Complementary Irrigation Effect on Seed Cotton Yield in North Côte d’Ivoire
Bibliographic record
Abstract
The study was set to assess a complementary irrigation effect on seed cotton yields in the Northern Côte d’Ivoire where the cotton is the main cash crop. Firstly, the soil samples were collected from the surface down to 30 cm depth and analyzed. The soil was sandy and silty. So, 65 kg of 46%urea and 285 kg of NPKSB15-15-15-6-1 were applied for its correction. Secondly, in a complete randomized blocks, four tests were conducted, within those were T0 (no complementary irrigation and no crop protection products and fertilizers), T1 (no complementary irrigation, with crop protection products and fertilizers, the cotton cultivation ongoing practice in the Northern Cote d’Ivoire, therefore the reference), T2 (complementary irrigation, along with crop protection products and fertilizers), T3 (only complementary irrigation, without any crop protection products and fertilizers). Thirdly, the tests were replicated in 3 blocks. As a result, from T1 to T2, the plants heights, the plants density at harvest period, bolls number per plant and seed cotton yields were respectively 88.58±1.78 vs 96.08±1.78 cm (+8.47%) at day 73; 53,934±1,260.78 vs 67,593±1,260.78 plants per ha (+25.32%); 23.11±0.81 vs 26.39±0.81 bolls per plant (+14.19%) and 1,616.26±67.86 vs 2,657.77±67.86 kg/ha (+64.44%). Conversely, the complementary irrigation led to higher pest damages on bolls, because 13±2.2% of T2 bolls were attacked, while just 4.6±2.2% of T1 bolls were damaged by insects’ pest. Looking for solutions linked to climate change effects, a complementary irrigation in cotton farms in the Northern Côte d’Ivoire could be profitable to the cotton growers. Nonetheless, the farmers should pay a great attention to the pest management.
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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".