Modelling the impacts of pests and diseases on agricultural systems.

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

A simple generic infection model for foliar fungal plant pathogens.

ABSTRACT In this study, a simple generic infection model was developed for predicting infection periods by fungal foliar pathogens. The model is designed primarily for use in forecasting pathogens that do not have extensive epidemiological data. Most existing infection models require a background epidemiological data set, usually including laboratory estimates of infection at multiple temperature and wetness combinations. The model developed in this study can use inputs based on subjective estimates of the cardinal temperatures and the wetness duration requirement. These inputs are available for many pathogens or may be estimated from related pathogens. The model uses a temperature response function which is scaled to the minimum and optimum values of the surface wetness duration requirement. The minimum wetness duration requirement (W(min)) is the number of hours required to produce 20% disease incidence or 5% disease severity on inoculated plant parts at a given temperature. The model was validated with published data from 53 controlled laboratory studies, each with at least four combinations of temperature and wetness. Validation yielded an average correlation coefficient of 0.83 and a root mean square error of 4.9 h, but there was uncertainty about the value of the input parameters for some pathogens. The value of W(min) varied from 1 to 48 h and was relatively uniform for species in the genera Cercospora, Alternaria, and Puccinia but less so for species of Phytophthora, Venturia, and Colletotrichum. Operationally, infection models may use hourly or daily weather inputs. In the case of the former, information also is required to estimate the critical dry-period interruption value, defined as the duration of a dry period at relative humidities <95% that will result in a 50% reduction in disease compared with a continuous wetness period. Pathogens were classified into three groups based on their critical dry-period interruption value. The infection model is being used to create risk maps of exotic pests for the U.S. Department of Agriculture's Animal Plant Health and Inspection Service.

Estimating disease risk at the whole plant level with General Circulation Models.

General Circulation Models (GCMs) have been developed to assess the impacts of potential global climate change. However, these models do not provide specific weather information at the whole-plant level and thus provide only very gross estimates of conditions that affect plant and disease development. Also, climatic change may increase the frequency of extreme events that influence plant production more than changes in daily or monthly averages. One solution is a simulation approach that can scale weather information from the global down to the plant scale. Over the last 4 years, we have been developing methods to hierarchically define current and forecast weather conditions down to the whole-plant level based on nested high-resolution atmospheric (mesoscale) numerical models. Two hierarchical mesoscale model approaches were tested to downscale weather data in a vineyard. The first, known as the Localized Mesoscale Forecast System (LMFS) uses surface databases to 'localize' mesoscale output. The second, known as the Canopy-Mesoscale Forecast System (CMFS), uses a boundary layer model to downscale mesoscale output. To illustrate the utility of this approach we focused on surface wetness duration (SWD), a variable with high spatial and temporal variability. SWD is also a critical variable for prediction of plant disease. Simulations of SWD with on-site input data were compared to those derived from the mesoscale models and to on-site sensors. Forecasts of atmospheric variables by the two systems were compared to on-site observations. Success in this effort leads us to extend this method to GCMs where factors such as temperature, rainfall, relative humidity, and surface wetness can be estimated within plant and crop canopies. We explore the implications of this work on evaluating the assessment of climate change on the risk of plant disease development.