Harnessing crop models and machine learning for a spatial-temporal characterization of irrigated rice breeding environments in Brazil

Field Crops Research, Год журнала: 2024, Номер 315, С. 109452 - 109452

Опубликована: Июнь 15, 2024

Язык: Английский

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Detecting environmental trends to rethink soybean variety testing programs

Crop Science, Год журнала: 2025, Номер 65(1)

Опубликована: Янв. 1, 2025

Abstract Variety testing programs (VTPs) use multi‐environment trials (MET) to evaluate and report the performance of commercially available pre‐commercial soybean ( Glycine max L. Merr.) varieties targeting a specific set environments. Adequate modeling environmental variability genotype–environment interactions (G × E) within VTP would help farmers seed companies decide which variety choose or recommend. We propose an approach characterize environments using data from University Missouri VTP. modeled trend (EnvT) based on phenotypic mean observed phenotype in each environment. The were classified into four different EnvT environment types, soil climate used as predictors through eXtreme Gradient Boosting (XGBoost) model. Temperature late vegetative flowering, soil‐saturated hydraulic conductivity, silt content key drivers EnvT. identified overrepresented (62%) increased ratio between G E variance. A simulation case study verified that random removal sites dataset quickly degraded analysis, implying increasing number underrepresented is recommended. Our results demonstrate characterization essential for optimizing resource allocation VTP, thereby supporting end goal aiding utilize best their production

Язык: Английский

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Using soybean historical field trial data to study genotype by environment variation and identify mega‐environments with the integration of genetic and non‐genetic factors

Agronomy Journal, Год журнала: 2025, Номер 117(1)

Опубликована: Янв. 1, 2025

Abstract Soybean [ Glycine max (L.) Merr.] provides plant‐based protein for global food production and is extensively bred to create cultivars with greater productivity in distinct environments through multi‐environment trials (MET). The application of MET assumes that trial locations provide representative environmental conditions are likely encounter when grown by farmers. A retrospective analysis data spanning 63 between 1989 2019 was conducted identify mega‐environments (ME) soybean seed yield the primary areas North America. ME were identified using from phenotypic values, geographic, soil, meteorological records at locations. Results indicate variation mostly explained location year interaction. due genotype interaction effects than effects. static portion environment variance represented 26.30% its total variation. observed sampled target population can be divided into two or three ME, thereby suggesting improvements response selection achieved selecting directly within clusters (i.e., regions ME) versus across all In addition, we published R package SoyURT contains datasets used this work.

Язык: Английский

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Yield environment changes the ranking of soybean genotypes

Field Crops Research, Год журнала: 2024, Номер 321, С. 109661 - 109661

Опубликована: Ноя. 28, 2024

Язык: Английский

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