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Frontiers of Agricultural Science and Engineering

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Front. Agr. Sci. Eng.    2024, Vol. 11 Issue (3) : 409-427    https://doi.org/10.15302/J-FASE-2024558
Greenhouse gas emissions during the COVID-19 pandemic from agriculture in China
Jianing TIAN1, Chuanhui GU1,2(), Yanchao BAI3
1. Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan 215316, China
2. Environmental Research Center, Duke Kunshan University, Kunshan 215311, China
3. College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225009, China
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Abstract

● Methane led China’s growth in net greenhouse gas emissions over the pandemic.

● N2O was linked to fertilizers and waste management.

● CO2 emissions varied by region, calling for tailored mitigation approaches.

● COVID-19 boosted methane from pig farming disruptions.

To study the impact of the COVID-19 pandemic on agricultural carbon emissions in China, the greenhouse gas emissions generated by crop and livestock production, and agricultural material and energy inputs in China from 2019 to 2021 were systematically calculated. It was found that from 2019 to 2021, Net greenhouse gas emissions (NGHGE) from agriculture in China had an increasing trend. Methane emissions ranked first in NGHGE, with an annual proportion exceeding 65% and an increasing annual trend. CH4 emissions were primarily influenced by enteric fermentation and rice production. Nitrous oxide emissions accounted for around 22% of annual NGHGE and decreased from 2019 to 2021. The main sources of N2O emissions were the use of nitrogen fertilizers and manure management. Carbon dioxide emissions accounted for about 18% annually, with diesel and agricultural electricity use contributing to over 60% of CO2 emissions. Soil carbon sequestration represented about a 6.1% lowering of NGHGE. The combined proportion of CH4 emissions from enteric fermentation and rice production accounted for over 50% of total GHG emissions. The changes in NGHGE were mainly caused by disturbance of the livestock industry during the pandemic.
Keywords Agricultural systems      emission factors      greenhouse gases      soil carbon sequestration     
Corresponding Author(s): Chuanhui GU   
Just Accepted Date: 28 April 2024   Online First Date: 17 May 2024    Issue Date: 17 July 2024
 Cite this article:   
Jianing TIAN,Chuanhui GU,Yanchao BAI. Greenhouse gas emissions during the COVID-19 pandemic from agriculture in China[J]. Front. Agr. Sci. Eng. , 2024, 11(3): 409-427.
 URL:  
https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2024558
https://academic.hep.com.cn/fase/EN/Y2024/V11/I3/409
GHG Carbon sources Emission factor reference Emission factor unit Formula Data composition of the formula
CH4 Rice production Recommended CH4 emission factors in rice field in China regions in 2005[14] kg·ha?1 ECH4,Rpro=∑EFi×ADi×10?4 ECH4,Rpro: CH4 emission of rice production per 10 kt;EF: emission factor;AD: sowing area per kha;i: rice field types
Enteric fermentation Animal enteric fermentationCH4 emission factors[14] kg per unit per year ECH4,Ent=∑EFCH4,Ent,j×APj×10?3 ECH4,Ent: CH4 emission of animal enteric fermentation per 10 kt;ECH4,Manu: CH4 emission of manure management per 10 kt;AP: number of standing stocks of livestock production at the end of the year/ten thousand;j: types of livestock, pigs, dairy cattle, non-dairy cattle, goats, sheep, horses, donkeys, mules, camels
Manure management (CH4 emission) Manure managementCH4 emission factors[14] kg per unit per year ECH4,Manu=∑EFCH4,Manu,j×APj×10?3
CO2 Pesticides 4.9341[14] kg·kg?1 C eqv. ECO2,Pest=MPest×4.9341×44/12 ECO2,Pest: CO2 emission of pesticide usage per 10 kt;MPest: pesticide usage per 10 kt
Plastic mulching 5.18[14] kg·kg?1 C eqv. ECO2,Plastm=MPlastm×5.18×44/12 ECO2,Plastm: CO2 emission of plastic mulching per 10 kt;MPlastm: plastic mulching usage per 10 kt
Agricultural Electricity 2019 annual emission reduction project China regional power grid baseline CO2 emission factors[15] t CO2 per MWh ECO2,Agrielect=MAgrielect×4.77%×EFCO2×10 ECO2,Agrielect: CO2 emission of agricultural electricity per 10 kt;MAgrielect: electricity use in rural area per TWh[16]
Diesel 0.5927[1] kg·kg?1 C eqv. ECO2,Dies=MDies×0.5927×44/12 ECO2,Dies: CO2 emission of diesel usage per 10 kt;MDies: diesel usage per 10 kt
Irrigation 25[17] kg·ha?1 C eqv. ECO2,Irrig=SIrrig×25×44/12×10?4 ECO2,Irrig: CO2 emission of irrigation per 10 kt;SIrrig: irrigation area per kha
N2O Fertilizer Parameters for estimating nitrogen input of crop straw turnover to field[14], the default values of N2O direct emission factor for different regions[14] and nitrogen, phosphorus, potassium ratio of complex fertilizer in different regions[18] kg N2O-N per kg N input EN2O,N-fert=NN-fert×EF EN2O,N-fert: N2O emission of nitrogen fertilizer usage per 10 ktNN-fert: total equivalent nitrogen in nitrogen fertilizer usage per 10 kt
EN2O,C-fert=NC-fert×EF EN2O,C-fert: N2O emission of complex fertilizer usage per 10 ktNC-fert: total nitrogen from complex fertilizer usage per 10 kt
Straw turnover EN2O,Stwto=∑NStwtok×EFNStwto = NGround field + NUnderground root= (Crop grain yield/Economic coefficient – Crop grain yield) × Straw turnover rate × Nitrogen content in straw+ Crop yield/Economic coefficient × Root shoot ratio × Nitrogen content in straw or rootNSugarcane = (Crop yield – Crop yield × Economic coefficient) × Straw turnover rate × Nitrogen content in straw+ Crop yield × Root shoot ratio × Nitrogen content in straw turnover rateNVegetable = Crop yield × 0.1 × Nitrogen content in straw × Straw turnover rate EN2O,Stwto: N2O emission of straw turnover per 10 ktNStwto: nitrogen content of straw turnover per 10 kt;k: rice, wheat, corn, sorghum, millet, soybean, rapeseed, peanut, sesame, seed cotton, sugar beet, sugarcane, hemp, potato, vegetables, tobacco
Manure return R>1,AAP=(Qnapa365)×TR<1,AAP=(Qpre+Qcur)/2Pfaeces(urine)=AAP×T×qfaeces(urine)/1000EN2O,Manurt=30%∑(Pfaecesj×rfaeces,Nj%+Purinej×rurine,Nj%)×EF R: off-take rate of livestock including poultry;AAP: average livestock production per year per ten thousand;Qnapa: annual livestock production per ten thousand;Qpre: standing stock of previous year;Qcur: standing stock of current year per ten thousand;T: feeding cycle of livestock per day;P: annual excretion per 10 kt;q: daily excretion per kg;r: nitrogen content of faeces and urine[18]EN2O,Manurt: N2O emission of manure return per 10 kt
Atmospheric deposition 0.01[14] kg N2O-N per kg N input EN2Q,Sed=[NManu×20%+(NN-fert+NC-fert+NStwto)×10%]×0.01 EN2O,Sed: N2O emission of atmospheric deposition per 10 ktNManu: total nitrogen excretion from livestock;NStwto: total nitrogen input of straw turnover;
Nitrogen leaching and runoff 0.0075[14] kg N2O-N per kg N input EN2O,Lea=(NN-fert+NC-fert+NStwto+NManurt)×20%×0.0075 EN2O,Lea: N2O emission of nitrogen leaching and runoff per 10 ktNStwto: total nitrogen input of straw turnover;NManurt: total nitrogen input of manure return
Manure management (N2O emission) Manure management N2O emission factor [14] kg per unit per year EN2O,Manu=∑EFN2O,Manu,i×APj×10?3 EN2O,Manu: N2O emission of manure management per 10 kt
Tab.1  Accounting ranges and data sources of GHG emissions from agriculture in China
Fig.1  Proportion of GHG emissions and NGHGE from agriculture in China from 2019 to 2021.
Fig.2  Proportion of carbon sources from agriculture in China in 2019 to 2021.
Fig.3  Proportion and total amount of each GHG from different sources from agriculture in China in 2019 to 2021.
Fig.4  Total CH4 emissions and proportion of CH4 emissions from manure management of livestock in China in 2019 to 2021
Region NGHGE(× 106 t C eqv.) Gross agricultural production(× 1011 yuan) Carbon footprint intensity(× 104 t C eqv. per 108 yuan)
2019 2020 2021 2019 2020 2021 2019 2020 2021
Qinghai 8.05 4.13 4.15 0.181 0.189 0.205 4.44 2.19↓ 2.03↓
Xizang 3.81 4.26 4.40 0.0949 0.104 0.115 4.01 4.10↑ 3.82↓
Shanghai 1.54 1.52 1.50 0.146 0.138 0.145 1.06 1.10↑ 1.03↓
Jilin 7.12 7.02 7.24 1.01 1.23 1.30 0.70 0.57↓ 0.56↓
Inner Mongolia 10.2 9.52 10.0 1.61 1.70 1.88 0.64 0.56↓ 0.53↓
Ningxia 1.67 1.68 2.10 0.331 0.398 0.413 0.50 0.42↓ 0.51↑
Jiangxi 7.43 8.23 7.95 1.62 1.69 1.80 0.46 0.49 0.44↓
Heilongjiang 16.8 16.4 17.2 3.77 4.04 4.10 0.44 0.41↓ 0.42↑
Gansu 5.59 6.10 6.31 1.31 1.43 1.62 0.43 0.43 0.39↓
Hunan 12.3 13.0 13.3 3.05 3.36 3.53 0.40 0.34↓ 0.38↑
Guangdong 12.0 11.8 11.8 3.53 3.77 3.95 0.34 0.31↓ 0.30↓
Anhui 7.85 8.07 8.11 2.37 2.53 2.80 0.33 0.32↓ 0.29↓
Zhejiang 5.15 5.35 4.53 1.60 1.59 1.70 0.32 0.34↑ 0.27↓
Yunnan 8.65 8.77 8.94 2.68 2.90 3.44 0.32 0.30↓ 0.26↑
Xinjiang 8.11 7.17 9.17 2.62 2.94 3.49 0.31 0.24↓ 0.26↓
Liaoning 5.42 5.90 5.60 1.91 2.06 2.22 0.28 0.29↑ 0.25↓
Guangxi 8.17 8.48 8.06 3.10 3.27 3.69 0.26 0.26 0.22↓
Sichuan 11.2 11.8 11.7 4.40 4.70 5.09 0.26 0.25↓ 0.23↓
Shanxi 2.33 3.07 2.64 0.937 1.08 1.22 0.25 0.29 0.22↓
Tianjin 0.497 0.489 0.589 0.203 0.229 0.258 0.25 0.21↓ 0.23↑
Hainan 1.95 2.14 1.58 0.82 0.875 1.05 0.24 0.25↑ 0.15↓
Hubei 7.73 7.66 8.15 3.26 3.49 3.91 0.24 0.22↓ 0.21↓
Jiangsu 9.04 9.53 9.69 3.83 4.10 4.43 0.24 0.23↓ 0.22
Fujian 3.81 3.87 3.90 1.77 1.82 1.91 0.21 0.21 0.20↓
Chongqing 2.94 3.18 3.17 1.40 1.60 1.76 0.21 0.20↓ 0.18↓
Hebei 6.37 6.59 6.91 3.11 3.41 3.65 0.20 0.19↓ 0.19
Beijing 0.197 0.224 0.230 0.102 0.108 0.123 0.19 0.21↑ 0.19↓
Guizhou 4.33 4.53 4.45 2.54 2.78 3.12 0.17 0.16↓ 0.14↓
Henan 8.58 8.55 8.91 5.41 6.24 6.56 0.16 0.14↓ 0.14
Shandong 7.15 6.80 6.61 4.91 5.17 5.81 0.15 0.13↓ 0.11↓
Shaanxi 2.86 3.37 2.91 2.45 2.81 3.04 0.12 0.12 0.10↓
Total 199 199 202 66.1 71.8 78.3 0.30 0.28↓ 0.26↓
Tab.2  NGHGE and carbon footprint intensity in agriculture in China
Fig.5  Proportion of different GHG emission sources from agriculture for the top four NGHGE provinces in China in 2020. (a) Heilongjiang, (b) Guangdong, (c) Hunan, and (d) Sichuan.
Region CH4 emission(× 107 t CO2 eqv.) CO2 emission(× 106 t CO2 eqv.) N2O emission(× 106 t CO2 eqv.) GHG emission(× 107t CO2 eqv.)
2019 2020 2021 2019 2020 2021 2019 2020 2021 2019 2020 2021
Henan 2.26 2.41 2.54 8.43 6.67 6.70 12.4 12.5 12.5 4.34 4.32 4.46
Hubei 2.30 2.23 2.47 4.28 4.22 4.17 8.11 8.78 8.32 3.54 3.53 3.71
Hunan 4.10 4.40 4.49 4.48 4.41 4.18 8.17 7.81 8.60 5.37 5.63 5.77
Guangdong 2.62 2.56 2.60 9.46 9.45 9.05 8.80 8.79 8.64 4.45 4.39 4.37
Guangxi 2.06 2.17 2.09 3.53 3.58 3.75 9.97 10.1 9.23 3.41 3.54 3.38
Hunan 0.296 0.519 0.316 1.12 1.22 1.23 3.02 1.41 1.36 0.709 0.782 0.574
Shandong 1.53 1.45 1.45 9.06 8.71 8.64 9.98 9.77 9.23 3.43 3.30 3.23
Jiangsu 1.76 1.89 1.95 11.8 12.0 12.1 7.64 7.90 7.83 3.70 3.88 3.94
Shanghai 0.0832 0.0816 0.0883 4.56 4.49 4.37 0.244 0.245 0.216 0.563 0.555 0.547
Zhejiang 0.760 0.836 0.832 9.24 9.23 6.62 2.50 2.46 2.10 1.93 2.01 1.70
Anhui 1.99 2.08 2.11 4.79 4.73 5.14 7.72 7.74 7.17 3.24 3.32 3.34
Fujian 0.563 0.603 0.624 4.76 4.65 4.64 3.96 3.90 3.80 1.43 1.46 1.47
Jiangxi 2.64 2.95 2.83 2.88 2.74 2.88 4.04 4.23 4.27 3.34 3.64 3.54
Guizhou 1.34 1.41 1.36 1.26 1.29 1.46 3.61 3.62 3.56 1.83 1.90 1.86
Yunnan 2.43 2.47 2.56 3.73 3.65 3.69 8.61 8.73 8.48 3.66 3.71 3.77
Xizang 1.19 1.24 1.29 1.63 1.63 1.63 0.440 1.66 1.67 1.40 1.57 1.62
Chongqing 0.657 0.719 0.713 1.53 1.52 1.52 2.71 2.94 2.98 1.08 1.16 1.16
Sichuan 3.03 3.22 3.19 4.25 4.25 4.28 9.97 10.0 9.93 4.45 4.64 4.61
Beijing 0.0177 0.0209 0.0245 0.418 0.425 0.414 0.111 0.140 0.161 0.0706 0.0773 0.0820
Tianjin 0.106 0.0945 0.127 0.319 0.401 0.421 0.440 0.446 0.464 0.182 0.179 0.216
Hebei 1.026 0.959 1.16 8.59 8.63 8.48 5.33 6.14 6.11 2.42 2.44 2.62
Shanxi 0.493 0.766 0.565 2.09 2.11 2.33 2.38 2.37 2.58 0.94 1.21 1.06
Inner Mongolia 2.91 2.74 2.84 4.12 4.12 4.38 7.25 6.48 6.98 4.05 3.80 3.98
Liaoning 0.904 1.08 0.967 3.86 3.77 3.81 5.02 5.06 5.05 1.79 1.97 1.85
Jilin 1.06 1.04 1.13 3.12 3.13 3.21 6.62 6.43 6.23 2.03 1.99 2.07
Heilongjiang 2.87 2.71 2.99 5.07 5.03 4.91 8.09 8.27 8.51 4.19 4.04 4.33
Xinjiang 1.87 1.94 2.18 7.60 3.49 8.22 5.29 5.27 5.49 3.16 2.82 3.56
Ningxia 0.450 0.461 0.596 0.807 0.810 0.821 1.04 0.995 1.17 0.635 0.642 0.795
Gansu 1.24 1.47 1.44 3.99 4.26 4.28 2.62 2.95 2.86 1.90 2.19 2.15
Shaanxi 0.590 0.610 0.611 3.26 3.20 3.27 3.69 3.70 3.69 1.29 1.30 1.31
Qinghai 2.78 1.33 1.32 0.315 0.308 0.323 1.31 1.55 1.55 2.94 1.51 1.51
Total 47.9 48.5 49.5 134 128 131 161 162 161 77.5 77.5 78.6
Tab.3  GHG emissions from agriculture in China in 2019 to 2021
Fig.6  The original characteristics of CH4 emission in Hunan (a), Inner Mongolia, Heilongjiang, and Sichuan (b) from 2019 to 2021.
Fig.7  Proportion of different N2O emission sources of top four N2O regions in China agriculture in 2020. (a) Henan, (b) Guangxi, (c) Sichuan, and (d) Shandong.
Fig.8  Proportion of different CO2 emission sources from agriculture in 10 regions of China with the greatest CO2 emissions in 2020.
Region CF from pigs(t per head) CF from cattle(t per head) Region CF from pigs(t per head) CF from cows(t per head)
Henan 0.1054 0.5835 Chongqing 0.0713 0.5290
Hubei 0.0964 0.4915 Sichuan 0.0653 0.4923
Hunan 0.0941 0.5247 Beijing 0.1605 0.4903
Guangdong 0.0824 0.5226 Tianjin 0.0739 0.4914
Guangxi 0.0946 0.5180 Hebei 0.0534 0.4416
Hainan 0.1116 0.8909 Shanxi 0.0632 0.7951
Shandong 0.0952 0.4951 Inner Mongolia 0.0637 0.5040
Jiangsu 0.0823 0.5482 Liaoning 0.0394 0.5747
Shanghai 0.0925 0.4644 Jilin 0.0452 0.4805
Zhejiang 0.1027 0.6925 Heilongjiang 0.0508 0.4709
Anhui 0.0722 0.5268 Xinjiang 0.0446 0.4835
Fujian 0.0771 0.5114 Ningxia 0.0543 0.5147
Jiangxi 0.0774 0.5829 Gansu 0.0557 0.5372
Guizhou 0.0774 0.4960 Shaanxi 0.0514 0.5298
Yunnan 0.0851 0.4904 Qinghai 0.0948 0.5243
Xizang 0.0631 0.4834
Tab.4  Carbon footprint from pigs and cattle in agriculture in China
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