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Abstract and Figures

There are relatively few studies that explicitly evaluate the performance of machine learning algorithms (MLAs) such as decision trees while varying conditions like data splitting strategies and feature selection methods in digital soil mapping (DSM). Since several more powerful black-box models such as Random forest (RF) exist, regular models like the Classification and Regression Tree (CART) are least applied despite being more intelligible than the former. We demonstrate a simple yet relevant way to improve the performance of a CART model for DSM while still benefiting from its intelligibility, interpretability and intuition potential. Soil organic carbon (SOC) levels across the Czech Republic are predicted at 30 m × 30 m resolution using selected covariates coupled with respective CART models. For this work, 440 topsoils (0 – 20 cm) for the Czech Republic were retrieved from the LUCAS soil database. Regarding the distinct CART models, data splitting strategies (Random, SPlit and Conditional Latin Hypercube Sampling: cLHS) and 7 feature selection methods were varied. Meanwhile, overall model results were compared using accuracy metrics including the root mean square error (RMSE). One of the satisfactory SOC model validation results based on SPlit has a root mean square error (RMSE) of 17.30 g/kg and a coefficient of determination (R²) of 0.52. The cLHS proves robust for model data splitting. Feature selection methods including stepwise regression (SWR), recursive feature elimination (RFE) and the genetic algorithm (GA) were considered computationally efficient for identifying relevant covariates. Generally, the study demonstrates the relevance and effectiveness of varying data splitting strategies and feature selection methods for improving SOC modelling via a decision tree (CART).
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Smart Agricultural Technology 3 (2023) 100106
Available online 10 August 2022
2772-3755/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Optimized modelling of countrywide soil organic carbon levels via an
interpretable decision tree
Ndiye M. Kebonye
a
,
b
,
*
, Prince C. Agyeman
c
, James K.M. Biney
d
a
Department of Geosciences, Chair of Soil Science and Geomorphology, University of Tübingen, Rümelinstr. 19-23, Tübingen, Germany
b
DFG Cluster of Excellence Machine Learning: New Perspectives for Science, University of Tübingen, AI Research Building, Maria-von-Linden-Str. 6, Tübingen 72076,
Germany
c
Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Kamýck´
a 129, Prague,
Suchdol 165 00, Czech Republic
d
Department of Landscape Ecology, The Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Lidick´
a 25/27, Brno 602 00, Czech Republic
ARTICLE INFO
Keywords:
Intelligible models
Model parsimony
Czech Republic
Generalization
Digital soil mapping (DSM)
ABSTRACT
There are relatively few studies that explicitly evaluate the performance of machine learning algorithms (MLAs)
such as decision trees while varying conditions like data splitting strategies and feature selection methods in
digital soil mapping (DSM). Since several more powerful black-box models such as Random Forest (RF) exist,
regular models like the Classication and Regression Tree (CART) are least applied despite being more intelli-
gible than the former. We demonstrate a simple yet relevant way to improve the performance of a CART model
for DSM while still beneting from its intelligibility, interpretability and intuition potential. Soil organic carbon
(SOC) levels across the Czech Republic are predicted at 30 m ×30 m resolution using selected covariates coupled
with respective CART models. For this work, 440 topsoils (020 cm) for the Czech Republic were retrieved from
the LUCAS soil database. Regarding the distinct CART models, data splitting strategies (Random, SPlit and
Conditional Latin Hypercube Sampling: cLHS) and 7 feature selection methods were varied. Meanwhile, overall
model results were compared using accuracy metrics including the root mean square error (RMSE). One of the
satisfactory SOC model validation results based on SPlit has a root mean square error (RMSE) of 17.30 g/
kg and a coefcient of determination (R
2
) of 0.52. The cLHS proves robust for model data splitting. Feature
selection methods including Stepwise Regression (SWR), Recursive Feature Elimination (RFE) and the Genetic
Algorithm (GA) were considered computationally efcient for identifying relevant covariates. Generally, the
study demonstrates the relevance and effectiveness of varying data splitting strategies and feature selection
methods for improving SOC modelling via a decision tree (CART).
1. Introduction
Preliminary large-scale decision-making depends on reliable yet ac-
curate digital soil property maps [14]. Even though all soil properties
are important, SOC which is a crucial indicator of soil health and quality
has been widely studied across distinct spatial extents. Fairly, the use of
machine learning algorithms (MLAs) [e.g. Random Forest (RF), Support
Vector Machines (SVM), Neural Networks (NN), etc.] is indeed popular
for mapping SOC with very promising results [3,5,6]. Thus, the
invaluable benet of machine learning algorithms cannot be overlooked
in digital soil mapping (DSM).
However, fear and scepticism remain that most MLAs are black-box
models that lack intuitiveness, intelligibility, interpretability as well as
contribute marginally towards our understanding of pedogenic func-
tions, processes and or activities. Black box models get this name from the
fact that they provide minimal knowledge and understanding of how the
model executes predictions or classications. Nonetheless, there are
some MLAs such as decision trees that allow for reasonable interpret-
ability and intelligibility. Decision trees have a clear history and track
[7]. In DSM, some studies including those by Taghizadeh-Mehrjardi
et al. [8], Henderson et al. [9] and Sun et al. [10] have explored deci-
sion trees for various purposes. Yet in DSM, seldom is decision trees
applied because most times researchers resort to using famous more
powerful black-box models (e.g. RF, SVM, etc.) at the cost of interpret-
ability. Moreover, relatively few to no studies have explicitly explored
decision trees, particularly on account to improve their performance or
* Corresponding author at: Department of Geosciences, Chair of Soil Science and Geomorphology, University of Tübingen, Rümelinstr. 19-23, Tübingen, Germany.
E-mail address: ndiyekeb@yahoo.com (N.M. Kebonye).
Contents lists available at ScienceDirect
Smart Agricultural Technology
journal homepage: www.journals.elsevier.com/smart-agricultural-technology
https://doi.org/10.1016/j.atech.2022.100106
Received 13 June 2022; Received in revised form 7 August 2022; Accepted 9 August 2022
Smart Agricultural Technology 3 (2023) 100106
2
accuracy while appraising distinct data splitting and feature selection
method combinations. These simple yet basic MLA tactics may be key
conduits to better improve the performance of decision trees for DSM.
Here, we applied a Classication and Regression Tree (CART) [11]
with the main question, can we be able to improve on SOC prediction
via a decision tree while varying data splitting strategies and feature
selection approaches?This way, not only is interpretability prioritized
but also some improvement in model performance is sure. We mainly
aim to: (i) compare SOC predictions for the Czech Republic while
varying CART models based on data splitting strategies [i.e. Random,
Fig. 1. Important maps in the study include a) soil organic carbon (SOC) sampling points distributed across the Czech Republic and b) the environmental covariates
applied in the prediction of soil organic carbon (SOC) all resampled or aggregated to approximately 30 m ×30 m. [Note: Covariate names in full are: Aspect (aspect),
Elevation (elevation), Enhanced Vegetation Index (evi), Two bands Enhanced Vegetation Index (evi2), Flow direction (owirection), Modied Soil Adjusted
Vegetation Index (msavi), Normalized Difference Vegetation Index (ndvi), Normalized Difference Water Index (ndwi), Roughness (roughness), Soil Adjusted
Vegetation Index (savi), Slope (slope), Topographic Position Index (tpi), Terrain Ruggedness Index (tri), XCoordinates/Longitude (x-axis) and YCoordinates/
Latitude (y-axis)].
N.M. Kebonye et al.
Smart Agricultural Technology 3 (2023) 100106
3
SPlit and the Conditional Latin Hypercube Sampling (cLHS)] and feature
selection methods [e.g. Boruta, LASSO, Genetic Algorithm (GA)] and (ii)
map the spatial distribution of SOC across the Czech Republic based on
promising CART model results. Appraising different data splitting stra-
tegies and feature selection method combinations while using CART
should improve SOC predictions. Specically, CART models with fewer
estimators are intuitively expected to yield better overall performances
while still rationally explaining soil relationships. With the need to un-
derstand soil processes and activities while making inferences on vari-
able interactions, interpretable models seem appropriate for complex
associations in DSM [12]. Hence, for this study, we only focus on a de-
cision tree (CART) as an example interpretable model.
2. Materials and methods
2.1. Soil samples and organic carbon measurement
The Czech Republic 2015 LUCAS topsoil (020 cm) dataset of
n =440 SOC measurements was used in this study. Mainly the samples
were distributed across the county following distinct land cover classes
including cropland, bareland, grassland, woodland, articial land,
water, wetland and shrubland. Therefore, issues of sample heterogeneity
are catered for through the LUCAS methodology and sampling design.
Safe to say, despite having few samples collected, these reasonably
represent most of the landscape being evaluated (Fig. 1A). For these SOC
measurements, a dry combustion elementary analysis method was
applied.
2.2. Modelling and feature selection
As previously mentioned, we applied a decision tree, particularly the
CART model [11]. This model imitates how people make decisions,
making it simple to comprehend. Moreover, according to Wu [13],
CART is one of the most important models. With CART models we can
assess non-linear associations that exist between variables while
beneting from its intuitiveness. A CART model often takes the form of a
tree with nodes (i.e., features), links (i.e., rules) as well as individual
leaves (i.e. nal decision) [14]. We applied terrain derivatives, spectral
indices and position features as the environmental covariates for the
models. A 30 m digital elevation model (DEM) of the area was obtained
from the USGS Earth Explorer (https://earthexplorer.usgs.gov/) and
used to generate the terrain derivatives in the software R. Conversely,
the USGS Earth Engine Data catalogue (https://developers.google.co
m/earth-engine/datasets/catalogue/landsat-8) was used to source for
the atmospherically corrected Landsat 8 data for the area. For the
Landsat 8 data, we averaged the images between the years 2014 to 2018.
The nal image was then imported to software R to extract the indi-
vidual bands and eventually compute for each of the spectral indices
used in this study (Fig. 1B).
The CART models being developed were built from a stack consisting
of 15 covariates that were all resampled or aggregated to approximately
30 m ×30 m (Fig. 1B). For the rst CART models, all covariates were
used to predict SOC using while splitting the data into calibration (80%)
and validation (20%) based on the Random, novel SPlit [15] or cLHS
[16] sampling strategies respectively. Using the calibration sets in each
case, 10-fold cross-validation was repeated ve times to identify the
optimal values for the hyperparameter cp. The relevant software R
packages used were caret [17], raster [18], SPlit [19], clhs [20].
Subsequent CART models involved systematically selecting cova-
riates based on 7 different feature selection methods, Boruta (B), LASSO
Regression (LR), Random Forest (RF), Recursive Feature Elimination
(RFE), Stepwise Regression (SWR), Relief Based Feature Selection
(RBAs) and Genetic Algorithm (GA). The Boruta method centres on the
RF algorithm in that it executes several iterations to eliminate unnec-
essary features [21]. The regularization method LASSO imposes a pen-
alty that causes the coefcients to shrink. The best choice of features is
then generated by the shrinkage in coefcients [22]. Random Forest is
an ensemble model composed of many decision trees that make pre-
dictions separately to obtain an average prediction [23]. During this
process, the relative importance of each feature can be quantied and
documented.
In the Recursive Feature Elimination (RFE) method, features are
ordered recursively based on their relevance [24]. For this study, we
implement a Random Forest selection function within the RFE method.
The SWR simply involves tting a simple regression model. Based on the
output of the regression model, each feature is ranked according to its
signicance [25]. Conversely, the RBAs method employs a lter-based
approach to selecting important features in the modelling procedure
[26]. A Genetic Algorithm selects important features in a model by
following biological principles. In general, features are chosen via the
natural selection method [27]. For the feature selection methods, soft-
ware R packages used were Boruta [28], GA [29], caret [17], mlbench
[30], FSelector [31], MASS [32] and leaps [33]. The feature selection
results are summarised in Fig. 2. However, for further information
regarding the feature selection plots and statistics, we refer readers to
the supplementary data associated with this study.
It should be noted that position covariates (i.e. x-axis and y-axis)
were not always included in the feature selection procedures but rather
during the modelling process. Mainly, this was to ensure that at all times
these covariates are included in the modelling process to ensure that the
CART models had spatial features and characteristics which some MLA
studies may ignore. All already stated were repeated, this time using cp
plus other hyperparameters including the minsplit, minbucket and
maxdepth at optimal values of 10, 5 and 15 respectively to further prune
the CART models. For the various CART models, a model with the least
root mean square error (RMSE) was considered promising although
other accuracy indicators were also used including the coefcient of
determination (R
2
), Lins concordance correlation coefcient (CCC),
mean square error (MSE) and bias (i.e. Eqs. (1) to (5)). The symbols
denoted by n,
ρ
,
σ
2 and
σ
reect the number of observations, correlation
coefcient between the predicted and the observed values, variance and
standard deviation correspondingly. All procedures were performed in
the software R.
R2=n
i=1(predicted observed)2
n
i=1(observed observed)2(1)
CCC =2
ρσ
predicted
σ
observed
σ
2
predicted +
σ
2
observed + (predicted observed)2(2)
MSE =1
n
n
i=1
(observed predicted)2(3)
RMSE =
1
n
n
i=1
(observed predicted)2
(4)
bias =1
n
n
i=1
(observed predicted)(5)
3. Results and discussion
3.1. Soil organic carbon predictions
The SOC levels ranged between 3.5215.7 g/kg which was a slightly
higher range than 0.839.9 g/kg by ˇ
ˇ
zala et al. [5]. This was reasonable
since ˇ
ˇ
zala et al. [5] only evaluate SOC levels in agricultural soils of the
Czech Republic. Conversely, the higher SOC levels connotate to forested
areas where most SOC stocks are expected (Fig. 1) [34]. The mean SOC
level was 26.12 g/kg, which is slightly lower (43.93 g/kg) [3] and
higher (13.00 g/kg) [6] than the mean levels recorded in other
large-scale monitoring studies in Europe. ˇ
ˇ
zala et al. [5] obtain 1.46%
N.M. Kebonye et al.
Smart Agricultural Technology 3 (2023) 100106
4
SOC at 030 cm in Czech agricultural soils. The various CART SOC
model performance results while applying different splitting strategies
and feature selection methods are documented in Tables 1 and 2.
Generally, different data splitting strategies, feature selection methods
and hyperparameter combinations resulted in varying outcomes. There
was some consistency in validation RMSE results obtained through
Random and SPlit strategies relative to the cLHS despite the feature
selection method applied. However, we observe some of the lowest
RMSE estimates while applying the cLHS strategy, an effect attributed to
its robust sampling approach [16]. In both Tables 1 and 2, SWR, RFE and
GA yield more parsimonious models that showed consistently better
overall results following the respective data splitting strategies. This
study validates the computational efciency of the abovementioned
feature selection methods. Also, applying fewer and optimally selected
estimators can improve CART model performance aside from maybe
using all covariates. Certainly, feature selection is necessary for a ma-
chine learning pipeline and ultimately for deriving reliable digital soil
maps.
3.2. Soil organic carbon spatial characteristics and patterns
The SOC prediction maps for the Czech Republic (020 cm and 30
m ×30 m resolution) based on promising CART models are shown in
Fig. 3. Generally, the spatial distribution patterns of SOC corroborate
those already observed by ˇ
ˇ
zala et al. [5] and Borůvka et al. [34]. High
SOC predictions are noticed in consistently forested areas of the Czech
Republic [34]. Moreover, these areas have the highest altitude (Fig. 1B)
and already Borůvka et al. [34] demonstrate the importance of elevation
as a key covariate for SOC prediction. Some studies also show consistent
effects of elevation on SOC spatial distribution at a large scale in
temperate zones [35,36]. There is consistently much lower than 40 g/kg
SOC across the Czech Republic associated with other soil types (i.e.
agricultural soils). Further assessing the SOC maps, particularly the
SWR.Random maps (Fig. 3), we observe some unrealistic orthogonal
artefacts [37] introduced by the CART models. In future, a solution to
this may be to apply oblique geographic coordinates proposed by Møller
et al. [37].
3.3. Future outlooks
Although the current study had demonstrated promising results,
there is yet a need to explore other perspectives regarding the problem.
For instance, directions towards evaluating the associated effects of (1)
sample size and spatial extent variation, (2) comparing other data
splitting strategies not current used (3) possible methodologies and
designs used for soil sampling as well as (4) varying the effects of both
global and local inuencing factors. For instance, the majority of
covariates applied in this work were mostly of global than local inu-
ence. Another option may be to (5) include other covariates such as soil
information from SoilGrids 2 (e.g., clay, silt, sand, soil type, etc.) and
climatic variables [38,39]. Digital soil mapping products are insufcient
without quantied uncertainty estimates; therefore these ought to be
Fig. 2. Schematic showing the covariate combinations concluded by each feature selection method.
N.M. Kebonye et al.
Smart Agricultural Technology 3 (2023) 100106
5
Table 1
Model validation results for the different splitting strategies versus feature selection method while using cp as a hyperparameter.
Selection criteria Model assessment metricscp as the only tuning parameter
Random sampling strategy SPlit sampling strategy cLHS sampling strategy
Data R
2
CCC MSE RMSE bias Data R
2
CCC MSE RMSE bias Data R
2
CCC MSE RMSE bias
ALL Cal (n =355) 0.50 0.66 314.19 17.73 0.00 Cal (n =352) 0.50 0.66 257.52 16.05 0.00 Cal (n =352) 0.44 0.61 297.67 17.25 0.00
Val (n =85) 0.11 0.31 417.74 20.44 4.04 Val (n =88) 0.17 0.38 615.68 24.81 0.19 Val (n =88) 0.10 0.27 603.19 24.56 2.27
B Cal (n =355) 0.49 0.65 320.76 17.91 0.00 Cal (n =352) 0.51 0.68 256.86 16.03 0.00 Cal (n =352) 0.40 0.57 303.89 17.43 0.00
Val (n =85) 0.11 0.31 405.22 20.13 3.71 Val (n =88) 0.10 0.30 644.59 25.39 0.10 Val (n =88) 0.24 0.39 538.12 23.20 1.56
LR Cal (n =355) 0.49 0.65 318.57 17.85 0.00 Cal (n =352) 0.38 0.55 330.41 18.18 0.00 Cal (n =352) 0.42 0.59 364.29 19.09 0.00
Val (n =85) 0.11 0.31 407.69 20.19 3.25 Val (n =88) 0.28 0.48 429.25 20.72 1.81 Val (n =88) 0.35 0.56 181.56 13.47 4.18
RF Cal(n =355) 0.50 0.66 314.19 17.73 0.00 Cal (n =352) 0.49 0.66 258.84 16.09 0.00 Cal (n =352) 0.42 0.59 371.86 19.28 0.00
Val (n =85) 0.11 0.31 417.74 20.44 4.04 Val (n =88) 0.15 0.36 662.05 25.73 0.48 Val (n =88) 0.42 0.58 146.33 12.10 5.26
RFE Cal (n =355) 0.49 0.65 318.69 17.85 0.00 Cal (n =352) 0.40 0.57 317.48 17.82 0.00 Cal (n =352) 0.43 0.60 303.03 17.41 0.00
Val (n =85) 0.11 0.31 406.11 20.15 3.17 Val (n =88) 0.33 0.50 410.24 20.25 0.99 Val (n =88) 0.32 0.49 404.25 20.11 2.78
SWR Cal (n =355) 0.48 0.65 321.05 17.92 0.00 Cal (n =352) 0.48 0.65 271.91 16.49 0.00 Cal (n =352) 0.43 0.60 292.39 17.10 0.00
Val (n =85) 0.11 0.31 403.41 20.09 3.67 Val (n =88) 0.16 0.38 587.56 24.24 0.78 Val (n =88) 0.30 0.44 470.92 21.70 0.10
RBAs Cal (n =355) 0.41 0.58 367.90 19.18 0.00 Cal (n =352) 0.37 0.54 316.25 17.78 0.00 Cal (n =352) 0.47 0.64 337.88 18.38 0.00
Val (n =85) 0.03 0.16 484.71 22.02 4.89 Val (n =88) 0.13 0.26 628.46 25.07 2.44 Val (n =88) 0.12 0.32 157.42 12.55 3.40
GA Cal (n =355) 0.50 0.67 310.93 17.63 0.00 Cal (n =352) 0.47 0.64 269.91 16.43 0.00 Cal (n =352) 0.43 0.60 296.21 17.21 0.00
Val (n =85) 0.11 0.31 423.66 20.58 3.36 Val (n =88) 0.13 0.33 640.66 25.31 0.47 Val (n =88) 0.43 0.48 396.97 19.92 0.26
Note: By selection criteria, we referred to the feature selection method. ALL =All covariates, B =Boruta selected covariates, LR =LASSO Regression selected covariates, RF =Random Forest selected covariates,
RFE =Recursive Feature Elimination selected covariates, SWR =Stepwise Regression selected covariates, RBAs =Relief Based Feature Selection covariates and GA =Genetic Algorithm covariates. Cal =Calibration
dataset and Val =Validation dataset.
Table 2
Model validation results for the different splitting strategies versus feature selection method while using cp and other hyperparameters.
Selection criteria Model assessment metricscp, minsplit (10), minbucket (5) and maxdepth (15) as tuning parameters
Random sampling strategy SPlit sampling strategy cLHS sampling strategy
Data R
2
CCC MSE RMSE bias Data R
2
CCC MSE RMSE bias Data R
2
CCC MSE RMSE bias
ALL Cal (n =355) 0.55 0.71 282.19 16.80 0.00 Cal (n =352) 0.53 0.69 238.29 15.44 0.00 Cal (n =352) 0.56 0.72 229.74 15.16 0.00
Val (n =85) 0.11 0.32 383.26 19.58 2.73 Val (n =88) 0.17 0.37 609.76 24.69 0.46 Val (n =88) 0.03 0.17 725.05 26.93 2.25
B Cal (n =355) 0.56 0.72 273.64 16.54 0.00 Cal (n =352) 0.58 0.74 219.32 14.81 0.00 Cal (n =352) 0.56 0.72 223.36 14.95 0.00
Val (n =85) 0.10 0.30 380.96 19.52 2.14 Val (n =88) 0.12 0.32 645.24 25.40 0.14 Val (n =88) 0.15 0.31 611.67 24.73 0.68
LR Cal (n =355) 0.56 0.72 273.64 16.54 0.00 Cal (n =352) 0.57 0.73 228.23 15.11 0.00 Cal (n =352) 0.53 0.69 297.13 17.24 0.00
Val (n =85) 0.10 0.30 380.96 19.52 2.14 Val (n =88) 0.48 0.67 326.43 18.07 2.82 Val (n =88) 0.47 0.65 130.14 11.41 3.70
RF Cal(n =355) 0.55 0.71 282.19 16.80 0.00 Cal (n =352) 0.53 0.69 238.15 15.43 0.00 Cal (n =352) 0.56 0.72 279.45 16.72 0.00
Val (n =85) 0.11 0.32 383.26 19.58 2.73 Val (n =88) 0.11 0.31 727.13 26.97 0.47 Val (n =88) 0.38 0.49 277.89 16.67 6.63
RFE Cal (n =355) 0.55 0.71 279.36 16.71 0.00 Cal (n =352) 0.60 0.75 213.33 14.61 0.00 Cal (n =352) 0.58 0.73 225.48 15.02 0.00
Val (n =85) 0.09 0.29 381.07 19.52 2.03 Val (n =88) 0.52 0.65 299.24 17.30 1.59 Val (n =88) 0.45 0.64 333.63 18.27 3.06
SWR Cal (n =355) 0.56 0.72 273.64 16.54 0.00 Cal (n =352) 0.52 0.69 249.75 15.80 0.00 Cal (n =352) 0.46 0.63 276.39 16.63 0.00
Val (n =85) 0.10 0.30 380.96 19.52 2.14 Val (n =88) 0.16 0.37 578.12 24.04 0.19 Val (n =88) 0.33 0.44 457.90 21.40 0.15
RBAs Cal (n =355) 0.57 0.72 268.66 16.39 0.00 Cal (n =352) 0.43 0.59 289.91 17.03 0.00 Cal (n =352) 0.51 0.68 313.49 17.71 0.00
Val (n =85) 0.00 0.04 573.37 23.95 4.38 Val (n =88) 0.07 0.20 702.23 26.50 1.56 Val (n =88) 0.04 0.20 187.10 13.68 2.80
GA Cal (n =355) 0.56 0.71 276.47 16.63 0.00 Cal (n =352) 0.54 0.70 237.40 15.41 0.00 Cal (n =352) 0.55 0.71 233.37 15.28 0.00
Val (n =85) 0.12 0.33 383.15 19.57 2.84 Val (n =88) 0.12 0.32 677.61 26.03 0.62 Val (n =88) 0.36 0.39 445.98 21.12 0.17
Note: By selection criteria, we referred to the feature selection method. ALL =All covariates, B =Boruta selected covariates, LR =LASSO Regression selected covariates, RF =Random Forest selected covariates,
RFE =Recursive Feature Elimination selected covariates, SWR =Stepwise regression selected covariates, RBAs =Relief Based Feature Selection covariates and GA =Genetic Algorithm covariates. Cal =Calibration
dataset and Val =Validation dataset.
N.M. Kebonye et al.
Smart Agricultural Technology 3 (2023) 100106
6
included in future studies as well. In general, since much of machine
learning is data-driven, it should be emphasized that soil expert
knowledge remains crucial in the DSM exercise particularly when it
comes to understanding soil functions and processes.
4. Conclusions
This study demonstrates how a decision tree (CART) performance
can be improved by simply varying data splitting strategies and feature
selection methods for DSM. A total of 15 global covariates (30 m ×30 m)
are used to predict surface SOC across the Czech Republic via CART
models following different conditions. These conditions include varying
data splitting strategies and feature selection methods. The cLHS data
splitting strategy shows some of the lowest RMSE estimates in the study
while also having the overall best performance while using cp as a
hyperparameter. Also, RMSE estimates seemed to decrease after inte-
grating tree pruning hyperparameters (minsplit, minbucket and max-
depth). Conversely, of the feature selection methods applied, we
observed better model performances while applying SWR, RFE and GA.
Soil organic carbon levels were more in high-altitude forested areas and
altogether the spatial distribution patterns corroborate with previous
studies. We can benet from interpretability while improving model
(CART) performance based on simple conditions such as data splitting
and feature selection.
Data and code availability
Data used in this work can be requested free from the European
Commission Joint Research Centre. The codes used in this study can also
be freely requested from the corresponding author.
CRediT authorship contribution statement
Ndiye M. Kebonye: Conceptualization, Methodology, Visualization,
Formal analysis, Investigation, Software, Data curation, Writing orig-
inal draft, Writing review & editing. Prince C. Agyeman: Methodol-
ogy, Conceptualization, Data curation, Writing review & editing.
James K.M. Biney: Methodology, Conceptualization, Data curation,
Writing review & editing.
Fig. 3. Soil organic carbon spatial distribution maps at 30 m ×30 m based on the best decision tree (CART) models derived using the different tuning parameters.
[Note: The SWR.Random is the Stepwise Regression based on a Random Split, RFE.SPlit is the Recursive Feature Elimination based on SPlit and GA.cLHS is the
Genetic Algorithm based on a Conditional Latin Hypercube Sampling. First column maps are those generated using cp alone and the second column are those
generated using cp coupled with other tuning parameters, minsplit (10), minbucket (5) and maxdepth (15)].
N.M. Kebonye et al.
Smart Agricultural Technology 3 (2023) 100106
7
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgments
The rst author acknowledges the funding provided through the
Central Ofce of the Cluster of Excellence Machine Learning: New
Perspectives for Science, University of Tübingen. Moreover, the authors
greatly appreciate the European Commission, Joint Research Centre for
allowing us to use the LUCAS 2015 topsoil dataset for the current study.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.atech.2022.100106.
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