| Grant number: | 16/06764-0 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | August 10, 2016 |
| End date: | February 09, 2017 |
| Field of knowledge: | Agronomical Sciences - Agronomy - Agricultural Meteorology |
| Principal Investigator: | Paulo Cesar Sentelhas |
| Grantee: | Gustavo Castilho Beruski |
| Supervisor: | Mark Lawrence Gleason |
| Host Institution: | Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil |
| Institution abroad: | Iowa State University, United States |
| Associated to the scholarship: | 14/05677-1 - Phytosanitary warning systems for the rational management of soybean rust: development and application in the States of São Paulo, Paraná and Mato Grosso, BP.DR |
Abstract Soybean rust, caused by fungus Phakopsora pachyrhizi, causes significant damage to the soybean crop in Brazil as well as many other countries. The disease is controlled by sequential applications of fungicides following a calendar-based system. This approach considers only aspects of the crop, disregarding the influence of local weather conditions on disease progress. The relationship between local weather and disease progress can be represented by mathematical models, which have been incorporated into warning systems in order to indicate more effective and efficient spray timing for disease control. A limitation for widespread implementation of soybean rust warning systems is that leaf wetness duration (LWD), a key weather variable for predicting risk of soybean rust, is seldom measured by weather stations. An alternative way to make LWD data available is to estimate it by physical or empirical models. The simplest empirical model is the one that considers the presence of wetness whenever the air relative humidity is above a particular threshold (NHRH). However, it is necessary to calibrate the threshold from place to place to improve the performance of the model and reduce errors. The objective of this project is to assess the feasibility of using LWD estimated by relative humidity as input data for an Asian soybean rust warning system. The experimental data used was obtained from field experiments conducted in Ponta Grossa, PR, Campo Verde, MT and Pedra Preta, MT throughout the 2014-15 and 2015-16 soybean growing seasons. An automatic weather station (AWS) installed. At each site was equipped with sensors to measure air temperature, relative humidity, rainfall and LWD. All sensors had been previously tested and calibrated under laboratory and field conditions. LWD will be estimated using the RH Threshold Model, which considers the presence of wetness whenever RH is above a given value (80, 85, 90 and 95 %). The estimated LWD will be input to the Soybean Asian Rust warning system proposed by Reis et al. (2004) so that daily values of estimated infection probability might be obtained and used to determine the number of fungicide sprays required. The expected results of this project are to define the optimal RH threshold for LWD estimation at different sites in Brazil, and therefore to apply LWD estimates based to help determine the recommended timing and number of sprays for Asian soybean rust. | |
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