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Resumen
Soybean monoculture is widespread across recently deforested areas in South America, leading to a decline in soil organic matter (SOM) and compromising the sustainability of the cropping system. Introducing cereals like maize into the crop rotation is necessary, but proper management knowledge to maximize its yield and profitability is needed. Our objectives were to quantify the impact of management and environmental variables influencing maize yield and [ver mas...]
dc.contributor.authorMadias, Andrés
dc.contributor.authorSimon, Carlos Gabriel
dc.contributor.authorStahringer, Nicolás I.
dc.contributor.authorBorrás, Lucas
dc.contributor.authorRubio, Gerardo
dc.contributor.authorGambin, Brenda L.
dc.date.accessioned2025-04-30T11:53:49Z
dc.date.available2025-04-30T11:53:49Z
dc.date.issued2025-07
dc.identifier.issn1161-0301
dc.identifier.issn1873-7331
dc.identifier.otherhttps://doi.org/10.1016/j.eja.2025.127612
dc.identifier.urihttp://hdl.handle.net/20.500.12123/22118
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S116103012500108X
dc.description.abstractSoybean monoculture is widespread across recently deforested areas in South America, leading to a decline in soil organic matter (SOM) and compromising the sustainability of the cropping system. Introducing cereals like maize into the crop rotation is necessary, but proper management knowledge to maximize its yield and profitability is needed. Our objectives were to quantify the impact of management and environmental variables influencing maize yield and estimate the potential to increase attainable yields in recently deforested fields of South American Gran Chaco. The analysis included a total of 62 on-farm trials across multiple environments, each including 9–28 hybrids. The mean site yields ranged from 2235 to 11141 kg ha−1. Using linear mixed models, we identified and tested a model with key management and environmental variables explaining yield variation. We used this model to estimate attainable yields across the region. Nitrogen availability, sowing date, and hybrid type (temperate or sub-tropical) were the most important management variables to predict yield (relative importance ≥ 0.80). Soil organic matter and soil water availability at sowing were the most important environmental yield predictors (relative importance of 0.71 and 0.66, respectively). The best model, tested against an independent dataset (n = 34 trials; RMSE=1722 kg ha−1; RRMSE=21 %) confirmed the influence of defined predictors. Our findings demonstrate that simple management adjustments can boost yields by ∼20 % (∼1500 kg ha−1). In this recently deforested region, the decline in SOM and its negative impact on yields highlight the importance of crop management strategies and policies aimed at improving current cropping systems.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevieres_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/es_AR
dc.sourceEuropean Journal of Agronomy 168 : 127612. (July 2025)es_AR
dc.subjectMaízes_AR
dc.subjectMaizeeng
dc.subjectRendimientoes_AR
dc.subjectYieldseng
dc.subjectManejo del Cultivoes_AR
dc.subjectCrop Managementeng
dc.subjectMateria Orgánica del Sueloes_AR
dc.subjectSoil Organic Mattereng
dc.subjectNitrógenoes_AR
dc.subjectNitrogeneng
dc.subjectFecha de Siembraes_AR
dc.subjectSowing Dateeng
dc.subject.otherRegión Chaqueña, Argentinaes_AR
dc.titleOn-farm insights in the South American Gran Chaco reveal the importance of soil organic matter and crop management decisions for boosting maize yieldses_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)es_AR
dc.description.origenEEA Las Breñases_AR
dc.description.filFil: Madias, Andres. AAPRESID; Argentinaes_AR
dc.description.filFil: Madias, Andres. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Campo Experimental Villarino; Argentinaes_AR
dc.description.filFil: Simon, Carlos Gabriel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Las Breñas. Agencia de Extensión Rural General Pinedo; Argentinaes_AR
dc.description.filFil: Stahfinger, Nicolás I. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Borrás, Lucas. Universidad Nacional de Rosario. Facultad de Ciencias Agrarias. Campo Experimental Villarino; Argentinaes_AR
dc.description.filFil: Rubio, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. INBA; Argentinaes_AR
dc.description.filFil: Rubio, Gerardo. Universidad de Buenos Aires. Facultad de Agronomía. INBA; Argentinaes_AR
dc.description.filFil: Rubio, Gerardo. Universidad de Buenos Aires. Facultad de Agronomía. Fertilidad y Fertilizantes; Argentinaes_AR
dc.description.filFil: Gambin, Brenda L. Iowa State University. Agronomy Department; Estados Unidoses_AR
dc.subtypecientifico


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