The Effect of In-Field Rain Water Harvesting on Orange-Fleshed Sweet Potato Biomass and Yield
Bibliographic record
Abstract
Water scarcity affects both food security and human nutrition. In-field rain water harvesting (IRWH) combines the advantages of rainwater harvesting, no-till, basin tillage and mulching on high drought risk clay soils. In this study, the IRWH system was customized to fit the cropping system of orange-fleshed sweet potato (OFSP). Field trials were conducted over two seasons to compare cultivation of OFSP using IRWH versus conventional tillage (CON). Data collection included plant survival, root initiation, marketable root yield, unmarketable root yield classes and biomass. Planting OFSP using the IRWH system resulted in significantly higher total biomass, higher marketable and total root yield per plant, as well as larger number of roots per plant compared to CON. Despite the relatively higher yield, total production (t/ha) was only significantly higher in season two at 4.6 t/ha vs 2.7 t/ha for CON. Subsistence farmers and households in semi-arid areas may grow small plots of orange-fleshed sweet potato in IRWH opposed to only growing maize and in that way add vitamin A to the diet. This is the first study on the application of IRWH to produce OFSP under rainfed conditions, and more research can be conducted to expand the knowledge on application and benefits of IRWH for OFSP production.
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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 | 0.000 |
| 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".