ADAPTING TO CLIMATE CHANGE BY IMPROVING WATER PRODUCTIVITY OF SOILS IN DRY AREAS
Bibliographic record
Abstract
ABSTRACT Considering extreme events of climate change and declining availability of appropriate quality water and/or highly productive soil resources for agriculture in dryland regions, the need to produce more food, forage and fibre will necessitate the effective utilization of marginal‐quality water and soil resources. Recent research and practices have demonstrated that effective utilization of these natural resources in dry areas can improve agricultural productivity per unit area and per unit water applied. This paper focuses on the following three case studies as examples: (1) low productivity soils affected by high levels of magnesium in soil solution and on the cation exchange complex; (2) degraded sandy soils under rainfed conditions characterized by low water‐holding capacity, organic matter and clay content and (3) abandoned irrigated soils with elevated levels of salts inhibiting growth of income generating crops. The results of these studies demonstrate that application of calcium‐supplying phosphogypsum to high‐magnesium soils, addition of clays to light textured degraded soils and phytoremediation of abandoned salt‐affected soils significantly improved productivity of these soils. Furthermore, under most circumstances, these interventions were economically viable, revealing that the efficient use of marginal‐quality water and soil resources has the potential to improve livelihoods amid growing populations in dry areas while reversing the natural resource degradation trend. However, considerably more investment and policy‐level interventions are needed to tackle soil degradation/remediation issues across both irrigated and dryland agricultural environments if the major challenge of producing enough food, forage and fibre is to be met. Copyright © 2011 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".