Fungicides Associated with Two Adjuvant Formulations for Preventive and Curative Soybean Rust Control

2009 ◽  
pp. 10-10-21
Author(s):  
D. C. Ferguson ◽  
J. R. Yowell ◽  
L. Larissa May-De Mio ◽  
Cláudia V. Godoy ◽  
Fabio da Cunha ◽  
...  
Keyword(s):  
Soybean Rust

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

Identification and classification of Asian soybean rust using leaf-based hyperspectral reflectance

2021 ◽  
Vol 42 (11) ◽  
pp. 4177-4198
Author(s):  
Renato Herrig Furlanetto ◽  
Marcos Rafael Nanni ◽  
Monica Sayuri Mizuno ◽  
Luís Guilherme Teixeira Crusiol ◽  
Camila Rocco da Silva
Keyword(s):  
Soybean Rust ◽  
Hyperspectral Reflectance ◽  
Asian Soybean Rust

Performance of dual and triple fungicide premixes for managing soybean rust across years and regions in Brazil: a meta‐analysis

2021 ◽  
Author(s):  
Jhonatan Paulo Barro ◽  
Kaique S. Alves ◽  
Cláudia V. Godoy ◽  
Alfredo R. Dias ◽  
Carlos A. Forcelini ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Soybean Rust

Registration of Asian Soybean Rust-Resistant Soybean Germplasm G01-PR16

2011 ◽  
Vol 5 (1) ◽  
pp. 118-122 ◽  
Author(s):  
H. Roger Boerma ◽  
Maria J. Monteros ◽  
Bo-Keun Ha ◽  
E. Dale Wood ◽  
Daniel V. Phillips ◽  
...  
Keyword(s):  
Soybean Rust ◽  
Soybean Germplasm ◽  
Asian Soybean Rust

scholarly journals Prediction of Short-Distance Aerial Movement of Phakopsora pachyrhizi Urediniospores Using Machine Learning

2017 ◽  
Vol 107 (10) ◽  
pp. 1187-1198 ◽  
Author(s):  
L. Wen ◽  
C. R. Bowen ◽  
G. L. Hartman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Short Distance ◽  
Soybean Rust ◽  
Machine Learning Techniques ◽  
Phakopsora Pachyrhizi ◽  
Primary Means ◽  
Soybean Plants ◽  
Selection Operator ◽  
Active Trap

Dispersal of urediniospores by wind is the primary means of spread for Phakopsora pachyrhizi, the cause of soybean rust. Our research focused on the short-distance movement of urediniospores from within the soybean canopy and up to 61 m from field-grown rust-infected soybean plants. Environmental variables were used to develop and compare models including the least absolute shrinkage and selection operator regression, zero-inflated Poisson/regular Poisson regression, random forest, and neural network to describe deposition of urediniospores collected in passive and active traps. All four models identified distance of trap from source, humidity, temperature, wind direction, and wind speed as the five most important variables influencing short-distance movement of urediniospores. The random forest model provided the best predictions, explaining 76.1 and 86.8% of the total variation in the passive- and active-trap datasets, respectively. The prediction accuracy based on the correlation coefficient (r) between predicted values and the true values were 0.83 (P < 0.0001) and 0.94 (P < 0.0001) for the passive and active trap datasets, respectively. Overall, multiple machine learning techniques identified the most important variables to make the most accurate predictions of movement of P. pachyrhizi urediniospores short-distance.

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