Adapt-N is an online software program that integrates 13 models — including hyper-local weather, soil, and crop models — to provide nitrogen planning, "what if" scenario tools, in-season whole field and variable rate nitrogen recommendations, and ways for agronomists to determine the economic and agronomic impact of field management decisions like nitrogen stabilizers, rates, and application timing. Adapt N was developed by Cornell University, and is exclusively operated by Agronomic Technology Corp, an independent agritech company. The program incorporates real-time weather data from 2.5 mile grids on the landscape to customize the estimate of nitrogen needs made by the multiple linked crop and soil models. Nitrogen rates for individual fields or areas within fields are estimated based on calculated N deficit or supply. As of 2016, Adapt-N is available in 35 states.
Maize; undergoing calibration for wheat; developing for additional crops.
Agricultural retailers, crop consultants, seed companies, land management firms, growers, co-ops, university extension agents, equipment companies, custom applicators, software vendors.
Adapt-N is purchased by service providers at a wholesale rate; those service providers then sell the service to Growers either directly or via bundles with other products and services. Pricing varies based on volume, partnership program (e.g., through SST Software), and use of other Agronomic Technology Corp products, such as the N-Insight nitrogen diagnostic tool.
Web-based, either access directly at the Adapt-N website or embedded into other agricultural software. Mobile alerts and API access also available.
Adapt-N has undergone extensive calibration continuously since 2011. Specific to NutrientStar, 104 replicated strip trials were conducted in 2011 and 2012 with growers that were already engaged in progressive nitrogen management practices, and comparing the farmer’s normal nitrogen program to the Adapt-N recommended program. As a result of following the Adapt-N recommendation, farmers were able to reduce nitrogen use by an average of 37 lb/ac while gaining an average increase in yield of 2 bu/ac. The results also showed that in 80-90% of the plots, Adapt-N was able to increase profits over the farmer’s normal nitrogen program, with an average gain of $30/ac.
The average partial factor productivity was calculated for farmer’s normal program and the for Adapt-N program at each of the 104 sites (pfp = [lbs. grain/ac]/[lbs. nitrogen/ac]). The pfp of the two programs were compared by determining the percent increase in NUE of Adapt-N over the farmer’s normal program (percent increase = ((NUEAdaptN – NUEFarmer)/NUEFarmer) * 100) at each site. The graph shows the number of sites within each percentage increase interval.
Note that new trial results are forthcoming.
Required data inputs
Field boundaries (can be selected on a map, uploaded via Shapefile, or integrated via API), cultivar, maturity class, expected harvest population, planting date, expected yield, N fertilizer applied (rate, timing, placement and source), enhanced efficiency fertilizer used, soil type, rooting depth, field slope, % SOM, tillage type, % residue, manure applied (rate, when, analysis, method – surface or incorporated), forage-sod crops in rotation, % legume in sod, method & timing of forage-sod management, prior crops.
Replicated strip trials were conducted on 104 fields on commercial farms at production scale under the guidance of certified crop advisors.
Grower N rate was compared with Adapt-N recommended rate in side-by-side comparisons and replicated 3 to 4 times.
Summary of the 4Rs across 104 trials
- 1970’s through early 2000’s: field research on space-time aspects of N response
- 1980’s to early 2000’s: initial software model development
- 2008-2013: Adapt-N prototype tool available as free web interface, supported by grant funding
- 2011-current: extensive field testing in the Corn Belt and the Northeast, and model revisions through on-farm trials
- 2013 through current: Adapt-N exclusively licensed and commercialized through Agronomic Technology Corporation, an independent agritech company.
Hutson, J.L. 2003. Leaching Estimation And Chemistry Model: A process-based model of water and solute movement, transformations, plant uptake, and chemical reactions in the unsaturated zone. Version 4. Dept. of Crop and Soil Sciences, Research series No. R03-1. Cornell University, Ithaca, NY, U.S.A.
Hutson, J.L. and R.J. Wagenet. 1992. LEACHM: Leaching Estimation And Chemistry Model: A process-based model of water and solute movement, transformations, plant uptake, and chemical reactions in the unsaturated zone. Continuum Vol. 2, Version 3. Water Resources Institute, Cornell University, Ithaca, NY, U.S.A.
Sinclair, T.R. and R.C. Muchow. 1995. Effect of nitrogen supply on maize yield: I. Modeling physiological responses. Agronomy J. 87:632-641.
Bianca Moebius-Clune, Greg Levow, & Harold van Es. 2014. Cornell Adapt-N Training Webinar: Cloud Computing Technology for Precision Nitrogen Management in Corn.
Harold van Es, Jeff Melkonian, Bianca Moebius‐Clune, Art DeGaetano, Laura Joseph. 2011(?). Adapt‐N: A Tool for Adaptive Nitrogen Management in Corn – Incorporating the Weather Component –.
Bianca Moebius-Clune and Harold van Es. Do It Yourself Trial Guide For Adapt-N Beta-Testing on Your Farm. 2011(?).
Bianca N. Moebius-Clune, Maryn Carlson, Harold M. van Es, Jeffrey J. Melkonian, Arthur T. DeGaetano, Laura Joseph. 2014. Adapt-N Training Manual: A tool for precision N management in corn. Edition 1.0. Extension Series No. E14-1. Cornell University.
Bianca Moebius-Clune, Harold van Es, Jeff Melkonian. 2011. How Adapt-N calculates field-specific sidedress N recommendations for corn. Adaptive N Management Fact Sheet 2.
T.W. Bruulsema. 2007(?). Proceedings of the Symposium “Integrating Weather Variability into Nitrogen Recommendations” Managing Crop Nitrogen for Weather.
Bianca Moebius-Clune, Maryn Carlson, Harold van Es, and Jeff Melkonian. 2013(?). Adapt-N Proves Economic and Environmental Benefits in Two Years of Strip-Trial Testing in New York and Iowa.
J.J. Melkonian and H.M. van Es, A.T. DeGaetano and L. Joseph. 2008. ADAPT-N: ADAPTIVE NITROGEN MANAGEMENT FOR MAIZE USING HIGH-RESOLUTION CLIMATE DATA AND MODEL SIMULATIONS.