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dc.contributor.author
Insua, Juan Ramón
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dc.contributor.author
Utsumi, Santiago A.
dc.contributor.author
Basso, Bruno
dc.date.available
2022-11-18T19:40:11Z
dc.date.issued
2019-03
dc.identifier.citation
Insua, Juan Ramón; Utsumi, Santiago A.; Basso, Bruno; Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models; Public Library of Science; Plos One; 14; 3; 3-2019; 1-21
dc.identifier.issn
1932-6203
dc.identifier.uri
http://hdl.handle.net/11336/178396
dc.description.abstract
Systematic monitoring of pasture quantity and quality is important to match the herd forage demand (pasture removal by grazing or harvest) to the supply of forage with adequate nutritive value. The aim of this research was to monitor, assess and manage changes in pasture growth, morphology and digestibility by integrating information from an Unmanned Aerial Vehicle (UAV) and two process-based models. The first model, Systems Approach to Land Use Sustainability (SALUS), is a process-based crop growth model used to predict pasture regrowth based on soil, climate, and management data. The second model, Morphogenetic and Digestibility of Pasture (MDP), uses paddock-scale values of herbage mass as input to predict leaf morphogenesis and forage nutritive value. Two field experiments were carried out on tall fescue- and ryegrass-based pastures under rotational grazing with lactating dairy cattle. The first experiment was conducted at plot scale and was used to calibrate the UAV and to test models. The second experiment was conducted at field scale and was used to test the UAV's ability to predict pasture biomass under grazing rotation. The Normalized Difference Vegetation Index (NDVI) calculated from the UAV's multispectral reflectance (n = 72) was strongly correlated (p < 0.001) to plot measurements of pasture biomass (R2 = 0.80) within the range of ~226 and 4208 kg DM ha-1. Moreover, there was no difference (root mean square error, RMSE < 500 kg DM ha-1) between biomass estimations by the UAV (1971±350 kg ha-1) and two conventional methods used as control, the C-Dax proximal sensor (2073±636 kg ha-1) and ruler (2017±530 kg ha-1). The UAV approach was capable of mapping at high resolution (6 cm) the spatial variability of pasture (16 ha). The integrated UAV-modeling approach properly predicted spatial and temporal changes in pasture biomass (RMSE = 509 kg DM ha-1, CCC = 0.94), leaf length (RMSE = 6.2 cm, CCC = 0.62), leaf stage (RMSE = 0.7 leaves, CCC = 0.65), neutral detergent fiber (RMSE = 3%, CCC = 0.71), digestibility of neutral detergent fiber (RMSE = 8%, CCC = 0.92) and digestibility of dry matter (RMSE = 5%, CCC = 0.93) with reasonable precision and accuracy. These findings therefore suggest potential for the present UAV-modeling approach for use as decision support tool to allocate animals based on spatially and temporally explicit predictions of pasture biomass and nutritive value.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Public Library of Science
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
DRONE
dc.subject
CROP MODELLING
dc.subject
PASTURE GROWTH
dc.subject
FORAGE DIGESTIBILITY
dc.subject.classification
Producción Animal y Lechería
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dc.subject.classification
Producción Animal y Lechería
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dc.subject.classification
CIENCIAS AGRÍCOLAS
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dc.title
Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-11-17T17:36:16Z
dc.journal.volume
14
dc.journal.number
3
dc.journal.pagination
1-21
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
San Francisco
dc.description.fil
Fil: Insua, Juan Ramón. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina
dc.description.fil
Fil: Utsumi, Santiago A.. Michigan State University; Estados Unidos
dc.description.fil
Fil: Basso, Bruno. Michigan State University; Estados Unidos
dc.journal.title
Plos One
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212773
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1371/journal.pone.0212773
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