Variable Rate Liquid Nitrogen Application for Cotton and Corn P R O D U C T I O N C. G. Bowers, Jr., Professor, Bio. & Ag. Engineering G. T. Roberson, Associate Professor, Bio. & Ag. Engineering D. K. Cassel, Professor, Soil Science G. C. Naderman, Associate Professor, Soil Science Cavell Brownie, Professor, Statistics AUTHORS: Variable rate technology was used in a cotton and corn rotation to apply a liquid nitrogen solution (33% N) based on yield and soil maps to evaluate precision agricultural, variable rate application production versus conventional field averaged, constant rate application production. A variable rate, liquid nitrogen applicator was designed and assembled to apply a 30% liquid nitrogen solution at layby based on the soil and yield variability in a 30+- acre field. Both cotton nitrogen use efficiency for each soil series and maximum yield 25-foot grids, from either year 1998 or 2000 yields, were utilized to generate a nitrogen application map. Nitrogen application and yield data were used to compare the two production systems for three-tillage treatments typically used in eastern North Carolina. Initial results for cotton nitrogen application in years 2000 and 2001 showed that approximately the same rates were applied on the average for both production systems. For year 2000, the cotton yields for variable rate nitrogen application on strip-till with wheat cover were significantly higher than the field averaged constant rate nitrogen for strip-till. There was no difference in nitrogen treatments with the chisel/disk treatment. For clean-till, subsoil, the yield for constant rate application was significantly higher than for the variable rate application. n Introduction Precision agriculture is being adopted to optimize crop yields, minimized their associated costs and reduce environmental impact of crop inputs on water quality. Precision agriculture management is provided through variable-rate application of inputs such as lime, fertilizers, pesticides, seeds and tillage. Variable rate technology is based on determining soil variability from georeferenced soil sampling, determining yield variability and potential yields from georeferenced yield monitoring and soil series, using agronomic recommendations to generate variable-rate application maps, and utilizing variable rate technology to apply inputs. This system will enable optimal potential yields to be reached and minimize environmental impacts. n Objectives To measure soil and yield variability in a 30+-acre sandy loam field and create yield potential maps. To conduct a 4-6 year, field study that compares precision agriculture, variable rate production to conventional, field averaged production for a cotton and corn rotation. To compare chisel, strip-till and subsoil tillage operations within the two production systems. An interdisciplinary, replicated field study was designed for to compare precision agriculture, variable rate production to conventional field-average, constant rate production in a cotton and corn rotation in eastern North Carolina. This field study is located on a 30+- acre sandy loam field at the Center for Environmental Farming Systems (CEFS) at Goldsboro, North Carolina. The experiment was designed for seven treatments randomized within 11 replications across the field. The treatments are (1) Chisel/disk with standard nitrogen (CDSN), (2) Chisel/disk with variable rate Standard agronomic practices were used to determine the inputs for lime, seed, fertilizer and pesticide application rates. Nitrogen efficiencies were determined for each soil series, and yield potentials determined from 1998 and 2000 yields. Georeferenced application rates were calculated for the variable rate application of a 30% nitrogen solution using the soil nitrogen efficiencies and yield potentials. This application map was then used with GPS and control software to provide variable rate application of nitrogen at layby of the crop. N (CDVN), (3) Wheat cover crop, no till with standard N (WCNTSN), (4) Wheat cover crop, strip till with standard N (WCSTSN), (5) Wheat cover crop, strip ‑ till with variable N (WCSTVN), (6) Clean-till, subsoil with standard N (SSSN), and (7) Clean-till, subsoil with variable rate N (SSVN). Figure 1. Experimental Plan for Precision Agriculture Study Figure 2. Liquid Nitrogen Applicator with Variable Rate Technology Equipment n Abstract n Material & Methods Biological & Agricultural Engineering

n RESULTS The process for generating the 2001 variable rate, nitrogen application map for cotton is shown below. The nitrogen efficiency soil series map (Figure 3) was generated using MapInfo TM by georeferencing a graphical soil series map and assigning efficiency numbers to each soil series using Red Hen Systems MapCalc TM. Yield maps (Figure 4) were generated with Ag. Leaders SMS Basic and Red Hen Systems MapCalc TM. The application map shown in Figure 5 was calculated for each 25-foot square grid in the field using the equation given below. The start-up nitrogen was 2.5 gallons of 30% nitrogen solution per acre. [(Max Yield x Lint Percent x Nitrogen Efficiency – Start-up Nitrogen) / (3.252 lbs. N/gallon 30% N solution)] Figure 3. Cotton Nitrogen Efficiencies for Soil Series Figure 4. Maximum Potential Cotton Yield Based on 1998 and 2000 Figure 5. Cotton Layby Nitrogen Application Map for 2001 Average layby nitrogen application rates for years 2000 and 2001 are in table 1 for variable and constant rates. The variable rate range was 6.4 to 45.2 gallons 30% N solution per acre in The two average application rates are practically equal. It is planned to use the GLEAMS model to estimate nitrogen loss to the environment and verify if variable rate application reduces environmental impact. The liquid N applicator was calibrated and had a 0.6% error at the median rate of 25.8 gallons per acre in Table 1. Comparison of Average Layby Nitrogen Application Rates YearVariable RateConstant Rate (gallons 30% N solution/acre) 2000* Note: Rates were slightly lower than normally recommended because of residual nitrogen in the soil from unharvested corn due to a hurricane. Yields were harvested and recorded with an AgLeader TM prototype yield monitor for cotton in The cotton yield map is shown in Figure 6 and was generated with SMS Basic TM and MapCalc TM using inverse square weighting of the nearest 6 points and smoothing with +/- 1 standard deviation. The yield monitor had a cumulative error of 1.6% with the individual treatment load errors varying from –6.6% to +7.7%. Actual seed cotton weights varied from 2,580 to 4,000 pounds for treatments. Figure 6. Cotton Yield Map for 2000 Using the cotton yield data for treatments and replications, a standard analysis of variance was done to compare treatments using SAS’s GLM. Results of this analysis are given in Table 2. Table 2. Comparison of Treatments Using ANOVA and Least Squares Means Treatment MeanYield LSMean LSD (Lbs. seed cotton/ac) Grouping CDSN ab CDVN b SSSN a SSVN c WCSTSN d WCSTVN c WCNTSN e Note: Means without the same letter in common differ significantly using the protected lsd procedure at a significance level.05. The variable rate nitrogen application on the wheat cover, strip-till produced significantly higher yields than the standard nitrogen. The chisel/disk tillage treatments were the same for both nitrogen rate application methods. The standard nitrogen application for the clean-till, subsoil treatment gave significantly higher yields than the variable rate nitrogen application. Biological & Agricultural Engineering

n Conclusions A variable rate, liquid nitrogen applicator was designed and assembled to apply a 30% liquid nitrogen solution at layby based on the soil and yield variability in a 30+- acre field. Nitrogen application and yield data were used to compare precision agriculture, variable rate liquid nitrogen application with field averaged, constant rate nitrogen application for three-tillage treatments. Initial results for cotton in years 2000 and 2001 showed that approximately the same rates were applied on the average for both nitrogen rate production systems across the three tillage treatments. the three tillage treatments, ANOVA and spatial statistical analysis show that strip-till with variable rate nitrogen had higher yields than constant rate nitrogen, that clean-till subsoil with constant rate nitrogen produced higher yields than variable rate nitrogen, and that chisel/disk tillage had no yield difference. Cotton yields for 2001 will be used to further analyze the treatments and variability. n Acknowledgements Funding for this research was provided by Cotton Inc., North Carolina Agricultural Research Service, North Carolina Department of Agriculture, and Deere Inc. Equipment, donated or loaned, came from John Blue Company (Squeeze pumps) and AgLeader (Prototype cotton yield monitor). The authors also acknowledge Todd Markham (Agricultural Research Technician, NCSU), Charles Collins (Engineering Research Technician, NCSU) and Clark Adams (Student, NCSU) for their technical help in this study and the Center for Environmental Farming Systems (NCDA) for land and equipment used in this study. The estimated yield map of Figure 7 was determined by spatially removing preliminary estimates of treatment effects from the yield map of Figure 6. Observations of the color trends between Figures 6 & 7 and the soil series map of Figure 1 generally agree. Figure 7. Estimated Yield Potential for Field From 2000 Cotton Yield Data Finally, a spatial analysis was made using SAS PROC MIXED with an isotropic exponential covariance structure to compare treatments using predicted yield from a treatment regression analysis of yield data of Figure 6 and the estimated yield potential of Figure 7. Results are shown in Figure 8. On the average, these results are the same as the treatment ANOVA results shown in Table 2. Expectations were that the higher yielding soils, which had more nitrogen applied with variable rate application, would have produced higher yields. Cotton yields for 2001 will be used to further evaluate treatments and yield spatial variability. Figure 8. Spatial Variability of Treatment Yields. n Soil Physical Properties Biological & Agricultural Engineering