Salt-affected soils is an important soil resource. Understanding fertilizer and salinity interaction are of great economic importance for improving crop yield and fertilizer use efficiency. A pot experiment was carried out to study the application of nitrogen (N) for ameliorating salt stress in wheat grown in the coastal saline soil of the Yellow River delta. Several controlling levels of salinity and nitrogen (0.7, 1.7, 2.7 g/kg, and 135, 270, and 405 kg/ha) were designed in a pot experiment in a stable water content state to investigate the N and salt interaction on soil properties and winter wheat growth characteristics. The results showed that the dry weight of winter wheat was promoted by salinity in the early growth stage (20 days), then it was gradually inhibited by nitrogen fertilizer. When winter wheat was grown by 54 days, the N and salinity had significant effects on the biomass of winter wheat. The nitrogen content of wheat shoot and root was mainly affected by N addition usage, and the largest value was obtained in 270 kg/ha N dosage treatments. The higher the salt content existed in the soil, the lower the growth rate shown in wheat cultivation. Under saline conditions, the N fertilizer application amount should be controlled to no more than 270 kg/ha, so that it could greatly promote wheat growth. Reasonable fertilizer usage could significantly contribute to crop yield and food quality of the saline agriculture in the Yellow River delta.
Coastal salt-affected soils account for a large area all around the world. Soil salinity and pH are two important parameters affecting soil quality. Investigating the correlation of electrical conductivity (EC) and pH at different soil depths in saline soil was useful for quickly assessing the saline–alkaline characteristics. During the natural desalination process in the field area of reclaimed lands, the phenomena of pH increase and nitrogen accumulation may occur. A field sampling experiment was conducted in slightly saline soil affected by natural desalination and newly reclaimed heavily saline soil. A series of soil–water ratio extracts consisting of 1:2.5, 1:5, 1:10, 1:20, and 1:40 was designed to measure the EC and pH for simulating the saline–alkaline characteristics during the soil desalination process. Meanwhile, for reasonable utilization of the naturally ameliorated slightly saline soil which consists of a high content of nitrogen, a plastic mulching (PM) accompanied with nitrogen (N) fertilizer addition experiment in maize cultivation plots was designed. Results showed that a significant correlation of EC and/or pH existed in all ratios of soil extracts, and the slightly saline soil had a higher nitrogen content (1.06 g kg−1). The EC was negatively correlated with pH at a depth of 0~100 cm in the coastal saline soil, which indicated the increase of pH value and alkalization during its natural desalination. Furthermore, PM treatments showed no significant difference with N treatments in soil bulk density and soil water content in the slightly saline soil. The PM and N treatments obtained similar grain yield, which was between 6.2 and 6.5 t ha−1. The soil salinity decreased in all treatments and the harvest index was largest in PM treated plots. Our study was beneficial for rapidly monitoring saline–alkaline characteristics and sustainable utilization of coastal saline soil resources. In addition, we should focus far more on pH improvement during the desalination process and rational utilization of chemical fertilizer for obtaining sustainable benefits in the coastal saline soil.
Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine learning algorithms and applied to cut-out-centered galaxies. However, according to the design and survey strategy of optical surveys by CSST, preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images. Moreover, given that multi-color images of strong lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong lensing systems in images with any number of channels. As evaluated using CSST mock data based on an Semi-Analytic Model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. 61 new strong lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.
Abstract Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong-lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine-learning algorithms and applied to cutout-centered galaxies. However, according to the design and survey strategy of optical surveys by the China Space Station Telescope (CSST), preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual transformer with a sliding window technique to search for strong-lensing systems within entire images. Moreover, given that multicolor images of strong-lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong-lensing systems in images with any number of channels. As evaluated using CSST mock data based on a semianalytic model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. A total of 61 new strong-lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.
The Yellow River Delta is an important grain production base in China, and it is a typical Yellow River irrigated area. Chemical fertilizer overuse has seriously affected grain production safety, and understanding the fertilizer application situation is useful for scientific agronomy management. In this study, we collected the data of the N, P, K fertilizers for crop cultivation in Donging City from 2011 to 2020, and we collected 185 investigating questionnaires to gather information on the fertilizer application rate for small farmers. The results showed that the amount of total fertilizer used has decreased from the year 2015, but the macro element fertilizer rate for crop cultivation exceeded the recommended dosage. The application of compound fertilizer increased during the investigated 10 years, and its proportion in 2020 was 1.65 times higher than in 2011. For obtaining an ideal grain yield, the N and P2O5 had relative reduction rates of 67.8% and 69.6% for wheat planting. Furthermore, the relative reduction rates of N, P2O5, and K2O were 25.9%, 69.6%, and 59.7%, respectively, for maize cultivation when compared to the recommended dosage. During wheat growth, the potassium fertilizer was needed to increase the dosage, although the K element content in the soil was high. Furthermore, the medium and trace elements are all important nutrients for improving crop yield and quality which need to be studied. More scientific measurements should be conducted to match chemical fertilizer reduction to constructing healthy and sustainable agriculture in the Yellow River irrigated area.
Context . Weak gravitational lensing is one of the most important probes of the nature of dark matter and dark energy. In order to extract cosmological information from next-generation weak lensing surveys (e.g., Euclid , Roman , LSST, and CSST) as much as possible, accurate measurements of weak lensing shear are required. Aims . There are existing algorithms to measure the weak lensing shear on imaging data, which have been successfully applied in previous surveys. In the meantime, machine learning (ML) has been widely recognized in various astrophysics applications in modeling and observations. In this work, we present a fully deep-learning-based approach to measuring weak lensing shear accurately. Methods . Our approach comprises two modules. The first one contains a convolutional neural network (CNN) with two branches for taking galaxy images and point spread function (PSF) simultaneously, and the output of this module includes the galaxy’s magnitude, size, and shape. The second module includes a multiple-layer neural network (NN) to calibrate weak-lensing shear measurements. We name the program F ORKLENS and make it publicly available online. Results . Applying F ORKLENS to CSST-like mock images, we achieve consistent accuracy with traditional approaches (such as moment-based measurement and forward model fitting) on the sources with high signal-to-noise ratios (S /N > 20). For the sources with S/N < 10, F ORKLENS exhibits an ~36% higher Pearson coefficient on galaxy ellipticity measurements. Conclusions . After adopting galaxy weighting, the shear measurements with F ORKLENS deliver accuracy levels to 0.2%. The whole procedure of F ORKLENS is automated and costs about 0.7 milliseconds per galaxy, which is appropriate for adequately taking advantage of the sky coverage and depth of the upcoming weak lensing surveys.
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