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Although Asian soybean rust occurs in a broad range of environmental conditions, the most explosive and severe epidemics have been reported in seasons with warm temperature and abundant moisture. Associations between weather and epidemics have been reported previously, but attempts to identify the major factors and model these relationships with field data have been limited to specific locations. Using data from 2002-03 to 2004-05 from 34 field experiments at 21 locations in Brazil that represented all major soybean production areas, we attempted to identify weather variables using a 1-month time window following disease detection to develop simple models to predict final disease severity. Four linear models were identified, and these models explained 85 to 93% of variation in disease severity. Temperature variables had lower correlation with disease severity compared with rainfall, and had minimal predictive value for final disease severity. A curvilinear relationship was observed between 1 month of accumulated rainfall and final disease severity, and a quadratic response model using this variable had the lowest prediction error. Linear response models using only rainfall or number of rainy days in the 1-month period tended to overestimate disease for severity <30%. The study highlights the importance of rainfall in influencing soybean rust epidemics in Brazil, as well as its potential use to provide quantitative risk assessments and seasonal forecasts for soybean rust, especially for regions where temperature is not a limiting factor for disease development.

Abstract:Much attention has recently been focused on the effects that climate variability and human activities have had on runoff. In this study, these effects are quantified using three methods, namely, multi-regression, hydrologic sensitivity analysis, and hydrologic model simulation. A conceptual framework is defined to separate the effects. As an example, the change in annual runoff from the semiarid Laohahe basin (18 112 km 2 ) in northern China was investigated. Non-parametric Mann-Kendall test, Pettitt test, and precipitation-runoff double cumulative curve method were adopted to identify the trends and change-points in the annual runoff from 1964 to 2008 by first dividing the long-term runoff series into a natural period (1964)(1965)(1966)(1967)(1968)(1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979) and a human-induced period . Then the three quantifying methods were calibrated and calculated, and they provided consistent estimates of the percentage change in mean annual runoff for the human-induced period. In 1980-2008, human activities were the main factors that reduced runoff with contributions of 89-93%, while the reduction percentages due to changes in precipitation and potential evapotranspiration only ranged from 7 to 11%. For the various effects at different durations, human activities were the main reasons runoff decreased during the two drier periods of 1980-1989 and 2000-2008. Increased runoff during the wetter period of 1990-1999 is mainly attributed to climate variability. This study quantitatively separates the effects of climate variability and human activities on runoff, which can serve as a reference for regional water resources assessment and management.