Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning
The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we examine the potential of “regenerative agricultu...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/12/6/1142 |
| _version_ | 1797593894878183424 |
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| author | Michael Gbenga Ogungbuyi Juan P. Guerschman Andrew M. Fischer Richard Azu Crabbe Caroline Mohammed Peter Scarth Phil Tickle Jason Whitehead Matthew Tom Harrison |
| author_facet | Michael Gbenga Ogungbuyi Juan P. Guerschman Andrew M. Fischer Richard Azu Crabbe Caroline Mohammed Peter Scarth Phil Tickle Jason Whitehead Matthew Tom Harrison |
| author_sort | Michael Gbenga Ogungbuyi |
| collection | DOAJ |
| description | The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while improving pasture productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 ha) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months), with each compared with controls characterised by lighter stocking rates for longer periods (2000 DSE/ha). Pastures were composed of wallaby grass (<i>Austrodanthonia species</i>), kangaroo grass (<i>Themeda triandra</i>), Phalaris (<i>Phalaris aquatica</i>), and cocksfoot (<i>Dactylis glomerata</i>), and were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We invoked a machine learning model forced with Sentinel-2 imagery to quantify TSDM, standing green and dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM, green biomass and recovery. However, regenerative treatments significantly impacted litterfall and trampled material, with high-intensity grazing treatments trampling more biomass, increasing litter, enhancing surface organic matter and decomposition rates thereof. Pasture digestibility and sward uniformity were greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were greater for the 15-month spelling treatment. TSDM prognostics from machine learning were lower than measured TSDM, although predictions from the machine learning approach closely matched observed spatiotemporal variability within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3–6 months) was more conducive to increasing pasture production under high rainfall conditions, and we speculate that – in this environment - high-intensity grazing with 3-month spelling is likely to improve soil organic carbon through increased litterfall and trampling. Our study paves the way for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling the management of pastures within small fields from afar. |
| first_indexed | 2024-03-11T02:15:59Z |
| format | Article |
| id | doaj.art-fad0a5eb91384edca1960410f218118e |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-11T02:15:59Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-fad0a5eb91384edca1960410f218118e2023-11-18T11:13:22ZengMDPI AGLand2073-445X2023-05-01126114210.3390/land12061142Enabling Regenerative Agriculture Using Remote Sensing and Machine LearningMichael Gbenga Ogungbuyi0Juan P. Guerschman1Andrew M. Fischer2Richard Azu Crabbe3Caroline Mohammed4Peter Scarth5Phil Tickle6Jason Whitehead7Matthew Tom Harrison8Tasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS 7248, AustraliaCibo Labs Pty Ltd., 15 Andrew St., Point Arkwright, QLD 4573, AustraliaInstitute for Marine and Antarctic Studies, University of Tasmania, Launceston, TAS 7248, AustraliaGulbali Institute, Charles Sturt University, Albury, NSW 2640, AustraliaTasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS 7248, AustraliaCibo Labs Pty Ltd., 15 Andrew St., Point Arkwright, QLD 4573, AustraliaCibo Labs Pty Ltd., 15 Andrew St., Point Arkwright, QLD 4573, AustraliaCape Herbert Pty Ltd., Blackstone Heights, TAS 7250, AustraliaTasmanian Institute of Agriculture, University of Tasmania, Launceston, TAS 7248, AustraliaThe emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while improving pasture productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 ha) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months), with each compared with controls characterised by lighter stocking rates for longer periods (2000 DSE/ha). Pastures were composed of wallaby grass (<i>Austrodanthonia species</i>), kangaroo grass (<i>Themeda triandra</i>), Phalaris (<i>Phalaris aquatica</i>), and cocksfoot (<i>Dactylis glomerata</i>), and were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We invoked a machine learning model forced with Sentinel-2 imagery to quantify TSDM, standing green and dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM, green biomass and recovery. However, regenerative treatments significantly impacted litterfall and trampled material, with high-intensity grazing treatments trampling more biomass, increasing litter, enhancing surface organic matter and decomposition rates thereof. Pasture digestibility and sward uniformity were greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were greater for the 15-month spelling treatment. TSDM prognostics from machine learning were lower than measured TSDM, although predictions from the machine learning approach closely matched observed spatiotemporal variability within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3–6 months) was more conducive to increasing pasture production under high rainfall conditions, and we speculate that – in this environment - high-intensity grazing with 3-month spelling is likely to improve soil organic carbon through increased litterfall and trampling. Our study paves the way for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling the management of pastures within small fields from afar.https://www.mdpi.com/2073-445X/12/6/1142machine learningsatellite imageryregenerative grazinggrassland biomasstotal standing dry matterdigital agriculture |
| spellingShingle | Michael Gbenga Ogungbuyi Juan P. Guerschman Andrew M. Fischer Richard Azu Crabbe Caroline Mohammed Peter Scarth Phil Tickle Jason Whitehead Matthew Tom Harrison Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning Land machine learning satellite imagery regenerative grazing grassland biomass total standing dry matter digital agriculture |
| title | Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning |
| title_full | Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning |
| title_fullStr | Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning |
| title_full_unstemmed | Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning |
| title_short | Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning |
| title_sort | enabling regenerative agriculture using remote sensing and machine learning |
| topic | machine learning satellite imagery regenerative grazing grassland biomass total standing dry matter digital agriculture |
| url | https://www.mdpi.com/2073-445X/12/6/1142 |
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