EVALUATING THE VALUE OF RECORD KEEPING IN DECISION MAKING ON COW/CALF OPERATIONS IN CANADA
Bibliographic record
Abstract
Research regarding adoption of best management practices and benefits is abundant in the agriculture industry. Specifically, recent Canadian research has shown that record keeping and benchmarking practices are associated with increased productivity (Manglai, 2016) and it is important that managers have prompt and correct information on costs and production to make good business decisions (Maqbool, 2017). However, there has been little research around which specific records and characteristics influence the cow/calf producer’s satisfaction and decision-making confidence regarding animal production. In this thesis, I look more closely at what influences a rancher’s propensity to keep different records, and what rancher characteristics influence a rancher’s decision-making confidence with replacement selection and satisfaction with animal production performance. In this thesis I conducted interviews with cow-calf producers who provincial industry associations considered leading adopters of record keeping and developed questions included in a Canada-wide survey (n=351) on record collection and application. I used satisfaction and confidence as proxies for value of records given the previously known lack and inconsistency of suitable production measures (wean weight and financial ratios). Being an analytical rancher (consulting data and using logical reasoning when making decisions) was shown to have a positive influence on a rancher’s likelihood of being confident in each different animal production decision area. My results show that for ranchers, being analytical by tracking and analyzing their operation’s production status with records, increases the likelihood that they are confident in decisions. Understanding this relationship helps industry make the case when they encourage greater analysis of records kept when making decisions (versus intuitive decision making that may be influenced by memories and limited experience). Further, my results show that a rancher’s satisfaction with animal production performance was significantly and positively influenced by having high decision confidence, meaning ranchers who indicated they analyzed records when making decisions were more likely to be more satisfied with their decision-making process and more satisfied with the production performance of their herd. As well, ranchers who considered themselves to have an internal locus of control (a strong belief that their success is reliant on their own actions) were more likely to have increased satisfaction which could be a result from them being incentivized to act in a way that they have control over their outcomes on the ranch. This knowledge can help industry continue to reassure ranchers that choosing to be intentional with their management styles will increase their satisfaction and therefore outcomes.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".