{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:21:54Z","timestamp":1775229714987,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government\u2019s Future Drought Fund and the University of Tasmania","award":["4-G37I1PA"],"award-info":[{"award-number":["4-G37I1PA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Effective agricultural management hinges upon timely decision-making. Here, we evaluated whether drone and satellite imagery could improve real-time and remote monitoring of pasture management. Using unmanned aerial systems (UAS), we quantified grassland biomass through changes in sward height pre- and post-grazing by sheep. As optical spectral data from Sentinel-2 satellite imagery is often hindered by cloud contamination, we assessed whether machine learning could help improve the accuracy of pasture biomass prognostics. The calibration of UAS biomass using field measurements from sward height change through 3D photogrammetry resulted in an improved regression (R2 = 0.75, RMSE = 1240 kg DM\/ha, and MAE = 980 kg DM\/ha) compared with using the same field measurements with random forest-machine learning and Sentinel-2 imagery (R2 = 0.56, RMSE = 2140 kg DM\/ha, and MAE = 1585 kg DM\/ha). The standard error of the mean (SEM) for the field biomass, derived from UAS-measured sward height changes, was 1240 kg DM\/ha. When UAS data were integrated with the Sentinel-2-random forest model, SEM reduced from 1642 kg DM\/ha to 1473 kg DM\/ha, demonstrating that integration of UAS data improved model accuracy. We show that modelled biomass from 3D photogrammetry has significantly higher accuracy than that predicted from Sentinel-2 imagery with random forest modelling (S2-RF). Our study demonstrates that timely, accurate quantification of pasture biomass is conducive to improved decision-making agility, and that coupling of UAS with satellite imagery may improve the accuracy and timeliness of agricultural biomass prognostics.<\/jats:p>","DOI":"10.3390\/rs16244688","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:08:53Z","timestamp":1734343733000},"page":"4688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Integration of Drone and Satellite Imagery Improves Agricultural Management Agility"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1745-2700","authenticated-orcid":false,"given":"Michael Gbenga","family":"Ogungbuyi","sequence":"first","affiliation":[{"name":"Tasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS 7248, Australia"}]},{"given":"Caroline","family":"Mohammed","sequence":"additional","affiliation":[{"name":"Tasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS 7248, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-6428","authenticated-orcid":false,"given":"Andrew M.","family":"Fischer","sequence":"additional","affiliation":[{"name":"Institute for Marine and Antarctic Studies, University of Tasmania, Launceston, TAS 7248, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3029-6717","authenticated-orcid":false,"given":"Darren","family":"Turner","sequence":"additional","affiliation":[{"name":"School of Geography, Planning, and Spatial Sciences, College of Sciences and Engineering, University of Tasmania, Private Bag 78, Hobart, TAS 7001, Australia"}]},{"given":"Jason","family":"Whitehead","sequence":"additional","affiliation":[{"name":"Cape Herbert Pty Ltd., Blackstone Heights, TAS 7250, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7425-452X","authenticated-orcid":false,"given":"Matthew Tom","family":"Harrison","sequence":"additional","affiliation":[{"name":"Tasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS 7248, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1890\/1540-9295(2006)4[408:UDPAOI]2.0.CO;2","article-title":"Using Digital Photographs and Object-Based Image Analysis to Estimate Percent Ground Cover in Vegetation Plots","volume":"4","author":"Luscier","year":"2006","journal-title":"Front. Ecol. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ogungbuyi, M.G., Mohammed, C., Ara, I., Fischer, A.M., and Harrison, M.T. (2023). Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review. Remote Sens., 15.","DOI":"10.3390\/rs15194866"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5726","DOI":"10.1111\/gcb.15816","article-title":"Carbon Myopia: The Urgent Need for Integrated Social, Economic and Environmental Action in the Livestock Sector","volume":"27","author":"Harrison","year":"2021","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Meier, E.A., Thorburn, P.J., Bell, L.W., Harrison, M.T., and Biggs, J.S. (2020). Greenhouse Gas Emissions From Cropping and Grazed Pastures Are Similar: A Simulation Analysis in Australia. Front. Sustain. Food Syst., 3.","DOI":"10.3389\/fsufs.2019.00121"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.jenvman.2014.05.028","article-title":"Modeling Vegetation Heights from High Resolution Stereo Aerial Photography: An Application for Broad-Scale Rangeland Monitoring","volume":"144","author":"Gillan","year":"2014","journal-title":"J. Environ. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2016.05.019","article-title":"Ultra-Fine Grain Landscape-Scale Quantification of Dryland Vegetation Structure with Drone-Acquired Structure-from-Motion Photogrammetry","volume":"183","author":"Cunliffe","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1038\/s43016-021-00387-6","article-title":"Climate Change Benefits Negated by Extreme Heat","volume":"2","author":"Harrison","year":"2021","journal-title":"Nat. Food"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107510","DOI":"10.1016\/j.ecolecon.2022.107510","article-title":"Improving Acceptance of Natural Capital Accounting in Land Use Decision Making: Barriers and Opportunities","volume":"200","author":"Fleming","year":"2022","journal-title":"Ecol. Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1007\/s00484-021-02167-0","article-title":"Negative Relationship between Dry Matter Intake and the Temperature-Humidity Index with Increasing Heat Stress in Cattle: A Global Meta-Analysis","volume":"65","author":"Harrison","year":"2021","journal-title":"Int. J. Biometeorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.ocecoaman.2014.02.009","article-title":"Constructing an Assessment Indices System to Analyze Integrated Regional Carrying Capacity in the Coastal Zones\u2014A Case in Nantong","volume":"93","author":"Wei","year":"2014","journal-title":"Ocean Coast. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1071\/CPv66n4_FO","article-title":"Dual-Purpose Cropping\u2014Capitalising on Potential Grain Crop Grazing to Enhance Mixed-Farming Profitability","volume":"66","author":"Bell","year":"2015","journal-title":"Crop Pasture Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ogungbuyi, M.G., Guerschman, J.P., Fischer, A.M., Crabbe, R.A., Mohammed, C., Scarth, P., Tickle, P., Whitehead, J., and Harrison, M.T. (2023). Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning. Land, 12.","DOI":"10.3390\/land12061142"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shahpari, S., Allison, J., Harrison, M.T., and Stanley, R. (2021). An Integrated Economic, Environmental and Social Approach to Agricultural Land-Use Planning. Land, 10.","DOI":"10.3390\/land10040364"},{"key":"ref_14","unstructured":"Bai, Z.G., Dent, D.L., Olsson, L., and Schaepman, M.E. (2008). Global Assessment of Land Degradation and Improvement 1. Identification by Remote Sensing, ISRIC\u2013World Soil Information."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.rama.2019.02.009","article-title":"Estimating Forage Utilization with Drone-Based Photogrammetric Point Clouds","volume":"72","author":"Gillan","year":"2019","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s10661-020-8216-3","article-title":"Integrating Drone Imagery with Existing Rangeland Monitoring Programs","volume":"192","author":"Gillan","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, H., Dahlgren, R.A., Larsen, R.E., Devine, S.M., Roche, L.M., O\u2019 Geen, A.T., Wong, A.J.Y., Covello, S., and Jin, Y. (2019). Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (SUAS) and Planetscope Satellite. Remote Sens., 11.","DOI":"10.3390\/rs11050595"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105880","DOI":"10.1016\/j.compag.2020.105880","article-title":"Predicting Pasture Biomass Using a Statistical Model and Machine Learning Algorithm Implemented with Remotely Sensed Imagery","volume":"180","author":"Basso","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1080\/15481603.2016.1221576","article-title":"Comparing the Spectral Settings of the New Generation Broad and Narrow Band Sensors in Estimating Biomass of Native Grasses Grown under Different Management Practices","volume":"53","author":"Sibanda","year":"2016","journal-title":"GIScience Remote Sens."},{"key":"ref_21","first-page":"101986","article-title":"Estimating Aboveground Biomass of the Mangrove Forests on Northeast Hainan Island in China Using an Upscaling Method from Field Plots, UAV-LiDAR Data and Sentinel-2 Imagery","volume":"85","author":"Wang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8056","DOI":"10.3390\/rs6098056","article-title":"Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types","volume":"6","author":"Zlinszky","year":"2014","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4876","DOI":"10.1002\/ece3.6240","article-title":"Detecting Shrub Encroachment in Seminatural Grasslands Using UAS LiDAR","volume":"10","author":"Madsen","year":"2020","journal-title":"Ecol. Evol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jensen, J.L.R., and Mathews, A.J. (2016). Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem. Remote Sens., 8.","DOI":"10.3390\/rs8010050"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1111\/2041-210X.12919","article-title":"Unmanned Aerial Systems Measure Structural Habitat Features for Wildlife across Multiple Scales","volume":"9","author":"Olsoy","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Swetnam, T.L., Gillan, J.K., Sankey, T.T., McClaran, M.P., Nichols, M.H., Heilman, P., and McVay, J. (2018). Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States. Front. Plant Sci., 8.","DOI":"10.3389\/fpls.2017.02144"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"026032","DOI":"10.1117\/1.JRS.10.026032","article-title":"Monitoring Grazing Intensity: An Experiment with Canopy Spectra Applied to Satellite Remote Sensing","volume":"10","author":"Li","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","article-title":"Urban Land Cover Classification Using Airborne LiDAR Data: A Review","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101517","DOI":"10.1016\/j.jocs.2021.101517","article-title":"Above-Ground Biomass Estimation from LiDAR Data Using Random Forest Algorithms","volume":"58","author":"Bastarrika","year":"2022","journal-title":"J. Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2179942","DOI":"10.1080\/22797254.2023.2179942","article-title":"UAV DTM Acquisition in a Forested Area\u2014Comparison of Low-Cost Photogrammetry (DJI Zenmuse P1) and LiDAR Solutions (DJI Zenmuse L1)","volume":"56","author":"Urban","year":"2023","journal-title":"Eur. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1139\/dsa-2023-0086","article-title":"Optimising Camera and Flight Settings for Ultrafine Resolution Mapping of Artificial Night-Time Lights Using an Unoccupied Aerial System","volume":"12","author":"Bhattarai","year":"2024","journal-title":"Drone Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"114880","DOI":"10.1016\/j.anifeedsci.2021.114880","article-title":"Using Multispectral Data from an Unmanned Aerial System to Estimate Pasture Depletion during Grazing","volume":"275","author":"Thomson","year":"2021","journal-title":"Anim. Feed Sci. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"120564","DOI":"10.1016\/j.jenvman.2024.120564","article-title":"Improvement of Pasture Biomass Modelling Using High-Resolution Satellite Imagery and Machine Learning","volume":"356","author":"Ogungbuyi","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, Y., Guerschman, J., Shendryk, Y., Henry, D., and Harrison, M.T. (2021). Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040603"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"diaa013","DOI":"10.1093\/insilicoplants\/diaa013","article-title":"Modelling Seasonal Pasture Growth and Botanical Composition at the Paddock Scale with Satellite Imagery","volume":"3","author":"Ara","year":"2021","journal-title":"In Silico Plants"},{"key":"ref_36","unstructured":"Franklin, M. (2019). Okehampton-Optimising Management of Production and Biodiversity Assets, Devonport TAS, University of Tasmania."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"77","DOI":"10.2989\/10220119.2017.1334706","article-title":"Grazing Management That Regenerates Ecosystem Function and Grazingland Livelihoods","volume":"34","author":"Teague","year":"2017","journal-title":"Afr. J. Range Forage Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Teague, R., and Kreuter, U. (2020). Managing Grazing to Restore Soil Health, Ecosystem Function, and Ecosystem Services. Front. Sustain. Food Syst., 4.","DOI":"10.3389\/fsufs.2020.534187"},{"key":"ref_39","unstructured":"(2022, October 25). Bureau of Meteorology Climate Statistics for Australian Locations, Available online: http:\/\/www.bom.gov.au\/climate\/averages\/tables\/cw_092027.shtml."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Whalley, R.D.B., and Hardy, M.B. (2000). Measuring Botanical Composition of Grasslands. Field and Laboratory Methods for Grassland and Animal Production Research, CABI Publishing.","DOI":"10.1079\/9780851993515.0067"},{"key":"ref_41","first-page":"81","article-title":"Dry Sheep Equivalents for Comparing Different Classes of Stock","volume":"1530","author":"White","year":"1981","journal-title":"Paper"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1071\/AN10078","article-title":"Whole-Farm Profit and the Optimum Maternal Liveweight Profile of Merino Ewe Flocks Lambing in Winter and Spring Are Influenced by the Effects of Ewe Nutrition on the Progeny\u2019s Survival and Lifetime Wool Production","volume":"51","author":"Young","year":"2011","journal-title":"Anim. Prod. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"233","DOI":"10.22499\/2.5804.003","article-title":"High-Quality Spatial Climate Data-Sets for Australia","volume":"58","author":"Jones","year":"2009","journal-title":"Aust. Meteorol. Oceanogr. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.geomorph.2016.11.021","article-title":"Optimising UAV Topographic Surveys Processed with Structure-from-Motion: Ground Control Quality, Quantity and Bundle Adjustment","volume":"280","author":"James","year":"2017","journal-title":"Geomorphology"},{"key":"ref_45","unstructured":"Harrison, M.T., Whitehead, J., Ogungbuyi, M.G., Ball, P., Guerschman, J.P., Tickle, P., Leverton, C., and Turner, D. (2023). Operationalising Satellite and Drone Imagery to Improve Decision-Making: A Case Study with Regenerative Grazing, University of Tasmania."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gillan, J.K., Karl, J.W., Elaksher, A., and Duniway, M.C. (2017). Fine-Resolution Repeat Topographic Surveying of Dryland Landscapes Using UAS-Based Structure-from-Motion Photogrammetry: Assessing Accuracy and Precision against Traditional Ground-Based Erosion Measurements. Remote Sens., 9.","DOI":"10.3390\/rs9050437"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud Detection Algorithm Comparison and Validation for Operational Landsat Data Products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1071\/CP17291","article-title":"Potential of Summer-Active Temperate (C3) Perennial Forages to Mitigate the Detrimental Effects of Supraoptimal Temperatures on Summer Home-Grown Feed Production in South-Eastern Australian Dairying Regions","volume":"69","author":"Langworthy","year":"2018","journal-title":"Crop Pasture Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.1071\/AN14421","article-title":"Modelling Pasture Management and Livestock Genotype Interventions to Improve Whole-Farm Productivity and Reduce Greenhouse Gas Emissions Intensities","volume":"54","author":"Harrison","year":"2014","journal-title":"Anim. Prod. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"107484","DOI":"10.1016\/j.ecolind.2021.107484","article-title":"Regenerative Rotational Grazing Management of Dairy Sheep Increases Springtime Grass Production and Topsoil Carbon Storage","volume":"125","author":"Epelde","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2018.09.028","article-title":"Application of Sentinel-2A Data for Pasture Biomass Monitoring Using a Physically Based Radiative Transfer Model","volume":"218","author":"Punalekar","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.agsy.2015.05.005","article-title":"Management Opportunities for Boosting Productivity of Cool-Temperate Dairy Farms under Climate Change","volume":"138","author":"Phelan","year":"2015","journal-title":"Agric. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1071\/CP18566","article-title":"Current and Future Direction of Nitrogen Fertiliser Use in Australian Grazing Systems","volume":"70","author":"Rawnsley","year":"2019","journal-title":"Crop Pasture Sci."},{"key":"ref_54","unstructured":"ter Braak, C.J.F., and Juggins, S. (September, January 30). Weighted Averaging Partial Least Squares Regression (WA-PLS): An Improved Method for Reconstructing Environmental Variables from Species Assemblages. Proceedings of the Twelfth International Diatom Symposium, Renesse, The Netherlands."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0016-7061(01)00074-X","article-title":"A Comparison of Prediction Methods for the Creation of Field-Extent Soil Property Maps","volume":"103","author":"Bishop","year":"2001","journal-title":"Geoderma"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.ecolmodel.2008.05.006","article-title":"How to Evaluate Models: Observed vs. Predicted or Predicted vs. Observed?","volume":"216","author":"Perelman","year":"2008","journal-title":"Ecol. Model."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?\u2013Arguments against Avoiding RMSE in the Literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model Dev."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"108081","DOI":"10.1016\/j.ecolind.2021.108081","article-title":"The Use of Machine Learning Methods to Estimate Aboveground Biomass of Grasslands: A Review","volume":"130","author":"Morais","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.1080\/01431160310001654923","article-title":"Narrow Band Vegetation Indices Overcome the Saturation Problem in Biomass Estimation","volume":"25","author":"Mutanga","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.eja.2019.02.003","article-title":"Examining the Yield Potential of Barley Near-Isogenic Lines Using a Genotype by Environment by Management Analysis","volume":"105","author":"Ibrahim","year":"2019","journal-title":"Eur. J. Agron."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1071\/AN15575","article-title":"Modelled Greenhouse Gas Emissions from Beef Cattle Grazing Irrigated Leucaena in Northern Australia","volume":"56","author":"Taylor","year":"2016","journal-title":"Anim. Prod. Sci."},{"key":"ref_64","unstructured":"Henry, B., Dalal, R., Harrison, M.T., and Keating, B. (2022). Creating Frameworks to Foster Soil Carbon Sequestration, Burleigh Dodds Science Publishing."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1111\/j.0906-7590.2004.04004.x","article-title":"Plant Responses to Livestock Grazing Frequency in an Australian Temperate Grassland","volume":"27","author":"Dorrough","year":"2004","journal-title":"Ecography"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Akhmadov, K.M., Breckle, S.W., and Breckle, U. (2006). Effects of Grazing on Biodiversity, Productivity, and Soil Erosion of Alpine Pastures in Tajik Mountains. Land Use Change and Mountain Biodiversity, CRC Press.","DOI":"10.1201\/9781420002874-17"},{"key":"ref_67","unstructured":"Blackburn, W.H. (2021). Impacts of Grazing Intensity and Specialized Grazing Systems on Watershed Characteristics and Responses. Developing Strategies for Rangeland Management, CRC Press."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2060","DOI":"10.1071\/AN16166","article-title":"Fetal and Lamb Losses from Pregnancy Scanning to Lamb Marking in Commercial Sheep Flocks in Southern New South Wales","volume":"57","author":"Allworth","year":"2016","journal-title":"Anim. Prod. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1071\/EA9900165","article-title":"The Performance of Short Scrotum and Wether Lambs Born in Winter or Spring and Run at Pasture in Northern Tasmania","volume":"30","author":"Hopkins","year":"1990","journal-title":"Aust. J. Exp. Agric."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.rse.2004.08.006","article-title":"Estimation of Pasture Growth Rate in the South West of Western Australia from AVHRR NDVI and Climate Data","volume":"93","author":"Hill","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.ecolind.2018.03.081","article-title":"Predicting Habitat Quality of Protected Dry Grasslands Using Landsat NDVI Phenology","volume":"91","author":"Weber","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"107161","DOI":"10.1016\/j.agwat.2021.107161","article-title":"Application, Adoption and Opportunities for Improving Decision Support Systems in Irrigated Agriculture: A Review","volume":"257","author":"Ara","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s11119-011-9221-x","article-title":"The Impact of Topography on Soil Properties and Yield and the Effects of Weather Conditions","volume":"12","year":"2011","journal-title":"Precis. Agric."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"An, S., Chen, X., Zhang, X., Lang, W., Ren, S., and Xu, L. (2020). Precipitation and Minimum Temperature Are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau. Remote Sens., 12.","DOI":"10.3390\/rs12030431"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e238","DOI":"10.1002\/fes3.238","article-title":"Genetic Factors Increasing Barley Grain Yields Under Soil Waterlogging","volume":"9","author":"Liu","year":"2020","journal-title":"Food Energy Secur."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1071\/AN14309","article-title":"Increasing Ewe Genetic Fecundity Improves Whole-Farm Production and Reduces Greenhouse Gas Emissions Intensities: 2. Economic Performance","volume":"54","author":"Ho","year":"2014","journal-title":"Anim. Prod. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.agsy.2018.09.003","article-title":"Advancing a Farmer Decision Support Tool for Agronomic Decisions on Rainfed and Irrigated Wheat Cropping in Tasmania","volume":"167","author":"Phelan","year":"2018","journal-title":"Agric. Syst."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"145031","DOI":"10.1016\/j.scitotenv.2021.145031","article-title":"Can Seasonal Soil N Mineralisation Trends Be Leveraged to Enhance Pasture Growth?","volume":"772","author":"Bilotto","year":"2021","journal-title":"Sci. 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