Data Aggregation Issues for Crop Yield Risk Analysis
Bibliographic record
Abstract
With increased emphasis on risk management in agriculture and a lack of disaggregated or farm‐level yield time series, decision makers are often faced with having to make adjustments to temporal yield risk measures obtained from readily available but aggregated yield data. This paper provides some empirical evidence on what type of aggregation bias to expect when measuring temporal yield risk using yield observations averaged across a region relative to yield risk estimated from quarter‐section yield time series in wheat. This study highlights some of the challenges faced when estimating aggregation distortions in measuring yield risk defined by temporal variance, especially given the nature of the empirical data set used. Cluster analysis, visual examination of relative frequency distributions and mapping of yield risk clusters suggest that using a readily available, aggregate temporal yield risk measure has the tendency to underestimate yield risk observed at the quarter‐section level and that clear, geographic yield risk boundaries do not exist in municipalities or across larger areas in this study. Further research on crops more risky than wheat appears promising. Avec un plus grand intéret sur la gestion du risk dans l'agriculture et un manque de données détaillees ou bien de collections de séries temporelles sur les rendements, les décideurs sont souvent tenus d'apporter des correctifs aux measures du risk obtenues a partir des données de rendements qui sont disponibles. Cet artcle apporte une preuve empirique du type de biais lie a l'agrégation qui peut être présent dans le calcul du risk de rendement temporel obtenu a partir de rendements moyens de blé observés au niveau régional en comparaison du risk de rendement qui est estimé a partir de données basées sur des quart‐de‐sections. Cette étude met en exergue quelques uns des obstacles qui se présentent dans l'estimation de distosions liées a l'aggrégation dans le calcul du risk de rendement défini par la variance temporelle, speciallement étant donne la charactère empirique des données utilisées. L'analyse de groupe, l'examen visual de la distribution des fréquences relatives, et la cartographie de classes de risk de rendement suggèrent que l'utilisation de la measure du risk de rendement basée sur des données disponibles de risk aggrège temporel a tendence a sousestimer le risk de rendement observe au niveau des quart‐de‐sections et qu'il n'y a pas de frontières de risk de rendement certaines, géographiques qui existent entre les municipalités ou bien a travers les zones plus larges examinées dans cette etude.
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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 | 0.118 | 0.378 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".