You can install the development version of soiltestcorr from GitHub with:
# install.packages("devtools")
devtools::install_github("adriancorrendo/soiltestcorr")2.
Modified Arcsine-Log Calibration Curve
The goal of soiltestcorr is to assist users on the
analysis of relationships between relative yield (ry) and soil test
values (stv) following different approaches.
Available functions (version 2.1.1, 05-10-2022):
The first calibration method available is the Modified Arcsine-log
Calibration Curve (mod_alcc()) originally described by
Dyson and Conyers (2013) and modified by Correndo et al. (2017). This
function produces the estimation of critical soil test values (CSTV) for
a target relative yield (ry) with confidence intervals at adjustable
confidence levels.
mod_alcc()
Instructions
Load your dataframe with soil test value (stv) and relative yield
(ry) data.
Specify the following arguments into the function -mod_alcc()-:
(a). data (optional),
(b). stv (soil test value) and ry (relative
yield) columns or vectors,
(c). target of relative yield (e.g. 90%),
(d). desired confidence level (e.g. 0.95 for 1 -
alpha(0.05)). Used for the estimation of critical soil test value (CSTV)
lower and upper limits.
(e). plot TRUE (produces a ggplot as main output) or
FALSE -default- (no plot, only results as list or data.frame),
(f). tidy TRUE (produces a data.frame with results) or
FALSE-default- (store results as list),
Run and check results.
Check residuals plot, and warnings related to potential leverage
points.
Adjust curve plots as desired.
Example of mod_alcc() output
soiltestcorr also allows users to implement the
quadrants analysis approach, also known as the Cate-Nelson analysis.
There are two versions of the Cate-Nelson technique:
Thus, the second alternative is based on Cate and Nelson (1965)
(cate_nelson_1965()). The first step of this method is to
apply an arbitrarily fixed value of ry as a target (y-axis) that divides
the data into two categories (below & equal or above ry target). In
a second stage, it estimates the CSTV (x-axis) as the minimum stv that
divides the data into four quadrants (target ry level combined with STV
lower or greater than the CSTV) maximizing the number of points under
well-classified quadrants (II, stv >= CSTV & ry >= ry target;
and IV, stv < CSTV & ry < RY target). This is also known as
the “graphical” version of the Cate-Nelson approach.
cate_nelson_1965()
Instructions
Load your dataframe with soil test value (stv) and relative yield
(ry) data.
Specify the following arguments into the function
-cate_nelson_1965()-:
(a). data (optional),
(b). stv (soil test value) and ry (relative
yield) columns or vectors,
(c). plot TRUE (produces a ggplot as main output) or
FALSE (no plot, only results as list or data.frame),
(d). tidy TRUE (produces a data.frame with results) or
FALSE (store results as list),
Run and check results.
Adjust plot as desired.
Example of cate_nelson_1965() output
The third alternative is based on Cate and Nelson (1971)
(cate_nelson_1971()). The first step of this alternative
version is to estimate the CSTV (x-axis) as the minimum stv that
minimizes the residual sum of squares when dividing data points in two
classes (lower or greater than the CSTV) without using an arbitrary ry.
This refined version does not constrains the model performance (measured
with the coefficient of determination -R2-) but the user has no control
on the RY level for the CSTV. This is also known as the “statistical”
version of the Cate-Nelson approach.
cate_nelson_1971()
Instructions
Load your dataframe with soil test value (stv) and relative yield
(ry) data.
Specify the following arguments into the function
-cate_nelson_1971()-:
(a). data (optional),
(b). stv (soil test value) and ry (relative
yield) columns or vectors,
(c). plot TRUE (produces a ggplot as main output) or
FALSE (no plot, only results as list or data.frame),
(d). tidy TRUE (produces a data.frame with results) or
FALSE (store results as list),
Run and check results.
Adjust plot as desired.
Example of cate_nelson_1971() output
The next calibration method available is the linear-plateau model
(linear_plateau()). This function fits the classical
regression response model that follows two phases: i) a first linear
phase described as y = b0 + b1 x, and ii) a second phase were the RY
response to increasing STV becomes NULL (flat), described as plateau = y
= b0 + b1 Xc, where Xc represents the CSTV. The function works
automatically with self starting initial values to facilitate the
model’s convergence.
linear_plateau()
Instructions
Load your dataframe with soil test value (stv) and relative yield
(ry) data.
Specify the following arguments into the function
-linear_plateau()-:
(a). data (optional),
(b). stv (soil test value) and ry (relative
yield) columns or vectors,
(c). target (optional) if want a CSTV for a different
`ry`` than the plateau.
(d). plot TRUE (produces a ggplot as main output) or
FALSE (no plot, only results as data.frame),
(e). resid TRUE (produces plots with residuals analysis)
or FALSE (no plot),
Run and check results.
Check residuals plot, and warnings related to potential
limitations of this model.
Adjust curve plots as desired.
Example of linear_plateau() output
The following calibration method available is the quadratic-plateau
model (quadratic_plateau()). This function fits the
classical regression response model that follows two phases: i) a first
curvilinear phase described as y = b0 + b1 x + b2 x^2, and ii) a second
phase were the RY response to increasing STV becomes NULL (flat),
described as plateau = y = b0 + b1 Xc + b2 * Xc, where Xc represents the
CSTV. The function works automatically with self starting initial values
to facilitate the model convergence.
linear_plateau()
Instructions
Load your dataframe with soil test value (stv) and relative yield
(ry) data.
Specify the following arguments into the function
-quadratic_plateau()-:
(a). data (optional),
(b). stv (soil test value) and ry (relative
yield) columns or vectors,
(c). target (optional) if want a CSTV for a different
`ry`` than the plateau.
(d). plot TRUE (produces a ggplot as main output) or
FALSE (no plot, only results as data.frame),
(e). resid TRUE (produces plots with residuals analysis)
or FALSE (no plot),
Run and check results.
Check residuals plot, and warnings related to potential
limitations of this model.
Adjust curve plots as desired.
Example of quadratic_plateau() output
Instructions
Load your dataframe with soil test value (stv) and relative yield
(ry) data.
Specify the following arguments into the function
-quadratic_plateau()-:
(a). data (optional),
(b). stv (soil test value) and ry (relative
yield) columns or vectors,
(c). target (optional) if want a CSTV for a different
`ry`` than the plateau.
(d). plot TRUE (produces a ggplot as main output) or
FALSE (no plot, only results as data.frame),
(e). resid TRUE (produces plots with residuals analysis)
or FALSE (no plot),
Run and check results.
Check residuals plot, and warnings related to potential
limitations of this model.
Adjust curve plots as desired.
Example of mitscherlich() output
References
Anderson, R. L., and Nelson, L. A. (1975). A Family of Models
Involving Intersecting Straight Lines and Concomitant Experimental
Designs Useful in Evaluating Response to Fertilizer Nutrients.
Biometrics, 31(2), 303–318. 10.2307/2529422
Bullock, D.G. and Bullock, D.S. (1994), Quadratic and
Quadratic-Plus-Plateau Models for Predicting Optimal Nitrogen Rate of
Corn: A Comparison. Agron. J., 86: 191-195.
10.2134/agronj1994.00021962008600010033x
Cate, R.B. Jr., and Nelson, L.A., 1965. A rapid method for
correlation of soil test analysis with plant response data. North
Carolina Agric. Exp. Stn., International soil Testing Series Bull.
No. 1.
Cate, R.B. Jr., and Nelson, L.A., 1971. A simple statistical
procedure for partitioning soil test correlation data into two classes.
Soil Sci. Soc. Am. Proc. 35:658-659
Correndo, A.A., Salvagiotti, F., García, F.O. and Gutiérrez-Boem,
F.H., 2017. A modification of the arcsine–log calibration curve for
analysing soil test value–relative yield relationships. Crop and Pasture
Science, 68(3), pp.297-304. 10.1071/CP16444
Dyson, C.B., Conyers, M.K., 2013. Methodology for online
biometric analysis of soil test-crop response datasets. Crop &
Pasture Science 64: 435–441. 10.1071/CP13009
Melsted, S.W. and Peck, T.R. (1977). The Mitscherlich-Bray Growth
Function. In Soil Testing (eds T. Peck, J. Cope and D. Whitney).
10.2134/asaspecpub29.c1
Warton, D.I., Wright, I.J., Falster, D.S., and Westoby, M., 2006.
Bivariate line-fitting methods for allometry. Biol. Rev. Camb. Philos.
Soc. 81, 259–291. 10.1017/S1464793106007007