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Cornell Computational Agriculture Initiative

Maize

Initially funded by the USDA, the Cornell Computational Agriculture Initiative continues today to accelerate the solution of agricultural research problems using the latest advanced computing and consulting services. For example, CAC improved weather data accessibility in order to help farmers make better crop management decisions. Today, farmers use Adapt-N to improve nitrogen management for corn. Adapt-N won a Best New Product of the Year award from Ag Professional magazine.

Select 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 grid
  • Development of a methodology to use radar estimated precipitation as a basis for interpolating daily rainfall
  • 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

Selected Papers

2013

Adapt-N Uses Models and Weather Data to Improve Nitrogen Management for Corn
B. Moebius-Clune, H. van Es, J. Melkonian
Better Crops

2011

A Case Study on the Use of Adapt-N
J. LaGioia, H. van Es, J. Melkonian, B. Moebius-Clune, D. Shearing

2010

Adapt-N Tool Helps Farmers Deal with Climate Change, Energy Consumption and Greenhouse Gas Emissions
H. van Es, J. Melkonian, B. Moebius-Clune, B. Schindelbeck, L. Joseph, A. DeGaetano
What’s Cropping Up - New York Field Crops & Soils Newsletter

NNY Research Supports Precision Farming Tool for Corn Growers
J. Melkonian, H. van Es, M. Davis
Northern New York Agricultural Program

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