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Functional diversity of soil microbial communities in response to supplementing 50% of the mineral N fertilizer with organic fertilizer in an oat field
ZHANG Mei-jun, JIA Ju-qing, LU Hua, FENG Mei-chen, YANG Wu-de
2021, 20 (8): 2255-2264.   DOI: 10.1016/S2095-3119(20)63331-7
Abstract （141）      PDF in ScienceDirect      
The effects of supplementing 50% of the mineral N fertilizer with organic fertilizer on the metabolism and diversity of soil microbial communities in an oat field were investigated using Biolog-Eco plates.  The experiment consisted of five treatments: no fertilizer (CK), mineral N fertilizer applied at 90 and 45 kg ha–1 N in the form of urea (U1 and U2, respectively), and U2 supplemented with organic fertilizer  in the form of sheep manure at 90 and 45 kg ha–1 N (U2OM1 and U2OM2, respectively).  Each treatment had three replications.  The experiment was conducted in 2018 and 2019 in Pinglu District, Shanxi Province, China.  The carbon source utilization by soil microbial communities, such as amino acids, amines, carbohydrates, carboxylic acids, and polymers, increased when 50% of the mineral N fertilizer was replaced with organic fertilizer in both years.  This result was accompanied by increased richness, dominance, and evenness of the microbial communities.  The utilization of amino acid, amine, and carboxylic acid carbon sources and community evenness were further improved when the organic fertilizer amount was doubled in both years.  Biplot analysis indicated that amines and amino acids were the most representative of the total carbon source utilization by the soil microbial communities in both years.  The highest oat yield was achieved at a total N application rate of 135 kg ha–1 in the treatment involving 45 kg ha–1 N in the form of urea and 90 kg ha–1 N in the form of sheep manure in both years.  It was concluded that the application of 50% of the conventional rate of mineral N fertilizer supplemented with an appropriate rate of organic fertilizer enhanced both the functional diversity of soil microbial communities and oat yield.  Amine and amino acid carbon sources may be used as a substitute for total carbon sources for assessing total carbon source utilization by soil microbial communities in oat fields in future studies.
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Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model
LIU Zheng-chun, WANG Chao, BI Ru-tian, ZHU Hong-fen, HE Peng, JING Yao-dong, YANG Wu-de
2021, 20 (7): 1958-1968.   DOI: 10.1016/S2095-3119(20)63483-9
Abstract （119）      PDF in ScienceDirect      
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 m2 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.
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Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution
DUAN Dan-dan, ZHAO Chun-jiang, LI Zhen-hai, YANG Gui-jun, ZHAO Yu, QIAO Xiao-jun, ZHANG Yun-he, ZHANG Lai-xi, YANG Wu-de
2019, 18 (7): 1562-1570.   DOI: 10.1016/S2095-3119(19)62686-9
Abstract （223）      PDF in ScienceDirect      
The use of remote sensing to monitor nitrogen (N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden.  In this study, we model the total leaf N concentration (TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution (VND).  The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model.  The results show that: (1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer.  (2) The effective layer for remote sensing detection varied at different growth stages.  The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively.  (3) The TLNC model considering the VND has high predicting accuracy and stability.  For models based on the greenness index (GI), mND705 (modified normalized difference 705), and normalized difference vegetation index (NDVI), the values for the determining coefficient (R2), and normalized root mean square error (nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively.  Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.
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Copyright © Journal of Integrative Agriculture
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Co-sponsored by China Association of Agricultural Science Societies (CAASS)
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