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Abstract Carbon-rich peat soils have been drained and used extensively for agriculture throughout human history, leading to significant losses of their soil carbon. One solution for rewetting degraded peat is wet crop cultivation. Crops such as rice, which can grow in water-saturated conditions, could enable agricultural production to be maintained whilst reducing CO₂and N₂O emissions from peat. However, wet rice cultivation can release considerable methane (CH₄). Water table and soil management strategies may enhance rice yield and minimize CH₄emissions, but they also influence plant biomass allocation strategies. It remains unclear how water and soil management influences rice allocation strategies and how changing plant allocation and associated traits, particularly belowground, influence CH₄-related processes. We examined belowground biomass (BGB), aboveground biomass (AGB), belowground:aboveground ratio (BGB:ABG), and a range of root traits (root length, root diameter, root volume, root area, and specific root length) under different soil and water treatments; and evaluated plant trait linkages to CH₄. Rice (Oryza sativaL.) was grown for six months in field mesocosms under high (saturated) or low water table treatments, and in either degraded peat soil or degraded peat covered with mineral soil. We found that BGB and BGB:AGB were lowest in water saturated conditions where mineral soil had been added to the peat, and highest in low-water table peat soils. Furthermore, CH₄and BGB were positively related, with BGB explaining 60% of the variation in CH₄but only under low water table conditions. Our results suggest that a mix of low water table and mineral soil addition could minimize belowground plant allocation in rice, which could further lower CH₄likely because root-derived carbon is a key substrate for methanogenesis. Minimizing root allocation, in conjunction with water and soil management, could be explored as a strategy for lowering CH₄emissions from wet rice cultivation in degraded peatlands. 

Abstract Rice is an important global crop while also contributing significant anthropogenic methane (CH₄) emissions. To support the future of rice production, more information is needed on the impacts of sustainability-driven management used to grow rice with lower associated methane emissions. Recent support for the impacts of different growing practices in the US has prompted the application of a regional methodology (Tier 2) to estimate methane emissions in different rice growing regions. The methodology estimates rice methane emissions from the US Mid-South (MdS) and California (Cal) using region-specific scaling factors applied to a region-specific baseline flux. In our study, we leverage land cover data and soil clay content to estimate methane emissions using this approach, while also examining how changes in common production practices can affect overall emissions in the US. Our results indicated US rice cultivation produced between 0.32 and 0.45 Tg CH₄annually, which were approximately 7% and 42% lower on average compared to Food and Agriculture Organization of the UN (FAO) and US Environmental Protection Agency (EPA) inventories, respectively. Our estimates were 63% greater on average compared to similar methods that lack regional context. Introducing aeration events into irrigation resulted in the greatest methane reductions across both regions. When accounting for differences between baseline and reduction scenarios, the US MdS typically had higher mitigation potential compared to Cal. The differences in cumulative mitigation potential across the 2008–2020 period were likely driven by lower production area clay content for the US MdS compared to Cal. The added spatial representation in the Tier 2 approach is useful in surveying how impactful methane-reducing practices might be within and across regions. 

Abstract 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 CH₄emissions and total global warming potential (GWP) (CH₄and nitrous oxide [N₂O]). 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 CH₄emissions in the growing season across the two fields and 5 years ranged from 41 to 123 kg CH₄‐C ha⁻¹for CF and 1 to 73 kg CH₄‐C ha⁻¹for AWD. On average, AWD reduced CH₄emissions by 73% relative to CH₄emissions in CF fields. Compared to N₂O emissions, CH₄emissions 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 N₂O emissions (−0.02 to 0.51 kg N₂O‐N ha⁻¹) between the irrigation treatments. The yield‐scaled GWP was 368 and 173 kg CO₂eq. Mg⁻¹season⁻¹for 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. 

Improved water management is a growing need in areas where rice production is intensive. In the state of Arkansas and other portions of the US, new irrigation practices are being implemented to conserve water during rice cultivation. The goal of this study was to evaluate canopy water use in two commercial rice fields using different irrigation practices across three growing seasons. Canopy water use was assessed across multiple metrics, including different representations of water use efficiency (WUE) as well as their contributing terms, gross primary production (GPP) and evapotranspiration (ET). Furthermore, we validated and employed a methodology for estimating transpiration from ET using the concept of underlying water use efficiency (uWUE) that includes a sensitivity to vapor pressure deficit (VPD). Periodic drying associated with the alternate wetting and drying irrigation practice did not result in decreased GPP, ET, or transpiration (T). Our findings indicated that approximately 43 to 56 % of ET is released as T during the growing season. The uWUE method improved the relationship between GPP and ET by accounting for the limitation of VPD on GPP during the afternoon periods. 

Abstract 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. 

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.