Managing grazing to increase ground cover in rangelands: using remote sensing to detect change
Bibliographic record
Abstract
Practices that improve the quantity, composition, and persistence of ground cover can contribute to a range of ecosystem services that support agricultural production, regulate climate, reduce erosion and support nutrient cycling. In rangeland grazing systems, incorporating periods of rest and matching stocking rates to feed availability is commonly used with the goal of improving land condition and productivity at a property scale. Understanding and quantifying differences in ground cover associated with changes to grazing management can provide livestock producers with greater confidence in the outcomes associated with their management. It can also demonstrate their nature positive activities which may be valued in emerging markets. This study sought to quantify any changes in ground cover resulting from changed grazing management (strategically managing the timing, intensity and duration of grazing events to maintain or improve land condition) across seven mixed grazing (cattle, sheep and/or goats) study sites in the semi-arid rangelands of western New South Wales, Australia. Time-series estimates of ground cover derived from Landsat imagery for each study site were compared with biophysically similar regional benchmark areas as controls. Overall, ground cover was found to have increased significantly (2–7%) following change in grazing management at four of the seven study sites, relative to control benchmark areas. It was apparent different land units varied in their response to the management change, and that the preceding 12 months rainfall (such as wet, intermediate or dry rainfall years) did not have a consistently significant effect on the relative response. Results of this study highlight that improvements in ground cover and land condition may be achieved through changes to grazing management, but also that there are complexities in both achieving and measuring any change. This study demonstrates the practical application of remotely sensed cover data and dynamic regional comparison techniques to document environmental outcomes at the property scale from grazing management in low input, extensive rangeland grazing systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".