U. S . Fores t Seroice&search P a p e r S E ’ - 3-4

March 1963 i

I n s t r u c t i o n s f o r U s i n g T r a f f i c C o u n t e r s

imate Recreatio

1_. Jnstructions f o r U s i n g T r a f f i c C o u n t e r s

t o E s t i m a t e R e c r e a t i o n V i s i t s a n d U s e

byGEORGE A. JAMES AND

THOMAS H. RIPLEY-j

INTRODUCTION

Every manager of a recreation site needsthree essential statistics: man-hours of use,number of visits, and peak loads. Man-hoursof use are a good gauge of site wear and tearand service reauirements. Visits reflect thenumber of impiessions gained by people andhence provide an index to public approvalor dissatisfaction, depending upon site con-dition. Peak load data are the basis of plansfor capacity or overload crowds.

Techniques described in the following pagesfor obtaining this necessary information aresimple, inexpensive, and’can produce reliableresults. They will provide good estimates ofthe number of visitors; total and componentman-hours of recreation uses such as camp-ing, picnicking, swimming, boating; averageparty size; and peak use loads. The methodshould be useful to all public agencies whereestimates of use and visits on unattendedrecreation sites are needed.

To obtain the estimates, we develop a ratiobetween the desired statistic (visits, totalrecreation use, etc.) and traffic counts bysimultaneously measuring both. This is calleddouble sampling. Pneumatic traffic counters,either recording or nonrecording, are placedon site entrances to tally total vehicle cross-ings. The counters are read daily. The num-ber of people visiting the area and the uselevels on recreational facilities are determinedhourly during a 12-hour period on a fewrandomly selected “sampling days” during

the season. On days when someone is noton the site counting people and recordingwhat they do, the traffic counters alone pro-vide the basis for use estimates. The dataare then analyzed, and estimates are calcu-lated showing visits and use for each sitefor the current season. The tables and chartsprepared from this sampling effort can beused to provide estimates during the nextseveral years from vehicle counts only, pro-vided there are no major changes in the sites.

The method was evolved from two studies.The first was an informal sampling conductedin 1961 on two National Forest recreationsites in the southern Appalachians. This brieftest determined what attributes could bestbe measured, sampling procedures, size ofsample, and relations to traffic counter read-ings, etc. Findings revealed a strong rela-tionship between pneumatic traffic countsand the amount of use the areas received.

As a result of early success, a second andcomprehensive study was planned and con-ducted June 3%September 3, 1962. This wasa cooperative venture between National For-est Administration and Southeastern ForestExperiment Station. Thirteen recreation siteswere involved: 11 in the Southern Region,and 2 in the Northeastern Region. Resultswere highly successful and have resulted insampling and analytical procedures with wideapplication.

~1 These are t h e cars that brought the swimmers, Alexander Spr ings , F lor idaNational Forest. Pnenmatio traffic counters were able to predict swimming use1

i season long with a high degree of accuracy.

1. The first step is to choose the period ofyear for which estimates are desired. In-formation will probably be needed for theentire year on some recreation sites. Inmost cases it will be necessary to divide*_ the year into at least two parts: high-useand off-season. Each must be sampledindependently because use estimates for :a specific period are generally accurateonly for that period. It is recommendedthat most forests in the southern and

PRELIMINARY STEPS

southeastern states divide the samplingyear into high-use (June 1 through LaborDay) and off-season (remainder of year)periods. Florida and other deep south for-ests might classify the high-use samplingperiod from May 15 through Labor Day.Other combinations are possible andshould be considered.

2. Each period chosen should be sampledby a minimum of 10 randomly selected12-hour satnpling days. Half the sampl-

ing days must be selected from all possibleweekends and holidays, and half from allposrible weekdays included in the sampl-ing season. It is necessary that all samplesbe selected in a random manner. A sampl-ing schedule must be prepared for eachsite in advance of study installation.

3 . Prior to study installation, traffic countersmust be installed at each entrance of thesite to be sampled. Counters need not,however, be placed on exits in areas havingl-way traffic only. Two counters installedat each site entrance provide good in-surance in case one of the counters failsto work. Two counters are recommendedfor single-entrance sites where use isheavy. Care must be exercised in select-ing roads on which counters are installed.Placement of counters is covered in detailunder “7’raff;c Counters” in a later sectionof this booklet.

1. All estimatescount retcurate r-tseasonalters mus

; are based on the traffic-zord, so it is imperative that ac-:cords be obtained. During thesampling period all traffic coun-t be read every 24 hours. Meters

must be read every day at the same timebetween 6:00 a.m. and 10:00 a.m., pre-ferably as near 10:00 a.m. as possible.Record actual daily axle counts,l notmeter readings, on Form 1 (see Appen-dix). A limited number of “missed” read-ings, though undesirable, will not invali-date the entire axle-count record. Thefollowing example shows how to enteraxle counts on Form 1 if one or more daysare missed :

Date Actual axle countJ une S 1,416J une 9 MissedJune 10 MissedJune 11June 12

3,::: (total for 3 days)

SAMPLING DETAILS

2. On each of the ten 12-hour sampling dayswhich have been randomly selected, ob-

’ Axle count as used throughout this manual means two..a.. ..,%“^^..-“- ..-I.:“!- ^_^ -^- 3 . ..I..-, l.-.r

3

server should begin making observationsat 10:00 a.m. All information to cotnpleteForm 2 (see Appendix) should he col-lected. On most recreation sites, one ob-server will be able to collect the necessaryinformation. Some sites will probably re-quire two observers during periods ofheavy recreation use. Summary instruc-tions for sampling and recording data onForm 2 are shown below:

1O:OO a.m.-Read traffic counter(s)and insert observation at top, opposite10:00 a.m. Example: if there are threecounters reading 00287, 12006, and 22943,record each reading.

lO:OO-lo:30 a.m.-Begin at site en-trance and subsample for exactly 30minutes, counting all people entering thearea regardless of tnode of travel. Recordopposite 10:00 a.m. at top of “Visits”column. In areas having more than oneentrance, it will be necessary to deter-mine starting entrance in a randommanner. This is done by writing num-bers, corresponding to numbers of en-trances, on single pieces of paper,placing them in a container and drawing asingle number. As an example: number2 is drawn for an area having three en-

4

trances. Consequently, the IO:00 a.m.sampling begins at entrance number 2,wit6 11:00 a.m. sampling at entrance 3,12:00 noon sampling at entrance 1, andcontinuing in sequence throughout thedav. The same random selection of start-

_I

ing entrance and subsequent proceduremust be followed for each 12-hour sampl-ing day of the season.

10: 30-l 1: 00 a.m.-Circulate the areaand determine (as of 10:30 a.m.) thenumber of people and parties (campingand picnicking) associated with each ma-jor use in the area. A party may consistof one or more persons. Record thesetotals in appropriate “Component use”columns opposite lo:30 a.m. These mustbe viewed as instantaneous estimates.

ll:OO-11:30 a.m.-Return to the singleentrance, or go to the next entrance in

sequence in multi-entrance sites, and de-termine visits for exactly 30 minutes.Record as before opposite 11:OO a.m. un-der “Visits.”

11: 30-12 : 00 noon.-Repeat proceduresfollowed between 10: 30-l 1: 00 a.m., tally-ing total people and parties as before.

Repeat these steps throughout the dayuntil 1O:OO p.m. Read traffic counters at1O:OO a.m. the following morning andrecord opposite “lO:OO a.m. next day.”

Form 2 data sheets should be sum-marized and checked for arithmetic erroras soon as possible and any pertinent re-marks concerning irregularities should benoted. This completes the work at theactual recreation site. The remainingsummarization and analysis can best beaccomplished by headquarters staff.

OFFICE COMPUTATIONS (FORM 2)

Total all counter readings in axle cross-ings for all entrances from 1O:OO a.m. onsampling day to 1O:OO a.m. on followingday. Record 24-hour total under item Aat bottom of form.

Add up the “Visits” column. Multiplythis total by 2 to convert the 30-minutereadings to an hourly basis. Next multi-ply by number of entrances involved.Example: If there were 3 entrances,each total should be multiplied by 3.Enter these expanded 12-hour valuesunder item R. Note that sampling doesnot provide an estimate of visitorsbetween 1O:OO p.m. and 1O:OO a.m. thefollowing morning. Visits during night(after 1O:OO p.m.) are generally few andhave little bearing on site management.Man-hours of use and number of partiesfor each 12-hour period should be totaled.Enter these values under “Totals” at bot-tom of form. A 24-hour value for useand parties must be determined. For mostuses, the 12-hour daytime tally gives anaccurate estimate, because few peopleswim, fish, or picnic at night. Campingis the exception and the only significantovernight use on most recreation sites;consequently, it must be figured on anight-time basis also. Two assumptions aremade with regard to campers: if they are

in the area with tents up at 6:00 p.m., theywill remain overnight; and most childrenand many adults retire sometime betweenthe hours of 6:00 p.m. and 10:00 p.m.Accordingly, the largest number of camp-ers and parties counted between the hoursof 6:00 p.m. and 10:00 p.m. is multipliedby 12 to account for camping man-hoursbetween 10:00 p.m. and 10:00 a.ni. thefollowing morning. These values areadded to the 12-hour daytime use to esti-mate 24-hour values. Record 24-hourcamping use and camping parties at thebottom of the form under (C).

Determine mean party size for campingand picnicking by dividing use by numberof parties associated with that use.

Example: Total 24-hour camping useis 500 hours; total number ofcamping parties is 125. Di-vide 500 by 12.5 to obtainthe average camping partysize, namely, 4 persons.

Double check entries, expansions, andtotals on Forms 1 and 2. The data are nowready to be examined by multiple regres-sion analysis. To b e most efficient thisshould be done using ADP (AutomaticData Processing) procedures for severalareas, say 10 or more at a time.

Punch data on ADP cards, or record onADP magnetic tapes. Insert F values onparameter cards to instruct machine toreject x variables which in final equationsare not significant at desired levels ofprobability. A probability level of 90 per-cent or higher is recommended. Consultappropriate statistical text for this step.

Run multiple regression analysis compu-tations on ADP computer.

Analyze ADP computations. Computepredicting equations and error terms foreach significant variable.

PREPARATION OF PREDICTING TABLES AND CHARTS

Two independent variables are examined each day. If the counter is read only oncein this analysis: axle counts (x) and axle during the period, 1,000 axle crossings arecounts squared (x”) . Estimates involving recorded. The above curve (and the tablesolely the x variable will produce a straight prepared from this curve) shows slightly lessline relationship between x and y (variate). than 500 picnicking hours during the S-dayEstimates containing the x2 variable will period. A daily reading of 200 axle cross-show a curved relationship between x and y. ings, however, shows 200 hours of picnickingExamples of the two types of situations are use daily, or a total of 1,000 hours duringshown below: the period.

Situations where the x’ variable alone bestexpresses the relationship between x. and ycan be expected to occur frequently. Of the71 predicting equations resulting from the1962 study, 46 percent involved the x2 vari-ate. The predicting equations for each sitewill generally contain some involving the xvariate only, and others containing the x2variate only. On only 2 of the 13 recreationsites sampled during 1962 were all site vari-ables best predicted by the x variate only.

Ninety-seven percent of the regressions re-sulting from the 1962 study were either simplelinear or simple curvilinear, i.e., involvingeither the x or x2 variates alone, not in com-bination. Less than 3 percent of the regres-sions were multiple curvilinear, involving xan d x2 in combination.

With these facts in mind, we are now readyto prepare predicting tables and charts. Pro-cedures for constructing the sheets summar-izing season-long visits and use estimates foreach site, such as shown in table 1 (Appen-dix). are as follows:

1,000E

L inao r re l a t i onsh ip be tween axle coun t

8 0 0 and visits

6 0 0

4 0 0

2 0 0

- y

-I I I I 14

0 200 400 600 800 1,000 1,500AXLE COUNT

CUrvilineOi relationship b e t w e e n axle

AXLE COUNT

Linear relationshiDs. such as shown above,A I

remain constant over the entire range ofvalues. In such cases it is necessary to obtaina record of total seasonal axle counts onlyduring the next several years to predict visitsor use.

Curvilinear relationships, such as shownabove, do not remain constant over the rangeof values. It can be readily seen from theabove relationshin that 24-hour axle counts

1

are necessary to produce reliable estimates.Example: consider a S-day continuous periodduring which 200 axle counts are recorded

1. Use appropriate formula for each variableto be predicted, plus seasonal record ofdaily axle counts. Plug axle count (x) orx2 into formula. For linear relationshipsuse axle counts directly from Form 1. Forcurvilinear relationships use axle countssquared. x’.

6

Example of linear relationshipBasic formula is Y= (a) times (number of

days in season)+ (b) times (total axle

count from Form 1) .Actual formula is Y--88 (66 days) + 1.7189

(35,000 axle crossings)= 5,808 + 60,162= 65,970 units (visits or

hours).Example of curvilinear relationship

Basic formula is Y=(a) times (number ofdays in season)

+ ( b ) t i m e s (ZxJ).To obtain (8x1) it isnecessary to square each24-hour axle count fromForm 1 and add thesquared values.

Actual formula is Y=7 (66 days) + 0.0003(2x12) o r 7(66) +0.0003 (5,000,ooo)

= 462 + 1,500= 1,962 units (visits or

hours).

2 . Compute sampling error for each predict-ing equation. Consult basic statisticaltext for calculation of standard errors andsampling errors. Note that the season-long summary tables thus prepared applyonly for the season and site sampled.

It may be disappointing to find thatreliable predicting equations are not al-ways produced for all desired variables.Due to certain site characteristics, or weakbasic data, it will occasionally be foundthat neither x nor x’ accurately predictssome site uses. However, good estimatesgenerally will result for most componentuses. In some cases where they do not,it may be possible to obtain close approxi-mations by simply subtracting predictedvalues of other variables from total use.

The next step is the preparation of REC-R E A T I O N V I S I T S A N D U S E E S T I -MATES tables for each site (table 2, Ap-pendix).

P R O C E D U R E

1) Insert various values of axle crossingsinto appropriate formulae. Somevalues will be x, others x2. Preparetable similar to table 2 (Appendix)for each area, as follows:

Example of linear relationshipBasic formula is Y = a + b(x)Actual formula is Y = 608 +

1.7638(x)Insert values of axle crossings intoabove formula as follows:Y = 6 0 8 + 1.7638(100) = 7 8 4Y = 6 0 8 + 1.7638(200) = 9 6 1Y = 608 + 1.7638(300) = 1137Y = 608 + 1.7638(400) = 1314

etc., until complete table isconstructed.

Example of curvilinear relationshipBasic formula is Y = a + b( x)zActual formula is Y = 188 +

0.0003 (x)2Insert squared values of axle countinto formula as follows:Y = 188 + 0.0003 (100)’ = 191Y = 188 + 0.0003(200)2 = 200Y = 188 + 0.0003(300)2 = 215Y = 188 + 0.0003(400)2 = 236

etc., until complete table isconstructed.

Note that the x values used in preparingthe tables should not be greater than themaximum, nor less than the minimum,actual axle crossings shown on Form 1for any area. Th ese tables can be usedto estimate future recreation visits anduse only during the season for whichsampling was done, i.e., either duringhigh use or off season.

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Two major steps have been accomplishedup to this point: Estimates of visits and usefor each sampled si te have been computed( t a b l e 1); and tables showing the relation-ships between axle crossings and the y vari-ables (uses and visits) have been prepared asshown in table 2. The use of table 1 hasal ready been discussed. RECREATIONVISITS AND USE ESTIMATES based onaxle crossing counts (table 2) are applicableduring the next several-year period. It isexpected that the relationships between axlecounts and associated visits and uses will re-

main fairly constant for the next S-yearperiod. It is necessary only to obtain an ac-curate record of 24-hour axle counts to obtainreliable estimates. These relationships willremain constant, however, only if no majorchange in recreational facilities is made on therespective site. Examples of major changewould be the construction of swimming poolor boat ramp where neither of these facilitiesexisted previously. Increasing the number ofpicnic tables or family camping units is notexpected to invalidate estimates.

SAMPLING PRECISION

The recommended sampling intensity of10 sampling days per site is expected to yielderror terms no larger than plus or minus 25percent of the estimated variable at the 67-percent level of probabili ty. Many errorterms will be considerably less than 2.5 per-cent (see table 1, Appendix). A few mayoccasionally be less accurate, depending onsuch things as site characteristics, accuracyof measurements, etc.

Example: If an equation estimates 100,000h o u r s o f c a m p i n g u s e , andsampling error is 3~ 25 percent,t h e n t h e t r u e v a l u e w i l l l i esomewhere between 75,000 and125,000 hours two out of threetimes.

If error terms consistently less than 2.5 per-cent are desired, a sharp increase in numberof 12-hour sampling days will be necessary.

OTHER INDEPENDENT VARIABLES

Several independent variables were assessed revealed that only two of the independentin the two sampling studies. These included variables, 24-hour axle count and 24-hourweather, period of week (weekday vs. week- axle count squared, were good predictors.end and holiday), 12-hour axle count, 24- The other independent variables proved to behour axle count, and the squared expressions poor estimators. Because they accounted forof these last two variables. Multiple regres- only a small proportion of total variation.sion analysis performed on all dependent they were dropped.variables (visits, total, and component uses)

STRATIFICATION OF RECREATION SITES

Multiple regression analyses were per-formed to determine whether recreation siteshaving similar recreational facilities could becombined and one set of predicting equationsapplied to several s i tes . I t was found thatthe predicting equations could be used onlyfor the site from which the sample was ob-tained, and that s t rat i f icat ion was not pos-sible. This does not mean, however, thatcertain areas cannot eventually be grouped.

It means only that the widely scattered sitesstudied in 1962 (no two si tes on any oneNational Forest) did not lend themselves tostratification. Site variation was apparentlytoo great to permit combining areas. Similarareas on a single National Forest or RangerDistrict might, however, lend themselves tosite stratification. If so, sampling require-ments could be reduced.

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TRAFFIC

Shortly prior to study installation, installtraffic counters at the entrance to each studysite. Counters should be installed on roadsleading directly to the recreation site, not onroads leading past the site to other destina-tions. An example of correct and incorrectplacement is shown in the accompanyingsketch.

Traffic counters in subsequent years mustbe kept in the same location as during thefirst sampling year. A change in counterlocation will change the relationship betweenaxle count and the y variate, and result inestimates that are inaccurate.

COUNTERS

An extra counter and spare counter bat-teries should be carried in the recreationvehicle, or furnished the concessionaire whereapplicable, so that faulty counters may berepaired each morning.

The use of self-recording counters shouldbe considered. These machines provide a con-tinuous record of axle crossings and peak use,and need to be serviced only periodically.Because most sites will require a 24-houraxle-count record, self-recording countersmight pay for themselves during a singleseason.

b

--TRAFFIC COUNTER

ARoUND LA@ -

9

APPENDIX

Region

Form 1

Record of daily axle counts (June 1 through Labor Day)

Forest District Site

Date Axle count Date Axle count Date Axle count

June 1

June 2

June 3

June 4

June 5

June 6

June 7

June 8

June 9

June 10

June 11

June 12

June 13

June 14

June 15

June 16

June 17

June 18

June 19

June 20

June 21

June 22

June 23

June 24

June 25

June 26

June 27

June 28

June 29

June 30

July 1

July 2

July 3

July 4

July 5

July 6

July 7

July 8

July 9

July 10

July 11

July 12

July 13

July 14

July 15

July 16

July 17

July 18

July 19

July 20

July 21

July 22

July 23

July 24

July 25

July 26

July 27

July 28

July 29

July 30

July 31

Aug. 1

Aug. 2

Aug. 3

Aug. 4

Aug. 5

Aug. 6

Aug. 7

Aug. 8

Aug. 9

Aug. 10

Aug. 11

Aug. 12

Aug. 13

Aug. 14

Aug. 15

Aug. 16

Aug. 17

Aug. 18

Aug. 19

Aug. 20

Aug. 21

Aug. 22

Aug. 23

Aug. 24

Aug. 25

Aug. 26

Aug. 27

Aug. 28

Aug. 29

Aug. 30

Aug. 31

Sept. 1

Sept. 2

Sept. 3

10

Region

Site

Form 2

Daily summary of axle count and recreation use

Forest District

Observer Date

Counter readings Component usesI I I 1

(as talliedfor 30

minuteseachhour)

BoatingPicnicking Swimming and/or Misc. Total

Fishing

(II4 22

oa 8

d k

(A) Total 24-hour axle count . . . . . . . . . . . . . . . . . . . . .

(B) Total visits, 1O:OO AM to 10:00 PM . . . . . . . . . . . . . . .

(C) Component uses

C a m p i n g - - Man-hours, 24-hour period . . . . . . . .Number of parties, 24-hour period . . . .Mean size ofparty. . . . . . . . . . . . .

P i c n i c k i n g - Man-hours, 12-hour period , . . . . . . .Number of parties, la-hour period . . . .Mean party size . . . . . . . . . . . . . .

S w i m m i n g - Man-hours, lZ-hour per iod . . . . . . . .

Boating and/ orFishing - Man-hours, 12-hour period . . . . . . . .

Miscellaneous - Man-hours, la-hour period . . . . . . . .

TOTAL USE (24-hour period) . . . . . . .

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Table 1. --Visits and use estimates

Site: Alexander Springs Forest: Florida Region: 8-

Period beginning: 10:00 AM June 30, 1962 Ending: 10:00 AM September 4 , 1962

Variable Estimate

Total visits (number) 62,877

Camping use (hours) 470 ,768

Average camping party size (number) 3.7

Picnicking use (hours) 24,892

Average picnicking party size (number) 4.5

Swimming use (hours) 71,535

Boating-fishing use (hours) 7,842

Total use (hours) 613,118

AWuracyg

Percent

” 3.0

i-11.0

i: 8.0

i: 9.0

211.0

+ 4.0

? 8.0

ir 8.0

1 True value lies between the plus percentage and minus percentage shown, at the 67-percent levelof probability.

1 2

Table 2. --Axle counts (24-hour) and associated recreation visits and use estimates

(June 1 through Labor Day)

Region Fores t Distr ic t Site

Axle crossings(x)

Number

Total visits Picnicking use Swimming use Total use

= -59+ 0.6349 (xl) = 188+0.0003 (x)2 = 56+ 0.0003 (xj2 = 608+ 1.7638 (xl)

Number Hours Hours Hours

1 0 0 4 1 9 1 5 9 7 8 41 5 0 3 6 1 9 5 6 3 8 7 32 0 0 6 8 2 0 0 6 8 9 6 12 5 0 1 0 0 2 0 7 7 5 1 , 0 4 93 0 0 1 3 1 2 1 5 8 3 1 , 1 3 73 5 0 1 6 3 2 2 5 9 3 1 , 2 2 54 0 0 1 9 5 2 3 6 1 0 4 1 , 3 1 44 5 0 2 2 7 2 4 9 1 1 7 1 , 4 0 25 0 0 2 5 8 2 6 3 1 3 1 1 , 4 9 05 5 0 2 9 0 2 7 9 1 4 7 1 , 5 7 86 0 0 3 2 2 2 9 6 1 6 4 1 , 6 6 66 5 0 3 5 4 3 1 5 1 8 3 1 , 7 5 47 0 0 3 8 5 3 3 5 2 0 3 1 , 8 4 37 5 0 4 1 7 3 5 7 2 2 5 1 , 9 3 18 0 0 4 4 9 3 8 0 2 4 8 2 , 0 1 98 5 0 4 8 1 4 0 5 2 7 3 2 , 1 0 79 0 0 5 1 2 4 3 1 2 9 9 2 , 1 9 59 5 0 5 4 4 4 5 9 3 2 7 2 , 2 8 4

1 , 0 0 0 5 7 6 4 8 8 3 5 6 2 , 3 7 21 , 1 0 0 6 4 0 5 5 1 4 1 9 2 , 5 4 81 , 2 0 0 7 0 4 6 2 0 4 8 8 2 , 7 2 41 , 3 0 0 7 6 6 6 9 5 5 6 3 2 , 9 0 11 , 4 0 0 8 3 0 7 7 6 6 4 4 3 , 0 7 71 , 5 0 0 8 9 3 8 6 3 7 3 1 3 , 2 5 41 , 6 0 0 9 5 7 9 5 6 8 2 4 3 , 4 3 01 , 7 0 0 1 , 0 2 0 1 , 0 5 5 9 2 3 3 , 6 0 61 , 8 0 0 1 , 0 8 4 1 , 1 6 0 1 , 0 2 8 3 , 7 8 31 , 9 0 0 1 , 1 4 7 1 , 2 7 1 1 , 1 3 9 3 , 9 5 92 , 0 0 0 1 , 2 1 1 1 , 3 8 8 1 , 2 5 6 4 , 1 3 62 , 1 0 0 1 , 2 7 4 1 , 5 1 1 1 , 3 7 9 4 , 3 1 22 , 2 0 0 1 , 3 3 8 1 , 6 4 0 1 , 5 0 8 4 , 4 8 82 , 3 0 0 1 , 4 0 1 1 , 7 7 5 1 , 6 4 3 4 , 6 6 52 , 4 0 0 1 , 4 6 5 1 , 9 1 6 1 , 7 8 4 4 , 8 4 12 , 5 0 0 1 , 5 2 8 2 , 0 6 3 1 , 9 3 1 5 , 0 1 8

Agricvlture Asheville