The first fine-resolution mapping of contour-levee irrigation using deep Bi-Stream convolutional neural networks

Lu, Liang; Meyarian, Abolfazl; Yuan, Xiaohui; Runkle, Benjamin R. K.; Mihaila, George A.; Qin, Yuchu; Daniels, Jacob; Reba, M. L.; Rigby, James R.

doi:10.1016/j.jag.2021.102631

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Tillage and burning data could help identify low and high residue areas for the following growing season (McCarty et al 2007, Hively et al 2018, Wang et al 2023b. Additionally, efforts to identify production areas and optimize irrigation management during the growing season would enhance the application of the Tier 2 approach at the field scale (Huang et al 2021, Liang et al 2021).…”

Section: Driving Factors For Regional Methane Emissions and Mitigatio...mentioning

confidence: 99%

Assessing the methane mitigation potential of innovative management in US rice production

Reavis,

Reba,

Shults

et al. 2023

Environ. Res. Lett.

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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 CH4 annually, 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.

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Section: Driving Factors For Regional Methane Emissions and Mitigatio...mentioning

confidence: 99%

Assessing the methane mitigation potential of innovative management in US rice production

Reavis,

Reba,

Shults

et al. 2023

Environ. Res. Lett.

View full text Add to dashboard Cite

show abstract

HIOS: Hierarchical Inter-Operator Scheduler for Real-Time Inference of DAG-Structured Deep Learning Models on Multiple GPUs

Kundu,

Shu

2023

2023 IEEE International Conference on Cluster Computing (CLUSTER)

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The first fine-resolution mapping of contour-levee irrigation using deep Bi-Stream convolutional neural networks

Cited by 2 publications

References 31 publications

Assessing the methane mitigation potential of innovative management in US rice production

Assessing the methane mitigation potential of innovative management in US rice production

HIOS: Hierarchical Inter-Operator Scheduler for Real-Time Inference of DAG-Structured Deep Learning Models on Multiple GPUs

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