Conversion to Organic Farming in the Continental United

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Conversion to Organic Farmingin the Continental UnitedStates: A GeographicallyWeighted Regression AnalysisAlina Taus a , Yelena Ogneva-Himmelberger a & JohnRogan aa Clark University

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Conversion to Organic Farming in the Continental United

States: A Geographically Weighted Regression Analysis

Alina Taus, Yelena Ogneva-Himmelberger, and John RoganClark University

Organic agriculture has expanded greatly over the past decades, but the rate of conversion has not been evenlydistributed across the United States. Measures of spatial concentration such as local Moran’s I show that thehighest rates of conversion are clustered in the Western United States, especially California, Washington,and Oregon, but also on the East Coast in New England. The influence of several variables on the spatialdistribution of organic conversion is first explored through ordinary least squares regression analysis andthen through a more localized technique called geographically weighted regression (GWR). Of the analyzedfactors, share of existing organic farms, prevalence of full-time operators, and average farm size were foundto be significant determinants of organic agriculture conversion rates. Furthermore, results show that spatialdependence is highly influential on the distribution of farms that are converting to organic production,suggesting the existence of relevant agglomeration effects. The GWR model suggests significant variationin the relationship between average farm size and conversion rates: The relationship is negative in mostof the country and positive only in the Northeast and parts of the Western United States. These resultshighlight the need to consider local models in conjunction with global regression techniques for a betterunderstanding of the spatial relationship between conversion to organic production methods and potentialdeterminants. Key Words: geographically weighted regression, location quotient, organic agriculture,organic conversion, spatial dependence.

La agricultura organica se ha extendido mucho durante las pasadas decadas, aunque la tasa de conversion noha estado uniformemente distribuida a traves de los Estados Unidos. Las medidas de concentracion espacial,tales como la local I de Moran, muestran que las tasas mas altas de conversion se apinan en los Estados Unidosoccidentales, especialmente en California, Washington y Oregon, aunque tambien en la Costa Este en NuevaInglaterra. La influencia de varias variables sobre la distribucion espacial de la conversion organica se exploraprimero mediante analisis de regresion ordinaria de mınimos cuadrados y despues por medio de una tecnicamas localizada que se denomina regresion geograficamente ponderada (GWR). Se encontro que de los factoresanalizados, la porcion de granjas organicas existentes, prevalencia de operarios de tiempo completo y promediodel tamano de la granja, son los determinantes significativos de las tasas de conversion a agricultura organica.Ademas, los resultados muestran que la dependencia espacial influye mucho en la distribucion de las granjasque estan transformandose hacia la produccion organica, lo cual sugiere la existencia de efectos relevantes deaglomeracion. El modelo GWR sugiere una variacion significativa en la relacion entre tamano promedio de

The Professional Geographer, XX(X) 20xx, pages 1–16 C© Copyright 20xx by Association of American Geographers.Initial submission, July 2010; revised submission, March 2011; final acceptance, August 2011.

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2 Volume XX, Number X, Xxxxx 20xx

la granja y las tasas de conversion: La relacion es negativa en la mayor parte del paıs y positiva solamenteen el nordeste y partes de los Estados Unidos occidentales. Tales resultados destacan la necesidad de tomaren cuenta modelos locales en conjuncion con tecnicas globales de regresion para una mejor comprensionde la relacion espacial entre la conversion a metodos de produccion organica y determinantes potenciales.Palabras clave: regresion geograficamente ponderada, cociente de localizacion, agricultura organica,conversion organica, dependencia espacial.

O rganic farming is becoming a worldwidemovement as it continues to play an in-

creasingly important role in modern agricul-ture. More and more farmers are turning tocertified organic farming systems because thesecan provide solutions to some of the problemsassociated with conventional agricultural prac-tices, such as environmental degradation, de-pletion of nonrenewable resources, and foodsafety issues (Lampkin and Padel 1994). Or-ganic farming is also perceived as a rewardingalternative that can lower input costs, capturehigh-value markets and premium prices, andboost farm income (Greene et al. 2009).

In the United States, organic farming firstemerged in the 1940s and has grown substan-tially during the past two decades (Dimitri andGreene 2002; Greene and Kremen 2002). Be-tween 1992 and 2003 certified organic croplandand pasture expanded from 935,000 acres to 2.3million acres, an increase of almost 150 percent(Yussefi and Willer 2003). Demand for organicproducts has also increased at an average rateof 20 percent each year since 1990 (Dimitri andGreene 2002) with retail sales reaching $19 bil-lion in 2007 (Greene et al. 2009).

Despite the rapid expansion of the organicsector, the recent growth rates have not beenuniform across the country (Lohr, Gonzalez-Alvarez, and Graf 2001; Eades and Brown2006). Although the U.S. Department of Agri-culture (USDA) reported that in 2005 all fiftystates had some certified organic farmland, thelargest concentration of organic cropland hasbeen observed in the Northeast, Upper Mid-west, and Western states (Greene et al. 2009).The spatial concentration of organic farmlandin certain regions suggests the existence ofclustering within the burgeoning industry. Astudy by Lohr, Gonzalez-Alvarez, and Graf(2001) that explored the spatial distributionand potential expansion of the organic marketidentified the Western and Northeast as theregions with the highest number of organicfarmers per county. In a more recent study,Eades and Brown (2006) used quantitativemeasures of industry concentrations such as

location quotients and local Moran’s I todetermine locations with high concentrationsof organic farms, organic acres, and organicsales. Using data from the USDA’s 2002Census of Agriculture, the study found thatcounties with the largest location quotient fororganic farms were located on the West Coast(particularly in California, Washington, andOregon), in the Great Plains states, and inNew England. Although these studies providea useful overview of the spatial distribution oforganic farming systems, one question that hasnot been addressed yet is what factors are cor-related with this spatial distribution of organicfarms. Pronounced regional concentration oforganic farming could indicate the existence ofcertain regional factors that make the industrymore viable in certain areas than in others. Forinstance, Eades and Brown (2006) analyzedthe correlation between organic farms in U.S.counties found to be significantly clustered,and various organic support establishments(e.g., retailers, distributors, manufacturers, andfarm input suppliers) in an attempt to identifyhow often these enterprises and organic farmswere likely to colocate. Comparing Californiaand New England, the authors found sig-nificant differences, with linkages betweenorganic production and support establishmentsbeing stronger in California than in NewEngland. These results were attributed todifferent market strategies pursued by organicproducers operating in the two regions. InCalifornia, where the emphasis is on industrialorganic production on a large scale, the supportestablishments are used as a tool to maintaineconomic advantage and to remain competitivewith other farmers, whereas in New Englandthese linkages are almost nonexistent becauseorganic farmers target consumers directlythrough farmers’ markets, roadside stands, andcommunity-supported agriculture (Eades andBrown 2006).

Regional studies have been conducted to an-alyze a broad range of factors and their influ-ence on the adoption of organic practices, butresearchers have come to different conclusions

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Conversion to Organic Farming in the Continental United States 3

regarding their significance as well as their rela-tionship with a given farmer’s decision to adoptorganic practices. For example, in a study byAnderson, Jolly, and Green (2005) where logis-tic regression was used to examine factors thatinfluence farmers in California to adopt organictechnology, farm size measured as number ofcultivated acres and the age of the farm op-erator were found to decrease the probabilityof adopting organic methods of production. Incontrast to these results, a study by D’Souza,Cyphers, and Phipps (1993) analyzed the adop-tion of sustainable farming practices amongfarmers in West Virginia and did not find farmsize to be a significant determinant of adop-tion. Similarly, D’Souza, Cyphers, and Phippsfound that a farmer’s level of education was pos-itively correlated with the decision to adopt sus-tainable practices, whereas Anderson, Jolly, andGreen (2005) concluded that this variable wasnot a significant predictor of adoption of or-ganic methods. D’Souza, Cyphers, and Phipps(1993) also found that the use of hired labor isnegatively associated with a farmer’s decisionto adopt sustainable practices, but their resultsshowed that this association was not statisti-cally significant. Perceived differences betweenorganic operations in different regions of theUnited States highlight the need for further re-search concerning the factors which drive theconversion to organic production and influencethe formation of clusters in organic agriculture.The lack of consensus regarding the signifi-cance and role of different drivers of organicconversion could indicate the existence of spa-tial variation in the relationship between adop-tion of organic practices and its determinants;for instance, differences in adoption rates couldbe explained by different factors in differentregions.

Although most studies have focused on var-ious socioeconomic characteristics as potentialdrivers of organic farming conversion, severalstudies conducted in Europe indicate that ag-glomeration effects also have a strong influenceon the spatial distribution of organic farms. Astudy conducted in Germany identified prox-imity to Switzerland as the main factor explain-ing the relatively high share of organic farmingin the area (Sick 1985). More recent studiesthat have addressed organic farming as a pro-cess of diffusion of innovation have noted thatthe presence of organic farmers in a region has

a positive impact, as these can offer the possibil-ity to nonorganic farmers to observe successfulorganic practices and feel reassured that the or-ganic system is feasible (Lohr and Salomonsson2000). Bichler et al. (2005) employed statisticalmethods to investigate the influence of severalfactors on the spatial distribution of organicfarming in Germany. The analysis revealed spa-tial contiguity as the strongest influence onthe distribution of organically managed land.More specifically, their model showed thatneighboring regions had a strong positive in-fluence on each other regarding the share oforganically managed land. The studies men-tioned previously highlight the importance ofconsidering agglomeration effects and there-fore spatial dependence when analyzing the dis-tribution of organic farms.

Research Objectives

The objective of this study is to analyze a seriesof explanatory factors and determine whetherthere is significant nonstationarity in therelationship between the presence of organicfarms and socioeconomic indicators across theUnited States. More specifically, the purposeof this research is twofold: (1) to examine thedistribution of farms that converted to organicproduction during 2007, the latest availableagricultural census; and (2) to explore globaland regional variation in the relationshipbetween organic farms and factors that promptfarmers to adopt organic farming practices.

Data

Data in this study are derived from the 2007USDA Census of Agriculture. This survey isconducted every five years by the NationalAgricultural Statistical Service and providesdata on U.S. farms and ranches as well as thepeople who operate them. The sample analyzedin this study includes the 3,069 counties withinthe United States. The number of farms thatadopted organic practices in 2007 was obtainedfrom Table 43 in “Organic Agriculture: 2007”available from the USDA Web site (USDA,2009). Specifically, the number represents thenumber of farms that indicated a certain num-ber of acres in answering the following ques-tion: “In 2007, how many acres were being

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converted to organic production?” (Question7 in section 22 of the Census form). Thisquestion did not exist in previous agriculturalcensuses. Using these data we calculated a lo-cation quotient (LQ) for each county (Cudjoeand Rees 1992). Commonly utilized in eco-nomic geography and locational analysis, thistechnique calculates an index that compares alocation’s share of a particular activity with thelocation’s share of a much larger reference oraggregate phenomena. In the context of thisresearch, this measure is utilized to express therelative concentration of farms that adopted or-ganic practices by comparing a county’s shareof farms that converted to organic methods tothe national rate of conversion for that partic-ular year. The strength of this index lies in itsability to overcome varying sizes of counties, anissue that if not taken into account could poten-tially result in misleading conclusions (Cudjoeand Rees 1992). LQs have formerly been usedby Beauchesne and Bryant (1999) and by Fred-eriksen and Langer (2004) to indicate the rel-ative share of existing organic farms. For thepurpose of this analysis, the formula for LQhad to be adjusted to reflect the relative con-centration of farms that became organic duringthe year 2007. The following formula was cal-culated for each observation:

LQc = Org Frmc /All Frmc

Org Frmn/All Frmn

where OrgFrm represents the number of farmsthat adopted organic practices in 2007 and All-Frm is the number of total farms reportedthat year. The subscript indicates whether thevalue represents a county sum (c) or a nationalsum (n). An LQ value of 1 would suggest thatthe proportion of farms that converted to or-ganic practices in a particular county equalsthe national rate of conversion. Similarly, anLQ above 1 would indicate that a county hasa greater share of organic farms than the na-tion as a whole does. The number of farms thatadopted organic practices in 2007, expressed asLQ, was regressed against a number of factorsreflecting farm and farmer characteristics. Def-initions for all potential explanatory variablestested are presented in Table 1. The variableswere chosen based on previous literature anddata availability.

Table 1 Variables associated with adoption oforganic farming production

Variable Definition

pctORGa Farms that adopted organic practicesin 2007 in a county (%)

pctCSAa Farms that sold produce throughcommunity supported agriculture(CSA) (%)

pctOpWoma Farms with a woman principaloperator (%)

pctOpFullTimea Farms where principal operator did notwork off-farm (%)

AvgSizea Average size of farm for a county(acres)

OpAge Average age of principal operator(years)

pctHired Farms with hired labor (%)

Note: aDenotes variable used in regression analysis.

Methods

Exploratory AnalysisThe first objective of this study was to examinethe current distribution of farms that adoptedorganic practices in the year 2007. Thisobjective was addressed by mapping LQ andcalculating the local Moran’s I coefficient.This technique allows for the identification ofregions where farms that adopted organic prac-tices are clustered and highlighting countieswhere the rate of conversion was greater thanwhat would be expected by national trends.

Modeling Adoption of Organic ProductionFor the second objective of this research threeregression models were developed. The modelsused the LQ as described earlier to representthe dependent variable. The LQ was preferredover a dependent variable representing thenumber of farms that adopted organic practicesin 2007 for the following reasons. First, thenumber of farms that converted to organic takesthe form of a count-based distribution, whichis highly skewed to the right with many obser-vations having a zero value. In this situation,the basic regression model would have beeninappropriate as the assumption of normalityof the dependent term would have been vio-lated. Generalized linear models (Nelder andWedderburn 1972) allow for the calibrationof regression models where the distributionof the dependent term is non-Gaussian, butthe basic regression model was preferred here

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as the transformation of the variable allowedfor a significant increase in the variance of thedependent variable. Second, the LQ providesa more interesting interpretation than theoriginal data because it reflects the relativeconcentration of farms that adopted organicpractices by comparing it to the national rate ofconversion.

Both ordinary least squares (OLS) andgeographically weighted regression (GWR)were used to model the spatial relationshipbetween the variables. Although OLS is themost common technique for carrying out aregression analysis, it is widely known thatsuch a method does not account for any spatialheterogeneity present in a relationship. AnOLS regression utilizes all observations withina data set to create a single set of parameterestimates, thus suggesting that relationshipshold everywhere equally within the study area.GWR is a statistical modeling technique thatattempts to overcome this limitation by allow-ing spatial nonstationarity to be modeled andmapped (Fotheringham, Brunsdon, and Charl-ton 2002). GWR attempts to capture spatialvariation by calibrating a regression equationfor each observation, weighting neighboringobservations by a function of distance from thesubject feature. Although various weightingschemes have been proposed, they are all basedon the assumption that areas close to a location iwill exert more influence on the parameters es-timated at location i than those located fartheraway from it. GWR produces a set of param-eters and statistics for each observation withina data set that can be mapped to further inves-tigate the spatial variation in the relationship.

Although GWR has often been used to ana-lyze local variations, this technique might pro-duce regression coefficients that are correlatedwith each other even when there is no collinear-ity among variables (Wheeler and Tiefels-dorf 2005). Regression coefficients could alsodemonstrate strong positive spatial autocorre-lation (Griffith 2008). These issues could re-sult in incorrect interpretation of the mean-ing of each coefficient. Diagnostic tools andremedial methods for identifying and over-coming parameter collinearity in GWR havebeen developed (Wheeler 2007, 2009). De-spite these issues, most researchers agree thatGWR is a valuable technique for exploring spa-

tially varying relationships. We used this ex-ploratory nature of the GWR technique in thisstudy.

Estimating determinants of organic farm-ing adoption using two global models.Initially, a standard OLS regression modelrelating the share of converted farms withfive explanatory variables was developed. Theequation for the model is given by:

LQi = α0 + α1 Pc tO RG + α2 pc tC SA

+ α3 pc tOpWom + α4 AvgSize

+ α5 Pc tOp Full + εi (1)

where LQi represents the location quotient ofconverted farms in county i,α0 through α5 areparameters to be estimated for the indepen-dent variables (see Table 1 for definitions), andεi is the error term. The model was imple-mented in GeoDA 0.9.5-I (Anselin, Syabri, andKho 2006) where several diagnostic tools re-vealed the presence of spatial dependence. Themodel was then reestimated using the spatiallag model, which is supported by means of amaximum likelihood method and controls forspatial dependence in the LQ variable (Anselinand Bera 1998).

The new model has the following equation:

LQi = α0+ρLQi +α1 Pc tO RG+α2 pc tC SA

+ α3 pc tOpWom + α4 AvgSize

+ α5 Pc tOp Full + εi (2)

Equation (2) is similar to Equation (1) but hasan added term ρLQi , where ρ is the scalar spa-tial lag coefficient that accounts for the impactof the share of farms that adopted organic pro-duction in neighboring counties. The modelwas reestimated in GeoDa using a distance-based spatial weight with a distance thresh-old of 250 km (see next section for details onthis threshold selection). Measures of fit suchas the log likelihood, Akaike information cri-terion (AIC) and the Schwartz criterion (SC)were used to compare the spatial lag model withthe classical OLS regression. Moran’s I statis-tic was computed for the residuals to determinethe extent to which spatial autocorrelation of

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residuals remained after the introduction of thespatially lagged term.

Estimating determinants of organic farmingadoption using a spatially lagged GWR. Asdescribed in the previous section, the GWRtechnique estimates local coefficients for eachindependent variable included in the regres-sion. The magnitude of these coefficients in-dicates the degree to which these factors con-tribute to explaining the local variation in thedependent variable. Because preliminary re-sults from the OLS model indicated the pres-ence of significant spatial dependence in thedata set, a GWR model that acknowledgedthis autocorrelation was developed. An indexthat expressed the local spatial dependence wasdeveloped and included with the other inde-pendent variables in the GWR model. TheGWR model was calculated for all observations(Equation 3) and represents the weighted aver-age of the LQ values of the counties located inthe neighborhood of a county i:

WAvg LQi =∑n

j=1 wi j · LQ j∑nj=1 wi j

(3)

where LQj is the location quotient of convertedfarms in one of the n neighbors of i, and wi jrepresents the weight assigned to that partic-ular observation. This weight is calculated asfollows:

wi j =[1 −

(di j

b

)2]2

if di j < b =0 otherwise

(4)where dij is the distance between points i and jand b is a distance threshold that controls thesize of the neighborhood around point i. Thisweighting scheme depicts the proximity of eachof the n neighbors to the location of point iwith closer observations carrying more weightthan further ones. It was implemented previ-ously by Brunsdon, Fotheringham, and Charl-ton (1998) and by Fotheringham, Brunsdon,and Charlton (2002) and provides a continu-ous, near Gaussian-function of di j , the distancebetween i and j.

Given that one of the main objectives ofthis study is to determine what causes clus-tering within organic agriculture, the distance

at which spatial dependence was most pro-nounced was considered appropriate for thesize of the neighborhood. The value of theparameter b was determined using a multidis-tance spatial cluster analysis based on Ripley’sK-function. By calculating a series of statis-tics at various distances, this function illustrateshow the spatial dependence—feature clusteringor feature dispersion—changes with increasingneighborhood size. The distance where the spa-tial processes promoting clustering were morepronounced (250 km) was used as the distancethreshold for the weighting scheme.

The following model calculates a unique setof parameters (an intercept and independentcoefficients) for each observation i defined by aset of geographic coordinates (ui , vi ):

LQi = α0(ui , vi ) + ρ(ui , vi )WAvg LQi

+ α1(ui , vi )Pc tO RG

+ α2(ui , vi )pc tC SA

+ α3(ui , vi )pc tOpWom

+ α4(ui , vi )AvgSize

+ α5(ui , vi )Pc tOp Full

+ εi (ui , vi ) (5)

Because counties are the unit of analysis inthis study, the geographic coordinates (ui, vi)correspond to the geographical center of eachcounty. The model was implemented usingtools in GWR 3.0 where a Gaussian modelwith a fixed kernel and a bandwidth of 250km was used for calibration. A bandwidth of250 km was used instead of automatic selectionmethod (by cross-validation on AIC) becauseclustering is the strongest at this distance. TheAIC was calculated and used to compare thismodel with the global regression technique. Adecrease in the AIC by at least 3 is regarded asan indication that the model is a better fit to thedata (Fotheringham, Brunsdon, and Charlton2002). Results from GWR were imported intoArcGIS 9.3 (ESRI 2009) where further analysiswas conducted to examine clustering of resid-uals and to explore the spatial variability in therelationship between explanatory factors andthe distribution of farms that adopted organicpractices. Clustering of residuals was evaluated

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at the local level by means of the local Moran’sI.

Results

Exploratory AnalysisTable 2 presents a series of descriptive statis-tics at the county level for the number of farmsthat converted to organic production as wellas for the explanatory variables considered forthe regression analysis. In 2007 the nationalrate of conversion to organic production was0.7 percent (USDA 2009). Although the LQranged from a minimum of 0 to a maximumof 32, more than 95 percent of the values fellbelow 3, a threshold observed in similar stud-ies by Goetz, Shields, and Wang (2004) andEades and Brown (2006). About 67 percent ofthe counties had a rate of conversion lower thanthe national rate (i.e., LQ was less than 1). Fromthese, one third had no reported farms that be-came organic in 2007.

Results of the LQ analysis were consistentwith the findings of Lohr, Gonzalez-Alvarez,and Graf (2001) and Eades and Brown (2006).High values were more often found to clusterin states in the Northeast, such as Maine, Mas-sachusetts, and New York; on the West Coast inCalifornia, Oregon, and Washington; and alsoin Utah, Colorado, and New Mexico (Figure1). To determine whether the observed patternof high LQ was statistically significant Moran’sI was implemented. A global Moran’s I of 0.14with a z score of 31.04 was obtained, indicatingthat the LQ values were clustered at the 99 per-cent confidence level. The multidistance clus-ter analysis based on the Ripley’s K-function

Table 2 Descriptive statistics on variables usedin regression models at the county level

Variable Minimum Maximum M SD

LQFarmsa 0 32.46 1.05 1.86pctORG 0 0.33 0.01 0.02pctCSA 0 0.2 0.008 0.01pctOpWom 0 3.5 0.2 0.14pctOpFullTime 0 2.66 0.49 0.14AvgSize 0 2,657.26 274.92 308.79OpAge 0 65.4 55.97 8.68pctHired 2.94 100 22.61 8.30

Note: aLQFarms = location quotient of farms that con-verted to organic practices in 2007 for a county.

revealed that spatial clustering was most pro-nounced at a distance of 250 km. This distancewas used in the cluster analysis as a threshold forthe inverse distance conceptualization of spatialrelationships.

The results of the local Moran’s I are pre-sented in Figure 2 where groups of adjacentcounties with high z scores indicate clustersof similar values. Negative z scores observedin several counties in Idaho, Nevada, and Ari-zona but also scattered throughout the Southindicate that surrounding features have verydifferent values than their neighbors. This im-plies that the conversion rates in these countiesare not affected by the conversion rates in thesurrounding counties. Other factors, such asproximity to markets with certain demograph-ics and population density, might play an im-portant role in these counties. The observedclusters in the Western United States and NewEngland were identified as significant clustersof high values confirming the patterns observedin Figure 1. In this case, the nearby countieshave a strong effect on the conversion rates.These large clusters might also be attributedto similarities in cultural views and the politicalsituation that exists in these counties.

Modeling Adoption of Organic ProductionA correlation analysis was undertaken to iden-tify any multicollinearity present among theindependent variables. Two variables that werecorrelated with one another (r > 0.4)—averageage of principal operator and percentage offarms with hired labor—were excluded fromthe regression analysis and their relationshipwith the dependent variable was assessed sepa-rately using bivariate local indicators of spatialassociation (LISA) analysis. Unlike globalmeasures of spatial correlation that evaluate anentire study area, bivariate LISA tests whetherlocal correlations between values of one vari-able at location i and the average neighboringvalue of a different variable are statisticallydifferent from what one would observe underconditions of spatial randomness.

Figure 3A illustrates the results of the bivari-ate LISA analysis performed on average age ofprincipal operator and percentage of farms ina county with hired labor. The first map showssignificant high–high clusters (p = 0.01) on theWest Coast, suggesting that counties where the

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Figure 1 Distribution of farms that converted to organic production during 2007. LQ = location quotient.

average age of principal farm operators is highare surrounded by counties where the rate ofconversion to organic production is also high.This might be attributed to the fact that theWestern United States has the longest historyof organic movement in the country. Olderfarmers decide to convert to organic productionbecause they have been farming for a long timeand have seen the economic advantages of sucha conversion (strong links to organic supportestablishments on the West Coast diminish therisk of economic failure). On the East Coastthere is no strong correlation between averageage of farmers and organic conversion rateswith the exception of several counties in Mas-sachusetts, Vermont, and New Hampshire,where higher average age is spatially correlatedwith lower neighboring conversion rates. Thisfinding indicates that younger farm operatorsin these counties are more likely to convertto organic farming than older farm operators.This could be related to a much weaker networkof organic support establishments on the East

Coast, which leads to higher level of risk associ-ated with organic conversion. Younger farmersmight be more risk-tolerant than older ones.

Figure 3B illustrates the spatial correlationbetween percentage of farms with hired laborand neighboring conversion rates. Significanthigh–high clusters (p = 0.01) are observed onthe West Coast, indicating that counties witha large number of farms that hire labor areadjacent to counties with a high organic pro-duction conversion rate. This finding mightindicate that farms that hire labor are finan-cially stable and are more likely to take therisks associated with conversion to organic pro-duction, when compared to farms that do nothire labor. Counties in New England appearto have no significant spatial relationship be-tween percentage of farms with hired laborand adoption rates. According to the USDA,about half of all hired farm workers live inonly five states—California, Texas, North Car-olina, Washington, and Oregon (USDA 2011),so these findings are not surprising.

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Figure 2 Significant clusters of counties with organic conversion. (Color figure available online.)

Figure 3 Bivariate local indicators of spatial association (BiLISA) cluster map of (A) average age ofprincipal operator and (B) percentage of farms with hired labor against rate of organic conversion. (Colorfigure available online.)

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Figure 4 Spatial distribution of standardized residuals for (A) ordinary least squares model; (B) spatiallylagged global model; and (C) spatially lagged geographically weighted regression. (Color figure availableonline.)

The results for the two global models andthe spatially lagged GWR model are presentednext. The model in Equation (1) is brieflydiscussed in terms of the spatial dependencepresent in the data.

OLS model. The initial model presented inEquation (1) explained 41 percent of the vari-ance in the number of farms that convertedto organic production. Significant variables in-cluded the proportion of existing organic farms,presence of full-time operators (both with pos-itive coefficients), and average farm size (witha negative coefficient). A low multicollinearitycondition number of 10.96 indicated no prob-lems with the stability of the regression resultsdue to possible correlations among the explana-tory variables. The Moran’s I score (0.08) wasstatistically significant (p = 0.002), indicatingstrong spatial autocorrelation of the residu-als. The spatial distribution of the standardizedresiduals further demonstrates the existence ofpatterns of over- and underprediction, partic-

ularly in the West and New England, whereorganic conversion was found to be more pro-nounced (Figure 4A). Results of several testsperformed to assess spatial dependence in themodel are presented in Table 3. The one-directional Lagrange multiplier (LM) test fora missing spatially lagged dependent variable(Lagrange multiplier [lag]) and the LM test forerror dependence (Lagrange multiplier [error])were both statistically significant, indicating thepresence of spatial dependence. This meansthat the organic farming conversion rates in a

Table 3 Diagnostics for spatial dependence inordinary least squares model

Test Value Probability

Moran’s I (error) 3.010 0.003Lagrange multiplier (lag) 24.952 0.000Robust Lagrange multiplier (lag) 18.314 0.000Lagrange multiplier (error) 7.031 0.008Robust Lagrange multiplier (error) 0.393 0.531

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given county are influenced by conversion ratesin the nearby counties and this must be ac-counted for in the model. The robust LM testswere further considered to determine the ap-propriate alternative spatial regression model.The low probability of the robust LM lag sug-gested the use of the spatial lag model over thespatial error model.

Spatially lagged global model. The modelpresented in Equation (2), which accounted forspatial dependence in the LQ variable, was animprovement over the previous model as indi-cated by an increase in the log likelihood from–5,445 (in OLS) to –5,435 and a decrease inboth the AIC and the SC. The magnitude of allof the estimated coefficients was lower than inthe classical regression model, indicating thatsome of the explanatory power of these vari-ables that was initially attributed to their in-county value can be accounted for by the valueof the dependent variable in neighboring loca-tions (Table 4). This is confirmed by the co-efficient of the spatially lagged variable (0.17),which was highly significant (p < 0.00001). Thiscoefficient can be interpreted in the followingway: If the relative share of farms that convertedto organic production (as measured by LQ)increases by 1 percent in the neighboring coun-ties, the share of converted farms within theconsidered county will rise by 0.17. The valueof the likelihood ratio test (19.7), which was alsostatistically significant, further confirmed theimportance of the spatial autoregressive term(Table 4).

The most influential explanatory variablewas the proportion of organic farms. Anincrease of 1 percent in the share of organicallymanaged farms increased the rate of conversionby 42.6 (Table 4). The prevalence of farmswhere operators worked full time was alsofound to have a positive influence on the depen-dent variable. Although statistically significant,this variable had a much lower coefficient thanthe proportion of existing organic farms, sug-gesting a smaller influence on the conversionrate. Another variable with relatively minoreffects was average farm size, whose coefficientindicates that with each additional acre, therate of conversion slightly decreased.

The Moran’s I value was closer to zero(0.004) and statistically significant at a lowerlevel (p = 0.05), indicating that the patterns ofover- and underpredictions observed in Figure4B are more random than those of the stan-dardized residuals from the previous model.Because the spatially lagged term eliminatedthe autocorrelation due to spatial dependency,the regional clusters of overprediction inthe West are most likely due to remainingautocorrelation in the error terms.

Spatially lagged local model. The modelpresented in Equation (5) explained on average52 percent of the variance in share of convertedfarms (Table 5). The AIC value decreasedfrom 10,884 in the OLS model to 10,362 in thelocal model, suggesting a better fit to the data.Analysis of variance tests the null hypothesis

Table 4 Results for the two global models: ordinary least squares and spatially lagged

Coefficient Standard error T value

Variable OLS Spatially lagged OLS Spatially lagged OLS Spatially lagged

y -Intercept 0.23 0.09 0.09 0.1 2.41 0.97Lag Coeff ρ N/A 0.17 N/A 0.04 N/A 4.26pctORG 46.03 42.6 1.2 1.44 38.31 29.4pctCSA 2.71 1.94 2.65 2.65 1.02 0.73pctOpFullTime 0.57 0.62 0.21 0.21 2.66 2.89pctOpWom 0.25 0.07 0.22 0.22 1.15 0.32AvgSize −0.3 −0.2 9.01E-05 9.04E-05 −2.87 −0.23

OLS – AIC = 10,902Spatially lagged – AIC = 10,884OLS – adjusted R2 = 0.41Likelihood ratio 19.7

Note: OLS = ordinary least squares; AIC = Akaike information criterion.

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Table 5 Regression coefficients for the geographically weighted regression model

Variable Minimum Lower quartile Median Upper quartile Maximum

y -Intercept –1.31848 –0.15743 0.0788 0.301178 4.77255pctORG 18.95495 36.669613 42.338 52.441095 94.11566pctCSA –67.6509 –8.794763 2.192 19.723883 70.61338pctOpFullTime –7.86431 –0.15297 0.3988 1.020061 9.645846pctOpWom –4.73578 –0.443955 0.294 0.783088 8.564586AvgSize –0.175 –0.0431 –3E-02 –0.0121 0.345WAvLQFrm –1.17014 0.001035 0.1764 0.385152 1.253812

Note: Akaike information criterion = 10,362. Adjusted R2 = 0.52.

that the local model represents no improve-ment over the OLS model. The resulting F =6.68 allowed the null hypothesis to be rejectedat the 99 percent level of confidence, thusindicating that the local model was a significantimprovement over the OLS model.

Figure 5 shows the spatial distribution of thelocal R2 values for each county, with valuesranging from as low as 0.1 to values as high as

0.94. A visual analysis suggests that more of thevariation in conversion rates is explained in theWest with the highest values observed in Ore-gon, Idaho, Montana, and Wyoming. Otherhigh values are observed in the Midwest in Min-nesota, Iowa, and Wisconsin and in counties lo-cated at the border of Texas and New Mexico.The local model had a low explanatory poweron the East Coast and mid-South but also in

Figure 5 The spatial distribution of local R2 from the geographically weighted regression model. (Colorfigure available online.)

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counties located in the Central United States.These regional differences in the explanatorypower of the model indicate that the farmand farmers’ characteristics alone are impor-tant conversion factors only in some parts of thecountry. In other parts of the country the de-gree of political liberalism or conservatism andcultural and political differences might havemore influence on organic conversion than thefarm and farmer’s characteristics alone.

The only explanatory factor that had a con-sistent relationship with the dependent variablewas percentage of organic farms (Table 5). Al-though the coefficients for the other explana-tory factors took both negative and positivevalues, suggesting a possible spatial variation inthe relationship with the dependent variable, aMonte Carlo test (Hope 1968) revealed that av-erage farm size was the only variable that exhib-ited significant spatial variation (Table 6). Thisresult was not surprising given that previousstudies that have examined this factor as a po-tential determinant of adoption of organic prac-

Table 6 Results of the Monte Carlo significancetest for spatial variability of parameters

Variables p Value

y -InterceptIntercept 0.49pctORG 0.96pctCSA 0.72pctOpFullTime 0.22pctOpWom 0.83AvgSize 0.00

∗

WAvLQFrm 0.36

Note:∗Significant at 0.1% level.

tices reached different conclusions regarding itsinfluence on conversion rates. The GWR coef-ficient for average farm size was negative in theeastern counties of Maine, New Hampshire,and Massachusetts; in central California; andin the Southeast region in Georgia and Florida(Figure 6). Areas where the relationship withadoption rates was positive are located in theNortheast and the West.

Figure 6 Spatial distribution of parameter for average farm size. (Color figure available online.)

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Although the standardized residuals were notrandomly distributed, the patterns of over- andunderprediction were not as pronounced as inthe OLS model (Figure 4C). Moran’s I had avalue of –0.01 and was statistically significantat a low level (p = 0.01). Although the GWRmodel also under- and overpredicts in certainlocations, it is worth noting that there were noclusters of overprediction on the West Coast.This indicates that the GWR model performedbetter than in the New England region whereclusters of both under- and overprediction werestill present.

Discussion and Conclusions

Although organic agriculture has expandedrapidly over the past decades, adoption of or-ganic practices has not been uniform across theUnited States. The results of the local Moran’sI analysis confirmed that organic conversionmeasured through the location quotient waslargely clustered in the Western United Statesand in New England. Clusters within the in-dustry suggest the possible existence of certainfactors that promote regional agglomerationwithin the organic sector. This study soughtto analyze the influence of several variables onthe spatial distribution of farms that adoptedorganic practices during 2007 in an attempt toidentify some of the factors that prompt farm-ers to adopt organic production methods incertain areas rather than others. This objec-tive was investigated by means of both a clas-sical regression analysis and a more localizedtechnique called GWR. The OLS model re-vealed that the proportion of organically man-aged farms in the county, the prevalence of full-time operators, and lower average farm sizeare significant determinants of the choice toadopt organic production. The spatially laggedmodel indicates that spatial dependence hasa significant influence on the spatial distribu-tion of farms that converted to organic produc-tion, suggesting the existence of local diffusionprocesses. These results confirm the conclu-sions reached by Lohr and Salomonsson (2000)and Bichler et al. (2005) regarding the ini-tial presence of organic farmers in a region,which reassures nonorganic farmers that or-ganic systems are successful and feasible in theirlocale.

The GWR model explained more variancethan the OLS model, but the explanatory powervaried across the nation. Higher R2 values wereobtained in Western states, whereas East Coaststates had some of the lowest R2 values. Theseresults indicate that the analyzed factors canbetter explain the organic clusters observed onthe West Coast. The localized model revealedthat the relationship between organic con-version and existing organic farms is positiveacross the entire United States. The prevalenceof full-time operators contributed to the re-gression model but was not spatially significant.In contrast, the relationship between organicadoption and farm size was found to benonstationary. This result confirms the earlierhypothesis that spatial nonstationarity was thereason why research has arrived at differentconclusions regarding the relationship betweenorganic conversion and farm size.

The spatial association between two otherfactors and the rate of conversion was testedthrough a bivariate analysis. Both average ageof principal operator and percentage of farmswith hired labor were found to be spatially as-sociated with high rates of conversion on theWest Coast. No significant correlations be-tween these variables and conversion rates werefound in counties located on the East Coast.

Limitations in the design of the study andlack of available data constrain the ability todraw significant conclusions regarding the fac-tors that determine clusters within the organicindustry. First, it is important to note that na-tionwide information on organic agriculture iscurrently very sparse. The year 2007 markedthe first U.S. Census of Agriculture when dataon farms converting to organic productionwere collected. The number of acres being con-verted to organic production and total organicproduct sales (in $1,000s) is also reported, butdue to federal disclosure rules these data are of-ten suppressed (represented by the letter D inthe tables) and thus cannot be used for a spatialanalysis. For example, the number of acres be-ing converted to organic production is reportedfor 36 percent of the counties and the totalorganic product sales (in $1,000s) is reportedfor only 18 percent of the counties. Second, awide range of other factors are known to signif-icantly influence the adoption of organic prac-tices. These extend beyond the socioeconomicfactors considered in this research and include

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cultural and political factors related to boththe producers and consumers of organic food.Whereas data on some of these variables suchas the existence of supportive advisory servicescan be compiled through tedious processes,other factors such as personal consideration areharder or sometimes impossible to quantify. Amore qualitative approach is needed to addressthe implication of some of these factors for theadoption of organic farming techniques.

Despite these limitations, this study con-tributes to the existing body of literature on theadoption of organic farming in the followingways. Whereas previous studies that haveaddressed this question from a geographicalperspective have focused solely on the spatialclustering of organic agriculture, this studyattempted to analyze some of the factors thatdrive conversion to organic production andinfluence the formation of clusters withinthe industry. The analysis revealed that localneighborhood effects such as the presence ofother organic farmers have a significant influ-ence on the decision to adopt organic farmingpractices. In addition to the widely used globalregression technique, a localized method calledGWR was implemented to analyze the localvariation in the relationship considered. Thelocal analysis confirmed that the relationshipbetween adoption of organic production andaverage farm size was nonstationary.

These results highlight the need to considerlocal models in conjunction with global re-gression techniques to identify possible spatialvariation in the relationship between conver-sion to organic practices and associated factors.Acknowledging the existence of spatial nonsta-tionarity could lead to a greater understandingof the various characteristics that enable theadoption of organic farming practices, which inturn could lead to more effective organic policyand educational efforts. Historically, changesin the organic agriculture policy were the resultof intensive lobbying by the organic industry.To ensure steady growth of the organic indus-try, however, the government should activelysupport the policy, at both the federal and statelevels. Some state governments have begun im-plementing programs that subsidize conversionto organic farming through state Environmen-tal Quality Assistant Programs (Klonsky andGreen 2005). Our findings suggest that theseprograms should be focusing on the areas with

few existing organic farms because the rate ofconversion in these areas is not high. Accordingto the 2008 Farm Bill, farmers can get up to$20,000 per year for implementation of conser-vation practices as part of the transition process(Organic Farming Research Foundation 2009).To successfully implement this policy, organicconversion should be actively promoted by thegovernment as a feasible farming strategy. �

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ALINA TAUS is a geographic information sys-tem (GIS) analyst at Greater Augusta Utility Dis-trict, 68 Court St. 2, Augusta, ME 04330. E-mail:[email protected]. She has a master’s degree inGIS from Clark University and her research inter-ests are in spatial data analysis and spatial statistics,organic farming, and local food systems.

YELENA OGNEVA-HIMMELBERGER is an As-sistant Professor in the International Develop-ment, Community, and Environment Departmentat Clark University, Worcester, MA 01610. E-mail:[email protected]. Her research focuses on GISapplications for public health and environmental jus-tice.

JOHN ROGAN is an Associate Professor in theGraduate School of Geography at Clark University,Worcester, MA 01610. E-mail: [email protected] research interests focus on the remote sensing offorest disturbance and land change.

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Documents

Conversion to Organic Farming in the Continental United