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Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China
Journal of Cleaner Production xxx (2017) 1e9
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Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China Lefeng Qiu a, *, Jinxia Zhu a, Yi Pan a, Wei Hu b, Gabriel S. Amable c a
Institute of Land and Urban-rural Development, Zhejiang University of Finance and Economics, Hangzhou, 310018, China Institute of Rural Development, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China c Department of Geography, University of Cambridge, Cambridge, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 December 2016 Received in revised form 4 May 2017 Accepted 7 July 2017 Available online xxx
Land suitability analysis is an important step in land use planning for livestock development because of its high efficiency to allocate livestock farms to the most suitable land areas and minimize adverse effects on environment. This study aimed to use two multi-criteria analysis approaches: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA), to comparatively evaluate and map land use suitability for livestock production in Hangzhou metropolitan area, China. The evaluation used eight factors as suitability criteria, relating to topographic, environmental, human factors, and socio-economic data. The analytic hierarchy process (AHP) method and GIS techniques were integrated into the evaluation models to create the land suitability map. The results of WLC approach showed that 11.4% of the total area was highly suitable for livestock production while 48.6% was unsuitable. Of these, areas located far from central city had higher suitability, while areas close to the urban district, drinking water source area, ecological and natural conservation area, had lower suitability. Sensitivity analysis indicated this expert suitability results for livestock production were robust. Indirect validations conducted by mutual comparisons of suitability maps computed using the WLC and OWA approaches demonstrated 90.4% of the overall agreement with a kappa coefficient of 0.857, indicating that both methods provide very similar spatial land use suitability distributions. Ecological fitness was evaluated by comparing the suitability analysis result with a previous livestock development planning and several recommendations aimed at improving the long-term livestock development plans were made for Hangzhou. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Livestock Land suitability evaluation Weighted linear combination Ordered weighted averaging Sensitivity analysis
1. Introduction Due to the pressure of growing demand for food and economic gain, livestock production has been developing rapidly in China since economic reforms began in 1978. In 2013, the year-end number of pigs and poultry stocks reached 0.47 and 5.71 billion, respectively (Li et al., 2016). Annual production of meat in China has increased from 10.0 million tons in 1980 to 83.5 million tons in 2015 (Wang et al., 2016). China's output of meat has been ranking first place globally for more than 20 years (Li et al., 2016). Additionally, it is predicted that the demand for meat products in China would be 437.34 million tons in 2020 (Wang et al., 2016). Meanwhile, the structure of livestock farming in the nation has seen rapid transformation, from traditional backyard rearing to large-scale and
* Corresponding author. E-mail address: [email protected] (L. Qiu).
intensive production, in order to increase the commodity and efficiency of animal products (Peng et al., 2014). However, this shift to large-scale and intensive development models has brought with it a number of environmental problems and complex social issues (Castellini et al., 2012; Jain and Tim, 1995), such as nitrogen and phosphorus eutrophication of surface water (Basnet et al., 2002), heavy metals and pathogens accumulation in soils (Pan et al., 2016) greenhouse gas emissions (Webb et al., 2014), social and public health issues (Sommer-Quabach et al., 2014). The expansion and intensification of livestock production has become commonplace and is helping to keep up with population growth and economic development goals set by government, particularly in metropolises (Wang et al., 2016). This is inevitably leading to potentially deleterious effects on environmental quality. Thus, land suitability evaluation (LSE) for livestock development has been proposed as an efficient methodology to minimize potential environmental impacts (Benson and Mugarura, 2013; Danuso et al., 2015). Recent years have witnessed an increasing use of land use
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Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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suitability analysis and many approaches have been applied to determine suitability of lands for agricultural use (Akıncı et al., 2013; Heumann et al., 2011); to develop habitat maps for wildlife (Reza et al., 2013); to explore multi-criteria decision strategies for regional planning (Debolini et al., 2015; Malczewski and Rinner, 2005); and to evaluate the environmental impacts for land use planning (Rojas et al., 2013). Meanwhile, a series of studies have discussed the application of LSE method on livestock development planning. In United States, an GIS-based spatial decision support system was developed by Jain and Tim (1995) to identify optimal land areas for livestock production, taking into account several environmental, economic, and aesthetic constraints. The design and implementation of the system as well as an example application involving several alternative livestock production strategies were then presented in southern Iowa. Moreover, Benson and Mugarura (2013) proposed a quantitative model to predict mean livestock stocking rates and identify areas in Uganda that were relatively understocked and those that were potentially overstocked using population density, agroecological factors, and market access as explanatory variables. Basnet et al. (2001, 2002) designed and developed a raster GIS-based multi-criteria model for selecting suitable sites for animal waste application to agricultural fields in livestock development planning in southeast of Queensland, Australia. The degree of land suitability for animal waste application was determined using a range of social, economic, environmental, and agricultural factors. Local researches in China on land suitability for livestock tend to focus on measurement and assessment of carrying capacity of the environment (Gao et al., 2014; Liang et al., 2013; Zheng et al., 2013). However, quantitative studies on land suitability evaluation for livestock development remain limited. A range of LSE techniques have been developed, based on soil properties, terrain and climatic data, and tested within a multicriteria decision making (MCDM) framework (Chen et al., 2010). The techniques include Weighted Linear Combination (WLC) (Akıncı et al., 2013), the Ideal Point Method (Liu et al., 2014), Analytic Hierarchy Process (AHP) (Zhang et al., 2015a), Ordered Weighted Averaging (OWA) (Romano et al., 2015), Analytic Network Process (Zabihi et al., 2015), Monte Carlo simulation
(Ligmann-Zielinska and Jankowski, 2014), and the Land Suitability Index (Park et al., 2011). These approaches rely heavily on the availability of precise input data. The MCDM framework within which these techniques operate is a multi-disciplinary and multistep methodology. It can result in many sources of uncertainty, e.g. in criteria selection, standardization, weight determination and aggregation. Thus, many LSE studies have suggested that more than one MCDM method should be applied to minimize the effect of technique bias, and a sensitivity analysis should be conducted to examine the robustness of LSE results (Carver, 1991; Liu et al., 2014; Romano et al., 2015). In the present paper, we have employed a land use suitability mapping approach for livestock development, which uses overlay mapping combined with WLC and OWA to generate suitability maps that are then compared to generate the resultant maps. WLC is one of the most commonly used MCDM aggregation operators because of its simplicity and efficiency, and the OWA is presented as an extension to the traditional MCDM approaches because it gives complete control alongside the risks and tradeoffs in the multicriteria decision-making process (Jiang and Eastman, 2000; Romano et al., 2015). These two MCDM approaches were selected because the multi-criteria involved were reasonably aggregated, and the results were applicable and convincing (Liu et al., 2014). The objectives of this study are to precisely assess land suitability for livestock production, and to provide suggestions and guidance for long-term livestock development planning in Hangzhou metropolitan area, and elsewhere in China. 2. Materials and methods 2.1. Site description Hangzhou, the capital of Zhejiang province in China, is located at the southern wing of the Yangtze River Delta. The Hangzhou metropolitan area (118 200 -120 70 E, 29110 -30 340 N) has eight districts, namely central Hangzhou, Tonglu, Lin'an, Jiande, Chun'an, Fuyang, Xiaoshan and Yuhang (Fig. 1). The area of Hangzhou is about 16,596 km2, with a landscape characterized by a mountainous topography, and with elevation ranging from 0 to 1758 m
Fig. 1. Location map of the study area.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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above the sea level (Zhang et al., 2015b). Within the total area of Hangzhou, hills and mountains cover 65.6%, the plains account for 26.4%, and water bodies take up 8.0%. It has a subtropical monsoon climate, with an average annual temperature of 17.3 C and annual rainfall of 1468 mm. Statistical data show that total production value of animal husbandry increased by 56 times from 1978 to 2014 in Hangzhou metropolitan area. As the most important representative of livestock products in Hangzhou, the year-end number of pig and poultry stocks reached 1.73 and 13.74 million in 2014, respectively (Zhejiang Bureau of Statistics, 2015). During the same period, the pigs and poultry sector was rapidly changing in structure toward larger and more intensive production systems. In 2012, the percentages of the annual pig and poultry production from intensive farms stood as 84.66% and 81.04% of all pig and poultry production in Hangzhou, respectively (Hangzhou Bureau of Agriculture, 2014). Currently, pig farms are mainly concentrated in Xiaoshan and Yuhang District, while Jiande and Yuhang District are the major poultry breeding bases (Zhejiang Bureau of Statistics, 2015). The latest official documentation indicated the government should continually encourage the development of large-scale and intensive livestock husbandry in Hangzhou (Hangzhou Bureau of Agriculture, 2014). Thus, Hangzhou is threatened by adverse ecological impacts resulting from rapid development of large-scale and intensive livestock production. However, as far as the pressure from the need to control environmental pollution of livestock farming is concerned, no comprehensive LSE study for livestock development has previously been conducted for the whole of Hangzhou metropolitan area other than some brief development strategies presented in the Ecological Development Plan of Animal Husbandry of Hangzhou (2014e2020) (Ecological plan for short) (Hangzhou Bureau of Agriculture, 2014).
overlay mapping combined with WLC and OWA approaches were used to generate LSE results that are then compared to generate the resultant maps. Sensitivity analysis stage was performed to examine the robustness of the LSE results, using one-at-a-time method. These techniques are described in further detail below. 2.2.1. Weighted linear combination A WLC is an aggregation method that can be used when dealing with multi-criteria decision making. It requires that criteria are standardized to a common numeric range, and then combined by weighted averaging. The equation used for LSE is given as follows (Xu and Zhang, 2013):
Sj ¼
n X
wi xi
The flowchart (Fig. 2) of GIS-based multi-criteria analysis we have used shows a series of steps. Based mainly on raster GIS modelling, it consists primarily of two stages: suitability evaluation and sensitivity analysis. Suitability evaluation stage includes a sequence of criteria datasets establishment, criteria weight calculations and final aggregation of criteria into the LSE results. First, relevant criteria were selected by the authors and experts in Hangzhou, who are familiar with livestock development of Hangzhou. Next, the weights of the criteria were calculated in the AHP by constructing a pair-wise comparison matrix. Finally, weighted
(1)
i¼1
Where Sj is the suitability score of the jth pixel by WLC, wi is the weight of criterion i, xi is the standard score of criterion i, and n is the total number of criteria. 2.2.2. Ordered weighted averaging The OWA was proposed by Yager (1988) as a parameterized family of combination operators. It involves two sets of weights: criteria importance weights and order weights. By changing the order weights, it has enough flexibility to generate a complete range of results of LSE (including the case of the WLC) (Boroushaki and Malczewski, 2008; Romano et al., 2015) and a large variety of decision support strategies. The OWA formula for LSE is defined as follows (Liu et al., 2014):
0 2.2. Methods
3
OWAj ¼
1
n B X i¼1
C B wi vi C BPn C xi @ i¼1 wi vi A
(2)
where OWAj is the suitability score of the jth pixel by OWA, xi is the standard score of criterion i, wi is the weight of criterion i; vi is the ith element of a set of order weights V ¼ (v1, v2,…,vn) such that vi2 P [0,1] and ði¼1;nÞ vi ¼ 1. The set of w is the same as the set of criteria weight w used in the WLC. v is central to the OWA combination procedure which controls the position of the aggregation operator on a continuum between the extremes of MIN and MAX, as well as incorporating a trade-off measure indicating the degree of compensation between criteria (Liu et al., 2014). With different sets of v, OWA can generate a wide range of decision strategies, in terms of risks and tradeoffs (Malczewski, 2004). We used a Regular Increasing Monotone (RIM) approach (Zarghami and Szidarovszky, 2008) to assign order weights. Yager (1988) introduced the following formula to obtain the order weights of an n-dimensional OWA operator from an RIM quantifier:
vj ¼ QRIM
j j1 QRIM ; j ¼ 1; 2; …; n; QRIMðrÞ ¼ r a n n (3)
Fig. 2. Flowchart of the land use suitability analysis for livestock development.
where vj is the jth element of a set of order weights, n is the total number of criteria. a can reflect the degree of risk and tradeoff by representing both increasing and decreasing quantifiers. When 0 < a < 1, the intersection operator (and) is very restrictive; a > 1, then the union operator (or) involved a risk and an entire area could be chosen as long as a single criterion meets its threshold (Malczewski and Rinner, 2005). Here, the choice of a ¼ 0.5 reflects a relatively strict standpoint that a site in Hangzhou is only included provided
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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that, for that site, most factors meet their thresholds and the order weights were calculated (Supplementary Table 1). 2.2.3. Analytical hierarchy process The AHP introduced by Saaty (1980) is one of the most widely known multi-criteria analysis approaches. It enables users to determine the weights of the parameters in the solution of a multicriteria problem. In the AHP method, the first step was to construct an AHP hierarchical model consisting of objectives, criteria, subcriteria, and alternatives (Saaty, 1990). In this study, the objective was the suitability, and the criteria consisted of 8 factors. The second step was to assign the relative importance of factors based on Saaty's scale (Saaty, 1980) (Supplementary Table 2) and derive the pair-wise comparison matrix (Table 1). The relative importance was assigned by using Delphi method (Humphrey, 1995), in which, a team of experts including local agronomists and faculty members working in Zhejiang Academy of Agricultural Sciences were invited to estimate the relative importance of the factors. Next, the degree of consistency of pairwise comparisons must be checked which was judged based on a consistency ratio index. The level of consistency is reasonably acceptable if consistency ratio is equal to or less than 0.1 (Akıncı et al., 2013; Zhang et al., 2015a). In this study, the consistency ratio was 0.0212, which indicated the matrix consistent. The third step was to compute the weight for each criterion by normalized geometric means of the rows in the matrix (Table 1). 2.2.4. One-at-a-time method The one-at-a-time method (Butler et al., 1997; Chen et al., 2010) was used for sensitivity analysis for the WLC method in the second stage of our framework. A series of evaluation runs was conducted where each criterion weight was altered in a step size of ±10% and the weights of the other criteria were adjusted proportionally to ensure that all criterion weights to sum to one. The range of percentage changes was from 100% to þ100% from the original criterion weight value. Thus, the sensitivity analysis simulation consists of 160 LSE runs where each run generates a single new suitability map. The equation used for this calculation is given as (Xu and Zhang, 2013): n X 1 wj ð1 þ crÞ R wj ; cr ¼ wj ð1 þ crÞ xj þ wi xi 1 wj isj
(4) where R is LSE result, w is the original weight of criterion from Eq. (1), cr is the change rate of the weight, x is the standard score of criterion, i and j are the i-th and j-th criterion, respectively, i.e., i s j. Next, the uncertainty of the evaluation results was represented by the change rate. Within GIS environment, the local area difference could be spatially visualized for a particular weight with a given change rate and identified for the sensitivity analysis. Furthermore, the mean of the absolute change rate of LSE (MACR) were calculated by the following equation (Xu and Zhang, 2013):
N X 1 Rk wj ; cr R0 MACR wj ; cr ¼ 100% N R0
(5)
k¼1
Where MACR(wj, cr) is the mean of the absolute change rate with k as the k-th pixel and N as the number of pixels of LSE map. High MACR values indicate high sensitivity. 2.3. Data sets Eight factors related to livestock suitability evaluation were selected according to the criteria outlined in Peng et al. (2014)
(Peng et al., 2014). These criteria were terrain slope (SL), soil fertility demand (SFD), land use (LU), and proximity to nature reserve (PNR), transport route (PTR), surface water (PSW), residential area (PRA), and existing large-scale livestock farms (PEF). They were derived from existing established data sets, the detailed data sources of the eight criteria were listed in Supplementary Table 3 and permission for their use for this research was provided by the agencies that provide the data. Slope was derived from the digital elevation model (DEM) of Hangzhou with spatial resolution of 30 m. Soil fertility demand was represented by the area of farmlands in different districts, which can be utilized to evaluate the regional carrying capacity for animal manure. The data sets for nature reserve, transport route, surface water, and residential area were extracted from land use map of Hangzhou in 2010 (1:10000). A total of 42 existing large-scale livestock farms were precisely located by global positioning systems. Euclidean distances were calculated for nature reserves, transport routes, surface water bodies, residential areas, and existing large-scale livestock farms in ArcGIS® (Version 9.2, ESRI Inc., USA). All data were processed and converted to raster format with spatial resolution of 30 m and projected coordinate system of Xian_1980. Each criterion was standardized to four suitability classes, i.e., highly suitable, moderately suitable, marginally suitable and unsuitable, based on the FAO system (FAO, 1976). The classification threshold values of the criteria and the standard scores for threshold given in Table 2 were obtained from the literature (Basnet et al., 2001; Benson and Mugarura, 2013; Jain and Tim, 1995; Peng et al., 2014) and expert opinions. The remaining values of the criteria were standardized using linear scaling equations (Basnet et al., 2002). When the largest value is best suited (as was the case for SFD, PNR, PSW, PRA and PEF)
Xi ¼
Ri Rmin Rmax Rmin
(6)
When the smallest value is best suited (for SL, PTR)
Xi ¼ 1
Ri Rmin Rmax Rmin
(7)
where Xi ¼ standard score of i-th cell, Ri ¼ value of i-th cell, Rmax ¼ maximum threshold value, Rmin ¼ minimum threshold value.
Table 1 Pairwise comparison matrix. Criteria
PSW
PRA
PTR
PNR
SFD
LU
SL
PEF
Weights
PSW PRA PTR PNR SFD LU SL PEF
1 1/2 1/3 1/4 1/4 1/5 1/6 1/7
2 1 1/2 1/3 1/3 1/4 1/5 1/6
3 2 1 1/2 1/2 1/3 1/4 1/5
4 3 2 1 1 1/2 1/3 1/4
4 3 2 1 1 1/2 1/3 1/4
5 4 3 2 2 1 1/2 1/3
6 5 4 3 3 2 1 1/2
7 6 5 4 4 3 2 1
0.3202 0.2193 0.1461 0.0934 0.0934 0.0600 0.0399 0.0278
Largest eigenvalue ¼ 8.2093. n ¼ 8. Consistency index ¼ (Largest eigenvalue - n)/(n - 1) ¼ 0.0299. Random index ¼ 1.41. Consistency ratio ¼ Consistency index/Random index ¼ 0.0212.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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Table 2 Classification threshold values of criteria and standard scores for suitability evaluation of livestock breeding. Criteria
Unsuitable
Marginally suitable
Moderately suitable
Highly suitable
SL ( ) SFD (km2) PNR (km) PTR (km) PSW (km) PRA (km) PEF (km) LU Score
>25 <100 <0.5 <0.1 <0.5 <0.5 <1 Water body 0
15e25 100e400 0.5e5 >10 0.5e2.5 0.5e5 1e5 Cropland or Woodland 30
2e15 400e700 5e10 1e10 2.5e5 5e10 5e10 Orchard or grassland 60
<2 >700 >10 0.1e1 >5 >10 >10 Vacant or built-up land 100
3. Results and discussion 3.1. Land suitability maps The resultant land suitability map for livestock farming was classified into four classes according to the land suitability classification of FAO (1976). The Jenks natural breaks tool in ArcGIS was used to classify the suitability classes, because once the number of classes was fixed, Jenks natural breaks method reduced the variance within classes and maximized the variance between classes (Jenks, 1967). Overall, 48.6% (8066 km2) of the study area was unsuitable for livestock production, 18.8% (3117 km2) was marginally suitable, 21.2% (3520 km2) was moderately suitable, and 11.4% (1890 km2) was highly suitable (Fig. 3). These results demonstrate that only a few areas were suitable for livestock in the study region. As would be expected, the region of highest suitability was located at the place far from central Hangzhou, whereas the region of lowest suitability roughly coincided with areas where livestock was prohibited by environmental policy including urban district, areas near drinking water sources, ecological and natural conservation areas. An overlay analysis between the standard criteria maps (Supplementary Fig. 1) and land suitability map (Fig. 3) indicated the relationships between the suitability for livestock and each criterion. Most of the suitable area (i.e. highly suitable, moderately
suitable) had slope less than 15 (88%). 98% of the suitable area was located on counties with more than 100 km2 area of farmland to carry animal manure, 93% was at a distance from main road less than 10 km and had good transport condition, and 70% and 91% was within 0.5e5 km away from nature reserve and surface water body, respectively. Suitability increased far away from areas with high density of population and livestock farms, and 76% and 95% of the land was more than 5 km away from residential area, and existing large-scale livestock farms, respectively. These results were in agreement with construction guidance for standardized intensive pig farm in China (Ministry of Agriculture of China, 2007). Land use suitability for livestock farming was assessed successfully by using WLC, AHP, and GIS. Combination of WLC, AHP, and GIS helped to reduce the problems resulting from the subjectivities, uncertainties, and hierarchy characteristics of the traditional land suitability assessment process. Due to a high demand for ecologicalization and sustainability, it is imperative that judicious land use planning be practiced on a regional basis in the livestock development process. Local government in Hangzhou should be aware of the different suitability levels in each district (Table 3) when they make policy for livestock and poultry farming planning. The total area of unsuitable and marginally suitable lands in each district exceeded that of moderately suitable and highly suitable lands. The total area of moderately suitable and highly suitable lands in Tonglu and Lin'an were
Fig. 3. Land suitability map for livestock generated by WLC approach.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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Table 3 Ratio of land areas of different suitability in each district in Hangzhou. District
Unsuitable (%)
Marginally suitable (%)
Moderately suitable (%)
Highly suitable (%)
Tonglu Lin'an Jiande Chun'an Fuyang Xiaoshan Yuhang Central Hangzhou
36.04 34.93 36.80 43.35 42.50 85.83 78.61 100.00
14.74 24.53 23.93 20.15 21.20 7.49 16.34 0.00
27.92 25.78 23.48 25.47 24.92 6.20 4.62 0.00
21.29 14.75 15.79 11.03 11.38 0.48 0.43 0.00
the top two highest ratios in the study area, at 49.22% and 40.54% respectively, whereas there were no suitable areas in central Hangzhou (which had the lowest ratio of 0.00%). Thus, priority of livestock development should be provided to Tonglu and Lin'an, while livestock development needs to be strictly prohibited in central Hangzhou. The ranking of priority of livestock development for the remaining districts is as follows: Jiande > Chun'an > Fuyang > Xiaoshan > Yuhang.
sensitive criterion. Within the ±100% of changes of weights, PSW and PEF had a MACR value of 19.16% and 1.49%, respectively. In general, all the MACR values of the simulated results were significantly lower than the rate of change of the weights (Fig. 4), which indicated that the LSE results were relatively robust and reliable (Xu and Zhang, 2013).
3.2. Sensitivity analysis
Fig. 5 showed the land suitability map for livestock generated by OWA approach. Figs. 3 and 5 exhibited almost the same distributions of level of suitability. Highly suitable and moderately suitable areas covered 4827 km2, i.e. 29.1% of the total area. The remaining 11766 km2 were unsuitable or marginally suitable for livestock farming. Similar relationships can be observed between the suitability for livestock development and each criterion in the resultant OWA map. Table 4 listed the results obtained from a comparison between the suitability maps generated using the WLC and OWA approaches. The overall agreement and kappa coefficient were used to assess the degree of agreement between the two maps. The former is the ratio of the area that had the same degree of suitability to the total area, and the latter uses all of the information in the error matrix and ranges from 0 to 1. A value of 1 implies perfect agreement, and values below 1 imply less than perfect agreements (Zhang et al., 2008). Fleiss (1981) characterized kappa coefficients below 0.40 as poor, 0.40 to 0.75 as fair to good, and over 0.75 as excellent. For a comparison involving four suitability levels, the overall agreement of 90.4% and the kappa coefficient of 0.857
Based on the one-at-a-time method, a sensitivity analysis was conducted by altering the weights of each criterion from -100% to 100% with a step size of 10%. Fig. 4 showed MACR values for the LSE obtained from 160 simulation runs for the WLC method. As the change rate of the weights increases, the MACR values showed a linear increase with different gradients for each criterion. The MACR values of the same criteria were almost equal using the same absolute rates of change but with positive and negative values, which indicates similar sensitivities for positive and negative weight changes. A higher gradient indicated a greater change in the LSE values with the changing weights and further suggested a higher sensitivity of the criterion for LSE (Xu and Zhang, 2013). The ranking of the MACR values for all criteria is as follows: PSW > PTR > PRA > LU > PNR > SFD > SL > PEF. This ranking is similar to the order of the criteria weights (Table 1), which revealed that the criteria weights calculated by AHP method were reliable. It was noted that PSW, which has the highest ranking, was the most sensitive, whereas PEF with the lowest ranking was the least
3.3. Comparison of WLC and OWA resultant maps
Fig. 4. Mean absolute values of the change rate for the LSE under simulations.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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Fig. 5. Land suitability map for livestock generated by OWA approach.
Table 4 Comparison of suitability maps between WLC and OWA. WLC map (km2)
OWA map (km2) Unsuitable
Marginally suitable
Moderately suitable
Highly suitable
Total
Unsuitable Marginally suitable Moderately suitable Highly suitable Total
8044 43 4 1 8092
4 2877 784 1 3666
3 202 2579 387 3171
1 0 160 1505 1666
8052 3122 3527 1894 16596
Overall agreement: 90.4%, kappa coefficient: 0.857.
confirm that using WLC and OWA resulted in very similar spatial distributions of land use suitability for livestock development. The WLC or OWA approaches combined with AHP and GIS are most likely applicable to other metropolitan areas, such as cities in southeastern China with similar livestock development processes, although the accuracies of the models may depend on regional characteristics. The combination model of WLC, AHP and GIS provides a convenient and reasonable method for land suitability analysis. However, when working with government managing departments, the OWA is the preferred method due to its introduction of order weights of criteria meant the results reflect not only the influence of criterion, but also the attitudes of the decision makes.
Table 5 Livestock development strategies from Ecological plan.
Pig
Poultry
Expansion
Reduction
Prohibition
Lin'an Tonglu Jiande Chun'an Tonglu Lin'an Jiande Chun'an Fuyang
Xiaoshan Fuyang Yuhang
Central Hangzhou
Xiaoshan Yuhang
Central Hangzhou
3.4. Guidance for livestock development planning in Hangzhou Using the suitability map, the Ecological plan was evaluated in terms of the ecological fit between their spatial distribution patterns. With regard to Ecological plan (see Table 5), most of the planned livestock development strategies were in accordance with priority ranking of livestock development for all districts, which confirmed Ecological plan has a satisfactory ecological fit to the LSE results. However, there were a few conflicts between the suitability map and current situation of livestock development in Hangzhou. Since the beginning of Ecological plan in 2014, a large number of existing large-scale livestock farms were still located in Xiaoshan, Yuhang, and Central Hangzhou (see Supplementary Fig. 1g), which were classified as breeding reduction or prohibition zone (see Table 5). The main reason for the conflicts is that animal breeding is one of the traditional economic sources of local farmers. Furthermore, animal husbandry has made tremendous development in the past few decades due to the convenient transportation to consumption markets for livestock products in Hangzhou metropolitan area. The Ecological plan has not been completely complied with in these three districts although local environmental capacity bears a heavy burden for livestock breeding. The following suggestions are made to address the issues arising from the lack of ecological fit between the planning documents for Hangzhou and the LSE results. The urban area of Hangzhou including the whole Central Hangzhou and most areas of Xiaoshan
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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and Yuhang districts should apply more strictly policy to completely prohibit livestock breeding. Where intensive livestock farms are retained, countermeasure must be taken to prevent the environmental pollution. For the west and southwest districts of Hangzhou, the priority should be relocate livestock development to highly suitable or moderately suitable areas in the suitability map. Then, countermeasures should be implemented, perhaps by substituting the prime cropland that would be lost from intensive livestock farms by vacant land on the low hill and gentle slope, by providing an effectively technical guidance in land use and livestock pollution management. 4. Conclusions WLC and AHP approaches combined within a GIS were used to assess land suitability for livestock development in Hangzhou, China. The study was conducted using eight criteria reflecting topographic, environmental, human, and economic factors of the area. The resultant land suitability map for livestock production was classified into four levels of land use suitability, namely highly suitable, moderately suitable, marginally suitable and unsuitable. Around 32.6% of the study area was estimated to be suitable for livestock production, mainly located at places that are far from central Hangzhou, whereas the remaining land was unsuitable due to the conflicts of environmental policy, topographic and economic factors. According to the ratios of different suitability levels in each district, priority of livestock development for all districts should be ranked as follows: Tonglu > Lin an > Jiande > Chun'an > Fuyang > Xiaoshan > Yuhang > Central Hangzhou. The land suitability maps generated by WLC and OWA approaches exhibited almost the same distributions of level of suitability, with the overall agreement of 90.4% and the kappa coefficient of 0.857. This result indirectly validated the effectiveness and stability of our LSE framework. A sensitivity analysis showed that our approach gave robust results according to MACR values of the simulated results, which were significantly lower than the rate of change of the weights. In general, the Ecological plan appeared to have taken ecological fitness properly into consideration, although the analysis indicated that there were a few regions whose current use conflicted with the suitability map and where future countermeasures may be required. Acknowledgments This study was financially supported by National Natural Science Foundation of China (No. 41401595), National Science and Technology Support Program (No. 2015BAL02B03), the National Natural Science Foundation of China (No. 41501190), and Natural Science Foundation of Zhejiang Province (No. LQ14D010003). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2017.07.053. References € Akıncı, H., Ozalp, A.Y., Turgut, B., 2013. Agricultural land use suitability analysis using GIS and AHP technique. Comput. Electron. Agr. 97, 71e82. Basnet, B.B., Apan, A.A., Raine, S.R., 2001. Selecting suitable sites for animal waste application using a raster GIS. Environ. Manag. 28, 519e531. Basnet, B.B., Apan, A.A., Raine, S.R., 2002. Geographic information system based manure application plan. J. Environ. Manag. 64, 99e113. Benson, T., Mugarura, S., 2013. Livestock development planning in Uganda: identification of areas of opportunity and challenge. Land Use Pol. 35, 131e139. Boroushaki, S., Malczewski, J., 2008. Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy
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Mapping land use competiDebolini, M., Valette, E., François, M., Che tion in the ruraleurban fringe and future perspectives on land policies: a case s (Morocco). Land Use Pol. 47, 373e381. study of Mekne FAO, 1976. A Framework for Land Evaluation, Soils Bulletin No. 32. Food and Agriculture Organization of the United Nations, Rome. Fleiss, J.L., 1981. Statistical Methods for Rates and Proportions, second ed. John Wiley, New York. Gao, M., Qiu, J., Li, C., Wang, L., Li, H., Gao, C., 2014. Modeling nitrogen loading from a watershed consisting of cropland and livestock farms in China using ManureDNDC. Agr. Ecosyst. Environ. 185, 88e98. Hangzhou Bureau of Agriculture, 2014. Ecological Development Planning of Animal Husbandry of Hangzhou. Hangzhou. Heumann, B.W., Walsh, S.J., McDaniel, P.M., 2011. Assessing the application of a geographic presence-only model for land suitability mapping. Ecol. Inf. 6, 257e269. Humphrey, W.S., 1995. A Discipline for Software Engineering. 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Liu, R., Zhang, K., Zhang, Z., Borthwick, A.G., 2014. Land-use suitability analysis for urban development in Beijing. J. Environ. Manag. 145, 170e179. Malczewski, J., 2004. GIS-based land-use suitability analysis: a critical overview. Prog. Plann 62, 3e65. Malczewski, J., Rinner, C., 2005. Exploring multicriteria decision strategies in GIS with linguistic quantifiers: a case study of residential quality evaluation. J. Geogr. Syst. 7, 249e268. Ministry of Agriculture of China, 2007. Construction Criterion for Standardized Intensive Pig Farm. Beijing. Pan, L.B., Ma, J., Wang, X.L., Hou, H., 2016. Heavy metals in soils from a typical county in Shanxi Province, China: levels, sources and spatial distribution. Chemosphere 148, 248e254. Park, S., Jeon, S., Kim, S., Choi, C., 2011. Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landsc. Urban Plan. 99, 104e114. Peng, L., Chen, W., Li, M., Bai, Y., Pan, Y., 2014. GIS-based study of the spatial distribution suitability of livestock and poultry farming: the case of Putian, Fujian. China. comput. Electron. Agr. 108, 183e190. Reza, M.I.H., Abdullah, S.A., Nor, S.B.M., Ismail, M.H., 2013. Integrating GIS and expert judgment in a multi-criteria analysis to map and develop a habitat suitability index: a case study of large mammals on the Malayan Peninsula. Ecol. Indic. 34, 149e158. Rojas, C., Pino, J., Jaque, E., 2013. Strategic environmental assessment in Latin America: a methodological proposal for urban planning in the metropolitan n (Chile). Land Use Pol. 30, 519e527. area of concepcio Romano, G., Dal Sasso, P., Trisorio Liuzzi, G., Gentile, F., 2015. Multi-criteria decision analysis for land suitability mapping in a rural area of Southern Italy. Land Use Pol. 48, 131e143. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw Hill, New York. Saaty, T.L., 1990. An exposition on the AHP in reply to the paper “remarks on the analytic hierarchy process”. Manag. Sci. 36, 259e268. Sommer-Quabach, E., Piringer, M., Petz, E., Schauberger, G., 2014. Comparability of separation distances between odour sources and residential areas determined by various national odour impact criteria. Atmos. Environ. 95, 20e28. Wang, X., Wu, X., Yan, P., Gao, W., Chen, Y., Sui, P., 2016. Integrated analysis on economic and environmental consequences of livestock husbandry on different scale in China. J. Clean. Prod. 119, 1e12. Webb, J., Audsley, E., Williams, A., Pearn, K., Chatterton, J., 2014. Can UK livestock production be configured to maintain production while meeting targets to reduce emissions of greenhouse gases and ammonia? J. Clean. Prod. 83, 204e211.
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Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China Lefeng Qiu a, *, Jinxia Zhu a, Yi Pan a, Wei Hu b, Gabriel S. Amable c a
Institute of Land and Urban-rural Development, Zhejiang University of Finance and Economics, Hangzhou, 310018, China Institute of Rural Development, Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, China c Department of Geography, University of Cambridge, Cambridge, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 December 2016 Received in revised form 4 May 2017 Accepted 7 July 2017 Available online xxx
Land suitability analysis is an important step in land use planning for livestock development because of its high efficiency to allocate livestock farms to the most suitable land areas and minimize adverse effects on environment. This study aimed to use two multi-criteria analysis approaches: Weighted Linear Combination (WLC) and Ordered Weighted Averaging (OWA), to comparatively evaluate and map land use suitability for livestock production in Hangzhou metropolitan area, China. The evaluation used eight factors as suitability criteria, relating to topographic, environmental, human factors, and socio-economic data. The analytic hierarchy process (AHP) method and GIS techniques were integrated into the evaluation models to create the land suitability map. The results of WLC approach showed that 11.4% of the total area was highly suitable for livestock production while 48.6% was unsuitable. Of these, areas located far from central city had higher suitability, while areas close to the urban district, drinking water source area, ecological and natural conservation area, had lower suitability. Sensitivity analysis indicated this expert suitability results for livestock production were robust. Indirect validations conducted by mutual comparisons of suitability maps computed using the WLC and OWA approaches demonstrated 90.4% of the overall agreement with a kappa coefficient of 0.857, indicating that both methods provide very similar spatial land use suitability distributions. Ecological fitness was evaluated by comparing the suitability analysis result with a previous livestock development planning and several recommendations aimed at improving the long-term livestock development plans were made for Hangzhou. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Livestock Land suitability evaluation Weighted linear combination Ordered weighted averaging Sensitivity analysis
1. Introduction Due to the pressure of growing demand for food and economic gain, livestock production has been developing rapidly in China since economic reforms began in 1978. In 2013, the year-end number of pigs and poultry stocks reached 0.47 and 5.71 billion, respectively (Li et al., 2016). Annual production of meat in China has increased from 10.0 million tons in 1980 to 83.5 million tons in 2015 (Wang et al., 2016). China's output of meat has been ranking first place globally for more than 20 years (Li et al., 2016). Additionally, it is predicted that the demand for meat products in China would be 437.34 million tons in 2020 (Wang et al., 2016). Meanwhile, the structure of livestock farming in the nation has seen rapid transformation, from traditional backyard rearing to large-scale and
* Corresponding author. E-mail address: [email protected] (L. Qiu).
intensive production, in order to increase the commodity and efficiency of animal products (Peng et al., 2014). However, this shift to large-scale and intensive development models has brought with it a number of environmental problems and complex social issues (Castellini et al., 2012; Jain and Tim, 1995), such as nitrogen and phosphorus eutrophication of surface water (Basnet et al., 2002), heavy metals and pathogens accumulation in soils (Pan et al., 2016) greenhouse gas emissions (Webb et al., 2014), social and public health issues (Sommer-Quabach et al., 2014). The expansion and intensification of livestock production has become commonplace and is helping to keep up with population growth and economic development goals set by government, particularly in metropolises (Wang et al., 2016). This is inevitably leading to potentially deleterious effects on environmental quality. Thus, land suitability evaluation (LSE) for livestock development has been proposed as an efficient methodology to minimize potential environmental impacts (Benson and Mugarura, 2013; Danuso et al., 2015). Recent years have witnessed an increasing use of land use
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Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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suitability analysis and many approaches have been applied to determine suitability of lands for agricultural use (Akıncı et al., 2013; Heumann et al., 2011); to develop habitat maps for wildlife (Reza et al., 2013); to explore multi-criteria decision strategies for regional planning (Debolini et al., 2015; Malczewski and Rinner, 2005); and to evaluate the environmental impacts for land use planning (Rojas et al., 2013). Meanwhile, a series of studies have discussed the application of LSE method on livestock development planning. In United States, an GIS-based spatial decision support system was developed by Jain and Tim (1995) to identify optimal land areas for livestock production, taking into account several environmental, economic, and aesthetic constraints. The design and implementation of the system as well as an example application involving several alternative livestock production strategies were then presented in southern Iowa. Moreover, Benson and Mugarura (2013) proposed a quantitative model to predict mean livestock stocking rates and identify areas in Uganda that were relatively understocked and those that were potentially overstocked using population density, agroecological factors, and market access as explanatory variables. Basnet et al. (2001, 2002) designed and developed a raster GIS-based multi-criteria model for selecting suitable sites for animal waste application to agricultural fields in livestock development planning in southeast of Queensland, Australia. The degree of land suitability for animal waste application was determined using a range of social, economic, environmental, and agricultural factors. Local researches in China on land suitability for livestock tend to focus on measurement and assessment of carrying capacity of the environment (Gao et al., 2014; Liang et al., 2013; Zheng et al., 2013). However, quantitative studies on land suitability evaluation for livestock development remain limited. A range of LSE techniques have been developed, based on soil properties, terrain and climatic data, and tested within a multicriteria decision making (MCDM) framework (Chen et al., 2010). The techniques include Weighted Linear Combination (WLC) (Akıncı et al., 2013), the Ideal Point Method (Liu et al., 2014), Analytic Hierarchy Process (AHP) (Zhang et al., 2015a), Ordered Weighted Averaging (OWA) (Romano et al., 2015), Analytic Network Process (Zabihi et al., 2015), Monte Carlo simulation
(Ligmann-Zielinska and Jankowski, 2014), and the Land Suitability Index (Park et al., 2011). These approaches rely heavily on the availability of precise input data. The MCDM framework within which these techniques operate is a multi-disciplinary and multistep methodology. It can result in many sources of uncertainty, e.g. in criteria selection, standardization, weight determination and aggregation. Thus, many LSE studies have suggested that more than one MCDM method should be applied to minimize the effect of technique bias, and a sensitivity analysis should be conducted to examine the robustness of LSE results (Carver, 1991; Liu et al., 2014; Romano et al., 2015). In the present paper, we have employed a land use suitability mapping approach for livestock development, which uses overlay mapping combined with WLC and OWA to generate suitability maps that are then compared to generate the resultant maps. WLC is one of the most commonly used MCDM aggregation operators because of its simplicity and efficiency, and the OWA is presented as an extension to the traditional MCDM approaches because it gives complete control alongside the risks and tradeoffs in the multicriteria decision-making process (Jiang and Eastman, 2000; Romano et al., 2015). These two MCDM approaches were selected because the multi-criteria involved were reasonably aggregated, and the results were applicable and convincing (Liu et al., 2014). The objectives of this study are to precisely assess land suitability for livestock production, and to provide suggestions and guidance for long-term livestock development planning in Hangzhou metropolitan area, and elsewhere in China. 2. Materials and methods 2.1. Site description Hangzhou, the capital of Zhejiang province in China, is located at the southern wing of the Yangtze River Delta. The Hangzhou metropolitan area (118 200 -120 70 E, 29110 -30 340 N) has eight districts, namely central Hangzhou, Tonglu, Lin'an, Jiande, Chun'an, Fuyang, Xiaoshan and Yuhang (Fig. 1). The area of Hangzhou is about 16,596 km2, with a landscape characterized by a mountainous topography, and with elevation ranging from 0 to 1758 m
Fig. 1. Location map of the study area.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
L. Qiu et al. / Journal of Cleaner Production xxx (2017) 1e9
above the sea level (Zhang et al., 2015b). Within the total area of Hangzhou, hills and mountains cover 65.6%, the plains account for 26.4%, and water bodies take up 8.0%. It has a subtropical monsoon climate, with an average annual temperature of 17.3 C and annual rainfall of 1468 mm. Statistical data show that total production value of animal husbandry increased by 56 times from 1978 to 2014 in Hangzhou metropolitan area. As the most important representative of livestock products in Hangzhou, the year-end number of pig and poultry stocks reached 1.73 and 13.74 million in 2014, respectively (Zhejiang Bureau of Statistics, 2015). During the same period, the pigs and poultry sector was rapidly changing in structure toward larger and more intensive production systems. In 2012, the percentages of the annual pig and poultry production from intensive farms stood as 84.66% and 81.04% of all pig and poultry production in Hangzhou, respectively (Hangzhou Bureau of Agriculture, 2014). Currently, pig farms are mainly concentrated in Xiaoshan and Yuhang District, while Jiande and Yuhang District are the major poultry breeding bases (Zhejiang Bureau of Statistics, 2015). The latest official documentation indicated the government should continually encourage the development of large-scale and intensive livestock husbandry in Hangzhou (Hangzhou Bureau of Agriculture, 2014). Thus, Hangzhou is threatened by adverse ecological impacts resulting from rapid development of large-scale and intensive livestock production. However, as far as the pressure from the need to control environmental pollution of livestock farming is concerned, no comprehensive LSE study for livestock development has previously been conducted for the whole of Hangzhou metropolitan area other than some brief development strategies presented in the Ecological Development Plan of Animal Husbandry of Hangzhou (2014e2020) (Ecological plan for short) (Hangzhou Bureau of Agriculture, 2014).
overlay mapping combined with WLC and OWA approaches were used to generate LSE results that are then compared to generate the resultant maps. Sensitivity analysis stage was performed to examine the robustness of the LSE results, using one-at-a-time method. These techniques are described in further detail below. 2.2.1. Weighted linear combination A WLC is an aggregation method that can be used when dealing with multi-criteria decision making. It requires that criteria are standardized to a common numeric range, and then combined by weighted averaging. The equation used for LSE is given as follows (Xu and Zhang, 2013):
Sj ¼
n X
wi xi
The flowchart (Fig. 2) of GIS-based multi-criteria analysis we have used shows a series of steps. Based mainly on raster GIS modelling, it consists primarily of two stages: suitability evaluation and sensitivity analysis. Suitability evaluation stage includes a sequence of criteria datasets establishment, criteria weight calculations and final aggregation of criteria into the LSE results. First, relevant criteria were selected by the authors and experts in Hangzhou, who are familiar with livestock development of Hangzhou. Next, the weights of the criteria were calculated in the AHP by constructing a pair-wise comparison matrix. Finally, weighted
(1)
i¼1
Where Sj is the suitability score of the jth pixel by WLC, wi is the weight of criterion i, xi is the standard score of criterion i, and n is the total number of criteria. 2.2.2. Ordered weighted averaging The OWA was proposed by Yager (1988) as a parameterized family of combination operators. It involves two sets of weights: criteria importance weights and order weights. By changing the order weights, it has enough flexibility to generate a complete range of results of LSE (including the case of the WLC) (Boroushaki and Malczewski, 2008; Romano et al., 2015) and a large variety of decision support strategies. The OWA formula for LSE is defined as follows (Liu et al., 2014):
0 2.2. Methods
3
OWAj ¼
1
n B X i¼1
C B wi vi C BPn C xi @ i¼1 wi vi A
(2)
where OWAj is the suitability score of the jth pixel by OWA, xi is the standard score of criterion i, wi is the weight of criterion i; vi is the ith element of a set of order weights V ¼ (v1, v2,…,vn) such that vi2 P [0,1] and ði¼1;nÞ vi ¼ 1. The set of w is the same as the set of criteria weight w used in the WLC. v is central to the OWA combination procedure which controls the position of the aggregation operator on a continuum between the extremes of MIN and MAX, as well as incorporating a trade-off measure indicating the degree of compensation between criteria (Liu et al., 2014). With different sets of v, OWA can generate a wide range of decision strategies, in terms of risks and tradeoffs (Malczewski, 2004). We used a Regular Increasing Monotone (RIM) approach (Zarghami and Szidarovszky, 2008) to assign order weights. Yager (1988) introduced the following formula to obtain the order weights of an n-dimensional OWA operator from an RIM quantifier:
vj ¼ QRIM
j j1 QRIM ; j ¼ 1; 2; …; n; QRIMðrÞ ¼ r a n n (3)
Fig. 2. Flowchart of the land use suitability analysis for livestock development.
where vj is the jth element of a set of order weights, n is the total number of criteria. a can reflect the degree of risk and tradeoff by representing both increasing and decreasing quantifiers. When 0 < a < 1, the intersection operator (and) is very restrictive; a > 1, then the union operator (or) involved a risk and an entire area could be chosen as long as a single criterion meets its threshold (Malczewski and Rinner, 2005). Here, the choice of a ¼ 0.5 reflects a relatively strict standpoint that a site in Hangzhou is only included provided
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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that, for that site, most factors meet their thresholds and the order weights were calculated (Supplementary Table 1). 2.2.3. Analytical hierarchy process The AHP introduced by Saaty (1980) is one of the most widely known multi-criteria analysis approaches. It enables users to determine the weights of the parameters in the solution of a multicriteria problem. In the AHP method, the first step was to construct an AHP hierarchical model consisting of objectives, criteria, subcriteria, and alternatives (Saaty, 1990). In this study, the objective was the suitability, and the criteria consisted of 8 factors. The second step was to assign the relative importance of factors based on Saaty's scale (Saaty, 1980) (Supplementary Table 2) and derive the pair-wise comparison matrix (Table 1). The relative importance was assigned by using Delphi method (Humphrey, 1995), in which, a team of experts including local agronomists and faculty members working in Zhejiang Academy of Agricultural Sciences were invited to estimate the relative importance of the factors. Next, the degree of consistency of pairwise comparisons must be checked which was judged based on a consistency ratio index. The level of consistency is reasonably acceptable if consistency ratio is equal to or less than 0.1 (Akıncı et al., 2013; Zhang et al., 2015a). In this study, the consistency ratio was 0.0212, which indicated the matrix consistent. The third step was to compute the weight for each criterion by normalized geometric means of the rows in the matrix (Table 1). 2.2.4. One-at-a-time method The one-at-a-time method (Butler et al., 1997; Chen et al., 2010) was used for sensitivity analysis for the WLC method in the second stage of our framework. A series of evaluation runs was conducted where each criterion weight was altered in a step size of ±10% and the weights of the other criteria were adjusted proportionally to ensure that all criterion weights to sum to one. The range of percentage changes was from 100% to þ100% from the original criterion weight value. Thus, the sensitivity analysis simulation consists of 160 LSE runs where each run generates a single new suitability map. The equation used for this calculation is given as (Xu and Zhang, 2013): n X 1 wj ð1 þ crÞ R wj ; cr ¼ wj ð1 þ crÞ xj þ wi xi 1 wj isj
(4) where R is LSE result, w is the original weight of criterion from Eq. (1), cr is the change rate of the weight, x is the standard score of criterion, i and j are the i-th and j-th criterion, respectively, i.e., i s j. Next, the uncertainty of the evaluation results was represented by the change rate. Within GIS environment, the local area difference could be spatially visualized for a particular weight with a given change rate and identified for the sensitivity analysis. Furthermore, the mean of the absolute change rate of LSE (MACR) were calculated by the following equation (Xu and Zhang, 2013):
N X 1 Rk wj ; cr R0 MACR wj ; cr ¼ 100% N R0
(5)
k¼1
Where MACR(wj, cr) is the mean of the absolute change rate with k as the k-th pixel and N as the number of pixels of LSE map. High MACR values indicate high sensitivity. 2.3. Data sets Eight factors related to livestock suitability evaluation were selected according to the criteria outlined in Peng et al. (2014)
(Peng et al., 2014). These criteria were terrain slope (SL), soil fertility demand (SFD), land use (LU), and proximity to nature reserve (PNR), transport route (PTR), surface water (PSW), residential area (PRA), and existing large-scale livestock farms (PEF). They were derived from existing established data sets, the detailed data sources of the eight criteria were listed in Supplementary Table 3 and permission for their use for this research was provided by the agencies that provide the data. Slope was derived from the digital elevation model (DEM) of Hangzhou with spatial resolution of 30 m. Soil fertility demand was represented by the area of farmlands in different districts, which can be utilized to evaluate the regional carrying capacity for animal manure. The data sets for nature reserve, transport route, surface water, and residential area were extracted from land use map of Hangzhou in 2010 (1:10000). A total of 42 existing large-scale livestock farms were precisely located by global positioning systems. Euclidean distances were calculated for nature reserves, transport routes, surface water bodies, residential areas, and existing large-scale livestock farms in ArcGIS® (Version 9.2, ESRI Inc., USA). All data were processed and converted to raster format with spatial resolution of 30 m and projected coordinate system of Xian_1980. Each criterion was standardized to four suitability classes, i.e., highly suitable, moderately suitable, marginally suitable and unsuitable, based on the FAO system (FAO, 1976). The classification threshold values of the criteria and the standard scores for threshold given in Table 2 were obtained from the literature (Basnet et al., 2001; Benson and Mugarura, 2013; Jain and Tim, 1995; Peng et al., 2014) and expert opinions. The remaining values of the criteria were standardized using linear scaling equations (Basnet et al., 2002). When the largest value is best suited (as was the case for SFD, PNR, PSW, PRA and PEF)
Xi ¼
Ri Rmin Rmax Rmin
(6)
When the smallest value is best suited (for SL, PTR)
Xi ¼ 1
Ri Rmin Rmax Rmin
(7)
where Xi ¼ standard score of i-th cell, Ri ¼ value of i-th cell, Rmax ¼ maximum threshold value, Rmin ¼ minimum threshold value.
Table 1 Pairwise comparison matrix. Criteria
PSW
PRA
PTR
PNR
SFD
LU
SL
PEF
Weights
PSW PRA PTR PNR SFD LU SL PEF
1 1/2 1/3 1/4 1/4 1/5 1/6 1/7
2 1 1/2 1/3 1/3 1/4 1/5 1/6
3 2 1 1/2 1/2 1/3 1/4 1/5
4 3 2 1 1 1/2 1/3 1/4
4 3 2 1 1 1/2 1/3 1/4
5 4 3 2 2 1 1/2 1/3
6 5 4 3 3 2 1 1/2
7 6 5 4 4 3 2 1
0.3202 0.2193 0.1461 0.0934 0.0934 0.0600 0.0399 0.0278
Largest eigenvalue ¼ 8.2093. n ¼ 8. Consistency index ¼ (Largest eigenvalue - n)/(n - 1) ¼ 0.0299. Random index ¼ 1.41. Consistency ratio ¼ Consistency index/Random index ¼ 0.0212.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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Table 2 Classification threshold values of criteria and standard scores for suitability evaluation of livestock breeding. Criteria
Unsuitable
Marginally suitable
Moderately suitable
Highly suitable
SL ( ) SFD (km2) PNR (km) PTR (km) PSW (km) PRA (km) PEF (km) LU Score
>25 <100 <0.5 <0.1 <0.5 <0.5 <1 Water body 0
15e25 100e400 0.5e5 >10 0.5e2.5 0.5e5 1e5 Cropland or Woodland 30
2e15 400e700 5e10 1e10 2.5e5 5e10 5e10 Orchard or grassland 60
<2 >700 >10 0.1e1 >5 >10 >10 Vacant or built-up land 100
3. Results and discussion 3.1. Land suitability maps The resultant land suitability map for livestock farming was classified into four classes according to the land suitability classification of FAO (1976). The Jenks natural breaks tool in ArcGIS was used to classify the suitability classes, because once the number of classes was fixed, Jenks natural breaks method reduced the variance within classes and maximized the variance between classes (Jenks, 1967). Overall, 48.6% (8066 km2) of the study area was unsuitable for livestock production, 18.8% (3117 km2) was marginally suitable, 21.2% (3520 km2) was moderately suitable, and 11.4% (1890 km2) was highly suitable (Fig. 3). These results demonstrate that only a few areas were suitable for livestock in the study region. As would be expected, the region of highest suitability was located at the place far from central Hangzhou, whereas the region of lowest suitability roughly coincided with areas where livestock was prohibited by environmental policy including urban district, areas near drinking water sources, ecological and natural conservation areas. An overlay analysis between the standard criteria maps (Supplementary Fig. 1) and land suitability map (Fig. 3) indicated the relationships between the suitability for livestock and each criterion. Most of the suitable area (i.e. highly suitable, moderately
suitable) had slope less than 15 (88%). 98% of the suitable area was located on counties with more than 100 km2 area of farmland to carry animal manure, 93% was at a distance from main road less than 10 km and had good transport condition, and 70% and 91% was within 0.5e5 km away from nature reserve and surface water body, respectively. Suitability increased far away from areas with high density of population and livestock farms, and 76% and 95% of the land was more than 5 km away from residential area, and existing large-scale livestock farms, respectively. These results were in agreement with construction guidance for standardized intensive pig farm in China (Ministry of Agriculture of China, 2007). Land use suitability for livestock farming was assessed successfully by using WLC, AHP, and GIS. Combination of WLC, AHP, and GIS helped to reduce the problems resulting from the subjectivities, uncertainties, and hierarchy characteristics of the traditional land suitability assessment process. Due to a high demand for ecologicalization and sustainability, it is imperative that judicious land use planning be practiced on a regional basis in the livestock development process. Local government in Hangzhou should be aware of the different suitability levels in each district (Table 3) when they make policy for livestock and poultry farming planning. The total area of unsuitable and marginally suitable lands in each district exceeded that of moderately suitable and highly suitable lands. The total area of moderately suitable and highly suitable lands in Tonglu and Lin'an were
Fig. 3. Land suitability map for livestock generated by WLC approach.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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Table 3 Ratio of land areas of different suitability in each district in Hangzhou. District
Unsuitable (%)
Marginally suitable (%)
Moderately suitable (%)
Highly suitable (%)
Tonglu Lin'an Jiande Chun'an Fuyang Xiaoshan Yuhang Central Hangzhou
36.04 34.93 36.80 43.35 42.50 85.83 78.61 100.00
14.74 24.53 23.93 20.15 21.20 7.49 16.34 0.00
27.92 25.78 23.48 25.47 24.92 6.20 4.62 0.00
21.29 14.75 15.79 11.03 11.38 0.48 0.43 0.00
the top two highest ratios in the study area, at 49.22% and 40.54% respectively, whereas there were no suitable areas in central Hangzhou (which had the lowest ratio of 0.00%). Thus, priority of livestock development should be provided to Tonglu and Lin'an, while livestock development needs to be strictly prohibited in central Hangzhou. The ranking of priority of livestock development for the remaining districts is as follows: Jiande > Chun'an > Fuyang > Xiaoshan > Yuhang.
sensitive criterion. Within the ±100% of changes of weights, PSW and PEF had a MACR value of 19.16% and 1.49%, respectively. In general, all the MACR values of the simulated results were significantly lower than the rate of change of the weights (Fig. 4), which indicated that the LSE results were relatively robust and reliable (Xu and Zhang, 2013).
3.2. Sensitivity analysis
Fig. 5 showed the land suitability map for livestock generated by OWA approach. Figs. 3 and 5 exhibited almost the same distributions of level of suitability. Highly suitable and moderately suitable areas covered 4827 km2, i.e. 29.1% of the total area. The remaining 11766 km2 were unsuitable or marginally suitable for livestock farming. Similar relationships can be observed between the suitability for livestock development and each criterion in the resultant OWA map. Table 4 listed the results obtained from a comparison between the suitability maps generated using the WLC and OWA approaches. The overall agreement and kappa coefficient were used to assess the degree of agreement between the two maps. The former is the ratio of the area that had the same degree of suitability to the total area, and the latter uses all of the information in the error matrix and ranges from 0 to 1. A value of 1 implies perfect agreement, and values below 1 imply less than perfect agreements (Zhang et al., 2008). Fleiss (1981) characterized kappa coefficients below 0.40 as poor, 0.40 to 0.75 as fair to good, and over 0.75 as excellent. For a comparison involving four suitability levels, the overall agreement of 90.4% and the kappa coefficient of 0.857
Based on the one-at-a-time method, a sensitivity analysis was conducted by altering the weights of each criterion from -100% to 100% with a step size of 10%. Fig. 4 showed MACR values for the LSE obtained from 160 simulation runs for the WLC method. As the change rate of the weights increases, the MACR values showed a linear increase with different gradients for each criterion. The MACR values of the same criteria were almost equal using the same absolute rates of change but with positive and negative values, which indicates similar sensitivities for positive and negative weight changes. A higher gradient indicated a greater change in the LSE values with the changing weights and further suggested a higher sensitivity of the criterion for LSE (Xu and Zhang, 2013). The ranking of the MACR values for all criteria is as follows: PSW > PTR > PRA > LU > PNR > SFD > SL > PEF. This ranking is similar to the order of the criteria weights (Table 1), which revealed that the criteria weights calculated by AHP method were reliable. It was noted that PSW, which has the highest ranking, was the most sensitive, whereas PEF with the lowest ranking was the least
3.3. Comparison of WLC and OWA resultant maps
Fig. 4. Mean absolute values of the change rate for the LSE under simulations.
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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Fig. 5. Land suitability map for livestock generated by OWA approach.
Table 4 Comparison of suitability maps between WLC and OWA. WLC map (km2)
OWA map (km2) Unsuitable
Marginally suitable
Moderately suitable
Highly suitable
Total
Unsuitable Marginally suitable Moderately suitable Highly suitable Total
8044 43 4 1 8092
4 2877 784 1 3666
3 202 2579 387 3171
1 0 160 1505 1666
8052 3122 3527 1894 16596
Overall agreement: 90.4%, kappa coefficient: 0.857.
confirm that using WLC and OWA resulted in very similar spatial distributions of land use suitability for livestock development. The WLC or OWA approaches combined with AHP and GIS are most likely applicable to other metropolitan areas, such as cities in southeastern China with similar livestock development processes, although the accuracies of the models may depend on regional characteristics. The combination model of WLC, AHP and GIS provides a convenient and reasonable method for land suitability analysis. However, when working with government managing departments, the OWA is the preferred method due to its introduction of order weights of criteria meant the results reflect not only the influence of criterion, but also the attitudes of the decision makes.
Table 5 Livestock development strategies from Ecological plan.
Pig
Poultry
Expansion
Reduction
Prohibition
Lin'an Tonglu Jiande Chun'an Tonglu Lin'an Jiande Chun'an Fuyang
Xiaoshan Fuyang Yuhang
Central Hangzhou
Xiaoshan Yuhang
Central Hangzhou
3.4. Guidance for livestock development planning in Hangzhou Using the suitability map, the Ecological plan was evaluated in terms of the ecological fit between their spatial distribution patterns. With regard to Ecological plan (see Table 5), most of the planned livestock development strategies were in accordance with priority ranking of livestock development for all districts, which confirmed Ecological plan has a satisfactory ecological fit to the LSE results. However, there were a few conflicts between the suitability map and current situation of livestock development in Hangzhou. Since the beginning of Ecological plan in 2014, a large number of existing large-scale livestock farms were still located in Xiaoshan, Yuhang, and Central Hangzhou (see Supplementary Fig. 1g), which were classified as breeding reduction or prohibition zone (see Table 5). The main reason for the conflicts is that animal breeding is one of the traditional economic sources of local farmers. Furthermore, animal husbandry has made tremendous development in the past few decades due to the convenient transportation to consumption markets for livestock products in Hangzhou metropolitan area. The Ecological plan has not been completely complied with in these three districts although local environmental capacity bears a heavy burden for livestock breeding. The following suggestions are made to address the issues arising from the lack of ecological fit between the planning documents for Hangzhou and the LSE results. The urban area of Hangzhou including the whole Central Hangzhou and most areas of Xiaoshan
Please cite this article in press as: Qiu, L., et al., Multi-criteria land use suitability analysis for livestock development planning in Hangzhou metropolitan area, China, Journal of Cleaner Production (2017), http://dx.doi.org/10.1016/j.jclepro.2017.07.053
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and Yuhang districts should apply more strictly policy to completely prohibit livestock breeding. Where intensive livestock farms are retained, countermeasure must be taken to prevent the environmental pollution. For the west and southwest districts of Hangzhou, the priority should be relocate livestock development to highly suitable or moderately suitable areas in the suitability map. Then, countermeasures should be implemented, perhaps by substituting the prime cropland that would be lost from intensive livestock farms by vacant land on the low hill and gentle slope, by providing an effectively technical guidance in land use and livestock pollution management. 4. Conclusions WLC and AHP approaches combined within a GIS were used to assess land suitability for livestock development in Hangzhou, China. The study was conducted using eight criteria reflecting topographic, environmental, human, and economic factors of the area. The resultant land suitability map for livestock production was classified into four levels of land use suitability, namely highly suitable, moderately suitable, marginally suitable and unsuitable. Around 32.6% of the study area was estimated to be suitable for livestock production, mainly located at places that are far from central Hangzhou, whereas the remaining land was unsuitable due to the conflicts of environmental policy, topographic and economic factors. According to the ratios of different suitability levels in each district, priority of livestock development for all districts should be ranked as follows: Tonglu > Lin an > Jiande > Chun'an > Fuyang > Xiaoshan > Yuhang > Central Hangzhou. The land suitability maps generated by WLC and OWA approaches exhibited almost the same distributions of level of suitability, with the overall agreement of 90.4% and the kappa coefficient of 0.857. This result indirectly validated the effectiveness and stability of our LSE framework. A sensitivity analysis showed that our approach gave robust results according to MACR values of the simulated results, which were significantly lower than the rate of change of the weights. In general, the Ecological plan appeared to have taken ecological fitness properly into consideration, although the analysis indicated that there were a few regions whose current use conflicted with the suitability map and where future countermeasures may be required. Acknowledgments This study was financially supported by National Natural Science Foundation of China (No. 41401595), National Science and Technology Support Program (No. 2015BAL02B03), the National Natural Science Foundation of China (No. 41501190), and Natural Science Foundation of Zhejiang Province (No. LQ14D010003). Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2017.07.053. References € Akıncı, H., Ozalp, A.Y., Turgut, B., 2013. Agricultural land use suitability analysis using GIS and AHP technique. Comput. Electron. Agr. 97, 71e82. Basnet, B.B., Apan, A.A., Raine, S.R., 2001. Selecting suitable sites for animal waste application using a raster GIS. Environ. Manag. 28, 519e531. Basnet, B.B., Apan, A.A., Raine, S.R., 2002. Geographic information system based manure application plan. J. Environ. Manag. 64, 99e113. Benson, T., Mugarura, S., 2013. Livestock development planning in Uganda: identification of areas of opportunity and challenge. Land Use Pol. 35, 131e139. Boroushaki, S., Malczewski, J., 2008. Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy
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