Enabling Regenerative Agriculture Using Remote Sensing and Machine Learning
نویسندگان
چکیده
The emergence of cloud computing, big data analytics, and machine learning has catalysed the use remote sensing technologies to enable more timely management sustainability indicators, given uncertainty future climate conditions. Here, we examine potential “regenerative agriculture”, as an adaptive grazing strategy minimise bare ground exposure while improving pasture productivity. High-intensity sheep treatments were conducted in small fields (less than 1 ha) for short durations (typically less day). Paddocks subsequently spelled allow biomass recovery (treatments comprising 3, 6, 9, 12, 15 months), with each compared controls characterised by lighter stocking rates longer periods (2000 DSE/ha). Pastures composed wallaby grass (Austrodanthonia species), kangaroo (Themeda triandra), Phalaris (Phalaris aquatica), cocksfoot (Dactylis glomerata), destructively sampled estimate total standing dry matter (TSDM), green biomass, trampled biomass. We invoked a model forced Sentinel-2 imagery quantify TSDM, Faced La Nina conditions, regenerative did not significantly impact productivity, all showing similar recovery. However, impacted litterfall material, high-intensity trampling increasing litter, enhancing surface organic decomposition thereof. Pasture digestibility sward uniformity greatest minimal spelling (3 whereas both senescent material greater 15-month treatment. TSDM prognostics from lower measured although predictions approach closely matched observed spatiotemporal variability within across treatments. root mean square error between modelled was 903 kg DM/ha, which field. conclude that (3–6 months) conducive production under high rainfall speculate – this environment - 3-month is likely improve soil carbon through increased trampling. Our study paves way using satellite at scales, enabling pastures afar.
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ژورنال
عنوان ژورنال: Land
سال: 2023
ISSN: ['2073-445X']
DOI: https://doi.org/10.3390/land12061142