Dissertation

Improving the Accuracy of Nitrogen Application and Removal Measurements in Manure Based Cropping Systems to Reduce Environmental Losses

Miller, Christine M. F

2019

Overview

Sustainable forage farming systems must precisely match nitrogen (N) applications to crop needs to minimize environmental N losses and meet crop yield goals. Successfully executing N management plans is especially challenging in manure fertilized cropping systems because manure is highly variable and contains a large proportion of organic N. To reduce N leaching to groundwater, California dairy farmers must limit the ratio of N applied to N removed from cropland (N Ratio) to 1.4. Farmers quantify their N application rates by sampling and analyzing the N concentration of their manures, and by measuring the weight or volume of manure when it is applied. This dissertation quantifies the extent and impacts of uncertainty in N application and removal measurements in manure based forage cropping systems. These measurements must be accurate enough to identify N Ratios that will support crop yields while preventing excess N leaching to groundwater. The validity of the data collected by dairy farmers to quantify their N application and removal rates was investigated using mass balance analysis in Chapter 2. First, the N, phosphorus (P), and potassium (K) excreted by cattle on 62 facilities was modeled and compared with the amount of nutrients recovered in manures exported off-farm or applied to cropland. Less N and P were recovered than expected given possible volatile losses during storage. Second, reported N Ratios were compared with N Ratios that simulated unavoidable environmental losses over three cropping years. Analyses showed that reported N application rates were too low to maintain reported crop yields. Overall, reported data was not consistent with mass balance analysis indicating substantial inaccuracies in measurements of N application and removal. The uncertainty in farmer measurements of the N removed in harvested forage and the N concentration of solid manure was quantified in Chapters 3 and 4, respectively. The N harvested from 9 forage fields and the N concentration of 10 solid manure piles to was intensively measured to estimate the 'true' value. Resampling simulations were used to assess the accuracy of less intensive measurement protocols relative to the 'true' value. Measurements of N removed in harvested forage were accurate within$\\pm$ 5\\% if every truckload of forage was weighed and 10 grab samples were composited for nutrient analysis. Measurements of N concentration of solid manure were accurate within$\\pm$ 10\\% if 10 grab samples were composited for nutrient analysis and 70\\% of samples were collected from greater than 40 cm into the manure pile. The uncertainty of total N application, N Ratios, and groundwater nitrate leaching rates was quantified in Chapter 5. A stochastic model of total N application was built using Monte Carlo methods to propagate the uncertainty from underlying measurements. The stochastic N application model was used to vary the N applications of two representative forage fields around farmer reported values for 5 years. The N Ratios and groundwater nitrate loading rates were simulated for the stochastic N application scenarios using HYDRUS 1D water and solute transport model and compared to monitoring well measurements. Results suggested that reducing uncertainty in process wastewater measurements is required to meaningfully judge the effectiveness of N Ratio limitations and reduce N losses to groundwater. Greater measurement accuracy of manure N application is required before nitrate leaching to groundwater can be further reduced. This dissertation provides many practical suggestions for efficiently improving the accuracy of N application and removal measurements in manure based cropping systems. Measurements that quantify N application must be accurate enough to identify and execute suggested management improvements before we will be able to reliably reduce environmental N losses.

• Publisher: ProQuest Dissertations & Theses
• Subject: Agriculture / Environmental science / Statistics
• ISBN: 1658413415, 9781658413411