Identifying Isoyield Environments for Field Pea Production
Bibliographic record
Abstract
Cultivars are often recommended to producers based on their averaged yields across sites within a geographic region. However, this geography‐based approach gives little regard to the fact that not all sites in a given region have the same level of production capacity. The objective of this paper was to describe a performance‐based approach to identifying groups of sites with similar yielding ability (i.e., isoyield groups), but not necessarily contiguous, and its use for analyzing the yield data from field pea (Pisum sativum L.) cultivar trials conducted across the Province of Alberta, Canada, from 1997 to 2001. Of 34 sites tested during the 5 yr, 11 were in 1997, 20 in 1998 and 2000, 22 in 1999, and 21 in 2001. The consecutive use of regression analysis and cluster analysis allowed for classification of test sites in individual years into different isoyield groups: six in 1997; 10 in 1998, 2000, and 2001; and 12 in 1999. However, the most meaningful isoyield groups were those based on the data across the 5 yr through a normalization procedure developed for averaging the multiyear unbalanced data. The use of such averages significantly lessens the impact of random year‐to‐year variation on the sites, resulting in only seven isoyield groups for the 34 test sites. The identification of isoyield environments (i) facilitates choosing appropriate cultivars for specific environments and (ii) provides a basis for scaling down the cultivar testing program in Alberta.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".