How do farmers adapt to agricultural risks in northern India? An agent-based exploration of alternate theories of decision-making
Bibliographic record
Abstract
Agricultural decision-making processes occur in complex and dynamic environments and are highly contextual. Despite evidence to the contrary, utility maximization is often the implicit theoretical assumption underlying agricultural decision-making processes. This study undertakes an exploratory approach to test alternative theories of human decision-making on the process of agricultural adaptation of farmers in India by synthesizing multiple sources of social and environmental data. We developed an empirical agent-based model (ABM) to simulate past adoption decisions of six agricultural adaptation strategies of 959 farmers in northern India. The model assessed the fit of four major decision-making rules - utility maximization, self-satisficing, social norms, and random choice for farmers differentiated by farm size. Scenario analysis was conducted to test whether (and which) alternative decision-making rules offered a better explanation of the adoption of (which) adaptation strategies. Results demonstrated that the utility-maximizing decision rule had a higher fit for productivity-enhancing adaptation strategies, such as adopting high yield varieties and enhanced fertilizer use, with model performance increasing, generally, with farm size. The adoption of climate tolerant varieties by farmers was most closely guided by self-satisficing and social norms decision-rules, with the model performance, under both scenarios, highest for marginal landholders. Marginal farmers are more likely to use these heuristics to adopt climate tolerant varieties as their decisions may not necessarily be geared towards increasing profit, unlike larger farmers. Social norms had a higher fit for the adoption of climate-related strategies, including enhanced irrigation, with model fit increasing, generally, with farm size. Agricultural policy and extension efforts that incorporate the varied motivations and heuristics of agricultural decision-making, rather than assuming adaptation as a utility maximization exercise, can better design, develop, and disseminate solutions to support the adaptive capacity of farmers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".