Methane Emission Reductions from the Alternate Wetting and Drying of Rice Fields Detected Using the Eddy Covariance Method

Simultaneously decreasing arsenic and cadmium in rice by soil sulfate and limestone amendment under intermittent flooding

Water management in paddy soils can effectively reduce the soil-to-rice grain transfer of either As or Cd, but not of both elements simultaneously due to the higher mobility of As under reducing and Cd under oxidizing soil conditions. Limestone amendment, the common form of liming, is well known for decreasing Cd accumulation in rice grown on acidic soils. Sulfate amendment was suggested to effectively decrease As accumulation in rice, especially under intermittent soil flooding. To study the unknown effects of combined sulfate and limestone amendment under intermittent flooding for simultaneously decreasing As and Cd in rice, we performed a pot experiment using an acidic sandy loam paddy soil. We also included a clay loam paddy soil to study the role of soil texture in low-As rice production under intermittent flooding. We found that liming not only decreased rice Cd concentrations but also greatly decreased dimethylarsenate (DMA) accumulation in rice. We hypothesize that this is due to suppressed sulfate reduction, As methylation, and As thiolation by liming in the sulfate-amended soil and a higher share of deprotonated DMA at higher pH which is taken up less readily than protonated DMA. Decreased gene abundance of potential soil sulfate-reducers by liming further supported our hypothesis. Combined sulfate and limestone amendment to the acidic sandy loam soil produced rice with 43% lower inorganic As, 72% lower DMA, and 68% lower Cd compared to the control soil without amendment. A tradeoff between soil aeration and water availability was observed for the clay loam soil, suggesting difficulties to decrease As in rice while avoiding plant water stress under intermittent flooding in fine-textured soils. Our results suggest that combining sulfate amendment, liming, and intermittent flooding can help to secure rice safety when the presence of both As and Cd in coarse-textured soils is of concern.

Toward Low-Carbon Rice Production in China: Historical Changes, Driving Factors, and Mitigation Potential

Under the "Double Carbon" target, the development of low-carbon agriculture requires a holistic comprehension of spatially and temporally explicit greenhouse gas (GHG) emissions associated with agricultural products. However, the lack of systematic evaluation at a fine scale presents considerable challenges in guiding localized strategies for mitigating GHG emissions from crop production. Here, we analyzed the county-level carbon footprint (CF) of China's rice production from 2007 to 2018 by coupling life cycle assessment and the DNDC model. Results revealed a significant annual increase of 74.3 kg CO2-eq ha-1 in the average farm-based CF (FCF), while it remained stable for the product-based CF (PCF). The CF exhibited considerable variations among counties, ranging from 2324 to 20,768 kg CO2-eq ha-1 for FCF and from 0.36 to 3.81 kg CO2-eq kg-1 for PCF in 2018. The spatiotemporal heterogeneities of FCF were predominantly influenced by field CH4 emissions, followed by diesel consumption and soil organic carbon sequestration. Scenario analysis elucidates that the national total GHG emissions from rice production could be significantly reduced through optimized irrigation (48.5%) and straw-based biogas production (18.0%). Moreover, integrating additional strategies (e.g., advanced crop management, optimized fertilization, and biodiesel application) could amplify the overall emission reduction to 76.7% while concurrently boosting the rice yield by 11.8%. Our county-level research provides valuable insights for the formulation of targeted GHG mitigation policies in rice production, thereby advancing the pursuit of carbon-neutral agricultural practices.

Fate of carbon influenced by the in-situ growth of phototrophic biofilms at the soil-water interface of paddy soil

Phototrophic biofilms (PBs) are commonly found in the sediment/soil-water interface of paddy soils and have a significant impact on carbon cycles. However, the specific carbon fate influenced by the in-situ growth of PBs in paddy soil remains unclear. In this study, we investigated the effect of in situ PBs growth on methane and carbon dioxide emissions, as well as dissolved organic matter (DOM) transformation. Our findings demonstrated a negative correlation between PBs growth and methane and carbon dioxide emissions, while showing a positive correlation with DOM composition. The in-situ growth of PBs reduced methane emissions by approximately 79 % and carbon dioxide emissions by approximately 33 % in the daytime, and also slowed down the degradation rate of dissolved organic matter from over 30.4 % to <16 %. Microsensor measurements revealed that these changes were attributed to the increased concentration and penetration depth of oxygen, as well as variations in pH caused by the growth of in situ PBs. Co-occurrence analysis indicated a robust correlation between DOM transformation and the significantly suppressed methanogenesis by methanogens such as Methanosaeta, Methanomassiliicoccus and Methanosarcina, and also the notably enhanced methane oxidation by methanotrophs including Methylobacterium, Methyloversatilis and Methylomonas, in response to the growth of PBs. These findings shed light on the impact of in situ PBs on methane and carbon dioxide emissions and DOM transformation, providing new insights for understanding carbon cycling in paddy soils.

Effects of alternate wetting and drying on oxyanion-forming and cationic trace elements in rice paddy soils: impacts on arsenic, cadmium, and micronutrients in rice

Rice is a global dietary staple and its traditional cultivation under flooded soil conditions leads to accumulation of arsenic (As) in rice grains. Alternate wetting and drying (AWD) is a widely advocated water management practice to achieve lower As concentrations in rice, water savings, and decreased methane emissions. It is not yet clear whether AWD leads to tradeoffs between concentrations of As and micronutrient elements (e.g., zinc, manganese, molybdenum) in rice grain. We analyzed pore water chemistry and rice grain composition data from a field experiment conducted in Arkansas, USA, in 2017 and 2018 to test the hypothesis that AWD will have diverging effects on oxyanion-forming (arsenic, molybdenum) vs. cationic (cadmium, zinc, manganese, copper) trace elements. This was hypothesized to occur via decreases in soil pH and/or precipitation of iron oxide minerals during oxidizing conditions under AWD. Solubility of all trace elements, except zinc, increased in more reducing conditions. Consistent with our hypothesis, AWD tended to increase grain concentrations of cationic elements while decreasing grain concentrations of oxyanionic elements. Decreases in total As in rice grains under AWD were mainly driven by changes in dimethylarsinic concentrations, with negligible changes in inorganic As. Linear mixed-effects modeling showed that effects of AWD on grain composition were more significant in 2017 compared to 2018. These differences may be related to the timing of dry-downs in the developmental stage of rice plants, with dry-downs during the heading stage of rice development leading to larger impacts on grain composition of certain elements. We also observed significant interannual variability in grain elemental composition from continuously-flooded fields and postulate the warmer temperatures in 2018 may have played a role in these differences.

Waterlogging increases greenhouse gas release and decreases yield in winter rapeseed (Brassica napus L.) seedlings

A sustainable future depends on increasing agricultural carbon (C) and nitrogen (N) sequestration. Winter rapeseeds are facing severe yield loss after waterlogging due to the effects of extreme rainfall, especially in the seedling stage, where rainfall is most sensitive. Uncertainty exists over the farming greenhouse gas (GHG) release of rapeseed seedlings following the onset of waterlogging. The effect of waterlogging on GHG release and leaf gas exchange in winter rapeseed was examined in a pot experiment. The experiment included waterlogging treatments lasting 7-day and 21-day and normal irrigation as a control treatment. According to our findings, (1) The ecosystem of rapeseed seedlings released methane (CH4) and nitrous oxide (N2O) in a clear up change that was impacted by ongoing waterlogging. Among them, N2O release had a transient rise during the early stages under the effect of seedling fertilizer. (2) The net photosynthetic rate, transpiration rate, stomatal conductance, plant height, soil moisture, and soil oxidation-reduction potential of rapeseed all significantly decreased due to the ongoing waterlogging. However, rapeseed leaves showed a significant increase in intercellular carbon dioxide (CO2) concentration and leaf chlorophyll content values after waterlogging. Additionally, the findings demonstrated an extremely significant increase in the sustained-flux global warming potential of the sum CO2-eq of CH4 and N2O throughout the entire waterlogging stress period. Therefore, continuous waterlogging can increase C and N release from rapeseed seedlings ecosystem and decrease yield. Therefore, we suggest increasing drainage techniques to decrease the release of agricultural GHGs and promote sustainable crop production.

Rice husk and husk biochar soil amendments store soil carbon while water management controls dissolved organic matter chemistry in well-weathered soil

Rice agriculture feeds over half the world's population, and paddy soils impact the carbon cycle through soil organic carbon (SOC) preservation and production of carbon dioxide (CO₂) and methane (CH₄), which are greenhouse gases (GHG). Rice husk is a nutrient-rich, underutilized byproduct of rice milling that is sometimes pyrolyzed or combusted. It is unresolved how the incorporation of these residues affects C dynamics in paddy soil. In this study, we sought to determine how untreated (Husk), low-temperature pyrolyzed (Biochar), and combusted (CharSil) husk amendments affect SOC levels, GHG emissions, and dissolved organic matter (DOM) chemistry. We amended Ultisol paddy mesocosms and collected SOC and GHG data for three years of rice grown under alternate wetting and drying (AWD) conditions. We also performed a greenhouse pot study that included water management treatments of nonflooded, AWD, and flooded. Husk, Biochar, and CharSil amendments and flooding generally increased SOC storage and CH₄ emissions, while nonflooded conditions increased N₂O emissions and nonflooded and CharSil treatments increased CO₂ emissions. All amendments stored ∼0.15 kg C m^-2 y^-1 more SOC than CH₄ emissions (as CO₂ equivalents), but the combustion of husk to produce CharSil resulted in the net release of CO₂ which negates any SOC storage. UV-visible absorption/fluorescence spectroscopy from the pot study suggests that nonflooded treatment decreased DOM aromaticity and molecular size. Our data show that flooding and amendment of Husk and Biochar maximized C storage in the highly weathered rice paddy soil under study despite Husk increasing CH₄ emissions. Water management affected dissolved organic matter chemistry more strongly than amendments, but this requires further investigation. Return of rice husk that is untreated or pyrolyzed at low temperature shows promise to close nutrient loops and preserve SOC in rice paddy soils.

Uncertainty in non-CO2 greenhouse gas mitigation contributes to ambiguity in global climate policy feasibility

Despite its projected crucial role in stringent, future global climate policy, non-CO₂ greenhouse gas (NCGG) mitigation remains a large uncertain factor in climate research. A revision of the estimated mitigation potential has implications for the feasibility of global climate policy to reach the Paris Agreement climate goals. Here, we provide a systematic bottom-up estimate of the total uncertainty in NCGG mitigation, by developing 'optimistic', 'default' and 'pessimistic' long-term NCGG marginal abatement cost (MAC) curves, based on a comprehensive literature review of mitigation options. The global 1.5-degree climate target is found to be out of reach under pessimistic MAC assumptions, as is the 2-degree target under high emission assumptions. In a 2-degree scenario, MAC uncertainty translates into a large projected range in relative NCGG reduction (40-58%), carbon budget (±120 Gt CO₂) and policy costs (±16%). Partly, the MAC uncertainty signifies a gap that could be bridged by human efforts, but largely it indicates uncertainty in technical limitations.

Multiyear methane and nitrous oxide emissions in different irrigation management under long-term continuous rice rotation in Arkansas

Rice paddies are one of the major sources of anthropogenic methane (CH₄ ) emissions. The alternate wetting and drying (AWD) irrigation management has been shown to reduce CH₄ emissions and total global warming potential (GWP) (CH₄ and nitrous oxide [N₂ O]). However, there is limited information about utilizing AWD management to reduce greenhouse gas (GHG) emissions from commercial-scale continuous rice fields. This study was conducted for five consecutive growing seasons (2015-2019) on a pair of adjacent fields in a commercial farm in Arkansas under long-term continuous rice rotation irrigated with either continuously flooded (CF) or AWD conditions. The cumulative CH₄ emissions in the growing season across the two fields and 5 years ranged from 41 to 123 kg CH₄ -C ha^-1 for CF and 1 to 73 kg CH₄ -C ha^-1 for AWD. On average, AWD reduced CH₄ emissions by 73% relative to CH₄ emissions in CF fields. Compared to N₂ O emissions, CH₄ emissions dominated the GWP with an average contribution of 91% in both irrigation treatments. There was no significant variation in grain yield (7.3-11.9 Mg ha^-1 ) or growing season N₂ O emissions (-0.02 to 0.51 kg N₂ O-N ha^-1 ) between the irrigation treatments. The yield-scaled GWP was 368 and 173 kg CO₂ eq. Mg^-1 season^-1 for CF and AWD, respectively, showing the feasibility of AWD on a commercial farm to reduce the total GHG emissions while sustaining grain yield. Seasonal variations of GHG emissions observed within fields showed total GHG emissions were predominantly influenced by weather (precipitation) and crop and irrigation management. The influence of air temperature and floodwater heights on GHG emissions had high degree of variability among years and fields. These findings demonstrate that the use of multiyear GHG emission datasets could better capture variability of GHG emissions associated with rice production and could improve field verification of GHG emission models and scaling factors for commercial rice farms.

Eddy covariance assessment of alternate wetting and drying floodwater management on rice methane emissions

Reducing methane emissions and water use is critical for combating climate change and declining aquifers on food production. Reductions in irrigation water use and methane emissions are known benefits of practicing alternate wetting and drying (AWD) over continuous flooding (CF) water management in lowland rice (Oryza sativa L.) production systems. In a two-year (2020 and 2021) study, methane emissions from large farm-scale (∼50 ha) rice fields managed under CF and AWD in soils dominated by Sharkey clay (Sharkey clay, clay over loamy, montmorillonitic non-acid, thermic Vertic halauepet) were monitored using the eddy covariance method (EC). In the EC system, an open-path laser gas analyzer was used to monitor air methane gas density in the constant flux layer of the atmosphere over the rice-crop canopies. Total water pumped into the field for floodwater management was higher in CF compared to AWD by 24 and 14% in 2020 and 2021, respectively. Considerable variations between seasons in the amount of methane emitted from the CF and AWD treatments were observed: CF emitted 29 and 75 kg ha^-1 and AWD emitted 14 and 34 kg ha^-1 methane in 2020 and 2021, respectively. Notwithstanding, the extent of reduction in methane emissions due to AWD over CF was similar for each crop season (52% in 2020 and 55% in 2021). Rice grain yield harvested differed by only ±2% between AWD and CF. This investigation of large-scale system-level evaluation, using the EC method, confirmed that by practicing AWD floodwater management in rice, water pumped from aquifers could be reduced by about a quarter and methane emissions from rice fields could be cut down by about half without affecting grain yields, thereby promoting sustainable water management and greenhouse gas emission reduction during rice production in the Lower Mississippi Delta.

Long-term non-phosphorus application increased paddy methane emission by promoting organic acid and methanogen abundance in Tai Lake region, China

Rice paddy is a significant source of atmospheric methane (CH₄), a major global warming source. CH₄ emission from paddy fields is greatly influenced by phosphorus (P) management, especially the long-term non-P application on CH₄ emission is largely unexplored. In the present study, long-term non-P application (NK) and P application (NPK) treatments of two paddy fields in Suzhou (from 1980) and Yixing (from 2009), Tai Lake region was done. The effect of P application on CH₄ emissions and related microorganisms (i.e., methanogens and methanotrophs) from 2019 to 2020 was analyzed. Results revealed that long-term NK treatment didn't alter the seasonal trend of CH₄ flux, but significantly promoted CH₄ emissions at the tillering stage. The non-P application for >12 years caused the cumulative CH₄ emissions of NK treatment in the whole rice season significantly increased by 41.9-221 % in two fields compared to NPK treatment in 2019 and 2020. NK treatment increased the abundance and diversity of methanogens, while reducing the abundance and diversity of methanotrophs. Community composition of soil pmoA gene differed in two experiment sites. Correlation analysis revealed that the CH₄ emission was significant and positively related to soil mcrA gene and C/P while negatively related to soil pmoA gene and P. Structure equation model analysis show the low soil available P content was the dominant driving factor for the high CH₄ emission under long-term non-P application through its direct impact on soil mcrA and pmoA genes. The increased soil organic acid content was another driver which was positively related to soil mcrA gene and negatively to soil pmoA gene. Our findings demonstrate the important role of soil P in regulating CH₄ emissions from paddy fields in the Tai Lake region, China, and suitable P application is necessary for ensuring the yield while reducing CH₄ emission.

Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia

Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040-2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040-2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

Informing Nature-based Climate Solutions for the United States with the best-available science

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Alternate Wetting and Drying in the Center of Portugal: Effects on Water and Rice Productivity and Contribution to Development

Rice irrigation by continuous flooding is highly water demanding in comparison with most methods applied in the irrigation of other crops, due to a significant deep percolation and surface drainage of paddies. The pollution of water resources and methane emissions are other environmental problems of rice agroecosystems, which require effective agronomic changes to safeguard its sustainable production. To contribute to this solution, an experimental study of alternate wetting and drying flooding (AWD) was carried out in the Center of Portugal in farmer’s paddies, using the methodology of field irrigation evaluation. The AWD results showed that there is a relevant potential to save about 10% of irrigation water with a reduced yield impact, allowing an additional period of about 10 to 29 days of dry soil. The guidelines to promote the on-farm scale AWD automation were outlined, integrating multiple data sources, to get a safe control of soil water and crop productivity. The conclusions point out the advantages of a significant change in the irrigation procedures, the use of water level sensors to assess the right irrigation scheduling to manage the soil deficit and the mild crop stress during the dry periods, and the development of paddy irrigation supplies, to allow a safe and smart AWD.

Unexpected Parabolic Temperature Dependency of CH4 Emissions from Rice Paddies

Global warming is expected to affect methane (CH₄) emissions from rice paddies, one of the largest human-induced sources of this potent greenhouse gas. However, the large variability in warming impacts on CH₄ emissions makes it difficult to extrapolate the experimental results over large regions. Here, we show, through meta-analysis and multi-site warming experiments using the free air temperature increase facility, that warming stimulates CH₄ emissions most strongly at background air temperatures during the flooded stage of ∼26 °C, with smaller responses of CH₄ emissions to warming at lower and higher temperatures. This pattern can be explained by divergent warming responses of plant growth, methanogens, and methanotrophs. The effects of warming on rice biomass decreased with the background air temperature. Warming increased the abundance of methanogens more strongly at the medium air temperature site than the low and high air temperature sites. In contrast, the effects of warming on the abundance of methanotrophs were similar across the three temperature sites. We estimate that 1 °C warming will increase CH₄ emissions from paddies in China by 12.6%─substantially higher than the estimates obtained from leading ecosystem models. Our findings challenge model assumptions and suggest that the estimates of future paddy CH₄ emissions need to consider both plant and microbial responses to warming.

Review: biological engineering for nature-based climate solutions

Nature-based Climate Solutions are landscape stewardship techniques to reduce greenhouse gas emissions and increase soil or biomass carbon sequestration. These mitigation approaches to climate change present an opportunity to supplement energy sector decarbonization and provide co-benefits in terms of ecosystem services and landscape productivity. The biological engineering profession must be involved in the research and implementation of these solutions-developing new tools to aid in decision-making, methods to optimize across different objectives, and new messaging frameworks to assist in prioritizing among different options. Furthermore, the biological engineering curriculum should be redesigned to reflect the needs of carbon-based landscape management. While doing so, the biological engineering community has an opportunity to embed justice, equity, diversity, and inclusion within both the classroom and the profession. Together these transformations will enhance our capacity to use sustainable landscape management as an active tool to mitigate the risks of climate change.

Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice.

Contribution of periphytic biofilm of paddy soils to carbon dioxide fixation and methane emissions

Rice paddies are major contributors to anthropogenic greenhouse gas emissions via methane (CH₄) flux. The accurate quantification of CH₄ emissions from rice paddies remains problematic, in part due to uncertainties and omissions in the contribution of microbial aggregates on the soil surface to carbon fluxes. Herein, we comprehensively evaluated the contribution of one form of microbial aggregates, periphytic biofilm (PB), to carbon dioxide (CO₂) and CH₄ emissions from paddies distributed across three climatic zones, and quantified the pathways that drive net CH₄ production as well as CO₂ fixation. We found that PB accounted for 7.1%-38.5% of CH₄ emissions and 7.2%-12.7% of CO₂ fixation in the rice paddies. During their growth phase, PB fixed CO₂ and increased the redox potential, which promoted aerobic CH₄ oxidation. During the decay phase, PB degradation reduced redox potential and increased soil organic carbon availability, which promoted methanogenic microbial community growth and metabolism and increased CH₄ emissions. Overall, PB acted as a biotic converter of atmospheric CO₂ to CH₄, and aggravated carbon emissions by up to 2,318 kg CO₂ equiv ha^-1 season^-1. Our results provide proof-of-concept evidence for the discrimination of the contributions of surface microbial aggregates (i.e., PB) from soil microbes, and a profound foundation for the estimation and simulation of carbon fluxes in a potential novel approach to the mitigation of CH₄ emissions by manipulating PB growth.

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH₄ ) to the atmosphere, they represent a large source of uncertainty in the global CH₄ budget due to the complex biogeochemical controls on CH₄ dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH₄ fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH₄ . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH₄ volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH₄ emissions.

An Ecosystem-Scale Flux Measurement Strategy to Assess Natural Climate Solutions

Eddy covariance measurement systems provide direct observation of the exchange of greenhouse gases between ecosystems and the atmosphere, but have only occasionally been intentionally applied to quantify the carbon dynamics associated with specific climate mitigation strategies. Natural climate solutions (NCS) harness the photosynthetic power of ecosystems to avoid emissions and remove atmospheric carbon dioxide (CO₂), sequestering it in biological carbon pools. In this perspective, we aim to determine which kinds of NCS strategies are most suitable for ecosystem-scale flux measurements and how these measurements should be deployed for diverse NCS scales and goals. We find that ecosystem-scale flux measurements bring unique value when assessing NCS strategies characterized by inaccessible and hard-to-observe carbon pool changes, important non-CO₂ greenhouse gas fluxes, the potential for biophysical impacts, or dynamic successional changes. We propose three deployment types for ecosystem-scale flux measurements at various NCS scales to constrain wide uncertainties and chart a workable path forward: "pilot", "upscale", and "monitor". Together, the integration of ecosystem-scale flux measurements by the NCS community and the prioritization of NCS measurements by the flux community, have the potential to improve accounting in ways that capture the net impacts, unintended feedbacks, and on-the-ground specifics of a wide range of emerging NCS strategies.

Rice Inundation Assessment Using Polarimetric UAVSAR Data

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model-based decomposition and machine learning to map inundated rice using time-series polarimetric, L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three-component model-based decomposition generated metrics representing surface-, double bounce-, and volume-scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double-bounce within total scattering, and the relative comparison between the double-bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L-band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas.

Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX

With population growth and resource depletion, maximizing the efficiency of soybean (Glycine max [L.] Merr.) and rice (Oryza sativa L.) cropping systems is urgently needed. The goal of this study was to shed light on precise irrigation amounts and optimal agronomic practices via simulating rice–rice and soybean–rice crop rotations in the Agricultural Policy/Environmental eXtender (APEX) model. The APEX model was calibrated using observations from five fields under soybean–rice rotation in Arkansas from 2017 to 2019 and remote sensing leaf area index (LAI) values to assess modeled vegetation growth. Different irrigation practices were assessed, including conventional flooding (CVF), known as cascade, multiple inlet rice irrigation with polypipe (MIRI), and furrow irrigation (FIR). The amount of water used differed between fields, following each field’s measured or estimated input. Moreover, fields were managed with either continuous flooding (CF) or alternate wetting and drying (AWD) irrigation. Two 20-year scenarios were simulated to test yield changes: (1) between rice–rice and soybean–rice rotation and (2) under reduced irrigation amounts. After calibration with crop yield and LAI, the modeled LAI correlated to the observations with R2 values greater than 0.66, and the percent bias (PBIAS) values were within 32%. The PBIAS and percent difference for modeled versus observed yield were within 2.5% for rice and 15% for soybean. Contrary to expectation, the rice–rice and soybean–rice rotation yields were not statistically significant. The results of the reduced irrigation scenario differed by field, but reducing irrigation beyond 20% from the original amount input by the farmers significantly reduced yields in all fields, except for one field that was over-irrigated.