Modelling the impacts of pests and diseases on agricultural systems

Abstract

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 Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate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.

Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models

“…Soils are often represented using fewer pools and processes in agricultural models than in biogeochemical ones, but plant processes may be represented more fully, e.g., grass-legume interaction [51], cold hardening and winter mortality of tillers [52] and the impacts of pests and diseases. Models accounting for biotic stress factors have mainly been reported for arable crops (e.g., [53]), whereas examples for grassland are rare. Models of grassland dynamics based on the explicit simulation of growth and development of different species in a community and of the competition among them can reflect detailed knowledge of the underlying system.…”

Is dry soil planting an adaptation strategy for maize cultivation in semi-arid Tanzania?

“…The DSSAT also has other embedded models that can simulate the flow of nutrients and water balance in the soil. Despite the complex array of processes simulated by the crop model, some important processes such as effects of pests and diseases are still not well depicted (Donatelli et al 2017), as are the impact of extreme events such as floods and hail (Alexandrov and Hoogenboom 2000) or high temperatures (above 35°C) during anthesis, which reduces pollen viability (Porter and Semenov 2005).…”

Pest Management using Machine Learning Algorithms, A Review

“…The improvement and application of pest and disease models to analyze and predict yield losses, including those due to climate change is still a challenge for the scientific community M. Donatelli et al, [7]. The need to estimate the impact of pests and diseases on agricultural production is an important element in the development and analysis of scenarios impacting farmers' income and food security.…”