Plant Soil (2013) 362:149–159 DOI 10.1007/s11104-012-1269-5
REGULAR ARTICLE
Nitrous oxide emissions and nitrate leaching from a rain-fed wheat-maize rotation in the Sichuan Basin, China Minghua Zhou & Bo Zhu & Klaus Butterbach-Bahl & Xunhua Zheng & Tao Wang & Yanqiang Wang
Received: 14 November 2011 / Accepted: 24 April 2012 / Published online: 12 May 2012 # Springer Science+Business Media B.V. 2012
Abstract Aims A 3-year field experiment (October 2004–October 2007) was conducted to quantify N2O fluxes and determine the regulating factors from rain-fed, N fertilized wheat-maize rotation in the Sichuan Basin, China. Methods Static chamber-GC techniques were used to measure soil N2O fluxes in three treatments (three replicates per treatment): CK (no fertilizer); N150 (300 kg N fertilizer ha−1 yr−1 or 150 kg Nha−1 per crop); N250 (500 kg N fertilizer ha−1 yr−1 kg or 250 kg Nha−1 per crop). Nitrate (NO3−) leaching losses were measured at nearby sites using free-drained lysimeters. Results The annual N2O fluxes from the N fertilized treatments were in the range of 1.9 to 6.7 kg Nha−1 yr−1
corresponding to an N2O emission factor ranging from 0.12 % to 1.06 % (mean value: 0.61 %). The relationship between monthly soil N2O fluxes and NO3- leaching losses can be described by a significant exponential decaying function. Conclusions The N2O emission factor obtained in our study was somewhat lower than the current IPCC default emission factor (1 %). Nitrate leaching, through removal of topsoil NO3−, is an underrated regulating factor of soil N2O fluxes from cropland, especially in the regions where high NO3- leaching losses occur. Keywords Nitrous oxide . Nitrate leaching . Rain-fed agriculture . Purple soil
Responsible Editor: Ute Skiba. M. Zhou : B. Zhu (*) : T. Wang : Y. Wang Key Laboratory of Mountain Environment Evolvement and Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, 610041 Chengdu, China e-mail:
[email protected] M. Zhou : K. Butterbach-Bahl Institute for Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany X. Zheng State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, 10083 Beijing, China
Introduction Atmospheric nitrous oxide (N2O) plays a significant role to global warming and the depletion of stratospheric ozone. The concentrations of N 2 O have increased from pre-industrial period of 270 ppbv to 319 ppbv in 2005 (IPCC 2007). Agricultural soils are acknowledged as the major source of anthropogenic N2O emissions, contributing about 60 % to the global anthropogenic N2O emissions currently (IPCC 2007; Syakila and Kroeze 2011). N2O is the byproduct of mainly two soil microbial processes e.g., nitrification under aerobic conditions and denitrification under anaerobic conditions (Davidson 1992; Skiba et al. 1993). The magnitude of nitrification and denitrification
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in agricultural systems is dependent on a multitude of environmental factors with soil O2, nitrogen and in the case of denitrification also C availability being the most important ones (Butterbach-Bahl et al. 2011). In the last three decades, N fertilizer application rates have increased rapidly both globally as well as specifically in China (FAO, 2011; Zhao et al. 2011), most likely strongly associated with increased losses of N2O from agricultural soils (Zheng et al. 2004). The Sichuan Basin (4.85×107 ha) is one of the most intensively managed agricultural regions of China, representing 7 % of the national cropland and producing 10 % the country’s total agricultural feed and food (Zhu et al. 2009). Rain-fed wheat-maize rotation is the most common cropping system in the Sichuan Basin. Information on N2O fluxes from agricultural soils is still limited, but a pre-requisite for a realistic estimation of regional N2O emissions is needed. Annual N2O fluxes from agricultural soils, however, are notoriously variable (by several times or even orders of magnitude) at spatial and temporal scales (Dobbie et al. 1999; Stehfest and Bouwman 2006), due to the strong dependency of soil N2O emissions on soil properties and field management (Snyder et al. 2009). Thus, extrapolating the findings of N2O fluxes from one place or time period to another is likely to result in under- or overestimation. Most of N2O measurements from rain-fed agricultural system were concentrated in the semiarid-arid (Barton et al. 2008) or temperate regions (Snyder et al. 2009; Stehfest and Bouwman 2006). Hence multi-year N2O measurements from rain-fed agroecosystem in the subtropical climate are necessary to decrease the uncertainties of regional and global N2O emission estimates (Wang et al. 2011). Nitrate (NO3−) leaching is another important pathway of N losses in agro-ecosystems, the extent of which depends on rates of N application and rainfall patterns (Di and Cameron 2002). NO3- leaching from cropland have been shown to decrease soil NO3- concentration (Zhu. et al., 2009), with soil NO3- being the substrate for denitrification, i.e. a major N2O producing process (Skiba et al. 1993). From this point of view, NO3- leaching losses may be a neglected regulating factor of soil N2O fluxes. Vallejo et al. (2005) found that irrigation management to reduce drainage and NO3- leaching losses could maintain high accumulated soil NO3- concentration. This may subsequently enhance denitrification and N2O emission. There are, however, few studies linking soil N2O
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fluxes and NO3- leaching losses from cropland to date. Since a large amount of N is lost from the rain-fed wheat-maize rotation via NO3- leaching in the study region (20–53 kg Nha−1 yr−1) (Zhu et al. 2009), we hypothesized that at periods with high rates of NO3leaching N2O emissions are potentially reduced. The objectives of this study, therefore, were to acquire (1) a three-year continuous N2O flux dataset and a regionalspecific N2O emission factors (EFd) for a regional typical wheat-maize rotation system, and (2) a better understanding of the relationship between N2O fluxes and NO3- leaching losses from rain-fed wheat-maize rotation in the Sichuan Basin, China.
Materials and methods Site and soil description The study site is located at the Yanting Agro-Ecological Station of Purple Soil, a member station of the Chinese Ecosystem Research Network (CERN), Chinese Academy of Sciences, in the central Sichuan Basin, southwestern China. The site is situated at N31°16′, E105°28′ at an altitude of 400–600 m. The study region has a subtropical climate with an annual mean temperature of 17.3°C and a mean precipitation of 826 mm (values for the period 1981 to 2009). The experimental soil is known locally as ‘purple soil’ and classified as Pup-Orthic Entisol using the Chinese Soil Taxonomy, Eutric Regosol using the FAO Soil Classification or Udorthen using the USDA Taxonomy (Gong 1999). Purple soil, a valuable agricultural soil resource in China, has a rich mineral composition, good cultivating capability, and high natural fertility and productivity (Zhu et al. 2008). The cropland on purple soil are mostly rain-fed due to topography and lack of irrigation infrastructure, with slopes of 5–30 % and soil thickness of 30– 80 cm. Specific soil characteristics for the plough layer (0–20 cm) at the present study site are summarized in Table 1. Experimental design The experiment was carried out on cropland at the Yanting Agro-ecological Station of Purple Soil. The fields at the Yanting Agro-ecological Station of Purple Soil have been continuously cropped with a wheat-
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Table 1 Chemical and physical properties of top soil at the study site (0–20 cm) Soil properties
N2O Plots Mean ± SE
Soil organic matter (g kg−1)
14.60±1.20
9.22±1.60
1.01±0.13
0.75±0.15
65. 90±8.20
39.44±8.40
Total soil P (g kg−1)
0.74±0.02
0.84±0.04
pH
8.3
8.3
Total soil N (g kg−1) Available soil N (mg kg−1)
−3
Lysimeter Plots Mean ± SE
Bulk density (g cm )
1.32±0.10
Clay (%)
6.5
19.2
Silt (%)
58.9
42.1
Sand (%)
34.6
38.7
Saturated hydrologic conductivity (mm h−1) Slope (%)
16.8±1.2
18.6±1.2
11
11.5
Table 2 Field experimental treatments and management practices from 2004 to 2007 Year
Crop season
Sowing and harvest dates
N fertilizer distribution
2004–2005
Wheat
19.10.04; 5.5.05
60 %,19.10.04
Maize
24.5.05; 13.9.05
40 %, 29.12.04 60 %, 24.5.05 40 %, 24.7.05 2005–2006
Wheat
25.10.05;10.5.06
Maize
25.5.06; 14.9.06
1.32±0.12
60 %, 25.10.05 40 %, 14.2.06 60 %, 25.5.06 40 %, 4.7.06
2006–2007
Wheat
25.10.06; 14.5.07
60 %, 25.10.06 40 %, 14.2.07
Maize
26.5.07; 21.9.07
60 %, 26.5.07 40 %, 10.7.07
SE Standard errors (n03)
maize rotation over the last two decades. The fieldbased study was arranged in a randomized block design with three N fertilizer rates (0, 150 and 250 kg N ha−1 per season or 0, 300 and 500 kg Nha−1 yr−1, hereafter referred to as N0, N150 and N250, respectively), with 3 replicates per treatment. The plot size was 6 m×4 m and the slope of the field was 11 %. To avoid horizontal leaching, we inserted hard plastic boards into soil to a depth of 40 cm around each plot and kept a distance of 2 m between each plot. For the N0 treatment, no N fertilizer was applied during the whole experimental period as well as the two previous years. In the fertilized treatments (N150 and N250), urea was applied in two split applications of 60 % and 40 % as basal and topdressing fertilizer, respectively, in both the wheat and maize seasons. Firstly, 60 % of the urea (basal fertilizer) were broadcast onto soil surface by hand and ploughed into soil (0–20 cm depth). The remaining fertilizer was topdressed on to surface soil approx. 2 months later (Table 2).A single application of calcium superphosphate (90 kg P2O5 ha−1) and potassium chloride (36 kg K2O ha−1) was applied with the basal application of urea. The wheat was planted with 0.1 m inter-row distance in the late of October and harvested in the early of May in the following year. Following local farmer’s practices maize was planted with 0.5 m inter-row spacing and 0.1 m inter-plant spacing in the late of May and harvested in the early of September (Table 2). In addition, the 2004–2005 cropping year, 2005–2006 cropping year and 2006–2007 cropping year represent the
periods October 2004–September 2005, October 2005– September 2006 and October 2006–September 2007 in the present study, respectively. N2O measurement Soil N2O fluxes were measured using static chamber techniques (Wang and Wang 2003; Zheng et al. 2008) from October 2004 to October 2007. The manual chambers consisted of two parts: base collar and a chamber cover, both parts made of stainless steel. The base collars were inserted a depth of 0.1 m in the center of each field plot and kept in place throughout the entire measurement period. The covers (0.5 m×0.5 m×0.5 m) were manually mounted onto the base collars for N2O flux measurements and manually removed after measurements. The chamber covers were equipped with a circulating fan to ensure a uniform N2O concentration within the chamber headspace. The chambers were wrapped with a layer of insulating material to minimize internal air temperature changes during the air sampling. During air sampling, the gas-tight chambers were temporarily mounted onto the frame and maintained gas-tight by filling the groove with water, but were moved at all other times to minimize the effects of chambers on soil environmental conditions. Flux measurements were made twice a week in each plot. On each sampling day, four gas samples were taken immediately after chamber closure and then every 10 min (i.e., 4 gas
152
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samples per flux measurement), and between 9:00 am and 11:00 am local time. Gas samples from the chamber were collected using 60 ml plastic syringes fitted with three-way stopcocks via a Teflon tube connected with the chamber. At the same time, soil temperature (5 cm depth) and soil water content (5 cm depth) and air temperature were measured. The measured volumetric soil water content was then converted into percentage water-filled pore space (%WFPS) by the measured soil bulk density of 1.32 gcm−3 and a theoretical particle density of 2.65 gcm−3. The daily precipitation and air pressure were collected from the meteorological station at the Yanting Agro-ecological Station of Purple Soil, approx. 200 m from the experimental fields. Gas samples were taken to the lab at the station and analyzed on the same day using a gas chromatograph (GC) equipped with an ECD (HP 5890II, The Hewlett-Packard Company, Palo Alto, California, USA) (Wang and Wang 2003). A filter column filled with ascarite was used as a pre-column prior to the analytical column (Hayesep N, 3 m, 1/8″) to remove CO2 and water vapor, as this was shown to interfere with the ECD determination of N2O (Zheng et al. 2008). The oven was operated at 55°C, and the ECD was operated at 330°C. N2O fluxes were calculated using the linear increase in N2O concentrations (four samples: 0, 10, 20, 30 min after chamber closure), headspace height, air temperature and air pressure (Zheng et al. 2000). The daily and seasonal or annual cumulative N2O fluxes from all the treatments were calculated directly from the measured fluxes using a linear interpolation method. Direct N2O emission factors (EFd %) of applied nitrogen fertilizer (N rate, kg Nha−1) was calculated using the cumulative N2O emissions from the N fertilized treatments (EF, kg Nha−1) and the unfertilized plots (E0, kg Nha−1). EFd % ¼ 100ðEF E0 Þ=N:
ð1Þ
NO3- leaching measurements In addition to measurements of N2O fluxes, we also measured soil NO3- leaching (Oct 2004 to Oct 2007). Recent studies, as e.g. reviewed by Weihermüller et al. (2007), indicated that most of the approaches used for measuring NO3- leaching in situ, such as porous cups,
resin boxes, etc., are usually inadequate to evaluate the NO3- transport processes and fluxes at the plot scale since observed fluxes may only be representative for small sampling areas. Therefore, in our study we used free drainage lysimeter plots covering an area of 8 m× 4 m, which were specifically designed and constructed based on the sloping cropland hydrological characteristics (patent: ZL2007100640686). Partition walls were inserted at least 60 cm into the bedrock in order to avoid unexpected seepage to other plots (Zhu et al. 2009). To avoid disturbance of NO3- leaching measurements we measured soil N2O fluxes at plots located approx.100 m apart. The site of the plots is fully comparable with the lysimeter plots with regard to aspect, slope, soil properties or field management practices. Statistical tests indicated that there were no significant differences in soil properties between the lysimeter plots for NO3- leaching monitoring and the plots for N2O measurement (multiple factor comparison, P>0.05). Due to the restrictions of high construction cost of lysimeter plots, NO3- leaching was measured from one N application rate only (150 kg Nha−1 per season) as well as from the unfertilized plots (little NO3- leaching losses across the entire measuring period). Interflow discharges and collected leachate NO3- were measured following each runoff event. The interflow water samples were stored at 4°C before being analyzed for NO3−. The water samples were filtrated through a 0.45 μm membrane and analyzed for NO3- concentrations using an Auto Analyzer-AA3 (Bran + Luebbe, Norderstedt, Germany). The NO3- leaching flux (Qi) of each single rain event was calculated as follows: Qi ¼ Ci qi =100
ð2Þ
Where Ci is the leachate concentration (mg L−1) and qi is the runoff discharge (mm). The monthly and annual cumulative NO3- leaching fluxes (Q) were calculated as follows: Xn Q¼ Q ð3Þ i¼1 i NO3-
(i01 to n, n is the number of interflow events in the calculated period) Statistical analysis All statistical analyses were performed with SPSS 13.0 (SPSS, Inc., USA) and Origin 7.0 (Origin Lab Corporation, USA). Non-linear regression analysis
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was used for identifying the relationship between soil N2O fluxes and NO3- leaching losses. Bivariate correlation analysis was used to identify correlations among soil N2O fluxes and soil temperature, soil moisture conditions (WFPS). The differences in N2O fluxes from different treatments were assessed using ANOVA, followed by the least significant difference test (LSD, P
