Soybean Rust: Five Years of Research

Abstract

Soybean rust (SR), Phakopsora pachyrhizi Sydow is an obligate biotrophic fungal pathogen causing rust on a wide range of hosts including many kind of beans (Glycine max, Phaseolus vulgaris var vulgaris, P. lunatus var lunatus, P. coccineus, Vicia faba, Lablab purpureus, Vigna radiata, V. unguiculata, Psophocarpus tetragonogobus, Pachyrhizus ahipa, P. erosus), few kind of peas (Pisum sativum, Cajanus cajan), many kinds of clover (Alisycarpus vaginalis, Trifolium repens, T. incarnatum, Melilotus officinalis), fenugreek, lupines, trefoil and kudzu. It originated in Asia and emerged in the continental United States (Louisiana) in 2004 (Scheinder et al., 2005) following hurricane Ivan which brought SR spores from Columbia. Presence of SR in South America since 2001 has been responsible for large yield losses in soybean, Glycine max L. Merr., fields. For example, in Brazil, a recent study depicted a two-year field trial where soybean rust was responsible for 37% to 67% of soybean seed yield losses (Kumudini et al., 2008). This study agreed with yield losses already observed in Asia, where the disease originated. Losses there can reach up to 80% (Miles et al., 2003). Based on a disease risk assessment study conducted by Pivonia and Yang (2005), climatic conditions in the soybean producing regions of the United States are suitable for similar yield losses. In 2009, soybean rust has been detected in sixteen states including 576 counties along the southeastern portion of the country (USDA Integrated Pest Management (IPM) web site; http://sbr.ipmpipe.org/cgi-bin/sbr/public.cgi). Its location boundaries in the US are mostly due to its warm location preference in addition to the presence of an alternative host, kudzu, in which it can overwintered. The invasive kudzu plant is an alternative hosts and its massive presence in several locations in South Louisiana and along the Mississippi river makes these locations of choice for soybean rust. Numerous studies have been conducted to build a system model allowing prediction, detection and evaluation of the presence of SR in soybean fields (Tao et al., 2009; Roberts et al., 2009).