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Carbon Neutrality in Agriculture: Greenhouse Gas Mitigation, Carbon Sequestration, and Pollution Control

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (27 March 2023) | Viewed by 15566

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Prof. Dr. Xiaobo Qin

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Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: climate change; GHG mitigation; carbon sequestration
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jianling Fan

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School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: soil carbon cycle; nitrogen cycle; greenhouse gas emission; agricultural management; carbon footprint

Special Issue Information

Dear Colleagues,

Climate change is affecting both agriculture and the environment. To achieve the goals of the Paris Agreement by limiting global warming to no more than 1.5 °C by 2050 and safeguard a livable climate, more than 130 countries and regions have now set or are considering a target of reducing greenhouse gas emissions to net zero by mid-century. President Xi Jinping pledged that China would achieve carbon neutrality by 2060 in an address to the United Nations General Assembly in September 2020, which sets a new target for China to deal with climate change and green and low-carbon development. Agriculture generates around a quarter of the global greenhouse gas emissions, including more than 40% of methane and 20% of nitrous oxide. On the other hand, the agricultural ecosystem, one of the planet’s largest reservoirs of carbon, has huge potential to expand its role of capturing carbon dioxide from the atmosphere. Agriculture can become carbon-neutral or even better, sequestering more carbon in the soil than it emits to the atmosphere. Therefore, it is essential to explore the roadmap for achieving carbon neutrality in agriculture, as well as boosting productivity, reducing nutrient losses, improving biodiversity and soil health, controlling agricultural pollution, increasing agricultural resilience, and adapting to climate change. This Special Issue welcomes theoretical discussions, innovative methods, applied case studies, review articles, and policy papers that connect carbon neutrality and agricultural management or pollution control.

Prof. Dr. Xiaobo Qin
Prof. Dr. Jianling Fan
Guest Editors

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Published Papers (6 papers)

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Research

28 pages, 1817 KiB

Open AccessArticle

Study on the Spatiotemporal Evolution and Influencing Factors of Agricultural Carbon Emissions in the Counties of Zhejiang Province

by Changcun Wen, Jiaru Zheng, Bao Hu and Qingning Lin

Int. J. Environ. Res. Public Health 2023, 20(1), 189; https://doi.org/10.3390/ijerph20010189 - 23 Dec 2022

Cited by 9 | Viewed by 2068

Abstract

The accurate measurement of agricultural carbon emissions and the analysis of the key influential factors and spatial effects are the premise of the rational formulation of agricultural emission reduction policies and the promotion of the regional coordinated governance of reductions in agricultural carbon emissions. In this paper, a spatial autocorrelation model and spatial Dubin model are used to explore the spatiotemporal characteristics, influential factors and spatial effects of agricultural carbon emissions (ACEs). The results show that (1) From 2014 to 2019, the overall carbon emissions of Zhejiang Province showed a downward trend, while the agricultural carbon emission density showed an upward trend. ACEs are mainly caused by rice planting and land management, accounting for 59.08% and 26.17% of the total agricultural carbon emissions, respectively. (2) The ACEs in Zhejiang Province have an obvious spatial autocorrelation. The spatial clustering characteristics of the ACEs are enhanced, and the “H-H” cluster is mainly concentrated in the northeast of Zhejiang, while the “L-L” cluster is concentrated in the southwest. (3) The results of the Dubin model analysis across the whole sample area show that the ACEs exhibit a significant spatial spillover effect. The disposable income per capita in the rural areas of the county significantly promotes the increase in the ACEs in the neighboring counties, and the adjustment of the industrial structure of the county has a positive effect on the agricultural carbon emission reductions in neighboring counties. (4) The grouping results show that there is heterogeneity between 26 counties in the mountainous areas and non-mountainous areas. In the 26 mountainous counties, the urbanization rate, rural population, mechanization level and industrial structure have significant negative spatial spillover effects on the carbon emissions. In the non-mountainous counties, the agricultural economic development level and disposable income per capita of the rural residents have significant spatial spillover effects on the agricultural carbon emissions. These research results can provide a theoretical basis for the promotion of the development of low-carbon agriculture in Zhejiang according to the region and category. Full article

(This article belongs to the Special Issue Carbon Neutrality in Agriculture: Greenhouse Gas Mitigation, Carbon Sequestration, and Pollution Control)

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14 pages, 2781 KiB

Open AccessArticle

Spatiotemporal Dynamics of Carbon Footprint of Main Crop Production in China

by Jianling Fan, Dengwei Guo, Lu Han, Cuiying Liu, Chuanhong Zhang, Jianan Xie, Junzhao Niu and Liwen Yin

Int. J. Environ. Res. Public Health 2022, 19(21), 13896; https://doi.org/10.3390/ijerph192113896 - 26 Oct 2022

Cited by 7 | Viewed by 2114

Abstract

As a major agricultural country, the comprehensive accounting of the dynamics and composition of the carbon footprint of major crops in China will provide a decision-making basis for environmental management and agricultural green development in the whole process of the major crop production system in China. To investigate the spatiotemporal dynamics of the carbon footprint for major crops in China, a life cycle-based carbon footprint approach was used to evaluate the carbon footprint per unit area (CF_A) and per unit yield (CF_Y) of eight crops for the period of 1990 to 2019. Our results showed that the CF_A for all major crops showed an increasing trend with time before 2016 but slowly decreased afterward, while the CF_Y decreased by 16–43% over the past 30 years due to the increase in crop yield. The three main grain crops, rice (4871 ± 418 kg CO₂-eq · ha⁻¹), wheat (2766 ± 552 kg CO₂-eq · ha⁻¹), and maize (2439 ± 530 kg CO₂-eq · ha⁻¹), showed the highest carbon footprint and contribution to the total greenhouse gas (GHG) emissions, mainly due to their larger cultivated areas and higher fertilizer application rates. CH₄ emission was the major component of the carbon footprint for rice production, accounting for 66% and 48% of the CF_A and CF_Y, respectively, while fertilizer production and usage were the largest components of carbon footprint for dryland crops, making up to 26–49% of the CF_A and 26–50% of the CF_Y for different crops. The present study also highlighted the spatial and temporal patterns of the carbon footprint for major crops in China, which could serve as references for the development of best management practices for different crop production in China, to mitigate agricultural GHG emission and to pursue low-carbon agriculture. Full article

(This article belongs to the Special Issue Carbon Neutrality in Agriculture: Greenhouse Gas Mitigation, Carbon Sequestration, and Pollution Control)

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18 pages, 856 KiB

Open AccessArticle

A Study on the Impact of Low-Carbon Technology Application in Agriculture on the Returns of Large-Scale Farmers

by Bingbing Huang, Hui Kong, Jinhong Yu and Xiaoyou Zhang

Int. J. Environ. Res. Public Health 2022, 19(16), 10177; https://doi.org/10.3390/ijerph191610177 - 17 Aug 2022

Cited by 4 | Viewed by 2345

Abstract

The relationship and mechanism between agricultural low-carbon technology application and farm household returns are not yet clear, especially the lack of evidence from developing countries. This paper takes large-scale farming households in Jiangxi Province, China, from 2019 to 2020 as the research object, and obtains relevant data from field research to explore the intrinsic impact of agricultural low-carbon technology application on the returns of large-scale farming households. Based on the relevant theoretical analysis, the division dimensions of agricultural low-carbon technologies were proposed, and agricultural low-carbon technologies were subdivided into ten specific low-carbon technologies according to six dimensions: tillage system, breeding, fertilization, irrigation, medicine application, and waste treatment. Relevant questions were designed and researched to obtain data on the application status of low-carbon technologies in agriculture and the income cost status of large-scale farmers. Based on the theoretical analysis, the research hypotheses were proposed, and an empirical analysis was conducted based on the obtained data from large-scale farmers. The application of seven low-carbon technologies in agriculture: conservation tillage system, direct sowing technology, selection of compound fertilizer/organic fertilizer/controlled-release fertilizer, soil formula fertilization technology, deep fertilization/irrigation fertilization, sprinkler/drip irrigation/wet irrigation/intermittent irrigation, and straw resourceization significantly improved the income level of large-scale farmers. Furthermore, the application of biodegradable agricultural membranes, biopesticides, and new pesticide-controlled release technologies did not have significant effects on the income level of large-scale farmers, due to their low application and penetration rate. Based on the findings of the paper, the government should strengthen the promotion and subsidies of agricultural low-carbon technologies, especially those technologies that have no significant impact on large-scale farmers’ income, such as biodegradable agricultural membranes, biopesticides, and new pesticide controlled-release technologies, so as to achieve a win-win situation of reducing carbon emissions and increasing farmers’ income. Full article

(This article belongs to the Special Issue Carbon Neutrality in Agriculture: Greenhouse Gas Mitigation, Carbon Sequestration, and Pollution Control)

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16 pages, 3320 KiB

Open AccessArticle

Investigation of Rice Yields and Critical N Losses from Paddy Soil under Different N Fertilization Rates with Iron Application

by Weishou Shen, Yaou Long, Zijian Qiu, Nan Gao, Yoko Masuda, Hideomi Itoh, Hirotomo Ohba, Yutaka Shiratori, Adharsh Rajasekar and Keishi Senoo

Int. J. Environ. Res. Public Health 2022, 19(14), 8707; https://doi.org/10.3390/ijerph19148707 - 17 Jul 2022

Cited by 10 | Viewed by 3171

Abstract

The application of iron powder stimulated the growth of iron-reducing bacteria as a respiratory substrate and enhanced their nitrogen (N)-fixing activity in flooded paddy soils. High N fertilization (urea) in the flooded paddy soils has caused adverse environmental impacts such as ammonia (NH₃) volatilization, nitrous oxide (N₂O) emissions, and nitrate (NO₃⁻) leaching. This study aims to investigate the effects of N fertilization rates in combination with an iron amendment on rice yields and N losses from flooded paddy fields. We performed a 2-year field plot experiment with traditional rice–wheat rotation in China’s Yangtze River Delta. The investigation consisted of seven treatments, including 100%, 80%, 60%, and 0% of the conventional N (urea and commercial organic manure) fertilization rate, and 80%, 60%, and 0% of the conventional N with the iron powder (≥99% purity) amendment. The rice yields decreased with a reduction in the conventional N fertilization rate, whereas they were comparable after the iron application under the 80% and 60% conventional N rate. The critical N losses, including NH₃ volatilization, N₂O emissions, and NO₃⁻ and NH₄⁺ leaching, generally decreased with a reduction in the conventional N fertilization rate. These N losses were significantly greater after the iron amendment compared with the non-amended treatments under the 80% and 60% conventional N fertilization rate in the first rice-growing season. However, it was comparable between the iron-amended and the non-amended treatments in the second season. Furthermore, NO₃⁻ leaching was the most significant N loss throughout the two rice seasons, followed by NH₃ volatilization. The iron amendment significantly increased soil Fe²⁺ content compared with the non-amended treatments irrespective of N fertilization, suggesting the reduction of amended iron by iron-reducing bacteria and their simultaneous N fixation. A combination of the iron application with 60–80% of the conventional N fertilization rate could maintain rice yields similar to the conventional N fertilization rate while reducing the critical N losses in the flooded paddy field tested in this study. Our study leads to the establishment of novel and practical rice cultivation, which is a step towards the development of green agriculture. Full article

(This article belongs to the Special Issue Carbon Neutrality in Agriculture: Greenhouse Gas Mitigation, Carbon Sequestration, and Pollution Control)

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22 pages, 1680 KiB

Open AccessArticle

Evaluating the Heterogeneity Effect of Fertilizer Use Intensity on Agricultural Eco-Efficiency in China: Evidence from a Panel Quantile Regression Model

by Mengyang Hou, Zenglei Xi and Suyan Zhao

Int. J. Environ. Res. Public Health 2022, 19(11), 6612; https://doi.org/10.3390/ijerph19116612 - 28 May 2022

Cited by 6 | Viewed by 2479

Abstract

Chemical fertilizer is one of the most important input factors in agricultural production, but the excessive use of fertilizer inevitably leads to the loss of agricultural eco-efficiency (AEE). Therefore, it is necessary to explore the impact of fertilizer use intensity (FUI) on AEE. However, ordinary panel regression, based on the assumption of parameter homogeneity may yield biased estimation conclusions. In this regard, a panel quantile regression model (QRM) was constructed with the provincial panel data of China from 1978–2020 to test the difference and variation of this impact under heterogeneous conditions. The model was then combined with the spatial econometric model to explore the effect of the spatial lag factor. The results are as follows: (1) The QSM has unveiled a great improvement space for AEE that remains low overall, despite displaying a rising trend; the highest AEE is in the eastern region. (2) The FUI has a significant negative effect on AEE with the rise in quantiles, this negative effect tended towards weakening overall, although it rebounded slightly; it was stronger in areas with low AEE. It is necessary to consider the heterogeneous conditions in comparison with the average treatment effect of ordinary panel econometric regressions. (3) The impact of FUI shows significant variability in different economic sub-divisions and different sub-periods. (4) After considering the spatial effect of fertilizer use, the negative influence on local AEE had a faster decay rate as the quantile rose, but could produce a positive spatial spillover effect on AEE in neighboring areas. Local governments should dynamically adjust and optimize their fertilizer reduction and efficiency improvement policies according to the level and development stage of their AEE to establish a complete regional linked agroecological cooperation mechanism. Full article

(This article belongs to the Special Issue Carbon Neutrality in Agriculture: Greenhouse Gas Mitigation, Carbon Sequestration, and Pollution Control)

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12 pages, 1327 KiB

Open AccessArticle

Land-Use Change Enhanced SOC Mineralization but Did Not Significantly Affect Its Storage in the Surface Layer

by Haikuo Zhang, Xuli Zheng, Yanjiang Cai and Scott X. Chang

Int. J. Environ. Res. Public Health 2022, 19(5), 3020; https://doi.org/10.3390/ijerph19053020 - 4 Mar 2022

Cited by 9 | Viewed by 2295

Abstract

To achieve carbon (C) neutrality and mitigate climate change, it is crucial to understand how converting natural forests to agricultural plantations influences soil organic C (SOC) mineralization. In this study, we investigated the impact of converting evergreen broadleaf forests (EBF) to extensively managed Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau) plantations (MBP) in subtropical China on SOC mineralization rate; the concentrations of labile SOC fractions such as dissolved organic C (DOC), microbial biomass C (MBC), and readily oxidizable C (ROC); the activities of C-degrading enzymes (cellobiohydrolase and phenol oxidase); and the abundance of C-degrading enzyme-encoding genes (cbhI and lcc). Three paired soil samples were taken from the surface layer (0–20 cm) of adjacent EBF-MBP sites in Anji County, Zhejiang province. Results showed that converting EBF to MBP significantly increased the SOC mineralization rate as well as soil pH, MBC, cellobiohydrolase, and phenol oxidase activities, and cbhI gene abundance, but did not change other soil properties described above. In addition, structural equation modelling (SEM) showed that the conversion increased SOC mineralization rate through increasing soil pH, cbhI gene abundance, MBC, and cellobiohydrolase and phenol oxidase activities. Our novel finding that converting EBF to extensively managed MBP enhanced SOC mineralization via increasing the activities of C-degrading enzymes suggests that C-degrading enzymes were a key factor regulating SOC mineralization in the extensively managed subtropical bamboo plantations. Full article

(This article belongs to the Special Issue Carbon Neutrality in Agriculture: Greenhouse Gas Mitigation, Carbon Sequestration, and Pollution Control)

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