The impact of El Niño Southern Oscillation on cropping season rainfall variability across Central Brazil The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications.
The sustainability of irrigated rice (Oryza sativa L.) production systems in Brazilian tropical region highly depends on the success of developing stable cultivars. To achieve this goal, many steps in product development must address the environmental variability and genotype by environment interactions (GE), which makes difficult the design and development of local-specific adapted cultivars. Thus, the adoption of new strategies for characterizing environmental-phenotype relations are the key for optimizing this process. In addition, it could also benefit post-breeding stages of seed production. To overcome this situation, we implemented a data-driven approach to link environmental characterization to yield clustering using historical data (1982-2017, 31 locations, 471 genotypes), 42 envirotyping covariables and machine learning (ML), combining two unsupervised (K-means and decision tree models, DTC) algorithms. Additionally, linear mixed models (LMM) were applied to explore the relations between the outcomes of our approach and GE analysis for irrigated rice yield in Brazilian tropical region. Four environments were identified: Very Low Yield (1.7 Mg.ha-1), Low Yield (5.1 Mg.ha-1), High Yield (7.2 Mg.ha-1), and Very High Yield (9.0 Mg.ha-1), considering all genotypes and regions. Our approach allows the prediction of environments (yield clusters) for a diverse set of growing conditions and revealed geographic and climatic causes of environmental quality, which differ according to each region and genotype group. From the LMM analysis, we found that the current relation between genetics (G), environmental variation (E), and GE for rainfed rice in Brazil is 1:6:2, but when we introduced our data-driven clusters (ME), the ratio decreased to 1:5:1. Consequently, the selection reliability for local adaptability across an extensive region increases. Our approach helps to identify mega-environments in Brazil that could be used as a target population of environments (TPE) of breeding programs. Additionally, it helps to identify more productive and stable seed production fields.
Precision N management using optical radiation sensors is a promising management strategy. Using a combination of three spectral reflectance bands, 22 vegetation indices (VIs) were calculated and evaluated for their efficiency in estimating N status in irrigated rice (Oryza sativa L.) during growth stages. The results obtained here have showed a promising strategy to include crop stages as covariate in generalist models to predict the N status parameters for irrigated rice. This approach is interesting because it reduces the need for specific models, with different structures, for each crop stage. In addition, including crop stages as a covariate in the prediction models allows knowing the rice N status according to the crop stage, which is essential for efficient N management in commercial crops. The results obtained here show the beginning of vegetative stage (V1–V9) significantly affects the prediction of all N status parameters. The dry leaf biomass (DLB), leaf area index (LAI), leaf nitrogen uptake (LNU), and nitrogen nutrition index (NNI) can be adequately predicted with combinations of just two VIs. These results show the importance of using active sensors with more than two fixed bands, preferably including a red‐edge band, for effective crop N status estimation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.