Assessing precision and accuracy of manure volatile solids, total nitrogen, and total ammoniacal nitrogen analyses across six commercial laboratories

Manure nutrient analyses inform nutrient management plans and GHG emission inventories, yet the magnitude and sources of measurement variability remain under-studied for several key nutrients.To assess uncertainty in manure nutrient measurements, we quantified intra-and inter-laboratory variation and analytical accuracy for volatile solids (VS), total nitrogen (TN) and total ammoniacal nitrogen (TAN) across 6 US laboratories certified by the Manure Analysis Proficiency Program.Fresh gutter-collected slurry subsamples (250 g) from a free-stall barn received either an equal amount of water (control), one of 2 spike levels of glucose (for VS), or one of 2 spike levels of NH 4 Cl (for TN and TAN).Repeatability (r, repeatability value; SD r , within-laboratory SD; RSD r , relative SD of repeatability) and leave-one-lab-out reproducibility (R, reproducibility value; SD R , between-laboratory SD; RSD R , relative SD of reproducibility), were calculated to evaluate precision.Spike recovery was used to assess accuracy.Repeatability values ranged from 0.30 to 3.49% for VS, 16.2 to 932 mg/kg for TN, and 28.0 to 1.65 10 3 mg/kg for TAN, while leave-one-lab-out R ranged from 2.56 to 6.08%, 405 to 683 mg/kg, and 363 to 593 mg/kg, respectively.Variability among laboratories was consistently higher than variability within laboratories but differed by nutrient: overall RSD R averaged 23.3% for VS, 6.32% for TN and 15.6% for TAN.Recovery rates across laboratories ranged from 64.1 to 118.7%, with all but 5 measurements falling within the literature recommended ranges (70 to 110% for VS and 85 to 115% for TN/TAN).Results confirm glucose and NH 4 Cl as effective positive-control spikes and highlight the impact of analytical method on VS and TN agreement across laboratories.These findings provide metrics of uncertainty in manure nutrient data that should be considered when measurements are used in research, modeling, and on-farm decision-making.