Combined Traditional Water Harvesting (Zai) and Mulching Techniques Increase Available Soil Phosphorus Content and Millet Yield
Bibliographic record
Abstract
Mismanagement of soil fertility is one of the major challenges for farmers in the Sahelian zone of Niger. This study, conducted in 2012 and 2013 in western part of Niger, aimed at examining the effects of combined Zai and Mulching techniques on soil fertility and millet productivity. The experimental design was a randomized Fischer block with four treatments (Zai, mulching, Zai + mulching and Control) and four replicates. In the Zai treatment, 200 g cattle manure was added per Zai hole (2.8 t/ha) and millet straw (2.0 t/ha) was spread in the mulching treatment. The control treatment did not receive cattle manure or millet straw. The measurements concerned grain and straw yield of millet (Pennisetum glaucum (L.) R. Br.) as well as physico-chemical soil characteristics. The results show that the Zai + mulching treatment improved soil fertility parameters and grain yield significantly. The content of available phosphorus and clay in the soil was doubled after two years. The soil organic carbon content had increased from 0.45 to 2.1 g kg-1. The cation exchange capacity and pH had increased by one compared to the control. The content of total nitrogen (0.1 to 0.2 g kg-1) and total potassium (8.6 to 57.8 mg kg-1) did not vary significantly between treatments. An increase of 250 kg ha-1 grain of millet compared to the control was obtained. Concerning the straw yield, the highest values were obtained by Zai treatment in both years (855±216 kg ha-1 in 2012 and 843±313 kg ha-1 in 2013) and Zai + mulching in 2013 (888±251 kg ha-1). The combination Zai + mulching improved the soil fertility and millet productivity and can be used to restore degraded soils.
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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".