The limits of increasing food production with irrigation in India
Bibliographic record
Abstract
Growing populations and dietary shifts to include higher proportions of meat are projected to double global food demand by 2050. Previous global studies have proposed and evaluated possible solutions by closing agricultural yield gaps, defined as the difference between current and potential crop yields. We compliment previous studies by developing a method for more accurately calculating potential changes in cereal grain production under different irrigation scenarios, explicitly incorporating yield differences associated with different sources of irrigation. Irrigating with groundwater often leads to higher crop yields than irrigating with surface water because of the greater facility to tailor both the volumes of water and the timing of application. Two possible scenarios for increasing production in India are examined, the first where all non-irrigated fields are irrigated proportionally to the State-specific distribution of irrigation sources, and the second where all non-irrigated fields are irrigated with groundwater: Rice production increases by 14 and 25 % in scenarios 1 and 2 respectively, but wheat production increases by only 3 % in both scenarios. Increased irrigation water consumption from irrigating fields that are currently non-irrigated is estimated at 31 % for rice and 3 % for wheat using the Global Crop Water Model. A third scenario estimates the potential loss in production without the use of irrigation: rice would be 75 % and wheat 51 % of current production. Our methodology and results can help policy makers estimate the current and potential contribution of irrigation sources to agricultural production and food security in India and can with facility be applied elsewhere.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".