Search

This study used nationally representative data from the China Longitudinal Aging Social Survey 2018 to investigate correlations between older people's productive aging activities and their life satisfaction. The results show that volunteering and grandparenting are positively associated with life satisfaction in older adults, although more so in the rural sample. Furthermore, working is positively associated with life satisfaction in urban older adults, while an opposite relationship was found among rural older adults. The findings imply that properly encouraging the productive engagement of older adults might be a win-win strategy in actively coping with the challenges of population aging. [ABSTRACT FROM AUTHOR]

Accurately obtaining the multi-year spatial distribution information of crops combined with the corresponding agricultural production data is of great significance to the optimal management of agricultural production in the future. However, there are still some problems, such as low generality of crop type mapping models and susceptibility to cloud pollution in large-area crop mapping. Here, the models were constructed by using multi-phase images at the key periods to improve model generality. Multi-phase images in key periods masked each other to obtain large-area cloud-free images, which were combined with the general models to map large areas. The key periods were determined by calculating the global separation index (GSI) of the main crops (wheat, maize, sunflower, and squash) in different growth stages in the Hetao Irrigation District (HID) in China. The multi-phase images in the key period were used to make the data set and were then combined with a variety of deep learning algorithms (U-Net, U-Net++, Deeplabv3+, and SegFormer) to construct general models. The selection of the key periods, the acquisition of regional cloud-free images, and the construction of the general crop mapping models were all based on 2021 data. Relevant models and methods were respectively applied to crop mapping of the HID from 2017 to 2020 to study the generality of mapping methods. The results show that the images obtained by combining multi-phase images in the key period effectively avoided the influence of clouds and aerosols in large areas. Compared with the other three algorithms, U-Net had better mapping results. The F1-score, mean intersection-over-union, and overall accuracy were 78.13%, 75.39% and 96.28%, respectively. The crop mapping model was applied to images in 2020, and its average overall accuracy was more than 88.28%. When we applied the model to map crops (county food crops, cash crops, and cultivated land area) from 2017 to 2019, the regression analysis between the mapping areas obtained by the model and the ground measurements was made. The R2 was 0.856, and the RMSE was 17,221 ha, which reached the application accuracy, indicating that the mapping method has certain universality for mapping in different years. [ABSTRACT FROM AUTHOR]

Water and fertilizer losses have often affected wheat yield per unit area in China and Pakistan. The 2-year field experiments were conducted at six research stations. The experimental sites in China were Tongzhou, Qiliying and Yuanyang. In Pakistan were Faialsaabad, TandoJam and Dokri. Fertilizer treatments were: N 275 , N 207 and N 135 kg ha−1 in Tongzhou and N 125 , N 105 and N 90 kg ha−1 in Faisalabad. While the irrigation treatments were: W 210–230 , W 160–190 and W 110–140 mm ha−1 in Qiyliying, W 350 , W 305 and W 250 mm ha−1 in Yuanyang, W 435 , W 400 and W 365 mm ha−1 in TandoJam and W 380 , W 340 and W 300 mm ha−1 in Dokri. The grain yields, water and N losses were simulated using the WHCNS model. The model showed a good ability to capture the effects of management practices and read the environmental diversity. The study revealed that major contributors to water and N losses were N leaching (r =0.98), evaporation and drainage (r =0.77–0.96) rates. In Tongzhou, the N 207 treatment maintained yield, NUE and decreased N loss. In Faisalabad, the N 125 increased yield but led to higher N loss and reduced NUE. Furthermore, the decline in surface soil water content was a common issue in Pakistani sites and wheat yields were 27.48–41.60 % lower than in China. In Qiliying, the W 160–190 and W 110–140 and in Yuanyang, the W 300 and W 250 treatments decreased the annual average water loss by 33.49–12.38 mm and 66.06–55.01 mm, respectively. In TandoJam, the W 400 and W 365 and in Dokri, the W 380 and W 340 treatments decreased water loss by 72.34–45.87 mm and 56.84–37.82 mm compared to the full irrigation amount. Overall, results showed that 17.39–23.81 % of water use could be decreased without yield penalty in Qiliying and 14.29 % in Yuanyang. In Pakistani sites of TandoJam and Dokri, a decreased irrigation amount of 10.5 % is recommended. • Determined water and N losses in China-Pakistani wheat production systems. • We demonstrate the potential use of the WHCNS model in China-Pakistani environment. • We measured wheat yield difference between China and Pakistani agriculture systems. [ABSTRACT FROM AUTHOR]

Abstract Social media is increasingly popular among LGBT (lesbian, gay, bisexual and transgender) who have a higher risk of depression and dis-identification in China. However, it is unclear whether psychological dependency on social media and social media use behavior have beneficial offline effects. We conducted an online survey of 1391 valid LGBT recruited in China and also interviewed five LGBT Weibo users to better explain some results. We found LGBT with higher levels of depression and dis-identification were more likely to be psychologically dependent on social media. Higher levels of social media dependency were predictive of social media use, and both online social support seeking and offering activities contributed individually as predictor of perceived online social support. Perceived online social support was not negatively related to offline depression and dis-identification, which meant what happens online may stay online. LGBT who has a longer history of using Weibo would be less dependent on social media, perceive less online social support and have better offline psychological wellbeing. The mechanism of how social media use affects offline life of LGBT needs to be further examined. Limitations, future directions and recommendations are discussed. Highlights • Worse mental health is positively related to social media dependency for LGBT. • Social media dependency is related to online support seeking and offering activities for LGBT. • Online support seeking and offering activities are positively correlated with perceived online social support for LGBT. • Perceived online support is not related to better offline mental health, while having a longer history of using Weibo predicts better mental health. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of crop evapotranspiration (ET c) is essential for efficient irrigation management at the farmland scale. However, effective decision-making for irrigation scheduling requires high spatiotemporal-resolution data to provide within-field heterogeneity information. The objective of this study was to evaluate the use of optical and thermal information obtained from an unmanned aerial vehicle (UAV) to quantify maize (Zea mays L.) ET c within the framework of the FAO-56 dual crop coefficient approach. Canopy temperature-based crop water stress index (CWSI) and number of degrees above canopy threshold (DACT) were used to determine the stress coefficient (K s). Three types of normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) were adopted to determine basal crop coefficient (K cb). Then the two forms of stress coefficient in combination with three vegetation indices (VIs) were evaluated to estimate daily crop ET c under different irrigation treatments over two years in the southwest region of Inner Mongolia, China. The results demonstrated that the combination of NDVI and CWSI produced the best estimates of maize ET c , with R2 of 0.84 and root mean square error (RMSE) of 0.50 mm/day. The DACT-based model also performed well, with R2 of 0.77–0.80 and RMSE of 0.53 mm/day. Although the results varied with irrigation levels, the daily mean bias error of ET c predictions over different years indicated acceptable accuracy, with a mean bias error (MBE) of 0.48 mm/day. Sensitivity analyses indicated that ET c models were most sensitive to the slope of the linear regression between K cb and VIs, followed by soil evaporation constant and influential factor in K s_DACT. From this study, the combinations of vegetation index and CWSI, as well as DACT, were recommended as alternative approaches for estimating ET c due to intrinsic simplicity and easy interpretation. These ET c models relying on UAV-based multi-sensor data thus show promising potential in farmland-scale applications. • The potential of low-cost UAV-based image data for maize ET c prediction was evaluated. • Three vegetation indices NDVI, SAVI, and EVI were used to determine K cb. • Two different forms of K s methods (CWSI and DACT) were compared. • The robustness and adaptability of UAV-based ET c estimation model across different irrigation treatments was discussed. [ABSTRACT FROM AUTHOR]

• The effect of imagery collected platform on the fixed-threshold method was assessed. • Machine learning regression algorithms were adopted to relate UAV VIs to FVC. • The established FVC estimation model was tested for different growing season. • RF model performed well for different growing seasons, stages, and water status. Crop water stress is an inevitable and increasing challenge for agriculture. To improve crop water use efficiency, management of water stress and accurate estimation of crop traits were required. Among crop traits used to detect crop growth status and predict yield, fractional vegetation cover (FVC) is of great significance. We conducted studies in a maize field located in Inner Mongolia, China with different irrigation levels during 2018 and 2019 growing seasons. UAV RGB imagery was captured to investigate the effect of image sensors on thresholds obtained by the fixed-threshold method (proposed in our recent study), and to provide reference FVC (FVC UAV_R) for FVC models based on UAV multispectral imagery. Five vegetation indices (VIs), calculated from UAV multispectral imagery, and three regression algorithms (RF: random forest, ANN: artificial neural network, and MLR: multivariate linear regression) were used to build the FVC model suitable for different growing seasons, growth stages, and crop water stress. The results showed that there was a change in thresholds obtained using the fixed-threshold method based on different image sensors, but this change did not make a big difference on the accuracy of FVC UAV_R , with the R2 difference of 0.01 and the RMSE difference of 0.01. As for the three FVC regression models, RF model was the most suitable model when these models established in 2018 were used to estimate maize FVC in 2019 for different growth stages and water stress. The low estimation accuracy for high FVC levels was the reason why MLR model could not be used in the other maize growing season. This study provides a low cost and easy way to estimate maize FVC and its inter-field variability under various water status in different maize growing seasons or growth stages. [ABSTRACT FROM AUTHOR]

An improved semantic segmentation method based on object contextual representations network (OCRNet) is proposed to accurately identify zucchinis intercropped with sunflowers from unmanned aerial vehicle (UAV) visible images taken over Hetao Irrigation District, Inner Mongolia, China. The proposed method improves on the performance of OCRNet in two respects. First, based on the object region context extraction structure of the OCRNet, a branch that uses the channel attention module was added in parallel to rationally use channel feature maps with different weights and reduce the noise of invalid channel features. Secondly, Lovász-Softmax loss was introduced to improve the accuracy of the object region representation in the OCRNet and optimize the final segmentation result at the object level. We compared the proposed method with extant advanced semantic segmentation methods (PSPNet, DeepLabV3+, DNLNet, and OCRNet) in two test areas to test its effectiveness. The results showed that the proposed method achieved the best semantic segmentation effect in the two test areas. More specifically, our method performed better in processing image details, segmenting field edges, and identifying intercropping fields. The proposed method has significant advantages for crop classification and intercropping recognition based on UAV visible images, and these advantages are more substantive in object-level evaluation metrics (mIoU and intercropping IoU). [ABSTRACT FROM AUTHOR]

• Ground measurements of canopy structure and chlorophyll content were adopted. • The annual change of relationships between UAV VIs and Gs was explored. • Non-linear regression algorithms were adopted to relate UAV VIs to Gs. • Multiple linear regression approach performed well and was considered very robust. To further assess the sensitivity of crop chlorophyll and structure based on UAV vegetation indices (VIs) to maize water stress, a study was carried out in a maize field located in Inner Mongolia, China, with various levels of deficit irrigation over the entire 2018 and 2019 growing seasons. Ground measurements of stomatal conductance (Gs), leaf area index and leaf chlorophyll were used as references for maize water status, canopy structure and chlorophyll content, respectively. Four structure VIs and two chlorophyll VIs, and three regression algorithms (multiple linear, random forest and artificial neural networks regression) were adopted. The results showed that canopy structure derived from VIs had a significant correlation (p < 0.001) with Gs with the highest r value of 0.64 (n = 270) in 2018 and 2019. The transformed chlorophyll absorption in reflectance index, chlorophyll VI, could only estimate severe maize water stress with an r value of −0.47 (p < 0.001, n = 270) for the drier 2019. The water stress sensitivity of chlorophyll and structure VIs maybe significantly influenced by different responses of canopy structure and chlorophyll concentration to water stress, and the different spectral resolution of UAV multispectral cameras. Compared to non-linear machine learning regression algorithms, the multiple linear regression was robust enough to relate UAV-based multispectral VIs to Gs with coefficients of determination of 0.48 and 0.45 (n = 270) for 2018 and 2019, respectively. Although stable significant correlations were found between UAV multispectral VIs and Gs, annual changes in these specific expressions were also observed. Overall, our results demonstrated the potential of using structure VIs derived from UAV multispectral images and multiple linear regression approach to estimate maize water status in field scale. [ABSTRACT FROM AUTHOR]

As the key principle of precision farming, variation of actual crop evapotranspiration (ET) within the field serves as the basis for crop management. Although the estimation of evapotranspiration has achieved great progress through the combination of different remote sensing data and the FAO-56 crop coefficient (Kc) method, lack of the accurate crop water stress coefficient (Ks) at different space–time scales still hinder its operational application to farmer practices. This work aims to explore the potential of multispectral images taken from unmanned aerial vehicles (UAVs) for estimating the temporal and spatial variability of Ks under the water stress condition and mapping the variability of field maize ET combined with the FAO-56 Kc model. To search for an optimal estimation method, the performance of several models was compared including models based on Ks either derived from the crop water stress index (CWSI) or calculated by the canopy temperature ratio (Tc ratio), and combined with the basal crop coefficient (Kcb) based on the normalized difference vegetation index (NDVI). Compared with the Ks derived from the Tc ratio, the CWSI-based Ks responded well to water stress and had strong applicability and convenience. The results of the comparison show that ET derived from the Ks-CWSI had a higher correlation with the modified FAO-56 method, with an R2 = 0.81, root mean square error (RMSE) = 0.95 mm/d, and d = 0.94. In contrast, ET derived from the Ks-Tc ratio had a relatively lower correlation with an R2 = 0.68 and RMSE = 1.25 mm/d. To obtain the evapotranspiration status of the whole maize field and formulate reasonable irrigation schedules, the CWSI obtained by a handheld infrared thermometer was inverted by the renormalized difference vegetation index (RDVI) and the transformed chlorophyll absorption in reflectance index (TCARI). Then, the whole map of Ks can be derived from the VIs by the relationship between CWSI and Ks and can be taken as the basic input for ET estimation at the field scale. The final ET results based on multispectral UAV interpolation measurements can well reflect the crop ET status under different irrigation levels, and greatly help to improve irrigation scheduling through more precise management of deficit irrigation. [ABSTRACT FROM AUTHOR]

With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images. [ABSTRACT FROM AUTHOR]

• Redundant information of the imagery was removed by the feature selection method. • U-Net algorithm is more suitable for crop mapping in the irrigation district. • The key early window period of major crop mapping was determined. • The crop mapping models with strong versatility in the early period were obtained. The information on spatial distribution pattern and area of different crops is particularly useful for monitoring and managing the sustainability of agricultural resources. However, there are still challenges in timely mapping of crop types and planting areas to support production during the present growing season. Here, we proposed to use time-series Sentinel-2 imagery and a deep learning method for major crop mapping (wheat, maize, sunflower, and squash) in the agricultural production areas in the Hetao Irrigation District (HID), Inner Mongolia Autonomous Region, China. A feature selection method that combines the global separability index and feature recursive elimination was proposed to optimize the band features of Sentinel-2 imagery. The Random Forest, Extreme Gradient Boosting, U-Net and deeplabv3+ algorithms were then used for image classification with all spectral bands and the selected band features, respectively. The selected bands and best method were used to determine the key time window for the early identification of four major crops. Integrate all image datasets in the early window period, and build mapping models suitable for images at different times in the window period by combining the selected bands and best method. The results showed that the proposed feature selection method effectively removed redundant features and improved the classification efficiency. The U-Net algorithm was more suitable for the classification of major crops in HID with a higher accuracy whether using all band features or the selected features. The key periods for early recognition of wheat, maize, sunflower, and squash in HID were mid-May, late June, early July, and late June, respectively. The constructed models for crop type mapping in the early period were tested in 2020 and 2021 in the HID, and the F1-score, the mean intersection-over-union and overall accuracy were 86.71 %, 84.66 %, and 98.89 %, respectively. The proposed feature selection method, U-Net classification algorithm, and screening of the early mapping window period in this study provide an efficient, accurate and timely crop mapping tool in HID. Future research can further verify the applicability of the early identification time window proposed in this study to support other agricultural management activities in the areas. [ABSTRACT FROM AUTHOR]

[Display omitted] During the era of global warming and highly urbanized development, extreme and high impact weather as well as air pollution incidents influence everyday life and might even cause the incalculable loss of life and property. Despite the vast development of atmospheric models, there still exist substantial numerical forecast biases objectively. To accurately predict extreme weather, severe air pollution, and abrupt climate change, numerical atmospheric model requires not only to simulate meteorology and atmospheric compositions simultaneously involving many sophisticated physical and chemical processes but also at high spatiotemporal resolution. Global integrated atmospheric simulation at spatial resolutions of a few kilometers remains challenging due to its intensive computational and input/output (I/O) requirement. Through multi-dimension-parallelism structuring, aggressive and finer-grained optimizing, manual vectorizing, and parallelized I/O fragmenting, an integrated Atmospheric Model Across Scales (iAMAS) was established on the new Sunway supercomputer platform to significantly increase the computational efficiency and reduce the I/O cost. The global 3-km atmospheric simulation for meteorology with online integrated aerosol feedbacks with iAMAS was scaled to 39,000,000 processor cores and achieved the speed of 0.82 simulation day per hour (SDPH) with routine I/O, which enabled us to perform 5-day global weather forecast at 3-km horizontal resolution with online natural aerosol impacts. The results demonstrate the promising future that the increasing of spatial resolution to a few kilometers with online integrated aerosol feedbacks may significantly improve the global weather forecast. [ABSTRACT FROM AUTHOR]