Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato
Canopy temperature (CT) as a surrogate of stomatal conductance has been highlighted as an essential physiological indicator for optimizing irrigation timing in potatoes. However, assessing how this trait could help improve yield prediction will help develop future decision support tools. In this stu...
| Main Authors: | , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | Inglés |
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MDPI
2023
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| Online Access: | https://hdl.handle.net/20.500.12955/2246 https://doi.org/10.3390/agronomy11071436 |
| _version_ | 1855028642157953024 |
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| author | Ninanya, Johan Ramírez, David A. Rinza, Javier Silva Díaz, Cecilia Cervantes, Marcelo García, Jerónimo Quiroz, Roberto |
| author_browse | Cervantes, Marcelo García, Jerónimo Ninanya, Johan Quiroz, Roberto Ramírez, David A. Rinza, Javier Silva Díaz, Cecilia |
| author_facet | Ninanya, Johan Ramírez, David A. Rinza, Javier Silva Díaz, Cecilia Cervantes, Marcelo García, Jerónimo Quiroz, Roberto |
| author_sort | Ninanya, Johan |
| collection | Repositorio INIA |
| description | Canopy temperature (CT) as a surrogate of stomatal conductance has been highlighted as an essential physiological indicator for optimizing irrigation timing in potatoes. However, assessing how this trait could help improve yield prediction will help develop future decision support tools. In this study, the incorporation of CT minus air temperature (dT) in a simple ecophysiological model was analyzed in three trials between 2017 and 2018, testing three water treatments under drip (DI) and furrow (FI) irrigations. Water treatments consisted of control (irrigated until field capacity) and two-timing irrigation based on physiological thresholds (CT and stomatal conductance). Two model perspectives were implemented based on soil water balance (P1) and using dT as the penalizing factor (P2), affecting the biomass dynamics and radiation use efficiency parameters. One of the trials was used for model calibration and the other two for validation. Statistical indicators of the model performance determined a better yield prediction at harvest for P2, especially under maximum stress conditions. The P1 and P2 perspectives showed their highest coefficient of determination (R2) and lowest root-mean-squared error (RMSE) under DI and FI, respectively. In the future, the incorporation of CT combining low-cost infrared devices/sensors with spatial crop models, satellite image information, and telemetry technologies, an adequate decision support system could be implemented for water requirement determination and yield prediction in potatoes. |
| format | info:eu-repo/semantics/article |
| id | INIA2246 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA22462025-05-26T00:57:45Z Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato Ninanya, Johan Ramírez, David A. Rinza, Javier Silva Díaz, Cecilia Cervantes, Marcelo García, Jerónimo Quiroz, Roberto Canopy temperature Crop modeling Irrigation management Model improvement https://purl.org/pe-repo/ocde/ford#4.01.06 Canopy temperature depression Depresión de la temperatura del dosel Crop modelling Modelización de los cultivos Irrigation management Gestión del riego Potatoes Papa Canopy temperature (CT) as a surrogate of stomatal conductance has been highlighted as an essential physiological indicator for optimizing irrigation timing in potatoes. However, assessing how this trait could help improve yield prediction will help develop future decision support tools. In this study, the incorporation of CT minus air temperature (dT) in a simple ecophysiological model was analyzed in three trials between 2017 and 2018, testing three water treatments under drip (DI) and furrow (FI) irrigations. Water treatments consisted of control (irrigated until field capacity) and two-timing irrigation based on physiological thresholds (CT and stomatal conductance). Two model perspectives were implemented based on soil water balance (P1) and using dT as the penalizing factor (P2), affecting the biomass dynamics and radiation use efficiency parameters. One of the trials was used for model calibration and the other two for validation. Statistical indicators of the model performance determined a better yield prediction at harvest for P2, especially under maximum stress conditions. The P1 and P2 perspectives showed their highest coefficient of determination (R2) and lowest root-mean-squared error (RMSE) under DI and FI, respectively. In the future, the incorporation of CT combining low-cost infrared devices/sensors with spatial crop models, satellite image information, and telemetry technologies, an adequate decision support system could be implemented for water requirement determination and yield prediction in potatoes. This research received financial support from “Programa Nacional de Innovación Agraria” (PNIA), with Project No. 016-2015-INIA-PNIA/UPMSI/IE “Uso efectivo del agua en el cultivo de papa en zonas áridas: Mejorando el manejo del riego mediante el monitoreo del estatus hídrico para enfrentar al Cambio Climático”. This research was undertaken as a part of, and funded by, the CGIAR Research Program on Roots, Tubers and Bananas (RTB) and supported by CGIAR Fund Donors. We thank all donors who supported this research through their contributions to the CGIAR Fund: http://www.cgiar.org/about-us/our-funders/ accessed on 1 June 2021. 2023-08-11T14:39:35Z 2023-08-11T14:39:35Z 2021-07-20 info:eu-repo/semantics/article Ninanya, J.; Ramírez, D. A.; Rinza, J.; Silva-Díaz, C.; Cervantes, M.; García, J.; & Quiroz, R. (2021). Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato. Agronomy, 11(7), 1436. doi: 10.3390/agronomy11071436 2073-4395 https://hdl.handle.net/20.500.12955/2246 https://doi.org/10.3390/agronomy11071436 eng urn:issn:2073-4395 Agronomy info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf MDPI CH Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | Canopy temperature Crop modeling Irrigation management Model improvement https://purl.org/pe-repo/ocde/ford#4.01.06 Canopy temperature depression Depresión de la temperatura del dosel Crop modelling Modelización de los cultivos Irrigation management Gestión del riego Potatoes Papa Ninanya, Johan Ramírez, David A. Rinza, Javier Silva Díaz, Cecilia Cervantes, Marcelo García, Jerónimo Quiroz, Roberto Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato |
| title | Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato |
| title_full | Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato |
| title_fullStr | Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato |
| title_full_unstemmed | Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato |
| title_short | Canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato |
| title_sort | canopy temperature as a key physiological trait to improve yield prediction under water restrictions in potato |
| topic | Canopy temperature Crop modeling Irrigation management Model improvement https://purl.org/pe-repo/ocde/ford#4.01.06 Canopy temperature depression Depresión de la temperatura del dosel Crop modelling Modelización de los cultivos Irrigation management Gestión del riego Potatoes Papa |
| url | https://hdl.handle.net/20.500.12955/2246 https://doi.org/10.3390/agronomy11071436 |
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