Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition

The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Assimilating Sentinel-2 images with the CERES-Wheat model can improve the precision of winter wheat yield estimates at a regional scale. To verify this method, we applied the ensemble Kalman filter(EnKF) to assimilate the leaf area index(LAI) derived from Sentinel-2 data and simulated by the CERES-Wheat model. From this, we obtained the assimilated daily LAI during the growth stage of winter wheat across three counties located in the southeast of the Loess Plateau in China: Xiangfen, Xinjiang, and Wenxi. We assigned LAI weights at different growth stages by comparing the improved analytic hierarchy method, the entropy method, and the normalized combination weighting method, and constructed a yield estimation model with the measurements to accurately estimate the yield of winter wheat. We found that the changes of assimilated LAI during the growth stage of winter wheat strongly agreed with the simulated LAI. With the correction of the derived LAI from the Sentinel-2 images, the LAI from the green-up stage to the heading–filling stage was enhanced, while the LAI decrease from the milking stage was slowed down, which was more in line with the actual changes of LAI for winter wheat. We also compared the simulated and derived LAI and found the assimilated LAI had reduced the root mean square error(RMSE) by 0.43 and 0.29 m^(2) m^(–2), respectively, based on the measured LAI. The assimilation improved the estimation accuracy of the LAI time series. The highest determination coefficient(R2) was 0.8627 and the lowest RMSE was 472.92 kg ha^(–1) in the regression of the yields estimated by the normalized weighted assimilated LAI method and measurements. The relative error of the estimated yield of winter wheat in the study counties was less than 1%, suggesting that Sentinel-2 data with high spatial-temporal resolution can be assimilated with the CERES-Wheat model to obtain more accurate regional yield estimates.

Reducing residues of tetracycline and its resistance genes in soil-maize system and improving plant growth:Selecting the best remediation substance

Tetracycline(TC)and tetracycline resistance genes(TRGs)in plant edible tissues pose a potential risk to the environment and then to human health.This study used a pot experiment to investigate the effects of different remediation substances(worm castings,fungal chaff,microbial inoculum,and biochar)on the physiological characteristics of maize and the residues of TC and TRGs in the soil-maize system under TC stress.The results showed that TC significantly inhibited growth,disrupted the antioxidant defense system balance,and increased proline and malondialdehyde contents of maize plants.Tetracycline residue contents were significantly higher in root than in shoot,and followed the order root>stem-leaf>grain,which was consistent with the distribution of bioconcentration factors in the different organs of maize plants.The TC residue content in the soil under different treatments was 0.013–1.341 mg kg-1.The relative abundances of different antibiotic resistance genes in the soil-maize system varied greatly,and in maize plants followed the order intI1>tetW>tetG>tet B>tetM>tetX>tetO.In the soil,tetX had the highest relative abundance,followed by tetG and tetW.A redundancy analysis(RDA)showed that TC was positively correlated with TRGs.The addition of different remediation substances alleviated the toxicity of TC on maize physiological characteristics and reduced the TC and TRG residues in the soil-maize system,with biochar being the best remediation substance.These results provide new insights into the effect of biochar on the migration of TC and TRGs from soil to plants.