The Effect of Biochar and Straw Return on N₂O Emissions and Crop Yield: A Three-Year Field Experiment

To evaluate the effects of application of biochar and straw return for consecutive years on N2O emissions and crop yields in North China, a three-year field experiment of applying biochar and straw following a ten-year application was conducted in a wheat–maize rotation system. Four treatments were set up, including F (NPK fertilizer only); FB (NPK fertilizer + 9.0 t·ha−1 biochar); FS (NPK fertilizer + straw); and FSB ((NPK fertilizer + 9.0 t·ha−1 biochar combined with straw). The results showed that compared with the F treatment, the FB treatment significantly reduced soil N2O emissions by 20.2%, while the FS and FSB treatments increased it by 23.7% and 41.4%, respectively. The FB treatment reduced soil N2O emissions by 15.1% in the wheat season and 23.2% in the maize season, respectively. The FS and FSB treatments increased the N2O emissions by 20.7% and 36.7% in the wheat season, respectively, and by 25.5% and 44.2% in the maize season, respectively. In the wheat season, the soil water content (SWC), NO3−-N content and pH were the main influencing factors of the soil N2O emissions. In the maize season, SWC and NO3−-N content were the main influencing factors. In addition, the FB, FS and FSB treatments increased the crop yield by 4.99%, 8.40% and 10.25% compared with the F treatment, respectively. In conclusion, consecutive application of biochar can significantly reduce N2O emissions and improve crop yield. Although FS and FSB treatments can also improve the crop yield, they are not beneficial to suppressing N2O emissions. Therefore, the successive application of biochar is an effective measure to reduce N2O emissions and maintain crop yield

Luminescence and microstructure of Sr2Mg(BO3)2 doped with Eu

Sr2Mg(B03)2 doped with Eu was synthesized respectively in air and weak reducing atmosphere (combustion of carbon particle), whose photoluminescence characteristics and structure were also studied at room-temperature. In air, the fluorescent body\u27s color was white for different synthesized temperatures. At room temperature, the sample was excited and showed red typical emission spectrum of Eu3+ whose emission apex were sharp near 612 nm and emission spect~m was made up of the charge transformation band (CTB) of Eu3 + and excitation spectrum of 4f&rarr;4f high energy level transition, then reached the area of VUV. However, under reducing atmosphere (combustion of carbon particles), the color of the sample yielded was yellow, whose color became deeper with increasing temperature and showed phase transition. Using UV excitation, the luminescence of yellow sample was very weak. In a complicated broad spectrum at visible light area, the red emission spectrum of Eu2+ was not observed. Crystal structure and luminescence of the sample were completely different from the results of Diaz and Keszler. Two samples were prepared under oxidation and reducing atmosphere at high temperature, which were different on crystal structure and microstructure. By studying Sr2Mg(B03)2:Eu3+ a series of directional faults or educts were found, because Eu3 + ions substituted for Sr2 + ions. However, microstructure of Sr2Mg(B03 )2: Eu2 + is more complicated, whose excitation spectrum might be excited by Eu2 +. By XRD patten of the samples, phase transitibn could be found. Twins and clusters that were formed from point defect such as interstitial atom and big angle crystal boundary could be found by TEM.<br /

Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process

The crop harvest index (HI) is of great significance for research on the application of crop variety breeding, crop growth simulation, crop management in precision agriculture and crop yield estimation, among other topics. To obtain spatial information on the crop dynamic HI (D-HI), taking winter wheat as the research object and fully considering the changes in crop biomass and the grain filling process from the flowering period to the maturity period, the dynamic fG (D-fG) parameter was estimated as the ratio between the aboveground biomass accumulated in different growth periods, from the flowering stage to the maturity stage, and the aboveground biomass in the corresponding periods. Based on the D-fG parameter estimation using unmanned aerial vehicle (UAV) hyperspectral remote sensing data, a technical method for obtaining spatial information on the winter wheat D-HI was proposed and the accuracy of the proposed method was verified. A correlation analysis was performed between the normalized difference spectral index (NDSI), which was calculated using pairs of any two bands of the UAV hyperspectral spectrum, and the measured D-fG. Based on this correlation analysis, the center of gravity of the local maximum region of R2 was used to determine the sensitive band center to accurately estimate D-fG. On this basis, remote sensing estimation of the D-fG was realized by using the NDSI constructed by the sensitive hyperspectral band centers. Finally, based on the D-fG remote sensing parameters and the D-HI estimation model, spatial information on the D-HI of winter wheat was accurately obtained. The results revealed five pairs of sensitive hyperspectral band centers (i.e., λ(476 nm, 508 nm), λ(444 nm, 644 nm), λ(608 nm, 788 nm), λ(724 nm, 784 nm) and λ(816 nm, 908 nm)) for D-fG estimation, and the results of the D-fG remote sensing estimation showed high precision. The root mean square error (RMSE) was between 0.0436 and 0.0604, the normalized RMSE (NRMSE) was between 10.31% and 14.27% and the mean relative error (MRE) was between 8.28% and 12.55%. In addition, the D-fG parameter estimation, using the NDSI constructed by the above five sensitive remote sensing band centers, yielded highly accurate spatial D-HI information with an RMSE between 0.0429 and 0.0546, an NRMSE between 9.87% and 12.57% and an MRE between 8.33% and 10.90%. The D-HI estimation results based on the hyperspectral sensitive band centers λ(724 nm, 784 nm) had the highest accuracy, with RMSE, NRMSE and MRE values of 0.0429, 9.87% and 8.33%, respectively. The proposed method of acquiring spatial information on the winter wheat D-HI in this study was shown to be feasible, and it might provide a technical reference toward developing satellite-based indices to monitor large-scale crop HI information

Dynamic Harvest Index Estimation of Winter Wheat Based on UAV Hyperspectral Remote Sensing Considering Crop Aboveground Biomass Change and the Grain Filling Process

The crop harvest index (HI) is of great significance for research on the application of crop variety breeding, crop growth simulation, crop management in precision agriculture and crop yield estimation, among other topics. To obtain spatial information on the crop dynamic HI (D-HI), taking winter wheat as the research object and fully considering the changes in crop biomass and the grain filling process from the flowering period to the maturity period, the dynamic fG (D-fG) parameter was estimated as the ratio between the aboveground biomass accumulated in different growth periods, from the flowering stage to the maturity stage, and the aboveground biomass in the corresponding periods. Based on the D-fG parameter estimation using unmanned aerial vehicle (UAV) hyperspectral remote sensing data, a technical method for obtaining spatial information on the winter wheat D-HI was proposed and the accuracy of the proposed method was verified. A correlation analysis was performed between the normalized difference spectral index (NDSI), which was calculated using pairs of any two bands of the UAV hyperspectral spectrum, and the measured D-fG. Based on this correlation analysis, the center of gravity of the local maximum region of R2 was used to determine the sensitive band center to accurately estimate D-fG. On this basis, remote sensing estimation of the D-fG was realized by using the NDSI constructed by the sensitive hyperspectral band centers. Finally, based on the D-fG remote sensing parameters and the D-HI estimation model, spatial information on the D-HI of winter wheat was accurately obtained. The results revealed five pairs of sensitive hyperspectral band centers (i.e., &lambda;(476 nm, 508 nm), &lambda;(444 nm, 644 nm), &lambda;(608 nm, 788 nm), &lambda;(724 nm, 784 nm) and &lambda;(816 nm, 908 nm)) for D-fG estimation, and the results of the D-fG remote sensing estimation showed high precision. The root mean square error (RMSE) was between 0.0436 and 0.0604, the normalized RMSE (NRMSE) was between 10.31% and 14.27% and the mean relative error (MRE) was between 8.28% and 12.55%. In addition, the D-fG parameter estimation, using the NDSI constructed by the above five sensitive remote sensing band centers, yielded highly accurate spatial D-HI information with an RMSE between 0.0429 and 0.0546, an NRMSE between 9.87% and 12.57% and an MRE between 8.33% and 10.90%. The D-HI estimation results based on the hyperspectral sensitive band centers &lambda;(724 nm, 784 nm) had the highest accuracy, with RMSE, NRMSE and MRE values of 0.0429, 9.87% and 8.33%, respectively. The proposed method of acquiring spatial information on the winter wheat D-HI in this study was shown to be feasible, and it might provide a technical reference toward developing satellite-based indices to monitor large-scale crop HI information

Spatial-temporal variation in soil respiration and its controlling factors in three steppes of Stipa L. in Inner Mongolia, China

National Natural Science Foundation of China [40730105, 40673067, 40973057]; National Key Technology Research and Development Program [2007BAC03A11