Reply to Wassmann et al.: More data at high sampling intensity from medium- and intense-intermittently flooded rice farms is crucial

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Here, we briefly respond to critique of our study (1) by Wassmann et al. (2). A detailed response to their letter is available online (edf.org/riceN2O)

High nitrous oxide fluxes from rice indicate the need to manage water for both long- and short-term climate impacts

Global rice cultivation is estimated to account for 2.5% of current anthropogenic warming because of emissions of methane (CH4), a short-lived greenhouse gas. This estimate assumes a widespread prevalence of continuous flooding of most rice fields and hence does not include emissions of nitrous oxide (N2O), a long-lived greenhouse gas. Based on the belief that minimizing CH4 from rice cultivation is always climate beneficial, current mitigation policies promote increased use of intermittent flooding. However, results from five intermittently flooded rice farms across three agroecological regions in India indicate that N2O emissions per hectare can be three times higher (33 kg-N2O⋅ha−1⋅season−1) than the maximum previously reported. Correlations between N2O emissions and management parameters suggest that N2O emissions from rice across the Indian subcontinent might be 30–45 times higher under intensified use of intermittent flooding than under continuous flooding. Our data further indicate that comanagement of water with inorganic nitrogen and/or organic matter inputs can decrease climate impacts caused by greenhouse gas emissions up to 90% and nitrogen management might not be central to N2O reduction. An understanding of climate benefits/drawbacks over time of different flooding regimes because of differences in N2O and CH4 emissions can help select the most climate-friendly water management regimes for a given area. Region-specific studies of rice farming practices that map flooding regimes and measure effects of multiple comanaged variables on N2O and CH4 emissions are necessary to determine and minimize the climate impacts of rice cultivation over both the short term and long term

Night-time lights: A global, long term look at links to socio-economic trends

We use a parallelized spatial analytics platform to process the twenty-one year totality of the longest-running time series of night-time lights data—the Defense Meteorological Satellite Program (DMSP) dataset—surpassing the narrower scope of prior studies to assess changes in area lit of countries globally. Doing so allows a retrospective look at the global, long-term relationships between night-time lights and a series of socio-economic indicators. We find the strongest correlations with electricity consumption, CO2 emissions, and GDP, followed by population, CH4 emissions, N2O emissions, poverty (inverse) and F-gas emissions. Relating area lit to electricity consumption shows that while a basic linear model provides a good statistical fit, regional and temporal trends are found to have a significant impact

Comparison of regression models between DMSP (logarithm) and electricity consumption (logarithm).

<p>Describes regression outputs when fixing effects for various dimensions in the data, both individually and in combination.</p

Correlation between area lit and a collection of socio-economic indicators.

<p>The matrix above shows links between logarithms of Area Lit, GDP, Electric Power Consumption, Population, CO₂ Emissions, N₂O Emissions, CH₄ Emissions, F-gas Emissions, and non-log Poverty Headcount Ratio, respectively. Numbers on the top-right side of the matrix denote Pearson’s <b><i>r</i></b> values (font size <b><i>∝</i></b> value), and stars represent significance level (***, <b><i>p</i> < 0.05</b>).</p

Comparison of predicted area lit values as a function of energy consumption, for different countries for the year 2012.

<p>We selected 7 countries from different regions and use the mean logarithm of energy consumption for each country for 2012 as the input to the six models described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174610#pone.0174610.t001" target="_blank">Table 1</a>. Horizontal bars represent the observed area lit values, while error bars depict a 95% confidence interval.</p