Water Quality in Irrigated Paddy Systems

Irrigated paddy rice (Oryza sativa L.) is a staple food for roughly half of the world’s population. Concerns over water quality have arisen in recent decades, particularly in China, which is the largest rice-producing country in the world and has the most intensive use of nutrients and water in rice production. On the one hand, the poor water quality has constrained the use of water for irrigation to paddy systems in many areas of the world. On the other hand, nutrient losses from paddy production systems contribute to contamination and eutrophication of freshwater bodies. Here, we review rice production, water requirement, water quality issues, and management options to minimize nutrient losses from paddy systems. We conclude that management of nutrient source, rate, timing, and placement should be combined with the management of irrigation and drainage water to reduce nitrogen and phosphorus losses from paddies. More research is needed to identify cost-effective monitoring approaches and mitigation options, and relevant extension and policy should be enforced to achieve water quality goals. The review is preliminarily based on China’s scenario, but it would also provide valuable information for other rice-producing countries

Evaluating the risk of phosphorus loss with a distributed watershed model featuring zero-order mobilization and first-order delivery

Many semi-distributed models that simulate pollutants' losses from watersheds do not handle well detailed spatially distributed and temporal data with which to identify accurate and cost-effective strategies for controlling pollutants issuing from non-point sources. Such models commonly overlook the flow pathways of pollutants across the landscape. This work aims at closing such knowledge gap by developing a Spatially and Temporally Distributed Empirical model for Phosphorus Management (STEM-P) that simulates the daily phosphorus loss from source areas to receiving waters on a spatially-distributed grid-cell basis. STEM-P bypasses the use of complex mechanistic algorithms by representing the phosphorus mobilization and delivery processes with zero-order mobilization and first-order delivery, respectively. STEM-P was applied to a 217km2 watershed with mixed forest and agricultural land uses situated in southwestern China. The STEM-P simulation of phosphorus concentration at the watershed outlet approximated the observed data closely: the percent bias (Pbias) was -7.1%, with a Nash-Sutcliffe coefficient (ENS) of 0.80 on a monthly scale for the calibration period. The Pbias was 18.1%, with a monthly ENS equal to 0.72 for validation. The simulation results showed that 76% of the phosphorus load was transported with surface runoff, 25.2% of which came from 3.4% of the watershed area (classified as standard A critical source areas), and 55.3% of which originated from 17.1% of the watershed area (classified as standard B critical source areas). The standard A critical source areas were composed of 51% residences, 27% orchards, 18% dry fields, and 4% paddy fields. The standard B critical source areas were mainly paddy fields (81%). The calculated spatial and temporal patterns of phosphorus loss and recorded flow pathways identified with the STEM-P simulations revealed the field-scale critical source areas and guides the design and placement of effective practices for non-point source pollution control and water quality conservation

Biostimulants as Plant Growth Stimulators in Modernized Agriculture and Environmental Sustainability

Plant growth stimulators (growth regulators + biostimulants; PGS) are chemical substances (organic/inorganic), helpful in plant growth and development. These are not considered as the replacement of fertilizers but can help in improved crop and soil quality. Both compounds can amplify the root biomass, nutrients translocation, enzymatic activities, crop yield, physiology, and nutrient uptake. Biostimulants are rich in minerals, vitamins, plant hormones, oligosaccharides, and amino acids. These compounds have a serious role to improve soil health, fertility, sorption, and desorption of nutrients. Hence, have a vital character in nutrients cycling, abiotic stress control, heavy metals bioavailability, and greenhouse gaseous emission. This chapter focuses on the discussions about the influence of plant growth regulators and biostimulants in crop production, soil health, heavy metal cycling, greenhouse gases emission with environmental sustainability. Whereas, the impact of biostimulants on greenhouse gases is a research gap

Nitrogen application rates need to be reduced for half of the rice paddy fields in China

This research was partially supported by the National Key Research and Development Program (2016YFD0800500), the Special Fund for Agro-scientific Research in the Public Interest (201003014, 201303089), National Natural Science Foundation of China (41773068) and the Newton Fund (Grant Ref: BB/N013484/1).Peer reviewedPostprin

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC

Informer-WGAN: High Missing Rate Time Series Imputation Based on Adversarial Training and a Self-Attention Mechanism

Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis. Therefore, data imputation needs to be performed first. Generally, data imputation methods include statistical imputation, regression imputation, multiple imputation, and imputation based on machine learning methods. However, these methods currently have problems such as insufficient utilization of time characteristics, low imputation efficiency, and poor performance under high missing rates. In response to these problems, we propose the informer-WGAN, a network model based on adversarial training and a self-attention mechanism. With the help of the discriminator network and the random missing rate training method, the informer-WGAN can efficiently solve the problem of multidimensional time series imputation. According to the experimental results under different missing rates, the informer-WGAN model achieves better imputation results than the original informer on two datasets. Our model also shows excellent performance on time series imputation of the key parameters of a spacecraft control moment gyroscope (CMG)

Nitrogen Transport/Deposition from Paddy Ecosystem and Potential Pollution Risk Period in Southwest China

Nitrogen (N) losses through runoff from cropland and atmospheric deposition contributed by agricultural NH3 volatilization are important contributors to lake eutrophication and receive wide attention. Studies on the N runoff and atmospheric N deposition from the paddy ecosystem and how the agriculture-derived N deposition was related to NH3 volatilization were conducted in the paddy ecosystem in the Erhai Lake Watershed in southwest China. The critical period (CP) with a relatively high total N (TN) and NH4+-N deposition occurred in the fertilization period and continued one week after the completion of fertilizer application, and the CP period for N loss through surface runoff was one week longer than that for deposition. Especially, the mean depositions of NH4+-N in the CP period were substantially higher than those in the subsequent period (p < 0.01). Moreover, agriculture-derived NH4+ contributed more than 54% of the total NH4+-N deposition in the CP period, being positively related to NH3 volatilization from cropland soil (p < 0.05). The N concentrations were higher in the outlet water of ditches and runoff in May than in other months due to fertilization and irrigation. Therefore, to reduce the agricultural N losses and improve lake water quality, it is important to both reduce agricultural NH4+-N deposition from NH3 volatilization and intercept water flow from the paddy fields into drainage ditches during the CP

Nitrogen Transport/Deposition from Paddy Ecosystem and Potential Pollution Risk Period in Southwest China

Nitrogen (N) losses through runoff from cropland and atmospheric deposition contributed by agricultural NH3 volatilization are important contributors to lake eutrophication and receive wide attention. Studies on the N runoff and atmospheric N deposition from the paddy ecosystem and how the agriculture-derived N deposition was related to NH3 volatilization were conducted in the paddy ecosystem in the Erhai Lake Watershed in southwest China. The critical period (CP) with a relatively high total N (TN) and NH4+-N deposition occurred in the fertilization period and continued one week after the completion of fertilizer application, and the CP period for N loss through surface runoff was one week longer than that for deposition. Especially, the mean depositions of NH4+-N in the CP period were substantially higher than those in the subsequent period (p 4+ contributed more than 54% of the total NH4+-N deposition in the CP period, being positively related to NH3 volatilization from cropland soil (p 4+-N deposition from NH3 volatilization and intercept water flow from the paddy fields into drainage ditches during the CP

Net Anthropogenic Nitrogen Input and Its Relationship with Riverine Nitrogen Flux in a Typical Irrigated Area of China Based on an Improved NANI Budgeting Model

Excessive nitrogen (N) inputs from human activities in the watershed have resulted in water quality deterioration and other biological hazards. It is therefore critical to fully understand the anthropogenic N inputs and their potential impacts on regional water quality. In this study, a modified net anthropogenic nitrogen input (NANI) budgeting model considering the irrigation N input was developed and applied to investigate spatial–temporal variations of anthropogenic N inputs and their relationship with riverine N flux from 2005 to 2019 in a semi-arid irrigated watershed, Ulansuhai Nur watershed (UNW), China. The results showed that the annual average anthropogenic N inputs reached 14,048.0 kg N km−2 yr−1 without a significant temporal change trend. Chemical N fertilizer was the major contributor for watershed NANI and accounted for 75.3% of total NANI. Hotspots for N inputs were located in the central part of the watershed. In this study, watershed NANI does not have a significant regression relationship with riverine N export during the study period. Riverine N export showed an obvious decreased trend, which mainly was attributed to human activities. In addition, approximately 1.92% of NANI was delivered into the water body. Additionally, the N inputs into the watershed by the irrigation water accounted for 9.9% of total NANI. This study not only expands the application range of the NANI model in irrigated watersheds, but also provides useful information for watershed N management strategies

Enhanced electrical performance in CaBi4Ti4O15 ceramics through synergistic chemical doping and texture engineering

High-temperature piezoelectric materials with excellent piezoelectricity, low dielectric loss and large resistivity are highly desired for many industrial sectors such as aerospace, aircraft and nuclear power. Here a synergistic design strategy combining microstructural texture and chemical doping is employed to optimize CaBi4Ti4O15 (CBT) ceramics with bismuth layer structure. High textured microstructure with an orientation factor of 80%–82% has been successfully achieved by the spark plasma sintering technique. Furthermore, by doping MnO2, both advantages of hard doping and sintering aids are used to obtain the excellent electrical performance of d33 = 27.3 pC/N, tanδ∼0.1%, Q31∼2,307 and electrical resistivity ρ∼6.5 × 1010 Ω·cm. Up to 600 °C, the 0.2% (in mass) Mn doped CBT ceramics still exhibit high performance of d33 = 26.4 pC/N, ρ∼1.5 × 106 Ω·cm and tanδ∼15.8%, keeping at an applicable level, thus the upper-temperature limit for practical application of the CBT ceramics is greatly increased. This work paves a new way for developing and fabricating excellent high-temperature piezoelectric materials