Silviculture Best Management Practices 

Implementation Monitoring 

A Framework for State Forestry Agencies 

Southern Group of State Foresters 

Water Resources Committee 

June 2007
Silviculture 

Best Management Practices 

Implementation Monitoring 

A Framework for State Forestry Agencies 

Southern Group of State Foresters 

Water Resources Committee 

June 2007

 Foreword 

The Federal Water Pollution Control Act Amendments recognized nonpoint source 

pollution, and called on states to develop and implement water quality management 

plans. Since then, state forestry and state water quality agencies have been working 

closely with the Environmental Protection Agency (EPA) to minimize silviculture-related 

sources of nonpoint source pollution. 

To address silviculture related water pollution in the southern states, a traditional 

regulatory approach was initially proposed. However, after further analysis and 

consultation with the forestry community, EPA and the states generally agreed that a nonregulatory approach was more effective. This approach was based primarily on 

education and field demonstration, with the following basic components: 

1. Identification of Best Management Practices (BMPs) to protect water quality 

during forestry operations; and 

2. Widespread education/training of forestry practitioners and forest landowners to 

facilitate the implementation of BMPs; and 

3. Routine monitoring of forestry operations to determine the level of BMP 

implementation. 

To date, all southern states have developed silviculture BMPs, which have been approved 

by EPA. Most of these states have recently revised or updated their BMPs to keep 

current with changing information and technology. Likewise, all southern states have 

developed and conducted education and training sessions for forestry practitioners, 

landowners, managers and loggers, which include the distribution of materials and 

emphasize BMP implementation. 

However, not all of these states have developed routine BMP monitoring procedures to 

measure actual implementation levels. In addition, no model procedure for conducting 

such monitoring exists. Thus, states with monitoring programs have measured and 

reported BMP implementation using significantly different methods. Consequently, 

monitoring results have been met with varying degrees of acceptance by the public and 

by regulatory agencies. Inconsistency among states with respect to statistical design, 

reproducibility, and general objectivity have been cited as areas of concern. 

In order to improve and maximize the integrity of BMP implementation monitoring in the 

South, the Southern Group of State Foresters appointed a Task Force to develop 

recommendations for a more consistent approach to BMP monitoring in the region. 

Specifically, the Task Force was charged with developing a framework to provide southwide guidance for monitoring BMP implementation that would be statistically sound, 

objective, and technically defensible. This framework would achieve analytical 

consistency and results would be generally comparable among states. 
The Task Force, composed of hydrologists and water quality specialists from state 

forestry agencies, U.S. Forest Service, and forestry industry, in consultation with EPA 

Region IV, met during 1996 – 1998 and completed the initial document. On March 25-

26, 2002, a subcommittee of that Task Force reconvened and completed this revision. 

Task Force Members 

Jeff Vowell – Chairman, Florida Division of Forestry* 

Frank Green, Georgia Forestry Commission* 

Tim Adams, South Carolina Forestry Commission 

Darryl Jones, South Carolina Forestry Commission* 

Robin Bible, Tennessee Division of Forestry 

Sam Austin, Virginia Department of Forestry 

Matt Poirot, Virginia Department of Forestry* 

Gary Cole, Alabama Forestry Commission* 

Burl Carraway, Texas Forest Service* 

Mike Sampson, Mississippi Forestry Commission* 

John Greis, U.S. Forest Service* 

David Hoge, U.S. Forest Service* 

Bruce Prud’homme, U.S. Forest Service* 

Rob Olszewski, Plum Creek Timber Company 

Jim Shepard, National Council of the Paper Industry for Air and Stream Improvement 

* Subcommittee members, 2002 
Table of Contents 

Introduction 5 

Implementation Monitoring Framework Protocol 

Monitoring Frequency 5 

Site Selection 5 

Practices to be Evaluated 6 

Basis for Practice Evaluation and Reporting 7 

Scoring Methodology 7 

Risk Assessment 8 

Follow-up Actions 9 

References 10 

Acknowledgements 10 

Glossary 10 

Appendix 

 Statistical Guide for BMP Implementation Monitoring 13 

 

5 

Introduction 

This document is presented as an Implementation Monitoring Framework within which 

state forestry agencies can build or revise their current monitoring programs. Widespread 

utilization of this document within the region is expected to improve consistency among 

states in the specific aspects of BMP monitoring listed below. In addition, the 

recommendations for each specific aspect are envisioned to be core elements of a 

credible evaluation and reporting process. 

Monitoring Frequency

Issue: How frequently should BMP implementation monitoring be conducted and 

reported? 

Alternatives Considered: Annual, biennial, every three years and continual monitoring. 

Recommendation: Statewide implementation monitoring should be conducted and 

reported at a minimum of every three years. 

Rationale: Due to the large number of forestry operations conducted annually, the 

number of sites necessary to achieve statistical reliability, and the logistics of locating, 

visiting and evaluating them, annual monitoring and reporting is often not practical. 

Further, there are no significant advantages of annual monitoring and reporting that 

justify the additional burdens. 

Monitoring and reporting on at least a three year basis is more logistically achievable, 

and is consistent with typical 319 funding cycles for states receiving federal grants. In 

addition, monitoring at this frequency is considered often enough to allow visual 

observations of on-site problems and take timely corrective action. 

Site Selection

Issue: What characteristics should a forestry site/operation exhibit in order to qualify as a 

BMP implementation monitoring site? 

Alternatives Considered: 

1. Minimum/no minimum area (acres) 

2. Presence/absence of surface water on site 

3. Time since treatment (years) 

4. Site selection methodology (to eliminate bias) 

5. Sample size (statistically valid confidence interval) 

Recommendations: 

1. No minimum area, but a site must be part of a normal, ongoing silvicultural operation, 

i.e., not in the process of conversion to another land use. 
6 

Rationale: Since forestry operations occur on tracts of all sizes and BMPs apply 

regardless of acres involved, all forestry operations should be eligible for monitoring. 

However, operations that include timber harvesting as part of a change in use, should be 

disqualified regardless of the size of the operation. Such activities would not accurately 

reflect normal silvicultural operations. 

2. The presence of surface water features is not necessary for a site to be eligible for 

BMP implementation monitoring. 

Rationale: BMP implementation in most states is not contingent upon the presence of 

surface water on-site. However, those states that have proximity restrictions associated 

with BMP implementation should select monitoring sites using the appropriate criteria. 

3. The most recent silviculture activity(s) on a site to which BMPs apply must not have 

been completed more than 2 years prior to implementation monitoring. 

Rationale: Forestry operations more than 2 years prior are increasingly difficult to 

evaluate because of rapid regrowth of vegetation and more difficult access. Likewise, 

evidence of erosion and sedimentation become less visible over time, as does the 

opportunity to correct such problems without "re-disturbing" sensitive areas. 

4. Sites for implementation monitoring may be located using aerial reconnaissance, 

severance tax records, notification logs, or other available sources of information. 

However, it is essential to achieve random, stratified random or randomized cluster 

statistical design to obtain an unbiased sample. 

Rationale: Several data sources can provide the information necessary to select a random 

sample of forestry operations sites. However, it is important that the sample population 

accurately reflect actual conditions in a given state. For example, portions of a state in 

which forestry operations are concentrated should be sampled accordingly, as should 

those with fewer operations. 

5. The sample size should be sufficient to achieve an estimate of implementation that is 

+ 5% within the 95% confidence interval. 

Rationale: To maximize the validity and credibility of the monitoring results, the number 

of sites evaluated for BMP implementation should be calculated to provide minimum 

error (+ 5%) and high confidence (95%). Designing a statistically valid sampling 

procedure for implementation monitoring and analyzing the results should be consistent 

with "Sampling and Estimating Compliance with BMPs" (1) and/or the Statistical Guide 

for BMP Implementation Monitoring found in the Appendix. 

Practices to be Evaluated

Issue: Which categories of practices should be evaluated for BMP implementation 

monitoring? 
7 

Alternatives Considered: Harvesting; Site Preparation (mechanical, chemical, burning); 

Forest Roads; Stream Crossings, Streamside Management Zones, Firebreaks, Forest 

Chemical Application (fertilization, herbicides). 

Recommendation: All of the above alternatives should be evaluated. 

Rationale: These BMP categories contain all practices that are generally associated with 

operational silviculture in the South. 

Basis for Practice Evaluation and Reporting

Issue: On what basis should BMPs be evaluated and reported? 

Alternatives Considered: Individual practices, Categories of practices, Overall site. 

Recommendation: Evaluation and reporting should include all three levels of BMPs 

listed above. 

Rationale: Evaluation of BMPs at the practice level provides the basic measure of onsite BMP implementation. This level of information also allows for comparison of a 

specific practice among all monitoring sites and against any other site variables. Such 

comparisons are useful for identifying those variables most often associated with nonimplementation. 

In addition, by evaluating categories of practices, monitoring can provide broader 

conclusions about BMP implementation for stream crossings, roads, etc. Also, this 

information can identify training needs for forestry agency personnel, and education 

needs for forestry practitioners. 

It is likewise useful to water quality agencies, other interested parties and particularly 

forest landowners to know the overall or cumulative level of BMP implementation for 

individual forestry operations. This is a primary and traditional measure of program 

success, and indicates the efficacy of the non-regulatory approach to controlling 

silvicultural related nonpoint source pollution. 

Scoring Methodology

Issue: How should BMP implementation monitoring be scored? 

Alternatives Considered: Pass/Fail; Graduated Scale; Percent Correct Implementation; 

Yes/No 

Recommendation: An individual practice should be scored as “Yes” when applied as 

specified in the state's BMP Manual. If a particular practice is not applicable, this should 

be noted as well. Any significant deviation from practice specifications should result in a 
8 

“No” answer for BMP implementation. Categories and overall scores should be 

expressed as a simple percentage of all applicable practices. For example, if 100 

practices were applicable but only 90 were actually implemented correctly, then the score 

would be 90% for that category or site, as the case may be. 

Rationale: Evaluating whether or not BMPs have been properly implemented, and their 

applicability to specific site conditions yields the most objective and reproducible method 

of implementation monitoring. While some judgment will always be necessary in 

questionable situations, objectivity can be maximized by training. In addition, 

subjectivity and confusion are minimized by avoiding practice evaluations based on 

graduated scales for partial implementation, or arbitrary "Pass/Fail" declarations. Simple 

“Yes/No” scoring of BMPs also facilitates the calculation, summarization and reporting 

of category and overall implementation levels on a percentage basis. 

Risk Assessment

Issue: How should the risk to water quality resulting from failure to implement BMPs be 

evaluated and documented? 

Alternatives Considered: No evaluation of risk; Risk evaluated and significant risk 

noted. 

Recommendation: Risk to water quality should be evaluated and significant risk 

documented. Significant risk may be attributed to non-implementation for a specific 

BMP, category of BMPs or the overall operation. The field evaluation of significant risk 

should be based on existing on-the-ground conditions resulting from failure to correctly 

implement BMPs, that if left unmitigated will likely result in an adverse change in the 

chemical, physical or biological condition of a waterbody. Such change may or may not 

violate water quality standards. 

Significant risk should also be considered as a situation or set of conditions that can be 

remedied or otherwise mitigated (2). In addition, failure to implement BMPs that results 

in risks to site productivity, road usability or other site values should not be considered a 

significant risk in the context of implementation monitoring. Significant risk should be 

directly and exclusively related to water quality impairment. 

Key site conditions often associated with significant risk include, but are not limited to: 

steep topography and highly erodible soils. Forestry operations conducted under one or 

more of these conditions without proper implementation of certain BMPs may have a 

high potential to result in significant risk to water quality. Some examples of forestry 

activities where significant risks have been identified are equipment operation in close 

proximity to surface waters, stream crossings, logging slash disposal and intensive 

mechanical site preparation. A comprehensive list of on-site indicators of significant 

risks to water quality is shown below: 
9 

On-Site Indicators of Significant Risk to Water Quality 

 

 Temporary stream crossings remain in channel following operation 

 Stream crossings and approaches not stabilized 

 Logging debris in waterbody affecting or obstructing flow 

 Evidence of excessive sediment entering waterbody from adjacent treated area 

 Canopy completely or almost completely removed from SMZ on perennial 

waterbody 

 Evidence of heavy equipment operation in stream channel 

 Waterbody banks compromised by equipment or skidding activities 

 Water diversion devices absent or severely compromised on roads or skid trails 

where runoff is likely to enter waterbody 

 Ruts or other excessive physical damage to soils and cover within the SMZ 

 Fill material in stream crossing without adequate means for conveyance of flow 

 Un-stabilized fireline tied directly into waterbody 

 Oil, chemicals, batteries or other hazardous materials leaking or remaining on site 

following operation 

 Road or skid trail too steep or so poorly located that stabilization is improbable 

 Excessive defoliation of riparian vegetation caused by herbicide application 

Rationale: Documenting the occurrence of significant risk serves a number of useful and 

practical purposes. First, risk assessment lends much credibility and integrity to the BMP 

monitoring process by recognizing that high risk conditions can occur, and that 

prevention and/or restoration is a high priority for state forestry agencies. Second, 

routine documentation of significant risk will determine whether such instances are the 

exception rather than the rule, and that lack of BMPs during a silviculture operation may 

not necessarily equate to or result in a water quality problem - this is particularly 

important as it relates to BMP effectiveness monitoring (3). Finally, providing forest 

landowners with an objective risk assessment is a valuable public service that not only 

protects the environment, but can also protect the landowner and/or operator from what 

might otherwise result in enforcement proceedings or other personal liability. 

Follow-up Actions

Issue: What specific actions should states take following BMP implementation 

monitoring? 

Alternatives Considered: No follow-up; Courtesy copies of monitoring results; Personal 

visit; Referral (where necessary) to regulatory agency. 

Recommendation: Landowners who have participated in the implementation monitoring 

should be provided a copy and explanation of the monitoring results. In addition, 

participating landowners should receive recommendations for any remedial actions 

deemed necessary by the field observer. In cases where a significant risk has been 

identified, state forestry personnel should attempt to schedule a follow-up site visit with 
10 

the landowner, to insure that recommendations were understood and implemented 

satisfactorily. 

Rationale: Follow-up activities with landowners and/or loggers serves as a useful 

educational opportunity, as well as a demonstration of cooperation and courtesy. The 

BMP monitoring data provides an excellent focal point for reviewing the performance of 

an operator and the responsibilities of the landowner, in terms of water quality and site 

protection. Remedial or other actions can also be recommended at this time, as can 

commendation for a job well done. 

Where a significant risk has been identified in the monitoring process, an on-site followup can be vital to insuring that the landowner/operator is aware of the seriousness of the 

situation and advised of remedial actions. Potential consequences of inaction can be 

explained and discussed at that time also, and should include environmental impacts as 

well as possible enforcement actions or other liabilities. This effort can provide the basis 

for fulfilling the responsibilities of the state forestry agency, and provide the landowner 

with the information from which to make an informed decision. 
11 

References 

(1) McNew, Ronald W. 1990. Sampling and Estimating Compliance with BMPs, 

in Workshop on Implementation of Forestry Best Management Practices. 

Southern Group of State Foresters and USDA Forest Service. Atlanta, GA. 

January 23-25, 1990. Edited by G. Dissmeyer 

(2) Vowell, Jeffery L. and Roy Lima, 2002. Results of the 2001 Silviculture BMP 

Compliance Survey. Florida Department of Agriculture and Consumer 

Services, Division of Forestry; Tallahassee, Florida. 

(3) Dissmeyer, George E. 1994. Evaluating the Effectiveness of Forestry Best 

Management Practices in Meeting Water Quality Goals or Standards. USDA 

Forest Service, Miscellaneous Publication 1520. 

Acknowledgements 

The Southern Group of State Foresters wishes to thank the Task Force members and 

acknowledge the assistance provided by the water resource specialists from other state 

forestry agencies in the Southern Region. In addition, appreciation is expressed to the 

U.S. Forest Service Southern Region and the U.S. EPA Region IV for considerable 

expertise in this effort. Finally a special thanks is expressed to George Dissmeyer 

(retired, U.S. Forest Service) for his leadership in helping organize and initiate this 

process. 

Glossary 

Implementation Monitoring – The process used to determine the proper application of 

BMPs according to the specifications in individual state BMP Manuals. 

Risk Assessment – The process and criteria used to identify a significant risk to the 

chemical physical or biological integrity of water quality. 

Significant Risk – An existing on-the-ground condition resulting from failure to correctly 

implement BMPs, that if left unmitigated will likely result in an adverse change in the 

chemical, physical or biological condition of a waterbody. Such change may or may not 

violate water quality standards. 
APPENDIX 

Statistical Guide for BMP Implementation Monitoring 

Statistical Guide for 

BMP Implementation Monitoring 

by 

Hughes Simpson, Texas BMP Coordinator 

John Greis, USDAFS Region 8 Hydrologist 

Dr. Ron McNew, Professor, University of Arkansas 

Southern Group of State Foresters 

Water Resources Committee 

October 2006 

Table of Contents 

Statistical Guide for BMP Implementation Monitoring 

Introduction 17 

Survey Design 

Determining the Number of Sites to Monitor 18 

Site Selection 19 

Data Storage 19 

Ensuring Randomness 19 

 Stratification of Field Sites 19 

Data Analysis 

 Margin of Error 20 

 Confidence Intervals 21 

BMP Trend Analysis 22 

 Area Weighting Results 23 

 Reporting 24 

17 

 

Introduction

Implementation monitoring is the process used to determine the proper 

application of Best Management Practices (BMPs) according to the specifications in 

individual state BMP Manuals. In 1999, the Southern Group of State Foresters (SGSF) 

endorsed a monitoring framework designed to provide regional guidelines for monitoring 

BMP implementation so that consistency and reliability of southern state efforts would be 

maximized. The framework calls for evaluations to be conducted on randomly selected 

forestry operations and to result in data that is statistically valid. 

Field evaluations consist of answering “yes”, “no”, or “not applicable” to 

questions regarding proper implementation of specific BMPs. These are typically broken 

down into several activity categories (roads, trails, stream crossings, etc.). Each question 

represents a specific BMP (“yes” means the BMP was implemented correctly and “no” 

means it was not). If a BMP listed on the evaluation form was not applicable to that site, 

“not applicable” is recorded. Additionally, the presence of a significant risk to water 

quality is noted for each question if, due to a lack of or malfunction of a BMP, water 

quality has been impacted or is clearly threatened. To determine the implementation rate, 

the total number of “yes” answers is summed and then divided by the total number of 

applicable answers (yes / yes + no) to determine the total BMP Implementation rate, 

expressed as a percent, for the site. 

After combining all results, BMP implementation may be reported for the state, 

regions of the state, landowner types, forestry activities, river basins or watersheds, and 

BMP groups or other categories of interest for reporting purposes. Strengths (BMPs 

along streams) and weaknesses (BMPs on roads) are generally identified from the results. 

In 2004, a task force of the SGSF Water Resources committee was formed to 

develop this statistical guidebook to assist the southern state forestry agencies with BMP 

implementation monitoring design and reporting. Included with this guidebook is an 

Excel spreadsheet created to help states determine how many sites are needed to conduct 

a statistically reliable survey, calculate the margin of error for each BMP evaluated and 

reported, and analyze statistical trends in BMP implementation. 

Major elements in the design of a statistically valid BMP implementation survey include: 

 

- sampling intensity (total number of sites needed for the survey) 

- methodology of choosing sites 

- how to ensure randomness of the samples 

- stratification of field sites (# of samples per county, landowner type, 

etc.) so that sound conclusions can be drawn from each. 

Key calculations for the analysis of a BMP implementation survey will include: 

- determining statistical significance of BMP trends 

- confidence intervals and margin of error 
18 

 

Survey Design

Determining the sample size, or number of sites to evaluate

 

 

Where n = the number of sites to evaluate 

 p = the estimated overall percent implementation in the state 

 m = the margin of error (5%) 

Notes: 

- p must be estimated because it is unknown (% implementation from the most recent 

round of monitoring may be used) 

- The closer the estimated value of p is to 100, the lower the value of n will be. 

- n is highest when p is estimated to be 50%. 

- m is the margin of error associated with the estimate of p. There is .95 (95%) 

probability that the sample taken will produce an estimate which differs from p by 

a value of m 

Example: 

 

Where p (overall BMP implementation) is estimated at 80% 

 n = 4 (80) * (100 – 80)

 52 

 n = 6400

 25 

 n = 256 

Using the spreadsheet: 

The spreadsheet is set up so that all that must be entered is the estimated value of 

p (Est. % BMP Impl). It will then automatically calculate the number of sites to evaluate 

based on an embedded formula and a margin of error equal to 5% (as recommended by 

the SGSF framework). 

n = 4p (100 - p)

 m2

n = 4p (100 - p)

 m2
19 

 

* These equations calculate the minimum number of sites necessary to 

evaluate. Increasing the sample size will yield an even more accurate 

estimate of BMP implementation. A minimum of 100 sites is recommended. 

Data Storage 

BMP implementation monitoring data can be stored in a number of different 

formats. The easiest is an Access database consisting of the individual state’s BMP 

monitoring form (checklist), data tables, queries, and reports. Site evaluations can then 

be entered directly into the database in a user friendly format. Queries and filters can be 

created to display the “target” data (i.e. implementation scores for tracts in which a 

professional forester was involved) for export to the Statistical Guidebook Spreadsheet. 

Reports can provide a quick glance at the results of the survey (i.e. % implementation by 

county). GIS programs can import data for geographical representation and further 

analysis. A sample database is available for states to customize to fit their needs. 

Site Selection 

 BMP field sites may be selected in a number of ways: aerial reconnaissance, 

severance tax records, timber deeds, drive-bys etc. To avoid bias, it is important that 

personnel involved in the site selection process do not contact consulting foresters, 

industry foresters, or large landowners to provide a list of recent harvesting operations. 

This could bias samples to the “good” sites. Of equal importance is to avoid selecting 

sites thought to be either “good” or “bad”. The SGSF framework calls for sites to be no 

older than 2 years after the most recent treatment activity. 

Ensuring Randomness 

 Ensuring randomness is critical in any type of sampling. One way to help achieve 

randomness is to identify twice as many sites as are needed for the survey, and use a 

random number generator to identify specific sites to monitor. 

Stratification of Field Sites by Ownership, Watershed, or Other Factors 

Stratifying the monitoring sites based on important characteristics such as 

ownership type, watershed, or physiographic region, can add substantial value to the 

survey’s results. It is important that the sample taken be reflective of the actual 

conditions. There are two ways to accomplish this: 

 Take a truly random sample from the population (this will solve 

the stratification but is extremely difficult). 

 Intentionally select sample sites based on their stratum 

Forest Inventory and Analysis (FIA) data may be used to estimate the number of 

sites undergoing forestry operations by landowner type. This percentage can then be 

used to estimate the number of monitoring sites each landowner group should comprise. 
20 

 

Data Analysis 

Margin of Error 

The margin of error expresses the maximum likely difference observed between 

the sample mean and the true population mean with 95% probability. It is an important 

statistical calculation and can be performed for an individual BMP evaluation question 

(i.e. SMZ width). The following formula is used to perform this calculation: 

Where m = margin of error for a single BMP 

 P = the percent implementation for a single BMP 

 n = the number of sites on which the BMP were evaluated 

Notes: 

- If the actual value of P is larger than the estimated value of P, then the actual margin of 

error will be smaller than m. 

- This equation is not valid for a subset of all possible sites (i.e. calculating margin of 

 error from the % BMP implementation for NIPF landowners.) 

- For a BMP that is not applicable to all sites, the actual margin of error will be larger 

than m. 

- Estimating the average % BMP implementation across all possible sites for a group of 

BMPs and then using this number of sites will produce a margin of error that is 

smaller than m. 

- If the value of P is 100%, the margin of error is not zero. No calculation can be made. 

Example: 

 

Where P (% BMP impl. for adequate SMZ width) was evaluated to be 89% on 125 sites 

 m = 2 √89 (100-89) 

 125 

 m = 2 √979 

 125 

 

 m = 2 √7.832 

 m = 5.597 
21 

 

Using the spreadsheet: 

The spreadsheet is designed to calculate the margin of error for a single BMP. All 

that must be entered is the % implementation for a single BMP (% for single BMP) and 

the number of sites on which that BMP was evaluated (# of sites). 

95% Confidence Interval 

The 95% confidence interval is a tool that statisticians use to demonstrate their 

confidence in the measured mean of a sample. It provides a range for which they are 

95% confident (i.e. 19 times out of 20) that the actual mean will be found within that 

range. To calculate the 95% confidence interval, you must also calculate the mean, 

variance, standard deviation, standard error, and margin of error. 

Example: 

Let’s calculate the 95% confidence interval for the following sample: 

95%, 80%, 88%, 100%, 77% 

First calculate the mean. 

95+80+88+100+77 = 440 = 88% 

5 5 

Then calculate the variance. 

Step 1: USS = 952 + 802 + 882 + 1002 + 772= 39,098

Step 2: SUM = 95 + 80 + 88 + 100 + 77 = 440 

Step 3: CF = 4402

/5 = 193,600/5 = 38,720 

Step 4: CSS = 39,098 – 38,720 = 378

Step 5: DF = 5 – 1 = 4 

Step 6: Variance = 378 / 4 = 94.5 

Next calculate the standard deviation. 

Std dev. = √variance = √94.5 = 9.721

After that, calculate the standard error. 

Std. error = (Std dev. / √number of sites) = 9.721 / √5 = 4.347

Next, calculate the margin of error. 

Margin of Error = 2(Std. error) = 2 (4.347) = 8.695

Finally, use the margin of error to calculate the 95% confidence interval. 

95% Confidence interval = Mean ± Margin of Error = (79.305, 96.695) 
22 

 

Using the Spreadsheet 

The spreadsheet is set up so that all that must be entered is the individual tract 

scores (Indiv. % Impl) and the total number of sites (# of sites). The spreadsheet 

automatically calculates the mean, variance, standard deviation, standard error, margin of 

error, and the 95% confidence interval (low and high ends). 

BMP Trend Analysis 

 Analyzing trends or patterns in BMP implementation can be useful to target areas 

or ownership types for concentrated educational efforts (i.e. additional logger training 

workshops). Commonly reported trends include higher BMP implementation rates when 

professional foresters are used, the landowner is familiar with BMPs, and the logger has 

attended BMP training. 

In order to determine trends in BMP implementation, several statistical analyses 

should be performed. First, a parametric two sample t-test is conducted because of the 

large sample size. This percentage data must undergo an arcsine square root 

transformation prior to analysis. Percentage data must be transformed because they are 

not normally distributed, which invalidates the normality assumption of the parametric 

test. A non-parametric test (Wilcoxon) may also be performed to add greater statistical 

validity. 

To determine statistical significance, the resulting P value was compared to the 

level of significance. The P value is the probability of observing a value of the test 

statistic as contradictory (or more) to the null hypothesis as the computed value of the test 

statistic. In these tests, a 0.05 (5%) level of significance was used. For the two 

implementation ratings to be significantly different, the P value must be lower than the 

level of significance. 

Using the spreadsheet: 

The spreadsheet is set up so that all you have to do is enter the individual scores 

for the tracts that answered “yes” to the particular trend question and likewise for those 

that answered “no” in the respective column. It will then perform the arcsine square root 

transformation and conduct a parametric two sample t-test on the new data, based on a 

level of significance of .05. This value will be used to determine whether the difference 

in implementation scores for that particular trend is statistically significant. This 

classification is noted by the answer “TRUE” found under the Stat. Diff column. 

**The arcsine square root transformation was conducted so that Microsoft 

Excel could perform the analysis. More robust tests (non-parametric tests 

like the Wilcoxon) may be conducted to add greater statistical validity. These 

tests are not included in basic Microsoft Excel programs but can be found in 

programs like JMP, SAS, or Statistica. 
23 

 

Area Weighting BMP Implementation Data 

Results are typically reported giving equal weight to all sites (i.e. a 20 acre tract 

counts the same as a 450 acre tract when compiling all data). Statistically, tracts could 

also be weighted based on their acreage, i.e. larger tracts would have a greater influence 

on the total % BMP implementation than the smaller tracts. This analysis can be 

performed to provide information on how the practices are impacting the total landscape. 

Both methods are useful in reporting BMP implementation rates, though the SGSF 

framework does not call for area-weighting. The following formula may be used to 

perform this calculation. 

AW % = Σ (((indiv A/Total A) *100)) * % Impl))) 

 Where AW % = area weighted BMP implementation % 

 A = area (acres) 

 % Impl = individual tract % BMP implementation 

 

Example: 

For this example, let’s use 5 individual tract scores and their respective size: 

 

95% - 100 acres, 80% - 35 acres, 88% - 70 acres, 100% - 275 acres, 77% – 20 acres 

Equal weighted % BMP Implementation = Sum of scores divided by number of sites 

 

95+80+88+100+77 = 440 = 88% 

 5 5 

Area weighted % BMP implementation = Sum of scores proportional to tract size 

% BMP Impl Tract Size % of Total AW % 

95 100 20 19 

80 35 7 5.6 

88 70 14 12.3 

100 275 55 55 

77 20 4 3.1 

Total 500 100 95 

 = 95% 

% of Total = Tract Size / Total Size 

AW % = % of Total * % BMP Implementation for each individual tract 

Area Weighted % BMP Implementation = Sum of individual AW % 
24 

 

Using the spreadsheet: 

The spreadsheet is set up so that all that must be entered are the individual percent 

BMP implementation rates and their respective tract sizes in acres. It will then 

automatically weight the BMP implementation scores based on the tract size. 

Reporting

Using the statistical procedures contained in this guide, BMP Implementation data can be 

reported in the following ways: 

 

- Overall % BMP implementation for the state 

- % BMP implementation by landowner group 

- % BMP implementation by BMP category 

- Area weighted % BMP implementation