Bibliographic record
Abstract
Crop production is highly dependent upon weather; therefore, future climate change could adversely affect the burgeoning global population. The primary objective of this study was to predict the consequences of climate change on agriculture. Since current climate projections use general circulation models (GCMs) on a global scale, a statistical downscaling model (SDSM) was used to downscale these outputs into a local scale, essential for reliable crop model simulations.By linking predicted changes in local climate to soil properties and crop characteristics through field and laboratory studies, thresholds of soil moisture content for efficient irrigation scheduling were defined, and an irrigation requirements model (IRM) was developed. Using the IRM, irrigation was triggered when soil moisture was 18 or 24 mm for peaches grown in clay and sandy soils, respectively, and was also triggered at 56 mm for grapes grown in clay soils. It was noteworthy that the IRM reduced irrigation needs by 20 to 25% without affecting yield of peaches (50 to 60 kg/tree). Regarding predicted increases in temperatures and variability in precipitation, the SDSM-HadCM3 A2 scenario forecast the greatest increases, namely ~3.5 and ~2.5°C in average monthly maximum and minimum temperatures, respectively, during the growing season, compared to a 1961-1990 base period. Moreover, precipitation events were also predicted to be more frequent (8 to 30%) and intense (10 to 50%) during crop growing months.With these future climate change scenarios, irrigated peach yield could increase 5 to 20%, since actual tree transpiration reached 0.8 kg/h (compared to a maximum of 0.4 kg/h without irrigation). Furthermore, with irrigation, fruit firmness, the best indicator of ripening and predictor of peach storage potential, is expected to improve by 20% over the current value of 340 kPa.The most novel aspect of this study was development of the IRM, and its prediction of optimal irrigation needed to sustain or increase crop yield and quality, and concurrently conserve water.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".