Table of Contents

Table of Figures

Table of Tables

1. Introduction

1.1. Background

1.2. Environmental Impacts of Nonpoint Source Pollution

2. Scientific Concepts

2.1. The Hydrologic Cycle

2.1.1. Description

2.1.2. The SCS-CN method

2.1.3. Curve Number Identification from Rainfall Runoff Records

2.1.3.1. CN determination for a single event

2.1.3.2. Determining catchment curve number from multiple events

2.1.3.2.1. The average curve number approach

2.1.3.2.2. The Hawkins-Hejlmfelt-Zevenbergen (HHZ) approach

2.1.3.2.3. The frequency-matching technique.

2.1.4. Estimating CNs from Handbooks for Ungaged Catchments

2.2 Water Erosion Processes

2.2.1. General Description

2.2.2. The Universal Soil Loss Equation (USLE)

2.2.3. Computation of USLE Factors

2.2.3.1. R, the rainfall-runoff erosivity index

2.2.3.2. K, the soil-erodibility factor

2.2.3.3. LS, the topographic factor

2.2.3.4. C, the crop-management factor:

2.2.3.5. P, the conservation-practice factor:

2.2.4. Nutrients and Chemical Loading

3. Results of the Data Collection Experiments

3.1. Study Site Description:

3.2. Rainfall Data

3.3. Conversion of Units and Data Correction

3.3.1. Runoff Measurements

3.3.2. Concentration of Chemicals and Sediments

3.4. Data Adjustment and Quality Monitoring

3.4.1. Data Adjustment

3.4.2. Data Quality:

4. Data Analysis

4.1. The Runoff Record and Rainfall-Runoff Relationships

4.2. Water-Quality Data Analysis

4.2.1. Introduction

4.2.2. A Comparison of Crops and Crop Management Practices

4.2.2.1. Total Dissolved Solids (TDS)

4.2.2.2. Oxidized Sulfate (SO4)

4.2.2.3. Nitrate (NO3)

4.2.2.4. Orthophosphate (PO4)

4.2.2.5. Other dissolved salts: Sodium (Na) and Potassium (K)

4.2.2.6. Soil loss (Sed.)

5. A Simple Method for Multiple-Objective Evaluation (MOE)

6. Recommendations

6.1. Recommendations Regarding Experimental Infrastructure

6.1.1. Runoff Collection Pits

6.1.2. Contributing Area

6.1.3. Raingages

6.1.4. Rainfall Intensity

6.2. Recommendations Regarding Data, Reporting, and Documentation

6.2.1. Nutrient Application Data

6.2.2. Full Reporting of Rainfall Events

6.2.3. Soils Data

6.2.4. Promptness, Initial Analysis, and Feedback

6.3. Future Directions

7. References

1. Locations of MEMP monitoring sites

2. The main components of the hydrologic cycle as applied to a representative column of soil

3. The SCS curve number relationship

4. Example of the rainfall-runoff relationship for Pit #1

5. A photographic sequence illustrating the impact of raindrop splash

6. Schematic diagram of the USLE standard plot used to determine K, the soil-erodibility factor

7. Break-point and hyetograph plots of a hypothetical storm for use in computing EI

7. The USDA soil texture triangle

8. The topographic factor LS for different slopes and slope lengths

9. Schematic representation of the effect of surface cover on soil erosion

10. The nitrogen cycle within the soil-plant-atmosphere system

11. Plant response to nutrient availability

12. The drainage system of Malawi

13. Actual locations of the field pits relative to one another in Chilindamaji Watershed

14. Schematics of field plots/pits and erosion-control plots

15. Daily precipitation time-series for Chilindamaji Watershed

16. Total monthly precipitation for Chilindamaji Watershed

17. Conceptual framework for runoff depth-adjustment

18. Comparison between monthly and total runoff values for different farm management systems in Chilindamaji Watershed

19. Rainfall-runoff relationships for the four field plots and the three erosion-control plots in Chilindamaji Watershed, January – April, 1995

20. A comparison of rainfall-runoff relationships between traditional cropping systems and management systems that aim to control erosion

21. Monthly and seasonal TDS losses from field and erosion-control plots in Chilindamaji Watershed

22. Monthly and seasonal SO4 losses from field and erosion-control plots in Chilindamaji Watershed

23. Monthly and seasonal NO3 losses from field and erosion-control plots in Chilindamaji Watershed

24. Monthly and seasonal PO4 losses from field and erosion-control plots in Chilindamaji Watershed

25. Monthly and seasonal Na losses from field and erosion-control plots in Chilindamaji Watershed

26. Monthly and seasonal K losses from field and erosion-control plots in Chilindamaji Watershed

27. Sediment yields based on the partial elimination method

28. Sediment yields based on the filtered sample

1. Sample CN calculations from a single rainfall-runoff event

2. An example average CN computation

3. Sample calculation of catchment CN using the HHZ approach

4. Soil hydrologic groups classified according to saturated hydraulic conductivity

5. Identification of Antecedent Moisture Conditions

6. Handbook CN Identification

8. Steps in the Calculation of Total Storm Energy, EI

9. K factors computed for average soil properties for USDA soil texture classes

10. Saturated hydraulic conductivity, permeability class, and hydrologic group of major USDA soil texture classes

11. List of the USLE crop-management factor C during different stages of various cropping cycles

Explanation of terms

12. P for various slopes and maximum slope lengths

13. Configuration of field pits and erosion-control plots in Chilindamaji Watershed

14. Excluded runoff events, Chilindamaji Watershed, Jan. - Apr., 1995

15. Water-quality parameters for included events, Chilindamaji Watershed, Jan. - Apr., 1995

Pit #1 (Burley tobacco)

Pit #4 (Burley tobacco)

Pit #2 (Maize)

Pit #3 (Maize)

Control Plot #1 (Burley tobacco)

Control Plot #2 (Fallow)

Control Plot #3 (Maize)

16. Demonstration of a simple MOE for the Chilindamaji Watershed

abstract	I + C
BOD	Biological Oxygen Demand.
C	The amount of rainfall intercepted by plants.
CN	The curve number coefficient in the SCS-CN method.
CNe	The estimated average curve number.
CNi	The computed curve number for the ith runoff producing event.
EPA	United States Environmental Protection Agency.
ET	Evapotranspiration.
HHZ	The Hawkins-Hejlmfelt-Zevenbergen approach for estimating CNs.
i	The rainfall event (storm) counter.
I	Water infiltration into soil.
Ia	The initial abstraction
MEMP	Malawi Environmental Monitoring Program.
MoREA	Ministry of Research and Environmental Affairs.
n	The total number of runoff producing events.
NPSP	Nonpoint Source Pollution.
P	Precipitation (for conditions in Malawi, equivalent to rainfall).
Pe	Deep percolation into soil.
PSP	Point Source Pollution.
Q	The total daily runoff volume.
Qei	The estimated runoff for each rainfall event.
R	Surface runoff.
S	Soil water storage
S	The maximum potential difference between P and Q at the beginning of a storm.
SCS-CN	The U.S. Soil Conservation Service Curve Number method.
Se	Estimated soil water storage.
Si	Lateral subsurface inflow into soil.
SM	Soil moisture.
So	Lateral subsurface outflow from soil.
USAID	United States Agency for International Development.
AMC	Antecedent moisture conditions.
USDA	United States Department of Agriculture.
USLE	The Universal Soil Loss Equation.
A	The USLE sediment yield.
R	The USLE rainfall-runoff erosivity index.
K	The USLE soil-erodibility factor.
L	The USLE length-of-slope factor.
S	The USLE degree-of-slope factor.
C	The USLE crop-management factor.
P	The USLE conservation-practice factor.
em	The kinetic energy of the mth intensity period for a unit rainfall.
im	The rainfall intensity period energy.
Em	The total kinetic energy for the intensity period.
I30	The maximum 30-minute rainfall intensity.
EI	The total kinetic energy of a storm.
im	The rainfall intensity for the mth period.
Pem	The cumulative rainfall at the end of the mth period.
Psm	The cumulative rainfall at the start of the mth period.
tem	The time at the end of the mth period.
tsm	The time at the start of mth period.
a	Percent soil organic matter
b	The soil structure code.
c	The soil profile permeability class.
SF	The soil structure factor.
TF	The soil texture factor.
PF	The soil permeability factor.
M	The soil texture parameter.
l	The slope length.
q	The slope angle.
m	The slope steepness parameter.
SPHA	The soil-plant-hydrosphere-atmosphere system.
	The ith event running average storage parameter.
Sj	The jth event storage parameter.
MOE	A Multiple-Objective Evaluation.
Sed.	Soil sediment loss.
TDS	Total Dissolved Solids
WQDSS	The USDA Water Quality Decision Support System.

1. Introduction

The United Sates Agency for International Development [USAID] and the government of Malawi are collaborating to provide the Malawi Environmental Monitoring Program [MEMP] with the necessary field, technical, and analytical training to carry out several environmental monitoring activities. MEMP was initiated to fulfill a threefold mission aimed at assisting the Ministry of Research and Environmental Affairs [MoREA] and other departments to:

1.Monitor the environmental impacts of policy reforms, in particular the impacts of smallholders' production of burley tobacco;

2.Establish a national capability to assess, monitor, and manage the environmental resources of Malawi; and

3.Provide equipment, training, and methods necessary for the fast and efficient production of maps, documents, and reports based on the results of MEMP's environmental monitoring activities.

To fulfill the above objectives, MEMP has been conducting monitoring programs in five small catchments near Nkhata Bay, Chikwawa, Dowa, Mangochi, and Kassungu (Figure 1). The monitoring activities include water sampling at catchment outlets, installation of erosion control-plots and pits, field monitoring pits, and the installation of automated samplers at two of the above-mentioned locations.

Although MEMP is a newly established program, it has the potential to become an integral part of the decisionmaking process at the level of national agricultural policy as well as at the level of operational farm-management. MEMP's capabilities can be enhanced through a combination of well-planned pilot data collection campaigns, training activities, scientific collaborations, and publications.

Providing the necessary training in the area of evaluating the environmental impacts of farm practices is at the heart of MEMP's objectives, and this report represents an effort in that direction. However, because the existing environmental record is too short to comprehensively evaluate and characterize the environmental impacts of different practices, this report concentrates on establishing guidelines for data analysis rather than providing policy recommendations. The report will attempt to make maximum use of the data collected from several erosion control-plots and field pits for the purpose of illustrating procedures such as (a) the identification of empirical rainfall-runoff relationships, (b) quality control and reduction of data, and (c) statistical analyses. Scientific and conceptual principals will be briefly illustrated, emphasizing operational aspects such as unit conversion, criteria-based selection of acceptable datasets, and data requirements for different environmental assessment objectives.

Environmental pollution of surface and ground water resources is classified as being either point source pollution [PSP] or nonpoint source pollution [NPSP]. PSP is defined as a concentrated effluent discharge at a given point. Examples of PSP include leakage from fuel tanks into groundwater, sewage effluent discharge into a lake or a stream, and other identifiable and usually measurable sources. On the other hand, NPSP is associated with the natural transport of dissolved and suspended materials carried by overland flow of storm water and/or irrigation water into streams and within porous soils. Major sources of NPSP include sediment, nutrients, pesticides, carbonaceous biological oxygen demand [BOD], nitrogenous BOD, and pathogens.

Figure 1. Map of Malawi depicting the locations of MEMP monitoring sites. Note that the sites are distributed in all three regions of the country (north, central, and south).

Transport and loading of nonpoint source pollutants have been occurring since geological times due to natural processes. However, transport and loading rates can be accelerated or decelerated by human activities that modify the natural properties of the watershed, thereby affecting the driving processes. Countless examples of the impacts of agricultural activities on the rate of erosion, nutrient loading, and pesticide loading can be found around the world. In 1991, The United States Environmental Protection Agency [EPA] has identified agriculturally-derived NPSP loading into surface water as the major cause of river and stream impairment in the continental U.S. In Malawi, erosion-caused siltation has reduced the effective head above the turbine from 6 m to 3 m in the Lower Shire River. Whether agricultural activities are responsible for the erosion can only be answered by quantifying and comparing the rate of erosion from different management systems.

Soil and nutrient losses have both on-site and off-site impacts. On-site impacts exist within the boundary of the field and/or the watershed, and include loss of productivity, soil degradation, and increased salinity. The economic loss associated with these impacts can be very high. For example, Pimental et al. (1995) reported that an estimated 4 billion tons of soil are lost from U.S. cropland each year. Furthermore, an estimated 1 million ha of cropland are lost each year in developing nations. The cost of associated on-site nutrient losses in the U.S. reaches $20 billion/year; another $7 billion/year is the estimated cost of lost soil.

Off-site impacts represent the consequences of (a) runoff mediated sediment discharge, nutrient loading, and pesticides loading of surface streams, and (b) chemical loading of groundwater via infiltration and deep percolation. Eutrophication of surface-water bodies is the most critical off-site impact of nutrient transport, and occurs as a direct result of excessive nutrient concentrations in the water body. Eutrophication significantly reduces dissolved oxygen, and eventually leads to the extinction of aquatic life in the water body. In addition to the degradation of water quality, the deposition of sediments can lead to the reduction of water storage capacities in watercourses and reservoirs. Termed "stream siltation," this can (a) cause an increase in the frequency and magnitude of floods, and (b) blanket the stream-bottom gravel beds necessary for the reproduction of some fish species.

Because of the significant adverse economic and environmental impacts of NPSP as well as the high costs of erosion control and abatement, selecting environmentally sound – yet profitable – farm-management practices is inherently a multiple-objective decisionmaking problem. Policymakers must have access to accurate and sufficient information regarding the various environmental impacts of existing and proposed agricultural management systems. This report aims to provide MEMP staff with the tools to improve the accuracy of the information conveyed both to policymakers and to those scientists who may be interested in analyzing the information contained in current or future datasets.

Informed agricultural policy decisions must be based on sound technical advice. Technicians, scientists, and farmers need to attain a close level of cooperation to ensure successful culmination of organized experimental studies and data collection campaigns. Furthermore, technical personnel need to be familiar with the basic scientific concepts underlying experimental studies. Such familiarity is an invaluable asset to any environmental monitoring program, because it equips project personnel with the necessary skills to:

1.Monitor the quality of data,

2.Take scientifically-based actions,

3.Perform initial data analyses with potential uses of the data in mind,

4.Be able to work within interdisciplinary teams; and above all,

5.Communicate these concepts to farmers.

To aid readers in attaining familiarity with these basic scientific concepts, Section 2 provides a brief description of the hydrologic cycle, erosion and sedimentation processes, and the nutrient cycle from an agricultural perspective. Detailed technical discussions are avoided whenever possible without compromising the quality of the information provided.

2.1.1. Description

As mentioned in Section 1.2, nonpoint source pollutants are suspended and dissolved materials carried by storm water over the land, into streams, and within the porous media of the soil. Clearly, water acts as the detaching agent of soil particles, dissolving agent of chemicals, and a transport agent of suspended and dissolved materials. It is imperative, therefore, to develop an understanding of the hydrologic cycle.

There are several possible conceptual models of the hydrologic cycle. These models are scale dependent and reflect the dominant hydrologic processes at that scale and for a specific situation. In this report, we focus on the agricultural aspects of the hydrologic cycle. Figure 2 is a schematic diagram of the components of the hydrologic cycle for agricultural lands.

Figure 2. Simplified diagram of the main components of the hydrologic cycle as applied to a representative column of soil. Note that infiltration is an output from the surface component as well as an input to the soil component of the cycle.

Figure 2 provides guidance for constructing a version of the conservation of mass equation. Conservation of mass for any confined volume is based on Equation (1), which states that the change of storage (S) within the volume equals the difference between the inflow to the volume and the outflow from the volume.

D S = Inflow – Outflow(1)

When the soil is the storage reservoir, water inputs to the profile comes from infiltration (I) which is the process by which storm water enter the profile, and lateral subsurface inflow (Si). The output processes are deep percolation (Pe), which is the vertical flow of excess moisture below the root zone, lateral subsurface outflow (So), and evapotranspiration (ET), a combination of plant transpiration and surface evaporation. The continuity equation describing change of soil moisture (D SM) is written as:

D SM = Si + I – ET – Pe – So (2)

D SM = Si + P – R – ET – Pe – So(4)

Equation 4 is a simple continuity equation for a daily soil moisture balance; it assumes a single-layered soil profile.

For the agricultural hydrologic cycle, precipitation, infiltration, and runoff are the most important processes for NPSP loading. Precipitation causes soil detachment (discussed in Section 2.2.1). Runoff carries suspended and dissolved materials overland, to streams, and hence to water bodies. Infiltration on the other hand, carries dissolved nutrients from the surface to the soil to be utilized by plants. However, some of these nutrients find their way to below the root zone, arriving eventually at the groundwater aquifer through the process of deep percolation.

Infiltration is the major source of the abstract from a rainfall event. The amount of infiltration during a storm determines the amount of rainfall available for runoff. Infiltration is a function of several factors, including soil properties, initial soil moisture at the beginning of the storm, and the intensity of rainfall during the storm. Soil properties such as the saturated conductivity (the rate at which water flows through saturated soil), field capacity (the maximum amount of water that can be held in the soil against gravitational pull), and porosity (the ratio of the volume of pores within the soil to the total soil volume), affect the total infiltration by determining the rate of water movement into the soil. Modeling the infiltration process in details is a complicated process that requires detailed information about the soil and the intensity rainfall events. As was the case in the plot/pit studies in Malawi, in the absence of rainfall intensity data there are simpler models that relate the total amount of runoff to the total amount of rainfall under given soil and cover conditions. One of these models is the U.S. Soil Conservation Service Curve Number (SCS-CN) method.

2.1.2. The SCS-CN method

Originally, the SCS-CN method was developed for extreme runoff events, and is based on the concept of "initial abstraction." Basically, the relationship in Equation 5 expresses the total daily runoff volume as a function of the initial abstraction and the total infiltration (Hawkins, 1978):

Ia = initial abstraction, and

Data from several locations indicate that there is a relationship between Ia and S. This relationship is expressed as:

Ia = b S(6)

2.1.3. Curve Number Identification from Rainfall Runoff Records

Equation (10) is the most commonly used form of the SCS rainfall runoff model. It can be solved for S directly using the quadratic formula, with Equation (9) then applied to evaluate the curve number. Alternatively, diagrams such as those presented in Appendix A can be used to identify the curve number directly. However, the first method is more appropriate when determining a curve number for a given catchment using several rainfall runoff events.

Q(P + 0.8S) = (P – 0.2S)2(12)

QP + 0.8QS = P2 – 0.4PS – 0.04S2(13)

It is important to recognize that a single event does not provide sufficient information about the rainfall-runoff relationship for a given catchment. For example, consider Figure 4, which illustrates the CN values for several rainfall events.

Figure 4. Example of the rainfall-runoff relationship for Pit #1 in Chilindamaji, near Nkhata Bay, Malawi. Note that the rainfall-runoff curve does not correspond with a unique CN.

2.1.3.2.1. The average curve number approach

There are several methods that can be used to determine the CN of a given catchment using multiple rainfall-runoff events. The first method is based on estimating the mean value of the CNs from all events, as follows:

Step 1Remove all non-runoff producing events from the sample. These events do not have the information that allows identification of CNs.

Step 2For the remaining runoff-producing events, compute the CN associated with each event using Equation (16) to calculate S and Equation (9) to estimate the CN.

Step 3Compute the average value of the sample using the relationship in Equation (17).

(17)

where

CNe = the average (estimated) curve number,

CNi = the computed curve number for the ith runoff producing event,

Step 4Compute Se (estimated soil water storage) associated with the average CN by substituting CNe from Equation (17) for CN in Equation (11).

Step 5.Compute the estimated runoff for each rainfall event by substituting Pi for P and Se for S in Equation (10). Be careful to check for the condition Pi > 0.2Se. Some of the events will not satisfy the condition; in these cases, = 0.00.

Step 6.Plot the observed Qi against the estimate . Notice if there is a spread about the 1:1 line (the line can be drawn by plotting Qi against itself on both axes). We also recommend using some measure of errors in the estimation, such as R2 and/or the error sum of squares.

The main problems with using the average value of all computed event CNs are discussed by Hawkins (1978), Hawkins et al. (1985), Hjelmfelt (1980), and Ponce and Hawkins (1996). Clearly, the method tends to weigh all events equally. Since runoff-producing low-rainfall events are associated with higher CNs, the method tends to overestimate the catchment CN. Higher rainfall events that also produce runoff are then modeled as catastrophic runoff events, as occurred for the 107 mm event in Table 2 above.

2.1.3.2.2. The Hawkins-Hejlmfelt-Zevenbergen (HHZ) approach

Hawkins et al. (1985) proposed calculating catchment CNs from historical rainfall-runoff records, a method based on probability assessments and first proposed by Hjelmfelt (1980). The HHZ approach identifies a subset of events that contains the necessary information about the catchment response. This subset corresponds to the condition P/Se > 0.456, which indicates a 90% probability of runoff occurrence. Such a set is primarily a set of the largest rainfall events (but not necessarily the highest runoff events). The procedure for obtaining a CN using HHZ is as follows:

Step 1 Remove all non-runoff producing events from the sample. These events do not have the information that allow CN identification.

Step 2 For the remaining runoff-producing events, sort all events in descending order of rainfall.

Step 3 Starting from the largest rainfall event, compute the storage parameter Si from Equation (16).

Step 4 Check for the cutoff value Pi/Si > 0.456.

Step 5 If Pi/Si > 0.456, add the next biggest storm to the calculation. Compute Si for this storm and compute Se, the average value corresponding to the storms that have entered the calculation. Go back to Step 4.

Step 6 Include all events where Pi/Se > 0.456. This means that if P/Se becomes < 0.456, continue the procedure until no more cases of P/Se are > 0.456. Once this subset of events is identified, from Equation (9) compute the CN from Se corresponding to the last incidence of P/Se > 0.456.

Step 7 Compute the estimated runoff for each rainfall event by substituting Pi for P and Se for S in Equation (10). Be careful to check for the condition Pi > 0.2Se. Some of the events will not satisfy the condition. In these cases, Qe = 0.00.

Step 8 Plot the observed Qi against the estimate . Notice if there is a spread about the 1:1 line (the line can be drawn by plotting the Qi against itself on both axes). We also recommend using some measure of errors in the estimation, such as R2 and/or the error sum of squares.

Note that not all datasets provide a good sample for this method. Some datasets will not contain any storms with Pi/Si > 0.456, and HHZ must not be used. Such "empty" datasets imply that the catchment has a low CN that cannot be identified from the available record (Hawkins et al., 1985). A longer record may be helpful, but there is no guarantee of determining a CN value. For such cases, the authors suggest trying to develop a catchment's rainfall-runoff relationship from regression analysis, or using the frequency-matching method (see Section 2.1.3.2.3). Table 3 illustrates the computations associated with HHZ for the same dataset as we use in Table 2. Note that only four events actually contributed to the computation of the catchment CN (CNe= 80.7).

2.1.4. Estimating CNs from Handbooks for Ungaged Catchments

Table 6 (continued)

2.2.1. General Description

2.2.2. The Universal Soil Loss Equation (USLE)

2.2.3. Computation of USLE Factors

R is a statistical measure calculated from a summation of rainfall energy in every storm over a fixed period of time (correlated with raindrop size) multiplied by its maximum 30-minute intensity. Empirically, R was found to have the highest correlation with soil erosion from experimental plots. For each intensity period, a rainfall energy em per unit intensity is computed (Foster, 1981) from:

em = the kinetic energy of the mth intensity period for a unit rainfall (MJoules/ha•mm),

im = the rainfall intensity period energy (mm/h).

Once the unit energy for each storm intensity period is obtained, the total energy for the intensity period is calculated by multiplying em by the total amount of rainfall that occurred during the interval. That is,

Em = empm(19)

The values obtained from Equation (19) are summed and then multiplied by the maximum 30-minute intensity, I30, giving EI, the total kinetic energy of the storm.

im = the intensity for the mth period (mm/h),

Pem = the cumulative rainfall at the end of the mth period (mm),

Psm = the cumulative rainfall at the start of the mth period (mm),

tem = the time at the end of the mth period (min),

tsm = the time at the start of mth period (min), and

The 30-minute intensities are estimated by dividing the storm into 30-minute intervals and interpolating the cumulative precipitation at the end of every interval. The procedure is repeated for each new interval and its associated cumulative rainfall in order to select I30. Note that the last interval may be less than 30 minutes, as is the case in the following example, where it is 10 minutes. The plots corresponding to these procedures are Figures 7c and 7d. Table 8 shows the calculation of the storm's energies.

Notes: im from Eqn. (21); em from Eqn. (18); Em from Eqn. (19); and EI from Eqn. (20).

Note: The calculations assume a value of vfs = (0.5´ sand). Source: Knisel, 1993.

Soil Texture Class		Saturated Hydraulic Conductivity			Permeability Class		Hydrologic Soil Group
	Lower Bound		Upper Bound
Silt Clay Clay			1.0	6		D
Silt Clay Loam Sandy Clay	1.0		2.0	5		C-D
Sandy Clay Loam Clay Loam	2.0		5.0	4		C
Loam Silt Loam	5.0		20.0	3		B
Loamy Sand Sandy Loam	20.0		60.0	2		A
Sand	60.0			1		A+

For convenience, the slope length factor L and slope steepness factor S are frequently conjoined into a single term because the effect of steeper slopes and longer slopes is similar. Steeper slopes produce higher overland flow velocities. Longer slopes allow for more accumulation of runoff from larger areas, also resulting in higher flow velocities. Both, therefore, nonlinearly increase erosion potential. For uniform slopes < 9% and longer than 5 m, the topographic factor is given by Equation (24), from which the nomogram of Figure 8 is constructed (Wischmeier and Smith, 1978; McCool et al., 1989a, 1989b).

l = the slope length (m),

q = the slope angle, and

2.2.4. Nutrients and Chemical Loading

Figure 10 shows that nutrients can reach streamflow in either soluble or insoluble form. They can also reach surface waters through attachment to soil particles. The affinity of various nitrogen and phosphorous forms to attach to soil particles varies, with nitrate (NO3) having the least and orthophosphate (PO4) having the highest affinities.

Factors affecting the nitrogen cycle include soil type, temperature, tillage system, moisture, vegetation type, and fertilizer amount and form. The external sources of nitrogen and phosphorus to the soil-plant system are rainfall, atmospheric fixation (nitrogen only), and application of fertilizers. Important internal sources are the decomposition of organic material, soil mineral weathering, and chemical desorption (the release of nutrients bonded to soil particles). Nutrients in soluble form are considered to be fully available to plants. On the other hand, only small amounts of insoluble nutrients are available to plants, an amount that is controlled by factors such as soil pH, temperature, and the soil's oxidation-reduction status. For nitrogen, important soluble forms are NO3, ammonium (NH4), and soluble organic nitrogen. For phosphorus, the important soluble forms are PO4 and soluble organic phosphorus. Figure 11 depicts the relationship of crop response to available nutrients. Note that there is an optimal range that is determined by climate conditions, soil, and – above all – genetically-determined plant physiology.

Pit/Plot No.	Crop			Contributing area								Collection pit
		Length, m			Breadth, m					Area, m2			Area, m2			Depth, m
Field Pits
1	Burley Tobacco			7.20			2.40		17.28			1.00			1.00
2	Maize			12.40			2.40		29.76			1.20			1.00
3	Maize			8.80			2.40		21.12			1.00			1.00
4	Burley Tobacco			3.20			5.20		16.64			1.00			0.70
Erosion-Control Plots
B	Burley Tobacco			10.00			5.00		50.00			1.00			1.00
M	Maize			10.00			5.00		50.00			1.00			1.00
F	Fallow			10.00			5.00		50.00			1.00			1.00