Elemental composition and moisture prediction in manure by portable X-ray fluorescence spectroscopy using random forest regression.

Manure elemental composition determination is essential to develop farm nutrient budgets and assess environmental risk. Portable X-ray fluorescence (PXRF) spectrometers could facilitate hazardous waste-free, rapid, and cost-effective elemental concentration determinations. However, sample moisture is a problem for elemental concentration determination by X-ray methods. The objective of this study was to quantify the effect of sample moisture content, predict moisture content, and correct for moisture effect on elemental concentration determinations in livestock manure. Oven-dried manure samples (n = 40) were ground and adjusted to five moisture ranges of (w/w moisture) <10%, 10-20%, 20-30%, 40-50%, and 60-70%. Samples were scanned by PXRF for 180 s using a vacuum (<1,333 Pa) and without a filter. The presence of moisture negatively affected elemental determination in manure samples. Calibrations (n = 200) were prepared using random forest regression with detector channel counts as independent variables. A three-step validation was performed using all the data, random cross-validation and external validation. The back end of the spectrum (14-15 keV) had strong predictive power (r2  = .98) for moisture content. The random forest approach increased r2 between PXRF and wet chemical methods from <.66 to >.90 for P, K, and Mg and from .78 to .98 for Fe, compared with linear, nonlinear, and Lucas-Tooth and Price equations. These results indicated that elemental concentration can accurately be measured in dried and moist manure samples using PXRF and expands the potential applications of PXRF to in situ elemental determinations for agricultural and environmental samples.

Portable X-Ray Fluorescence Spectroscopy for Rapid and Cost-Effective Determination of Elemental Composition of Ground Forage.

The recent development of portable X-ray fluorescence spectrometers (PXRF) has created new avenues for rapid plant elemental concentration determination at reduced cost while avoiding hazardous chemicals. A few studies have indicated the potential use of PXRF for homogenous plant tissue analysis. However, there is a lack of information for analysis of heterogeneous plant samples like livestock forage, which consists of a mixture of several species and plant parts, each varying in elemental concentration. Our objective was to evaluate PXRF for forage analysis, specifically the effect of forage particle size and scan time on important elements including P, K, Ca, and Fe determination. Hay samples (n = 42) were oven dried (60°C for 3 days) and ground into three particle sizes (≤0.5 mm, 0.25-0.5 mm and 1-2 mm). Prepared samples were scanned by PXRF using a vacuum (<10 torr) without a filter. Samples were placed in cups over thin prolene X-ray film and scanned for 180 s. A subset (n = 29) were also scanned for 60 and 120 s. PXRF counts for P, K, Ca, and Fe were compared with laboratory Inductively Coupled Plasma Optical Emission Spectroscopy (ICP) determinations, using regression models. Results indicated that these elements could potentially be determined with PXRF (r 2 ≥ 0.70) in heterogeneous forage samples. Relationship strength increased with decreasing particle size, however, the relationship was still strong (r 2 ≥ 0.57) at the largest particle size. Scanning time did not affect the relationship with ICP concentration for any of the particle sizes evaluated. This work demonstrated that with the right sample preparation PXRF can obtain results comparable to acid digestion and ICP regardless of sample composition, and suggests the potential for in situ determinations.

Strontium isotopes and the reconstruction of the Chaco regional system: evaluating uncertainty with Bayesian mixing models.

Strontium isotope sourcing has become a common and useful method for assigning sources to archaeological artifacts.In Chaco Canyon, an Ancestral Pueblo regional center in New Mexico, previous studiesusing these methods have suggested that significant portion of maize and wood originate in the Chuska Mountains region, 75 km to the West [corrected]. In the present manuscript, these results were tested using both frequentist methods (to determine if geochemical sources can truly be differentiated) and Bayesian methods (to address uncertainty in geochemical source attribution). It was found that Chaco Canyon and the Chuska Mountain region are not easily distinguishable based on radiogenic strontium isotope values. The strontium profiles of many geochemical sources in the region overlap, making it difficult to definitively identify any one particular geochemical source for the canyon's pre-historic maize. Bayesian mixing models support the argument that some spruce and fir wood originated in the San Mateo Mountains, but that this cannot explain all 87Sr/86Sr values in Chaco timber. Overall radiogenic strontium isotope data do not clearly identify a single major geochemical source for maize, ponderosa, and most spruce/fir timber. As such, the degree to which Chaco Canyon relied upon outside support for both food and construction material is still ambiguous.