WQIag -- Water Quality Index for Runoff Water from
Agricultural Fields
1.
Introduction:
Water quality index (WQI) is a dimensionless number that
combines multiple water quality
factors into a single number by normalizing values to subjective rating curves (Miller et al., 1986). Conventionally it has been used for evaluating the quality of water for water resources such as rivers, streams and lakes, etc. Factors included in WQI vary depending upon the designated water uses of the waterbody and local preferences. Some of the factors include dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total coli form bacteria, temperature, and nutrients (nitrogen and phosphorus), etc. These parameters are measured in different ranges and expressed in different units. The WQI takes the complex scientific information of these variables and synthesizes into a single number. Several authors have worked on the this concepts and presented examples with case scenarios in the literature (Bolton et al. 1978; Bhargave, 1983; House 1989; Mitchell and Stapp, 1996; Pesce and Winderlin, 2000; Cude, 2001; Liou et al. 2004; Said et al. 2004; Nasiri et al., 2007, NSF, 2007). Lal, 2011 reviewed and summarized the work of these authors and presented a case scenario for different WQI models using an example dataset. This paper also recognizes the need for a WQI model for the surface runoff water generated from agricultural fields.
factors into a single number by normalizing values to subjective rating curves (Miller et al., 1986). Conventionally it has been used for evaluating the quality of water for water resources such as rivers, streams and lakes, etc. Factors included in WQI vary depending upon the designated water uses of the waterbody and local preferences. Some of the factors include dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total coli form bacteria, temperature, and nutrients (nitrogen and phosphorus), etc. These parameters are measured in different ranges and expressed in different units. The WQI takes the complex scientific information of these variables and synthesizes into a single number. Several authors have worked on the this concepts and presented examples with case scenarios in the literature (Bolton et al. 1978; Bhargave, 1983; House 1989; Mitchell and Stapp, 1996; Pesce and Winderlin, 2000; Cude, 2001; Liou et al. 2004; Said et al. 2004; Nasiri et al., 2007, NSF, 2007). Lal, 2011 reviewed and summarized the work of these authors and presented a case scenario for different WQI models using an example dataset. This paper also recognizes the need for a WQI model for the surface runoff water generated from agricultural fields.
The Natural Resources Conservation Service of the US
Department of Agriculture (USDA/NRCS) provides technical assistance (TA) and
financial assistance (TA) cost shares that enable agricultural producers to be
good stewards of the Nation’s soil, water, and related natural resources on non-Federal
lands. One of the key goals of
implementing conservation practices is to maintain and improve water quality
within the watershed. The NRCS/USDA is always looking for approaches
and techniques to evaluate the effects of its programs on the environment – for
example the CEAP Program (USDA/NRCS, 2011).
The WQI can serve as a simple first step tool in these efforts of
evaluating the effects of the conservation practices in improving and/or
sustaining the quality of water in the watershed. However,
the structure and components of the conventional WQI models discussed by Lal,
2011 would need to be modified for them to be appropriate for evaluating the
water quality of the runoff from agricultural landscape.
This paper describes such a model -- referred to as WQIag --
developed for evaluating the quality of the runoff water from agricultural
fields. In addition to describing
different components of WQIag and how they are integrated into a single
dimensionless number, the paper also presents a hypothetical scenario for
Clackamas County in Oregon.
2.
Components and Composition of WQIag
The factors influencing the WQIag could be divided into four
broad categories: 1) Field sensitivity/physical factors, 2) Nutrient management
factors, 3) Tillage management factors, and 4) Pest management factors. The field sensitivity/physical factors such
as slope, soil texture, etc. control the quantity of runoff. Based upon these factors the precipitation
falling on a field generates runoff that moves both dissolved nutrients as well
as entraining particles in overland flow.
This portion of the index addresses the inherent characteristics of the
field which is not expected to change dramatically over time without
application of conservation measures.
On the other hand tillage, nutrient, and pesticide
management practices control the quality of water flowing out of the
field. The application and management of
nutrients are critical to the index as well as the ultimate load of nutrients
potentially entering a water body. The
nutrient management is composed of four variables: the rate, timing, form, and
method of application of fertilizers. The
primary objective of nutrient management is to balance nutrient inputs for the
vegetative requirement to achieve sustainable crop yields while minimizing off
site loss of nutrients. The USDA/NRCS
Practice Standard 590 (Nutrient Management Practice Standard) describes these
variables in much greater details (USDA/NRCS, 2006).
2.1 Field Sensitivity/Physical
Factors (WQI-fs)
2.1.1 Slope (WQI-fsl)
The slope plays an important role in generating runoff from
a field. Higher the slope more
susceptible it is to generate runoff and soil erosion. Thus, the highest WQI-fsl is assigned to the
flat land with slope of less than 0.5 percent.
On the other hand, very-very steep soils of more than 10% are assigned
the WQI-fsl value of 1 (Table 1) thus implying that they are more detrimental
to water quality. Proximity of the field
to a waterbody (river, stream, lake, etc.) receiving runoff is an important consideration.
It could significantly influence the runoff water quality entering the water
body. However, it does not influence runoff
water quality at the edge of the field which is the primary objective of this
analysis.
2.1.2
Soil Erodibility Factor (WQI-fkf)
The soil erodibility is defined as the susceptibility of a
soil to sheet and rill erosion by water. It is generally expressed as K factor -- one
of the six factors used in the Universal Soil Loss Equation (USLE) and the
Revised Universal Soil Loss Equation (RUSLE) that are used to predict the
average annual rate of soil loss. The K
factor ranges from 0.02 to 0.69 is based primarily on percentage of silt, sand
and organic matter, and on the soil structure and the saturated hydraulic
conductivity. The other factors being
equal, higher the K value, more susceptible the soil is to sheet and rill
erosion by water thus leading to decreasing the quality of runoff water. Table 2 presents the ranking of WQI-fkf for
different ranges of K-factors. The
K-factor in the range of 0.02 to 0.10 is assigned the highest WQI-fkf value of
10. The least WQI-fkf value of 2 is
assigned to K-factor above 0.56.
The K-factor value for a field can be obtained from the NRCS
Web Soil Survey Website by using the
following procedure:
- Go to the Web Soil Survey website by clicking on the following website link (http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx).
- Zoom into the region of your field using one of the “Quick Navigation” options (Address, State & County, Latitude & Longitude, etc) within the Area of Interest (AOI) tab.
- Delineate your field using one of the two “Area of Interest AOI” buttons on the “Area of Interactive Map” window.
- Get the Soil Map Unit Symbol(s) and Soil Map Name(s) with their acreage(s) for the delineated AOI by clicking on the “Soil Map” tab.
- Get the report on K-Factor, Rock Free within the Soil Erosion Factors from the “Soil Properties and Qualities” tab within the “Soil Explorer” tab. "Erosion factor Kf (rock free)" indicates the erodibility of the fine-earth fraction, or the material less than 2 millimeters in size.
- You can use the K-Factor of the most predominant soil(s) or develop an average K-Factor value for the AOI using the weighted average method for different Map Unit Acreages.
- Based upon the calculated K value, estimate WQI-fkf value from the table 2.
2.1.3
Organic Matter (OM) content
(WQI-fom)
Organic matter content of the soil can significantly
influence the quality of water running off from a field. Soil
organic matter can hold 10 to 1000 times more water and nutrients than the same
amount of soil. The presence of OM in
soils reduces sediment and nutrient load in the runoff and improves the water
quality. Thus, Table 3 assigns the
highest WQI-fom value of 10 to the soil with the OM content of more than 8
percent and then reducing for the soils with lesser percentage OM content.
2.1.4
Rainfall / Vegetation Factor
(WQI-fvr)
Rainfall falling on a field generates runoff and soil
erosion. However, vegetative cover (live
and dead) present during the rainfall can significantly reduce its impact on
runoff generation. Thus, this section
evaluates the combined effect of these two factors on the water quality index.
We classify vegetative cover (live or dead) and rainfall in
three categories (low, medium and high).
We suggest the following heuristics for the vegetative cover (Live or dead).
Less than 30% vegetative cover = Low Vegetation (Vl)
31 to 80% vegetative cover = Medium Vegetation (Vm)
More than 80% vegetative cover = High Vegetation (Vh)
The heuristic for the rainfall categories would be
site-specifics as explained for the example scenario later in this
section.
With three levels of vegetative covers: low (Vl), medium
(Vm), and High (Vh); and the three levels of rainfall categories: low (Rl),
medium (Rm), and high (Rh), we developed a 3 by 3 matrix to generate WQI-fvr as
depicted in Table 4a and 4b. In this
matrix, the combination of high vegetation (Vh) and low rainfall (Rl) gets the
highest WQI-fvr rating of 9 as it would generate minimum amount of runoff. On the
other extreme the combination of the low vegetation (Vl) and high rainfall (Rh)
gets the least WQI-fvr rating of 1 as it would generate high runoff and erosion.
The table 4c presents an example for estimating the WQI-fvr
for a field in Clackamas county of Oregon.
It shows the average monthly precipitation ranging from 0.7 inch in July
to 7.09 inches in December. A rainfall
ranking factor is assigned for each month using the following heuristics:
Rainfall less than 2 in = Low Rainfall (Rl)
Rainfall between 2.0 to 4.0 in = Medium Rainfall
(Rm)
Rainfall more than 4 in = High Rainfall (Rh)
The table 4c also shows monthly vegetation ranking for the
test case scenario. The months of June to September are
assigned high vegetative cover (Vh) followed by October and November getting
the medium vegetative cover (Vm), and December to March getting the low
vegetative cover (Vl). This assignment is arbitrary taking into
account the cropping season of the region.
It could vary significantly in real condition based upon the land use
and land cover type.
Based upon the rainfall and vegetation ranking, a value of
WQI-fvr is assigned for each month from the decision matrix tables 4a and 4b. The months of June to September get the
highest ranking of 9 because of high vegetation (Vh) and low rainfall
combination during these months. Table
4c provides WQI-fvr value for each month which can be combined into a single
value for entire year by simple averaging these numbers.
2.1.5 Integrating
Soil Sensitivity/Physical factors into a single value (WQI-fs)
The field sensitivity/physical factors (slope, K-factor, OM,
and vegetative/rainfall) are combined into a single WQI-fs value using a simple
arithmetic mean with a weighing factor assigned to each value. This technique permits adjusting contribution
of each component in the overall WQI-fs based upon the local preferences as
demonstrated in Table 5. In this
example, all four components get equal weight of 0.25 totaling to 1. If different weights need to be assigned to
different factors, the sum total of all the weights should always equal to 1.
2.2 Nutrient Management (WQI-nm)
2.2.1 Application Rate
(WQI-nar)
Nutrient management components that affect runoff water
quality from a field include: rate,
form, timing and method of application of fertilizers. Higher fertilizer application rate leads to
increasing water quality concerns. Farmers
generally apply fertilizers using the State Land Grand University (LGU)
recommendations or the nutrient budgeting process (NBP). Both of these approaches aim to maximize
production with little consideration to the environmental quality. Thus as depicted in Table 6 the LGU/NBP
fertilizer rate applications are considered mediocre for the water quality and are
awarded the rating of 5 in the scale of 1-10.
Anything less that LGU/NBP application rate gets higher points (between
5 to10) and anything more gets lower WQI-nar ranking.
2.2.2 Application
Type and Timing (WQI-ntt)
The timing of fertilizer application plays an important role
in the effectiveness of the plant to uptake the applied nutrients. If applied at the optimum time, large
percentage of nutrients are taken up by the plants, thus minimizing negative
impact on the water quality. In addition plants need nutrients at different
growth stages. It is a well established
that split applications works better than a single application of nitrogen
fertilizer both for the environment as well as for the plant growth. It minimizes lateral movement, volatilization
and deeper percolation of the nutrients.
Table 7 presents rating for this component of nutrient management for
the water quality index. The split application of synthetic normal fertilizers
during the growing season is assigned the highest rating of 10 and the single
application of un-composted manure during the pre-growing season gets the
minimum rating of 2. The slow releasing fertilizers are applied in a single
application in advance of the cropping season so that they become available
when they are needed by the plants. This technique causes the least damage to
the water quality thus getting the highest rating of 10.
On the other hand, P-fertilizers can be applied in single
application as P is not subjected to volatilization and can stay in the soils
for an extended period of time. However,
applying synthetic P fertilizers during growing season when needed would cause
less damage to water quality thus getting much higher rating of 7 compared to 2
if it is applied during the pre-growing season.
2.2.3 Application Method and Soil Condition (WQI-nms)
Application method and soil condition at the time of
fertilizer application are two additional factors that play key role in plant
nutrient uptake and impact on water quality.
Fertilizer directly injected into dry/well drained soils condition is
best for plant uptake and also causes minimal impact on the water quality, thus
getting the highest ranking of 10 (Table 8). However, anhydrous ammonia, widely used in
American agriculture as a source of nitrogen, needs to be applied in slightly
moist conditions with appropriate coverage mechanism to minimize volatilization. This method also gets high rating of 9. Applying anhydrous ammonia in dry soils would
lead to significant volatilization thus this technique is given a low rating of
2 similar to the fertilizer broadcasted on the frozen soils (Table 8)
2.2.4 Integrating Nutrient Management factors into
a single WQI-nm
The nutrient management factors are combined into a single
WQI-nm value using a simple arithmetic weighted mean of their values. A
weighting factor is assigned to each component.
This technique permits adjusting relative contribution of each component
in the overall WQI based upon the local preferences as demonstrated in Table 9.
In this example, the application rate, and N-Source and application timing get
the highest weight of 0.30 each; followed by the application method and soil
condition with the weight of 0.25. The
P-source and application timing is given the least weigh of 0.15. The total of four weights should always equal
to 1.
2.3 Tillage Management (WQI-tm)
The effect of soil tillage on soil erosion is well
established. More the soil is tilled
more susceptible it becomes for erosion.
Thus, it is an important factor in evaluating the quality of runoff
water from a field. Soil Tillage
Intensity Rating (STIR) is a tool that has been widely used for evaluating the
soil disturbance as well as the severity of the disturbance caused by tillage
operations (Al-Kaisi, 2007 and Boetger, undated). Specific components of STIR value include:
-
Operational speed of tillage
operation
-
Tillage type
-
Depth of tillage operation
-
Percentage of soil surface area
disturbed
The STIR value can range between 0-200. Low STIR value reduces likelihood of sheet
rill erosion. By definition, No-Till operation gets the STIR value of 30. Table 10 presents different tillage systems
with their possible STIR ranges and associated WQI-tm values. You can use this
table by identifying the tillage system most representative of your condition
and then selecting corresponding WQI-tm value; or you can use the RUSLE2
database to obtain the STIR value for your tillage system and then selecting
the corresponding WQI-tm value.
2.4 Pest Management (WQI-pm)
Pests (weeds, insects, and diseases) are expected elements
of a farming system. Considerable amount
of efforts and resources are devoted on controlling and/or managing them. Modern pest management approach uses
combination of practices generally referred to as Integrated Pest Management
(IPM). They incorporate crop rotations,
cultural practices, scouting, crop selections, and other field practices to
prevent pest problems from occurring.
When pest infection do occur at damaging levels they are controlled
using chemicals in the most effective way with minimum risk to environmental including
water quality. Table 11 employs this
criterion and presents the WQI-pm ranking for different levels of pest
management options. Highest rating of 10
is given when IPM is followed with no chemical suppression, and lowest rating
of 2 is awarded to the system of pest control using chemicals with no
mitigation.
3
Combining sub-indices into a single
number of WQIag
Table 12 presents a hypothetical scenario for WQIag
calculations by aggregating values of different WQI sub-indices such as WQI-fs,
WQI-nm, WQI-tm and WQI-pm. The WQI-fs is
arrived by combining four field sensitivity/physical components namely slope
(WQI-fsl, K-factor (WQI-kfk, OM factor (WQI-fom, and Rainfall/Vegetation
interaction (WQI-fvr) as illustrated Table 5
The WQI-nm integrates components of nutrient management namely
application rate (WQI-nar, N-source and application timing (WQI-ntt, P-Source
and application time (WQI-ntt), and application method and soil condition
(WQI-nms) as demonstrated in Table 9.
The overall WQIag is then arrived at combining the WQI-fs, WQI-nm,
WQI-tm and WQI-pm. A weighting factor is
assigned to each of these sub-indices to account for the local and
site-specific preferences. In the
present example, equal weight of 0.25 is assigned to each of the factors. If un-equal weight needs to be assigned to
one or more factors, make sure the sum total of all weights should always equal
to 1.
For the hypothetical scenario, the overall WQIag is arrived
at 6.025 in the scale of 1-10 which is equivalent of 60.25 on the scale of
1-100 generally used in conventional WQI models (Lal, 2011). In these models, the WQI ranking of 60 is
classified as poor water quality which is expected for the runoff water from
the agricultural fields. Part of the
contaminants (nutrients and sediments) in the runoff water is assimilated in
the pathway from the edge of the field to the waterbody receiving the
runoff. Thus, the distance of the
waterbody from the field is another important factor that influences the
quality of the water of the receiving waterbody. If you need to generate the WQI for water
entering the waterbody, the field level WQIag would need to be adjusted by the
distance of the field generating the runoff to the body of water receiving the
water.
4
Concluding
Remarks
Water quality index (WQI) takes information from a number of
sources and combines them into single number that represents an overall
snapshot of the quality of the water at a particular time and location. Traditionally WQI has been developed and used
for evaluating water quality of water resources such as streams, rivers and
lakes (Lal, 2011). It is the first
attempt to define a WQI model, referred to as WQIag, for evaluating the quality
of runoff water from agricultural fields.
WQIag incorporates subjective judgment on ranking different factors and
how they influence the model. In
addition, a concept of weighting factor
have been introduced to weigh different factors for site-specific local
preference for each factor.
The NRCS/USDA is always looking for approaches and
techniques to evaluate the effects of its programs on the environment including
water quality. It is hoped that WQIag
could serve as a tool for evaluating the success of conservation practices for
improving water quality. It could provide answers to commonly asked
questions: how effective a conservation practice – cost-shared by NRCS -- has
been in improving the water quality. The simplicity of WQIag in expressing the
water quality would be much more appreciated by farmers and inspire them to
serve as better stewards of the conservation practices.
WQIag could also serve as a proxy for water quality
monitoring which is a rather tedious and expensive phenomenon. Tracking changes in the WQIag value for a
site over time could serve as a surrogate to water quality monitoring at much
less cost. It could be used for trend analysis and provide
a direction, context and qualitative indication of nutrients leaving the field
and potentially entering water courses.
3.
References:
Al-Kaisi. M. 2007. Conservation Systems: Challenges and Benefits. In Iowa Learning Farm. Vol. 3 Issue 1.
Bhargave D.S. 1983.
Use of water quality index for river classification and zoning of Ganga
River, Environmental Poll. Serv. B: Chem. Phys. 6, 51-76
Boetger, S. undated.
RUSLE2 Soil Erosion Calculations on Conservation Tillage System. USDA/NRCS, FL
Bolton, P.W., Currie, J. C., Tervet D. J, Welch, W. T.
1978. An index to improve water quality
classification. Water Pollution Control. 77, 271-284
Cude, C. G. 2001. Oregon Water Quality
Index: A Tool for evaluating water
quality management effectiveness. J. of
the Am. Water Resources Assco. Vol 37 No. 1 (125-137)
House, M. A. 1989. A
Water quality index for river
management. J. Inst.Water Environ. Manage. 3, 336-344.
Lal, H. 2011.
Introduction to Water Quality Index.
National Water Quality and Quantity Team, USDA/NRCS-WNTSC, Portland, OR
(under preparation)
Liou, S., Lo, S., Wang S.,
2004. A generalized water quality index for Taiwan. Environ. Monitor. Assess. 96, 35-52
Miller W. W., Joung, H. M., Mahannah C. N. and Garrett J. R.
1986. Identification of water quality
differences Nevada
through index application. J.
Environmental Quality 15, 265-272.
Mitchell, M. K. and Stapp, W.B. 1996. Field Manual for Water Quality Monitoring: An
Environmental Educational Program for Schools, Thomson-Shore, Inc., Dextor, MI, pp 277
Nasiri, F., Maqsiid, I., Haunf, G. Fuller, N., 2007. Water quality index: a fuzzy river pollution
decision support expert system. J. Water
Resou. Plan. Manage. 133, 95-105.
[NSF] National Sanitation Foundation International. 2007.
Pesce, S. F., Wunderlin, D.A., 2000.
Use of water quality indices to verify the impact of Cordoba City
(Argentina)
on Suquira river. Water Res.
34:2915-2926
Said, A., Stevens, D., , Selke, G., 2004. An innovative index for water quality in
streams. Environ. Manage. 34, 406-414.
USDA/NRCS. 2006.
Nutrient Management—Conservation Practice Standard Practice Standard Code 590, pg 8
USDA/NRCS. 2011. Assessment of the Effects of Conservation
Practices on Cultivated Cropland in the Chesapeake Bay region. Conservation Effects Assessment Project
(CEAP) pp 160
Table 1: Soil slope description / ranges and associate
WQI-fsl
Slope
Description
|
Slope
Range
|
WQI-fsl
|
Very flat to flat
|
< 0.5%
|
10
|
Flat to gentle slopping
|
0.5 to 2%
|
8
|
Gentle to moderate slope
|
2.0 to 5%
|
6
|
Moderate to steep
|
5.0 to 7.5%
|
4
|
Steep to very steep
|
7.5 to 10%
|
2
|
Very-Very Steep
|
>10
|
1
|
Table 2: Soil K-factor ranges and
associated WQI-fkf
K-Factor Range
|
WQI-fkf
|
0.02 to 0.10
|
10
|
0.11 to 0.25
|
8
|
0.26 to 0.40
|
6
|
0.41 to 0.55
|
4
|
0.56 to 0.69
|
2
|
Table 3: Percentage
Organic Matter (OM) and associated WQI-fom
% OM Range
|
WQI-om
|
>8%
|
10
|
6-8%
|
9
|
4-6%
|
7
|
2-4%
|
6
|
0.5-2%
|
4
|
<.5%
|
2
|
Table 4a: Decision
Matrix for Rainfall / Vegetative (Live or Dead) cover
Vegetative cover
|
Rainfall
|
||
Vh*Rl
(9)
|
Vh*Rm
(8)
|
Vh*Rh
(7)
|
|
Vm*Rl
(6)
|
Vm*Rm
(5)
|
Vm*Rh
(4)
|
|
Vl*Rl
(3)
|
Vl*Rm
(2)
|
Vl*Rh
(1)
|
|
Vegetative Cover Range: low (Vl), medium (Vm), high (Vm)
Rainfall Range: low (Rl),
medium (Rm), high (Rm)
Table 4b: Combination of rainfall and vegetative (live or
dead) and associated WQI-fvr
VegCover*Rainfall
|
WQI-fvr
|
Vl*Rh
|
1
|
Vl*Rm
|
2
|
Vl*Rl
|
3
|
Vm*Rh
|
4
|
Vm*Rm
|
5
|
Vm*Rl
|
6
|
Vh*Rh
|
7
|
Vh*Rm
|
8
|
Vh*Rl
|
9
|
Table 4c: An Example of estimating WQI-fvr based upon the
monthly rainfall and expected vegetative (Live or dead) for a field in
Clackamas county in Oregon
Jan
|
Feb
|
Mar
|
Apr
|
May
|
Jun
|
Jul
|
Aug
|
Sep
|
Oct
|
Nov
|
Dec
|
|
Rainfall (in)
|
6.17
|
4.39
|
3.99
|
2.64
|
2.17
|
1.73
|
0.7
|
0.94
|
1.84
|
3.11
|
6.02
|
7.09
|
Rainfall Factor
|
Rh
|
Rh
|
Rm
|
Rm
|
Rm
|
Rl
|
Rl
|
Rl
|
Rl
|
Rm
|
Rh
|
Rh
|
Veg Cover
|
Vl
|
Vl
|
Vl
|
Vm
|
Vm
|
Vh
|
Vh
|
Vh
|
Vh
|
Vm
|
Vm
|
Vl
|
Veg* Rainfall Factor
|
Vl*Rh
|
Vl*Rh
|
Vl*Rm
|
Vm*Rm
|
Vm*Rm
|
Vh*Rl
|
Vh*Rl
|
Vh*Rl
|
Vh*Rl
|
Vm*Rm
|
Vm*Rh
|
Vl*Rh
|
WQI- fvr
|
1
|
1
|
2
|
5
|
5
|
9
|
9
|
9
|
9
|
5
|
4
|
1
|
Table 5: Integrating Soil Sensitivity/Physical factors into
a single WQI-fs valu
Soil
Sensitivity Component
|
Soil
Sensitivity Option
|
WQI
Ranking (WR)
|
Weighing
Factor (WF)1
|
WR*WF
|
|
Slope
|
Flat to Gentle Slopping (0.5 to 2%)
|
8
|
0.25
|
2.0
|
|
K-Factor
|
0.30
|
6
|
0.25
|
1.5
|
|
OM
Content
|
5%
|
7
|
0.25
|
1.75
|
|
Rainfall/Vegetation
|
Annual Mean Average for the example case
|
5
|
0.25
|
1.25
|
|
Mean
WQI-fs (Total of all four rows)
|
1.00
|
6.5
|
|||
1The sum total of rows should equal 1
Table 6: Fertilizer
application rate and associated WQI-nar
Application Rate
|
WQI-nar
|
No Fertilizer Applied
|
10
|
50% of the LGU/NBP
recommendation
|
7.5
|
LGU/NBP recommendation
|
5
|
10-20% over the LGU/NBP
recommendations
|
3
|
Ø 20% over the LGU/NBP recommendations
|
1
|
Table 7: Fertilizer
Source, application type and timing (single or split) and associated WQI-ntt
Application Type
|
N-Source
& Application Timing
|
||||
Synthetic
Fertilizers
|
Composted
Organics
|
Un-composted
Manure
|
|||
Normal
Fertilizers
|
Slow Releasing Fertilizers
|
||||
During Growing Season
|
Pre-growing Season
|
Pre-growing Season
|
Pre growing Season
|
||
Single
|
8
|
6
|
10
|
4
|
2
|
Split Application
|
10
|
8
|
NA
|
6
|
4
|
P-Source
& Application Methods
|
|||||
Single
|
7
|
2
|
NA
|
4
|
2
|
Table 8: Fertilizer
application method and soil condition, and associated WQI-nms
Soil Condition
|
Anhydrous Ammonia
|
Other forms of N-Fertilizer
(Solids or Liquids)
|
|||
Application Method
|
|||||
Injected
|
injected
|
Surface
Banded
|
Broadcasted
& Incorporated
|
Broadcasted
|
|
Dry/Well
Drained
|
2
|
10
|
8
|
7
|
6
|
Moist
(25% FC in upper 24 in soil depth)
|
9
|
8
|
6
|
5
|
4
|
Frozen
|
NA
|
NA
|
NA
|
NA
|
2
|
Table 9: Integrating
Nutrient Management factors into a single WQI-nm
Nutrient
Management Component
|
Nutrient
Management Option
|
WQI
Ranking (WR)
|
Weighing
Factor (WF)1
|
WR*WF
|
Application
Rate
|
LGU/NBP Recommendation
|
5
|
0.30
|
1.50
|
N-Source
and Application timing
|
Single application Synthetic Fertilizer
during pre-growing season
|
6
|
0.30
|
1.80
|
P-Source
and Application Timing
|
Single application Synthetic Fertilizer
during pre-growing season
|
2
|
0.15
|
0.30
|
Application
Method & Soil Condition
|
Injected
in the moist soil
|
8
|
0.25
|
2.0
|
Mean
WQI-nm (Sum total of all four rows)
|
1.00
|
5.60
|
||
1The sum total of rows should equal 1
Table 10: Tillage
description / STIR ranges and associated WQI-tm
Tillage
Description
|
STIR Value
|
WQI-tm
|
No Till
|
< 30
|
10
|
Mulch Till
|
31 to 60
|
7.5
|
Conventional Till
|
60 to 100
|
5
|
Intensive Till
|
Ø 100
|
2
|
Table 11: Pest
management practices and associated WQI-pm
Description of
Practice
|
WQI-pm
|
Follow IPM with No Chemical Suppression Needed
|
10
|
Follow IPM with Suppression using Low Risk Chemicals
|
7.5
|
Follow IPM and Suppress using Chemicals and Mitigation
|
7.0
|
Suppress with Chemicals and Mitigate
|
5
|
Chemical Suppression and No Mitigation
|
2
|
Table 12: Integrating
field sensitive/physical and management (nutrient, tillage and pest) factors
into a single WQIag
Factors
|
Description
|
Ranking
|
|||||||
WQI
Ranking
(WR)
|
Weighing
Factor
(WF)
|
WR*WF
|
|||||||
Field Sensitivity Factors (WQI-fs)
|
|||||||||
Slope
|
Flat
to Gentle Slopping (0.5 to 2%)
|
8
|
0.25
|
2.0
|
|||||
K-Factor
|
0.30
|
6
|
0.25
|
1.5
|
|||||
OM
Content
|
5%
|
7
|
0.25
|
1.75
|
|||||
Rainfall/Vegetation
|
Annual
mean average for the example case
|
5
|
0.25
|
1.25
|
|||||
WQI-fs (Aggregated value of slope,
K-factor, OM Content, and Rainfall /vegetation rankings)
|
1.00
|
6.5
|
|||||||
Nutrient
Management (WQI-nu)
|
|||||||||
Application
Rate
|
LGU/NBP
Recommendation
|
5
|
0.30
|
1..5
|
|||||
N-Source
Application Timing
|
Single
Application of Synthetic Fertilizer during pre-growing season
|
6
|
0.30
|
1.8
|
|||||
P-Source
and Application Timing
|
Single
Application synthetic Fertilizer during pre-growing season
|
2
|
0.15
|
0.3
|
|||||
Application
method & Soil Condition
|
Non-hydrous
Fertilizer into moist soils
|
8
|
0.25
|
2.0
|
|||||
WQI-nu (Aggregated value of Application
rate, N-Source Application & timing, P-Source Application & Timing, and
Application methods & Soil Condition)
|
1.0
|
5.6
|
|||||||
Tillage
Management (WQI-tm)
|
Conventional
Till with STIR Value between 60-100
|
5.0
|
|||||||
Pest
Management
(WQI-pm)
|
Follow
IPM with suppression using Low Risk Chemicals
|
7.5
|
|||||||
Overall WQIag
Weighted
mean of WQI-fs, WQI-Nu, and Tillage
& Pest Management ranking factors
|
WQI-fs
|
WQI-nm
|
WQI-tm
|
WQI-pm
|
|||||
WQI Ranking (WR)
|
6.5
|
5.6
|
5
|
7.5
|
|||||
Weight Factor (WF)
|
0.25
|
0.25
|
0.25
|
0.25
|
|||||
Weighted Value (WR*WF)
|
1.625
|
1.400
|
1.250
|
1.875
|
|||||
WQIag
(Aggregate of weighted value of WQI-fs, WQI-nm, WQI-tm, & WQI-pm)
|
6.150
|
||||||||
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