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

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 668
Author(s):  
Nayara Longo Sartor Zagui ◽  
André Krindges ◽  
Anna Diva Plasencia Lotufo ◽  
Carlos Roberto Minussi
Keyword(s):  
Environmental Conditions ◽  
Fuzzy System ◽  
Plant Disease ◽  
Real Data ◽  
Soybean Rust ◽  
Advection Equation ◽  
Mato Grosso ◽  
Temporal Modeling ◽  
Asian Soybean Rust ◽  
Spatio Temporal

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.

scholarly journals An Overview of the Epidemiology of Phakopsora pachyrhizi in the State of Mato Grosso, Brazil

2021 ◽  
Vol 13 (6) ◽  
pp. 110
Author(s):  
Erlei Melo Reis ◽  
Wanderlei Dias Guerra ◽  
Mateus Zanatta ◽  
Laércio Zambolim
Keyword(s):  
Environmental Effect ◽  
The State ◽  
Soybean Rust ◽  
Economic Sustainability ◽  
Phakopsora Pachyrhizi ◽  
Airborne Spores ◽  
Mato Grosso ◽  
Asian Soybean Rust ◽  
Weed Plants ◽  
Soybean Plants

This review seeks to expand the knowledge about the epidemiology of Asian sybean rust in the state of Mato Grosso and contribute to ensuring the economic sustainability of soybean crop. It is discussed the Phakopsora pachyrhizi potential of dispersal from Asia to South America and finally to Mato Grosso state. The origin of the Asian soybean rust inoculum within Mato Grosso is addressed by the survival in volunteer and soybean weed plants (Pitelli, 2015) in other crops such as cotton. Data on the adverse environmental effect on the soybean plants survival are shown mainly the water deficit from June to August. Reports on the effect air temperature and mainly solar radiation on the mortality of airborne spores during their anemophilous spread on sunny days are also discussed. This increase of knowledge aims to make the soybean-free period more efficient by the knowledge on the soybean plants survival and on the fungus viability in the month of August. Due to the proximity of soybean farms, during the soybean-free period, in other states (Tocantins, Goiás, Rondônia, etc.) and in other neighbor countries we discuss the likelihood that inoculum in the state may also originate in out-of-state crops during the Mato Grosso soybean-free period.

scholarly journals Predicting Asian Soybean Rust Epidemics Based on Off-Season Occurrence and El Niño Southern Oscillation Phenomenon in Paraná and Mato Grosso States, Brazil

2018 ◽  
Vol 10 (11) ◽  
pp. 562 ◽  
Author(s):  
C. A. Minchio ◽  
L. H. Fantin ◽  
J. H. Caviglione ◽  
K. Braga ◽  
M. A. Aguiar e Silva ◽  
...  
Keyword(s):  
Polynomial Regression ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Three Dimensional ◽  
Variable Number ◽  
Soybean Rust ◽  
Three Dimensional Model ◽  
Mato Grosso ◽  
Asian Soybean Rust ◽  
Macro Scale

The study aimed to propose models to predict Asian soybean rust epidemics based on both the occurrence of the disease in the period between seasons and the climate variability index, which is influenced by the El Niño Southern Oscillation (ENSO) phenomenon. The data used to develop these models were obtained from 11 crop seasons, distributed among six regions of Paraná and twelve regions of Mato Grosso which was determined by the National Institute for Space Research (INPE). The three-dimensional model was obtained from linear and quadratic polynomial regression analyses, considering the following climatic variables as independent (Y axis): Rainfall (PP), Standardized Precipitation Index (SPI), Southern Oscillation Index (SOI) and Temperature on the sea surface (SST Niño 3.4). The independent variable (X axis) was the number of occurrences of rust in the off-season, and the dependent variable (Z axis) was defined as rust occurrences during the season, which were reported by the Anti-rust Consortium. The best model that explains the epidemic of the disease during the season in Paraná state was composed by Rainfall or SST Niño 3.4 variable as the Y axis. The best model for Mato Grosso state used SST Niño 3.4 or SOI variable. The variable number of occurrences in the off-season significantly influenced the model, indicating the potential use of this variable and meteorological variables on a macro scale to predict epidemics even before the start of the season.

scholarly journals Asian Soybean Rust Severity Sowed in Different Seasons

2020 ◽  
Vol 12 (10) ◽  
pp. 240
Author(s):  
Erlei Melo Reis ◽  
Luana Maria de Rossi Belufi ◽  
Wanderlei Dias Guerra ◽  
Laércio Zambolin ◽  
Mateus Zanatta
Keyword(s):  
Insect Pests ◽  
Soybean Rust ◽  
Management Systems ◽  
Phakopsora Pachyrhizi ◽  
Mato Grosso ◽  
Asian Soybean Rust ◽  
Different Seasons ◽  
Rust Severity ◽  
Sowing Period ◽  
On Farm

In on-farm trials, the foliolar severity of Asian soybean rust was evaluated in 44 areas, in three regions of Mato Grosso sown in December (2019) and February (2020). Several susceptible cultivars were used in different crop systems; insect pests and weeds were controlled with different management systems by the farmers. Forty soybean leaflets from four plots replications, demarcated at random in each field were taken. In laboratory foliolar severity was appraised. For rust control in the trials conducted in February, fungicides with efficiency greater than 60% were used consisting of DMIs, QoIs and SDHIs in double or triple mixtures, always adding multisites (chlorothalonil, mancozeb, copper oxychloride). The severity was greater in the fields sown in December (4.84% than in February 0.68%). The number of fungicides spraying/ha in December was 6.4 and February 4.6. It is discussed that through the use of multisites fungicides, the mutation potential in Phakopsora pachyrhizi is reduced and that the spores from areas cultivated in February, probably due to unfavorable environment, do not survive during the soybean free-period. Our results indicate that the sowing period can be changed from the end of December to February, since multisites fungicides are always used.

scholarly journals Comparative Severity Analysis of Asian Soybean Rust in Soybean Farms Sown in December and February in Mato Grosso, 2018/19 Growing Season

2020 ◽  
Vol 12 (9) ◽  
pp. 130
Author(s):  
Erlei Melo Reis ◽  
Rodrigo Marcelo Pasquali ◽  
Luana Maria de Rossi Belufi ◽  
Wanderlei Dias Guerra ◽  
Mateus Zanatta
Keyword(s):  
Linear Regression Analysis ◽  
The State ◽  
Soybean Rust ◽  
Phenological Stages ◽  
Mato Grosso ◽  
North West ◽  
The North ◽  
Asian Soybean Rust ◽  
Progress Curve ◽  
Seeding Time

The objective of this work was to compare the leaflet severity of Asian soybean rust in farms sown in December and February in the state of Mato Grosso. In the survey, 28 fields were sampled in 14 counties in the North, West and South regions of the state. A total of 40 leaflets were collected per plot, in randomized treatments with four replication and three crop phenological stages. Leaflet severity was assessed according to a diagrammatic scale. The data were expressed as leaflet severity, submitted to linear regression analysis, calculated the area under the disease progress curve (AUDPC) considering the three phenological stages sampled, and the means compared by the Tukey’s test. Leaflet severity was significantly higher in the fields sown in December than in February, as well the number of fungicides sprayings. Our results indicate that the proposed change in seeding time from December to February can be implemented by significantly reducing risks and in compliance with the principles of IN 002/2015.

scholarly journals Asian Soybean Rust

EDIS ◽  
2007 ◽  
Vol 2007 (17) ◽  
Author(s):  
Wayne M. Jurick II ◽  
Dario F. Narvaez ◽  
Carrie L. Harmon ◽  
James J. Marois ◽  
David L. Wright ◽  
...  
Keyword(s):  
Plant Pathology ◽  
Plant Disease ◽  
Soybean Rust ◽  
Causal Organism ◽  
Web Based ◽  
Disease Cycle ◽  
Diagnosis And Management ◽  
Asian Soybean Rust ◽  
The Us ◽  
Fungal Plant Disease

PP-235, a 4-page illustrated fact sheet by Wayne M. Jurick II, Dario F. Narvaez, Carrie L. Harmon, James J. Marois, David L. Wright, and Philip F. Harmon, describes this fungal plant disease new to the US since 2004, its symptoms, causal organism, disease cycle and epidemiology, diagnosis, and management. Includes links to web-based resources and references. Published by the UF Department of Plant Pathology, July 2007.

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

2021 ◽  
Vol 13 (11) ◽  
pp. 127
Author(s):  
Erlei Melo Reis ◽  
Wanderlei Dias Guerra ◽  
Laércio Zambolim ◽  
Fernando C. Juliatti ◽  
José Otávio Menten ◽  
...  
Keyword(s):  
Soybean Rust ◽  
Copper Oxychloride ◽  
Sowing Time ◽  
Mato Grosso ◽  
Asian Soybean Rust ◽  
Progress Curve ◽  
Commercial Farms ◽  
Rust Severity ◽  
Crop Sustainability ◽  
On Farm

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.

Asian Soybean Rust Sentinel Plots

2006 ◽  
Author(s):  
Ralph von Qualen ◽  
Xiao-Bing Yang
Keyword(s):  
Soybean Rust ◽  
Asian Soybean Rust

Asian Soybean Rust Sentinel Plots

2006 ◽  
Author(s):  
Ralph von Qualen ◽  
Xiao-Bing Yang
Keyword(s):  
Soybean Rust ◽  
Asian Soybean Rust

scholarly journals Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

2021 ◽  
Vol 10 (3) ◽  
pp. 188
Author(s):  
Cyril Carré ◽  
Younes Hamdani
Keyword(s):  
Conceptual Models ◽  
Future Generations ◽  
Temporal Data ◽  
Temporal Modeling ◽  
Temporal Models ◽  
Gis Data ◽  
Spatio Temporal ◽  
New Interpretation ◽  
Conventional Literature

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

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