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Abstract
Estimation of crop nutrient demand, seed nutrient removal, and nutrient use efficiency (yield to nutrient uptake ratio) are crucial for pursuing both balanced nutrition and more sustainable farming systems. However, the estimation of the nutrient requirements as the nutrient uptake per unit of seed yields is impaired in many situations due to the narrow variation of the dataset used to obtain these values or by the overgeneralization of considering a [ver mas...]
dc.contributor.authorSalvagiotti, Fernando
dc.contributor.authorMagnano, Luciana
dc.contributor.authorOrtez, Osler
dc.contributor.authorEnrico, Juan Martin
dc.contributor.authorBarraco, Miriam Raquel
dc.contributor.authorBarbagelata, Pedro Anibal
dc.contributor.authorCondori, Alicia Adelina
dc.contributor.authorDi Mauro, Guido
dc.contributor.authorManlla, Amalia Graciela
dc.contributor.authorRotundo, Jose Luis
dc.contributor.authorGarcía, Fernando O.
dc.contributor.authorFerrari, Manuel Carlos
dc.contributor.authorGudelj, Vicente Jorge
dc.contributor.authorCiampitti, Ignacio A.
dc.date.accessioned2021-05-03T13:21:42Z
dc.date.available2021-05-03T13:21:42Z
dc.date.issued2021-07
dc.identifier.issn1161-0301
dc.identifier.otherhttps://doi.org/10.1016/j.eja.2021.126289
dc.identifier.urihttp://hdl.handle.net/20.500.12123/9247
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S1161030121000617
dc.description.abstractEstimation of crop nutrient demand, seed nutrient removal, and nutrient use efficiency (yield to nutrient uptake ratio) are crucial for pursuing both balanced nutrition and more sustainable farming systems. However, the estimation of the nutrient requirements as the nutrient uptake per unit of seed yields is impaired in many situations due to the narrow variation of the dataset used to obtain these values or by the overgeneralization of considering a constant value for the nutrient demand at varying yield levels. Past studies focused on other crops and using linear models for estimation of the nutrient requirements, but not yet for soybeans (Glycine max L.). The aims of this research study were to: (i) quantify nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) requirements in soybean and (ii) compare linear and non-linear (spherical) models in their relationship between plant and seed nutrient content all relative to seed yield at varying probabilities utilizing quantile regression. A large dataset from different studies conducted between 2009–2018 period, including data of seed yield, total biomass at physiological maturity, and N, P, K, and S uptake. Soybean seed yield ranged from 955 to 6525 kg ha−1, aboveground biomass from 1990 to 15,814 kg ha−1, and harvest index from 0.16 to 0.57. On average, nutrient uptake was 261 kg N ha−1, 25 kg P ha−1, 133 kg K ha−1, and 16 kg S ha−1 (N:P:K:S ratio = 17:1.6:8.5:1), while nutrient content in seeds averaged 191 kg N ha−1, 17 kg P ha−1, 54 kg K ha−1, and 9 kg S ha−1 (N:P:K:S ratio = 21:1.8:5.8:1). The spherical model described better than the linear model the relationship between plant nutrient uptake or nutrient content in seeds with seed yield in soybean, and thus, nutrient requirements per unit of yield decreased as seed yield increased. A relationship between nutrient internal efficiency and seed yield for the different percentiles as determined by the non-linear quantile regression offered probabilistic values for estimating nutrient uptake in soybean, providing useful information for obtaining more reliable estimates of nutrient balances at the system-level.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherElsevieres_AR
dc.relationinfo:eu-repograntAgreement/INTA/PNCER-022421/AR./Diagnostico, reposición de macronutrientes y tecnología de la fertilización.es_AR
dc.relationinfo:eu-repograntAgreement/INTA/PNCYO-1127033/AR./Manejo nutricional de cereales y oleaginosas para la intensificación sustentable de los sistemas productivoses_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourceEuropean Journal of Agronomy 127 : 126289 (July 2021)es_AR
dc.subjectSojaes_AR
dc.subjectSoybeanseng
dc.subjectNutrienteses_AR
dc.subjectNutrientseng
dc.subjectNitrógenoes_AR
dc.subjectNitrogeneng
dc.subjectFósforoes_AR
dc.subjectPhosphoruseng
dc.subjectPotasioes_AR
dc.subjectPotassiumeng
dc.subjectAzufrees_AR
dc.subjectSulphureng
dc.subjectAbsorción de Sustancias Nutritivases_AR
dc.subjectNutrient Uptakeeng
dc.titleEstimating nitrogen, phosphorus, potassium, and sulfur uptake and requirement in soybeanes_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.origenEEA Oliveroses_AR
dc.description.filFil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentinaes_AR
dc.description.filFil: Salvagiotti, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: Magnano, Luciana. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentinaes_AR
dc.description.filFil: Ortez, Osler. University of Nebraska-Lincoln. Department of Agronomy and Horticulture; Estados Unidoses_AR
dc.description.filFil: Enrico, Juan Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentinaes_AR
dc.description.filFil: Barraco, Mariam Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria General Villegas; Argentina.es_AR
dc.description.filFil: Barbagelata, Pedro Anibal. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentinaes_AR
dc.description.filFil: Condori, Alicia Adelina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentinaes_AR
dc.description.filFil: Di Mauro, Guido. Corteva Agriscience. Predictive Agriculture; Estados Unidoses_AR
dc.description.filFil: Manlla, Amalia Graciela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentinaes_AR
dc.description.filFil: Rotundo, José Luis. Corteva Agriscience. Predictive Agriculture; Estados Unidoses_AR
dc.description.filFil: García, Fernando O. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Ferrari, Manuel Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentinaes_AR
dc.description.filFil: Gudelj, Vicente Jorge. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentinaes_AR
dc.description.filFil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unidoses_AR
dc.subtypecientifico


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