Estimating Nitrogen Mineralization from Cover Crop Mixtures Using the Precision Nitrogen Management Model. (1st September 2017)

• Record Type: Journal Article
• Authors: Melkonian, J.; Poffenbarger, H. J.; Mirsky, S. B.; Ryan, M. R.; Moebius‐Clune, B. N.
• Is Part Of: Agronomy Journal. Volume 109:Number 5(2017)
• Page Start: 1944
• Page End: 1959
• Publication Date: 2017-09-01
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.2134/agronj2016.06.0330
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract : Core Ideas: The precision N management model performed reasonably well for estimating cover crop N mineralization. Model performance was sufficient to justify incorporation into an N decision support tool. Cover crops as a best management practice can be assessed with the calibrated model. Cover crops influence soil N dynamics and N availability to a subsequent crop. Dynamic simulation models, if properly calibrated and tested, can simulate C and N dynamics of a terminated cover crop and estimate crop‐available N over diverse production environments. We calibrated and tested a dynamic simulation model modified to simulate C and N cover crop residue dynamics in maize ( Zea mays L.) production systems. Data from a 2‐yr field study of different cover crop residue mixtures, fertilizer rates, and tillage practices were used in model calibration and testing. First order rate constants governing cover crop residue decomposition were calibrated so that statistical measures of model best fit were optimized. Calibration resulted in a good fit between measured and modeled N release from the terminated cover crop mixtures (root mean square error [RMSE] = 14 kg N ha −1 ; Willmott's index of agreement (IA) = 0.92). The calibrated model performed reasonably well in the testing phase (RMSE = 25; IA = 0.88) with significantly better performance for the no‐till (NT) treatments compared to the incorporated treatment. Accounting for cover crop components (leaf, stem proportions), calibration of the temperature and moisture response functions that modify the calibrated rate constants, and testing over a wider range of soils, management practices and climates are potential areas for model improvement. The revised, calibrated model will be used in a decision support tool for N management in maize production and in studies of N dynamics in maize agro‐ecosystems that include cover crops.