Different Approaches for Improvement of Nitrogen Management in Alabama Corn Grain Production
Torino Isasmendi, Miguel Severo
Type of Degreethesis
DepartmentAgronomy and Soils
MetadataShow full item record
Nitrogen (N) fertilization management in corn (Zea mays L.) production requires more consideration in Alabama. This is due to high spatial variability in soil texture across the state and within fields, and to the high temporal variability of rainfall patterns among growing seasons. Remote sensing using vegetation spectral indices (VIs) can be calculated from data collected using active remote sensors, providing the ability to perform on-the-go variable N rate application and to assess in-season N rate to achieve maximum economic yield potential. Analysis of N response under different rainfall scenarios was considered in this study as an additional tool to increase nitrogen use efficiency (NUE). The objectives of this study were to: (i) identify VIs that best correlate with field measurements of plant leaf area index (LAI) and chlorophyll (Chl) content at early corn growth stages (V6); (ii) evaluate well-correlated VIs for in-season corn yield potential predictability; and, (iii) evaluate the impact that in-season changes in rainfall have on simulated corn yield, N leaching (NL), inorganic N in the soil at maturity (IN), and nitrogen use efficiency (NUE). The data were collected in three different regions of Alabama; at Baldwin (south AL), Macon (central AL), and Limestone (North AL) counties. A complete randomized block design (r = 5) including different combinations of N rates at planting and side-dress N was implemented during 2009 to 2012. Canonical correlation analysis was performed to evaluate which VIs were best correlated with LAI and Chl at different growth stages. In addition, VIs were evaluated for their corn yield predictability goodness. A crop simulation study was conducted for the same experiment at two of the locations in central and north Alabama. Soil and plant measurements were collected to calibrate and validate the CSM-CERES-Maize model. Different scenarios of rainfall amount and distribution were selected based on the abundant and well distributed rain index (AWDR) calculated for 61 years. The goal for using the AWDR index was to characterize periods during the year that were either wet or dry. Having a better understanding of corn N response under different soil and weather conditions will assist producers in better decision making concerning the N rate and application timing. The CSM-CERES-Maize model was used to simulate and assess corn yield, N leaching, inorganic N in the soil at maturity, and grain N use efficiency for each rainfall scenario. Results from the first study indicated that VIs including the red-edge wavelength are better at assessing LAI and Chl content at early growth stages than other VIs. Furthermore, the red-edge VIs performed better for mid-season yield predictability assessment. When comparing VI models for yield potential prediction, the normalized difference red-edge vegetation index (NDRE) resulted in higher yield potential predictability than the normalized difference vegetation index (NDVI). The NDRE exhibited R2 of 0.37, 0.42, and 0.67 at V6, V8, and V10, respectively, while the NDVI resulted in 0.26 at V6, 0.30 at V8 and 0.36 at V10 growth stage. Other VIs including the RE resulted in similar yield potential prediction as the NDRE. Although the yield potential predictability in red-edge VIs was higher than NDVI at V6, the root mean square error (RMSE) were not considerably different between NDVI and red-edge indices. Results from model simulation for years corresponding to either rainfall scenario indicated that corn response to N fertilization changed based on the rainfall conditions and soil type (silt loam in North and loamy sand in Central Alabama). In both locations, for scenario A, the crop response to N rates under wet May-June years was higher than the one during dry May-June years. In Central Alabama, the yield response curve under rainfall conditions reached a plateau at 56 and 112 kg N ha-1 at dry and wet May-June, respectively. The North Alabama location, also under rainfall conditions, resulted in higher N response. During dry May-June years no N was needed to achieve the maximum yield, while in wet May-June years, 56 kg N ha-1 was sufficient to reach the plateau where yield did not significantly increase with higher N rates. Rainfall patterns for scenario B (wet/dry March-June and dry/wet July-August) March-June wet and July-August dry combination resulted in higher N response given that plants were under to prolonged time under wet conditions as opposed to March-June dry and July-August wet combination. Results from this study could be used by farmers as a decision support tool to improve N management in corn under the Alabama growing conditions.
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