Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize

GIS-based G × E modeling of maize hybrids through enviromic markers engineering

Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

An allometry perspective on crops

Understanding trait-trait coordination is essential for successful plant breeding and crop modeling. Notably, plant size drives variation in morphological, physiological, and performance-related traits, as described by allometric laws in ecology. Yet, as allometric relationships have been limitedly studied in crops, how they influence and possibly limit crop performance remains unknown. Here, we review how an allometry perspective on crops gains insights into the phenotypic evolution during crop domestication, the breeding of varieties adapted to novel conditions, and the prediction of crop yields. As allometry is an active field of research, modeling and manipulating crop allometric relationships can help to develop more resilient and productive agricultural systems to face future challenges.

Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, classical machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

Genetic, molecular and physiological crosstalk during drought tolerance in maize (Zea mays): pathways to resilient agriculture

MAIN CONCLUSION

This review comprehensively elucidates maize drought tolerance mechanisms, vital for global food security. It highlights genetic networks, key genes, CRISPR-Cas applications, and physiological responses, guiding resilient variety development. Maize, a globally significant crop, confronts the pervasive challenge of drought stress, impacting its growth and yield significantly. Drought, an important abiotic stress, triggers a spectrum of alterations encompassing maize's morphological, biochemical, and physiological dimensions. Unraveling and understanding these mechanisms assumes paramount importance for ensuring global food security. Approaches like developing drought-tolerant varieties and harnessing genomic and molecular applications emerge as effective measures to mitigate the negative effects of drought. The multifaceted nature of drought tolerance in maize has been unfolded through complex genetic networks. Additionally, quantitative trait loci mapping and genome-wide association studies pinpoint key genes associated with drought tolerance, influencing morphophysiological traits and yield. Furthermore, transcription factors like ZmHsf28, ZmNAC20, and ZmNF-YA1 play pivotal roles in drought response through hormone signaling, stomatal regulation, and gene expression. Genes, such as ZmSAG39, ZmRAFS, and ZmBSK1, have been reported to be pivotal in enhancing drought tolerance through diverse mechanisms. Integration of CRISPR-Cas9 technology, targeting genes like gl2 and ZmHDT103, emerges as crucial for precise genetic enhancement, highlighting its role in safeguarding global food security amid pervasive drought challenges. Thus, decoding the genetic and molecular underpinnings of drought tolerance in maize sheds light on its resilience and paves the way for cultivating robust and climate-smart varieties, thus safeguarding global food security amid climate challenges. This comprehensive review covers quantitative trait loci mapping, genome-wide association studies, key genes and functions, CRISPR-Cas applications, transcription factors, physiological responses, signaling pathways, offering a nuanced understanding of intricate mechanisms involved in maize drought tolerance.

Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions

KEY MESSAGE

Green Leaf Area Index dynamics is a promising secondary trait for grain yield and drought tolerance. Multivariate GWAS is particularly well suited to identify the genetic determinants of the green leaf area index dynamics. Improvement of maize grain yield is impeded by important genotype-environment interactions, especially under drought conditions. The use of secondary traits, that are correlated with yield, more heritable and less prone to genotype-environment interactions, can increase breeding efficiency. Here, we studied the genetic basis of a new secondary trait: the green leaf area index (GLAI) dynamics over the maize life cycle. For this, we used an unmanned aerial vehicle to characterize the GLAI dynamics of a diverse panel in well-watered and water-deficient trials in two years. From the dynamics, we derived 24 traits (slopes, durations, areas under the curve), and showed that six of them were heritable traits representative of the panel diversity. To identify the genetic determinants of GLAI, we compared two genome-wide association approaches: a univariate (single-trait) method and a multivariate (multi-trait) method combining GLAI traits, grain yield, and precocity. The explicit modeling of correlation structure between secondary traits and grain yield in the multivariate mixed model led to 2.5 times more associations detected. A total of 475 quantitative trait loci (QTLs) were detected. The genetic architecture of GLAI traits appears less complex than that of yield with stronger-effect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one fifth of yield variability across a larger environmental network of 11 water-deficient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efficiency through a marker-assisted approach.

Effects of Climate Change and Drought Tolerance on Maize Growth

Climate change is affecting all regions of the world with different climates, and the scale of damage is increasing due to the occurrence of various natural disasters. In particular, maize production is highly affected by abnormal climate events such as heat waves and droughts. Increasing temperatures can accelerate growth and shorten the growing season, potentially reducing productivity. Additionally, enhanced temperatures during the ripening period can accelerate the process, reducing crop yields. In addition, drought stress due to water deficit can greatly affect seedling formation, early plant growth, photosynthesis, reproductive growth, and yield, so proper water management is critical to maize growth. Maize, in particular, is tall and broad-leaved, so extreme drought stress at planting can cause leaves to curl and stunt growth. It is important to understand that severe drought can have a detrimental effect on the growth and reproduction of maize. In addition, high temperatures caused by drought stress can inhibit the induction of flowering in male flowers and cause factors that interfere with pollen development. It is therefore important to increase the productivity of all food crops, including maize, while maintaining them in the face of persistent drought caused by climate change. This requires a strategy to develop genetically modified crops and drought-tolerant maize that can effectively respond to climate change. The aim of this paper is to investigate the effects of climate change and drought tolerance on maize growth. We also reviewed molecular breeding techniques to develop drought-tolerant maize varieties in response to climate change.