a b s t r a c tIncreasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson's r < 0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1-5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield (r > 0.40, p < 0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts.

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Online dictionary learning (ODL) is an emerging and efficient dictionary learning algorithm, which can extract fault features information of fault signals in most occasions. However, the typical ODL algorithm fails to consider the interference of noise and the structural features of the fault signals, which leads to the fault features of weak fault signals that are difficult to extract. For that, a novel feature enhancement method based on an improved constraint model of an ODL (ICM-ODL) algorithm has been proposed in this paper. For the stage of dictionary learning, the elastic-net constraint is used to promote the anti-noise performance of the dictionary atoms. For the stage of signals sparse coding, the l 2,1 norm constraint is added to learn the structural features of fault signals. In addition, a variational mode decomposition algorithm is used to reduce the impact of noise on the signal initially. Taking the weak fault signals of bearing as examples for analysis, the results show that the feature enhancement of the weak fault signals is fulfilled by using the ICM-ODL algorithm. Compared with the typical ODL method, the ICM-ODL algorithm can not only improves the anti-noise performance of the dictionary atoms, but also removes the noise compositions of the reconstructed signal significantly. INDEX TERMS Online dictionary learning, sparse representation, elastic-net, l 2,1 norm, feature enhancement.

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This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the erecting stage to the maturity stage of winter wheat (early March to late May in each year at a ten-day interval) of the years from 2000 to 2009. We take the study area overlying on the administration map around the study area, and divide the study area into 17 parts where at least one weather station is located in each part. The pixels where the 17 weather stations are located are firstly chosen and studied for their fitting models, and then the best models for all pixels of the whole area are determined. According to the procedures for the models' development, the selected best models for the 17 pixels are identified and the forecast is done with three steps. The forecasting results of the ARIMA models were compared with the monitoring ones. The results show that with reference to the categorized VTCI drought monitoring results, the categorized forecasting results of the ARIMA models are in good agreement with the monitoring ones. The categorized drought forecasting results of the ARIMA models are more severity in the northeast of the Plain in April 2009, which are in good agreements with the monitoring ones. The absolute errors of the AR(1) models are lower than the SARIMA models, both in the frequency distributions and in the statistic results. However, the ability of SARIMA models to detect the changes of the drought situation is better than the AR(1) models. These results indicate that the ARIMA models can better forecast the category and extent of droughts and can be applied to forecast droughts in the Plain.

Application of sewage effluent or sewage sludge to cropland may increase Ni content in soil and plants, and affect food quality. In this study, soil and winter wheat (Triticum aestivum L.) samples from 14 sites of an area of 12 000 km2 were collected. One selected soil was treated with sewage sludge at the rates of 0, 7.5, 15, 30, 60, 120, and 210 Mg ha−1 (0, 2.5, 5, 10, 20, 40, and 70 kg Ni ha−1). The soil was incubated, potted, and planted to winter wheat that was grown until harvest. All soil samples were then extracted by the DTPA method for Ni, and the soil samples were also fracfionated into exchangeable (EXC), carbonate (CAB), oxide (OXD), organic (ORG), and residual (RES) fractions. Wheat parts from both field and pot studies were analyzed for Ni. With application of sewage effluent, Ni was retained in the surface soil, and was mainly present in the OXD and CAB fractions. The amendment of sewage sludge increased Ni in the OXD, CAB, and EXC fractions. Principal component analysis indicated that Ni content in the EXC fraction was an intensity factor, while Ni contents in the OXD, CAB, and ORG fractions were capacity factors, which represented a potential source of available Ni in the soils. In the field study, wheat leaves showed higher Ni concentrations than the seeds and stem. In the pot experiments, sludge application rates higher than 60 Mg ha−1 caused Ni accumulation in the wheat seeds.