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.

Association of hydrogen peroxide with commercial fungicide formulations in the control of Asian soybean rust

ABSTRACT: In recent years, there have been reductions in the efficacy of the fungicidal control of Phakopsora pachyrhizi, thereby hindering the management of soybean rust and compromising crop yield. This study evaluated the effects of incorporating hydrogen peroxide (H2O2) in commercial fungicide formulations on the control of soybean rust. We conducted two experiments, one of which was performed in a greenhouse environment and the other under field conditions. In both environments, we examined the following four control programs using commercial fungicide formulations: (I) azoxystrobin + cyproconazole (quinone outside inhibitor [QoI] + demethylation inhibitor [DMI]); (II) picoxystrobin + cyproconazole (QoI + DMI); (III) pyraclostrobin + epoxiconazole + fluxapyroxad (QoI + DMI + succinate dehydrogenase inhibitor); and (IV) water (H2O) (program without fungicide application), combined with the incorporation of (i) H2O2; (ii) mancozeb (positive control I); (iii) chlorothalonil (positive control II); or (iv) water (H2O) alone. Analyses of infected leaf area and grain yield revealed that the addition of H2O2 to the formulations of DMI and QoI fungicides led to a reduction in disease severity of between 33% and 44% relative to the effects of these products used alone, as well as an increase in yield and SPAD values. The use of H2O2 and multi-site fungicides alone failed to provide effective control of soybean rust. In addition to enhancing the efficacy of disease control, the use of H2O2 associated with commercial fungicide mixtures was shown to be a potential tool for the management of fungicide resistance and reduction in losses from Asian soybean rust.

Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease

Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is often limited to human-defined and static features. Here, we introduce a novel framework that can characterize shape changes (morphodynamics) for cell-drug interactions directly from images, and use it to interpret perturbed development of Phakopsora pachyrhizi, the Asian soybean rust crop pathogen. We describe population development over a 2D space of shapes (morphospace) using two models with condition-dependent parameters: a top-down Fokker-Planck model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. We discover a variety of landscapes, describing phenotype transitions during growth, and identify possible perturbations in the tip growth machinery that cause this variation. This demonstrates a widely-applicable integration of unsupervised learning and biophysical modeling.

Fungi Resistance to Multissite Fungicides

Multisite fungicides have been used for many years in fruit and vegetable crops worldwide. Cases of the fungi resistance development to these fungicides have been rare. From the 2002 season onwards, with the outbreak of Asian soybean rust in Brazil, caused by Phakopsora pachyrhizi, site-specific fungicides became the main weapon for its control. From 2002 to 2011, penetrant mobile site-specific fungicides were used and until today in double (DMI + QoI) or triple (DMI + QoI + SDHI) co-formulatoons in an area of more than 30 million hectares and with three sprays per area. This resulted, as expected, in the fungus sensitivity reduction, today with cross and multiple resistance to those site-specific fungicides. From the 2011 season in an attempt to recover control that for some chemicals and mixtures reached < 30%, research was started with site-specific + multi-site mixtures, taking as example Phytophthora infestans resistance development to metalaxyl in Europe showinig long-lasting solution found by the addition of multisite mancozeb. It is expected that the effective life of site-specific + multi-site mixtures may be as long in controlling soybean rust as it has been for potato, tomato and grape downy mildews. This review presents the concepts involved in the sensitivity reduction to fungicides. Some fungal species and fungicides involved are listed. Considering the P. pachyrhizi sporulation potential, the great soybean area sprayed and the number of sprays per area mainly with site-specific co-formulations and the reduced area sprayed with multisites, we discuss the need for annual monitoring of P. pachyrhizi sensitivity to the these chemicals.

Asian Rust Severity in Soybean Sown in December and February in Mato Grosso State

The objective of this work was to assess the effect of December sowing time with February on the Asian soybean rust severity. In on-farm trials two soybean treatments sowing in December (2020) (DSS.) and February (2021) (FSS) were assessed for Asian soybean rust severity in 24 sites, in three regions of Mato Grosso state. The DSS treatment was established in the growers commercial farms and the FSS in a 5 ha area sown specifically for this treatment. The DSS treatment was conducted in 16 sites and the FSS in eigth. For rust control fungicides with efficacy higher than 60% were sprayed consisting of DMIs, QoIs and SDHIs in double or triple mixtures, always added by multisites (chlorothalonil, mancozeb, or copper oxychloride). About eighty soybean leaflets from four plots repetitiond, demarcated at random in each field, were taken in each smpling. In laboratory leaflet severity was appraised and area under disease progress curve (AUDPC) calculated. Related to DSS, the AUDPC overall mean was 174 units and receiving 6.9 fungicide spraying and for FSS 26 units with 4.8 fungicide sprayings. Our results reinforce that the sowing time can be changed from the end of December to February to maintain soybean crop sustainability.

Potential of the Dithiocarbimate Fungicides on the Control of Coffee Leaf Rust and Asian Soybean Rust

Coffee leaf rust (Hemileia vastatrix) and Asian soybean rust (Phakopsora pachyrhizi) are diseases that cause great losses in the productivity of these crops, not only in Brazil but on a global scale. Coffee and soybean varieties grown are susceptible to these diseases. Thus, it is necessary to search for efficient compounds for their chemical control, mainly from the group of protectors or residuals so that they can be formulated with systemic fungicides to control the diseases. This allows not only the efficient management of diseases but also the prevention of the emergence of resistant mutants in the populations of these pathogens. In this context, the present study aimed to evaluate the sensitivity and effect of bis(N-R-sulfonyldithiocarbimato)zincate(II) salts fungicides on the epidemiological components of pathosystems coffee × H. vastatrix and soybean × P. pachyrhizi. Initially, four zinc(II) complexes salts (1A, 2A, 1B, and 2B) with N-R-sulfonyldithiocarbimates were synthesized. In the first experiment, the in vitro sensitivity of H. vastatrix and P. pachyrhizi was studied for the four compounds synthesized and mancozeb at 0.5, 5.0, 50.0, 100.0 and 200.0 µmol L-1. All the compounds synthetized in this study had inhibitory effects on H. vastatrix and P. pachyrhizi. In the greenhouse it was studied the effect of bis(N-R-sulfonyldithiocarbimato)zincate(II) salts on the epidemiological components of coffee leaf rust and Asian soybean rust. For the pathosystem coffee × H. vastatrix, there were no differences in the values obtained for the bis(N-R-sulfonyldithiocarbimato)zincate(II) salts and mancozeb for the latent period. For the sporulated lesion variable, the control treatment had a mean value of 149.0 lesions/leaf, differing significantly from the other treatments. The mean value of compound 2B was estimated as 25.0 lesions/leaf, differing significantly from treatments 1A, 1B, 2B, and mancozeb. Treatments 1A, 1B, 2B, and mancozeb did not differ significantly from each other. For the Asian soybean rust, the area under the disease progress curve had a mean value of 75.8 for the control, while for the 2A treatment the value was 4.1, differing from the other compounds. The treatments 1A, 1B, 2A, and mancozeb did not differ significantly from each other. In conclusion, compounds 1A, 2A, and 1B were more efficient in the control of the coffee leaf rust, while compound 2A was efficient in the control of the Asian soybean rust.