Duration: 10/01/2009 to 09/30/2014

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

Climate change is in the forefront of the agricultural community. Current predictions on crop and animal performance are based on research literature and accepted understanding of the current biological systems. At present, published research is one the few options available to policy makers and producers alike to predict the potential impacts of climate change over the next 30 years. Many of these issues are extremely complex and cannot be evaluated in the field because of not only this complexity, nut also bescause the changes will be gradual. In addition, the magnitude of these changes is uncertain and difficult to predict, especially at the local scale.

Yet the need for substantive information to help guide policies and actions regarding climate change are clear. Item 6 of the CSREES Strategic Plan (Anonymous, 2007) states, Support the development, dissemination, and implementation of science-based knowledge tools and technology to assess the consequences of land use and climate change on soil and ecosystem functions. In its final report to Congress the U.S. Climate Change Science Program (Backlund et al., 2008a) described existing observational and monitoring networks necessary to assess climate change, However, many key biological and physical indicators are not currently monitored, are monitored haphazardly or with incomplete spatial coverage, or are monitored only in some regions. In addition, the information from these disparate networks is not well integrated.

A range of climate scenarios exist that attempt to describe how the climate will change over the next three to four decades (Backlund et al., 2008b). These scenarios vary in basic interpretation, but all tend to predict increased temperatures in the range of 1 to 2°C for most of the Great Plains and Midwest. The current literature is replete with results regarding the impact of temperature or drought on crop performance, but seldom are the two evaluated simultaneously and few research frameworks exist that allow for climate changes to be evaluated on a large scale, both geographically and temporally.

Numerous groups have sought to evaluate the impact of climate change on crop performance and the subsequent impacts that this may have on global food, fiber and fuel supplies. Hatfield et al. (2008) indicated that high night temperatures will reduce crop yields as a result of higher respiration requirements and carbon loss. Higher temperatures are also likely to cause more rapid crop development which may result in lower yields by shortening the grain filling period (Muchow, 1990). In fact, Lobell and Asner (2003) predict a 17 percent yield reduction in corn and soybean yield for each 1°C increase in air temperatures when temperature effect was confounded with rainfall limitation. However, when the effect of rainfall deficit was accounted for separately, then Lobell and Field (2007) reported a smaller 8.3% reduction in corn yield and a 1.3% decline in soybean yield per 1ºC rise in temperature. Backlund et al. (2008) also predict that precipitation variability will increase along with the increased incidence of heat waves. This rainfall pattern will likely result in longer periods of water stress followed by intense rainfall events. Wolfe et al. (2008) estimate that crops in the Northeast US will experience higher frequency of temperature stress, perennial crops will have inadequate chilling in the winter and all crops will be exposed to increase pest and weed pressure. Crop, soil, pest, and economic models that are used singularly or simultaneously exist in systems that can rapidly test the impacts of drought and temperature stress on crop species.

Corn yields are currently increasing at a rate of approximately 95 kg ha-1 year-1. However, predictions suggest that because of increased night temperatures and greater incidence of water stress, this trend will decline to little or no annual improvement. Such predictions assume that plant adaptation, both natural and through human interaction (breeding), will not occur as climate change is occurring. Evaluating the impact of a breeding program, which may focus on performance under high temperatures, is difficult with current field and growth chamber technology, because most controlled environment systems, like SPAR or FACE, are too small to allow for germplasm evaluation or traditional breeding techniques (Hatfield et al., 2008). Crop models exist in systems that can rapidly test the impacts of improved drought or temperature tolerance on crop productivity as climate change occurs or they can be used to provide plant breeders with areas of focus to address this challenge.

Along with increased variability in precipitation, it has been predicted that total rainfall amounts in the Midwest and Great Plains will be reduced approximately 10% with a shift toward more precipitation being received during the winter months and less during the summer months (Backlund et al., 2008). Increased precipitation variability will increase the risk associated with crop production through longer periods of drought and higher intensity thunderstorms. These events result in lower yields as well as greater opportunities for soil erosion and crop damage from hail and damaging winds.

Soil organic matter levels affect water infiltration rates, soil water holding capacity (Hudson, 1994), crop productivity, and is a regarded as one of the primary measures of soil quality. Increases in temperatures, reduced crop productivity and increased soil water deficits (because of increased precipitation variability) could reduce soil organic matter levels if management practices such as reduced tillage and the use of cover crops are not adopted. The effects of reduced tillage and the use of cover crops [cite] are well documented as methods of increasing soil organic matter, but the effects of increased temperatures and reduced soil water content are not and will be difficult to assess with current field and growth chamber techniques and technology. Integrated or coupled crop, soil, and economic models can be deployed to address these problems. The advantage of a modeling approach is the range of treatments that can be tested is nearly unlimited and is only constrained by the model functionality.

A great deal of focus has been placed on the impacts that increased temperatures and precipitation variability may have on crop productivity. However, these climate scenarios may equally affect disease, insect and weed pests. Increased temperatures will likely alter insect, disease and weed lifecycles as well. Recent research has illustrated that some weed species require greater quantities of herbicide to control them when CO2 increases (Ziska et al., 1999). However the change for many pests can be positive, negative or neutral depending on the local environment change (Coakley et al., 1999). The impact is even more complicated since climate change not only affects the pest response, but equally affects the host response to disease and insect attack (Garrett et al., 2006). Until climate change scenarios can be down-scaled to the level where pest impact can be modeled and assessed, uncertainty will remain (Seem, 2004).

To complicate the challenges associated with climate change, the Renewable Fuel Standard Program mandate to quadruple bioenergy contributions to the U.S. fuel supply by 2010 (U.S. Dep. of Energy, 2007; http://www.epa.gov/oms/renewablefuels/). Biofuel production has many production challenges, but protecting soil and water simultaneously is also important to society as a whole. Many agricultural practices developed over the past decade (nutrient and manure management, cropping systems, reduced tillage systems) have made great strides in improving and protecting soil and water resources that benefit the general public as well as the crop producer. However, if pressure to remove residue from production fields to produce bioenergy is not properly balanced, then these soil quality gains could be lost along with soil through erosion and water quality through impairments via runoff. Developing crop management systems that provide bioenergy feedstocks while protecting soil and water resources will be difficult to study in the field because runoff studies require large watersheds, numerous locations and many years to encompass a wide range of environments. These types of studies require a large amount of financial support and will span five to ten years before tangible results will be available. Once again, integrated models can address these questions rapidly and can evaluate a larger number of treatments than field experiments. Coupled or integrated crop, soil and economic models are poised to address questions that may arise because of expected climate change

Other questions related to bioenergy production include the most appropriate species to be produced at a given location and the evaluation of perennial, annual or mixed cropping systems to address the needs of the bioenergy industry. Perennial crops have the advantage that after establishment, annual production costs and energy requirements are lower then annual crops. Traditional perennial grass production has largely occurred in marginal areas, which means that there is not a clear understanding of their production potential if grown in higher production environments (i.e. switchgrass on deep silt loam soils or under irrigation). It would be within the framework of the existing NC-1018 database and crop models to evaluate switchgrass or other perennial crops both spatially and temporally across the ten-state region.

The advantage of annual crop systems for bioenergy production is they allow producers flexibility to respond to the food, fiber and fuel needs of society rapidly. It is conceivable that future bioenergy systems that adequately balance food and fuel needs while simultaneously protecting soil and water resources will include both food and fuel crops with the use of cover crops during fallow periods to reduce the negative impacts of annual crop production. Developing such a system can be easily evaluated with integrated models across a wide range of environments and soils types (Hoogenboom et al., 2004).

North Central Regional Committee 1018 has not only developed a spatial-temporal database of soils, weather, and crop information for the ten north central states, it has developed a research model that can easily be replicated in other regions of the U.S. or the world. The methodologies used to aggregate these data to a county resolution could be applied to other regions to develop similar databases. The system also has the ability to create input files for use in integrated crop-soil-environment suites such as the Decision Support System for Agrotechnology Transfer (DSSAT; Hoogenboom et al., 2004), but also has lower level soil and crop simulation models programmed within the Modeling Applications Integrated Framework (MASIF) database at the Computational Ecology and Visualization Laboratory at Michigan State University. The future plans are to use the existing measured weather data to develop climate change scenarios which will add a new suite of capabilities to this system that can aid in answering production, economic and policy oriented questions.

Also, most climate change scenario predictions are at very large scales (several 1000 km2); however, the NC-94/1018 database has a county level spatial resolution. It has an existing temporal resolution of 30 year with the tools and templates in place to expand temporally as more data is measured. The importance of these data is they first can serve as baseline for measured data comparisons over the next 30 to 40 years AND can serve as a baseline from which to develop climatologies from the climate change model predictions. An integrated modeling approach is needed in order to develop effective policies, management programs that secure our food, fuel, and fiber supply as well as protect our natural resources. Often these systems need to be evaluated simultaneously for the biological, agronomic, ecologic and economic impacts. Since all of these variables are intertwined, one model type (crop model, soil model, etc) will seldom accomplish -the objectives set forth here. The scientists who participate in NC-1018 encompass a wide range of disciplines and include crop modelers, soil scientists, climatologists, and agricultural economists. This integrated team has the capabilities to address all of the issues described above and objectives stated below through large scale, integrated modeling efforts.

Related, Current and Previous Work

The current NCR committee, NC-1018 and its predecessor NC-94, have been in existence for nearly 60 years working on research activities related to the role of weather and climate on agriculture in the North Central Region and other member states. The committee has helped move forward several major agro-climatological innovations, including some of the first successful efforts to collect and electronically enter data for agroclimatological studies and as well as proposing and championing the formation of regional climate centers which have now successfully provided data to the general public and performed research activities on regional climate for almost 30 years. They have also conducted research activities on regional climate and its impact on agricultural production and resource use. Over the course of this past reassignment, the committee has been responsible for the publication of over 80 scientific publications to communicate and disseminate their efforts.

The intent of NC1018 has been to collaboratively study the impact of climate and soils on crop 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, land use information, and recently socioeconomic data. 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 associated 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.

The scientists in the group have experience in analyzing large scale climate data and potential climate change scenarios. Growing season precipitation variations in central and western U.S. have been examined and found that the interannual variations in summer rainfall in these regions have been strongly influenced by the SST anomalies associated with the Atlantic Multidecadal Oscillation (Hu et al,, 2008; Feng et al., 2008; Feng and Hu, 2007; Feng and Hu, 2008). In addition, the major persistent summer droughts in central and western U.S. in the past century (e.g., the dust bowls in the 1930s and droughts in the 1950s) have been severely affected by the abnormally warmer SST in the North Atlantic (Hu and Feng, 2007; Hu and Feng, 2008). These results indicate that further understanding the causal links between the Atlantic SST anomalies, which could be predicted by appropriate ocean circulation models, and summer circulation and precipitation variations can lead to better prediction of the growing season rainfall and droughts in the central and western U.S.

Data collection remains a responsibility of many member of NC-1018. Over the past five years, the weather station networks have expanded in Georgia, North Dakota, Michigan, South Dakota, Kansas and Nebraska. However, a more significant accomplishment has been the development of web sites to deliver the information to clientele. Web sites are available for data retrieval from Georgia, North Dakota, South Dakota, Iowa, Missouri, and Kansas. All of these web sites enable users to down load raw data, but many have weather-related products that are also available. These include advisories for planting, pest control and irrigation scheduling routines that use measured weather data to assist clientele in their daily lives. 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.

Other impacts from this group include the use of measured weather data to address plant-soil-environment inter-related questions. In Georgia, computer models combined with historic climate and current weather conditions 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. In 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 and 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. In Kansas, work verified 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. In Michigan, MASIF provides an interface to regional models. This allowed users to couple crop growth and carbon models into MASIF. Crop models allow for net primary production (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. In 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. In 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. In 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. In South Dakota, PET forecasts 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. 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. The research on yield and precipitation relationships results were presented in response 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. 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.

Another outcome from the data compilation has been a unique publication, the North Central Region Agricultural Climate Atlas (Gage et al. 2003b). This collection of pertinent climate, soil-and crop information is derived from the existing NC-94 database. These will be published soon in a hard-copy color publication. The committee has included updating this information and provision of the data via an interactive web site as part of the new project. The web site would allow users to access, view, and analyze pieces of the data set on-line. This would be the first web site of its kind to analyze agriculture in a region.

This regional committee has a long history of collecting and aggregating spatial and temporal data with regards to crop production, soil physical and chemical properties, and weather variables important to crop growth and development. During the past five years, this group has continued this mission as well as the mission of utilizing these data as inputs for crop, soil and pest models, to analyze crop responses to the environment and to develop risk management tools for crop producers and policy makers.

Crop simulation models were integrated into this group over 10 years ago. Initial work focused on modeling crop productivity and interfacing the regional dataset with models such as CERES-Maize and SORKAM. Over the past five years, crop modeling efforts can be categorized into model development, evaluating crop-soil-environment interactions and using crop models to guide production decisions. Model development during this period has resulted in the integration of biological models to address increasingly more complex issues such as water and soil quality. Several crop models have been linked with the Root Zone Water Quality Model (RZWQM) to develop a tool that can evaluate the impacts of crop management practices on water quality (Ma et al., 2006; Ma et al., 2007). Examples of the utility of coupled models are simulations of tile drainage and nitrate leaching in the Georgia Piedmont (Abrahamson et. al., 2005) and crop management effects on water quality (Saseendran et al., 2007). Water Quality with RZWQM-crop models and nitrogen and phosphorous levels in streams were evaluated using the Soil and Water Assessment Tool (SWAT) in Minnesota (Green et al., 2005).

The NC-1018 group has illustrated its ability to rapidly respond to emerging issues. As carbon sequestration became a topic of general interest, an array of research was conducted to simulate soil carbon levels as well as evaluate crop management impacts on soil carbon sequestration (Gage et al, 2006). The linkage of the DSSAT-CENTURY models (Gijsman et al., 2002; Jones et al., 2003) provided another tool for study of soil organic matter issues under crop rotations. Colunga-Garcia et al. (2005) evaluated the impact that urbanization could have on carbon sequestration by agroecosystems in the North Central Region. Grace et al., (2005a) and Hoogenboom (2006) evaluated soil carbon sequestration levels base on the plant/soil dynamics in the face of climate change. Other used models to evaluate ways to maximize net carbon sequestration (Grace et al., 2005b; and Bostick et al., 2007).

Crop modeling and risk assessment is further complimented by the inclusion of simulating pest incidence, development and damage. Disease forecasting will have a tremendous impact on predicting disease levels as well as estimating the impacts that climate change may have on disease activity (Colunga-Garcia et al., 2005; Seem, 2004). This group has been instrumental in developing models for diseases such as spotted wilt (Olatinwo et al., 2008.), grape downy mildew (Kim et al., 2005) and more recently soybean rust (Isard et al., 2005). Also, the estimation of important environmental factors affecting disease development and impacts have been developed (Grant and Gao, 2006; Grant and Slusser. 2005; Magarey et al., 2005). These developments provide frameworks to study existing diseases or insects or can be applied to emerging issues very rapidly since the methodology and dataset have been developed.

The history of this group began with data collection and archiving. The use of the archived data was later added as an additional focus for the group, but continuing to collect and archive data remains one of the objectives for this group and their methods have also evolved. Improving existing data sets (Nelson et al., 2005) or improving the accuracy of soil input data (Gijsman et al., 2007; Gunal and Ransom, 2006 a and b; Karlstrom et al., 2007; Olson et al., 2005 a and b; and Staggenborg et al., 2007) and weather data estimations (Bannayan and Hoogenboom, 2008 a and b; Garcia y Garcia et al., 2005; Garcia y Garcia et al., 2008; Garcia y Garcia and Hoogenboom. 2005; Shank et al., 2008a and b; Soltani and Hoogenboom, 2007; White et al., 2007) continue to be a focus of this group. These techniques will be useful in developing the daily climates based on climate change scenarios. Data collection methods continue to be evaluated to improve model input data available, its quality and resolution (Alfieri et al., 2007; Chen et al., 2007). In more recent years, socioeconomic data were included in the NC 94/1018 database so that temporal impacts of crop yield fluctuations can be evaluated for their impacts on farm size, number and profitability.

Crop based food, fiber, and fuel production is determined by human manipulation of the crop-soil-environment interaction, which is often referred to as crop management. Decades of field research have been dedicated to studying this complex interaction as a means to maximize natural resource use efficiency. This approach will also be important in studying the impact of expected climate change over the next decade. In recent years, biological models have matured to levels capable of being important tools in this research area (Staggenborg and Vanderlip, 2005). In the past five years, the many efforts by NC-1018 have focused in the area of crop-soil-environment interaction. The current NC-94/1018 database was used to asses the impact of climatic factors on crop productivity and yield trends across the region (Todey and Shukla, 2005; Staggenborg et al., 2008). Attempts have been made to predict the impact of drought and frost events on crop production (Boken et al., 2007; Lopez-Cedron et al., 2005, 2008; Jain et al., 2006; Prabhakaran and Hoogenboom, 2008; Sau et al., 2004; Smith et al., 2006) as well as the effects of management decisions on yields of maize (Fang et al., 2008; Tojo Soler et al., 2007), sweet corn (Lizaso et al., 2007), pearl millet (Tojo Soler et al., 2007), peanut (Dangthaisong et al., 2006), and grain sorghum (Staggenborg et al., 2008). Models have also been used in crop improvement and plant breeding programs (Anothai et al., 2008; Boote et al., 2003; Suriharn et al., 2007; White and Hoogenboom, 2005).

Crop models have been used to develop tools for producers to improve their management and maximize profits. An area of focus for NC-1018 over the past five years has been water use as illustrated by several studies that were conducted and products that were developed. An irrigation support tool was developed for peanut production in the southeastern U.S. (Paz et al., 2007) which can be modified for use in irrigated corn production in the Great Plains or Mississippi Delta regions. Crop models were used to study impact of deficit irrigation in cotton (Suleiman et al., 2007) the development of on-farm irrigation management (Guerra et al., 2005) and crop water use (Guerra et al., 2007; Suleiman and Hoogenboom, 2007). These approaches resulted in information that producers could use immediately and would have required three to five years to collect using field research techniques. In the western states of the region, the impact of irrigation on crop yields has recently been evaluated to assist in water buy-out policy analysis.

More recently, crop models have been used to assess yield risk that results from year to year crop yield fluctuations, an important tool in the face of increased uncertainty as a result of global warming. Deng et al. (2008) used crop models to develop crop indices for alternative crops and crop models have been used to evaluate the risk associated with irrigation (Lin et al., 2008). Crop models have been used to evaluate yield stability has been evaluated (Banterng et al., 2006; Garcia y Garcia et al., 2006) and if forecast information can be used as a risk management tool. (Fraisse et al., 2006). Results from these efforts pave the way for continued assessment of crop responses to climate change with the use of crop models. Other efforts in this area include evaluating the impacts of estimated weather variables (Garcia y Garcia et al., 2005; Garcia y Garcia et al., 2008; Garcia y Garcia and Hoogenboom, 2005; Shank et al., 2008a and b; White et al., 2007) and other potential climate impacts such as increased aerosols (Greenwald et al., 2006) and increased CO2 levels (Heinemann et al., 2006).

As climate change unfolds, crop simulation models will assist producers in altering their crop management practices to maintain crop productivity and profitability. Crop models will be key in developing these new management systems through crop selection (Mullen et al., 2005; Pathak et al., 2007; White et al., 2007), evaluation of the new management strategy before capital is invested in it adoption (Soltani and Hoogenboom, 2007), the evaluation of integrated production systems (Herrero et al., 2007), and potential land use changes (Ashish et al., 2008) that may occur.

In summary, NC-1018 has continued its history of collecting and aggregating spatial and temporal data throughout the region. Other efforts have focused on improving the quality of the data, expanding the data available, and developing new techniques to exploit existing datasets. Modeling efforts continued in the development and evaluation of crop and pest models as well as the development of soil carbon models. One area of new innovation is integration of models from different disciplines. The best examples from this group were the integration of soil water quality model (RZWQM) and models from the DSSAT suite. This integration enabled researchers to model the impacts of crop management on soil water quality. This approach will also serve to stimulate other innovations such as the integration of soils, crop and economic models resulting in powerful policy development tools especially in areas focused on natural resource management.

Several outcomes from this group will enable them to provide insight on the impacts of climate change on food, fiber, and fuel production. These outcomes include experience in risk management assessment, new techniques for predicting environmental stresses and the ability to assist producers in managing inputs such as irrigation water and selecting the most appropriate crop for a given scenario to achieve the goals of securing adequate food and fiber supplies while assisting a fledgling bioenergy industry. These results will also serve as the framework to expand these efforts to other regions of the U.S. and other regions of the world.

Objectives

1. Enhance the understanding of crop-climate-soil interaction at a regional scale
   
2. Application of risk assessment tools, including the existing NC-1018 database, for the crop-climate-soils interface on a regional scale.
   
3. Enhance the understanding of potential bioenergy production systems.
   
4. Disseminate the research outcomes on the potential effects of climate variability and climate change effects on crop production resource use and adaptation options to users and stakeholders.
   

Methods

Objective 1: Enhance the understanding of crop-climate-soil interaction on a regional scale Objective 1a. Enhance existing database by developing new agro-climatological variables for risk assessment of crop production in the region. We propose to add variables that are necessary inputs for many of the emerging models or enhancements to existing models. These variables may be necessary to properly simulate pests or necessary to calibrate current crop models. We intend to begin collecting measurements on leaf wetness from the automated weather stations throughout the region. Sensors that record these data have been available for many decades, but are beginning to be installed on more automated weather stations in the region. Similarly to leaf wetness, soil moisture content to depths of 40 to 60 cm are installed on many stations in the region. The SCAN sites (UDSA-NCRS) have included soil moisture sensors since their inception over five years ago. Additional data variables could include wind velocity at 2 and 10 m, wind vector, and relative humidity. Existing data will be aggregated to the county level if more than one station exists. Counties without measurement sites will have data estimated on a daily basis through either spatial interpolation or through proxy variables. Objective 1b. Maintain and update current database and metadata with climate, crops and soils variables for enhanced analysis and dissemination to clientele. We propose to update current database variables with the data that has been measured from 2000 through 2010 to keep in time with decadal calculations. Climate variables that will be updated will include daily maximum and minimum air temperatures, precipitation, and solar radiation. Crop yields will be updated as well with the inclusion of data being separated into dryland and irrigated since NASS has expanded their database as well. It is not anticipated that the soils data will change significantly. Socioeconomic data will also be updated based on USDA databases information. After the data have been updated, many of the previous analyses regarding crop responses to the environment and socioeconomic responses to crop production will be re-evaluated. Objective 1c. Enhance the current NC-1018 database with climate data that will be developed based on climate change projection models. We propose to evaluate the projected changes to air temperature and precipitation patterns across the region based on the A1B climate change scenario. Since most of these values are currently published at relatively coarse spatial and temporal resolutions, an evaluation will be made on the best possible means to develop daily weather variables at the county level in the current database to project for the next 30 years. One proposed method is to use existing NC-1018 database data as a baseline and increasing temperature data by the proposed amount (~1 to 2°) and using Monte Carlo simulations to redistribute the rainfall to match expected variability patterns. Another proposed method would be to use the existing database data as the input into a weather generator to create the new climatology. The new climatology will be at the county level and span from 2010 to 2040. Objective 2: Application of risk assessment tools, including the existing NC-1018 database, for the crop-climate-soils interface on a regional scale. Objective 2a. Crop production risks due to the variability of crop-climate-soils. We propose to continue to develop and enhance plant, soil, and pest models that are capable of evaluating crop production systems responses to environmental challenges. These challenges are likely to include current diseases and insects such as wheat stripe rust, soybean rust or soybean aphid. Crop responses to climate change will be evaluated with the crop models included in the DSSAT suite. Soybean rust and other important diseases with use FLAIR models as described by Kim et al. (2005). Insect modeling will employ models for specific pests, with soybean aphid of particular interest. Soybean aphids populations will be estimated using the techniques similar to those of McCornack et al (2004) and Onstad et al. (2005). Another area of focus over the next decade will be to improve precipitation use efficiency and irrigation water use efficiency as well as allocation. A new area of research for this group will include the integration of soil-plant-economic or plant-pest-economic models since many of the issues mentioned here require simultaneous evaluation of many interlinked variables. Objective 2b. Potential effects of climate change on crop production at a regional scale We propose to use similar techniques and models as mentioned under 2a with the new climatology developed under 1c to determine the potential risk that our food, fiber and fuel production systems will face if the predicted climate change scenarios hold true. These results can then be used to develop potential management systems or indicate crop improvement needs to address the production challenges society will face if climate change occurs as predicted. Results from this objective will not only be potential crop yields, but probabilities and variability around crop yields. This work will also result in a framework to expand these analyses to other regions of the US and the world. Objective 2c Evaluate potential and active strategies for crop management adaptation on a regional basis in the face of climate change. We propose to use models, data, and results from Objective 2b to evaluate potential management strategies to reduce the yield losses associated with increased temperatures and increased precipitation variability as a result of climate change. Under this objective, crop shifts, conservations practices, potential policy impacts and crop improvement can be evaluated a priori to give indications of what are the appropriate management systems to use, what policies need to be implemented and how crops need to be improved to sustain productivity levels as temperatures increase and precipitation becomes more variable. Objective 3: Enhance the understanding of potential bioenergy production systems. Objective 3a. Use existing crop simulation models to determine the appropriate bioenergy species for sub-regions based on climate. We propose to use existing and new crop models to evaluate crop performance of major bioenergy crops such as corn, soybean, forage sorghum, switchgrass, short lifecycle poplars, and other bioenergy crops across the region. Biomass production for most annual crops will be estimated with the crop models included in the DSSAT suite. Yields for other biomass crops such as switchgrass and poplars will be estimated using the EPIC model. These results will enable producers, bioenergy producers, and policy makers to determine which species are most appropriate for each environment across the 10 state region. This work will also result in a framework to expand these analyses to other regions of the US and the world. Objective 3b. Develop cropping systems to include both food, fiber, and fuel crops that maintain food security, supply desired bioenergy feedstocks, and protect ecosystem services. We propose to use existing and new crop, soil, pest, and economic models to evaluate cropping systems that include bioenergy crops. This will be to identify the most profitable systems to provide food, fiber, and fuel feedstocks at desired or near desired levels while simultaneously protecting natural resources and maintaining ecosystem services. The results from this work will help producers, bioenergy producers, and policy makers determine the appropriate use of annual and perennial crops across the region that will achieve the desired outcomes that society demands. Linked or integrated model systems will be necessary to perform these evaluations because of the complexity of the system. The models used for these simulations will include the crop simulation models previously mentioned as well as economic and potentially groundwater models that will be coupled so as to capture the integrated effects each model on the other coupled model(s). Peterson and Steward (2006) and Bulatewicz et al. (2009) have demonstrated the use of AEM and OpenMI, respectively as effective ways to couple unrelated models with minimal programming. These methods allow each model to use outputs from the other models to serve as inputs for each other and then through an iterative process, solve more policy oriented problems. Objective 4. Develop products that disseminate information about climate variability and climate change effects on crop production and selection. We propose to make available the results of our work to our peers, crop producers, bioenergy producers, and policy makers through appropriate communication channels. This will include publishing peer reviewed research articles, extension publications, and web based publications and applications. The intended audience would be extension agents, policy makers, crop consultants, state and regional climatologists, and crop production researchers.

Measurement of Progress and Results

Outputs

• An updated regional database that will include weather and crop data from 2000 through 2010.
• New variables added to the database that will enhance its utility as an input source for crop, soil, economic, and pest models.
• A new climatology built based on data in the current database and climate change model A1B predictions.
• A reassessment of crop responses to soil and environmental influence based on new measured data.
• Potential impact of climate change on food, fiber, and fuel production systems.
• Output 6; Management options for to maintain food, fiber, and fuel production in the face of climate change.

Outcomes or Projected Impacts

• Improved yield forecasts will improve crop marketability and ultimately lead to better economic decision-making based on better information.
• Better decision-making for bioenergy crop selection and systems management will improve profitability for farmers growing bioenergy feedstocks.
• Provide decision tools for better soil and water conservation practices to protect soil from erosion and water from impairments.
• Provide decision tools that will allow both irrigated and dryland producers to conserve the water that is applied or received.
• Knowledge of how climate change could potentially affect crop yields.
• Outcome 6: Knowledge regarding the level of crop improvement needed maintain productivity during the next 30 years as temperatures increase and precipitation variability increases. Outcome 7: A framework to study climate change across the U.S using crop, soil and pest models. Outcome 8: An assessment of bioenergy crop performance across the region to determine the appropriate crop to be grown. Outcome 9: An assessment of the appropriate crops and cropping systems needed to maintain food, fiber and fuel supplies while protecting the soil and other ecosystem services. Outcome 10: Frameworks to study climate change and bioenergy systems that can be easily transferred throughout the US and the world. Outcome 11: A framework for socioeconomic responses to crop production and crop production changes. Outcome 12: Educate policy-makers by providing scientifically-based assessments of climate change impact on major US food, fiber and fuel crops. Outcome 13: Educate stakeholders and students about climate and climate change through access to the agricultural weather monitoring networks in participant states and the array of resources available on the network web sites.

Milestones

(2009): Develop a framework for testing or creating a new climatology based on climate change prediction scenario.

(2010): Create or adopt a climate change scenario for the North Central Region based on the A1B scenario or other scenario deemed appropriate by the committee. Addition of three new variables (leaf wetness, soil moisture content, wind speed) to the existing database

(2011): Incorporate new variables into models that forecast disease, insect, weed and other stresses. Update current database with measured data for the period of the end of the current database through 2010.

(2012): Update current database with measured data for the period of the end of the current database through 2010. Evaluation and comparison of modeled crop responses to environmental stress and responses to predicted climate change. Evaluation of the best locations in the North Central Region to establish new biofuel crops based on soil-crop-environmental systems, including water resources.

(2013): Evaluation and comparison of modeled crop responses to environmental stress and responses to predicted climate change. Evaluation of bioenergy feedstocks and cropping systems that supply food, fiber and feedstocks while protecting soil and water quality. Evaluation of the socioeconomic responses to crop production systems in the region and the change dynamics that impact where people live and work.

Projected Participation

View Appendix E: Participation

Outreach Plan

As discussed in the outcomes, an interactive web site and data archive will be developed to display and serve the regional crop-soil-climate data collected as part of the research work. Users will be able to access and plot information here. A hard-copy publication of the main climate, crop and soil maps and trends is in preparation. Refereed publications and conference papers will be used to report research results. Several members of the committee are state climatologists for their respective states. Because of this responsibility to serve the public, many results will be incorporated in products, web sites and information provided to people of the respective states. Members of the project recognize their responsibility to provide science-based information and impact assessments to students, stakeholders and policy-makers.

Organization/Governance

Literature Cited

Literature Cited:
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Literature Cited from NC 1018 participants

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.

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.

Alfieri J G, D. Niyogi, M. A. LeMone, F. Chen, S. Fall, 2007, A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Datasets Used with Land Surface Models, Pure and Applied Geophysics (Invited), 164, 1789 - 1809. DOI 10.1007/a00024-007-0241-4.

Anothai, J., A. Patanothai, K. Pannangpetch, S. Jogloy, K.J. Boote, and G. Hoogenboom. 2008. Reduction in data collection for determination of cultivar coefficients for breeding applications. Agricultural Systems 96(1-3):195-206.

Ashish, D., G. Hoogenboom, and R.W. McClendon. 2008. Land-use classification of
mutispectral aerial images using artificial neural networks. International Journal of Remote Sensing. (Accepted for publication).

Bannayan, M., and G. Hoogenboom. 2008a. 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.

Bannayan, M., and G. Hoogenboom. 2008b. 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.

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.

Boken, V.K., C. E. Haque, and G. Hoogenboom. 2007. Predicting drought using pattern recognition, Annals of the Arid Zone 46(2):133-144.

Boote, K. J., J. W. Jones, W. D. Batchelor, E. D. Nafziger, and O. Myers. 2003. Genetic coefficients in the CROPGRO-soybean model: Links to field performance and genomics. Agron. J. 95: 32-51.

Bostick, W.M., V.B. Bado, A. Bationo, C. Tojo Soler, G. Hoogenboom, and J.W. Jones. 2007. Soil carbon dynamics and crop residue yields of cropping systems in the Northern Guinea Savannah of Burkina Faso. Soil and Tillage Research 93(1):138:151.

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

Colunga-Garcia M, S Gage, and G Safir. 2005. Development and integration of temporal/spatial information into plant pest and disease forecasting systems. Survey Detection & Identification & Biological Control National Science Program/Center for Plant Health Science & Technology Annual Report 2004. p. 9-12.

Colunga-Garcia M., P.R. Grace, S.H. Gage, G.P. Robertson, G.R. Safir. 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.

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.

Deng, X., B.J. Barnett, G. Hoogenboom, Y. Yu, and A. Garcia y Garcia. 2008. Alternative crop insurance indices. Journal of Agricultural and Applied Economics 40(1): 223-237.

Fang, H., S. Liang, G. Hoogenboom, J. Teasdale and M. Cavigelli. 2008. Corn yield estimation of remotely sensed data into the CSM-CERES-Maize model. International Journal of Remote Sensing 29(10):3011-3032.

Feng, S., R. Oglesby, C. Rowe, D. Loope, and Q. Hu, 2008: Pacific and Atlantic SST influences on Medieval drought in North America simulated by Community Atmospheric Model. J. Geophys. Res. J. Geophys. Res., 113, D11101, doi:10.1029/2007JD009347.

Feng, S, and Q. Hu, 2008: How the North Atlantic multidecadal oscillation may have influenced the Indian summer monsoon during the past two millennia. Geophys. Res. Lett., 35, L01707, doi:10.1029/2007GL032484.

Feng, S., and Q. Hu, 2007: Changes in winter snowfall/precipitation ratio in the contiguous United States. J. Geophys. Res. 112, D15109, doi:10.1029/2007JD008397.

Fraisse, C.W., N.E. Breuer, D.Zierden, J.G. Bellow, J. Paz, V.E. Cabrera, A. Garcia y Garcia, K.T. Ingram, U. Hatch, G. Hoogenboom, J.W. Jones and JJ. O'Brien. 2006. AgClimate: A climate forecast information system for agricultural risk management in the southeastern USA. Computers and Electronics in Agriculture 53(1):13-27.

Gage, S.H., M. Colunga-Garcia, P.R. Grace, H. Yang, G.R. Safir, G.P. Robertson, A. Shortridge, A Prasla, A. Ali, S. Del Grosso, P. Wilkins, S. Rowshan. 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.

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.

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.

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 and Biological Engineers 49(6):2005-2015.

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.

Gijsman A. J., G. Hoogenboom, W. J. Parton, and P. C. Kerridge. 2002. Modifying DSSAT crop models for low-input agricultural systems using a soil organic matter-residue module from CENTURY. Agron. J. 94:462-474.

Grace, P.R., M. Colunga-Garcia, S.H. Gage, G.R. Safir, G.P. Robertson. 2005. The potential impact of climate change on North Central Regions soil organic carbon resources. Ecosystems.

Grace, P.R., S.H. Gage, M. Colunga-Garcia, G.P. Robertson, G.R. Safir. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.
Heinemann, A.B., A.de H.N. Maia, D. Dourado_Neto, K.T. Ingram and G. Hoogenboom. 2006. Soybean (Glycine Max [L.] Merr.) growth and development response to CO2 enrichment under different temperature regimes. European Journal of Agronomy 24(1):52-61.

Heisler, G., B. Tao, J. Walton, R. Grant, R. Pouyat, I. Yesilonis, D. Nowak, and K. Belt 2006. Land cover influences on below-canopy temperatures in and near Baltimore, MD., In: Proceedings of the 6th Symposium on the Urban Environment, American Meteorological Soc. (In press).

Herrero, M., E. Gonzalez-Estrada, P.K. Thornton, C. Quiros, M.M. Waithaka, R. Ruiz and G. Hoogenboom. 2007. IMPACT- Generic household-level databases and diagnostic tools for integrated crop-livestock analysis. Agricultural Systems 92 (1-3):240-265.

Hoogenboom, G. 2006. Plant/soil interface and climate change: carbon sequestration from the production perspective. In: p. 93-126. [J.S. Bhatti, R. Lal, M. J.. Apps and M.A.. Price, editors] Climate Change and Managed Ecosystems. CRC Press. Boca Raton, Florida.

Hoogenboom, G., J.W. Jones, P.W. Wilkens, C.H. Porter, W.D. Batchelor, L.A. Hunt, K.J. Boote, U. Singh, O. Uryasev, W.T. Bowen, A.J. Gijsman, A. du Toit, J.W. White, and G.Y. Tsuji. 2004. Decision Support System for Agrotechnology Transfer Version 4.0 [CD-ROM]. University of Hawaii, Honolulu, HI.

Hosamane. S., 2005. Using National Weather Service forecasts and model output statistics (MOS) to forecast evapotranspiration. Masters Thesis. South Dakota State University. 120pp.

Hu, Q., and S. Feng, 2008: Variation of the North American summer monsoon regimes and the Atlantic Multidecadal Oscillation. J. Climate, 21, 2371-2383.

Hu, Q. and S. Feng, 2007: Decadal variation of the southwest U.S. summer monsoon circulation and rainfall in a regional model. J. Climate, 20, 4702-4716.

Isard, S. A., Gage, S.H., Comtois, P. and Russo, J. 2005. Principles of the atmospheric pathway for invasive species applied to soybean rust. BioScience: 851-861.

Jain, A., R.W. McClendon, and G. Hoogenboom. 2006. Freeze prediction for specific locations using artificial neural networks. Transactions of the American Society of Agricultural and Biological Engineers 49(6):1955-1962.

Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. DSSAT Cropping System Model. Europ. J. Agron. 18:235-265.

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.

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.

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.

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.

Lopez-Cedron, F. X, K. J. Boote, B. Ruiz-Nogueria, and F. Sau. 2005. Testing CERES-Maize versions to estimate maize production in a cool environment. Europ. J. of Agronomy 23: 89-102.

Lopez-Cedron, F. X, K. J. Boote, J. Pineiro, and F. Sau. 2008. Improving the CERES-Maize model ability to simulate water deficit effects on maize production and yield components. Agron. J. 100:296-307.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Prabhakaran, T., and G. Hoogenboom. 2008. Evaluation of the weather research and forecasting model for two frost events. Computers and Electronics in Agriculture 64:234-247.

Presley, DeAnn Ricks. 2007. Ph. D. Dissertation. Genesis and spatial distribution of upland soils in east central Kansas. Kansas State Univ.

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.

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.

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.

Seem, R.C. 2004. Forecasting plant disease in a changing climate: A question of scale. Can. J. Plant Pathol. 26:274-283.

Shank, D.B., G. Hoogenboom, and R.W. McClendon. 2008. Dew point temperature prediction using artificial neural networks. Journal of Applied Meteorology and Climatology 47(6):1757-1769.

Shank, D.B., R.W. McClendon, J.O. Paz, and G. Hoogenboom. 2008. Ensemble artificial neural networks for prediction of dew point temperature. Applied Artificial Intelligence 22(6):523-542.

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.

Soltani, A., and G. Hoogenboom. 2007. Assessing crop management options with crop simulation models based on generated weather data. Field Crops Research 103:198-207

Staggenborg, S.A., M. Carignano, and L. Haag. 2007. Predicting soil pH and buffer pH with a real-time sensor. Agron. J. 99:854-861.

Staggenborg, S.A., W.B. Gordon, K.C. Dhuyvetter. 2007. Grain sorghum and corn comparisons: Yield, economic and environmental responses. Agron. J. (accepted).

Staggenborg, S.A., and R.L. Vanderlip. 2005. Crop simulation models can be used as dryland cropping systems research tools. Agron. J. In Press.

Suleiman, A.A., and G. Hoogenboom. 2007. Comparison of Priestley-Taylor and Penman-Monteith for daily reference evapotranspiration estimation in Georgia. Journal of Irrigation and Drainage Engineering 133(2):175-182.

Suleiman, A..A., C.M. Tojo Soler, and G. Hoogenboom. 2007. Evaluation of FAO-56 crop coefficient procedures for deficit irrigation management of cotton in a humid climate. Agricultural Water Management 91(1-3):33-42.

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.

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.

Tojo Soler, C.M., P.C. Sentelhas, and G. Hoogenboom. 2007 Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal of Agronomy 27(2-4):165-177.

Tojo Soler, C.M., N. Maman, X. Zhang, S.C. Mason and G. Hoogenboom. 2008. Determining optimum planting dates for pearl millet for two contrasting environments using a modeling approach. Journal of Agricultural Science 146(4):445-459.

United States Department of Energy. 2007. Fact Sheet: Energy Independence and Security Act of 2007 http://www.whitehouse.gov/news/releases/2007/12/20071219-1.html (Accessed on October 24, 2008)

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.
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.

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.

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.

Attachments

Land Grant Participating States/Institutions

FL, GA, IL, IN, KS, MI, MO, MS, MT, ND, NE, NY, SD, TN, TX, WA, WY

Non Land Grant Participating States/Institutions

USDA Midwest Climate Hub