Irrigated Agriculture as an Adaptation Strategy Against Climate Change: A Review
Bibliographic record
Abstract
Agriculture evolved to increase crop productivity and diminish plague effects. As a negative outcome of the human footprint by agriculture and industrialization, overall economic practices have led to substantial alterations in the environment (i.e., greenhouse gas production, elevated atmospheric temperature, more extreme climatic events), collectively known as climate change. The fast-changing environment due to climate change is most common in the tropics, the impacts of this phenomenon are perceived in different regions of the world, where most agricultural activity occurs. These facts reinforce the requirement for diminishing climate change producing activities and implementation of adaptive practices for long-term agricultural productivity and sustainability. Albeit may sound counter-intuitive, agroecological systems and traditional knowledge may provide alternatives to mitigate climate change effects in the context of agriculture. This review comprehensively describes the development of irrigated agriculture, major effects of climate change on irrigation, and further explores alternative practices stemming from agroecological systems or traditional knowledge, which could improve agricultural productivity and sustainability. Among some strategies, it is proposed to establish climate risk planning, agricultural producers must modify the application of their inputs to adjust to the new water and thermal requirements; implement conservation techniques to reduce the loss of soil moisture and thus ensure the development of crops in a drier and warmer environment as indicated by climate change projections. Likewise, the implementation of varieties tolerant to water stress is one more adaptation action that would allow continuing cultivating in lower regions, where the largest irrigated area is concentrated and which would receive the greatest impact from an increase in temperature. In this way, it will be necessary to implement new approaches, technologies and policies to learn from the past, following the new climate scenarios, conserving and making rational use of natural resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".