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Envirometrix · Mar 17, 2019 · 5d08a36 · 5d08a36
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diff --git a/01-Introduction.Rmd b/01-Introduction.Rmd
@@ -370,7 +370,7 @@ $o$ for organisms (including humans), $r$ is relief, $p$ is parent
 material or geology and $t$ is time. The Eq. \@ref(eq:clorpt) is the
 CLORPT model originally presented by Jenny [-@jenny1994factors].
 
-@MCBRATNEY20033 re-conceptualized and extended the CLORPT model via the
+@McBratney2003Geoderma re-conceptualized and extended the CLORPT model via the
 *“scorpan”* model in which soil properties are modeled as a function of:
 
 -   (auxiliary) **s**oil classes or properties,

diff --git a/03-Soil_variables.Rmd b/03-Soil_variables.Rmd
@@ -1792,7 +1792,7 @@ TT.plot(class.sys = "USDA.TT", tri.data = tdf,
 
 This shows that not all positions in the triangle have the same prior probability. So probably a more sensitive way to estimate uncertainty of converting soil texture classes to fractions would be to run simulations using a density image showing the actual distribution of classes and then, by using the `rpoint` function in the [spatstat package](http://spatstat.org), we could also derive even more realistic conversions from texture-by-hand classes to texture fractions.
 
-## Converting Munsell color codes to other color systems
+### Converting Munsell color codes to other color systems
 
 In the next example we look at the Munsell color codes and conversion algorithms from a code to RGB and other color spaces. Munsell color codes can be matched with RGB values via the [Munsell color codes conversion table](http://www.cis.rit.edu/mcsl/online/munsell.php). You can load a table with 2350 entries from the book repository:
 

diff --git a/04-Soil_covariates.Rmd b/04-Soil_covariates.Rmd
@@ -6,8 +6,12 @@
 
 ### Types of soil covariates
 
-Soils (and vegetation + ecosystems) form under complex interactions between climate, living organism and anthropogenic influences, modified by relief and hydrological processes and operating over long periods of time. 
-This has been clearly identified first by @jenny1994factors with his CLORPT factors of soil formation and subsequently extended by @MCBRATNEY20033 with the SCORPAN formulation (see section \@ref(soil-mapping-theory)).
+Soils (and vegetation + ecosystems) form under complex interactions between 
+climate, living organism and anthropogenic influences, modified by relief and 
+hydrological processes and operating over long periods of time. 
+This has been clearly identified first by @jenny1994factors with his CLORPT 
+factors of soil formation and subsequently extended by @McBratney2003Geoderma 
+with the SCORPAN formulation (see section \@ref(soil-mapping-theory)).
 
 The following groups of covariates are commonly considered for use in 
 Predictive Soil Mapping:

diff --git a/index.Rmd b/index.Rmd
@@ -93,7 +93,7 @@ learning techniques to fit models for the purpose of producing spatial and/or
 spatiotemporal predictions of soil variables, i.e. maps of soil properties and 
 classes at different resolutions. It is a multidisciplinary field combining 
 statistics, data science, soil science, physical geography, remote sensing, 
-geoinformation science and a number of other sciences [@Scul01; @MCBRATNEY20033; @Henderson2004Geoderma; @Boettinger2010Springer; @Zhu2015PSM]. *Predictive Soil Mapping with R* 
+geoinformation science and a number of other sciences [@Scul01; @McBratney2003Geoderma; @Henderson2004Geoderma; @Boettinger2010Springer; @Zhu2015PSM]. *Predictive Soil Mapping with R* 
 is about understanding the main concepts behind soil mapping, mastering R 
 packages that can be used to produce high quality soil maps, and about 
 optimizing all processes involved so that production costs can also be reduced.