Building capacity for assessing spatial-based sustainability metrics in agriculture
Bibliographic record
Abstract
Crop yield is influenced over time and space, namely, by a wide range of variables linked with crop genetics, agronomic management practices and the environment under which the crop dynamically responds to maximize growth potential and survival. Such variability can pose substantial uncertainty and risks in the use of agricultural sustainability decision-making frameworks that include crop yield as a leading metric. Here, decision analytics can play a vital role by guiding the use of statistical-based analytics to build in a higher degree of intelligence to enable better predictive (i.e., crop yield forecasting both over the growing season and inter-annually) and prescriptive (optimization across crop areas and subdivisions) approaches. While inter-annual variability in yield can be modelled based on a deterministic trend with stochastic variation, quantifying the variability of yield and how it changes across different spatial resolutions remains a major knowledge gap. To better understand how yield scales spatially, we integrate in this study, for the first time, multi-scale crop yield of spring wheat and its variance (i.e., field to district to region) obtained within the major wheat growing region of the Canadian Prairies (Western Canada). We found large differences between the mean and variance from field to district to regional scales, from which we determined spatially-dependent (i.e., site specific) scaling factors for the mean and variance of crop yield. From our analysis, we provide several key recommendations for building capacity in assessing agricultural sustainability using spatial-based metrics. In the future, the use of such metrics may broaden the adoption and consistent implementation of new sustainable management protocols and practices under a precautionary, adaptive management approach.
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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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".