Challenges in Plant Breeding Under Climate Change: A Review

Опубликована: Янв. 1, 2024

Climate change challenges plant breeding, requiring a reassessment of methods. This review explores obstacles due to shifting climates, impacting crop predictability and productivity. Breeders must focus on robust, adaptable plants address these issues, emphasizing genetic diversity, adaptive traits, global food security. Priorities need shift toward traits that resist climate-related stressors, like drought heat. Advanced technologies, such as genomics gene editing, hold promise for climate-resilient crops. However, ethical, regulatory, socioeconomic concerns be addressed. Collaboration, ethics, holistic planning are crucial sustainable, crops

Язык: Английский

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

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Сен. 20, 2024

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

Язык: Английский

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Modeling interactions of planting date and phenology in Louisiana rice under current and future climate conditions

Crop Science, Год журнала: 2023, Номер 64(4), С. 2274 - 2287

Опубликована: Июнь 9, 2023

Abstract The performance of novel genetic combinations under untested environmental scenarios and management practices can be virtually examined using process‐based crop models. Indeed, there has been a long‐standing interest in the modeling community to expand utility models broader germplasm panels (e.g., breeding lines or diversity panels). Yet, is often misalignment between data needed parameterize routinely collected by programs. To address this gap, we leverage dataset from long‐term trial on advanced experimental released varieties Louisiana rice program calibrate evaluate decision support for agrotechnology transfer (DSSAT) CSM‐CERES‐Rice model. Next, use same large collection generate numerous silico genotypes their across different (different planting dates) three climatic conditions (current climate two future based CMIP6‐SSP5‐8.5 projections). Our simulations indicate that shifting current date (i.e., March) back 1–2 months (to January) moderate warming (+1.3°C warmer 41% higher CO 2 level), 2–3 December) extreme (+4.1°C 133% level) could potentially offset negative impacts increased temperature. Given earlier planting, shorter duration those with growing degree day requirements during vegetative grain filling periods) are found more favorable supporting high yields. Such thermal time anthesis remain just outside pool variation trait. Opportunities challenges leveraging derive insights into adaptation strategies climates further discussed.

Язык: Английский

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Plant Breeding in the face of climate change

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Окт. 8, 2022

ABSTRACT Climate change will have a net negative and inequitable impact on agriculture. Genetics for crop improvement ranks in the top set of technologies that can contribute to human adaptation climate change. However, framework how breed crops is lacking. Here we propose develop new genotype (G) x management (M) (G M) adapt change, transition from current future G M way food security does not come at expense security. The integrate genomic, agronomic, environmental (E) predictors accomplish two critical goals: 1-predict emergent phenotypes stems dynamic interplay between G, E M, thus enable breeder consider behavior genetic trait combinations environments plants been exposed or tested before, 2-identify could increase nutritional while regenerating natural production resources. We highlight need invest artificial intelligence information breeders harness multiple sources create address diverse cultural geographically granular societal needs.

Язык: Английский

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Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators

Plant Methods, Год журнала: 2024, Номер 20(1)

Опубликована: Июнь 6, 2024

The selection of highly productive genotypes with stable performance across environments is a major challenge plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing superiority and/or stability genotype environments. However, these are traditionally estimated using phenotypic values only and not well suited an unbalanced design in which observed all objective this research was propose evaluate new estimators following GE metrics: Ecovalence, Environmental Variance, Finlay-Wilkinson regression coefficient, Lin-Binns measure. Drawing from multi-environment genomic prediction model, we derived best linear unbiased for each metric. These derivations included both squared expectation variance term. To assess effectiveness our estimators, conducted simulations varied traits environment parameters. In results, consistently outperformed traditional phenotype-based terms accuracy. By incorporating term into addition term, were able improve precision estimates, particularly Ecovalence situations where heritability low sparseness high. All methods implemented R-package: GEmetrics. genomic-based enable estimating designs predicting genotypes, should help efficiency high-performance

Язык: Английский

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GIS-FA: An approach to integrate thematic maps, factor-analytic and envirotyping for cultivar targeting

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июль 15, 2023

Abstract Key message: We propose an enviromics prediction model for cultivar recommendation based on thematic maps decision-makers . Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important breeding programs. Understanding the causes and factors of GEI allows utilization genotype adaptations target population environments through environmental features Factor-Analytic (FA) models. Here, we present a novel predictive approach called GIS-FA integrates geographic information systems (GIS) techniques, FA models, Partial Least Squares (PLS) regression, Enviromics to predict phenotypic performance untested environments. The allows: (i) tested genotypes environments; (ii) select best-ranking their over-all stability using selection tools; (iii) draw showing overall or pairwise decision-making. exemplify usage two datasets rice [ Oryza sativa (L.)] soybean Glycine max (L.) Merr.] MET spread over tropical areas. In summary, our method identification new scenarios by pinpointing groups where have superior predicted facilitates/optimizes utilizing maps.

Язык: Английский

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Métodos estatísticos na avaliação da repetibilidade genotípica em lima ácida ‘Tahiti’

Опубликована: Март 30, 2023

Métodos estatísticos na avaliação da repetibilidade genotípica em lima ácida ‘Tahiti’. Orientador: Leonardo Lopes Bhering. Frutíferas perenes como a ‘Tahiti’ tiveram sua área de cultivo aumentada nos últimos anos devido ao acréscimo no consumo dos seus frutos preparação alimentos e bebidas. Para atender demanda pela produção, utilização variedades com alto potencial produtivo recebe destaque um método potencializar o alta eficiência sustentabilidade. A perenidade ‘Tahiti’, assim outras espécies, requer métodos seleção que isolem efeitos ambientais possibilitem identificação apenas fração genética entre os candidatos. Portanto, busca análise possam corroborar para aumentar confiabilidade dados experimentos é suma importância progresso melhoramento genético. Diferentes foram aplicados conjunto fim investigar cultura. Sendo assim, 24 genótipos, constituídos 12 copa enxertados 2 híbridos porta enxerto avaliados longo 4 características produtivas, vegetativas qualidade frutos. Em primeiro artigo, objetivou-se estimar parâmetros genéticos coeficiente através modelo linear misto, determinar número ótimo medidas se avaliar genótipos acurácia precisão. resumo, quatro colheitas foi recomendado identificar combinações base produtivas. várias simultaneamente processo importante necessário ser realizado, porém desafiador, dado diversidade genes controlam essas variadas magnitudes interação destes ambiente. Deste modo, segundo capítulo, aplicou-se metodologia regressão aleatória propôs-se índice as áreas abaixo das curvas valores preditos, obtidos pelos coeficientes aleatórios produtivas vegetativas. Constatou-se modelos lidam adequadamente repetidas, desbalanceados são recomendados lidar interações ambientais. aplicada permitiu predição genotípicos medições não avaliadas recomendação superiores caracteres simultaneamente. Ao selecionar ou recomendar superiores, conceitos probabilidade, advindos inferência bayesiana podem confiabilidade, permitindo estáveis, aumentando programa melhoramento. terceiro estudo, testou-se aplicabilidade probabilístico bayesiano performance estabilidade. Ajustou-se por meio algoritmo amostrador Monte Carlo Hamiltoniano. Calculou-se probabilidade superioridade do valor genético cada genótipo contexto geral colheita, bem inferioridade x colheitas. Os resultados mostraram componentes variância acurados, comparações estabilidade intervalos credibilidade obtidos. Palavras-chave: Citrus latifolia. Dados longitudinais. Modelos mistos. Inferência bayesiana.

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