Attainable yield and soil texture as drivers of maize response to nitrogen a synthesis analysis for Argentina
The most widely used approach for prescribing fertilizer nitrogen (N) recommendations in maize (Zea Mays L.) in Argentina is based on the relationship between grain yield and the available N (kg N ha-1), calculated as the sum of pre-plant soil NO3--N at 0 - 60 cm depth (PPNT) plus fertilizer N (Nf)...
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| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2021correndo1.pdf Material complementario LINK AL EDITOR |
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| 024 | |a 10.1016/j.fcr.2021.108299 | ||
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| 245 | 1 | 0 | |a Attainable yield and soil texture as drivers of maize response to nitrogen |b a synthesis analysis for Argentina |
| 520 | |a The most widely used approach for prescribing fertilizer nitrogen (N) recommendations in maize (Zea Mays L.) in Argentina is based on the relationship between grain yield and the available N (kg N ha-1), calculated as the sum of pre-plant soil NO3--N at 0 - 60 cm depth (PPNT) plus fertilizer N (Nf). However, combining covariates related to crop N demand and soil N supply at a large national scale remains unexplored for this model. The aim of this work was to identify yield response patterns associated to yield environment (crop N demand driver) and soil texture (soil N supply driver). A database of 788 experiments (1980-2016) was gathered and analyzed combining quadratic-plateau regression models with bootstrapping to address expected values and variability on response parameters and derived quantities. The database was divided into three groups according to soil texture (fine, medium and coarse) and five groups based on the empirical distribution of maximum observed yields (from Very-Low = minor to 8.5 Mg ha- 1 to Very-High =minor to 13.1 Mg ha- 1) resulting in fifteen groups. The best model included both, attainable yield environment and soil texture. The yield environment mainly modified the agronomic optimum available N (AONav), with an expected increase rate of ca. 21.4 kg N Mg attainable yield- 1, regardless of the soil texture. In Very-Low yield environments, AONav was characterized by a high level of uncertainty, related to a poor fit of the N response model. To a lesser extent, soil texture modified the response curvature but not the AONav, mainly by modifying the response rate to N (Fine minor to Medium minor to Coarse), and the N use efficiencies. Considering hypothetical PPNT levels from 40 to 120 kg N ha-1, the expected agronomic efficiency (AENf) at the AONav varied from 7 to 31, and 9–29 kg yield response kg fertilizer N (Nf)- 1, for Low and Very-High yield environments, respectively. Similarly, the expected partial factor productivity (PFPNf) at the AONav ranged from 62 to 158, and 55–99 kg yield kg Nf-1, for the same yield environments. These results highlight the importance of combining attainable yield environment and soil texture metadata for refining N fertilizer recommendations. Acknowledging the still low N fertilizer use in Argentina, space exists to safely increasing N fertilizer rates, steering the historical soil N mining profile to a more sustainable agro-environmental scenario in the Pampas. | ||
| 650 | |2 Agrovoc |9 26 | ||
| 653 | |a CORN | ||
| 653 | |a FERTILIZER | ||
| 653 | |a SOIL FERTILITY | ||
| 653 | |a NITROGEN USE EFFICIENCY | ||
| 653 | |a PARTIAL FACTOR PRODUCTIVITY | ||
| 700 | 1 | |9 32879 |a Correndo, Adrián Alejandro |u Kansas State University. Department of Agronomy. Manhattan. USA. | |
| 700 | 1 | |a Gutiérrez Boem, Flavio Hernán |u Universidad de Buenos Aires. Facultad de Agronomía. Buenos Aires, Argentina. |u CONICET. Buenos Aires, Argentina. |9 6387 | |
| 700 | 1 | |9 9707 |a García, Fernando Oscar |u Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias. Buenos Aires, Argentina. | |
| 700 | 1 | |a Alvarez, Carolina |u Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi (EEA Manfredi). Córdoba. Argentina. |9 31267 | |
| 700 | 1 | |9 40178 |a Alvarez, Cristian |u Instituto Nacional de Tecnología Agropecuaria (INTA). AER General Pico, La Pampa, Argentina. | |
| 700 | 1 | |a Angeli, Ariel |u CREA. Buenos Aires, Argentina. |9 74016 | |
| 700 | 1 | |a Rimski Korsakov, Helena |u Universidad de Buenos Aires. Facultad de Agronomía. Buenos Aires, Argentina. |9 11233 | |
| 700 | 1 | |a Zubillaga, María de las Mercedes |u Universidad de Buenos Aires. Facultad de Agronomía. Buenos Aires, Argentina. |9 7188 | |
| 773 | 0 | |t Field crops research |g Vol.273 (2021), art.108299, 27 p., tbls., grafs., mapas |w (AR-BaUFA)SECS000083 | |
| 856 | |q application/pdf |f 2021correndo1 |i En reservorio |u http://ri.agro.uba.ar/files/intranet/articulo/2021correndo1.pdf |x ARTI202209 | ||
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| 856 | |u https://www.elsevier.com |z LINK AL EDITOR | ||
| 942 | |c ARTICULO | ||
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| 976 | |a AAG | ||