Crop production management practices as a cause for low water productivity at Zanyokwe Irrigation Scheme
Bibliographic record
Abstract
Generally, smallholder irrigation schemes (SIS) in South Africa have performed poorly and have not delivered on their development objectives of increasing crop production and improving rural livelihoods. Limited knowledge of irrigated crop production among farmers has been identified as one of the constraints to improved crop productivity, but research that investigates the relationship between farmer practices and productivity is lacking. A monitoring study was therefore conducted at the Zanyokwe Irrigation Scheme (ZIS) in the Eastern Cape to identify cropping systems and management practices used by farmers and to determine how these were related to performance. Evidence from 2 case studies showed that water management limited crop productivity. Irrigation application and system efficiencies were below the norm and irrigation scheduling did not take crop type and growth stage into account. Monitoring of 20 farmers over a 3-yr period showed that cropping intensity averaged only 48% and that the yields of the 2 main summer crops, grain maize (Zea mays L.) and butternut (Cucurbita moschata) averaged only 2.4 and 6.0 t∙ha-1, respectively. In addition to poor water management, other main constraints to crop productivity were inadequate weed and fertiliser management and low plant populations. The results indicated that a lack of basic technical skills pertaining to irrigated crop production among farmers was a possible cause of inadequate management. In this regard, it is expected that farmers could benefit from ‘back to basics’ training programmes in the areas of crop and irrigation water management. Research needs to focus on labour-saving production technologies, establishing farm-specific fertiliser recommendations, the identification and use of affordable sources of nutrients, as well as strategies to improve plant population in maize by preventing bird damage to newly-planted stands. Keywords: smallholder irrigation schemes, cropping pattern, constraints to crop productivity, research agenda
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".