Predicting crop water requirements and yield for tomato under a humid climate
Bibliographic record
Abstract
Methodologies to predict crop water requirements in arid and semi-arid areas are well known. Humid areas pose a challenge, because irrigation is normally required only for short periods of a few weeks or months, during the peak of the summer growing season. The amount of irrigation water is also much less compared to the arid and semi-arid regions and is supplementary to rainfall. The objective of this study was to assess the usefulness of the AquaCrop (V 6.1) to estimate irrigation requirements for field grown tomato in a humid region of Eastern Canada. Input to the model was obtained from two years of field trials conducted at the Macdonald Farm of McGill University, Quebec, Canada. There were three irrigation treatments in 2017: 100%, 70 %, and 30 % of plant available water (AWC); and in 2019: 85 %, 60 % and 30 % of plant available water (AWC). The model was calibrated with the 2017 field results and validated with the 2019 field results. The calibrated and validated parameters were evapotranspiration, dry yield, total biomass and water productivity (kg of dry yield/m3 of water transpired). In the calibration phase, AquaCrop estimated dry yield and total biomass with a R2 of 0.94 and 0.86, respectively. For the validation phase, AquaCrop estimated dry yield and total biomass with a R2 of 0.84 and 0.96 respectively. The model overestimated biomass under water limiting conditions (30 % AWC) and underestimated the dry yield of tomato in general. There was no statistical difference in water productivity irrespective of the irrigation treatment. Overall, the model is suitable for predicting irrigation water requirement, crop yield, total biomass, and water productivity for tomato under humid climate. The validated AquaCrop model was then used to predict the irrigation water requirements and fruit yield for the once – in- 35-year dry year and the year of average growing season rainfall, for three different soil types (silty clay, sandy loam, heavy clay) under four irrigation regimes (90%, 75%, 65% and 50% of AWC). The irrigation water requirement for the once-in-35 dry year of 2001 under 90 % AWC was ~ 250 mm irrespective of the soil type and the observed water productivity was 2.32 Kg/m3. The year of average growing season rainfall (1993) with a total rainfall of 500 mm required ~ 165 mm under 90 % AWC with a water productivity of 2.38 Kg/m3. The sandy loam soil has the highest water productivity irrespective of the irrigation treatment for both the dry year of 2001 and the average year of 1993. Under water scarce conditions, 75 % AWC it could be used instead of 90 % AWC, with no significant decrease in crop yield and water productivity
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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 | 0.000 |
| 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 | 0.000 |
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 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".