Brief Summary of Minutes of Annual Meeting

Nebraska: We analyzed the observed and predicted drought variability in the continental U.S. In the analysis we downscaled the present-day control simulations and predictions of future climate in the 21st century from 16 fully coupled atmosphere-ocean models included in the IPCC AR4 from the models grid resolutions to a 1/8 degree grid system. This statistically-downscaled dataset covers the continental U.S. for the period from 1950 to 2099. The predictions of future climate were made with low, median and high greenhouse gas emission scenarios (SRES B1, A1b, and A2). Using the downscaled temperature and precipitation data we calculated the PDSI and used it to evaluate the drought variability. Major results suggest that the High Plains will become drier in the future warmer climate. Significant increases in drought intensity and durations are projected for this region. An article on the changes in drought variability in the continental U.S. is currently in preparation. We modified a hybrid soil temperature model by combining empirical and mechanistic approaches, and evaluated the model in an agroecosystem and also a tallgrass prairie in the Great Plains. This model simulated soil temperatures on a daily basis from meteorological inputs of maximum and minimum air temperatures and soil and plant properties. The agroecosystem consisted of a no-till corn (Zea mays L.) and soybean (Glycine max Merr. (L)) rotation system. In the agroecosystem, the root mean square error of the modified model simulation varied from 1.41 to 2.05 oC for the four depths of 0.1, 0.2, 0.3 and 0.5 m beneath the surface. The mean absolute error varied from 1.06 to 1.53 oC. The root mean square error and mean absolute error of the modified model were about 0.1-0.3 oC less than the original model at the 0.2-0.5m depths. For the tallgrass prairie, the mean absolute errors of the simulated soil temperatures were slightly greater than the agroecosystem varying from 1.48-1.7 oC for all years and from 1.09-1.37 oC during the active growing seasons for all years. Advantages of this model are its relative simplicity using readily available daily temperature data. The new model can be incorporated into a larger crop model where soil temperatures are required. Given the applied nature of this hybrid model, it would be well suited to simulate soil temperatures in the first 50 cm of soil over a vegetated surface for processes related to soil respiration, soil organic matter decomposition and soil-borne pests. Kansas We have developed analyses of plant disease risk in the context of global change. As part of an international collaboration linking researchers in Latin America, Europe, Southeast Asia, and Pacific Asia, we have evaluated key components of climate change analyses that should be considered in planning for climate change adaptation (Garrett et al. 2011). We have demonstrated the effects of temperature on disease resistance genes and their potential contributions to the durability of resistance (Webb et al. 2010). As part of a global analysis of potato late blight risk under climate change, we have developed a framework for applying disease risk models that require high resolution input in a broader range of scenarios (Sparks et al., in review). We have developed integrated analyses of risk that incorporate the range of factors important for small-scale farmers (Perez et al. 2010). We have evaluated disease ecology in tallgrass prairie and potential spill-over between agricultural and natural systems (Saleh et al. 2010). We have also characterized the distribution of a resistance gene homolog in the dominant grass species of tallgrass prairie (Rouse et al., in review). In a paper that was the Journal of Ecology Editors Choice for March 2010, we presented an analysis of variation in this dominant grass species in response to altered environmental conditions associated with climate change (Travers et al. 2010). We have interpreted risk factors involved in emerging plant diseases for a general audience (Garrett et al. 2010). We have published the first of a series of analyses of microbial communities using high-throughput sequencing techniques, in a comparison of soil communities and their response to tillage and crop rotation in Kansas (Yin et al. 2010). As part of our ongoing work in applying the R programming environment in plant disease ecology, we have reviewed a new text on the subject (Garrett 2010). Our work shows that crop models are useful tools in studying cropping system performance within a region. The results from the simulations will allow producers and policy makers to develop programs aimed at maintaining rural economic viability as ground water supplies decline and as warming and drying occurs. The coupling of a crop model, a soil drainage model, and an economic model resulted in initial evaluations of crop selection and irrigation practices on recharge in the Ogallala Aquifer. The addition of soil moisture sensors to the mesonet has expanded soil moisture monitoring efforts. Iowa The outputs described above resulted in changes in knowledge of stakeholders, agricultural economists, and field extension specialists. At the Iowa State University hosted one-day workshop, Global Climate Change and Its Impact on Food Production and Biofuels, an audience of agricultural thought leaders, including agribusiness representatives, individual producers, and agricultural associations, were informed on the following topics: (1) evidence of global climate change and implications of global climate projections for global crop production, (2) overview of uncertainties in climate model projections arising from poor representation of physical processes like thunderstorms and landscape heterogeneity, (3) overview of emerging developments in climate models and their projection ns, emphasizing the potential for outlooks with 10-year lead time, (4) analysis of the regional climate mechanisms that have prevented a warming trend in the Midwest annual temperature over the past 20 years, interpretation within the context of global change, and implications for crop productivity In May, an audience of ISU Extension Farm Management Field Specialists was provided a webinar presentation on climate change with follow-up discussion on implications for farm management. The presentation included information on the following: (1) In depth analysis of recent trends in maximum/minimum temperature and precipitation in Iowa, and explanation of causes for downward trend in growing season maximum temperature, (2) description of projected climate changes for the next 25 years in Iowa, including growing season length, precipitation, maximum/minimum temperature, humidity, and freeze/thaw cycles. In July through September, a series of eleven public outreach seminars targeted an audience of landowners, community leaders, and local government officials to discuss how Iowa communities and rural landowners can plan for future floods, with emphasis on land use choices and community development that can be made. The total number of attendees exceeded 800. The climate knowledge presented included the following: (1) recent increasing trend of high annual statewide precipitation and increasing trend of days with rainfall > 1.25" at many communities, (2) overview of the causes for rainfall increase: the low drought risk phase of the Pacific Decadal Oscillation, warmer Gulf of Mexico sea surface temperature, water holding capacity of Midwest soils, and (3) overview of the outlook of future flood risk: risk remains at recent levels for the next 10-15 years; an increased risk of drought in the subsequent 20-30-yr period with isolated days of rainfall exceeding even the rare events experienced recently. The subsequent discussion period often centered on an important implication for landowners of the continuation of high rainfall years and high number of high rainfall days within a year. The implication is they will have a growing need to manage surface runoff. The increased runoff from high rainfall days not only threatens soil quality for agricultural productivity but also increases flood risk. Michigan During 2010, significant progress was made in the simulation of two representative crops growing in the region, corn, and wheat, under historical and projected future climates. The simulations included areas within the region where agricultural activities historically have been limited by climatological and soil constraints, but which could become more favorable for agriculture in the future given a warmer climate. CERES-Maize and CERES-Wheat crop models, part of the Decision Support for Agrotechnology Transfer (DSSAT) model software system were used for all simulations. Five locations across the Great Lakes region (1 in WI, 3 in MI, and 1 in NY) were chosen for the study on the basis of climatological series record length and homogeneity, as well as geographical coverage across the region and its major land use zones. The study was divided into two major categories; historical and future. Historical scenarios were based on observed daily weather data at each of the locations. The individual lengths of the data series ranged from 85-109 years and all but 1 of the series began prior to 1915. Daily solar radiation series were derived stochastically at each site with the WGEN synthetic weather generator. Climate model-derived scenarios developed for a previous research project for the period 19902099 were used to develop projected future weather scenarios for locations at or very near to each of the historical climate station sites. An ensemble of scenarios for each location was created by using simulations from four GCMs each driven with A2 and B2 SRES greenhouse gas emission scenarios, four different downscaling methods, and two greenhouse gas emission scenarios, resulting in a total of 32 individual projected future scenarios per site, 1990-2099. In general, mean annual temperatures across the region warmed 2-6°C (relative to the 1990-2009 period) in the ensemble of projected future time series by the 2090-2099 decade. Projected annual precipitation totals in the ensembles generally remained in a range between 70-140 percent of the control period totals, with a slight overall average increase by the 2090-2099 decade. Regionally, low precipitation and moisture stress were chief limitations to simulated crop yields during the historical period. Simulated corn and wheat yield series were found to increase with time since the late 1930's at most of the study sites, largely the result of wetter, less stressful growing season weather conditions. In the projected future simulations, the warmer climate suggested by the GCMs led to some initial early-century increases in simulated non-CO2 enriched crop yields relative to historical yields. By late in the century, however, relative yield declines were found for most of the projected scenarios. With CO2 enrichment included, increases in simulated wheat yield were noted for most scenarios. Largest percentage increases in yield during 2010-2099 were found at northern locations. The ratios of the future scenarios with and without increases in CO2 concentration suggest that the majority of wheat yield increases during this period are due to CO2 enrichment. Missouri Missouri crop producers use the commercial agriculture automated weather station network and horizon point system to: "Improve fertilizer efficiency "Reduce applied pesticides "Make springtime planting decisions "Reduce spray drift "Plant crops at strategic times "Spray crops at strategic times "Practice prudent water mangement through irrigation scheduling tools Specifically, some outcomes derived from a survey given to horizon point participants include the following responses: " We do a lot of hay and this is handy in helping us look ahead to harvesting of the hay. " I advise farmers using these data for fall N applications. Farmers participating in federal and state incentive programs must follow " I used the rainfall information to make better informed decisions on when to irrigate this summer. " Make bin fan decisions based on bin drying estimates. " Look at all the pest scouting reports to see if I need to scout a little more than normal. " I like to check out the wind speed predictions as we do a lot of custom application and it helps me line out our work load. New York Our work has several impacts. In terms of fundamental understanding of the host-pathogen relationship, we have established that even very short periods of cold temperatures can alter the response of a host plant to attack by a host-specific pathogen. In this case we hypothesize that the cold actually causes a generalized stress response that is manifest as implementation of generalized resistance. The molecular mechanisms of this response should provide new insight into stress responses by plants. For growers, this new information can mean better prediction of disease development by incorporation of the effect of acute cold events. Finally, for climate researchers, this work presents a challenge in assessing the impact of climate change on plant disease development. If very short periods of punctuated cold can elicit a defense response from a crop plant, how will climate models and assessments account for this effect when predicted weather conditions are only expressed in very general (average) terms? North Dakota Relationships between long term change in growing degree units and yield forecast has been studied. Improved yield forecasts will improve marketability of crops and ultimately lead to better economic decision-making based on better information. The North Dakota Agricultural Weather Network implemented an irrigation scheduler with which farmers can create a field on a GIS based map. Our interface would collect soil data. Farmers can read the amount of water needed once the type of crop is selected. It saves money and water. Better decision-making for irrigation will improve profitability for farmers using irrigation. It will improve the use efficiency of a scarce commodity. We developed herbicide timing and pesticide applications in North Dakota Agricultural Weather Network (NDAWN). This will allow more efficient use of pesticides and herbicides and hopefully limit overuse of chemical application. The NDAWN system records hourly atmospheric moisture conditions. Atmospheric moisture information will provide improved understanding of moisture conditions in spatial and temporal detail, which are necessary for improved disease forecasts and ultimately more precise use of chemicals and improved profitability. The NDAWN system records hourly temperature, relative humidity, rainfall, atmospheric moisture, wind speed and direction, atmospheric pressure, soil temperature at 4 inch depth for bare soil and turf. Better access to weather and soils data, in combination with crop yield histories for model testing, will result in improved crop model capabilities.