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
- Institución
- Instituto Nacional de Tecnología Agropecuaria
- OAI Identificador
- oai:localhost:20.500.12123/17645
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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)2025-09-04T09:50:21Zoai: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:l2025-09-04 09:50:22.084INTA 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) |
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INTA Digital (INTA) |
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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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