IDENTIFICATION AND MANAGEMENT OF RISK IN PRODUCTION AGRICULTURE: THE CASE OF SASKATCHEWAN GRAIN AND OILSEED FARMERS
Bibliographic record
Abstract
Agricultural risks and uncertainty play a significant role in determining the stability of farm income. Successful farm managers are those who are able to identify and manage risks they face in the production process. Farmers have different perception of sources of risk and the risk management strategies adopted to manage risk also differ based on the perception of the importance the strategy in managing risk. Therefore, it is important to understand the risk perception of specific group of farmers to provide guidance in designing appropriate risk management strategies. This study uses survey of grain and oilseed farmers in Saskatchewan to identify their most important sources of risk, risk management strategies and model how farm and producer characteristics affect the perception and management of risks in production Best-Worst Scaling and latent Class cluster analysis were the tools employed to analyse the data. The results suggest production and marketing risks such as variation in output prices, rainfall variability, change in input prices, diseases and pests, accidents and health/disability, natural disasters, unable to meet quality requirements and risk management strategies including producing at low cost, keeping financial reserve, pests and diseases control, reducing debt level, buying crop insurance, diversification, getting market information and forward contracting as important sources of risk and risk management strategies to grain and oilseed producers in Saskatchewan. The cluster analysis also showed the existence of two unique clusters based on perception of sources of risk and three unique segments in relation to their perception of important risk management strategies. The regression analysis also suggests farmers use different risk management strategies based on a particular risk faced. Several socio-economic variables including off-farm income, sales, experience, debt to asset ratio, education household income and age were found to influence farmers’ perception of risks and risk management strategies. The study should guide policy makers and service providers for appropriate targeting in the design of risk management strategies to help producers cope with risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".