Seasonal soil freeze-thaw events may enhance soil nitrogen transformation and thus stimulate nitrous oxide （N2O） emissions in cold regions. However, the mechanisms of soil N2O emission during the freeze-thaw cycling...Seasonal soil freeze-thaw events may enhance soil nitrogen transformation and thus stimulate nitrous oxide （N2O） emissions in cold regions. However, the mechanisms of soil N2O emission during the freeze-thaw cycling in the field remain unclear. We evaluated N2O emissions and soil biotic and abiotic factors in maize and paddy fields over 20 months in Northeast China, and the structural equation model （SEM） was used to determine which factors affected N2O production during non-growing season. Our results verified that the seasonal freeze-thaw cycles mitigated the available soil nitrogen and carbon limitation during spring thawing period, but simultaneously increased the gaseous N2O-N losses at the annual time scale under field condition. The N2O-N cumulative losses during the non-growing season amounted to 0.71 and 0.55 kg N ha 1 for the paddy and maize fields, respectively, and contributed to 66 and 18% of the annual total. The highest emission rates （199.2- 257.4 μg m-2 h-1） were observed during soil thawing for both fields, but we did not observe an emission peak during soil freezing in early winter. Although the pulses of N2O emission in spring were short-lived （18 d）, it resulted in approximately 80% of the non-growing season N2O-N loss. The N2O burst during the spring thawing was triggered by the combined impact of high soil moisture, flush available nitrogen and carbon, and rapid recovery of microbial biomass. SEM analysis indicated that the soil moisture, available substrates including NH4＋ and dissolved organic carbon （DOC）, and microbial biomass nitrogen （MBN） explained 32, 36, 16 and 51% of the N2O flux variation, respectively, during the non-growing season. Our results suggested that N2O emission during the spring thawing make a vital contribution of the annual nitrogen budget, and the vast seasonally frozen and snow-covered croplands will have high potential to exert a positive feedback on climate change considering the sensitive response of nitrogen biogeochemical cycling to the freeze-thaw disturbance.展开更多
The excessive nitrogen （N） fertilizer input coupled with flood irrigation might result in higher N leaching and lower nitrogen recovery efficiency （NRE）. Under an intensive rice system in the Ningxia irrigation re...The excessive nitrogen （N） fertilizer input coupled with flood irrigation might result in higher N leaching and lower nitrogen recovery efficiency （NRE）. Under an intensive rice system in the Ningxia irrigation region, China, environmental friendly N management practices are hreavily needed to balance the amount of N input for optimum crop production while minimize the nitrogen loss. The objective of this study was to determine the influences of side-dressing （SD） technique in mechanical transplanting systems on the NRE, N leaching losses and rice yield in anthropogenic-alluvial soil during two rice growing seasons （2010-2011）. Four fertilizer N treatments were established, including conventional urea rate （CU, 300 kg ha-1 yr-1）; higher SD of controlled-release N fertilizer rate （SD1,176 kg ha-1 yr-1）; lower SD of controlled-release N fertilizer rate （SD2, 125 kg ha-1 yr-1）; and control （CK, no N fertilizer）. Field lysimeters were used to quantify drainage from undisturbed soil during six rice growing stages. Meanwhile, the temporal variations of total nitrigen （TN）, NO3--N, and NH4＋-N concentrations in percolation water were examined. The results showed that SD1 substantially improved NRE and reduced N leaching losses while maintaining rice yields. Across two years, the averaged NRE under SD1 treatment increased by 25.5% as relative to CU, but yet the rice yield was similar between two treatments. On average, the nitrogen loss defined as TN, NH4＋-N, and NO3--N under the SD1 treatment reduced by 27.4, 37.2 and 24.1%, respectively, when compared with CU during the study periods. Although the SD2 treatment could further reduce N leaching loss to some extent, this technique would sharply decline rice yield, with the magnitude of as high as 21.0% relative to CU treatment. Additionally, the average NRE under SD2 was 11.2% lower than that under SD1 treatment. Overall, the present study concluded that the SO technique is an effective strategy to reduce N leaching and increase NRE, thus potentially mitigate local environmental threat. We propose SD1 as a novel alternative fertilizer technique under an irrigated rice-based system in Ningxia irrigation region when higher yields are under consideration.展开更多
As one of the key parameters for characterizing crop canopy structure, Leaf Area Index(LAI) has great significance in monitoring the crop growth and estimating the yield. However, due to the nonlinearity and spatial h...As one of the key parameters for characterizing crop canopy structure, Leaf Area Index(LAI) has great significance in monitoring the crop growth and estimating the yield. However, due to the nonlinearity and spatial heterogeneity of LAI inversion model, there exists scale error in LAI inversion result, which limits the application of LAI product from different remote sensing data. Therefore, it is necessary to conduct studies on scale effect. This study was based on the Heihe Oasis, Zhangye city, Gansu province, China and the following works were carried out: Airborne hyperspectral CASI(Compact Airborne Spectrographic Imager) image and LAI statistic models were adopted in muti-scale LAI inversion. The overall difference of muti-scale LAI inversion was analyzed in an all-round way. This was based on two aspects, "first inversion and then integration" and "first integration and then inversion", and on scale difference characteristics of three scale transformation methods. The generation mechanism of scale effect was refined, and the optimal LAI inversion model was expanded by Taylor expansion. By doing so, it quantitatively analyzed the contribution of various inversion processes to scale effect. It was found that the cubic polynomial regression model based on NDVI(940.7 nm, 712 nm) was the optimal model, where its coefficient of determination R2 and the correlation coefficient of test samples R reached 0.72 and 0.936, respectively. Combined with Taylor expansion, it analyzed the scale error generated by LAI inversion model. After the scale effect correction of one-dimensional and twodimensional variables, the correlation coefficient of CCD-LAI(China Environment Satellite HJ/CCD images) and CASI-LAI products(Compact Airborne Spectro graphic Imager products) increased from 0.793 to 0.875 and 0.901, respectively. The mean value, standard deviation, and relative true value of the two went consistent. Compared with onedimensional variable correction method, the twodimensional method had a better correction result. This research used the effective information in hyperspectral data as sub-pixels and adopted Taylor expansion to correct the scale error in large-scale and low-resolution LAI product, achieving large-scale and high-precision LAI monitoring.展开更多
基金supported by the National Science and Technology Major Project of China (2014ZX07201-009)
文摘Seasonal soil freeze-thaw events may enhance soil nitrogen transformation and thus stimulate nitrous oxide （N2O） emissions in cold regions. However, the mechanisms of soil N2O emission during the freeze-thaw cycling in the field remain unclear. We evaluated N2O emissions and soil biotic and abiotic factors in maize and paddy fields over 20 months in Northeast China, and the structural equation model （SEM） was used to determine which factors affected N2O production during non-growing season. Our results verified that the seasonal freeze-thaw cycles mitigated the available soil nitrogen and carbon limitation during spring thawing period, but simultaneously increased the gaseous N2O-N losses at the annual time scale under field condition. The N2O-N cumulative losses during the non-growing season amounted to 0.71 and 0.55 kg N ha 1 for the paddy and maize fields, respectively, and contributed to 66 and 18% of the annual total. The highest emission rates （199.2- 257.4 μg m-2 h-1） were observed during soil thawing for both fields, but we did not observe an emission peak during soil freezing in early winter. Although the pulses of N2O emission in spring were short-lived （18 d）, it resulted in approximately 80% of the non-growing season N2O-N loss. The N2O burst during the spring thawing was triggered by the combined impact of high soil moisture, flush available nitrogen and carbon, and rapid recovery of microbial biomass. SEM analysis indicated that the soil moisture, available substrates including NH4＋ and dissolved organic carbon （DOC）, and microbial biomass nitrogen （MBN） explained 32, 36, 16 and 51% of the N2O flux variation, respectively, during the non-growing season. Our results suggested that N2O emission during the spring thawing make a vital contribution of the annual nitrogen budget, and the vast seasonally frozen and snow-covered croplands will have high potential to exert a positive feedback on climate change considering the sensitive response of nitrogen biogeochemical cycling to the freeze-thaw disturbance.
基金supported by the National Science and Technology Major Project of China (2014ZX07201009)the Special Foundation for Basic Scientific Research of Central Public Welfare Institute of China (BSRF201306)the Sustainable Agricultural Technique Research and Development Project Phase II between China and Japan
文摘The excessive nitrogen （N） fertilizer input coupled with flood irrigation might result in higher N leaching and lower nitrogen recovery efficiency （NRE）. Under an intensive rice system in the Ningxia irrigation region, China, environmental friendly N management practices are hreavily needed to balance the amount of N input for optimum crop production while minimize the nitrogen loss. The objective of this study was to determine the influences of side-dressing （SD） technique in mechanical transplanting systems on the NRE, N leaching losses and rice yield in anthropogenic-alluvial soil during two rice growing seasons （2010-2011）. Four fertilizer N treatments were established, including conventional urea rate （CU, 300 kg ha-1 yr-1）; higher SD of controlled-release N fertilizer rate （SD1,176 kg ha-1 yr-1）; lower SD of controlled-release N fertilizer rate （SD2, 125 kg ha-1 yr-1）; and control （CK, no N fertilizer）. Field lysimeters were used to quantify drainage from undisturbed soil during six rice growing stages. Meanwhile, the temporal variations of total nitrigen （TN）, NO3--N, and NH4＋-N concentrations in percolation water were examined. The results showed that SD1 substantially improved NRE and reduced N leaching losses while maintaining rice yields. Across two years, the averaged NRE under SD1 treatment increased by 25.5% as relative to CU, but yet the rice yield was similar between two treatments. On average, the nitrogen loss defined as TN, NH4＋-N, and NO3--N under the SD1 treatment reduced by 27.4, 37.2 and 24.1%, respectively, when compared with CU during the study periods. Although the SD2 treatment could further reduce N leaching loss to some extent, this technique would sharply decline rice yield, with the magnitude of as high as 21.0% relative to CU treatment. Additionally, the average NRE under SD2 was 11.2% lower than that under SD1 treatment. Overall, the present study concluded that the SO technique is an effective strategy to reduce N leaching and increase NRE, thus potentially mitigate local environmental threat. We propose SD1 as a novel alternative fertilizer technique under an irrigated rice-based system in Ningxia irrigation region when higher yields are under consideration.
基金This research was supported by the National Natural Science Foundation of China(41701499)the Sichuan Science and Technology Program(2018GZ0265)+3 种基金the Geomatics Technology and Application Key Laboratory of Qinghai Province,China(QHDX-2018-07)the Major Scientific and Technological Special Program of Sichuan Province,China(2018SZDZX0027)the Key Research and Development Program of Sichuan Province,China(2018SZ027,2019-YF09-00081-SN)Technology Planning Project of Guangdong Province(NO.2018B020207012)。
文摘As one of the key parameters for characterizing crop canopy structure, Leaf Area Index(LAI) has great significance in monitoring the crop growth and estimating the yield. However, due to the nonlinearity and spatial heterogeneity of LAI inversion model, there exists scale error in LAI inversion result, which limits the application of LAI product from different remote sensing data. Therefore, it is necessary to conduct studies on scale effect. This study was based on the Heihe Oasis, Zhangye city, Gansu province, China and the following works were carried out: Airborne hyperspectral CASI(Compact Airborne Spectrographic Imager) image and LAI statistic models were adopted in muti-scale LAI inversion. The overall difference of muti-scale LAI inversion was analyzed in an all-round way. This was based on two aspects, "first inversion and then integration" and "first integration and then inversion", and on scale difference characteristics of three scale transformation methods. The generation mechanism of scale effect was refined, and the optimal LAI inversion model was expanded by Taylor expansion. By doing so, it quantitatively analyzed the contribution of various inversion processes to scale effect. It was found that the cubic polynomial regression model based on NDVI(940.7 nm, 712 nm) was the optimal model, where its coefficient of determination R2 and the correlation coefficient of test samples R reached 0.72 and 0.936, respectively. Combined with Taylor expansion, it analyzed the scale error generated by LAI inversion model. After the scale effect correction of one-dimensional and twodimensional variables, the correlation coefficient of CCD-LAI(China Environment Satellite HJ/CCD images) and CASI-LAI products(Compact Airborne Spectro graphic Imager products) increased from 0.793 to 0.875 and 0.901, respectively. The mean value, standard deviation, and relative true value of the two went consistent. Compared with onedimensional variable correction method, the twodimensional method had a better correction result. This research used the effective information in hyperspectral data as sub-pixels and adopted Taylor expansion to correct the scale error in large-scale and low-resolution LAI product, achieving large-scale and high-precision LAI monitoring.
