Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms

Autores
Gargiulo, Juan; Lyons, Nicolas; Masia, Fernando; Beale, Peter; Insua, Juan Ramón; Correa Luna, Martín; Garcia, Sergio
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.
EEA Balcarce
Fil: Gargiulo, Juan. NSW Department of Primary Industries; Australia
Fil: Gargiulo, Juan. University Of Sidney. Faculty of Science; Australia
Fil: Lyons, Nicolas. NSW Department of Primary Industries; Australia
Fil: Masia, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Fil: Masia, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Beale, Peter. Local Land Services Hunter; Australia
Fil: Insua, Juan Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina
Fil: Insua, Juan Ramón. Universidad Nacional de Mar del Plata. Facultad de ciencias Agrarias; Argentina
Fil: Correa Luna, Martín. University Of Sidney. Faculty of Science; Australia
Fil: Garcia, Sergio. University Of Sidney. Faculty of Science; Australia
Fuente
Remote Sensing 15 (11) : 2752. (May 2023)
Materia
Automatización
Productividad
Calibración
Australia
Automation
Productivity
Calibration
Remote Sensing
Teledetección
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/17645
Acceder
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oai_identifier_str oai:localhost:20.500.12123/17645
network_acronym_str INTADig
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network_name_str INTA Digital (INTA)
spelling Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy FarmsGargiulo, JuanLyons, NicolasMasia, FernandoBeale, PeterInsua, Juan RamónCorrea Luna, MartínGarcia, SergioAutomatizaciónProductividadCalibraciónAustraliaAutomationProductivityCalibrationRemote SensingTeledetecciónSystematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.EEA BalcarceFil: Gargiulo, Juan. NSW Department of Primary Industries; AustraliaFil: Gargiulo, Juan. University Of Sidney. Faculty of Science; AustraliaFil: Lyons, Nicolas. NSW Department of Primary Industries; AustraliaFil: Masia, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Masia, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Beale, Peter. Local Land Services Hunter; AustraliaFil: Insua, Juan Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; ArgentinaFil: Insua, Juan Ramón. Universidad Nacional de Mar del Plata. Facultad de ciencias Agrarias; ArgentinaFil: Correa Luna, Martín. University Of Sidney. Faculty of Science; AustraliaFil: Garcia, Sergio. University Of Sidney. Faculty of Science; AustraliaMultidisciplinary Digital Publishing Institute, MDPI2024-05-07T10:29:02Z2024-05-07T10:29:02Z2023-05-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/17645https://www.mdpi.com/2072-4292/15/11/27522072-4292https://doi.org/10.3390/rs15112752Remote Sensing 15 (11) : 2752. (May 2023)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:46:30Zoai:localhost:20.500.12123/17645instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:46:31.321INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
title Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
spellingShingle Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
Gargiulo, Juan
Automatización
Productividad
Calibración
Australia
Automation
Productivity
Calibration
Remote Sensing
Teledetección
title_short Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
title_full Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
title_fullStr Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
title_full_unstemmed Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
title_sort Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms
dc.creator.none.fl_str_mv Gargiulo, Juan
Lyons, Nicolas
Masia, Fernando
Beale, Peter
Insua, Juan Ramón
Correa Luna, Martín
Garcia, Sergio
author Gargiulo, Juan
author_facet Gargiulo, Juan
Lyons, Nicolas
Masia, Fernando
Beale, Peter
Insua, Juan Ramón
Correa Luna, Martín
Garcia, Sergio
author_role author
author2 Lyons, Nicolas
Masia, Fernando
Beale, Peter
Insua, Juan Ramón
Correa Luna, Martín
Garcia, Sergio
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Automatización
Productividad
Calibración
Australia
Automation
Productivity
Calibration
Remote Sensing
Teledetección
topic Automatización
Productividad
Calibración
Australia
Automation
Productivity
Calibration
Remote Sensing
Teledetección
dc.description.none.fl_txt_mv Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.
EEA Balcarce
Fil: Gargiulo, Juan. NSW Department of Primary Industries; Australia
Fil: Gargiulo, Juan. University Of Sidney. Faculty of Science; Australia
Fil: Lyons, Nicolas. NSW Department of Primary Industries; Australia
Fil: Masia, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentina
Fil: Masia, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Beale, Peter. Local Land Services Hunter; Australia
Fil: Insua, Juan Ramón. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible; Argentina
Fil: Insua, Juan Ramón. Universidad Nacional de Mar del Plata. Facultad de ciencias Agrarias; Argentina
Fil: Correa Luna, Martín. University Of Sidney. Faculty of Science; Australia
Fil: Garcia, Sergio. University Of Sidney. Faculty of Science; Australia
description Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.
publishDate 2023
dc.date.none.fl_str_mv 2023-05-25
2024-05-07T10:29:02Z
2024-05-07T10:29:02Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/17645
https://www.mdpi.com/2072-4292/15/11/2752
2072-4292
https://doi.org/10.3390/rs15112752
url http://hdl.handle.net/20.500.12123/17645
https://www.mdpi.com/2072-4292/15/11/2752
https://doi.org/10.3390/rs15112752
identifier_str_mv 2072-4292
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute, MDPI
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute, MDPI
dc.source.none.fl_str_mv Remote Sensing 15 (11) : 2752. (May 2023)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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