Brief Summary of Minutes of Annual Meeting

Georgia Investigators: Gerrit Hoogenboom and David Stooksbury Project Report: The Georgia Automated Environmental Monitoring Network (www.Georgiaweather.net) was expanded to 71 automated stations in 2006. To integrate research with information delivery and outreach, artificial intelligence systems are being developed for specific crop management applications. The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the amount of prior weather data to include in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40 C was 0.20 C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17 C and 0.16 C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons.Available climate information can be used by growers to assess different scenarios and alternative management strategies. An irrigation decision tool for peanut production was developed to provide probability distributions of the seasonal cost to irrigate peanuts under different El Niño-Southern Oscillation (ENSO) forecasts.Yields were simulated for both irrigated and rainfed peanuts using the CSM-CROPGRO-Peanut model. The tool was used to examine the effects of different planting dates, soil types and climate forecasts. Results of a case study are presented for the Georgia Green variety grown in Miller County, Georgia. The probability of obtaining a high net return under irrigated conditions increased when planting dates were delayed for El Niño years. Dryland peanut production was profitable in a La Niña year if peanuts were planted between mid-April and early May. The peanut irrigation decision support tool will be deployed as a web-based tool on the AgClimate web site (www.agclimate.org). Indiana Investigator: Richard Grant Project Report: Understanding the dry down of soybean canopies is important in evaluating the potential for infect and emission of soybean rust (Phakopsora pachyrhizi) and other fungal disease spores. Further study of the dry-down time and wetness duration for the lower, midsection, and upper soybean canopy were explored. Results indicate that the duration of wetness varies widely within the canopy and depends on the source of wetness- precipitation or dew. Rain penetrates a soybean canopy much more quickly than dew. The time required for dew to wet the canopy was much lower at the top of the canopy than for the middle or upper layer. The duration of dew was least in the mid-canopy compared to the top of the canopy. This effect may have been due to the difficulty of dewfall penetration in a dense canopy. Precipitation duration and dry-down in the soybean canopy was marked by a nearly equal duration and drying throughout the top two layers of the canopy, with a definite increase in duration and dry-down time at the bottom of the canopy. Rain events in general had longer wetness durations than dew events due to the tendency of summer rain to come in the early evening-- long before dew initiation occurs on other nights. Observations of sensor accuracy showed a need for increased spatial area covered by sensors in order to account for the wide spatial variation in the amount of dew formation. Sensors were designed and produced to further explore the conditions surrounding the wetting and drying of a soybean canopy. Kansas Investigators: Scott Staggenborg and Michel Ransom Project Report: This project uses crop simulation models to examine the impacts of cropping systems within the 10 states of the North Central Region. Since a systems approach is the desired variable to examine, DSSAT 4.0 is used to simulate the appropriate cropping systems throughout the region. Previous work was completed using only corn and soybean simulations on three soils in three selected counties in each state. This approach is limited because the corn-soybean rotation, which dominates much of the eastern two-thirds of the region, does not represent the western portion of the region where irrigated agriculture and diverse dryland cropping systems occur. As a result, a different approach will be taken during the next phase of our project. Field studies were initiated in 2005 and repeated in 2006 to calibrate the corn, grain sorghum and cotton simulation models found in DSSAT 4.0. The initial results suggest that the CERES-Maize performs adequately for the range of environmental conditions to be studied. It does appear that CERES-Sorghum and CROPGRO-Cotton need further calibration as simulated yields were routinely lower than measured values. The USDA has subdivided the US into Major Land Resource Areas (MLRAs). Approximately, 50 MLRAs of variable size are encompassed within the 10 states of the North Central Region. Simulations are being conducted on three cropping systems, where applicable, on the three predominant soil series for each MLRA. Soils are identified using the State Soil Geographic (STATSGO) database for each state. Soil physical properties to be used for simulations are obtained from the NRCS Soil Survey Laboratory Database. Historic weather data is selected from a location within each MLRA in order to maintain uniform coverage of the region. Michigan Investigators: Stuart Gage, Gene Safir, Jeff Andresen Project Report: The North Central Regional Daily Climate Database was utilized to develop a derived plant stress index based on daily maximum and minimum temperatures and daily precipitation. The period of record was 1971-2000 and the number of locations was 1055. Monthly degree-day and precipitation accumulations were computed for each of the 1055 locations. The equation used to compute the plant stress index was heat accumulation / precipitation +1 for each month of the year. Patterns of stress were mapped and plotted to examine plant stress throughout the 30 year period of record. A threshold of plant stress was examined to evaluate years and locations where stress influenced crop growth and yield. Probabilities of plant stress are under development. The plant stress values were overlaid on a digital eco-region map developed by Bailey to examine the patterns of stress in relation of the ecological classification of the region. A hierarchical clustering analysis was preformed to examine the co-occurrence of eco-regional classification and plant stress. A similar approach was taken to examine corn yield. The analysis revealed very interesting patterns illustrating the co-occurrence of poor yields with high incidence of plant stress values computed Further research is required to refine the analysis and this work in ongoing. Next steps will include completing the analysis of the plant stress index, further linking stress with crop yield and production, comparing the plant stress index with other drought indices and developing a plant stress probability index for the region. We will also attempt to relate the index to pest and pathogen incidence. Work has proceeded to investigate regional patterns of soybean rust and application of the principles of aerobiology to forecast rust distribution and establishment. Work has continued on development of the North Central Regional Atlas. The atlas is ready for final review by committee members as well as external reviewers. To supplement the Crop, Climate and Soils variables currently included in the Atlas, we have begun to build a socioeconomic database in collaboration with the Land Policy Institute at Michigan State University. With collaboration from economists and analysts in the Institute we have added 15 variables of annual socioeconomic data including human population, employment and financial values for each of the 1055 counties in the region. Mapping these variables not only reveals interesting patterns but also reveals intricate and lagged associations between the bio-physical patterns of climate, crop production and soil characteristics. Over the next year we will be collaborating with members of committee to develop a strategic analysis of this exciting spatial temporal database. Research Activities:

• Development of a soybean rust forecast model.
• Development and application of a plant stress index to assess regional patterns and stress probabilities
• Analysis of the Crop, Climate and Soils Database to enhance regional decision making
• Incorporation of socioeconomic data into the regional database
• Analysis of regional socioeconomic patterns in the north central region

Missouri Investigator: Patrick Guinan Project Report: Using information from NOAAs National Digital Forecast Database and combining the data with historical data provided by Missouris Commercial Agriculture Automated Weather Station network, products were developed in 2006 to supplement the existing Horizon Point system. Horizon Point is an enrollment e-mail delivery system where producers are provided with historical weather information, forecasted weather information, and customized products that are specific to their latitude/longitude and farm characteristics. This year, continued collaborative efforts among university faculty have resulted in additional products including animal comfort tables for cattle and poultry and grain drying tables for corn and soybean. Additionally, a weed emergence table was activated earlier this spring to be used as a general guide in weed scouting and weed management decisions. Research Activities: Paper birch decline in the Niobrara Valley: Interactions of weather, microclimate and genetics New York Investigator: Robert Seem Project Report: Testing and development of the computer modeling system continues. The Local-area Agricultural Atmospheric Simulation System (LAWSS), running on the Cornell supercomputer cluster, has been updated for better performance and for solving various compiler and model issues. LAWSS was operated for regional simulations at Finger Lakes area down to 333m resolution for three grape growing seasons. During the operational runs, we found several cases of mathematical problems, which are due to limited upper layer boundary conditions. To accommodate various wetness and plant disease models, the LAWSS output data has been transformed to a common data (NetCDF) format. Based on NetCDF weather data, following models were implemented for spatiotemporal simulations: SWEB - leaf wetness model, and DMCast - grape downy mildew model, ACC - Asiatic citrus canker model, and FuzzyLWD - Fuzzy logic leaf wetness model. The raw survey data of individual Asiatic citrus canker (ACC) trees from Florida Miami area were obtained from collaborators in Florida. The data were previously analyzed with using daily weather observations from the Miami airport. However, due to high magnitude of wind and rainfall variance, higher spatiotemporal weather data are needed for wind channel effects and trajectory analyses. The LAWSS system was suitable for high resolution wind vector data but lacks of rainfall data, which are essential for ACC modeling. Therefore Mesoscale Atmospheric Simulation System (MASS), which is a complete mesoscale weather model has been implemented in the Cornell supercomputer cluster and will be used in the ACC simulation and analyses. It will also solve the mathematical problems related to upper layer boundary conditions. The ACC model is composed of three sub-models  source strength, transport and risk assessment. It re-samples observed tree data according to the input weather data. The 1km and 333m resolution weather data from LAWSS were re-gridded up to 10m resolution for test runs of the model and it presented reasonable results. South Dakota Investigator: Dennis Todey Project Report: Drought Response - Throughout the spring and summer of 2006, Dr. Todey did programming efforts to respond to drought in the Midwest relating yield variability, precipitation totals and historical comparison. Deployed another eight new automated weather stations across South Dakota to collect data and provide temperature and precipitation and other derived information across the state. Funded a graduate student conducting research on data comparison between automated stations and cooperative observer stations in South Dakota. Yield Database and Analysis - Updated NC-1018 yield database to be complete over the years 1970-2005 for corn soybean and wheat. Developed yield trends over the whole period of record for all three commodities. Compared yield trends and improvement of corn yields over the last 10 and 5 years. Determined significance of linear relationship across the region. Assessed variability in yields of crops based on variability normalized by average yield to determine a level of risk across the region.

Abrahamson, D.A., D.E. Radcliffe, J.L. Steiner, M.L. Cabrera, D.M. Endale and G. Hoogenboom. 2006. Evaluation of the RZWQM for simulating tile drainage and leached nitrate in the Georgia Piedmont. Agronomy Journal 98(3):644-654. Banterng, P., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2006. Yield stability evaluation of peanut lines: a comparison of an experimental versus a simulation approach. Field Crops Research 96(1):168-175. Bostick, W.M., V.B. Bado, A. Bationo, C. Tojo Soler, G. Hoogenboom and J.W. Jones. 2006. Soil carbon dynamics and crop residue yields of cropping systems in the Northern Guinea Savannah of Burkina Faso. Soil and Tillage Research. (In Press). Colunga-Garcia M, S Gage, and G Safir. 2005. Development and integration of temporal/spatial information into plant pest and disease forecasting systems. Survey Detection & Identification & Biological Control National Science Program/Center for Plant Health Science & Technology Annual Report 2004. p. 9-12. Colunga-Garcia M., P.R. Grace, S.H. Gage, G.P. Robertson, G.R. Safir. Urbanization and its Impact on the Carbon Sequestration Potential of Agroecosystems in the North Central Region. Third USDA Symposium on Greenhouse Gases & Carbon Sequestration in Agriculture and Forestry, March 21 - 24, 2005, Baltimore, MD. Dangthaisong, P., P. Banterng, S. Jogloy, N. Vorasoot, A. Patanothai and G. Hoogenboom. 2006. Evaluation of the CSM-CROPGRO-Peanut model in simulating responses of two peanut cultivars to different moisture regimes. Asian Journal of Plant Sciences 5(6):913-922. Fraisse, C.W., N.E. Breuer, D.Zierden, J.G. Bellow, J. Paz, V.E. Cabrera, A. Garcia y Garcia, K.T. Ingram, U. Hatch, G. Hoogenboom, J.W. Jones and JJ. O'Brien. 2006. AgClimate: A climate forecast information system for agricultural risk management in the southeastern USA. Computers and Electronics in Agriculture 53(1):13-27. Gage, S.H., M. Colunga-Garcia, P.R. Grace, H. Yang, G.R. Safir, G.P. Robertson, A. Shortridge, A. Prasla, A. Ali, S. Del Grosso, P. Wilkins, S. Rowshan. A Modeling Application Integrative Framework for Regional Simulation of Crop Productivity, Carbon Sequestration and Greenhouse Gas Emissions. Third USDA Symposium on Greenhouse Gases & Carbon Sequestration in Agriculture and Forestry, March 21 - 24, 2005, Baltimore, MD. Garcia y Garcia, A., G. Hoogenboom, L.C. Guerra, J.O. Paz and C.W. Fraisse. 2006. Analysis of the interannual variation of peanut yield in Georgia using a dynamic crop simulation model. Transactions of the American Society of Agricultural Engineers. (In Press). Grace, P.R., M. Colunga-Garcia, S.H. Gage, G.R. Safir, G.P. Robertson. 2005. The potential impact of climate change on North Central Regions soil organic carbon resources. Ecosystems. Grace, P.R., S.H. Gage, M. Colunga-Garcia, G.P. Robertson, G.R. Safir. Maximizing Net Carbon Sequestration in Agroecosystems of the North Central Region. Third USDA Symposium on Greenhouse Gases & Carbon Sequestration in Agriculture and Forestry, March 21 - 24, 2005, Baltimore, MD. Grant, R.H. and W. Gao. 2006. Distribution of diffuse UV-B radiation in a maize canopy. 17th Conf. on Biometeorol. and Aerobiology, Amer. Meteorol. Soc. Greenwald, R., M.H. Bergin, J. Xu, D. Cohan, G. Hoogenboom and W.L. Chameides. 2006. The influence of aerosols on crop production: A study using the CERES model. Agricultural Systems 89(2-3):390-413. Gunal, H., and M.D. Ransom. 2006. Genesis and micromorphology of loess-derived soils from central Kansas. Catena 65:222-236. Gunal, H., and M.D. Ransom. 2006. Clay illuviation and calcium carbonate accumulation along a precipitation gradient in Kansas. Catena 68:59-69. Heinemann, A.B., A.de H.N. Maia, D. Dourado_Neto, K.T. Ingram and G. Hoogenboom. 2006. Soybean (Glycine Max [L.] Merr.) growth and development response to CO2 enrichment under different temperature regimes. European Journal of Agronomy 24(1):52-61. Heisler, G., B. Tao, J. Walton, R. Grant, R. Pouyat, I. Yesilonis, D. Nowak, and K. Belt 2006. Land cover influences on below-canopy temperatures in and near Baltimore, MD., In: Proceedings of the 6th Symposium on the Urban Environment, American Meteorological Soc. (In press). Herrero, M., E. Gonzalez-Estrada, P.K. Thornton, C. Quiros, M.M. Waithaka, R. Ruiz and G. Hoogenboom. 2007. IMPACT- Generic household-level databases and diagnostic tools for integrated crop-livestock analysis. Agricultural Systems 92 (1-3):240-265. Isard, S. A., Gage, S.H., Comtois, P. and Russo, J. 2005. Principles of the atmospheric pathway for Invasive species applied to soybean rust. BioScience: 851-861. Jain, A., R.W. McClendon and G. Hoogenboom. 2006. Freeze prediction for specific locations using artificial neural networks. Transactions of the American Society of Agricultural Engineers. (In Press). Kim, K.R., Seem. R.C., Park. E.W., Zack, J.W., and Magarey, R.D. 2005 Simulation of grape downy mildew across geographic areas based on mesoscale weather data using supercomputer. Plant Pathol. J. 21:111-118. Ma, L., G. Hoogenboom, L. R. Ahuja, J.C. Ascough, and S.A. Saseendran. 2006. Development and evaluation of the RZWQM-CERES-Maize hybrid model for maize production. Agricultural Systems 87(3):274-295. Magarey, R.D., Russo, J.M., Seem, R.C., and Gadoury, D.M. 2005. Surface wetness duration under controlled environmental conditions. Ag. For. Meterol. 128:111-122. Paz, J.O., C.W. Fraisse, L.U. Hatch, A. Garcia y Garcia, L.C. Guerra, O. Uryasev, J.G. Bellow, J.W. Jones and G. Hoogenboom. 2006. Development of an ENSO-based irrigation decision support tool for peanut production in the southeastern US. Computers and Electronics in Agriculture. (In Press). Schmitz, H. and R.H. Grant 2006. Precipitation and dew in soybean canopies: An In depth look at the differences in wetness with canopy height.. 17th Conf. on Biometeorol. and Aerobiology, Amer. Meteorol. Soc. Smith, B.A., R.W. McClendon and G. Hoogenboom. 2006. Improving air temperature prediction with artificial neural networks. International Journal of Computational Intelligence 3(3):179-186. Suleiman, A., and G. Hoogenboom. 2007. Comparison of Priestley-Taylor and Penman-Monteith for daily reference evapotranspiration estimation in a humid climate. Journal of Irrigation and Drainage Engineering. (In Press). Suriharn, B., A. Patanothai, K. Pannangpetch, S. Jogloy and G. Hoogenboom. 2007. Determination of cultivar coefficients of peanut lines for breeding applications of the CSM-CROPGRO-Peanut model. Crop Science. (Accepted for publication). White, J.W., K.J. Boote, G. Hoogenboom and P.G. Jones. 2007. Regression-based evaluation of ecophysiological models. Agronomy Journal 99(2). (In Press).