Effects of Diversification of Rainfed Cropping Systems to Enhance Sustained Yields for Semi-Arid Tropical Agroecosystems: Effects of 11–Years of Farmers’ Practice
Bibliographic record
Abstract
Semi-arid tropical (SAT) soils cover approximately 11 million hectares (Mha) globally. Soils in SAT regions are prone to degradation if poorly managed, and approximately 38% of the developing countries’ poor who depend on these soils for their livelihood are at risk of malnutrition. Agronomic practices that can improve soil quality, and can sustain or improve crop productivity are critical for SAT agroecosystems. The objective of this study was to investigate the 11-year effects of farmer-imposed agronomic practices (cropping system and fertilization) on soil organic carbon (SOC), nitrogen (N), phosphorus (P), sulphur (S), and micronutrients [zinc (Zn) and boron (B)], and crop productivity in the Kothapally watershed, Andhra Pradesh, India. Rainfed diversified cropping systems with legumes in rotation or intercropping systems were compared with rainfed monoculture cotton systems. Soil samples were collected from each field of the participating twenty-three farmers in June 2010 and were compared with soil data collected in June 1999 from the same farmer's fields. All soil samples were analyzed (with 10% replication) in the laboratory for physical and chemical characteristics using analysis of variance, where we tested the effects of crop (independent variable) on the dependent variables, e.g. SOC stocks, at p = 0.05. Differences in the means were compared with a Tukey test. Relationships between SOC stocks and yield were evaluated with Pearson correlation analysis, and the sample size and correlation coefficients were reported when p = 0.05. Increased SOC stocks were observed in rainfed diversified cropping systems with legumes in rotation or intercropping systems compared to rainfed monoculture cotton cropping system (p = 0.0283), and SOC stocks (in 2010) were correlated with 2010 crop yields (r = 0.384, n = 23, p = 0.05). Overall, the 11-year study showed sustained crop productivity in rainfed diversified cropping systems compared to rainfed cotton cropping 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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".