Detecting environmental trends to rethink soybean variety testing programs

Crop Science, Journal Year: 2025, Volume and Issue: 65(1)

Published: Jan. 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

Language: Английский

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A Guide to Metabolic Network Modeling for Plant Biology

Plants, Journal Year: 2025, Volume and Issue: 14(3), P. 484 - 484

Published: Feb. 6, 2025

Plants produce a diverse array of compounds that play crucial roles in growth, development, and responses to abiotic biotic stresses. Understanding the fluxes within metabolic pathways is essential for guiding strategies aimed at directing metabolism crop improvement plant natural product industry. Over past decade, network modeling has emerged as predominant tool integration, quantification, prediction spatial temporal distribution flows. In this review, we present primary methods constructing mathematical models systems highlight recent achievements using modeling. Furthermore, discuss current challenges applying flux analysis plants explore potential use machine learning technologies The practical application expected provide significant insights into structure regulation networks.

Language: Английский

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Characterization of common bean production regions in Brazil using machine learning techniques

Agricultural Systems, Journal Year: 2024, Volume and Issue: 224, P. 104237 - 104237

Published: Dec. 12, 2024

Language: Английский

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Effect of sulfur- and zinc-containing fertilizers on soybean yield and analysis of spatial and seasonal yield variability in Ghana, West Africa

European Journal of Agronomy, Journal Year: 2024, Volume and Issue: 164, P. 127461 - 127461

Published: Dec. 9, 2024

Language: Английский

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