Cornell Computational Agriculture Initiative
Funded by the USDA, the Cornell Computational Agriculture Initiative is a collaboration between CAC
and the College of Agriculture and Life Sciences. Its goal is to accelerate the solution of agricultural
research problems using the latest high performance computing, database, and Web-based technologies.
For example, CAC worked with the Northeast Regional Climate Center to improve weather data accuracy and
accessibility in order to help farmers make better crop
selection and management decisions.
Systems and Consulting Support
The Computational Agriculture Initiative is led by Harold van Es,
Professor of Soil Sciences, and David Lifka, CAC director. CAC provides HPC systems and technical consulting to support
faculty research and outreach projects that benefit the agricultural community.
Research Projects
Development of high-resolution climate data for the Northeast (A. DeGaetano)
- Development of methodology for hourly and daily maximum and minimum air temperature to a high-resolution 4km grid
- Development of a methodology to use radar estimated precipitation as a basis for interpolating daily rainfall to a 4 km grid
- Development of a Web-accessible SQL Server database at CAC of historical high-resolution daily temperature and precipitation fields
- Linking high-resolution temperature and precipitation data to agricultural-environmental decision support tools
Real-time N management recommendations using a dynamic simulation model (J. Melkonian, H. van Es)
- Development of a server-based tool for precise nitrogen management under maize production using real-time high-resolution climate information and a dynamic simulation model
- Development of computational methods to assess the impact of agricultural N management on N2O emissions and greenhouse gas impacts
Use of hyperspectral sensing and data mining for rapid soil assessment (H. van Es)
- Development of methodology to simultaneously assess multiple soil and plant characteristics using visible near-infrared sensing technology for applications in soil management, health assessment and survey
- Development of a SQL Server database on VNIR data and use of data mining methods to analyze such data
Utilizing interpolated climate surfaces and simulated nitrogen dynamics for spatially-distributed predictions of weed competitiveness (S. Riha)
- Characterize growth of the major weeds in the Northeast and the effects of resource availability on their competitive traits
- Elucidate and implement appropriate methods to simulate crop-weed competition and facilitate precision herbicide application using a dynamic simulation model
Data mining of space-time information (P. Sullivan)
- Mining of Northeast Regional Climate Center (NRCC) data available from the CAC SQL Server using exploratory, geostatistical, neural network and wavelet analyses
- Find spatial patterns in NRCC data that can lead to better prediction of weather events and improvement in climate data
Selected Papers
2008
High-Resolution Spatial Interpolation of Weather Generator Parameters Using Local Weighted Regressions
D. Wilks
Agricultural and Forest Meteorology
Hyperspectral Analysis of Long-term Tillage Effects on Soil Reflectance, Nutrition, and Aggregate Stability
W. Hively, H. van Es, R. Shindelbeck, B. Moebius, D. Grantham, T. Owiyo, A. Bilgili, W. Philpot, S. DeGloria
Soil and Tillage Research
Nitrous Oxide Losses under Maize Production as Affected by Soil Type, Tillage, Rotation, and Fertilization
I. Tan, H. van Es, J. Duxbury, J. Melkonian, R. Schindelbeck, L. Geohring, W. Hively, B. Moebius
Soil and Tillage Research
2007
Doe Soil Nitrogen Affect Early Competitive Traits of Annual Weeds in Comparison with Maize?
A. Berger, A. McDonald, S. Riha
Weed Research
Managing Crop Nitrogen for Weather
Application of Dynamic Simulation Modeling for Nitrogen Management in Maize
J. Melkonian, H. van Es, A. DeGaetano, J. Sogbedji, L. Joseph
Nitrogen Management under Maize in Humid Regions: Case for a Dynamic Approach
H. van Es, B. Kay, J. Melkonian, J. Sogbedji
International Plant Nutrition Institute
Spatial Interpolation of Daily Maximum and Minimum Air Temperature based on Meteorological Model Analyses and Independent Observations
A. DeGaetano, B. Belcher
Journal of Applied Meteorology and Climatology
Spatially-Balanced Complete Block Designs for Field Experiments
H. van Es, C. Gomes, M. Sellmann, C. van Es
Geoderma
2006
A Method to Infer Time of Observation at US Cooperative Observer Network Stations Using Model Analyses
B. Belcher, A. DeGaetano
International Journal of Climatology
Patterns of Early Root Development for Maize and Four Common Weeds as Influenced by Competitive Environment
A. Berger, A. McDonald, S. Riha
Functional Ecology
Soil Test, Aerial Image and Yield Data as Input for Site-Specific Fertility and Hybrid Management under Maize
A. Magri, H. van Es, M. Glos, W. Cox
Precision Agriculture
2004
Economics of Purchasing a Yield Monitor for Split-Planter Corn Hybrid Testing
W. Cox, W. Knoblauch, H. van Es, T. Katsvairo, M. Glos
Agronomy Journal
Spatial Analysis of Maize Response to Nitrogen Fertilizer in Central New York
J. Kahabka, H. van Es, E. McClenahan, W. Cox
Precision Agriculture
The Challenge of Generating Spatially Balanced Scientific Experiment Designs
C. Gomes, C. Sellmann, C. van Es, H. van Es
Lecture Notes in Computer Science
Thesis
Computational and Experimental Approaches Related to Nitrous Oxide Emissions and Economic Analysis of Private and Social Returns from Maize Fertilization
I. Tan
2007 Ph.D. Dissertation, Cornell University
Root Development and Soil Nitrogen Availability as Drivers of Maize-Weed Competition
A. Berger
2006 M.S. Thesis, Cornell University
Adapting to Climate Change
K. Kotani
2005 Ph.D. Dissertation, Cornell University
Corrections to Radar-Estimated Daily Precipitation Using Observed Gauge Data
E. Ware
2005 M.S. Thesis, Cornell University
