The global agricultural emissions were marked by significant disparities among top emitters’ (Fig 2). Countries such as China, Brazil, EU 27 region, Indonesia, India and the USA are responsible for more than 50 per cent of global agriculture emission. China’s and Brazil’s contribution of 12.24 and 12.04 per cent to global emission are mainly due to its excessive use of fertilizers and livestock farming. Livestock farming contributed significant proportion of CH
4 and N
2O emissions in both nations, in Brazil it accounted for 63 percent of total agricultural emissions. Paddy cultivation also accounted for 22 to 38 percent of CH
4 emission in China. Extensive use of fertilizers also contributed to N
2O emissions
(Brentrup et al., 2018; FAO, 2022;
Tian et al., 2024; Gao et al., 2025).
Indonesia contributes seven per cent of total global GHG emission from agriculture mainly due to rise in Paddy and Oil Palm cultivation; Paddy and Oil palm contributed nine per cent to Indonesia’s agricultural GHG emission in 2020
(Purba et al., 2023). India stands fifth with 6.97 per cent contribution to global GHG emission from agriculture. Enteric fermentation, Paddy cultivation, excessive use of fertilizers and manures, crop residue burning especially in Northern part of India are the major sources of GHG emission (
Kumar and Aravindakshan, 2022).
Amongst the top emitters, China had the highest agriculture emissions, agriculture GDP, Gross production value in agriculture, agricultural land and labor employed. China recorded the highest CV in agricultural GDP (37.2%) and substantially lower CV for agricultural land (0.7%) due to strict regulation regarding land use policy (Table 2). For agriculture emissions, Indonesia (32.36%) exhibited the highest CV though ranked fourth in agriculture emission. While China and Brazil respectively ranked the first and the second in emission, had substantially lower CV of 19.11 and 18.65 per cent for agricultural emissions. On account of matured economies with stable production structure, USA, Canada and Japan exhibited lowest CV of agricultural emissions. The EU 27 region had the second highest agricultural GDP with lowest CV (5.6%). India, EU 27 region and USA had the large agricultural sector after China. For agricultural GDP, EU 27 region had the lowest CV (4.3%), Brazil had the second highest (30.5%) followed by Indonesia (30.42%).
The gross production value in agriculture sector was second highest in EU 27 followed by USA and India. The Brazil had the highest CV of 31.92 percent followed by India with CV of 30.23 percent in gross production value reflecting higher volatility in production and prices. The lowest CV in gross production value is in EU 27 region reflecting mature production structure and stable prices. The Australia had the third highest agricultural land with highest CV (10.7%) while India had the lowest (0.5%).
After China, India had the highest number of labors employed in agriculture globally with lowest CV (5.7%) on account of growing population with higher dependency of rural population on agriculture. Though EU 27 region had substantially lower number of labors employed in agricultural, had the highest CV (27.9%) on account of inter-industry migration and declining interest of youth towards agriculture.
The changes in emission were driven by agricultural emission intensity, structure effect, productivity effect, mechanization effect and labor effect (Table 3). China recorded the largest increase in its emissions in past three decades while Japan had recorded the lowest increase in emission. Japan had the highest CV of change in emissions while China had substantially lower CV (152%). The lowest CV for change in emission was exhibited by India (109%). Indonesia recorded the largest decrease in emissions among all top agricultural emission emitters followed by Brazil and Russia. For the World, agricultural emissions increased over the last three decades. China and India contributed 80 per cent of this increase in emission from agriculture.
In the World, emission intensity effect was the largest component of change in emissions. It was negative for the World and for top emitters with exception of Japan which recorded positive value for this effect. The negative value of this effect indicate that agricultural GDP had grown at faster pace than the agricultural emissions. Thus, Japanese agriculture is becoming unsustainable with fast paced emissions coupled with slow value additions (
World Bank, 2025). Brazil, Indonesia and China together contributed 50 percent of emission intensity effect in the World. Argentina had the highest CV of 985 per cent indicating significant fluctuation in agriculture emission per unit of agriculture output (Table 3).
The agriculture structure effect was the third largest component of change in emissions in the World. China, Indonesia and USA accounted for one third of this effect in the World. The structure effect and agricultural economic growth were the drivers of agriculture emission for China with 5.14 MT and 0.35 MT from 2000 to 2018, respectively (
Sui and Lv, 2021) Argentina, EU 27, Japan, Russia and Brazil had the negative values for this effect. These countries had slow pace of value addition in agriculture sector compared to increase in gross production value in agriculture. China had the lowest CV of 174 per cent while Canada had the highest CV (7497%) with lowest positive structure effect. Intensive production practices can undermine the sustainability while intensive value addition would lead to greater sustainability by making better use of resources thus enhancing the sustainability of agri-food system
(Sial et al., 2021) (Table 3).
The agricultural productivity effect was the second largest component of change in emissions in the World. Brazil, China and India accounts for 50 per cent of this effect in the World. Japan, Russia, Canada, Australia and Argentina had the lowest value for this effect. These countries had slower pace of increase in gross production value in agriculture as compared to increase in agricultural land. Japan had the highest CV of 2874 per cent while China had the lowest CV (51%) (Table 3).
The mechanization and labor effect are the smallest component of change in emission in the World. China is the only country to surpass the global mean for mechanization effect. Large tractors and advanced equipment uplifted the technological advancement in China’s agriculture sector which led to promotion of modernized agriculture in China from 2002 to 2012
(Meng et al., 2024). While India was the only country with a negative value for this effect; for India, gross value production in agriculture had slower pace than the increase in agricultural labors. Australia had the highest CV (2020%) with lowest positive value for this effect while China had the lowest CV (87%). India and Indonesia were the only countries with positive value of labor effect which indicated increase in labors employed in agriculture. Indonesia had the highest CV (719%) while EU 27 region had the lowest CV (76%) (Table 3).
For India, compared to the base period (1992-94), the current period (2019-21) showed a decline of 2.2 per cent point in contribution of agriculture emission intensity effect (Table 4). This indicated a faster growth in agriculture GDP as compared to agriculture emission. The National Mission on Sustainable Agriculture, a part of National Action Plan on Climate Change, had directly focused on making India sustainable. The development of climate resilient technologies, diversifying cropping pattern and many other practices has led to slight reduction in the agriculture emission intensity effect (
Padhee and Whitbread, 2022). Similarly, all other effects except mechanization and labor effect showed a decline in their respective contribution. As opposed to India, China showed an increase in contribution of agriculture emission intensity effect by 5.38 percent point. The total CO2 emission from agricultural land of China increased with an average growth rate of 1.82 percent annually due to agricultural diesel, plastic films and fertilizers from 1995 to 2020
(Liu et al., 2023). While, the mechanization effect had no significant change, the productivity effect showed a decline of 20.56 percent point in its contribution.
While comparing Indonesia and Japan, agriculture emission intensity and labor effect had contrasting absolute contributions. For Indonesia, agriculture emission intensity effect showed an increase in its contribution by 40 per cent point. In 2022, Indonesia had the highest emission intensity mainly due to land-use changes
(Tubiello et al., 2024). While Japan showed a significant decline of 22.77 per cent point. Due to loss of area and interest of youth in agricultural sector, the capability of the sector had declined in 2020-21. This led to decrease in agricultural emission (
Kazuya, 2023). For labor effect, Indonesia exhibited a notable decline in its contribution marking a 12.2 per cent point decline relative to base period while Japan showed significant increase of 21.02 per cent point. For the agriculture structure and productivity effects, both the countries exhibited a notable decline in their contributions. For Indonesia and Japan, mechanization effect showed a significant increase in the contribution respectively (Table 4).
For Russia and EU 27 region, agriculture emission intensity effect, agriculture structure effect and the mechanization effect exhibited a significant decline in their respective contribution. The contribution of productivity effect for both the countries had remarkably increased from base to current period whereas the labor effect, for Russia, declined by 7.63 per cent point and increased by 17.31 per cent point for EU 27 region (Table 4).
The agriculture emission intensity effect showed significant increase in its contribution for Argentina by 12.3 percent point relative to base period while significantly declined for Brazil by 33 per cent point. The agriculture structure effect declined in its contribution in absolute change in agriculture emission for both Argentina and Brazil. While, the productivity effect remained same for Brazil, its contribution significantly increased for Argentina. The contribution of mechanization effect notably declined by 22.59 per cent point relative to base period for Argentina whereas increased for Brazil by 22.29 per cent point while the labor effect increased significantly for both the countries (Table 4).
While the contribution of agriculture emission intensity effect significantly declined by 24 per cent point for the USA, it increased by 13.13 per cent point for Canada. The contribution of agriculture structure effect and the mechanization effect significantly increased for both countries while the productivity effect notably declined. The contribution of labor effect declined significantly for USA and increased for Canada (Table 4).
On comparing Australia with the World, the contribution of agriculture emission intensity effect declined notably by 83.13 per cent point for Australia and globally increased by 23.34 per cent point from base to current period. The contribution of agriculture structure effect and the labor effect notably increased for Australia and the World. While, the contribution of the productivity effect remained same for Australia, it significantly declined by 12.3 per cent point for the World. The contribution of the mechanization effect declined for both Australia and the World (Table 4).
The triennium ending sustainability index (SI) for India showed that the sustainability index has increased till 2013-15 and decreased thereafter (Table 5).
Suresh et al. (2022) found that the agricultural sustainability showed an overall improvement for all states of India from 1971 to 2011. While the exact opposite holds true for Canada and Australia. The sustainability index of rest of the countries and the World increased over the years indicated unsustainable development. Globally, the lowest index value was observed for 2019-21 due to high-emission agricultural practices worldwide particularly excessive use of fertilizers to support the rising population, deforestation and net forest conversions. China, Indonesia, Japan, Argentina and USA experienced steep reduction in sustainability. While Russia, EU 27 and Brazil experienced moderate decline in sustainability.
Equation 11 of Simultaneous Equation Model (SEM) showed that changes in emission intensity effect and agriculture structure effect were causing nearly equal amount of effect on agricultural emissions (Table 6). India was the benchmark country in the regression model. While EU 27 had lower mean emission change compared to India by 4958.51 Co2 equivalent kilo tonnes, Indonesia had mean emission change higher by 9348.85 Co2 equivalent kilo tonnes than India. Rest of the countries didn’t have any significant difference in mean changes in agricultural emissions with respect to India. The regressors in the model accounted for 99.7 per cent of the variation in change in emission.
Equation 12 showed that for every one unit increase in the structure effect and productivity effect, the intensity effect decreases by 0.778 and 0.845 units respectively, keeping other variables constant. The intensity effect for all the other countries were not significantly different from that of India, ceteris peribus. Equation 13 showed that, on an average, India experienced 10526.4 units increase in the structure effect, ceteris peribus. For every one unit increase in the productivity effect, the structure effect decreases by 0.239 unit, ceteris peribus. However, for every one unit increase in mechanization effect, the structure effect increases by 0.144 units, ceteris peribus. The structure effect of China is 14617.9 units higher than that of India whereas its lower by 14750.9 units for EU 27 region (Table 6).
Equation 14 showed that, on an average, India experienced 34378.6 units increase in the productivity effect, independent of any influence from mechanization effect. For every one unit increase in the mechanization effect, the productivity effect decreases by 0.091-unit, ceteris peribus. As for each country-specific dummy, the productivity effect of China and Brazil are 27997.7 units and 33397.4 units higher than that of India, respectively, ceteris peribus. However, the productivity effect of the USA, EU 27 region, Indonesia, Japan, Canada, Argentina and Australia are 20947.9, 23409.8, 16399.4, 33956.7, 28423.5, 28227.5 and 28535.5 units lower than that of India, respectively, ceteris peribus (Table 6).
Among all drivers of greenhouse gas emission from agriculture, Mechanization effect was common to all but for intensity effect. For intensity effect, the mechanization effect had indirect influence through productivity and structure effect.
× (ΔEIt × ΔStrt × Prodt-1 × Mecht-1 × AgLabt-1 + ΔEIt × Strt-1 ×
× (ΔEIt × ΔStrt × ΔProdt × Mecht-1 × AgLabt-1+ ΔEIt × ΔStrt ×
(ΔEIt × ΔStrt × ΔProdt × ΔMecht × AgLabt-1+ ΔEIt × Strt -1×
(ΔEIt × ΔStrt × ΔProdt × ΔMecht × ΔAg_Labt)
