Environmental clusters defining breeding zones for tropical irrigated rice in Brazil

Abstract

Geographic and seasonal effects are important in driving selection decisions in rice breeding research. Adopting new strategies for characterizing environmental‐phenotype associations is critical to understanding these effects, and the outcomes of their study could reflect the benefits of developing locally adapted cultivars. This study aimed to characterize Brazil's tropical irrigated rice environment, Latin America's largest rice production system. We integrated unsupervised (K‐means clustering) and supervised (decision tree classifier, DTC) algorithms to identify environmental clusters (EC) based on historical yield data. The dataset included 31 locations and 471 genotypes from 1982 to 2017. We used environmental features (EF), such as weather and geography, as input variables for our analysis, assuming the model as EC ∼ f (EF). Results indicate that the tropical irrigated rice production region can be divided into four primary breeding zones, with temperature emerging as a significant factor in the study area. After employing a linear mixed model (LMM) analysis, we observed that the current relationship between genetics (G), environmental variation (E), and their interaction (GE) in Brazil's tropical irrigated rice has a 1:6:2 ratio. However, when introducing our data‐driven model based on environmental clusters, we reduced this ratio to 1:5:1. Therefore, the selection for local adaptability across a large region became more reliable. Our approach successfully identified environmental clusters in Brazil's tropical production region of irrigated rice, providing valuable insights for defining breeding zones and identifying more productive and stable seed production fields.This article is protected by copyright. All rights reserved

Optimizing Multi-Environment Trials in The US Rice Belt via Smart-Climate-Soil Prediction Based-Models and Economic Importance

“…Gevartosky et al (2023) improved the response to selection by 145% when they considered environmental covariates in the design of optimized training sets for genomic prediction. In the same way, Costa-Neto et al (2023) achieved a GxE variance decrease from 22% to 15% when environmental covariates were considered, showing a more effective GxE effect capture. Conversely, the heritability of the MET and MET_EC scenarios (0.9 5 and 0.9 5, respectively) remained the same even when using the Ω matrix in the last scenario.…”

“…The advantage of this is that environmental information is broad and free (Costa-Neto et al, 2021), while the traditional GxE matrix is highly dependent on the genotypes and environment combinations used. Therefore, with historical weather data and genotypes reaction norms, it is possible to recommend the best and most stable varieties for each target region and even work to discover potential new producing regions (Araújo et al, 2024; Callister et al, 2024; Cooper & Messina, 2021; Costa-Neto et al, 2023).…”

“…To avoid the yield gaps between the expected yield potential in the TPE and the real on-farm yield that farmers achieve, it is important to characterize better and select the locations used for testing/selection and to understand how they are related (mega-environments) by a process called enviromic or environtyping. Specifically, in this process, the environmental covariates are collected and processed in appropriate MET groupings, which are analyzed concerning the alignment with TPE and, thus, used to capture genotype reaction norms models (Callister et al, 2024; Cooper & Messina, 2021; Costa-Neto et al, 2023).…”