Modelling crop yield, water consumption, and water use efficiency for sustainable agroecosystem management

Journal Pre-proof Modelling crop yield, water consumption, and water use efficiency for sustainable agroecosystem management

Yaqun Liu, Wei Song PII:

S0959-6526(19)34810-3

DOI:

https://doi.org/10.1016/j.jclepro.2019.119940

Reference:

JCLP 119940

To appear in:

Journal of Cleaner Production

Received Date:

10 July 2019

Accepted Date:

30 December 2019

Please cite this article as: Yaqun Liu, Wei Song, Modelling crop yield, water consumption, and water use efficiency for sustainable agroecosystem management, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro.2019.119940

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Journal Pre-proof

Modelling crop yield, water consumption, and water use

efficiency

for

sustainable

agroecosystem

management

Yaqun Liua,b, Wei Songa,* a

Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and

Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, PR China b University

*

of Chinese Academy of Sciences, Beijing 100049, PR China

Corresponding author at: Key Laboratory of Land Surface Pattern and Simulation, Institute of

Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, No. 11(A), Datun Road, Chaoyang District, Beijing 100101, PR China. E-mail address: [email protected] (W. Song).

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Journal Pre-proof

Modelling crop yield, water consumption, and water use

efficiency

for

sustainable

agroecosystem

management ABSTRACT

Agroecosystems provide food, energy, and essential services, while accounting for 80% of the world's total water use. Producing more food with limited water resources or improving water use efficiency (WUE) is an urgent task, especially in arid and semi-arid areas with fragile ecosystems and severe water shortages. This study integrated crop type distribution, irrigation data, remote sensing imagery, and meteorological factors to simulate the crop yield, water consumption, and WUE in the Heihe River Basin of China during 2007–2012. We found that variations in water consumption had little impact on crop yield because the value far exceeded the actual water requirement. From 2007–2012, the water consumption of corn increased by 11.24%, while its yield decreased by 0.63%. In contrast, the water consumptions of wheat, oilseed rape, and other crops decreased by 15.45%, 10.32%, and 4.61%, while their yields increased by 2.41%, 8.91%, and 1.13%, respectively. Consequently, the WUE of corn decreased by 10.68%, while that of oilseed rape, wheat, barley, and other crops increased by 21.44%, 21.13%, 4.96%, and 6.02%, respectively. The expansion of the area planted with corn led to a 3.36% increase in the WUE of the basin. However, this agricultural development is clearly unsustainable because it increased agricultural water consumption and decreased the ecological and domestic water supply. On-demand irrigation is therefore necessary to reduce water wastage and improve WUE. This would have reduced agricultural water consumption in 2007 and 2012 by 32.58% and 30.13%,

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Journal Pre-proof and the WUEs would have increased by 48.33% and 43.12%, respectively. Sustainable water-efficient management should comprehensively consider the economic benefits and environmental burdens in crop structure adjustment, irrigation practices, and field management. Additionally, a knowledge-exchange system among experts, resource managers, and farmers is crucial for improving WUE strategies for sustainable agroecosystem development.

Keywords: Water use efficiency; Crop yield simulation; Crop water requirement; On-demand irrigation; Water-efficient management; Knowledge-exchange system

1. Introduction Agroecosystems provide essential goods (e.g., food, fiber, and fuels) and services (e.g., carbon sequestration, climate regulation, water and soil conservation, and maintaining biodiversity) for the social-ecological systems (Liu et al., 2017). Water is critical to agroecosystem development, and agricultural water use accounts for 80% of the world’s total water consumption (Song et al., 2018). However, in an attempt to meet the growing human demand for food and bioenergy, the reclamation and high-intensity use of cropland (Song and Pijanowski, 2014; Song and Deng, 2017) has led to increased water consumption (Lang et al., 2018; Liu et al., 2017) and the degradation of natural ecosystems (Lang and Song, 2018). In addition, ongoing climate change has exacerbated the uneven spatial distribution of global water resources (MA, 2005; Lang et al., 2017), resulting in increasingly serious food and water shortages (Smajgl et al., 2016). Improvements in water use efficiency (WUE) are crucial for regional and global food, water, and ecological security (Deng et al., 2006). Therefore, large-scale, long-term, and reliable monitoring

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Journal Pre-proof of WUE is urgently needed if sustainable agroecosystem development is to be achieved. Agroecosystem WUE is generally defined as the ratio of crop carbon gain (e.g., net primary production (NPP), net ecosystem production (NEP), gross primary production (GPP), or yield) to water consumption (e.g., evapotranspiration (ET), or actual water consumption) (Han et al., 2013; Lu et al., 2015; Wagle et al., 2016a). Water use efficiency is a key indicator of agroecosystem sustainability (Mo et al., 2005; Heydari, 2014), and links the coupled carbon and water cycles in the “food-energy-water” nexus (Smajgl et al., 2016). Increasing WUE will simultaneously lead to crop yield improvements and water consumption minimization (i.e., more crop per drop), which is important for adapting to the growing food demand and water scarcity in both irrigated and rainfed agroecosystems (Marris, 2008; Monaghan et al., 2013). Thus, a more accurate simulation of WUE would provide fundamental information that can greatly benefit WUE-improving management practices. Although field observations of WUE (Mo et al., 2005; Miriti et al., 2012; Wagle et al., 2016b) are generally more accurate than model simulations, it is difficult to determine the spatial heterogeneity of WUE at the macroscale. Conversely, remote sensing (RS)-derived NPP (e.g., MOD17A3) and ET (e.g. MOD16A3) products are commonly used to understand the large-scale spatiotemporal variations in WUE (Xia et al., 2015). However, these coarse-spatial-resolution (500 m) RS products cannot provide crop-specific WUE-improvement strategies because their simulations did not consider crop spatial distribution (Liu et al., 2016). Multi-temporal or time-series vegetation indices (e.g., the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), land surface water index (LSWI), and Normalized Difference Vegetation-Water Index (NDVWI)), which are based on the

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Journal Pre-proof Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) sensors on Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Huanjing (HJ) charge coupled device (CCD) or other RS images have proven to be effective in identifying crop distribution (Arvor et al., 2011; Hao et al., 2014; Dong et al., 2016). Previous WUE simulations have generally ignored the key anthropic factor of irrigation, due to the lack of spatial irrigation data (Zhu et al., 2006; Mo et al., 2009). Nevertheless, optimizing the crop distribution and irrigation system is essential to WUE improvement (Levidow et al., 2014; Xu et al., 2018). Therefore, it is critical to integrate the crop type and irrigation distribution to accurately simulate the spatiotemporal WUE of different crops. Accurate WUE information depends on crop yield and water consumption simulations. The models used for crop yield simulations are mainly empirical statistical, crop growth, and light use efficiency (LUE) models. Empirical statistical models (e.g., Miami (Lieth, 1972), Thornthwaite Memorial (Cramer et al., 1999), and Chikugo (Uchijima and Seino, 1985)) generally simulate crop yield based on its linear or nonlinear statistical relationships with climatic or biological factors (e.g., solar radiation (SOL), temperature, precipitation, NDVI, and leaf area index (LAI)) (Cramer et al., 1999; Becker-Reshef et al., 2010). These models are easy-to-use but have an unsatisfactory accuracy because they do not consider the biochemical processes of biomass accumulation and the concomitant feedbacks from environmental changes. Conversely, by introducing the processes and mechanisms of different growth periods into model frameworks (Liu et al., 1997), crop growth models (e.g., CENTURY (Parton et al., 1993), TEM (McGuire et al., 1997), BIOME-BGC (Han et al., 2016), CARAIB (Warnant et al., 1994), DLEM (Tian et al., 2010), and ORCHIDEE (Piao et al., 2012)) can perform more accurate crop yield simulations. However, uncertainties still remain

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Journal Pre-proof in large-scale applications based on these models, mainly because matched spatiotemporal multi-parameters data is difficult to obtain. Because of the ability of RS to access real-time and accurate spatialized parameters, LUE models (e.g., CASA (Zhu et al., 2006), C-Fix (Yan et al., 2016), GLO-PEM (Prince and Goward, 1995), MODIS-PSN (Ruimy et al., 1994), and VPM (Xiao et al., 2004)) have been widely used for large-scale and long-term crop yield simulation (Potter et al., 2012). These LUE models take into account key processes (e.g., photosynthesis and respiration); however, previous studies have generally ignored crop type and irrigation distribution. The models used for crop water consumption (CWC) simulation include CROPWAT (Luo et al., 2015), AquaCrop (Pohankova et al., 2018), and PolyCrop (Tan and Zheng, 2017). However, these models cannot provide the spatiotemporal water consumption for different crops due to the lack of crop type distribution data (Liu et al., 2017). Using the same parameters as those used to simulate the yield, CWC and WUE of different crops will inevitably compromise model accuracy. Moreover, previous WUE studies have generally been performed at coarse-spatial-resolutions (250–8000 m), meaning they are unable to support decision making in fragmented agroecosystems. Due to the lack of crop-specific spatiotemporal WUE information, it is difficult to understand the underlying causes of WUE variations and develop scientific crop and irrigation optimization strategies. Therefore, there is an urgent need to incorporate crop type and irrigation distribution into the modelling of high-spatial-resolution crop-specific WUE to develop efficient water use strategies for sustainable agroecosystem management. Water and food shortages in arid and semi-arid areas are more serious than in wetter areas (MA, 2005). The Heihe River Basin (HRB) is an important grain base in the arid and semi-arid

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Journal Pre-proof areas of China, and its irrigated oasis agriculture consumes over 85% of the total water consumption in the basin (Liu et al., 2017). Mainly due to the huge irrigation consumption in the middle reaches of HRB, the Juyan Lake and downstream rivers have dried up several times. The Chinese government has therefore initiated many studies and projects to ensure sustainable water use (Liu et al., 2017). Although the downstream rivers and lakes have been restored, the irrigation requirement in HRB has increased from 23.60 × 108 m3 in 2007 to 26.93 × 108 m3 in 2012, due to agroecosystem expansion and structural changes in crop planting (Song and Zhang, 2015; Liu et al., 2017). Investigating the spatiotemporal patterns of crop-specific WUE is therefore critical to reduce agricultural water waste and develop efficient water management strategies (Wang et al., 2015). In this context, the aims of this study were to: (1) model the spatiotemporal NPP, yield, water consumption, and WUE of different crops in the HRB in 2007 and 2012; (2) analyze their spatiotemporal heterogeneity and variations; and (3) provide practical WUE-improvement policy recommendations for sustainable agroecosystem development.

2. Materials and Methods 2.1. Study area The HRB is a continental river basin that extends across arid and semi-arid areas of northwestern China. It is located in the central part of the “silk road economic belt” and the Hexi Corridor (97°30′E−101°43′E, 37°55′N–40°00′N) (Fig. 1). The HRB has an area of 12.80 × 106 ha, covering the 11 counties of Qilian, Sunan, Shandan, Minle, Ganzhou, Linze, Gaotai, Suzhou, Jiayuguan, Jinta, and Ejina. The HRB is one of the ten largest grain bases in China, with extensive solar-thermal resources and sufficient irrigation water. The main crops include corn, wheat, barley

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Journal Pre-proof and oilseed rape, and they are mainly distributed in the oasis agroecosystem in the middle reaches of the river basin (Liu et al., 2016). The region has a temperate continental climate, with frequent droughts and a scarcity of rain. The average annual precipitation shows a gradually decreasing trend from upstream (350 mm) to midstream (200 mm) and downstream (45 mm). The average annual temperature, annual sunshine duration, annual total SOL, and annual potential ET are 6–8℃, 3000 h, 6000 MJ/m2, and 2700 mm, respectively (Liu et al., 2017). The water resources are mainly derived from melting ice and snow in the Qilianshan Mountains and rainfall, of which over 85% is consumed by the agroecosystem. [Insert Fig. 1 here]

2.2. Data sources The WUE of this study was calculated based on crop yield and water consumption. The main data sources used for simulating crop yield and water consumption were the crop spatial distribution, monthly NDVI, monthly meteorological factors, monthly crop irrigation (CI), and a digital elevation model (DEM). These data were obtained for 2007 and 2012, with a spatial resolution of 30 m. The crop spatial distribution dataset identified five crop types (corn, wheat, barley, oilseed rape, and other crops), with an overall accuracy of 89.38%, using the multi-temporal NDVWI (Liu et al., 2016) based on 169 scenes from Landsat TM/ETM+ images. The NDVWI combines the advantages of NDVI and NDWI by comprehensively considering the spectral characteristics of crops in the red, near infrared, and short-wave infrared bands, resulting in a better crop classification performance than the NDVI (Liu et al., 2016). Monthly NDVI data was obtained by the pan-sharpening fusion (Vivone et al., 2015) of

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Journal Pre-proof MODIS NDVI (i.e., MOD13Q1) and TM/ETM+ NDVI. MOD13Q1 time-series data (Didan, 2015), with a spatial resolution of 250 m and a temporal resolution of 16 d, was initially processed by the maximum value composites (MVC) method, and then used in the fusion. Monthly meteorological factors (precipitation; average, maximum, and minimum temperatures; relative humidity; wind speed; and pressure) were interpolated using the thin plate spline method in the ANUSPLIN software (Liu et al., 2017), based on the monitoring data from 20 meteorological sites in the HRB and its surroundings. Monthly CI data was obtained by integrating two datasets: a monthly irrigation dataset, with 30 s spatial resolution over the HRB (Zeng et al., 2016) and an irrigation ditch map for Zhangye City (Liu and Ma, 2010). The distance to an irrigation ditch was used as a covariate to assist in interpolating monthly irrigation from 30 s spatial resolution to 30 m using the thin plate spline method. A DEM at 30 m spatial resolution was obtained from the aster global digital elevation model (ASTER GDEM) Version 2 dataset (Tachikawa et al., 2011), and was used for calculating total SOL. The data sources used for the verification of CWC, NPP, and yield in 2007 and 2012 were RS-based actual ET, the MODIS MOD17A3H product, and statistical yield data, respectively. An annual actual ET dataset, with 1000 m spatial resolution, was derived from multisource RS data using the ETWatch model (Wu et al., 2012). Actual ET was used as an approximate value for the assessment of CWC. The MOD17A3H version 6 product provides yearly NPP at 500 m spatial resolution, with values derived from the sum of 8-day net photosynthesis, i.e., the difference between GPP and maintenance respiration (Running et al., 2015). Statistical crop yield data at the

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Journal Pre-proof county scale was obtained from multiple statistical yearbooks (ZBSC, 2007, 2012; GBSC, 2008, 2013).

2.3. Methods We initially used the CASA model to map crop NPP based on the crop spatial distribution, NDVI, meteorological factors, and CI (Fig. 2). Several process-based parameters were associated with NPP to simulate crop yield. We then calculated effective rainfall (ER) based on meteorological factors, and simulated CWC in combination with irrigation data. Finally, we simulated WUE based on the definition of crop yield per unit water consumption. [Insert Fig. 2 here] 2.3.1. CASA model for NPP mapping The CASA model is a representative LUE model that has been widely used for NPP simulation, and its reliability has been verified in both global and regional studies (Potter et al., 2003, 2012; Zhu et al., 2006). The CASA model-based NPP in month t (NPPt) was determined by the absorbed photosynthetically active radiation (APARt) and the actual efficiency of radiation use (εt) (Potter et al., 1993). Approximately half of the total SOL (with wavelengths of 0.4 ~ 0.7 μm) is available for most vegetation, and the radiation that is actually used by vegetation is determined by the fraction of photosynthetically active radiation absorbed by vegetation (FPAR). The FPAR is calculated from a linear function of the simple ratio (SR), and SR is determined by the NDVI (Potter et al., 1993; Zhu et al., 2006). The εt is restricted by the stress factors of high temperature (Tmax,t), low temperature (Tmin,t), and water (Wt) in month t. The maximum ε (εmax) value for agroecosystems in China is 0.542 gC·MJ−1 (Zhu et al., 2006). The formulas used to calculate Tmax,t,

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Journal Pre-proof Tmin,t, and Wt have been documented previously (Potter et al., 1993). Tmax,t and Tmin,t were determined by the annual optimal growth temperature (Topt) and the mean temperature in month t (Tmean,t), and Wt was determined by the actual ET (ETa,t) and potential ET (ETo,t) in month t. The ETa,t was calculated by the advection-aridity approach (Brutsaert and Stricker, 1979), and ETo,t was calculated by the FAO Penman-Monteith Formula (Allan et al., 2005).

NPPt  APARt   t

(1)

APARt  SOLt  FPARt  0.5

(2)

FPARt 

SRt 

SRt  SRmin   FPARmax  FPARmin   FPARmin SRmax  SRmin

1  NDVI t 1  NDVI t

(4)

 t   max  Tmax,t  Tmin,t  Wt

(5)

2 Tmax,t  0.8  0.02  Topt  0.0005  Topt

Tmin,t 

1.184

(6)



1

1  exp 0.2  Topt  10  Tmean,t   1  exp 0.3  Tmean,t  10  Topt  

Wt  0.5  0.5 

ETa ,t

(7)

(8)

ETo ,t

ETa   2  1 

(3)

    Rn  G    0.35  0.5  0.54u2    es  ea      900  u2   es  ea  Tmean  273     1  0.34u2 

(9)

0.408     Rn  G     ETo 

(10)

where SRmin and SRmax are the minimum and maximum SR, respectively; FPARmin and FPARmax represent the FPARs with no vegetation coverage and full vegetation coverage, and had values of 0.001 and 0.95, respectively; Rn is the crop surface net radiation (MJ*m-2*d-1); G is the soil heat flux (MJ*m-2*d-1); △ is the slope of the saturated water pressure curve (kPa/℃); γ is the

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Journal Pre-proof psychrometer constant (kPa/℃); u2 is the wind speed at 2 m (m/s); Tmean is mean temperature (℃);es and ea are the saturated (kPa) and actual water pressure (kPa), respectively; and α is the Priestley-Taylor empirical coefficient, which varies with land cover and micro-meteorological conditions (Brutsaert and Stricker, 1979). In this study, the α value of the agroecosystem in the HRB was set at 1.10.

2.3.2. Process-based parameters for crop yield simulation The total biomass of crop i was determined by its NPP and the carbon-to-biomass coefficient (ai). For most crops, only a portion of the aboveground biomass is harvested. The yield of crop i (Yieldi) refers to its harvested aboveground biomass after drying.

Yieldi  NPPi  HI i  pi  ai / (1  wi ) NPPi 

(11)

th ,i

 NPP

t ts ,i

(12)

t

where HIi, pi, and wi represent the harvesting index, the ratio of aboveground biomass to total biomass, and the water content of crop i, respectively; and NPPi is the NPP of crop i, i.e., the accumulated NPP in its growth period, from sowing date (ts,i) to harvesting date (th,i). The values of these process-based parameters (Table 1) were determined based on the results of previous studies (Xie et al., 2011; Liu et al., 2017), statistical data (DCPMOAC, 2007, 2012; ZBSC, 2007, 2012; GBSC, 2008, 2013), and our field survey. [Insert Table 1 here]

2.3.3. Crop water consumption Crop water consumption includes ER and CI, where ER is the rainfall effectively used by

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Journal Pre-proof crops, not including that lost through runoff and infiltration. The ER was calculated using the Soil Conservation Service method proposed by the United States Department of Agriculture (USDA-SCS Method) (Allan et al., 2005).

CWC  ER  CI

(13)

 ARmonth * 125  0.2* ARmonth  /125 ERmonth   125  0.1* ARmonth

for ARmonth  250 mm for ARmonth  250 mm

(14)

where ERmonth and ARmonth are the monthly ER and actual rainfall, respectively.

2.3.4. Water use efficiency modelling Crop WUE in this study was defined as crop yield (Yield) per CWC (Mo et al., 2009).

WUE 

Yield CWC

(15)

2.3.5. Accuracy assessment method The modelling accuracy of CWC and NPP were assessed by RS-based actual ET and MOD17A3H using 1000 random samples, respectively. Crop yield was verified by statistical data at the county scale. The root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the reliability of modelling results.

1 n 2  Si  Vi   n i 1

RMSE 

  2 R    

S n

i 1

S n

i 1

i

i

(16)



 S Vi  V

S



  V  V  2

n

i 1

i

2

     

2

(17)

where Si and Vi represent the simulated and verification values of a sample i, respectively; S and V represent the mean simulated and verified values of all samples, respectively; and n is the

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3. Results

3.1. Validation of CWC, NPP, and yield We evaluated the performance of CWC, NPP, and yield modelling using the actual ET, MOD17A3H NPP, and statistical yield (Fig. 3). The relationship between our simulated CWC and the actual ET estimated by the ETWatch model was similar over time, with R2 and RMSE values of 0.5570 and 130.68 mm in 2007, and 0.5561 and 129.87 mm in 2012, respectively (Fig. 3a,b). Most CWC values were greater than the actual ET, with the largest values found in corn. The simulated crop-type-specific NPP was generally greater than the MOD17A3H NPP, with R2 and RMSE values of 0.5676 and 34.12 gC/m2 in 2007, and 0.5890 and 31.90 gC/m2 in 2012, respectively (Fig. 3c,d). However, despite our NPP being greater, our simulated yield was less than the statistical yield, with R2 and RMSE values of 0.7072 and 1940.61 kg/ha in 2007, and 0.7933 and 1802.19 kg/ha in 2012, respectively (Fig. 3e,f). [Insert Fig. 3 here]

3.2. Spatiotemporal variations in crop NPP Crop NPP in 2007 and 2012 had significant spatial heterogeneity and varied among crop types (Fig. 4). Crops with a high NPP (>450 gC/m2) were mainly distributed in the piedmont plain (e.g., Ganzhou), while crops with a low NPP (>300 gC/m2) were located in the downstream area of the HRB where there are high evaporation rates and low rainfall (e.g., Ejina), and the

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Journal Pre-proof high-altitude mountainous areas with low accumulated temperature (e.g., Minle and Shandan) (Fig. 4a,b,c). The NPP in the areas of Linze, Gaotai, Suzhou, Jinta, Shandan, and Minle where corn planting has expanded increased from 2007 to 2012 (Fig. 4d). The largest NPP in 2007 was found in Ganzhou, but it had declined by 2012. The NPP in the urbanized regions of Ganzhou and Linze decreased significantly. Moreover, the NPP increased in the corn-expansion and barley-to-wheat transfer areas of Shandan and Minle, while it decreased in southeastern Shandan, where corn planting contracted over the study period. The NPPs of wheat, oilseed rape, and other crops increased to different extents from 2007 to 2012 (Table 2). The NPP of oilseed rape had the largest increase of 8.91% (34.97 gC/m2), while that of wheat and other crops increased by 2.41% and 1.13%, respectively. The NPPs of barley and corn decreased slightly by 2.97% (9.79 gC/m2) and 0.63% (2.87 gC/m2), respectively. Consequently, the average NPP of the HRB increased by 1.69% (6.71 gC/m2), from 397.38 to 404.09 gC/m2. [Insert Fig. 4 here] [Insert Table 2 here]

3.3. Spatiotemporal variations in crop yield The corn yield increased over the study period, while the yields of wheat, barley, and oilseed rape plantations decreased (Fig. 5a,b,c). In 2007, the higher-crop-yield regions were mainly concentrated in Ganzhou, while Linze, Gaotai, Suzhou, Jiayuguan, and Jinta had medium yields. Lower yields were observed in Minle, Shandan, and Ejina (Fig. 5a). In 2012, the higher-crop-yield regions expanded to the higher-latitude regions of Linze and Jinta, and to the high-altitude regions

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Journal Pre-proof of the southeastern border between Shandan and Minle (Fig. 5b). The crop yield of Ganzhou in 2012 decreased compared with the yield in 2007 (Fig. 5d). The low-crop-yield regions in Minle and Shandan decreased, while the low-crop-yield regions in Suzhou, Jiayuguan, and Jinta expanded to some extent. The crop yield of Ejina in the downstream area of the HRB was relatively low, and declined slightly over the study period. Similarly to the changes of NPP, the yields of oilseed rape, wheat, and other crops increased by 8.91% (192.72 kg/ha), 2.41% (75.46 kg/ha), and 1.13% (40.25 kg/ha), respectively, from 2007 to 2012 (Table 2). In contrast, the yields of corn and barley decreased by 0.63% (39.49 kg/ha) and 2.97% (87.24 kg/ha), respectively. As a result, the average crop yield in the HRB increased by 133.70 kg/ha (3.40%), from 3932.75 to 4066.45 kg/ha. [Insert Fig. 5 here]

3.4. Spatiotemporal variations in CWC From 2007 to 2012, the average ER decreased from 203.81 to 154.33 mm in the HRB agroecosystem (Fig. 6a). Qilian and Sunan at higher altitudes had the largest decrease (>60 mm). The ER decrease in most midstream areas of HRB was between 50 and 60 mm, and the lowest (<20 mm) decrease occurred in the arid region of Ejina in the downstream areas. The ERs of corn, wheat, barley, oilseed rape, and other crops decreased by 43.50, 22.79, 36.62, 48.53, and 51.17 mm, respectively (Table 3). The average CI in the HRB increased from 513.92 mm in 2007 to 561.80 mm in 2012 (Fig. 6b). Most regions experienced an increased CI, with values between 50 and 300 mm. Ganzhou and Linze had a CI increase greater than 300 mm. The CIs in the cropland-reclaimed and

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Journal Pre-proof barley-to-wheat transfer areas increased. The CI decrease was highest in Gaotai, reaching above 300 mm in some parts of the county. The CIs of corn and other crops increased by 139.14 and 18.75 mm, respectively, whereas the CIs of wheat, oilseed rape, and barley decreased by 72.99, 10.18, and 4.01 mm, respectively (Table 3). [Insert Fig. 6 here] [Insert Fig. 7 here] [Insert Table 3 here] Because CI accounted for more than 70% of CWC (Table 3), the CWCs in 2007 and 2012 presented a spatial heterogeneity dominated by CI (Fig. 7). The CWCs in some high-CI regions of Ganzhou, Linze, and Ejina exceeded 2000 mm (Fig. 7a,b,c). Compared with 2007, the CWC in the higher-CWC regions of Ganzhou and Linze continuously increased in 2012, while the CI in Gaotai and Ejina decreased (Fig. 7d). The plain oasis areas in the middle reaches of the HRB had CWC values higher than 1000 mm. The CWC increased in most regions, with CWC values between 500 and 1000 mm. The regions with CWC values <500 mm were mainly located in Minle, Shandan, central Ganzhou, and Jinta and generally presented a decreasing trend. The CWC of corn was the largest, while that of barley was the smallest (Table 3). From 2007 to 2012, the CWC of corn increased by 11.24% (95.64 mm), while that of wheat, oilseed rape, barley, and other crops decreased by 15.45% (95.78 mm), 10.32% (58.71 mm), 7.56% (40.63 mm), and 4.61% (32.60 mm), respectively. Thus, the average CWC in the HRB decreased slightly by 0.22%, from 717.73 to 716.13 mm.

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Journal Pre-proof 3.5. Spatiotemporal variations in crop WUE In 2007 and 2012, the higher WUE values were mainly located in the lower-CWC regions, while the lower WUE values were mainly distributed in the higher-CWC regions (Fig. 8). The regions with a WUE higher than 1500 g/m3 were scattered throughout the corn plantation area (Fig. 8a,b,c), while the low-value regions, with WUE <200 g/m3 were mainly distributed in the corn plantation areas with a high CWC. The WUE in Ejina was relatively low due to the high CWC. The WUEs of the wheat, barley, and oilseed rape plantations in Minle and Shandan were generally between 500 and 1500 g/m3, and showed an increasing trend, with an increase of over 500 g/m3 in some regions (Fig. 8d). The CWC-surged areas showed a decreasing trend in WUE. The WUE in cropland expansion areas increased, while in areas of urban expansion it declined. Although the water consumption of corn was highest, its yield was almost twice that of the other major crops, resulting in the highest WUE for corn (Table 4). From 2007 to 2012, the WUE of corn decreased by 10.68% (78.58 g/m3), whereas the WUEs of wheat, oilseed rape, barley, and other crops increased by 21.13% (106.54 g/m3), 21.44% (81.54 g/m3), 4.96% (27.12 g/m3), and 6.02% (30.22 g/m3), respectively. As a consequence, the average WUE in the HRB agroecosystem increased by 3.36% (19.90 g/m3), from 547.94 to 567.84 g/m3. [Insert Fig. 8 here] [Insert Table 4 here]

3.6. Agricultural water-saving potential and WUE improvement Because the actual CWC was significantly higher than the crop water requirement, 32.58% (1619.24 × 106 m3) and 30.13% (1619.24 × 106 m3) of the total CWC in the HRB was wasted in

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Journal Pre-proof 2007 and 2012, respectively (Table 5). With constant crop yields, the WUEs in 2007 and 2012 could have increased by 48.33% (264.83 g/m3) and 43.12% (244.83 g/m3), respectively, if crop irrigation was performed according to the actual crop water requirement. Consequently, the WUE of corn in 2007 and 2012 could have increased by 71.80% (528.49 g/m3) and 81.44% (535.51 g/m3), respectively. [Insert Table 5 here]

4. Discussion 4.1. Spatial heterogeneity of WUE The spatial heterogeneity of WUE in the HRB was jointly affected by crop type distribution, climatic conditions, irrigation facilities, field management, and farmers’ perception. Although the plain oasis areas in the middle or lower reaches of the HRB have lower precipitation, the conditions are more conducive for irrigation and more agricultural water can be supplied than in the mountainous areas in the middle or upper reaches (Song et al., 2018). In addition, the plain oasis areas have a higher accumulated temperature than the mountainous areas. Thus, thermophilic and water-intensive corn was mainly distributed in the plain oasis areas, while hardy wheat, barley, and oilseed rape was mainly grown in mountainous areas (Liu et al., 2016). Accordingly, both the CWC and crop yield in plain oasis areas were higher than in mountainous areas. However, a high yield does not imply a high WUE, because farmers have an inadequate knowledge of crop water requirements, and tend to perform over-irrigation. Agricultural practices generally develop in pursuit of higher economic benefits; however, input-intensive farming is not always economical, and can degrade soil and water resources. For example, in the HRB, although CWC substantially

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Journal Pre-proof exceeds the crop water requirement, crop yields have not increased significantly and have even decreased in some areas. Water-inefficient practices (e.g., flood irrigation) not only waste a substantial volume of water but also enhance the environmental burden of agriculture. Unfortunately, low irrigation costs and inadequate supervision have further aggravated over-irrigation, especially in the irrigation-conducive plain oasis areas. Thus, for the same crop type, its WUE in mountainous areas is generally higher than that in plain oasis areas. Moreover, differences in field management practices other than irrigation (e.g., fertilizing, breeding, and spraying pesticides) can also lead to the spatiotemporal heterogeneity of WUE (Lu and Fan, 2013); however, these factors were not investigated in this study. Crop structure adjustments and climate variability in the HRB have further altered the heterogeneity of WUE. From 2007 to 2012, mainly driven by economics, the number of corn and oilseed rape plantations increased, while the number of wheat and barley plantations decreased (Liu et al., 2016, 2017). Climate warming has expanded the area of corn plantation into the high-latitude or high-altitude areas; consequently, these areas have experienced an increase in WUE. However, the WUE in untransformed corn planting areas has decreased, due to the increased proportion of seed corn, which has a lower yield and higher water consumption than normal corn (Tan and Zheng, 2017). In addition, the increased temperature and reduced rainfall in the HRB from 2007 to 2012 has increased crop irrigation (Liu et al., 2017). In terms of crop type, farmers tend to irrigate more for water-intensive corn because it provides greater economic benefits. Consequently, the CWC of corn increased, while that of wheat, barley, oilseed rape, and other crops reduced. However, the yield of corn did not increase significantly, and the yields of the other four crops did not decrease but rather increased, due to their CWCs being far higher than

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Journal Pre-proof their actual water requirements. Hence, the WUE of corn decreased and the WUEs of other crops increased.

4.2. Implications for improving WUE Water use efficiency can be improved by crop structure adjustments, water-pricing policies, administrative water allocations, on-demand irrigation systems, and effective field management. Although the replacement of other crops by corn has slightly increased the average WUE and farmers’ income in the HRB (Tan and Zheng, 2019), it has also sharply increased agricultural water consumption, and concomitantly reduced the ecological and domestic water supply (Song et al., 2018). Sustainable water-efficient management strategies should combine farmers’ economic income with wider environmental benefits (Levidow et al., 2014), i.e., integrate both the economic and environmental efficiencies of water use. Therefore, crop structure adjustments should aim to simultaneously improve WUE and ensure an ecological water supply in the basin. Specifically, it is crucial to ensure food-water-ecology security by transferring from water-intensive crops to water-saving and high-value crops, and importing high virtual water content (VWC) crops from other water-rich areas (Liu et al., 2016, 2017). Previous studies have confirmed that water-price increases can induce farmers to adopt water-conserving practices (Wu et al., 2018). However, raising water prices is often ineffective in reducing water demand because economic income is more of a priority for most farmers than saving water (Molden et al., 2010). Moreover, it will increase irrigation costs and reduce farmers’ incomes. Conversely, administrative water allocation has been proven to be an effective way to improve WUE, because an inelastic water supply generally leads to water-efficient agricultural

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Journal Pre-proof practices (Molden et al., 2010). It is critical to allocate agricultural water on the basis of the crop area and natural conditions across multiple irrigated districts. However, it is difficult to meet the conflicting water demands from multiple self-interest-oriented stakeholders (e.g., agriculture, industry, urban residents, and ecosystems). Although ecological water use should be given a high priority to ensure sustainable development, few farmers voluntarily save water for ecological protection without compensation. Thus, effective ecological compensation measures should be developed to encourage farmers to improve WUE and earn extra-income from saving water. Additionally, it is essential to strengthen the supervision of water allocation and reallocation (Deng et al., 2015). Innovative irrigation practices and field management can enhance WUE and reduce environmental burdens. In arid and semi-arid areas, more than 50% of irrigation and rainfall evaporates into the atmosphere or infiltrates into the soil (Molden et al., 2010). Accordingly, reducing evaporation and infiltration, while increasing productive transpiration, is the key to improving WUE. On-demand irrigation scheduling is an effective way to reduce water wastage and improve yields (Xu et al., 2018). Drip and sprinkler irrigation systems have proven to be more efficient in reducing evaporation than flood irrigation (Liu and Shen, 2018). Innovative irrigation technologies, such as supplemental irrigation and deficit irrigation, can achieve high water productivity (Molden et al., 2010). Moreover, water harvesting technologies (e.g., terracing farming, and drainage ditches) can increase the additional water available for crops, and prevent water and soil erosion. It is also crucial to reduce delivery losses by canal lining or pipe-delivery. Although individual farmers and farming organizations have already made significant investments in well-equipped irrigation systems, the potential for WUE improvements has not been fulfilled

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Journal Pre-proof due to the lack of relevant knowledge among farmers. It is therefore critical to establish a knowledge-exchange system among experts, resource managers and farmers regarding crop water requirements, irrigation scheduling, and crop yield response to different irrigation practices (Levidow et al., 2014; Deng et al., 2015). In terms of WUE-improving field practices, effective breeding, mulching, and fertilizing can improve soil properties and water retention capacity, and close crop yield gaps (Lu and Fan, 2013). Crop breeding improves WUE by triggering biophysical vigor to increase the harvest index and enhance resistance to drought (Molden et al., 2010). Mulching with crop residues or plastic film can raise early-growth temperature, reduce soil evaporation, and increase water retention and productive transpiration (Waraich et al., 2011). Fertilizing with macronutrients (e.g., carbon, nitrogen, and phosphorus) and micronutrients (e.g., zinc, copper, and iron) can reduce photo-oxidative damage, maintain osmotic potential, and enhance photosynthetic capacity (Liao et al., 2018; Liu et al., 2019). Most importantly, these effective field management strategies should also be incorporated into a knowledge-exchange system to enable farmers to improve WUE.

4.3. Comparisons with other studies Our crop-type-specific NPP had similar values and distributions to previous simulated and observed results in the HRB (Cui et al., 2016), but had higher values than MOD17A3H NPP (Running et al., 2015). The underestimation of MODIS NPP for agroecosystems can be attributed to the use of the same LUE parameter and the coarse spatial distribution (MCD12Q1, 500 m) used for all crop types (Cui et al., 2016; Xiao et al., 2019). However, our simulated average yields were lower than the statistical (ZBSC, 2007, 2012; GBSC, 2008, 2013) or experimental yields (Yang et

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Journal Pre-proof al., 2011). This was because statistical or experimental yields were generally estimated based on a few samples at the field-scale, ignoring the low-yield samples impacted by droughts, diseases, or pests. In contrast, our RS-based simulations, supported by fine resolution crop distributions, considered all crops in the HRB. Moreover, our simulated CWC was similar to that of previous studies in the HRB (Yang et al., 2011; Cui et al., 2016; Tan and Zheng, 2019). Consequently, our simulated WUE was slightly smaller than census-based (Tan and Zheng, 2019) or experiment-based WUEs (Yang et al., 2011). Similarly to previous studies, we found that an expansion in the area planted with corn increased the WUE in the HRB (Tan and Zheng, 2019). In general, most previous WUE studies in the HRB have been performed based on a limited number of samples from statistical data or field experiments, and few RS-based simulations have not been supported by fine resolution crop and irrigation distribution information. Future research should integrate multi-scale methods (e.g., field experiments, fixed-point observations, and RS) to more accurately simulate large-scale and long-term WUE for sustainable agroecosystem management.

5. Conclusions In summary, the present study provided high-spatial-resolution crop-type-specific WUE information and WUE-improvement strategies for sustainable agroecosystem management. We found that WUE varies with crop structure adjustments, irrigation, and climate variability. The CWC in the HRB far exceeded the crop water requirement, and its changes had a limited impact on crop yield but altered the spatial heterogeneity of WUE. Accordingly, the WUE of corn decreased and the WUEs of other crops increased. Because corn has the highest WUE, its expansion has led to a 3.36% increase in the average WUE of the HRB. However, this

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Journal Pre-proof unsustainable increase in the WUE has resulted in a surge in agricultural water consumption and has concomitantly reduced the ecological and domestic water supply. Thus, by performing on-demand irrigation, the CWC could have been reduced by 32.58% and 30.13% in 2007 and 2012, and the WUE could have been increased by 48.33% and 43.12%, respectively. In order to achieve sustainable WUE-improving management, water-efficient practices (e.g., planting structure, irrigation systems, administrative water allocation, and field management) should comprehensively consider the economic benefits and environmental burdens. Most importantly, all of these WUE strategies should be incorporated into a knowledge-exchange system to enable farmers to improve their water use.

Abbreviations Crop irrigation, CI Crop water consumption, CWC Effective rainfall, ER Evapotranspiration, ET Gross primary production, GPP Remote sensing, RS Light use efficiency, LUE Net primary production, NPP Normalized difference vegetation index, NDVI Normalized difference vegetation-water index, NDVWI Water use efficiency, WUE

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Acknowledgments This research was supported by the National Natural Science Foundation of China (Grant No. 41671177), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20040201) and the Key Research and Development Program of China (Grant No. 2016YFA0602402).

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Journal Pre-proof 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens Environ 91, 256-270. Xiao, X.Y., Li, X.B., Jiang, T., Tan, M.H., Hu, M.Y., Liu, Y.Q., Zeng, W., 2019. Response of net primary production to land use and climate changes in the middle-reaches of the Heihe River Basin. Ecol Evol 9, 4651-4666. Xie, G.H., Han, D.Q., Wang, X.Y., Lu, R.H., 2011. Harvest index and residue factor of cereal crops in China. Journal of China Agricultural University 16, 1-8. (In Chinese) Xu, X.X., Zhang, M., Li, J.P., Liu, Z.Q., Zhao, Z.G., Zhang, Y.H., Zhou, S.L., Wang, Z.M., 2018. Improving water use efficiency and grain yield of winter wheat by optimizing irrigations in the North China Plain. Field Crop Res 221, 219-227. Yan, H.M., Zhan, J.Y., Yang, H.C., Zhang, F., Wang, G.F., He, W.J., 2016. Long time-series spatiotemporal variations of NPP and water use efficiency in the lower Heihe River Basin with serious water scarcity. Phys Chem Earth 96, 41-49. Yang, C.H., Huang, G.B., Chai, Q., Luo, Z.X., 2011. Water use and yield of wheat/maize intercropping under alternate irrigation in the oasis field of northwest China. Field Crop Res 124, 426-432. Zeng, Y.J., Xie, Z.H., Yu, Y., Liu, S., Wang, L.Y., Jia, B.H., Qin, P.H., Chen, Y.N., 2016. Ecohydrological effects of stream-aquifer water interaction: a case study of the Heihe River basin, northwestern China. Hydrol Earth Syst Sc 20, 2333-2352. Zhangye Bureau of Statistics of China (ZBSC), 2007, 2012. Zhangye Statistical Yearbook, Zhangye Qinghua Press, Zhangye. Zhu, W.Q., Pan, Y.Z., He, H., Yu, D.Y., Hu, H.B., 2006. Simulation of maximum light use efficiency for some typical vegetation types in China. Chinese Sci Bull 51, 457-463.

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Journal Pre-proof Author Contributions: Conceptualization, W.S.; methodology, Y.L. and W.S.; formal analysis, Y.L.; investigation, W.S. and Y.L.; resources, W.S.; writing—original draft preparation, Y.L.; writing—review and editing, W.S.; supervision, W.S.

Journal Pre-proof Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, nor in the decision to publish the results.

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Fig. 1. Geographical location, elevation and counties of the HRB.

Journal Pre-proof Crop spatial distribution

NDVI

FPAR

SOL

Stress factors

Tmax

ε

APAR CASA model

Crop irrigation

DEM

USDA-SCS Method

W

εmax

Crop yield

NPP

Tmin

Meteorological factors

Process-based parameters

Fig. 2. Modelling framework of water use efficiency.

Effective rainfall

Crop water consumption

Water use efficiency

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Fig. 3. Comparison of simulated and verification data in the HRB: simulated CWC and ETWatch-based actual ET in 2007 (a) and 2012 (b), simulated NPP and MOD17A3H NPP in 2007 (c) and 2012 (d), simulated yield and statistical yield in 2007 (e) and 2012 (f).

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Fig. 4. Spatiotemporal variations of crop NPP in HRB: (a) 2007, (b) 2012, (c) average in 2007–2012, (d) change in 2007–2012.

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Fig. 5. Spatiotemporal variations of crop yield in the HRB: (a) 2007, (b) 2012, (c) average in 2007–2012, (d) change in 2007–2012.

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Fig. 6. Spatiotemporal variations of (a) effective rainfall (ER) and (b) crop irrigation (CI) in the HRB during 2007–2012.

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Fig. 7. Spatiotemporal variations of crop water consumption in the HRB: (a) 2007, (b) 2012, (c) average in 2007–2012, (d) change in 2007–2012.

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Fig. 8. Spatiotemporal variations of crop water use efficiency in the HRB: (a) 2007, (b) 2012, (c) average in 2007–2012, (d) change in 2007–2012.

Journal Pre-proof A modelling framework of crop-type-specific water use efficiency (WUE) is presented. WUE heterogeneity varied with planting structure changes and climate variability. Expansion of corn with a high WUE and water consumption was unsustainable. On-demand irrigation can enhance WUE and reduce environmental burdens. A knowledge-exchange system is the key to efficient water management.

Journal Pre-proof Table 1. Key parameters during the growth process of different crops. Crop type

Sowing date Harvesting date

HIi

pi

ai

wi

Corn

15 April

26 September

0.444

0.9

3.00

13.00%

Wheat

20 March

26 July

0.387

0.9

2.22

12.50%

Barley

20 March

19 July

0.390

0.9

2.22

12.50%

Oilseed rape

10 April

21 August

0.251

0.9

2.00

18.00%

Other crops

1 April

14 August

0.369

0.9

2.36

14.00%

Abbreviations: HIi, pi, ai, and wi represent the harvesting index, the ratio of aboveground biomass to total biomass, the conversion coefficient of carbon flux to biomass, and the water content of crop i, respectively.

Journal Pre-proof Table 2. Average crop NPP (gC/m3) and crop yield (kg/ha) in 2007 and 2012. Crop type

NPP in 2007

NPP in 2012

Yield in 2007

Yield in 2012

Corn

454.39

451.52

6261.12

6221.63

Wheat

353.73

362.27

3125.84

3201.30

Barley

329.85

320.06

2937.45

2850.21

Oilseed rape

392.73

427.70

2163.85

2356.57

Other crops

391.00

394.36

3553.74

3593.99

Journal Pre-proof Table 3. Average effective rainfall (ER), crop irrigation (CI), and crop water consumption (CWC) in 2007 and 2012 (mm). 2007

2012

Crop type ER

CI

CWC

ER

CI

CWC

Corn

183.83

666.75

850.58

140.33

805.89

946.22

Wheat

229.78

390.13

619.91

206.99

317.14

524.13

Barley

250.20

287.39

537.59

213.58

283.38

496.96

Oilseed rape 262.08

306.87

568.95

213.55

296.69

510.24

Other crops

505.50

707.54

150.87

524.07

674.94

202.04

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Table 4. Average water use efficiency (g/m3) of crops in the HRB in 2007 and 2012. Crop type

2007

2012

Corn

736.10

657.52

Wheat

504.24

610.78

Barley

546.41

573.53

Oilseed rape

380.32

461.86

Other crops

502.27

532.49

Journal Pre-proof Table 5. Crop water requirement (CWR), crop water consumption (CWC), planting area (PA), and crop water waste (CWW) in the HRB in 2007 and 2012. CWR (mm)

CWC (mm)

PA (103 ha)

CWW (106 m3)

2007

2012

2007

2012

2007

2012

2007

2012

Corn

495.11

521.50

850.58

946.22

118.21

149.52

420.20

635.04

Wheat

418.94

411.39

619.91

524.13

45.92

33.68

92.29

37.97

Barley

377.89

393.53

537.59

496.96

23.51

15.43

37.55

15.96

Oilseed rape 394.26

417.03

568.95

510.24

16.37

20.02

28.60

18.66

Other crops 495.32

506.58

707.54

674.94

490.34

508.85

1040.60

856.70

Crop type

Note: CWR and PA data are from Liu et al. (2017).