Effect of topographical and soil complexity on potato yields in irrigated fields
Bibliographic record
Abstract
Spatial variation of soil moisture as affected by topography and soil textural patterns is an important control on variability of yields in agricultural fields. Site-specific irrigation management could be a way of increasing water use efficiency and evening out yield variability. A better understanding of regional landscapes is required to identify which types of fields could benefit from SSIM. The causal influence of landscape characteristics on yields under irrigated conditions is poorly understood. Here, a new approach is used to infer the causal impact of topography and soil properties on yields of irrigated potatoes. The analysis comprises a four-year long dataset of potato yield, soil texture, hydrological, topographical, and meteorological variables collected from 99 sites within 19 fields in southern Alberta, Canada, from 2019 to 2022 inclusive. Using a Bayesian linear mixed model, we quantified the effect of topographical complexity (median equal −3.39 MT ha −1 ), soil texture complexity (median equal −1.97 MT ha −1 ), and the cases where both were true (median equal −4.47 MT ha −1 ), on potato yields. Using the same method, we quantified the effect on initial soil water storage with medians equal to −13.1 mm (topographical complexity), 1.7 mm (soil complexity), and −6.7 mm (both). The topographical and soil complexity metrics applied could be used to identify fields that are suitable for SSIM-VRI. Findings are likely specific to the geographical and weather conditions encountered in the study area. We encourage implementation of our method in different regions to determine the generality of our results. • Causal analysis and Bayesian Hierarchical Model used to quantify the effect of topographical complexity on potato yields under irrigation. • Flatter topography was revealed to be wetter than non-wet, affecting initial soil moisture which in turn negatively impacted yields. • Soil texture complexity had smaller negative effects on potato yield and wasn’t related to soil moisture.
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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) | 0.000 | 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 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".