Improving Crop Yield and Water Productivity by Ecological Sanitation and Water Harvesting in South Africa
Bibliographic record
Abstract
This study quantifies the potential effects of a set of technologies to address water and fertility constraints in rain-fed smallholder agriculture in South Africa, namely in situ water harvesting (WH), external WH, and ecological sanitation (Ecosan, fertilization with human urine). We used the Soil and Water Assessment Tool to model spatiotemporally differentiated effects on maize yield, river flow, evaporation, and transpiration. Ecosan met some of the plant nitrogen demands, which significantly increased maize yields by 12% and transpiration by 2% on average across South Africa. In situ and external WH did not significantly affect the yield, transpiration or river flow on the South Africa scale. However, external WH more than doubled the yields for specific seasons and locations. WH particularly increased the lowest yields. Significant water and nutrient demands remained even with WH and Ecosan management. Additional fertility enhancements raised the yield levels but also the yield variability, whereas soil moisture enhancements improved the yield stability. Hence, coupled policies addressing both constraints will likely be most effective for improving food security.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".