Revisiting the relationship between nitrogen nutrition index and yield across major species
- Autores
- Rodriguez, Ignacio; Lacasa, Josefina; van Versendaal, Emmanuela; Lemaire, Gilles; Belanger, Gilles; Jégo, Guillaume; Sandaña, Patricio; Soratto, Rogério; Djalovic, Ivica; Ata-Ul-Karim, Syed Tahir; Reussi Calvo, Nahuel Ignacio; Giletto, Claudia Marcela; Zhao, Ben; Ciampitti, Ignacio
- Año de publicación
- 2024
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Crop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested for several crop species, but a cross-species comparison of that relationship has not been performed yet. This study aimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on the yield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently published global dataset to parametrize critical dilution curves, we calculated integrated NNI, instantaneous NNI, relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. We obtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observations to fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat). Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of the relationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesis demonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This finding contributed to improving the understanding of the association between relative yield and NNI with implications for breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is a contribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparative approach.
EEA Balcarce
Fil: Rodriguez, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos.
Fil: Rodriguez, Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.
Fil: Rodriguez, Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Lacasa, Josefina. Kansas State University. Department of Agronomy; Estados Unidos.
Fil: van Versendaal, Emmanuela. Kansas State University. Department of Agronomy; Estados Unidos.
Fil: Lemaire, Gilles. Institut National de la Recherche Agronomique; Francia.
Fil: Belanger, Gilles. Agriculture and Agri-Food Canada, Quebec Research and Development Centre; Canadá.
Fil: Jégo, Guillaume. Agriculture and Agri-Food Canada, Quebec Research and Development Centre; Canadá.
Fil: Sandaña, Patricio. Universidad Austral de Chile. Institute of Plant Production and Protection; Chile.
Fil: Soratto, Rogério. São Paulo State University. College of Agricultural Sciences; Brasil.
Fil: Djalovic, Ivica. National Institute of the Republic of Serbia. Institute of Field and Vegetable Crops; Serbia.
Fil: Ata-Ul-Karim, Syed Tahir. The Pennsylvania State University. Department of Plant Science; Estados Unidos.
Fil: Reussi Calvo, Nahuel Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.
Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Giletto, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.
Fil: Giletto, Claudia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.
Fil: Zhao, Ben. Chinese Academy of Agricultural Sciences. Ministry of Agriculture, Farmland Irrigation Research Institute; China
Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos. - Fuente
- European Journal of Agronomy 154 : 127079 (March 2024)
- Materia
-
Nitrógeno
Rendimiento de Cultivos
Aplicación de Abonos
Nutrición de las Plantas
Nitrogen
Crop Yield
Fertilizer Application
Plant Nutrition - Nivel de accesibilidad
- acceso restringido
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/17009
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Revisiting the relationship between nitrogen nutrition index and yield across major speciesRodriguez, IgnacioLacasa, Josefinavan Versendaal, EmmanuelaLemaire, GillesBelanger, GillesJégo, GuillaumeSandaña, PatricioSoratto, RogérioDjalovic, IvicaAta-Ul-Karim, Syed TahirReussi Calvo, Nahuel IgnacioGiletto, Claudia MarcelaZhao, BenCiampitti, IgnacioNitrógenoRendimiento de CultivosAplicación de AbonosNutrición de las PlantasNitrogenCrop YieldFertilizer ApplicationPlant NutritionCrop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested for several crop species, but a cross-species comparison of that relationship has not been performed yet. This study aimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on the yield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently published global dataset to parametrize critical dilution curves, we calculated integrated NNI, instantaneous NNI, relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. We obtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observations to fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat). Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of the relationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesis demonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This finding contributed to improving the understanding of the association between relative yield and NNI with implications for breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is a contribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparative approach.EEA BalcarceFil: Rodriguez, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos.Fil: Rodriguez, Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Rodriguez, Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Lacasa, Josefina. Kansas State University. Department of Agronomy; Estados Unidos.Fil: van Versendaal, Emmanuela. Kansas State University. Department of Agronomy; Estados Unidos.Fil: Lemaire, Gilles. Institut National de la Recherche Agronomique; Francia.Fil: Belanger, Gilles. Agriculture and Agri-Food Canada, Quebec Research and Development Centre; Canadá.Fil: Jégo, Guillaume. Agriculture and Agri-Food Canada, Quebec Research and Development Centre; Canadá.Fil: Sandaña, Patricio. Universidad Austral de Chile. Institute of Plant Production and Protection; Chile.Fil: Soratto, Rogério. São Paulo State University. College of Agricultural Sciences; Brasil.Fil: Djalovic, Ivica. National Institute of the Republic of Serbia. Institute of Field and Vegetable Crops; Serbia.Fil: Ata-Ul-Karim, Syed Tahir. The Pennsylvania State University. Department of Plant Science; Estados Unidos.Fil: Reussi Calvo, Nahuel Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Giletto, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Giletto, Claudia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Zhao, Ben. Chinese Academy of Agricultural Sciences. Ministry of Agriculture, Farmland Irrigation Research Institute; ChinaFil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos.Elsevier2024-03-12T18:04:53Z2024-03-12T18:04:53Z2024-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/17009https://www.sciencedirect.com/science/article/pii/S11610301230034771161-0301 (Print)1873-7331 (Online)https://doi.org/10.1016/j.eja.2023.127079European Journal of Agronomy 154 : 127079 (March 2024)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-16T09:31:32Zoai:localhost:20.500.12123/17009instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:31:32.327INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse |
| dc.title.none.fl_str_mv |
Revisiting the relationship between nitrogen nutrition index and yield across major species |
| title |
Revisiting the relationship between nitrogen nutrition index and yield across major species |
| spellingShingle |
Revisiting the relationship between nitrogen nutrition index and yield across major species Rodriguez, Ignacio Nitrógeno Rendimiento de Cultivos Aplicación de Abonos Nutrición de las Plantas Nitrogen Crop Yield Fertilizer Application Plant Nutrition |
| title_short |
Revisiting the relationship between nitrogen nutrition index and yield across major species |
| title_full |
Revisiting the relationship between nitrogen nutrition index and yield across major species |
| title_fullStr |
Revisiting the relationship between nitrogen nutrition index and yield across major species |
| title_full_unstemmed |
Revisiting the relationship between nitrogen nutrition index and yield across major species |
| title_sort |
Revisiting the relationship between nitrogen nutrition index and yield across major species |
| dc.creator.none.fl_str_mv |
Rodriguez, Ignacio Lacasa, Josefina van Versendaal, Emmanuela Lemaire, Gilles Belanger, Gilles Jégo, Guillaume Sandaña, Patricio Soratto, Rogério Djalovic, Ivica Ata-Ul-Karim, Syed Tahir Reussi Calvo, Nahuel Ignacio Giletto, Claudia Marcela Zhao, Ben Ciampitti, Ignacio |
| author |
Rodriguez, Ignacio |
| author_facet |
Rodriguez, Ignacio Lacasa, Josefina van Versendaal, Emmanuela Lemaire, Gilles Belanger, Gilles Jégo, Guillaume Sandaña, Patricio Soratto, Rogério Djalovic, Ivica Ata-Ul-Karim, Syed Tahir Reussi Calvo, Nahuel Ignacio Giletto, Claudia Marcela Zhao, Ben Ciampitti, Ignacio |
| author_role |
author |
| author2 |
Lacasa, Josefina van Versendaal, Emmanuela Lemaire, Gilles Belanger, Gilles Jégo, Guillaume Sandaña, Patricio Soratto, Rogério Djalovic, Ivica Ata-Ul-Karim, Syed Tahir Reussi Calvo, Nahuel Ignacio Giletto, Claudia Marcela Zhao, Ben Ciampitti, Ignacio |
| author2_role |
author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Nitrógeno Rendimiento de Cultivos Aplicación de Abonos Nutrición de las Plantas Nitrogen Crop Yield Fertilizer Application Plant Nutrition |
| topic |
Nitrógeno Rendimiento de Cultivos Aplicación de Abonos Nutrición de las Plantas Nitrogen Crop Yield Fertilizer Application Plant Nutrition |
| dc.description.none.fl_txt_mv |
Crop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested for several crop species, but a cross-species comparison of that relationship has not been performed yet. This study aimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on the yield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently published global dataset to parametrize critical dilution curves, we calculated integrated NNI, instantaneous NNI, relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. We obtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observations to fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat). Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of the relationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesis demonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This finding contributed to improving the understanding of the association between relative yield and NNI with implications for breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is a contribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparative approach. EEA Balcarce Fil: Rodriguez, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos. Fil: Rodriguez, Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Fil: Rodriguez, Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Fil: Lacasa, Josefina. Kansas State University. Department of Agronomy; Estados Unidos. Fil: van Versendaal, Emmanuela. Kansas State University. Department of Agronomy; Estados Unidos. Fil: Lemaire, Gilles. Institut National de la Recherche Agronomique; Francia. Fil: Belanger, Gilles. Agriculture and Agri-Food Canada, Quebec Research and Development Centre; Canadá. Fil: Jégo, Guillaume. Agriculture and Agri-Food Canada, Quebec Research and Development Centre; Canadá. Fil: Sandaña, Patricio. Universidad Austral de Chile. Institute of Plant Production and Protection; Chile. Fil: Soratto, Rogério. São Paulo State University. College of Agricultural Sciences; Brasil. Fil: Djalovic, Ivica. National Institute of the Republic of Serbia. Institute of Field and Vegetable Crops; Serbia. Fil: Ata-Ul-Karim, Syed Tahir. The Pennsylvania State University. Department of Plant Science; Estados Unidos. Fil: Reussi Calvo, Nahuel Ignacio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Fil: Giletto, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Fil: Giletto, Claudia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Fil: Zhao, Ben. Chinese Academy of Agricultural Sciences. Ministry of Agriculture, Farmland Irrigation Research Institute; China Fil: Ciampitti, Ignacio. Kansas State University. Department of Agronomy; Estados Unidos. |
| description |
Crop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested for several crop species, but a cross-species comparison of that relationship has not been performed yet. This study aimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on the yield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently published global dataset to parametrize critical dilution curves, we calculated integrated NNI, instantaneous NNI, relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. We obtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observations to fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat). Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of the relationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesis demonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This finding contributed to improving the understanding of the association between relative yield and NNI with implications for breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is a contribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparative approach. |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2024-03-12T18:04:53Z 2024-03-12T18:04:53Z 2024-03 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
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publishedVersion |
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http://hdl.handle.net/20.500.12123/17009 https://www.sciencedirect.com/science/article/pii/S1161030123003477 1161-0301 (Print) 1873-7331 (Online) https://doi.org/10.1016/j.eja.2023.127079 |
| url |
http://hdl.handle.net/20.500.12123/17009 https://www.sciencedirect.com/science/article/pii/S1161030123003477 https://doi.org/10.1016/j.eja.2023.127079 |
| identifier_str_mv |
1161-0301 (Print) 1873-7331 (Online) |
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eng |
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eng |
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info:eu-repo/semantics/restrictedAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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restrictedAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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application/pdf |
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Elsevier |
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Elsevier |
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European Journal of Agronomy 154 : 127079 (March 2024) reponame:INTA Digital (INTA) instname:Instituto Nacional de Tecnología Agropecuaria |
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INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria |
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tripaldi.nicolas@inta.gob.ar |
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