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In aquaculture, co-culturing rice with fish may mitigate greenhouse-gas emissions. In this study, co-culture of four rice cultivars in a laboratory-scale rice–fish system reduced CH4 and N2O emissions relative to fish monoculture. Differences in CH4 and N2O emissions among rice cultivars primarily stem from the differential effects of rice plants on plant-mediated CH4 transport, CH4 oxidation and nitrogen absorption.

This article is devoted to solving full-wave electromagnetic inverse scattering problems (EM-ISPs), which determine the geometrical and physical properties of scatterers from the knowledge of scattered fields. Due to the intrinsic ill-posedness and nonlinearity of EM-ISPs, traditional non-iterative and iterative methods struggle to meet the requirements of high accuracy and real-time reconstruction. To overcome these issues, we propose a two-step contrast source learning approach, cascading convolutional neural networks (CNNs) into the inversion framework, to tackle 2D full-wave EM-ISPs. In the first step, a contrast source network based on the CNNs architecture takes the determined part of the contrast source as input and then outputs an estimate of the total contrast source. Then, the recovered total contrast source is directly converted into the initial contrast. In the second step, the rough initial contrast obtained beforehand is input into the U-Net for refinement. Consequently, the EM-ISPs can be quickly solved with much higher accuracy, even for high-contrast objects, almost achieving real-time imaging. Numerical examples have demonstrated that the proposed two-step contrast source learning approach is able to improve accuracy and robustness even for high-contrast scatterers. The proposed approach offers a promising avenue for advancing EM-ISPs by integrating strengths from both traditional and deep learning-based approaches, to achieve real-time quantitative microwave imaging for high-contrast objects.

Synthetic-aperture radar (SAR) can work in all weather conditions and at all times, and satellite-borne radar has the characteristics of short revisiting period and large imaging width. Therefore, satellite-borne synthetic-aperture radar has been widely deployed, and the SAR images have been widely used in geographic mapping, radar interpretation, ship detection, and other fields. Satellite-borne synthetic-aperture radar is also susceptible to various types of intentional or unintentional interference during the imaging process, and because the interference is a direct wave, its power is much stronger than the wave reflected by targets. As a common interference pattern, radio-frequency interference widely exists in various satellite-borne synthetic-aperture radars, which seriously deteriorates SAR image quality. In order to solve the above problems, this paper proposes a feature decomposition network to suppress interference based on regularization optimization. The contributions of this work are as follows: 1. By analyzing the performance limitations of the existing methods, this work proposes a novel regularization method for radio-frequency interference suppression tasks. From the perspective of data distribution histograms and residual components, the proposed method eliminates the variable components introduced by common regularization, greatly reduces the difficulty of data mapping, and significantly improves its robustness and performance. 2. This work proposes a feature decomposition network, where the feature decomposition module contains two parts; one part only represents the interference signal, and the other part only represents the radar signal. The neurons representing the interference signal are discarded, and the neurons representing the radar signal are used as input for the subsequent network. A cosine similarity constraint is used to separate the interference from the network as much as possible. Finally, this method is validated on the MiniSAR dataset and Sentinel-1A dataset.

With respect to the detection and recognition of an Artificial Modulation Target (AMT) with different modulated types, the state-of-the-art methods generally suffer the deficiencies of overfitting and insufficient generalization of existing neural network solutions. To address these problems, this paper proposes a multi-scale amplitude-phase feature discrimination method for AMTs in SAR images. First, a multi-type modulated AMT Dataset is generated (AMT Detection and Modulation Type Recognition Dataset, ADMTR Dataset), wherein the factors of jamming position, jamming-to-signal ratio (JSR), and the modulated parameter are considered to enhance the generalization. Second, a Multi-Input Multi-Output Fusion Wavelet Neural Network (MIMOFWTNN) is established, which not only uses the amplitude information of the scene but also adequately makes use of the phase and high-frequency information. This empowers us to detect the AMT in a higher dimensional feature space such that the type recognition can be implemented with more certainty. Analysis and discussions conducted on comparison experiments and ablation experiments demonstrate that the proposed network can achieve an average accuracy of 96.96% on the cross-validation set and a correct rate of 99.0% on the completely independent test set, which outperforms the compared methods.

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

To better understand the effects of tillage practice and fertilization management on greenhouse gas emissions and yields, a four-year field experiment was conducted to assess the effects of tillage practices (rotary tillage (RT) and no tillage (NT)) on the emissions of methane (CH4) and nitrous oxide (N2O) and rice yield under four fertilization management strategies (no fertilizer without straw (CK), inorganic fertilizer without straw (F), inorganic fertilize with biochar (FB), and inorganic fertilizer with straw (FS)). The results showed that NT significantly reduced CH4 emissions by 21.1% and 52.6% compared to RT in early and late rice, respectively. Conversely, NT led to a significant increase in N2O emissions by 101.0%, 79.0%, and 220.8% during the early rice, late rice, and fallow periods. Nevertheless, global warming potential (GWP) and greenhouse gas intensity (GHGI) were significantly mitigated, respectively, by 36.4% and 35.9% in NT, compared to RT treatment. There were significant interactions between tillage practice and fertilization management. Compared with CK, the F and FB treatments significantly reduced the GWP, respectively, by 40.4% and 53.8%, as well as the GHGI, respectively, by 58.2% and 69.9% in the RT condition; however, no significant difference was found under the NT condition. In contrast, the FS treatment significantly increased GWP and GHGI in both the RT and NT conditions. Overall, FB treatment had the same significantly low GHGI rating, with a value of 0.44 kg CO2-eq kg−1 yield year−1 in RT and NT. Thus, the conversion of straw to biochar and its application to rice fields is a potentially sustainable agricultural strategy for mitigating GHG emissions and increasing yields. This study provides theoretical and practical support for double-season rice production in climate-smart agriculture.

Freshwater aquaculture is an important source of greenhouse gas (GHG) emissions. GHG emissions are expected to lead to global warming and climate change. A reduction in GHG emissions is urgently required for the sustainable development of freshwater aquaculture. In this study, a laboratory-scale experiment was conducted to analyze the effects of a vegetable–fish co-culture on CH4 and N2O emissions from a freshwater aquaculture pond. The results show that the co-culturing of yellow catfish with pak choi (PC-F) or water spinach (WS-F) significantly reduced the N2O emission from the aquaculture pond by 60.20% and 67.71%, respectively, as compared with a yellow catfish monoculture (F). However, the co-culture of these two vegetables did not affect the level of CH4 emissions. The reduction in N2O emissions was primarily attributed to the decrease in the concentration of N2O and NO3− in the water. The overall global warming potential (GWP) of CH4 and N2O was significantly reduced by 19.1% with PC-F compared to F, but it did not significantly differ between WS-F and F. PC and WS cultivation improved the food yield by 1555.52% and 419.95% compared to F, respectively. Consequently, the GHG emissions intensity (GHGI) under PC-F and WS-F decreased by 96.15% and 80.77% compared to F, respectively. Altogether, the results highlight that a vegetable–fish co-culture is likely an efficient system for mitigating GWP per unit of food yield in freshwater aquaculture ponds. These results can provide a reference for the mitigation of GHG emissions from freshwater aquaculture.

Enhanced-efficiency nitrogen fertilizer (EENF) is a recommend nitrogen fertilizer for rice production because of its advantage on improving nitrogen use efficiency. However, its efficacy on CH4, the dominant greenhouse gas, emission from rice fields showed great variation under field conditions. And the factors influencing its efficacy are still unclear. We synthesized the results of 46 field studies and analyzed the impact of product type, rice variety, and primary agronomic measures (rice cropping system, nitrogen (N) application rate, and water management options) on the effectiveness of EENF on the CH4 emission and rice yield. Overall, EENF, including inhibitors (IS) and slow/control-released fertilizer (S/CRF), significantly reduced CH4 emission by 16.2% and increased rice yield by 7.3%, resulting in a significant reduction in yield-scaled CH4 by 21.7%, compared with conventional N fertilizer. Nitrapyrin, DMPP (3,4-dimethylpyrazole phosphate), and HQ (Hydroquinone) + Nitrapyrin showed relative higher efficacy on the mitigation of CH4 emission than other EENF products; and HQ showed relative lower efficacy on rice yield than other EENF products. The reduction in CH4 emission response of hybrid rice varieties to IS and S/CRF was greater than that of inbred rice varieties. IS significantly reduced the CH4 emission and increased the rice yield under all three rice cropping systems, and showed the highest efficacy in the late rice season of double rice cropping system. Whereas, S/CRF did not significantly reduce the CH4 emission from rice seasons of single rice cropping system and rice-upland crops rotation system. IS did not reduce the CH4 emission when N application rate less than 100 kg ha−1, and S/CRF did not affect the CH4 emission when N application rate less than 100 kg ha−1 or above 200 kg ha−1. Continuous flooding was unfavorable for IS and S/CRF to mitigate CH4 emission and enhance rice yield. These results emphasized the necessary to link EENF products with rice varieties and agronomic practices to assess their efficacy on CH4 emissions and rice yield.

Rice-fish co-culture has gained increasing attention to remediate the negative environmental impacts induced by intensive aquaculture. However, the effect of rice-fish co-culture on oxygen depletion has rarely been investigated. We constructed a rice-fish co-culture system in yellow catfish (Pelteobagrus fulvidraco) and freshwater shrimp (Macrobrachium nipponense) ponds using a new high-stalk rice variety, and conducted a field experiment to investigate the effect of rice-fish co-culture on water parameters and oxygen consumption. The results showed that rice-fish co-culture reduced the nutrients (total nitrogen, ammonia-N, total phosphorous and potassium) and the dissolved oxygen content in fish and shrimp ponds. However, they showed similar seasonal change of dissolved oxygen in the water of fish and shrimp ponds. Rice-fish co-culture reduced the total amount of oxygen consumption and optimized the oxygen consumption structure in pond. The respiration rates in water and sediment were significantly reduced by 66.1% and 31.7% in the catfish pond, and 64.4% and 38.7% in the shrimp pond, respectively, by additional rice cultivation. Rice-fish co-culture decreased the proportions of respiration in sediment and water, and increased the proportion of fish respiration. These results suggest that rice-fish co-culture is an efficient way to reduce hypoxia in intensive culture pond. Keywords: rice-fish co-culture, oxygen depletion, respiration, pond aquaculture, yellow catfish, freshwater shrimp

Life cycle assessments (LCA) of staple food (rice, wheat and corn) production and assessments of the associated greenhouse gas (GHG) and reactive nitrogen (Nr) releases from environmental and ecological perspectives help to develop effective mitigation options. Therefore, carbon (C) and nitrogen (N) footprint reductions in agroecosystems have become an increasingly popular topic related to global climate change and agricultural adaptation. The LCA method was used to calculate the product and farm C footprint (CF) and N footprint (NF) of double rice, rice-wheat and wheat-maize production based on governmental statistical datasets and published results. The spatial and temporal patterns of CF and NF were analyzed in China during 2004–2017, and their driving factors were analyzed to identify potential mitigation strategies. The results showed that of the various inputs, fertilizer application and diesel oil consumption contributed the most to both GHG and Nr emissions from farm inputs in the grain crop production process. The CFs for double rice, rice-wheat and wheat-maize were 0.83, 0.74, and 0.37 kg CO2-eq kg−1 year−1 at the yield scale, respectively. In addition, the NFs were 11.6, 13.4, and 15.4 gN-eq kg−1 year−1 at the yield scale for double rice, rice-wheat and wheat-maize, respectively. The largest fraction of CF and NF was the share of CH4 emissions and NH3 volatilization from the field soil, respectively. The annual CFs and NFs of the multiple crop in the southwestern provinces were higher than those in the central and northern provinces. The annual GHG and Nr emissions from the multiple crop maintained a relatively stable state from 2004 to 2017. The proportion of CO2/Nr production was partly reflected a soil C/N ratio, suggesting a higher C abundance for the double rice production, which could be used as a reference parameter for crop structure adjustment in the future. Based on our results and other studies, some effective solutions, especially optimized fertilization, farm machinery operation efficiencies and changes in regional allocation of grain cropping areas, are needed to mitigate the impacts of climate change and eutrophication on the main grain crop production in China.

The effect of no- and reduced tillage (NT/RT) on greenhouse gas (GHG) emission was highly variable and may depend on other agronomy practices. However, how the other practices affect the effect of NT/RT on GHG emission remains elusive. Therefore, we conducted a global meta-analysis (including 49 papers with 196 comparisons) to assess the effect of five options (i.e. cropping system, crop residue management, split application of N fertilizer, irrigation, and tillage duration) on the effect of NT/RT on CH4 and N2O emissions from agricultural fields. The results showed that NT/RT significantly mitigated the overall global warming potential (GWP) of CH4 and N2O emissions by 6.6% as compared with conventional tillage (CT). Rotation cropping systems and crop straw remove facilitated no-tillage (NT) to reduce the CH4, N2O, or overall GWP both in upland and paddy field. NT significantly mitigated the overall GWP when the percentage of basal N fertilizer (PBN) >50%, when tillage duration > 10 years or rainfed in upland, while when PBN