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.

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

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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.

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The K-factor value for a field can be obtained from the NRCS Web Soil Survey Website by using the following procedure:

1. Go to the Web Soil Survey website by clicking on the following website link (http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx).

2. 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.

3. Delineate your field using one of the two “Area of Interest AOI” buttons on the “Area of Interactive Map” window.

4. 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.

5. 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.

6. 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.

7. 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.

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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

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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)

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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.

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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

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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

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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

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Table 1: Soil slope description / ranges and associate WQI-fsl

Slope Description Slope Range WQI-fslVery flat to flat < 0.5% 10

Flat to gentle slopping 0.5 to 2% 8

Gentle to moderate slope 2.0 to 5% 6Moderate 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

Table 3: Percentage Organic Matter (OM) and associated WQI-fom

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K-Factor Range WQI-fkf0.02 to 0.10 100.11 to 0.25 80.26 to 0.40 60.41 to 0.55 40.56 to 0.69 2

% OM Range WQI-om>8% 106-8% 94-6% 72-4% 6

0.5-2% 4<.5% 2

Table 4a: Decision Matrix for Rainfall / Vegetative (Live or Dead) cover

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

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

JanFeb

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 VlVeg* 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

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Vege

tativ

e co

ver 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)

VegCover*Rainfall WQI-fvrVl*Rh 1Vl*Rm 2Vl*Rl 3

Vm*Rh 4Vm*Rm 5Vm*Rl 6Vh*Rh 7Vh*Rm 8Vh*Rl 9

Table 5: Integrating Soil Sensitivity/Physical factors into a single WQI-fs valuSoil Sensitivity

ComponentSoil Sensitivity

OptionWQI

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 1.75Rainfall/Vegetation Annual Mean Average

for the example case5 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

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Application Rate WQI-narNo Fertilizer Applied 1050% of the LGU/NBP

recommendation 7.5LGU/NBP recommendation 510-20% over the LGU/NBP

recommendations 3 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 FertilizersComposted

Organics

Un-composted

ManureNormal Fertilizers Slow Releasing FertilizersDuring

Growing Season

Pre-growing Season

Pre-growing Season

Pre growing Season

Single 8 6 10 4 2Split

Application 10 8 NA 6 4P-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 injectedSurface 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 4Frozen NA NA NA NA 2

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Table 9: Integrating Nutrient Management factors into a single WQI-nm

1The sum total of rows should equal 1

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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

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 5Intensive Till 100 2

Table 11: Pest management practices and associated WQI-pm

Description of Practice WQI-pm

Follow IPM with No Chemical Suppression Needed 10Follow IPM with Suppression using Low Risk Chemicals 7.5Follow IPM and Suppress using Chemicals and Mitigation 7.0Suppress with Chemicals and Mitigate 5Chemical Suppression and No Mitigation 2

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Table 12: Integrating field sensitive/physical and management (nutrient, tillage and pest) factors into a single WQIag

Factors DescriptionRanking

WQIRanking

(WR)

WeighingFactor(WF)

WR*WF

Field Sensitivity Factors (WQI-fs)Slope Flat to Gentle Slopping (0.5 to 2%) 8 0.25 2.0K-Factor 0.30 6 0.25 1.5OM Content 5% 7 0.25 1.75Rainfall/Vegetation Annual mean average for the example case 5 0.25 1.25WQI-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 WQIagWeighted 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 7.5Weight Factor (WF) 0.25 0.25 0.25 0.25

Weighted Value (WR*WF) 1.625 1.400 1.250 1.875WQIag (Aggregate of weighted

value of WQI-fs, WQI-nm, WQI-tm, & WQI-pm)

6.150

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