Reducing the carbon footprint per unit of economic benefit is a new method to accomplish low‐carbon agriculture. A case study: adjustment of the planting structure in Zhangbei County, China

Wang, Zhanbiao; Zhang, Jizong; Zhang, Lifeng

doi:10.1002/jsfa.9714

Cited by 27 publications

(9 citation statements)

References 25 publications

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“…The conclusion of the research on the level of GHG emissions and food production is that the lowest possible emission rate per unit of output should be pursued [24]. In the literature, this indicator is called emission intensity or carbon intensity; it is related to the concept of carbon footprint, which in its general form assumes that the carbon footprint is a certain amount of GHG emissions that are considered to be related to an activity, for example, production [25].…”

Section: Introductionmentioning

confidence: 99%

Greenhouse gas emissions intensity of food production systems and its determinants

et al. 2021

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It is estimated that about 1/4th of all greenhouse gas (GHG) emissions may be caused by the global food system. Reducing the GHG emissions from food production is a major challenge in the context of the projected growth of the world’s population, which is increasing demand for food. In this context, the goal should be to achieve the lowest possible emission intensity of the food production system, understood as the amount of GHG emissions per unit of output. The study aimed to calculate the emission intensity of food production systems and to specify its determinants based on a panel regression model for 14 countries, which accounted for more than 65% of food production in the world between 2000 and 2014. In this article, emission intensity is defined as the amount of GHG emissions per value of global output. Research on the determinants of GHG emissions related to food production is well documented in the literature; however, there is a lack of research on the determinants of the emission intensity ratio for food production. Hence, the original contribution of this paper is the analysis of the determinants of GHG emissions intensity of food production systems. The study found the decreased of emission intensity from an average of more than 0.68 kg of CO2 equivalent per USD 1 worth of food production global output in 2000 to less than 0.46 in 2014. The determinants of emission intensity decrease included the yield of cereals, the use of nitrogen fertilizers, the agriculture material intensity, the Human Development Index, and the share of fossil fuel energy consumption in total energy use. The determinants of growth of emission intensity of food production systems included GDP per capita, population density, nitrogen fertilizer production, utilized agriculture area, share of animal production, and energy use per capita.

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Section: Introductionmentioning

confidence: 99%

Greenhouse gas emissions intensity of food production systems and its determinants

et al. 2021

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“…However, when changes in nitrogen emissions were calculated to assess the ecological benefits, NF a , NF y , NF v , and NF e in the CFO system were higher than those in the CWF system, while NF decreased with reduced nitrogen fertilizer application rates. These results indicated that the ecological benefits of the different farming measures might vary under different ecological objectives (Wang et al 2019).…”

Section: The Effects Of the Cropping System And Nitrogen Application Rate On The Ecological And Economic Benefitsmentioning

confidence: 92%

“…The CF values were estimated based on the LCA method, which involved the following equations (Gan., 2012;Wang et al 2019;Yang et al 2014): where CF a , CF y , CF v , and CF e represent the CF of cotton production per unit of sown area (kg CO 2 eq ha −1 ), cotton yield (kg CO 2 eq kg −1 ), production value (kg CO 2 eq CNY −1 ), and economic benefit (kg CO 2 eq CNY −1 ) per year, respectively; AI i represents the agricultural inputs applied during cotton production, which are presented in Table 1; EF i represents the GHG emission factor of each agricultural input, which are presented in Table 2; ΔC represents the GHG emissions from the change in SOC in the field soil; and yield, production value, and economic benefit are the respective seed cotton yield (kg ha −1 ), production value (CNY ha −1 ), and economic benefit (CNY ha −1 ) of cotton production each year. The values for the yield, production value, and economic benefit in this study are presented in Table S1.…”

Section: The Cf Calculationmentioning

confidence: 99%

February orchid cover crop improves sustainability of cotton production systems in the Yellow River basin

Wang

Zhai

Xiong

et al. 2021

Agron. Sustain. Dev.

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Cotton-winter fallow is the major cropping system of cotton in the Yellow River basin of China, which not only leads to a considerable waste of land and natural resources, but also high greenhouse emissions and a loss of reactive nitrogen. Replacing winter bare fallow in cotton production with February orchid as a cover crop is a new cropping system in this area, but its sustainability is still unknown. Therefore, a field experiment was conducted with two cropping systems (cotton-winter fallow and cotton-February orchid) under four nitrogen application rates (0, 112.5, 168.75, and 225 kg N ha−1). Field observations were incorporated into a life cycle assessment to estimate the carbon footprint, nitrogen footprint, net ecosystem economic benefits, and economic benefits. The estimated carbon footprint per unit of sown area was 43.6–76.1% lower in the cotton-February orchid system than in the cotton-winter fallow system, mainly because of the increase in soil organic carbon. The cotton-February orchid system significantly increased the nitrogen footprint per unit of sown area by 6.7–11.5% under different application rates mainly because of the increase in N2O emissions. The nitrogen application rate significantly impacted the carbon and nitrogen footprints. After accounting for changes in the nitrogen and carbon footprints, the cotton-February orchid system with 168.75 kg N ha−1, which resulted in the highest net ecosystem economic benefits and economic benefits, resulted in a 25.0% reduction in nitrogen fertilizer applied and a 9.5% increase in net ecosystem economic benefits compared with the conventional cotton-winter fallow system and nitrogen fertilizer application rate (225.75 kg N ha−1). Thus, adopting an integrated strategy combining February orchid as a cover crop and a reduced nitrogen fertilizer application contributes to improvements in green and sustainable cotton production systems in the Yellow River basin and other regions with similar ecological conditions.

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“…Carbon emission intensity refers to the amount of carbon emissions per unit of agricultural output value or per unit of planting area, reflecting the costs and eco-efficiency of agricultural production systems [36]. According to [37][38][39], we chose carbon emissions per unit of planting area and carbon emissions per unit of agricultural output value to quantify the carbon emission intensity of the inputs of agricultural production in China, respectively. The formula is as follows:…”

Section: Calculating Carbon Emission Intensitymentioning

confidence: 99%

Carbon Emissions from Agricultural Inputs in China over the Past Three Decades

Song

Zhang

et al. 2023

Agriculture

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Global warming has become one of the major threats to the security of human survival, security, and sustainable development. Agricultural production has been widely suspected as one of the main sources of anthropogenic carbon emissions. Analyzing the changing characteristics and influencing factors of agricultural carbon emissions is of great significance for the mitigation of global climate change and the sustainable development in agriculture. Taking China, a large agricultural country, as an example, this study used the empirical model to quantify carbon emissions from agricultural inputs from 1991 to 2019, and analyzed the driving factors using ridge regression. We found that agricultural carbon emissions in China have been on the rise in the past 30 years, but at a markedly slower pace. From 2008 to 2019, the average annual growth rate of agricultural carbon emissions was 1.47%, down significantly from 2.92% between 1991 and 2007. The carbon emissions per unit of planting area showed an overall increasing trend, which grew from 179.35 t ce/km2 to 246.26 t ce/km2, with an average annual growth rate of 1.13%. The carbon emissions per unit of agricultural output mainly showed a decreasing trend, which decreased from 0.52 kg ce/CNY to 0.06 kg ce/CNY, with an average annual rate of change of −7.42%. China’s agricultural carbon emissions were closely related to macro-policies. Fertilizer inputs, agricultural industry structure, and energy use intensity were significantly positively correlated with carbon emission intensity. The degree of urban feedback to rural areas, public investment in agriculture, and large-scale planting were significantly negatively correlated with carbon emission intensity, but the impacts of these factors had a “lag effect”. In order to reduce carbon emissions from agriculture and promote development in green agriculture, we suggest that the government should further increase the degree of urban feedback to rural and public investment in the agricultural sector. In addition, large-scale agricultural production should be encouraged to increase resource efficiency and reduce carbon emissions.

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Reducing the carbon footprint per unit of economic benefit is a new method to accomplish low‐carbon agriculture. A case study: adjustment of the planting structure in Zhangbei County, China

Cited by 27 publications

References 25 publications

Greenhouse gas emissions intensity of food production systems and its determinants

Greenhouse gas emissions intensity of food production systems and its determinants

February orchid cover crop improves sustainability of cotton production systems in the Yellow River basin

Carbon Emissions from Agricultural Inputs in China over the Past Three Decades

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