Sustainable intensification with irrigation raises farm profit despite climate emergency
Bibliographic record
Abstract
Societal Impact Statement Despite comprising a small proportion of global agricultural land use, irrigated agriculture is enormously important to the global agricultural economy. Burgeoning food demand driven by population growth—together with reduced food supply caused by the climate crisis—is polarising the existing tension between water used for agricultural production versus that required for environmental conservation. We show that sustainable intensification via more diverse crop rotations, more efficient water application infrastructure and greater farm area under irrigation is conducive to greater farm business profitability under future climates. Summary Research aimed at improving crop productivity often does not account for the complexity of real farms underpinned by land‐use changes in space and time. Here, we demonstrate how a new framework— WaterCan Profit —can be used to elicit such complexity using an irrigated case study farm with four whole‐farm adaptation scenarios ( Baseline , Diversified , Intensified and Simplified ) with four types of irrigated infrastructure ( Gravity , Pipe & Riser , Pivot and Drip ). Without adaptation, the climate crisis detrimentally impacted on farm profitability due to the combination of increased evaporative demand and increased drought frequency. Whole‐farm intensification—via greater irrigated land use, incorporation of rice, cotton and maize and increased nitrogen fertiliser application—was the only adaptation capable of raising farm productivity under future climates. Diversification through incorporation of grain legumes into crop rotations significantly improved profitability under historical climates; however, profitability of this adaptation declined under future climates. Simplified systems reduced economic risk but also had lower long‐term economic returns. We conclude with four key insights: (1) When assessing whole‐farm profit, metrics matter: Diversified systems generally had higher profitability than Intensified systems per unit water, but not per unit land area; (2) gravity‐based irrigation infrastructure required the most water, followed by sprinkler systems, whereas Drip irrigation used the least water; (3) whole‐farm agronomic adaptation through management and crop genotype had greater impact on productivity compared with changes in irrigation infrastructure; and (4) only whole‐farm intensification was able to raise profitability under future climates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".