Impacts of fragmentation and neighbor influences on farmland conversion: A case study of the Edmonton-Calgary Corridor, Canada

Land Use Policy 48 (2015) 482–494

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Land Use Policy journal homepage: www.elsevier.com/locate/landusepol

Impacts of fragmentation and neighbor influences on farmland conversion: A case study of the Edmonton-Calgary Corridor, Canada Feng Qiu a,∗ , Larry Laliberté b , Brent Swallow a , Scott Jeffrey a a b

Department of Resource Economics and Environmental Sociology, University of Alberta, Edmonton, Alta. T6G 2H1, Canada Digital Initiatives, University of Alberta, Edmonton, Alta. T6G 2H1, Canada

a r t i c l e

i n f o

Article history: Received 7 April 2014 Received in revised form 8 June 2015 Accepted 25 June 2015 Keywords: Farmland conversion Fragmentation Neighbor influences Spatial regression

a b s t r a c t Under heavy development pressure, farmland is rapidly being converted to non-agricultural uses such as houses, roads, and industrial uses. A great deal of research has investigated these farmland losses and their associated drivers. However, the existing empirical studies have neglected two important issues related to farmland conversion: spillover effects from neighboring areas and the impacts of farmland fragmentation. This study incorporates fragmentation and neighboring impacts into the farmland conversion analysis and provides new insights for the land-use/cover change literature. Empirical results indicate that increases in fragmentation further encourage farmland conversion to urban uses, but the effects are not linear with decreasing marginal influences. Land-use activities and decisions have strong spillover effects on neighboring areas. Ignoring this externality could result in biased estimates and marginal effects and thus misleading policy decisions and recommendations. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Agricultural land provides a wide array of benefits to both local communities and broader social groups. In addition to producing food, fiber and fuel, farmland supplies a stream of environmental amenities such as wildlife habitat and scenic open spaces (Uematsu et al., 2013). However, under heavy development pressure, farmland in many places is rapidly being converted to non-agricultural uses such as residences, industry, and infrastructure. A number of studies have investigated these farmland losses and associated drivers in specific contexts (e.g., Deng et al., 2006; Irwin and Geoghegan, 2007; Lichtenberg and Ding, 2008; Baumann et al., 2011; Prishchepov et al., 2013; Corbelle-Rico and Crecente-Maseda, 2014). Even so, the existing empirical studies have neglected two important issues related to farmland conversion: spillover effects from neighboring areas and impacts of farmland fragmentation. Conventional wisdom and theoretical analyses (e.g., Segerson et al., 2006; Irwin and Geoghegan, 2007) both suggest that farmland conversion is often a consequence of urbanization and population growth in nearby cites and suburban areas. Improvements in infrastructure facilities, construction of roads, growth of markets, enhanced employment opportunities, and policy changes

∗ Corresponding author. Fax: +1 780 492 0268. E-mail addresses: [email protected] (F. Qiu), [email protected] (L. Laliberté), [email protected] (B. Swallow), [email protected] (S. Jeffrey). http://dx.doi.org/10.1016/j.landusepol.2015.06.024 0264-8377/© 2015 Elsevier Ltd. All rights reserved.

in neighboring areas are likely to influence local land-use decisions (see Crecente et al., 2002; Long et al., 2007; Drummond et al., 2012 for discussions). The ability to quantify spillovers from neighboring areas is important, especially for policy design. For example, regional governments are often thought to consider the actions of neighboring governments when designing local policies related to taxes and zoning, and determining the provision of local public services (Wilson, 1986). Additionally, agglomeration economies might be another important reason for spatial interactions among nearby agri–businesses and farm input services (Boland, 2010; Olson and Boehlje, 2010). With the continuous process of agricultural industrialization and intensification, agriculture as a fairly location-dependent industry shows a developing tendency of regional agglomeration (Gruber and Soci, 2010). Ignoring spillover effects can result in biased and misleading results that may in turn lead to poor policy recommendations. In addition to spillovers from neighboring areas, fragmentation of farmland through division of land into smaller parcels is another significant problem that is often overlooked in studies of farmland conversion. Rural residential development, transportation routes, and energy and utility corridors can fragment farmland, resulting in plots of land that are less suitable for some agricultural uses. Fragmentation can impose new costs on agricultural businesses such as bylaws that restrict odors, dust, light, noise, or limit the ability to conduct operations (e.g., seeding, spraying) to certain times of the day or week, which can also contribute to conflicts between farm businesses and new residents. On the other hand, land fragmentation and conversion caused by urban sprawl, road construction,

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Fig. 1. Study area: Edmonton- Calgary Corridor, Canada.

and population growth might also have some positive effects on conserving nearby farmland as new residents create new business opportunities for local producers (Segerson et al., 2006). Local farmers may shift their business enterprises to satisfy nearby market demand for fresh vegetables and fruits as well as other perishable commodities or provide recreational on-site farm activities. This ambiguous relationship between fragmentation and farmland conversion needs empirical study for specific contexts. The objectives of this study are twofold. First land fragmentation within the context of agricultural land uses is quantified. Although general landscape fragmentation caused by urbanization has been studied in several empirical papers (e.g., Hahs and McDonnell, 2006; Jaeger et al., 2007), little research has been conducted to quantify fragmentation in the context of agriculture. Second, the effects of fragmentation and neighboring influences on farmland conversion activities are examined. This is accomplished through the use of remote sensing data and spatial econometrics. In

undertaking this analysis new insights into empirical research on farmland conversion and new information on farmland conversion and fragmentation in an area of highly dynamic land-use change are provided. The rest of the paper is organized as follows. Section 2 discusses the study area and sample period. Section 3 presents the data and descriptive results. Sections 4 and 5 discuss the empirical strategy and present the results. Section 6 summarizes policy implications, and Section 7 offers concluding remarks. 2. Study area and period The analysis undertaken in this paper is for the Edmonton- Calgary Corridor (ECC) area in the province of Alberta, Canada. The ECC provides an interesting case study as it is rapidly becoming one of the most urbanized areas in Canada. At the same time, Alberta is Canada’s second largest agricultural producer after Ontario (Gov-

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ernment of Alberta 2014). In 2010 farm cash receipts earned by Alberta farmers totaled $9.0 billion and accounted for 20.3% of the national total (AAFC 2013). According to (AAFC) (2014), Alberta’s agriculture and food processing industries contribute to 15.5% of the provincial GDP and ranked number three after Ontario (32.3%) and Quebec (22.2%) in 2012. Alberta has undergone significant economic and population growth in recent years and this is predicted to continue into the foreseeable future. Rapid growth and development is putting significant pressure on the agricultural base. The issue of farmland conversion is particularly relevant in the ECC area (Government of Alberta 2008), with the primary driver being urban development pressure from residential development and the secondary driver being industrial development. The ECC is the most developed area in Alberta and one of Canada’s top four urbanized regions (Government of Alberta 2012). Measured from north to south, the region covers a distance of approximately 400 km with a total area of 39,639 km2 . The region includes two metropolitan cities, Calgary and Edmonton, and 12 other counties (see Fig. 1). The Queen Elizabeth II Highway (referred to as Highway 2) acts as the central spine of the corridor. Between 2006 and 2011, the population in the ECC grew by 291,004 residents, representing a growth rate of 12.1%. This is the most densely populated region in the province and as of 2011 74.2% of Albertans lived in this region (Government of Alberta 2012). The ECC covers about 6% of the total province area, but has 25% of the province’s best land for agriculture (i.e., belongs to Land Suitability for Agriculture class 2). Between 2000 and 2012, about 625 km2 of land within the ECC were converted to developed uses; about 83% of the land converted to development came from the agricultural land base. In addition, approximately 90% of the land converted from agricultural to developed uses was of high-quality soil.1 Recently there has been growing debate in the province regarding the economic, social, and environmental consequences associated with fragmentation and conversion of agricultural land within the ECC, and across the province. At the provincial level, Alberta has gone through phases of centralization, decentralization, and now re-centralization of responsibility for land use planning. From the late 1940s until 1990, provincial and regional planning authorities had significant power to manage and guide land use in the province (Taylor et al., 2014). The Municipal Government Act of 1994 devolved authority for most land use decisions to individual municipalities, the lowest level of governance in Alberta (Alberta Agriculture, Food and Rural Development (ARD), 2002). Concerns about the complex cumulative effects of uncoordinated land use led the Alberta provincial government to give more attention to the need for broader regional growth strategies and land use plans. The Land Use Framework (2008) and Alberta Land Stewardship Act (2009) mandated the development and implementation of regional land use plans for the 7 major watersheds in the province. To date, regional plans have been approved for the Lower Athabasca and South Saskatchewan regions. As of January 2015, the Alberta Government was in the midst of a systematic review of the Municipal Government Act. Government of Alberta (2015) presents a summary of key issues including regional collaboration. Partners and other stakeholders emphasized “the need to support regional decision making,” with the provincial government encouraging, facilitating, and incentivizing voluntary regional collaboration. In addition, given the rapid population and economic growth around the Edmonton area and their substantial impacts on nearby

Table 1 Definition of agricultural land and developed land. Developed land

Agriculture land Cropland

Hay and pasture

Land predominantly built up or developed. Includes road surfaces, railway surfaces, buildings and paved surfaces, urban areas, and industrial sites Annually cultivated cropland and woody perennial crops. Includes annual field crops, vegetables, summer fallow, orchards, and vineyards Periodically cultivated cropland. Includes tame grasses and other perennial crops such as alfalfa and clover grown alone or as mixtures for hay, pasture, or seed

municipalities’ economic, environmental, and social settings, a regional agency – the Capital Region Board (CRB) – was created in 2008 to manage growth and development for the 24 municipalities in the region and to coordinate the fragmented and often conflicting efforts from neighboring municipalities. The CRB regional growth plan, Growing Forward, was established in 2009 minimize development foot prints and strengthen communities throughout the entire region (CRB 2009). In September 2014 the Capital Region Board expressed the common desire by municipalities in the region to conserve farmland, but noted the need to develop new policies to act on that desire (Ma 2014). The Calgary Regional Partnership is a voluntary network of 12 municipalities that formed in 1999 to coordinate land use in the Calgary region. Several of the rural municipal districts that surround Calgary have either refused to join or have withdrawn to protest Calgary’s domination of the organization (Taylor et al., 2014). Appropriate quantification of farmland fragmentation and conversion, as well as impact analyses associated with major drivers and neighborhood spillovers will provide valuable information to better understand these issues. These analyses will also support future policy design targeting land-use planning, sustainable agriculture, farmland reservation, development of local food networks, and food security. How to best respond to this information is likely to be a matter of considerable debate. 3. Descriptive analysis 3.1. Land-use/cover data ECC land cover data are derived from the Land Cover for Agricultural Regions of Canada, circa 2000, and the Annual Space-Based Crop Inventory for Canada, 2012. Both sets of land cover data are at a 30-m resolution and were classified by Agriculture and Agri-Food Canada (AAFC). The original raster dataset includes 9 land-use/cover classes: Water, Exposed, Developed (or Built-Up), Shrubland, Wetland, Grassland, Annual Crops, Hay and Pasture, and Forests (see Fig. 2). This study focuses on agriculture (combined Annual Crops and Hay and Pasture classes) and the developed class. Table 1 provides a brief definition of the relevant land-use/cover classes. The township shapefile is obtained from AltaLIS Ltd. In Alberta, any parcel of land can be located by its legal land description, which is based on the Alberta Township Survey (ATS) system. The ATS system is a grid network dividing the province into equalsize parcels of land (approximately 9.656 km by 9.656 km in area) that are defined between range lines and township lines. Many existing land-use and environmental studies are based on the ATS system, and its use facilitates policy recommendations and evaluations. 3.2. Farmland conversion to developed land

1 These numbers are calculated using the land cover and soil suitability data obtained from Agriculture and Agri-Food Canada and Alberta Agriculture and Rural Development.

Researchers and policy-makers tend to be interested in exploring the drivers and consequences of farmland losses and urbanization (e.g., Lambin et al., 2001; Long et al., 2007; Deng

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Fig. 2. Land use/cover for the Edmonton-Calgary Corridor: 2000 (left) and 2012 (right). Data source: AAFC.

et al., 2008; Baumann et al., 2011; Wang et al., 2012; Prishchepov et al., 2013). These types of in-depth analyses depend on careful measurement and description of land conversion and fragmentation. Summary tables and maps are presented in this subsection to illustrate detailed numbers and the spatial pattern associated with agricultural land converted to developed uses as well as fragmentation. A summary of the changes in agricultural land and developed land uses from 2000 to 2012 in the ECC is presented in Table 2.2 The total net loss of agricultural land in the ECC between 2000 and 2012 was almost 180,000 ha, of which 40% was converted to developed

2 In addition to land conversion from agriculture to developed uses, a series of complete transition tables are created that reveal the detailed number of landuse/cover changes from each existing category to each of the other categories over the study period. This allows for the identification of transitions between specific land-use classes (e.g., forest to cropland, grassland to pasture, wetland to pasture). These tables (and associated underlying data) are available from the authors upon request.

uses.3 In 2000, total developed area in the ECC was approximately 159,000 ha, with that value increasing to more than 221,000 ha in 2012. This represents an increase of almost 40%. Of the land converted to developed use over that period, about 83% was converted from agriculture (see Fig. 3) and approximately 90% was in Land Suitability classes 2 and 3 (i.e., the best quality farmland in Alberta).4 To get a more nuanced view of the spatial patterns of farmland conversion, the 30 m resolution results were aggregated to the township level. Fig. 4 provides a summary of the agricultureto-developed land use changes (left) and changes in developed area (right). Both are expressed as a percentage of total township area. The two land-use change pictures exhibit nearly identical spatial

3 The other 60% was converted mainly to shrubland, grassland, and forestland, according to the AAFC land cover data. 4 Land suitability class represents the suitability ranking of land for spring seeded agricultural crops. It is a nation- wide classification system, and the highest suitability class in Alberta is Class 2, due to climatic limitations.

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Fig. 3. Land use sources of newly developed land: 2000–2012.

Fig. 4. Conversion of agricultural land to developed uses (left) and changes of developed land (right) in the Edmonton- Calgary Corridor: 2000–2012.

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Table 2 Agricultural and developed land cover/land-use (LCLU) changes in Edmonton-Calgary Corridor: 2000–2012.a

Total Land Agriculture Developed

LCLU 2000 (Ha)

(%)

LCLU 2012 (Ha)

(%)

Net change (Ha)

3,963,873 2,665,735 158,941

100 67.25 4.01

3,963,873 2,487,227 221,478

100 62.74 5.59

0 −178,508 62,537

As % of total land

As % of own classb

0 −4.50 1.58

0 −6.7 39.35

Source: Authors’ compilation from land cover/land use data obtained from AAFC. a The percentage (%) columns for 2000 and 2012 LCLU values do not sum to 100 because non-agricultural and non-developed uses are not reported here. b Reported as a percentage of 2000 LCLU values.

patterns.5 This is consistent with the results presented earlier; that is, most newly-developed land was converted from agriculture. Farmland conversion is greatest around the Edmonton and Calgary, the region around the city of Red Deer in the middle of the ECC, and in other areas along Highway 2 (i.e., down the center of the corridor). The results can be explained by the rapid development and population growth in these areas. For example, during the second half of the period covered by the data (i.e., from 2006 to 2011), the Edmonton census metropolitan area (CMA) added 124,924 residents (12.1% increase), while the Calgary CMA grew by 12.6%, an increase of 135,529 people. These two CMAs accounted for almost three-quarters of Alberta’s total population growth. Calgary and Edmonton ranked fourth and fifth in population size, and placed first and second in growth among CMAs in Canada (Government of Alberta 2012).

3.3. Hot spot analysis To identify areas at greatest risk for conversion, hotspot (Getis and Ord, 1992; Ord and Getis, 1995) analysis is undertaken. This analysis, which uses the nearest neighbor hierarchical clustering method, identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots) of farmland conversion. The threshold for defining “neighbors” for the purposes of representing neighbor impacts in the hot spot analysis is modeled using fixed distances. Twenty different threshold values ranging from 10 km to 50 km (approximately 1–5 township lengths) were considered; the 30 km threshold was chosen on the basis of intensity of spatial clustering. Results from the hot spot analysis are presented in Fig. 5. It is interesting to note that, although Calgary and Edmonton had similar population growth rates, farmland conversion was much higher in the Edmonton CMA than in the Calgary CMA. One reason for this could be the availability of prime farmland for outward expansion. The large counties surrounding Edmonton (e.g., Strathcona and Parkland) have among the best quality soils in Alberta, and are very suitable for both farming and residential uses. There are also many local farms in the neighboring areas, which could act as an incentive (e.g., local food, open space, recreational on-farm activities) for people choosing to live and work there. There are more spaces available for conversion by these neighboring areas. In contrast, in the Calgary region the two neighboring counties (Rocky View and Foothills) have among the poorest soil quality and the rocky land in the west of the region is poorly suited for either sustainable agricultural or residential uses.

5 The negative number is mainly because of the land reclamation. There was a small portion of previously developed land from well sites that has been reclaimed and added into the agricultural land base. The land cover analysis indicates that 11,217 ha of developed land was converted to cropland in the ECC region between 2000 and 2012.

Fig. 5. Hotspot analysis for conversion of agricultural to developed land in the Edmonton-Calgary Corridor: 2000–2012. Note: A high z-score indicates a spatial clustering of high values (e.g., significantly higher than expected >1.96 standard deviations) and a low negative z-score indicates a spatial clustering of low values (e.g., significantly higher than expected < − 1.96 standard deviations).

3.4. Fragmentation of agricultural land To examine farmland fragmentation and its changes over time, a set of fragmentation indices are identified that capture different dimensions of farmland fragmentation for the ECC area. These are (1) patch density, (2) mean patch size, (3) edge density (or alternatively perimeter-to-area ratio), (4) mean perimeter-to-area ratio, and (5) effective mesh size. Index definitions and the relationship with fragmentation are presented in Table 3. Irwin and Bockstael (2007) also used (1), (2), and (4) to investigate the land-use fragmentation and urbanization in Maryland. The edge density (3) is the measurement proposed by Alberta Agriculture and Rural Development (ARD) (ARD 2013) to depict fragmentation under the system used to monitor land use under the Land Use Framework.

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Table 3 Fragmentation indexes. Fragmentation index

Formula

Mean patch size (−)

Mean perimeter-to-area ratio (+)

 aik lik 

Effective mesh size (−)

a2i

Edge density (+)

a b

Explanation

nk A aik nk  lik

Patch density (+)a

The total n patches of land use k divided by the total landscape area Ab The sum of land use k’s patch area divided by total n patches of land use k The total length of the patches perimeter divided by total patch area of land use k

The sum of perimeter-to-area ratio for land use k divided by total n patches of land use k

aik

The sum of each patch’s area squared and divided by the total area of land use k

Ak

The signs in parentheses indicate the indices relationships to fragmentation. Land use k in our application refers to agricultural land use.

Table 4 Fragmentation change measurements. Frag index Patch density Mean patch size Edge density Mean perimeter-to-area Effective mesh size

ECC-2000 0.018 36.97 84.45 630.00 7,095.74

ECC-2012 0.012 52.72 69.01 538.88 74,944.31

(%) −33.33 42.60 −18.40 −14.46 956.19

Hwy2-2000

Hwy2-2012

0.024 33.38 85.93 762.50 1,321.78

0.014 53.03 70.11 591.17 14,676.28

(%) −41.67 58.87 −15.81 −22.47 1010.34

Note: ECC-2000 and ECC-2012 present fragmentation indices for the Edmonton-Calgary Corridor area in 2000 and 2012, respectively. %  denotes the percentage change of fragmentation indices from 2000 to 2012. Hwy2 refers to the region that includes Edmonton and Calgary cities and a 5 km buffering area along Highway 2 connecting the two cities.

The first and second indices in Table 3 express the number and the size of patches within the ECC per km2 . A decrease in the mean patch size reflects increased fragmentation when the total amount of agricultural land is relatively constant. Edge density, in km/km2 is the length of all borders of agricultural patches divided by the total area of agricultural land use. Increases in edge density or mean perimeter-to-area ratio reflect increases in farmland shape perimeter per unit of agricultural land, which may indicate increased fragmentation. The mean perimeter-to-area ratio captures the mean size and shape of patches, holding the total number of patches constant. Finally, effective mesh size indicates the likelihood that any two randomly chosen points in the region are physically connected. The more barriers (e.g., roads, railroads, urban areas) erected in the landscape, the smaller the chance that two points will be connected. Table 4 provides 2000 and 2012 fragmentation index measures (and percentage changes 2000–2012) for the ECC as well as for a smaller area labeled as “Hwy2”. Hwy2 includes the area immediately surrounding Edmonton and Calgary (10 km buffer) and within a 5 km buffer along Highway 2, which connects the two cities. According to the results presented in Table 4, farmland fragmentation in the ECC region did not increase from 2000 to 2012. To a certain degree, all indices display a decreasing trend in fragmentation. Similarly, farmland fragmentation in the Hwy2 buffering region has not increased in the past 12 years.6 However, if the fragmentation indices for the entire ECC are compared to those for the Hwy2 buffering region, it becomes clear that fragmentation was higher in the buffer regions surrounding Edmonton/Calgary and along Highway 2 than for the larger ECC region. Most of the measures indicate higher fragmentation for Hwy2 in each period. The only exception is the mean patch

6 The fragmentation indices might be underestimated because of data limitations. Remote sensing data, even for high-resolution data (e.g., 30 m × 30 m = 900 m2 ), are usually not able to appropriately capture the low-density residential land uses (often less than 200 m2 ) (Uuemaa et al., 2013). The failure to take this into consideration can result in underestimating the fragmentation conditions, especially in those areas experiencing fast urban sprawl. There is no obvious solution to resolve this problem.

size in 2012 where the values for the two regions are very similar. A cross-regional comparison indicates that for the 2000–2012 period, the Edmonton-Calgary-Highway 2 buffering area experienced more farmland fragmentation. This is likely a result of urban sprawl, population growth, and other forms of development. Farmland fragmentation caused by urban sprawl and population growth is often a regional and small-scale event. Indeed, it may occur only in certain areas on the fringe of urban centers and not through the entire ECC region or even the Hwy2 buffer region. In most rural areas within Alberta, average farm size increased significantly and farm numbers decreased correspondingly over the study period, primarily due to technological change that has saved labor and increased the use of material inputs (Veeman and Gray, 2010; p. 138). This has contributed to the decreased fragmentation. The number of farms in Canada fell 17.0% from 246,923 to 205,730 between 2001 and 2011. For the ten year period ending in 2011, the average size of Canadian farms increased by 15.4% from 273 ha to 315 ha. Alberta has experienced an even larger rate of increase. The 2011 Census of Agriculture reported 43,234 farms in Alberta, a 19.4 % decrease since 2001. As the number of farms decreased, average farm size increased. From 2001 to 2011, the average size of Alberta farms increased by 20.1 % from 393 hectares to 492 hectares (Government of Alberta 2012).7 To further examine the local fragmentation issue, three fragmentation indices (patch density, mean patch size and edge density) are calculated for each township. Fig. 6 presents the changes in those fragmentation indices, by township, for the study period 2000–2012. Changes in the patch density index show that the number of agricultural patches increased significantly in the northeast (northeast of Sturgeon County) and southwest (east Parkland County and the center of Leduc County) areas surrounding Edmonton. Meanwhile, the mean patch size index map indicates a decrease in average agricultural operation size in these areas. Together, the

7 Census farm numbers and average farm size, Alberta. https://osi.alberta.ca/osicontent/Pages/OfficialStatistic.aspx?ipid=844 (accessed January 2, 2015).

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Fig. 6. Changes in township- level fragmentation indices for the Edmonton-Calgary Corridor (2000–2012): patch density, mean patch size, and edge density.

increase in the number of agricultural land patches together with decreases in farm size serve as evidence of growing fragmentation. Similar patterns can be observed in many parts of Red Deer County, in the eastern part of Rocky View County, and in some small areas surrounding Calgary. Changes in the edge density index reveal a clear pattern of increased farmland fragmentation in the Edmonton CMA, Red Deer County, many areas in the Calgary CMA, and areas around Highway 2. The fragmentation investigation reveals an interesting phenomenon for agriculture in the ECC region. Specifically, around the urban fringe there is considerable conversion and fragmentation of agricultural land due to the rapid economic development while in rural areas more distant from cities, average farm size keeps growing. In areas surrounding city boundaries and major transportation centers, it is commonly perceived that there are increased agricultural land losses and fragmentation. Conversely, in many rural counties it is often observed that new natural land has been

cultivated. The same land cover data are used to investigate the conversion of grassland and forestland to agricultural land, with a finding that 41.3 % of grassland (87,632 ha) and 9.3% forestland (59,291 ha) in ECC region were converted into farmland. Most of the conversions occurred in the rural areas far away from city centers and Highway 2. For example, large amount of farmland increases were detected in the west part of Mountain View and Rocky View counties. These areas are mainly composed of marginal to unsuitable land (e.g., rocky land) for crop agriculture. Most of the areas are natural grassland or forestland. In recent years, losses in prime agricultural land and increasing agricultural commodity prices have caused a significant amount of the grassland and shrubland in this region to be converted to crop agriculture. This is an important contributing factor to the pattern of increased fragmentation indices. Together with the trend of increasing farm size and decreasing farm numbers, it is thus not unusual to observe fragmentation measures decrease in these agricultural areas overall.

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4. Empirical methods and results The main research interest of this study is to investigate the impacts of fragmentation and spillover effects on conversion of land from agricultural to non- agricultural uses (i.e., development). Linear regression is a common tool for quantifying the influence of one variable on another. However, use of simple linear regression in this study might cause serious bias and result in inconsistent findings. This section outlines the obstacles that must be overcome to identify the effect of fragmentation on farmland conversion. As well, the corresponding empirical results are presented and discussed. 4.1. Endogeneity Many existing studies (e.g., Lambin et al., 2001, 2003) have highlighted the complexity of land-use/cover change. In empirical applications, if only fragmentation is included as an explanatory variable with all the other relevant determinants being ignored, correlation between these unobserved factors (e.g., population density and soil quality) and fragmentation can cause an endogeneity problem that will bias the estimated coefficient for the fragmentation variable. To deal with this potential endogeneity in the current study, variables for seven other factors, suggested by previous studies (e.g., Jaeger et al., 2007; Deng et al., 2011; Begue et al., 2011; Upton et al., 2014) are added to the analysis; specifically, population growth (i.e., change in population density), land suitability for agriculture (i.e., percentage of the two highest suitability classes), road density, farmland value, growing season (April–September) mean temperature, growing season cumulative precipitation, and elevation. The 2001 and 2011 census subdivision (CSD) level population density data were obtained from Statistics Canada. CSD is the general term for municipalities or areas in Canada that are treated as municipal equivalents for statistical purposes. In 2011, Alberta had 453 CSDs with a total population of 3,629,380.8 This study adjusts the land cover and population density variables (and other explanatory variables) to the township level to conduct the spatial regression analysis.9 Township-level daily weather data for 2000–2012 were provided by ARD. A 13-year average growing season mean daily temperature and growing season cumulative precipitation are calculated and used in the analysis as two most relevant weather factors influencing agricultural production. Land suitability for agricultural uses is generated based on the Land Suitability Rating System (LSRS) obtained from ARD. The proportion of land with the highest two classes (i.e., most suitable for agricultural uses) for each township are used in the analysis. Agricultural land value for each township is calculated based on the agricultural-land transaction-market value obtained from ARD. Road network data for 2012 are provided by AltaLIS Ltd., with road density being calculated as the length of roads for each township divided by the respective township area. The estimation of farmland conversion, taking fragmentation and those relevant determinants into consideration, may be expressed as: Li = ˛ + Fragi + Z  i  + ui

(1)

where Li is the land converted from agriculture to developed land in township i between 2000 and 2012 and Fragi is the corresponding change in fragmentation from 2000 to 2012. In this part of the empirical investigation, township-level edge density

is used as the fragmentation index. ARD (2013) proposed using this index as an official measurement of farmland fragmentation for Alberta, and so by using it here the results can potentially be used directly as input into policy discussion and evaluation. Z represents the vector of other explanatory variables discussed earlier, including farmland value (LandValue), growing season mean temperature (Temperature), growing season cumulative precipitations (Precipitation), land suitability for agricultural uses (Suitability), road density (RoadDensity), and elevation (Elevation); ˛, , and  are parameters and u is a vector of i.i.d. disturbances. Of particular interest is  as it reflects the effect of increasing fragmentation on agricultural land conversion to developed land uses. There is a potential endogeneity problem if land conversion and fragmentation are determined by the system simultaneously. To investigate the potential impacts of land conversion on fragmentation, a linear regression model with robust standard deviations was run using land conversion as an independent variable (together with other relevant explanatory variables) for edge-density changes. The estimated coefficient for developed land-use changes was not significant. A Dubin–Wu–Hausman test was conducted to exam whether fragmentation is endogenous. Proximity to the nearest town is adopted as the IV variable, the argument being that proximity to a non-city suburban area does not necessarily encourage farmland conversion. However, it increases land fragmentation through road construction and other infrastructure (i.e., irrigation) development. The result of the test indicates that the null hypothesis that fragmentation is exogenous cannot be rejected. Based on the above extra determinants and the test result, it is concluded that there was no endogeneity problem for the sample dataset.

4.2. Neighbor impacts As has been discussed, individual households and regional decision makers are both affected by their neighbors’ land-use activities and decisions. Ideally, these effects should be incorporated into a model of land use changes. In terms of modeling, an equation that includes the average increase in neighboring farmland conversion in (1) can be expressed as: L = ˛n + WL + Frag + Z + u

Eq. (2) is often referred to as a spatial autoregressive (SAR) model (LeSage and Pace, 2009). The term WL is called the spatial lag, since it represents a linear combination of values of the newlyconverted land constructed from observations from neighboring areas. W is a n × n weight matrix in which wij > 0 if observation j is a neighbor to observation i, and wij = 0 otherwise. If the unobserved u is spatially dependent, SAR estimates may be inconsistent. A potential method to take this spatial dependence into consideration is to assume that the disturbance term follows a spatial autoregressive process: u = Wu +

(3)

u = (In − W ) −1

Table 5 Robust LM tests for spatial dependence.

8

The 2000 and 2012 population data are unavailable, and so 2001 and 2011 Census data are used to proxy the corresponding population information for 2000 and 2012, respectively. 9 An area-weighted average method is used to aggregate the CSD population data to the township level.

(2)

Statistic P-value

Robust LM spatial lag

Robust LM spatial error

Robust LM lag and error

7.85 0.01

2.49 0.12

20.60 0.00

Note: The weights matrix is based on a 30 km threshold distance.

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Table 6 Results from OLS, SAR, and SAC Models (n = 422). OLS

SAR

SAC

Variable

Coef.

Std. err

Coef.

Std. err

Marg. effect

Coef.

Std. err

Frag Frag2 Pop Pop2 LandValue ($100) Precipitation (1000 mm) Temperature (◦ C) Suitability (% of 1or 2) RoadDensity (m/ha) Elevation (km) Constant   Adj. R2

22.04*** −3.56*** 5.72*** −0.01*** 1.40*** −0.19 −121.52*** −158.23*** 7.01*** −0.83*** 2216.05*** – – 0.630

8.61 0.54 0.97 0.00 0.16 0.36 32.17 46.90 1.67 0.13 567.62 – – 0.682

22.08*** −3.66*** 5.38*** −0.01*** 1.26*** 0.21 −129.30*** −103.42*** 6.20*** −0.70*** 1984.91*** 0.32*** – 0.669

6.75 0.50 0.54 0.00 0.15 0.35 35.78 44.61 1.18 0.13 555.21 0.07 –

32.47*** −5.38*** 7.91*** −0.01*** 1.85*** 0.31 −190.15*** −152.09*** 9.12*** −1.03*** – – –

22.71*** −3.67*** 5.46*** −0.01*** 1.30*** 0.16 −129.61*** −112.41*** 6.65*** −0.73*** 2036.12*** 0.25*** 0.005

8.40 0.53 0.93 0.00 0.18 0.33 31.86 44.64 1.67 0.12 541.64 0.13 0.21

Note: The weights matrix is based on a 30 km threshold distance; ***, **, and * mean the coefficient is significant at 1%, 5%, and 10%, respectively.

where  is a real scalar parameter and is an n × 1 vector of i.i.d. disturbances. Substituting (3) into (2) yields a spatial autocorrelation model (SAC): L = ˛n + WL + Frag + Z + u u = Wu +

(4)

5. Results 5.1. Spatial regressions As is standard in most spatial studies, the analysis begins by starting with a non-spatial linear regression model, and then testing whether or not the spatial error and/or lag model needs to be used. In this study the robust LM tests developed by Anselin et al. (1996) are used, with the LM test results being presented in Table 5. The weight matrix is based on a threshold distance of 30 km. The results show very strong evidence of residual spatial autocorrelation and indicate lag-type dependence. For the error-type spatial dependence, the LM tests give contradictory results. As a result both the SAR and the SAC models are estimated for further investigation and comparisons. Results from OLS, SAR, and SAC are provided in Table 6. With respect to the OLS results, the overall fit is satisfactory, with an adjusted R2 of 0.630. Coefficients for the fragmentation index and other explanatory variables are all significant (with the exception of Precipitation), and have the expected signs in accordance with intuition and earlier findings in the literature. Increased fragmentation results in higher levels of farmland development; however, the relationship between fragmentation and farmland development is nonlinear, as indicated by the statistically significant squared term for change in the fragmentation index. It indicates that the marginal effects on farmland conversion decreases as the fragmentation change gets larger. This is mainly because the availability of farmland that could be developed is limited in these areas (e.g., around the city centers) that have experiences huge fragmentation increases. Increases in fragmentation are most significant within the boundaries of Edmonton and Calgary and some of their immediate neighboring areas. However, the marginal conversion effects in these areas are not as dramatic as those in suburban and rural regions where the increases in fragmentation are relatively smaller. In terms of growth pressure, the results confirm that an increase in population density has a large positive effect on land development. Specifically, the OLS coefficient indicates that an increase of one person per km2 will result in 5.57 ha (equivalently, 0.0557 km2 ) of

land being converted to urban uses.10 Impacts of population density changes are also nonlinear and the marginal effect on farmland conversion decreases as the population growth increases. One possible explanation is that, with large increases in population there might be a structural change to the residential development strategy (e.g., from low density to high density) that result in less agricultural land (per person) being converted. For example, increases in population density in the cities of Edmonton and Calgary will not have an equal marginal effect on farmland conversion as population increases in suburban or country residential areas. Farmland value is found to have a positive effect on farmland conversion. Agricultural land value often reflects not only agricultural rents, but also the value from potential future urban development (Plantinga and Miller, 2001). This positive coefficient reflects the development premium incorporated into the value of agricultural land. Results further confirm that areas with high suitability agricultural land and growing season temperature are less likely to be converted to developed uses, as the two factors reflect the higher opportunity costs for conversion. As expected, road density is found to have a positive effect on farmland conversion. Next the results taking into account spatial dependence are considered, including a spatially-lagged dependent variable (the SAR model) and combining both spatial lag and spatial error (the SAC model). The overall fits from both models are slightly better than for the OLS model. For the SAC model, the estimated spatial coefficient  is insignificant and the overall goodness-of-fit is similar to the SAR model. Further discussion of results therefore focuses on the SAR model. In general, the estimated coefficients remain fairly stable across methods for all three models, with only marginal differences being exhibited. However, as the model diagnostics indicate (Table 5), OLS suffers from spatial dependence. Ignoring the spatial interactions could lead to biased and inconsistent estimates. Although the biases for the estimates do not seem to be substantial in the current application, interpreting parameter estimates from fragmentation and other individual drivers could be quite different between OLS and SAR models, which are discussed in the next section. 5.2. Marginal effects Linear regression coefficients have a straightforward interpretation as the partial derivative of the dependent variable with respect to the explanatory variable. For y =

10

k

x ˇ r=1 r r

+ , the par-

ˆ Pop − ˇ ˆ ¯ Marginal effect of Pop = ˇ

Pop2 ∗ pop = 5.72 − 0.01 ∗ 15 = 5.57.

492

F. Qiu et al. / Land Use Policy 48 (2015) 482–494

tial derivatives of yi with respect to xir are simply ∂yi /∂xir = ˇr ; and ∂yi /∂xjr = 0. Therefore, in the above OLS model, the implicit marginal effects of any explanatory variables read directly from the estimated coefficients which are the derivatives of the land conversion equation with respect to the relevant drivers. For those variables that involve squared terms, the marginal effects also depend on evaluated values of the explanatory variables. In a SAR model which contains spatial lags of the dependent variable, interpretation of the coefficients becomes more complicated. To interpret the SAR model, first rearrange as:

effects associated with the SAR model. The spatial multiplier measures the sum of each row of the inverse matrix of spatial weights given a unit change is induced at each location (see Eq. (7)). Kim et al. (2003) have demonstrated that the spatial multiplier can be solved to be 1/(1 − ). Pace and LeSage (2009) (pp 38) showed the spatial multiplier is equivalent to the Average Total Impact to an Observation (based on summing the total impacts over the rows of the matrix Sr (W), and then taking an average over all regions) for a SAR model. The average total marginal effects for each explanatory variable, using spatial multiplier, can thus be calculated as:

(1 − W )y = Xˇ + , which can be rearranged as

MEk = (1 − ) ˆ

y = (1 − W )−1 Xˇ + (1 − W )−1

(5)

Setting (1 − W )−1 = S(W ) and (1 − W )−1 = v, Eq. (5) can be expressed in detail as (LeSage and Pace, 2009; Eq. (2.42))

⎛

y1

⎞

⎛

⎜ ⎟ ⎜ ⎜ .. ⎟ = ⎜ ⎝. ⎠ ⎝ yn

S(W )11

...

S(W )1n

.. .

..

.. .

S(W )n1

⎛

v1

.

···

⎞

⎞ ⎛

x11

⎟ ⎜ ⎟ · ⎜ .. ⎠ ⎝ .

xn1

S(W )nn

...

x1k

..

.. .

.

···

⎞ ⎛

ˇ1

⎞

⎟ ⎜ ⎟ ⎟ · ⎜ .. ⎟ ⎠ ⎝. ⎠

xnk

ˇk

⎜ ⎟ + ⎜ .. ⎟ ⎝. ⎠

(6)

vn Define xk as a n × 1 column vector for one explanatory variable (e.g., changing population density). The derivative of y with respect to xk can then be defined as:

⎛

∂y 1 /∂x1k ⎜ ∂y2 /∂x2k ∂y = ⎜.  ⎝ .. ∂x k ∂y n /∂x1k

⎛

ˇk S(W )11 ⎜ ˇk S(W )21 = ⎜. ⎝ .. ˇk S(W )n1

⎞

∂y 1 /∂x2k · · · ∂y 1 /∂xnk ∂y 2 /∂x2k · · · ∂y 2 /∂xnk ⎟

.. .

∂y n /∂x2k ˇk S(W )12 ˇk S(W )22 .. . ˇk S(W )n2

..

. ··· ··· ··· .. . ···

⎟ ⎠

.. .

∂y n /∂xnk

⎞

ˇk S(W )1n ˇk S(W )2n ⎟ ⎟ .. ⎠ . ˇk S(W )nn

= ˇk S(W ) = ˇk (1 − W )−1 = ˇk (In + W + 2 W 2 + 3 W 3 + · · ·) (7) The marginal effect of the SAR model is therefore ˇk (1 − W )−1 . The implication is that a change in a single observation associated with any given explanatory variable will affect the region itself (a direct impact) as well as (potentially) all other regions indirectly (an indirect impact). Eq. (6) implies for a single observation i that: yi =

k

ˇr (Sr (W )i1 x1r + Sr (W )i2 x2r +, · · ·, +Sr (W )in xnr ) + vi

r=1

The total impact of a change in xk on yi is the sum of direct impacts plus the induced/ indirect cumulative impacts from other ∂yi /∂xik

∂yi /∂xjk . Impacts arising from a change in the explana-

regions

i= / j

tory variables will influence immediate neighbors (the first order) more than higher-order distant neighbors. In general, the impact of changes in an explanatory variable differs over all regions. The tradition of regional science literature on spatial hedonic models (Kim et al., 2003; Anselin and Lozano-Gracia, 2008; Small and Steimetz, 2012) is followed here in that a desirable summary measure, the spatial multiplier, is utilized to illustrate the average total marginal

−1 ˆ

ˇSAR,k

(8)

Marginal effects calculated from the SAR estimates are presented in Table 6. The marginal effects of the fragmentation and other explanatory variables are affected by the estimation method. Specifically, marginal effects for Frag and Pop vary considerably across the models. The effects of incorporating the induced neighbor effects are large, resulting in a change between OLS and SAR of 22.00–32.41 for Frag and of 5.57 –7.76 for Pop, also taking the nonlinear squared terms into consideration. For other relevant drivers, the induced spatial spillovers effects are also substantial. For example, the OLS model indicates that an increase in 1 m of road per ha in a township will result on average in 7.01 ha farmland being converted to developed uses. However, the marginal impact is much larger under the SAR model (i.e., 9.12 ha), which is consistent with the fact that improved road constructions and infrastructure in the neighboring areas can result in more land been developed for residential and industrial/commercial uses. 5.3. Summary In summary, including a spatially-lagged dependent variable filters out biases caused by simultaneous spatial feedback effects, and yields more accurate estimates and marginal effects. The estimate of the lagged dependent variable indicates that if the mean land conversion in a neighboring area increases by 1 ha, on average it will result in an additional 0.32 ha of land in the current area being converted to developed uses. The large spillover effects are consistent with conventional wisdom and intuition. Previous research also finds that faster development in urban areas, such as more commercial and industrial construction and roads, results in more land conversion in nearby rural or suburban areas (e.g., for residential uses) (Rounsevell and Reay, 2009). Furthermore, the SAR model can further quantify the induced effects of a neighborhood’s factor changes, since the weighted farmland conversions are an explanatory variable for each observation. The traditional linear regression model does not capture these induced effects because it only allows own area factors to have impacts on the dependent variable. Therefore, a traditional land use model cannot capture spillover effects that are generated by a change in neighborhoods’ land-use determinants. Ignoring the neighbor influences and relying on the OLS estimates would result in biased coefficients and inaccuracy in interpreting the marginal effects and predicting/forecasting landuse changes. 6. Policy implications Two key policy implications can be drawn from the positive neighborhood interactions. First, land use, land conversion, and land development should not be treated as isolated local issues. Neighborhood impacts (from land-uses and more fundamentally, factors such as population density and road and infrastructure constructions) are significant and strong and should be taken into account when local, regional, and provincial policymakers make land use planning decisions. Collaboration among neighboring municipalities and creation of regional land use plans can

F. Qiu et al. / Land Use Policy 48 (2015) 482–494

help internalize the externality (i.e., the neighboring spillovers) which will facilitate determination of the optimal social, or at least regional, allocation of land uses. In the Alberta policy context, this provides a rationale for cooperative planning of farmland conservation by neighboring municipalities. Second, effective land use management requires collaboration from a variety of governmental agencies at both the intraand inter-municipal levels. Farmland conversion (or broadly, land use) decisions are influenced by many factors such as population growth, road construction, soil quality, and water availability. Meanwhile, land use changes can cause extensive economic, environmental, and social consequences. Furthermore, these influences and consequences often take place beyond municipal boundaries. Lack of coordination among agencies could result in serious undesired externalities, especially if one or more of the agencies which are involved in a potential conflict situation are not legally associated with the planning process. For example, agriculture in Alberta is typically recognized as a responsibility of the Canadian federal and provincial governments and very few municipalities have distinct agricultural strategies. Edmonton’s food and agriculture strategy, Fresh (City of Edmonton, 2012), is an exception. The result is that proposed zoning actions for industrial and commercial development (Area Structure Plans and Neighborhood Area Structure Plans) usually do not account for the impacts on agriculture. Industrial and commercial development, which is associated with population growth, road construction, and soil degradation, can have substantial influences on land conversion. This analysis shows that these influences can be beyond the boundaries of particular municipalities. There might be some coordination among relevant agencies within the municipality. However, it is rare to include different agencies from other municipalities. In the ECC, for example, Sturgeon County is an area that is experiencing increasing fragmentation and significant farmland conversion (Fig. 5). Industrial development in the northeast part of Edmonton (e.g., under the city’s Horse Hills Area Structure Plan) and in Sturgeon County itself (Sturgeon County Land Use Bylaw No. 118/07) is the single largest cause of the dramatic land-use changes. Agricultural and other governmental agencies from Sturgeon County were not formally involved in the industrial development process of the city of Edmonton, although Sturgeon’s farming sector will be largely affected by these development and plans. The spatial model utilized in this study can help examine and predict the effects of certain specific drivers on land-use/cover changes and can also decompose the effects into direct (local) and indirect (spillover) impacts. It should therefore be especially useful for planning purposes. One important policy recommendation from this study is to encourage cross-discipline agencies to coordinate planning activities, both intra-municipality and across neighboring municipalities. The average marginal effects from various factors might provide general information on how land conversions can be affected by neighboring factors. For more detailed and localized policy recommendation, the SAR model provides an opportunity to calculate specific marginal effects in certain areas, under alternative scenarios (e.g., the marginal effects of increasing the road density by 10% in Edmonton on Sturgeon’s farmland conversion). The new initiative of the Alberta Capital Region Board to collaborate to promote farmland conservation should take this into account. There may be a need to discuss the extension of Edmonton’s Food and Agriculture Strategy to the wider Capital Region area. A similar approach may also be appropriate for the area around Calgary. 7. Concluding remarks This study incorporates farmland fragmentation and neighboring impacts into an analysis of farmland conversion and provides new insights for studies of land- use/cover change and spatial

493

spillovers. Empirical results provide useful information regarding the evaluation of these influences, and in particular, the results indicate that fragmentation has positive effects on farmland conversion. Increases in fragmentation further encourage farmland conversion to urban uses, but the effects are nonlinear with decreasing marginal influences. Land-use activities and decisions have strong spillover effects on neighboring areas. Ignoring these externalities could result in underestimated marginal effects and thus misleading policy/decision recommendations. Therefore, this paper’s main recommendation is that to design or implement effective strategies, municipal, regional, and provincial policymakers (both land use planning agency and other relevant agencies) need to collaborate and coordinate with each other. Any attempts to isolate one area or one agency from neighboring activities when designing land development, conservation, or growth policies may lead to inefficiencies and ineffective policy. The inclusion of the spatial lag variable has important consequences for the other parameters in the model (e.g., the change in population density in the current case) and so should not be ignored. From a policy perspective, even if the sole concern is to estimate the impact of fragmentation on conversion, with no interest in neighboring effects, the results from a model that does not account for spillovers will be biased and could be misleading. More generally, spatial spillovers can result from many factors, including government intervention, technology improvements, reductions in transportation costs, and social norms. For example, spatial technology spillover, which refers to the benefits of new technological knowledge on the productivity gained from investment or innovation progress of the businesses in neighboring areas/regions, is a classic topic in regional economics (Feldman, 1999). In the transportation literature, recent studies (e.g., Lall, 2007) argue that the positive economic benefits accruing from improvement in transportation infrastructure come not only from these investments made by individual states/provinces, but also positive externalities from network expansions and improvements made by neighboring regions. According to the sociology literature (e.g., Coleman, 2007), people often conform their behavior to a widely accepted social norm. When people see or learn about neighbors’ behavior, they may begin to act like them because of their propensity for social conformity. Spatial regression models can be used to empirically examine the magnitude and statistical significance of the spatial spillovers, which should be particularly useful from a public policy perspective. These models are thus useful extensions and generalizations of the non-spatial impact assessment approaches (e.g., OLS, logit, and Tobit models) commonly used in the land-use literature. Acknowledgements The authors thank Xiaofeng Ruan for his excellent assistant work on data collection. We thank the two anonymous reviewers and the editor Dr. Guy M. Robinson for their insightful comments on this article. We also greatly appreciate the Alberta Land Institute (ALI) for providing financial support for this study. References Alberta Agriculture, Food and Rural Development (ARD), 2002. Loss and Fragmentation of Farmland. Government Report. Alberta Agriculture, Food and Rural Development (ARD). Anselin, L., Bera, A.K., Florax, R., Yoon, M.J., 1996. Simple diagnostic tests for spatial dependence. Reg. Sci. Urban Econ. 26 (1), 77–104. Anselin, L., Lozano-Gracia, N., 2008. Errors in variables and spatial effects in hedonic house price models of ambient air quality. Empirical Economics, 34(1. Springer, pp. 5–34. Baumann, M., Kuemmerle, T., Elbakidze, M., Ozdogan, M., Radeloff, V.C., Keuler, N.S., Prishchepov, A.V., Kruhlov, I., Hostert, P., 2011. Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy 28, 552–562.

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Further reading Agriculture and Agri-Food Canada (AAFC). 2013. Canada’s Farm Income Forecast for 2011 and 2012: http://www.agr.gc.ca/ eng/about-us/publications/economic-publications/alphabeticallisting/canada-s-farm-income-forecast-for-2011-and-2012/ ?id=1328906101616#tb-b9 [Last accessed: June 6, 2015]. Agriculture and Agri-Food Canada (AAFC). 2014. An Overview of the Canadian Agriculture and Agri-Food System: http:// publications.gc.ca/collections/collection 2014/aac-aafc/A38-1-12014-eng.pdf [Last accessed: June 6, 2015]. Alberta Agriculture and Rural Development (ARD). 2013. A Balancing Act – the Policy Approach toFragmentation and Conversion of Agricultural Land https://docs. mackenziecounty.com/docushare/dsweb/Get/Document-4788/ [Last AAMDC%20Fragmentation%20Conversion%20Policy.pdf accessed: June 6, 2015]. Boland, M. (2010). Increasing coordination in the plant and plant product processing and handling sector. Choices, 25. www.choicesmagazine.org/magazine/article.php?article=153 [Last accessed: June 6, 2015]. Capital Region Board (CRB). 2009. Growing Forward: The Capital Region Growth Plan. http://capitalregionboard.ab.ca/-/media/ 3-Capital-Region-Growth-Plan.pdf [Last accessed: June 6, 2015]. Government of Alberta. 2008. Land Use Framework Report. https://www.landuse.alberta.ca/Documents/LUF Land-use Framework Report-2008-12.pdf [Last accessed: June 6, 2015]. Government of Alberta. 2012. 2011. Census of Canada, Population and Dwelling Release, file:///C:/Users/fq/Dropbox/ Working%20papers/2.%20Alberta%20Farmland%20Fragmentation/ 2/2011-census-population-and-dwelling-counts.pdf [Last accessed: June 6, 2015]. Government of Alberta. 2014. Industry and Economy, http://alberta.ca/industryandeconomy.cfm[Last accessed: June 6, 2015] Land Use Framework (2008): https://www.landuse.alberta. ca/PLANFORALBERTA/LANDUSEFRAMEWORK/Pages/default.aspx [Last accessed: June 6, 2015]. Alberta Land Stewardship Act (2009): https://www.landuse. alberta.ca/Governance/ALSA/Pages/default.aspx [Last accessed: June 6, 2015]. Ma, K., 2014. CRB to conserve farmland. St. Alberta Gazette, Wednesday September 14. http://www.stalbertgazette.com/ article/20140917/SAG0801/309179998/crb-to-conserve-farmland [Last accessed: June 6, 2015]. Olson, K. and Boehlje, M. (2010). Theme Overview: Fundamental Forces Affecting Agribusiness Industries, Part I. Choices, 25. www.choicesmagazine.org/magazine/article.php?article=150 [Last accessed: June 6, 2015]. Taylor, Z., Burchfield, M. and Kramer, A., 2014. Alberta cities at the crossroads: urban development challenges and opportunities in historical and comparative perspective. The School of Public Policy SPP Research Papers 7(12). www.policyschool.ca [Last accessed: June 6, 2015].