Bibliographic record
Abstract
The successful diffusion of new agricultural biotechnologies depends on widespread producer acceptance and uptake. The assessment of the key factors that can influence producer decision making is fundamental to the understanding of the rate of uptake, the attainable rate of potential benefits and the effectiveness of different measures that can stimulate the diffusion of these innovations. This dissertation examines three related aspects of cow-calf producer decision making with regards to the uptake of genomic selection for feed efficiency in beef cattle production in Canada. Improvements in feed efficiency can have significant economic and environmental impacts on beef cattle production through the reductions in feed costs and greenhouse gas emissions. Specifically, the following objectives are addressed: (i) the evaluation of the factors affecting cow-calf producer willingness to pay (WTP) for genomically improved feed efficient bulls (ii) the assessment of how supply chain linkages can influence cow-calf producer decision making (iii) the assessment of environmental outcomes from different decisions made by cow-calf producers and the extent to which the opportunity to obtain additional revenue from these environmental externalities can influence these decisions. In the first paper, cow-calf producers’ private valuation of genomic information on feed efficiency in their bull purchase decision is assessed. The analysis is situated in a multi-trait context that accounted for both conventional and genomic breeding information and cow-calf producer heterogeneity due to attitudes and farm practices. The results indicated that willingness to pay (WTP) for genomic information is positive; cow-calf producer valuation of conventional breeding technologies is relatively higher. The results further showed evidence of heterogeneity in cow-calf producer preferences according to characteristics such as risk perceptions, calf retention practices and familiarity with genomics. The results of the second paper highlight the potential supply chain issues that can impact the widespread diffusion of the innovation. From the stylized industry framework outlined, the allocation of benefits from the genomic selection for feed efficiency is skewed towards feedlot operators who typically do not incur the cost of bull purchases in fragmented systems. The results suggest that in the absence of a mechanism that rewards cow-calf producers for the additional cost associated with the genomic bull, the diffusion of the innovation is likely to be slow. The results of the third paper show that breeding for feed efficient cattle is associated with positive environmental outcomes across the three agroecological zones considered. The simulation analysis showed that these environmental benefits differ spatially and are highest when the selection for feed efficiency is combined with limits on stocking rates. While the participation in a carbon offset scheme is an additional source of revenue which can possibly change cow-calf producer incentives, the results show that revenue from the offset scheme is inadequate given the low level of emissions per farm and the examined price of carbon. Overall, the empirical results of this study suggest that genomic selection for feed efficiency can improve the economic and environmental performance of the Canadian beef cattle industry. The potential supply chain bottlenecks and the spatial heterogeneity in cow-calf production must however be accounted for in the design of mechanisms to stimulate cow-calf producer uptake.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".