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). H...
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| Format: | Artículo |
| Language: | Inglés |
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Elsevier
2021
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/10372 https://www.sciencedirect.com/science/article/abs/pii/S0378429021002458 https://doi.org/10.1016/j.fcr.2021.108299 |
| _version_ | 1855484576398311424 |
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| author | Correndo, Adrián A. Gutiérrez Boem, Flavio Hernán García, Fernando O. Alvarez, Carolina Alvarez, Cristian Angeli, Ariel Barbieri, Pablo Barraco, Miriam Raquel Berardo, Angel Boxler, Miguel Calviño, Pablo Capurro, Julia Ester Carta, Hector Guillermo Caviglia, Octavio Ciampitti, Ignacio A. Díaz-Zorita, Martín Díaz-Valdez, Santiago Echeverría, Hernan E. Espósito Goya, Gabriel Pablo Ferrari, Manuel Carlos Ferraris, Gustavo Nestor Gambaudo, Sebastian Gudelj, Vicente Jorge Ioele, Juan Pablo Melchiori, Ricardo Jose Molino, Josefina Orcellet, Juan Manuel Pagani, Agustín Pautasso, Juan Manuel Reussi Calvo, Nahuel Ignacio Redel, Matías Rillo, Sergio Nestor Rimski-Korsakov, Helena Sainz Rozas, Hernan Rene Saks, Matías Telleria, María Guadalupe Ventimiglia, Luis Alberto Zorzín, Jose L. Zubillaga, Ma. Mercedes Salvagiotti, Fernando |
| author_browse | Alvarez, Carolina Alvarez, Cristian Angeli, Ariel Barbieri, Pablo Barraco, Miriam Raquel Berardo, Angel Boxler, Miguel Calviño, Pablo Capurro, Julia Ester Carta, Hector Guillermo Caviglia, Octavio Ciampitti, Ignacio A. Correndo, Adrián A. Díaz-Valdez, Santiago Díaz-Zorita, Martín Echeverría, Hernan E. Espósito Goya, Gabriel Pablo Ferrari, Manuel Carlos Ferraris, Gustavo Nestor Gambaudo, Sebastian García, Fernando O. Gudelj, Vicente Jorge Gutiérrez Boem, Flavio Hernán Ioele, Juan Pablo Melchiori, Ricardo Jose Molino, Josefina Orcellet, Juan Manuel Pagani, Agustín Pautasso, Juan Manuel Redel, Matías Reussi Calvo, Nahuel Ignacio Rillo, Sergio Nestor Rimski-Korsakov, Helena Sainz Rozas, Hernan Rene Saks, Matías Salvagiotti, Fernando Telleria, María Guadalupe Ventimiglia, Luis Alberto Zorzín, Jose L. Zubillaga, Ma. Mercedes |
| author_facet | Correndo, Adrián A. Gutiérrez Boem, Flavio Hernán García, Fernando O. Alvarez, Carolina Alvarez, Cristian Angeli, Ariel Barbieri, Pablo Barraco, Miriam Raquel Berardo, Angel Boxler, Miguel Calviño, Pablo Capurro, Julia Ester Carta, Hector Guillermo Caviglia, Octavio Ciampitti, Ignacio A. Díaz-Zorita, Martín Díaz-Valdez, Santiago Echeverría, Hernan E. Espósito Goya, Gabriel Pablo Ferrari, Manuel Carlos Ferraris, Gustavo Nestor Gambaudo, Sebastian Gudelj, Vicente Jorge Ioele, Juan Pablo Melchiori, Ricardo Jose Molino, Josefina Orcellet, Juan Manuel Pagani, Agustín Pautasso, Juan Manuel Reussi Calvo, Nahuel Ignacio Redel, Matías Rillo, Sergio Nestor Rimski-Korsakov, Helena Sainz Rozas, Hernan Rene Saks, Matías Telleria, María Guadalupe Ventimiglia, Luis Alberto Zorzín, Jose L. Zubillaga, Ma. Mercedes Salvagiotti, Fernando |
| author_sort | Correndo, Adrián A. |
| collection | INTA Digital |
| description | 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 = <8.5 Mg ha−1 to Very-High = >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 > Medium > 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. |
| format | Artículo |
| id | INTA10372 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA103722024-08-21T13:20:03Z Attainable yield and soil texture as drivers of maize response to nitrogen : A synthesis analysis for Argentina Correndo, Adrián A. Gutiérrez Boem, Flavio Hernán García, Fernando O. Alvarez, Carolina Alvarez, Cristian Angeli, Ariel Barbieri, Pablo Barraco, Miriam Raquel Berardo, Angel Boxler, Miguel Calviño, Pablo Capurro, Julia Ester Carta, Hector Guillermo Caviglia, Octavio Ciampitti, Ignacio A. Díaz-Zorita, Martín Díaz-Valdez, Santiago Echeverría, Hernan E. Espósito Goya, Gabriel Pablo Ferrari, Manuel Carlos Ferraris, Gustavo Nestor Gambaudo, Sebastian Gudelj, Vicente Jorge Ioele, Juan Pablo Melchiori, Ricardo Jose Molino, Josefina Orcellet, Juan Manuel Pagani, Agustín Pautasso, Juan Manuel Reussi Calvo, Nahuel Ignacio Redel, Matías Rillo, Sergio Nestor Rimski-Korsakov, Helena Sainz Rozas, Hernan Rene Saks, Matías Telleria, María Guadalupe Ventimiglia, Luis Alberto Zorzín, Jose L. Zubillaga, Ma. Mercedes Salvagiotti, Fernando Rendimiento de Cultivos Maíz Textura del Suelo Fertilidad del Suelo Eficiencia en el Uso de los Nutrientes Nitrógeno Abonos Nitrogenados Crop Yield Maize Soil Texture Soil Fertility Nutrient Use Efficiency Nitrogen Nitrogen Fertilizers 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 = <8.5 Mg ha−1 to Very-High = >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 > Medium > 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. EEA Pergamino Fil: Correndo, Adrián A. Kansas State University. Department of Agronomy; Estados Unidos Fil: Gutiérrez-Boem, Flavio H. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Gutiérrez-Boem, Flavio H. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: García, Fernando O. Consultor privado; Argentina Fil: García, Fernando O. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina Fil: Alvarez, Carolina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Argentina Fil: Alvarez, Cristian. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Anguil. Agencia de Extensión Rural General Pico; Argentina Fil: Angeli, Ariel. Consorcio Regional de Experimentación Agrícola. Investigación + Desarrollo (I+D); Argentina Fil: Barbieri, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Barraco, Miriam Raquel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria General Villegas; Argentina Fil: Berardo, Angel. Laboratorio de Suelos Fertilab; Argentina Fil: Boxler, Miguel. Consultor privado; Argentina Fil: Calviño, Pablo. Consultor privado; Argentina Fil: Capurro, Julia Ester. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros. Agencia de Extensión Rural Cañada de Gómez; Argentina Fil: Carta, Hector Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Agencia de Extensión Rural 9 de Julio; Argentina Fil: Caviglia, Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Caviglia, Octavio. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias Entre Ríos; Argentina Fil: Ciampitti, Ignacio A. Kansas State University. Department of Agronomy; Estados Unidos Fil: Díaz-Zorita, Martín. Universidad Nacional de La Pampa. Facultad de Agronomía; Argentina Fil: Díaz-Valdez, Santiago. Bayer Crop Science; Argentina Fil: Echeverría, Hernán E. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina Fil: Espósito, Gabriel. Universidad Nacional de Rio Cuarto. Facultad de Agronomía y Veterinaria; Argentina Fil: Ferrari, Manuel Carlos. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Departamento de Suelo y Fertilidad; Argentina Fil: Ferraris, Gustavo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Desarrollo Rural; Argentina Fil: Gambaudo, Sebastián. Consultor privado; Argentina Fil: Gambaudo, Sebastián. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentina Fil: Gudelj, Vicente Jorge. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez; Argentina Fil: Ioele, Juan Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Marcos Juárez. Agencia de Extensión Rural Corral de Bustos; Argentina Fil: Melchiori, Ricardo Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná; Argentina Fil: Molino, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agencia De Extensión Rural Oncativo; Argentina Fil: Orcellet, Juan Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concepción del Uruguay; Argentina Fil: Pagani, Agustín. Clarion; Argentina Fil: Pautasso, Juan Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná. Agencia de Extensión Rural Diamante; Argentina Fil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Reussi Calvo, Nahuel Ignacio. Laboratiorio de Suelos Fertilab; Argentina Fil: Redel, Matías. Consultor privado; Argentina Fil: Rillo, Sergio Nestor. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Agencia de Extensión Rural 9 de Julio; Argentina Fil: Rimski-Korsakov, Helena. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Sainz Rozas, Hernan Rene. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina Fil: Sainz Rozas, Hernan Rene. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Matías Saks, Matías. Kansas State University. Department of Agronomy; Estados Unidos Fil: Telleria, María Guadalupe. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Agencia de Extensión Rural Junín; Argentina Fil: Ventimiglia, Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Agencia de Extensión Rural 9 de Julio; Argentina Fil: Zorzín, José. Consultor privado; Argentina Fil: Zubillaga, Ma. Mercedes. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Salvagiotti, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Argentina Fil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentina 2021-09-28T10:42:21Z 2021-09-28T10:42:21Z 2021-09 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/10372 https://www.sciencedirect.com/science/article/abs/pii/S0378429021002458 0378-4290 https://doi.org/10.1016/j.fcr.2021.108299 eng info:eu-repo/semantics/restrictedAccess application/pdf Argentina .......... (nation) (World, South America) 7006477 Elsevier Field Crops Research 273 : 108299. (Noviembre 2021) |
| spellingShingle | Rendimiento de Cultivos Maíz Textura del Suelo Fertilidad del Suelo Eficiencia en el Uso de los Nutrientes Nitrógeno Abonos Nitrogenados Crop Yield Maize Soil Texture Soil Fertility Nutrient Use Efficiency Nitrogen Nitrogen Fertilizers Correndo, Adrián A. Gutiérrez Boem, Flavio Hernán García, Fernando O. Alvarez, Carolina Alvarez, Cristian Angeli, Ariel Barbieri, Pablo Barraco, Miriam Raquel Berardo, Angel Boxler, Miguel Calviño, Pablo Capurro, Julia Ester Carta, Hector Guillermo Caviglia, Octavio Ciampitti, Ignacio A. Díaz-Zorita, Martín Díaz-Valdez, Santiago Echeverría, Hernan E. Espósito Goya, Gabriel Pablo Ferrari, Manuel Carlos Ferraris, Gustavo Nestor Gambaudo, Sebastian Gudelj, Vicente Jorge Ioele, Juan Pablo Melchiori, Ricardo Jose Molino, Josefina Orcellet, Juan Manuel Pagani, Agustín Pautasso, Juan Manuel Reussi Calvo, Nahuel Ignacio Redel, Matías Rillo, Sergio Nestor Rimski-Korsakov, Helena Sainz Rozas, Hernan Rene Saks, Matías Telleria, María Guadalupe Ventimiglia, Luis Alberto Zorzín, Jose L. Zubillaga, Ma. Mercedes Salvagiotti, Fernando Attainable yield and soil texture as drivers of maize response to nitrogen : A synthesis analysis for Argentina |
| title | Attainable yield and soil texture as drivers of maize response to nitrogen : A synthesis analysis for Argentina |
| title_full | Attainable yield and soil texture as drivers of maize response to nitrogen : A synthesis analysis for Argentina |
| title_fullStr | Attainable yield and soil texture as drivers of maize response to nitrogen : A synthesis analysis for Argentina |
| title_full_unstemmed | Attainable yield and soil texture as drivers of maize response to nitrogen : A synthesis analysis for Argentina |
| title_short | Attainable yield and soil texture as drivers of maize response to nitrogen : A synthesis analysis for Argentina |
| title_sort | attainable yield and soil texture as drivers of maize response to nitrogen a synthesis analysis for argentina |
| topic | Rendimiento de Cultivos Maíz Textura del Suelo Fertilidad del Suelo Eficiencia en el Uso de los Nutrientes Nitrógeno Abonos Nitrogenados Crop Yield Maize Soil Texture Soil Fertility Nutrient Use Efficiency Nitrogen Nitrogen Fertilizers |
| url | http://hdl.handle.net/20.500.12123/10372 https://www.sciencedirect.com/science/article/abs/pii/S0378429021002458 https://doi.org/10.1016/j.fcr.2021.108299 |
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