Location: Livestock Nutrient Management Research
Project Number: 3090-31630-006-051-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 1, 2022
End Date: May 31, 2025
There are at least two conservation practices in NRCS’ arsenal to manage NH3 emissions from open-lot livestock production facilities. These include: Practice 375 – Dust control from animal activity on open lot surfaces; and Practice 591 – Amendments for the treatment of agricultural waste. Many of the specific details including timing, amount, frequency, mode, and operational variations of these conservation practices are not well established. The objectives of this research are: 1) Establish a common, scientific basis for two management practices designed to suppress NH3 emissions; 2) Replicate and extend the findings of others to evaluate the relative importance of microscale influences on NH3 emissions via pen sprinkling; 3) Demonstrate at field scale the effectiveness of pen sprinkling for short-term NH3 control; and 4) Demonstrate at field scale the relative effectiveness of freshwater blending for NH3 emissions control from irrigated livestock wastewater.
A substantial portion of NH3 emissions from open-lot livestock facilities is associated with recently excreted urine patches. For the experiments devoted to emissions from open lots, we will use a synthetic urine mixture adapted from previous research. When applied to a feedlot surface, this synthetic urine mixture has been verified to emulate NH3 and N2O emissions from real cattle urine. Our chamber-based experiments will follow previously developed methods. The experimental apparatus consists of one or more discrete chambers in which either simulated feedyard surfaces or simulated cropland soils have been installed. The chambers are installed on a rail system such that both indoor (highly controlled environment) and outdoor (field) conditions can be simulated. A real-time, chemiluminescence NH3 analyzer (Model 17C, Thermo Environmental Inc., Franklin, MA) with 0.5% precision at full scale, a 0 to 90% response time of 120 s, and a 10 s averaging time will be used to measure NH3 concentrations within the chambers. The analyzer will be calibrated daily with zero air and 45 ppmv NH3 gas in air. All chambers will be shaded to reduce heating inside the chamber area. Chamber temperature, ambient shaded temperatures, and absolute barometric pressure will be measured continuously. Temperatures of both simulated feedyard surfaces and simulated soil surfaces will be measured both inside and outside the chamber using subsurface thermistors/dataloggers and an infrared thermometer. Where applicable, automated chambers and a custom-made multiplexer will be used to facilitate rapid, short-term emission rate measurements among chambers on a 24/7 basis, as described previously. Mean short-term and cumulative emission rates will be compared statistically by ANOVA and subsequent mean separation tests using SAS. Demonstration at field scale of the effectiveness of pen sprinkling for short-term NH3 control in feedyards requires quantification of emissions. We will quantify ammonia emissions from sprinkled and unsprinkled feedyard source areas using upwind and downwind measurements of ammonia concentration in the air coupled with an atmospheric inverse dispersion model. The inverse dispersion model requires gas concentration downwind of an emission source area, upwind (background) concentration, wind information, and accurate mapping of the source area. Ammonia concentrations will be measured with open path lasers (OPL) specifically tuned to detect NH3. The deployment of OPL will depend on the geometry of the demonstration feedyard, but the principle is to locate (a) two OPL mounted on fixed, programmable positioners on the corners of the feedyard to measure perimeter concentrations and (b) one OPL mounted on a fixed tower to measure the interior concentration between the sprinkled and unsprinkled source areas. A three-dimensional, sonic anemometer (Model CSAT3, Campbell Scientific, Logan, UT) will collect wind speed, wind direction, friction velocity, turbulence statistics, and Monin–Obukhov length data needed to drive the inverse-dispersion model.