Current Situation and Policy Recommendations for Itinerant Animal Handling Events, including Mobile Zoos

Karewlin Furtoak^*
*Correspondence: Karewlin Furtoak, Department of Soil and Crop Science, Texas A and M University, Olsen Blvd, USA, Email:

Introduction

Two high-level fusion and two sequential hybrid strategies were also tested. MIR outperformed XRF when it came to inorganic properties (RPIQV for clay=3.4, silt=3.0, and sand=1.8) in the field under investigation, while MIR was superior for organic properties (RPIQV for total OC=7.7 and N=5.0). For these properties, there was little to no improvement in accuracy with even the optimal fusion approach. The large number of elements with variable importance in the projection scores >1 (Fe, Ni, Si, Al, Mg, Mn, K, Pb (clay only), and Cr) and strong spearman correlations (0.57 rs 0.90) with clay and silt account for the high XRF accuracy for these materials. When comparing the best single spectrometer to the optimal fusion method, relative improvements in spectrally inactive properties based on indirect prediction mechanisms were marginal for pH (3.2% increase in RPIQV versus MIR alone), but more pronounced for labile OC (9.3% versus MIR) and CEC (12%). Performance was worse when a subpar spectrometer dominated in a fusion approach compared to the best single spectrometer.

Description

In the field of soil science, the use of Mid-Infrared Spectroscopy (MIRS) is well-established. The fundamental vibrations of many organic molecules containing soil Organic Carbon (OC) and Nitrogen (N) as well as minerals in the clay (such as kaolinite, smectite) and sand (such as quartz) particle size fractions are captured by MIRS using radiation in the range of 2500-25,000 nm (4000-400 cm^-1). Quantitative spectral models are based on the proportionality and specificity of spectrally-active molecules in relation to the soil property of interest, and as a result, model accuracy is affected. Reviews, for instance, have found that complex properties related to both organic and inorganic soil fractions, such as pH and CEC, have lower and more inconsistent estimation accuracy than OC, total N, clay, and sand content. Secondary soil properties that are indirectly estimated by covariations with primary soil properties (such as OC and clay) are described in other infrared studies. As prediction mechanisms employ not only unique spectral signatures, such as aliphatic peaks, but also covariations with clay minerals, fraction OC contents of various residence times may also be considered indirectly estimated properties. The incorporation of additional predictors in addition to MIRS ought to be looked into because, frequently, the robustness and accuracy of the model for these indirectly estimated soil properties are insufficient.

Methods

The dirt being scrutinized was an arable, sediment soil Haplic Luvisol (16% earth, 80% residue, and 4% sand) in Luttewitz (Saxony, Germany). High base saturation and high activity clays characterize this loess-derived soil. The location has a height of 290 meters, an annual average temperature of 8.6ºC, and 572 millimeters of precipitation. Management followed standard agricultural methods, such as tillage with a moldboard plow to a depth of 30 centimeters. According to Greenberg et al., sampling took place in September 2016 over the course of five days. There were a total of 120 sample points distributed in a grid across a 52.5 m × 600 m field. Because three outlier sample units were identified and removed, n=117 are included in the current study. A 15 cm × 15 cm area of wheat stubble on the surface of the soil was cleared at each sampling point, and soil was collected to a depth of 2 cm.

The fact that amphibian species that are phylogenetically related tend to live in similar combinations of climatic variables could partially account for the lack of correlation between CT_max and environmental variables at this regional level. This is made possible by the strong spatial autocorrelation that exists between environmental variables and species' low dispersal ability, which results in these closely related species living in close proximity to one another in space. Additionally, the species' CT_max may be significantly influenced by additional factors. Kellerman et al., for example discovered that as precipitation decreased, CT_max increased in Drosophila. As a result, CT_max may be influenced more by water-related environmental factors than by high temperatures alone, particularly in extremely wet environments like the Tropical Andes. However, climate warming will have more than just a temperature increase: Ectotherms, particularly tropical amphibians that rely on water, could also be put under stress by lower humidity and less cloud cover.

Conclusion

According to an examination of the XRF prediction mechanisms, the contents of clay and silt contained a significant number of elements with VIP scores greater than one and strong correlations with clay and silt, which could be the reason for their superior XRF estimation accuracy in comparison to other properties. For complex soil properties influenced by both organic and inorganic soil fractions, the best single spectrometer had a marginal advantage over the optimal model fusion approach for pH, but it was more pronounced for labile OC and CEC. Therefore, labile OC and CEC may benefit most from model fusion.

Model fusion approaches should be used with knowledge of the spectral prediction mechanisms because the dominance of a suboptimal spectrometer in a fusion approach harmed performance in comparison to the best single spectrometer. Without this, the most robust method for capturing the potential advantages of multiple sensors is GR averaging.