Simulation of apparent infection rate to predict severity of soybean rust using a fuzzy logic system

Understanding Phakopsora pachyrhizi in soybean: comprehensive insights, threats, and interventions from the Asian perspective

Soybean (Glycine max L.) is an important crop in Asia, accounting for 17% of global soybean cultivation. However, this crop faces formidable challenges from the devastating foliar disease, Asian Soybean Rust (ASR), caused by Phakopsora pachyrhizi, a biotrophic fungus with a broad host range, causing substantial yield losses (10-100%) in Asia. This comprehensive review consolidates knowledge on ASR, encompassing its impact, historical perspectives, genetic diversity, epidemic drivers, early detection, risk assessment, and sustainable management strategies of ASR in the region. ASR has expanded globally from Asia, reaching Africa and Americas, driven by wind-dispersed urediniospores. Genetic diversity studies reveal the complexity of P. pachyrhizi, with distinct populations exhibiting varying virulence patterns. Factors affecting ASR epidemics in Asia include host susceptibility, landscape connectivity, climate, and environmental conditions. Understanding the interplay of these factors is essential for early intervention and control of ASR in soybean fields. Effectively managing ASR can exploit the utilization of diverse intervention strategies, encompassing disease forecasting, automated early detection, disease resistance, fungicide application, and biological control. A pivotal aspect of successful, sustainable disease management lies in reducing the ASR pathogen virulence and preventing it from developing fungicide resistance, while the highpoint of effectiveness in disease control is attained through a synergistic approach, integrating various strategies. In summary, this comprehensive review provides insights into multifaceted approaches that contribute to the development of sustainable and economically impactful soybean production in the face of the persistent threat of ASR in Asia.

Fuzzy model for quantitative assessment of the epidemic risk of African Swine Fever within Australia

African Swine Fever (ASF) has spread rapidly across different continents since 2007 and caused huge biosecurity threats and economic losses. Establishing an effective risk assessment model is of great importance for ASF prevention, especially for those ASF-free countries such as Australia. With a vast territory and an economy heavily relying on primary industry, Australia faces a threat from the spread of ASF. Although ordinary quarantine measures have been well-performed throughout Australia, there is still a need to develop an effective risk assessment model to understand the spread of ASF due to the strong transmission ability of ASF. In this paper, via a comprehensive literature review, and analyzing the transmission factors of ASF, we provide a fuzzy model to assess the epidemic risk of Australian states and territories, under the assumption that ASF has entered Australia. As demonstrated in this work, although the pandemic risk of ASF in Australia is relatively low, there is a risk of irregular and scattered outbreaks, with Victoria (VIC) and New South Wales (NSW) - Australia Capital Territory (NSW-ACT) showed the highest risk. The reliability of this model was also systematically tested by a conjoint analysis model. To our knowledge, this is the first study to comprehensively analyze the ASF epidemic risk in a country using fuzzy modeling. This work can provide an understanding of the risk ASF transmission within Australia based on the fuzzy modeling, the same methodology can also provide insights and useful information for the establishment of fuzzy models to perform the ASF risk assessment for other countries.

Evasive Planning for the Management of Eucalyptus Rust Austropuccinia psidii for Espírito Santo State, Brazil

Eucalyptus is one of the most exploited forest genera on the planet. Eucalyptus has a variety of uses, mainly because of its great diversity and versatility. Brazil is among the main producers of cellulose, paper, and wood panels in the world. One of the factors limiting the production of Eucalyptus spp. is the occurrence of diseases such as rust caused by the fungus Austropuccinia psidii. This work aimed to map areas at risk of eucalyptus rust in the state of Espírito Santo, Brazil. The study was carried out in two stages: (i) mapping the rust risk areas in the state through the Geographic Information System (GIS) and (ii) applying fuzzy standardization to the infection index to generate a risk index. It was found through GIS and fuzzy standardization that most of the areas surveyed presented medium to high risk of rust occurrence. Thus, it becomes necessary to adopt complementary management measures to control the disease, especially for the months of April to November. The methodology used in this study can be implemented for other diseases and forest species in other parts of the world.

Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System

Mato Grosso, Brazil, is the largest soy producer in the country. Asian Soy Rust is a disease that has already caused a lot of damage to Brazilian agribusiness. The plant matures prematurely, hindering the filling of the pod, drastically reducing productivity. It is caused by the Phakopsora pachyrhizi fungus. For a plant disease to establish itself, the presence of a pathogen, a susceptible plant, and favorable environmental conditions are necessary. This research developed a fuzzy system gathering these three variables as inputs, having as an output the vulnerability of the region to the disease. The presence of the pathogen was measured using a diffusion-advection equation appropriate to the problem. Some coefficients were based on the literature, others were measured by a fuzzy system and others were obtained by real data. From the mapping of producing properties, the locations where there are susceptible plants were established. And the favorable environmental conditions were also obtained from a fuzzy system, whose inputs were temperature and leaf wetness. Data provided by IBGE, INMET, and Antirust Consortium were used to fuel the model, and all treatments, tests, and simulations were carried out within the Matlab® environment. Although Asian Soybean Rust was the chosen disease here, the model was general in nature, so could be reproduced for any disease of plants with the same profile.

Modeling the Effects of Cloudy Weather on Regional Epidemics of Soybean Rust

This study simulated daily development of soybean rust (SBR), caused by Phakopsora pachyrhizi, based on cloud cover conditions. Results from a previous study that determined the relationship between shading and apparent infection rates were applied in this study to simulate SBR progress on a regional scale using a semi-empirical logistic model parameterized according to the observed cloud cover conditions. Depending on local weather data availability, cloudy days were assumed to be either (i) the days with less than 2 h of full sun or (ii) the days with complete cloud cover as measured by three daily observations. Estimated disease progress and final estimates of epidemic intensity were verified by 30 reports of seasonal disease progress in 11 regions of Brazil and South Africa from 2002 to 2007. The model predicted final disease severity and the observed final severity fall into a linear relationship with correlation coefficient r = 0.96 and a slope close to 1. Severe SBR epidemics occurred when 19.5 or more cloudy days were recorded during the period from initial disease detection to the date of final disease assessment near the end of a growing season in Brazil and South Africa. Mild epidemics were observed with less than eight cloudy days in a season.

Use of a climatic rule and fuzzy sets to model geographic distribution of climatic risk for European canker (Neonectria galligena) of apple

A rule-based model was developed to assess climatic risk of European canker (Neonectria galligena), which is a major disease of apple in some temperate zones. A descriptive rule was derived from published observations on climatic conditions favorable for European canker development. Fuzzy set theory was used to evaluate the descriptive rule quantitatively. The amount and frequency of rainfall and the average number of hours between 11 and 16°C/day were used as input variables whose values were matched with terms in the rule, e.g., 'high' or 'low'. The degree of a term, e.g., the state of being high or low, to a given input value was determined using a membership function that converts an input value to a number between 0 and 1. The rule was evaluated by combining the degree of the terms associated with monthly climate data. Monthly risk index values derived using the rule were combined for pairs of consecutive months over 12 months. The annual risk of European canker development was represented by the maximum risk index value for 2 months combined. The membership function parameters were adjusted iteratively to achieve a specified level of risk at Talca (Chile), Loughgall (Northern Ireland), East Malling (UK), and Sebastopol (USA), where European canker risk was known. The rule-based model was validated with data collected from Canada, Ecuador, Denmark, Germany, Norway, Poland, Sweden, the Netherlands, New Zealand, and the Pacific Northwest (USA), where European canker has been reported to occur. In these validation areas, the model's risk prediction agreed with reports of disease occurrence. The rule-based model also predicted high risk areas more reliably than the climate matching model, CLIMEX, which relies on correlations between the spatial distribution of a species and climatic conditions. The combination of a climatic rule and fuzzy sets could be used for other applications where prediction of the geographic distribution of organisms is required for climatic risk assessment.

Viability of Phakopsora pachyrhizi Urediniospores Under Simulated Southern Louisiana Winter Temperature Conditions

Soybean rust, caused by Phakopsora pachyrhizi, originally occurred in Asia. It has now spread to South America and the continental United States. This disease has the potential to cause severe economic losses to U.S. soybean growers, especially in the south, where the environmental conditions are more favorable to P. pachyrhizi survival during winter. In the present study, the effect of simulated southern Louisiana winter temperature conditions (12°C, 14-h days and 1°C, 10-h nights with 75% relative humidity) on soybean rust urediniospore viability was examined. It was found that urediniospore viability declined rapidly from 72 to 40% after 1 day and then decreased gradually to 17% after 7 days and 11% after 60 days. Spores stored under southern Louisiana winter conditions for 60 days still produced pustules on inoculated leaves. In comparison, the viability of spores stored at room temperature decreased gradually and reached 0% at the end of 60 days. Winter temperature treatment not only reduced spore viability but also decreased germ tube growth. In addition, soybean rust spores recovered from overwintered dry kudzu leaves were also found viable. This study indicates that soybean rust spores could survive southern Louisiana winter conditions and initiate a new cycle of infection in the next growing season.

Predicting severity of asian soybean rust epidemics with empirical rainfall models

ABSTRACT Although Asian soybean rust occurs in a broad range of environmental conditions, the most explosive and severe epidemics have been reported in seasons with warm temperature and abundant moisture. Associations between weather and epidemics have been reported previously, but attempts to identify the major factors and model these relationships with field data have been limited to specific locations. Using data from 2002-03 to 2004-05 from 34 field experiments at 21 locations in Brazil that represented all major soybean production areas, we attempted to identify weather variables using a 1-month time window following disease detection to develop simple models to predict final disease severity. Four linear models were identified, and these models explained 85 to 93% of variation in disease severity. Temperature variables had lower correlation with disease severity compared with rainfall, and had minimal predictive value for final disease severity. A curvilinear relationship was observed between 1 month of accumulated rainfall and final disease severity, and a quadratic response model using this variable had the lowest prediction error. Linear response models using only rainfall or number of rainy days in the 1-month period tended to overestimate disease for severity <30%. The study highlights the importance of rainfall in influencing soybean rust epidemics in Brazil, as well as its potential use to provide quantitative risk assessments and seasonal forecasts for soybean rust, especially for regions where temperature is not a limiting factor for disease development.