The DOI matches its Crossref record.
Risk, uncertainty, and learning in adoption of a crop innovation
Abstract
Risk and uncertainty have often been suggested as causes of poor adoption of rural innovations, but empirical evidence has been scarce. This study focuses on a new crop‐type, chickpeas, in Western Australia to gather such evidence. The empirical models developed are based on a theoretical framework that conceptualizes adoption as a dynamic decision process involving information acquisition and learning‐by‐doing by growers who vary in their managerial abilities, risk preferences, and their perceptions of the profitability and riskiness of the innovation. Learning encompasses improvements in skill as well as reductions in uncertainty. An annual face‐to‐face survey of over 100 farmers was conducted over 3 years, eliciting the farmers' risk attitudes and their subjective distributions of yields and prices. Two limited dependent variable models, Tobit and Probit, are used to estimate the empirical model. There is a high degree of goodness‐of‐fit for both models. The study provides strong empirical support for the primarily economic character of the adoption decision, and highlight the importance of economic risk in the process. The two risk‐related factors with greatest impact on the adoption decision were risk aversion and relative riskiness of the innovation. Risk aversion tended to reduce adoption, and to do so to a greater extent as relative riskiness and scale increased. Results also reveal the key role that trialing of the innovation plays in adoption.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Seeing is believing? Evidence from an extension network experiment
Kondylis, F., Mueller, V., & Zhu, J. (2017) · Journal of Development Economics · doi:10.1016/j.jdeveco.2016.10.004 · Cited by 191
Psychological factors influencing technology adoption: A case study from the oil and gas industry
Roberts, R., Flin, R., Millar, D., & Corradi, L. (2021) · Technovation · doi:10.1016/j.technovation.2020.102219 · Cited by 121
Information quality and effectiveness for more rapid adoption decisions by farmers
Llewellyn, R. S. (2007) · Field Crops Research · doi:10.1016/j.fcr.2007.03.022 · Cited by 79
Canales, E., Bergtold, J. S., & Williams, J. R. (2024) · American Journal of Agricultural Economics · doi:10.1111/ajae.12414 · Cited by 38
Aubert, C., Raineau, Y., & Raynal, M. (2026) · European Review of Agricultural Economics · doi:10.1093/erae/jbaf065
Metadata
- Authors
- Ghadim, A. K. A., Pannell, D. J., & Burton, M. P.
- Venue
- Agricultural Economics
- Publisher
- Wiley
- Published
- 2005-06-14
- Volume
- 33
- Issue
- 1
- Pages
- 1-9
- ISSN
- 0169-5150 (ISSN-L)
- 1574-0862
- OpenAlex
- W2030198620
- Cited by
- 288
- References
- 0
- FWCI
- 32.27
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Risk, uncertainty, and learning in adoption of a crop innovation. https://app.cochat.ai/papers/10.1111/j.1574-0862.2005.00433.x
- The paper
- Ghadim, A. K. A., Pannell, D. J., & Burton, M. P. (2005). Risk, uncertainty, and learning in adoption of a crop innovation. Agricultural Economics. https://doi.org/10.1111/j.1574-0862.2005.00433.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.