Financial Benefit of Using Crop Protection Decision Rules Over Systematic Spraying Strategies

Abstract

Decision rule models are considered to be one of the main cornerstones of the implementation of integrated pest management (IPM) programs. Even if the need for such programs to offer cost advantages over conventional strategies is a major incentive for IPM adoption, few studies focus on this financial dimension. In this article, a modeling approach of the response of a pathosystem to a disease control method and of the predictive performance of decision rules is used to explore how some basic factors act on the likelihood of adoption of decision rule models strategies (such as using an IPM system) over systematic strategies (such as systematic-spraying and never-spraying strategies). Even if the average cost of using the decision rule strategies is always lower than the average cost of systematic strategies in several different scenarios, the models developed here showed strong effects of different pathosystems and decision rules on financial benefits. The number of production situations where decision rules are of interest is highly correlated with their accuracy. However, because of the inescapable trade-offs between decision rule accuracy and limiting factors such as its user-friendly characteristics, the use of decision rules is unlikely to reduce costs to <70% of the costs of systemic strategies. In more general terms, this study provides quantitative guidelines on the financial advantage that decision rules can offer in plant protection as well as a better understanding of their potential usefulness.

Economics of a decision–support system for managing the main fungal diseases of winter wheat in the Grand-Duchy of Luxembourg

“…Optimising fungicide sprays by risk analysis and early warning systems is thus of great importance. The profitability of a crop protection strategy for diseases depends on various factors, namely the basic biological parameters of the pathosystem, the technical details of management tools, and historical and real-time information, so as to provide the basis for an effective and reliable forecasting system (Fabre et al, 2007). Although various disease forecast systems exist, the precise control time or economic injury threshold is not always clearly available (Shtienberg, 2013).…”

“…DSS help by limiting potentially harmful side effects of fungicide applications while ensuring economic benefits (Fabre et al, 2007;Shtienberg, 2013). Indeed, a major incentive in adoption of DSS by farmers is related to the cost advantages over conventional strategies (Wearing, 1988;Langvad and Noe, 2006;Fabre et al, 2007).…”

“…DSS help by limiting potentially harmful side effects of fungicide applications while ensuring economic benefits (Fabre et al, 2007;Shtienberg, 2013). Indeed, a major incentive in adoption of DSS by farmers is related to the cost advantages over conventional strategies (Wearing, 1988;Langvad and Noe, 2006;Fabre et al, 2007). Decision rules (i.e., tactical models) designed to provide farmers with binary advice ("treatment is needed" or "it is not worth the cost") are considered to be cornerstones for the implementation of DSS in integrated disease management programs (Hughes, 1999;Way and van Emden, 2000;McCown, 2002).…”

Wireless in-situ Sensor Network for Agriculture and Water Monitoring on a River Basin Scale in Southern Finland: Evaluation from a Data User’s Perspective

“…This leads to applications that monitor mostly local weather and soil characteristics [4]. Agricultural sensor networks have been developed for frost [3] or crop pest warning [12]. They are also an essential component in more advanced decision support systems (DSS) for crop protection [13,14].…”

Epidemiología de la mancha marrón de las mandarinas causada por Alternaria alternata. Desarrollo de estrategias de control eficientes

“…En la evaluación biológica se valora si el modelo estima correctamente la progresión de la enfermedad en condiciones de campo. En cambio, en la validación comercial debe comprobarse también qué el modelo permite tomar decisiones adecuadas sobre la aplicación de las medidas de control (Fabre et al, 2007;McRoberts et al, 2011;Gent et al, 2013).…”