Influence of Crop Growth Stages and Management Practices on Soil Water Content at Different Soil Depths under Dryland Conditions
Bibliographic record
Abstract
Soil water loss through evaporation plays a role on low crop productivity and this is due to poor cropping systems and soil surface coverage. The study was carried out at three locations of North-West province of South Africa, which were Potchefstroom, Taung and Rustenburg during 2011/12 and 2012/13 planting seasons. The experimental design was a factorial experiment laid out in a randomised complete block design (RCBD) with three replicates. The experiment consisted of five cropping systems, which were monocropping cowpea, monocropping maize, cowpea followed by maize in rotation, maize followed by cowpea in rotation and intercropping maize-cowpea. The three crop growth stages compared in this study were before tasselling/flowering, during tasselling/pod formation and during physiological maturity of maize and cowpea. Soil was sampled for the 0-0.15, 0.15-0.3, 0.3-0.6 and 0.6-0.9 m depth increments and soil water content determined using the Gravimetric method. The crop growth stage before tasselling/flowering in maize/cowpea had significantly (P < 0.05) higher water content of 10.2, 10.8, 12.5 and 13.3% at the depth of 0-0.15, 0.15-0.3, 0.3-0.6 and 0.6-0.9 m respectively. Soil collected at Rustenburg and Potchefstroom had significantly (P < 0.05) higher water content of 13.5 and 10.2; 15.9 and 10.9; 18.3 and 12.8; 18.4 and 14.5% at the depths of 0-0.15, 0.15-0.3, 0.3-0.6 and 0.6-0.9 m respectively. Monocropping cowpea plots had significantly (P < 0.05) higher water content of 12.4% than other cropping systems at the soil depth of 0.3-0.6 m. Monocropping plots of cowpea had the ability to hold soil water and this depends on the type of cowpea cultivar and canopy cover. The stage before tasselling/flowering of maize-cowpea (V10/Vn) was found to have high soil water content. Soil water content differs across locations due to different soil physical properties.
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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".