IMPACTS OF SOIL MOISTURE AND TILLAGE ON SHORT-TERM EROSION IN AGRICULTURAL LANDS OF NORTH CENTRAL MEXICO
Bibliographic record
Abstract
Background: Soil erosion is a natural process accelerated by anthropogenic activities such as agriculture, leading to increased runoff and erosion, resulting in global environmental and economic losses. Addressing this issue through conservation agriculture is critical, particularly in arid regions where soil degradation is prevalent. This study adds value by evaluating the combined effects of tillage practices and antecedent soil moisture conditions (AMC) on runoff and soil erosion under controlled rainfall simulation. Objective: To assess the effects of tillage practices and AMC on runoff and soil erosion, hypothesizing that conservation-oriented practices would reduce erosion and runoff. Methodology: A randomized complete block design experiment was conducted in an arid zone of North-central Mexico. Four tillage treatments were evaluated: 1) no crop (NC), 2) maize with conventional tillage and crop residues (CTR), 3) maize with conventional tillage (CT), and 4) maize sown by handspike (HS). Each treatment was tested under two AMC scenarios: dry and wet. Runoff and soil erosion were measured, and results were analyzed using ANOVA. Results: Dry AMC significantly reduced erosion in HS (p ≤ 0.01) and CTR (p ≤ 0.05) compared to wet AMC. CT and CTR produced the lowest erosion under wet AMC (p ≤ 0.05). For total runoff, CTR and HS produced the lowest values under dry AMC. These findings highlight the effectiveness of crop residue cover in CTR and no-tillage cropping (HS) in reducing both erosion and runoff. Implications: The study demonstrates the importance of soil moisture conditions and tillage practices in managing erosion. Limitations include the use of simulated rainfall, which may not fully capture natural variability. However, the findings provide valuable insights for conservation agriculture in arid regions. Conclusion: Crop residue cover and no-tillage cropping are effective in reducing soil erosion and runoff, especially under dry AMC. These practices are crucial for sustainable soil management in arid agroecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".