Rice paddies and math tests pdf

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Outliers: The Story of Success - Part 2, Chapter 8, Rice Paddies and Math Tests Summary & Analysis

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Matters Arising to this article was published on 19 February Agriculture e.

We find strong spatial consistencies between rice paddy area and XCH 4 and seasonal consistencies between rice plant growth and XCH 4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since , which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH 4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial—temporal dynamics of XCH 4 in monsoon Asia.

Atmospheric methane CH 4 concentration has increased substantially since early , after a hiatus during — 1 , 2 , 3 , 4 , 5 ; however, there is no consensus on the possible causes for this observed increase 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , Recent studies suggested that biogenic sources may have contributed most to the ongoing increase of CH 4 emission 4 , 5 , 12 , especially the expansion of tropical agriculture 4 , 5.

Rice paddy CH 4 emissions are predicted to increase by the end of the 21st century due to the enhancement of rice plant productivity from a warmer climate and atmospheric carbon fertilization 15 , and increased rice paddy area driven by an increasing market demand for rice as a food staple Hence, it is critical to understand the role of rice paddies in the observed spatial distribution, seasonal dynamics, and interannual variation of atmospheric CH 4 concentration.

Advanced satellite measurements of column-averaged concentration of CH 4 XCH 4 provide large-scale constraints for CH 4 emission estimates 17 , 18 , 19 and are used to detect surface CH 4 emission hotspots from space 20 , Satellite data sets for methane production-related variables e. Bloom et al.

Hayashida et al. Nevertheless, the relationship between rice cultivation and atmospheric CH 4 concentration in rice paddy regions based on the statistical data of rice harvest areas cannot accurately characterize the role of rice paddies in the seasonal fluctuations of XCH 4. Although numerous measurements and analyses of CH 4 emission from rice paddies at the site scale have been done 24 , 25 , 26 , 27 , the influence of rice paddies on the spatial distribution and seasonal dynamics of atmospheric CH 4 concentration is still poorly understood at the continental scale, in part due to the lack of moderate to high-spatial resolution maps of paddy rice croplands.

The accurate spatial and temporal pattern of rice paddies is critical for understanding the contribution of rice paddies to atmospheric CH 4 concentration. The resultant annual maps of rice paddies show the spatial—temporal changes of rice paddy areas in monsoon Asia during —, including the hot spots and interannual trends. The results show that geographically those regions with relatively larger proportion of rice paddies have high XCH 4.

In those areas dominated by single- or double-paddy rice cropping systems, the seasonal dynamics of XCH 4 also has one or two peaks in a year, corresponding well with the seasonal dynamics of paddy rice growth. Third, we assess the interannual dynamics of rice paddy area and XCH 4 in monsoon Asia. The results show a decreasing trend of rice paddy area and a renewed growth of XCH 4 in monsoon Asia since We generated the annual paddy rice maps during — and quantified the spatial—temporal changes in rice paddy area in monsoon Asia.

China and India had the largest total area of rice paddies, and together accounted for over half the total rice paddy area in monsoon Asia Supplementary Fig. The rice paddies in monsoon Asia substantially increased from to , but then decreased from to Supplementary Fig.

Geographically, those regions with significant decreasing trends in rice paddy area during — included the Yangtze Plain of southern China and eastern Thailand, while Northeast China and India had significantly increasing trends in rice paddy area Fig. These annual maps provide improved data and knowledge of the spatial distribution and interannual variation of rice paddy in monsoon Asia.

The small and red polygons in a are the samples illustrated in Fig. Source data are provided as a Source Data file. The spatial distributions of rice paddies were consistent with those of atmospheric CH 4 concentration over six 3-year moving-window periods —, —, —, —, —, and —, Fig.

Those regions with high densities of rice paddies also had high XCH 4. The three periods —, —, and — were selected in the main text to illustrate the spatial relationships between rice paddies and XCH 4. The 3-year mean XCH 4 increased as the density of rice paddies rose for all these 3-year periods Fig. Similar relationships were also found in different seasons in these periods Supplementary Figs.

It should be noted that clouds and shadows in the inter-tropical convergence zone e. In those areas with frequent cloud cover and shadows, there is potentially a multicollinearity issue in the rice paddy area and XCH 4 data. We re-ran the SEM analysis for the other monsoon Asia countries after excluding Indonesia and Malaysia, and the results still showed strong spatial consistency between rice paddy area and XCH 4 Supplementary Figs.

These results suggested that the spatial distribution of rice paddies was one of the major factors that determine the spatial distributions of atmospheric CH 4 concentration in monsoon Asia.

First, we analyzed the seasonal dynamics of XCH 4 and paddy rice growth in four typical ROIs with a high density of rice paddies and different cropping systems single- and double-cropping systems Fig. We calculated mean EVI for all pixels within 0. Remarkably, both XCH 4 Fig. Northern India and northern Bangladesh Fig. The seasonality of XCH 4 Fig. The Poyang Lake region of southern China Fig. The annual XCH 4 Fig.

It should be noted that there were no obvious double peaks for EVI all-pixel and EVI all-rice in one year at a coarse spatial gridcell resolution of 0. These results suggested that the growth cycle of paddy rice contributed significantly to the seasonality of XCH 4. The column-averaged methane concentration XCH 4 and the enhanced vegetation index EVI were analyzed in four regions of interest ROIs with dense rice paddies and different cropping systems.

The four ROIs are shown in Fig. It is well recognized that rice plants play a critical role in the processes of methane production 37 , Several recent in situ studies found a strong correlation between daily CH 4 flux and rice plant biomass, which suggested that methane flux in rice paddies is affected by rice growth and development 37 , 38 , 39 , 40 , 41 , 42 , 43 , Here, we compared the observed CH 4 emission and EVI at eight paddy rice sites 25 , 26 , 27 , 41 , 42 , 45 , 46 , 47 , and the results confirmed their consistency in terms of seasonality Supplementary Fig.

The synchrony of rice growth and CH 4 emissions is due to the fact that the rhizodeposition from current season photosynthesis and plant growth controlled the organic matter of the flooded soils, and subsequently determined the methanogenesis in paddy soils 38 , 48 , Rice plants provide root exudates, which is the important organic matter used by soil microbes for CH 4 production in rice paddies 37 , The quantity of root exudates varies during the rice plant growing season, which rises as the rice plants grow, reaches a maximum during the flowering stage with a peak in root biomass, and then decreases Supplementary Fig.

Although this synchronized peak has been previously found at field site, our results using satellite data show synchrony at large scales for the first time with the aid of high-resolution paddy rice maps. Moreover, the rice cropping systems affected the peaks in XCH 4 , that is, the XCH 4 variation was controlled by the paddy rice growth cycle regardless of whether it was a single- or double-cropping system wheat—rice, rice—rice Fig.

The ROI-scale analyses suggested the modeling of the seasonal dynamics of atmospheric CH 4 concentration need to consider the rice cropping system single rice, double rice, or rice plus other crop rather than general cropping intensity single, double, or multiple cropping. Regions with the same or similar cropping system and planting schedule demonstrated clear relationships between paddy rice growth and XCH 4. Most 0. The 0. The insets in a — d are the corresponding frequency diagrams of Figure a — d.

The relationships between EVI all-rice and XCH 4 data in regions with heterogeneous annual cropping systems and rotation schedules were more complicated and irregular, and the 0.

For example, the Mekong Delta of Vietnam has single-, double-, and triple-rice cropping systems that are either rain-fed or irrigated The EVI and XCH 4 profiles from this region with mixed cropping systems had irregular seasonal patterns and accordingly weak or negative correlations Supplementary Fig.

A similar result was found for the period of —, —, and — Supplementary Figs. The significant correlations between EVI and XCH 4 in rice paddy areas with the same or similar cropping systems and planting schedules Figs. Our analyses demonstrate that the satellite-observed XCH 4 is indicative of CH 4 emissions from rice paddies, and paddy rice cultivation dominates the spatial distribution and seasonal dynamics of atmospheric CH 4 concentration over those regions with dense rice paddies.

This study also shows the importance of annual paddy rice maps for assessing the effects of rice paddies on the spatial pattern and seasonal dynamics of atmospheric CH 4 concentration by providing more details on the location and area proportion within gridcells. On one hand, these maps could show area fractional information within the 0.

On the other hand, they allow us to capture the seasonal dynamics of vegetation indices for individual rice paddies pixels when analyzing seasonal relationship between them. The temporal statistics of vegetation indices for all pixels within individual gridcells often differ from those for the pure rice paddy pixels e.

The pure rice paddy pixel-based analyses had clear double peaks EVI all-rice in Fig. Therefore, it is necessary to use accurate paddy rice maps at moderate spatial resolution as masks to track seasonal fluctuations of atmospheric CH 4 concentration.

In addition to the density of rice paddies within gridcells, our results also demonstrate the importance of the information about rice cropping intensity single or double and timing of rice crop calendar within gridcells. Together, the accurate information on the locations of rice paddies allow us to fingerprint the effects of rice paddies on atmospheric CH 4 concentration, which can reduce influence from other land cover types in the gridcells.

Furthermore, the spatial distribution of rice paddies in monsoon Asia has changed substantially since , including a northward shift of rice paddies in China Recent increases in atmospheric CH 4 concentration since are not well understood as evidenced by many hypotheses currently debated 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , Some studies reported that biogenic sources, most notably agriculture, may be the key contributor to renewed growth in atmospheric CH 4 4 , 5.

Rice paddy is one of the main agricultural sources of CH 4 emission. In this study, we also investigated whether interannual variations in rice paddy area contributed to the renewed growth of atmospheric CH 4 concentration since at the national and continental scales. We analyzed the interannual variations of XCH 4 during — in monsoon Asia, especially China and India, the two countries with the largest rice paddy areas.

The interannual variations in XCH 4 in the rice paddy-dominated areas of monsoon Asia, China, and India were relatively stable during —, but changed into an increasing trend beginning in Fig. The rice paddy area increased during — in China, India, and monsoon Asia, but after rice paddy area decreased in China and monsoon Asia and remained stable in India Fig. The total rice paddy area in monsoon Asia has declined since over the time period of renewed XCH 4 growth.

Similar results were also found for the whole region rice paddy and non-rice paddy areas of monsoon Asia, China, and India Supplementary Fig. The decreasing trends of rice paddy area and the increasing trends of atmospheric CH 4 concentration in both the rice paddy-dominated areas and the whole area since suggested that the change in rice paddy area was not the main driver for the renewed increase of atmospheric CH 4 concentration.

A study using atmospheric methane observations and an atmospheric transport model reported that the annual methane emission has not significantly changed in India during — and the major CH 4 sources ruminants, rice paddies, waste, and fossil fuels did not much change 57 , which is in line with the stable rice paddy areas in India from this study during the same period Fig.

Another country-scale study showed that rice paddies did not contribute to the increase of atmospheric CH 4 concentration in China and India during — using the emission estimates from the inverse model and the spatial distribution of different source sectors within the EDGAR emissions inventory 58 , which agrees with our result from — Fig.

Our study at monsoon Asia scale suggests that the renewed growth of atmospheric CH 4 concentration was unlikely attributed to the dynamics of rice paddy area. Temporal seasonal and interannual dynamics of atmospheric column-averaged methane concentration XCH 4 over rice paddy-dominated regions and interannual variations in MODIS-based rice paddy areas during — in monsoon Asia a , China b , and India c.

The XCH 4 outliers in winter have been removed. The black lines and black dashed lines below indicate trends of rice paddy area for different periods in monsoon Asia, China, and India. The rice paddy-dominated regions are shown in Supplementary Fig. The linear trends and its significance levels for different periods are shown below the panels; the formulas in black color are for paddy rice planting area, and the formula in red is for XCH 4.

What factors drove the interannual variations of atmospheric CH 4 concentration since is still a hotly debated issue 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , as CH 4 emissions are controlled by multiple sources and sinks.

Outliers: The Story of Success - Part 2, Chapter 8, Rice Paddies and Math Tests Summary & Analysis

Tokyo University of Agriculture and Technology. Uncertainty assessments of herbicide losses from rice paddies in Japan associated with local meteorological conditions and water management practices were performed using a pesticide fate and transport model, PCPF-1, under the Monte Carlo MC simulation scheme. First, MC simulations were conducted for five different cities with a prescribed water management scenario and a year meteorological dataset of each city. The effectiveness of water management was observed regarding the reduction of pesticide runoff. However, a greater potential of pesticide runoff remained in Western Japan. Secondly, an extended analysis was attempted to evaluate the effects of local water management and meteorological conditions between the Chikugo River basin and the Sakura River basin using uncertainty inputs processed from observed water management data.

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Melioidosis, a severe infection with the environmental bacterium Burkholderia pseudomallei , is being recognised increasingly frequently. What determines its uneven distribution within endemic areas is poorly understood.


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Outliers: The Story of Success Chapter Eight "Rice Paddies & Math Tests" NO PREP

Outliers Sparknotes Chapter 5. Moderate Positive Outliers 6. It'll show you how to go from average to world-class performer.

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To print the story please do so via the link in the story toolbar. Gladwell starts the chapter off by describing the Chinese countryside and an explanation of rice farming. The rice is grown in paddies which are small but require close monitoring and coordination.

Outliers Sparknotes Chapter 5

On page , several quotes from penniless peasants were given and the one that struck me most was, "No food without blood and sweat". While back in those times this quote applied to working people, this can easily be compared to students. To earn the grades, recognition, program acceptance, etc.

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