Productivity of sorghum and millets under different in-field rainwater management options on soils of varying fertility status in Zimbabwe
Bibliographic record
Abstract
Traditional cereal crops are important for food and nutrition security in rural communities of southern Africa, but their productivity is often constrained by low soil water largely linked to low seasonal rainfall and long intra-seasonal dry spells. Planting basins (PB), tied ridges (TR), and conventional ploughing (CP) were evaluated, over two cropping seasons (2020/2021 and 2021/2022), for their effects on sorghum [Sorghum bicolor (L.), Moench], pearl millet [Pennisetum glaucum (L.) R.Br.], and finger millet [Eleusine coracana (L.) Gaertn] productivity on degraded (<0.4% soil organic carbon) and productive (>0.6% soil organic carbon) fields under rainfed conditions in Mbire (<450 mm rainfall year−1) and Mutasa (>800 mm rainfall year−1) districts in Zimbabwe. Field trials were established on degraded and productive field sites in each district, with sorghum, pearl millet, and finger millet either sown as monocrops or intercropped with cowpea. The experiments were laid out in a 2 × 3 × 3 factorial in a randomized complete block design (RCBD). The highest sorghum grain yield response of 2100 kg ha−1 was attained under PB on productive soils. Overall, PB and TR increased sorghum, finger millet, and pearl millet grain yields by 43% to 58% compared with CP. Growing sorghum, finger millet, and pearl millet on productive soils increased grain yields by 64%, 33%, and 43%, respectively, compared with degraded soils. Intercropping sorghum, pearl millet, and finger millet with cowpea increased cereal yields by between 23% and 42% over the sole crops. Rainwater use efficiency averaged 1 kg grain mm−1 on productive fields and 0.4 kg grain mm−1 on degraded fields. PB produced the highest net profit of $US408 on a productive field. Overall, production of sorghum and millets on productive soils gave positive economic returns irrespective of rainwater management option and cropping system. Conversely, 63% of the treatments on degraded soils recorded negative economic returns in both districts. We conclude that in-field rainwater management technologies combined with other agronomic practices like intercropping increase the productivity of sorghum and millets under rainfed conditions. However, degraded soils remain a challenge for the increased productivity of traditional cereal crops.
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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".