Productivity enhancement of salt‐affected environments through crop diversification
Bibliographic record
Abstract
Abstract Recent trends and future demographic projections suggest that the need to produce more food and fibre will necessitate effective utilization of salt‐affected land and saline water resources. Currently at least 20 per cent of the world's irrigated land is salt affected and/or irrigated with waters containing elevated levels of salts. Several major irrigation schemes have suffered from the problems of salinity and sodicity, reducing their agricultural productivity and sustainability. Productivity enhancement of salt‐affected land and saline water resources through crop‐based management has the potential to transform them from environmental burdens into economic opportunities. Research efforts have led to the identification of a number of field crops, forage grasses and shrubs, aromatic and medicinal species, bio‐fuel crops, and fruit tree and agroforestry systems, which are profitable and suit a variety of salt‐affected environments. Several of these species have agricultural significance in terms of their local utilization on the farm. Therefore, crop diversification systems based on salt‐tolerant plant species are likely to be the key to future agricultural and economic growth in regions where salt‐affected soils exist, saline drainage waters are generated, and/or saline aquifers are pumped for irrigation. However, such systems will need to consider three issues: improving the productivity per unit of salt‐affected land and saline water resources, protecting the environment and involving farmers in the most suitable and sustainable crop diversifying systems to mitigate any perceived risks. This review covers different aspects of salt‐affected land and saline water resources, synthesizes research knowledge on salinity/sodicity tolerances in different plant species, and highlights promising examples of crop diversification and management to improve and maximize benefits from these resources. Copyright © 2008 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 | 0.001 | 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".