While international pest management programs have long relied on farmer cooperation to spread pest control information at larger scales, a study by French researchers published in the open-access journal PLoS Computational Biology on Thursday 13th October 2011 reveals that slow information diffusion within farmer communities gives rise to significant lags in implementation of pest management procedures.
Food security of millions of people in the developing world has faced a growing number of challenges in recent years, including risks associated with emergent agricultural pests. While pest management programs have a larger place than ever on the international policy agenda, the debate concerning their efficiency at large scales has remained unresolved. Pest management practices that rely on farmer cooperation to share pest control information have been favoured, but the efficiency of such methodologies has been questioned due to incomplete knowledge of variation in farmers' practices, and their complex interactions with pest dynamics. A modeling framework, integrating both social and ecological perspectives, was therefore needed to better predict the efficiency of pest management programs.
The modeling framework developed by the authors was comprised of an agent-based model combining social (information diffusion theory) and biological (pest population dynamics) models to study the roles played by cooperation and sharing of pest management information among small-scale farmers in controlling an invasive pest. The model was implemented with field data from large-scale surveys of approximately 300 farmer households in the Ecuadorian Andes, and was undertaken within a regional pest management program funded by the French Institute for Research and Development (IRD) and the McKnight Foundation.
Though the slow learning process places restrictions on the knowledge that can be generated using cooperative pest management practices, the authors conclude that if individuals learn from others about the benefits of early prevention of pests, then a temporary educational effort may have a sustainable long-run impact on pest control.
FINANCIAL DISCLOSURE: This work was conducted within the project ''Innovative Approaches for Integrated Pest Management in changing Andes'' (C09-031) funded by the McKnight Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
COMPETING INTERESTS: The authors have declared that no competing interests exist.
CITATION: Rebaudo F, Dangles O (2011) Coupled Information Diffusion–Pest Dynamics Models Predict Delayed Benefits of Farmer Cooperation in Pest Management Programs. PLoS Comput Biol 7(10): e1002222. doi:10.1371/journal.pcbi.1002222
Institut de Recherche pour le Développement,
Telephone: 00 593 9494 5853
Preferred contact: Email
This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by journal staff, or by the article authors and/or their institutions. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.
PLoS Journals publish under a Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/), which permits free reuse of all materials published with the article, so long as the work is cited (e.g., Brinkworth RSA, O'Carroll DC (2009) Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology. PLoS Comput Biol 5(11): e1000555. doi:10.1371/journal.pcbi.1000555). No prior permission is required from the authors or publisher. For queries about the license, please contact the relative journal contact indicated here: http://www.plos.org/journals/embargopolicy.php
About PLoS Computational Biology
PLoS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained.
About the Public Library of Science
The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.plos.org.
AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert! system.