Annual/Termination Reports:

[11/15/2006] [01/08/2007] [04/22/2008] [09/26/2008] [08/10/2009]

Report Information

Participants

Dennis Todey, South Dakota State University;Stuart Gage, Michigan State University;Robert Seem, Cornell University (Geneva);Scott Staggenborg, Kansas State University;Mickey Ransom, Kansas State University;Dong Wang, University of Minnesota ;Rich Grant, Purdue University;David Greenland, Louisana State University;Gerrit Hoogenboom, University of Georgia;Ken Boote, University of Florida;Pat Guinan, University of Missouri;Jim Zandlo, State Climatologist, Minnesota;Daryl Herzmann, Iowa State University

Brief Summary of Minutes

President's Room, Radisson Hotel, Minneapolis, MN
February 27-28th, 2006
Meeting was called to order by Dennis Todey at 8:30 a.m., February 27.

Daryl then volunteered to act as secretary for the duration of the meeting.

Dennis discussed the formation of a nominating committee to pick next year's officers. Stuart and Rich agreed to be on this committee and present their nominations during the business meeting Tuesday morning.

Dennis discussed the formation of a location committee to pick the location of our next meeting. Gerrit, Scott, and Stuart agreed to look into it. It was agreed to discuss this more during the business meeting on Tuesday.

Dennis mentioned the conference call scheduled for Tuesday morning. Forrest Chumley, Jeff Andreson, and Bart Freeland were mentioned as being in on this call. Dennis would follow up further with the new CEREES adviser to see if he could join the call as well.

Dennis brought up discussion on changing the terms of the executive positions. It was discussed to extend the appointment to a 2 year term with the secretary continuing to roll into the chair position after the 2nd year. It was noted that this would involve a 4 year commitment of the secretary and perhaps this was too long. It was discussed that perhaps the secretary position would not roll into the chair position. The chair entertained a motion to move to 2 year terms for the chair, an annual term for the secretary and to discontinue the ascension of secretary into chair. Stuart moved and Scott seconded. No discussion and the motion passed by voice vote.

New members where then discussed. Questions were asked about the exact procedure to get new members in. This would be asked of Forrest during the conference call on Tuesday. It was noted to make sure the new members were listed on the appendix.

Dong noted that University of Minnesota would be picking up the cost of the conference room and perhaps the conference call on Tuesday.

With that, introductory talks were given by committee members.

Stuart, Scott, Rich, Gerrit, Ken, David, Bob, Dong, Pat, Daryl, and Dennis presented. It was discussed to perhaps send the power point presentations to Daryl for inclusion on a website? Some of the members handed out copies of their research reports.

Dennis brought up the importance of including all of the presented material in the mid term reports that will be due soon. It is important to include grant monies that may not directly relate with 1018, but include the use of NC1018 derived datasets. We should ask Forrest about this more in the morning. Mickey proposed that we have some sort of standard format for our reports.

Discussion was had about the geographic coverage of our group. Should we look to expand west? It was pointed out that confining ourselves to a particular region is probably not required. It is natural for an active group to look to bring more energetic folks in. Groups tend to be more topically organized than geographical.

Stuart then presented information on the atlas. The atlas is a contiguous dataset for the 1055 counties in the north central region. It is an important dataset to support modeling over the area. The need to place this information on a website is becoming apparent. There also needs to be clear meta data and scientific interpretation available on the site as well. A copy of the atlas was given to a few external folks for review and their comments are appreciated. The question of financial support for placing the atlas on the website was discussed. Clearly this is something that can't just be done without money to support someone to spend quality time on the effort. Someone would also need to write the descriptions for the website.

The issue of our research funding model was discussed. This was something that would need further discussion with Forrest.

Discussion was made on shepherding the NC1018 dataset. Clearly we need to work together and make sure everyone has a copy of the latest and greatest data. Having a website would probably help with this process. Stuart mentioned talking with Steve Hollinger about getting some meta data for how some of the datasets were created.

It was mentioned if NCDC or the climate centers would be interested in storing this dataset. Perhaps they could provide the small amounts of funding to expand the domain.

Steve Hollinger's phenology database was discussed. Some states don't have a copy of it. It was discussed to talk with Steve about it.

A question was asked if a dataset of reported events was maintained on the county level. Previous efforts to accomplish this were noted with little success also noted. The dataset doesn't appear to exist.

The agenda for the conference call was discussed. Topics to be addressed:

 Tell Forrest about what we have been doing as a committee.
 Inform Forrest that we have switched to a 2 year appointment for the chair.
 Raise concerns over the funding model.
 Ask about the process to include new members or states.

Further discussion was had about other research ideas. Irrigation decision support was mentioned along with Carlson's aridity index. The need for real time versus historical tools was also discussed.

The need for an economist to join our group was mentioned. Dennis mentioned a lady at SDSU that could be interested.

Meeting was adjourned for the day at 5:10 PM.

The meeting reconvened at 8:15 a.m., February 28.

Meeting started with a discussion of the conference call and other items relating to the business meeting.

The nominating committee approached Mickey to serve as secretary and Bob as chair. Both members agreed and their nomination was accepted by the group with no decent.

Meeting location and time was discussed. It was noted that having a meeting in between semesters is probably not a good idea, since that is usually a busy time at the University. It was noted that a Monday meeting may be a bad idea, since some folks don't like to travel on the weekends. Everyone agreed that no time/place will accommodate everyone.

It was mentioned to have Daryl send out an email to the list detailing how the email list works. Daryl would also make sure that everyone is on it, including Carl Bernaki (sp) from Illinois.

The topic of new members was discussed. New members should just talk with the AES director to get approval. Nothing more should be necessary.

More discussion was had on where to have the meeting next year.

Conference call.

 Forrest Chumley, Bart Freeland, Brian Slater, Dev Nygan (sp) joined the discussion. Some trouble was encountered getting the conference call set up, but technical issues were resolved.
 Forrest presented his report. He commended the committee on its progress and working together. He reminded members to review Appendix I on the regional website. Members are encouraged to show linkages in their reports. Mentioning individual grants in these reports are relevant. Members should also mention any technology or data transfer. Be sure to mention impacts and how they address research objectives. We need to submit the SAES 422 form. He recommended the 2 year term for the executive positions. He said the midterm report could be turned in shortly after a midterm meeting in October.
 Forrest was asked about new members. Approval of Experiment station director and approval of the committee is all that is needed.
 Forrest was asked about geographical expansion of our committee. He noted that any successful committee will naturally grow and include others. It is a positive thing. He noted how well the special session at the ASA meeting was attended.
 Forrest was asked about the status of multi-state research. He mentioned that the current thought was to make the process competitive with a similar amount of dollars available. The time line of this was uncertain with issues remaining in congress. At this time, there is no concern about the elimination of the funding.
 Forrest was asked if there is money floating around for work to be done with the atlas. Dennis mentioned sending a proposal to the E-extension effort. Forrest mentioned the importance of having the atlas on line with a number of query features available to the user. He would like to see it as a tool for research and policy directors. He mentioned Bob talking with the AES directors and that the directors have a small pool of funds to do something. Forrest said this amount may be 2,500 dollars, but later discussion seemed to think this was 25,000 dollars.
 Stuart asked about opportunities to do carbon research. It was generally agreed that our committee should look into such matters.
 Bart Freeland talked about making sure states that have soil temperature data to please send him a copy for publication in the weekly crop bulletin.
 Dennis mentioned repacking a submission to the E-extension RFP. Others agreed to help out.
 It was stressed that all members should have their reports ready before the October meeting. Mickey emailed out a format that others could follow.
 Could the atlas be on a website by October? Perhaps, but funding was needed for this effort.

After the call, a number of issues were discussed. Bob will look into accessing this small pot of money that apparently exists with the AES directors.

A plethora of issues were discussed. Including soil databases. The SCAN network and its data. The need for PAN evaporation data. Risk management and yield research.

A discussion on where to have the next meeting was had. The two locations most prominently mentioned were Lincoln, NE and Kansas City, MO. Nothing definite was decided.

It was mentioned that a subset of committee members could meet to discuss smaller issues and that there are funds for that.

Meeting adjourned by Dennis at approximately 10:30 a.m.

Minutes submitted by Daryl Herzmann.

Accomplishments

Georgia<br /> <br /> Investigators: Gerrit Hoogenboom and David Stooksbury<br /> <br /> Project Report:<br /> It is common practice to use crop simulation models and long-term weather data to study the impact of climate variability on yield. Thus, the simulated yield mainly reflects the weather variability but not the adoption of new technologies. Therefore, long-term observed yield data, if available, cannot be readily used for evaluation of crop models. The objectives of this study were to analyze the impact of climate variability on long-term simulated peanut yields and to assess the applicability of using long-term average county yield determined from statistical estimates for evaluation of the simulated yield. Observed yields obtained from State variety trials and yield estimates obtained from the USDA-National Agricultural Statistics Service (USDA-NASS) for three counties in the Georgia peanut belt from 1934 to 2003 were used for evaluating the simulated yield series. Simulated yields based on the CSM-CROPGRO-Peanut model were categorized into three technological periods (TP). A weighted average based on the acreage of the soil type, the peanut type, and the irrigated land in each county was calculated to obtain a unique simulated yield. Then yields and weather data of the 70-year period were grouped with respect to El Niño Southern Oscillation phases, and TPs. The Pearson's coefficient of correlation, the LSD, and the t test were used to evaluate the results. The simulated yields clearly reflected the seasonal variability in weather. The NASS yield estimates were adequate to assess simulated yields for the 70-year, but failed to reflect the weather variability at the beginning of the period. The results from this study showed that crop models can be a useful tool to help understand the inter-annual variation of yield due to climate variability if appropriate adjustments are made to account for changes and improvements in agrotechnology.<br /> <br /> Indiana<br /> <br /> Investigator: Richard H. Grant<br /> <br /> Project Report:<br /> Two derived variables were worked on during 2005: solar ultraviolet-A (UV-A) and canopy wetness. The estimation of ultraviolet-A radiation across the earths surface is needed to model plant productivity and future impacts of ultraviolet-B radiation to plant productivity. We have evaluated the quality of broadband UV-A irradiance measurements within a UV climate monitoring network in the USA and developed a model to estimate the UV-A irradiance from measurements of the global spectral irradiance at 368-nm. The model was developed from ½ hour interval measurements made during 2000 at three locations across the United States and evaluated from ½ hour measurements made during 2000 through 2002 at seven locations. The stability of the UV-A irradiance sensors across the two year period was evaluated by comparison of changes in UV-A sensor response to changes in 368 nm AOD across years on the same (+/-3) day referenced to the change in UV-A response to changes in 368 nm AOD on sequential days during 2000. Most of the seven UV-A sensors installed during 1999 and 2000 appear to have remained stable (within detectable bounds) through 2004. UV-A irradiance was modeled using measured global 368-nm irradiance and empirical functions defining UV-A and 368-nm irradiance relationships derived from a radiative transfer model. The theoretical pseudo two stream discrete ordinates radiative transfer model provided baseline irradiance relationships between UV-A irradiance and 368-nm spectral irradiance. The model estimated the UV-A irradiance at seven locations across the USA with a mean bias error of 0.5 W m-2 and a root mean squared error of 1.5 W m-2. The model error was comparable to the combined effect of previously-estimated UV-A and 368-nm irradiance measurement errors but greater than that of the UV-A sensor alone.<br /> <br /> <br /> Kansas<br /> <br /> Investigators: Staggenborg, S. and M. Ransom<br /> <br /> Project Report: <br /> 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. <br /> <br /> 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 will be conducted on three cropping systems, where applicable, on the three predominant soil series for each MLRA. Soils will be identified using the State Soil Geographic (STATSGO) database for each state. Soil physical properties to be used for simulations will then be selected from the NRCS Soil Survey Laboratory Database. Historic weather data will be selected from a location within each MLRA in order to maintain uniform coverage of the region.<br /> <br /> Michigan<br /> <br /> Investigators: Stuart Gage, Gene Safir, Jeff Andresen <br /> <br /> Project Report:<br /> Agricultural production is a highly dynamic process intimately linked to management strategy, and climate. Agricultural systems managed using the principles of ecology, have a better chance of sustaining the productivity of the land and sustaining the ecological services derived from harvesting the crops. We have examined the regional characteristics of the agricultural production system in Michigan and the North Central Region. We have continued to develop the Modeling Applications Integrative Framework (MASIF) to process the large amounts of spatial-temporal outputs from regional scale simulation experiments. We have pursued two levels of utilization of MASIF for supporting regional analyses. One level was to incorporate models that project carbon outputs directly into MASIF, and to utilize it as a primary geospatial data I/O and data analysis platform. The second level is the development of procedures to couple model output streams from geospatial simulation models to the MASIF system. This system enables comparison of model outputs with observed data (e.g. remotely sensed information, agricultural yield statistics), cross-model comparisons and evaluations and to utilize the powerful suite of data analysis and visualization capabilities incorporated in MASIF. Another aspect of our research has been to develop a socio-ecological and geographical analysis of agriculture conducted aimed toward development of a tool for policy and planning for sustainable agriculture. This research links geographical and ecological systems to the social domains of production agriculture. This will contribute to the conceptual and practical aspects of sustainable agriculture including contributions to multi-disciplinary studies, and mission-based policy-making at local, state and regional levels. We have applied the principles of geographic information science as a basis for developing a holistic framework for an integrated socio-ecological analysis of agricultural sustainability. We have constructed a spatial database by acquiring, processing, and maintaining selected sets of ecological, agricultural, socio-economic, and demographic information.<br /> <br /> Research Activities:<br /> " Simulation of carbon sequestration in the North Central Region<br /> " Analysis of climate characteristics of the North Central Regional <br /> <br /> Climate Database<br /> " Further development of a Modeling Applications Integrative Framework<br /> " Incorporation of the SOCRATES carbon simulation model into MASIF<br /> <br /> Minnesota<br /> <br /> Investigator: Wang, D.<br /> <br /> Project Report:<br /> The watershed modeling project has finished the second year of data collection for model validation purposes. One of the main focuses of the modeling project was to evaluate changing precipitation regimes (i.e. rainfall frequency, intensity, and annual mean) as a result of global climate change on terrestrial and aquatic N and P balances. Flow and water quality samples were measured at the lower Tamarack river watershed. Significant progress was also made in modeling. The SWAT model was calibrated with 18 years of USGS data and validated with the last 2 years data from the field measurements. Based on the lower Tamarack data, a paper was presented at the fall AGU meeting in San Francisco, CA.<br /> <br /> Missouri<br /> <br /> Investigators: Patrick Guinan and J. Travlos<br /> <br /> Project Report:<br /> Support from the University of Missouris Integrated Pest Management program and technological advancements in wireless communication have provided the opportunity to bring real-time weather conditions via the Internet to 10 more weather stations in the Commercial Agriculture Automated Weather Station network. With real-time weather the level of application increases significantly. For example, real-time weather provides the latest wind conditions for spray applicators. Drift is an issue in Missouri with the Missouri Department of Agricultures Pesticide Bureau receiving about a dozen reported drift complaints annually. Additionally, agricultural producers access the latest soil temperature data to aid in spring time planting decisions or autumn application of anhydrous ammonia. Health hazard information is also provided for farmers with livestock. Other benefits derived from real-time weather include the ability to monitor the latest temperature trends during periods when vegetation becomes vulnerable to extreme or sub-freezing temperatures and using real-time relative humidity information for baling decisions. <br /> <br /> Research Activities:<br /> " Paper birch decline in the Niobrara Valley: Interactions of weather, microclimate and genetics<br /> <br /> New York<br /> <br /> Investigator: Robert Seem<br /> <br /> Project Report:<br /> Disease forecasts are usually generated from irregularly dispersed weather stations. We developed a system to simulate local-scale, high-resolution weather and plant disease which incorporates a simplified mesoscale boundary layer model for the estimation of local air temperature and RH. The system also integrates models for surface wetness duration and special disease forecast, can incorporate current upper air conditions for true weather forecasts, or it can recreate weather forecasts utilizing a 55-yr database. Maximum horizontal resolution of 150m was achieved by running 5-step nested child grids inside coarse mother grids. The system simulated several growing seasons to drive the DMCast model, and thereby estimate the risk of grape downy mildew (Plasmopara viticola) with 333m resolution in two regions of NY. Outputs were represented as maps or as graphs for specific locations. While resolution is greatest over North America, the system is functional globally and may be useful for site selection and reanalysis of historical epidemics. <br /> <br /> Investigator: Dennis Todey<br /> <br /> Project Report:<br /> Dr. Todey and S. Hosamane (graduate student) completed an irrigation forecast study where operational National Weather Service forecast information was used to forecast potential evapotranspiration (PET) out to three days in advance for three locations in South Dakota. These forecasts were compared to PET values based on observed data at these locations. The forecasts were able to correctly predict potential evapotranspiration for corn, soybean, and alfalfa with correlations ranging from 0.3 to 0.8 up to two days in advance across the state. <br /> <br /> Several members of the NC-1018 committee (including J. Andresen, G. Hoogenboom) and a collaborator at ISU (D. Herzmann) wrote a proposal to an eXtension call for engagement to leverage previous committee work and database development. The goal of the proposal was to use existing committee-collected data and committee expertise to directly aid producers in decision-making through eXtension. The proposal was not funded.<br /> <br /> Dr. Todey wrote a proposal ($25,000) to be submitted to the NCR committee for assistance in publishing a regional county-level ag climate atlas. This paper copy will be printed and distributed throughout the region. The proposal was accepted. Review of the atlas is under way for final publishing to be completed in 2006. Additional database work was done to update county-level information and correct errors in the database. These data will be developed into an interactive web site for any user to view pictures or collect raw county level data.<br /> <br /> For a paper presented at the American Association of Applied Climatology conference, presented results confirming the relationship between corn and soybean production and yield. Correlations between growing season precipitation and yield on a county basis were 0.5 to 0.8 for the western corn belt of eastern South Dakota, Nebraska and Kansas. In contrast additional precipitation in the eastern corn belt was found to hinder yield where correlations were negative reaching as much as -0.5 in some counties. <br /> <br /> As part of the effort to develop new climatologies for agriculture, Dr. Todey completed a collaborative project to develop an evaporation climatology and maps across the South Dakota. These activities are proposed to be expanded over the Upper Midwest.<br /> <br />

Publications

Abrahamson, D.A., D.E. Radcliffe, J.L. Steiner, M.L. Cabrera, J.D. Hanson, K.W. Rojas, H.H. Schomberg, D.S. Fisher, L. Schwartz, and G. Hoogenboom. 2005. Calibration of the Root Zone Water Quality Model for simulating tile drainage and leached nitrate in the Georgia Piedmont. Agronomy Journal 96(6):1584-1602.<br /> <br /> Colunga-Garcia M., P.R. Grace, S.H. Gage, G.P. Robertson, G.R. Safir. 2005. 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.<br /> <br /> 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. 2005. 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.<br /> <br /> Garcia y Garcia, A., and G. Hoogenboom. 2005. Evaluation of an improved daily solar radiation generator for the southeastern USA. Climate Research 29:91-102.<br /> <br /> Grace, P.R., S.H. Gage, M. Colunga-Garcia, G.P. Robertson, G.R. Safir. 2005. 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.<br /> <br /> Grant, R.H. and J.R. Slusser. 2005. Estimation of ultraviolet-A irradiance from measurements of 368-nm spectral irradiance. J. Atmos. & Ocean. Technology 22: 2853-2863.<br /> <br /> Grant, R.H. and J.R. Slusser. 2005. The measurement and modeling of broadband UV-A irradiance. In: G. Bernhard, J.R. Slusser, J.R. Herman and W. Gao, Eds., Symposium on UV Ground- and Space-based Measurements, Model, and Effects V, Proceedings of SPIE Vol. 5886.<br /> <br /> Green, M., Wang, D., Murphy, M., and J. Almendinger. 2005. Sensitivity of simulated stream water N and P concentrations and N:P ratios to precipitation regimes in a central Minnesota watershed. Eos Trans. AGU, 86(52), Fall Meet. Suppl., Abstract H31B-1302, 2005 AGU winter meeting, San Francisco, CA.<br /> <br /> Guerra, L.C., G. Hoogenboom, J.E. Hook, D.L. Thomas, V.K. Boken, and K.A. Harrison. 2005. Evaluation of the model EPIC for simulating on-farm irrigation applications. Irrigation Science 23:171-181.<br /> <br /> Gunal, H., and M.D. Ransom. In press. Genesis and micromorphology of loess-derived soils from central Kansas. Catena.<br /> <br /> Gunal, H., and M.D. Ransom. 2005. Clay mineralogy, specific surface area and micromorphology of polygenetic soils from eastern Kansas. Archives of Agronomy and Soil Science 51:459-468.<br /> <br /> Hoogenboom, G. 2005. Plant/soil interface and climate change: carbon sequestration from the production perspective. In: [J.S. Bhatti, R. Lal, M. Apps and M. Price, editors] Climate Change and Managed Ecosystems. CRC Press. (In Press).<br /> <br /> Hosamane. S., 2005. Using National Weather Service forecasts and model output statistics (MOS) to forecast evapotranspiration. Masters Thesis. South Dakota State University. 120pp.<br /> <br /> Ma, L.,G. Hoogenboom, L.R. Ahuja, D.C. Nielsen and J.C. Ascough II. 2005. Evaluation of the RZWQM-CROPGRO Hybrid model for soybean production. Agronomy Journal 97(4):1172-1182.<br /> <br /> Mullen, J.D., C. Escalante, G. Hoogenboom and Y. Yu. 2005. Determinants of irrigation farmers crop choice and acreage allocation decisions: Opportunities for extension delivery service. Journal of Extension [on-line] 43(5). Available at http://www.joe.org/joe/2005october/rb3.shtml. <br /> <br /> Nelson, B.R., W.F. Krajewski, J.A. Smith, E. Habib and G. Hoogenboom. 2005. Archival precipitation data set for the Mississippi river basin: evaluation. Geophysical Research Letters 32: L19403,doi:10.1029/2005GL023334.<br /> <br /> Olson, K.R., T.E. Fenton, N.E. Smeck, R.D. Hammer, M.D. Ransom, C.W. Zanner, R. McLeese, and M.T. Sucik. 2005. Identification, mapping, classification, and interpretation of eroded Mollisols in the U.S. Midwest. Soil Survey Horizons 46:23-35.<br /> <br /> Olson, K.R., T.E. Fenton, N.E. Smeck, R.D. Hammer, M.D. Ransom, C.W. Zanner, R. McLeese, and M.T. Sucik. 2005. Proposed modifications of mollic epipedon thickness criteria for eroded conditions and potential impacts on existing soil classifications. Soil Survey Horizons 46:39-47.<br /> <br /> Seem, R.C. 2004. Forecasting Plant Disease in a Changing Climate: A Question of Scale. Can. J. Plant Pathol. 26:274-283. <br /> <br /> Staggenborg, S.A., and R.L. Vanderlip. 2005. Crop Simulation Models Can be Used as Dryland Cropping Systems Research Tools. Agron. J. In Press.<br /> <br /> Todey, D.P. and C. Shukla, 2005. Climate factors impacting productivity and yield trends in the Midwest. 15th Annual Conference on Applied Climatology. Savanna, GA. American Meteorological Society.<br /> <br /> White, J.W., and G. Hoogenboom. 2005. Integrated viewing and analysis of phenotypic, genotypic, and environmental data with GenPhEn arrays. European Journal of Agronomy 23:170-182.<br /> <br /> White, J.W, G. Hoogenboom, and L.A. Hunt. 2005. A structured procedure for assessing how crop models respond to temperature. Agronomy Journal 96(2):426-439.<br />

Impact Statements

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  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.

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Report Information

Participants

Scott Staggenborg - Kansas State University;
Mickey Ransom - Kansas State University, Secretary;
Adnan Akyuz - National Weather Service (moving to North Dakota State University effective 1-07);
Dennis Todey - South Dakota State University;
Gerrit Hoogenboom - University of Georgia;
Daryl Herzmann - Iowa State University;
Stewart Gage - Michigan State University;
Jeff Andresen - Michigan State University;
Bob Seem - Cornell University (Geneva), Chair;
Forrest Chumley - Administrative Advisor, Kansas State University

Brief Summary of Minutes

Accomplishments

Georgia<br /> <br /> Investigators: Gerrit Hoogenboom and David Stooksbury<br /> <br /> Project Report:<br /> 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<br /> C was 0.20<br /> C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17<br /> C and 0.16<br /> 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.<br /> <br /> 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).<br /> <br /> <br /> Indiana<br /> <br /> Investigator: Richard Grant<br /> <br /> Project Report:<br /> 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.<br /> <br /> <br /> <br /> Kansas<br /> <br /> Investigators: Scott Staggenborg and Michel Ransom<br /> <br /> Project Report:<br /> 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.<br /> <br /> 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.<br /> <br /> <br /> <br /> Michigan<br /> <br /> Investigators: Stuart Gage, Gene Safir, Jeff Andresen <br /> <br /> Project Report:<br /> 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. <br /> <br /> 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. <br /> <br /> Research Activities:<ul><br /> <li>Development of a soybean rust forecast model.<br /> <li>Development and application of a plant stress index to assess regional patterns and stress probabilities<br /> <li>Analysis of the Crop, Climate and Soils Database to enhance regional decision making<br /> <li>Incorporation of socioeconomic data into the regional database<br /> <li>Analysis of regional socioeconomic patterns in the north central region</ul><br /> <br /> <br /> <br /> Missouri<br /> <br /> Investigator: Patrick Guinan<br /> <br /> Project Report:<br /> 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. <br /> <br /> Research Activities:<br /> Paper birch decline in the Niobrara Valley: Interactions of weather, microclimate and genetics<br /> <br /> <br /> <br /> New York<br /> <br /> Investigator: Robert Seem<br /> <br /> Project Report:<br /> 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. <br /> <br /> 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.<br /> <br /> <br /> <br /> South Dakota<br /> <br /> Investigator: Dennis Todey<br /> <br /> Project Report:<br /> 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.<br /> <br /> 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.<br /> <br /> <br />

Publications

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.<br /> <br /> 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.<br /> <br /> 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).<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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).<br /> <br /> 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.<br /> <br /> 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.<br /> 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.<br /> <br /> 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.<br /> <br /> Gunal, H., and M.D. Ransom. 2006. Genesis and micromorphology of loess-derived soils from central Kansas. Catena 65:222-236.<br /> <br /> Gunal, H., and M.D. Ransom. 2006. Clay illuviation and calcium carbonate accumulation along a precipitation gradient in Kansas. Catena 68:59-69.<br /> <br /> 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.<br /> <br /> 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).<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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).<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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).<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> 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).<br /> <br /> 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).<br /> <br /> 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).<br />

Impact Statements

1. GA-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. IN-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.
3. KS-Our work shows 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. Simulation models can now be used to evaluate cropping systems that will require less water.
4. MI-The development of plant stress indices based on the climate record to evaluate the association with ecosystem attributes will enable important decisions regarding the capacity of the region to support new cropping strategies. This is particularly critical as new uses of existing crops are explored as well as new crops for developing a bio-economy in the region. The integration of socioeconomic variables with existing bio-physical attributes of the region will provide new insight into the economics of changing agriculture value as the economy shifts to bio-based fuels. Drought could override all of the gains made in increased productivity due to genetic enhancement and thus disable the region from supporting the emerging bio-energy economy.
5. MO-Horizon Point is designed to make precise weather information available for farmers and to provide customized products using weather and climate data that can aid in management decisions associated with their crops and livestock.
6. NY-This project is designed to assist US efforts to insure plant biosecurity by creating new tools to assist in the early detection of outbreaks of exotic plant pathogens or new strains of endemic pathogens. The project will produce a very high resolution weather simulation system that can be used to estimate disease development and spread within an outbreak zone. The unique features of the forecast system permits rapid reanalysis of archived weather information to create estimates of the weather conditions within the outbreak zone without the needs for on-site weather instruments. Ultimately, this reanalysis tools will permit a rapid response any anomalous disease outbreaks.
7. SD-Yield trends and climate relationships are of great importance currently because of a volatile corn market relating the expansion of ethanol production in the Midwest. Understanding large scale changes in yield and their trend over shorter and longer terms is necessary to predict the amount of potential corn yield available to be made into ethanol. Yield trends continue to increase across the Midwest for all commodities. The trend increase varies by crop and location. The largest corn yield trends over the last 35 years continue to be in eastern South Dakota and western Minnesota with increases of 157 to 205 kg/ha/yr based on a linear regression. This compares with a more common 63-157 bu/ac/yr across the corn belt. But the more recent trends are larger over several areas extending from eastern South Dakota through western Iowa into northeast South Dakota. These trends are widely over 315 bu/ac/yr to many places over 630 kg/ha/yr.
8. SD-Variability of yield was assessed by summing the absolute deviations of each year from the regression line. This was normalized by dividing this summed value by the average yield for that county. The resultant number has no value, but is labeled a risk unit. The higher the resulting number (more inter-annual variability with lower average yield) the larger your risk. Risk units of 3-4 were consistent across the main part of the corn belt from northwest Iowa eastward. Not unsurprisingly, the larger risk values (7-10 RU) were found on the western edge of the corn area. Some what surprisingly, though, RU values of 6-9 were found across northern Missouri, further east than we expected. Soybean yield slopes were very consistent across the Midwest ranging from 13  26 kg/ha/yr.
9. SD-The consistent area with the largest slope continues to be Wisconsin where yields are increasing at 26  39 bu/ac/yr. Bean yield risk was calculated in the same manner as corn yields to produce RU values. The lowest risk values (2-3 RU) were in central Illinois and scattered counties in Iowa and Indiana. The core soybean areas (Iowa to Indiana) had slightly higher RUs (3-4). The RU values continued to increase as you moved out of this core area. Kansas interestingly has the highest yield average risk of anywhere in the Midwest. Further research on these risk values working with production numbers can help determine the viability of ethanol and other value added production facilities.

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Report Information

Participants

Scott Staggenborg - Kansas State University


Mickey Ransom - Kansas State University, Secretary


Bob Seem - Cornell University (Geneva), Chair


Marjorie McGwirk - NOAA National Climatic Data Center, Asheville, NC


Rich Grant - Purdue University


Gene R. Safir - Michigan State University


Charles McKeown - Michigan State University

Brief Summary of Minutes

Accomplishments

Please refer to attached meeting minutes document.

Publications

Impact Statements

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Report 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)

Brief Summary of Minutes

Accomplishments

Accomplishments:<br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> 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. <br /> <br />

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. <br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> Boken, V.K., C. E. Haque, and G. Hoogenboom. 2007. Predicting drought using pattern recognition, Annals of the Arid Zone 46(2):133-144.<br /> <br /> <br /> 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<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> 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.<br /> <br /> <br /> Presley, DeAnn Ricks. 2007. Ph. D. Dissertation. Genesis and spatial distribution of upland soils in east central Kansas. Kansas State Univ.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> 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<br /> <br /> <br /> Staggenborg, S.A., W.B. Gordon, K.C. Dhuyvetter. 2007. Grain sorghum and corn comparisons: Yield, economic and environmental responses. Agron. J. (accepted). <br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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.<br /> <br /> <br /> 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).<br /> <br /> <br /> 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.<br /> <br />

Impact Statements

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.

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Report Information

Participants

Akyuz Adnan ND State University;
Staggenborg Scott Kansas State University;
Machado Stephen Oregon State University;
Todey Dennis SD State University;
Guinan Pat University of Missouri;
Stevens Gene University of Missouri;
Stooksbury David University of Georgia;
Holman John Kansas State University;
Fraisse Clyde_W. University of Florida;
Hubbard Ken University of Nebraska;
Hoogenboom Gerrit University of Georgia;
Schmitt Mike University of Minnesota;
Seem Robert Cornell University;
Andresen Jeff Michigan State University;
Grant Rich Purdue University;
Anderson Chris Iowa State University;
Taylor Elwynn Iowa State University;

Brief Summary of Minutes

Day1: Monday July 6, 2009
" 8:30 AM: Welcoming statement by the president Scott Staggenborg
" Introduction of the attendees (see the list above)
" Advisor Mike Schmitts comments: His second year being involved with this project. Having read the other proposal he ranks this proposal to be a very good one. The atlas receives a special attention among the appropriating committee. He posed a question if we needed the appropriated money. If we do we need to determine how we need to spend it. The final report is due this fall (Later e-mail from Christina Hamilton, NCRA Assistant Director and NIMSS System Administrator, on July 16, 2009 indicated that the due date to submit the final report is March 31, 2010). Mike also indicated that the NC1018 is now NC-Temp1018. We have a new number designated for us which is NC1179. The NC1179 has a value in terms of mix of expertise in the participants. He explained how these multistate projects value institutional involvement. The more projects an institution is involved with the better it is for that institution in order to maintain federal funding. Most institutions use these involvements as a justification to maintain and get extra federal dollars to the state.
" Regional Climate Centers: Ken Hubbard, High Plains Regional Climate Center. Ken talked about the National Climate Services. It passed the house and is becoming more reality with a new climate program structure. RCCs are writing a white paper. He also talked about the following topics:
o ACIS (Applied Climate Information System). It is synchronized across the nation. ACIS allows you to write your own program.
o Paperless WxCoder III (Weather-coder 3). It adds coop data in-real-time.
o Ag-ACIS: Specifically designed data access for agricultural community such as NRCS.
o HCN-M: Modernized Historical Climate Network. It proposes 1000 automated weather network in the country paired with the existing HCN stations.
" Break
" State Reports:
o Kansas Report (Scott Staggenborg): He talked about the following topics:
§ Modeling Soil-Carbon
§ Cellulosic biomass removal
§ Simulating Soil-Carbon
§ Kansas Mesonet Plan
" Groundwater Management Districts
" K-State
" NWS
" Stations configured similar to NRCSs SCAN (Soil and Climate Analysis Network)
§ Coupled Model taking into account:
" Crop Model
" Ground Water (Irrigation Water)
" Economic Model (Crops Chosen for a year)
" Crop Model
§ Ongoing research
o New York Report (Robert Seem): He gave information about the Vineyard Site Evaluation web site: www.nyvineyardsite.com which is a collaborative project of the College of Agriculture and Life Sciences at Cornell University and the Institute for the Application of Geospatial Technologies with funding from the New York Wine and Grape Foundation. He explained how to use this interactive web site for decision making. The web site contains the following main pages:
§ Educational information
" Climate
" Soil
" Topography
" Proximity to water
§ Data Layers
" Climate Layer
" Lake Erie Layer
" Finger Lakes Layer
§ Information Resources (Such as NRCS, Cornell Coop Extension, and other helpful links)
§ Tutorial (How to use the site)
o Missouri Report (Pat Guinan): Pat talked about the following topics:
§ Getting MO-Mesonet into near-real-time.
§ Network Evolution
§ Mesonet specification
§ 7-Variables: hourly and daily
§ Supplemental variables:
" Soil moisture
" Pressure
" Fuel moisture
" Leaf wetness
§ Data delivery: AgEBB-Agricultural Electronic Bulletin Board (http://agebb.missouri.edu/)
§ Data QC
§ Sustainability ($3000/year per station to be shared equally with AgEBB)
§ Real time web template: http://agebb.missouri.edu/weather/realtime/mizzou.asp
§ Benefits to real time:
" Spray application
" Planting decision
" Heat stress and wind-chill
" Control fire burns
" Flood assessment
" MADIS (Meteorological Assimilation Data Ingest System): http://www-frd.fsl.noaa.gov/mesonet/
" Damaging wind
" Energy distribution
" Icing potential
o South Dakota Report (Dennis Todey): Dennis talked about the following topics:
§ Statewide temperatures (Average Weather)
§ Soil moisture (Hydra probe) at 11 locations
§ 37 stations in the network (South Dakota Automatic Weather Data Network): http://climate.sdstate.edu/awdn/archive/daily.asp
§ GDD until freezing module
§ Research:
" Irrigation forecasting
" Disease forecasting
" Mycotoxin occurrence on corn
" Lawn watering
" Climate impact on grape growing in SD
" Trend analysis in winter and spring for various locations for various temperature thresholds in SD
o Florida Report (Clyde W. Fraisse): Clyde talked about the following topics:
§ Agro-Climate web site: (http://agroclimate.org/)
" Climate forecast
" Ag tools
" Decision aid tools
" Adaptation strategies
§ Long-term database
§ Soil, DSSAT ready files
§ FAWN Database
§ Application of risk assessment tools (crop-yield-disease)
" ENSO specific yield composites for different applications:
o Peanut
o Potato
o Tomato
o Blueberry
o Peach
o Strawberry
o Forage and livestock
" Chill accumulation hours
" Strawberry disease tool
" Modeling leaf wetness
o Penman-Monteith
o RH Threshold
" Number of fungicide applications required to control Anthracnose based on ENSO phases
" Drought monitoring
" Carbon footprint of Ag. Operation
" Outreach and education
" Begin discussion of milestones for the new project: We discussed the objectives of the project starting from Objective 1, which is to enhance the understanding of crop-climate-soil interaction on a regional scale We discussed Objective 1-a which is to enhance existing database by developing new agro-climatologically variables for risk assessment of crop production in the region. We talked about what additional variables that we can consider adding. Among the list, we talked about the following variables:
" Dew point temperature
i. Discussion: It is available at county-based NC108 data set
" Snow depth (CoCoRaHS even though not all observers are reporting this variable)
" (Soil) Organic matter
" Solar radiation
" Soil moisture
i. Discussion: We talked about how we can improve the soil moisture network. One option is modeling it. The other is to measure. Someone pointed out the NRCSs SCAN (Soil and Climate analysis Network). Pat G. mentioned that 3 out of 4 SCAN sites in MO are not working. Scott questioned whether if measuring at 5 depths is necessary.
" Leaf wetness (hours of wetness derived from Penman-Monteith)
" Wind speed (needed for evaporation)
i. Discussion: observation height of 10m is safer to use than trying to estimate it from 10
" MRCC database was discussed. It is grid-based for the entire US. Ken suggested we contact Steve Hilberg, Director, HPRCC, to see if we can acquire the database for NC1018.
" We also discussed what the format of the data (i.e. grid vs. county base). Grid based data has advantage over county in terms of describing variability within a county. However, county based data is desirable in terms of making political decision for a county.

ACTION 1: Dennis will work with Steve H. and Ken H. to see if MRCC database can be acquired. ACTION TEAM: Dennis Todey and Ken Hubbard to complete ACTION 1

ACTION 2: Dennis will work with Pat G. and Ken H. to determine what additional variables can be added to the database. (Milestone 2010)
ACTION TEAM: Dennis Todey, Ken Hubbard and Pat Guinan to complete ACTION 2

" Scott questioned where the data currently resides. Dennis answered that it resides in multiple places. He also said that the data is available on-line.
" We also discussed the Objective 2 which is the application of risk assessment tools, including the existing NC-1018 database, for the crop-climate-soils interface on a regional scale. We did not identify any action item for this objective at this time.
" We looked at the Atlas and asked ourselves whether if we should expand the coverage to the entire US. Mike Schmitt reminded us that the Atlas receives a special attention (in the appropriation committee).
" Continued discussion of the detailed milestones for the new project: We discussed the objectives of the project starting from Objective 1-c, which is to enhance current NC-1018 database with climate data that will be developed based on climate change projection models. Chris Anderson (Iowa State University) said Program for Climate Model Diagnosis and Inter-comparison (PCMDI), daily archived data is available to public and therefore to us to utilize. Jeff said coarse gridded data is sometime not usable for local analysis. Downscaling could be a method to remedy this problem but it is not reliable. Chris said the decision tool that models provide a wide range of scenarios or a middle scenario for the end-users to select. He added that he prefers the farmer, the end-user, should be able to decide what probability level they would like to be confident by selecting the risks they would like to take. Scott said he would like to see what this group is going to decide with the climate change projection models. Chris suggested that there is a model out there by University of California Santa Barbara (UCSB) that incorporates 121 scenarios. Chris is currently working on a RISA proposal that will deal with climate change adaptation. He offered to share the downscaled data regionally. He expects to get funded in the coming fall. He added that we can use the UCSB model right away.
" Continued discussion of the detailed milestones for the new project: (2009): Develop a framework for testing or creating a new climatology based on climate change prediction scenario. Scott asked a question: Should we talk about it now or should we pull the trigger next year?

ACTION 3: Scott will contact UCSB, Rich will contact NOAA to pursue completing the 2009 milestone (Develop a framework for testing or creating a new climatology based on climate change prediction scenario)
ACTION TEAM: Scott Staggenborg and Rich Grant to complete ACTION 3

ACTION 4: To pursue fulfilling Objective 1-c which is to enhance current NC-1018 database with climate data that will be developed based on climate change projection models.
ACTION TEAM: Scott Staggenborg, Rich Grant, Jeff Anderson, Garret Hoogenboom, Chris Anderson to complete ACTION 4

ACTION 5: Meet Tuesday afternoon with as many participants as possible to discuss finalizing the text in the Atlas.
ACTION TEAM: Everyone who can be there in the afternoon after the meeting is adjourned (See Tuesday afternoon minutes) to complete ACTION 5

" Adjourned for the day

Day2: Tuesday July 7, 2009
" North Dakota Report (Adnan Akyüz): Adnan gave an historic perspective of the about the Fargo flood of 2009 and his services that he provided during the flood. He talked about the following specifics:
" Historic Red-River crests at Fargo location
" 2008-2009 weather season in Fargo and how it led to the flood
" Satellite coverage of the flood before and after
" Historic comparisons
" Prelude to the flood
" 2009 timetable
" Aerial photos
" Flood fight
" Anatomy of the Red River Valley
" Nebraska Report (Ken Hubbard): Ken Hubbard also reported on behalf of Nebraska as well as the regional climate center. He talked about the following topics:
" Solar and dew-point expansion
" Seasonal bias on estimated data: Ken and Steve Hu created a better r²
" Bio-fuels: a graduate student identified bio-fuel locations of the state
" Soil moisture: 60 sites were accessorized with Hydra-Probe. 4 Layers were sampled.
" NE Carbon sequestration project
" ET project: It has not been published yet
" ENSO Composites with various climate variables in Nebraska
" Michigan Report (Jeff Anderson): Jeff talked about the following specifics of his current projects:
" Weather and climate risk management in agriculture
" Historical and projected future trends
" Impacts of weather/climate on agriculture
" Landscape change and regional climate change
" Wind energy
" Enviro-Weather (http://www.enviroweather.msu.edu/ )
i. Weather based pest, natural resources and production management tool
ii. Irrigation Scheduler V4.0
iii. Influence of land use and land cover change on climate (CLIP Project) in east Africa.
" Missouri Report (Gene Stevens): Gene was the second participant representing Missouri. He reported on the following topics:
" Rice growth and irrigation
" Degree-day 50
" Herbicide timing
" Insect scouting
" Nitrogen rates
" Flood draining
" Arkansas irrigation scheduling model
" Business Meeting
" Adding names to the list
" How to become a member of the NC108 participant
" Web site: Who will host and maintain. Current Climate Data site: http://mesonet.agron.iastate.edu/GIS/apps/nc1018/fe.phtml

ACTION 6: Dennis will talk to Daryl to see if he can add the atlas, the proposal and the minutes to the web site
ACTION TEAM: Dennis Todey to complete ACTION 6

ACTION 7: Adnan will type and distribute the minutes within 60 days of the meeting
ACTION TEAM: Adnan Akyüz to complete ACTION 7

ACTION 8: Project (termination) report needs to be submitted by March 31, 2010 (per Christina Hamilton, NCRA Assistant Director and NIMSS System administrator, e-mail on 7/16/2009)
ACTION TEAM: The Secretary of the NC1179 to complete ACTION 8

" Discussion: Officer Election every 2 year vs. 1 year. Garret brought up the discussion. He point out that waiting 2 years might hurt those faculties who are in the tenure track. Scott mentioned that we decided to elect officers every 2 years because 1) we have such a small group and 2) it takes a year for an officer to get comfortable with the duties. Ken motioned that we reduce election frequency from once every 2 years to every year. Garret seconded the motion.
" Bob nominated Adnan be the chair next year. Garret was nominated to be the secretary. No one objected the nominations and the officer lineup for the coming year is as follows:
i. Chair: Adnan Akyuz, ND State University
ii. Secretary: Garret Hoogenboom, U of Georgia
" Next years meeting time and place
i. Several time options we discussed. Fiscal calendar was an issue
ii. Clyde volunteered to host the meeting in Florida during spring break so that the teaching faculty would be able to come to the meeting without missing a class.
" As a closing remark Mike Schmitt reminded that there is a $6000 fund available to be used in printing the atlas. We need to communicate with the budget office to let them know if we will use the money.
" Meeting adjourned at 11:30 AM

Day2 (Afternoon): Tuesday July 7, 2009 (Atlas Group Meeting)
" The atlas group met at lunch after the regular business meeting
" Participants:
i. Adnan Akyuz
ii. Scott Staggenborg
iii. Dennis Todey
iv. Pat Guinan
v. Ken Hubbard
vi. Jeff Andresen
" Dennis asked if we need to continue with the atlas and the group responded with Yes.
" Steward has the original version of the maps and the program. Dennis said he will talk to Steward and Steve Hollinger to see if they are willing to complete the atlas. We have some money to pay them for their service. Jeff reminded that the new climatological averaging period will start in 2011. It will look bad to release 1971-2000 averages this late.
" PeT and the Trend are the only 2 variables that (still) need to be calculated.
" Jeff suggested to add the trend along with 11-yr running mean

ACTION 9: Ken will talk to Steve Hilberg to see if MRCC would be willing to provide continues data feed to NC1018 database
ACTION TEAM: Ken Hubbard to complete ACTION 9

ACTION 10: Jeff will talk to Steve Hollinger and Dennis will talk to Stew and Daryl to see if they would be willing to continue service to complete the Atlas.
ACTION TEAM: Jeff Anderson and Dennis Todey to complete ACTION 10

Accomplishments

During the past year, this group continued to accomplish activities related to modeling and weather data collection and dissemination. Biotic system models have been used to investigate soil carbon dynamics, the performance of cellulosic biofuel crops, and the impact of biomass removal on soil quality and soil erosion. 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. <br /> <br /> Crop models have been used to determine the impact of irrigation scheduling and the value of irrigation in the region. Activities related to disease forecasting in corn and determining grape yields and winter survival in several northern states. In Florida, work regarding the impact that ENSO activities have on crop yields and fungicide applications have continued. One area of significant accomplishment occurred with the revelation that leaf wetness can be modeled with greater accuracy and with less expense than attempting to measure it. Related to these simulations, work continues to supplement older climate data sets with estimated solar radiation and dew points. <br /> <br /> In regards to weather data collection and dissemination, mesonets expanded in Kansas and Missouri with new stations being deployed and in Nebraska and South Dakota with soil moisture sensors being deployed on a selected set of stations within each state. The addition of these sensors will expand the soil moisture monitoring efforts that were initiated several years ago. <br /> <br /> From a weather data dissemination perspective, Cornell and other organizations in New York have developed and launched a web site to assist grape growers in evaluating potential vineyard sites. This GIS based website www.nyvineyardsite.com aims to reduce winter damage to grape vines. The system uses GIS layers of climate, soil, topography, and proximity to water.<br /> <br /> Missouri continues to be a leader in collecting and providing real time weather data from their network of stations to the public. Their website is open to the public http://agebb.missouri.edu/ . They also have demonstrated the ability to sustain their weather stations through local sponsors. <br /> <br /> Other data dissemination products include a GDD to freeze calculator, disease prediction applets many other products that add value to weather data collected in many states. These applications extend the value of weather data farther into the public realm. <br /> <br /> <br /> The group published over 35 refereed journal articles, developed countless new web pages, made a wide range of outreach presentations and served as advisers to a wide range of group during weather episodes throughout the region.

Publications

Publications<br /> <br /> Akyüz, F. A., T. Scherer, D. Morlock, 2008: Automated Irrigation Scheduling Application of the North Dakota Agricultural Weather Network. International Conference on Soil Fertility, Land Management, and Agroclimatology. Preceedings, p 3. Kusadasi, Turkey. October 29-November 1, 2008. <br /> <br /> Akyüz, F. A., B. Mullens, D. Morlock, 2008: Agricultural Application of the North Dakota Agricultural Weather Network. International Conference on Soil Fertility, Land Management, and Agroclimatology. Preceedings, p 104. Kusadasi, Turkey. October 29-November 1, 2008. <br /> <br /> <br /> Akyuz, F. A, M. Ewens, R. Carcoana, and B. Mullins : 2007. NWS frost depth observation with liquid-in probes performance: Two-year review. Journal of Service Climatology. In-review, 2007.<br /> <br /> 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.<br /> <br /> Anothai, J., A. Patanothai, S. Jogloy, K. Pannangpetch, K.J. Boote, and G. Hoogenboom. 2008. A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of crop performance trials. Field Crops Research 108:169-178.<br /> <br /> 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 28(10):1357-1368.<br /> <br /> 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.<br /> <br /> Baumhardt, R.L., S.A. Staggenborg, P.H. Gowda, P.D. Colaizzi, and T.A. Howell. 2009. Modeling Irrigation Management Strategies to Maximize Cotton Lint Yield and Water Use Efficiency. Agron. J. 101:460-468. <br /> <br /> Berkheimer, S.F., J.K. Potter, J.A. Andresen, and E.J. Hanson, 2007. Flower Bud Mortality and Salt Levels in Blueberry Fields Adjacent to Michigan Highways Treated with Deicing Salt. HortTechnology 16: 508 512. <br /> <br /> Ben-Asher, J., A. Garcia y Garcia, and G. Hoogenboom. 2008. Effect of high temperature on photosynthesis and transpiration of sweet corn (Zea mays L. var. rugosa). Photosynthetica.46(4):595-603.<br /> <br /> Boken, V.K., G. Hoogenboom, J.H. Williams, B. Diarra, S. Dione, and G.L. Easson. 2008. Monitoring peanut contamination in Mali (Africa) using AVHRR satellite data and a crop simulation model. International Journal of Remote Sensing 29(1):117-129.<br /> <br /> 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.<br /> <br /> Evert, S, S.A. Staggenborg, B,L.S. Olson. 2009. Soil Temperature and Planting Depth Effects on Tef Emergence. J Agron. And Crop Sci. 195:232-236.<br /> <br /> 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.<br /> <br /> 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.<br /> <br /> Getter, K.L., D.B. Rowe, and J.A. Andresen. 2007. Quantifying the effect of slope on extensive green roof stormwater retention. Ecological Engineering 31:225-231.<br /> <br /> Guerra, L.C., G. Hoogenboom, A. Garcia y Garcia, P. Banterng, and J.P. Beasley, Jr. 2008. Determination of cultivar coefficients for the CSM-CROPGRO-Peanut model using variety trial data. Transactions of the American Society of Agricultural and Biological Engineers 51(4):1471-1481.<br /> <br /> Isard, S.A., R.J. Schaetzl, and J.A. Andresen, 2007. Soils Cool as Climate Warms in the Great Lakes Region, USA: 1951-2000. Annals Assoc. Am. Geographers 97:467-476.<br /> <br /> Lin, S., J.D. Mullen, and G. Hoogenboom. 2008. Farm-level risk management using irrigation and weather derivatives. Journal of Agricultural and Applied Economics 40(2):485-492.<br /> <br /> Olatinwo R.O., J.O. Paz, S.L. Brown, R.C. Kemerait, A.K. Culbreath, J.P. Beasley, Jr., and G. Hoogenboom. 2008. A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the tomato spotted wilt virus risk index. Phytopathology 98(10):1066-1074.<br /> <br /> Phakamas, N., A. Patanothai, K. Pannangpetch, S. Jogloy and G. Hoogenboom. 2008. Dynamic patterns of components of genotpye x environment interaction for pod yield of peanut over multiple years: a simulation approach. Field Crops Research 106(1):9-21.<br /> <br /> Phakamas, N., A. Patanothai, K. Pannangpetch, S. Jogloy and G. Hoogenboom. 2008. Seasonal responses and genotype-by-season interactions for the dynamic growth and development traits of peanut. Journal of Agricultural Science 146:311-323.<br /> <br /> Phakamas, N., A. Patanothai, S. Jogloy, K. Pannangpetch, and G. Hoogenboom. 2008. Physiological determinants for pod yield of peanut lines. Crop Science 48(6):2351-2360.<br /> <br /> Prabhakaran, T.V., and G. Hoogenboom. 2008. Evaluation of the weather research and forecasting model for two frost events. Computers and Electronics in Agriculture 64:234-247.Putto, W., A. Patanothai, S. Jogloy, and G. Hoogenboom. 2008. Determination of mega-environments for peanut breeding using the CSM-CROPGRO-Peanut model. Crop Science 48(3):973-982.<br /> Presley, D.R., P.E. Hartley, and M.D. Ransom. In press. Mineralogy and morphological properties of buried polygenetic paleosols formed in Late Quaternary sediments on upland landscapes of the Central Plains, USA. Geoderma.<br /> <br /> Schmitz, Hans F. 2008. Physical Processes of Soybean Rust, M.S. Thesis, Purdue University, May, 2008. Major Professor: Richard H. Grant.<br /> <br /> 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.<br /> <br /> 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(6):523-542.<br /> <br /> Soler, C.M.T., 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 146(4):445-459.<br /> <br /> Staggenborg, S.A., K.C. Dhuyvetter, W.B. Gordon. 2008. Grain Sorghum and Corn Comparisons: Yield, Economic, and Environmental Responses. Agron. J. 100. 1600-1604.<br /> <br /> Suriharn, B., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2008. Yield performance and stability evaluation of peanut breeding lines with the CSM-CROPGRO-Peanut model. Crop Science 48(4):1365-1372.<br /> <br /> White, J.W., M. Herndl, L.A. Hunt, T. S. Payne and G. Hoogenboom. 2008. Simulation-based analysis of effects of Vrn and Ppd loci on flowering in wheat. Crop Science 48(2):678-687.<br /> <br /> White, J.W., G. Hoogenboom, P.W. Stackhouse and, J. M. Hoell. 2008. Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US. Agricultural and Forest Meteorology 148(10):1574-1584.<br /> <br /> Willson, G. D., F. A. Akyuz: 2007. Survival of the western prairie fringed orchid at Pipestone National Monument. The 21st North American Prairie Conference. August 4-8, 2008. Winona, MN. <br /> <br /> Zavareh, M., G. Hoogenboom, H.R. Mashhadi, and A. Arab. 2008. A decimal code to describe the growth stages of sesame (Sesamum orientale L.). International Journal of Plant Production 2(3):193-206.<br /> <br /> <br /> <br /> <br /> <br /> <br />

Impact Statements

1. Knowledge of how climate change could potentially affect local climate and crop yield in turn. Detailed climatic data designed for agriculture help provide crop, insect, and disease management information, pest outbreak predictions and control recommendations, and/or decision support aids. Farmers used many agricultural applications provided for the economic and environmental benefits. Climate data are used in the high school through graduate school classroom for education. Students had hands-on experience with the true climate data that were observed in their local areas
2. 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. Our work shows that crop models are useful tools in studying cropping system performance within a region. The results from the irrigation simulations will help producers and policy makers develop programs aimed at maintaining rural economic viability while protecting the Ogallala aquifer. The soil quality and biofuel simulations have indicated that grain-biomass alternating rotations have the potential to maintain soil organic matter. These simulations will be expanded to include simulations of soil erosion as well.
3. The impacts of work in Indiana illustrate that UV-B light exposure will play an important role in predicting Asian Soybean Rust (SBR; Phakopsora pachyrhizi) survival and subsequent spread. Relationships were developed to improve the prediction of SBR survival and re-exposure. A related SBR experiment illustrated that rain and wind transport are important in the movement of SBR spores. Leaf wetness conditions because of rainfall in the Cornbelt were found to be sufficient to promote SBR survival. Results also indicate that modeling susceptibility for initial infection in the lower canopy by rainfall wetness should probably be modeled differently than for disease spread by rainfall and/or dew.
4. Maps were developed of heat unit accumulation and evapotranspiration for Indiana These maps are used to monitor and predict the life stages of field crop insects to help farmers plan pesticide applications. A web map archive of past daily ET estimates for Indiana has been created for the current growing season. D. Niyogi is working on integrating the traditional Coop network with CoCoRaHS high density precipitation network. Recent large scale flooding in the Midwest has underscored the importance of a dense and high quality precipitation observation network. The Indiana State Climate Office merged coop and CoCoRaHS data into GIS maps.
5. Relatively little is known about specifics of the hydrological balance in the Great Lakes Region, mainly due to the lack of data and difficulty making direct measurements. The SWAT model allows detailed, physically-based estimates of the hydrological balance over long periods of time, helping to quantify the impacts of changing land use on sustainable water use. In recent times, increasing demand for water resources from agricultural and non-agricultural users in Michigan has led to controversy over water rights, as evidenced by recent water-related legal battles and new water use laws and regulations. Results from this research may help frame the increasing discussion on water rights and water use between the public, state officials, industrial water users, the state s agriculture industry, and policy makers.

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