Useful to Usable logo
What's New whats

Featured Resources FeaturedResources

U2U Corn Split N DST
Designed to help farmers determine the feasibility and profitability of using in-season nitrogen application.

FREE online tool  that uses historical weather, fieldwork data, and user-specific equipment and economic consideration to help you:
  • Increase corn yields
  • Reduce nitrogen costs
  • Reduce nitrogen losses to the environment
  • Determine the likelihood of completing in-season fieldwork 
The Corn Split N DST now covers all 12 north central U.S. states! Need help navigating the Corn Split N DST ? Check out the updated

Would you like to test drive new product features or even all new tools before they're publically available? Sign up to become a tool tester
Tool Tips and Updates tool

Did you know......

You can now leave feedback on any of the U2U decision support tools. Simply click on the "Feedback?" link to the left of the About tab.  From there you can tell us what you like/dislike about the tool, what enhancements you want see in the future, and provide your contact information (optional) for more details.  If you think an image would be helpful here please add one. 

HPRCC hosting new tool based on U2U research

The High Plains Regional Climate Center has launched a new tool, Corn Belt
Climate Trends , based on research conducted by U2U PhD student Juliana Dai at the University of Nebraska-Lincoln. With this new tool users can find out on a monthly, seasonal, or annual basis how the temperature and precipitation has changed in the last three decades. Learn more about the tool in the October 2015 edition of The Prairie Post .

Upcoming Events upcoming

ASA-CSSA-SSSA Annual Meeting
Nov 17, 2015 | Minneapolis, MN
The U2U team will be hosting TWO special events on Tuesday November 17, 2015 at the ASA-CSSA-SSSA Annual Meeting.

  • FREE Workshop: U2U Decision Tool Training
      • 2:30-4:30 PM 
      • Approved for 1.0 CEUs
      • This training includes an overview of the four online tools available through the Useful to Usable project; AgClimate View, Corn Growing Degree Day, Climate Patterns Viewer, and Corn Split N and small group hands-on experience with these tools to work through scenarios. 
      • Presenters: Dennis Todey (South Dakota State University), Hans Schmitz (Purdue University)

AgriClimate Connection agriclimate

AgriClimate Connection is an interactive blog where farmers and scientists across the Corn Belt can learn about and discuss cutting-edge farm management strategies, weather and climate conditions, and much more. It is jointly-managed by and U2U

Recent Posts:

Posted on 11/3/2015 by Lynn Laws
Now farmers and advisors in all 12 Corn Belt states can use the free Corn-Split N decision support tool, developed by the USDA-funded Useful to Useful climate initiative....... Read more
Posted on 10/7/2015 by Lynn Laws
Learn about research being conducted, by the Sustainable Corn Project's graduate students and post-doctoral students, in a new booklet now available for viewing online...... Read more
Posted on 9/18/2015 by Pat Guinan
This time of year questions about frost/freeze potential are common as producers look for a little more time for crops to mature, or gardeners and horticultural interests...... Read more

Be sure to subscribe to our blog for the latest updates.  

Reaching Out reaching

New Atlas Provides Insight into Corn Belt Farmers' Climate Beliefs and Behaviors

The U2U and CSCAP teams have released their second atlas in a series featuring watershed-level results from their 2012 survey of Midwestern farmers. The newly published atlas focuses on farmers' specific behaviors, beliefs about climate and weather, and the tools they utilize to make farm decisions.
Church, S., Haigh, T., Widhalm, M., Prokopy, L., Arbuckle, J., Hobbs, J., Knoot, T., Knutson, C., Loy, A., Mase, A., Mcguire, J., Morton, L., Tyndall, J. (2015). Farmer Perspectives on Agricultural Practices, Information, and Weather Variability in the Corn Belt: A Statistical Atlas, Volume 2. Purdue University Research Repository. doi:10.4231/R79W0CFS

Two New Articles in  Earth Interactions
Kellner, O. and D. Niyogi. 2015. "Climate Variability and the U.S. Corn Belt: ENSO and AO Episode-dependent Hydroclimatic Feedbacks to Corn Production at Regional and Local Scales."  Earth Interactions 19(6): 1-32.

El Niño-Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980-2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt.  This study is unique in that it utilizes the oceanic Niño index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Niño events on yields compared to La Niña events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production. ( Access Full Article )

Liu, X., J. Andresen, H. Yang, and D. Niyogi. 2015.  "Sensitivity analysis and validation the Hybrid-Maize simulation model across the Midwest."  Earth Interactions , 19(9): 1-16.

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, and Mead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days' accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre
−1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing crop-specific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies. ( Access Full Article)
New Publication in the International Journal of Climatology

Dai, S., M. Shulski, K. Hubbard, and G. Takle. In Press. "A spatiotemporal analysis of Midwest U.S. temperature and precipitation trends during the growing season from 1980 to 2013." International Journal of Climatology DOI: 10.1002/joc.4354
Since late 1970s, climate warming has been widely recognized. In the Midwest, farmers cannot rely on the normal calendar anymore, and it has become critically necessary to evaluate the most recent climate trends relative to growing season in order to conduct adaptation efforts for agriculture. Based on the homogenized historical monthly temperature and precipitation records during the period of 1980-2013 from 302 observing stations in the 12 Midwestern US states, we investigate the climate trends on four timescales: monthly, early growing season, late growing season, and the entire growing season. The climate metrics include maximum temperature, minimum temperature, average temperature, diurnal temperature range, and precipitation. Nonparametric Sen's Slope together with the nonparametric Mann-Kendall test is used to estimate the decadal trend and to detect the statistical significance. The results show that growing season average temperature has increased at a rate of 0.15 C decade -1 over the Midwest United States. Within the growing season, minimum temperature is increasing faster in the early growing season, especially in June, while maximum temperature is increasing faster in the late growing season, especially in September. Spatially, statistically significant ( p ≤ 0.05) growing season warming is more focused in the southern part of the region in the early growing season but in the northern part of the region in the late growing season. Over the Midwest, dominant trends in diurnal temperature range are decreasing during most months, with the exception of September. The majority of the locations show increasing trends in growing season precipitation, yet few are statistically significant. Furthermore, precipitation has been increasing in the early growing season but decreasing in the late growing season. This within-season reversing trend in precipitation is found in 8 of 12 Corn Belt states: Illinois, Iowa, Michigan, Minnesota, Missouri, Nebraska, North Dakota, and Wisconsin. ( Access Full Article)
New Article in  Environmental Management
Prokopy, L.S., J.G. Arbuckle, A.P. Barnes, V.R. Haden, A. Hogan, M.T. Niles, and J. Tyndall. 2015. "Farmers and Climate Change: A Cross-National Comparison of Beliefs and Risk Perceptions in High-Income Countries."  Environmental Management. 56(2): 492-504.
Climate change has serious implications for the agricultural industry-both in terms of the need to adapt to a changing climate and to modify practices to mitigate for the impacts of climate change. In high-income countries where farming tends to be very intensive and large scale, it is important to understand farmers' beliefs and concerns about climate change in order to develop appropriate policies and communication strategies. Looking across six study sites-Scotland, Midwestern United States, California, Australia, and two locations in New Zealand-this paper finds that over half of farmers in each location believe that climate change is occurring. However, there is a wide range of beliefs regarding the anthropogenic nature of climate change; only in Australia do a majority of farmers believe that climate change is anthropogenic. In all locations, a majority of farmers believe that climate change is not a threat to local agriculture. The different policy contexts and existing impacts from climate change are discussed as possible reasons for the variation in beliefs. This study compared varying surveys from the different locations and concludes that survey research on farmers and climate change in diverse locations should strive to include common questions to facilitate comparisons. ( Access Full Article)

Researcher Spotlight research
The Scientific Solutions group within Purdue University's Rosen Center for Advanced Computing is responsible for developing and maintaining all decision support tools for the U2U project. This dedicated group is continually adding new features based on stakeholder needs, and they work closely with team members to ensure tool usability and top-notch functionality. 
Carol Song
Carol is a senior research scientist and leader of the Scientific Solutions group.  In addition to U2U, Carol is the principal investigator on sever al NSF projects, including the Data Infrastructure Building Blocks project to develop geospatial computation, data analysis and visualization capability for the HUBzero platform, and the Purdue participation in the national-scale computing infrastructure project XSEDE. Carol received her Ph.D. in computer science from the University of Illinois at Urbana-Champaign, and she worked at the National Center for Supercomputing Applications (NCSA) creating one of the earliest scientific visualization tools used by astrophysics researchers to investigate their simulation data.
Carol enjoys reading, especially biographies and mystery/action novels, on her way to completing 25+ books in 2015. She loves traveling around the world, and visiting her parents in China. She and her husband, Zhiyuan, a fellow computer scientist, are proud of their son Beilin who is studying math and computer science at Carnegie Mellon University. Their daughter, Claudia, 16, is an avid distance runner, proudly representing the Red Devils as a member of the school's varsity cross country team. 
Larry Biehl

Larry Biehl, a Purdue University graduate, joined the Purdue staff as a research engineer with the Laboratory for Application of Remote Sensing (LARS) in 1974. Over the years Larry has participated in Skylab, Landsat MSS and Thematic Mapper research, and was involved with several NASA-sponsored field research programs including field spectral data acquisition and calibration procedures, data preprocessing and software development. In addition to his work with U2U, Larry is currently involved with managing the Purdue Terrestrial Observatory, directing the IndianaView consortium and participating in other campus research projects.
Larry is married with four kids who have all graduated from Purdue University. His hobbies are gardening, woodworking, programming, helping the kids in their house projects and four grandkids. He and his wife Donna are very involved with activities at their church.
Christopher Panza

Chris joined the U2U team as a Software Engineer in 2014. He has been working inwebsite development for nearly a decade with experience in both software testing and development, which is a perfect match for U2U's web based tools. Chris brings years of experience in website usability, design, and the development of clean, organized and functional code to the project. He is passionate about the development of open source software and is a regular contributor on various open source projects. Chris has been working hard developing the newest U2U tool, Irrigation Investment
DST, which will be available in the coming months.
Outside of software development, Chris enjoys running and cycling and has participated in many long distance races. His wife is an Assistant Professor at Purdue and they recently welcomed a baby boy into the world.

Other News... other
Congratulations to the U2U Team for receiving the 2015 USDA NIFA Partner ship Award for  Mission Integration! Linda Prokopy and Melissa Widhalm were able to attend the award ceremony on Oct 22, 2015.


U2U Team 2015


About Us: 

Useful to Usable (U2U) is a multi-institution research and extension project focused on improving the resilience and profitability of farms in the North Central U.S. amid a more variable and changing climate. Through the development and dissemination of decision support tools, resource materials and training, we strive to transform existing climate information into actionable knowledge for more effective decision making. 

Melissa Widhalm, Project Manager

Click here to join our mailing list.

This project is supported by Agriculture and Food Research Initiative Competitive Grant no. 2011-68002-30220 from the USDA National Institute of Food and Agriculture.