A Systematic Map of the Research on Disease Modelling for Agricultural Crops Worldwide

Abstract

In this work, we developed a systematic map to identify and catalogue the literature pertaining to disease modelling for agricultural crops worldwide. Searches were performed in 2021 in the Web of Science and Scopus for papers reporting any type of disease model for 103 crops. In total, 768 papers were retrieved, and their descriptive metadata were extracted. The number of papers found increased from the mid-1900s to 2020, and most of the studies were from North America and Europe. More disease models were retrieved for wheat, potatoes, grapes, and apples than for other crops; the number of papers was more affected by the crop’s economic value than by its cultivated area. The systematic map revealed an underrepresentation of disease models for maize and rice, which is not justified by either the crop economic value or by disease impact. Most of the models were developed to understand the pathosystem, and fewer were developed for tactical disease management, strategic planning, or scenario analysis. The systematic map highlights a variety of knowledge gaps and suggests questions that warrant further research.

Summary of the worldwide available crop disease risk simulation studies that were driven by climate change scenarios and published during the past 20 years

“…Wheat (37 simulations) was most often investigated, followed by rice (15 simulations), grapevine (13 simulations) and potato (11 simulations) (Table 2). This is in agreement with Fedele et al (2022) who reported that most articles related to crop disease modelling in general are related to wheat, potato, grapevine and apple, whereby apple is obviously under-represented in climate change impact research (Table 1). Many other important crops such as barley and soybean are also under-represented in crop disease risk simulations, or completely "set aside" such as oat and rye (Table 1).…”

Development of a model for Colletotrichum diseases with calibration for phylogenetic clades on different host plants

“…Mathematical models can help growers schedule fungicide sprays ( Pertot et al., 2017 ; Rossi et al., 2019 ; Fedele et al., 2022 ). Models have been developed to understand the epidemiology of anthracnose on specific crops and to support infection-risk-based fungicide applications ( Dodd et al., 1991 ; Park et al., 1992 ; Monroe et al., 1997 ; Moral et al., 2012 ; Singh, 2020 ).…”