Quantifying the influences of grazing, climate and their interactions on grasslands using Landsat TM images
Bibliographic record
Abstract
Abstract Appropriate grazing management ensures sustainable productivities of grassland ecosystems while maintaining grassland services. Thus, it is important to understand the influences of grazing management on grassland ecosystems, which can be monitored by measuring grassland response (e.g. leaf area index [LAI]) to grazing management. However, the measured grassland response includes the impact not only of grazing management, but also of other factors and their interactions, such as climate variability and fire. Therefore, to better study the effects of grazing management, grassland response to grazing needs to be quantified separately from that of other factors which influence grasslands and their interactions. The aim of our research was to quantify these interactions using Landsat thematic mapper (TM) images with long‐term datasets at a regional scale. We studied vegetation using a manipulative grazing experiment that applied a range of low to high cattle stocking rates from 2008 to 2011 in a northern native mixed‐prairie in Saskatchewan. Results show that precipitation, temperature, interaction between temperature and precipitation, cattle density, interaction between temperature and cattle density, and the interaction among cattle density and climate parameters explained 65.5, 14.5, 9.8, 1.7, 1.4 and 0.5% of the variation in grassland LAI, respectively; thus, precipitation has the dominant effect in mixed‐grass prairies, while temperature and the interaction between temperature and precipitation have only moderate effects, and grazing intensity and the interaction between grazing intensity and climate variations have relatively low effects. The results also suggest that grassland response to grazing can be quantitatively separated from that of climate variability with the prior knowledge of grazing intensity, even though the influences of precipitation on LAI overrode the effects of short term grazing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".