Climate change is a vital environmental issue that significantly affects rice productivity. Rice paddy fields are one of the greatest anthropogenic sources of methane (CH 4) and nitrous oxide (N 2 O) emissions. To evaluate the combined effects of manure amendment and water management on GHG emissions, grain yield and water productivity per rice yield in a lowland rice field with a sandy clay loam soil in Myanmar, this study was conducted with a split-plot design. Two water management practices (continuous flooding [CF] and alternate wetting and drying [AWD]) and four levels of cow dung manure (0, 2.5, 5.0 and 7.5 t ha −1) were applied with three replications in the dry (February-May) and wet (July-October) seasons in 2017. In the dry season, significantly higher cumulative methane (CH 4) emissions (50.5%) were recorded in CF than in AWD, while cumulative nitrous oxide (N 2 O) emissions were 70% higher in AWD than in CF, although the difference was not significant. Manure application showed no effect on CH 4 and N 2 O emissions compared with the no-manure control, irrespective of application level. In the wet season, significantly higher cumulative CH 4 emissions (65.2%) were again recorded in CF than in AWD; however, the cumulative N 2 O emissions were similar between CF and AWD. Methane and N 2 O emissions in the wet season were 65.8 and 35.8% higher, respectively, than those in the dry season. In both seasons, higher grain yields (1.8% in dry and 7.6% in wet) and higher water productivity (130% in dry and 31% in wet) were recorded in AWD than in CF. Increased grain yields (18.9% in dry and 7.7% in wet) and water productivity (25.5% in dry and 15.8% in wet) were recorded in the manure treatments compared to those in the no-manure treatment. This study presents quantitative data on how manure amendment and water management affected GHG emissions in a paddy field in Myanmar.

The study is focused on impact of manure application, rice varieties and water management on greenhouse gas (GHG) emissions from paddy rice soil in pot experiment. The objectives of this study were a) to assess the effect of different types of manure amendments and rice varieties on greenhouse gas emissions and b) to determine the optimum manure application rate to increase rice yield while mitigating GHG emissions under alternate wetting and drying irrigation in paddy rice production. The first pot experiment was conducted at the Department of Agronomy, Yezin Agricultural University, Myanmar, in the wet season from June to October 2016. Two different organic manures (compost and cow dung) and control (no manure), and two rice varieties; Manawthukha (135 days) and IR-50 (115 days), were tested. The results showed that cumulative CH4 emission from Manawthukha (1.084 g CH4 kg-1 soil) was significantly higher than that from IR-50 (0.683 g CH4 kg-1 soil) (P<0.0046) with yield increase (P<0.0164) because of the longer growth duration of the former. In contrast, higher cumulative nitrous oxide emissions were found for IR-50 (2.644 mg N2O kg-1 soil) than for Manawthukha (2.585 mg N2O kg-1 soil). However, IR-50 showed less global warming potential (GWP) than Manawthukha (P<0.0050). Although not significant, the numerically lowest CH4 and N2O emissions were observed in the cow dung manure treatment (0.808 g CH4 kg-1 soil, 2.135 mg N2O kg-1 soil) compared to those of the control and compost. To determine the effect of water management and organic manures on greenhouse gas emissions, second pot experiments were conducted in Madaya township during the dry and wet seasons from February to October 2017. Two water management practices {continuous flooding (CF) and alternate wetting and drying (AWD)} and four cow dung manure rates {(1) 0 (2) 2.5 t ha-1 (3) 5 t ha-1 (4) 7.5 t ha-1} were tested. The different cow dung manure rates did not significantly affect grain yield or greenhouse gas emissions in this experiment. Across the manure treatments, AWD irrigation significantly reduced CH4 emissions by 70% during the dry season and 66% during the wet season. Although a relative increase in N2O emissions under AWD was observed in both rice seasons, the global warming potential was significantly reduced in AWD compared to CF in both seasons (P<0.0002, P<0.0000) according to reduced emission in CH4. Therefore, AWD is the effective mitigation practice for reducing GWP without compromising rice yield while manure amendment had no significant effect on GHG emission from paddy rice field. Besides, AWD saved water about 10% in dry season and 19% in wet season.

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To assess the effect of different organic manures and rice cultivars on methane emissions, a pot experiment was conducted at the Yezin Agricultural University, Nay Pyi Taw, Myanmar during the wet season of 2016. Organic manures (control, compost, and cow dung) and two rice cultivars (Manawthukha and IR 50) were tested. For both rice cultivars, high grain yield was observed in the control, and the minimum grain yield was observed in the cow dung treatment. The rate and cumulative CH4 emissions in Manawthukha were higher than those in IR 50, in accordance with the yield, because of the longer growth duration. Although not significant, the lowest methane emissions were observed in the cow dung manure treatment (0.808 g CH4 kg−1 soil) against the control (0.893 g CH4 kg−1 soil) and compost (0.951 g CH4 kg−1 soil) treatments. Based on these results, a field experiment was conducted at Madaya Township, Mandalay region, Myanmar during the dry and wet seasons of 2017 to determine the effects of water management and different rates of cow dung manure on methane emission and yield of IR 50. Higher methane emissions were recorded for continuous flooding (CF) than for alternative wetting and drying (AWD). In both seasons, higher grain yields (1.8% in dry and 7.6% in wet) were recorded for AWD than for CF. Higher methane emissions were recorded from OM3 and lower emissions from OM0 in both water management practices. In AWD, methane emissions were restricted under aerated soil conditions, although a higher amount of manure was added.

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The primary point of this research is to design a road extraction algorithm for processing National Aeronautics and Space Administration satellite pictures. Roadway network detection is one of the important appointments for calamity emergency response, smart shipping structures, and real-time modify roadway network. Everyone is trying to detect road; this system is useful for urban or rural developing schedule. The development of a town / village depends not only on the building and population density of the town or village, but also in the systematic development of roads. The research focused on finding ways to use morphological image processing primarily. As an application area, we use National Aeronautics and Space Administration imagery obtained from 2009-2020 in Monywa, Upper Myanmar to find out how the roads have been developed and how the city has been developed. Extraction road from planet pictures is hard matter with many realistic application programs. The primary points in the model are the advancement of the picture, the segmentation of that picture, the application of the morphological operators, and finally the detection of the roadway network. Use Google Earth Pro to get the necessary data photos and search for road improvements. After collecting images from different seasons and years, we can find precise answers by combining them with precise algorithms. In addition to significant, benefits of Google Earth Pro, this research demonstrates the ability to make good use of satellite imagery and to integrate it with outside experts to save money, save time, and provide accurate answers. It is simulated with MATLAB programming language.

Building change detection makes it is easy to locate buildings from a distance in the sky. They can also observe the development of rural, or urban areas between 10 decade and present. So, higher resolution satellite and aerial pictures are needed to detect buildings. Building shape varies from one to another over the world. Rural areas are sparsely populated, but densely and complexly populated in urban areas. And it is difficult to detect separate buildings from them. To solve obstacles, non-linear filter, line extracting and region thresholding method is used in this research. The test images from the last decade and images of current year are acquired by using google earth pro, and have different spatial resolutions. Detection area is Hlaingthaya Township, Yangon, Myanmar. This system is simulated with MATLAB programming language