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POTATOPEST: Swedish scientists develop a new model for studying and predicting the impact of late blight on potato crops

This article was written by Jorge Luis Alonso G., an information consultant specializing
in the potato crop.

A study conducted by a team of scientists at the Swedish University of Agricultural Sciences and The Rural Economy and Agricultural Society in Sweden was published recently in the journal Field Crops Research. The study focused on creating a streamlined dynamic yield loss simulation model that couples a simple epidemiological model of late blight disease with a basic model of potato crop growth. The model uses 17 years of field data on disease progress of potato late blight and potato yield in southern Sweden.

This article is an overview of the results of the study.

The UN’s 2030 Agenda sets ambitious targets for food security, but population growth, resource depletion and climate change pose formidable threats. In addition, crop pests and diseases (CPDs) cause significant production losses.

As a result, there is a need to sustainably increase primary food production, with a particular focus on reducing CPD-induced yield losses through improved pest management. As a solution, agricultural systems modeling can provide a deeper understanding of this complex problem, although traditional statistical models are limited when applied to different environmental conditions.

Alternatively, mechanistic, process-based models are emerging as promising tools that reflect the dynamic interactions between crops and their environment.

Understanding complex biological systems

Mechanistic simulation models serve as invaluable tools for understanding complex biological systems such as crop-pathogen-environment systems. These models empower users by providing a means to measure the extent of damage in any given agroecosystem and assist in making both tactical and strategic decisions regarding disease and pest management.

For example, potatoes, a major food crop, are under constant siege from both abiotic and biotic factors, with the oomycete Phytophthora infestans, the causal agent of the dreaded late blight disease, being a major threat.

Although a number of potato growth models are available, unfortunately, most of them only consider abiotic factors and do not take into account the confounding effects of CPDs. Against this background, the aim of this study was to develop a user-friendly mechanistic model to assess potato yield losses caused by P. infestans.

The influence of disease growth rate and epidemic on yield loss was investigated and the sensitivity of the model to climatic changes was evaluated. Ultimately, this will pave the way for improved integrated pest management strategies for more sustainable global food production.

The POTATOPEST model

The model used in this study, named POTATOPEST, is an adapted version of WHEATPEST and RICEPEST, two mechanistic, process-based models originally developed for wheat and rice. In this particular context, the POTATOPEST model is used to meticulously investigate the devastating effects of P. infestans on potato crops.

This robust model, which relies on a relatively small set of agronomic input variables confidently provides reasonable estimates of tuber yield and its reduction caused by this formidable pathogen. It is also a powerful tool for assessing the role of disease growth rate and infection onset in yield reduction.

Furthermore, the model explores the impact of rising temperatures, suggesting an interesting scenario where yield losses may decrease as the pathogen deviates from its optimal developmental conditions. Demonstrating its inherent versatility, the model shows an ability to be readily adapted to other cultivars and regions, with a particular affinity for those at higher latitudes.

According to the Swedish scientists, the POTATOPEST model allows the assessment of the damage caused by P. infestans in potato crops. They conclude that the model produced reasonable estimates of tuber yield and its reduction caused by P. infestans based on relatively small number of agronomical input variables.

The model allowed the assessment of the role that the rate of disease growth and the time of the start of the infection play in yield reduction. In addition, the effect of rising temperatures was also evaluated, suggesting that P. infestans might cause less yield loss as the pathogen deviates from its optimum development.

The model can be adapted to other cultivars and regions, especially at high latitudes.

Tool for studying new potato varieties

In the current landscape, where late blight is mainly controlled by repeated fungicide applications, POTATOPEST is emerging as a crucial tool for studying the productivity, resistance and tolerance of new potato varieties. This mission aligns with the ongoing efforts of the scientific and grower communities to reduce fungicide use in light of its adverse environmental impacts.

The predictive results of the model could prove to be a valuable asset for agricultural advisors and find their place as part of Integrated Pest Management (IPM) programs. As a mechanistic model, POTATOPEST paves the way for the inclusion of other pests and diseases in future research efforts.

Thus, it sets the stage for detailed analysis of specific combinations of pests and diseases — those currently present or possibly future — characteristic of a region of interest, thereby facilitating the evaluation of improved and more efficient management strategies.

Source: González-Jiménez, J., Andersson, B., Wiik, L., & Zhan, J. (2023). Modelling potato yield losses caused by Phytophthora infestans: Aspects of disease growth rate, infection time and temperature under climate change. Field Crops Research, 299, 108977. https://doi.org/10.1016/j.fcr.2023.108977
Photo: Late blight lesions on potato leaves. Credit Utah State University

Editor & Publisher: Lukie Pieterse


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