Regional prevalence of soybean Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum, was modeled using tillage practices, soil texture, and weather variables (monthly air temperature and monthly precipitation from April to August) as inputs. Logistic regression was used to estimate the probability of stem rot prevalence with historical disease data from four states of the north-central region of the United States. Potential differences in disease prevalence between states in the region were addressed using regional indicator variables. Two models were developed: model I used spring (April) weather conditions and model II used summer (July and August) weather conditions as input variables. Both models had high explanatory power (78.5 and 77.8% for models I and II, respectively). To investigate the explanatory power of the models, each of the four states was divided into small geographic areas, and disease prevalence in each area was estimated using both models. The R(2) value of the regression analysis between observed and estimated SSR prevalence was 0.65 and 0.71 for models I and II, respectively. The same input variables were tested for their significance to explain the within-field SSR incidence by using Poisson regression analysis. Although all input variables were significant, only a small amount of variation of SSR incidence was explained, because R(2) of the regression analysis between observed and estimated SSR incidence was 0.065. Incorporation of available site-specific information (i.e., fungicide seed treatment, weed cultivation, and manure and fertilizer applications in a field) improved slightly the explained amount of SSR incidence (R(2) = 0.076). Predicted values of field incidence generally were overestimated in both models compared with the observed incidence. Our results suggest that preseason prediction of regional prevalence would be feasible. However, prediction of field incidence would not, and a different site-specific approach should be followed.

Plant disease epidemiology requires expansion of its current methodological and theoretical underpinnings in order to produce full contributions to global food security and global changes. Here, we outline a framework which we applied to farmers' field survey data set on rice diseases in the tropical and subtropical lowlands of Asia. Crop health risks arise from individual diseases, as well as their combinations in syndromes. Four key drivers of agricultural change were examined: labor, water, fertilizer, and land availability that translate into crop establishment method, water shortage, fertilizer input, and fallow period duration, respectively, as well as their combinations in production situations. Various statistical approaches, within a hierarchical structure, proceeding from higher levels of hierarchy (production situations and disease syndromes) to lower ones (individual components of production situations and individual diseases) were used. These analyses showed that (i) production situations, as wholes, represent very large risk factors (positive or negative) for occurrence of disease syndromes; (ii) production situations are strong risk factors for individual diseases; (iii) drivers of agricultural change represent strong risk factors of disease syndromes; and (iv) drivers of change, taken individually, represent small but significant risk factors for individual diseases. The latter analysis indicates that different diseases are positively or negatively associated with shifts in these drivers. We also report scenario analyses, in which drivers of agricultural change are varied in response to possible climate and global changes, generating predictions of shifts in rice health risks. The overall set of analyses emphasizes the need for large-scale ground data to define research priorities for plant protection in rapidly evolving contexts. They illustrate how a structured theoretical framework can be used to analyze emergent features of agronomic and socioecological systems. We suggest that the concept of "disease syndrome" can be borrowed in botanical epidemiology from public health to emphasize a holistic view of disease in shifting production situations in combination with the conventional, individual disease-centered perspective.

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Regional prevalence of soybean Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum, was modeled using management practices (tillage, herbicide, manure and fertilizer application, and seed treatment with fungicide) and summer weather variables (mean monthly air temperature and precipitation for the months of June, July, August, and September) as inputs. Logistic regression analysis was used to estimate the probability of stem rot prevalence with disease data from four states in the north-central region of the United States (Illinois, Iowa, Minnesota, and Ohio). Goodness-of-fit criteria indicated that the resulting model explained well the observed frequency of occurrence. The relationship of management practices and weather variables with soybean yield was examined using multiple linear regression (R 2 = 0.27). Variables significant to SSR prevalence, including average air temperature during July and August, precipitation during July, tillage, seed treatment, liquid manure, fertilizer, and herbicide applications, were also associated with high attainable yield. The results suggested that SSR occurrence in the north-central region of the United States was associated with environments of high potential yield. Farmers' decisions about SSR management, when the effect of management practices on disease prevalence and expected attainable yield was taken into account, were examined. Bayesian decision procedures were used to combine information from our model (prediction) with farmers' subjective estimation of SSR incidence (personal estimate, based on farmers' previous experience with SSR incidence). MAXIMIN and MAXIMAX criteria were used to incorporate farmers' site-specific past experience with SSR incidence, and optimum actions were derived using the criterion of profit maximization. Our results suggest that management practices should be applied to increase attainable yield despite their association with high disease risk.

The effects of fluctuating soil temperature and water potential on sclerotial germination and apothecial production by Sclerotinia sclerotiorum were investigated in growth chamber experiments. In the temperature experiments, temperature fluctuations of 4, 8, 12, and 16°C around a median of 20°C, and a constant of 20°C, were tested. Daily temperature fluctuations of 8°C resulted in highest levels of sclerotial germination and apothecial production. The earliest appearance of apothecia occurred in the 8°C fluctuation treatment, 24 days after the start of the experiment. Sclerotia in the 12°C fluctuation treatment germinated last; its first sclerotium germinated 44 days after experiment initiation. For the soil water potential experiments, constant saturation (approximately –0.001 MPa) and three levels of soil water potential fluctuation from saturation—“low” (–0.03 to –0.04 MPa), “medium” (–0.06 to –0.07 MPa), and “high” (–0.09 to –0.1 MPa)—were tested. Constant saturation yielded the highest number of germinated sclerotia and apothecia. All soil water potential fluctuations were detrimental to sclerotial germination and apothecial production, with sclerotial germination under fluctuating moisture conditions less than a tenth of that occurring under constant saturation. The first sclerotium in the constant saturation treatment germinated in 35 days; however, 76 days were required in the high soil water potential fluctuation treatment.