ABSTRACT
In production agriculture, energy efciency can be signicantly improved by matching the crop’s N fertilizer needs and requirements. Current approaches for estimating optimum nitrogen fertilizer rates for maize (Zea mays L.) are generally based on regionalized mass-balance equations or expected economic returns. However, N losses occur from dynamic and complex interactions among weather, soil organic matter (SOM) mineralization and hydrology, crop water and N uptake, and management practices. This results in spatially and temporally variable fertilizer N needs. Studies have documented that early-season weather impacts changes between the inorganic and organic soil N pools, which contributes to variability in calculated maize economic optimum in-season N rate values. These interacting and complex spatiotemporal processes can be simulated by well-calibrated models. This chapter discusses the integration of multiple data sources for improved estimates of maize N fertilizer needs. Data from an 11 ha eld located in Iowa, United States, will be used in this study. Equipment-mounted near-infrared (NIR) spectroscopy was used to estimate soil organic carbon (SOC) content at 1342 locations and kriged for 136 blocks. Soil texture and hydrology were determined from soil survey information. Using daily weather data and soil information, 24-year simulations were conducted using the precision nitrogen management (PNM) model to estimate late spring rootzone inorganic N content. This was combined with information on crop N uptake potential, mid-season N mineralization, and price ratio corrections to determine optimum sidedress N rates. Spatial and temporal variability in optimum N rate had a range of 60 kg ha−1 and eld-scale maps were derived for 10th and 90th percentile climate scenarios. This approach provides a framework for integration of relevant spatiotemporal processes to create more precise and locally adapted N fertilizer recommendations for maize.
