Can the Adaptive Multi-Paddock Grazing System Increase Carbon Sequestration in Alberta's Grassland Soils?
Bibliographic record
Abstract
Natural grasslands cover around 40% of the Earth’s surface and play an important role as a source of ecological goods and services. By sequestering around 30% of terrestrial global carbon, grasslands play a critical part in the alleviation of climate change. Despite their ecological significance, grasslands have been reduced to a fraction of their original extent. In Canada, up to 70% of grasslands have been destroyed, making it the most endangered ecosystem in North America. What remains is often intensely grazed and a diverse ecosystem of wild animals is replaced by domestic livestock. The continuous application of poor grazing management by ranchers is one of the main causes for the depletion of natural grasslands, resulting in the release of stored soil carbon back into the atmosphere. Fortunately, 60-70% of the depleted carbon can be re-sequestered through the adoption of improved grazing management, thus improving grassland ecosystems. The Adaptive Multi-Paddock (AMP) grazing system is an example of improved grazing management. AMP grazing is a system in which livestock is frequently rotated between multiple fenced paddocks. Compared to conventional grazing practices (Non-AMP), the AMP system is a favorable solution which can improve carbon sequestration in world wide grasslands soils– and in turn, contribute to the mitigation of climate change. By regenerating grassland ecosystems, AMP grazing could potentially aid in creating a more sustainable, resilient agroecosystem. Our goal is to study the effect of AMP grazing on soil organic carbon (SOC) sequestration in Canadian grasslands. First, we collected soil cores from 30 study sites located throughout the grassland ecoregions in Canada. Each site consisted of a pair of ranches: one AMP and one Non-AMP. Second, we analyzed the soil cores for total carbon using an elemental analyzer. There does not seem to be any substantial difference in total carbon between AMP and Non-AMP systems, however we have yet to differentiate between soil organic carbon and soil inorganic carbon. Once we distinguish the two variables we will be able to confirm the effectiveness of the AMP grazing system in increasing carbon sequestration in Canadian grasslands. Literature Cited: Derner, J. D., & Schuman, G. E. (2007). Carbon sequestration and rangelands: a synthesis of land management and precipitation effects. Journal of soil and water conservation, 62(2), 77-85. Gauthier, D. A., & Wiken, E. B. (2003). Monitoring the conservation of grassland habitats, Prairie Ecozone, Canada. Environmental Monitoring and Assessment, 88(1-3), 343-364. Samson, F., & Knopf, F. (1994). Roundtable: prairie conservation in North America. BioScience, 44(6), 418-421 Kraus, D. (2016). Why Canada’s Prairies are the World’s Most Endangered Ecosystem. Retrieved from: http://www.natureconservancy.ca/en/blog/archive/grasslands-the-most.html#.XUnsE-hKi70 Lal, R. (2002). Soil carbon dynamics in cropland and rangeland. Environmental pollution, 116(3), 353-362 Teague, W. R. (2018). Forages and pastures symposium: Cover crops in livestock production: Whole-system approach: Managing grazing to restore soil health and farm livelihoods. Journal of animal science, 96(4), 1519-1530.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".