Quantifying the impacts of climate-smart farming practices for improved management and long-term carbon storage
Bibliographic record
Abstract
Within agricultural production there is tension between feeding a rapidly growing population and conserving the finite resources at the foundation of our agroecosystems. Fortunately, in recent decades there has been a growing focus on farming practices that promote long-term soil health, land productivity, and resilience to climate change. The term ‘conservation agriculture’ encompasses practices that 1) promote minimum soil disturbance, 2) maintain permanent soil cover, and 3) diversify plant species. This research evaluated several conservation agriculture practices for their ability to deliver desired agroecosystem services across the Northeastern US. In the first study, a cover crop mixture field experiment was implemented in seven states to evaluate how climatic, edaphic, and management conditions affected the performance of cover crop bicultures that included species with varying functional traits. Seeding rate recommendations for mixtures are typically developed at the regional level, thus cover crop performance is highly variable due to site-level conditions and competition among species. Our results indicated that expected spring growing degree days and baseline soil fertility (i.e., inorganic N) are the most significant variables to consider when designing site-specific cover crop mixtures. The second study assessed the effects of long-term management on soil organic carbon (SOC) dynamics in mid-Atlantic grain cropping systems. At the time of sampling, five unique systems (two conventional, three organic) had been continuously managed for 25 years, representing a range of tillage and fertility practices and rotational complexities. Results showed SOC loss in all systems over time regardless of management, likely because of high baseline SOC stocks from long-term perennial forage production prior to research plot establishment. However, cropping systems that best maintained SOC over time included management with minimal soil disturbance, frequent manure inputs, and/or greater rotational diversity through perennial cropping or cover cropping. Both studies increase our understanding of the ability of specific conservation practices to support agroecosystem biodiversity, long term soil health, and potential carbon sequestration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".