Effects of Irrigation Systems on Physicochemical Properties of Soil at Different Depths: A Case Study at a Farm Near Ziway Lake, Ethiopia
Bibliographic record
Abstract
Abstract Irrigation, regardless of its possible negative impacts on the environment, is the prime choice to combat poverty and sustain food security. Changes in soil physicochemical properties and subsequent loss of productivity are the main challenges associated with irrigation practices. This study investigated the effect of furrow and basin irrigation systems on surface and subsurface soil physicochemical properties in comparison with properties in non‐irrigated fields. The soil properties were analysed as repeated measures using appropriate covariance structure. The results showed significant interaction between irrigation and depth on electrical conductivity, pH, Na+, Ca2+, Mg2+, soluble cations and organic carbon; but only the main effect of irrigation system was significant on K+, HCO3−, Cl−, SO42−, soluble anions and sodium adsorption ratio. Both irrigation systems had lower soluble salt content on the surface than at the subsurface layer with basin irrigation having slightly higher, but the non‐irrigated fields showed the opposite. The soil pH was high, particularly in the non‐irrigated fields, which could affect the availability of important soil constituents like Ca, Mg, Co, Cu, Fe, Mn and Zn, and increase Na hazard and boron (B) toxicity that would impair crop productivity. Therefore, the effects of the irrigation method on the soil properties should be well understood and appropriate precautions should be taken when choosing irrigation methods. © 2018 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".