Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using geographically weighted principal component analysis‐geographically weighted regression kriging (GWPCA‐GWRK)

Soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) are important indicators of soil fertility when undertaking a quality evaluation. Obtaining a high-precision spatial distribution map of soil nutrients is of great significance for the differentiated management of nutrient resources and reducing non-point source pollution. However, the spatial heterogeneity of soil nutrients lead to uncertainty in the modeling process. To determine the best interpolation method, terrain, climate, and vegetation factors were used as auxiliary variables to participate in the investigation of soil nutrient spatial modeling in the present study. We used the mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and accuracy (Acc) of a dataset to comprehensively compare the performance of four different geospatial techniques: ordinary kriging (OK), regression kriging (RK), geographically weighted regression kriging (GWRK), and multiscale geographically weighted regression kriging (MGWRK). The results showed that the hybrid methods (RK, GWRK, and MGWRK) could improve the prediction accuracy to a certain extent when the residuals were spatially correlated; however, this improvement was not significant. The new MGWRK model has certain advantages in reducing the overall residual level, but it failed to achieve the desired accuracy. Considering the cost of modeling, the OK method still provides an interpolation method with a relatively simple analysis process and relatively reliable results. Therefore, it may be more beneficial to design soil sampling rationally and obtain higher-quality auxiliary variable data than to seek complex statistical methods to improve spatial prediction accuracy. This research provides a reference for the spatial mapping of soil nutrients at the farmland scale.

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Influence of environmental factors on the detection of blood in sheep faeces using visible–near-infrared spectroscopy as a measure of Haemonchus contortus infection

Parasites & Vectors ◽

10.1186/s13071-020-04468-6 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Elise A. Kho ◽

Jill N. Fernandes ◽

Andrew C. Kotze ◽

Glen P. Fox ◽

Maggy T. Sikulu-Lord ◽

...

Keyword(s):

Principal Component Analysis ◽

Haemonchus Contortus ◽

Prediction Accuracy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Component Analysis ◽

Diagnostic Methods ◽

Calibration Models ◽

Good Prediction Accuracy

Abstract Background Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, thereby preventing losses in production and flock welfare. We previously demonstrated the ability of visible–near-infrared (Vis–NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we report our investigation of whether variation in sheep type and environment affect the prediction accuracy of Vis–NIR spectroscopy in quantifying blood in faeces. Methods Visible–NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales, Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387–609 nm) using partial least squares regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). Samples from QLD were quantified using Hemastix® test strip and FAMACHA© diagnostic test scores. Results Principal component analysis showed that location, class of sheep and pooled versus individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity 57–94%, specificity 44–79%). The models were not predictive for blood in the naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of the QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion This study demonstrates the potential of Vis–NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture sufficient environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

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Application of geographically weighted principal component analysis and fuzzy approach for unsupervised landslide susceptibility mapping on Gish River Basin, India

Geocarto International ◽

10.1080/10106049.2020.1778105 ◽

2020 ◽

pp. 1-24 ◽

Cited By ~ 4

Author(s):

Tirthankar Basu ◽

Arijit Das ◽

Swades Pal

Keyword(s):

Principal Component Analysis ◽

River Basin ◽

Landslide Susceptibility ◽

Principal Component ◽

Component Analysis ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Fuzzy Approach ◽

Weighted Principal Component

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Optimization of Process Parameters for Submerged Arc Welding by Weighted Principal Component Analysis Based Taguchi Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.622-623.45 ◽

2012 ◽

Vol 622-623 ◽

pp. 45-50 ◽

Cited By ~ 1

Author(s):

Joydeep Roy ◽

Bishop D. Barma ◽

J. Deb Barma ◽

S.C. Saha

Keyword(s):

Principal Component Analysis ◽

Taguchi Method ◽

Arc Welding ◽

Principal Component ◽

Traverse Speed ◽

Component Analysis ◽

Submerged Arc Welding ◽

Bead Geometry ◽

Weighted Principal Component ◽

Submerged Arc

In submerged arc welding (SAW), weld quality is greatly affected by the weld parameters such as welding current, traverse speed, arc voltage and stickout since they are closely related to weld joint. The joint quality can be defined in terms of properties such as weld bead geometry and mechanical properties. There are several control parameters which directly or indirectly affect the response parameters. In the present study, an attempt has been made to search an optimal parametric combination, capable of producing desired high quality joint in submerged arc weldment by Taguchi method coupled with weighted principal component analysis. In the present investigation three process variables viz. Wire feed rate (Wf), stick out (So) and traverse speed (Tr) have been considered and the response parameters are hardness, tensile strength (Ts), toughness (IS).

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