Land Use Policy 101 (2021) 105145

Contents lists available at ScienceDirect

Land Use Policy

journal homepage: www.elsevier.com/locate/landusepol

Delineation of a basic farmland protection zone based on spatial connectivity and comprehensive quality evaluation: A case study of City,

Yanming Chen a,b, Mengru Yao c, Qiqi Zhao a,b, Zhenjie Chen a,b, Penghui Jiang a,b, Manchun Li a,b,*, Dong Chen a,b a Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China b Collaborative Innovation Center of South China Sea Studies, Nanjing, Jiangsu 210023, China c Urban & Rural Planning and Design Institute, Guangzhou, Guangdong 510200, China

ARTICLE INFO ABSTRACT

Keywords: The continuous reduction of farmland poses a serious threat to national security. Delineating essential basic Farmland delimitation farmland for special protection is urgently needed in China and is an important measure to stabilize food pro­ Spatial connectivity duction capacity and ensure regional food security. Currently, research on basic farmland delineation is mostly Comprehensive quality based on the evaluation of the characteristics of the farmland and lacks the integration of multiple factors such as Changsha City spatial connectivity and scale constraints. Solving these issues will help improve the rationality and scientificity of basic farmland delineation. This study performed a case study of Changsha City based on land use, spatial planning, economic and social factors, natural geography, and other multi-source data. The index method and the food demand method were used to predict the scale of basic farmland protection to determine the basic farmland scale thresholds. Buffer analysis method is used to measure the spatial connectivity of farmland. This study implemented the LESA evaluation system to construct a comprehensive quality evaluation index system for farmland. Finally, based on the technical framework of basic farmland delimitation of “serial priority, quality screening, and scale constraint,” the basic farmland scale was determined to be 23,104,701 ha.

1. Introduction Ministry of Agriculture and Rural Affairs signaled the need to consoli­ date the results of permanent basic farmland delineation and to coor­ Farmland is the land resource on which humans depend for survival dinate ecological construction and farmland protection. Many countries and development and has become a global and strategic issue affecting across the world have made various efforts to protect increasingly economic and social development. Since the Chinese economic reform precious farmland resources. and its opening up to trade, industrialization and urbanization at the In the rapidly urbanizing United States, a large amount of high- expense of farmland resources have made the reversal of the non- quality and high-yield farmland was converted to urban land. The agriculturalization trend difficult in some areas (Liu et al., 2014). In government subsequently drafted a series of public land management recent years, the continuous reduction of farmland area has limited the regulations to curb the spread of cities and to protect farmland (Smith, potential for further grain production, which seriously threatens na­ 2002; Furuseth, 2006). Additionally, the U.S. Soil Administration had tional food security (Liu, 2018). In 2016, the Ministry of Natural Re­ proposed a “Land Evaluation and Site Assessment” (LESA) system, sources and the Ministry of Agriculture and Rural Affairs jointly issued a collaborating with the state governments to determine the type and report requiring all the localities to implement a permanent basic scope of farmland protection (Wright, Zitzmann et al. 1983; Brabec and farmland demarcation and to strengthen the protection of permanent Smith, 2002). Japan had formulated and implemented a number of laws basic farmlands. In 2019, the Ministry of Natural Resources and the and regulations to protect land and resources and to solve the current

* Corresponding author at: Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China. E-mail address: [email protected] (M. Li). https://doi.org/10.1016/j.landusepol.2020.105145 Received 1 February 2020; Received in revised form 2 September 2020; Accepted 4 October 2020 Available online 3 November 2020 0264-8377/© 2020 Elsevier Ltd. All rights reserved. Y. Chen et al. Land Use Policy 101 (2021) 105145 land shortage issue to protect farmland and to improve the utilization section focuses on the evaluation of the socio-economic conditions of rate of resources (Sorensen, 2000a; Aizaki, Sato et al. 2006). Japanese agricultural land (Steiner et al., 1987; Dung and Sugumaran, 2005; laws stipulate that farmland must be contiguous and prohibit converting Braun et al., 2019). Owing to the recent development of research theory farmland to non-farmland (Sorensen, 2000b). The British government and technical resources, the evaluation index has been further encourages industry to occupy wasteland and prohibits the industrial expanded, and economic benefits( Zeng et al., 2017), ecological quality use of high-quality farmland. Thus, different levels of farmland protec­ (Han et al., 2019), agricultural natural risk (Ren et al., 2018), landscape tion prevent industry from occupying large amounts of cultivated land aesthetic function (Guo et al., 2014), and other factors are considered for (Pretty et al., 2000; Robinson and Sutherland, 2002). the evaluation of farmland quality. Scientists in China and abroad have extensively researched basic It is essential for all the countries to further develop the techniques, farmland delineation and more diversified approaches have been methods, and scientific basis required for basic farmland delimitation. developed with a quantitative and spatial focus, thereby establishing a The LESA system has become the most widely used evaluation system. solid theoretical foundation for basic farmland delimitation. Although this system has certain advantages, it does not consider the A demarcation model of permanent basic farmland has been pro­ ecological value of farmland. Research on the delimitation of basic posed by constructing a systematic classification model to analyze the farmland is mostly based on one or more farmland attributes, and the continuity and fragmentation of the farmland from a perspective of work needed before and after the evaluation is seldom discussed. In this spatial continuity and high productivity (Cheng et al., 2017). A GIS grid context, a “combined” basic farmland comprehensive delineation search–based analysis system of farmland continuity was developed to method system must be constructed by linking a feature evaluation with rapidly identify contiguous areas of high-quality farmland (Guo and spatial optimization and scale constraints to provide a clearer farmland Yang, 2010). Huang et al. analyzed the farmland contiguousness of the delineation definition. Guangxi Zhuang autonomous region using the spatial aggregation We performed a case study on Changsha City based on the current function of the ArcGIS software (Haizhou and Yong, 2018). Some re­ state of land use in 2016. The index and the grain demand methods are searchers rely on the LESA system to conduct comprehensive evalua­ used to predict the scale of basic farmland in the study area and to tions of farmland, which takes into account the natural quality and land determine the basic farmland scale threshold. The spatial connectivity conditions (Feng et al., 2014; Nosrati and Collins, 2019). This system and comprehensive quality of farmland were coupled to the basic consists of two segments: 1) land evaluation (LE) and 2) site analysis farmland delimitation, and the technical framework for basic farmland (SA). LE focuses on the natural conditions of agricultural land. The SA delimitation of "serial priority, quality screening, and scale constraint"

Fig. 1. Study area.

2 Y. Chen et al. Land Use Policy 101 (2021) 105145 was designed and executed. This study formulates basic farmland 100.08 %. Based on spatial distribution characteristics, the basic farm­ delineation rules, delineates basic farmland protection patches, analyzes land of Changsha City is primarily distributed in Yuelu, Wangcheng, the basic farmland spatial distribution and landscape pattern charac­ Changsha, Ningxiang, and . teristics, and provides guidance and reference for basic farmland delineation in China and other countries. 2.2. Data sources

2. Materials and methods The data used in this study primarily involves four categories, i.e., land use data (vector data), spatial planning data (vector data), natural 2.1. Study area geographic data (raster data), and economic and social data (raster and vector data). Table 1 summarizes the data sources and uses. Changsha City is located in the northeast Province, in the lower reaches of the Xiangjiang River and the western edge of the ◦ ′ ◦ ′ 2.3. Research methodology Changliu Basin (Fig. 1). Its geographical range is 111 53 ~114 15 E, ◦ ′ ◦ ′ 27 51 ~28 41 N. The land area covered by the city is 11,815.96 km2. We set 2035 as the target year to correspond to the latest round of Changsha has a mild subtropical monsoon humid climate with four spatial planning. The indicator farm method and the grain demand distinct seasons, abundant precipitation, and simultaneous rain and method were used to predict the basic farmland scale in the study area, heat. It is a significantarea for the development of the core growth pole followed by which the basic farmland scale threshold was determined. of the transition zone between the eastern coastal zone and the central This study combines the spatial connectivity and comprehensive quality and western regions, the open economic belt of the Yangtze River and evaluation of the farmland, formulates basic farmland delineation rules, the coastal open economic belt. and delineates basic farmland protection patches. Changsha has diverse soil types, which can be divided into 9 soil types and 21 subtypes. Among them, red soil and paddy soil are the main 2.3.1. Basic farmland scale prediction ones, which account for 70 % and 25 % of the total soil area respectively, Regarding existing research methods, the index method and the food and are suitable for the growth of a variety of crops. In 2017, Changsha demand method were used to predict the scale of basic farmland pro­ City completed a regional GDP of 1053.55 billion yuan. Based on the tection to determine the basic farmland scale thresholds. permanent population, the per capita GDP reached 135,388 yuan. The The index method for farmland scale prediction was performed by city’s total permanent population is 7.92 million, the urbanization rate multiplying the total amount of farmland predicted by the basic farm­ is 77.59 %, and the per capita disposable income of urban residents is land protection rate to obtain the basic farmland amount (Xiewenfeng, 46,948 yuan. 2008). With the acceleration of urbanization, by analogy with similarly From the Changsha Land Use Master Plan (2006–2020) (revised in developed cities, the total amount of farmland in Changsha will 2016), the existing scale of 232,232.50 ha of the protected farmland area decrease. This reduction will inevitably lead to an increase in the basic in Changsha City in 2016 was planned to increase to 232,040.00 ha in farmland protection rate, which is consistent with the development 2020 with the implementation of a basic farmland protection level of trends of other large cities (Huang et al., 2011; Wang et al., 2014; Yuan

Table 1 Research data.

Data Category Data Details Data Sources Application

Changsha City Land Use Change Survey Data in Changsha Municipal Bureau of Natural Resources Extraction of farmland, urban land, 2016 and Planning transportation land, waters, and other factors Results of the demarcation of permanent basic Changsha Municipal Bureau of Natural Resources Comparison of basic farmland delineation results farmland in Changsha City in 2016 and Planning Land Use Data Changsha City farmland quality supplement and Changsha Municipal Bureau of Natural Resources Evaluation of Natural Quality and Site Conditions improvement results in 2015 and Planning of Farmland Bulletin on Primary Data Results of Changsha City Changsha Municipal Bureau of Natural Resources Farmland area statistics Land Change Survey (2001–2017) and Planning Changsha Land Use Master Plan (2006–2020) Changsha Municipal Bureau of Natural Resources Analysis of basic farmland protection status (revised in 2016) and Planning Self-evaluation report of Changsha Land Use Master Changsha Municipal Bureau of Natural Resources Analysis of basic farmland protection status Spatial Planning Plan (2006–2020) and Planning Data Changsha Municipal Bureau of Natural Resources Compare the scale of population forecasting and Changsha City Master Plan (2017–2035) and Planning master the direction of urban development Changsha Municipal Bureau of Ecological Changsha City Ecological Protection Red Line Evaluation of the ecological value of farmland Environment Geospatial Data Cloud Changsha DEM data (30 m) Farmland water calculation http://www.gscloud.cn/ World soil database Natural Changsha City Soil Attributes Data (1 km) http://www.crensed.ac.cn/portal/metadata Farmland water calculation Geographic /a948627d-4b71-4f68-b1b6-fe02e302af09 Data Resource and Environment Data Cloud Platform Global Drought and Potential Evasive Database Changsha Meteorological Data (500 m, 1 km) Farmland water calculation https://cgiarcsi.community/data/global-aridity- and-pet-database/ Extract indicators for basic farmland scale Changsha Statistical Yearbook (2001–2017) Changsha Municipal Bureau of Statistics prediction Statistical Communique of National Economic and Extract indicators for basic farmland scale Changsha Municipal Bureau of Statistics Economic and Social Development of Changsha City (2017) prediction Social Data Changsha City Population Spatial Distribution Resource and Environment Data Cloud Platform Create an ecological threat layer Kilometer Grid Data in 2015 Changsha City GDP Spatial Distribution Kilometer Resource and Environment Data Cloud Platform Create an ecological threat layer Grid Data in 2015

3 Y. Chen et al. Land Use Policy 101 (2021) 105145 et al., 2016). According to previous research, the basic farmland pro­ Where Dc is the basic farmland demand, Qc is the total grain demand, N tection rate will reach 90 % in Changsha City in 2035. is the food self-sufficiency rate, Y is the grain crop yield, P is the pro­ The change in farmland area represents a complex nonlinear system portion of planted grain crops, and I is the multiple cropping index. that is affected by multiple factors, most of which are difficult to accu­ The prediction results of the above methods were summarized, and rately quantify, making prediction results more uncertain. Therefore, we the two prediction results were then combined to determine the basic regard the n-year farmland area as a consequence of the (n-1)-year farmland scale threshold in Changsha City in 2035. farmland area after the effect of several factors. Based on the time series data of farmland area, the metabolic Grey Model (GM) is used to explore 2.3.2. Identification of farmland spatial connectivity the mathematical evolution of farmland area to predict the target annual In this study, the "spatial connectivity of farmland" is definedas the farmland scale (H., 1994; Wang et al., 2012; Zhou and He, 2013). Based relative connectivity of farmland plaques; farmland plaques are on the farmland area data of Changsha City from 2000 to 2016, this considered connected when the distance between them is less than the study used the GM to predict the farmland area of Changsha City in specified threshold. 2035. Model construction and data operation were conducted using the Railways and large rivers generally block the connectivity and affect Matlab 2010b software. the productivity of farmland (Cui et al., 2007; Zhang et al., 2007). Ac­ The construction process of the GM model) is as follows: cording to the "Highway Engineering Standards" (JTG B01-2014), the ( ) ( ) ( ) ( ) Let X 0 = x 0 (1), x 0 (2),…, x 0 (n) ) total width of a first-level highway exceeds 20 m. Therefore, if the dis­ ( ) ( ) ( ) ( ) ( ) ,if X 1 = x 1 (1), x 1 (2),…, x 1 (n) is an accumulative sequence of X 0 , tance between the cultivated plaques was less 20 m, the farmlands were the differential equation for the GM model is established by sequence communicated. According to the “Local Standard for the Construction of ( ) X 1 as follows: High Standard farmland in Hunan Province” (DB43/T 876.1-2014), the minimum contiguous area for the plain river network, hilly and sloping, (1) dx (1) + ax = b (1-1) and hilly mountainous areas should be 66.67, 13.33, and 6.67 ha, dt respectively. Because Changsha City has a large number of farmlands n ( ) ∑ ( ) distributed in hilly mountain areas, the study selected 6.67 ha as the In the formula x 1 (i) = x 0 (i), i = 1, 2,…, n; a is the development i=1 farmland scale threshold. coefficient; b is the gray action amount. ( ) ( ) ( ) ( ) Sequence Z 1 = (z 1 (2), z 1 (3),…, z 1 (n)) generates a sequence for 2.3.3. Comprehensive quality evaluation of farmland (1) This study implemented the principles of leading, stability, practi­ the immediate mean )of a cumulative X , where ( ) ( ) ( ) cality, measurability, and local conditions based on the LESA evaluation Z 1 k = 1 x 1 (k) + x 1 (k + 1) , k = 2, 3,…, n. The values of parameters 2 system. Moreover, three aspects were considered to construct a a and b in formula 1-1 can be obtained by the least square method: comprehensive quality evaluation index system for farmland: 1) natural ) T T 1 T ̂a = [a, b] = B B B Y (1-2) quality, 2) land condition, and 3) ecological value of farmland. The ⎡ ⎤ ⎡ ⎤ natural quality of farmland emphasizes the background conditions and ( ) ( ) Z 1 (2) 2 x 0 (2) forms the basis of farmland productivity (Tang et al., 2019; Jiang et al., ⎢ (1) ⎥ ⎢ (0) ⎥ ⎢ Z (3) 1 ⎥ ⎢ x (3) ⎥ 2020a). This study refers to the "Grading Regulations for Farmland B = ⎣ ⎦, Y = ⎣ ⎦ ⋮ ⋮ ⋮ Quality" (GB/T28407-2012) and the research results of domestic and (1) ( ) (0) ( ) Z n 1 x n foreign scholars (Song et al., 2015; Liu et al., 2019) to choose six Then the solution of Eq. 1-1 can be obtained as follows: indexes—surface soil texture, soil organic matter content, soil acidity [ ] and alkalinity, effective soil thickness, terrain slope, and surface rock (0) b ak b ̂x(1)(k + 1) = x (1) e + (1-3) a a outcrops—to characterize the natural quality of farmland. The land condition factors of farmland are non-soil quality factors The available restore value is: that have an important influence on agricultural productivity and ac­ tivities (Cao et al., 2020). Good natural quality conditions and stable site (0) (1) (1) a (0) b ak ̂x (k + 1) = ̂x (k + 1) ̂x (k) = (1-e )[x (1) ]e (1-4) a environmental conditions are necessary for delineating basic farmland (Jiang et al., 2020b; Liu et al., 2020). This study selected six indicators Eq. 1-4 are the prediction formulas. that affect the farming environment and utilization effect of farmland: Additionally, we use the food demand method to predict the basic irrigation guarantee rate, drainage conditions, machine-accessible road farmland scale in 2035. Without considering the flow of food, the food accessibility, distance to arterial traffic road, distance to rural settle­ demand of an area is determined by the total population of the region ments, and urban influence. and the per capita food consumption. Therefore, the total grain demand Farmland ecological value refers to a series of products, services and of Changsha City in 2035 was obtained by predicting the total popula­ environment generated by the farmland ecosystem that can support tion and the per capita grain consumption. The total population pre­ human survival and development, including the value of climate regu­ diction also employed the metabolic GM model. The Chinese Academy lation, water conservation, soil conservation, environment purification, of Agricultural Sciences group reported that "400 kg of grain per capita is biodiversity maintenance, recreation, and other aspects (Bernues´ et al., essential". Therefore, 400 kg was chosen as the per capita food con­ 2016; Tzilivakis et al., 2019). As demand for a high-quality ecological sumption standard. environment grows and the country assigns importance to the con­ The formula for calculating the total food demand is indicated below: struction of an ecological civilization, the importance of the ecological

Qc = Pn × Qr (2-1) function of farmland is becoming increasingly prominent. Thus, this study used four indicators to quantitatively evaluate the ecological value Where Qc is the total food demand, Pn is the target population (i.e., the of farmland: habitat quality, water production, field regularity, and total population predicted for 2035), and Qr is the per capita food ecological management level. demand. Fig. 2 shows the evaluation index system for comprehensive farm­ The basic farmland demand to ensure food security is calculated land quality. The service value of farmland ecosystems was further using the following formula: considered in the process of calculating the ecological value of farmland, and regional representative ecosystem services, water production, and Qc × N Dc = habitat quality were selected. The corresponding results were calculated Y × P × I (2-2)

4 Y. Chen et al. Land Use Policy 101 (2021) 105145

Evaluation index system for comprehensive farmland quality

Natural quality of Land condition of Ecological value of farmland farmland farmland s nt roa d t e rate op linity o n r level ne c roads

t c pe lity ettlement s alk a s o u texture thickness nditions tte r d sl o k mach i al qu a main c o r n

control m a a n

influence u o c

soil guarantee of soil regularity production to r r

g e e itat o ni c t lit y ace rban ield ater T erra i g a f cidity Ha b F U a ib i W o r s istance Surfa c D rain a ffective cologica l S u r tance rrigation D oil E I s E oil i S S D Acces

Fig. 2. Evaluation index system for comprehensive farmland quality. using the InVEST model (Sharp et al., 2015). Considering the different attribute characteristics of each evaluation Table 3 Weight of indexes for the comprehensive evaluation of farmland quality. factor, this study employed a [0, 100] closed interval to achieve the conversion between the index attribute value and the farmland quality Guideline Layer Weight Indicator Layer Weight evaluation score (Ting et al., 2014). A score of 100 indicates optimal surface soil texture 0.06 conditions, a score of 0 indicates inappropriate conditions, and a score soil organic matter content 0.04 Natural quality of soil acidity and alkalinity 0.04 between 0 and 100 indicates an intermediate state. The factor quanti­ 0.42 farmland effective soil thickness 0.12 zation method is summarized in Table 2. terrain slope 0.11 This study uses the analytic hierarchy process (Veisi et al., 2016; Ma surface rock outcrop 0.05 et al., 2018) and the Delphi method (Ocampo et al., 2018; Toumbourou irrigation guarantee rate 0.09 and Tessa, 2018). Six experts were selected according to professional drainage conditions 0.07 machine-accessible road level; the weight given to each expert was [1/6, 1/6, l /6, l/6, 1/6, 1/6]. Site condition of 0.03 0.36 accessibility farmland Table 3 shows the weight value of the comprehensive evaluation index distance to arterial traffic road 0.07 of farmland in Changsha City. distance to rural settlements 0.05 The quality evaluation of farmland to delimit basic farmland is a urban influence 0.05 multi-objective decision-making process. Multi-objective system opti­ habitat quality 0.05 Ecological value of water production 0.05 mization and ranking decision-making are the most suitable methods for 0.22 farmland field regularity 0.03 this purpose (Wang et al., 2015; Kritikos and N., 2017). This study used ecological management level 0.09 the Technique for Order Preference by Similarity to Ideal Solution

(TOPSIS) to calculate the comprehensive quality of farmlands. The Table 2 Quantization methods for evaluation indicators. comprehensive quality of farmland was then sorted from highest to lowest and classifiedinto highest, high, medium, and low quality areas. Quantization Evaluation Factor Calculation Formula Fig. 3 shows our delineation principle. Method

Hierarchical Surface soil texture, Direct assignment 2.3.4. Basic farmland delineation principle division soil organic matter “ content, soil acidity According to the principles of serial priority, quality screening, and and alkalinity, scale constraint,” the spatial connectivity of farmland is the firstdecision effective soil factor in the screening of basic farmland plaques; the second decision thickness, terrain factor is the comprehensive quality of farmland, and the basic farmland slope, surface rock scale is the final criterion and constraint condition. The basic farmland outcrop, irrigation guarantee rate, delineation process is shown in Fig. 4. drainage conditions, ecological control 3. Results level Linear Machine accessibility di S fi = M × (1-r), r = , d = attenuation roads, distance to d 2L 3.1. Result of farmland scale prediction method main roads √̅̅̅̅̅̅ Exponential Distance to rural 1-r di S According to the forecast results of the area of farmland and the basic fi = M , r = , d = attenuation settlements, urban d nπ farmland protection rate, it can be calculated that the scale of basic method influence farmland in 2035 is 239091.88 ha. According to the forecast results of Extreme value Habitat quality, f = 100*(xi xmin)/(xmax xmin) method water production, Positive correlation indicator f = the total food demand, the basic farmland demand with the goal of field regularity 100*(xi xmax)/(xmin xmax) Negative ensuring food security Based on this calculation, the demand for basic correlation indicator farmland in 2035 is 231,383.60 ha. Summarizing the prediction results

5 Y. Chen et al. Land Use Policy 101 (2021) 105145

Fig. 3. Basic farmland delineation process.

Fig. 4. Spatial distribution of contiguous farmland.

6 Y. Chen et al. Land Use Policy 101 (2021) 105145 of the above two methods, this study believes that the basic farmland 4. Discussion scale of Changsha in 2035 is reasonable within the range of 231,383.60–239, 09.88 ha. 4.1. Analysis of basic farmland spatial distribution

3.2. Result of farmland spatial connectivity From a topographic map of Changsha City, most basic farmland is densely distributed along the river. In Ningxiang, a considerable amount A series of farmlands feature a distance between farmland plaques of of basic farmland has formed along the banks of the Oushui, Liusha, and less than 20 m and a total farmland plaque area of more than 6.67 ha. Wujiang rivers. In Wangcheng, basic farmland is primarily distributed Statistics on contiguous farmland areas are shown in Table 4. The spatial along the . In , the banks of the Laodao distribution of contiguous farmland areas is shown in Fig. 5. River are the areas with the strongest concentration of basic farmland in the county. Moreover, the Liuyang, Laodao, and Daxi rivers exhibited 3.3. Result of comprehensive quality evaluation concentrated distribution areas of basic farmlands. The area where these farmlands are located is flat and open, with sufficient light, which is The comprehensive quality of farmland was sorted from highest to conducive to the accumulation of dry plant materials. As a result of the lowest and classifiedinto highest, high, medium, and low quality areas. long-term cultivation done by the local residents, the agricultural fa­ The highest, high, medium, and low quality areas covered 42180.64 cilities and farming potential are optimal. Therefore, compared with the 179310.80, 87230.61, and 3726.70 ha, and accounted for 13.50 %, scattered distribution of farmland in urban space, these arable lands can 57.39 %, 27.92 %, and 1.19 % of the total farmland, respectively. form a relatively independent farmland system, which is less disturbed High-quality farmland is primarily concentrated and distributed in by other land types. These croplands have a high degree of continuity Wangcheng, Ningxiang, and Changsha in northwest Changsha City and stability, and the risk of occupation is low. Allocating them to basic (Fig. 6). Additionally, much high-quality farmland is scattered farmlands will help protect Changsha’s agricultural production throughout Liuyang. Low-quality farmland is primarily distributed in capacity. Ningxiang, the west and south of Yuelu, and northeast Liuyang. The scale of farmland within the central urban area is small, and the 4.2. Landscape pattern analysis of basic farmlands comprehensive quality is low. The landscape pattern index refers to the highly concentrated land­ 3.4. Basic farmland delineation results scape pattern information, which is a quantitative index used to reflect landscape structural composition and spatial dominance characteristics After screening and sorting the spatial connectivity and the (Wan et al., 2011) and is a relevant method for landscape pattern comprehensive quality conditions of the farmland, the total area of analysis. The basic farmland landscape pattern index primarily relates to farmland plaques assigned to basic farmland was calculated cumula­ the structure and shape of basic farmland patches. Based on ecology tively and compared with the established threshold of basic farmland research (Wu, 2004; Shao and Wu, 2008; Brooks and Lee, 2019; Huang scale. Based on the plaque screening results, a total of 149,627 basic et al., 2019), the spatial pattern and spatial morphology indexes were farmland plaques were delineated, covering an area of 231,407.01 ha selected to characterize the basic farmland landscape pattern. and accounting for 74.06 % of the total area of farmland. The spatial The spatial pattern index can highly concentrate landscape pattern distribution is illustrated in Fig. 6. The demarcated basic farmland is information with simple quantitative indicators to reflect structural distributed outwards around the central city, concentrated in the composition and important spatial formulation characteristics (Yang, northern and western parts of Changsha City. Zheng et al. 2014, Schmiedel and Culmsee, 2016). This study selected This study calculated the area of basic farmland delineated within the PD, LPI, AI, and COHESION indexes to represent the spatial pattern each jurisdiction. As indicated in Table 5, Ningxiang and Liuyang are of basic farmland. A better patch shape is conducive to maintaining the key basic farmland areas, and a total of 186,774.03 ha of basic farmland stability of the internal ecosystem of the basic farmland and is conve­ have been demarcated, accounting for 80.72 % of the total basic farm­ nient for mechanized production operations (Cook and Lier, 1994; Zhou land area in the city. The scale of farmland in these areas is relatively and Li, 2015). The LSI, PARA, and FRAC indexes were selected to large, the overall quality is high, and many continuous arable land characterize the spatial pattern index of basic farmland. distributions were observed, which are ideal areas for farmland devel­ Table 6 summarizes the spatial pattern index and spatial shape index opment. The regions within the central urban areas such as Hibiscus, of basic farmlands. The spatial distribution of basic farmland plaques is Yuhua, and Tianxin are the primary areas for urban construction ac­ more concentrated; the maximum plaque index reached 88.41, the tivities. The farmland is less evenly distributed, and the degree of con­ plaque aggregation index and cohesion index are above 90, the overall centration is low. Therefore, only high-quality farmlands were classified aggregation was enhanced, and the fragmentation degree was low. as basic farmlands for effective protection. 4.3. Discussion on demarcation area and demarcation threshold

If the designated cultivated area reaches the minimum size threshold of basic farmland and is between 231,383.60 and 239, 091.88 ha, all high-quality concentrated farmland that meets the conditions will be

Table 4 Statistics on contiguous farmland areas.

Contiguous Level Contiguous Scale (ha) Number of Contiguous Pieces (sheet) The Total Area (ha) Percentage of Total Farmland (%)

Primary contiguous ≥ 666.67 61 176,138.07 56.37 Secondary contiguous 333.33~666.67 50 23,259.92 7.45 Third-level contiguous 133.33~333.33 144 28,755.71 9.20 Fourth-level contiguous 66.67~133.33 196 18,328.65 5.87 Fifth-level contiguous 33.33~66.67 312 14,410.33 4.61 Sixth-level contiguous 6.67~33.33 1622 22,813.41 7.30 Total — 2385 28,3706.09 90.80

7 Y. Chen et al. Land Use Policy 101 (2021) 105145

Fig. 5. Evaluation result of farmland comprehensive quality. allocated to basic farmland. comprehensive quality score of the farmland determined by the original If the designated area of farmland is greater than the maximum size plan was 0.54, with a maximum value of 0.79 and a minimum value of threshold of basic farmland (i.e., 239,091.88 ha), the removal of the last 0.22. The average comprehensive quality score of basic farmland patch prevents the total area of farmland patches from reaching the delineated in this study was 0.58, with a maximum value of 0.84 and a minimum size threshold of basic farmland. All farmland patches shall be minimum value of 0.40. More high-quality farmland with higher retained, and the total delineated area of farmland shall be used as the comprehensive quality was classified as basic farmland. protection scale of basic farmland. From the perspective of basic farmland spatial distribution, the basic If all the suitable farmland in the first to sixth consecutive pieces of farmland delineated in this study had an 82.64 % agreement with the farmland is allocated to basic farmland and the demarcated area is still original planning, which is roughly consistent with the actual demar­ less than the minimum size threshold of basic farmland (i.e., 231,383.60 cation results. In the process of demarcation, connectivity is used as the ha), the following methods are used to increase the farmland area: priority standard for the delineation of basic farmland, and high-level contiguous farmland is prioritized into basic farmland, solving the 1) The connected distance threshold is maintained, the continuous scale problem of basic farmland space fragmentation. This study simulta­ threshold is canceled, and the farmland patches with a distance of neously provided further spatial optimization of the original results. less than 20 m are considered as connected patches and constitute Therefore, the degree of spatial continuity of basic farmland was small-scale continuous patches. First, the sum of the connected patch improved, landscape morphology was optimized, and the basic farmland areas is sorted from high to low. Then, the basic farmland is assigned patch shape was made more regular. to the farmland in order of quality priority, until the area of the This study builds a combined basic farmland comprehensive delin­ patched farmland reaches the basic farmland protection target. eation method system, effectively connecting the three links of feature 2) If the basic farmland protection target is not reached after the pre­ evaluation, space optimization, and scale constraints to form a set of vious operation, the remaining unclassified farmland will be technical processes to provide clearer technical guidance for basic extracted with an area of no less than 0.33 ha. According to their farmland delineation. Farmland allocated to basic farmland is more in comprehensive quality, they are sorted from high to low and are line with the requirements of "high quality concentration" and contrib­ designated as basic farmland, until the area of the farmland patch utes to the long-term management and protection of basic farmlands. reaches the basic farmland protection target. As discussed above, this study process can be further promoted in China or other parts of the world. The scale of farmland and the selected connectivity threshold must be predicted according to the actual situa­ 4.4. Comparative analysis of delineated results versus existing planning tion of each region in the promotion process. In the comprehensive quality evaluation, the selection of index factors should also be localized In order to verify the rationality and validity of the basic farmland to achieve more accurate evaluation results. delimitation model in this study, the results of the demarcation of this study were compared with those of the Changsha Land Use Master Plan. The results are shown in Table 7. 4.5. Limitations From the perspective of basic farmland delimitation quality, this study replaces the previous relatively single evaluation perspective and Research needs to be improved in the following two aspects: 1) the incorporates farmland ecosystem services capabilities into evaluation influence of factors such as roads, rivers, and mountains on the con­ indicators. According to the evaluation system of this study, the average nectivity of farmland patches has not been taken into account in the

8 Y. Chen et al. Land Use Policy 101 (2021) 105145

Fig. 6. Spatial distribution of basic farmland. (a) Ningxiang, (b) Wangcheng, (c) Changsha, and (d) Liuyang. evaluation of farmland spatial connectivity. The acquisition and evalu­ 5. Conclusions ation of connectivity indicators can be therefore optimized; 2) in the field of basic farmland delineation, the research adopted a patch This study focuses on Changsha as the research area and "high screening method, which failed to accurately consider the boundary quality concentration" as the core concept of basic farmland delineation. shape of basic farmlands. Therefore, it is still important to strengthen the Based on multi-source data, the ideal point approximation and gray “control line” role of the basic farmland protection red line in the new prediction methods were used to analyze the spatial connectivity of land space planning era, fully consider its rigid constraint on urban farmland, to evaluate the comprehensive quality of farmland, to predict space, and promote smart city growth. the scale of basic farmland protection, and to systematically perform basic farmland delineation research. Based on the principles of “serial priority, quality screening, and scale constraint,” the basic farmland was demarcated at 23,104,701 ha. The demarcated basic farmland spreads

9 Y. Chen et al. Land Use Policy 101 (2021) 105145

Table 5 Declaration of Competing Interest Statistics on the area of basic farmland.

District Number of basic Basic farmland Proportion of the basic The authors report no declarations of interest. farmland plaques area (ha) farmland area in the city (%) Appendix A. Supplementary data Furong 278 168.19 0.07 Kaifu 2190 3,221.00 1.39 Supplementary material related to this article can be found, in the Yuhua 433 174.69 0.08 online version, at doi:https://doi.org/10.1016/j.landusepol.2020.10 Tianxin 1041 718.66 0.31 5145. Yuelu 2803 6,380.17 2.75 Wangcheng 12,305 33,970.27 14.68 Changsha 52,082 53,889.83 23.29 References Ningxiang 36,685 67,938.22 29.36 Liuyang 41,810 64,945.98 28.07 Aizaki, H., Sato, K., Osari, H., 2006. Contingent valuation approach in measuring the Total 149,627 231,407.01 100.00 multifunctionality of agriculture and rural areas in Japan. Paddy Water Environ. 4 (4), 217–222. Bernu´es, A., Tello-García, E., Rodríguez-Ortega, T., Ripoll-Bosch, R., Casasús, I., 2016. Agricultural practices, ecosystem services and sustainability in High Nature Value Table 6 farmland: unraveling the perceptions of farmers and nonfarmers. Land Use Policy – Landscape pattern indexes of basic farmland in Changsha. 59, 130 142. Brabec, E., Smith, C., 2002. Agricultural land fragmentation: the spatial effects of three Index Category Index Name Index Value land protection strategies in the eastern United States. Landsc. Urban Plan. 58 (2–4), 0–268. PD 1.06 Braun, A.B., Trentin, A.W.d.S., Visentin, C., Thome, A., 2019. Proposal for an optimized LPI 88.41 Spatial pattern index method for sustainable remediation evaluation and application: implementation of a AI 90.14 multi-criteria process. Environ. Sci. Pollut. Res. - Int. 26 (35), 35996–36006. COHESION 99.92 Brooks, B.-G.J., Lee, D.C., 2019. Feasibility of pattern type classification for landscape LSI 72.13 patterns using the AG-curve. Landsc. Ecol. 34 (9), 2149–2157. Spatial shape index PARA 351.34 Cao, Y., Bai, Y., Zhang, L., 2020. The impact of farmland property rights security on the PAFRAC 1.62 farmland investment in rural China. Land Use Policy 97, 104736. Cheng, Q., Jiang, P., Cai, L., Shan, J., Zhang, Y., Wang, L., Li, M., Li, F., Zhu, A., Chen, D., 2017. Delineation of a permanent basic farmland protection area around a city centre: Case study of Changzhou City, China. Land Use Policy 60, 73–89. Table 7 Cook, E.A., Lier, H.N.V., 1994. Landscape Planning and Ecological Networks. Elsevier. Comparison of basic farmland delineation results. Cui, B.X., L. A. J, Su, G.H., 2007. Algorithm for filtering traffic video images based on mathematical morphology. J. Shenyang Univ. Technol. 44 (2), 76–80. Comparative indicators Changsha land use The delineation Dung, E.J., Sugumaran, R., 2005. Development of an agricultural land evaluation and master plan results of this study site assessment (LESA) decision support tool using remote sensing and geographic delineation information system. J. Soil Water Conserv. 60 (5), 228–235. Feng, T., Zhang, F., Li, C., Qu, Y., Zhu, F., 2014. Spatial distribution of prime farmland Demarcated area (ha) 232,232.50 231,407.01 based on cultivated land quality comprehensive evaluation at county scale. Trans. Average comprehensive quality of 0.54 0.58 Chin. Soc. Agric. Eng. 30 (1), 200–210. farmland Furuseth, O.J., 2006. Public attitudes toward local farmland protection programs. Maximum value of the 0.79 0.84 Growth Change 18 (3), 49–61. comprehensive quality of Guo, Z., Yang, Y., 2010. GIS-based farmland connectivity analysis methods research and – cultivated patches system implementation. Geogr. Geo-Inf. Sci. 26 (3), 59 62, 113. Minimum value of the 0.22 0.40 Guo, B., Jin, X., Yang, X., Zhou, Y., 2014. Study on zoning approach for well-facilitied capital farmland: based on a comprehensive assessment of agricultural natural comprehensive quality of disaster risk. J. Nat. Resour. 29 (3), 377–386. cultivated patches H, H.G., 1994. Defining and Using Reference Evapotranspiration. PD 1.05 1.06 Haizhou, H., Yong, L., 2018. The analysis and application of Guangxi’s concentrated LPI 87.00 88.41 arable land based on ArcGIS: on boundary line of Guangxi’s arable land (Research AI 88.90 90.14 Paper III). J. Nanning Normal Univ. (Philos. Social Sci. Ed.) 39 (06), 148–154. COHESION 99.91 99.92 Han, Y., Guo, X., Jiang, Y., Rao, L., Sun, K., Li, J., Wang, L., 2019. Cultivated land LSI 80.95 72.13 landscape ecological security: influencing factors and spatial differences in the hilly PARA 342.32 351.34 region of South China. Acta Ecol. Sin. 39 (17), 6522–6533. PAFRAC 1.63 1.62 Huang, C., Tang, Z., Zhou, Q., Cao, Y., 2011. Application of improved error GM (1, 1) model on predicting of cultivated land in . Energy Procedia 5, 1172–1176. Huang, X., Cao, X.-Z., Zhang, M., Zou, X., 2019. Construction of landscape ecological outward around the central urban area, of which Ningxiang, Liuyang, security pattern of shengli coalfield in inner mongolia based on the minimum cumulative resistance model. J. Ecol. Rural Environ. 35 (1), 55–62. and Changsha County are the key basic farmland demarcated areas. In Jiang, P., Li, M., Cheng, L., 2020a. Dynamic response of agricultural productivity to terms of landscape pattern characteristics, the spatial distribution of landscape structure changes and its policy implications of Chinese farmland basic farmland plaques is more concentrated, the overall polymerization conservation. Resour. Conserv. Recycl. 156, 104724. Jiang, P., Li, M., Sheng, Y., 2020b. Spatial regulation design of farmland landscape was enhanced, the degree of fragmentation was low, and the shape of around cities in China: a case study of Changzhou City. Cities 97. the plaques was more regular. The relationship between the scale of Kritikos, N, M., 2017. A full ranking methodology in data envelopment analysis based on farmland, the comprehensive quality of farmland, and the spatial con­ a set of dummy decision making units. Expert Syst. Appl. 77, 211–225. Liu, Y., 2018. Introduction to land use and rural sustainability in China. Land Use Policy nectivity of farmland achieved a better unity in this study, which em­ 74, 1–4. phasizes the usefulness of adopting a “trinity” approach by considering Liu, Y., Fang, F., Li, Y., 2014. Key issues of land use in China and implications for policy farmland protection in terms of scale, quality, and spatial connectivity. making. Land Use Policy 40, 6–12. Liu, L., Liu, Z., Gong, J., Wang, L., Hu, Y., 2019. Quantifying the amount, heterogeneity, Thus, this study provides a practical basis for the demarcation of basic and pattern of farmland: implications for China’s requisition-compensation balance farmlands. of farmland policy. Land Use Policy 81, 256–266. Liu, Y., Liu, L., Zhu, A.X., Lao, C., Hu, G., Hu, Y., 2020. Scenario farmland protection Funding zoning based on production potential: a case study in China. Land Use Policy 95, 104581. Ma, H., Li, S., Chan, C.-S., 2018. Analytic Hierarchy Process (AHP)-based assessment of This work was supported by Natural Science Foundation of Jiangsu the value of non-World Heritage Tulou: a case study of Pinghe County, – Province of China (BK20180348, SBK2020022823), National Natural Province. Tourism Manage. Perspect. 26, 67 77. Science Foundation of China (42071440, 41801298, 41571378).

10 Y. Chen et al. Land Use Policy 101 (2021) 105145

Nosrati, K., Collins, A.L., 2019. A soil quality index for evaluation of degradation under Toumbourou, Tessa, 2018. Using a Delphi approach to identify the most efficacious land use and soil erosion categories in a small mountainous catchment, Iran. J. Sci. interventions to improve Indonesia’s forest and land governance. Land Use Policy. 16 (11), 2577–2590. S0264837716307463. Ocampo, L., Ebisa, J.A., Ombe, J., Geen Escoto, M., 2018. Sustainable ecotourism Tzilivakis, J., Warner, D.J., Holland, J.M., 2019. Developing practical techniques for indicators with fuzzy Delphi method – A Philippine perspective. Ecol. Indic. 93, quantitative assessment of ecosystem services on farmland. Ecol. Indic. 106, 105514. 874–888. Veisi, H., Liaghati, H., Alipour, A., 2016. Developing an ethics-based approach to Pretty, J.N., Brett, C., Gee, D., Hine, R.E., Bijl, G.V.D., 2000. An Assessment of the Total indicators of sustainable agriculture using analytic hierarchy process (AHP). Ecol. External Costs of UK Agriculture. Agric. Syst. 65 (2), 113–136. Indic. 60, 644–654. Ren, S., Li, E., Deng, Q., He, H., Li, S., 2018. Analysis of the impact of rural households’ Wan, Q., Jin, X., Zhou, Y., 2011. Dynamic analysis of coastal region cultivated land behaviors on heavy metal pollution of arable soil: taking Lankao County as an landscape ecological security and its driving factors in Jiangsu. Acta Ecol. Sin. 31 example. Sustainability 10 (12), 4368. (20), 5903–5909. Robinson, R.A., Sutherland, W.J., 2002. Post-war changes in arable farming and Wang, J., Ma, X., Wu, J., Dong, Y., 2012. Optimization models based on GM (1,1) and biodiversity in Great Britain. J. Appl. Ecol. 39 (1), 157–176. seasonal fluctuation for electricity demand forecasting. Int. J. Electr. Power Energy Schmiedel, I., Culmsee, H., 2016. The influenceof landscape fragmentation, expressed by Syst. 43 (1), 109–117. the’ Effective Mesh Size Index’, on regional patterns of vascular plant species Wang, Y., Liu, Q., Tang, J., Cao, W., Li, X., 2014. Optimization approach of background richness in Lower Saxony, Germany. Landsc. Urban Plan. 153, 209–220. value and initial item for improving prediction precision of GM(1,1) model. J. Syst. Shao, G., Wu, J., 2008. On the accuracy of landscape pattern analysis using remote Eng. Electron. 25 (1), 77–82. sensing data. Landsc. Ecol. 23 (5), 505–511. Wang, X., Yang, F., Wei, H., Zhang, L., 2015. A new ranking method based on TOPSIS Sharp, R., Ricketts, T., Guerry, A.D., et al., 2015. InVEST 3.2.0 Beta User’s Guide. The and possibility theory for multi-attribute decision making problem. Opt. – Int. J. Natural Capital Project, Stanford, pp. 114–131. Light Electron. Opt. 126 (24), 4852–4860. Smith, E.B.C., 2002. Agricultural land fragmentation: the spatial effects of three land Wright, L.E., Zitzmann, W., Young, K., Googins, R., 1983. LESA - agricultural Land protection strategies in the eastern United States. Landsc. Urban Plan. 58 (2–4), Evaluation and Site Assessment. J. Soil Water Conserv. 38 (2), 82–86. 255–268. Wu, J., 2004. Effects of changing scale on landscape pattern analysis: scaling relations. Song, W., Pijanowski, B.C., Tayyebi, A., 2015. Urban expansion and its consumption of Landsc. Ecol. 19 (2), 125–138. high-quality farmland in , China. Ecol. Indic. 54, 60–70. Xiewenfeng, 2008. Study on Cultivated Land and Basic Farmland Protection in the Sorensen, A., 2000a. Conflict, consensus or consent: implications of Japanese land Revision of General Land-Use Planning of Changsha City. Hunan Agricultural readjustment practice for developing countries. Habitat Int. 24 (1), 51–73. University. Sorensen, A., 2000b. Land readjustment and metropolitan growth: an examination of Yuan, C., Liu, S., Fang, Z., 2016. Comparison of China’s primary energy consumption suburban land development and urban sprawl in the Tokyo metropolitan. Prog. forecasting by using ARIMA (the autoregressive integrated moving average) model Plann. 53 (4), 217–330. and GM(1,1) model. Energy 100, 384–390. Steiner, F., Dunford, R., Dosdall, N., 1987. The use of the agricultural land evaluation Zeng, T., Lyu, J., Shi, J., Han, X., Zhao, S., Sun, Y., 2017. Benefitanalysis and evaluation and site assessment system in the United States. Landsc. Urban Plan. 14 (3), of the key land consolidation and readjustment projects. Acta Agriculturae 183–199. Universitatis Jiangxiensis 39 (6), 1234–1243. Tang, H., Yun, W., Liu, W., Sang, L., 2019. Structural changes in the development of Zhang, Y.L., Cao, L.G.X., Wudongwan, D., 2007. Basic operators of mathematical China’s farmland consolidation in 1998–2017: changing ideas and future morphology and application in image preprocessing. Adv. Sci. Technol. Eng. Syst. J. framework. Land Use Policy 89, 104212. 7 (3), 356–359. Ting, F., Fengrong, Z., Can, L., Yanbo, Q., Fengkai, Z., 2014. Spatial distribution of prime Zhou, W., He, J.-M., 2013. Generalized GM (1,1) model and its application in forecasting farmland based on cultivated land quality comprehensive evaluation at county scale. of fuel production. Appl. Math. Model. 37 (9), 6234–6243. Trans. Chinese Soc. Agric. Eng. 30 (1), 200–210. Zhou, Z.X., Li, J., 2015. The correlation analysis on the landscape pattern index and hydrological processes in the Yanhe watershed, China. J. Hydrol. 524, 417–426.

11