Geographical adaptability for optimizing the recommendation of soybean cultivars in the Brazilian Cerrado

• Published in: Scientific reports Vol. 14; no. 1; pp. 13076 - 12
• Main Authors: Corbellini, Marcos, Bobek, Daniel Vicente, de Toledo, José Francisco Ferraz, Ferreira, Lenio Urzeda, Santana, Dthenifer Cordeiro, Gilio, Thiago Alexandre Santana, Teodoro, Larissa Pereira Ribeiro, Teodoro, Paulo Eduardo, Tardin, Flavio Dessaune
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/449; 704/172; Adaptability; Adaptation; Adaptation, Physiological; Brazil; Crop yield; Cultivars; Genotype; Genotypes; Geography; Glycine max - genetics; Glycine max - growth & development; Humanities and Social Sciences; Moisture content; multidisciplinary; Relative humidity; Science; Science (multidisciplinary); Soil - chemistry; Soil temperature; Soil water; Soybeans; Brazil
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-63809-y

Yield multi-location trials associated to geostatistical techniques with environmental covariables can provide a better understanding of G x E interactions and, consequently, adaptation limits of soybean cultivars. Thus, the main objective of this study is understanding the environmental covariables effects on soybean adaptation, as well as predicting the adaptation of soybean under environmental variations and then recommend each soybean cultivar to favorable environments aiming maximize the average yield. The trials were carried out in randomized block design (RBD) with three replicates over three years, in 28 locations. Thirty-two genotypes (commercial and pre-commercial) representing different maturity groups (7.5–8.5) were evaluated in each trial were covering the Edaphoclimatic Region (REC) 401, 402 and 403. The covariables adopted as environmental descriptors were accumulated rainfall, minimum temperature, mean temperature, maximum temperature, photoperiod, relative humidity, soil clay content, soil water avaibility and altitude. After fitting means through Mixed Linear Model, the Regression-Kriging procedure was applied to spacialize the grain yield using environmental covariables as predictors. The covariables explained 32.54% of the GxE interaction, being the soil water avaibility the most important to the adaptation of soybean cultivars, contributing with 7.80%. Yield maps of each cultivar were obtained and, hence, the yield maximization map based on cultivar recommendation was elaborated.