Impact assessment of common bean availability in Brazil under climate change scenarios

Antolin, Luís Alberto Silva; Heinemann, A. B.; Marin, Fábio Ricardo

doi:10.1016/j.agsy.2021.103174

Cited by 15 publications

(12 citation statements)

References 45 publications

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“…Conversely, benchmark reaction norm models try to reproduce the observable reaction norm in a linear way. Both approaches can achieve adequate results (e.g., Cooper et al, 2016;Ly et al, 2018;Heinemann et al, 2019;Millet et al, 2019;Monteverde et al, 2019;Jarquin et al, 2020;Toda et al, 2020;Antolin et al, 2021), although, to the best of our knowledge, we have observed three key issues: (1) the quality of the linear modeling of a reaction norm depends on the diversity of METs, and thus, on the range of environmental conditions evaluated, which consequently implicates that the screened impact of environmental factors is MET-specific (not TPEspecific) and varies across years; (2) A CGM demands greater phenotyping effort for training genotype-specific parameters capable of reproducing the achievable phenotypic plasticity, from a reduced core of phenotypic records collected from field trials in near-iso environments (e.g., well-watered conditions vs. waterlimited conditions for same planting date and management), which, for some regions or crops, can limit the applicability of the method, even if it is a biologically accurate way to reproduce yield plasticity for certain scenarios such as drought stress; (3) the use of reaction norm models trained from high technological and G2). The range of the environmental gradient is delimited by the space between the two vertical green lines.…”

Section: Theory: Adapting the Shelford Law Of Minimummentioning

confidence: 99%

“…Developing climate-smart solutions in a time-reduced and cost-effective manner is crucial to minimize economic and environmental impacts in farm fields (Tigchelaar et al, 2018;Cortés et al, 2020;Ramirez-Villegas et al, 2020). All these strategies must be linked with the characterization of growing conditions of crops (Xu, 2016) because it allows for a deeper understanding of how the environmental signal is a driver to shape the past, present, and future phenotypic variations observed in farm fields (e.g., Cooper et al, 2014;Ramirez-Villegas et al, 2018;Heinemann et al, 2019;de los Campos et al, 2020;Antolin et al, 2021;Costa-Neto et al, 2021b). In plant breeding research, mostly based on the selection of bestevaluated genotypes in a certain experimental network, this approach discriminates which genetic and non-genetic factors affect adaptative responses and yield performance.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, new ways to establish a biologically accurate approach for predicting a given growing environment, as well as its relationship with TPE major conditions, have been better understood by quantifying the impact and frequency of the major environment-types (envirotypes) across years or locations (e.g., Chenu et al, 2011; Abbreviations: A, Additive effects; b, Coefficient of yield adaptability from Finlay-Wilkinson; BD, Block-diagonal matrix of the genomic by environment effect; D, Dominance effects; EC, Environmental covariate; E-GP, Enviromic-aided genomic prediction including envirotype markers; Envirotype, Environmental-type; FW, Finlay-Wilkinson adaptability model; GBLUP, Genomic best linear unbiased predictions; G×E, Genotype by environment interaction; GP, Genomic prediction; MET, Multi-environment trials; MSE, Mean squared error; N GE , Minimum core of genotype-environment combinations; OTS, Optimized training sets for genomic prediction; r, Predictive ability given by the average linear correlation between observed and predicted trait values; RN, Enviromic by genomic matrix for reaction norm effects; T, Typology matrix of envirotype markers (qualitative covariables and their frequencies); W, Environmental covariable matrix (quantitative covariables); W-GP, Enviromic-aided genomic prediction using quantitative environmental covariates. Heinemann et al, 2019;Antolin et al, 2021;. Furthermore, this might also lead to a better understanding of the quality of a certain environment (e.g., a field trial) in providing representative phenotypic records to support selection purposes or as a training population set in predictive breeding approaches.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

2021

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Quantitative genetics states that phenotypic variation is a consequence of the interaction between genetic and environmental factors. Predictive breeding is based on this statement, and because of this, ways of modeling genetic effects are still evolving. At the same time, the same refinement must be used for processing environmental information. Here, we present an “enviromic assembly approach,” which includes using ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as enviromic-aided genomic prediction, E-GP). We propose that the quality of an environment is defined by the core of environmental typologies and their frequencies, which describe different zones of plant adaptation. From this, we derived markers of environmental similarity cost-effectively. Combined with the traditional additive and non-additive effects, this approach may better represent the putative phenotypic variation observed across diverse growing conditions (i.e., phenotypic plasticity). Then, we designed optimized multi-environment trials coupling genetic algorithms, enviromic assembly, and genomic kinships capable of providing in-silico realization of the genotype-environment combinations that must be phenotyped in the field. As proof of concept, we highlighted two E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity exerting optimized phenotyping efforts. Our approach was tested using two tropical maize sets, two types of enviromics assembly, six experimental network sizes, and two types of optimized training set across environments. We observed that E-GP outperforms benchmark GP in all scenarios, especially when considering smaller training sets. The representativeness of genotype-environment combinations is more critical than the size of multi-environment trials (METs). The conventional genomic best-unbiased prediction (GBLUP) is inefficient in predicting the quality of a yet-to-be-seen environment, while enviromic assembly enabled it by increasing the accuracy of yield plasticity predictions. Furthermore, we discussed theoretical backgrounds underlying how intrinsic envirotype-phenotype covariances within the phenotypic records can impact the accuracy of GP. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

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Section: Theory: Adapting the Shelford Law Of Minimummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

2021

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“…Environmental changing scenarios challenge agricultural research to deliver climate-smart solutions in a time-reduced and cost-effective manner (Tigchelaar et al, 2018;Ramírez-Villegas et al 2020;Cortés et al, 2020). Characterizing crop growth conditions is crucial for this purpose (Xu, 2016) , allowing a deeper understanding of how the environment shapes past, present, and future phenotypic variations (e.g., Ramírez-Villegas et al 2018;Heinemann et al, 2019;Cooper et al, 2014;de los Campos et al, 2020;Costa-Neto et al, 2021b;Antolin et al, 2021). For plant breeding research, mostly based on selecting the best-evaluated genotypes for a target population of environments (TPE), this approach is useful to discriminate genomic and non-genomic sources of crop adaptation.…”

Section: Introductionmentioning

confidence: 99%

“…From envirotyping, it is possible to check the quality of a certain environment, which is directly related to how the observed growing conditions in a particular field trial could be related to the most frequent environment-types (envirotypes) that occur in the breeding program TPE or target region (e.g., Heinemann et al, 2019;Cooper et al, 2021;Antolin et al, 2021). In agricultural research, the quality of a certain environment is directly related to how it can limit the expression of the genetic potential of the certain crop for a certain trait, such as suggested by the movement called 'School of de Wit' since 1965 (see Bouman et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity

Costa‐Neto

Crossa

Fritsche‐Neto

2021

Preprint

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Quantitative genetics states that phenotypic variation is a consequence of genetic and environmental factors and their subsequent interaction. Here, we present an enviromic assembly approach, which includes the use of ecophysiology knowledge in shaping environmental relatedness into whole-genome predictions (GP) for plant breeding (referred to as E-GP). We propose that the quality of an environment is defined by the core of environmental typologies (envirotype) and their frequencies, which describe different zones of plant adaptation. From that, we derive markers of environmental similarity cost-effectively. Combined with the traditional genomic sources (e.g., additive and dominance effects), this approach may better represent the putative phenotypic variation across diverse growing conditions (i.e., phenotypic plasticity). Additionally, we couple a genetic algorithm scheme to design optimized multi-environment field trials (MET), combining enviromic assembly and genomic kinships to provide in-silico realizations of the future genotype-environment combinations that must be phenotyped in the field. As a proof-of-concept, we highlight E-GP applications: (1) managing the lack of phenotypic information in training accurate GP models across diverse environments and (2) guiding an early screening for yield plasticity using optimized phenotyping efforts. Our approach was tested using two non-conventional cross-validation schemes to better visualize the benefits of enviromic assembly in sparse experimental networks. Results on tropical maize show that E-GP outperforms benchmark GP in all scenarios and cases tested. We show that for training accurate GP models, the genotype-environment combinations' representativeness is more critical than the MET size. Furthermore, we discuss theoretical backgrounds underlying how the intrinsic envirotype-phenotype covariances within the phenotypic records of (MET) can impact the accuracy of GP and limits the potentialities of predictive breeding approaches. The E-GP is an efficient approach to better use environmental databases to deliver climate-smart solutions, reduce field costs, and anticipate future scenarios.

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Characterization of common bean production regions in Brazil using machine learning techniques

Justino,

Heinemann,

Matta

et al. 2025

Agricultural Systems

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Impact assessment of common bean availability in Brazil under climate change scenarios

Cited by 15 publications

References 45 publications

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize

Enviromic assembly increases accuracy and reduces costs of the genomic prediction for yield plasticity

Characterization of common bean production regions in Brazil using machine learning techniques

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