Sowing date, genotype choice, and water environment control soybean yields in central Argentina
Soybean [Glycine max (L.) Merr.] is one of the most important crops worldwide, and Argentina is the third largest global grain producer and the worlds´ largest meal exporter. Under the continuous challenge of increasing crop yields, especially in the central temperate region of the country, there is...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/184587 |
| Acceso en línea: | http://hdl.handle.net/11336/184587 |
| Access Level: | acceso abierto |
| Palabra clave: | soybean yield predicting yield sowing date https://purl.org/becyt/ford/4.1 https://purl.org/becyt/ford/4 |
| Sumario: | Soybean [Glycine max (L.) Merr.] is one of the most important crops worldwide, and Argentina is the third largest global grain producer and the worlds´ largest meal exporter. Under the continuous challenge of increasing crop yields, especially in the central temperate region of the country, there is a growing need to optimize management in relation to the environment that each specific farm and paddock presents. Understanding the impact of available technologies and management options can help optimize crop design. Here, we identify and quantify the effect of the most relevant variables affecting soybean yield by analyzing a database that includes 53 field trials with four common commercial genotypes, reporting 50 management and environmental variables. Linear mixed-effect models revealed that two management decisions (genotype and sowing date selection) and three environmental variables (rainfall during the reproductive crop period from R1 to R7, soil type [Hapludoll vs. Argiudoll], and water table presence above or below 2 m of depth from the surface) helped explain ∼40% of total yield variability, which ranged from 1,675 to 7,226 kg ha−1 and averaged 5,133 kg ha−1. Water table presence generated higher and more stable yields particularly in coarse-textured Hapludolls and under low-rainfall conditions. Results highlight specific management and environmental conditions that affect soybean crop yields in the region, pointing to effective pathways toward yield gap reductions. |
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