Nitrogen management poses myriad challenges to the sustainable production of corn. In typical corn production systems, N fertilizer is the most expensive input, the largest contributor to greenhouse gas emissions, the nutrient most limiting to crop growth, and the nutrient with the most pathways to become a pollutant in the environment (4). Research and extension efforts in recent decades have focused on various methods to improve N fertilizer management, especially the 4 R’s- the right rate, in the right form, in the right place, at the right time. However, between 2003 and 2011, there was an overall decline in the acreage receiving appropriate N fertilizer management in the Chesapeake Bay watershed and a 9% increase in the commercial N fertilizer application rate (5).
Concurrent with research on improved N fertilizer management, precision agriculture technology rapidly evolved. Precision agriculture uses the ability for global position satellite (GPS) receivers to interface with machinery controls and crop and soil sensors to record data and vary management practices, such as fertilizer application rates, within a field. While improved N fertilizer management was an early promise of precision agriculture, widespread success and implementation of precision N fertilizer management in corn has been elusive (6). One reason for the lack of success and adoption of these fertilizer N management practices is that N fertility recommendations that take into account the ecological factors controlling N cycling are not well developed.
Despite current limitations in N fertility recommendations, the advent of the soil health movement in mainstream agriculture has led to increasing farmer interest in using cover crops to recycle N and build soil organic matter (7). Along with this interest has come the need to credit the N supply potential from cover crops and soil organic matter when calculating fertilizer N application rates. Recently, methods to predict N supply from cover crop residues and soil organic matter have been developed from research experiments across Pennsylvania (2, 3). There is potential for these methods to be paired with precision agriculture technologies to develop field maps of biological N supply that could inform variable rate N fertilizer applications.
Our goal is to decrease N pollution in the environment and increase farmer profitability by improving NUE in corn production. Our research question is whether the spatially explicit crediting of biological N supply from cover crops and soil organic matter when calculating a variable rate N fertilizer prescription can improve fertilizer NUE.
The first objective is to use precision agriculture technologies to map cover crop biomass N content and delineate soil sampling zones for the measurement of soil organic matter and soil texture. The second objective is to use the maps of cover crop biomass N, soil organic matter, and soil texture to calculate spatially explicit biological N supply credits. The third objective is to use the map of biological N supply credits to calculate variable rate N fertilizer prescriptions and measure whether NUE improves and overall corn yields can be maintained by using a variable rate application compared to the standard practice of a fixed N rate.
To establish the variable rate nitrogen experiment, a triticale cover crop was planted after corn silage harvest. Corn silage was harvested on 26 September 2017, a burndown herbicide was applied to control weeds on 1 October 2017, and the Hy Octane triticale variety was drilled on 7.5 inch row spacing with a seeding rate of 90 lbs/acre on 5 October 2017.
Prior to cover crop planting, on 3 October 2017 the 12 acre field was mapped with an electric conductivity (EC) sensor (Veris 3100) using a 50′ pass width and a driving speed of ~ 15 miles/hr. The sensor logged georeferenced EC measurements for the 0-1′ and 0-3′ depth ranges at a frequency of 1/sec. A preliminary soil map was created based on interpolated EC measurements categorized into 3 groups (Figure 1).
Soil samples were collected from 24 points in the field on 17 November 2017. A stratified random sampling approach was used to determine sampling points that were representative of the range of mapped EC values. Each soil sampling point was georeferenced (< 2 meter GPS accuracy) and a composite sample of 8 cores (3/4″ diameter, 8 inch deep) was collected from random spots within a 5′ radius of the georeferenced point. Soil samples were air dried, ground in a flail mill, sieved to < 2 mm, and sent to the Penn State Agricultural Analytical Services Lab to measure nutrients, soil organic matter and soil texture.