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Decision-making for pest management in agriculture is often assisted by sampling plans that guide users in determining the need for an intervention. Even though Tuta absoluta is easily recognizable by most tomato growers and that several sampling plans have been developed, adoption of decision-making systems for this pest is still incipient. Two potential obstacles for adoption are market uncertainty and farmer's risk aversion. Both obstacles could be tackled by adopting sampling plans that allow farmers to plan interventions according to rough estimations of economic thresholds and the intuition and experience gained by farmers. In this study, we evaluated four sampling plans using computer simulations and field trials. We compared the efficiency and the ability of each plan to both estimate the actual mean number of larvae per plant and to classify pest populations according to a predefined economic threshold. We also analyzed the time spent, and plants examined by human subjects applying each plan on a tomato crop with a T. absoluta infestation slightly over a predefined economic threshold. We show that sampling plans that deliver the most precise classifications, are poorest in delivering pest density estimations and vice versa. Our findings are consistent for both human subjects and computer simulations. However, the average number of samples required by sampling plans does not reflect the time spent by humans sampling real plants. Our results show that sampling plans based on counts, as opposed to those based on binary data, can efficiently provide reliable information on a current level of T. absoluta infestation relative to an estimated decision threshold. We suggest that sampling plans that promote the creation of farmer's memory, such as those based on counts, may be more suitable to both reduce risk aversion and increase adaptability to market uncertainty.

PurposeThis study aims to identify the most relevant causal factors and the feedback loops of the dynamics between Tuta absoluta incidence in tomato crops and farmers' reactions to the problem. The authors seek to develop a conceptual model based on farmers' know-how to address crop damage by T. absoluta at a local and regional levels in order to determine how to confront this problem in the tomato-growing region of Sáchica, Colombia.Design/methodology/approachCommunity-Based System Dynamics (CBSD) is a participatory research methodology in which a group of stakeholders identifies relevant variables and the cause-effect relations among them which are then arranged into a causal loop diagram. The authors implemented this methodology in a workshop, focused on the farmers' insights related to the pest situation at the local and regional level, to achieve a causal loop diagram that explained pest dynamics and their potential management.FindingsThe relevant factors for the presence of T. absoluta, seen in the causal loop diagram, vary regionally and locally. At the local level, the pest impacts tomato production, farmers' well-being and their cash flow, while at the regional level, it affects market dynamics and environment and promotes regional coordination among farmers. Farmers propose product innocuity as a key regional objective. They also proposed establishing a planting calendar and census of greenhouses to control the pest throughout the region and the tomato supply.Research limitations/implicationsFirst, the synthesized model could not be validated with the farmers due to the COVID 19 epidemic. However, the authors held sessions with experts to analyze each result. Second, decision-makers from the local government did not participate in the workshop. Nevertheless, the approach of the workshop was aimed at understanding the mental models of the farmers since they are the ones who decide how pests are managed. Finally, even though farmers showed interest in projects aimed at proposing area-wide, long-term and wide pest control strategies, there is a risk that they will not adopt the proposed changes, due to risk aversion.Originality/valueCBSD has not been applied to agricultural systems to analyze impacts from pests at the local and regional levels. The results of this study contribute to designing future interventions for pest control in the region, along with the factors which may turn out to be “side effects” or unwanted results. To design pest control interventions at a regional level, a sound understanding of the variables or factors that control the system dynamics at various levels is required. This study represents the first step towards that end.

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