为探究氮肥减量条件下添加生物质炭对农田土壤微生物碳源利用及春小麦产量的影响,采用随机区组试验设计,设置0(N0)、300(N1)、255 kg·hm^(-2)(N2)3个氮肥水平和0(B0)、10×10^(3)(B1)、20×10^(3)(B2)、30×10^(3)kg&#...为探究氮肥减量条件下添加生物质炭对农田土壤微生物碳源利用及春小麦产量的影响,采用随机区组试验设计,设置0(N0)、300(N1)、255 kg·hm^(-2)(N2)3个氮肥水平和0(B0)、10×10^(3)(B1)、20×10^(3)(B2)、30×10^(3)kg·hm^(-2)(B3)4个生物质炭水平,共计12个处理,研究氮肥减量配施生物质炭对麦田土壤微生物群落碳源代谢特征和小麦产量的影响。结果表明,与对照(N0B0)相比,各处理平均颜色变化率(average well color development,AWCD)和Mcintosh指数(U)均呈上升趋势,其中氮肥常规用量配施中量生物质炭(N1B2)处理土壤微生物AWCD、U值最高,分别为0.93、5.83,分别比N0B0处理提高52.5%、36.3%;氮肥减量水平下,随生物质炭用量增加土壤微生物Shannon指数呈增加趋势;土壤微生物主要利用酯类碳源,对醇类碳源利用整体偏低,不同处理下微生物对不同碳源的利用能力有所不同。主成分分析显示,不同处理间土壤微生物群落对6类可利用碳源利用差异主要在于氨基酸类、糖类、酸类和醇类;土壤微生物群落功能多样性指标与春小麦产量呈正相关,当减量氮肥配施中量生物质炭(N2B2处理)时,春小麦产量可达8301.35 kg·hm^(-2),与常规施用氮肥(B0N1处理)相比增产22.1%,综上所述,氮肥配施生物质炭能够提高土壤微生物活性,改善土壤微生物环境,促进春小麦生长,提高产量。研究结果可为生物质炭在北疆灌区的应用和推广提供依据。展开更多
The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate ...The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.展开更多
文摘为探究氮肥减量条件下添加生物质炭对农田土壤微生物碳源利用及春小麦产量的影响,采用随机区组试验设计,设置0(N0)、300(N1)、255 kg·hm^(-2)(N2)3个氮肥水平和0(B0)、10×10^(3)(B1)、20×10^(3)(B2)、30×10^(3)kg·hm^(-2)(B3)4个生物质炭水平,共计12个处理,研究氮肥减量配施生物质炭对麦田土壤微生物群落碳源代谢特征和小麦产量的影响。结果表明,与对照(N0B0)相比,各处理平均颜色变化率(average well color development,AWCD)和Mcintosh指数(U)均呈上升趋势,其中氮肥常规用量配施中量生物质炭(N1B2)处理土壤微生物AWCD、U值最高,分别为0.93、5.83,分别比N0B0处理提高52.5%、36.3%;氮肥减量水平下,随生物质炭用量增加土壤微生物Shannon指数呈增加趋势;土壤微生物主要利用酯类碳源,对醇类碳源利用整体偏低,不同处理下微生物对不同碳源的利用能力有所不同。主成分分析显示,不同处理间土壤微生物群落对6类可利用碳源利用差异主要在于氨基酸类、糖类、酸类和醇类;土壤微生物群落功能多样性指标与春小麦产量呈正相关,当减量氮肥配施中量生物质炭(N2B2处理)时,春小麦产量可达8301.35 kg·hm^(-2),与常规施用氮肥(B0N1处理)相比增产22.1%,综上所述,氮肥配施生物质炭能够提高土壤微生物活性,改善土壤微生物环境,促进春小麦生长,提高产量。研究结果可为生物质炭在北疆灌区的应用和推广提供依据。
基金Under the auspices of National Natural Science Foundation of China(No.42101414)Natural Science Found for Outstanding Young Scholars in Jilin Province(No.20230508106RC)。
文摘The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.
