Greenhouse gas emissions intensity of global croplands

Infectious Diseases, Livestock, and Climate: A Vicious Cycle?

Life-cycle analysis on energy consumption and GHG emission intensities of alternative vehicle fuels in China

Greenhouse gas emission intensities of grass silage based dairy and beef production: A systems analysis of Norwegian farms

Mitigation of greenhouse gas emissions from beef production in western Canada – Evaluation using farm-based life cycle assessment

Recent regulatory and voluntary initiatives to estimate supply chain greenhouse gas (GHG) emission intensity of liquefied natural gas (LNG) have emphasized the use of direct measurements as activity-based national inventories tend to systematically underestimate GHG emission intensities. In this work, we demonstrate how measurement data can be integrated into a life cycle assessment (LCA) framework for assessing the GHG emissions of LNG supply chains by synthesizing results from a three year measurement campaign over 54 sites across production, midstream, and liquefaction stages. Measurement-informed GHG emission intensity ranges from 13.8 to 17.2 g of CO2 equiv/MJ of LNG produced, about 19-39% higher than those derived from an activity-based inventory assessment. We also observe large variation in the contribution of each stage to the total supply chain emission intensity. Critically, we find that stages downstream of gas production account for up to 73% of the production to liquefaction GHG emission intensity of LNG. Thus, relying on aggregate production-only emission intensities as the basis to assess the emission impact of the LNG is likely to underestimate emissions, leading to potentially ineffective public or corporate policies. Finally, we find that supply-chain-specific GHG intensities can significantly differ from basin-level, representative GHG intensities; thus, assessments of the GHG intensity of LNG can benefit from being tied to individual transactions and specific gas pathways where data allow.

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Effects of nitrogen fertilizer substitution by cow manure on yield, net GHG emissions, carbon and nitrogen footprints in sweet maize farmland in the Pearl River Delta in China

Balancing crop production and greenhouse gas (GHG) emissions from agriculture soil requires a better understanding and quantification of crop GHG emissions intensity, a measure of GHG emissions per unit crop production. Here we conduct a state-of-the-art estimate of the spatial-temporal variability of GHG emissions intensities for wheat, maize, and rice in China from 1949 to 2012 using an improved agricultural ecosystem model (Dynamic Land Ecosystem Model-Agriculture Version 2.0) and meta-analysis covering 172 field-GHG emissions experiments. The results show that the GHG emissions intensities of these croplands from 1949 to 2012, on average, were 0.10-1.31kgCO2 -eq/kg, with a significant increase rate of 1.84-3.58×10-3 kgCO2 -eqkg-1 year-1 . Nitrogen fertilizer was the dominant factor contributing to the increase in GHG emissions intensity in northern China and increased its impact in southern China in the 2000s. Increasing GHG emissions intensity implies that excessive fertilizer failed to markedly stimulate crop yield increase in China but still exacerbated soil GHG emissions. This study found that overfertilization of more than 60% was mainly located in the winter wheat-summer maize rotation systems in the North China Plain, the winter wheat-rice rotation systems in the middle and lower reaches of the Yangtze River and southwest China, and most of the double rice systems in the South. Our simulations suggest that roughly a one-third reduction in the current N fertilizer application level over these "overfertilization" regions would not significantly influence crop yield but decrease soil GHG emissions by 29.60%-32.50% and GHG emissions intensity by 0.13-0.25kgCO2 -eq/kg. This reduction is about 29% and 5% of total agricultural soil GHG emissions in China and the world, respectively. This study suggests that improving nitrogen use efficiency would be an effective strategy to mitigate GHG emissions and sustain China's food security.

A whole farm systems analysis of greenhouse gas emissions of 60 Tasmanian dairy farms

Livestock are by far the greatest contributor to Australian agricultural greenhouse gas (GHG) emissions and are projected to account for 72% of total agricultural emissions by 2020. This necessitates the development of GHG mitigation strategies from the livestock sector. Currently there are many research streams investigating the efficacy of GHG mitigation technologies, though most are at the individual animal level. Here we examine the effect of a promising animal-scale intervention - increasing ewe fecundity - on GHG emissions at the whole farm scale. This approach accounts for seasonal climatic influences on farm productivity and the dynamic interactions between variables. The study used a biophysical model and was based on real data from a property in south-eastern Australia that currently runs a self-replacing prime lamb enterprise. The breeding flock was a composite cross-bred genotype segregating for the FecB gene (after the 'fecundity Booroola' trait observed in Australian Merinos), with typical lambing rates of 150-200% lambs per ewe. Lambs were born in mid-winter (July) and were weaned and sold at 18 weeks of age at the beginning of summer (December). Livestock continuously grazed pastures of phalaris, cocksfoot and subterranean clover and were supplied with barley grain as supplementary feed in seasons when pasture biomass availability was low. Biophysical variables including pasture phenology and flock dynamics were simulated on a daily time-step using the model GrassGro with historical weather data from 1970 to 2012. Whole farm GHG emissions were computed with GrassGro outputs and methodology from the Australian National Greenhouse Accounts Inventory (DCCEE, 2012). Increasing ewe fecundity from 1.0 lamb per ewe at birth (equivalent to scanning rates at pregnancy of 80% of ewes with single lambs, 17% with twins and 3% empty) to 1.5 (scanning rates of 20% ewes with singles, 51% with twins, 26% with triplets and 3% empty as observed at the property) reduced mean emissions intensity from 9.3 to 7.3 t CO2-equivalents/t animal product and GHG emissions per animal sold by 32%. Increasing fecundity reduced average lamb sale liveweight from 42 to 40 kg, but this was offset by an increase in annual sheep sales from 8 to 12 head/ha and an increase in average annual meat production from 410 to 540 kg liveweight/ha. A key benefit associated with increasing sheep fecundity is the ability to increase enterprise productivity whilst remaining environmentally sustainable. For the same long-term average annual stocking rate as an enterprise running genotypes with lower fecundity, it was shown that genotypes with high fecundity such as those on the property could either increase meat and wool productivity from 449 to 571 kg/ha (clean fleece weight plus liveweight at sale) with little change in net GHG emissions, or reduce net GHG emissions from 4.1 to 3.2 t CO2-equivalents/ha for similar average annual farm productivity. In either case, GHG emissions intensity was reduced by about 2.1 t CO2-equivalents/t animal product. From a methodological perspective, this study revealed that differences in computing the relative effect of increased fecundity on total farm production, GHG emissions or emissions intensity either within or across years were relatively small. For example, the mean difference in emissions intensity of an enterprise obtaining 1.5 lambs per ewe relative to an enterprise obtaining 1.0 lamb per ewe computed within years was -25%, whereas the relative difference in mean emissions intensity across years was -27%. Such findings justify the traditional approach of previous GHG mitigation studies which compare differences (e.g. abatement potential) between values averaged across multiple-year simulation runs, as opposed to the method of computing the differences between intervention strategies within years then comparing the average difference.

Ratoon rice can improve rice yield by increasing the multiple cropping index in China. However, the greenhouse gas (CH4 and N2O) emission characteristics from ratoon rice fields and the cultivation methods to reduce CH4 and N2O emissions are rarely reported. This study first conducted the analysis of genotype differences in greenhouse gas emission fluxes using five strong ratoon ability rice varieties in 2020. Second, water management methods, including alternating the wet–dry irrigation (AWD) pattern and conventional flooding irrigation (CF) during the main season, were carried out in 2021. CH4 and N2O emission flux, agronomic traits, and rice yield during both main and ratoon seasons were investigated. The results showed that the CH4 emission flux during the main and ratoon seasons was 157.05–470.73 kg·ha–1 and 31.03–84.38 kg·ha–1, respectively, and the total N2O emission flux was 0.13–0.94 kg·ha–1 in the ratoon rice system over the two seasons (RRSTS). Compared with the main season, the CH4 emission flux during the ratoon season was significantly reduced, thus decreasing the greenhouse gas global warming potential (GWP) and greenhouse gas emission intensity (GHGI) in the ratoon rice system. Cliangyouhuazhan (CLYHZ) showed a high yield, and the lowest GWP and GHGI values among the five rice varieties in RRSTS. Compared with CF, the AWD pattern reduced the CH4 emission flux during the main and ratoon seasons by 67.4–95.3 kg·ha–1 and 1.7–5.1 kg·ha–1, respectively, but increased the N2O emission flux by 0.1–0.6 kg·ha–1 during the RRSTS. Further, compared with CF, the AWD pattern had a declined GWP by 14.3–19.4% and GHGI by 30.3–34.3% during the RRSTS, which was attributed to the significant reduction in GWP and GHGI during the main season. The AWD pattern significantly increased rice yield by 21.9–22.9% during the RRSTS, especially for YX203. Correlation analysis showed that CH4, GWP, and GHGI exhibited significant negative correlations with spikelet number per m2 and the harvest index during the main and ratoon seasons. Collectively, selecting the high-yield, low-emission variety CLYHZ could significantly reduce greenhouse gas emissions from ratoon rice while maintaining a high yield. The AWD pattern could reduce total CH4 emission during the main season, reducing the GWP and GHGI while increasing the ratoon rice system yield. It could be concluded that a variety of CLYHZ and AWD patterns are worthy of promotion and application to decrease greenhouse gas emissions in the ratoon rice area in the upper reaches of Yangtze River, China.

Expectations and drivers of future greenhouse gas emissions from Canada's oil sands: An expert elicitation

Irrigation reduces the negative effect of global warming on winter wheat yield and greenhouse gas intensity

CONTEXTSheep production industries face the challenge of increasing farm production and profit while reducing environmental impacts. OBJECTIVESGenetic selection using multi-trait breeding indices can be used to improve flock productivity, profitability, and greenhouse gas (GHG) emissions intensities (kg CO2-eq /kg of product), however validation of the improved performance of animals ranked higher on breeding indices at a flock level is required. METHODSPhenotypic data from 387,580 production records of animals born between 2018 and 2020 of known genetic merit in commercial flocks were inputted to an established bio-economic model. Two contrasting flocks were compared, a flock of ewes ranked High (top 20%) on the Irish replacement Index bred with rams ranked High on the replacement and terminal indices, and a flock of ewes ranked Low (bottom 20%) on the Irish replacement Index bred with rams ranked Low on the replacement and terminal indices. The two flocks were then simulated using life cycle assessment to estimate the GHG emissions profile for both systems. RESULTS AND CONCLUSIONFlock weaning rates were 1.70 and 1.53 lambs weaned per ewe presented for breeding for the High and Low genetic merit flocks, respectively. The flock of High genetic merit ewes sold 0.17 more lambs per ewe, equating to 3.29 kg more lamb carcass per ewe, than the flock of Low genetic merit ewes; lambs from the High genetic merit flock were also sold at an earlier age. The greater production of the High genetic merit flocks resulted in an additional €18/ewe net profit than the Low genetic merit flock. Although total flock GHG emissions were higher for the High genetic merit flock, GHG emissions intensities were lower at 21.7 and 23.3 kg CO2-eq /kg lamb carcass sold for the High and Low genetic merit flocks, respectively. The lower emissions intensity of the High genetic merit flock was due to the dilution effect of higher lamb production and lambs being drafted for slaughter ealier. SIGNIFICANCEThe results suggest Irish sheep producers can make substantial profit gains through selection according to the national breeding indices while also reducing their environmental impact, and farmers should consider genetic merit when purchasing their rams, particularly sires of replacement ewe lambs.

Coupled effects of straw and nitrogen management on N2O and CH4 emissions of rainfed agriculture in Northwest China