Progress 05/01/10 to 04/30/12
Outputs
OUTPUTS: This is the final report for a special grant on computational agriculture that has had several very successful and impactful accomplishments. 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.cals.cornell.edu/. It provides location-based service where farmers and consultants can access such climate data, 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 has been operational since the 2010 growing season for the Northeast and IA, and since 2012 also for IL, WI, MN, and IN. 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 and extension agents in the Northeast and Iowa. Additional funding (NRCS-CIG and NY State) has been obtained for implementation and testing of the tool. Field and laboratory experiments have been 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 all areas east of the 100th meridian. 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: This technology is explored as an alternative or complementary tool to more costly field and laboratory procedures for assessment of soil quality. Recent activities focused on applications to soil salinity parameter assessment (Turkey), and carbon and soil quality assessment (New York, Kenya, Costa Rica, and Iowa). Spatially-balanced experimental designs: Spatially balanced complete block designs have been developed using a heuristic computational method, Simulated Annealing. Objective functions were based on spatial balance and relative position of treatments in blocks. The method was evaluated and performed well for cases with less than 15 treatments or replications, which was presented at the 2004 JSM meetings. PARTICIPANTS: Nothing significant to report during this reporting period. TARGET AUDIENCES: Nothing significant to report during this reporting period. 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 recent 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 extensive national training on the new Adapt-N tool for stakeholders, and the model is being used by consultants and extension agents in the Northeast and Midwest. The tool has received extensive attention in the farm press and received the 2012 Best New Product Award from AgProfessional magazine. Additional funding has been acquired for field demonstrations and encouragement of adoption. Results from 2011-12 strip trials (84 total) in NY and IA have been very encouraging, and the tool has shown to be win-win, i.e., increase farmer profit while reducing environmental impacts. 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 Norteats Region Climate Center web server. The data are also accessible through web services, 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. Finally, we developed improved, spatially balanced experimental designs that are increasingly adopted as part of field research projects.
Publications
- Wilks, D.S. 2008. High-resolution spatial interpolation of weather generator parameters using local weighted regressions. Agricultural and Forest Meteorology 148:111-120.
- van Es, H.M., B.D. Kay, J.J. Melkonian, and J.M. Sogbedji. 2007. Nitrogen Management Under Maize in Humid Regions: Case for a Dynamic Approach. In: T. Bruulsema (ed.) Managing Crop Nutrition for Weather.. Intern. Plant Nutrition Institute Publ. pp. 6-13.
- Melkonian, J., H.M. van Es, A.T. DeGaetano, J.M.Sogbedji, and L. Joseph. 2007. Application of Dynamic Simulation Modeling for Nitrogen Management in Maize. In: T. Bruulsema (ed.) Managing Crop Nutrition for Weather. Intern. Plant Nutrition Institute Publ. pp. 14-22.
- van Es, H., Gomes, C, Sellmann, M, and van Es, C. 2007. Spatially-Balanced Designs for Experiments on Autocorrelated Fields. Geoderma 140:346-352.
- Ware, E.C., D.S. Wilks and A.T. DeGaetano. 2006. Corrections to radar-estimated daily precipitation using observed gauge data. J. Hydrology.
- Berger, A.B., McDonald, A.J., Riha, S.J. 2006. Patterns of Early Root development for maize and four common weeds as influenced by competitive environment. Functional Ecology 20:770-777.
- Belcher, B.N. and A.T. DeGaetano, 2005: A method to infer time of observation at US Cooperative Observer Network Stations using model analyses. Int. J. of Climatol, 25, 1237-1251.
- Sogbedji, J.M., H.M. van Es and K.M. Agbeko. 2006. Modeling Nitrogen Dynamics under Maize in Ferralsols in Western Africa. Accepted for Nutrient Cycling in Agroecosystems.
- Sogbedji, J.M., H.M. van Es and K.M. Agbeko. 2006. Cover Cropping and Nutrient Management Strategies for Maize Production in Western Africa. Accepted for Agronomy Journal.
- Sogbedji, J.M., H.M. van Es and K.M. Agbeko. 2006. Optimizing N fertilizer use under maize on West African Ferralsols. Accepted for Nutrient Cycling in Agroecosystems.
- Sogbedji, J.M., H.M. van Es, J.M. Melkonian, and R.R. Schindelbeck. 2006. Evaluation of the PNM model for simulating drain flow nitrate-N concentrations under manure-fertilized maize. Accepted for Plant and Soil.
- Magri, A., H.M. van Es, M.Glos, and W.J. Cox. 2005. Integrated assessment of the use of crop, soil, and remote sensing information for precision management of maize. Precision Agric. 6:87-110.
- Gomes, C, M. Sellmann, C. van Es, and H. van Es. 2004. The Challenge of Generating Spatially Balanced Scientific Experiment Designs. Lecture Notes in Computer Science 3011: 387-394.
- van Es, H., Gomes, C, Sellmann, M, and van Es, C. 2004. 'Spatially-Balanced Designs for Experiments on Autocorrelated Fields". 2004 Proceedings of the American Statistical Association, Statistics & the Environment Section [CDROM], Alexandria, VA: American Statistical Association p. 3000-3003.
- van Es, H.M, C.L. Yang, and L.D. Geohring. 2005. Maize nitrogen response as affected by drainage variability and soil type. Precision Agriculture 6:281-295.
- Xue, Y.D., H.M. van Es, R.R. Schindelbeck, B.N. Moebius-Clune, P.L Yang. 2013. Effects of carbon profile, N placement and temperature on N2O emissions in clay loam and loamy sand soils. Soil Use&Manag. doi: 10.1111/sum.12037.
- Kinoshita, R., H.M. van Es, B.N. Moebius-Clune, W.D. Hively, and A.V. Bilgili. 2012. Strategies for Soil Quality Assessment Using VNIR Hyperspectral Spectroscopy in a Western Kenya Chronosequence. Soil Sci. Soc Am. Journal. 76: 1776-1788. doi:10.2136/sssaj2011.0307.
- 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.
- 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.
- 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: 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.
- 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.
- Wu, C.-Y., A. Jacobson, M. Laba, and P. Baveye. 2009. Alleviating moisture content effects on the near-infrared diffuse-reflectance sensing of soils. Soil Science 174:456-465.
- Wu, C.-Y., A. Jacobson, M. Laba, and P. Baveye. 2009. Surface roughness and near-infrared reflectance sensing of soils. Geoderma 152:171-180.
- DeGaetano, A.T. and D.S. Wilks, 2009: Radar-guided interpolation of climatological precipitation data, International Journal of Climatology, 29, 185-196.
- McDonald, A.J., Riha, S.J., DiTommaso, A., DeGaetano, A., 2009. Climate change and the geographic of weed damage: analysis of U.S. maize systems suggests the potential for significant range transformations. Agriculture, Ecosystems, & Environment 130:131-140.
- 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.
- Idowu, O.J., H.M. van Es, G.S. Abawi, D.W. Wolfe, J.I. Ball, B.K. Gugino, B.N. Moebius, R.R. Schindelbeck, and A.V. Bilgili. 2008. Farmer-Oriented Assessment of Soil Quality using Field, Laboratory, and VNIR Spectroscopy Methods. Plant and Soil 307:243-253.
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Progress 05/01/10 to 04/30/11
Outputs
OUTPUTS: Note: This annual report is the same as the final report for NYC-125577 for FY2009-11 as it pertains to the same project. 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. In 2011, 40 on-farm strip trials are being conducted. Field and laboratory experiments are being conducted to improve model representation and parameterization on nitrous oxide losses. N2O losses have been qualified in three experiments: (i) manured and inorganic fertilizer additions, (ii) under different cover cropping systems on a dairy farm, and using shallow and deep N injection in sand and clay soils under different tillage regimes. 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. 2011 activities focused on carbon and soil quality assessment (New York, Kenya, Costa Rica, and Iowa). PARTICIPANTS: Nothing significant to report during this reporting period. TARGET AUDIENCES: Nothing significant to report during this reporting period. 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, and (v) the largest monetary crop input for most grain corn production systems. 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 and provided numerous presentations on the new Adapt-N tool for crop advisors, agency personnel and farmers. Currently, 180 users are registered for the tool. Additional funding has been acquired for field demonstrations and education. Adaptive N recommendations were developed for fields in Iowa in collaboration with MGT Consultants, with very encouraging results High-resolution precipitation and temperature data are 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 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 like soil salinity, active carbon, organic matter, and P sorption capacity. VNIRRS is can be effectively combined with traditional soil chemical testing to provide a comprehensive soil assessment. This project also suggests that combinations of VNIRRS and geostatistical methods can be effectively used to map soil properties.
Publications
- 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 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 (DOI: 10.1080/15324982.2010.528153).
- 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.
- 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., F. Akbas, and H.M. van Es. 2011. Combined Use of Hyperspectral VNIR Spectroscopy and Kriging Methods to Predict Soil Variables Spatially. Precision Agriculture 12:395-420. 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.
- 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.
- 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.
- Meyers, J. G.L. Sacks, H.M. van Es, and J. vandenHeuvel. 2011. Maximizing Operational Efficiency via Dynamic Spatially-Explicit Optimization. Australian Journal of Grape and Wine Research. doi: 10.1111/j.1755-0238.2011.00152.x
- 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.
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