Progress 09/01/08 to 08/31/10

Outputs
OUTPUTS: The integrated activities component of the project involves project management and the Computational Agriculture Initiative is incorporated into the Center for Advanced Computing structure as a participating program. N Modeling: Adapt-N, a next-generation tool for precise management of nitrogen, is now fully operational and provides adjustments to sidedress N recommendations on a farm-specific basis. This server-based (cloud) computing tool allows for continuing upgrades, and is accessible at http://adapt-n.eas.cornell.edu/. It provides location-based service where farmers and consultants can access such climate data at CAC, run a dynamic simulation model on N fate, and receive a recommendation for N fertilizer application based on the most up-to-date field-specific climate data and crop development. It was operational for the 2010 growing season for the Northeast and Iowa. This interface is being linked to the PNM model and simulations can be generated using inputs provided by the user. Adapt-N accesses the high-resolution precipitation and temperature data. Adaptations of the tool for Midwest conditions have been made in collaboration with crop consultants at MGT Envirotec in Iowa. Additional funding (NRCS-CIG and NY State) has been obtained for implementation and testing of the tool. A space-time analysis of N recommendations was developed for fields in Iowa using spectroscopy-based estimates of N mineralization, soil survey data, 24 years of weather data, and the PNM model. Field and laboratory experiments are being conducted to quantify nitrous oxide losses under manured and inorganic fertilizer additions to improve model representation and parameterization. High-resolution Climate Data: High-resolution climate data were expanded to include Iowa. Weed Modeling: We modified an existing crop-weed competition model to incorporate the influence of nitrogen acquisition on plant growth and resource partitioning in mixed vegetation systems. This model (COMPETE) builds on several other models within a spatially-explicit framework that allows individual plants to compete for solar radiation, soil water and soil N. The COMPETE model was used to explore the effects of soil N on the competitive interactions of velvetleaf and maize in a rainfed environment across multiple growing seasons. Results indicate that there is considerable year-to-year variability in weed-free maize yield and that weed-induced maize yield losses also vary significantly across years at all N levels. VNIR Reflectance Spectroscopy: VNIRRS is explored as an alternative or complementary tool to more costly field and laboratory procedures for assessment of soil quality. 2010 activities focused on applications to soil salinity parameter assessment (Turkey), and carbon and soil quality assessment (New York, Kenya, Costa Rica, and Iowa). PARTICIPANTS: Project Director Harold van Es, Department of Crop and Soil Sciences Co-Investigators Art DeGaetano, Department of Earth and Atmospheric Sciences Susan Riha, Department of Earth and Atmospheric Sciences Jeffrey Melkonian, Department of Crop and Soil Sciences Graduate Students Chris Graham, Department of Crop and Soil Sciences Rintaro Kinoshita, Department of Crop and Soil Sciences Programmer Laura Joseph, Department of Earth and Atmospheric Sciences TARGET AUDIENCES: Farmers, consultants, society. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
This initiative started in 2003 and project accomplishments and new initiatives have been presented in departmental seminars, at national/international meetings, and at meetings of the project team. The premier outcome of the current efforts are the integration of high-resolution climate data with crop and soil models, combined into the user-friendly Adapt-N tool, to improve the precision of N management in corn. This has major societal significance as nitrogen on corn is (i) the primary source of groundwater contamination in agricultural areas, (ii) the primary source of hypoxia problems in the Gulf of Mexico and Chesapeake Bay, (iii) the largest energy input component in corn production, and (iv) the primary source of greenhouse gas emission in agriculture. The lack of incorporation of local information, especially site-specific weather information, severely limits the precision in N management and creates large inefficiencies and environmental impacts. Adapt-N addresses these concerns. We performed training on the new Adapt-N tool for stakeholders, notably Certified Crop Advisors and the model has been used by consultants and extension agents in New York. Additional funding has been acquired for field demonstrations and encouragement of adoption. Adaptive N recommendations were developed for fields in Iowa in collaboration with MGT Consultants. Results were very encouraging, and several other Iowa consultants and the Iowa Soybean Association are adopting the Adapt-N approach. This required the adaptation of the model to Midwest conditions and the availability of high-resolution climate data for the region. We also performed a proof-of-concept on integrated space-time N recommendations using VNIRRS and the PNM model for an Iowa field. High-resolution precipitation and temperature data are being made available on the CAC web server at http://www.cac.cornell.edu/maps/zoom.aspx . The data are also accessible through web services (http://rain.nrcc.cornell.edu/~laura/web_service.html), which can be used when providing input for simulation models like Adapt-N. We determined that relative maize yield losses from weed competition were frequently greater under low compared to high soil N conditions, but the magnitude of this difference was strongly dependent on weather. In the more favorable years (i.e. higher yield potentials), relative yield losses due to weed competition generally increased at low N levels. On the other hand, soil N had little effect on relative yield losses in years with lower yield potential. These findings illustrate the dynamic and complex nature of competitive interactions and indicate why it is difficult to draw definitive conclusions about the importance of factors like soil N to crop yield loss based on short-term field experimentation. We determined that VNIR reflectance spectroscopy has good potential for rapid and inexpensive assessment of soil quality indicators - soil salinity, active carbon, organic matter, and P sorption capacity. This project also suggests that combinations of VNIRRS and geostatistical methods can be effectively used to map soil properties.

Publications

• Bilgili. A.V., H.M. van Es, F. Akbas, A. Durak, W.D. Hively, T. Owiyo, and S.D. DeGloria. 2010. Visible-Near- Infrared Reflectance Spectroscopy for Assessment of Soil Properties in Semi-Arid Turkey. J. Arid Environment 74:229-238.
• McDonald, AJ, Riha, SJ, Ditommaso, A. 2010. Early season height differences as robust predictors of weed growth potential in maize: new avenues for adaptive management Weed research 50: 110-119.
• Berger, A., McDonald, A.,Riha, S. 2010. A coupled view of above and below-ground resource capture explains different weed impacts on soil water depletion and crop water productivity in maize. Field Crops Research 119:314-321.
• Bilgili. A.V., M.A. Cullu, H.M. van Es, A. Aydemir, and S.K. Dikilitas. 2010. Using Hyperspectral VNIR Spectroscopy for the Characterization of Soil Salinity. In: M. Qadir et al. Sustainable Management of Saline Waters and salt-Affected Soils and Agriculture. ICARDA-USAID-IWMI workshop Aleppo, Syria, 2009.
• Melkonian, J. L.D. Geohring, H.M. van Es, P.E. Wright, T.S. Steenhuis and C. Graham. 2010. Subsurface drainage discharges following manure application: Measurements and model analyses. Proc. XVIIth World Congress of the Intern. Commission of Agric. Engineering, Quebec City, Canada.
• Bilgili. A.V., M.A. Cullu, H.M. van Es, A. Aydemir, and S Aydemir. 2011. The Use of Hyperspectral Visible and Near Infrared Reflectance Spectroscopy for the Characterization of Salt-Affected Soils in the Harran Plain, Turkey. Arid Land Research and Management 25: 19 -37.
• Bilgili. A.V., F. Akbas, and H.M. van Es. 2010. Combined Use of Hyperspectral VNIR Spectroscopy and Kriging Methods to Predict Soil Variables Spatially. Precision Agriculture (DOI 10.1007/s11119-010-9173-6).
• Graham, C.J., H.M. van Es, J.J. Melkonian, and D.A. Laird. 2010. Improved nitrogen and energy use efficiency using NIR estimated soil organic carbon and N simulation modeling. In: D.A. Clay and J. Shanahan. GIS Applications in Agriculture: Nutrient Management for Improved Energy Efficiency. pp 301-325, Taylor and Francis, LLC.
• van Es, H.M. 2010. Historical and Emerging Soil and Water Conservation Issues in the Northeastern USA. In: T. Zobeck and W. Schillinger. Soil and Water Conservation Advances in the US. Pp. 163-182. Soil Science Soc. America. Special Publ. 60. Madison, WI.

Progress 09/01/08 to 08/31/09

Outputs
OUTPUTS: The integrated activities component of the project involves project management and data mining. N Modeling: PNM model simulations have been performed on the CAC batch machines. The Adapt-N web interface was developed for providing adjustments to sidedress N recommendations on a farm-specific basis. This interface is being linked to the PNM model and simulations can be generated using inputs provided by the user. Adapt-N also accesses the high-resolution precipitation and temperature data. A space-time analysis of N recommendations was developed for fields in Iowa. High-resolution Climate Data: Our activities have been focused on making the procedures we have developed over the last years operational. The RUC based temperature interpolations have been compared to observed station data using cross validation. Comparisons have been made between our technique and the classic approaches of interpolation: multiquadric interpolation, inverse-distance weightings, and kriging. All three improve on (i.e., correct biases in) the original radar-based fields, but simple inverse-distance weighting was the best for the operational products. Weed Modeling: We modified an existing crop-weed competition model to incorporate the influence of nitrogen acquisition on plant growth and resource partitioning in mixed vegetation systems. This model (COMPETE) builds on several other models, including MAESTRA (for light interception), GECROS (for photosynthesis and respiration), and PNM (for soil processes) within a spatially-explicit framework that allows individual plants to compete for solar radiation, soil water and soil N. The COMPETE model was then used to explore the effects of soil N on the competitive interactions of velvetleaf and maize in a rainfed environment across multiple growing seasons. Results indicate that there is considerable year-to-year variability in weed-free maize yield and that weed-induced maize yield losses also vary significantly across years at all N levels. VNIR Reflectance Spectroscopy: VNIRRS is explored as an alternative or complementary tool to more costly field and laboratory procedures for assessment of soil quality. Soil samples were analyzed according to the Cornell soil quality assessment protocol for fifteen indictors, as well as with VNIRS (350-2500nm). Predictions were then classified according to threshold values established for New York soils and Kappa and Effectiveness statistics were used to evaluate the predictability of VNIRS. The methodology is also tested for its feasibility to quantify soil P sorption capacity of soils for New York farm soils and to estimate soil salinity parameters of soils from Southeastern Turkey. VNIRRS was combined with geostatistical methods to optimize sampling efficiency and spatial estimation. PARTICIPANTS: Harold van Es and David Lifka: Integrated Activities, GIS applications and Data Mining. Jeffrey Melkonian: Adaptive N management recommendations. Art DeGaetano: High-resolution climate data. Susan Riha: Weed modeling. Harold van Es: VNIR spectroscopy of soils. The project supports services from professionals in CAC (Linda Woodard and Susan Mehringer) and EAS (Laura Joseph and Andrew McDonald) and two Ph.D. students (Andres Berger-Ricca and Christopher Graham). Collaboration include NY stakeholders and MGT Consultants in Iowa. TARGET AUDIENCES: Farmers and other residents (environmental impacts) in New York and Iowa. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
This initiative started in 2003 and project accomplishments and new initiatives have been presented in departmental seminars, at national/international meetings, and at meetings of the project team. A Web site has been developed for the initiative (http://www.cac.cornell.edu/clients/CompAg.aspx), which is being enhanced with new tools as the project further develops. PNM model adjustments to the current New York State sidedress N recommendations were generated over a three week period in early-mid June 2005-2009, when sidedress N is typically applied to maize in New York. Adapt-N has been made into a user friendly tool and is reliable for access by stakeholders at http://adapt-n.eas.cornell.edu. We performed training on the new Adapt-N tool for stakeholders, notably Certified Crop Advisors and the model has been used by consultants and extension agents in New York. In addition, in 2009, adaptive N recommendations were developed for fields in Iowa in collaboration with MGT Consultants. Results were very encouraging, and several other Iowa consultants and the Iowa Soybean Association want to adopt the Adapt-N approach. This requires the adaptation of the model to Midwest conditions and the availability of high-resolution climate data for the region, which is being pursued as part of this project. We also successfully performed a proof-of-concept on integrated space-time N recommendations using VNIRRS and the PNM model for an Iowa field. Daily high-resolution temperature and precipitation fields are now stored in SQL-Server databases at CAC. High-resolution precipitation and temperature data are being made available on the CAC web server at http://www.cac.cornell.edu/maps/zoom.aspx . The data are also accessible through web services (http://rain.nrcc.cornell.edu/~laura/web_service.html), which can be used when providing input for simulation models like Adapt-N. We determined that relative maize yield losses from weed competition were frequently greater under low compared to high soil N conditions, but the magnitude of this difference was strongly dependent on weather. In the more favorable years (i.e. higher yield potentials), relative yield losses due to weed competition generally increased at low N levels . On the other hand, soil N had little effect on relative yield losses in years with lower yield potential. These findings illustrate the dynamic and complex nature of competitive interactions and indicate why it is difficult to draw definitive conclusions about the importance of factors like soil N to crop yield loss based on short-term field experimentation. We determined that VNIR reflectance spectroscopy has good potential for rapid and inexpensive assessment of soil quality indicators - active carbon, organic matter, and magnesium, notably salinity and P sorption capacity. This project also suggests that combinations of VNIRRS and geostatistical methods can be effectively used to map soil properties.

Publications

• Schindelbeck, R.R., H.M. van Es, G.S. Abawi, D.W. Wolfe, T. L. Whitlow, B.K. Gugino, O.J. Idowu, and B.N. Moebius. 2008. Comprehensive Assessment of Soil Quality for Landscape and Urban Management. Landscape and Urban Planning.88:73-80. doi:10.1016/j.landurbplan.2008.08.006.
• McDonald, A., S. Riha, A. DiTommaso and A. DeGaetano. 2009. Climate change and the geography of weed damage: Analysis of U.S. maize systems suggests the potential for significant range transformations. Agriculture, Ecosystems and Environment 130:131-140.
• Chun-Yu Wu, Astrid R. Jacobson, Magdeline Laba, and Philippe C. Baveye. 2009. Alleviating Moisture Content Effects on the Visible Near-Infrared Diffuse-Reflectance Sensing of Soils. Soil Science 174:456-465.
• Chun-Yu Wu, Astrid R. Jacobson, Magdeline Laba, and Philippe C. Baveye. 2009. Accounting for surface roughness effects in the near-infrared reflectance sensing of soils. Geoderma 152: 171-180.
• Bilgili. A.V., H.M. van Es, F. Akbas, A. Durak, W.D. Hively, T. Owiyo, and S.D. DeGloria. 2009. Near- Infrared Reflectance Spectroscopy for Assessment of Soil Properties in Semi-Arid Turkey. J. Arid Environment doi:10.1016j.aridenv.2009.08.011.
• Tan, I.Y.S., H. M. van Es, J. M. Duxbury, J. J. Melkonian, R. R. Schindelbeck, L.D. Geohring, W.D. Hively, and B. N. Moebius. 2009. Nitrous Oxide Losses under Maize Production as Affected by Soil Type, Tillage, Rotation, and Fertilization. Soil&Tillage Research 102:19-26