SAES-422 Multistate Research Activity Accomplishments Report

Sections

Status: Approved

Basic Information

Participants

Richard Grant (Purdue University), Mickey Ransom (Kansas State University), Dennis Todey (South Dakota State University), Pat Guinan (University of Missouri), Bob Seem (Cornell University - Geneva), Kathy Vreeland (Cornell University - Ithaca), Scott Staggenborg (Kansas State University) Mike Schmitt (Administrative Advisor-University of Minnesota), Jeff Andresen (Michigan State University), Stuart Gage (Michigan State University), Adnan Akyuz (North Dakota State University), Gerrit Hoogenboom (University of Georgia), Alan Lakso (Cornell University, visitor)

[Minutes]

Accomplishments

Accomplishments: Many of the participating states have established and maintain weather networks specifically for use in agriculture and education. The Georgia Automated Environmental Monitoring Network now consists of more than 75 automatic weather stations across the state and its web site (www.GeorgiaWeather.net) receives more than 500,000 hits per month. The North Dakota Agricultural Weather Network (ndawn.ndsu.nodak.edu) has nearly 75 automatic weather stations across the state that provides real-time weather information and can run advisory models for planting, pest control and irrigation scheduling. Similarly, the Michigan Automated Weather Network (www.agweather.geo.msu.edu/mawn/) has joined with several other in-state weather monitoring systems to create over 70 automatic weather reporting stations whose information can be accessed and used to run agricultural models. South Dakota Climate and Weather network (climate.sdstate.edu/climate_site/climate.htm) has 27 automated stations. The information is not only used by the agricultural community, but is of interest to anyone who needs to know local weather conditions. These networks have proved especially useful in K-12 education programs where students can monitor the local weather and climate conditions to learn more about the world around them. A significant accomplishment of NC-1018 is the support of crop simulation, especially Decision Support System for Agrotechnology Transfer (DSSAT). DSSAT is a single software package that facilitates the application of crop simulation models in research, teaching, decision making, outreach & service, and policy & planning. It includes: more than seven crop simulation models (CERES, CROPGRO, SUBSTOR, CANEGRO, CROPSIM, AROID, OILCROP, and others); Utilities and tools for data handing (experimental, soil, weather, economics); and Application programs (seasonal, crop rotational, and spatial analysis). A major new release of DSSAT (Version 4.5) will be released in the fall of 2008. Improvements include: New crops (cotton, sweet corn, sugarcane, cassava, and green bean); Crop model improvements (sorghum, wheat, maize, grain legumes [soybean, peanut, dry bean, velvet bean, faba bean, chickpea]); A generic soil module and modules for tillage, soil evaporation, soil temperature, tile drainage, and organic residue; GenCalc (an estimator of cultivar coefficients); Sequence/crop rotation analyzer (with an economic option and the ability to handle multiple experiments); AEGIS/Win (a spatial analysis program based on ArcView v3.x); WeatherMan (with data quality control and estimation of solar radiation; Linkage to SimCLIM; and Climate Impact Analysis (CIA Tool). The intent of NC1018 is to collaboratively study the impact of climate and soils on crops production with a special intent to assist crop modeling efforts. The work of this project created a singularly unique database comprising the data necessary to assess crop production at the county level across all of the North Central states. It consists of more than thirty years of crop data overlaid with soils, climate and land use information. All these data have been scaled to the county level and represent the best available data from which to conduct meaningful analyses of climate change, cropping changes and associate social and economic impacts. This data base comes at a time when climate modelers are clamoring for data sets that allow their global climate models to be down-scaled and validated. The NC-1018 data base has secured a new and highly important role in helping to determine the effects of climate change on crop production in the North Central states.

Impacts

  1. Georgia: Computer models combined with historic climate and current weather conditions can play a critical role in providing farmers with state-of-the-art technologies to help determine optimum management practices that reduce the use of natural resources, protect the environment, as well as provide long-term economic sustainability.
  2. Indiana: The prediction of crop yield depends in part on accurate descriptions of the environment. Two stressors of crops are fungal infestations and ultraviolet radiation. Research has provided the means to estimate the ultraviolet-A radiation reaching crops across the USA. The duration of plant wetness strongly influences the potential for fungal infections such as Asian rust on soybean. Ongoing studies of the wetting up drying down of soybean canopies is critical to determining if fungal infections can take hold and spread within soybean canopies under the climatic conditions of Indiana.
  3. Kansas: Our work will verify that crop models are useful tools in studying cropping system performance within a region. These results will provide an excellent baseline for future cropping systems simulation.
  4. Michigan: MASIF provides an interface to regional models. This allowed users to couple crop growth and carbon models into MASIF. Crop models allow for NPP determinations that can be interfaced with carbon models for estimates of soil organic carbon. Several datasets have been used to derive new datasets to identify critical components of agricultural sustainability, e.g., indicators of crop diversity, ecoregion-watershed intersections, crop stress zones, etc. A significant component of our analysis in this project focuses on the potential impact of land use on agriculture. We have identified the high priority clusters of agriculture and rural development that warrant special preservation measures. We are pursuing the development of a policy-relevant, multi-dimensional framework that contributes to the establishment of state land use goals and to more effective regional planning that includes farmland preservation and economic transformation.
  5. Minnesota: Changing precipitation regimes as a result of global climate change can affect basic nutrient balances in terrestrial systems. A modeling approach can address many possible scenarios of climate change and interactions between dominant climate and landscape parameters.
  6. Missouri: Bringing real-time weather conditions to rural locations and using the Internet as a resource for access to this information supports high technology agriculture and aids in farm management decisions.
  7. New York: With continuing expansion of wine industry in the Great Lakes grape growers need assistance in siting new vineyards. High-resolution simulations of local weather conditions provide estimates of the risk of cold events that can severely damage sensitive grape vines. Risk assessment of where extreme cold events are most likely will allow growers to optimally site new vineyards.
  8. South Dakota: The PET forecasts will allow irrigators to predict crop water use in advance of peak water use days when they are often shut-off due to electrical load management issues. This will allow them to better manage water resources.
  9. South Dakota: The regional crop climate atlas will take collected committee data and present it in a printed format for people related to agriculture to be able to see spatial depiction of climate and agriculture in the Upper Midwest. Further work will couple the paper publication with a more interactive and updatable web site.
  10. South Dakota: Data relating precipitation and yield can be used to help forecast final yield in mid-year based on amounts of precipitation. This can allow producers to make use of these forecasts to make better marketing decisions.
  11. South Dakota: The research on yield and precipitation relationships results were presented to respond to a Science article linking most of recent trends in crop yields to lower temperatures, neglecting the impact of additional precipitation throughout much of the last 15 years across the corn belt.
  12. South Dakota: The evaporation climatology provides engineers and producers with averages and extremes of evaporation from pan evaporation stations. This will particularly help with development of lagoon construction in balancing precipitation and evaporation from such lagoons.

Publications

Alfieri J G, D. Niyogi, M. A. LeMone, F. Chen, S. Fall, 2007, A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Datasets Used with Land Surface Models, Pure and Applied Geophysics (Invited), 164, 1789 - 1809. DOI 10.1007/a00024-007-0241-4. Anothai, J., A. Patanothai, K. Pannangpetch, S. Jogloy, K.J. Boote, and G. Hoogenboom. 2008. Reduction in data collection for determination of cultivar coefficients for breeding applications. Agricultural Systems 96(1-3):195-206. Ashish, D., G. Hoogenboom, and R.W. McClendon. 2008. Land-use classification of mutispectral aerial images using artificial neural networks. International Journal of Remote Sensing. (Accepted for publication). Bannayan, M., and G. Hoogenboom. 2008. Weather Analogue: A tool for lead time prediction of daily weather data realizations based on a modified k-Nearest Neighbor approach. Environmental Modeling 23(6):703-713. Bannayan, M., and G. Hoogenboom. 2008. Predicting realizations of daily weather data for climate forecasts using the non-parametric nearest-neighbor re-sampling technique. International Journal of Climatology. (Accepted for publication). Boken, V.K., C. E. Haque, and G. Hoogenboom. 2007. Predicting drought using pattern recognition, Annals of the Arid Zone 46(2):133-144. Chen F., K. W. Manning, M. A. LeMone, S.B. Trier, J. G. Alfieri, R. Roberts, M. Tewari, D. Niyogi, T. W. Horst, S. P. Oncley, J. B. Basara, P. D. Blanken, 2007, Description and Evaluation of the Characteristics of the NCAR High-Resolution Land Data Assimilation System, Journal of Applied Meteorology and Climatology, 46, 694-713, DOI: 10.1175/JAM2463.1 Deng, X., B.J. Barnett, G. Hoogenboom, Y. Yu, and A. Garcia y Garcia. 2008. Alternative crop insurance indices. Journal of Agricultural and Applied Economics 40(1): 223-237. Fang, H., S. Liang, G. Hoogenboom, J. Teasdale and M. Cavigelli. 2008. Corn yield estimation of remotely sensed data into the CSM-CERES-Maize model. International Journal of Remote Sensing 29(10):3011-3032. Garcia y Garcia. A., L.C. Guerra, and G. Hoogenboom. 2008. Impact of generated solar radiation on simulated crop growth and yield. Ecological Modeling 210(3):312-326. Gijsman, A.J., P.K. Thornton, and G. Hoogenboom. 2007. Using the WISE database to parameterize soil inputs for crop simulation models. Computers and Electronics in Agriculture 56:85-100. Guerra, L.C., A. Garcia y Garcia, J.E. Hook, K.A. Harrison, D.L. Thomas, D.E. Stooksbury, and G. Hoogenboom. 2007. Irrigation water use estimates based on crop simulation models and kriging. Agricultural Water Management 89(3):199-207. Hartley, Paul, DeAnn Presley, and Michel D. Ransom. 2007. Mineralogy of polygenetic soils from the Bluestem Hills of East-Central Kansas, USA. In Annual Meetings Abstracts [CD-ROM]. ASA, CSSA, and SSSA, Madison, WI. Karlstrom, E.T., Oviatt, C.G., and. Ransom, M.D. 2007. Paleoenvironmental interpretation of multiple soil-loess sequence at Milford Reservoir, northeastern Kansas. Catena 72:113-128. Lin, S., J.D. Mullen, and G. Hoogenboom. 2008. Farm-level risk management using irrigation and weather derivatives. Journal of Agricultural and Applied Economics. (Accepted for publication). Lizaso, J.I. , K.J. Boote, C.M. Cherr, J.M.S. Scholberg, J.J. Casanova, J. Judge, J.W. Jones, and G. Hoogenboom. 2007. Developing a sweet corn simulation model to predict fresh market yield and quality of ears. American Journal of Horticultural Science 132(2):415-422. Olatinwo R.O., J.O. Paz, S.L. Brown, R.C. Kemerait, A.K. Culbreath, J.P. Beasley, Jr., and G. Hoogenboom. 2008. Predicting spotted wilt severity in peanut based on local weather conditions and the tomato spotted wilt virus risk index. Phytopathology. (Accepted for publication). 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. 2007. Development of an ENSO-based irrigation decision support tool for peanut production in the southeastern US. Computers and Electronics in Agriculture 55(1):28-35. Pathak, T.B., C.W. Fraisse, J.W. Jones, C.D. Messina, and G. Hoogenboom. 2007. Use of global sensitivity analysis for CROPGRO cotton model development. Transactions of the American Society of Agricultural Engineers 50(6):2295-2302. Prabhakaran, T., and G. Hoogenboom. 2008. Evaluation of the weather research and forecasting model for two frost events. Computers and Electronics in Agriculture. (In Press). Presley, DeAnn, Michel D. Ransom, and Paul Hartley. 2007. Mineralogy and stratigraphy of polygenetic soils on different geomorphic surfaces of the Bluestem Hills of East-Central Kansas. In Annual Meetings Abstracts [CD-ROM]. ASA, CSSA, and SSSA, Madison, WI. Presley, DeAnn Ricks. 2007. Ph. D. Dissertation. Genesis and spatial distribution of upland soils in east central Kansas. Kansas State Univ. Saseendran, S.A., L. Ma, R. Malone, P. Heilman, L. R. Ahuja, R. S. Kanwar , D. L. Karlen, and G. Hoogenboom. 2007. Simulating management effects on crop production, tile drainage, and water quality using RZWQM-DSSAT. Geoderma 140:297-309. Shank, D.B., G. Hoogenboom, and R.W. McClendon. 2008. Dew point temperature prediction using artificial neural networks. Journal of Applied Meteorology and Climatology 47(6):1757-1769. Shank, D.B., R.W. McClendon, J.O. Paz, and G. Hoogenboom. 2008. Ensemble artificial neural networks for prediction of dew point temperature. Applied Artificial Intelligence 22(7). (Accepted for publication). Soltani, A., and G. Hoogenboom. 2007. Assessing crop management options with crop simulation models based on generated weather data. Field Crops Research 103:198-207 Staggenborg, S.A., W.B. Gordon, K.C. Dhuyvetter. 2007. Grain sorghum and corn comparisons: Yield, economic and environmental responses. Agron. J. (accepted). Staggenborg, S.A., M. Carignano, and L. Haag. 2007. Predicting soil pH and buffer pH with a real-time sensor. Agron. J. 99:854-861. Suleiman, A.A., and G. Hoogenboom. 2007. Comparison of Priestley-Taylor and Penman-Monteith for daily reference evapotranspiration estimation in Georgia. Journal of Irrigation and Drainage Engineering 133(2):175-182. Suleiman, A..A., C.M. Tojo Soler, and G. Hoogenboom. 2007. Evaluation of FAO-56 crop coefficient procedures for deficit irrigation management of cotton in a humid climate. Agricultural Water Management 91(1-3):33-42. Tojo Soler, C.M., P.C. Sentelhas, and G. Hoogenboom. 2007 Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal of Agronomy 27(2-4):165-177. Tojo Soler, C.M., N. Maman, X. Zhang, S.C. Mason and G. Hoogenboom. 2008. Determining optimum planting dates for pearl millet for two contrasting environments using a modeling approach. Journal of Agricultural Science. (In Press). White, J.W., G. Hoogenboom, P.W. Stackhouse and, J. M. Hoell. 2008. Evaluation of daily temperature data for the continental US modeled from satellite data. Agricultural and Forest Meteorology. (In Press).White, J.W., K.J. Boote, G. Hoogenboom, and P.G. Jones. 2007. Regression-based evaluation of ecophysiological models. Agronomy Journal 99(2):419-427.
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