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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 [ver mas...]
dc.contributor.authorGargiulo, Juan
dc.contributor.authorLyons, Nicolas
dc.contributor.authorMasia, Fernando
dc.contributor.authorBeale, Peter
dc.contributor.authorInsua, Juan Ramón
dc.contributor.authorCorrea Luna, Martín
dc.contributor.authorGarcia, Sergio
dc.date.accessioned2024-05-07T10:29:02Z
dc.date.available2024-05-07T10:29:02Z
dc.date.issued2023-05-25
dc.identifier.issn2072-4292
dc.identifier.otherhttps://doi.org/10.3390/rs15112752
dc.identifier.urihttp://hdl.handle.net/20.500.12123/17645
dc.identifier.urihttps://www.mdpi.com/2072-4292/15/11/2752
dc.description.abstractSystematic 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.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherMultidisciplinary Digital Publishing Institute, MDPIes_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/es_AR
dc.sourceRemote Sensing 15 (11) : 2752. (May 2023)es_AR
dc.subjectAutomatizaciónes_AR
dc.subjectAutomationeng
dc.subjectProductividades_AR
dc.subjectProductivityeng
dc.subjectCalibraciónes_AR
dc.subjectCalibrationeng
dc.subjectAustraliaes_AR
dc.subjectTeledetección
dc.subjectRemote Sensingeng
dc.titleComparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farmses_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)es_AR
dc.description.origenEEA Balcarcees_AR
dc.description.filFil: Gargiulo, Juan. NSW Department of Primary Industries; Australiaes_AR
dc.description.filFil: Gargiulo, Juan. University Of Sidney. Faculty of Science; Australiaes_AR
dc.description.filFil: Lyons, Nicolas. NSW Department of Primary Industries; Australiaes_AR
dc.description.filFil: Masia, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; Argentinaes_AR
dc.description.filFil: Masia, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: Beale, Peter. Local Land Services Hunter; Australiaes_AR
dc.description.filFil: 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; Argentinaes_AR
dc.description.filFil: Insua, Juan Ramón. Universidad Nacional de Mar del Plata. Facultad de ciencias Agrarias; Argentinaes_AR
dc.description.filFil: Correa Luna, Martín. University Of Sidney. Faculty of Science; Australiaes_AR
dc.description.filFil: Garcia, Sergio. University Of Sidney. Faculty of Science; Australiaes_AR
dc.subtypecientifico


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