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

Effects of legume intercropping and nitrogen input on net greenhouse gas balances, intensity, carbon footprint and crop productivity in sweet maize cropland in South China

The environmental sustainability of food production systems, including net greenhouse gas (GHG) emissions, is of increasing importance. In Norwegian pork production, animal performance is high in terms of reproduction, growth, and health. The development and use of an IPCC methodology-based model for estimating GHG emissions from pork production could be helpful in identifying the effects of progress in genetics and management. The objective was to investigate whether an IPCC methodology-based model was able to reflect the effects of the progress in genetics and management in pork production on the GHG emissions per kg carcass weight (CW). It is hypothesized that this progress has led to low GHG emissions intensities in Norwegian pork compared to global levels and that expected improvements will give a lasting reduction in GHG emissions intensities. A model ‘HolosNorPork’ for estimating net farm gate GHG emissions intensities was developed, including allocation procedures, at the pig production unit level. The model was run with pig production data from in average 632 farms from 2014 to 2019. The estimates include emissions of enteric and manure storage methane, manure storage nitrous oxide emissions, as well as GHG emissions from production and transportation of purchased feeds, and direct and indirect GHG emissions caused by energy use in pig-barns. The model was able to estimate the effects on net GHG emissions intensities from pork production on the basis of production characteristics. The estimated net GHG emissions intensity was found to have decreased from on average 2.49 to 2.34 kg CO2 eq. kg−1 CW over the investigated period. For 2019 the net GHG emission for the one-third lower performing farms was estimated to 2.56 kg CO2 eq. kg−1 CW, whereas for the one-third medium and one-third best performing farms the estimates were 2.36 and 2.16 kg CO2 eq. kg−1 CW, respectively. The net GHG emissions intensity for pork carcasses from boars was estimated to be 2.07 kg CO2 eq. kg−1 CW. For the health regimes investigated, Conventional and Specific-Pathogen Free (SPF), the estimated GHG emissions intensities for 2019 were 2.37 and 2.24 kg CO2 eq. kg−1 CW, respectively. The effects on net GHG emissions intensities of breeding and management measures were estimated to be profound, and this progress in pig production systems contributes to an on-going strengthening of pork as a sustainable source for human food supply.

Biochar combined with N fertilization and straw return in wheat-maize agroecosystem: Key practices to enhance crop yields and minimize carbon and nitrogen footprints

Peanut and rapeseed oil, prominent edible oils in China, significantly contribute to greenhouse gas and reactive nitrogen emissions. A comprehensive examination of their environmental footprints is foundational for developing green and low-carbon products. Using a cradle-to-factory gate life cycle assessment, we quantified the carbon footprint (CF) and nitrogen footprint (NF) associated with the oil production of peanut and rapeseed from 2004 to 2023 in China. The results showed that peanut oil has a lower environmental impact than rapeseed oil, with a CF of 3,312.2 kg CO2eq t−1 oil and NF of 28.5 kg reactive nitrogen (Nr) t−1 oil, respectively, compared to 3,722.4 kg CO2eq t−1 oil and 43.3 kg Nr t−1 oil for rapeseed oil. It corresponded to less than 11.0% in CF and 34.2% in NF of peanut oil than that of rapeseed oil. The cropping phase was the primary source of disparity between the two oil products, with peanut exhibiting consistently lower yield-based CF and NF than rapeseed. Fertilizer application, primarily nitrogen (N) and compound fertilizers, accounted for 63.7% (peanut) and 91.4% (rapeseed) of CF, meanwhile N runoff and ammonia (NH3) volatilization were dominant in NF. Moreover, regions such as Jiangxi (peanut) and Yunnan, Shaanxi, and Gansu (rapeseed) exhibited high CF and NF but low productivity, suggesting the need for cropping layout optimization. Our findings highlight the environmental advantages of peanut oil, and recommend improved fertilizer management in agricultural stage and cleaner oil processing production to promote low-carbon, sustainable edible oil production in China.

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.

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.

Rice paddy ecosystems contribute greatly to the anthropogenic greenhouse gas (GHG) emissions and reactive nitrogen (Nr) losses. Understanding the impacts of agricultural management practices on GHG and N losses is critical for implementing optimal farming practices and maintaining agricultural productivity. This study examines the influence of agricultural management on GHG and N losses in Tai-Lake Paddy soils of China using the Denitrification-Decomposition (DNDC) model.   The DNDC model was validated through the field experiment conducted in Changshu, China. A regional model was constructed for the entire Tai-Lake watershed. Sensitivity analysis was conducted using single-factor analysis.  To comprehensively analyze combined carbon and nitrogen footprints and economic benefits, multiple quantitative indicators, including Net Global Warming Potential (NGWP), Total Nr losses (Nrloss), Carbon Footprint (CF), Nr Footprint (NrF), and Net Economic and Environmental Benefits (NEEB), were calculated.  The results indicate that the DNDC model can accurately simulate the yield and total greenhouse gas emissions during the wheat-rice rotation period in the target farmland. The NGWP for the Tai Lake Basin ranges from 6035 to 12104 kg CO2-eq/hm2, and Nrloss ranges from 227.37 to 380.82 kg N-eq/hm2. The sensitivity analysis results indicate a positive correlation between CF and Nrf with fertilizer amount, soil pH value, soil organic carbon content, clay proportion, and precipitation. Among these factors, the fertilizer amount has the most significant impact on CF and Nrf, followed by pH. Additionally, the NEEB index is negatively affected by higher fertilizer amount and pH. Other factors have no significant effect on NEEB. Based on these results, it is recommended to reduce the fertilizer amount in agricultural production and maintain a relatively acidic soil pH level. This approach may can help reduce GHG and nitrogen loss, while improving the NEEB.

Carbon and Nitrogen Footprints of Major Cereal Crop Production in China: A Study Based on Farm Management Surveys

Effects of Long-Term Straw-Return Modes on Soil Organic Carbon Content and Carbon Footprint in Wheat–Maize Rotation System

Combined applications of organic and synthetic nitrogen fertilizers for improving crop yield and reducing reactive nitrogen losses from China's vegetable systems: A meta-analysis.

Carbon Footprint of Rubber/Sugarcane Intercropping System in Sri Lanka: A Case Study

This study used a meta-analytic approach to systematically examine changes in greenhouse gas (GHG) emissions intensities (i.e., carbon footprints) between pulse-containing and pulse-free crop rotations in western Canada. A systematic literature review was conducted to identify published literature relevant to the goal of the analysis and meta-analysis was conducted to determine statistically significant differences in GHG emissions between pulse-free and pulse-containing crop rotations. Four pulse-free reference rotations (cereal-cereal [CC]; oilseed-cereal [OC]; oilseed-oilseed [OO]; and cereal-oilseed [CO]) were compared to rotations where the first crop in each two-year sequence was replaced with either dry pea (Pisum sativum L.) or lentil (Lens culinaris M.). Two scenarios were considered. The first scenario investigated the effects of dry peas and lentils when synthetic nitrogen (N) applied to cereal and oilseed crops grown after pulses was not reduced (i.e., no change) (NN). The second scenario (NCR) investigated the effect of dry peas and lentils when synthetic N application rates were reduced to the maximum extent possible (i.e., credit) to maintain subsequent crop yields. Pooled analyses demonstrated that, in general, cereal and oilseed crops grown after a dry pea or lentil crop had similar or reduced GHG emissions compared to those grown after a cereal or oilseed. The GHG emissions from cereal and oilseed crops grown after dry peas and lentils were higher in NN (888–987 kg CO2e/ha; 286–598 kg CO2e/t) than in NCR (311–978 kg CO2e/ha; 116–598 kg CO2e/t), suggesting that emissions were reduced to a greater extent when pulse crops offset the N fertilizer requirements of a subsequent crop compared to when they were used to provide N to maximize crop yields. In two-year rotations, the inclusion of pulses reduced GHG emissions compared to all reference rotations in both NN (savings of 475–719 kg CO2e/ha over two years [area basis]; 164–496 kg CO2e/t over two years [yield basis]) and NCR (savings of 489–1185 kg CO2e/ha over two years [area basis]; 335–610 kg CO2e/t over two years [yield basis]), mostly as a result of reduced synthetic N requirements of the whole rotation. The results of the analysis are presented by crop for each pulse-free and pulse-containing cropping sequence for each scenario to allow for flexibility in comparing GHG emissions from various rotations.

Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world.

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

Rationale: Greenhouse gas (GHG) emissions from crop agriculture are of great concern in the context of changing climatic conditions; however, in most cases, data based on lifecycle assessments are not available for grain yield variations or the carbon footprint of maize. The current study aimed to determine net carbon emissions and sequestration for maize grown in Bangladesh. Methods: The static closed-chamber technique was used to determine total GHG emissions using data on GHG emissions from maize fields and secondary sources for inputs. A secondary source for regional yield data was used in the current study. GHG emission intensity is defined as the ratio of total emissions to grain yield. The net GHG emission/carbon sequestration was determined by subtracting total GHG emissions (CO2 eq.) from net primary production (NPP). Results: Grain yields varied from 1590 to 9300 kg ha−1 in the wet season and from 680 to 11,820 kg ha−1 in the dry season. GHG emission intensities were 0.53–2.21 and 0.37–1.70 kg CO2 eq. kg−1 grain in the wet and dry seasons, respectively. In Bangladesh, the total estimated GHG emissions were 1.66–4.09 million tonnes (MT) CO2 eq. from 2015 to 2020, whereas the net total CO2 sequestration was 1.51–3.91 MT. The net CO2 sequestration rates were 984.3–5757.4 kg ha−1 in the wet season and 1188.62–5757.39 kg ha−1 in the dry season. This study observed spatial variations in carbon emissions and sequestration depending on growing seasons. In the rice–maize pattern, maize sequestered about 1.23 MT CO2 eq. per year−1, but rice emitted about 0.16 MT CO2 eq. per year−1. This study showed potential spatiotemporal variations in carbon footprints. Recommendation: Special care is needed to improve maize grain yields in the wet season. Fertiliser and water use efficiencies need to be improved to minimise GHG emissions under changing climatic conditions. Efforts to increase the area under cultivation with rice–maize or other non-rice crop-based cropping systems are needed to augment CO2 sequestration. The generation of a regional data bank on carbon footprints would be beneficial for combating the impact of climate change.