Whom Do Urban Agriculture Parks Provide Landscape Services to and How? A Case Study of Beijing, China

Abstract

An urban agriculture park (UAP) is a mixture of various kinds of urban agriculture and has a group of administrators to plan and manage its landscapes. Thus, the relationships between users and the ability of the UAPs to provide services are crucial. This study investigated the user profiles of three kinds of UAPs in Beijing, China. Investigation of 345 interviewees suggested that most of the users have an upper-middle level income and are well educated. Social connections vary across different types of UAPs. An assessment matrix of landscape services was introduced for a pioneer of UAP, Little Donkey Farm, based on questionnaires, a field survey, and indicators of landscape patterns. Pearson correlations between service demands and users’ characters showed age, companions, and education level were significantly correlated to the needs of scenery and education services. The landscape with the highest supply value was the vegetable plots planted by members. The scenery service was not adequately supplied, and 31.5% of the areas did not meet the demand. Based on the budgets of supply and demand, six types of landscape should be optimized. This study provides an approach to understand the path of landscape service provision in UAP and supports basic knowledge on how to better involve urban agriculture in sustainable development.

1. Introduction

Due to their multiple benefits to cities, urban agriculture (UA) and periurban agriculture meet the goals of the UN’s 2030 agenda for Sustainable Development (SDGs): no poverty; zero hunger; sustainable cities and communities, and life on land in Goals [1,2,3], and especially urban sustainability [4]. UA appears when agricultural land is divided and surrounded by urban areas in the process of urban expansion. UA has evolved into various forms, including urban agricultural sightseeing parks, allotment gardens, controlled environmental agriculture garden, community gardens, home gardens, and so on [5]. One typical type of UA is an urban agriculture park (UAP). UAP is a mixture of several types of UA elements, like allotment gardens, picking-up gardens, and scenic view parks [6]. The function of UA has developed from simple food production to include multiple functions [5]. In practice, urban agricultural areas have faced the threat of rapid urbanization, and some cities ignored them or converted them to real estate [3]. Constructing the edible city and promoting involvement in nature-based solutions (NBS) can raise the awareness of UA. UA, in an edible city framework, contributes to food security, poverty alleviation, and other societal challenges [2,7,8]. Some researchers have developed an assessment framework for NBS [5]. However, further assessment on different kinds of UA are needed to provide evidence for practice.

Analysis of ecosystem services (ES) can better clarify the benefits of UA to residents [9]. ES includes the benefits human beings get from natural ecosystems. Based on studies from the USA, the UK, Austria, New Zealand, Germany, and Canada, etc., UA can provide ecosystem services, such as pollination, pest control, temperature regulation, soil health, water storage, and carbon storage [10,11]. UA also helps cities mitigate and adapt to climate change by reducing greenhouse gas emissions [12,13]. Additionally, UA improves biodiversity: the richness and diversity of bird communities is greater in UAPs than in either urban parks or rural agricultural areas [14]. Environmental concerns are emphasized by educated residents, while access to food is more important for low-income and unemployed urban gardeners [15]. Besides these environmental regulation services, UA also provides important cultural services [5,9]. Among 274 articles that analyzed the supply of ES by UA, 111 focused on cultural services, which thus received the greatest attention among 10 study fields [5]. Educational functions focus on environmental and social–ecological learning [16]. Local communities get actively involved at agricultural sites, which improves social cohesion [17]. Most studies focused on ES provision, such as on the different kinds of ecosystem provided by different types of UA [9], or on comparing UA to other green spaces [14]. Incorporating UA into cities in urban planning and design requires more scientific evidence concerning benefits and supply [17]. Studies have shown that the benefits of UA rely on the users’ characteristics and different types of UA [9,15].

Compared to other types of UA, a UAP has a more complicated landscape structure. It has a group of administrators to manage the landscapes: it provides both agricultural production and sociocultural services for residents. A UAP is different to an urban park, because urban parks lack agricultural activity. It is also less intensive of food production than an agricultural area, whether urban or nonurban. The sustainability of a UAP is more sensitive to the relationship between supply and demand of ES. Spatial patterns and relationships can be better captured by a specification of ES and landscape services. Landscape services are provided by the landscape, which flow from the ecosystem to society [18]. These are closely associated with the landscape pattern [19]. UAP is a semi-natural system, which is modified by humans, and its functions rely on the landscape or the infrastructures. According to the seminatural characters of UA, landscape services are more suitable than ES for UPA research [20].

There are several assessment methods for landscape services. The participatory mapping of landscape services is more relevant to local actors [19,21]. The public participation method has been used to evaluate the spiritual value, educational value, aesthetic value, and social cohesion of landscape services; researchers conducted an expert or stakeholder scoring matrix, and mapped the spatial characteristics of landscape services using a Geographic Information System [22]. Burkhard introduced the supply–demand matrix method to show the relationship between services supply and demand for each land cover patch, describing 26 kinds of ES corresponding to the 44 kinds of different land cover types [23]. This research method in supply and demand analysis can better involve stakeholders, and is applicable to small study sites.

To make UAPs sustainably developed, treating a UAP as a point in the UA network is not enough, the analysis about landscape structure and relationship between supply and demand inside an UAP is necessary. Beijing, China was chosen as a case study, where the urban agricultural areas are mainly UAPs. Despite the importance of agriculture for the nation, as well as for global agriculture, few articles on Chinese UA have yet been published in English [24,25,26]. After solving the hunger issues, agriculture in urban areas or periurban areas has become more important in resilient cities, and for large numbers of urban residents [27]. This study aimed to clarify the relationship of demand and supply in UAPs using a supply–demand matrix of landscape services analysis, and provides suggestions for landscape planning inside them. We combined a field survey and questionnaires to describe the characteristics of 33 UAPs in Beijing and probed into the relationship between users’ demand and their preferences in a case study area of UAP.

2. Methods

The user profiles of UAPs in Beijing were investigated by questionnaires in three representatives of 33 UAPs (Figure 1). The relationship between users’ characteristics and their demands were analyzed based on landscape service demand and questionnaires. One typical UAP was selected after a user profiles analysis. The spatial variations of landscape service demand–supply were showed by landscape structure mapping based on questionnaires for users, landscape metrics, and questionnaires for managers.

2.1. Study Area and Sampling

Beijing is the capital of the People’s Republic of China. It is the nation’s political, economic, cultural, educational, and international communications center. Beijing is located in northern China near the Yan Mountains, with a total area of 16,410.54 square kilometers. Beijing had a resident population of 21.542 million at the end of 2018 [28]. The topography of Beijing is high in the northwest and low in the southeast [29]. Beijing has a semihumid climate with clear-cut seasons. Winter and summer are long, while spring and autumn are short. The average temperature in Beijing from 1981 to 2010 was 11.5 degrees Celsius, and the extreme temperatures were 37.2 degrees Celsius and −16.9 degrees Celsius. The annual average rainfall was close to 511.1 mm [30].

As a pioneer in the development of UA in China, Beijing has developed various kinds of urban agricultural and periurban agriculture [31]: sightseeing urban agricultural parks; civic gardens; controlled environmental agriculture gardens; community gardens, and so on. To examine the user characteristics and landscape services, we chose the most popular UAPs from internet information using “flower garden .app” [32]. The closed UAPs and those with bad conditions were deleted from our investigation list. Thirty-three selected UAPs were located in the urban or periurban areas of Beijing (Figure 2a).

To investigate the profile of users of different UAPs, we used stratified random sampling and purposive sampling based on classification of 33 UAPs (Figure 1 and Figure 2b). This ensured that samples covered different types of UAPs. We investigated the main function of each UAP by a network information query and by a field survey of 33 agricultural parks. These were categorized as picking; single rental; multifunctional rental; sightseeing and popular science; catering accommodation; and other. Fractional vegetation cover (FVC) and area were selected as indicators of heterogeneity in UAPs’ landscape characteristics and functions. FVC was estimated using the dimidiate pixel model, based on Landsat 8 OLI imagery data in 2017 [33]. The area was determined based on Worldview II remote sensing images (captured on 2 June, 2016). UAPs were then categorized using hierarchical cluster analysis, based on main function, vegetation coverage, and areas.

Functional differences distinguished two UAP types—multifunction and single function. Two UAPs (Anlilong Heights and Chestnut Orchard) were ignored in classification because they were a great distance away from the other areas. The multi-functional UAPs then were classified into two types by area and FVC. Hierarchical cluster analysis classified thirty-three UAPs into these three main types: large multifunction UAPs, small multifunction UAPs, and single-function UAPs (Figure 2b). To investigate the users’ characteristics in different types of UAPs, we chose Apple Theme Park from the large multifunction UAPs, Jingling (Fairy) Garden from the small multifunction UAPs, and Little Donkey Farm from the single-function UAPs (Figure 2b).

2.2. User Profiles

We obtained the basic characteristics of users using questionnaires and a detailed face-to-face interview. The questionnaires focused on creating a portrait of the users, including their age, income, and education. The interviewees were also asked what kind of transportation they took and with whom they came. These questions helped us understand their purpose in visiting an UAP. The questionnaires were conducted face-to-face during the spring and summer of 2017 by three master’s students. In Apple Theme Park, all users were tourists; in Little Donkey Farm, all users were gardeners, there to plant vegetables; in Jingling Garden, there were both tourists and gardeners. Therefore, four groups of people completed the questionnaires (n = 345): tourists in large multifunction UAPs, tourists and gardeners in small multifunction UAPs, and gardeners in single-function UAPs.

2.3. User Demand of Landscape Services and Preference Analysis

Due to the differences between distinguished types of UAPs, in order to avoid uncertainty, we chose one type to carry out the match analysis of supply and demand of landscape services. Little Donkey Farm was chosen as the case study. It was constructed in April 2008 and is located at the foot of Fenghuangling, a famous natural scenic spot in the west of Haidian District of Beijing, next to the Beijing–Miyun diversion channel (Figure 2a). It was the first community-supported agriculture (CSA) farm in China, and it was a pioneer of UA in Beijing. Since its construction, Little Donkey Farm has adhered to the concept of an ecological and organic farm, on account of its good soil and water quality. Little Donkey Farm covers about 22 ha, of which about 36% is cultivated, mainly to produce “green, organic, healthy” vegetables. There are several vegetable plots rented by users who plant their own vegetables [34]. The vegetable field is 30 square meters for each member, and the members pay about 1500 CNY per year (approximately 214 US dollars/year) to the administrator.

2.3.1. Unit of Landscape Services Assessment

Landscape patches were taken as the units for landscape service assessment and mapping [35]. The landscape of Little Donkey Farm was classified by the combination of land-use type, vegetation cover, and function (

Table A1. The description of landscape classifications in Little Donkey Farm.

Landscape Classification	Land Cover	Function
Vegetable plot planted by member	Farmland	Land where members grow their own vegetables
Vegetable plot planted by manager	Farmland	Administrators plant fresh vegetables for members
Vegetable greenhouse	Farmland	A land for growing vegetables in a greenhouse
Apple orchard	Garden	A garden for planting apple trees
Woods	Forest land	A land that only grows trees
Woods and grass	Forest land	A land that grows trees and is covered with flowers and herbal plants
Shrubs	Forest land	A land that grows only shrubs
Shrubs and grass	Forest land	A land that grows only shrubs and is covered with flowers and herbal plants.
Woods and shrubs	Forest land	Land covered by trees and shrubs
Woods, shrubs, and grass	Forest land	A land planted with trees and shrubs and covered with flowers and plants
Dining room	Commercial land	The land for providing catering services to visitors
Site of activity	Administration and public services	Land used for activities organized by agricultural administrators or members
Field classroom	Administration and public services	Land used by agricultural administrators for teaching of agricultural knowledge
Rest area	Administration and public services	A small patch to take a break from agriculture activities
Service center	Administration and public services	Land used by agricultural managers for instruction, self-promotion, etc.
Tool house	Land for warehouses	For reserve tools and materials
Zoo	Administration and public services	Land used for raising various animals and for visitors’ viewing (the donkey where Little Donkey Farm’s name comes from lives there)
Filed road	Land for roads and transport facilities	Roads for cultivation and rest
The road for viewing	Land for roads and transport facilities	Roads for visitors’ viewing
Landscape lake	Water and water facilities’ land	The lake formed by artificial excavation to provide beautiful waterscape
Unused land	Other sites	Has not been used
Wetland	Other sites	A natural or artificial marsh near the lake
Beijing–Miyun diversion channel	Water and water facilities’ land	A channel used to transport water from reservoir to city
Kiwifruit orchard	Garden	A garden for planting kiwifruit trees
Other vegetable field	Farmland	Providing vegetables for sales or the dining room
Floodway	Water and water facilities’ land	A channel used to help increase the flood-fighting capacity by helping the main channel to disperse flood pressure
Compost heap	Land for warehouses	The land used for stacking cow dung and dead branches and leaves
Parking lot	Land for roads and transport facilities	Land for parking
Storage room	Land for warehouses	For reserve materials

in the Appendix A). The land cover classification was performed using Worldview II remote sensing images (captured on 2 June, 2016), which have a spatial resolution of 0.4 m. Information about vegetation cover and function was collected in the field study and was used in the landscape classification system. Though the main function of Little Donkey Farm is vegetable planting, land is also used in other ways, such as for basic infrastructure and scenery.

2.3.2. Landscape Services Demand Assessment

According to the literature, education, exercise, natural experience, social cohesion, recreation, countryside life, and scenery services were identified as landscape services provided by UAPs [5] (

Table 1. Landscape services and definitions in urban agriculture parks.

Landscape Services	Definition
Education	Enables people to consciously increase their knowledge about agricultural culture through popular science information boards and close contact with agriculture landscape.
Exercise	Improves health and physical strength through agricultural activities.
Natural experience	Provides a place that makes people feel connected to nature.
Social cohesion	Enables people to enhance their relationship with companions through cooperation in agricultural cultivation.
Recreation	Creates a suitable atmosphere through landscaping to regulate and relax people’s bodies and minds.
Countryside life	Provides a place away from the hustle and bustle of the city for a while, and allows self-sufficiency.
Scenery services	The landscape created is natural and beautiful.

).

At Little Donkey Farm, the demands were assessed by a demand matrix based on questionnaires. The demand matrix shows the value of the landscape services provided by different landscape types from the perspective of users [23]. Each interviewee was asked to score their demand for certain landscape functions from 0 to 5 (0: don’t need this function; 5: we need this function the most). We designed short and concise questions to describe the different functions of each landscape type (Appendix B). Pictures of each landscape type were provided to eliminate misunderstanding of landscape names. The questionnaires were conducted during spring and summer from 2017 to 2018 by five master’s students. The five interviewers were specialized in landscape ecology and ecosystem services and were trained before administering the questionnaires. Fifty-six valid questionnaires on Little Donkey Farm were collected, accounting for 28% of the rental members.

The results were summarized in a demand assessment matrix. The rows of the assessment matrix correspond to each landscape type, and the columns correspond to types of landscape services [23]. Where the rows and columns intersect, each cell includes the value of a certain service for each landscape type, calculated as the average score over 56 questionnaires. Where the value is higher, there was more demand for that service from the landscape type.

2.3.3. Preference Analysis

To clarify differences in demand between distinct groups of people, basic personal information and reasons for choosing the farm were also collected during the interview. The questions included personal characteristics (age, income, education), numbers of visit per week, length of stay, transportation types, and visit companions. These indicators are preferences factors, which may influence demand for landscape services. Pearson correlation analysis was used to quantitively analyze the relationship between the demand and preferences factors.

2.4. Match Between Supply and Demand

2.4.1. Supply Assessment

The questionnaires administered to managers show their willingness to provide particular services to users. We interviewed four farm managers (out of six) to rate the seven types of services for various landscape types. As in the demand questionnaires, ratings were from 0 to 5 (0: no supply of the landscape service; 5: the supply of the landscape service is sufficient). The values from the questionnaires were used to calculate the average value for the four managers. The questionnaire used pictures to represent each type of landscape to eliminate the inconsistent understandings of landscape names.

To reduce the influence of subjectivity in the managers’ questionnaires, we combined landscape metrics and questionnaires in assessing the supply of landscape services (

Table 2. Indicators of landscape service.

Indicators	Education	Exercises	Natural Experience	Social Cohesion	Recreation	Countryside Life	Scenery
SIMI			√				√
LSI			√			√	√
CA		√	√
LPI							√
MSI				√
Questionnaires	√	√	√	√	√	√	√

). The landscape metrics reflect landscape patterns, which include composition and spatial configuration of different landscape types. This influences people’s aesthetic feelings, the convenience of leisure activities, the diversity of their experiences, and the degree of ES [36]. Following relevant literature, landscape shape index (LSI), class area (CA), largest patch index (LPI), mean shape index (MSI), and similarity index (SIMI) were selected and were calculated by Fragstates software [37]. SIMI describes the resemblance of different patches in the field [38]. The higher the SIMI, the more monotonous the landscapes will be. SIMI relates to some services that require landscape diversity and heterogeneity: it was used to assess scenery services and nature experience (Table 2). Where the patch similarity is higher, so is enjoyment of the scenery and nature experience. The LSI reflects the complexity of the patch shape. The more regular the shape is, the more human participation [39]. This index reflects the value of natural experience, enjoyment of the scenery, and pursuit of the countryside life, all of which are related to nature. CA measures the composition of different landscape types [39]. Generally, the larger the patch type is, the more related landscape services there are. Indicators of area-related landscape services include physical exercise and nature experience, with emphasis on the intensity of the experience within the same type of landscape. LPI measures the dominance of a certain type of patch [37]. Generally, the greater the dominance, the greater the value of scenery services from this kind of patch will be. MSI determines whether patch shapes of a certain type of landscape are closer to a square or a circle. The more circular the patches are, the better they will communicate with each other [40]. Therefore, social cohesion value is related to MSI index. The five metrics were calculated at class level in Fragstats [37]. With the maximum and minimum values as the critical values, each landscape metric was divided into five equal grades, and a corresponding score (1–5 points) was assigned to each grade. For the service supply value, with more than two landscape metrics, the final score was calculated as the average.

Based on the rating scales of landscape metrics and the values from questionnaires, the final supply assessment matrix was calculated using the following formula (Equation (1)).

$$G_{ij}=\frac{1}{2}\left(L_{ij}+D_{ij}\right)$$

(1)

where G_ij is the value of landscape type i corresponding to each service type j; L_ij is the rating score of landscape metrics for each landscape type, corresponding to each service; D_ij is the score from the UPA managers for each landscape type corresponding to each service. Due to the fact that L_ij and G_ij are all ranks and standardized values, we gave them as the same weights. Although the values of G_ij are of no practical significance, a comparison of landscape services from different landscape types can indicate which landscape type has better service provision.

2.4.2. Budgets of Supply and Demand

The supply–demand budgets determine whether the landscape services provided by each landscape patch meets the needs of users. The supply of each landscape service type was subtracted from the demand, and the calculation results were aggregated into a supply–demand budget matrix. Each service in the budget matrix corresponded to a certain landscape patch. The budget values for each landscape type were assigned to the corresponding landscape patches to obtain the landscape service mapping. According to Natural Breaks, the supply–demand budget was divided into five levels: values between 0–1 and −1–0 show a basic balance of supply and demand; values between 4–5 and −5–−4 show a serious imbalance of supply and demand [23].

The total supply and demand were calculated by summing the values of each service for all the landscape types. The summed variables of the different services do not represent the actual variable of total services. They are used to compare which landscape types can provide the most high-value landscape services. If one landscape type has many kinds of services with high values, the sum for the landscape services will be high. After summation, a four-quadrant chart was drawn using a middle-digit standardized ranking of services values.

3. Results

3.1. Users’ Characteristics in Various Types of UAPs

The average area of large multifunction UAPs was 91.40 ha, located in periurban areas at the edge of urban areas (Figure 2). These have diverse functions, such as picking up, sightseeing, and even, in some of the UAPs, accommodation. The average area of small multifunction UAPs was 10.99 ha, and most of them were located in periurban areas and rural areas (Figure 2). Single-function UAPs focused on just one main function, such as planting vegetables or picking up.

There were no significant differences in income, education, and age between the users in different types of UAPs. Thus, we analyzed these three variables together. Results showed that most of the users were middle class, with an upper-middle level income and a good education. Forty-two percent (n = 345) of the interviewees’ incomes were between 12,000 and 18,000 CNY/month (1690–2634 dollars/month). Most of them were well educated, with 89% of the interviewees holding bachelor’s, master‘s, or doctoral degrees. The users were mostly middle-aged, between 31 and 50 years old (

Table 3. Users’ characteristics (income, education, and age).

Types of Prosperities	Categories	Total (%), n = 345
Income (CNY)	0–6000	0.0
	6000–12,000	27.7
	12,000–18,000	42.6
	18,000–24,000	16.6
	24,000–30,000	12.5
	Above 30,000	0.7
Education	Technical secondary school	3.5
	College degree	7.0
	Bachelor’s degree	57.0
	Master’s degree	23.3
	Doctor’s degree	9.3
Age	21–30	9.3
	31–40	31.4
	41–50	39.5
	51–60	14.0
	>60	5.8

).

There were significant differences between three types of UAPs in companion structure, i.e., in whom the interviewees came to the UAPs with (Figure 3). The companion structures help us to understand the purposes of visits to UAPs. Families prefer single-function UAPs and small multifunction UAPs. People prefer to participate in planting activities with their spouse. In single-function UAPs, the main functions were renting a plot, planting vegetables, picking up vegetables, and taking them home. For the gardeners in multifunction UAPs, the main function was planting activities. In these two groups, the largest portion of interviewees visited with a spouse (Figure 3). In contrast, most of the interviewees at large, multifunction UAPs (67.4%) said they came to the UAPs with their friends.

3.2. Services Demand and Preference Analysis in Little Donkey Farm

3.2.1. Landscape Structure

A landscape structure map is the essential way of mapping landscape services. The main area of Little Donkey Farm is the vegetable plots, including vegetable plots planted by members, a vegetable greenhouse, and a vegetable plot planted by the managers (Figure 4). There were 14 patches of vegetable plots planted by members, accounting for 17.6% of the total areas. Every member has a 30 m² plot, where they can plant their favorite vegetables. There are also plots designed to provide infrastructure for vegetable cultivation, such as a storage room, tool house, and compost heap. Some built-up lands are used for member services, such as a rest area, parking plot, service center, and dining room, which occupy only 1.8% of the total areas. The zoo, landscape lake, and wetland provide a greater range of functions in a single-function garden. These services are only for members who rent a plot. Some posters hang on the sides of the field roads to provide knowledge of vegetables. The other fields supply fruit picking up, vegetable picking up, activities and sight viewing for members.

3.2.2. Demand of Landscape Services

The vegetable plots planted by members were the landscape type with the highest demand from users in Little Donkey Farm (

Table A2. Landscape services demand for each landscape class.

Landscape Structure	Education	Exercise	Natural Experience	Social Cohesion	Recreation	Countryside life	Scenery Services
Vegetable plot planted by member	3.21	4.43	4.29	3.36	4.20	4.29	2.39
Vegetable plot planted by manager	2.57	1.12	3.54	2.55	2.12	3.24	2.23
Vegetable greenhouse	2.23	2.87	2.89	2.64	1.39	2.87	1.54
Picking area	2.97	3.13	3.76	3.65	4.02	2.77	1.63
Kiwifruit orchard	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Apple orchard	0.43	1.22	2.26	0.00	1.97	1.83	2.11
Woods	0.00	0.00	1.29	0.00	1.12	0.34	3.56
Woods and grass	0.00	0.00	1.53	0.00	1.41	0.75	4.03
Shrubs	0.00	0.00	1.31	0.00	1.15	0.43	2.96
Shrubs and grass	0.00	0.00	1.63	0.00	1.52	0.92	3.43
Woods and shrubs	0.00	0.00	1.87	0.00	1.71	1.24	3.76
Woods, shrubs, and grass	0.00	0.00	1.93	0.00	2.02	1.48	3.82
Dining room	0.00	0.00	0.96	4.38	1.42	1.25	0.00
Warehouse	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Compost place	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Activity square	0.83	0.92	0.15	3.53	4.31	0.00	2.25
Field classroom	0.79	0.00	1.74	0.93	1.03	0.00	0.71
Rest area	0.00	0.00	0.00	4.02	4.79	3.12	3.33
Agricultural service area	3.85	0.00	0.00	0.00	0.00	0.00	0.00
Tool room	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Zoo	2.41	0.00	3.18	2.42	2.30	1.16	0.12
Field Road	0.00	0.00	3.13	0.00	0.00	0.00	2.86
The rood for viewing	0.00	0.00	1.89	0.00	0.00	0.00	2.14
Parking lot	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Landscape lake	0.00	0.00	2.63	0.53	0.84	0.00	0.21
Headrace channel	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Flood discharge channel	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Unused land	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Wetland	0.00	0.00	2.63	0.53	0.84	0.00	0.21

Note: The highest value is in bold for each service. The value of 0 means users don’t need this service; the value of 5 means the need of this certain service is greatest.

in the Appendix A). The highest demand value for four out of seven services were found in the vegetable plots planted by members. The area with the greatest demand for recreation was not in the vegetable patch, but in the rest areas, where users preferred more recreation services. The dining room was the landscape where users required the most social cohesion services. The “woods and grass” responded to the highest demand of scenery service.

If service demand is associated with the areas of different landscape types, the value of the service will grow continuously with an increase in landscape areas. The demand values for the ten types of landscape were higher than the values provided by landscapes with smaller areas. The rest area was the smallest landscape, while users demanded the most recreation service from it (Table A2 in the Appendix A, Figure 5). Vegetable plots planted by members represented a landscape type where service demand was the greatest and occupied the largest area. Among landscape types whose size was between that of the rest areas and the vegetable plots planted by members, some were linked directly with agricultural activities and their area was proportional to demand (Figure 5: picking area; landscape lake; activity square; shrubs and grass; vegetable greenhouse; apple orchard; vegetable plot planted by manager; woods, shrubs, and grass). However, a few landscape types did not fit the growing trend (Figure 5), which exposed the mismatch between demand for services and landscape area. The three categories in which service declined significantly compared to the landscapes of similar sizes were the zoo, field classroom, and the woods (Figure 5).

3.2.3. Relationship between Users’ Characteristics and Service Demand

Correlation analysis between the basic situation of users and their demand for landscape services showed that there were different factors closely related to various landscape services (

Table 4. Correlations between preferences factors and landscape service demand.

	Age	Education	Frequency of Visiting per Week	The Duration of Stay	The Way to Visit the Garden	Companion
Education	0.003	0.385 **	0.038	−0.094	0.057	−0.047
Exercise	0.446 **	0.013	−0.136	0.323 **	−0.230 *	−0.216 *
Natural experience	0.113	0.201 *	0.057	0.071	−0.180	0.042
Social cohesion	0.140	0.001	−0.066	0.287 **	−0.021	−0.037
Recreation	0.290 **	0.097	−0.026	0.169	−0.003	0.002
Countryside life	0.341 **	0.112	−0.071	0.297 **	−0.183 *	−0.126
Scenery service	0.025	0.153	0.196 *	0.285 **	−0.121	0.041

Note: * correlation is significant at the 0.05 level; ** correlation is significant at the 0.01 level.

). The correlation between age and service demand showed that retired persons were sensitive to services of exercise, pursuit of countryside life, and scenery services. For older people, UAP requires more diverse landscapes and more convenient transportation. Parents and highly educated people demand more education services provided by information boards and popular science activities (Table 4). Beautiful views may attract people to visit UAPs more frequently and stay longer, as the correlation coefficient between scenery services and stay time was higher (Table 4). Duration of stay was significantly positively correlated with four types of landscape services (exercise, social cohesion, countryside life, scenery service), and was the factor most correlated with the services (Table 4).

3.3. Budget of Supply and Demand

3.3.1. Supply of Landscape Services

Overall, the greatest supply of landscape services was from the vegetable plots planted by members (

Table A3. Landscape services supply for each landscape class.

Landscape Structure	Education	Exercise	Natural Experience	Social Cohesion	Recreation	Countryside Life	Scenery Services
Vegetable plot planted by member	4.38	5.00	4.50	4.75	4.38	4.50	3.25
Vegetable plot planted by manager	2.50	4.13	4.13	4.00	3.00	3.38	3.00
Vegetable greenhouse	1.88	3.25	3.84	3.88	2.38	4.38	2.50
Picking area	2.00	3.50	4.50	5.00	2.38	4.50	3.13
Kiwifruit orchard	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Apple orchard	0.50	2.50	3.21	0.00	1.00	2.63	3.21
Woods	0.00	0.00	3.63	0.00	4.00	2.75	4.13
Woods and grass	0.00	0.00	2.92	0.00	2.63	1.38	2.75
Shrubs	0.00	0.00	2.79	0.00	1.63	2.25	2.88
Shrubs and grass	0.00	0.00	2.42	0.00	2.00	2.38	3.29
Woods and shrubs	0.00	0.00	3.09	0.00	2.63	2.88	3.71
Woods, shrubs and grass	0.00	0.00	3.38	0.00	3.88	2.88	4.17
Dining room	0.00	0.00	0.00	4.88	2.13	3.50	0.00
Warehouse	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Compost place	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Activity square	2.00	3.88	3.63	4.50	2.75	2.38	1.84
Field classroom	1.38	0.00	3.04	3.38	2.00	0.00	2.46
Rest area	0.00	0.00	2.92	4.38	3.00	4.13	3.00
Agricultural service area	2.13	0.00	0.00	0.00	0.00	0.00	0.00
Tool room	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Zoo	2.38	0.00	2.96	2.50	2.00	3.38	1.96
Field Road	0.00	0.00	3.88	0.00	0.00	0.00	2.13
The rood for viewing	0.00	0.00	3.88	0.00	0.00	0.00	3.71
Parking lot	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Landscape lake	0.00	0.00	2.34	0.00	1.13	0.00	0.00
Headrace channel	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Flood discharge channel	0.00	0.00	0.00	0.00	0.00	0.00	0.00
Unused land	0.00	0.00	1.88	0.00	0.00	0.00	0.00
Wetland	0.00	0.00	2.67	0.00	0.50	0.00	0.00

Note: The highest value is in bold for each service. The value of 0 means this landscape does not provide the service; the value of 5 means the provision of the certain service is greatest.

in the Appendix A). The greatest supply of education, exercise, natural experience, recreation, and enjoyment of countryside life was from the vegetable plots planted by members. The natural experience and enjoyment of countryside life in the picking area were tied for the highest score with the vegetable plots planted by members. The social cohesion service in the picking area was the highest among all landscape types. The most valuable type of landscape for scenery services was the woods.

3.3.2. Match Between Supply and Demand

The supply of landscape services for exercise and enjoyment of countryside life met the demand in various landscapes, but the supply of education, recreation, and scenery services did not meet the demand in some landscapes (Figure 6). Overall, 31.5% of the area did not meet the demand for scenery services—this mainly included the field road, the woods, and the landscape lake. The supply of education services was not enough to meet the demand in 11% of the study area—mainly including the zoo, the agricultural service center, the vegetable plots planted by managers, and the vegetable greenhouse. The landscapes where recreation services did not meet the demand included the zoo, the rest areas, activity square, and apple orchards, accounting for 6.9% of the total area of the study area.

By comparing total supply and total demand, landscapes can be divided into three types: high supply–high demand, high supply–low demand, and low supply–low demand (Figure 7). There was no landscape with high demand–low supply in the study area. The main parts of the study area, vegetable plots planted by members and picking areas, had high supply and demand levels. Vegetable plots planted by managers, the activity square, and the vegetable greenhouse had high supply–low demand (Figure 7). The remaining landscape types were mostly located in the low supply–low demand quadrant (Figure 7). Except for the wetland, the landscape lake, and the agriculture center, demand for landscape services in the low demand–low supply quadrant was lower than supply (Figure 7). The supply for the wetland, landscape lake, and service center did not meet the overall demand.

4. Discussion

This study investigated the user profile of UAPs in Beijing and provided an approach to understand the relationship between users’ characteristics and UA from a landscape service perspective. It employed a supply and demand matrix using landscape indicators and questionnaires.

4.1. Differences of User Profiles

There is a significant difference between the characteristics of users of Chinese UAPs and users of UA in other countries. Among 345 interviewees in Beijing, there was a lower proportion of elderly people than in the studies from Spain and Australia, where 80% and 65% of the gardeners were over 50 [9,41]. Middle-aged people (41–50) were the highest proportion of users in Beijing’s UAP (Table 3). Our surveys in Beijing showed that people who use UAPs have higher incomes than previous studies, which found users of UA generally had low incomes [2,9,42,43]. Most of the users had middle- to upper-level incomes and high education level (Table 3). The interviewees’ incomes were much higher than the average monthly income of Beijing residents in 2019 (3435 CNY). The member fee costs may be the reason [44]. In Beijing, a 30 m² site rents for more than 1500 CNY (214 US dollars) per year. Some of the allotment gardens are close to residents in Europe and Latin America [9], while in Beijing, most of them are far away from residential areas. This is why 70% of the gardeners need to drive a car to the UAP (Figure 3).

One possible reason for the differences is the function of UAPs in Beijing. We compared the research results in Europe, South America, and Asia, with the corresponding functions of different types of UAPs in Beijing. The single-function UAPs are like allotment gardens in Europe and pick-your-own farms [45,46,47], and their main function is for planting vegetables and picking fruits. Multifunction UAPs are more like urban farms, institutional gardens, agriculture parks, tourism farms, and farming experience farms [45,47]. UAPs in Beijing have a broader range of functions. Food production is not the greatest need of users. Even the relatively single-function agriculture has evolved into multifunctional UA areas, providing more recreation and ES for urban residents [9,46,48]. Compared to the original aims of food provision, UA has begun to fulfill multiple functions for residents [5,45]. Among all the cultural services, recreation and physical exercise were what users valued most in our case study (Table A2 in the Appendix A). Social cohesion is a function that is heavily involved in both our research and previous research [46,49].

Another reason for the differences is the urbanization progress in China. The characteristics and development process of China’s UA are closely linked with China’s urbanization process, population migration, agricultural development, and land system. As a country that has historically faced doubt about whether it can feed its entire population through agriculture, China has made food security one of its most important state policies, and agricultural development is centered on ensuring food security. With the development of globalization and urbanization, China’s agriculture has begun to show versatility. A statistical assessment indicated that 62.9% of the urban residents come from rural areas [50]. Although living in the city, these people and their descendants have a strong desire for rural life. Therefore, the edge of the city and surrounding areas have become popular spots of UA. This background is greatly different from the original purpose of allotment gardens: poverty alleviation in the 18th century [46]. However, they have much in common in terms of function and landscape structure. UAPs in Beijing mainly serve the middle class, who have larger incomes and more education (Table 3). Their desire for UA is mainly to exercise and enjoy the rural life.

4.2. Implications of Landscape Services Analysis of UAPs

Analysis of user sources and preferences provides a basis for development for potential users of UAPs. The values of different services mainly depend on users’ age and education. In the investigation, people wanted to get physical exercise by engaging in agricultural activities, especially older people (Table 4). This demand put higher requirements on the size of each members’ allocated patch. In a relatively small garden, people tend to need less energy to complete vegetable planting. The users valued vegetable plots greatly, where they also required multiple services. The managers mostly sought a combination of elements, like parks, allotment gardens, and picking-up gardens, to gain more economic benefits. What was previously a win–win situation—farmers’ financial gain and gardeners’ recreational enjoyment—is now a problem. Research into mixed-use land and the right proportion of built-up and infrastructure will be key.

Our research used demand and supply assessment of landscape services based on questionnaires, and can inform design of UAPs fully considering the intentions of users and administrators [27]. Among the supply and demand of landscape services, the supply and demand for countryside life, exercise, and social cohesion were balanced (Figure 6). The areas with the greatest value for promoting social cohesions are picking areas, vegetable plots planted by members, rest areas, and dining room (Table A2 in the Appendix A). The construction of the rest area in Little Donkey Farm is distinctive: the rest area was built around the vegetable plots and is connected to the surrounding landscape. This design has promoted communication between people and meets the need for social cohesion.

There are some gaps between supply and demand, especially in the education and scenery services in Little Donkey Farm (Figure 6). The space utilization of the farm is not perfect: a lot of places have disorderly compost, affecting the beauty, so the users’ needs cannot be met. In the field interviews, many users expressed hope that the farm could improve its management and strengthen the constructions. The concept of autonomy can also be introduced, to allow members to participate in the overall planning and construction of agricultural parks to achieve more sustainable development [46,51]. In Little Donkey Farm, woods, shrubs, and grass are the landscape types that offer significant scenery services (Table A2 in the Appendix A). As these landscapes are separated from people’s main activity areas, people’s satisfaction in the pursuit of scenery services is affected. Administrators in single-function UAPs can optimize landscape types by connecting vegetable plots to some relatively natural fields, such as woods, shrubs, and grass.

From the perspective of overall landscape services from different landscape types, landscapes needed to be optimized to include two types: types that users valued highly, and types that do not currently supply sufficient services (Figure 7). According to the statistics of total service value, the most important landscape types are the picking area and the vegetable plot, which should be the focus of the planning and design of the whole agricultural park. The areas with supply deficits included the landscape lake, the wetland, and the agriculture service center, most of which provide scenery services. To better meet users’ needs, UAP should be built with small but sophisticated scenic spots and beautiful environments, rather than neglecting these because of the lack of direct economic benefits. Some landscape patches occupy larger areas but demand less services, such as zoo, field classrooms, and woods, which also need to be optimized (Figure 5).

4.3. Limitation and Future Research

The approach we used requires a lot of field surveys and interviews; these take a lot of time, which makes it hard to conduct questionnaires for each UAP. In this study, UAPs in Beijing were classified and sampled before the questionnaire to increase the universality of the research results. However, it is uncertain whether the user characteristics may be different from the results of larger-range surveys. Little Donkey Farm, selected in the study, was a pioneer of single-function (rental) farming, which was imitated by many subsequent UAPs. Therefore, the results of the landscape services analysis represent the general situation of single-function UAPs. Beijing’s agricultural parks have been developing rapidly, with the ES value of UA growing from 48.206 billion USD in 2012 to 52.625 billion USD in 2018 [31]. Although the numbers of UAPs cannot be counted precisely, it must have been increasing, and the 33 agricultural parks selected in this study are not representative of all agricultural parks.

International studies have shown that UA as an approach to NBS is an important trend in sustainable development [5,18]. Although we studied some site of urban agricultural parks in Beijing, our potential contribution is to provide basic research data for future consideration of UA as an approach to NBS. From the perspective of a city as a whole, the inclusion of UA into the NBS framework requires a careful consideration of local management policies, climate change conditions, and resource utilization characteristics, and a balance between ecosystem services and ecosystem disservices [27]. The model should be multilevel, and it is necessary to analyze the ecological, cultural, and social values that may be generated by different types of UA [51]. Different types of UA have various functions, with focuses including cultural value, food production, and environmental regulation. Thus, a suitable “niche” can be found to replace some functions of green space, while land-use policy and owner’s equity can be guaranteed.

At present, the management of agricultural parks in Beijing is bottom-up and relatively isolated, and there are no well-established projects, as in Europe [49,52]. Our interviewees were people who have used urban agricultural landscape services, even for many years. From a planning perspective on how to develop UA, information on people who do not use UAPs is particularly important, as their demands may differ from existing users. Current planning and design may not meet their demands. They may be unable or unwilling to use UAPs due to factors like distance and cost. In the process of implementing NBS and edible urbanism 5.0, current agricultural parks in China cannot support the food security of low-income people and their demand should be explored in future studies [53]. To provide social cohesion and food security to more low-income groups in the city, one possible approach is to switch low-efficiency, neglected community green space to community gardens, and study how to provide broader and more equitable ES through more intensive and easily accessible ways [17,46]. Meanwhile, policymakers should pay close attention to whether UA will cause water consumption problems in the process of climate change.

5. Conclusions

We have applied a landscape services assessment to UA, based on questionnaires and an analysis of landscape structure. Users’ characteristics and preferences were also investigated. The results indicated the main users of UAPs in Beijing are middle-income class, and most of them are well educated, which is different from UA users in other countries. Preferences analysis showed that user characteristics greatly influence the services demand. Comparing with a previous study [15], the values of different services mainly depend on users’ income, age, and education.

The case study in Little Donkey Farms showed that the match between supply and demand has spatial variation inside a UAP. The supply of landscape services for exercise and enjoyment of countryside life met demand in various landscapes, while the supply of education, recreation, and scenery services for some landscape types did not meet the demand. Overall, 31.5% of the areas did not meet the demand for scenery services. Both landscape services analysis and preferences analysis confirmed the lack of scenery services. The fields with supply deficits are landscapes where people expected scenery services, which need more design and administration. The landscapes with agriculture activities, such as vegetable plots planted by members and picking areas, are the high demand–high supply sites, which satisfy users’ needs well. Landscapes that can provide scenery services, such as woods and grass, need to be better configured. Some infrastructure with popular science propaganda and recreation can be improved to provide more education and recreation services.

This study applied an approach to landscape services assessment in UA areas, and also created potential for landscape planning within the agriculture parks to balance demand and supply. Our research focused on a pioneer of single-function (rental) farming; future application to other kinds of UAPs may clarify the supply–demand relationship in UAs.