Dynamic response of agricultural productivity to landscape structure changes and its policy implications of Chinese farmland conservation

Resources, Conservation & Recycling 156 (2020) 104724

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Full length article

Dynamic response of agricultural productivity to landscape structure changes and its policy implications of Chinese farmland conservation

T

Jiang Penghuia,b,c,*, Li Manchuna,b,c, Cheng Lianga,b,c a

School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China 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 c Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources b

A R T I C LE I N FO

A B S T R A C T

Keywords: Landscape structural classification model Landscape evolution Potential productivity Farmland protection

Farmland landscape systems are crucial to sustainable agriculture, but they can readily be disturbed by nonagricultural activities. However, the conclusion that agricultural landscape system would affect farmland productivity remains uncertain and lacks quantitative evidence. To construct a healthy farmland ecosystem to avoid farmland fragmentation and support a sustainable agricultural production, we built a structural classification model for the farmland landscape system to analyse spatiotemporal evolutionary trends in China's farmland landscape system at the pixel level. Then, long-term grain yield monitored by stations and potential productivity of farmland calculated using a global agro-ecological zone (GAEZ) model were applied to explore the relationship between agriculture production and farmland landscape system variations. The results showed that China's farmland landscape tends to be fragmented. Model analyses showed that the fragmentation of farmland will cause an average 55.33 % cumulative decline in potential farmland productivity and that fragmentation of core farmland landscape will cause a 0.85 % decline in potential productivity. Based on these findings, we offer a farmland protection strategy based on the spatial optimum allocation of natural and human landscapes.

1. Introduction With ongoing socio-economic development and in response to natural disasters, wars, and institutional reforms, the extent of farmland worldwide is decreasing, with a continues decline in farmland quality (Foley et al., 2005; Lobell et al., 2012; Renwick et al., 2013; Queiroz et al., 2014; Deng et al., 2015; Pribadi and Pauleit, 2015). It is estimated that by 2030, 3.7 % of global farmland will disappear due to urbanisation (d’Amour et al., 2017). Notably, one-quarter of this farmland will be in China (d’Amour et al., 2017). China has the highest global human population and consequently a decrease in farmland will inevitably threaten grain security. Particularly, substantial decreases in the extent of farmland will pose a considerable challenge to maintaining China's grain security in the context of substantial changes in the natural environment dominated by climate change (Hanjra and Qureshi, 2010; Piao et al., 2010). The principle food of people in China mainly is wheat and rice, and food security particularly refers to grain security, and abundant farmlands are required to guarantee this. Although most of the lands suited for agriculture have been reclaimed as farmlands during the past

⁎

thousand years, China is still facing the risk of food security because of the release of second child policy and the huge population base (Cheng et al., 2017a). The population scale of China in 2016 reached up to 1.37 billion and the per capita farmland was only 0.98 ha (MLR, 2016). Furthermore, the conversion from farmland to non-agricultural purposes tends to grow in intensity with rapid urbanisation and modernisation, making it difficult for the local government in China to balance the amount of farmland loss and supplement. It is estimated that China’s total crop yield decreased by 6.52 % because of the land conversion from farmland to urban land (Liu et al., 2015). However, farmland landscape change is characterised by landscape fragmentation, restricting China to improve the grain production capacity with the application of advanced technologies. Agricultural mechanisation will expend more in a fragmented farmland landscape, and Chinese peasants can hardly afford this cost. Since the reform and opening-up policy of China in 1978, structural features of the farmland landscape have been significantly altered by the different modes of land utilisation (e.g. infrastructure construction, urban expansion, and return of farmland to forests or grassland) in the ongoing urbanisation of China (Hu et al., 2014; Huang et al., 2019).

Corresponding author. E-mail addresses: [email protected] (J. Penghui), [email protected] (L. Manchun), [email protected] (C. Liang).

https://doi.org/10.1016/j.resconrec.2020.104724 Received 5 July 2019; Received in revised form 13 January 2020; Accepted 24 January 2020 Available online 12 February 2020 0921-3449/ © 2020 Elsevier B.V. All rights reserved.

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Within the context of the aforementioned issues, in this study, we developed a pixel-based structural classification model for the farmland landscape system based on the patch-corridor-matrix pattern in landscape ecology and the law of distance decay in geography, thus taking a lead in defining and determining the structural decomposition of the farmland landscape system. Based on long-term data on grain yield (wheat and rice) and global agro-ecological zone (GAEZ)-based evaluation of the potential productivity of farmland, we analysed the influence of the structure of farmland landscape system on the yield of agricultural crops and potential productivity of the farmland system. Furthermore, Furthermore, from the perspectives of concentrated protection of contiguous farmland landscape, restriction of human activities, and ecological protection, we propose a farmland protection mode characterised by the spatial optimum allocation of natural and human landscapes.

Regarding considerable changes in China’s farmland landscape, several scholars have analysed the ecological effect of farmland landscape changes using various index models and conducted numerous studies on farmland protection policies (Chen et al., 2009; Su et al., 2014; Gao et al., 2015; Weissteiner et al., 2016). However, for a farmland ecosystem, changes in certain features (including shape, quantity, and type) represent the external pattern of farmland, which can be explicitly manifested (Fischer et al., 2012; Lausch et al., 2015). Roschewitz et al. (2005) concluded that a farmland system with simple landscape structure possibly indicates a higher crop yield. Rahman and Rahman (2009) found that an increase in farmland fragmentation by 1 % will lead to a 0.05 % decline in rice yield. Carvalheiro et al. (2011) concluded that farmland landscape design with the protection of flowering plant patches can improve crop productivity. The structural features of a farmland landscape system are the internal pattern of farmland functions, and they represent the implicit process of farmland change, which is difficult to observe (LaFevor, 2015; Lausch et al., 2015). The method widely used to explore landscape structural changes is the landscape metrics based on the mathematical analysis results of shape, area, and quantity (Lausch et al., 2015). However, it is difficult to visualise the spatial heterogeneity and ecological functions of agricultural landscape precisely by using patch-based landscape metrics or other methods (Kupfer, 2012). Scale dependency and land classification system also increase the difference in the results of ecological process analysis, leading to various conclusions on the same question (Dale and Kline, 2013; Frazier and Kedron, 2017). The uncertainties of landscape metrics confuse most scholars in making a decision on selecting the most suitable landscape for analysing the difference in landscape structure changes (Uuemaa et al., 2011). Therefore, the accuracy of landscape metrics in explaining ecological processes has been questioned (Li and Wu, 2004; Uuemaa et al., 2011; Kupfer, 2012; Dale and Kline, 2013; Frazier and Kedron, 2017). Therefore, scholars have developed new analysis tools to make a good connection between landscape structure changes and landscape function variations. Soille and Vogt (2009) developed a morphological segmentation method to analyse the landscape structure of and pattern change in forest ecosystems. Riitters et al. (2007, 2009) further improved the mathematic segmentation method of landscape structure and pattern using a neutral model analysis. However, this methodology is mainly applied in forest landscape research and therefore whether it is appropriate for agricultural landscape function analysis is still controversial. Studies on the landscape effect of farmland have tended to focus on the quantitative description of explicit features (e.g. presentational scale and spatial layout) (Li et al., 2007; Liu and Deng, 2010; Deng et al., 2017). However, there are no studies on the ecological effects of farmland landscape systems focusing on the spatiotemporal evolution of its implicit features (e.g. inner structure and functions) (Cheng et al., 2015; Lee et al., 2015; Jiang et al., 2018). Due to a high degree of spatial overlap between urban development land and premium-quality farmland resources, there exists an intense conflict between urban development and farmland protection (Song et al., 2015; Cheng et al., 2017b). Moreover, the existing modes of farmland protection in national policies are categorised as dynamic balance of farmland amount (quantity) and prime farmland protection (quality) and are deficient in overall consideration of regional urban development and protection of ecological security. Additionally, at least 60 % of China’s farmland reserves (nonagricultural lands that can be farmed) are distributed in regions with a fragile ecological environment, which are very difficult to develop and utilise. After several years of development and construction, China’s farmland reserves have decreased to approximately 5.3 million ha, and the farmland reserves in certain regions (e.g. Shanghai City, Beijing City, Tianjin City, and Zhejiang Province) have almost been exhausted, posing a substantial challenge for farmland protection. Therefore, object-oriented studies on farmland protection modes alone are insufficient to meet the comprehensive management needs of land resources in the new era (Liu et al., 2014).

2. Material and methods 2.1. Hypotheses (1) Technical efficiency is a key factor to improve grain yield under different environment conditions. However, land fragmentation because of irrational conversion or disorder ownership allocation prevents the adoption of modern agricultural technical widely. Rahman and Rahman (2009) performed a research based on farm level survey data and found that land fragmentation significantly decreases technical efficiency in rice farming. According to their estimation, a 1 % increase in farmland parcels will lead to a 0.03 % reduction in technical efficiency. Consequently, a 1 % increase in the degree of farmland fragmentation will cause a 0.05 % decline in rice yield. Therefore, we present the following hypothesis based on the idea that concentrated farmlands will have a higher productivity than the fragmented: in the same geographical environment (e.g. landform and climate), the potential productivity of farmland declines in the spatial gradient sequence of "interior of farmland system > fringes of farmland system > nonagricultural matrix." (2) It is generally accepted that concentrated farmland landscape indicates exceptional agricultural advantages, and the fragmented farmland landscape indicates the opposite. Jiang et al. (2018) explored the evolution of farmland landscape structure and its driving forces, which indicated that farmland productivity is significantly related to its landscape structure. It is concluded that farmland landscape with high continuity and large area are usually characterised by stable yield. Contrarily, a high degree of farmland landscape fragmentation amounts to a low stability of grain yield (Jiang, 2018). Consequently, we present the following hypothesis: in the same geographical environment, stability of grain production of regional farmland will decline, whereas core farmland will be degraded into other types of farmland landscape. 2.2. Methodology According to the definitions of different farmland landscape types (Table1) and spatial adjacency criteria, we developed a structural classification model for the farmland landscape system using a combination of various morphological image processing technologies. Using the model, the farmland landscape system was structurally divided into core, perforated, edge, and patch farmlands. Based on long-term remote-sensing monitoring data for farmland (1980s, 1995, 2000, 2005, and 2010), we analysed the spatiotemporal evolution of China’s farmland landscape system from the 1980s to 2010. To determine the influence of evolution of China's farmland landscape system on agricultural production, we used GIS-based spatial statistics technology to determine the potential productivity of different farmland landscape types according to the potential productivity data 2

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Table 1 Definitions of farmland landscape types (Jiang et al., 2018, 2019, 2020; Riitters et al., 2007, 2009; Soille and Vogt, 2009). Types

Definitions

Core Farmland

Core farmland refers to the farmland that is spatially isolated from the nonfarmland landscape and is homogeneous and internally contiguous. Core farmland provides a stable internal environment for the existence and functioning of the farmland ecosystem. The scale of core farmland directly determines the capacity and potential scale of regional agricultural production. Edge farmland exists together with core farmland and is spatially distributed in the peripheral areas of core farmland. Edge farmland is the buffer area or transitional zone between core farmland and non-farming land. There is a complex environmental difference between edge farmland and core farmland. Edge farmland reflects the impact of external nonagricultural activities on the farmland ecosystem. An increase in edge farmland implies that core farmland has undergone a significant contraction. Perforated farmland refers to the farmland that is surrounded by core farmland spatially and is adjacent to non-farming land. The scale of perforated farmland reflects the degree to which the core farmland landscape is internally eroded. An increase in perforated farmland will accelerate the structural collapse of core farmland. Fringe patch farmland refers to farmland with one end adjacent to edge farmland and the other end located in a nonagricultural environment. Fringe patch farmland is a peninsular extension of edge farmland in a nonagricultural environment, and is also a buffer and transitional zone between farmland and non-farming land. It reflects the effects that fringe erosion of nonagricultural landscape have on the farmland landscape. Discrete patch farmland refers to the farmland that is spatially isolated from any other type of farmland landscape and is surrounded by a nonagricultural environment. For discrete patch farmland, the interior habitat area is small, and there is no environmental difference between core and fringe areas. Patch farmland reflects the degree to which regional farmland landscape is fragmented. If regional patch farmland becomes the dominant farmland landscape, this indicates that the regional farmland landscape is to a large extent fragmented. Corridor farmland refers to farmland that is linearly and continuously distributed with both ends connected to a farmland system. Corridor farmland is mostly residual landscape corridors, and results from the failure of nonagricultural activities to completely erode the farmland landscape. Its ecological function is mainly to filter out or block the disturbance of nonagricultural activities to the farmland ecosystem.

Edge Farmland

Perforated Farmland

Patch Farmland Patch farmland refers to farmland that is not adjacent to core farmland spatially and exists independently in the peripheral areas of core farmland. Patch farmland reflects the degree to which the regional farmland landscape is fragmented. If regional patch farmland becomes the dominant farmland landscape, this indicates that the regional farmland landscape is to a large extent fragmented. In terms of spatial position, structure, and functionality, patch farmland can be further classified into fringe patch farmland, discrete patch farmland, and corridor farmland.

Fringe Patch Farmland

Discrete Patch Farmland

Corridor farmland

Notes: From a macroscopic perspective, farmland is suitable for classification into patch farmland, whereas from a meso-scale perspective, farmland is suitable for classification into edge farmland and discrete patch farmland. From a microscopic perspective, farmland is suitable for classification into corridor farmland. Parts of the definitions, including the core, edge, perforated and patch farmland are improved based on the research of Jiang et al. (2018).

conditions (kg/ha), i refers to the proportion of irrigated farmlands to the total area of farmlands (Tatsumi et al., 2011; Liu et al., 2015). Details regarding model building and operation are presented in Liu et al. (2015), and the data are available from the website1 . From the perspective of spatial differentiation, we analysed the influence of structural variation in the farmland system on the potential productivity of the farmland system. Based on long-term observational data2 for the yield of rice (in 111 monitoring stations) and wheat (123 monitoring stations) from 1999 to 2013 (Supporting Information-Extended Fig. 1), we discuss the influence of structural evolution of farmland landscape system on the stability of grain production from the perspective of temporal variation in the farmland landscape system. The geographical environment of farmland comprises the comprehensive agricultural zones where the yield monitoring stations are located, soil types, geomorphology types, and administrative divisions (Supporting Information-Extended Fig. 1). Using GIS-based spatial overlay analysis technology, the verification points were endowed with natural and human geographical attributes. The comprehensive agricultural zones of Grade 1 were used to reflect the regional features of agricultural production and eliminate primary regional differences in agricultural production, ensuring that the verification points are consistent in agricultural production features (e.g.

of China's farmland in 1980, 1990, 2000, and 2010, along with the structural classification results of farmland landscape system in these years. Farmland productivity is generally determined under natural conditions with agricultural technologies, and it is mostly evaluated using census and remote-sensing data (Tan et al., 2005; Tao et al., 2012; Shi et al., 2013; Tian and Qiao, 2014). However, it is difficult to evaluate the variations in potential productivity under changing environment using such regular methods. The agro-ecological zone model (GAEZ) issued by the FAO/IIASA provides a solution with full consideration of socio-economic scenarios and different climatic circulation models (Fischer et al., 2005). The potential productivity data for this study were obtained by performing the following steps. (1) The GAEZ model was used to calculate the effects of light, temperature, water, and climate on five agricultural crops (wheat, corn, paddy rice, soybean, and sweet potato) within a 1-km2 grid. (2) The potential productivity of each grid was calculated by giving overall consideration to restrictive factors associated with agricultural production (light, temperature, water, soils, geomorphology, cropping system, and agricultural production techniques). The GAEZ model can be represented with the following equation (Liu et al., 2015): Yield

total

= yield

rain-fed

(1-i) + yield

irrigated

×I

(1)

where, yield total refers to the total potential productivity of farmlands (including rain-fed and irrigation farmlands) (kg/ha), yield rain-fed refers to the potential productivity of rain-fed farmlands (kg/ha), yield irrigated refers to the potential productivity of farmlands under irrigated

1 2

3

Web address: http://www.resdc.cn/ Web address: http://data.cma.cn/

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Fig. 1. Spatiotemporal evolution of the structure of China's farmland landscape system. PF, patch farmlands; CF, core farmlands; PEF, perforated farmlands; EF, edge farmlands.

cropping system and cultivation mode). Soil and geomorphology types (Supporting Information-Extended Figs. 1 and 2) were used to unify the basic natural conditions for agricultural production of the verification points and administrative division was used to unify the socio-economic environment for agricultural production of the verification points. To further reduce the influence of natural background differences between verification points from the law of zonality, the longitudinal and latitudinal differences between verification points were rigorously controlled, such that they were less than 2° in group comparisons. Accordingly, we developed two indices—the stability of grain production and degree of farmland landscape degradation—to build comparative verification groups, and then assessed the influence of structural evolution of the farmland landscape system on the stability of grain production according to the degree of consistency between the stability of grain production and degree of farmland landscape degradation. The details are as follows.

period; if S→—∞, this indicates that grain production is confronted with a higher risk and that it is difficult to effectively ensure grain yield.

(1) Stability of grain production

where, L refers to the degree of landscape degradation, A(C→P) refers to the area of the core farmland degraded into patch farmland, A(C→ PE) refers to the area of the core farmland degraded into perforated farmland, and A(T) refers to the total area of the farmland in the region. Both A(C→P) and A(C→PE) are implemented through the Markov switching model and are used to reflect the dynamic process of switching between different farmland landscape types at the start and end of a period. The underlying theoretical concept is as follows:

(2) Degree of farmland landscape degradation The degree of landscape degradation is used to measure the degree to which the core part of a landscape type is degraded. Specifically, it is equal to the proportion of the scale of the core farmland degraded into patched landscape and punched landscape to the total scale of core landscape in a region (in relation to the monitoring period for grain yield, the analysis period for the degree of landscape degradation is from 2000 to 2010, and the analysed areas are the county-level regions in which the monitoring stations for grain yield are located).

L=

Stability of grain production is intuitively manifested in the fluctuation of grain yield in a specific period. If the grain yield in a good or bad year is substantially different from the average grain yield of the period, this indicates that grain production is highly unstable. Based on the sequential observation data acquired at 123 monitoring stations for wheat yield (1999–2013) and 111 monitoring stations for rice yield (1999–2013), we analysed the stability of grain production according to the difference ratio among the annual grain yield per mu in good years, annual grain yield per mu in bad years, and average grain yield per mu in the observation period using the following equation:

Max (g ) Min (g ) ⎤ − S=1−⎡ ⎢ ( ) Avg g Avg (g ) ⎥ ⎦ ⎣

A (C → P ) + A (C → PE ) A (T )

⎡ A11 … A1n ⎤ Aij = ⎢ ⋮ ⋮ ⋮ ⎥ ⎢ An1 … Ann ⎥ ⎣ ⎦

(3)

(4)

where, A refers to area, n refers to the number of farmland landscape types before and after the switching process, i and j refer to the farmland landscape type before and after the switching process, respectively, and Aij refers to the area of type-i farmland landscape that is switched to type-j farmland landscape. Each row of elements in the matrix represents the information flow direction of type-i farmland landscape switched to other types of farmland landscape, and each column of elements in the matrix represents the source information of type-j farmland landscape switched from other types of farmland landscape.

(2)

where, S refers to the stability index of grain production, g refers to the grain yield per mu, Max(g) refers to the average grain yield per mu in good years, Min(g) refers to the average grain yield per mu in bad years (excluding the years in which the grain yield per mu is zero), and Avg(g) refers to the average grain yield per mu in a certain period (excluding the years in which the grain yield per mu is zero). If S→1, this indicates that grain production is very stable during the 4

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Fig. 2. The spatiotemporal change in potential productivity of the Chinese farmland system in 1980, 1990, 2000, and 2010 (units: kg/ha); source: http://www.resdc. cn/.

used the consistency index (C) to assess the response relationship between these factors in light of the aforementioned theoretical hypotheses combined with the response relationship of "high degree of landscape degradation leads to low stability of grain production." If C ≤ 1, this indicates that there is a response relationship between the degree of landscape degradation and stability of grain production. Otherwise, it indicates that there is no response relationship as proposed in this study.

(3) Consistency judgment Based on the verification points with a unified geographical background, we selected 29 and 26 verification points to monitor wheat and rice yields, respectively. We built 17 and 19 comparative verification groups for the degree of consistency between the stability of wheat and rice production, respectively, and degree of landscape degradation. These comparative verification groups covered China's different types of agricultural zones and included China’s main types of cultivated soils and landforms, and the provinces or municipalities with varying levels of economic development. Therefore, the selected verification points are of regional representativeness in a strict sense. On this basis, we

C=

Ln + 1 (a) − Ln (a) −1 Sn + 1 (b) − Sn (b)

(5)

where, L(a) refers to the degree of landscape degradation, and S(b) 5

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farmland, respectively, which is consistent with Hypothesis 1. Particular, the potential productivity of core farmland decreased by 0.85 % because the core farmland continued to be fragmented (Fig. 3). Among the 36 verification groups, the index of consistency between the stability of grain production and degree of farmland landscape degradation was on an average equal to 0.97, which is generally consistent with Hypothesis 2 (Tables 2 and 3). The C results regarding the wheat groups are all consistent with the response relationship between the degree of farmland landscape degradation and stability of grain production set forth in Hypothesis 2 (the C was equal to or less than 1; Table 2). With the exception of certain deviations in the results of the two groups (where C was greater than 1), the C results regarding the rice groups are consistent with Hypothesis 2 (Table 3). The abnormal values (e.g. C was equal to 9.60 in the 15th group) in the rice groups are primarily attributable to the fact that the groups concerned are located in highland or hilly regions in Southwest China (Table 3). In such regions, the geographical environment is characterised by mountainous terrain, four distinct seasons, and different weather at 5 km distance. Accordingly, there are significant environmental differences within the area even from a microscopic perspective. Given the lack of more refined data regarding geographical background, we were unable to unify the geographical environment of the verification points more accurately in this study. Therefore, certain deviations remained in the verification results for such regions.

refers to the stability of grain production. 3. Results 3.1. Spatial evolution of Chinese farmland landscape structures Farmland landscape pattern is significantly affected by the landform, leading to a variety of farmland landscapes. It is widely accepted that farmland in plain area will be concentrated. Instead, farmland landscapes in mountainous region or hilly area are usually fragmented. As China’s landform is characterised by mountainous west and flat east, the landscape structure of China's farmland is characterised by contiguous core farmland in the east and discrete patch farmland in the west, which collectively account for at least 60 % of China's total farmland. However, rapid urbanisation and large-scale transportation construction lead to the farmland landscape in China becoming seriously fragmented (Cheng et al., 2015). Therefore, patch farmland accounts for the highest proportion of the total farmland (on an average at least 36 %, Fig. 1), and the proportion of this type of farmland increased during the study period. In terms of the proportion of China’s total farmland, the core farmland is ranked second to patch farmland, accounting for at least 30 % on an average (Fig. 1). However, the extent by which the core farmland decreased was 0.91 % between 1980 and 2010, and most of this occurred after 1995 (Fig. 1). The decrease in the proportion of core farmland and the increase in the proportion of patch farmland imply that China’s farmland landscape system is tending to become more fragmented. The increase in the proportion of edge farmland and decrease in the proportion of perforated farmland imply that marginal erosion due to nonagricultural activities is still the main cause of farmland fragmentation in China and that the outward expansion of core farmland is on the decrease. Overall, farmland fragmentation has become the main form of variation in China’s farmland landscape system, and this system is susceptible to direct disturbance attributable to the nonagricultural landscape system.

4. Discussion and policy implications This study reveals that the production functions of farmland landscapes vary during the spatiotemporal evolution of the farmland landscape system. In addition, the stability of grain production fluctuates according to the spatial difference between farmland landscape types, implying that in the same geographical environment, the spatial positions of landscape units determine their functional orientation in the farmland landscape system. Furthermore, it is of considerable importance to classify farmland landscape from the perspective of spatial position differences and adjacency relationships between farmland landscape units. The results of structural classification can reflect the variation in farmland landscape pattern and the consequent ecological effect. Furthermore, we believe that the influence of fragmentation on the farmland landscape is primarily manifested in the series of brokenwindow effects arising from the decomposition of core farmland. When core farmland landscape decomposes into multiple farmland patches that are monotonous in terms of ecological features (for example, structure and functionality), the immediate consequences are a reduction in farmland scale and irregularity in farmland shape. This has various negative effects, for example, the development of a more significant fringe effect on farmland arising from nonagricultural production activities and ecosystems, an increase in the quantity of factors that can potentially disturb the farmland landscape, and a reduction in the stability of the agricultural production environment (Tscharntke et al., 2005; Fahrig et al., 2011). Moreover, a fragmented farmland landscape pattern promotes increase in agricultural production costs and impedes the agricultural mechanisation process, and it is unfavourable to the realisation of agricultural modernisation (Zhang and Yang, 2012; Hartvigsen, 2014). In contrast, fragmentation of farmland landscape has a certain positive influence in terms of increasing the diversity of agricultural production and reducing the risk of agricultural disease and pest transmission (Carsjens and Van Der Knaap, 2002; Di Falco et al., 2010; Deng et al., 2017). However, fragmentation of farmland landscape does more harm than good to agricultural production from the perspectives of promoting the modernisation of agricultural production, ensuring the stability of grain production, and attaining high and stable yields of agricultural crops (Brabec and Smith, 2002; Di Falco et al., 2010). To accomplish the strategic goal of protecting the quality, scale, and

3.2. Response of farmland productivity to landscape structure change According to the spatial distribution characteristics and spatial statistical analysis of potential productivity of China's farmland calculated using the GAEZ model, the potential productivity of farmland decreases in a sequence from the interior to the exterior of the farmland landscape system (Fig. 2). Particular, the potential productivity of fragmented farmland is the lowest. From 1980–2010, the potential productivity of patch farmland was 60.97 %, 58.19 %, and 46.83 % lower than that of perforated farmland (Fig. 3), core farmland, and edge

Fig. 3. Potential productivity of different farmland landscape types (unit of potential productivity of farmlands axis: kg/ha). 6

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Table 2 Response relationship between the stability of wheat production and degeneration of farmland landscape under the same geographic background. ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34

SN

Lon

Lat

GM

ST

AZ

LD

S

C

CJ

50353 50468 51828 51931 51709 51811 54849 54936 54525 54619 54749 54852 58002 57096 58015 58102 58118 58203 58040 58141 58049 58141 58040 58049 53956 53958 57044 57048 56748 56751 58158 58255 58243 58356

126.65 127.45 79.93 81.66 75.98 77.27 120.00 118.83 117.28 116.91 119.93 120.70 115.55 114.78 116.33 115.77 116.53 115.73 119.13 119.03 119.81 119.03 119.13 119.81 110.83 110.72 109.23 108.71 99.18 100.18 120.48 120.50 119.83 120.95

51.72 50.25 37.13 36.86 39.47 38.43 36.30 35.58 39.70 38.91 37.18 36.93 34.81 34.53 34.42 33.87 33.28 32.87 34.83 33.67 34.03 33.67 34.83 34.03 35.40 35.15 34.40 34.40 25.12 25.70 33.20 32.38 32.98 31.41

Platform with low elevation

Meadow soil

Northeast China zone

√

Irrigation-silting soil

Gansu-Xinjiang zone

0.98

√

Plain with medium elevation

Irrigation-silting soil

Gansu-Xinjiang zone

0.95

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.99

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.81

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.92

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.91

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.43

√

Plain with low elevation

Lime concretion black soils

Huang-Huai-Hai zone

0.52

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.64

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.89

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.71

√

Platform with low elevation

Loessal soil

Loess plateau zone

0.64

√

Platform with low elevation

Lou soil

Loess plateau region

0.19

√

Plain with medium elevation

Paddy soil

Southeast China zone

0.66

√

Platform with low elevation

Fluvoaquic soils

Lower-Middle reaches of the Yangtze

0.75

√

Platform with low elevation

Take off the latent paddy soil

Lower-Middle reaches of the Yangtze

0.20 −0.34 0.42 0.53 0.46 0.57 0.54 0.60 0.29 0.36 0.42 0.17 0.34 0.58 0.45 0.29 0.38 0.23 0.26 0.74 0.80 0.74 0.26 0.80 −0.11 0.25 −0.34 0.66 0.22 0.54 0.62 0.45 0.67 0.66

0.20

Platform with medium elevation

0.00 0.45 6.11 0.00 2.30 0.12 8.47 2.45 2.18 2.55 4.75 1.66 3.32 0.66 1.30 1.02 1.95 2.26 2.23 0.91 0.34 0.91 2.23 0.34 0.24 0.46 3.90 3.06 1.92 2.45 0.76 1.44 3.96 25.78

1.00

√

Notes: SN-Sample Numbers, Lon-Longitude, Lat-Latitude, GM-Geomorphology, ST-Soil Types, AZ-Agricultural Zoning, LD-Landscape Degradation Degree, S- Stability of Grain Production, C-Consistency, CJ- Consistency Judgment.

beneficial for constructing a continuous farmland landscape (Fig.4-b). However, using only the function separation method cannot prevent non-agricultural disturbance nor the interaction effects of pollution dispersal on local ecosystems. Thus, an ecological buffer between settlements and agricultural landscapes, which can consist of trees and shrubs, is needed to restrict the diffusion of pollutants and prevent soil erosion by intercepting precipitation (Fig.4). Moreover, the ecological buffer can also be used to provide habitats for wildlife and contribute to improving regional biodiversity. For the terrace landscapes, afforestation for the purpose of the soil and water conversation in particular is worth special mention but is far from sufficient (Fig.4-a). Owing to the large variation in terrain, farmlands are easily ruined by high levels of surface runoff during the rainy season in China, which results in water and soil erosion and farmland quality deterioration. In this case, combining biological measures with engineering construction will be key to creating a stable farmland landscape with high productivity (Fig.4-a). As the practices in the Loess Plateau areas of China and the results of related studies, levelling the slope and reinforcing the ridge of the terrace through land consolidation engineering and planting shrubs will be more effective than implementing either engineering or biological methods individually (LaFevor, 2015; Shi et al., 2019). The agricultural facilities combining natural laws with human invention (including irrigation system and water and soil conservation engineering) will not only contribute to protecting farmland from natural disaster but also act as crucial parts of the ecological cycle in farmland landscape systems. To design a productive agricultural landscape pattern and further coordinate the relationship between farmland protection and urban development, more attention should be paid on exploring the effects of landscape structure and non-agricultural interference on agricultural

ecology of farmland, it is imperative to minimise the fragmentation of the farmland landscape system. To achieve this, it will be necessary to coordinate the relationship between three strategies of national land development and protection, namely, farmland protection, socio-economic development, and ecological protection (Wang et al., 2018). To protect the quality, scale, and ecology of China's farmland, the agricultural landscape system should be reconstructed based on the spatial optimum allocation of natural and human landscapes (Fig.4). Firstly, since rural areas are the basic units for Chinese agricultural production and farmland management, contradictions between human lives and resource conservation should be coordinated through the spatial governance of rural areas. If rural management can be efficiently regulated, then the goal of protecting the quality, scale, and ecology of farmland will be achieved through a bottom-up approach. The key point here is that an efficient rural governance system should be constructed to resist irrational development behaviours and effectively implement protection measures. The major function of this management system aims to construct a self-renewal mechanism through the improvement of farmer awareness and sustainable development. In this way, rural governance will be beneficial for China to achieve both a rural renaissance and farmland protection. However, farmland protection measures without an effective organization system which takes into consideration the lives and will of native people will be ineffective. Currently, an urgent task for Chinese rural governance is spatial optimum allocation, especially for villages on flat terrains (Fig.4-b). The overlapping of farmland, public service land, residential land, and even rural factories lead to agricultural landscapes becoming easily segmented, resulting in landscape fragmentation. The spatial function separation method through the concentration of agricultural functions and non-agricultural functions will be 7

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Table 3 Response relationship between the stability of rice production and degeneration of farmland landscape under the same geographic background. ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

SN

Lon

Lat

GM

ST

AZ

LD

S

C

CJ

57882 57978 57574 57662 57682 57574 57682 57662 57978 57853 58547 58567 58646 58547 58567 58646 57899 58612 57992 58806 56188 56196 56284 56288 56187 56288 56187 56284 56748 56751 57328 57318 57307 57318 54348 54332 58040 58141

113.55 112.55 112.40 111.68 113.58 112.4 113.58 111.68 112.55 110.63 119.18 121.43 119.92 119.18 121.43 119.92 114.91 116.68 114.70 115.83 103.66 104.68 103.48 103.92 103.83 103.92 103.83 103.48 99.18 100.18 107.5 106.55 105.08 106.55 123.32 122.72 119.13 119.03

26.78 25.28 29.36 29.05 28.70 29.36 28.70 29.05 25.28 26.73 29.03 29.30 28.45 29.03 29.30 28.45 26.80 28.70 25.68 26.36 30.98 31.47 30.42 30.58 30.70 30.58 30.70 30.42 25.12 25.70 31.20 31.07 31.10 31.07 41.42 41.52 34.83 33.67

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

√

Paddy soil

Lower-Middle reaches of the Yangtze

0.91

√

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

0.81

√

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

0.97

√

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

1.03

×

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

0.41

√

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

0.69

√

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

0.24

√

Plain with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

0.49

√

Hills with low elevation

Paddy soil

Lower-Middle reaches of the Yangtze

0.93

√

Plain with low elevation

Paddy soil

Southeast China zone

0.88

√

Plain with low elevation

Water loggogenic paddy soil

Southeast China zone

0.97

√

Plain with low elevation

Water loggogenic paddy soil

Southeast China zone

0.91

√

Plain with low elevation

Water loggogenic paddy soil

Southeast China zone

0.99

√

Plain with medium elevation

Paddy soil

Southeast China zone

9.60

×

Hills with low elevation

Acid Purple Soil

Southeast China zone

0.96

√

Hills with low elevation

Acid Purple Soil

Southeast China zone

0.25

√

Plain with low elevation

Meadow soil

Northeast China zone

0.89

√

Plain with low elevation

Fluvoaquic soils

Huang-Huai-Hai zone

0.18 0.81 0.25 0.61 0.45 0.25 0.45 0.61 0.81 0.20 0.06 0.64 0.45 0.06 0.64 0.45 0.64 0.36 0.07 0.76 0.31 0.60 0.69 0.43 0.60 0.43 0.60 0.69 0.24 0.29 0.49 0.68 0.41 0.68 −0.30 0.40 0.15 0.67

0.40

Plain with low elevation

0.45 0.00 2.70 6.90 1.64 2.70 1.64 6.90 0.00 0.30 6.65 5.66 5.41 6.65 5.66 5.41 0.62 1.17 24.55 14.64 5.50 2.99 0.49 8.54 10.53 8.54 10.53 0.49 1.92 2.45 5.19 0.75 1.11 0.75 0.11 0.48 2.23 0.91

0.61

√

Notes: SN-Sample Numbers, Lon-Longitude, Lat-Latitude, GM-Geomorphology, ST-Soil Types, AZ-Agricultural Zoning, LD-Landscape Degradation Degree, S- Stability of Grain Production, C-Consistency, CJ- Consistency Judgment.

spatial planning system to maintain farmland area and the scale of farmland conversion at an optimal proportion (Dramstad and Fjellstad, 2011; Primdahl, 2014; Xiao et al., 2017).

landscape system in the future. Although this study proves that farmland landscape structure changes affect agricultural productivity significantly, there are a series of questions unanswered. Land-use data with high resolution and accuracy should be interpreted to improve the landscape structure segmentation result. Wickham and Riitters (2019) reported that using low resolution data in the process of landscape structure segmentation will lead to more heterogeneous landscape information and inaccurate landscape pattern analysis. Therefore, landuse data with high resolution will benefit a more accurate agricultural landscape pattern analysis and design. A reasonable and applicable farmland landscape pattern planning should be precisely designed to coordinate the relationship between farmland landscape construction, urban development, and ecological protection (Albert et al., 2016; Sanz et al., 2016). As the farmland landscape systems in China are mostly distributed in the middle of the urban system and natural system, farmland landscape pattern planning determines the effects of agricultural non-point source pollution on natural system and the disturbing intensity of non-agricultural activities on farmland productivity (Zhang et al., 2015; Chai et al., 2019). Land management policy system related to spatial regulation of farmland landscape system should be proposed to protect productive agricultural ecosystem. As widely known, the farmland landscape structure is mostly affected by land management polices because of its regulation of human activities (Cheng et al., 2015; Jiang et al., 2019). However, no special laws or policies for farmland landscape protection and design have been legislated against non-agricultural damage. Therefore, it is urgent to bring farmland landscape into legal regulation and regional or national

5. Conclusions Farmland landscape system change plays a crucial part in agriculture production. Through the classification of farmland landscape structures, fragmentation or continuity can easily be recognised. As the essence of farmland system, core farmlands usually indicate stable productivity. However, patch farmlands are caused by the disturbance due to human activities or other natural factors and their productivity is hardly ensured. With the application of geographical analysis methods and morphological image processing technologies, we explored the response of agriculture productivity change to farmland landscape system evolution. According to the spatial statistical analyses, farmland landscape system tended to be fragmented during the study period. Correspondingly, we found that farmland fragmentation will cause an average 55.33 % cumulative decline in potential farmland productivity, especially the fragmentation of core farmland landscape, which can cause a 0.85 % decline in potential productivity. Therefore, we propose a method of farmland landscape system protection, which covers the conservation of quality, scale, and ecology. To achieve this goal, the spatial allocation of naturale and human landscapes should be optimized to regulate nonagricultural disturbances by all kinds of human activities. 8

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Fig. 4. Sketch map of the spatial optimum allocation of natural and human landscapes (left is for terrace agriculture landscape and right is for flat terrain agriculture landscape). (a) Profile of terrace and the spatial layout of terraces, settlements and ecological lands; (b) Profile of farmlands on flat terrains and the spatial layout of farmlands, settlements and ecological lands.

Conflict of interest statement

Province. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data CRediT authorship contribution statement Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.resconrec.2020. 104724.

Jiang Penghui: Conceptualization, Formal analysis, Methodology, Funding acquisition, Validation, Visualization, Writing - original draft. Li Manchun: Conceptualization, Investigation, Resources, Supervision. Cheng Liang: Conceptualization, Resources, Writing - review & editing.

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Acknowledgements This work was supported by the National Natural Science Foundation of China [grant number 41801298]; the Natural Science Foundation of Jiangsu Province of China [grant number BK20180348]; the Fundamental Research Funds for the Central Universities [grant number 020914380057], and the "Double-Creation Plan" of Jiangsu 9

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10