Single Rice Research Articles

RNA Extraction from Rice Immature Embryo Using Laser Capture Microdissection.

Laser capture microdissection (LCM) enables the selective isolation of organs, tissues, and cells from surrounding tissues. Total RNA extracted from small tissue sections can be used for a variety of subsequent analysis such as RNA-seq analysis. Here, we describe a method for isolating embryos from rice ovary sections using LCM and extracting total RNA. We successfully obtained sufficient amount of total RNA from a single rice embryo at the globular embryo stage for RNA-seq analysis.

Responses of yield, CH4 and N2O emissions to ratoon rice cropping and different management practices

Context or problemConversion from single rice (SR) or double rice (DR) to ratoon rice (RR) is gaining growing popularity in China. Yet, a quantitative synthesis of their impact on greenhouse gas (GHG, including methane (CH4) and nitrous oxide (N2O)) emissions and grain yield has not been conducted. Objective or research questionThe objective was to evaluate the effects of conversion from SR or DR to RR on CH4 and N2O emissions, grain yield, global warming potential (GWP), and greenhouse gas intensity (GHGI) and to investigate the potential responses to different operating practices [alternate wetting-drying irrigation, nitrogen management, rice variety selection, and their multiple treatments (multiple measures)] in RR fields (oRR). MethodsIn this study, a comprehensive meta-analysis of 571-paired measurements from ratoon rice fields was conducted. ResultsOur results showed that the conversion from SR to RR significantly increased CH4 emissions, grain yield, and GWP by 35.4 %, 30.6 %, and 43.3 %, respectively. In contrast, the conversion from DR to RR decreased CH4 emissions, grain yield, and GWP by 23.2 %, 7.4 %, and 30.0 %, respectively. Interestingly, both conversions from SR or DR to RR did not affect N2O emissions but reduced GHGI in paddy fields, suggesting that RR provided an economically and ecologically sustainable rice planting model. Furthermore, on average, oRR further decreased CH4 and N2O emissions and GHGI from RR fields but did not affect grain yield. Among the existing management practices, the overall effect of multiple measures was better than that of alternate wetting-drying irrigation, nitrogen management, and rice variety selection. ConclusionsOverall, ratoon rice cropping decreased CH4 emissions and maintained rice grain yield. However, it is also necessary to further implement comprehensive cultivation strategies in the future to maximize the benefits of grain yield and GHG emissions reduction.

Optimization of phosphorus and potassium use efficiency of rice (Oryza sativa L) under different flood depths within the Everglades Agricultural Area

Rice cultivation in South Florida, especially within crop rotation systems, presents a promising avenue for mitigating phosphorus (P) levels in drainage water by optimizing nutrient absorption across varying flooding conditions. This study aimed to assess the impacts of different flooding depths on rice growth and nutrient efficiency while exploring the interrelationships among these factors. Conducted over a two-year period, the field study involved planting a single rice variety (Diamond: 95 kg ha−1, long-grain, short-season) at four distinct flooding depths (5, 10, 15, and 20 cm) without fertilizer application. Results revealed minimal effects of flooding depths on root and shoot length or dry matter accumulation. Additionally, concentrations of total P and potassium (K) in both shoot and root tissues remained consistent across the different flooding depths. Despite the absence of P and K fertilizer inputs, no significant changes were observed in P use efficiency (PUE), K use efficiency (KUE), or water use efficiency (WUE). Cultivating rice under optimal flood depths of 5 cm within the Everglades Agricultural Area (EAA) demonstrated potential for conserving water and essential nutrients such as P and K. These findings highlight the feasibility of rice cultivation in fertile soils as a means to decrease nutrient (P and K) concentrations in drainage water, thereby promoting sustainable agricultural practices in the region.

Game analysis of future rice yield changes in China based on explainable machine-learning and planting date optimization

ContextGlobal warming's escalating severity necessitates sophisticated approaches for predicting rice yield. Research QuestionCombining crop models with data-driven techniques, such as machine learning, can more effectively grasp the complex interplay of variables influencing crop growth. It remains a significant challenge to balance accuracy and interpretability in such hybrid models. MethodsThe research integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with statistical and machine learning models respectively, to assess rice yield changes in China under four future Shared Socio-economic Pathway (SSP). SSPs are scenarios that integrate socioeconomic trends with greenhouse gas emissions and radiative forcing pathways, which affect the phenology and yield of rice. The Shapley Additive Explanation (SHAP) method was employed to interpret the model, effectively determining the interplay among variables influenced rice yields. Mitigated the negative impacts of climate change on rice yield through the planting date optimization. ResultsProjections indicate significant rice yield losses in China without CO2, worsening with increased radiative forcing (p < 0.001). Considering rising CO2, single-season rice yields are projected to increase by 0.1–3.6 %, early rice by 4.6–9.5 %, while late rice yields are still decrease by 2.3–8.8 %. The rising CO2 can offset yield losses for single and early rice but not for late rice. The hybrid approach which combined the Random Forest (RF) with the DSSAT performed best in predicting rice yield. Studies showed that rising temperatures caused rice yield losses in China, yet we found that Growing Degree Days (GDD) exerted a more negative impact (p < 0.001). In high-precipitation regions, deep soil moisture is more influential than shallow soil moisture, whereas the reverse was true in drier areas (p < 0.001). Advancing planting dates for early and single rice and delaying for late rice can increase yields (p < 0.001). Adjusting to optimal planting dates, single-season rice yields increased by 3.3–6.3 %, early rice increased by 9.7–18.3 %, while late rice still decreased by 1.0–4.7 %. ConclusionsWithout considering the impact of CO2, significant rice yield losses in China are projected. Even with the fertilization effect of CO2, rice yields remain negatively impacted by climate change. However, implementing appropriate measures, such as optimizing planting dates, can help Chinese rice production benefit under changing climate. ImplicationsThis study offers insights into balancing accuracy and interpretability in hybrid models and provides guidance for local policymakers to address future climate change.

Silicate Application Alleviated the Depressive Impacts of Nighttime Warming on the Rice Growth, Yield and Quality in Southern China

ABSTRACTNighttime warming decreased single rice production in southern China, while silicate supply increased the yield and stress resistance. It is still unclear regarding the impacts of silicate application on the growth, yield and quality in rice under nighttime warming. A field experiment was conducted to investigate the impacts of silicate application on the growth, yield and quality during rice growing period under nighttime warming. The warming was set at two levels, that is, ambient temperature (CK) and nighttime warming (NW). The open passive nighttime warming was used in this study, that is, rice canopy was covered with aluminium foil reflective film at night (19:00–6:00). Silicate fertiliser (steel slag) was applied at two levels, that is, Si0 (0 kg SiO2·hm−2) and Si1 (200 kg SiO2·hm−2). The results indicated that, compared with the control, the average nighttime temperature on rice canopy and at 5 cm soil layer increased by 0.51°C–0.59°C and 0.28°C–0.41°C during the rice growing period, respectively. Under nighttime warming, silicate supply increased the dry weight of shoot, total dry weight of the whole plant and yield by 64.1%, 55.3% and 7.1% at the filling‐maturity stage, respectively. Silicate application significantly reduced dry matter translocation rate with 15%–18%, but significantly increased the root‐shoot ratio with 0.39–0.41 in rice. Under nighttime warming, silicate supply significantly increased milled rice rate, head rice rate and total starch content by 2.3%, 2.5% and 41.8%, respectively. Nighttime warming reduced the yield by decreasing the number of effective panicle, seed setting rate and 1000‐grain weight, but increasing empty chaff grain. Silicate supply increased the yield by increasing the number of effective panicle, filled grains per panicle, seed setting rate and 1000‐grain weight, but reducing empty chaff grain. This study suggests that silicate supply can effectively alleviate the suppressive effects of nighttime warming on single rice growth, yield and quality in Southern China.

Fertilization management and greenhouse gases emissions from paddy fields in China: A meta-analysis

ContextThe contribution of rice paddy soils to greenhouse gases (GHGs) emissions and global warming has attracted widespread attention. ObjectivesThere are studies on the local effects of fertilizers on soil GHGs emissions, but few have looked at the comprehensive and comparative effects of fertilization management, considering soil and environmental characteristics. MethodsOur meta-analysis used data from 83 Chinese paddy fields to investigate the effects of NPK (nitrogen, phosphorus and potassium), NPKS (NPK with straw return), NPKM (NPK with manure), and NPKB (NPK with biochar) on CO2, CH4 and N2O emissions compared to the unfertilized fields (control) under single rice in the south (SRS), single rice in the north (SRN) and double rice (DR). ResultsThe order of increased global warming potential (GWP) was NPKB (10 %) < NPK (20 %) < NPKM (70 %) < NPKS (140 %). The highest GHG increases were 60 % for CO2 and 270 % for N2O under NPKM, and 180 % for CH4 under NPKS, while the lowest were 25 % for CO2, 2 % for CH4, and 5 % for N2O under NPKB compared to control. The NPKB positively increased CH4 by 38 % in SRN, while NPK resulted in a 285 % increase in N2O emission in SRS. The CO2, CH4, and N2O reached the highest increased values of 18×103, 500, and 2 kg ha−1, respectively when the N input under NPKM and NPKS was 200–300 kg ha−1. Such a relatively high N input only increased the yield by 1–4 t ha−1. In contrast, a 30 %-50 % reduction in N input (equivalent to 130–260 kg C kg−1), with a yield increase of about 4–5 t ha−1, decreased CO2, CH4, and N2O emissions by 86 %, 66 %, 75 % respectively (GWP by 12 %) under NPK and NPKB. The soil properties are also the main controllers of GHGs from paddy soils, where the highest GHGs were associated with soils with clay loam, pH=5–6, and C/N=10. The estimated cumulative emissions of CO2, CH4, and N2O under NPK in China in 2021 were 430, 7.5, and 0.038 Tg, respectively, while the addition of NPKB led to a reduction of the mentioned GHGs by 23 %, 43 %, and 47 % (GWP by 30 %), respectively. ConclusionReducing inorganic nitrogen inputs and incorporating biochar, but cautiously applying manure and straw in fertilization management are win-win strategies in paddy fields to reduce GHGs and ensure rice yield. Implications/significanceOur study highlights the importance of proper fertilization management according to rice zones and soil properties, which could decrease the GHGs emissions and bring yield and environmental benefits.

Rotary Tillage Plus Mechanical Transplanting Practices Increased Rice Yields with Lower CH4 Emission in a Single Cropping Rice System

As the main contributor to greenhouse gas (GHG) in paddy soil, information on methane (CH4) emission characteristics under different tillage and cultivation practices are limited. A five-year field trial was conducted from 2019 in a single-cropping rice system in Taihu Lake region, east of China. The experiment had a completely randomized block design, and the treatments included rotary tillage plus rice dry direct seeding (RD), rotary tillage plus rice mechanical transplanting (RT), and plowing tillage plus rice mechanical transplanting (PT). We determined the rice yield, GHG emission, soil traits, and methanogens and methanotrophs in 2022 and 2023. The results revealed that PT and RT significantly increased rice yield compared to RD, whereas PT simultaneously increased CH4 emissions. The year-averaged cumulative CH4 emissions in PT were increased by 38.5% and 61.4% higher than RT and RD, respectively. Meanwhile, yield-scaled global warming potentials (GWPs) in RT and RD were lower than those in PT. Tillage and cultivation practices shifted mcrA and pmoA abundances, and PT significantly decreased pmoA abundance. The community structure and diversity of the methanogens and methanotrophs were not significantly affected. Structural equation model analyses illustrated that CH4 emissions were regulated by mcrA and pmoA directly, which in turn, regulated by soil carbon and nitrogen. Overall, rotary tillage plus mechanism transplanting was a feasible agronomic technology in a single-cropping rice system in Taihu Lake region, exhibiting higher and more stable rice productivity, accompanied with lower CH4 emissions and yield-scaled GWP.

Hyperaccumulators' Diversity Enhances Cd-Contaminated Soil Restoration and Reduces Rice Cd Uptake under an Intercropping System.

Cd accumulation in rice-cultivated soils across China is a major problem that needs to be tackled. A plot experiment was carried out using heavy metal (HM) hyperaccumulators Amaranthus hypochondriacus L. and Perilla frutescens (L.) Britt. intercropped with low-accumulation rice to obtain safe edible rice while reducing the soil Cd concentration. It was found that Cd concentration in soil was decreased by 7.43 and 2.86% under rice intercropped with Amaranthus hypochondriacus L. and Perilla frutescens (L.) Britt., respectively, compared to single cropped rice. In addition, enhanced effects were noted under the combination of Amaranthus hypochondriacus L., Perilla frutescens (L.) Britt, and rice in which a 20.35% decrease in soil Cd content was recorded compared to single-cultivated rice soil. In addition, the available Cd in soil was reduced by 4.00 and 5.00% under rice/Amaranthus and rice/Perilla, respectively, and 12.00% under rice/Amaranthus/Perilla mixed culture. Moreover, the concentration of Cd in various parts of rice was under permissible limits. However, rice biomass was decreased by the presence of hyperaccumulators. This study suggests that combining HM hyperaccumulator plants and low-accumulation rice provides efficient Cd extraction results and could be a crucial option for restoring Cd-contaminated soil without reducing rice production.

Simulation of rice grain breakage process based on Tavares UFRJ model

Understanding the breakage characteristics of rice grains is an important means to reduce rice breakage rate. However, the dynamic breakage mechanism of rice grain is unclear due to the lack of a reasonable breakage model. In this study, the uniaxial compression test and drop weight test of single rice were carried out, the breakage model of rice grain was constructed, the reliability of rice model was verified by the experiment and simulation results. The results showed that the fracture energy distribution of rice can be obtained by uniaxial compression test, the specific fracture energy of rice accords with a lognormal distribution, and the median specific fracture energy of rice is 479.75 J/kg. The damage accumulation coefficient and fragment size distribution of rice can be acquired by drop test, the result of damage accumulation coefficient of rice was 4.3. Rice grain breakage mainly occurs in the milling section of the vertical circulation rice mill.

Modeling the spread of rice blast in rice plant group based on cellular automata

Studying and modeling the process of the disease and spread of blast can guide fertilization and reduce the amount of pesticide application. Since most of the existing research focuses on the pathogenesis of the rice blast fungus and the interaction between the plant structure and the fungus, this paper presents a method to simulate the spread of rice blast among rice populations. The method is divided into the following processes. First, based on the cellular automata grid dynamics model, the cell state corresponds to the degree of infection of the rice blast fungus, and its ability to simulate the spatiotemporal evolution of complex systems is used to simulate the spread of disease spots on the surface of rice leaves. Second, according to the biological mechanism of the rice blast fungus and the position of the first infected leaf in a single rice plant, the normal distribution function is used to calculate the infection probability of the remaining leaves, and then the transmission mode of the disease inside a single rice plant is simulated. Then, in view of the natural phenomenon that the blast fungus spores spread with the wind, the wind factor is added to propose a method for the spread of the rice blast fungus among the population of rice plants, and thus a model for the spread of the pathogen among the entire plant population was established. Finally, we set up a simulation system for the spread of rice blasts in rice fields and visualized the spread and spread trend of rice blasts in rice populations. In the simulation experiment, the infection process of rice blast on the surface of the rice leaf was simulated, and the change process of the shape of the diseased spot can be visually seen. This paper not only provides a new idea for the research on pathogen propagation and diffusion between plants in agricultural production but also provides a reference for how to realistically visualize the pathogen propagation model in the virtual agricultural scene.

Real-time monitoring abscisic acid release from single rice protoplast by amperometry at microelectrodes modified with abscisic acid receptor PYL2

It was previously reported that stress induces a cellular production of abscisic acid in plants, but no direct method shows the evidence. Here, an electrochemical microsensor involving an abscisic acid receptor PYL2 modified carbon fiber microelectrode was fabricated by self-assembly method, where the Cu2+ combined with the histidine tag of PYL2 on the surface of microelectrode was used as the detection probe, the mediated reaction between Cu+ and ferricyanide realized the amplification responses and provided the microsensor with a high sensitivity for detection of abscisic acid with a detection limit of 0.8 nM. With use of this microsensor, an increase of extracellular abscisic acid from single rice protoplast induced by sulfate, osmotic and salinity stress was real-time monitored. Direct measurement of free extracellular abscisic acid in single plant cells might offer important new insights into its role in plants challenged by abiotic stresses.

Infrared microspectroscopy and machine learning: A novel approach to determine the origin and variety of individual rice grains

Accurately distinguishing the origin and variety of rice types is of paramount importance to conducting research on this staple crop. While various methods are currently employed for this purpose, few approaches can verify the identity of single grains rapidly and accurately. In this study, we present a method that integrates machine learning with infrared (IR) microspectroscopy for swift detection of the origin and variety of a single rice grain. To establish the validity of our approach, we assembled a diverse collection of rice samples, comprising 14 distinct types with different origins or varieties. Each rice sample yielded 100 microspectroscopy spectra, resulting in 1400 spectra. We applied two deep learning algorithms, deep neural network (DNN) and convolutional neural network (CNN), for spectral analysis. The 1400 spectra were randomly partitioned into calibration and validation sets at a ratio of 3:1. These datasets were subjected to both DNN and CNN analysis for classification of samples by origin and variety. Following 10,000 iterations, we selected optimal DNN and CNN models. The predication accuracies of the optimal DNN model for calibration and validation sets were 95.4% and 90.0%, respectively. In comparison, the optimal CNN model demonstrated superior accuracy, with 99.8% for the calibration set and 92.0% for the validation set. Based on these results, we selected the CNN model as the final model for field use in rice grain classification.

Optimizing the rate of straw returning to balance trade-offs between carbon emission budget and rice yield in China

Rice paddies are a prominent contributor to greenhouse gases (GHGs) emissions, accounting for 22 % of the overall agricultural methane (CH4) emissions. Although returning rice straw to the field can increase yield and soil organic carbon (SOC), the significant GHGs released during this process often negate these positive effects. Thus, understanding the impact of straw returning on the relationship between rice yield and GHGs is crucial for farming adaptation and climate change mitigation. This study aims to investigate the effects of varying straw returning rates on rice yield and GHGs in China and identify the optimal rates on a provincial scale. This study utilized county-level meteorological, soil, and field management data. The Dynamic Nitrogen and Carbon Model (DNDC) model was used to simulate the impacts on rice yield, GHGs, and SOC changes under different straw returning rates in China from 2010 to 2022, including no straw, 1/3, 2/3, and full straw returning. The results showed that 1/3, 2/3, and full straw incorporation increased rice yield per unit area by 1.07 %, 2.25 %, and 3.26 % for single rice cropping, 1.10 %, 2.24 %, and 3.52 % for early rice, and 1.92 %, 3.91 %, and 5.95 % for late rice, respectively. Compared with no straw returning, the total SOC content (0–50 cm) increased by 3.88 % (78.07 Tg), 7.75 % (156.09 Tg), and 11.65 % (234.62 Tg) under 1/3, 2/3, and full straw returning, respectively. However, the total GHGs were increased by 23.32 %, 43.50 %, and 61.48 % for 1/3, 2/3, and full straw incorporation compared to no straw returning. While the overall benefits of straw returning varied across regions, in certain areas like northeastern China, Xinjiang, and Yunnan, an increase in straw returning rates leads to a significant increase in yield with a relatively smaller increase in GHGs. This can be attributed to factors such as lower temperatures, decreased SOC, and lower soil pH, restricting CH4 production. Conversely, in regions with higher soil pH, such as north China and the Sichuan Basin, as well as in warmer southern areas, the increase in GHGs is quite pronounced. In summary, implementing straw returning significantly increases rice production and SOC but simultaneously increases GHGs. In China, the most widespread utilization of rice straw remains straw returning. We have summarized the average impact of straw returning on yield, SOC, and GHGs, and proposed straw returning rates tailored to the comprehensive benefits in different regions, to enhance the carbon sequestration and emission reduction rate of rice straw returning.

Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

Upland crop-rice cropping systems (UCR) facilitate sustainable agricultural intensification. Accurate UCR cultivation mapping is needed to ensure food security, sustainable water management, and rural revitalization. However, datasets describing cropping systems are limited in spatial coverage and crop types. Mapping UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples, which limits its applications over large regions. We describe a novel algorithm (RRSS) for automatic mapping of upland crop–rice cropping systems using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) data. One indicator, the VV backscatter range, was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed UCR. The RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic conditions. This study developed 10-m UCR maps of a major rice bowl in South China, the Xiang-Gan-Min (XGM) region. The performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites, with an overall accuracy of 91.92%. There were 7348 km2 areas of UCR, roughly one-half of them located in plains. The UCR was represented mainly by oilseed-UCR and tobacco-UCR, which contributed respectively 69% and 15% of UCR area. UCR patterns accounted for only one-tenth of rice production, which can be tripled by intensification from single rice cropping. Application to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm, which could be further applied to generate 10-m UCR datasets for application at national or global scales.

Test and application of abrasion characteristics of brown rice

The abrasion characteristics of brown rice in the polishing unit of the rice mill are not clearly understood, which greatly limits the new development need of moderate milling. Therefore, a single rice abrasion characteristic test platform with adjustable normal force, grit number of abrasive surface and sliding distance was set up. The results showed that both the normal load and the grit number significantly influence mass loss and specific abrasion area. It was found that the abrasion coefficient decreases with increasing load and increases with larger grit number. The abrasive surface of brown rice grain was inspected to understand abrasion behavior. The rice kernels lying on the plate milled by abrasive plane is more conducive to the restoration of nutritious embryo. An application of abrasion characteristics combining the milling information from simulation was proposed, aiming at provided a method for the digital design of the rice mill.

Integrated Effects of Straw Incorporation and N Application on Rice Yield and Greenhouse Gas Emissions in Three Rice-Based Cropping Systems

Crop straw and N fertilizer applications impact paddy rice yield and greenhouse gas (GHG) emissions. However, their interactive effects have not been well documented. This study investigated the effects of straw (S), no straw incorporation (NS), and three levels of N fertilization rates (N0, N1, and N2) on single rice (SR), double rice (DR), and rice-wheat (RW) cropping systems. Straw incorporation significantly increased total CH4 emissions by 118.6%, 8.0%, and 79.0% in the SR, DR, and RW, respectively, compared to the NS. The total GHG emissions in DR are significantly 72.6% and 83.5% higher than those in RW and SR, respectively. Compared to NS, straw incorporation significantly increased yield-scaled emissions by 27.8%, 15.0%, and 89.0% in SR, DR, and RW, respectively. Straw with N application significantly increased average rice yield over N1 and N2 by 39.4%, 50.0%, and 6.7% in SR, DR, and RW, respectively. There was a significant correlation between methyl coenzyme M reductase (mcrA) and CH4 emissions in rSR = 0.87 (p &lt; 0.05) and rRW = 0.85 (p &lt; 0.05), except in rDR = 0.06 (p &gt; 0.05). This study scientifically supports straw incorporation combined with a moderate N application rate in rice-based cropping systems to maintain high rice yields and mitigate GHG emissions.

Modeling denitrification nitrogen losses in China's rice fields based on multiscale field-experiment constraints.

Denitrification plays a critical role in soil nitrogen (N) cycling, affecting N availability in agroecosystems. However, the challenges in direct measurement of denitrification products (NO, N2 O, and N2 ) hinder our understanding of denitrification N losses patterns across the spatial scale. To address this gap, we constructed a data-model fusion method to map the county-scale denitrification N losses from China's rice fields over the past decade. The estimated denitrification N losses as a percentage of N application from 2009 to 2018 were 11.8 ± 4.0% for single rice, 12.4 ± 3.7% for early rice, and 11.6 ± 3.1% for late rice. The model results showed that the spatial heterogeneity of denitrification N losses is primarily driven by edaphic and climatic factors rather than by management practices. In particular, diffusion and production rates emerged as key contributors to the variation of denitrification N losses. These findings humanize a 38.9 ± 4.8 kg N ha-1 N loss by denitrification and challenge the common hypothesis that substrate availability drives the pattern of N losses by denitrification in rice fields.

Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

Addressing the pressing need for sustainable bioenergy sources amid the growing climate crisis, this study investigates potential crop bioenergy output in the Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict the accumulated net primary productivity (NPP) of various crops, i.e., single rice, double rice, maize, and wheat, in Jianghan Plain. The models highlight a strong predictive performance, with the SE model achieving an R2 of 0.83 and wheat model an R2 of 0.90, implying these models can be utilized reliably for future NPP forecasting. Our evaluation of past and future NPP values reveals a negative correlation between the increase in NPP values and observed temperature trends. This emphasizes the significant impact of temperature on crop bioenergy potential. For future NPP predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields a favourable outcome for all crops, particularly SE. Additionally, the results suggest that aggressive decarbonization and global warming mitigation strategies are vital for enhancing NPP values. However, the study also highlights potential trade-offs and stresses the necessity for region-specific climate reconciliation strategies; for instance, midland counties benefit from temperature control, whereas the sideline favours CO2 mitigation. The study further investigates the spatial distribution of potential bioenergy, with the southwestern part of the plain holding more bioenergy potential, providing an essential reference for future energy planning. By 2030, in Jianghan Plain, when fully integrated with reasonable climate responses, there could be substantial bioenergy potential from crop residues reaching (1.2–1.9) × 109 MJ, which is 11% less than the current.

Single-Season Rice Area Mapping by Combining Multi-Temporal Polarization Decomposition Components and the Two-Stage Segmentation Method

Recently, Synthetic Aperture Radar (SAR) data, especially Sentinel-1 data, have been increasingly used in rice mapping research. However, current studies usually use long time series data as the data source to represent the differences between rice and other ground objects, especially other crops, which results in complex models and large computational complexity during classification. To address this problem, a novel method for single season rice mapping is proposed, based on the principle that the scattering mechanism of rice paddies in the early flooding period is strongly influenced by water bodies, causing the volume scattering to be lower than that of other crops. Thus, a feature combination that can effectively and stably extract rice planting areas was constructed by combining multi-temporal volume scattering in the early flooding period of rice using dual-polarization SAR data, so that a simple semantic segmentation model could realize high-precision rice mapping tasks. A two-stage segmentation structure was introduced to further improve the mapping result with the Omni-dimensional Dynamic Convolution Residual Segmentation model (ODCRS model) as the bone model. In the experiment, Suihua City, Heilongjiang Province was selected as the study site, and the VH/VV polarized data of Sentinel-1 satellite in 2022 was used as the data source. The mapping accuracy of the ODCRS model was 88.70%, and the user accuracy was 84.19% on the field survey data. Furthermore, experiments with different years and regions also proved the effectiveness and stability of the proposed method.

Co-application of concentrated biogas slurry and pyroligneous liquor mitigates ammonia emission and sustainably releases ammonium from paddy soil

Biogas production causes vast amounts of biogas slurry (BS). Application of BS to croplands can substitute chemical fertilizers while result in higher ammonia emissions. Tremendous variation of ammonium concentration in different BSs induces imprecise substitution, while concentrated BS holds higher and more stable ammonium. Pyroligneous liquor, an acidic aqueous liquid from biochar production, can be used with concentrated BS to reduce ammonia emission. However, the effects of combining concentrated BS with pyroligneous liquor on ammonia emission and soil (nitrogen) N transformation have been poorly reported. In this study, a field experiment applying concentrated BS only, or combining with 5 %, 10 %, and 20 % pyroligneous liquor (v/v) for substituting 60 % N of single rice cultivation was conducted by contrast with chemical fertilization. The results showed that substituting chemical N fertilizers with concentrated BS increased 24.6 % ammonia emission. In comparison, applying 5 %, 10 %, and 20 % pyroligneous liquor with concentrated BS reduced 4.9 %, 20.3 %, and 24.4 % ammonia emissions, respectively. Applying concentrated BS with more pyroligneous liquor preserved higher ammonium and dissolved organic carbon in floodwater, and induced higher nitrate concentration after fertilization. Whereas soil ammonium and nitrate contents were decreased along with more pyroligneous liquor application before and after the topdressing and exhibited sustainable release until rice harvest. In comparison, the soil N mineralization and nitrification rates were occasionally elevated, while the activities of soil urease, protease, nitrate reductase, and nitrite reductase had multiple responses. Applying concentrated BS only, or combining with 5 %, 10, and 20 % pyroligneous liquor, have little effect on soil basic properties but inorganic N. In summary, applying concentrated BS with >10 % pyroligneous liquor could preserve more N with sustainable release and potentially lower N loss to the atmosphere, and we proposed that applying 13.5 % pyroligneous liquor in concentrated BS could achieve maximum soil fertility and minimum ammonia emission.