Tradeoffs between agricultural production and ecosystem services: A case study in Zhangye, Northwest China

Journal Pre-proof Tradeoffs between agricultural production and ecosystem services: A case study in Zhangye, Northwest China

Zhihui Li, Xiangzheng Deng, Gui Jin, Alnail Mohmmed, Aisha Olushola Arowolo PII:

S0048-9697(19)36028-0

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136032

Reference:

STOTEN 136032

To appear in:

Science of the Total Environment

Received date:

18 October 2019

Revised date:

5 December 2019

Accepted date:

8 December 2019

Please cite this article as: Z. Li, X. Deng, G. Jin, et al., Tradeoffs between agricultural production and ecosystem services: A case study in Zhangye, Northwest China, Science of the Total Environment (2018), https://doi.org/10.1016/j.scitotenv.2019.136032

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© 2018 Published by Elsevier.

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Tradeoffs between agricultural production and ecosystem services: A case study in Zhangye, Northwest China Zhihui Lia,b,c, Xiangzheng Denga,b,c*, Gui Jind, Alnail Mohmmeda,b, Aisha Olushola Arowoloe a. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b. Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Beijing 100101, China

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c. University of Chinese Academy of Sciences, Beijing 100149, China

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d. College of Urban and Environmental Science, Central China Normal University, Wuhan, 430079, China

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e. Department of Agricultural Economics and Farm Management, Federal University of

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Abstract

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Agriculture Abeokuta (FUNAAB), Ogun State, Nigeria.

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Humans have increasingly intervened in the nature to advance socioeconomic development at the expense of ecosystem services. Tradeoffs between ecosystem

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services and socioeconomic development are inevitable and should be considered in sustainable ecosystem management. This is no exception in Zhangye where intensive agricultural activities have significantly affected its ecological conditions. Thus, this study evaluated the tradeoffs between agricultural production and key ecosystem services along with their spatial distributions at the watershed level in Zhangye based on multisource observation data. The key ecosystem services, including net primary productivity (NPP), water yield, and soil conservation, were evaluated for the years 2000, 2010, and 2015 using remote sensing data and the InVEST model. The Morishima elasticity of substitution (MES) between these ecosystem services and 1

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agricultural production were then estimated by applying a quadratic directional output distance function, and mapped to determine the tradeoffs. The results showed that the average NPP and annual water yield respectively increased by 22% and 24%, while annual soil conservation decreased by 22% during 2000-2015. The average MES values for agricultural production with NPP, water yield, and soil conservation were

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0.14, −0.69, and −0.56, respectively. This indicated the existence of a synergetic relationship between agricultural production and NPP as well as tradeoff relationships

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between agricultural production and water yield/soil conservation. Differences in the

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spatial patterns of the relationships between agricultural production and these

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ecosystem services were observed. Significant tradeoff relationships were observed

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for agricultural production with water yield and soil conservation in the upper reach

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of Zhangye. It indicated that increasing agricultural production would be at the cost of decreased water yield and soil conservation, especially in the upper reach area. The

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quantification and spatial pattern determinations of tradeoffs between ecosystem services and agricultural production is useful for the development of regional ecological conservation policy and sustainable ecosystem management. Keywords: Ecosystem services; Agricultural production; Tradeoffs and synergies; Spatial pattern; Morishima elasticity of substitution; Watershed Highlights • Spatial dynamics of key ecosystem services in Zhangye were analyzed from 2000–2015.

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• A directional output distance function was applied to quantify tradeoffs. • Agricultural production and water yield/soil conservation exhibited tradeoffs. • Agricultural production and NPP showed weak synergy. • Tradeoff hotspots were identified in the upper reach of Zhangye. 1. Introduction

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Ecosystems around the world provide services such as air and water purification,

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climate modulation, food production and nutrient recycling among others, which are

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essential for sustaining human society (MEA, 2005). However, due to the pressures

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from global change and human interventions, the provisioning of ecosystem services

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is declining (Mace et al., 2012; Nagendra et al., 2015). At the same time, it leads to complex tradeoffs and synergies between competing economic and environmental

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goals in ecosystem management (Jin et al., 2019; Power, 2010; Sanon et al., 2012).

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Human interventions in natural ecosystems over the years have aimed to maximize economic benefits and utility at the expense or loss of some ecosystem services (Butler et al., 2013). Such tradeoffs have resulted in adverse outcomes for the environment and human society (Rodríguez et al., 2006; Zheng et al., 2014). Managing ecosystem services in the context of complex human-nature interactions requires reliable and effective data support. Multisource observation data, including Earth observation together with socioeconomic information, that enables spatially continuous, regular, and repeatable observations has become an indispensable tool to support this need (Cord et al., 2017; Skidmore and Pettorelli, 2015). Hence, to

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maintain healthy ecosystems and foster sustainable socioeconomic development, it is crucial to gain a better understanding of tradeoffs between economic and environmental goals related to ecosystem services in the presence of multisource observation data. Numerous studies have recognized and quantified the tradeoffs/synergies between

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economic development and ecosystem services using various approaches and on

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different scales (Bostian et al., 2015; Lester et al., 2013; Lu et al., 2015; Power, 2010;

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Ruijs et al., 2013; Zheng et al., 2016). While correlation analysis has been widely

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applied to ascertain interactions between pairs of ecosystem services (Maes et al., 2012; Maskell et al., 2013; Turner et al., 2014; Zhao et al., 2018), multi-criteria

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decision analysis that considers both ecological and socioeconomic criteria has been a

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useful tool for analyzing the tradeoffs between conflicting conservation and

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socio-economic goals (Favretto et al., 2016; Fontana et al., 2013; Sanon et al., 2012). In recent years, production theory-based economic valuation models have been applied to examine tradeoffs, bearing in mind the complex interdependences and highly nonlinear relationships among the conservation and economic benefits of ecosystem services (Balbi et al., 2015; Mastrangelo and Laterra, 2015). These economic valuation models incorporate quantitative ecosystem services and can be applied to analyze tradeoffs between production activities and the environment (Bostian and Herlihy, 2014). Compared to other approaches, the output of this type of model is expected to be more familiar and practically useful to environmental managers. 4

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Oasis areas in arid regions are critical landscapes that are of importance to the human–environment system (Xie et al., 2014). Zhangye, an oasis area with a fragile and sensitive ecological environment spanning over semi-arid and arid regions, is located in the upper and middle reaches of the Heihe River Basin (HRB). The oasis area has spurred socioeconomic development in the region and is the main source of

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livelihood in the entire HRB. Due to rapid agricultural development in the past three decades, the adverse effects of human activities on natural ecosystems are particularly

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severe in arid regions, where the ecology is fragile because of limited water resources

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(Lu et al., 2015; Zhou et al., 2015). Long-term irrigation-based agriculture and other

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human activities in the Zhangye oasis area have led to the overexploitation of land

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and water resources. The effects of the complex interactions among the water, land,

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climatic, ecological, and human systems have changed ecosystems and their inherent services (Zhou et al., 2015). However, few comprehensive studies have focused on

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the quantification of the tradeoffs between agricultural production and ecosystem services at the watershed scale in Zhangye oasis area. The identification of tradeoffs between competing goals related to agricultural development and ecosystem conservation in Zhangye is important to support effective management strategies that will reduce the adverse effects of human activities on the ecosystem and promote sustainability (Kalbus et al., 2012; Lu et al., 2015; Luo et al., 2019; Peng et al., 2018). Net primary production (NPP), water yield, and soil conservation are important ecosystem services (Fan et al., 2018; Kong et al., 2018; Pan et al., 2014) and are also components of the most pressing environmental – socioeconomic challenges in 5

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Zhangye Oasis area. NPP is an effective indicator of ecosystem sustainability as it responds quickly to land use and climate change (Xiao et al., 2019b). On the other hand, water yield is essential to ecosystem function, development of irrigation-based agriculture, resident quality of life, and economic activity (Little et al., 2009; Yang et al., 2019). Meanwhile, soil conservation indicates the ability of vegetation cover to

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prevent soil erosion and retain soil fertility (Fan et al., 2018). In this study, considering the significance of ecosystem services and data availability, we selected

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net primary production, water yield, and soil conservation as key ecosystem services,

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aiming to examine the tradeoff relationships between them and agricultural

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production at the watershed level in Zhangye. The identification of spatial patterns of

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these tradeoffs at the watershed level is expected to be useful for guiding ecosystem

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2 Study area

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conservation efforts in this region.

Zhangye is located in the upper and middle reaches of the HRB between latitude 37°28'N to 39°57'N and longitude 97°20'E to 102°12'E. The HRB is located in Northwest China (Fig. 1a) and is the second largest inland river basin in the country, covering the Qilian mountain area (upper reach), the Hexi corridor (middle reach), and the Ejina Banner Gobi desert area (lower reach) (Fig. 1b). Zhangye covers an area of approximately 42,000 km2 and is administratively comprised of Ganzhou district, Linze county, Gaotai county, Minyue county, Shandan county, and Su’nan county (Fig. 1c). The elevation of Zhangye ranges from 1249 to 5542 m, decreasing from the southeast to the northwest. According to the geomorphological characteristics, 6

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Zhangye can be divided into three parts: the upper reach in the Qilian mountain area, the middle reach in the corridor plain area, and the lower reach in the barren area. The Qilian mountain area is located in the southern part of Zhangye and consists of a series of parallel mountains and intermountain basins with elevation ranging from 2000m to 5500m. The upper reach area accounts for 52.2% of the total area of Zhangye. The part of the mountain area with elevation greater than 4200m has

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perennial snow cover, which serves as a natural ‘solid’ reservoir. The part of the

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mountain area with elevation in the range of 2600m to 3600m is mostly covered by

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forestland and grassland and serves as the headwater area of the basin. The middle

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reach in the corridor plain area with elevation range of 1300m to 2000m is part the

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Qilan mountain diluvial fan and accounts for 38.3% of the total area of Zhangye. The

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plain is characterized by flat terrain along with rich light and heat resources, making it suitable for agriculture. The lower reach in the barren area is located in the northern

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part of Zhangye and is covered primarily by desert grassland and the Gobi desert. The barren area has a semi-arid to arid climate and accounts for only 9.5% of the total area of Zhangye.

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Journal Pre-proof Fig. 1. Location of Zhangye in the Heihe River Basin, Northwest China Zhangye is in the arid continental climate zone and has an annual precipitation of approximately 200 mm, an average annual temperature of around 3°C, and an annual sunshine duration of more than 3000 h. However, the climate varies with the elevation and landscape. Moving from the southeast to the northwest, the elevation decreases, the average annual precipitation gradually decreases, and the average annual

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temperature increases. Statistical analyses of meteorological data have shown large

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differences in precipitation and temperature between the mountain and corridor plain

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areas of Zhangye (Fig. 2). From 1980 to 2015, the average annual temperature in the

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upper reach Qilian mountain area ranged from −0.07°C to −2.4°C, and the annual

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precipitation ranged from 290mm to 527mm. During this same period, the average

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annual temperature in the middle reach corridor plain area ranged from 7.3°C to 10.0°C, and the annual precipitation was between 75mm and 205 mm (Fig. 2a).

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Obvious differences in temperature and precipitation were also observed between seasons. In Zhangye, high temperatures occur mainly from May to September, with the average monthly temperature ranging from 10°C to 20°C. The temperature in the middle reach is significantly higher than in the upper reach (Fig. 2b), and 80% to 90% of the annual precipitation is concentrated within the rainy season from May to September (Fig. 2c). Given its sufficient light and heat resources along with the large temperature difference between day and night, Zhangye is the largest oasis in the HRB; it supplies 83% of the runoff, provides 95% of the cultivated land, is home to 91% of the 8

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population, and contributes more than 80% of the gross domestic product of the HRB (Xiao et al., 2019a). Intensive agricultural production occurs in Zhangye, and water

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resources are consumed for both irrigation and domestic activities.

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Fig. 2. Annual precipitation and average temperature (a), multi-year average monthly temperature (b), and multi-year average monthly precipitation (c) in Zhangye from

3.1 Data

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3. Data and Methods

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1980 to 2015.

The evaluation of ecosystem services and tradeoffs analysis was based on multisource observation data, including GIS remote sensing data, climatic and environmental monitoring data, and socioeconomic data. Three key ecosystem services (NPP, water yield, and soil conservation) were examined to ascertain their relationships with agricultural production at the watershed level in Zhangye. NPP was determined as the MOD17A3 annual NPP product from the Numerical Terradyamic Simulation Group (NTSG). The physical quantities of water yield and soil 9

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conservation were determined using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Agricultural production was evaluated based on grain production data obtained for each county from the Zhangye local statistical yearbooks. The county-level grain production was scaled to the watershed level according to the percentage of the watershed’s cultivated land area out of the county’s total cultivated

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land area. The data for the study corresponded to the years 2000, 2010, and 2015, and all grid data used in this study followed a 1km × 1km resolution. Table 1 lists the

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details of input data for ecosystem services assessment.

Table 1 Input data for ecosystem services assessment and tradeoff analysis Data source and description

Land use

Land use data were derived from the Data Center for Resources

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Data type

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and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn/). The daily meteorological data, including average precipitation,

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temperature, evapotranspiration, and sun hours, were collected

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Meteorological

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from the National Meteorological Information Center (http://data.cma.cn/).

Digital elevation

Shuttle Radar Topography Mission DEM data were provided by

model (DEM)

the Cold and Arid Regions Science Data Center (http://westdc.westgis.ac.cn/).

Soil types and

Soil data, including soil depth, clay content, silt content, sand

properties

content, clay content, organic carbon content, gravel content, and electrical conductivity were derived from the Harmonized World Soil Database (Nachtergaele et al., 2009).

NPP

The MOD17A3 annual NPP product from NTSG (http://www.ntsg.umt.edu/).

Grain production

County-level grain production data were obtained from the Statistical Yearbooks of Zhangye. 10

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3.2 Quantifying key ecosystem services Water yield module. Water yield reflects the supply of water resources, which are determined by various factors such as land use type, climate, topographic conditions, and soil characteristics. The InVEST water yield module was used to quantify the annual water yield at the watershed level. Water yield is equal to the difference

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between precipitation and actual evapotranspiration (Peng et al., 2018). Using the

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module, annual water yield was calculated for each 1 km × 1 km grid cell on pixel i as

AETi ) + Pi , Pi

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Y𝑖 = (1 -

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follows:

(1)

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where Yi is the annual water yield, AETi is the actual annual evapotranspiration, and Pi is the average annual precipitation. The ratio

AETi Pi

, which reflects the effects

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of soil moisture, the water holding capacity of litter, and canopy interception, was

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calculated using the Budyko curve proposed by Zhang et al. (2004). The specific data for assessing water yield, including land use type, precipitation, potential evapotranspiration, soil depth, plant available water content (PAWC), root depth, constant Z (set as 0.8, which characterizes the local precipitation pattern and additional hydrogeological conditions), and plant evapotranspiration coefficient (K), are presented in Table A1 and Fig. A1.

Soil conservation module. Soil conservation reflects the different resistances to soil erosion of each land use types and the various soil retention capacities under different conditions. Soil conservation is equal to the difference between potential soil erosion and actual soil erosion. The InVEST soil conservation module, which is 11

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theoretically based on the universal soil loss equation (USLE) (Wischmeier and Smith, 1978), was applied to calculate annual soil conservation in each 1 km × 1 km grid cell on pixel i as follows: USLEi = Ri ×Ki ×LSi ×𝐶i ×𝑃i {RKLSi =Ri ×Ki ×LSi , when C=1, P=1 , SCi =RKLSi -USLEi

(2)

where USLEi is the actual soil erosion, RKLSi is potential soil erosion, SCi is soil

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conservation, Ri is the rainfall erosivity, Ki is the soil erodibility factor, LSi is the

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slope length-gradient factor, 𝐶i is the crop management factor, and 𝑃i is the support

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practice factor. The specific data required for the calculation of SC were DEM, land

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use type, precipitation (used to calculate the rainfall erosivity index, Fig. A1g), soil

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properties (used to calculate soil erodibility, Fig. A1h), the crop management factor (usle_c), and support practice (usle_p, Table A1). In this module, the crop

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management and support practice factors were modified based on the relevant

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literature (Hamel et al., 2015; Keller et al., 2015; Li et al., 2012; Qiang et al., 2016). 3.3 Determination of tradeoff relationships between agricultural production and ecosystem services

3.3.1 Theoretical framework The DODF was applied to quantify the tradeoff relationships between agricultural production and ecosystem services at the watershed level. We modelled their joint production on land within a watershed and calculated substitution elasticities between agricultural production and each ecosystem service. Following the distance function developed by Shephard (1970), we let 𝑃(𝑥) denote the output possibility set for the 12

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that 𝑃(𝑥) = *𝑦: 𝑥 can produce y+, 𝑥 ∈ 𝑅𝑁+ . (3) In this context, the outputs include the agricultural production and three ecosystem services, and land area constitutes the shared input in the joint production. The

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production function is assumed to satisfy the set of axioms depicted by Färe and Grosskopf (2000), including convexity, compactness, and free-disposability. Given

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these assumptions, the DODF measures the distance from the production unit to the

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efficiency boundary along a directional vector 𝑔𝑦 , providing a complete

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representation of the feasible output set and individually measuring the performance

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for each included output observation. The DODF is defined as ⃗⃗⃗⃗⃗ 𝐷𝑂 (𝑥, 𝑦; 𝑔𝑦 ) = max{𝛽: (𝑦 + 𝛽𝑔𝑦 ) ∈ 𝑃(𝑥)}, (4)

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+ where 𝑔𝑦 ∈ 𝑅𝑀 is the director vector that specifies the path of output expansion. The

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vector ⃗⃗⃗⃗⃗ 𝐷𝑂 (𝑥, 𝑦; 𝑔𝑦 ) measures the distance of each observation to the production frontier in a particular direction, which can be interpreted as a measure of inefficiency for each observation (Bostian and Herlihy, 2014). Thus, for observations on the frontier,

⃗⃗⃗⃗⃗ 𝐷𝑂 (𝑥, 𝑦; 𝑔𝑦 ) = 0 ,

and

for

observations

below

the

frontier,

⃗⃗⃗⃗⃗𝑂 (𝑥, 𝑦; 𝑔𝑦 ) > 0. The properties of the DODF fulfill all the properties described 1 ≥𝐷 in Chambers et al. (1998) and Färe et al. (2005).

We prefer to specify the DODF in a quadratic form as this is the only known flexible functional form that has specifications for both first- and second-order terms and satisfies the axiomatic properties of the directional distance function (Chambers 13

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et al., 2013; Färe et al., 2010): ⃗⃗⃗⃗⃗ 𝐷𝑂 (𝑥𝑘 , 𝑦𝑘 ; 𝑔𝑦 ) 𝑁

𝑀

𝑛=1

𝑚=1

𝑁

𝑁

1 = 𝛼0 + ∑ 𝛼𝑛 𝑥𝑛𝑘 + ∑ 𝛽𝑚 𝑦𝑚𝑘 + ∑ ∑ 𝛼𝑛𝑛′ 𝑥𝑛𝑘 𝑥𝑛′ 𝑘 2 ′ 𝑀

+

𝑛=1 𝑛 =1

𝑀

𝑁

𝑀

1 ∑ ∑ 𝛽𝑚𝑚′ 𝑦𝑚𝑘 𝑦𝑚′ 𝑘 + ∑ ∑ 𝛾𝑛𝑚 𝑥𝑛𝑘 𝑦𝑚𝑘 , 2 ′ 𝑛=1 𝑚=1

of

𝑚=1 𝑚 =1

(5)

where 𝑘 = 1,2, … , 𝐾 and indicates the unit or observation watershed; and

translation property include: 𝑀

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𝛼, 𝛽, and 𝛾 are the parameters to be estimated. The restrictions required to fulfill the

𝑚=1

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𝑀

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∑ 𝛽𝑚 𝑔𝑦𝑚 = −1, (6) ∑ 𝛽𝑚𝑚′ 𝑔𝑦𝑚′ = 0, 𝑚 = 1, … , 𝑀, (7) 𝑚′ =1

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and 𝑀

∑ 𝛾𝑛𝑚 𝑔𝑦𝑚 = 0, 𝑛 = 1, … , 𝑁. (8)

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𝑚=1

The following are set to ensure symmetric cross-input and cross-output effects: 𝛼𝑛𝑛′ = 𝛼𝑛′ 𝑛 , 𝑛 ≠ 𝑛′ , 𝑛, 𝑛′ = 1, … , 𝑁,

and

(9)

𝛽𝑚𝑚′ = 𝛽𝑚′ 𝑚 , 𝑚 ≠ 𝑚′ , 𝑚, 𝑚′ = 1, … , 𝑀. (10)

Moreover, the resulting output frontier reveals the physical tradeoff relationship between each ecosystem service and agricultural output in the watershed. Thus, the Morishima elasticity of substitution (MES) between outputs (Blackorby and Russell, 1989; Bostian and Herlihy, 2014), which measures how the ratio of shadow prices between two outputs changes as the output intensity (ratio of outputs) changes, was

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calculated for each of the key ecosystem services (Kumar et al., 2007). MES is defined as 𝑀𝑚𝑚′ =

𝜕ln(𝑝𝑚 ⁄𝑝𝑚′ ) . (11) 𝜕ln(𝑦𝑚′ ⁄𝑦𝑚 )

Given the shadow price ratio for two outputs ym and ym' : ⃗⃗⃗⃗⃗𝑂 (𝑥, 𝑦; 𝑔𝑦 )/𝜕𝑦𝑚 𝜕𝐷 𝑝𝑚 = ∀𝑚, 𝑚′ ∈ 𝑀, 𝑝𝑚′ 𝜕𝐷 ⃗⃗⃗⃗⃗𝑂 (𝑥, 𝑦; 𝑔𝑦 )/𝜕𝑦𝑚′

(12)

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where Mmm' is the substitution elasticity of output m' to output 𝑚; pm and pm'

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represent the shadow prices of outputs 𝑚 and m' , respectively; and ym and ym' are

𝑀

=

∗ 𝑦𝑚 ′

𝜕 2 ⃗⃗⃗⃗⃗ 𝐷𝑂 (𝑥, 𝑦; 𝑔𝑦 )⁄𝜕𝑦𝑚 𝜕𝑦𝑚′ 𝜕 2 ⃗⃗⃗⃗⃗ 𝐷𝑂 (𝑥, 𝑦; 𝑔𝑦 )⁄𝜕𝑦𝑚′ 𝜕𝑦𝑚′ [ − ], (13) ⃗⃗⃗⃗⃗𝑂 (𝑥, 𝑦; 𝑔𝑦 )⁄𝜕𝑦𝑚 ⃗⃗⃗⃗⃗𝑂 (𝑥, 𝑦; 𝑔𝑦 )⁄𝜕𝑦𝑚′ 𝜕𝐷 𝜕𝐷

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𝑚𝑚′

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DODF, Mmm' can be estimated as

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the physical production quantities of outputs 𝑚 and m' , respectively. Using the

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∗ ⃗⃗⃗⃗⃗𝑂 (𝑥, 𝑦; 𝑔𝑦 ) is the production frontier. With the quadratic where 𝑦𝑚 ′ = 𝑦𝑚′ +𝐷

specification of the DODF, Mmm' can be simplified to 𝛽𝑚𝑚′ 𝑁 𝛽𝑚 + ∑𝑀 𝑚′ =1 𝛽𝑚𝑚′ 𝑦𝑚′ + ∑𝑛=1 𝛾𝑛𝑚 𝑥𝑛

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∗ 𝑀𝑚𝑚′ = 𝑦𝑚 ′ [

−

𝛽𝑚′ 𝑚′ ] . (14) 𝑁 𝛽𝑚′ + ∑𝑀 𝑚=1 𝛽𝑚𝑚′ 𝑦𝑚 + ∑𝑛=1 𝛾𝑛𝑚′ 𝑥𝑛

The sign of Mmm' is determined by the magnitude and sign of βmm' . If Mmm' is negative, the outputs 𝑚′ and 𝑚 are substitutes; as the magnitude of Mmm' increases, it becomes more costly to increase 𝑦𝑚 . Conversely, for a positive Mmm' , the outputs 𝑚′ and 𝑚 are complements, and a greater elasticity implies that it is less costly to increase 𝑦𝑚 . 3.3.2 Empirical model specification 15

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Empirically, we applied the DODF to value the tradeoffs between agricultural production and key ecosystem services at the watershed level in Zhangye. The quadratic form of the DODF in our case is given by Eq. (15): ⃗⃗⃗⃗⃗ 𝐷𝑂 (𝑥𝑘 , 𝑦𝑘 ; 𝑔𝑦 ) 1

4

1

1

1 = 𝛼0 + ∑ 𝛼𝑛 𝑥𝑛𝑘 + ∑ 𝛽𝑚 𝑦𝑚𝑘 + ∑ ∑ 𝛼𝑛𝑛′ 𝑥𝑛𝑘 𝑥𝑛′ 𝑘 2 ′ 𝑛=1

4

𝑛=1 𝑛 =1

1

4

1 ∑ ∑ 𝛽𝑚𝑚′ 𝑦𝑚𝑘 𝑦𝑚′ 𝑘 + ∑ ∑ 𝛾𝑛𝑚 𝑥𝑛𝑘 𝑦𝑚𝑘 , 2 ′

of

+

𝑚=1

4

𝑚=1 𝑚 =1

(15)

𝑛=1 𝑚=1

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where 𝑚 = 1,2, … ,4 indicates five outputs, and 𝑛 = 1 indicates only one input. The DODF was applied to model agricultural production (𝑌1 ), NPP (𝑌2 ), water

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yield (𝑌3 ), and soil conservation (𝑌4 ) per unit area as outputs that can be jointly

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produced on land. For computational purposes, each output value was divided by its

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respective sample mean to ensure independence from the unit of measurement and

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correct for differences in scale. Referring to the studies of Ruijs et al. (2013) and Bostian and Herilihy (2014), the shared input in this application was only land and was normalized to 1 km2 so that the outputs were determined on the scale of one unit area of land. For estimation purposes, a constant input (in this case, one unit area of land) was equivalent to modeling production without inputs (Lovell and Pastor, 1997). Further, assuming 𝑔𝑦 = (1,1,1,1,1), the quadratic form can be derived as follows: 4

4

4

1 ⃗⃗⃗⃗⃗ 𝐷𝑂 (1, 𝑦𝑘 ; 1) = 𝛼0 + ∑ 𝛽𝑚 𝑦𝑚𝑘 + ∑ ∑ 𝛽𝑚𝑚′ 𝑦𝑚𝑘 𝑦𝑚′ 𝑘 . (16) 2 ′ 𝑚=1

𝑚=1 𝑚 =1

Subsequently, the form of 𝑀𝑚𝑚′ can be derived as 𝛽𝑚𝑚′ 𝛽𝑚′ 𝑚′ ∗ 𝑀𝑚𝑚′ = 𝑦𝑚 − ]. (17) ′ [ 𝑀 𝛽𝑚 + ∑𝑚′ =1 𝛽𝑚𝑚′ 𝑦𝑚′ 𝛽𝑚′ + ∑𝑀 𝑚=1 𝛽𝑚𝑚′ 𝑦𝑚 16

Journal Pre-proof To calculate 𝑀𝑚𝑚′ , the parameters in Eq. (16) must be estimated. As ⃗⃗⃗⃗⃗ 𝐷𝑂 (1, 𝑦𝑘𝑡 ; 1) is not observable, we applied stochastic frontier analysis to estimate the parameters. Specifically, the producer is efficient at a given direction vector 𝑔𝑦 if ⃗⃗⃗⃗⃗𝑂 (1, 𝑦𝑘 ; 1) + 𝜀𝑘 =𝐷 ⃗⃗⃗⃗⃗𝑂 (1, 𝑦𝑘 ; 1) + 𝑣𝑘 − 𝑢𝑘 , (18) 0 =𝐷 where 𝜀𝑘 = 𝑣𝑘 − 𝑢𝑘 , 𝑣𝑘 ~ i. i. d. 𝑁(0, 𝜎𝑣2 ) and 𝑢𝑘 ~ 𝑁(𝜇𝑘 , 𝜎𝑢2 )+ , i = 1,2,…,K. For the estimation of Eq. (18), we used the translation property of the directional distance

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function as follows:

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⃗⃗⃗⃗⃗𝑂 (1, 𝑦𝑘 + 𝛺𝑘 · 1; 1) + 𝑣𝑘 − 𝑢𝑘 . (19) −𝛺𝑘 =𝐷

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This property indicates that the producer decreases the distance to the efficiency

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boundary by the scalar factor 𝛺𝑘 , while the output is simultaneously improved by

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𝛺𝑘 · 1, assuming that the technology is available. By choosing an observation-specific value for the translation property (we chose 𝑦1𝑘 as the value for 𝛺𝑘 ), variation in the

5

5

5

1 ′ ′ ′ = 𝛼0 + ∑ 𝛽𝑚 𝑦𝑚𝑘 + ∑ ∑ 𝛽𝑚𝑚′ 𝑦𝑚𝑘 𝑦𝑚 ′ 𝑘 + 𝑣𝑘 − 𝑢𝑘 , 2 ′

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−𝑦1𝑘

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stochastic output distance function can be obtained as follows:

𝑚=1

(20)

𝑚=1 𝑚 =1

′ where 𝑦𝑚 = 𝑦𝑚 + 𝑦1 . We estimated the parameters of Eq. (20) using maximum

likelihood estimation (Aigner et al., 1977) and then calculated the efficiency of the joint production of agriculture with the key ecosystem services. We also calculated the MES values for agricultural production with NPP, water yield, and soil conservation. 4. Results 4.1 Spatial-temporal dynamics of ecosystem services 4.1.1 Spatial-temporal dynamics of NPP

17

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The annual average NPP value increased by 22% during the study period from 106.84 gC·m−2 in 2000 to 130.57 gC·m−2 in 2015 (Table 3). Fig. 3 illustrates the spatial distribution and changes in NPP from 2000 to 2015. The spatial distribution of NPP exhibited regional differences, whereby it decreased from the southeast to northwest, with the highest values distributed primarily in the upper and middle

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reaches of Zhangye (Fig. 3). NPP values were higher in the middle reach area than in the upper reach area. This can be attributed to the fact that the Qianlian mountain area

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in the upper reach is mostly forestland and grassland, while the middle reach area is

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an oasis plain that is dominated by crops with higher photosynthesis potential. In

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contrast, the lowest NPP was recorded in the northern Gobi desert area, where

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vegetation coverage is relatively low. The pattern of NPP change between 2000 and

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2015 showed that NPP increased in the south and central parts of Zhangye (approximately 57% of the total area of Zhangye), while a decrease was witnessed in

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the northern part of the study region (Fig. 3).

Fig. 3. Spatial distributions of NPP in 2000, 2010, and 2015, and variation in NPP from 2000 to 2015. 18

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4.1.2 Spatial-temporal dynamics of water yield The annual water yield increased by 24% from 28.38×108 m3 in 2000 to 35.2×108 m3 in 2015 (Table 3). The spatial distribution of water yield remained relevantly consistent from 2000 to 2015 and generally followed the distribution of precipitation. Both precipitation and annual water yield increased progressively from the northwest

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to southeast (Fig. 4). The highest values of water yield were mainly located in Su’nan

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county along the Qilian mountain area. Although Su’nan county is mainly covered by

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forest and grassland, it lies within the main rainfall area of Zhangye with a cold and

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humid climate. Water yield in Su’nan county is therefore relatively higher than in other areas of Zhangye. Water yield generally increased in the southeast from between

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in the northwest.

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2000 and 2015, where the increase exceeded 300mm (Fig. 4). However, it decreased

Fig. 4. Spatial distributions of water yield in 2000, 2010, and 2015, and variation in water yield from 2000–2015.

4.1.3 Spatial-temporal dynamics of soil conservation

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Soil conservation had an overall decreasing trend during the study period, fluctuating from 19.96×108 t in 2000 to 15.55×108 t in 2015 (Table S2). Soil conservation values were high in the upper reach mountainous region while low figures were recorded in the relatively flat terrain of the central and northwest parts of Zhangye (Fig. 5). Soil conservation is influenced by numerous factors such as climate,

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topography, land use, and human activities. The spatial distributions of land use and rainfall erosivity (Fig. A1a and A1g) show that while rainfall erosivity was higher in

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the southeast mountain area than in the central and northwest areas, the forest and

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grassland effectively intercepted rainfall, resulting in high soil conservation. As

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precipitation became more abundant from 2000 to 2015, soil conservation generally

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decreased in the study region, with the largest decreases (exceeding 500 t/hm−2)

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concentrated primarily in the northeastern area of Zhangye (Fig. 5).

Fig. 5. Spatial distributions of soil conservation in 2000, 2010, and 2015, and variation in soil conservation from 2000 to 2015. 4.2 Spatial tradeoffs between agricultural production and ecosystem services 20

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Watersheds are spatially explicit landscape units that contain a range of interacting physical, ecological, and social attributes and provide a range of ecosystem services that are valued by society (Costanza et al., 2014; Flotemersch et al., 2016). Zhangye had 178 watersheds for which the values of grain production and the three ecosystem services were calculated (Fig. 6). Grain production grew steadily, increasing from

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88.94×104 t in 2000 to 128.28×104 t in 2015 (Table S2). Most grain production occurred in the middle stream oasis area, which accounts for approximately 90% of

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cultivated land in Zhangye (Fig. 6).

Fig. 6. Spatial distribution of average NPP (a), water yield (b), soil conservation (c), and grain production (d) per unit area at the watershed level in Zhangye for the year 2015. A panel data of grain production, NPP, water yield, and soil conservation for 178 watersheds in the years 2000, 2010, and 2015 were constructed and the MES values between grain production and each ecosystem service in each watershed were calculated. Table 2 lists the MES values based on all the watersheds. The average 21

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MES values between grain production and NPP (MES12), water yield (MES13), and soil conservation (MES14) were 0.14, −0.69 and −0.56 respectively. This indicates that in Zhangye as a whole, substitute relationships (i.e., tradeoffs) existed between grain production and water yield and between grain production and soil conservation. In contrast, a complementary or synergetic relationship was observed between grain

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production and NPP.

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Table 2. Estimated MES values between grain production and key ecosystem services

Variable

NPP

MES12

Water yield

MES13

Soil conservation

MES14

Grain

Std. Dev.

Min

Max

0.14

0.35

−0.26

1.93

−0.69

12.23

−114.91

73.36

−0.56

4.97

−28.77

18.43

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production

Mean

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Ecosystem services

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in Zhangye based on the 178 watersheds

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Tradeoff hotspots were identified by mapping the spatial distribution of MES values for grain production with NPP, water yield, and soil conservation at the

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watershed level in Zhangye (Fig. 7). According to the definition of MES, higher magnitude of MES values corresponds to stronger tradeoff or synergy between ecosystem services. Thus, Fig. 7 indicates the extent to which it becomes more or less costly to increase grain production at the expense of other ecosystem service outputs in different areas of Zhangye. Grain production and NPP presented a synergetic relationship mainly in the upper and middle reaches of Zhangye, while they showed a tradeoff relationship in the lower reach (Fig. 7a). However, the MES magnitudes for most tradeoffs and synergies were less than 1, indicating that NPP was relatively unresponsive to changes in grain production for most watersheds in this study. The 22

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MES values for grain production and water yield (MES13) were highly negative (MES13 < −10) in the upper reach, which is characterized by abundant precipitation, high elevation, and high water yield. In contrast, the middle and lower reaches exhibited low-magnitude negative or positive MES13 values (Fig. 7b). These results indicate strong tradeoff relationships between grain production and water yield in the

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mountainous upper reach. This implies that increasing grain production would quickly become very costly in this region. The upper reach area of Zhangye is therefore the

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least suitable zone for further development, and ecological conservation is needed to

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ensure the sustainable supply of ecosystem services. Similarly, the MES values

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between grain production and soil conservation (MES14) were highly negative

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(MES14 < −10) in Su’nan county, which is located in the forested and mountainous

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upper reach that characterized by high rainfall erosivity and soil conservation. In contrast, low-magnitude negative and positive MES14 values were observed in the

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middle and lower reaches (Fig. 7c). These negative MES14 values indicate that if the tradeoffs between grain production and soil conservation occurred in the upper reach region, the higher the magnitude of MES14 is, the faster opportunity cost of soil conservation increase to meet grain production development. Overall, spatial heterogeneities were observed in the tradeoffs or synergies between grain production and NPP, water yield, and soil conservation in Zhangye. Due to various factors including land use type, climatic and topographical conditions, and soil properties, the MES values of adjacent watersheds can be different and even possess opposite signs. In regions with highly negative MES values, the opportunity 23

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costs of ecosystem services will increase as agricultural production increases further; thus, these regions are identified as tradeoff hotspots that require ecological conservation. In contrast, increasing agricultural production would be more cost-effective in regions with low-magnitude MES values (either negative or positive). Therefore, the spatial distribution maps of MES for grain production with ecosystem

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services are helpful for prioritizing conservation policies in Zhangye.

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Fig. 7. Spatial distribution of MES values for grain production with NPP (a), water

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yield (b), and soil conservation (c) at the watershed level in Zhangye.

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5. Discussion and Conclusions

This study evaluated the tradeoffs between agricultural production and key ecosystem services along with their spatial distributions at the watershed level in Zhangye based on multisource observation data. Specifically, NPP, water yield, and soil conservation were selected as key ecosystem services to clarify their relationships with grain production. In the valuation procedure, a quadratic DODF was applied to estimate the parameters and calculate MES values. The MES values for grain production with three ecosystem services were then mapped, demonstrating that the tradeoffs varied greatly among watersheds. In practice, this study evaluated ecosystem services and examined the tradeoff 24

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relationships between grain production and ecosystem services at the watershed scale in Zhangye. The mapping of spatial distribution of MES values for grain production with NPP, water yield, and soil conservation showed that there existed strong tradeoff relationships for grain production with water yield and soil conservation in the upper reach area where covered by forest and grassland and characterized by abundant

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precipitation and high rainfall erosivity. In contrast, the tradeoff or synergetic relationships between grain production and ecosystem services were relatively weak

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in the middle and lower reaches of Zhangye, where the terrain is flat. The findings of

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the tradeoff analyses in this study indicated that upper reach area should be

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conservation and restoration hotspots for sustainable ecological and economic

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development. It provides the government managers with information about specific

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location of where loss of ecosystem services due to increase in agricultural production would be minimized and where decreased production would result in significant gains

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in ecosystem services.

The DODF method used to evaluate tradeoffs in this study offers potential advantages for ecosystem management. As the relationships between ecosystem services are not linear, and the nonlinearity can vary depending on the geographic characteristics and natural conditions. Therefore, linking the production frontier theory-based tradeoff analysis of competing economic and environmental goals with the biophysical model and remotely sensed data makes it possible to clarify tradeoff relationships among ecosystem services in a spatially explicit manner. While, there are still some limitations of the tradeoff analysis in the study that could be further 25

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explored in the future. For instance, the tradeoff analysis could be further extended to determine opportunity cost and conduct cost–benefit analyses by including the production prices. Another limitation of this study is that agricultural development was reflected solely by grain production. However, the diversification of agricultural production in Zhangye will increase the proportion of commercial crops with high

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returns. The indicators therefore can be expanded to provide information for effective

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decision-making related to future land use and ecosystem service management.

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Acknowledgments

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This work was supported by the Young Scientists Fund of the National Natural

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Science Foundation of China [Grant No. 71804175] and the State Key Program of

Conflicts of interest

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National Natural Science Foundation of China [Grant Nos. 91325302 and 91425303].

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The authors declare no conflicts of interest.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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