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No specific permissions were required for the 23 sample plots in this study. We confirmed that the field studies did not involve endangered or protected species. The specific location of the sample plots is provided in the manuscript (Fig 1).
Figure data removed from full text. Figure identifier and caption: 10.1371/journal.pone.0168249.g001 Location of study site and spatial distributions of sample plots.The figure in the left bottom corner of Fig 1 is similar to Figure 1 in the reference [43] but not identical to the Figure 1 in the reference [43]. The box in the left bottom corner refers to the South China Sea islands.
The field experiment was conducted at Xiaotangshan Precision Agriculture Experimental Base, Changping District, Beijing, North China (40° 10.6′ N, 116° 26.3′ E). This experimental base has been operational since 2001 and is used for precision agriculture research. This site is located in a warm temperate zone with a mean annual rainfall of 507.7 mm and a mean annual temperature of 13°C [44]. The soil at this site is a silt-clay loam [44]. The double cropping system of winter wheat (Triticumaestivum L.) and summer maize (Zeamays L.) is the farming practice at this site and is the dominant farming style in the North China Plain. Winter wheat is usually sowed in October and harvested in June of the following year. Approximately 10 cm-high wheat residuals are left on the field surface after harvest. Summer maize is sowed in June without tillage and then harvested in October. When precipitation is scarce, crops are irrigated depending on soil water status.
A very flat sample area was established at this site with slopes less than 1°and a size of 140 m × 100 m. Land leveling was conducted in this sample area about 2 years ago (November 2013). A single crop cultivar was then planted in this area, which is under uniform water and fertilizer management. Before land leveling, the sample area was subjected to different treatments, such as using different fertilizers and crop cultivars to meet different research needs [44], [45], [46]. Therefore, soil properties showed spatial variations in the sample area because of long-term differences in farm managements (i.e., fertilizer, irrigation and cultivar), which might have led to variations in the vegetation growth and Rs. To determine the spatial variation of Rs, we employed a grid sampling method, where the distance between each sample plot was approximately 20 m (Fig 1). This design was in accordance with the research results on spatial autocorrelation of soil properties at this site [45]. The field experiments were conducted at two continuous sunny days at the late jointing stages of winter wheat (April 20 to 22, 2015) and summer maize (August 3 to 5, 2015), which mostly corresponded to the period of the highest biological activity because of the maximum crop growth rate [18]. On April 13, 2015, the sample plots were fully irrigated to meet the water requirements of winter wheat growth. A heavy rain event occurred on July 27, 2015, approximately 1 week before the summer maize experiment. Therefore, the soil water content was considered to be suitable for crop growth at the time when we conducted the two filed experiments. Field experiments were conducted at 23 sample plots (Fig 1). We conducted the summer maize experiment at the same sample plots where the winter wheat experiment was conducted using high-precision GPS positioning. Each plot size was 1.5 m × 1.5 m. In each plot, we measured the variables that might explain the spatial variation of Rs: these variables include (1) Rs; (2) biotic factors measured by aboveground biomass (AGB), leaf area index (LAI), and canopy chlorophyll content (Chlcanopy); (3) environmental factors encompassing soil water content at 0–20 cm depth (SWC20) and soil temperature at 10 cm depth (Ts10); (4) soil property factors, including soil total nitrogen (STN) content, soil total carbon (STC) content, soil carbon/nitrogen (C/N), and soil organic carbon (SOC) content; and (5) canopy spectral reflectance of winter wheat and summer maize. It is noteworthy that we only measured soil property factors during the winter wheat experiment because of the short interval between the winter wheat and summer maize experiment (3 months). We assumed that the soil properties in such a short time could be considered constant. Therefore, the measurement data for soil properties were used to analyze the spatial variation of Rs in the winter wheat and summer maize.
Measurements of soil respiration and environmental factors: In each sample plot, Rs was measured using a Rs chamber (6400–09; LiCor, Lincoln, Nebraska, USA) connected to a portable photosynthesis system (LI-6400; LiCor, Lincoln, Nebraska, USA). The Rs chamber was mounted on a PVC soil collar that was sharpened at the bottom. Four soil collars were randomly distributed in each plot for the winter wheat experiment. Six soil collars were installed in each plot for the summer maize experiment. Each Rs measurement was performed between 09:00 h and 12:00 h (local time) because fluxes measured during this time interval usually represent the daily mean flux [18]. Rs measurement procedures, soil collar placement, and Rs data processing were described in previous studies [18], [43]. After the Rs measurement on the PVC soil collar in each plot (S1 Table), the soil temperature at 10 cm depth (Ts10) and soil moisture at 0–20 cm (SM20) were measured in the collar to minimize sample difference. Detailed procedures for soil temperature and soil moisture measurements were previously described by Huang et al. [43].
Canopy reflectance measurements and vegetation index calculation: Canopy reflectance was measured after the installation of soil collars. A portable spectroradiometer (FS-FR2500, ASD, USA) was used to measure winter wheat and summer maize canopy radiance between 350 and 2500 nm with a 1 nm waveband width. The procedures for canopy reflectance measurements were described in detail by Huang et al. [18].
Based on the measured canopy reflectance data, three VIs, namely, normalized difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), and enhanced vegetation index (EVI), were calculated to analyze their relationships to the biotic factors of winter wheat and summer maize. Three formulas used for the calculation of these VIs were described by Huang et al. [18].
LAI was measured with a LAI-2000 plant canopy analyzer (LI-COR Inc., Lincoln, Nebraska, USA). In each plot, five representative positions were selected for LAI measurement, and two repeated measurements were performed at each position. Chlleaf was obtained with a portable chlorophyll meter (SPAD-502, New Jersey, USA). The Chlleaf measurement procedures and Chlcanopy calculation were described in detail by Huang et al. [18].
AGB was measured by randomly harvesting the aboveground fresh winter wheat plants in three subplots (0.2 m×0.2 m) and three maize plants in each plot. The fresh samples were oven dried at 65°C until the mass of the sample became constant. AGB measurement damaged the samples. Thus, we conducted this measurement when all the other measurements were finished. To reduce spatial sampling and measurement errors, we averaged the LAI, Chlleaf, and AGB derived from each plot for both winter wheat and summer maize for further analysis.
Soil inside the four PVC collars in each plot was destructively sampled after measuring Rs, soil temperature, and soil moisture in the winter wheat experiment. The collected soil samples were stored at room temperature and rapidly transported to the nearby laboratory (approximately 200 meters from the sampling site) for analysis. Soil sampling procedures and soil sample processing were described elsewhere [43]. SOC content was estimated by the standard Mebius method [47]. STN and STC content were measured by an elemental analyzer (Isoprime-EuroEA3000, Milan Italy). Soil C/N was calculated from the ratio of the STC and STN content.
Correlation analysis was employed to examine the relationships among Rs, biotic factors, environmental factors, and soil properties. The coefficient of variation (CV) was used to represent the spatial variation of Rs and its various affecting factors. The relationships between biotic factors (i.e. LAI, AGB, and Chlcanopy) and VIs (i.e., NDVI, CIred edge, and EVI) were examined using regression analysis. The optimum VI was selected based on the determination coefficient (R2). Previous studies [45], [46] revealed that soil properties in our experimental area exhibited spatial variance. Spatial clustering of sample plots based on soil property factors is advisable to detect the possible confounding effects of soil properties on the relationship between Rs and other biotic or abiotic factors, and elucidate the relationship between Rs and VIs. In the present study, cluster analysis was performed based on the soil property factors to quantify the similarity in the 23 sample plots. Hanesch et al. [48] demonstrated that using all the variables causes over-information in cluster analysis and leads to insufficiently distinguishable samples from one another. The high correlation among input variables will over-represent one variable and bias the cluster results [49], [50]. Correlation analysis of the soil properties (Table 1) demonstrated that the SOC content highly correlated with the STN content (Pearson’s correlation coefficient r = 0.83, p<0.001) and STC content (r = 0.86, p < 0.001). Soil C/N displayed no significant correlation (p > 0.05) with the SOC, STN, and STC content (Table 1). Therefore, the SOC content and soil C/N were considered in the cluster analysis. Moitinho et al. [51] also demonstrated that SOC and soil C/N ratio are the two most important soil property variables that affect spatial variation of Rs in a sugarcane field. Before cluster analysis, the variables were standardized using the methods of Jiang et al. [37]. Based on the results of the cluster analysis, linear regression between Rs and optimal VI was used to detect the possible relationship between Rs and the photosynthesis proxy factor derived from remote sensing data in each cluster. One-way ANOVA with the least significant difference (LSD) multiple comparison test was used to analyze differences in Rs, biotic factors, environmental factors, and soil properties among different clusters. All the statistical analyses were performed with the Statistical Package for the Social Sciences (SPSS, Chicago, Illinois, USA).
Table data removed from full text. Table identifier and caption: 10.1371/journal.pone.0168249.t001 Correlation coefficients among soil respiration (Rs), leaf area index (LAI), aboveground biomass (AGB), canopy chlorophyll content (Chlcanopy), soil water content at 0–20 cm depth (SWC20), soil temperature at 10 cm depth (Ts10, °C), soil total nitrogen (STN) content, soil total carbon (STC) content, soil carbon/nitrogen (C/N) ratio, and soil organic carbon (SOC) content at the late jointing stage of winter wheat and summer maize in North China plain. Significance levels***p < 0.001Bold signal means the correlation analysis results for winter wheat, and the no mark values describe the results for summer maize.
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