Conservation agriculture enhances ecosystem services and sustainability of the system over conventional agriculture
Bibliographic record
Abstract
Ecosystem services (ES) provision and variation over spatial scales in agricultural land are often the result of interactions between agricultural management and ecological structures. Our goal was to evaluate the effect of conservation agriculture (CA) and conventional tillage (CT) practices on different ES and ecosystem disservices (DES) to evaluate their spatial variability, and to develop a new index to determine the sustainability of CA and CT systems in farmers’ fields. Under conservation agriculture and conventional tillage, the ecosystem services supplied by wheat-based cropping systems was measured, and the inverse distance weighted (IDW) interpolation technique was used to create maps of spatial variability. A new index, i.e., ecosystem service sustainability index (ESSI) was developed to assess the sustainability of the study area. The gain in food was higher in some parts of the Nilokheri and Taraori villages of Karnal district, where CA was practiced over the years. The gain in regulating service, i.e., SOC stock in the study area ranged between 1.99 and 5.54 Mg ha –1 . The study revealed approximately 178% increase in supporting service, i.e., soil formation in Nilokheri block over the study area. The ESSI of Karnal ranged between 1.48 and 7.60, and for Kaithal district it was between 1.22 and 6.24. In the study area, 67 villages were reported as degraded, 28 villages as vulnerable, 38 villages as sustainable but input intensive, 17 villages as sustainable. By incorporating ecosystem service concepts into conservation agriculture, wheat agroecosystems can be transformed into more robust and sustainable production 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".