Study of Statistical Methods in the Spatial Variability Measurement of Climatic Variables within Broiler Houses

2008 ◽  
Author(s):  
Thayla Morandi Ridolfi de Carvalho ◽  
Daniella Jorge Moura ◽  
Irenilza Alencar Nääs
Keyword(s):  
Spatial Variability ◽  
Statistical Methods ◽  
Climatic Variables

scholarly journals Assessment of Influencing Factors on the Spatial Variability of SOM in the Red Beds of the Nanxiong Basin of China, Using GIS and Geo-Statistical Methods

2021 ◽  
Vol 10 (6) ◽  
pp. 366
Author(s):  
Ping Yan ◽  
Kairong Lin ◽  
Yiren Wang ◽  
Xinjun Tu ◽  
Chunmei Bai ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Statistical Methods ◽  
Influencing Factors ◽  
Land Degradation ◽  
Ph Value ◽  
Southern China ◽  
Great Influence ◽  
Significant Negative Correlation ◽  
Crop Productivity ◽  
Soil Parameters

Understanding the spatial variability of soil organic matter (SOM) is crucial for implementing precise land degradation control and fertilization to improve crop productivity. Studying spatial variability provides a scientific basis for precision fertilization and land degradation control. In this study, geostatistics and classical statistical methods were used to analyze the spatial variability of SOM and its influencing factors under various degrees of land degradation in the red bed area of southern China. The results demonstrate a declining trend for SOM content with increasing land degradation. The SOM content differs profoundly under different land degradation degrees. The coefficient of variation ranges from 13.61% for extreme land degradation to 8.98% for mild land degradation, 7.96% for moderate land degradation, and 5.64% for severe land degradation. A significant positive correlation is displayed between the altitude and the SOM (p < 0.01) under mild and moderate land degradation conditions. Bulk density and pH value have a significant negative correlation with SOM (p < 0.01). It can be observed that terrain factors, as well as physical and chemical soil parameters, have a great influence on SOM.

Spatial variability of the trends in climatic variables across China during 1961–2010

2014 ◽  
Vol 120 (3-4) ◽  
pp. 773-783 ◽  
Author(s):  
Hanbo Yang ◽  
Dawen Yang ◽  
Qingfang Hu ◽  
Huafang Lv
Keyword(s):  
Spatial Variability ◽  
Climatic Variables

Statistics and parallaxes

1978 ◽  
Vol 48 ◽  
pp. 7-29
Author(s):  
T. E. Lutz
Keyword(s):  
Experimental Design ◽  
Statistical Methods ◽  
Review Paper ◽  
Systematic Errors ◽  
Past Experience ◽  
Random Errors ◽  
Trigonometric Parallaxes ◽  
Systematic And Random Errors

This review paper deals with the use of statistical methods to evaluate systematic and random errors associated with trigonometric parallaxes. First, systematic errors which arise when using trigonometric parallaxes to calibrate luminosity systems are discussed. Next, determination of the external errors of parallax measurement are reviewed. Observatory corrections are discussed. Schilt’s point, that as the causes of these systematic differences between observatories are not known the computed corrections can not be applied appropriately, is emphasized. However, modern parallax work is sufficiently accurate that it is necessary to determine observatory corrections if full use is to be made of the potential precision of the data. To this end, it is suggested that a prior experimental design is required. Past experience has shown that accidental overlap of observing programs will not suffice to determine observatory corrections which are meaningful.

Review of Statistical Methods for Rates and Proportions.

1973 ◽  
Vol 18 (11) ◽  
pp. 562-562
Author(s):  
B. J. WINER
Keyword(s):  
Statistical Methods
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