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Abstract

Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.

Item Type:	Article
Authors/Creators:	Chen, Y and Guerschman, J and Shendryk, Y and Henry, D and Harrison, MT
Keywords:	remote sensing, deep learning, digital agriculture, dairy farming, grazing, grassland 28 biomass, satellite imagery, Sentinel-2, Planet Labs, pasture, native grass, Themeda triandra, phalaris, cocksfoot, wool, stocking rate, pasture variability
Journal or Publication Title:	Remote Sensing
Publisher:	Molecular Diversity Preservation International
ISSN:	2072-4292
DOI / ID Number:	10.3390/rs13040603
Copyright Information:	Copyright 2021 Molecular Diversity Preservation International
Related URLs:	Google Scholar: Harrison, MT UTAS Profile: Harrison, MT
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