Enviromics: bridging different sources of data, building one framework

Abstract

Enviromics is the field of applied data science that integrates databases of environmental factors into biostatistics and quantitative genetics. It can leverage plant ecophysiology knowledge to bridge the gaps about environment interactions with systems biology (genes, transcripts, proteins, and metabolites), which also boosts the ability to understand and model the phenotypic plasticity of the main agronomic traits. Recently, the plant breeding community has experienced reduced costs for acquiring environmental sensors to be installed in the field trials while increasing the reliability and resolution of the remote sensing techniques. The combination of those two factors has started the spring of enviromics-aided breeding in recent years. However, the use of environmental information in plant breeding is not a novelty approach developed a few years ago, but a core of efforts originated in the last 60 years, yet some basic ideas traced back to early 20 th century attempts to establish a relationship between phenotypic and environmental variation. This review highlights the main concepts surrounding the construction of the "modern enviromics science", tracing back to its origins in the last decades. Finally, we present how this field has helped integrate different data sources in predictionbased models or build one framework.

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques

“…Here, we have shown how integrating multi-trait selection for mean performance (within mega-environments) and stability (across years) with detailed environmental typology may be useful to identify specific adaptations (such as tolerance to warmer environments), increasing the sustainability of breed programs mainly under the climate changes in view ( Lopes et al., 2015 ). Therefore, our results can leverage plant ecophysiology knowledge aiding in identifying the primary sources of the genotype-environment interaction in plant breeding programs ( Costa-Neto and Fritsche-Neto, 2021 ; Resende et al., 2021 ). The use of this approach becomes particularly interesting due to the dynamism and exhaustivity of the data available (climate information available for all points of the globe) that make it possible to replicate the procedure anywhere, anytime, and the possibility of including additional information such as soil proprieties, crop management, companion organisms, and crop canopy ( Xu, 2016 ).…”

“…While in 2019 most of the flowering period in ME1 had a high vapor pressure deficit ( Figure 4A ) and maximum temperatures between 34.5°C and °C 39.3°C ( Supplementary Figure S15A ), 2020 had milder temperatures and a smaller vapor pressure deficit. This approach can leverage plant ecophysiology knowledge aiding to identify the main sources of the genotype-environment interaction to select stress-resilient hybrids ( Costa-Neto and Fritsche-Neto, 2021 ; Resende et al., 2021 ; Carcedo et al., 2022 ).…”

Using soybean historical field trial data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors

“…Logically, plant breeders are motivated to ask: What is an environment? C osta -N eto and F ritsche -N eto (2021) defined environment as “… an emergent property derived from the balance of inputs and frequency across the plant’s lifetime,” and from an agronomic point of view, “… a certain time window between planting date and harvesting.” Over 60 years ago, C omstock , R. E. and M oll (1962) described the differences between micro and macro-environment, and explained that GEI is the result of fluctuations in the macro-environment during a crop’s lifetime. More recently, introduction of enviromics/envirotyping (C osta -N eto et al .…”

“…All analyses were implemented using “Asreml-R” version 4 (B utler et al . 2017) in the R programming environment (R Core Team 2021). Variance components of linear mixed models were estimated with REML followed by estimation/prediction of the fixed and random effects in Henderson’s mixed models (H enderson 1950, 1963).…”

Editorial: Harnessing genebanks: High-throughput phenotyping and genotyping of crop wild relatives and landraces

“…Therefore, an improved roadmap to address these bottlenecks with modern technologies is a prerequisite for their effective utilization (Figure 1). A first pivotal research avenue worth considering is mining phenotypic and genetic variation hidden within CWR and landraces (Singh et al, 2022) through extended sampling targeting isolated pockets of cryptic diversity (Ramirez-Villegas et al, 2020), robust ecological data curation (Waldvogel et al, 2020b) [e.g., targeting specific abiotic stresses such as drought (Corteś and Blair, 2018) and heat tolerance (Loṕez-Hernańdez and Corteś)], dense linkage disequilibrium (LD) guided genomic characterizations (Blair et al, 2018), and geographicwide agronomical (Osterman et al 2022) and physiological (Conejo-Rodriguez et al) trials across diverse germplasm and environments [recently referred to as enviromics (Costa-Neto and Fritsche-Neto, 2021;Crossa et al, 2021;Resende et al, 2021)].…”