP methods considered.

Despite improvements to grain yield in maize ( Zea mays L.) during the Green Revolution, maize remained predominantly tall unlike other major cereals such as wheat ( Triticum aestivum L.) and rice ( Oryza sativa L.). Although short‐statured maize (SSM) has been a topic of interest for many decades, historical efforts to introduce it commercially have remained unsuccessful. Commercial interest in SSM has recently returned mainly due to their lodging resistance, potential for high‐density planting, and better in‐season accessibility for pest and disease management. In this article, we conduct a systematic review to examine the limitations of past SSM hybrids in regard to genetic backgrounds and traits associated with agronomic performance, and renewed interest in SSM. Our objectives are to (i) identify the limiting factors of early SSM to understand why maize lagged behind in adopting dwarf traits compared to other cereal crops, and (ii) analyze the drivers behind renewed interest in SSM to assess its agronomic significance and potential role in future crop improvement strategies. This study analyses 45 studies and 17 patents and patent applications published between 1965 and 2024. Linear regression models were used to analyze changes in yield, height, harvest index, and lodging over time in both short and tall hybrids. Based on our review results, traits used to reduce height in maize introduced undesirable defects in plant reproductive development and architecture in early SSM hybrids. Modern improvements in SSM hybrid performance are due to less severe dwarfing traits or through concentrating trait effects away from reproductive structures.

Corn (Zea mays L.) yield productivity is driven by a multitude of factors, specifically genetics, environment, and management practices, along with their corresponding interactions. Despite continuous monitoring through proximal or remote sensors and advanced predictive models, understanding these complex interactions remains challenging. While predictive models are improving with regard to accurate predictions, they often fail to explain causal relationships, rendering them less interpretable than desired. Process-based or biophysical models such as the Agricultural Production Systems sIMulator (APSIM) incorporate these causalities, but the multitude of interactions are difficult to tease apart and are largely sensitive to external drivers, which often include stochastic variations. To address this limitation, we developed a novel methodology that reveals these hidden causal structures. We simulated corn production under varied conditions, including different planting dates, nitrogen fertilizer amounts, irrigation rules, soil and environmental conditions, and climate change scenarios. We then used the simulation results to rank features having the largest impact on corn yield through Random Forest modeling. The Random Forest model identified nitrogen uptake and annual transpiration as the most influential variables on corn yield, similar to the existing research. However, this analysis alone provided limited insight into how or why these features ranked highest and how the features interact with each other. Building on these results, we deployed a Causal Bayesian model, using a hybrid approach of score-based (hill climb) and constraint-based (injecting domain knowledge) models. The causal analysis provides a deeper understanding by revealing that genetics, environment, and management factors had causal impacts on nitrogen uptake and annual transpiration, which ultimately affected yield. Our methodology allows researchers and practitioners to unpack the “black box” of crop production systems, enabling more targeted and effective model development and management recommendations for optimizing corn production.