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Multiple critical N dilution curves [CNDCs] have been previously developed for potato; however, attempts to directly compare differences in CNDCs across genotype [G], environment [E], and management [M] interactions have been confounded by non-uniform statistical methods, biased experimental data, and lack of proper quantification of uncertainty in the critical N concentration [%Nc]. This study implements a partially-pooled Bayesian hierarchical method to
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dc.contributor.author | Bohman, Brian J. | |
dc.contributor.author | Culshaw-Maurer, Michael | |
dc.contributor.author | Abdallah, Feriel Ben | |
dc.contributor.author | Giletto, Claudia | |
dc.contributor.author | Bélanger, Gilles | |
dc.contributor.author | Fernández, Fabián G. | |
dc.contributor.author | Miao, Yuxin | |
dc.contributor.author | Mulla, David J. | |
dc.contributor.author | Rosen, Carl J. | |
dc.date.accessioned | 2023-03-30T12:01:17Z | |
dc.date.available | 2023-03-30T12:01:17Z | |
dc.date.issued | 2023-03 | |
dc.identifier.issn | 1161-0301(print) | |
dc.identifier.issn | 1873-7331(online) | |
dc.identifier.other | https://doi.org/10.1016/j.eja.2023.126744 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12123/14366 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1161030123000126 | |
dc.description.abstract | Multiple critical N dilution curves [CNDCs] have been previously developed for potato; however, attempts to directly compare differences in CNDCs across genotype [G], environment [E], and management [M] interactions have been confounded by non-uniform statistical methods, biased experimental data, and lack of proper quantification of uncertainty in the critical N concentration [%Nc]. This study implements a partially-pooled Bayesian hierarchical method to develop CNDCs for previously published and newly reported experimental data, systematically evaluates the difference in %Nc [∆%Nc] across G × E × M effects, and directly compare CNDCs from the Bayesian framework to CNDCs from conventional statistical methods. The partially-pooled Bayesian hierarchical method implemented in this study has the advantage of being less susceptible to inferential bias at the level of individual G × E × M interactions compared to alternative statistical methods that result from insufficient quantity and quality of experimental datasets (e.g., unbalanced distribution of N limiting and non-N limiting observations). This method also allows for a direct statistical comparison of differences in %Nc across levels of the G × E × M interactions. Where found to be significant, ∆%Nc was hypothesized to be related to variation in the timing of tuber initiation (e.g., maturity class) and the relative rate of tuber bulking (e.g., planting density) across G x E × M interactions. In addition to using the median value for %Nc (i.e., CNDC), the lower and upper boundary values for the credible region (i.e., CNDClo and CNDCup) derived using the Bayesian framework should be used in calculation of N nutrition index (and other calculations) to account for uncertainty in %Nc. Overall, this study provides additional evidence that%Nc is dependent upon G × E × M interactions; therefore, evaluation of crop N status or N use efficiency must account for variation in %Nc across G × E × M interactions. | eng |
dc.format | application/pdf | es_AR |
dc.language.iso | eng | es_AR |
dc.publisher | Elsevier | es_AR |
dc.rights | info:eu-repo/semantics/restrictedAccess | es_AR |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | es_AR |
dc.source | European Journal of Agronomy 144 : 126744 (March 2023) | es_AR |
dc.subject | Nitrógeno | es_AR |
dc.subject | Nitrogen | eng |
dc.subject | Papa | es_AR |
dc.subject | Potatoes | eng |
dc.subject | Eficiencia en el Uso de Nutrientes | es_AR |
dc.subject | Nutrient Use Efficiency | eng |
dc.subject | Concentración | es_AR |
dc.subject | Concentrating | eng |
dc.subject | Métodos Estadísticos | es_AR |
dc.subject | Statistical Methods | eng |
dc.title | Quantifying critical N dilution curves across G × E × M effects for potato using a partially-pooled Bayesian hierarchical method | es_AR |
dc.type | info:ar-repo/semantics/artículo | es_AR |
dc.type | info:eu-repo/semantics/article | es_AR |
dc.type | info:eu-repo/semantics/publishedVersion | es_AR |
dc.rights.license | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | es_AR |
dc.description.origen | EEA Balcarce | es_AR |
dc.description.fil | Fil: Bohman, Brian J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos. | es_AR |
dc.description.fil | Fil: Culshaw-Maurer, Michael J. University of Arizona. CyVerse; Estados Unidos. | es_AR |
dc.description.fil | Fil: Abdallah, Feriel Ben. Walloon Agricultural Research Centre. Productions in Agriculture Department, Crop Production Unit, Bélgica. | es_AR |
dc.description.fil | Fil: Giletto, Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Unidad Integrada Balcarce. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. | es_AR |
dc.description.fil | Fil: Bélanger, Gilles. Science and Technology Branch, Agriculture and Agri-Food Canada; Canadá. | es_AR |
dc.description.fil | Fil: Fernández, Fabián G. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos. | es_AR |
dc.description.fil | Fil: Miao, Yuxin. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos. | es_AR |
dc.description.fil | Fil: Mulla, David J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos. | es_AR |
dc.description.fil | Fil: Rosen, Carl J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos. | es_AR |
dc.subtype | cientifico |
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