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Long-term water balance and sustainable production of Miscanthus energy crops in the Loess Plateau of China
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
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Long-term water balance and sustainable production of Miscanthus energy crops in the Loess Plateau of China Wei Liu a,1, Jia Mi b,1, Zhihong Song b, Juan Yan c, Jianqiang Li c, Tao Sang a,b,* a
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China b Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China c Key Laboratory of Plant Germplasm Enhancement and Speciality Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, Hubei 430074, China
article info
abstract
Article history:
Sustainable production of second-generation energy crops on marginal land holds a great
Received 13 August 2013
potential for renewable energy development. Because a vast area of marginal land is
Received in revised form
located in the arid and semiarid regions of the world, water shortage is the most serious
13 January 2014
environmental limitation. In this study, we developed a water balance model to address
Accepted 17 January 2014
the question of whether Miscanthus energy crops can be sustainably produced in the Loess
Available online 12 February 2014
Plateau of China, a region of more than 60 million hectares particularly abundant in semiarid marginal land. The simulation of 20-year soil water content in bare soil, the
Keywords:
winter wheat field, and the Miscanthus field across the Loess Plateau suggested that the
Bioenergy
long-term production of Miscanthus would not cause water depletion in deep soil. This
Miscanthus lutarioriparius
finding addressed a serious concern that growing high-biomass plants in the Loess Plateau
Soil water content
might lead to deep-soil water depletion, which was suggested to be the cause of previous
Sustainable production
failure of afforestation. Planting Miscanthus was effective in reducing surface runoff and
Water balance model
consequently preventing water and soil loss in this heavily eroded region. The model and analyses illustrated where in the Loess Plateau this perennial energy crop could be produced with stable and sufficient yield. ª 2014 Elsevier Ltd. All rights reserved.
1.
Introduction
Bioenergy contributes to the global sustainability by providing a source of renewable energy and by mitigating climate change. Second-generation energy crops that are developed to
grow on marginal land have the great potential to contribute to regional sustainability [1e3]. The Loess Plateau of China is such a region facing serious threat of environmental and economic problems. Long-term human disturbance, especially the overuse of the land for food crop and feedstock production, has turned the Loess Plateau into one of the most seriously eroded
* Corresponding author. Tel.: þ86 10 62836172; fax: þ86 10 62590843 E-mail address: [email protected] (T. Sang). 1 These authors contributed equally to this work. 0961-9534/$ e see front matter ª 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biombioe.2014.01.018
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places of the world [4]. About two-thirds of the region suffers from severe soil erosion due to lack of natural vegetation cover, which has created major problems such as landscape degradation, soil nutrient depletion, and water and soil loss [5,6]. As a result, the economic and social development level was far behind the level of the national average [7]. Over the past couple of decades, there has been considerable effort to afforest the region for preventing further erosion and water and soil loss. The effort, however, had limited success because in many areas with precipitation too low to support high evapotranspiration required by trees and large shrubs [8e10]. Although the climatic and soil conditions in the Loess Plateau favor grasses over trees for restoration [4,6], there is still a major hurdle for the grassland restoration, i.e., the lack of income for local farmers once the government subsidies are over [4]. Growing second generation energy crops, particularly Miscanthus that naturally occurs in part of the region, offers opportunities for the sustainable development of economy and environment [11]. Miscanthus is a C4 plant capable of maintaining high photosynthetic rates and producing high biomass in the cool climate [12,13]. As a perennial grass with high water and nutrient use efficiencies, Miscanthus can be grown on marginal land without the requirement of irrigation or heavy fertilization [14]. The previous studies demonstrated that Miscanthus lutarioriparius, an endemic species in central China, was able to adapt to the semiarid regions and produced high biomass in the Loess Plateau of China [15]. On the basis of the field experiment, a radiation model was utilized to estimate the potential yield of Miscanthus energy crop across the Loess Plateau [16]. While the model considered parameters including the annual incident photosynthetically active radiation (PAR), accumulative temperature, and annual precipitation, one critical factor potentially limiting energy crop production, soil water balance has yet to be analyzed. Previous works suggested that trees and large shrubs planted in the previous afforestation effort had relative short life span and their deep roots coupled with high transpiration led to water depletion in the deep soil, which was difficult to recover from precipitation [17,18]. Therefore, in what extent soil water dynamic will affect the sustainable production of Miscanthus in the Loess Plateau becomes a critical question that has to be addressed before large-scale plantation is initiated. With the increased attention on Miscanthus as a potential energy crop, a growing number of studies have been conducted to evaluate the hydrologic impact of its production around the world. It was found that planting the highly productive Miscanthus crop could substantially reduce soil water content [19e23]. In order to evaluate long-term soil water balance over an area of more than 60 million hectares, we developed a water balance model and took advantage of the availability of detailed 20-year climatic data from the 46 national meteorological stations across the Loess Plateau as well as the field experimental data of M. lutarioriparius. We also considered the interaction of soil water content and biomass production of the perennial crop on the yearly basis, and consequently estimated the fluctuation of both variables. According to model validation by observed soil water contents in nine locations, we could answer the questions of whether planting
Miscanthus energy crops might cause soil water depletion and whether the yield might be sufficient and stable over 20 years of production.
2.
Materials and methods
2.1.
The study region
The Loess Plateau is located in the central to northwestern China (34 e45 50 N, 101 e114 330 E) and consists of a vast area of semiarid land. It belongs to the warm or temperate continental monsoon climate with high interannual precipitation variability. The average annual temperature ranges from 6 to 10 C from northwest to southeast and the annual precipitation is largely between 300 and 600 mm [4]. Especially, annual precipitation is unevenly distributed in the Loess Plateau and most of the rain falls between June and September. Underground water is usually buried too deep in the Loess Plateau to be utilized by plants [24]. There are 46 national meteorological stations distributed across the Loess Plateau (Fig. S1) and the latitude, longitude, and the soil type of each site are listed in Table. S1.
2.2.
The model
According to the water balance model [25,26], the net change in soil water content (SWC) between two moments at a given location (DW) was controlled by two processes: precipitation as the only source of water input in the Loess Plateau (P), and water output including plant interception (I), evapotranspiration (E), root water uptake (S), drainage (D) between two soil layers, and surface runoff (R) (for explanations in detail see Section 2.3). Thus, DW ¼ Wtþ1 Wt ¼ Pt ðIt þ Et þ Dt þ St þ Rt Þ
(1)
where the subscript t denoted the tth month, and Dt ¼ 1 month interval. In the model, three types of surfaces were studied, including bare soil, winter wheat, and M. lutarioriparius. With regard to the growing season, winter wheat was planted in October and harvested in June of the next year in the Loess Plateau. As a perennial, Miscanthus emerged in May and the aboveground part senesced in October. In consideration of different hydrological processes in different soil layers, we divided the soil up to 1-m into three zones: top zone (0e20 cm), middle zone (20e50 cm), and deep zone (50e100 cm) [27,28]. For the top zone, the input was rainfall and the output included interception, drainage, runoff, and evaporation for bare soil or transpiration due to root water uptake in the top zone for crops. Assumed that the evaporation from the soil was omitted compared with the transpiration due to root water uptake for crops. For the middle zone, the input was drainage from the top zone and the output included root water uptake and drainage to the deep zone. For the deep zone, the input was drainage from the middle zone and the output included root water uptake and drainage to the deeper soil. Therefore, the following equations described the SWC dynamics of the three soil zones at the sth location in the t þ 1 month:
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s1 Wtþ1 ¼ Wts1 þ Pst 1 Ist 1 Est 1 þ Dst 1 þ Rst 1 s2 ¼ Wts2 þ Dst 1 Sst 2 þ Dst 2 Wtþ1 s3 Wtþ1 ¼ Wts3 þ Dst 2 Sst 3 þ Dst 3
(2)
where subscripts 1, 2, 3 denoted the top, middle, and deep zones, respectively. Note that E denoted evaporation for bare soil and E denoted the transpiration due to root water uptake for crops in the top zone in the Eq. (2).
2.3.
Parameters and data sources
There were 46 national meteorological stations distributed across the Loess Plateau with monthly climatic data over the 20-years period (January 1986eDecember 2005) and nine stations among them had the uninterrupted record of SWC available for validation (see additional explanations below) (Fig. S1). These climatic data included precipitation, air temperature, relative air humidity, wind speed, sunshine hours, and incident solar radiation. The climate data and observed SWC in each of the three soil layers in nine locations planting winter wheat were obtained from the China Meteorological Data Sharing Service System [29]. The soil types at the 46 locations were determined by Data Sharing Infrastructure of Earth System Science [30]. These included aeolian sandy, fluvo-aquic soil, orthic chestnut soil, orthic dark loessial soil, orthic sierozem, stratified old manured loessial soil, and yellow cultivated loessial soil. Physical parameters of these types of soil, including wilting point, field capacity, and infiltration rate, were obtained from China Soil Scientific Database [31]. The initial SWC at the 46 locations was determined according to Water resource of Clouds and Soil in China [32] and Atlas for Spatialized Information for Terrestrial Ecosystem in China [33]. Because the interception rate of M. lutarioriparius was not yet available, the previously measured interception rate of M. giganteus, 25% of gross rainfall was adopted [34]. The interception rate of winter wheat was reported to be 15% [35]. The evapotranspiration was the product of the potential evapotranspiration and crop coefficient. The potential evapotranspiration of three surfaces (bare soil, winter wheat, and Miscanthus) was calculated by the PenmaneMonteith equation [36]. Crop coefficients (the ratio of water loss from the crop to Penman potential evaporation) of winter wheat and Miscanthus were set at 1.05 and 1.5, respectively [37e40]. Leaf area indexes of winter wheat and Miscanthus were set at 3 and 6, respectively [41, 42]. Root water uptake was calculated according to the relative root density in each soil layer, and then multiplied by the evapotranspiration. As for winter wheat, the parameter values of the relative root density were 53%, 30%, and 17% in the top, middle, and deep zones, respectively [40,43,44]. As for M. lutarioriparius, we measured its root system in the experimental field in Qingyang (Fig. S2), which is located near the Qingyang meteorological station in the Gansu Province. The total of 93 populations of three Miscanthus species have been grown in the experimental field since 2009. Three sampling points were chosen in random in the field and a total of seven layers of soil core (0e10 cm, 10e20 cm, 20e30 cm, 30e40 cm, 40e50 cm, 50e70 cm, and 70e100 cm) were collected with a 10 10 cm square soil
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acquisition drill. The collected soil cores were rinsed with root bag (bore diameter ¼ 0.25 mm). The roots of M. lutarioriparius were picked and sucked dry with paper towel. Root length was measured using the WinRHIZO system, which was used to calculate the relative root density. When SWC was lower than wilting point (Table S2), the root water uptake was set as 0. Drainage occurred when SWC exceeded the field capacity (Table S2), and was equal to the difference between SWC and the field capacity. According to the principle of runoff generation, surface runoff occurred when rainfall exceeded infiltration (Table S2), and was equal to the difference between rainfall and the infiltration [26]. Due to the plant root effect, the infiltration rate of soil increases by 10% and 20% for wheat and Miscanthus than bare soil, respectively [43,45]. Miscanthus is a perennial crop that can produce biomass for up to 20 years after the initial planting. Thus the interaction between crops growth and SWC as a continuous process needs to be incorporated into the water balance model. In a given year, higher biomass production consumes more water and leads to more severe reduction of SWC. The lowered SWC subsequently limits biomass production in the next year. We considered the following three scenarios of water shortage as yield limiting factors. If the average SWC in the growing season was less than 15% in the t-th year, the aboveground and belowground biomass decreased by 20% in the t þ 1th year [46], and the interception, evapotranspiration, and water uptake decreased accordingly. If the average SWC was less than 5% in the t-th year, the biomass decreased by 50% in the t þ 1th year [46]. If the average SWC was below 5% for successive three years, Miscanthus could not survive and the biomass was set to zero in the following year. Using the previous radiation model and the 20-year means of climatic data [16], we calculated the mean yield over the 20year period, which was referred as to static yield. We used the same model to estimate a yearly yield using climatic data of each year and then adjusted with the yield limiting factors. Thus, we obtained yearly yield during the 20-year period at each location and the average of the 20 yields was referred as to dynamic yield.
2.4.
Simulation and analysis
Given all parameters described above, SWC of the three soil layers at each of the 46 locations were calculated for 20 years using the recession process described by the water balance model (Eq. (2)). Based on the simulated 20-year SWC dynamics, we calculated the yearly average in each zone at each location. If the average was less than 5%, the year was defined as a drought year for this zone. We counted the number of the drought years as the indication of drought levels. Furthermore, the number of the successive drought years was counted and the largest number of successive drought years at a location was considered as the indication of the severity of drought. Wilcoxon paired sample test was performed to evaluate the effect of different surfaces on SWC during 20-year period. Paired t-test was performed for the number of drought years and successive drought years between two surfaces in each of the three soil layers. Paired t-test was also performed for differences in 20-year mean runoff and evapotranspiration
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Fig. 1 e Soil water content of the three soil layers simulated for the 20-year period in Qingyang. Blue, red, and green curves indicate monthly water content variation in top, middle, and deep zones. (a) Bare soil, (b) winter wheat field, and (c) Miscanthus field. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
between two surfaces. Wilcoxon paired sample test and paired t-test were conducted using R 2.13.0 for Windows [47]. The performance of the model was validated by comparing the simulated SWC with the measured values in each soil zone for winter wheat. Specifically, the NasheSutcliffe efficiency for the simulated and observed SWC anomalies was obtained in three soil zones of winter wheat field at nine of the locations in the Loess Plateau, including Qingyang. It was calculated as: NS ¼ 1
n X i¼1
ðOi Si Þ2 =
n X i¼1
Oi O
2
with O observed and P predicted values. The values ranged between 1.0 (perfect fit) and N.
3.
Results
3.1. Soil water content simulated for the 20-year period in Qingyang At first, we took Qingyang as an example to illustrate the simulated results. Based on root length measured from each soil layer, the relative root density was estimated at 61%, 32%,
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Fig. 2 e The average of soil water content simulated over the 20-year period at each of the 46 locations across the Loess Plateau in the three soil layers: (a) top, (b) middle, and (c) deep zones. B: bare soil, W: winter wheat, and M: Miscanthus. The scale bar indicates 10% of soil water content. The differences of soil water content during 20-year period between bare soil and winter wheat (BeW), between bare soil and Miscanthus (BeM), and between winter wheat and Miscanthus (WeM) were tested statistically for (d) top, (e) middle, and (f) deep zones. The full and half bars indicate significant (P < 0.05) and insignificant (P > 0.05) difference, respectively. The landscape represents 62 million hectares of the Loess Plateau, and the background colors from yellow to blue indicate the gradient of the 20-year average of annual precipitation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
and 7% in the top, middle, and deep zones, respectively (Fig. S3). According to the 20-year simulations of soil water balance, the results of simulated monthly soil water content (SWC) of three soil layers (top, middle, and deep zones) in three types of surface (bare soil, winter wheat, and Miscanthus) over the 20-year period were shown in Fig. 1.
For bare soil (Fig. 1a), SWC mainly varied with annual precipitation, which was most conspicuous for the top zone and least for the deep zone. SWC in the top zone ranged from 7% to 74%, followed by the middle zone ranging from 5% to 28% and then the deep zone ranging from 5% to 17%. In comparison with bare soil, SWC in the top and middle zones of
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Fig. 3 e The numbers of years with average soil water content less than 5% during the 20-year period at each of the 46 locations in the Loess Plateau in the three soil layers: (a) top, (b) middle, and (c) deep zones. The numbers of successive years with yearly average soil water content lower than 5% during the 20-year period at each of the 46 locations in the Loess Plateau in the three soil layers: (d) top, (e) middle, and (f) deep zones. The scale bar indicates 10 years. The landscape represents 62 million hectares of the Loess Plateau, and the background colors from yellow to blue indicate the gradient of the 20-year average of annual precipitation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the winter wheat field decreased dramatically in the most active growing season from April to June and even approached to zero in some years, leading to a wider range of fluctuation between 0 and 73% for the top zone and 0 and 29% for the middle zone (Fig. 1b). The deep zone was least affected, with a fluctuation ranging from 4% to 16%.
For the Miscanthus field (Fig. 1c), SWC in the top and middle zones also decreased dramatically during the growth season, reaching 0 in certain years. The recovery, however, was slower than winter wheat because Miscanthus had a longer active growing season that spanned nearly the entire rain season in the Loess Plateau from May to October. The fluctuation ranges
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Fig. 4 e (a) The average of Miscanthus yield estimated over the 20-year period at each of the 46 locations across the Loess Plateau. SY, DY, and S.E. of DY represent static yield, dynamic yield, and the standard error of dynamic yield, respectively. The scale bar indicates 10 ton haL1 yL1. (b) The number of year with dynamic yield lower than 5 ton haL1 yL1 (LY) and the number of successive years with dynamic yield lower than 5 ton haL1 yL1 (SLY). The scale bar indicates 10 years. The landscape represents 62 million hectares of the Loess Plateau, and the background colors from yellow to blue indicate the gradient of the 20-year average of annual precipitation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
of the top and middle zones were much narrower than bare soil and winter wheat, varying from 0 to 45% and from 0 to 17%, respectively. The deep zone was still the least variable in SWC among different surfaces, with Miscanthus field ranging from 3% to 14%.
3.2.
Soil water content across the Loess Plateau
The monthly SWC was simulated over the 20-year period for the three soil layers on the three surfaces at the 46 locations with meteorological data fully available. To present an overview of SWC across the Loess Plateau, the 20-year average of monthly SWC was mapped at the 46 locations (Fig. 2aec). For the top zone, SWC decreased in all cases from the southeast to northwest along with the trend of annual precipitation (Fig. 2a). Of the three surface types, bare soil had the highest water content, which was especially conspicuous in the southeast where mean annual precipitation was high. Differences between surface types decreased toward northwest where annual precipitation was the lowest. We further tested the differences between two surfaces and mapped the results in the Loess Plateau (Fig. 2d). SWC was significantly higher in the top zone of bare soil than planted surfaces. The differences between winter wheat and Miscanthus surfaces were significant at 37 of the 46 locations, and 9 locations showing no significant difference were mainly found in the southeast. For the middle zone, the average SWC and the difference of SWC between surfaces were generally lower than those of the top zone (Fig. 2b). Differences in SWC between surfaces clearly decreased from the southeast to northwest. The difference between bare soil and the winter wheat field was significant at 32 locations, most of which lied in northwest (Fig. 2e). The difference between bare soil and the Miscanthus field was also significant at 32 locations mostly in northwest. The difference
between the winter wheat and Miscanthus fields was significant at only one location in south. In the deep zone, the average SWC was the lowest and least variable between surfaces (Fig. 2c). In fact, none of the difference between any two types of the surfaces was statistically significant (Fig. 2f). The model was validated by calculating the NasheSutcliffe efficiency for the simulated and observed SWC anomalies in three soil zones of winter wheat field. In Qingyang, the values were 0.69, 0.64 and 0.63 for the top zone, middle zone, and deep zone, respectively. For the remaining eight locations across the Loess Plateau (Table S3), the overall average values were 0.68, 0.64 and 0.62 for the top zone, middle zone, and deep zone, respectively, indicating a satisfactory model performance.
3.3.
Drought years
For the three soil zones, the number of drought years (yearly average of SWC lower than 5%) increased with the decrease of annual precipitation from southeast to northwest (Fig. 3aec). The number of drought years also increased from bare soil to winter wheat field and then to Miscanthus field. The trend of increase was especially conspicuous in middle and deep zones in southeast. The increase was not significant from bare soil to winter wheat field or from winter wheat to Miscanthus field, but was significant from bare soil to Miscanthus field (P < 0.05) (Table S4). The distributions of drought years in the three soil layer of the Miscanthus field were shown in Fig. S4. The highest frequency of the drought years was 2, 4, and 6 for the top, middle, and deep zones, respectively. The number of successive drought years, an indication of severity of drought, also increased from southeast to northwest and from bare soil to winter wheat field and then to Miscanthus field in each of the three zones (Fig. 3def). The
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increase was not significant from bare soil to winter wheat field or from winter wheat to Miscanthus field, but was significant from bare soil to Miscanthus field (P < 0.05) (Table S5). The distributions of successive drought years had clear shifts from toward higher numbers from the top to deep zones of the Miscanthus field (Fig. S5). Nearly half of locations had no successive drought years. The majority of locations had more than 3 or more successive drought years in the deep zone.
3.4.
Dynamic yield
As a result of variation in water availability from both precipitation and the SWC of the previous year, the yield of Miscanthus as a perennial crop fluctuated over years. The yearly yield was estimated using the radiation model with adjustment according to yearly SWC, and the average of the 20-year yield, namely dynamic yield, was mapped in the Loess Plateau (Fig. 4a). There was a clear trend of yield increase from northwest to southeast along with the increase of annual precipitation. This was similar to the static yield estimated based on the averages of 20-year climatic data [9]. In terms of yield levels, dynamic yield was lower than the static yield, especially in northwest. The standard errors of dynamic yield ranged from 3.2 to 7.79 ton ha1 y1 (Fig. 4a). To gain an overview of yield stability, we counted the number of low-yield year (<5 ton ha1 y1) and the longest of successive low-yield years, and mapped them at the 46 locations in the Loess Plateau (Fig. 4b). The highest number of lowyield years at a location was 12 and the longest successive low-yield years lasted for 4. There were 5 locations with five or more low-yield years and 7 locations with two or more successive low-yield years, which were all located in northwest.
4.
Discussion
To evaluate the sustainable production of Miscanthus energy crop in the Loess Plateau rich of semiarid marginal land, we developed the water balance model and estimated 20-year SWC at multiple locations across regions. The water balance model was chosen for this study because it could be used to simulate the long-term change of SWC at locations with markedly different climatic and soil conditions across a large area of more than 60 million hectares [26,48]. More detailed analyses at a finer special scale may be further conducted using other hydrodynamic and hydrological models, such as the Soil and Water Assessment Tool (SWAT) [22,49] or the multilayer canopy-rootesoil system model (MLCan) [19]. An important character of the model in this study was the consideration of the growing properties of a perennial crop and its impact on hydrologic dynamics over a long period. In contrast with annual crops, perennial crops have living root systems all year around, which is more effective in increasing infiltration and reducing surface runoff. Treating SWC and biomass yield as interacting variables was also necessary for perennial crops. Dividing the soil into three layers for analysis allowed for more precise description of hydrologic processes in different layers. The soil of up to 1-m deep was considered because 93% of the roots of M. lutarioriparius were found in top
0.5 m (Fig. S3), which was similar to that of M. giganteus [37,45]. With regard to model validation, observed SWC available in the China meteorological database was utilized. For many of the 46 locations in the Loess Plateau, data were either unavailable or discontinuous for the 20-year period examined. For locations with continuous record of SWC, winter wheat field had the most complete record, from which field without irrigation was selected for model validation. Finally, nine locations across the arid, semiarid, and semihumid regions of the Losses Plateau met data quality control (Fig. S1). Using these data from locations with distinct climates, the goodness-of-fit measurement of the NasheSutcliffe efficiency supported the simulation (Table. S3), indicating that the model was valid [50]. In our analysis, we compared three land use scenarios, bare soil, winter wheat field and Miscanthus field. Bare soil represents an increasingly large area of recently abandoned farmland where food crop yield had been low and unstable. Winter wheat is the main food crop grown in the Loess Plateau. A serious concern of growing highly productive plants in the semiarid regions was that this may cause soil water depletion and consequently unsustainable production. However, the results of this study showed that the most dramatic impact of planting Miscanthus was on the top zone of soil, where SWC was significant reduced at all locations. The middle zone was less severely affected, with little difference between planting wheat and Miscanthus. For the deep zone, it is striking that there was no impact at all no matter winter wheat or Miscanthus was planted. We analyzed the changes of parameters in our water balance model and found that the main reason for causing this somewhat surprising result was the reduction in runoff. With increases in interception and infiltration, the 20-year mean runoff of Miscanthus field was significantly lower than that of bare soil (P < 0.01) and wheat (P < 0.05). The reduced runoff offset the evapotranspiration of Miscanthus, which was significantly higher than that of bare soil (P < 0.01) and wheat (P < 0.05) [19,20,23,51]. Because the evapotranspiration extracted most of water from the top zone where 60% of Miscanthus roots were found, the mean SWC was significantly reduced. The deep zone was not significantly affected because of the combined effect of increased filtration and less severe root extraction. Although the top zone became drier during the growing season of Miscanthus, the water content of this zone recovered most easily from precipitation. This is different from other plants previously utilized for ecological restoration in the Loess Plateau, including Robinia pseudoacacia and Astragalus adsurgens, which have much deeper root system both exceeding 7 m [52,53]. Water utilized by these plants from the deep zones of soil is harder to be recharged with precipitation than shallower zones. Consequently, there have been reports that the plants stopped growing or died after a few years and left dry layers of soil that took years to recover [52,53]. Apparently, Miscanthus with the relatively shallow root system will not suffer from the problem and can be a good choice for ecological restoration. In the Loess Plateau, annual precipitation is unevenly distributed, with 70e80% of it occurring between June and September, which overlaps with the growing season of
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Miscanthus. Heavy summer rain often leads to large runoff in a short period, which causes serious soil erosion combined with loose soil. Growing Miscanthus can contribute to soil and water conservation due to its biological characters. On one hand, Miscanthus has large aboveground cover, which to some extent prevents rain from scouring the surface of soil. On the other hand, Miscanthus has dense and relatively shallow root systems, which can effectively adhere to soil. According to our simulation, planting Miscanthus could decrease runoff by 47% in comparison with bare soil and 35% in comparison with winter wheat. Thus, the simulation suggested that Miscanthus energy crops would not cause soil water depletion in the majority of areas in the Loess Plateau. Furthermore, it should generate a positive effect on water and soil conservation. With this encouraging result, the study further provided us with guidelines of producing Miscanthus energy crops in the Loess Plateau. The number of drought years and especially the number of successive drought years would most likely become the limiting factor for the energy crop production. In this regard, the two top soil layers of the Miscanthus field matter most because they contain more than 90% of the roots (Fig. S3). According to Fig. 3b and d, the majority of the Loess Plateau in east and south can support sustainable Miscanthus production as far as SWC is concerned. The most northwestern part with very low precipitation and long successive drought years should not be chosen for energy crop production. The transitional zone with relatively low precipitation should be utilized with caution. In finer scales, local climates, landscape, and other environmental factors should be carefully evaluated and field tests should be conducted to determine whether the production can be sustainable and ecologically benefit. However, if the estimated yield is very low or unstable over years at a location, it should not be considered as a viable place for supporting bioenergy industry. Particularly, the locations with frequent low-yield years and successive lowyield years should be ruled out (Fig. 4b). These areas were actually those with too little precipitation to support Miscanthus production as far as our previous study concerned. The result thus suggests that the previous estimate of yield potential should still hold for the majority of the areas of the Loess Plateau [7]. The dynamic yield data also suggest that the majority of the Loess Plateau should sustain reasonable levels of yield of Miscanthus energy crop. The water balance model developed in this study provided a useful tool for analyzing long-term soil water balance of energy crop production in the landscape featured with arid and semiarid marginal land. From this point on, climate change scenarios can be incorporated into the model to evaluate the environmental impact and sustainability of future bioenergy development.
5.
Conclusions
In this study, we developed a water balance model and conducted a 20-year simulation of soil water content for the field growing Miscanthus energy crops across the Loess Plateau of China, where more than 30 million hectares of marginal land was potentially available. The results
55
indicated that planting Miscanthus would not cause water depletion in deep soil, which addressed one of the most serious concerns of growing high-biomass plants in this region. Additionally, the substantial reduction in surface runoff in the Miscanthus field would generate a positive impact on water and soil conservation in the Loess Plateau. The estimate of yield levels provided a valuable guideline for bioenergy development in a large landscape rich in semiarid marginal land. Thus Miscanthus production may embarks on the sustainable path of the economy and society in the Loess Plateau.
Acknowledgments We thank Jian Wang of the Xifeng Water and Soil Conservation Experimental Station, the Yellow River Committee, for technical assistance. The work was supported by the Key Program of the National Natural Science Foundation of China (91131902) and the Knowledge Innovation Program of the Chinese Academy of Sciences (KSCX2-EX-QR-1).
Appendix A. Supplementary data Supplementary data related to this article can be found at doi: 10.1016/j.biombioe.2014.01.018.
references
[1] Sang T. Toward the domestication of lignocellulosic energy crops: learning from food crop domestication. J Integr Plant Biol 2011;53(2):96e104. [2] Somerville C, Youngs H, Taylor C, Davis SC, Long SP. Feedstocks for lignocellulosic biofuels. Science 2010;329(5993):790e2. [3] McLaughlin SB, Kszos LA. Development of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States. Biomass Bioenergy 2005;28(6):515e35. [4] Chen LD, Wei W, Fu BJ, Lu YH. Soil and water conservation on the Loess Plateau in China: review and perspective. Prog Phys Geogr 2007;31(4):389e403. [5] Fu BJ, Meng QH, Qiu Y, Zhao WW, Zhang QJ, Davidson DA. Effects of land use on soil erosion and nitrogen loss in the hilly area of the Loess Plateau, China. Land Degrad Dev 2004;15(1):87e96. [6] Fu BJ, Wang YF, Lu YH, He CS, Chen LD, Song CJ. The effects of land-use combinations on soil erosion: a case study in the Loess Plateau of China. Prog Phys Geogr 2009;33(6):793e804. [7] Cao SX. Impact of China’s large-scale ecological restoration program on the environment and society in arid and semiarid areas of China: achievements, problems, synthesis, and applications. Crit Rev Environ Sci Technol 2011;41(4):317e35. [8] Cao SX, Tian T, Chen L, Dong X, Yu X, Wang G. Damage caused to the environment by reforestation policies in arid and semi-arid areas of China. Ambio 2010;39(4):279e83. [9] Cao SX. Why large-scale afforestation efforts in China have failed to solve the desertification problem. Environ Sci Technol 2008;42(6):1826e31.
56
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
[10] Cao SX, Chen L, Yu XX. Impact of China’s Grain for Green Project on the landscape of vulnerable arid and semi-arid agricultural regions: a case study in northern Shaanxi Province. J Appl Ecol 2009;46(3):536e43. [11] Sang T, Zhu W. China’s bioenergy potential. Glob Change Biol Bioenergy 2011;3(2):79e90. [12] Dohleman FG, Long SP. More productive than maize in the midwest: how does Miscanthus do it? Plant Physiol 2009;150(4):2104e15. [13] Naidu SL, Moose SP, Al-Shoaibi AK, Raines CA, Long SP. Cold tolerance of C-4 photosynthesis in Miscanthusgiganteus: adaptation in amounts and sequence of C-4 photosynthetic enzymes. Plant Physiol 2003;132(3):1688e97. [14] Lewandowski I, Schmidt U. Nitrogen, energy and land use efficiencies of miscanthus, reed canary grass and triticale as determined by the boundary line approach. Agric Ecosyst Environ 2006;112(4):335e46. [15] Yan J, Chen W, Luo F, Ma H, Meng A, Li X, et al. Variability and adaptability of Miscanthus species evaluated for energy crop domestication. Glob Change Biol Bioenergy 2012;4(1):49e60. [16] Liu W, Yan J, Li J, Sang T. Yield potential of Miscanthus energy crops in the Loess Plateau of China. Glob Change Biol Bioenergy 2012;4(5):545e54. [17] Wang Y, Shao MA, Shao H. A preliminary investigation of the dynamic characteristics of dried soil layers on the Loess Plateau of China. J Hydrol 2010;381(1e2):9e17. [18] Wang Y, Shao MA, Zhu Y, Liu Z. Impacts of land use and plant characteristics on dried soil layers in different climatic regions on the Loess Plateau of China. Agric Forest Meteorol 2011;151(4):437e48. [19] Le PVV, Kumar P, Drewry DT. Implications for the hydrologic cycle under climate change due to the expansion of bioenergy crops in the Midwestern United States. Proc Natl Acad Sci USA 2011;108(37):15085e90. [20] McIsaac GF, David MB, Mitchell CA. Miscanthus and switchgrass production in Central Illinois: impacts on hydrology and inorganic nitrogen leaching. J Environ Qual 2010;39(5):1790e9. [21] Richter GM, Riche AB, Dailey AG, Gezan SA, Powlson DS. Is UK biofuel supply from Miscanthus water-limited? Soil Use Manage 2008;24(3):235e45. [22] Schilling KE, Jha MK, Zhang Y-K, Gassman PW, Wolter CF. Impact of land use and land cover change on the water balance of a large agricultural watershed: historical effects and future directions. Water Resour Res 2008;44(7):W00A09. [23] Vanloocke A, Bernacchi CJ, Twine TE. The impacts of Miscanthusgiganteus production on the Midwest US hydrologic cycle. Glob Change Biol Bioenergy 2010;2(4):180e91. [24] Li Y. Effects of forest on water circle on the Loess Plateau. J Nat Resour 2001;16(5):427e32. [25] Alley WM. On the treatment of evapotranspiration, soilmoisture accounting, and aquifer recharge in monthly water-balance models. Water Resour Res 1984;20(8):1137e49. [26] Conway D. A water balance model of the Upper Blue Nile in Ethiopia. Hydrol Sci J 1997;42(2):265e86. [27] Baier W, Dyer JA, Sharp WR. The versatiie soil moisture budgeIn Technical Bulletin 87. Ottawa: Agrometeorology Section, Research Branch, Agriculture Canada; 1979. [28] Baier W, Robertson GW. Soil moisture modelling e conception and evolution of the VSMB. Can J Soil Sci 1996;76(3):251e61. [29] China Meteorological Data Sharing Service System. Available from: http://cdc.cma.gov.cn/; 2014 [accessed 10.01.14].
[30] Data Sharing Infrastructure of Earth System Science. Available from: http://www.geodata.cn/; 2014 [accessed 10.01.14]. [31] China Soil Scientific Database. Available from: http://www. soil.csdb.cn/; 2014 [accessed 10.01.14]. [32] Guo J. Water resource of clouds and soil in China. Beijing: Meteorological Press; 2001. [33] Yu G, He H, Liu X. Atlas for spatialized information for terrestrial ecosystem in China e volume of climatological elements. Beijing: Meteorological Press; 2004. [34] Finch JW, Riche AB. Interception losses from Miscanthus at a site in south-east England-an application of the Gash model. Hydrol Process 2010;24(18):2594e600. [35] Leuning R, Condon AG, Dunin FX, Zegelin S, Denmead OT. Rainfall interception and evaporation from soil below a wheat canopy. Agric Forest Meteorol 1994;67(3e4):221e38. [36] Zhao CY, Feng ZD, Chen GD. Soil water balance simulation of alfalfa (Medicago sativa L.) in the semiarid Chinese Loess Plateau. Agric Water Manage 2004;69(2):101e14. [37] Beale CV, Morison JIL, Long SP. Water use efficiency of C4 perennial grasses in a temperate climate. Agric Forest Meteorol 1999;96(1e3):103e15. [38] Liu Y, Luo Y. A consolidated evaluation of the FAO-56 dual crop coefficient approach using the lysimeter data in the North China Plain. Agric Water Manage 2010;97(1):31e40. [39] Liu Y, Teixeira JL, Zhang HJ, Pereira LS. Model validation and crop coefficients for irrigation scheduling in the North China plain. Agric Water Manage 1998;36(3):233e46. [40] Zhang X, Chen S, Sun H, Shao L, Wang Y. Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades. Agric Water Manage 2011;98(6):1097e104. [41] Heaton EA, Flavell RB, Mascia PN, Thomas SR, Dohleman FG, Long SP. Herbaceous energy crop development: recent progress and future prospects. Curr Opin Biotechnol 2008;19(3):202e9. [42] Zhang S, Loevdahl L, Grip H, Jansson P-E, Tong Y. Modelling the effects of mulching and fallow cropping on water balance in the Chinese Loess Plateau. Soil Till Res 2007;93(2):283e98. [43] Chen W, Shen YY, Robertson MJ, Probert ME, Bellotti WD. Simulation analysis of lucerne-wheat crop rotation on the Loess Plateau of Northern China. Field Crop Res 2008;108(2):179e87. [44] Kang SZ, Zhang L, Liang YL, Hu XT, Cai HJ, Gu BJ. Effects of limited irrigation on yield and water use efficiency of winter wheat in the Loess Plateau of China. Agric Water Manage 2002;55(3):203e16. [45] Monti A, Zatta A. Root distribution and soil moisture retrieval in perennial and annual energy crops in Northern Italy. Agric Ecosyst Environ 2009;132(3e4):252e9. [46] Liu L, Chen P. Triarrhena. Beijing: China Light Industry Press; 1989. [47] R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2009. [48] Dugan JT, Zelt RB. Simulation and analysis of soil-water conditions in the Great Plains and adjacent areas, central United States, 1951e80. Reston: U.S. Geological Survey; 2001. Water-Supply Paper 2427. [49] Ng TL, Eheart JW, Cai XM, Miguez F. Modeling Miscanthus in the soil and water assessment tool (SWAT) to simulate its water quality effects as a bioenergy crop. Environ Sci Technol 2010;44(18):7138e44. [50] Krause P, Boyle DP, Ba¨se F. Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 2005;5:89e97.
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
[51] Hickman GC, Vanloocke A, Dohleman FG, Bernacchi CJ. A comparison of canopy evapotranspiration for maize and two perennial grasses identified as potential bioenergy crops. Glob Change Biol Bioenergy 2010;2(4):157e68. [52] Chen J, Wan H, Wang J, Yong S. Over depletion and recovery of soil moisture on Astragalus adsurgens grasslands in the
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loess hilly-gully region. Sheng Tai Xue Bao 2004;24(12):2979e83. [53] Shan C, Liang Z, Hao W. Review on growth of locust and soil water in Loess Plateau. Xibei Zhiwu Xuebao 2003;23(8):1341e6.
Available online at www.sciencedirect.com
ScienceDirect http://www.elsevier.com/locate/biombioe
Long-term water balance and sustainable production of Miscanthus energy crops in the Loess Plateau of China Wei Liu a,1, Jia Mi b,1, Zhihong Song b, Juan Yan c, Jianqiang Li c, Tao Sang a,b,* a
State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China b Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China c Key Laboratory of Plant Germplasm Enhancement and Speciality Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, Hubei 430074, China
article info
abstract
Article history:
Sustainable production of second-generation energy crops on marginal land holds a great
Received 13 August 2013
potential for renewable energy development. Because a vast area of marginal land is
Received in revised form
located in the arid and semiarid regions of the world, water shortage is the most serious
13 January 2014
environmental limitation. In this study, we developed a water balance model to address
Accepted 17 January 2014
the question of whether Miscanthus energy crops can be sustainably produced in the Loess
Available online 12 February 2014
Plateau of China, a region of more than 60 million hectares particularly abundant in semiarid marginal land. The simulation of 20-year soil water content in bare soil, the
Keywords:
winter wheat field, and the Miscanthus field across the Loess Plateau suggested that the
Bioenergy
long-term production of Miscanthus would not cause water depletion in deep soil. This
Miscanthus lutarioriparius
finding addressed a serious concern that growing high-biomass plants in the Loess Plateau
Soil water content
might lead to deep-soil water depletion, which was suggested to be the cause of previous
Sustainable production
failure of afforestation. Planting Miscanthus was effective in reducing surface runoff and
Water balance model
consequently preventing water and soil loss in this heavily eroded region. The model and analyses illustrated where in the Loess Plateau this perennial energy crop could be produced with stable and sufficient yield. ª 2014 Elsevier Ltd. All rights reserved.
1.
Introduction
Bioenergy contributes to the global sustainability by providing a source of renewable energy and by mitigating climate change. Second-generation energy crops that are developed to
grow on marginal land have the great potential to contribute to regional sustainability [1e3]. The Loess Plateau of China is such a region facing serious threat of environmental and economic problems. Long-term human disturbance, especially the overuse of the land for food crop and feedstock production, has turned the Loess Plateau into one of the most seriously eroded
* Corresponding author. Tel.: þ86 10 62836172; fax: þ86 10 62590843 E-mail address: [email protected] (T. Sang). 1 These authors contributed equally to this work. 0961-9534/$ e see front matter ª 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biombioe.2014.01.018
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b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
places of the world [4]. About two-thirds of the region suffers from severe soil erosion due to lack of natural vegetation cover, which has created major problems such as landscape degradation, soil nutrient depletion, and water and soil loss [5,6]. As a result, the economic and social development level was far behind the level of the national average [7]. Over the past couple of decades, there has been considerable effort to afforest the region for preventing further erosion and water and soil loss. The effort, however, had limited success because in many areas with precipitation too low to support high evapotranspiration required by trees and large shrubs [8e10]. Although the climatic and soil conditions in the Loess Plateau favor grasses over trees for restoration [4,6], there is still a major hurdle for the grassland restoration, i.e., the lack of income for local farmers once the government subsidies are over [4]. Growing second generation energy crops, particularly Miscanthus that naturally occurs in part of the region, offers opportunities for the sustainable development of economy and environment [11]. Miscanthus is a C4 plant capable of maintaining high photosynthetic rates and producing high biomass in the cool climate [12,13]. As a perennial grass with high water and nutrient use efficiencies, Miscanthus can be grown on marginal land without the requirement of irrigation or heavy fertilization [14]. The previous studies demonstrated that Miscanthus lutarioriparius, an endemic species in central China, was able to adapt to the semiarid regions and produced high biomass in the Loess Plateau of China [15]. On the basis of the field experiment, a radiation model was utilized to estimate the potential yield of Miscanthus energy crop across the Loess Plateau [16]. While the model considered parameters including the annual incident photosynthetically active radiation (PAR), accumulative temperature, and annual precipitation, one critical factor potentially limiting energy crop production, soil water balance has yet to be analyzed. Previous works suggested that trees and large shrubs planted in the previous afforestation effort had relative short life span and their deep roots coupled with high transpiration led to water depletion in the deep soil, which was difficult to recover from precipitation [17,18]. Therefore, in what extent soil water dynamic will affect the sustainable production of Miscanthus in the Loess Plateau becomes a critical question that has to be addressed before large-scale plantation is initiated. With the increased attention on Miscanthus as a potential energy crop, a growing number of studies have been conducted to evaluate the hydrologic impact of its production around the world. It was found that planting the highly productive Miscanthus crop could substantially reduce soil water content [19e23]. In order to evaluate long-term soil water balance over an area of more than 60 million hectares, we developed a water balance model and took advantage of the availability of detailed 20-year climatic data from the 46 national meteorological stations across the Loess Plateau as well as the field experimental data of M. lutarioriparius. We also considered the interaction of soil water content and biomass production of the perennial crop on the yearly basis, and consequently estimated the fluctuation of both variables. According to model validation by observed soil water contents in nine locations, we could answer the questions of whether planting
Miscanthus energy crops might cause soil water depletion and whether the yield might be sufficient and stable over 20 years of production.
2.
Materials and methods
2.1.
The study region
The Loess Plateau is located in the central to northwestern China (34 e45 50 N, 101 e114 330 E) and consists of a vast area of semiarid land. It belongs to the warm or temperate continental monsoon climate with high interannual precipitation variability. The average annual temperature ranges from 6 to 10 C from northwest to southeast and the annual precipitation is largely between 300 and 600 mm [4]. Especially, annual precipitation is unevenly distributed in the Loess Plateau and most of the rain falls between June and September. Underground water is usually buried too deep in the Loess Plateau to be utilized by plants [24]. There are 46 national meteorological stations distributed across the Loess Plateau (Fig. S1) and the latitude, longitude, and the soil type of each site are listed in Table. S1.
2.2.
The model
According to the water balance model [25,26], the net change in soil water content (SWC) between two moments at a given location (DW) was controlled by two processes: precipitation as the only source of water input in the Loess Plateau (P), and water output including plant interception (I), evapotranspiration (E), root water uptake (S), drainage (D) between two soil layers, and surface runoff (R) (for explanations in detail see Section 2.3). Thus, DW ¼ Wtþ1 Wt ¼ Pt ðIt þ Et þ Dt þ St þ Rt Þ
(1)
where the subscript t denoted the tth month, and Dt ¼ 1 month interval. In the model, three types of surfaces were studied, including bare soil, winter wheat, and M. lutarioriparius. With regard to the growing season, winter wheat was planted in October and harvested in June of the next year in the Loess Plateau. As a perennial, Miscanthus emerged in May and the aboveground part senesced in October. In consideration of different hydrological processes in different soil layers, we divided the soil up to 1-m into three zones: top zone (0e20 cm), middle zone (20e50 cm), and deep zone (50e100 cm) [27,28]. For the top zone, the input was rainfall and the output included interception, drainage, runoff, and evaporation for bare soil or transpiration due to root water uptake in the top zone for crops. Assumed that the evaporation from the soil was omitted compared with the transpiration due to root water uptake for crops. For the middle zone, the input was drainage from the top zone and the output included root water uptake and drainage to the deep zone. For the deep zone, the input was drainage from the middle zone and the output included root water uptake and drainage to the deeper soil. Therefore, the following equations described the SWC dynamics of the three soil zones at the sth location in the t þ 1 month:
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
s1 Wtþ1 ¼ Wts1 þ Pst 1 Ist 1 Est 1 þ Dst 1 þ Rst 1 s2 ¼ Wts2 þ Dst 1 Sst 2 þ Dst 2 Wtþ1 s3 Wtþ1 ¼ Wts3 þ Dst 2 Sst 3 þ Dst 3
(2)
where subscripts 1, 2, 3 denoted the top, middle, and deep zones, respectively. Note that E denoted evaporation for bare soil and E denoted the transpiration due to root water uptake for crops in the top zone in the Eq. (2).
2.3.
Parameters and data sources
There were 46 national meteorological stations distributed across the Loess Plateau with monthly climatic data over the 20-years period (January 1986eDecember 2005) and nine stations among them had the uninterrupted record of SWC available for validation (see additional explanations below) (Fig. S1). These climatic data included precipitation, air temperature, relative air humidity, wind speed, sunshine hours, and incident solar radiation. The climate data and observed SWC in each of the three soil layers in nine locations planting winter wheat were obtained from the China Meteorological Data Sharing Service System [29]. The soil types at the 46 locations were determined by Data Sharing Infrastructure of Earth System Science [30]. These included aeolian sandy, fluvo-aquic soil, orthic chestnut soil, orthic dark loessial soil, orthic sierozem, stratified old manured loessial soil, and yellow cultivated loessial soil. Physical parameters of these types of soil, including wilting point, field capacity, and infiltration rate, were obtained from China Soil Scientific Database [31]. The initial SWC at the 46 locations was determined according to Water resource of Clouds and Soil in China [32] and Atlas for Spatialized Information for Terrestrial Ecosystem in China [33]. Because the interception rate of M. lutarioriparius was not yet available, the previously measured interception rate of M. giganteus, 25% of gross rainfall was adopted [34]. The interception rate of winter wheat was reported to be 15% [35]. The evapotranspiration was the product of the potential evapotranspiration and crop coefficient. The potential evapotranspiration of three surfaces (bare soil, winter wheat, and Miscanthus) was calculated by the PenmaneMonteith equation [36]. Crop coefficients (the ratio of water loss from the crop to Penman potential evaporation) of winter wheat and Miscanthus were set at 1.05 and 1.5, respectively [37e40]. Leaf area indexes of winter wheat and Miscanthus were set at 3 and 6, respectively [41, 42]. Root water uptake was calculated according to the relative root density in each soil layer, and then multiplied by the evapotranspiration. As for winter wheat, the parameter values of the relative root density were 53%, 30%, and 17% in the top, middle, and deep zones, respectively [40,43,44]. As for M. lutarioriparius, we measured its root system in the experimental field in Qingyang (Fig. S2), which is located near the Qingyang meteorological station in the Gansu Province. The total of 93 populations of three Miscanthus species have been grown in the experimental field since 2009. Three sampling points were chosen in random in the field and a total of seven layers of soil core (0e10 cm, 10e20 cm, 20e30 cm, 30e40 cm, 40e50 cm, 50e70 cm, and 70e100 cm) were collected with a 10 10 cm square soil
49
acquisition drill. The collected soil cores were rinsed with root bag (bore diameter ¼ 0.25 mm). The roots of M. lutarioriparius were picked and sucked dry with paper towel. Root length was measured using the WinRHIZO system, which was used to calculate the relative root density. When SWC was lower than wilting point (Table S2), the root water uptake was set as 0. Drainage occurred when SWC exceeded the field capacity (Table S2), and was equal to the difference between SWC and the field capacity. According to the principle of runoff generation, surface runoff occurred when rainfall exceeded infiltration (Table S2), and was equal to the difference between rainfall and the infiltration [26]. Due to the plant root effect, the infiltration rate of soil increases by 10% and 20% for wheat and Miscanthus than bare soil, respectively [43,45]. Miscanthus is a perennial crop that can produce biomass for up to 20 years after the initial planting. Thus the interaction between crops growth and SWC as a continuous process needs to be incorporated into the water balance model. In a given year, higher biomass production consumes more water and leads to more severe reduction of SWC. The lowered SWC subsequently limits biomass production in the next year. We considered the following three scenarios of water shortage as yield limiting factors. If the average SWC in the growing season was less than 15% in the t-th year, the aboveground and belowground biomass decreased by 20% in the t þ 1th year [46], and the interception, evapotranspiration, and water uptake decreased accordingly. If the average SWC was less than 5% in the t-th year, the biomass decreased by 50% in the t þ 1th year [46]. If the average SWC was below 5% for successive three years, Miscanthus could not survive and the biomass was set to zero in the following year. Using the previous radiation model and the 20-year means of climatic data [16], we calculated the mean yield over the 20year period, which was referred as to static yield. We used the same model to estimate a yearly yield using climatic data of each year and then adjusted with the yield limiting factors. Thus, we obtained yearly yield during the 20-year period at each location and the average of the 20 yields was referred as to dynamic yield.
2.4.
Simulation and analysis
Given all parameters described above, SWC of the three soil layers at each of the 46 locations were calculated for 20 years using the recession process described by the water balance model (Eq. (2)). Based on the simulated 20-year SWC dynamics, we calculated the yearly average in each zone at each location. If the average was less than 5%, the year was defined as a drought year for this zone. We counted the number of the drought years as the indication of drought levels. Furthermore, the number of the successive drought years was counted and the largest number of successive drought years at a location was considered as the indication of the severity of drought. Wilcoxon paired sample test was performed to evaluate the effect of different surfaces on SWC during 20-year period. Paired t-test was performed for the number of drought years and successive drought years between two surfaces in each of the three soil layers. Paired t-test was also performed for differences in 20-year mean runoff and evapotranspiration
50
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Fig. 1 e Soil water content of the three soil layers simulated for the 20-year period in Qingyang. Blue, red, and green curves indicate monthly water content variation in top, middle, and deep zones. (a) Bare soil, (b) winter wheat field, and (c) Miscanthus field. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
between two surfaces. Wilcoxon paired sample test and paired t-test were conducted using R 2.13.0 for Windows [47]. The performance of the model was validated by comparing the simulated SWC with the measured values in each soil zone for winter wheat. Specifically, the NasheSutcliffe efficiency for the simulated and observed SWC anomalies was obtained in three soil zones of winter wheat field at nine of the locations in the Loess Plateau, including Qingyang. It was calculated as: NS ¼ 1
n X i¼1
ðOi Si Þ2 =
n X i¼1
Oi O
2
with O observed and P predicted values. The values ranged between 1.0 (perfect fit) and N.
3.
Results
3.1. Soil water content simulated for the 20-year period in Qingyang At first, we took Qingyang as an example to illustrate the simulated results. Based on root length measured from each soil layer, the relative root density was estimated at 61%, 32%,
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
51
Fig. 2 e The average of soil water content simulated over the 20-year period at each of the 46 locations across the Loess Plateau in the three soil layers: (a) top, (b) middle, and (c) deep zones. B: bare soil, W: winter wheat, and M: Miscanthus. The scale bar indicates 10% of soil water content. The differences of soil water content during 20-year period between bare soil and winter wheat (BeW), between bare soil and Miscanthus (BeM), and between winter wheat and Miscanthus (WeM) were tested statistically for (d) top, (e) middle, and (f) deep zones. The full and half bars indicate significant (P < 0.05) and insignificant (P > 0.05) difference, respectively. The landscape represents 62 million hectares of the Loess Plateau, and the background colors from yellow to blue indicate the gradient of the 20-year average of annual precipitation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
and 7% in the top, middle, and deep zones, respectively (Fig. S3). According to the 20-year simulations of soil water balance, the results of simulated monthly soil water content (SWC) of three soil layers (top, middle, and deep zones) in three types of surface (bare soil, winter wheat, and Miscanthus) over the 20-year period were shown in Fig. 1.
For bare soil (Fig. 1a), SWC mainly varied with annual precipitation, which was most conspicuous for the top zone and least for the deep zone. SWC in the top zone ranged from 7% to 74%, followed by the middle zone ranging from 5% to 28% and then the deep zone ranging from 5% to 17%. In comparison with bare soil, SWC in the top and middle zones of
52
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Fig. 3 e The numbers of years with average soil water content less than 5% during the 20-year period at each of the 46 locations in the Loess Plateau in the three soil layers: (a) top, (b) middle, and (c) deep zones. The numbers of successive years with yearly average soil water content lower than 5% during the 20-year period at each of the 46 locations in the Loess Plateau in the three soil layers: (d) top, (e) middle, and (f) deep zones. The scale bar indicates 10 years. The landscape represents 62 million hectares of the Loess Plateau, and the background colors from yellow to blue indicate the gradient of the 20-year average of annual precipitation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the winter wheat field decreased dramatically in the most active growing season from April to June and even approached to zero in some years, leading to a wider range of fluctuation between 0 and 73% for the top zone and 0 and 29% for the middle zone (Fig. 1b). The deep zone was least affected, with a fluctuation ranging from 4% to 16%.
For the Miscanthus field (Fig. 1c), SWC in the top and middle zones also decreased dramatically during the growth season, reaching 0 in certain years. The recovery, however, was slower than winter wheat because Miscanthus had a longer active growing season that spanned nearly the entire rain season in the Loess Plateau from May to October. The fluctuation ranges
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
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Fig. 4 e (a) The average of Miscanthus yield estimated over the 20-year period at each of the 46 locations across the Loess Plateau. SY, DY, and S.E. of DY represent static yield, dynamic yield, and the standard error of dynamic yield, respectively. The scale bar indicates 10 ton haL1 yL1. (b) The number of year with dynamic yield lower than 5 ton haL1 yL1 (LY) and the number of successive years with dynamic yield lower than 5 ton haL1 yL1 (SLY). The scale bar indicates 10 years. The landscape represents 62 million hectares of the Loess Plateau, and the background colors from yellow to blue indicate the gradient of the 20-year average of annual precipitation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
of the top and middle zones were much narrower than bare soil and winter wheat, varying from 0 to 45% and from 0 to 17%, respectively. The deep zone was still the least variable in SWC among different surfaces, with Miscanthus field ranging from 3% to 14%.
3.2.
Soil water content across the Loess Plateau
The monthly SWC was simulated over the 20-year period for the three soil layers on the three surfaces at the 46 locations with meteorological data fully available. To present an overview of SWC across the Loess Plateau, the 20-year average of monthly SWC was mapped at the 46 locations (Fig. 2aec). For the top zone, SWC decreased in all cases from the southeast to northwest along with the trend of annual precipitation (Fig. 2a). Of the three surface types, bare soil had the highest water content, which was especially conspicuous in the southeast where mean annual precipitation was high. Differences between surface types decreased toward northwest where annual precipitation was the lowest. We further tested the differences between two surfaces and mapped the results in the Loess Plateau (Fig. 2d). SWC was significantly higher in the top zone of bare soil than planted surfaces. The differences between winter wheat and Miscanthus surfaces were significant at 37 of the 46 locations, and 9 locations showing no significant difference were mainly found in the southeast. For the middle zone, the average SWC and the difference of SWC between surfaces were generally lower than those of the top zone (Fig. 2b). Differences in SWC between surfaces clearly decreased from the southeast to northwest. The difference between bare soil and the winter wheat field was significant at 32 locations, most of which lied in northwest (Fig. 2e). The difference between bare soil and the Miscanthus field was also significant at 32 locations mostly in northwest. The difference
between the winter wheat and Miscanthus fields was significant at only one location in south. In the deep zone, the average SWC was the lowest and least variable between surfaces (Fig. 2c). In fact, none of the difference between any two types of the surfaces was statistically significant (Fig. 2f). The model was validated by calculating the NasheSutcliffe efficiency for the simulated and observed SWC anomalies in three soil zones of winter wheat field. In Qingyang, the values were 0.69, 0.64 and 0.63 for the top zone, middle zone, and deep zone, respectively. For the remaining eight locations across the Loess Plateau (Table S3), the overall average values were 0.68, 0.64 and 0.62 for the top zone, middle zone, and deep zone, respectively, indicating a satisfactory model performance.
3.3.
Drought years
For the three soil zones, the number of drought years (yearly average of SWC lower than 5%) increased with the decrease of annual precipitation from southeast to northwest (Fig. 3aec). The number of drought years also increased from bare soil to winter wheat field and then to Miscanthus field. The trend of increase was especially conspicuous in middle and deep zones in southeast. The increase was not significant from bare soil to winter wheat field or from winter wheat to Miscanthus field, but was significant from bare soil to Miscanthus field (P < 0.05) (Table S4). The distributions of drought years in the three soil layer of the Miscanthus field were shown in Fig. S4. The highest frequency of the drought years was 2, 4, and 6 for the top, middle, and deep zones, respectively. The number of successive drought years, an indication of severity of drought, also increased from southeast to northwest and from bare soil to winter wheat field and then to Miscanthus field in each of the three zones (Fig. 3def). The
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increase was not significant from bare soil to winter wheat field or from winter wheat to Miscanthus field, but was significant from bare soil to Miscanthus field (P < 0.05) (Table S5). The distributions of successive drought years had clear shifts from toward higher numbers from the top to deep zones of the Miscanthus field (Fig. S5). Nearly half of locations had no successive drought years. The majority of locations had more than 3 or more successive drought years in the deep zone.
3.4.
Dynamic yield
As a result of variation in water availability from both precipitation and the SWC of the previous year, the yield of Miscanthus as a perennial crop fluctuated over years. The yearly yield was estimated using the radiation model with adjustment according to yearly SWC, and the average of the 20-year yield, namely dynamic yield, was mapped in the Loess Plateau (Fig. 4a). There was a clear trend of yield increase from northwest to southeast along with the increase of annual precipitation. This was similar to the static yield estimated based on the averages of 20-year climatic data [9]. In terms of yield levels, dynamic yield was lower than the static yield, especially in northwest. The standard errors of dynamic yield ranged from 3.2 to 7.79 ton ha1 y1 (Fig. 4a). To gain an overview of yield stability, we counted the number of low-yield year (<5 ton ha1 y1) and the longest of successive low-yield years, and mapped them at the 46 locations in the Loess Plateau (Fig. 4b). The highest number of lowyield years at a location was 12 and the longest successive low-yield years lasted for 4. There were 5 locations with five or more low-yield years and 7 locations with two or more successive low-yield years, which were all located in northwest.
4.
Discussion
To evaluate the sustainable production of Miscanthus energy crop in the Loess Plateau rich of semiarid marginal land, we developed the water balance model and estimated 20-year SWC at multiple locations across regions. The water balance model was chosen for this study because it could be used to simulate the long-term change of SWC at locations with markedly different climatic and soil conditions across a large area of more than 60 million hectares [26,48]. More detailed analyses at a finer special scale may be further conducted using other hydrodynamic and hydrological models, such as the Soil and Water Assessment Tool (SWAT) [22,49] or the multilayer canopy-rootesoil system model (MLCan) [19]. An important character of the model in this study was the consideration of the growing properties of a perennial crop and its impact on hydrologic dynamics over a long period. In contrast with annual crops, perennial crops have living root systems all year around, which is more effective in increasing infiltration and reducing surface runoff. Treating SWC and biomass yield as interacting variables was also necessary for perennial crops. Dividing the soil into three layers for analysis allowed for more precise description of hydrologic processes in different layers. The soil of up to 1-m deep was considered because 93% of the roots of M. lutarioriparius were found in top
0.5 m (Fig. S3), which was similar to that of M. giganteus [37,45]. With regard to model validation, observed SWC available in the China meteorological database was utilized. For many of the 46 locations in the Loess Plateau, data were either unavailable or discontinuous for the 20-year period examined. For locations with continuous record of SWC, winter wheat field had the most complete record, from which field without irrigation was selected for model validation. Finally, nine locations across the arid, semiarid, and semihumid regions of the Losses Plateau met data quality control (Fig. S1). Using these data from locations with distinct climates, the goodness-of-fit measurement of the NasheSutcliffe efficiency supported the simulation (Table. S3), indicating that the model was valid [50]. In our analysis, we compared three land use scenarios, bare soil, winter wheat field and Miscanthus field. Bare soil represents an increasingly large area of recently abandoned farmland where food crop yield had been low and unstable. Winter wheat is the main food crop grown in the Loess Plateau. A serious concern of growing highly productive plants in the semiarid regions was that this may cause soil water depletion and consequently unsustainable production. However, the results of this study showed that the most dramatic impact of planting Miscanthus was on the top zone of soil, where SWC was significant reduced at all locations. The middle zone was less severely affected, with little difference between planting wheat and Miscanthus. For the deep zone, it is striking that there was no impact at all no matter winter wheat or Miscanthus was planted. We analyzed the changes of parameters in our water balance model and found that the main reason for causing this somewhat surprising result was the reduction in runoff. With increases in interception and infiltration, the 20-year mean runoff of Miscanthus field was significantly lower than that of bare soil (P < 0.01) and wheat (P < 0.05). The reduced runoff offset the evapotranspiration of Miscanthus, which was significantly higher than that of bare soil (P < 0.01) and wheat (P < 0.05) [19,20,23,51]. Because the evapotranspiration extracted most of water from the top zone where 60% of Miscanthus roots were found, the mean SWC was significantly reduced. The deep zone was not significantly affected because of the combined effect of increased filtration and less severe root extraction. Although the top zone became drier during the growing season of Miscanthus, the water content of this zone recovered most easily from precipitation. This is different from other plants previously utilized for ecological restoration in the Loess Plateau, including Robinia pseudoacacia and Astragalus adsurgens, which have much deeper root system both exceeding 7 m [52,53]. Water utilized by these plants from the deep zones of soil is harder to be recharged with precipitation than shallower zones. Consequently, there have been reports that the plants stopped growing or died after a few years and left dry layers of soil that took years to recover [52,53]. Apparently, Miscanthus with the relatively shallow root system will not suffer from the problem and can be a good choice for ecological restoration. In the Loess Plateau, annual precipitation is unevenly distributed, with 70e80% of it occurring between June and September, which overlaps with the growing season of
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Miscanthus. Heavy summer rain often leads to large runoff in a short period, which causes serious soil erosion combined with loose soil. Growing Miscanthus can contribute to soil and water conservation due to its biological characters. On one hand, Miscanthus has large aboveground cover, which to some extent prevents rain from scouring the surface of soil. On the other hand, Miscanthus has dense and relatively shallow root systems, which can effectively adhere to soil. According to our simulation, planting Miscanthus could decrease runoff by 47% in comparison with bare soil and 35% in comparison with winter wheat. Thus, the simulation suggested that Miscanthus energy crops would not cause soil water depletion in the majority of areas in the Loess Plateau. Furthermore, it should generate a positive effect on water and soil conservation. With this encouraging result, the study further provided us with guidelines of producing Miscanthus energy crops in the Loess Plateau. The number of drought years and especially the number of successive drought years would most likely become the limiting factor for the energy crop production. In this regard, the two top soil layers of the Miscanthus field matter most because they contain more than 90% of the roots (Fig. S3). According to Fig. 3b and d, the majority of the Loess Plateau in east and south can support sustainable Miscanthus production as far as SWC is concerned. The most northwestern part with very low precipitation and long successive drought years should not be chosen for energy crop production. The transitional zone with relatively low precipitation should be utilized with caution. In finer scales, local climates, landscape, and other environmental factors should be carefully evaluated and field tests should be conducted to determine whether the production can be sustainable and ecologically benefit. However, if the estimated yield is very low or unstable over years at a location, it should not be considered as a viable place for supporting bioenergy industry. Particularly, the locations with frequent low-yield years and successive lowyield years should be ruled out (Fig. 4b). These areas were actually those with too little precipitation to support Miscanthus production as far as our previous study concerned. The result thus suggests that the previous estimate of yield potential should still hold for the majority of the areas of the Loess Plateau [7]. The dynamic yield data also suggest that the majority of the Loess Plateau should sustain reasonable levels of yield of Miscanthus energy crop. The water balance model developed in this study provided a useful tool for analyzing long-term soil water balance of energy crop production in the landscape featured with arid and semiarid marginal land. From this point on, climate change scenarios can be incorporated into the model to evaluate the environmental impact and sustainability of future bioenergy development.
5.
Conclusions
In this study, we developed a water balance model and conducted a 20-year simulation of soil water content for the field growing Miscanthus energy crops across the Loess Plateau of China, where more than 30 million hectares of marginal land was potentially available. The results
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indicated that planting Miscanthus would not cause water depletion in deep soil, which addressed one of the most serious concerns of growing high-biomass plants in this region. Additionally, the substantial reduction in surface runoff in the Miscanthus field would generate a positive impact on water and soil conservation in the Loess Plateau. The estimate of yield levels provided a valuable guideline for bioenergy development in a large landscape rich in semiarid marginal land. Thus Miscanthus production may embarks on the sustainable path of the economy and society in the Loess Plateau.
Acknowledgments We thank Jian Wang of the Xifeng Water and Soil Conservation Experimental Station, the Yellow River Committee, for technical assistance. The work was supported by the Key Program of the National Natural Science Foundation of China (91131902) and the Knowledge Innovation Program of the Chinese Academy of Sciences (KSCX2-EX-QR-1).
Appendix A. Supplementary data Supplementary data related to this article can be found at doi: 10.1016/j.biombioe.2014.01.018.
references
[1] Sang T. Toward the domestication of lignocellulosic energy crops: learning from food crop domestication. J Integr Plant Biol 2011;53(2):96e104. [2] Somerville C, Youngs H, Taylor C, Davis SC, Long SP. Feedstocks for lignocellulosic biofuels. Science 2010;329(5993):790e2. [3] McLaughlin SB, Kszos LA. Development of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States. Biomass Bioenergy 2005;28(6):515e35. [4] Chen LD, Wei W, Fu BJ, Lu YH. Soil and water conservation on the Loess Plateau in China: review and perspective. Prog Phys Geogr 2007;31(4):389e403. [5] Fu BJ, Meng QH, Qiu Y, Zhao WW, Zhang QJ, Davidson DA. Effects of land use on soil erosion and nitrogen loss in the hilly area of the Loess Plateau, China. Land Degrad Dev 2004;15(1):87e96. [6] Fu BJ, Wang YF, Lu YH, He CS, Chen LD, Song CJ. The effects of land-use combinations on soil erosion: a case study in the Loess Plateau of China. Prog Phys Geogr 2009;33(6):793e804. [7] Cao SX. Impact of China’s large-scale ecological restoration program on the environment and society in arid and semiarid areas of China: achievements, problems, synthesis, and applications. Crit Rev Environ Sci Technol 2011;41(4):317e35. [8] Cao SX, Tian T, Chen L, Dong X, Yu X, Wang G. Damage caused to the environment by reforestation policies in arid and semi-arid areas of China. Ambio 2010;39(4):279e83. [9] Cao SX. Why large-scale afforestation efforts in China have failed to solve the desertification problem. Environ Sci Technol 2008;42(6):1826e31.
56
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
[10] Cao SX, Chen L, Yu XX. Impact of China’s Grain for Green Project on the landscape of vulnerable arid and semi-arid agricultural regions: a case study in northern Shaanxi Province. J Appl Ecol 2009;46(3):536e43. [11] Sang T, Zhu W. China’s bioenergy potential. Glob Change Biol Bioenergy 2011;3(2):79e90. [12] Dohleman FG, Long SP. More productive than maize in the midwest: how does Miscanthus do it? Plant Physiol 2009;150(4):2104e15. [13] Naidu SL, Moose SP, Al-Shoaibi AK, Raines CA, Long SP. Cold tolerance of C-4 photosynthesis in Miscanthusgiganteus: adaptation in amounts and sequence of C-4 photosynthetic enzymes. Plant Physiol 2003;132(3):1688e97. [14] Lewandowski I, Schmidt U. Nitrogen, energy and land use efficiencies of miscanthus, reed canary grass and triticale as determined by the boundary line approach. Agric Ecosyst Environ 2006;112(4):335e46. [15] Yan J, Chen W, Luo F, Ma H, Meng A, Li X, et al. Variability and adaptability of Miscanthus species evaluated for energy crop domestication. Glob Change Biol Bioenergy 2012;4(1):49e60. [16] Liu W, Yan J, Li J, Sang T. Yield potential of Miscanthus energy crops in the Loess Plateau of China. Glob Change Biol Bioenergy 2012;4(5):545e54. [17] Wang Y, Shao MA, Shao H. A preliminary investigation of the dynamic characteristics of dried soil layers on the Loess Plateau of China. J Hydrol 2010;381(1e2):9e17. [18] Wang Y, Shao MA, Zhu Y, Liu Z. Impacts of land use and plant characteristics on dried soil layers in different climatic regions on the Loess Plateau of China. Agric Forest Meteorol 2011;151(4):437e48. [19] Le PVV, Kumar P, Drewry DT. Implications for the hydrologic cycle under climate change due to the expansion of bioenergy crops in the Midwestern United States. Proc Natl Acad Sci USA 2011;108(37):15085e90. [20] McIsaac GF, David MB, Mitchell CA. Miscanthus and switchgrass production in Central Illinois: impacts on hydrology and inorganic nitrogen leaching. J Environ Qual 2010;39(5):1790e9. [21] Richter GM, Riche AB, Dailey AG, Gezan SA, Powlson DS. Is UK biofuel supply from Miscanthus water-limited? Soil Use Manage 2008;24(3):235e45. [22] Schilling KE, Jha MK, Zhang Y-K, Gassman PW, Wolter CF. Impact of land use and land cover change on the water balance of a large agricultural watershed: historical effects and future directions. Water Resour Res 2008;44(7):W00A09. [23] Vanloocke A, Bernacchi CJ, Twine TE. The impacts of Miscanthusgiganteus production on the Midwest US hydrologic cycle. Glob Change Biol Bioenergy 2010;2(4):180e91. [24] Li Y. Effects of forest on water circle on the Loess Plateau. J Nat Resour 2001;16(5):427e32. [25] Alley WM. On the treatment of evapotranspiration, soilmoisture accounting, and aquifer recharge in monthly water-balance models. Water Resour Res 1984;20(8):1137e49. [26] Conway D. A water balance model of the Upper Blue Nile in Ethiopia. Hydrol Sci J 1997;42(2):265e86. [27] Baier W, Dyer JA, Sharp WR. The versatiie soil moisture budgeIn Technical Bulletin 87. Ottawa: Agrometeorology Section, Research Branch, Agriculture Canada; 1979. [28] Baier W, Robertson GW. Soil moisture modelling e conception and evolution of the VSMB. Can J Soil Sci 1996;76(3):251e61. [29] China Meteorological Data Sharing Service System. Available from: http://cdc.cma.gov.cn/; 2014 [accessed 10.01.14].
[30] Data Sharing Infrastructure of Earth System Science. Available from: http://www.geodata.cn/; 2014 [accessed 10.01.14]. [31] China Soil Scientific Database. Available from: http://www. soil.csdb.cn/; 2014 [accessed 10.01.14]. [32] Guo J. Water resource of clouds and soil in China. Beijing: Meteorological Press; 2001. [33] Yu G, He H, Liu X. Atlas for spatialized information for terrestrial ecosystem in China e volume of climatological elements. Beijing: Meteorological Press; 2004. [34] Finch JW, Riche AB. Interception losses from Miscanthus at a site in south-east England-an application of the Gash model. Hydrol Process 2010;24(18):2594e600. [35] Leuning R, Condon AG, Dunin FX, Zegelin S, Denmead OT. Rainfall interception and evaporation from soil below a wheat canopy. Agric Forest Meteorol 1994;67(3e4):221e38. [36] Zhao CY, Feng ZD, Chen GD. Soil water balance simulation of alfalfa (Medicago sativa L.) in the semiarid Chinese Loess Plateau. Agric Water Manage 2004;69(2):101e14. [37] Beale CV, Morison JIL, Long SP. Water use efficiency of C4 perennial grasses in a temperate climate. Agric Forest Meteorol 1999;96(1e3):103e15. [38] Liu Y, Luo Y. A consolidated evaluation of the FAO-56 dual crop coefficient approach using the lysimeter data in the North China Plain. Agric Water Manage 2010;97(1):31e40. [39] Liu Y, Teixeira JL, Zhang HJ, Pereira LS. Model validation and crop coefficients for irrigation scheduling in the North China plain. Agric Water Manage 1998;36(3):233e46. [40] Zhang X, Chen S, Sun H, Shao L, Wang Y. Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades. Agric Water Manage 2011;98(6):1097e104. [41] Heaton EA, Flavell RB, Mascia PN, Thomas SR, Dohleman FG, Long SP. Herbaceous energy crop development: recent progress and future prospects. Curr Opin Biotechnol 2008;19(3):202e9. [42] Zhang S, Loevdahl L, Grip H, Jansson P-E, Tong Y. Modelling the effects of mulching and fallow cropping on water balance in the Chinese Loess Plateau. Soil Till Res 2007;93(2):283e98. [43] Chen W, Shen YY, Robertson MJ, Probert ME, Bellotti WD. Simulation analysis of lucerne-wheat crop rotation on the Loess Plateau of Northern China. Field Crop Res 2008;108(2):179e87. [44] Kang SZ, Zhang L, Liang YL, Hu XT, Cai HJ, Gu BJ. Effects of limited irrigation on yield and water use efficiency of winter wheat in the Loess Plateau of China. Agric Water Manage 2002;55(3):203e16. [45] Monti A, Zatta A. Root distribution and soil moisture retrieval in perennial and annual energy crops in Northern Italy. Agric Ecosyst Environ 2009;132(3e4):252e9. [46] Liu L, Chen P. Triarrhena. Beijing: China Light Industry Press; 1989. [47] R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2009. [48] Dugan JT, Zelt RB. Simulation and analysis of soil-water conditions in the Great Plains and adjacent areas, central United States, 1951e80. Reston: U.S. Geological Survey; 2001. Water-Supply Paper 2427. [49] Ng TL, Eheart JW, Cai XM, Miguez F. Modeling Miscanthus in the soil and water assessment tool (SWAT) to simulate its water quality effects as a bioenergy crop. Environ Sci Technol 2010;44(18):7138e44. [50] Krause P, Boyle DP, Ba¨se F. Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 2005;5:89e97.
b i o m a s s a n d b i o e n e r g y 6 2 ( 2 0 1 4 ) 4 7 e5 7
[51] Hickman GC, Vanloocke A, Dohleman FG, Bernacchi CJ. A comparison of canopy evapotranspiration for maize and two perennial grasses identified as potential bioenergy crops. Glob Change Biol Bioenergy 2010;2(4):157e68. [52] Chen J, Wan H, Wang J, Yong S. Over depletion and recovery of soil moisture on Astragalus adsurgens grasslands in the
57
loess hilly-gully region. Sheng Tai Xue Bao 2004;24(12):2979e83. [53] Shan C, Liang Z, Hao W. Review on growth of locust and soil water in Loess Plateau. Xibei Zhiwu Xuebao 2003;23(8):1341e6.