12 CALIFORNIA AGRICULTURE, VOLUME 54, NUMBER 3

Modeling vineyard expansion,potential habitat fragmentationEmily Heaton ❏ Adina M. Merenlender

We used a statistical modelingtechnique called logistic regres-sion analysis, and a geographicinformation system (GIS), to mapareas of possible future vineyardexpansion in Sonoma County,based on data about vineyard de-velopment from 1990 through1997. The goal of this researchwas to develop a model thatwould improve our understandingof vineyard expansion patterns ata landscape scale (for instance,including an entire county). Theapproach involved identifyinglandscape characteristics thatwere associated with vineyard de-velopment and mapping the areaswith similar characteristics thatwere undeveloped in 1997. Weused the results to map wherehabitat removal and fragmentationcould result from vineyard expan-sion. This method, although stillunder development, is designedfor county- or regional-scaleanalysis to assist land-use plan-ners, natural resource protectionagencies and land conservationprograms in protecting valuableenvironmental resources whilesustaining a vital agriculturaleconomy.

While recognizing that the wine in-dustry represents a significant portionof California’s agricultural economy —

$1.3 billion was paid for Californiawine grapes in 1999 (CASS 2000) — itis also important to preserve the eco-logical integrity of the natural re-sources upon which we all depend forservices ranging from basic sustenanceto aesthetic pleasure. Mapping andanalyzing land use at the county scalecan facilitate community efforts to bal-ance economic growth and environ-mental protection. Land-use analysisand models can also aid planners inprioritizing undeveloped and agricul-tural land for possible protectionthrough conservation easements andother methods.

Recently, vineyard development isexpanding into rangelands, wood-lands and forestlands (forests that in-clude commercially harvested andregulated species such as Douglas fir),resulting in deforestation. We used re-sults of our model to examine the po-tential for habitat loss and fragmenta-tion. Model results can also be used toexamine the possible effects of newregulations, such as those that restrictfuture vineyard development on steepslopes (see sidebar, p. 19).

Modeling approach

Our analyses have sought to iden-tify areas that are most susceptible tofuture vineyard development inSonoma County. Our primary as-sumption is that areas that share im-portant characteristics with recentlyplanted land are more likely to be de-veloped into vineyards in the future.The results of our modeling efforts arestrongly influenced by the develop-ment trends mapped for SonomaCounty from June 1990 through June1997. The model does not specify sitesthat will necessarily support the suc-cessful propagation of wine grapes,since some newer vineyards may havebeen planted in less productive places.Furthermore, site-specific characteris-tics, such as microclimate and some

This is the first time that areas poten-tially suited to agriculture have beenmodeled using GIS for an entirecounty in California. Predictions madeby the model are derived from the cur-rently available data, which does notinclude such information as microcli-mate and soil profiles for specific sites.Scientists hope to add more informa-tion in the future. — Ed.

CALIFORNIA AGRICULTURE, MAY-JUNE 2000 13

soil properties, could notbe included.

We used logistic re-gression analysis(Hosmer and Lemeshow1989), a statistical model-ing technique, to quan-tify the correlation be-tween geographic factorsand recent vineyard de-velopment. The combi-nation of logistic regres-sion and a geographicinformation system (GIS)has been used to modelhabitat suitability forwildlife species (Carroll et al. 1999;Clark et al. 1999; Massolo and Meriggi1998). However, it is less commonlyused to model land-use change, thenotable example being the second gen-eration of Landis and Zhang’s (1998a,1998b) California Urban FuturesModel for urban expansion in BayArea counties.

Logistic regression allowed us toidentify which landscape characteris-tics were associated with sites that re-cently became vineyard versus thosethat did not. For a given version of themodel, we applied an equation result-ing from regression analysis to landthat remained available for vineyardexpansion and calculated the relativeprobability that each available sitemay be suitable for vineyard plantinggiven its characteristics (fig. 1).

Vineyard locations representing11,663 acres of post-1990 (from 1990through 1997) vineyard and 36,337acres of pre-1990 vineyard were incor-porated into a GIS. Our statisticalanalysis of recent vineyard develop-ment was based on vineyard sites de-veloped between 1990 and 1997. Ex-planatory variables were extractedfrom digital data on topography, veg-etation, land use and distances to vari-ous features (table 1) (see sidebar, p. 8).Although factors such as soil proper-

ties, water availability, zoning andmarket forces are also important, theywere not incorporated because corre-sponding GIS layers were not yetavailable.

We divided the county into 2.5-acrecells with a corresponding elevation,vegetation type and so on. The aver-age size of spatially distinct, post-1990vineyard blocks in Sonoma Countywas 24 acres, and the majority (400 outof 489) were 2.5 acres or larger. There-fore treating the landscape as 2.5-acreplots reflects the scale at which vine-yard development occurs. Due to thelimitations of the digital vineyardmap, we did not use a smaller cell size.

To gain an accurate understandingof expansion patterns, we identifiedland that was not available for vine-yard development in 1990 and ex-cluded corresponding cells from ourmodel. Land that we considered un-available for vineyard developmentincluded pre-1990 vineyards, 1990 ur-ban areas, state or federally ownedland, county and regional parks, waterbodies, and land with a slope valuegreater than 50% or elevation greaterthan 2,625 feet. After removal of theselands, 537,995 acres were left formodel-building. High elevation sites

were excluded because these areas areoften too cold to grow wine grapes.Planting on steep slopes (greater than50%) is generally forbidden by newregulations so these areas were alsoexcluded.

To test the ability of our methodol-ogy to predict vineyard expansion atthe county level, we used two inde-pendent data sets, one for modelbuilding and one for model testing.Each data set included observations(the 2.5-acre cells) representing post-1990 vineyard sites and available un-developed areas. To assign individualvineyards to a data set, they were firstclassified by appellation area and size:large (≥ 250acres), medium (< 250acres and ≥ 25 acres), and small (< 25acres). Half of the vineyards in eachappellation-size class were then ran-domly assigned to each data set.

Multiple versions of the modelwere constructed using different com-binations of variables and observa-tions from the model building set. Fora given version of the model, cells inthe test data set were classified as ei-ther suitable for vineyard or unsuit-able. The classification of each cell wasthen compared to its actual status(vineyard or undeveloped). The per-

Oak woodlands support a variety of wildlife including tule elk. By modeling vineyardexpansion, researchers can show where habitat may become too fragmented to supportviable populations of all its original inhabitants in the future.

Rob

ert

J. K

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er

14 CALIFORNIA AGRICULTURE, VOLUME 54, NUMBER 3

Fig. 1. Probability surface showing one version of the vineyard expansion model.

centage of cells that were correctlyclassified using different versions ofour model averaged 73%.

Vineyard development trends. Be-tween 1990 and 1997, vineyard expan-sion occurred primarily on gentle,nonforested slopes that were alreadyunder cultivation. The probability thata site was a post-1990 vineyard de-creased as slope increased. Also, grass-land and shrub were more likely to beconverted than woodland or forest.Farmland designated as prime, uniqueor of state or local importance wasmore closely associated with vineyarddevelopment as compared to otherland-use designations such as grazingland. Distance to the nearest pre-1990vineyard was more strongly correlatedwith vineyard development than wasdistance to the nearest 1990 urban area.According to these trends, Knights Val-

ley, Dry Creek and Sonoma appella-tions, and areas neighboring Sebastopolhave high potential suitability for vine-yard development (fig. 1, also see p. 9).

Geographic factors. In our analy-sis, the likelihood of a cell being apost-1990 vineyard decreased as slopeincreased. This result is understand-able because the cost and difficulty ofestablishing and maintaining a vineyardgenerally increase with increasing slope;development on steeper slopes requireserosion-control measures and in somecases recontouring and terracing.

The tendency to develop non-forested land can be explained prima-rily by economic considerations andpossibly by disease prevention. Clear-ing trees can be expensive, and re-maining tree roots can transmitArmellaria root rot (also known as oakroot fungus) to planted vines

(Merenlender and Crawford 1998).Also, clearing native vegetation forvineyard development has met withsome community resistance (see p. 4).

Distance to the nearest preexistingvineyard may also affect a grower’sdecision to plant a piece of land. Con-noisseurs often seek wines from recog-nized appellation areas and thereforethese wines can fetch a higher priceper bottle. Even in regions that are lesswell known, the presence of healthygrapevines nearby suggests that newvineyards will also do well.

Land value may very well explainwhy the probability scores in ourmodel decrease as distance to urbanarea decreases. Sonoma County citiesare growing, and the resulting oppor-tunity for urban development can in-crease real estate values, which makesland near urban areas more expensive

CALIFORNIA AGRICULTURE, MAY-JUNE 2000 15

TABLE 1. Variables included in logistic regression analysis, whether continuous or binary, and digital source of data

Factors examined Related variables Variable definition Data type* Data source

Topography 30-m USGS digital elevation models (DEMs)

Elevation ELEV Elevation (m) above sea level ContinuousSlope SLP Slope (%) ContinuousElevation×slope† ELEV×SLP Interaction term: elevation multiplied by slope ContinuousAspect N North facing slope Binary

NE Northeast facing slopeE East facing slopeSE Southeast facing slopeS South facing slopeSW Southwest facing slopeW West facing slopeNW Northwest facing slopeFLAT Slope = 0%

Aspect×slope N×SLP, NE×SLP, Interaction terms: aspect variable (N, NE, E, SE, ContinuousE×SLP, SE×SLP, S, SW, W, or NW) multiplied by slopeS×SLP, SW×SLP,W×SLP, NW9×SLP

Land use PFARM Prime farmland‡ Binary California Department of Conservation’sSFARM Farmland of statewide importance‡ Farmland Mapping and Monitoring ProjectUFARM Unique farmland‡ (FMMP) “important farmland” map forLFARM Farmland of local importance‡ Sonoma CountyGRAZE Land suitable for grazingOTHUSE Use other than urban or those above

Vegetation type CON Conifer Binary 25-m “CDF hardwood pixel” vegetation mapMHWD Montane hardwood based on 1990 Landsat Thematic Mapper

imageryCOW Coast live oak woodlandBOW Blue oak woodlandUHWD Unspecified hardwood§SHRUB ShrubGRASS GrassURBAN Urban¶OTHVEG Land cover type other than those above

Location VINDIS Distance to nearest pre-1990 vineyard Continuous Vineyard data originally compiled by Circuit Rider Productions; vineyard locations provided by Sonoma County Grape Growers Association

URBDIS Distance to nearest 1990 urban area Continuous FMMP “important farmland” mapRDDIS Distance to nearest road Continuous 1:100,000 TIGER94 line files

*Binary variables were assigned a value of 1 if the condition was met and a value of 0 if it was not (e.g. a cell classified as conifer received a value of 1 for the vari-able CON and a value of 0 for all other variables related to vegetation type).†Interaction between factors is indicated by ‘×’ between variables‡Prime farmland is irrigated farmland with physical and chemical features that allow sustainable production of agricultural crops and high yields. Farmland of state-wide importance is similar to prime farmland but has minor shortcomings, such as greater slopes or poorer ability to store soil moisture. Unique farmland is irrigatedor nonirrigated farmland with lesser quality soils used for the production of the state’s leading agricultural crops. Farmland of local importance is land of importanceto the local agricultural economy as determined by each county’s board of supervisors and a local advisory committee.§Unspecified hardwood is vegetation that was identified as hardwood but not classified into a more specific hardwood category (e.g. BOW, COW or MHWD).¶1990 urban areas identified as land unavailable for vineyard expansion were extracted from the FMMP important farmland map for Sonoma County. The FMMPurban classification is based entirely on building density derived from aerial photographs, field verification and public review. The CDF classification of urban landwas based on spectral radiance data obtained from satellites. Thus, ‘urban’ areas in the CDF vegetation map may have buildings, or they may not. For example,some roads in the middle of nowhere may show up as ‘urban’ due to their reflectance characteristics. Thus, cells classified as urban on the FMMP layer may not beclassified as urban on the CDF layer and vice versa.Note: Significant variables were identified using backward regression and Akaike Information Criteria (AIC) values. Binary variables representing the same factor(e.g. vegetation type) were combined into a single variable when the AIC value was improved. For example, COW and UHWD were combined into COW+UHWDbecause their quantitative relationship to vineyard development was effectively the same, as determined by looking at the AIC value when the variables were in-cluded separately versus when they were combined. Wald χ2 and p-values were also calculated to examine significance.

for agriculture. On the other hand, ag-ricultural land that is far from trans-portation corridors may contend withincreased transportation costs and islikely to fall outside of well-recognizedwine-grape growing regions inSonoma County, which can affect theprice per ton. This trend is partially re-flected by the negative correlation be-tween probability score and the dis-tance to the nearest road.

Modeling limitations

We used data based on informationmapped across a large geographic ex-tent, meaning that site-specific detailsare missing from the information. Forexample, the elevation data is derivedfrom a 98-foot digital elevation modelthat would not be able to detect fine-scale topographical changes such asrocky outcrops that can make a siteimpossible to farm. Our approach is

meant to aid in identifying general ar-eas (for example, the Sebastopol vicin-ity or areas in southern Alexander Val-ley, see p. 9) that have characteristicssimilar to sites where vineyards haverecently been established. It does notallow us to examine a particular acreon a given probability surface and de-termine with certainty whether thatpiece of land is suitable for vineyarddevelopment. The model also does not

16 CALIFORNIA AGRICULTURE, VOLUME 54, NUMBER 3

address how much vineyard is likelyto be planted in the future.

The model does not yet include allof the variables that are important indetermining site suitability for grow-ing grapes. We are collecting GeneralPlan land-use data from county mapsand soil characteristics from thecounty soil survey. However, datarepresenting important microphysicalvariables is essentially unobtainable indigital format for the entire county,given limited academic research funds(for example, microhabitat variablesrelated to climate, site-specific soilproperties and high-resolution topog-raphy). In addition, economic vari-ables such as the future value ofSonoma County’s wine grapes and theavailability of land for sale are veryimportant but were not considered inthis first modeling exercise.

Vineyards were not mapped for thewestern third of the county in theoriginal mapping effort because it wasnot an important grape-growing re-gion from 1990 through 1997. How-ever, some vineyards have recentlybeen planted in western SonomaCounty, where coastal forestlands rep-

resent the majority of the land cover.This fact, and the knowledge that pat-terns have changed over the past 10years, indicates that trends couldchange again as long as the demandfor high-quality wines and new vari-eties continues to grow. Neverthe-less, landscape characteristics con-sidered favorable in the past willprobably continue to be favorable,and parcels with these characteristicswill remain attractive for vineyarddevelopment if growing wine grapescontinues to be profitable.

The method of selecting whichvineyard and nonvineyard cells to in-clude in the model affects the correla-tions between certain landscape vari-ables (e.g., elevation) and vineyarddevelopment. To build the version ofthe model presented here, we used allthe available cells from the selectedvineyards; however, future work shouldinclude analysis on the effects of weight-ing the number of samples from a givenvineyard according to its size.

Habitat fragmentation impacts

Biodiversity is reduced when natu-ral habitats are converted into urban,

suburban and agricultural land. Thisproblem is compounded by the frag-mentation of contiguous natural areasinto an increasing number of smallerfragments, each of which may not belarge enough to support viable popu-lations of all the original inhabitants.

Fragmentation has been linked toa number of environmental conse-quences, in Sonoma County(Merenlender et al. 1998) and else-where (Saunders et al. 1991; Forman1995; Turner 1996; Harrison and Bruna1999). These include physical effectsdue to increased amounts of forestedge that cause changes in microcli-mate (Chen et al. 1995) and can resultin tree mortality (Ferreira andLaurance 1997; Esseen 1994). The bio-logical effects of fragmentation includea decline in species requiring largeamounts of connected habitat (Beir1993; Stouffer and Bierregaard 1995)and increased predation of nativefauna (Andren 1995; Sieving and Karr1997).

To identify where vineyard expan-sion may lead to future habitat frag-mentation, we identified predictedchanges in habitat connectivity result-ing from one expansion scenario. First,as a baseline, we mapped continuouspatches of tree cover that existed in1990 using a vegetation map ofSonoma County based on 1990 satelliteimagery (82-foot resolution) providedby the California Department of For-estry and Fire Protection. A continu-ous patch of tree cover was defined asbeing at least 164 feet away from ur-ban areas, and all cells comprising apatch had to contain or be within 82feet of a forest or woodland habitattype (blue oak woodland, coast liveoak woodland, conifer or montanehardwood). Furthermore, a continu-ous patch had to be a minimum size of250 acres in the north part of thecounty or 50 acres in the south, withthe exception of the Laguna de SantaRosa area, where all areas mapped astree cover were analyzed as habitatpatches. A smaller minimum patchsize was used for the more developedsouthern county because largerpatches of woodland are not commonthere, making smaller habitat patchesessential for wildlife conservation.

Using a modeling technique to analyze GIS data enables scientists to map areas ofpossible future vineyard expansion.

Gre

gory

A.

Giu

sti

CALIFORNIA AGRICULTURE, MAY-JUNE 2000 17

10,000

1,000

100

10

10-2.5

50-250

250-500

2.5-50

500-1,000

1,000-2,500

2,500-5,000

5,000-10,000

>10,000

3,3932,532

199 155

35 28

1012

4 5

24

0

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1 0

43

Patches in 1990Patches after maximum- development scenario

Size classes (acres)

Nu

mb

er o

f p

atch

es

Laguna

Laguna

South

County

Fig. 2. Patches of continuous tree cover before (A) and after (B) the maximum vineyarddevelopment scenario for the Sonoma County study area. Each patch is shown by aunique color.

Fig. 3. Distribution of patches of continuous treecover before (1990) and after the maximum devel-opment scenario. Patches less than 50 acres weremapped only for southern Sonoma County.Patches greater than 250 acres were mapped forthe entire Sonoma County study area.

The oaks in the Laguna de SantaRosa area are primarily valley oaks,now considered a rare communitytype. Tracking changes to this habitatat the highest resolution possible wasof prime importance. These patch-sizecriteria were developed with inputfrom researchers and land managersfamiliar with Sonoma County’s oakwoodlands.

Each patch of connected tree coverresulting from this analysis is repre-sented by a different color in figure 2a.Within the study area, we calculatedthat there were 3,648 continuouspatches of tree cover prior to conver-sion, totaling 252,067 acres. Five ofthese patches were larger than 5,000acres, 16 were 250 to 5,000 acres, and3,627 were less than 250 acres (fig. 3).

Using the results from the versionof the model mapped in figure 1, wethen mapped all “available” non-vineyard land that had a probabilityvalue greater than 0.5 as vineyard. In asample with an equal number of vine-yard and nonvineyard observations(as was used in the version of themodel presented in fig. 1), the prob-ability of drawing a vineyard cell atrandom is 0.5. In such a sample, a cellwith an estimated probability valuegreater than 0.5 is more likely to be apost-1990 vineyard than randomly ex-pected, and a cell with a value lessthan 0.5 is less likely to be a post-1990vineyard. For the version of the modelmapped in fig. 1, 78% of all post-1990vineyard cells had a probability greaterthan 0.5, indicating that although cellswith a probability value less than 0.5may be suitable for vineyard develop-ment, cells with a value greater than 0.5were developed more often.

Using an expansion scenario withprobability greater than 0.5, an addi-tional 133,581 acres of vineyard weremapped. This is not to say that this en-tire acreage is plantable. For example,in some areas the temperature or thechemical composition of the soil mayprevent the production of winegrapes. It is unlikely that 133,581 acresof new vineyard will ever be plantedwithin the study area, considering thatin 1997 there were 48,000 acres of vine-yard. However, the model would pre-dict that the 9,000 acres of vineyard

currently being added (and registeredby October 1999 in the AgriculturalCommissioner’s Office) will morelikely be planted within the areas des-ignated by this expansion scenario.

Use of this “maximum develop-ment scenario” allows for a conserva-tive estimate of woodland areas thatmay be desirable for vineyard expan-sion. Land-use planners can use thisscenario to identify whichwoodlands may warrant pro-tection, given zoning and landsales patterns. One of the mosteffective uses of this analysis isto learn where continuouspatches of tree cover and areasof high vineyard suitabilityoverlap. This is where fragmen-tation of ecologically valuablewoodland habitat is more likelyto occur. According to our re-sults, areas that may receive sig-nificant habitat loss and frag-mentation include hillsides eastof Healdsburg, Sonoma Valley,and the west side of Santa Rosa(fig. 2a, 2b, also p. 9).

The habitat type mostthreatened by vineyard expan-sion in the Mayacmas range

running along the east side of SonomaCounty and Sonoma Mountains westof Sonoma Valley is absent from themore coastal or steeper sites inSonoma County (fig. 4). The overstoryis predominantly Oregon, valley,black, blue and/or coast live oak andis accompanied by a diverse under-story of native perennial grasses andshrubs such as toyon and manzanita.

18 CALIFORNIA AGRICULTURE, VOLUME 54, NUMBER 3

Fig. 4. Patches of continuous tree cover in Sonoma Valley before (A) and after (B) themaximum vineyard development scenario. Each patch is shown by a unique color.

Moister sites may include Douglas firand redwood, while flatter areas suchas the Laguna de Santa Rosa area pri-marily support remnant stands of val-ley oaks.

Figure 2b displays the continuouspatches of tree cover that would per-sist after the “maximum vineyard de-velopment” scenario. After applyingthe conversion scenario, there were2,742 patches, totaling 222,868 acres.Three of these were larger than 5,000acres, 24 were 250 to 5,000 acres, and2,715 were smaller than 250 acres. Theincrease in the number of medium-sized patches after conversion is dueto the fragmentation of larger forestedareas into several smaller ones. Over900 small patches, primarily in the La-guna de Santa Rosa area, are lost alto-gether when the maximum vineyarddevelopment scenario is run. The re-moval of habitat using the maximumdevelopment scenario resulted in asize reduction of the largest patches,deletion of the smallest patches and anincrease in the number of medium-sized patches (fig. 3).

Future directions

Recent developments in computerpower and mapping analysis softwarehave made it possible for more re-searchers to model land-use change

across large geographic regions(Goodchild et al. 1996). This approachhas allowed us to map areas that maybe suitable for vineyard expansion andareas where conversions could causehabitat loss and fragmentation. Wewould like to continue to monitorwhere vineyard expansion occurs todetermine if the areas identified are in-deed converted from woodland tovineyard.

We would like to expand our inves-tigations in this area by using remotesensing to monitor vineyard develop-ment more regularly and across alarger geographic region. Active col-laborations have been initiated to in-vestigate how changes in vineyard de-velopment costs and wine-grapevalues may affect development pat-terns. We also hope to improve ourcurrent model by including additionalenvironmental variables, such as soilcharacteristics and water availability.

Although vineyard expansion canhave negative environmental impacts,other land-uses changes such as urbanand exurban development also haveconsequences for habitat fragmenta-tion and biodiversity. Integrating ur-ban expansion models with our vine-yard expansion model will allow usto explore changes in the urban-agriculture-wildland interface, re-

sulting in a better understanding ofthe forces driving these changes andthe relative impacts of different landuses on various ecological processes. Itwill also be important to incorporatethe county planning process in futurestudies, because areas suitable forvineyard development may be morevalued for other land uses such as ur-ban or rural residential.

We have presented this initialmodel and its results to county plan-ners, agencies and citizens, generatingdiscussion at the local level (seesidebar, p. 19). Further, this methodhas provided open-space acquisitionplanners with some direction for pro-tecting oak woodlands, demonstratingthat this information can be used tosupport science-based planning andpolicy decision-making.

E. Heaton is Graduate Student, and A.M.Merenlender is Cooperative ExtensionSpecialist, Integrated Hardwood RangeManagement Program, Department of En-vironmental Science, Policy and Manage-ment, UC Berkeley. This research wassupported by the UC Sustainable Agricul-ture Research and Education Program.We would like to thank Circuit Rider Pro-ductions, Inc. and the Sonoma CountyGrape Growers Association for the digitalvineyard data. Dave Newburn, PengGong and Greg Biging provided valuablediscussion on the topic. We are grateful toGreg Greenwood and Sean Saving, whoprovided the program for analyzing forestfragmentation, and to Colin Brooks forrunning this analysis. Discussions withCharles Cooke, Jeff Opperman, DanielRoberts and two anonymous reviewers im-proved this manuscript.

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