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| Filter results3 paper(s) found. |
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1. Adapt-N: A Computational Tool for Precise N Management in CornCurrent approaches to estimation of optimum N fertilizer rates are based on mass balances, average expected economic return based on field experiments, soil N tests, an d crop leaf or canopy sensing. However, denitrification and leaching losses of nitrogen may occur from dynamic and complex interactions among weather, soil hydrology, crop water and N uptake, and management practices , and result in high variability in annual crop N needs in maize ( Zea mays L.) production. W eather impacts the soil... |
2. Relationship of in-season soil nitrogen concentration with corn yield and potential nitrogen lossesModeled or measured soil mineral N (SMN) levels during the corn growing season have been used to set sidedress N rates, but there has been little research linking SMN levels at different growth stages to yield to help guide this process. The degree to which SMN level influences the risk of N losses is also not known. Data from 32 site-years of field experiments in Illinois (2015–2018) that included 12 combinations of N fertilizer rate, timing, and source, were used to evaluate... G. Preza-fontes, E. Nafziger, L. Christianson, C. Pittelkow |
3. The MRTN Approach: Past, Present, and FutureThe method of using crop N response data assimilation, now called the “MRTN approach”, was conceived at a September, 2004 meeting in Bettendorf, Iowa of scientists from several Corn Belt states. This meeting was prompted by findings in field trials that showed substantially lower optimum N rates than those based on using corn yield goal to predict crop N needs; most also showed no correlation between EONR and yield at EONR across trials. The MRTN method is straightforward: yield data... E. Nafziger, J. Sawyer |