Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum (S. sclerotiorum), one of the most destructive diseases in many crops including Brassica napus L. The extensive use of fungicides to control S. sclerotiorum caused severe damage to the environment in the long term. Increasing study reported that selenium (Se) is a beneficial element for plant by promoting growth and enhancing disease resistance. In this study, it was found that Se in soil shortened lesion length by 19.14% on rape stem infected with S. sclerotiorum. While resistance mechanism of rape stem against S. sclerotiorum remains unknown. Transcriptomic analysis of rape stem was performed and the results indicated that genes related to antifungal pathways were up-regulated. Moreover, metabonomic analysis was carried out to study the inhibitive effect of the dissolved organic matter derived from rape straw with Se pretreatment in soil (RSDOM) on S. sclerotiorum mycelium, results showed that RSDOM caused severe damage to energy metabolism of mycelium. Further study indicated that RSDOM decreased the pathogenicity of mycelium on rape leaves significantly, and enhanced content of chlorophyII, carotenoids, OD phenol and activities of phenylalanine ammonia-lyase (PAL), polyphenol oxidase (PPO) in rape leaves, which suggested that RSDOM plays a positive role in regulating oxidative stress responses of plant when infected with S. sclerotiorum. In addition, when compared with dimcthachlon (DIM) treatment alone, DIM combined with RSDOM resulted in higher inhibition on mycelial growth of S. sclerotiorum (the inhibition ratio of nearly 60%). Results in this study suggested that Se enhanced the resistance of rape stem against S. sclerotiorum because of the up-regulated genes related to antifungal pathways, and RSDOM improved the mycelial growth inhibition and decreased the pathogenicity of mycelium on rape leaves. Overall, Se as well as Se-enrich byproducts, possessed great potential to be developed as ecological fungicides for controlling S. sclerotiorum.Copyright © 2020 Elsevier Ltd. All rights reserved.

The conservation and restoration of soil organic matter are often advocated because of the generally beneficial effects on soil attributes for plant growth and crop production. More recently, organic matter has become important as a terrestrial sink and store for C and N. We have attempted to derive a monetary value of soil organic matter for crop production and storage functions in three contrasting New Zealand soil orders (Gley, Melanic, and Granular Soils). Soil chemical and physical characteristics of real-life examples of three pairs of matched soils with low organic matter contents (after long-term continuous cropping for vegetables or maize) or high organic matter content (continuous pasture) were used as input data for a pasture (grass-clover) production model. The differences in pasture dry matter yields (non-irrigated) were calculated for three climate scenarios (wet, dry, and average years) and the yields converted to an equivalent weight and financial value of milk solids. We also estimated the hypothetical value of the C and N sequestered during the recovery phase of the low organic matter content soils assuming trading with C and N credits. For all three soil orders, and for the three climate scenarios, pasture dry matter yields were decreased in the soils with lower organic matter contents. The extra organic matter in the high C soils was estimated to be worth NZ$27 to NZ$150 ha(-1) yr(-1) in terms of increased milk solids production. The decreased yields from the previously cropped soils were predicted to persist for 36 to 125 yr, but with declining effect as organic matter gradually recovered, giving an accumulated loss in pastoral production worth around NZ$518 to NZ$1239 ha(-1). This was 42 to 73 times lower than the hypothetical value of the organic matter as a sequestering agent for C and N, which varied between NZ$22,963 to NZ$90,849 depending on the soil, region, discount rates, and values used for carbon and nitrogen credits.

采用GRNN（Generalized Regression Neural Network）和RF（Random Forest）2种机器学习方法构建土壤有机质预测模型,以提高稀疏样本情况下的土壤有机质估算精度。依据北京市大兴区农用地2007年的土壤有机质采样数据,按MMSD准则（Minimization of the Mean of the Shortest Distances）抽稀为8种不同采样密度的样本（分别为2703、1352、676、339、169、85、43、22个样本）,分别采用GRNN、RF和Ordinary kriging对各采样密度下的未知采样点进行预测,采用交叉检验的方式验证各采样密度下未知样点的预测精度。随着采样点密度的下降,样点间的空间自相关性逐渐减弱,半变异函数的拟和精度变差,预测点结果误差增大,预测的置信度降低。当抽稀到43个和22个采样点时,样点间的空间自相关性接近歼灭,半变异函数的决定系数较低且残差较大。普通克里格受到采样点数量和采样密度、样点的空间结构的影响比较明显,其预测精度随采样点数量的下降而下降。在85个采样点及以下时,其预测值与观测值之间没有显著的相关性。GRNN和RF的预测精度受采样密度的影响不大,其预测精度在一个较小的范围内波动,其预测值围绕观测值在一定阈值空间内震荡波动,具有较好的相关性,在85个及以下的采样密度时,预测精度相对普通克里格有较大的提升。普通克里格法不适合在稀疏样本条件下空间插值计算,尤其是在空间自相关性比较弱的情况下。机器学习模型能充分学习土壤间环境信息、样点空间邻近效应信息,兼顾属性相似性和空间自相关,具有更好的稳定性和适应性,不容易受到采样点数量、构型和采样密度等因素的影响,即使在采样点空间自相关性很弱的情况下也能做出稳定预测精度。

Spectroscopy is regarded as a quick and nondestructive method to classify and analyze quantitatively many of elements of the soil. Visible and near -infrared re? ectance spectroscopy offers a conductive tool for investigating soil heavymetal pollution. In this work, 51 soil samples with depths of 0-10 cm were collected, which were in the Eastern Junggar coal -field mining area, Xinjiang. The soil organic matter (SOM) content, Arsenic (As) content and indoor hyperspectra were measured in the laboratory. The significant relationship between As content and hyperspectral data was conductive analysis of NPDIs, which were calculated from Vis-NIR region. For calculating the indices, on the basis of the raw spectral reflectance (R), its three mathematical transformations were calculated, i. e., the reciprocal (1/R), logarithm (lgR) and root mean square method ( sqrt-R/IT2), respectively. The two band combination of optimized indices software V1.0 (No: 2018R11S177501, independently developed based on the JAVA) was used during the calculation of the indices. NPDIs were calculated using all possible combinations of available bands (i nm and j nm) in the full spectral region (400-2 400 nm). In the optimal spectral indices (1r 73 and p= 0. 001), an index of VIP_>. -1 was further selected as a model independent variable by the Variable importance in projection (VIP) selection method. The main goal of this work is to obtain optimized spectral index (NPDI) related to soil heavy metal As, to estimate As concentration in soil based on geographically weighted regression (GWR) model, and to investigate the plausibility of using optimized spectral index for hyperspectral detection of heavy metal Arsenic in soil of coal mining areas. To assess the performance of the soil heavy metal contents prediction models, four cross -validation metrics were used; Residual Prediction Deviation (RPD), the Coefficient of Determination (R2) the Root Mean Square Error (RMSE) and Akaike Information Criterion (ACI). The results of this study are as follows: (1) As has the largest dispersion in the study area, SOM contents in all samples are less than 2%, and the As concentration has no significant correlation with the SOM content at a significance level of 0. 01 (1r =0. 113). (2) Single-bandreflectance shows low correlation with As contents, lower than 0. 228. However, the highest correlation coefficient and lowest p -values (1r1...>.-0. 73 and p=0. 001) between As and NPDIs calculated by original and transformed reflectance (R, 1/R, lgR, /1-2) are found in theNear-infrared (NIR, 780-1 100 nm) and Shortwave -infrared (SWIR, 1 100-1 935 nm) long wavelength infrared. The original spectral region formed with long wave length near -infrared (LW-NIR) regions show highest correlation with As contents (17'1=0. 74). (3) VIP value of NPDIR( 417/1 246) NPD11/R(799/953, 825/947), NPDisqrt-R(1023/1 257, 1 008/1 249, 1 021/1 250, 1 020/1 247) and NPDl15R(801/953, 811/953, 817/951, 825/947, 828/945) higher than 1, thus these NPDIs are chosen as independent variables. (4) From the four prediction model (GWR) performances it can be seen, the Model -a (R) showed superior performance to other three models (Model -b (1/R), Model -c (/) and Model -d (1gR)), and it has the highest validation coefficients (R-2 =0. 831, RMSE=4. 912 mu g. g(-1), RPD=2. 321) and lowest AIC value (ATC=179. 96). The hyperspectral optimized index NPDIR(417/1 246) may help to quickly and accurately evaluate Arsenic contents in soil, furthermore, the results provide theoretical and data support to accesse the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.

Considering the hypothesis that the predictive capacity of models is tied to soil characteristics, the stratification of a spectral library into groups is a strategy to improve the accuracy of the predictions. Thus, the objective of this study was to i) characterize and identify differences among spectra obtained for subtropical soils samples, ii) evaluate different pre-processing techniques and multivariate methods to propose SOC prediction models from the spectral data and iii) evaluate the performance of SOC prediction models calibrated from the stratification of a local library. A local spectral library of soils (n = 841 samples) was used in the Planalto region of the State of Rio Grande do Sul, Brazil. Soil classes that occur in the area are: Rhodic Ferralsol (FR) and Dystric Gleysol (GL). Land uses are: native forest (NFo), native field (NFi) and crops in no -tillage system (CTS). SOC was determined via wet combustion with sulphochromic solution. Spectral reflectance measurements were performed in the laboratory with a spectroradiometer in the range of 350-2500 nm. Six pre-processing techniques were applied to the spectra (including derivatives, normalization and non-linear transformations) and four multivariate calibration methods, namely, partial least squares regression (PLSR), multiple linear regression (MLR), support vector machines (SVM) and random forest (RF), were used with the objective of identifying the best combination to predict SOC. After determining the best combination, the spectral library was stratified into groups based on soil class, land use, sample layer and spectral characteristics. The models were built with 70% of the samples for calibration and 30% for independent validation. The coefficient of determination (R-v(2)), root mean square error (RMSEV) and ratio of performance to interquartile range (RPIQ(v)) of the independent validation were used to evaluate the performance of the models. The spectral curves presented different absorption characteristics in relation to soil classes and land uses. SGD pre-processing technique had the highest R-V(2) and RMSEV values for all models. Among the multivariate methods, PLSR had the best performance for SOC prediction for the total set of samples (R-v(2) = 0.74, RMSEV, = 0.52% and RPIQ(v) = 2.23), followed by models SVM, MLR, and RF. The FR -CTS (n = 445) group showed the best model performance after stratification, with R-v(2) = 0.82, RMSEv = 0.29% and RPIQ(v) = 2.60. For some stratified groups, the use of a smaller number of samples to build the model reduced the performance of the models, suggesting that one must be careful when working with small datasets. This study highlights the potential for the application of VIS-NIR-SWIR spectroscopy as a reliable and economical tool to quantify SOC concentrations for subtropical soils with high levels of iron oxides and clay on a local scale. Predictive models can be improved when the variation in soil characteristics is considered, underscoring the need for a preliminary study examining the grouping of the sample set to validate the use of local spectral libraries for the prediction of soil properties.

The abundance of Juncus effusus (soft rush) and Juncus conglomeratus (compact rush) has increased in coastal grasslands in Norway over recent decades, and their spread has coincided with increased precipitation in the region. Especially in water-saturated, peaty soils, it appears from field observations that productive grasses cannot compete effectively with such rapidly growing rush plants. In autumn-winters of 2012-2013 and 2013-2014, a four-factor, randomised block greenhouse experiment was performed to investigate the effect of different soil moisture regimes and organic matter contents on competition between these rush species and smooth meadow-grass (Poa pratensis). The rush species were grown in monoculture and in competition with the meadow-grass, using the equivalent of full and half the recommended seed rate for the latter. After about three months, above- and below-ground dry matter was measured. J. effusus had more vigorous growth, producing on average 23-40% greater biomass in both fractions than J. conglomeratus. The competitive ability of both rush species declined with decreasing soil moisture; at the lowest levels of soil moisture, growth reductions were up to 93% in J. conglomeratus and 74% in J. effusus. Increasing water level in peat-sand mixture decreased competivitiveness of meadow-grass, while pure peat, when moist, completely impeded its below-ground development. These results show that control of rush plants through management may only be achieved if basic soil limitations have been resolved.