Water Air Soil Pollut (2015) 226:25
DOI 10.1007/s11270-014-2218-0
Relative Importance Analysis of a Refined Multi-parameter
Phosphorus Index Employed in a Strongly Agriculturally
Influenced Watershed
Bin Zhou & Rolf D. Vogt & Xueqiang Lu & Chongyu Xu &
Liang Zhu & Xiaolong Shao & Honglei Liu &
Meinan Xing
Received: 27 July 2014 / Accepted: 5 November 2014
# Springer International Publishing Switzerland 2015
Abstract Eutrophication is a main cause for impairment of freshwater ecosystems, and diffuse phosphorus
(P) loss from agricultural land is usually the main cause
for freshwater eutrophication. The P index is a simple
and practical tool for estimating the potential P loss risk.
In a preceding study, a refined P index scheme was
developed and validated. In the current study, the relative importance of the 14 input variables used is
assessed in order to determine their relative significance
to the final P index value. The backpropagation network
with Garson’s algorithm was employed in order to capture the significance of interactions among the input
variables. The study clearly shows the source factors,
especially the degree of P saturation (DPS), along with
management practices regarding application of inorganic P fertilizer and livestock manure, are the most important factors governing the P loss in the very high and
high risk areas. Conversely, the transportation factors
governed P loss risk in the low and very low risk areas.
Recommended management strategies for mitigation of
P loss from the different risk zones are proposed based
B. Zhou : R. D. Vogt : L. Zhu
Department of Chemistry, University of Oslo,
0315 Oslo, Norway
C. Xu
Department of Geosciences, University of Oslo,
0315 Oslo, Norway
B. Zhou (*) : X. Lu : X. Shao : H. Liu : M. Xing
Tianjin Academy of Environmental Sciences,
Tianjin 300191, China
e-mail: zhoubin19821214@gmail.com
on the relative importance analysis and practical constraints. A scenario analysis, based on a gradient reduction of DPS, through decreased application of both
inorganic P fertilizer and P emissions factors from livestock manure, gave a reduction of average P index from
7.3 to 57 %. Moreover, the proportion of high- and veryhigh-risk area may be reduced from 38 to 23 % and 24 to
13 %, respectively.
Keywords P index . Relative importance analysis .
Garson algorithms . Targeted best management practices
1 Introduction
Phosphorus (P) is usually the main growth-limiting
factor in aquatic systems; the fluxes of bio-available P
to surface waters have a governing role in the water
eutrophication process (Brown et al. 1999; Carpenter
et al. 1998). Numerous studies have documented that
diffuse P losses from agricultural land is the major cause
for fresh water eutrophication, especially in developed
countries (Elliott et al. 2006; Jarvie et al. 2013; Wu et al.
2012).
Identifying and assessing risk for diffuse P loss from
agricultural areas, and thereby improving our capability
to control the P fluxes, especially at the watershed scale,
are crucial prerequisites for selecting the most costefficient abatement strategies towards eutrophication.
Lemunyon and Gilbert (1993) initiated the development
of a modeling tool for assessing potential loss of P, later
referred to as the P index. Since then, P index model has
25
Page 2 of 13
grown more comprehensive by incorporating more
governing factors (García et al. 2011; Good et al.
2012; Marjerison et al. 2011). This index provides a
semi-quantitative estimation of the susceptibility for P
losses from agricultural land by considering both source
and transport factors. There are likely strong synergistic
and antagonistic effects among these explanatory parameters, especially between the source and transport
factors. These combined effects were incorporated in a
refined phosphorus index, developed and validated in
our previous work (Zhou et al. 2014). Integrated risk
values for potential P loss and their spatial distribution
characteristics can thereby be readily assessed.
However, knowledge of relative importance of the
individual input variables is lacking. Knowledge
regarding the relative importance of the input factors
guides the watershed manager to focus on the most
relevant factors thereby improving the explanatory
ability of P index. As powerful interpretive methods,
relative importance and sensitivity analysis elaborate the
influence of the explanatory variables on the response
variable. To our best knowledge, only a few studies on
this topic have been performed. Brandt and Elliott
(2005) conducted a sensitivity analysis of eight input
variables employed in a P index assessment in Pennsylvania, using a partial differentiation algorithm between
output and input variables. Similarly, Beaulieu et al.
(2006) performed a sensitivity analysis of ten explanatory variables used in their P index assessment in Quebec by means of the Monte Carlo model and stepwise
regression algorithm. Although these sensitivity analysis methodologies are different, both of these studies
were based on the assumption that only one input variable varied at a time, while the others were kept constant. The real-world interdependence among P index
input variables were thus not taken into consideration in
these sensitivity analysis (Gevrey et al. 2006;
Mastrorillo et al. 1998). This is problematic as the
refined P index clearly demonstrated the importance of
these interactions (Zhou et al. 2014). Furthermore, such
an approach is not compatible with the concept of the
refined P index.
The artificial neural networks (ANNs) and relevant
weight algorithms have provided new thoughts in this
field. ANNs are imitations of biological neural networks, akin to the vast network of neurons in human
brain. Over the last couples of decades, ANN models
have received increased attention and wide application
as an intelligent, powerful data analytical and forecasting
Water Air Soil Pollut (2015) 226:25
technique in the field of agricultural, environmental, and
ecological sciences (Lek and Guégan 1999). At present,
multi-layer feed-forward neural networks, trained by
backpropagation algorithm (BPN), have gained popularity and are applied more often than other networks types
(Mumtaz et al. 2008). In the BPN, all neurons are arranged in successive layers, and the information flows
unidirectionally from input layer to output layer, through
hidden layer(s) with connection weights among adjacent
layers (Lek and Guégan 1999). Garson (1991) proposed
an algorithm based on the neural network connection
weights in order to determine the relative importance of
each input variable, similar to general sensitivity analysis, striving to quantify relationships between explanatory and response variables. However, the main difference
lies in the consideration of potential interactions among
variables. Using BPN together with Garson’s algorithm,
all input variables are allowed to vary simultaneously.
The magnitude and sign of the relationship between
input variables are managed, in compliance with our
conceptual understanding. This is thus more equivalent
to the real-world condition compared with traditional
sensitivity analysis approach using partial derivative
algorithm.
The current study assesses relative importance of the
14 input variables used in the refined P index and
quantifies their individual contributions to the final P
index (PI) value within each risk class using BPN with
Garson’s algorithm. Based on the identified order of
relative importance within different risk areas, a series
of scenario analysis was subsequently conducted identifying the effect of targeted P loss control strategies in a
practical manner.
2 Material and Methods
2.1 Study Area
The study area (∼436 km2) is a strongly agriculturally
influenced watershed (117°25′–117°43′E, 39°56′–
40°18′N) located in the north of Tianjin municipality,
in northern China (Fig. 1). The region has a sub-humid
continental monsoon climate featuring four distinct seasons, with an annual mean temperature of 14 °C and an
average annual precipitation of 653 mm. Nearly threefifths of precipitation occurs in the summer period between July and September (JCBS 2011). The study area
is characterized by a varied topography: The lowlands
Water Air Soil Pollut (2015) 226:25
Page 3 of 13 25
Fig. 1 Location of studied watershed and distribution of risk areas based on the P index (Bin Zhou et al. 2014)
(with an average gradient of less than 2°) and flat plains
(2-6°) account for 22 % and 25 % of study area, respectively, together constituting nearly half of the watershed.
Hilly land with gradients of 6–15°, low mountain region
with gradients of 15–25°, and mountain region (>25°)
accounts for 23 %, 19 %, and 11 % of the watershed,
respectively. On the macro-scale, the overall elevation
declines from the north to the south, where a large water
reservoir (Yuqiao) is situated. There is a mix of predominantly Gleysols in the lowland and lithosols in the
mountain. The former is derived from deltaic alluvial
sediments, while the latter is formed through weathering
25
Water Air Soil Pollut (2015) 226:25
Page 4 of 13
of the parent sedimentary bedrock consisting of sandstone and limestone. Approximately 130,000 residents
live within the watershed. Agricultural crop production
and livestock breeding are the main sources of income
and employment, although there are also several small
businesses, such as hotels, catering, and clothing factories. The local environmental protection bureau (JCEPB
2012) reported extensive and long-term excess application of inorganic P fertilizer and discard of livestock
manure.
In a synoptic soil survey, a total of 126 top soil
samples (0–15 cm) were collected throughout the watershed, capturing the span in soil types and land-use,
and analyzed for bio-available P (BAP) and P sorption
index (PSI). Based on soil pH, either the Olsen (Olsen
et al. 1954) or Bray-1 (Bray and Kurtz 1945) method
was used for BAP analysis, complying with the recommendation by U.S. Department of Agriculture (Elrashidi
2010). PSI was determined and calculated by Eq. 1
according to Bache and Williams (1971):
2.2 Description of the P Index
X
PSI L kg−1 ¼
logC
2.2.1 The Refined P Index Model
A refined P index model (Bin Zhou et al. 2014) was
evolved from a previous P index model (Zhang et al.
2003) applied in the same watershed. The main
explanatory input factors and their weightings to the
model are summarized in Table 1. Agricultural
management and environmental factors, such as P
fertilizer application, manure and sewage P production, and irrigation factor, were extracted from the
yearbook data of each individual village and divided
by its agricultural land area. The factor values were
assigned to their corresponding agricultural land
patch in an ArcGIS platform.
ð1Þ
where X is the amount of sorbed P (milligrams P per
kilogram) on the soil and C is the P concentration in
solution at equilibrium (milligrams P per liter). Degrees
of P saturation (DPS) in soils were determined based on
the BAP and PSI by Eq. 2:
BAP
DPSð%Þ ¼
100
ð2Þ
BAP þ PSI
The final values of the DPS factor were assigned to a
grid (250×250 m) of the study area through inverse
distance weighted interpolation (Childs 2004). Soil erosion factors for each grid were calculated using Revised
Universal Soil Loss Equation (RUSLE) (Renard et al.
Table 1 Factor weightings and loss ratings applied in the refined P index scheme
Factors
Unit/range
Weight
Risk rating value
Very low
2
Low
4
Medium
6
High
8
Very high
10
Source factors
DPS
%
1.0
<5
5–10
10–15
15–25
>25
P fertilizer
kg P ha–1 year−1
0.75
0
1–30
30–90
90—150
>150
Livestock manure
kg P ha—1 year—1
0.75
0
1–30
30–90
90–180
>180
Daily life sewage
kg P ha–1 year–1
0.75
0
0–8
8–16
16–24
>24
Soil erosion: A=R×K×LS×C×P
ton ha–1 year–1
1.0
<5
5–10
10–15
15–25
>25
Runoff amount
mm year–1
1.0
<195
195–210
210–225
225–240
>240
Irrigation amount
mm ha−1 year−1
0.75
0
0–380
380–420
420–460
>460
Soil drainage class
0–5
0.85
5
4
3
2
0
Transportation factors I:
erosion process
Transportation factors II:
migration distance
Water course erosion
0–1
0.85
0–0.2
0.2–0.4
0.4–0.6
0.6–0.8
>0.8
Flow length
km
0.85
>50
38–50
28–38
18–28
<18
Water Air Soil Pollut (2015) 226:25
Page 5 of 13 25
1997). Runoff factors were determined by mean annual
rainfall (millimeters) and the annual runoff coefficient.
Daily metrological data for the period 2006 to 2012
from local weather stations was used to derive the mean
annual rainfall. Annual runoff coefficients for different
land-use were adopted from a previous study in the
same area (Chen and Zhu 1991). The soil drainage class
factors were extracted from the Harmonized World Soil
Database (Nachtergaele and Batjes 2012). Water course
factors were based on a river network map and an
improved watercourse erosion model by introducing a
nonlinear watercourse erosion factor (Sivertun and
Prange 2003). Flow length factors were calculated using
data from a digital elevation model by means of the
ArcGIS 10 hydrological analyst module.
In order to capture the real-world interactions among
the factors, the applied structure of P index scheme
(Eq. 3) had been re-organized relative to the former P
index study by Zhang et al. (2003). The migration
distance factors were extracted from the transportation
scheme due to the restriction to other erosion-based
transportation factors.
(forest and shrub). This reflects that the source factors are
the primary drivers for P loss within the high-risk zones.
PI ¼ ½∑ S α wα ∑ T Dβ wβ ∑ T E γ wγ
2.3.2 Multi-layer Feed-Forward Neural Network
2.3 Relative Importance Analysis
2.3.1 Data Preparation
A spatial database was built consisting of 6,983 grid
points (250×250 m) covering the study area. All explanatory factors and final PI values were added into the
database. Prior to the neural network exercise, the original data set (dependent and independent variables) were
normalized using the Eq. 4:
Ri ¼
S i −minðS Þ
maxðS Þ− minðS Þ
ð4Þ
where Ri is the normalized value for observation i, Si is
the original value for observation i, and min(S) and
max(S) represent the minimum and maximum values
of targeted data set S, respectively.
ð3Þ
where Sα and wα are the source factor α rating, and its
weighting value, TDβ, and wβ are the transportation
factor β rating and its weighting value, which are based
on migration distance, TEγ and wγ are the transportation
factor γ rating and its weighting values, which are based
on erosion process, PI is the P index value.
The PI results were categorized into five risk rating
classes (Table 1) based on Jenks natural breaks classification method (McMaster 1997) and presented in Fig. 1.
2.2.2 Spatial Distribution Characteristics of PI
The algorithm of backpropagation in neural networks
consists of the following steps (Lek and Guégan 1999):
1. Number of nodes (input, hidden, and output layer) is
set relative to the number of input and output variables.
2 Learning rates and the maximum iterations (set all
weights and thresholds to small random values) are
initialized.
3. Input vectors are given to the input nodes, and the
output vectors are presented to the output node.
4. Input values for the hidden nodes are calculated
based on Eq. 5:
n
S j ¼ ∑ xi W i j
The percentages of area with specific geographical factors found within each risk class were assessed in order to
study their relationship with the PI values (Fig. 2). The
results show that the areas in the vicinity of rivers
(<100 m) and low elevation zones (i.e., <35 m above
the reservoir) are strongly represented in the very-highand high-risk zones. This reflects that the plain lowland
area and riverside zones are the main hot-spots for potential P loss. With regards to land-use structure, there is a
clear increasing potential for P loss with increasing share
of farmland or decreasing proportion of natural land-use
ð5Þ
i¼1
where xi is the input variable at the node I and Wij is
the weight from input node i to hidden node j.
Then the output was derived from the hidden
nodes according to Eq. 6:
Y j ¼ f Sj ¼
1
ð6Þ
1 þ e−S j
where Yj is the output variable from hidden node j.
The same algorithm was employed to calculate the
inputs to the output nodes.
5. Error term for the output node was calculated.
25
Water Air Soil Pollut (2015) 226:25
Page 6 of 13
Fig. 2 Percentage of related geographic factors within each P loss risk zones
6. Iteration ending condition was determined: When
the network errors were larger than predefined
threshold or the number of iterations was less than
the maximum iterations, then the calculation process continued (repeat steps 3–5) till one of these
criteria was met.
In this study, a three-layered feed-forward neural network (one input layer, one hidden layer, and one output
layer) was employed (Fig. 3). A cross-validation method
(Olden and Jackson 2000) was applied to determine the
optimal number of hidden neurons. It was found that the
lowest RMS error was achieved when the number of
neurons in the hidden layer was set at 4.
The Neural Interpretation Diagram was plotted to
show the structure of the structure of the multilayer
perception neural network used in this study and its
connections between layers in Fig. 3.
by Goh (1995). The details of the algorithm are given in
Eq. 7:
Qik ¼
L
N
X
X
jwi j v jk j= jwr j j
j¼1
N X
L
X
i¼1 j¼1
r¼1
N
X
jwi j v jk j= jwr j j
!
ð7Þ
r¼1
where wij is the connection weight between the input
neuron i and the hidden neuron j, vjk is the connection
weight between the hidden neuron j and the output
neuron k, and ∑Nr¼1 wr j is the sum of the connection
weights between the N input neurons and the hidden
neuron j. Qik represents the percentage of influence of
the input variable on the output. In order to avoid the
counteracting influence due to positive and negatives
values, all connection weights were given their absolute
values in the modified Garson algorithm.
3 Results and Discussion
2.3.3 Garson Algorithm
Garson (1991) proposed a method of partitioning the
neural network connection weights in order to determine
the relative importance of each input variable within the
network. The same idea has been modified and applied
3.1 Relative Importance of Input Parameters to the P
index Model
The very-high-risk area is strongly governed by the source
factors, e.g., high DPS and large fertilizer application
Water Air Soil Pollut (2015) 226:25
Page 7 of 13 25
Fig. 3 Neural interpretation
diagram for neural network
interpreting the final PI values as
a function of 14 input variables
revealed relatively high sensitivity scores (Fig. 4). This
mainly reflects that the agricultural land (Farmland and
Orchards) dominate this very-high-risk area (94 %)
(Fig. 2). The excess application of inorganic P fertilizers
and manure from the extensive livestock and poultry
breeding, as well as sewage to the fields (JCBS 2012;
JCEPB 2012) jointly contribute to the high level of P
enrichment in the top soil. In addition, the degree of P
saturation (DPS %) had generally a high risk level (average value ca. 21.5 %) due to high concentration of bioavailable P and the limited capacity of the soil to sorb P.
Where there is a very high risk for P loss, the runoff level
and water course erosion factors are shown to have the
greatest relative importance in the transportation scheme.
The extent of low-lying terrain and intensive agricultural
draining networks has a strong influence on the potential
risk for P loss. The riverine region should therefore be
prioritized as the main target area for control strategies
abating P loss.
The high-risk zone was also significantly influenced
by agricultural activities, since agricultural land compromised 66 % of its land-use. The PI score was therefore mainly governed by the DPS (%), as well as the
application of P fertilizer and livestock manure. The
relative importance of DPS (%) and application of P
fertilizers factors are correlated in the high- and veryhigh-risk areas, most likely because that P sorption
capacity maintained a relatively uniform P sorption
capacity while soil bio-available P generally determined
by P fertilizer usage. The point that water course and
runoff level factors had relatively high sensitivities in
the high-risk areas emphasizes the significance of riverine agricultural areas in regards to controlling P
transportation.
Fig. 4 Relative importance of the 14 input variables on the PI score based on Garson’s algorithm (Garson 1991)
25
Page 8 of 13
Medium-risk areas are generally distributed in the
intermediate region situated between the mainly natural
and predominantly human-affected regions.
Compared with the high-risk areas, the proportion of
farmland is significantly lower (19 %) in the medium-risk
areas, leading to lower sensitive scores for DPS (%) and
application of P fertilizer. Similarly, the relative importance of application of manure was significantly lower
than in the high-risk areas due to less livestock farming.
On the other hand, the application of sewage showed the
highest sensitivity in the source factor scheme. This is
due to relatively dense population in this area, with the
largest residential region (15.36 km2). In regards to the
transportation factors, vegetation coverage (RUSLE-C),
slope-length (RUSLE-LS), and runoff level factors mainly govern the PI in the median-risk zone, implying that
the natural factors associated with soil erosion begin to
take control of the potential for P loss.
The influences of human activities in the low-risk
zone are less than in the medium-risk zone due to a
larger proportion of natural forest and shrub land (48 %).
This led to a drop in sensitivity of source factors on the
PI, especially the application of P fertilizers and livestock manure. The analysis instead shows that the transportation factors govern the potential for P loss in this
zone. The draining class and soil texture (RULSE-K)
factors associated with soil physical properties, especially, gained higher sensitivities. This is likely reflecting a
greater diversity of soil types in this low-risk area.
Similar to the low-risk area, source factors have
relatively weak sensitivity in the very-low-risk area.
Due to its relatively rugged morphology, the influences
of human activities are low, with farmland and residential areas constituting only 2 % and 5 % of the very-lowrisk area, respectively. Instead, the transportation factors
governed the potential risk for P loss: Flow length,
vegetation coverage (RUSLE-C), and water course erosion showed high sensitive scores, with a record high
score in relative importance for the flow length factor.
This factor is the measure of the actual migration distance of surface runoff, which becomes larger as the
terrain complexity increases.
3.2 Differentiated BMPs for Controlling P loss
The dominant factors influencing the PI in each P loss
risk area varied. Differentiated best management practices (BMP) controlling P losses need therefore to be
tailored for each risk area class. With this perspective,
Water Air Soil Pollut (2015) 226:25
relevant background factors and recommended abatement strategies for reducing P losses in each of the risk
zones are presented in Table 2.
Although only accounting for 8 % of the watershed,
the very-high-risk area is considered the most important
abatement zone due to its large P losses from intensive
agricultural activities, augmented by numerous rivers and
agricultural channels and its proximity to the targeted
Yuqiao reservoir. Relatively stringent P loss strategies
focusing on the agricultural management practices are
essential for this area. Excessive P fertilization is widespread in the study area according to the survey from
local environmental and agricultural departments (JCBS
2012; JCEPB2012). This heavy loading of excessive P
input, especially in fields used for vegetable farming
(around 224 P kg ha−1 year−1) (JCBS 2012), needs to
be radically reduced. Special focus should be given to
agricultural fields already possessing a high degree of P
saturation (DPS) in the top soil. A main P source is dung
from extensive livestock and poultry breeding used as
manure or simply disposed of in a manner that enhances
its negative effects. This should be strictly controlled or a
system for collection of the dung needs to be established.
As for the transportation part, the agricultural fields
draining directly into the rivers and Yuqiao reservoir need
to be given special attention. Vegetative buffer strips have
been demonstrated by several studies (Dillaha et al. 1988;
Lee et al. 1998; Syversen 2005) to be an efficient abatement action in such areas, showing average removal
efficiencies of P from 37 to 89 %.
Radical management policies, such as prohibiting or
limiting the agricultural practice in the high-risk zone, is
not considered feasible considering the large local population dependent on their agricultural production for
their livelihood. Instead, P-based nutrient management
plans (NMPs), based on environment and economic
benefits (Weld et al. 2002), are worth considering. It is
possible to fully meet crop requirements while at the
same time significantly reduce the excessive nutrient
input through soil P testing, providing formulated advice regarding P fertilization to the farmer. Additionally,
the potential risk for soil P loss can be greatly suppressed through more optimal timing of fertilizer application avoiding heavy rain periods, deep application of
P fertilizer, and leaving crop residues (Dudenhoeffer
et al. 2013; Fawecett 2009). The dung from livestock
and poultry farming contributes substantially to the P
load due to the lack of any alternative for disposal of the
dung. Moreover, large amounts of animal dung are
Main governing factors
1. P fertilizer application 2. DPS (%)
3. Livestock breeding P emission
4. Water course erosion
1. Soil drainage class
2. RUSLE-C 3. Water course
erosion 4. RUSLE-K
Very low (130) 1. Flow length 2. RUSLE-C
3. Water course erosion 4.
RUSLE-R
Low (88)
Medium (106) 1. RUSLE-C 2. Runoff level
3. Daily life sewage P emission
4. P fertilizer application
High (97)
Very high (35) 1. DPS (%) 2. Runoff level 3. P
fertilizer application 4. Livestock
breeding P emission
Risk ranking
(area, km2)
5.1
4.1
Loamy sand
Sandy loam
15
7.8
18
12
10
7.1
5.7
303
122
73
56
33
Average
Average Average
shortest
slope
elevation (m)
distance to
(degree)
Yuqiao
reservoir (km)
Sandy clay loam 6.3
Loam
Clay-loam
Leading
soil texture
class
Related background factors
Table 2 Differentiated best management practices within different P loss risk zone
1. Prohibit field cultivation in this region; 2. Limit the development
of orchard in the ecologically vulnerable area; 3. Use berms, silt
fencing and erosion control blankets to stabilize the soil at the high
slope area
1. Implement natural forest conservation; 2. Install vegetation
filter strips along stream banks to keep soil out of the stream;
3. Encourage to develop terrace for orchard farming
1. Vegetation restoration for bare soil, especially on the critical
riverine area; 2. Reduce tillage, apply P fertilizer in the
sub-soil and mulch bare soil with dry branches and fallen
leaves for orchard filed; 3. Set up household sewage treatment plant
1. Carry out P-based nutrient management; 2. Reduce tillage
behaviors and leaving crop residues; 3. Use vegetative buffer
strips along the river bank and agricultural channel
1. Prohibit or restrict agricultural production requiring high P
fertilizer inputs (i.e., vegetable); 2. Prohibit or restrict size
of livestock and large scale poultry breeding; 3. Use vegetative
buffer strips along the in the riverine area and the shore of
Yuqiao reservoir
Targeted BMPs controlling P loss
Water Air Soil Pollut (2015) 226:25
Page 9 of 13 25
25
Page 10 of 13
carelessly disposed of directly into the agricultural channel or on wasteland along the river banks (JCEPB
2012). A system for manure collection should thus be
established in this zone, ensuring a sound disposal of the
animal dung. Alternatively, the scale of livestock and
poultry farming should be regulated dependent on the
carrying capacity of crop farming, as commonly practiced in most western countries.
In the medium-risk zone, the main potential source of
P loss is from sewage and by P fertilization in orchards.
According to the local statistical data (JCBS 2012), approximately 47,000 residents live in this risk zone. Sewage from household tanks empties into the nearest channels or is used as fertilizers in the low-lying farm land or
fishpond, without prior treatment. Sewage collection network and treatment system is not an option in such rural
areas due to high construction cost. Again, a system for
collection of sewage which, along with the animal dung,
can be utilized in a large-scale biogas plant may be a
solution. The sludge from the bioreactor is enriched in
bioavailable P and is therefore refined for use as fertilizer
in other regions. Horticulture of orchards comprises more
than 20 km2 in this risk zone. The synoptic studies of
BAP documented that the P fertilization in the orchards is
an important potential P source (Ojwando 2014). A series
of BMPs focusing on orchard garden, such as reducing
fertilizing, applying P fertilizer in the sub-soil, and leaving branches and litterfall to increase the soil organic
content and thereby the soils ability to hold P, is therefore
suggested. As for the transportation part, the vegetation
cover factor (RUSLE-C) showed high sensitivity in this
risk zone. It has been reported that enhanced vegetation
cover generally is considered as one of the most effective
manners to improve soil nutrient-holding capacity (Li
and Shao 2006) and limit erosion. Vegetated filter strips
consisting of perennial plants is therefore recommended
as an amendment policy for limiting the P transport in
this risk zone.
Low- and very-low-risk area had a similar set of
important sensitivity factors (Table 2). The transportation factors are the main governing factors for potential
P loss due to the rugged topography and low anthropogenic influence. Developing effective soil erosion control strategies is thus the main key to control potential P
loss in this region. As for orchard farming, terraces
should be considered as the suggested cultivation way
in order to reduce the steepness and length of slopes. For
crop planting, a relatively strict management policy
should be implemented due to the fragile ecological
Water Air Soil Pollut (2015) 226:25
environment with relatively shallow skeleton soil on
the steep slopes. In addition, natural forest conservation
and restoration are also important measures to improve
the ecosystems capacity to retain nutrients. In the steep
slope area, berms, silt fencing, and erosion control blankets can be used as measures for controlling soil erosion.
Converting the large number of decommissioned fish
farms ponds along the shore of the reservoir into constructed wetlands by redirecting the small channels and rivers
through these ponds will also serve to significantly reduce
the flux of P into the reservoir. Numerous studies have
documented a significant removal of nutrients in constructed wetlands receiving urban or agricultural stormwater
(Carleton et al. 2001), and downstream from agricultural
hot spots (Gottschall et al. 2007; Dunne et al. 2005).
3.3 Effect Scenarios of Abatement Actions
in the Critical Areas
Application of inorganic P fertilizers and livestock manure in the very-high- and high-risk areas were selected
as objects for the scenario analysis based on their strong
governing effect on the PI in these risk zones and their
practical operability in regards to agricultural
management.
The results from nine scenarios for each risk area are
listed in Table 3, according to their overall effect. Reduction gradients ranging from 0 to 85 % were set for
the application of P fertilizer and livestock manure P
emission factors. The change in DPS was determined
relative to ‘business as usual’ based on the current
amount of applied P fertilizer and the number and type
of livestock in each village. DPS is determined using
Eq. 2, based on measured BAP (bioavailable P) and
PSC (phosphorus sorption capacity). PSC generally depends on the physico-chemical characteristics of soil
and maintains a relatively constant level within the
considered timeframe (Pinto et al. 2013). On the other
hand, BAP is mainly governed by agricultural management practices and the properties of the soil (Menon and
Chien 1995). The application of P fertilizer and emissions of P from livestock manure jointly contributed
nearly 75 % of the total P loss (TMWA 2010), and the
background BAP level is relatively low (less than
3 mg kg−1) (Wang 1982). All of the applied P fertilizers
were considered as BAP sources, while the BAP
amount from different types of manure were determined
based on previous studies (Dagna 2012; Laboski et al.
2006). The mean DPS reduction rates achieved by the
Water Air Soil Pollut (2015) 226:25
Page 11 of 13 25
Table 3 Scenario analysis based on different P controlling strategies
Risk area (area
proportion)
P fertilizer
reduction (%)
Livestock P emission
reduction (%)
Mean DPS
reduction (%)
Mean PI
reduction (%)
∑PI highðveryhighÞ
100
∑PI overall
High-risk area
(21.33 %)
0
0
0
0
38
25
0
20
9.6
32
55
0
50
20
29
85
0
65
36
27
0
25
6.6
7.3
31
0
55
16
15
30
0
85
21
22
29
25
25
25
16
31
55
55
55
34
27
85
85
85
57
23
0
0
0
0
24
25
0
15
11
20
55
0
38
17
19
85
0
61
37
16
0
25
1.3
6.0
20
0
55
9.6
13
19
0
85
18
24
18
25
25
25
18
20
55
55
55
32
17
85
85
85
63
13
Very-high-risk area
(7.64 %)
different combined scenarios of abatement actions are
listed in Table 3.
The P fertilizer application factor shows a stronger
sensitivity to the DPS and PI values compared with the
livestock manure P emission factor. This is in accordance
with the sensitivity analysis results and is conceptually
sound since the P fertilizer generally possesses a higher
environmental risk due to its relatively high mobility and
bioavailability. In contrast, the release of BAP from livestock manure usually takes place over a relatively longer
period due to the slow decomposition processes. Compared with the high-risk area, a more pronounced effect of
the abatement actions was observed on the PI values in
the very-high-risk areas. This reflects that the source
factors possess more significant influences on the PI
value in the very-high-risk area than in the high-risk area.
The combined effect of the abatement scenarios on
the total risk for P loss was quantified by taking the ratio
of the accumulated PI grid value for the critical area
(high- and very-high-risk areas) over the accumulated
PI grid value for the overall study area. In the high- and
very-high-risk areas, the risk level for P loss was reduced
from 38 to 23 % and from 24 to 13 %, respectively
(Table 3). This may serve as a quantitative indicator for
decision-makers in terms of setting up cost-effective P
control plans.
4 Summary and Conclusions
A multi-parameter sensitivity analysis of a refined P
index model was performed for an intensely agriculturally influenced watershed using a BPN network model
with Garson algorithms. The following conclusions are
made from this study:
1. Source scheme factors generally show more significant sensitivities in the very-high- and high-risk ranking areas. DPS factor showed a similar trend to the P
fertilizer application. In addition, as the main hydrological process of P transportation, runoff level and
water course erosion factors also exhibit relatively
significant sensitivities in the transportation scheme.
This clearly reflects the importance of considering
25
2.
3.
4.
5.
Page 12 of 13
the combined effects of explanatory variables in the
revised P index.
In the transition zone, between predominantly natural and human-affected regions, the average sensitive score of source factors and transportation factors were similar. P emission from sewage and
orchard P fertilization were the main source factors
governing the PI. The most important transportation
factors were vegetation coverage and runoff level.
In the low- and very-low-risk zones the potential risk
for P loss was mainly governed by transportation
factors, especially the vegetation coverage
(RUSLE-C) and flow length. This is mainly due to
strong erosion factors and overall low source factors.
Relatively comprehensive P control strategies are
required in order to curb the excessive losses of P.
The extensive livestock breeding needs to be restricted, especially in the vicinity of reservoir and
riverine zone. Alternatively, a system for collection
of dung from the livestock (as well as sewage from
the population) needs to be established. In addition,
the implementation of P-based NMPs is required,
especially for agricultural crops demanding high P
fertilizer inputs (such as vegetables). Due to the
dominance of transportation scheme factors in the
low- and very-low-risk areas, the corresponding P
control strategies in these regions should mainly focus
on avoiding soil erosion by vegetation restoration.
The scenario analysis indicates that decreasing the
application of P fertilizer and manure by up to 85 %
will lead to a decrease in the proportion of high- and
very-high-risk area from 38 to 23 % and 24 to 13 %,
respectively.
Acknowledgment The authors are grateful to the Research
Council of Norway for funding the SinoTropia Project (Project
no. 209687/E40). The Ji County Environmental Protection Bureau, Ji County Meteorological Bureau, and Ji County Statistics
Bureau are highly acknowledged for their valuable assistances in
providing the essential background data.
References
Bache, B., & Williams, E. (1971). A phosphate sorption index for
soils. Journal of Soil Science, 22, 289–301.
Beaulieu, L., Gallichand, J., Duchemin, M., & Parent, L. (2006).
Sensitivity analysis of a phosphorus index for Quebec.
Canadian Biosystems Engineering, 48, 1.
Bin Zhou, R. D. V., Chongyu Xu, Xueqiang Lu, Hongliang Xu,
Joshi P. Bishna, Liang Zhu: 2014, 'Establishment and
Water Air Soil Pollut (2015) 226:25
validation of an amended phosphorus index: refined phosphorus loss assessment of a agriculture watershed in northern
China', Water Air and Soil Pollution(Accepted).
Brandt, R., & Elliott, H. (2005). Sensitivity analysis of the
Pennsylvania phosphorus index for agricultural recycling of
municipal biosolids. Journal of Soil and Water Conservation,
60, 209–219.
Bray, R. H., & Kurtz, L. (1945). Determination of total, organic,
and available forms of phosphorus in soils. Soil Science, 59,
39–46.
Brown, S., Schreier, H., Shah, P., & Lavkulich, L. (1999).
Modelling of soil nutrient budgets: an assessment of agricultural sustainability in Nepal. Soil Use and Management, 15,
101–108.
Carleton, J., Grizzard, T., Godrej, A., & Post, H. (2001). Factors
affecting the performance of stormwater treatment wetlands.
Water Research 35, 1552–1562.
Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W.,
Sharpley, A. N., & Smith, V. H. (1998). Nonpoint pollution
of surface waters with phosphorus and nitrogen. Ecological
Applications, 8, 559–568.
Chen, Y. & Zhu, X.: 1991, Research on the Yuqiao reservoir's
eutrophication and its control measure, Tianjin WaterPower
Press.
Childs, C. (2004). Interpolating surfaces in ArcGIS spatial analyst
(pp. 32–35). July-September: ArcUser.
Dagna, N. (2012). Application of soil test methods to study the
availability of phosphorus in beef cattle manure.
Dillaha, T., Sherrard, J., Lee, D., Mostaghimi, S., & Shanholtz, V.
(1988). Evaluation of vegetative filter strips as a best management practice for feed lots. Journal Water Pollution
Control Federation, 1231–1238.
Dudenhoeffer, C. J., Nelson, K. A., Motavalli, P. P., Burdick, B.,
Dunn, D., & Goyne, K. W. (2013). Utility of phosphorus
enhancers and strip-tillage for corn production. Journal of
Agricultural Science, 1916–9752, 5.
Dunne, E., Culleton, N., O’Donovan, G., Harrington, R., & Olsen,
A. (2005). An integrated constructed wetland to treat contaminants and nutrients from dairy farmyard dirty water.
Ecological Engineering 24, 219–232.
Elliott, H., Brandt, R., Kleinman, P., Sharpley, A., & Beegle, D.
(2006). Estimating source coefficients for phosphorus site
indices. Journal of Environmental Quality, 35, 2195–2201.
Elrashidi, M. (2010). Selection of an appropriate phosphorus test
for soils. Lincoln, NE: USDA NRCS.
Fawecett, R.: 2009, ‘A review of bmps for managing crop nutrients and conservation tillage to improve water quality',
Conservation Technology Information Center. Available at
http://www.conservationinformation.org. Accessed 16
García, A. M., Hoos, A. B., & Terziotti, S. (2011). A regional
modeling framework of phosphorus sources and transport in
streams of the Southeastern United States. JAWRA Journal of
the American Water Resources Association, 47, 991–1010.
Garson, G. D. (1991). Interpreting neural-network connection
weights. AI Expert, 6, 46–51.
Gevrey, M., Dimopoulos, I., & Lek, S. (2006). Two-way interaction of input variables in the sensitivity analysis of neural
network models. Ecological Modelling, 195, 43–50.
Goh, A. (1995). Back-propagation neural networks for modeling
complex systems. Artificial Intelligence in Engineering, 9,
143–151.
Water Air Soil Pollut (2015) 226:25
Good, L. W., Vadas, P., Panuska, J. C., Bonilla, C. A., & Jokela, W.
E. (2012). Testing the Wisconsin phosphorus index with
year-round, field-scale runoff monitoring. Journal of
Environmental Quality, 41, 1730–1740.
Gottschall, N., Boutin, C., Crolla, A., Kinsley, C., & Champagne,
P. (2007). The role of plants in the removal of nutrients at a
constructed wetland treating agricultural (dairy) wastewater,
Ontario, Canada. Ecological Engineering 29, 154–163.
Jarvie, H. P., Sharpley, A. N., Withers, P. J., Scott, J. T., Haggard,
B. E., & Neal, C. (2013). Phosphorus mitigation to control
river eutrophication: murky waters, inconvenient truths, and
“postnormal” science. Journal of Environmental Quality, 42,
295–304.
JCBS. (2011). 'Ji county Statistical yearbook 2011', Ji county
Bureau of Statistics.
JCBS. (2012). 'Ji county Statistical yearbook 2012', Ji county
Bureau of Statistics.
JCEPB. (2012). 'Ji County Environmental Quality Report (2012)',
Ji county Environmental Protection Bureau.
Laboski, C. A., Peters, J. B., & Bundy, L. G. (2006). Nutrient
application guidelines for field, vegetable, and fruit crops in
Wisconsin. Division of Cooperative: Extension of the
University of Wisconsin-Extension.
Lee, K., Isenhart, T., Schultz, R., & Mickelson, S. (1998). Nutrient and
sediment removal by switchgrass and cool-season grass filter
strips in Central Iowa, USA. Agroforestry Systems, 44, 121–132.
Lek, S., & Guégan, J.-F. (1999). Artificial neural networks as a
tool in ecological modelling, an introduction. Ecological
Modelling, 120, 65–73.
Lemunyon, J., & Gilbert, R. (1993). The concept and need for a
phosphorus assessment tool. Journal of Production
Agriculture, 6, 483–486.
Li, Y., & Shao, M. (2006). Change of soil physical properties
under long-term natural vegetation restoration in the Loess
Plateau of China. Journal of Arid Environments, 64, 77–96.
Marjerison, R., Dahlke, H., Easton, Z., Seifert, S., & Walter, M.
(2011). 'A phosphorus index transport factor based on variable source area hydrology for New York State', Journal of
Soil and Water Conservation 66, 149–157.
Mastrorillo, S., Dauba, F., Oberdorff, T., Guégan, J.-F., & Lek, S.
(1998). Predicting local fish species richness in the Garonne
River basin. Comptes Rendus de l'Académie des SciencesSeries III-Sciences de la Vie, 321, 423–428.
McMaster, R. (1997). In Memoriam: George F. Jenks (1916–1996).
Cartography and Geographic Information Systems, 24, 56–59.
Menon, R., & Chien, S. (1995). Soil testing for available phosphorus in soils where phosphate rock-based fertilizers are
used. Fertilizer Research, 41, 179–187.
Mumtaz, K., Sheriff, S., & Duraiswamy, K. (2008). Evaluation of
neural network based classification systems for clinical cancer data classification. Journal of Computer Applications, 1,
10.
Page 13 of 13 25
Nachtergaele, F., & Batjes, N. (2012). Harmonized world soil
database, FAO.
Ojwando, W. O. (2014) Monitoring of phosphorous fractions–
Understanding the hydrogeochemical processes governing
mobilization and transfer of phosphorous in an agricultural
watershed in north-eastern China. Published dissertation,
University of Oslo. Oslo, Norway
Olden, J. D., & Jackson, D. A. (2000). Torturing data for the sake
of generality: how valid are our regression models?
Ecoscience, 7, 501–510.
Olsen, S. R., Cole, C., Watanabe, F. S., & Dean, L. (1954).
Estimation of available phosphorus in soils by extraction
with sodium bicarbonate. DC: US Department of
Agriculture Washington.
Pinto, F. A., Souza, E. D. D., Paulino, H. B., Curi, N., & Carneiro,
M. A. C. (2013). 'P-sorption and desorption in savanna
Brazilian soils as a support for phosphorus fertilizer management. Ciencia e Agrotecnologia, 37, 521–530.
Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D., &
Yoder, D. (1997). 'Predicting soil erosion by water: a guide
to conservation planning with the revised universal soil loss
equation (RUSLE)', Agriculture handbook (Washington).
Sivertun, Å., & Prange, L. (2003). Non-point source critical area
analysis in the Gisselo watershed using GIS. Environmental
Modelling & Software, 18, 887–898.
Syversen, N. (2005). Effect and design of buffer zones in the
Nordic climate: the influence of width, amount of surface
runoff, seasonal variation and vegetation type on retention
efficiency for nutrient and particle runoff. Ecological
Engineering, 24, 483–490.
TMWA. (2010). 'The implementation scheme of pollution source
treatment project around Yuqiao Reservoir', Tianjin municipal water administration.
Wang, J. (1982). Ji County soil census report. Ji County agricultural committee, 137.
Weld, J., Parsons, R., Beegle, D., Sharpley, A., Gburek, W., &
Clouser, W. (2002). Evaluation of phosphorus-based nutrient
management strategies in Pennsylvania. Journal of Soil and
Water Conservation, 57, 448–454.
Wu, H., Yuan, Z., Zhang, L., & Bi, J. (2012). Eutrophication
mitigation strategies: perspectives from the quantification of
phosphorus flows in socioeconomic system of Feixi, Central
China. Journal of Cleaner Production, 23, 122–137.
Zhang, S., Chen, L., Fu, B., & Li, J. (2003). The risk assessment of
nonpoint pollution of phosphorus from agricultural lands: a
case study of Yuqiao reservoir watershed. Quaternary
Science, 23, 262–269.
Zhou, B., Vogt, R. D., Xu, C., Lu, X., Xu, H., Bishnu, J. P., & Zhu,
L. (2014). Establishment and validation of an amended phosphorus index: refined phosphorus loss assessment of an
agriculture watershed in Northern China. Water, Air, & Soil
Pollution, 225, 1–16.