FarmCan: A Physical, Statistical, and Machine Learning Model to Forecast Crop Water Deficit at Farm Scales
Bibliographic record
Abstract
Abstract. In the coming decades, a changing climate, growing global population, and rising food prices will have significant yet uncertain impacts on both water and food security. The loss of high-quality land, the slowing in annual yield of major cereals, and increasing fertilizer use, all indicate that strategies are needed for monitoring and predicting ongoing and future water deficits on farms for better agricultural water management decisions. Most such activities are based on in-situ measurements which are costly, hard to scale, and ignore the wealth of spatial and temporal information from remotely-sensed data. In this study, we designed FarmCan, a novel and robust climate-informed machine learning (ML) framework to predict crop water demand at the farm scale with up to 14 days lead time. We use a diverse set of simulated and observed near-real-time (NRT) remote sensing data coupled with inputs from farmers, a Random Forest (RF) algorithm, and precipitation (P) prediction from MSWEP to predict the amount and timing of evapotranspiration (ET), potential ET (PET), soil moisture (SM), and root zone soil moisture (RZSM). Our study shows that SM and RZSM are the variables that are more correlated with P, while PET and ET do not show a strong correlation with P, SM, and RZSM. Our case study of 4 farms in the Canadian Prairies Ecozone (CPE) using R2, RMSE, and KGE indicators, shows that our algorithm was able to forecast crop water requirements 14 days in advance reasonably well. We also found that during 2020, RF forecasted ET and PET and needed irrigation (NI) with more accuracy than SM and RZSM, although this might vary based on the soil type, location, year of study, and crop type. Due to the forecasting capability and transferability of the mechanism developed, FarmCan is a promising tool for use in any region of the world to help stakeholders make decisions during prolonged periods of drought or waterlogged conditions, schedule cropping and fertilization, and address local government' policy concerns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".