Global Rice Array: From environmental diversity to climate-smart breeding strategies
Abstract
Background: Rice breeding for climate adaptation requires a robust framework to evaluate genotype performance across diverse and interacting production environments. However, most breeding pipelines remain constrained by limited environmental representation and insufficient understanding of genotype by environment (G x E) interactions. Objectives: This study introduces the Global Rice Array (GRA), a coordinated multi-environment trial network specifically designed to capture major sources of environmental heterogeneity in lowland rice systems and to support environment-targeted breeding decisions. Unlike conventional location-based testing, the GRA integrates standardized trials across Asia, Africa, and Latin America, enabling a functional characterization of environments and genotypes at global scale. Methods: A total of 58 diverse rice genotypes nominated by breeders across Asia, Africa, and Latin America were evaluated. The set includes genotypes tolerant to heat, drought, cold, salinity, and major biotic stress. Fifty-one field trials were conducted across 12 countries in three continents from 2018 to 2020. Grain yield, days to flowering, and plant height were measured across genotypes, sites and seasons. Results: Using the additive main effect and multiplicative interaction (AMMI) model, environment explained 56% of grain yield variation, followed by G x E interaction (26%) and genotype (18%), confirming the limited predictability of performance from single-environment trials. Importantly, environment grouping revealed seven functional environment groups (EGs) structured by climate, water regime, and biotic pressures rather than geographic origin. This demonstrates that lowland rice production environments can be more effectively defined as integrated stress environments rather than administrative or regional units. Low-yielding EGs achieved only 58% of the yield of high-yielding EGs and reflected climate-water and biotic stress interaction constraints (higher night temperatures >24 °C, lower solar radiation, higher pest and disease pressure). Cluster analysis further identified four genotype groups with contrasting adaptation strategies, including broadly adapted high-yielding genotypes and specifically adapted stress-resilient genotypes. Broadly adapted genotypes contributed to yield stability in favorable environments, whereas specific adaptation was associated with stress tolerance, including escape strategies associated with variation in flowering time. Phenology analyses further showed that maturity groups were stable across environments, but that stress consistently delayed development and reduced yield across genotypes. Conclusions: Overall, our results demonstrate that climate-smart rice breeding should integrate environment characterization, genotype × environment analysis, and complementary use of broadly and specifically adapted germplasm. Breeding pipelines must explicitly target functional environmental groups and combine stress-resilient traits with appropriate maturity types to ensure productivity and stability under evolving climate conditions.
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BibTeX
@article{ranaivoson2026global,
title = {{Global Rice Array: From environmental diversity to climate-smart breeding strategies}},
author = {Lalaina Bakotiana Ranaivoson and Kazuki Saito and Myriam Adam and Gatien Falconnier and Ibrahim Ali and Gaurav Joshi and Mona Liza Jubay and B. P. Mallikarjuna Swamy and M. Akhlasur Rahman and Alexis Ndayiragije and Nguyen T.K. Tien and Krishnendu Chattopadhyay and Vikas Kumar Singh and Khady Nani Dramé and S.L. Krishnamurthy and Bertrand Muller and Hsu Myat Noe Hnin and Eliel Enrique Petro Paez and Indra Deo Pandey and Raj Gautam and Pankaj Kumar Singh and Sunil Verma and Tomizuddin Ahmed and Frederico Molia and Issa Wonni and Agnieszka Klonowska and Rakesh Kumar Singh and Maria Camila Rebolledo},
journal = {Field Crops Research},
year = {2026},
month = oct,
doi = {10.1016/j.fcr.2026.110727},
url = {https://hal.science/hal-05841565v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Global Rice Array: From environmental diversity to climate-smart breeding strategies
AU - Lalaina Bakotiana Ranaivoson
AU - Kazuki Saito
AU - Myriam Adam
AU - Gatien Falconnier
AU - Ibrahim Ali
AU - Gaurav Joshi
AU - Mona Liza Jubay
AU - B. P. Mallikarjuna Swamy
AU - M. Akhlasur Rahman
AU - Alexis Ndayiragije
AU - Nguyen T.K. Tien
AU - Krishnendu Chattopadhyay
AU - Vikas Kumar Singh
AU - Khady Nani Dramé
AU - S.L. Krishnamurthy
AU - Bertrand Muller
AU - Hsu Myat Noe Hnin
AU - Eliel Enrique Petro Paez
AU - Indra Deo Pandey
AU - Raj Gautam
AU - Pankaj Kumar Singh
AU - Sunil Verma
AU - Tomizuddin Ahmed
AU - Frederico Molia
AU - Issa Wonni
AU - Agnieszka Klonowska
AU - Rakesh Kumar Singh
AU - Maria Camila Rebolledo
PY - 2026
DA - 2026/10/30
JO - Field Crops Research
DO - 10.1016/j.fcr.2026.110727
UR - https://hal.science/hal-05841565v1
AB - Background: Rice breeding for climate adaptation requires a robust framework to evaluate genotype performance across diverse and interacting production environments. However, most breeding pipelines remain constrained by limited environmental representation and insufficient understanding of genotype by environment (G x E) interactions. Objectives: This study introduces the Global Rice Array (GRA), a coordinated multi-environment trial network specifically designed to capture major sources of environmental heterogeneity in lowland rice systems and to support environment-targeted breeding decisions. Unlike conventional location-based testing, the GRA integrates standardized trials across Asia, Africa, and Latin America, enabling a functional characterization of environments and genotypes at global scale. Methods: A total of 58 diverse rice genotypes nominated by breeders across Asia, Africa, and Latin America were evaluated. The set includes genotypes tolerant to heat, drought, cold, salinity, and major biotic stress. Fifty-one field trials were conducted across 12 countries in three continents from 2018 to 2020. Grain yield, days to flowering, and plant height were measured across genotypes, sites and seasons. Results: Using the additive main effect and multiplicative interaction (AMMI) model, environment explained 56% of grain yield variation, followed by G x E interaction (26%) and genotype (18%), confirming the limited predictability of performance from single-environment trials. Importantly, environment grouping revealed seven functional environment groups (EGs) structured by climate, water regime, and biotic pressures rather than geographic origin. This demonstrates that lowland rice production environments can be more effectively defined as integrated stress environments rather than administrative or regional units. Low-yielding EGs achieved only 58% of the yield of high-yielding EGs and reflected climate-water and biotic stress interaction constraints (higher night temperatures >24 °C, lower solar radiation, higher pest and disease pressure). Cluster analysis further identified four genotype groups with contrasting adaptation strategies, including broadly adapted high-yielding genotypes and specifically adapted stress-resilient genotypes. Broadly adapted genotypes contributed to yield stability in favorable environments, whereas specific adaptation was associated with stress tolerance, including escape strategies associated with variation in flowering time. Phenology analyses further showed that maturity groups were stable across environments, but that stress consistently delayed development and reduced yield across genotypes. Conclusions: Overall, our results demonstrate that climate-smart rice breeding should integrate environment characterization, genotype × environment analysis, and complementary use of broadly and specifically adapted germplasm. Breeding pipelines must explicitly target functional environmental groups and combine stress-resilient traits with appropriate maturity types to ensure productivity and stability under evolving climate conditions.
SN - 0378-4290
ER -