Assessment of Precision Irrigation on Potatoes in Southern Alberta
Bibliographic record
Abstract
Precision irrigation, in which water is applied at different times and rates across and within farm fields according to environmental and soil conditions, offers a promising solution for effective water resource management. Excess irrigation can lead to disease and lower yields while under irrigation leads to a deficit often resulting in reduced yields and quality. Understanding water requirements is key to making informed irrigation decisions. Optimizing water usage for potato crops in southern Alberta is important in the face of water scarcity challenges. This project evaluates precision irrigation scheduling performance, including creation of management zones within a field, to determine which field variables have the most effect on potato yield, and analyze the effectiveness of predictive software. The data collected from five irrigated potato fields in Southern Alberta from 2019 to 2022, as well as from the Integrated Agriculture Technology Center (IATC) in 2021 and 2022, were analyzed. Annually, soil parameters, topography, moisture usage, and yield were evaluated at 5-6 monitoring points per field to represent variations within that field. Soil moisture at each point was monitored using moisture sensors and the Alberta Irrigation Management Model (AIMM) software was used to estimate evapotranspiration (ET) and soil moisture changes at the IATC site. It was revealed that topographic complexity had the most significant influence on soil moisture dynamics, resulting in significant effects on potato yield. Soil moisture had a significant positive effect on yield during the tuber bulking stage, especially at a depth of 0-35cm, but a significant negative impact at a depth of 35-60cm. While variations in growing degree days and soil complexity did not consistently affect yield, there was a tendency towards a negative effect. Moisture content variations among points at the IATC sites had no significant relationship with yield, indicating success in the ability of predictive scheduling and VRI in reducing this source of yield variability. The AIMM model demonstrated higher reliability in prediction of irrigation requirements in 2022 than 2021, possibly due to differences in factors such as soil organic matter, bulk density of the soil, soil texture, weather, topography, and subsoil constraints, which affect model performance, but were not measured in this study. Precision irrigation offers a potential solution to address water scarcity challenges by optimizing water use efficiency and enhancing crop yield and quality through informed irrigation practices and technology integration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".