Mungbean (Vigna radiata) is a fast-growing, warm-season legume crop that is primarily cultivated in developing countries of Asia. Here we construct a draft genome sequence of mungbean to facilitate genome research into the subgenus Ceratotropis, which includes several important dietary legumes in Asia, and to enable a better understanding of the evolution of leguminous species. Based on the de novo assembly of additional wild mungbean species, the divergence of what was eventually domesticated and the sampled wild mungbean species appears to have predated domestication. Moreover, the de novo assembly of a tetraploid Vigna species (V. reflexo-pilosa var. glabra) provides genomic evidence of a recent allopolyploid event. The species tree is constructed using de novo RNA-seq assemblies of 22 accessions of 18 Vigna species and protein sets of Glycine max. The present assembly of V. radiata var. radiata will facilitate genome research and accelerate molecular breeding of the subgenus Ceratotropis.

The ability of empirical models to enhance accuracy of site-specific estimates of leaf wetness duration (LWD) was assessed for 15 sites in Iowa, Nebraska, and Illinois during May to September of 1997, 1998, and 1999. Enhanced estimation of LWD was obtained by applying a 0.3-m height correction to SkyBit wind-speed estimates for input to the classification and regression tree/stepwise linear discriminant (CART/SLD) model (CART/SLD/Wind model), compared to either a proprietary model (SkyBit wetness) or to the CART/SLD model using wind speed estimates for a 10-m height. The CART/SLD/Wind model estimated LWD more accurately than the other models during dew-eligible (20:00 to 9:00) as well as dew-ineligible (10:00 to 19:00) periods, and for the period 20:00 to 9:00 regardless of rain events. Improvement of LWD estimation accuracy was ascribed to both the hierarchical structure of decision-making in the CART procedure and wind speed correction. Accuracy of the CART/SLD/Wind model identifying hours as wet or dry varied little among the 15 sites, suggesting that this model may be desirable for estimating LWD from site-specific data throughout the midwestern United States.

Months of the year with high risk of European canker (Neonectria galligena) development in areas of the United States, Chile, England, and Northern Ireland were determined from published data. Moving-window analysis of long-term climatic data was used to classify disease risk in these areas in relation to rainfall and temperature conditions using the degree of agreement statistic. Greatest agreement occurred when it both rained on >30% of days/month and there was an average of >8 h/day with temperature of 11 to 16°C. When these thresholds were applied in eight validation areas in New Zealand, Australia, the United States, The Netherlands, and Denmark, areas with reported higher risk of disease tended to be areas where the thresholds were exceeded more often and by greater amounts. Areas at higher latitudes (>52°) with frequent summer rainfall appeared to be most prone to European canker, including the fruit rot phase of the disease, probably because summer temperatures were more favorable than at lower latitudes. The climatic thresholds derived for European canker could be useful for studies of disease establishment risk, surveillance, eradication, climate change impact assessment, and, possibly, for disease risk forecasting. The methods used in this study allowed conditions favorable for disease development to be identified even though quantitative regional disease data were lacking, and they could be useful for similar geoclimatic studies of other diseases.

Few biologically based models to assess the risk of soybean rust have been developed because of difficulty in estimating variables related to infection rate of the disease. A fuzzy logic system, however, can estimate apparent infection rate by combining meteorological variables and biological criteria pertinent to soybean rust severity. In this study, a fuzzy logic apparent infection rate (FLAIR) model was developed to simulate severity of soybean rust and validated using data from field experiments on two soybean cultivars, TK 5 and G 8587. The FLAIR model estimated daily apparent infection rate of soybean rust and simulated disease severity based on population dynamics. In weekly simulation, the FLAIR model explained >85% of variation in disease severity. In simulation of an entire epidemic period, the FLAIR model was able to predict disease severity accurately once initial values of disease severity were predicted accurately. Our results suggest that a model could be developed to determine apparent infection rate and an initial value of disease severity in advance using forecasted weather data, which would provide accurate prediction of severity of soybean rust before the start of a season.