Development of simulation models for strategic decision making in plant disease management

• Grant number: 16/13415-2
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2016
• End date: September 30, 2018
• Field of knowledge: Agronomical Sciences - Agronomy - Plant Health
• Principal Investigator: Armando Bergamin Filho
• Grantee: Armando Bergamin Filho
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated researchers: Alice Kazuko Inoue Nagata ; Jorge Alberto Marques Rezende ; José Belasque Junior ; Laetitia Willocquet ; Lilian Amorim ; Serge Savary

Abstract

Disease management entails two broad types of decision: tactical or strategic. Tactical decisions are made in the course of a growing season, and involve disease management instruments of short term; a typical example is the use of pesticide. Because tactical decisions are made in the course of a growing season, the corresponding framework of decision-making is often reactive, and is dependent on running information. Typical running information in the course of a growing season includes the current level of disease, current weather conditions, or weather forecasts. Strategic decisions, by contrast, are proactive; these decisions are made before a growing season starts, and very often concern a number of successive growing seasons; the choice of variety when an orchard is about to be established, the adoption of no-till practice, or area-wide management are typical strategic decisions. Strategic decisions therefore have long-term implications, may sometimes entail important costs, and actually produce the background against which tactical decisions are made. In this proposal we intend to address strategic decisions in three important pathosystems: Asiatic soybean rust, begomovirus and crinivirus of tomato, and huanglongbing of citrus. Implementing, assessing, and comparing different strategies for disease management through formal field experiments in the real world is impossible. This is mainly because the management options to be tested involve large areas and long time lines, which cannot be practically implemented in experiments. We propose the use of systems analysis to estimate outcomes of different management strategies, mainly because it allows upscaling processes over time and space. (AU)