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This study aimed to improve the seed quality during the deterioration period of rough rice (Oryza sativa L.), cultivar ‘Khoa Dawk Mali 105’ (KDML 105), using an automatic soaking and germination accelerator unit (ASGA) together with stimulation via infrared radiation treatment (IRT) to stimulate seed quality (germination rate and γ-aminobutyric acid (GABA) content). This study used a general full factorial design, and the independent variables were the storage period (10, 11 and 12 months), methods of germinated rough rice preparation (conventional method (CM) and an automatic soaking and germination accelerator unit (ASGA)), and stimulation with IRT. The initial grain moisture content did not exceed 14% (wet basis (wb)). The germination rate of the rough rice by CM and ASGA with stimulation with IRT was significantly higher than non-stimulated rice, by 6.56 and 8.11%, respectively, in each storage period. The GABA contents of the germinated rough rice using CM and ASGA stimulated with IRT were significantly higher than ungerminated rough rice, by 19.52 and 21.24% (10 months), respectively; 16.36 and 23.58% (11 months), respectively; and 69.88 and 67.69% (12 months), respectively.

This study was conducted to investigate the effects of high hydrostatic pressure on the functional compounds of germinated rough rice. Rough rice was germinated at 37 °C over a 2-day period and then subjected to 0.1, 10, 30, 50, and 100 MPa for 24 h. Before-germination (BG) and after-germination (AG) samples were prepared and analyzed for functional compounds. The highest total polyphenol content (1.5 ± 0.2 mg/g) was found after 30 MPa. The highest γ-aminobutyric acid (GABA) content (111.4 ± 4.1 mg/g), total arabinoxylan content (5.4 ± 0.6 %), and γ-oryzanol content (46.1 ± 4.1 mg/100 g) were found after 50 MPa. Phytic acid decreased with pressure from 4.0 ± 0.1 mg/g (0.1 MPa) to 1.0 ± 0.1 mg/g (100 MPa). These results suggest that high hydrostatic pressure treatments may increase the functional properties of germinated rough rice.

Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.

Rice is a staple food by an increasing number of people in China. As more issues have arisen in China due to rice contaminated by cadmium (Cd), Cd contamination in arable soils has become a severe problem. In China, many studies have examined Cd contamination in arable soils on a national scale, but little studies have focused on the distribution of Cd in paddy fields. This study explored the spatial pattern of Cd in paddy soils in China, made a preliminary evaluation of the potential risk, and identified the most critically contaminated regions based on the domestic rough rice trade flow. The results showed that Cd concentrations in paddy soils in China ranged from 0.01 to 5.50 mg/kg, with a median value of 0.23 mg/kg. On average, the highest Cd concentrations were in Hunan (0.73 mg/kg), Guangxi (0.70 mg/kg), and Sichuan (0.46 mg/kg) provinces. Cd concentrations in paddy soils in central and western regions were higher than those in eastern regions, especially the southeastern coastal regions. Of the administrative regions, Cd standard exceedance rate was 33.2 %, and the heavy pollution rate was 8.6 %. Regarding to Cd of paddy soil, soil environmental quality was better in Northeast China Plain than in Yangtze River Basin and southeastern coastal region. Mining activities were the main anthropogenic pollution source of Cd in Chinese paddy soil. Based on rice trade, more of the Chinese population would be exposed to Cd through intake of rice produced in Hunan province. Certain regions that output rice, especially Hunan province, should be given priority in the management and control of Cd contamination in paddy soil.

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVITian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R2 can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option.

The resource and environmental challenges faced by rice production call for resource-saving and environment-friendly rice varieties. Water-saving and drought-resistance rice (WDR) is a new type of cultivated rice combining both high yield potential and acceptable grain quality as a current lowland paddy rice, as well as water-saving and drought resistance as a traditional upland rice. The lowland and upland rice are two ecotypes adapted to contrasting soil water status, originating mainly because of their differentiated drought resistance. Upland rice, domesticated in a water–limited environment and experiencing a bidirectional selection process, has better drought resistance and especially better drought avoidance. Though the potential tradeoff between drought resistance and productivity is very common in rice, the bidirectional selection could overcome this tradeoff and accumulate recombination genotypes. It is very important to choose elite parents on the basis of studies on the great genetic diversity of rice yield and drought resistance among the rice germplasm resources and adapt the bidirectional selection strategies to especially integrate drought avoidance, drought tolerance, high water use efficiency, and productivity in WDR breeding. The breeding history and genomic studies indicated that lowland paddy rice and upland rice hybridization breeding with suitable selection in different environments is an effective approach to improving complex traits such as yield potential and drought resistance. Meanwhile, molecular technology shows higher efficiency on value-added breeding such as transferring and pyramiding pest- and disease-resistant genes, which helps WDR obtain other green characters. Twenty-two WDR varieties were registered and distributed to farmers in recent years and could be planted in both irrigated and rainfed ecosystems, thus showing promising application prospects. The major crop management technology of WDR in lowland paddy fields with water-saving cultivation and in rainfed fields by dry seeding with aerobic cultivation were also discussed in this article.

Timely and accurate estimation of the area and distribution of crops is vital for food security. Optical remote sensing has been a key technique for acquiring crop area and conditions on regional to global scales, but great challenges arise due to frequent cloudy days in southern China. This makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could bridge this gap since it is less affected by clouds. The recent availability of Sentinel-1A (S1A) SAR imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to fully utilize phenological information to improve early crop classification. In deep learning methods, one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown to efficiently extract temporal features for classification tasks. However, due to the complexity of training, these three deep learning methods have been less used in early crop classification. In this work, we attempted to combine them with an incremental classification method to avoid the need for training optimal architectures and hyper-parameters for data from each time series. First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed an incremental classification process to train each classifier using all of the previous data, and obtained a classification network with all parameter values (including the hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang City, China. To verify the effectiveness of this method, we also implemented the classic random forest (RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient (0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900 for pineapple before harvest. All results indicated the effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification. This method is expected to provide new perspectives for early mapping of croplands in cloudy areas.

Aims To assess 1) the effect of the combination of flooding (winter flooding vs. non-winter flooding; WFL vs NWF) and timing of straw incorporation (early vs late straw incorporation; ESI vs LSI) in the post-harvest of paddy agrosystem, on a year-round global balance of greenhouse gases (GHG) exchanges, i.e. methane (CH 4 ), carbon dioxide (CO 2 ) and nitrous oxide (N 2 O); 2) the impact on the net ecosystem carbon balance (NECB) and 3) the resulting net global warming potential (GWP). Methods A field experiment was conducted with fortnightly samplings of main GHG emissions. Effect of the studied factors on GHG emissions was seasonally assessed. The net GWP is estimated from the balance between GHG (CH 4 and N 2 O) and NECB. Results NWF-LSI reduced net GWP by 206% compared to conventional post-harvest management (WFL-ESI). NECB was similar in all treatments. Avoiding winter flooding reduced CH 4 emissions significantly in the post-harvest and next growing seasons, while delay straw incorporation prevented CH 4 and CO 2 emissions during post-harvest. None of the treatments increased N 2 O emission. Environmental implications of post-harvest management options are discussed. Conclusions Post-harvest management affects net GWP of the paddy rice cultivation by modifying GHG emissions in post-harvest and next growing season without compromise sequestration C budget. The combination of non-winter flooding and late straw incorporation strategies were more effective in reducing both CH 4 and CO 2 emissions, due to avoiding higher temperatures at the time of the straw incorporation during post-harvest and increasing soil Eh conditions at the following growing season.

Soil microorganisms play an important role in determining nutrient cycling. The integration of fish into rice fields can influence the diversity and structural composition of soil microbial communities. However, regarding the rice–fish co-culture (RF) farming system in Thailand, the study of the diversity and composition of soil microbes is still limited. Here, we aim to compare the microbial diversity, community composition, and functional structure of the bacterial communities between RF and rice monoculture (MC) farming systems and identify the environmental factors shaping bacterial community composition. Bacterial taxonomy was observed using 16s rRNA gene amplicon sequencing, and the functional structures of the bacterial communities were predicted based on their taxonomy and sequences. The results showed that soil organic carbon, total nitrogen (TN), organic matter, available phosphorous, and clay content were significantly higher in RF than in MC. The most dominant taxa across both paddy rice fields belonged to Actinobacteria, Chloroflexi, Proteobacteria, Acidobacteria, and Planctomycetes. The taxa Nitrosporae, Rokubacteria, GAL15, and Elusimicrobia were significantly different between both rice fields. At the genus level, Bacillus, Anaeromyxobacter, and HSB OF53-F07 were the predominant genera in both rice fields. The most abundant genus in MC was Anaeromyxobacter, whereas RF belonged to Bacillus. The community composition in MC was positively correlated with magnesium and sand content, while in RF was positively correlated with pH, TN, and clay content. Nitrogen fixation, aromatic compound degradation, and hydrocarbon degradation were more abundant in RF, while cellulolysis, nitrification, ureolysis, and phototrophy functional groups were more abundant in MC. The enzymes involved in paddy soil ecosystems included phosphatase, β-glucosidase, cellulase, and urease. These results provide novel insights into integrated fish in the paddy field as an efficient agricultural development strategy for enhancing soil microorganisms that increase soil fertility.

In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.