Leaf wetness duration estimation and its influence on a soybean rust warning system

Abstract

A limitation for widespread implementation of Asian soybean rust (ASR) warning system is the scarcity of leaf wetness duration (LWD) data. The objective of this study was to assess the feasibility of using LWD estimated by different empirical methods as input data for an ASR-warning system. The study was carried out with data from field experiments conducted in Ponta Grossa, Parana State, Campo Verde and Pedra Preta, both in Mato Grosso State, Brazil, throughout the 2014–15 and 2015–16 seasons. The estimated LWD values were used as input in an ASR-warning system. More reliable estimations of LWD were obtained using the Number of Hours with Relative Humidity above 90% (NHRH≥90%). The use of estimated LWD resulted in disease overestimation in Ponta Grossa, underestimation in Pedra Preta and moments of under and overestimation in Campo Verde. Analyzing the ASR-warning system with the more conservative threshold, five sprays per season were recommended for all sites and the mean fractions of correct estimates (θ1) in Ponta Grossa were higher (θ1 = 0.876) than those obtained in Pedra Preta (θ1 = 0.632) and Campo Verde (θ1 = 0.662). Otherwise, for the less conservative spray threshold, a reduction of one spray was observed in Ponta Grossa (θ1 = 0.912) when compared to Pedra Preta (θ1 = 0.826) and Campo Verde (θ1 = 0.767), where five sprays were still recommended. Thus, LWD estimated by NHRH≥90% has enough accuracy and precision for being used as input in the ASR-warning system in the most traditional soybean regions of Brazil.