Improving Crop Yield and Water Productivity by Ecological Sanitation and Water Harvesting in South Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it