research paper on sericulture in india

Sericulture Industry in India – A Review

India Science and Technology (Online) 2008

6 Pages Posted: 15 Sep 2009

Debnirmalya Gangopadhyay

CSIR-HRDC - National Institute of Science, Technology & Development Studies (NISTADS)

Date Written: September 14, 2009

The reduction of rural poverty continues to be a paramount goal of the developing countries like India as the majority of the poor population still resides in the countryside. The World Bank, for example, estimates that more than 70 % of the world poor lives in rural areas. So far, various strategies have been pursued to address this concern and among the major ones is rural employment creation. The agriculture sector, however, has been contending with a number of factors that have limited its further potential for generation new jobs in rural areas. Those factors may include the small land holdings, insufficient capital and investment incentives, the inadequate farm infrastructure, limited market and stagnant prices of agricultural products. It is therefore necessary to focus on a broader spectrum of the rural economy. The establishment of rural based industries like sericulture, in particular, can be very effective in creating new job opportunities and providing supplemental income. Being a rural agro-based labour intensive industry this sector can also play vibrant role to check migration from rural to urban areas. In this article, the present status of the sericulture industry in India, its trends, position in global sericulture and science and technological achievements have been reviewed. Besides, some of the critical issues like potentiality of the sector in national economy, rural development, women empowerment and employment generation have been identified. An attempt has been made to draw a strategic model to strengthen and promote sericulture industry in India to enhance productivity and quality of silk etc. This article would be helpful to recognize the potential, strength and challenges of the sericulture industry in India to formulate certain policies and measures for socio-economic development.

Keywords: potential, strengths, challenges, rural development, sericulture, S&T and trends

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Sericulture Industry in India: An Overview

The reduction of rural poverty continues to be a paramount goal of the developing countries like India as the majority of the poor population still resides in the countryside. The World Bank, for example, estimates that more than 70 % of the world’s poor live in rural areas. So far, various strategies have been pursued to address this concern and among the major ones is rural employment creation. Being a rural agro-based labour intensive industry this sector can also play vibrant role in checking migration from rural to urban areas. In this article, the present status of the sericulture industry in India, its trends, position in global sericulture and science and technological achievements have been reviewed. This article would be helpful in recognizing the potential, strength and challenges of the sericulture industry in India so as to formulate certain policies and measures for socio-economic development.

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Sericulture is a cottage based industry which combines both the features of agriculture and industry. India has tremendous potential for silk development but yet unexploited, however, development is not far away. It is one of the major employment generating sectors in the state and its growth has immense employment generation potential, particularly in rural Karnataka. This studyis an attempt to analyze the agribusiness potential of sericulture in Karnataka. The study explored that there is a huge improvement in area, production and also employment from 2008-09 to 2015-16. The cultivated area was only 177943 hectares in 2008-09, which has improved over the years and has reached 208947 hectares in 2015-16 with a compound annual growth rate of 2 per cent. Similarly, the production in Karnataka was also increased from 5949MT in 2003-04 to 9645MT in 2014-15 with CAGR of 4 per cent. 27 per cent of the families of Karnataka are contributing to the total families engaged in Indian sericulture industry. In the year 2014-15, earnings from export were about Rs.2829.94 crore but it was reduced to Rs.2495.99 crore in the year 2015-16. The import earnings during the year 2015-16 was Rs.1389.10 crore. Hence, it is revealed from the study that sericulture has a very high employment potential. It is the biggest employer in the country only next to handloom industry. It is ideally suited to generate jobs in the rural areas and particularly in the drought prone areas.Sericulture gains added importance in the context of growing unemployment, both disguised and seasonal. Most of the farmers in India are poor and are not employed throughout the year. Sericulture can provide subsidiary employment to such farmers and augment their incomes.

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Bhattarcharjya, D., Alam, K., Bhuimali, A. & Saha, S. (2020). Status, potentials, constrains and strategies for development of sericulture farming system in West Bengal state of India (review). Bulg. J. Agric. Sci., 26 (4), 709–718 West Bengal Sericulture and rural areas are facing multiple socio-economic changes, including a transition from an agriculture-based to a service-based economy. This restructuring forces agricultural and rural sector-networks to reformulate their (self-) definitions. Farming is an important and key factor for the economy of developing countries. Since last several years, farmers once again welcomed many traditional farming systems to get crop yield for completion of food requirement. Although, traditional techniques are not much sufficient in new agriculture world because farming is getting new challenges from biotic and abiotic factors. New techniques and systems are getting popularity in present farming because of their important benefits which can solv...

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Sericulture is an eco-friendly agro-based labor intensive and commercially attractive economic activity, falling under cottage and small-scale sector. Sericulture enterprise in its totality is a long chain industry from mulberry cultivation to fabric making. India stands second in silk production; next to China. Sericulture is the only cash crop, which provides frequent and attractive returns in the tropical states of the country through year. Sericulture Industry in India has classified the employment generation pattern of the industry into two major types: Direct Employment ? (a) Mulberry Cultivation; (b) Leaf Harvesting; (c) Silk Worm Rearing; Indirect Employment ? (a) Reeling; (b) Twisting; (c ) Weaving; (d) Printing & Dyeing; (e) Finishing; (f) Silk Waste Processing. The production of raw silk and silk fabrics are limited to only a few countries in the World of which China (1,03,620 MT; 81.95%) occupies the first place and India (19,690 MT; 15.44%), the second. India is the second largest producer of silk in the world next only to China. Karnataka is the leading sericulture state which contributes around 50 per cent of the total silk production in India. It is estimated that the indirect effect of sericulture to the farm income is about 25 per cent. Sericulture is practiced in about 52,360 villages all over the country and employment to about 7.56 million people, most of them being small and marginal farmers in rural areas, creating employment to at least for 12-13 people per hectare of mulberry. Raigarh district stand first in area under plantation of host plant for silkworm rearing. Raigarh district has total area of 2022.6 ha Daba tasar farming under with production of 15,93,7,216 lakh cocoons 63 6375 beneficiaries. In view of the importance of sericulture enterprise, the paper tries to enlighten and discuss the significance of sericulture and strategies to be taken for the employment generation in Indian sericulture industry. Present paper explores the possible employment opportunities derived from problem analysis in the study area. The study concludes with some suggestions to improve the feasibility of sericulture in long term.

Jasmeen Qadir

1 P. G. Department of Sericulture, Poonch Campus, University of Jammu, Jammu & Kashmir, 185101, India. 2 Temperate Sericulture Research Institute, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir-Mirgund, Jammu & Kashmir,193121, India. 3 Division of Sericulture, Sher-eKashmir University of Agricultural Sciences and Technology of Jammu, Chatha, India -180009. 4 Department of Zoology, Govt. Degree College Poonch, University of Jammu, Jammu & Kashmir, 185101, India

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Agriculture in India, since ancient times is the most crucial sector for ensuring food and nutrition security. It is the key sector in India for generating employment opportunities for the vast majority of the population particularly in rural areas, agriculture is the backbone of the Indian economy, despite major emphasis on industrial development during the last for decades. Growth in agriculture not so good and it is real reason of worry. Need more investment, specially in water, agro-R & D, farm mechanization etc. Unfortunately the gross capital formation in agriculture which was 18.3% of agriculture GDP in 2012-13 has fallen to 14.8% in 2014-15. Agricultural development is the necessity to improve productivity, generate employment and provide a source of income to the poor segments of population. The pace of adoption of modern technology in India is slow and the farming practices are too haphazard and unscientific. Some of the basic issues for development of Indian agriculture sector are revitalization of cooperative institutions, improving rural credits, research, human resource development, trade and export promotion, land reforms and education. Future Prospects and Solution for India Agriculture sector is an important contributor to the Indian economy around which socioeconomic privileges and deprivations revolve and any change in its structure is likely to have a corresponding impact on the existing pattern of social equity. Sustainable agricultural production depends upon the efficient use of soil, water, livestock, plant genetics, forest, climate, rainfall and topology. Indian agriculture faces resource constraints, infrastructural constraints, institutional constraints, technological constraints and policy induced limitations.

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Abstract: Sericulture is one of the important potential agro- based rural industry in the world. This paper analyzed that socio- economic development through sericulture sector in the world and in India. Majority of the people are engaged in various sericulture related activities in the country. This paper mainly focused on socio- economic development, employment generation, and sericulture sector activities in the state. This sector expected low investment with higher returns in short gestation, due to this rural economy mainly concentrated on this sector. This paper mainly focused on sericulture as a eco-friendly, helps to soil conservation and foreign exchange earning opportunity for the developing countries

Indian journal of pure and applied biosciences

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Eri-Culture: The Drive from Tradition to Innovation

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• Umme Hani 4 &
• Amarendra Kumar Das 4  

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 135))

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Assam, a north-eastern state of India is a land of variety of silkworms. Eri-Culture is an age-old traditional practice of rearing of Eri silkworm. Eri is a kind of silk with thermal quality and hence, the poor and the underprivileged used it to replace the woolen clothes. The tribal folks of Assam mostly practised it. Eri silk was also known as the “Poor Men’s Silk” since it was the cheapest and the sturdiest among all the available silks. Today, the tradition took a drive towards the global market with a different grace. The Eri silk fabrics that are being constructed by the handloom weavers are in huge demand not only in India but also outside the nation. The involvement of the Designers and Industrialists led to a remarkable revolution through intervention and commerce. The discussion laid here is about the power of Design and Innovation, which could easily turn a Cultural Heritage of Handlooms into a commercial industry. The argument additionally concerns the need of an understanding for those who are performing interventions in the Eri silk sector to focus on certain aspects so that the Tradition and Heritage are not being harmed. The interference of Design professionals into this sector has undeniably helped the weavers’ community towards crafting a better livelihood. But sometimes, age-old traditional designs with a touch of contemporary drifts also turned out to be extra rewarding.

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Hani, U., Das, A.K. (2019). Eri-Culture: The Drive from Tradition to Innovation. In: Chakrabarti, A. (eds) Research into Design for a Connected World. Smart Innovation, Systems and Technologies, vol 135. Springer, Singapore. https://doi.org/10.1007/978-981-13-5977-4_70

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• Agriculture

Sericulture in India: The Past, Present and Future

The cultivation of silkworms for the production of silk has a long history in India and continues to be an important industry

New Delhi: India is the second-largest producer of silk in the world, and sericulture plays a significant role in the country’s rural economy. The industry employs around 9.76 million people in rural and semi-urban areas.

The sericulture activities in india are spread across 52,360 villages. india produces four types of natural silks: mulberry, eri, tasar, and muga. the country produces silk garments, made-ups, fabrics, yarns, carpets, shawls, scarves, cushion covers, and accessories through the raw material., as per various reports, during april-december 2021, india produced 26,587 metric tonnes (mt) of silk. the total silk production in india during 2021-2022 was 34,903 mt, an increase of 3.4% yoy over the previous year (33,770 mt). the share of mulberry production is the largest among other types of silk produced in the country., the major silk-producing states in the country are andhra pradesh, assam, bihar, gujarat, jammu & kashmir, karnataka, chhattisgarh, maharashtra, tamil nadu, uttar pradesh, and west bengal. karnataka contributed around 32% of the total silk production in the country during 2021-22. this was followed by andhra pradesh which had a share of 25% in the overall silk production during 2021-22., strong exports, sericulture is one of the largest foreign exchange earners for the country. the silk fabrics and made ups, and silk readymade garments are the most exported silk products from india with 2021-22 exports share of 45.3%, and 36.3%, respectively. the share of other products in india’s total silk exports are as follows – silk waste (11.3%), silk carpets (4.3%), and natural silk yarn (2.8%). india enjoys a unique global position in terms of production and exports of all the commercially useful varieties of silk and the government has initiated various trade shows and fairs in order to promote the exports of silk products across the world., india’s silk and silk products are highly demanded throughout the world. the country exports to more than 30 countries in the world. some of the top importers are usa, uae, china, uk, australia, germany, france, italy, spain, malaysia, nepal, japan, belgium, canada, south africa, and singapore., usa is the top importer of silk products from india with a share of 24.3% as of 2021-22. uae was the second largest importer of indian silk after usa, with a share of 22.6%. these countries are followed by china, australia, and uk which have 9.1%, 5%, and 4.7% of the total exports share, respectively., silk carpets constituted the majority of share in the exports to usa, with 40% of all the silk products. this was followed by readymade garments which constituted 33% of the total. natural silk yarn, fabrics, and made ups were 27% of the total share exported to usa., natural silk yarn, fabrics, and made ups comprised the majority of the products exported to uae with a share of 55%% in the total exported products. silk carpets constituted 25.8% and silk readymade garments constituted 19.2% of the total exported products to uae. silk waste was the major product exported to china with a share of 92.1% of the total exported products., government initiatives, realizing the huge employability potential spawning across the value chain of the silk industry, the government of india established the central silk board (csb). with low capital requirement and remunerative nature of production from rural on-farm and off-farm activities, silk industry provides employment to 9.4 million people in rural and semi-urban areas., the government has implemented various schemes and programs to support sericulture farmers. for the development and growth of the silk industry in india, the government has implemented several research & development, training, transfer of technology and it initiatives. the focus is to develop new technologies, train more workforce, provide education, and enhance connectivity between scientists, experts, and developers., there are various schemes such as the sericulture development in north-eastern states (nertps), tribal sub-plan (tsp), silk samagra and scheduled caste sub-plan (scsp) are implemented for the development of the industry. silk samagra silk samagra is an integrated scheme for development of silk industry (isdsi) which the government of india introduced through csb. this scheme will have a total outlay of rs. 2,161.68 crore (us$ 272.8 million) for 3 years (2017-18 to 2019-20) and is aimed at the complete development of the silk industry in india. this scheme will aid in scaling up silk production by improving the quality and productivity. sericulture development in the north-eastern states (nertps)., the government of india has launched the sericulture development scheme within an umbrella scheme, namely “north-east region textile promotion scheme”. the objective of this scheme is the revival, expansion, and diversification of sericulture in the state with a special focus on eri and muga silks., challenges to be addressed, despite its historical significance and economic importance, the sericulture industry in india faces challenges such as disease outbreaks among silkworms, fluctuations in cocoon prices, competition from synthetic fabrics, and technological advancements affecting demand for silk., sericulture farmers may face challenges such as disease outbreaks among silkworms, fluctuating cocoon prices, lack of access to modern technology and infrastructure, and competition from synthetic fabrics. integration with weavers: in some cases, sericulture farmers are closely connected to handloom weavers. the silk produced by sericulture farmers is often used by weavers to create a wide range of silk textiles and products., as per research & markets, the indian sericulture market size reached rs 451.6 billion in 2022. the experts at the consulting firm project the market to reach rs 1,194.5 billion by 2028, exhibiting a growth rate (cagr) of 17.7% during 2023-2028., sericulture is a significant source of livelihood in many rural areas of the country., such farming provides direct employment and income to farmers and their families. it is often a seasonal activity, with specific periods dedicated to various stages of silkworm rearing. the sector has been practiced for generations in many regions of india, and there is a wealth of traditional knowledge and expertise among sericulture farmers regarding silkworm rearing and cocoon production., with growing emphasis on sustainable and eco-friendly practices in the sericulture industry, the focus on organic silk production and responsible supply chain management. the technology interventions could further work wonders for this niche industry., related articles more from author, dr v k paul emphasizes prioritizing creation of a strong primary healthcare system, karnataka unveils its biotech policy, plans to generate 30,000 high-quality jobs by 2029, “all our services are backed up by qualified & experienced technical..., nanotherapy for cancer treatment could help reduce chemotherapy doses, university of leeds scientists develop nanosurgical tool to combat cancer, new delhi to host 4th edition of global bio-india 2024 on..., biofach india 2024: india showcases 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Bharathi, Prof D. "Sericulture Industry in India - A Source of Employment Generation." International Journal of Advanced Engineering Research and Science , vol. 3, no. 10, Oct. 2016.

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• Published: 28 August 2024

Carbon emissions from the 2023 Canadian wildfires

• Brendan Byrne   ORCID: orcid.org/0000-0003-0619-3045 1 ,
• Junjie Liu   ORCID: orcid.org/0000-0002-7184-6594 1 , 2 ,
• Kevin W. Bowman 1 , 3 ,
• Madeleine Pascolini-Campbell 1 ,
• Abhishek Chatterjee   ORCID: orcid.org/0000-0002-3680-0160 1 ,
• Sudhanshu Pandey 1 ,
• Kazuyuki Miyazaki   ORCID: orcid.org/0000-0002-1466-4655 1 ,
• Guido R. van der Werf   ORCID: orcid.org/0000-0001-9042-8630 4 ,
• Debra Wunch 5 ,
• Paul O. Wennberg   ORCID: orcid.org/0000-0002-6126-3854 2 , 6 ,
• Coleen M. Roehl   ORCID: orcid.org/0000-0001-5383-8462 2 &
• Saptarshi Sinha   ORCID: orcid.org/0009-0009-3308-0311 7  

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The 2023 Canadian forest fires have been extreme in scale and intensity with more than seven times the average annual area burned compared to the previous four decades 1 . Here, we quantify the carbon emissions from these fires from May to September 2023 on the basis of inverse modelling of satellite carbon monoxide observations. We find that the magnitude of the carbon emissions is 647 TgC (570–727 TgC), comparable to the annual fossil fuel emissions of large nations, with only India, China and the USA releasing more carbon per year 2 . We find that widespread hot–dry weather was a principal driver of fire spread, with 2023 being the warmest and driest year since at least 1980 3 . Although temperatures were extreme relative to the historical record, climate projections indicate that these temperatures are likely to be typical during the 2050s, even under a moderate climate mitigation scenario (shared socioeconomic pathway, SSP 2–4.5) 4 . Such conditions are likely to drive increased fire activity and suppress carbon uptake by Canadian forests, adding to concerns about the long-term durability of these forests as a carbon sink 5 , 6 , 7 , 8 .

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Canadian forests cover a vast area of nearly 362 million ha (ref. 9 ), amounting to 8.5% of the global forested area 10 . These forests are an important sink of carbon, absorbing fossil carbon dioxide (CO 2 ) from the atmosphere and slowing the pace of climate warming 11 , 12 . However, climate change is increasing forest fire activity, acting to suppress the carbon uptake capacity of these forests 13 . Although more frequent fires have been widespread, 2023 has seen forest fires on an extreme scale. With 15 million ha of Canadian forests burned (about 4% of forest area) 1 , 2023 saw more than seven times (8  σ ) the average burned area over the preceding 40 years (1983–2022 mean, 2.2 million ha; range, 0.2–7.1 million ha) 1 . The adverse societal impacts of these fires are clear: 232,000 evacuations and poor air quality affecting millions 14 . However, the carbon emissions from the fire events remain uncertain. In this study, we quantify these emissions through inverse modelling of satellite observations of carbon monoxide (CO). Then, we examine concurrent climate anomalies and projected changes in the prevalence of hot–dry weather under climate change. Finally, we discuss the implications of fires for the Canadian carbon budget.

Fire emissions

Fire carbon emissions can be tracked from space using bottom-up and top-down approaches. Bottom-up approaches use satellite observations to track fire activity, such as burned area 15 or fire radiative power 16 . Emissions of CO 2 , CO and other trace gases are then estimated by combining the estimates of fire activity with quantities such as fuel loads and emission factors. Although these bottom-up estimates are continually improving, inventories can vary significantly in global and regional trace gas and aerosol emission estimates 15 , 17 . Top-down approaches provide a method for refining bottom-up trace gas emission estimates by optimally scaling emission estimates to be consistent with the observed concentrations of trace gases in fire plumes. A strength of this approach is that it integrates emissions from both flaming and smouldering combustion to capture net emissions.

In this study, we perform top-down estimates of CO emissions from the 2023 Canadian fires based on observational constraints from the TROPOspheric monitoring instrument (TROPOMI) space-based CO retrievals (Fig. 1a,b ). These estimates are performed using three different bottom-up fire emission inventories: the global fire emissions database (GFED4.1s) 15 , the global fire assimilation system v.1.2 (GFAS) 16 and the quick fire emissions dataset v.2.6r1 (QFED) 18 . For each inversion, the combined carbon emissions released as CO and CO 2 (CO 2  + CO) are then estimated using the CO 2 /CO emission factors from the same bottom-up database. The CO 2 /CO emission ratios can be highly variable, adding uncertainty to our analysis. We incorporate some of this uncertainty here as each bottom-up database has different mean emission ratios for Canadian forests (range, 7.7–10.8 gC of CO 2 per gC of CO 2 ). Details for these inversions are provided in the methods and a description of the inversion results and evaluation of the performance of the top-down estimates are provided in Supplementary Information sections 1 and 2 ). We find the top-down estimates are relatively insensitive to choices about inversion configuration but do show sensitivity to prescribed hydroxyl radical (OH) abundances 19 , which determine the atmospheric lifetime of the CO emitted (Supplementary Information section 1 and Supplementary Fig. 1 ).

a – c , May–September TROPOMI dry-air mole fractions of CO ( X CO ) averaged over 2019–2022 ( a ) and for 2023 ( b ) aggregated to a 2° × 2.5° grid. c , Canadian forest fire carbon emissions (from CO and CO 2 ) for the 2023 May–September fire season, compared with fire emissions during 2010–2022 (distribution shown by box-and-whisker plots). Top-down emissions over 2010–2022 are estimated from MOPITT (2010–2021) and TROPOMI (2019–2022) CO retrievals. d , A comparison of May–September Canadian fire emissions with 2022 territorial fossil carbon emissions for the ten largest emitting countries, obtained from Global Carbon Budget 2022 2 .

Figure 1c shows the bottom-up and top-down CO 2  + CO carbon emissions from fires during May–September 2023. The bottom-up datasets show large differences, ranging from 234 to 735 TgC (mean of 469 TgC). This range is reduced by 69% in the top-down estimates (570–727 TgC), which also give a larger mean estimate of 647 TgC. Emissions during 2023 far exceed typical Canadian forest fire emissions, with 2010–2022 average emissions of 29–82 TgC for the bottom-up inventories and 121 TgC for top-down estimates (Supplementary Fig. 2 ). To contextualize these numbers, we compare the top-down estimates to annual national fossil fuel emissions for the ten largest emitters (Fig. 1d ). The 5 month 2023 emissions are more than four times larger than Canadian annual fossil fuel emissions (149 TgC yr −1 ) and comparable to India’s annual emissions (740 TgC yr −1 ).

Fire activity is affected by several complex drivers, including fuel traits 20 and ignition probability 21 . However, fire weather—hot and dry conditions—has been shown to be extremely important in driving fire behaviour 22 . Climate data show an exceptionally hot and dry fire season for Canadian forests during 2023 (Fig. 2 ). This was the driest January–September period for Canadian forests since at least 1980, with about 86% of forested area having below-average precipitation and about 52% being more than 1 s.d. below the 2003–2022 average (Supplementary Fig. 4 ). May–September 2023 was the warmest since at least 1980, with about 100% of the forest area above average and about 90% being more than 1 s.d. above the 2003–2022 average. Similarly, the vapour pressure deficit (VPD), which is closely associated with fire activity 22 , 23 , 24 , was the third highest since 1980, including 85% of the forest area being above average and about 54% being more than 1  s.d. above the 2003–2022 average.

a – d , Maps (left) and time series (right) of CPC global unified gauge-based cumulative precipitation (∑P) ( a ), MERRA-2 2 m temperature (with 2 week running mean) ( b ), MERRA-2 VPD (with 2 week running mean) ( c ) and fire CO 2  + CO emissions from the GFED4.1s database ( d ). All maps are shown at a spatial resolution of 0.5° × 0.625° and Z -scores are for the area-mean of Canadian forests. Note that GFED4.1s is shown instead of the inversion results because those are at a coarser spatial resolution and cover a shorter time period, maps of prior and posterior mean fire emissions are shown in Supplementary Fig. 14 . Months are shown from January (J) to December (D).

Although hot–dry conditions were widespread across Canadian forests, there are two notable regional patterns. Western Quebec (49°–55° N, 72°–80° W), which is typically relatively wet (Supplementary Fig. 5a ), had exceptionally dry conditions during 2023, with precipitation through September being 23.7 cm (49%) below average. Coupled with extreme heat and VPD during June–July, fire emissions in this region contributed about 15% of the national total (Supplementary Fig. 6 ). The other notable region was northwestern Canada near the Great Slave Lake (57°–62° N, 110°−125° W). This region is drier than western Quebec on average, with about half the annual precipitation. However, 2023 was exceptional, with both a large precipitation deficit of 8.1 cm (27% of January–September total) and exceptionally warm conditions throughout May–September (+2.6 °C) (Supplementary Fig. 6 ). This region contributed about 61% of the total Canadian forest fire emissions.

Fires and climate

The relationship between climate variability and fire emissions for Canadian forests is examined in Fig. 3 , which shows fire emissions as a function of temperature and precipitation Z -scores over 2003–2023 for the 0.5° × 0.625° grid cells, in which Z -scores are the anomalies divided by the standard deviation. May–September emissions are lowest for combined cool–wet conditions (5.2 gC m −2 ), whereas emissions increase when either temperature is above average (19.5 gC m −2 ) or precipitation is below average (9.2 gC m −2 ). However, emissions are largest for combined warm–dry conditions (35.7 gC m −2 ). In particular, fire emissions are much increased during exceptionally hot and dry conditions (99.6 gC m −2 , temperature Z  > 1 and precipitation Z  < −1). These hot–dry conditions were much more prevalent in 2023 than in preceding years, with a mean May–September T2M Z -score of 2.3 and a precipitation Z -score of −1.1 across grid cells, explaining why fire emissions were extreme during 2023. Notably, the number of individual fires during 2023 was not unusual, with 6,623 relative to a 10 yr average of 5,597 (ref. 25 ). Yet, probably primarily driven by these hot–dry conditions 24 , many of these fires grew to enormous sizes with hundreds of megafires (greater than 10,000 ha) recorded.

Mean May–September GFED4.1s CO 2  + CO fire emissions as a function of May–September T2M Z -score and January–September precipitation Z -score for each 0.5° × 0.625° forested grid cell during 2003–2023 (using 2003–2022 as a baseline). For individual years, the mean Z -scores across forested grid cells are shown with ‘X’. The projected decadal-mean temperature and precipitation Z -scores for the median CMIP6 model under SSP 2–4.5 are shown by the circles. The CMIP6 Z -scores are calculated using the 2000–2019 period as a baseline but use the reanalysis 2003–2022 standard deviations (see section on ‘Climate data’). The historical and projected T2M and precipitation over the Canadian boreal forests simulated by the CMIP6 ensemble are shown in Supplementary Fig. 12 .

Next, we examine future climate conditions in the region and how they compare to the concurrent climate conditions that led to the massive fires. Figure 3 shows the decadal mean temperature and precipitation Z -scores for the median of 27 models from the coupled model intercomparison project phase 6 (CMIP6) 26 under the moderate-warming shared socioeconomic pathway (SSP) 2–4.5 (ref. 4 ). Large projected temperature increases are found to occur, with average temperatures in the 2050s similar to 2023. More modest increases in precipitation are projected, indicating a ‘speeding up’ of the water cycle, in which both evaporation and precipitation rates increase (Supplementary Fig. 12 shows ensemble distribution). Studies indicate that the combined effect will result in regional increases in moisture deficits for Canadian forests through the end of the twenty-first century 6 , 27 , 28 . Beyond the 2050s, average temperature and precipitation conditions are projected to exceed the historical range. These changes will impact the boreal carbon cycle in many ways, such as changing fuel loads and species composition, which complicates projections of future fire activity. However, increases in boreal fire emissions linked to warming have been reported over recent decades 13 , 27 , 29 , 30 and several studies have projected further increases in Canadian fire activity with future warming 5 , 6 , 7 , 8 . Thus, we find that warming, coupled with regionally increasing moisture deficits, is likely to drive increased fire carbon emissions from Canadian forests.

Canadian carbon budget implications

As a party to the Paris Agreement, Canada is obligated to track economy-wide greenhouse gas (GHG) emissions and removals in a national GHG inventory (NGHGI). This includes tracking emissions and removals from ‘managed’ lands, for which human interventions and practices have been applied to perform production, ecological or social functions 31 . However, the 2006 Intergovernmental Panel on Climate Change (IPCC) guidelines for national GHG inventories 31 and Canadian NGHGI 32 differ in how emissions and removals over managed lands are categorized. The IPCC guidelines treat all emissions and removals on managed land as anthropogenic, whereas the Canadian NGHGI treats ‘natural disturbances’ as non-anthropogenic. This difference in categorization leads to large differences between the Canadian NGHGI and an estimate using the IPCC guideline definitions.

Figure 4 shows that NGHGI removals on managed forest land are almost exactly compensated by emissions from harvested wood products, such that the total CO 2 emissions for Canada are dominated by the energy sector (more than 90% of net emissions). However, we see that natural disturbances are shown to be of considerable magnitude, amounting to nearly 60% of total CO 2 emissions in 2021. The 2023 CO + CO 2 fire emissions across managed Canadian forests (see section on ‘Managed land’) are estimated to be 421 (388–461) TgC, amounting to 2.5–3 years of economy-wide CO 2 emissions.

Lines show the annual net emissions or removals from managed forest land (green), harvested wood products (brown), natural disturbances that are not counted towards Canada’s emissions (red) and the economy-wide net CO 2 emissions (grey). The top-down estimates of the 2023 CO 2  + CO fires emissions over managed land are shown in black. Total CO 2 emissions, harvested wood products and forest land emissions and removals were obtained from Table A11-1 of the NGHGI 32 , whereas natural disturbances were obtained from Table 6-5 of the NGHGI. All quantities presented are in units of teragrams of carbon (1 TgC = 1 MtC = 1,012 gC), which can be converted to units of megatonnes of CO 2 (MtCO 2 ) by multiplying by a factor of 3.664.

Regardless of their characterization, fire carbon emissions will affect the growth rate atmospheric CO 2 . As such, monitoring changes in the carbon budget across both managed and unmanaged land is important. Including all land in the Canadian carbon budget, top-down estimates find that Canadian ecosystems are a sink of CO 2 when constrained by either in situ or space-based CO 2 observations. Using both data types, an ensemble of atmospheric CO 2 inversion systems report that Canadian carbon stocks increased 366 ± 88.6 TgC yr −1 over 2015–2020 11 , contributing about 30% of the net land carbon sink. Similarly, space-based biomass estimates find carbon accumulation in Canadian boreal forests, although smaller in magnitude 13 , 33 , 34 . Thus, Canadian forests play an important role in mitigating anthropogenic emissions, slowing the rise of atmospheric CO 2 . The large carbon release resulting from the 2023 Canadian fires puts into question the durability of this sink. Others 13 showed that fires have acted to suppress the carbon uptake potential of Canadian forests over the past 30 years. Although Canadian forests have historically experienced large stand-replacing fires at infrequent intervals of 30 to more than 100 years 35 , 36 , 37 , increases in fire frequency will probably reduce biomass recovery and affect species composition 37 , 38 , 39 , 40 . It has also been argued that fire, insects and droughts may already be driving Canadian forests into a carbon source 41 , 42 . In the extreme case that expansive fires, such as that of 2023, become the norm (burning 4% of Canadian forest area), all Canadian forests could burn every 25 years. So, although the magnitude is uncertain, it is likely that increasing fire activity in Canadian forests will reduce the capacity of these Canadian forests to continue to act as a carbon sink.

The role of Canada’s fire management strategy in managing fire carbon emissions also deserves some discussion. Fire management strategies require balancing several considerations, including socioeconomic costs, ecological impacts and carbon emissions. Canada’s present strategy adopts a risk-based approach, for which decisions on whether or not to suppress fires are made on a fire-by-fire basis 43 , with differing priorities across provinces and territories. Understanding how fire regimes will change with climate change is thus of high importance, for future decision criteria and costing.

Conclusions

The 2023 fire season was the warmest and driest for Canadian forests since at least 1980, resulting in vast carbon emissions from forest fires. Using TROPOMI CO retrievals, we estimate the total May–September CO 2  + CO emissions from these fires to be 647 TgC (range 570–727 TgC), comparable in magnitude to India’s annual fossil fuel CO 2 emissions. The 2023 warmth was exceptional based on the last 44 years but CMIP6 climate models project that the temperatures of 2023 will become normal by the 2050s. Such changes are likely to increase fire activity 5 , 6 , 7 , 8 , risking the carbon uptake potential of Canadian forests. This will impact allowable emissions for reaching warming targets, as reduced carbon sequestration by ecosystems must be compensated for by adjusting anthropogenic emissions reductions.

Climate data

Precipitation estimates were derived from Climate Prediction Center (CPC) global unified gauge-based analysis of daily precipitation data provided by the National Oceanic and Atmospheric Administration from their website at https://psl.noaa.gov (refs. 44 , 45 ). MERRA-2 2 m temperature (T2M) and dew point temperature at 2 m (T2MDEW) were obtained from the single-level diagnostics file 3 . VPD was calculated from these quantities using:

where es is the saturation vapour pressure and ea is the vapour pressure, calculated from T2MDEW and T2M, respectively, using the formulation of ref. 46 . The Z -scores for precipitation, T2M and VPD were calculated relative to the 20-year baseline of 2003–2022; for example, for T2M this is calculated as:

where T2M year is the May–September mean T2M for a given year and T2M 2003–2022 is the 20-element ensemble of May–September mean T2Ms during 2003–2022.

CMIP6 data were downloaded from the Canadian Climate Data and Scenarios website ( https://climate-scenarios.canada.ca/?page=cmip6-scenarios ). We examine the ensemble median of 27 models provided on a 1° × 1° grid (technical documentation at https://climate-scenarios.canada.ca/?page=pred-cmip6-notes ). The models included are based on data availability and are tabulated at https://climate-scenarios.canada.ca/?page=cmip6-model-list . T2M and precipitation are analysed for the historical and future scenarios. We combine the historical simulation with SSP 2–4.5 (ref. 4 ), shown in the main text or SSP 5–8.5 as shown in Supplementary Fig. 7 .

The Z -scores are calculated from the median of the CMIP6 ensemble by calculating the temporal mean of the median model mean over 2000–2019 for a given grid cell, whereas the reanalysis data are used to estimate internal variability. Therefore, the T2M Z -score is calculated as:

Sources and sinks

Fossil co emissions.

Anthropogenic CO emissions are obtained from the community emissions data system (CEDS) for historical emissions 47 ; specifically we use version CEDS-2021-04-21 (ref. 48 ).

Prior fire CO 2 and CO emissions

Fire CO 2 and CO emissions are obtained from the GFED, GFAS and QFED databases. GFED4.1s 15 provides estimates of biomass burning using a biogeochemical model ingesting MODIS 500 m burned area 49 in combination with 1 km thermal anomalies and 500 m surface reflectance observations to estimate burned area associated with small fires using a statistical model 50 . These data were downloaded from https://www.globalfiredata.org/ . GFAS v.1.2 provides estimates of daily biomass burning emissions by assimilating MODIS fire radiative power observations 16 . These data were downloaded from the atmosphere data store ( https://ads.atmosphere.copernicus.eu ). We use v.2.6 of the QFED gridded emission estimates 18 . These data were downloaded from https://portal.nccs.nasa.gov/datashare/iesa/aerosol/emissions/QFED/v2.6r1/0.25/QFED/ . For all biomass burning datasets, we release fire emissions at the model surface but incorporate a 3 hourly diurnal cycle based on ref. 51 . Year-specific emissions are used for the prior in the atmospheric CO inversions.

Biogenic emissions, atmospheric CO production and OH data

Biogenic emissions, atmospheric CO production and OH data were all derived from the outputs of the MOMO-Chem chemical data assimilation 52 . An updated version of the tropospheric chemistry reanalysis v.2 (TCR-2) 53 produced using MOMO-Chem is used to evaluate the atmospheric production and loss of CO. The reanalysis is produced through the assimilation of several satellite measurements of ozone, CO, NO 2 , HNO 3 and SO 2 . The chemical loss of CO was estimated using the reanalysis OH fields. Because of the multiconstituent data assimilation, the reanalysis OH shows improved agreements in global distributions over remote oceans in comparison with the ATom aircraft measurements from the surface to the upper troposphere 53 . Constraints obtained for OH profiles have a large potential to influence the chemistry of the entire troposphere, including oxidation of non-methane hydrocarbons (NMHCs) to estimate the chemical production of CO. The biogenic emissions at the surface were obtained from the model of emissions of gases and aerosols from Nature v.2.1 (MEGAN2.1) 54 . Year-specific fields were only available through 2018 and estimates for that year are repeated for more recent years. We also perform a supplemental sensitivity analysis for the impact of prescribed OH abundances on inferred emissions using the fields of ref. 55 , which are commonly used for GEOS-Chem methane inversions 56 .

CO retrievals

TROPOMI is a grating spectrometer aboard the ESA Sentinel-5 Precursor (S5P) satellite which measures Earth-reflected radiances 57 . CO total column densities are retrieved in the shortwave infrared (around 2.3 μm) using the shortwave infrared CO retrieval algorithm 58 , 59 . TROPOMI CO retrievals 60 were downloaded from the Copernicus data space ecosystem ( https://dataspace.copernicus.eu/ ). We use S5P RPRO L2 CO (processor v.2.4.0) through 25 July 2022, then switch to S5P OFFL L2 CO for more recent data (processor v.2.5.0 or 2.5.0). Retrieved CO total column densities are then converted to dry-air mole fractions of CO ( X CO ) using the dry-air surface pressure and hypsometric equation. The column averaging kernel is similarly converted to mole-fraction space. Individual retrievals (quality flag ≥ 0.5) from each orbit are aggregated into super-observations using the model grid (2° × 2.5°).

The retrieval uncertainty on super-observations is taken to be the mean uncertainty on all retrievals in a given super-observation. This approach is used because systematic errors may exist between retrievals, such that assuming random errors would underestimate the true retrieval error. For assimilation into NASA carbon monitoring system-flux (CMS-Flux), we calculate observational errors that incorporate error in the atmospheric transport model. For this, we follow the approach of ref. 61 . First, we perform a forward model simulation with the prior fluxes for 2019–2023. Then we take the observational uncertainty to be the standard deviation between the simulated and real TROPOMI super-observations over a moving window of 30° latitude, 30° longitude and 30 days (across all years). The uncertainties estimated using this approach range over 3.5–14.3 ppb (5–95 percentiles), whereas retrieval errors range over 1.4–4.9 ppb. Thus, the observational errors are dominated by representativeness errors.

We use the MOPITT (measurements of pollution in the troposphere) satellite thermal-infrared–near-infrared (TIR–NIR) CO retrieval. Version 9 (L2V19.9.3) 62 is used from 2009 to 31 October 2022, whereas L2V19.10.3.beta is used from 1 November 2022 onwards. These data were downloaded from the EarthData ASDC ( https://asdc.larc.nasa.gov/data/MOPITT/ ). As with TROPOMI, profile retrievals were converted into dry-air mole fractions of CO ( X CO ) for assimilation; however, unlike TROPOMI, we do not generate super-observations but instead assimilate individual observations. This is because the footprint of MOPITT retrievals (22 × 22 km 2 ) is much coarser than TROPOMI retrievals (3 × 7 km 2 ).

The total carbon column observing network (TCCON) consists of ground-based Fourier transform spectrometers which retrieve X CO , X CO 2 and other species from observations of solar radiation 63 . In this study, we examine GGG2020 (ref. 64 ) TCCON data from Park Falls 65 and East Trout Lake 66 . These data were obtained from the TCCON Data Archive hosted by CaltechDATA at https://tccondata.org . Super-observations are created for each site as hourly averages; we only include hours with five or more observations.

Atmospheric CO inversions

We perform a series of CO inversion analyses using the CMS-Flux atmospheric inversion system. This inversion model is descended from the GEOS-Chem adjoint model 67 and has been used for CO 2 (refs. 68 , 69 ) and CO inversion analyses 70 . The inversions in this study are all performed globally at 2° × 2.5° spatial resolution using MERRA-2 reanalysis. CEDS anthropogenic emissions, biogenic atmospheric CO production, direct biogenic CO emissions and fire emissions (from GFED4.1s, GFAS or QFED) and atmospheric OH fields are all prescribed in the forward simulations (see section on ‘Sources and sinks’). Four-dimensional variational data assimilation (4D-Var) is used to optimize scaling factors on the net surface flux for each grid cell (combined anthropogenic, fire and direct biogenic CO flux). The posterior CO fluxes are then decomposed into anthropogenic, fire and biogenic fluxes using the fractional contribution of the prior (an approach widely used for CO inversions).

A series of MOPITT X CO inversions are performed over 2010–2021. Weekly fluxes are optimized over the period 7 November of the preceding year (YYYY − 1) to 1 February of the next year (YYYY + 1), the optimized fluxes in the desired year are retained (YYYY) and the fluxes outside this period are discarded as spin-up or spin-down. These inversions are performed using the GFED4.1s fire inventory. Prior uncertainties on emissions are assumed to be proportional to the emissions, with a scale factor uncertainty of 200%.

TROPOMI X CO inversions are performed over 2019–2023. These inversions are performed over a truncated period of 1 April to 30 September, with April then being discarded as spin-up. Several different inversion configurations are used to quantify the uncertainty in posterior fluxes due to both Bayesian posterior uncertainties and systematic choices about error specification and inversion configuration, both of which have been shown to contribute significantly to inversion error estimates 11 .

Three ensembles of inversions are performed on the basis of the three different prior fire inventories: GFED4.1s, GFAS or QFED (Extended Data Fig. 1a ). Each prior inventory was subjected to four different experimental configurations (Extended Data Fig. 1b ). In one case, the X CO super-observations error is taken to be the mean retrieval uncertainty across all retrievals included in a given super-observation. This approach typically gives an uncertainty of 1.3–4.9 ppb. The other case uses an observational error estimate that incorporates representativeness errors (see section on ‘TROPOMI’), which are typically between 3.5 and 14.3 ppb. The experimental configurations also differ by the treatment of prior uncertainties on the fluxes. These uncertainties are not well known a priori, thus we use two very different approaches. In the first approach, we assume that the errors on fluxes are equal to 200% of the prior flux estimate. In the second approach, we assume that flux uncertainties are near constant in flux units (scale factor uncertainty times control flux is constant, this is truncated to scale factors uncertainties between 0.25 and 1,000). Finally, we also vary the temporal optimization to either 3 or 7 days. As with the prior flux uncertainties, there are many possible choices for temporal optimization, so we choose two reasonable estimates to quantify the sensitivity to this choice. The spread in maximum a posteriori estimates across these different set-ups gives an indication of the uncertainty in estimated fluxes due to the set-up decisions.

We also estimate the Bayesian posterior uncertainty (Extended Data Fig. 1c ), which derives from uncertainties in the prior fluxes and observations. This uncertainty is estimated using the Monte Carlo method introduced by ref. 71 and formalized by ref. 72 We perform the experiment during 2023 for each prior inventory and use 40 inversion ensemble members using the inversion configuration with TROPOMI X CO representativeness errors and 7 day optimization.

Finally, for each prior inventory, we calculate the posterior best estimates and uncertainties from the experiments described above (Extended Data Fig. 1d ). The best estimate is taken to be the mean across the four different inversion configurations. The uncertainty on this estimate is taken as the square-root of the sum of the variances resulting from the different inversion configurations and Monte Carlo posterior covariance estimate. The overall best estimate is taken to be the average across the best estimates for the prior inventory ensembles and the overall uncertainty is taken to be the range of 1  σ uncertainties across the three prior inventory ensembles.

We estimate posterior CO 2 fluxes from the posterior CO emissions using the CO 2 /CO emission ratios provided by the prior GFED4.1s, GFAS and QFED inventories. Each inventory has different CO 2 /CO emission; thus, we use the emission ratio to estimate the posterior CO 2 from the same inventory that was used as the prior inventory. This incorporates some uncertainty CO 2 /CO emission ratio into the CO 2 emission estimates.

Regional masks

Forest area.

Forest area is defined using v.6.1 of the MODIS MCD12C1 product 73 . On the basis of the type 1 majority land cover, we define forests to include the categories evergreen needleleaf forests, evergreen broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, woody savannas and savannas.

Managed land

The map of managed lands 74 was accessed through personal communication with M. Hafer and A. Dyk (the map was only created for cartographic communication purposes). The extent of land considered managed forest in Canada for the purposes of GHG reporting to the United Nations Framework Convention on Climate Change cannot be mapped in detail. That information comes from provincial/territorial forest inventories that are not spatially explicit and cannot be mapped. Supplementary Fig. 13 shows the managed land map and the fractional managed/unmanaged for 2° × 2.5° grid cells.

Data availability

The dataset produced for this study can be accessed at JPL Open Repository, https://doi.org/10.48577/jpl.V5GR9F .

Code availability

The Python and Bash codes used in this study are available at Zenodo ( https://doi.org/10.5281/zenodo.12709398 ) 75 .

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Acknowledgements

The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with the National Aeronautics and Space Administration. Resources supporting this work were provided by the NASA High-End Computing programme through the NASA Advanced Supercomputing Division at Ames Research Center. Authors B.B., A.C., J.L. and K.B. acknowledge the support from NASA Orbiting Carbon Observatory Science Team Program and the Carbon Monitoring System Program (grant no. NNH20ZDA001N-CMS). We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access and the many funding agencies who support CMIP6 and ESGF. GFAS is generated using Copernicus Atmosphere Monitoring Service Information 2020; neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains. The East Trout Lake TCCON station is funded through an infrastructure grant from the Canada Foundation for Innovation (grant no. 35278) and the Ontario Research Fund (grant no. 35278). The Park Falls TCCON site was supported by NASA (grant no. 80NSSC22K1066). We thank J. L. Laughner for guidance with the TCCON data. We thank M. Hafer and A. Dyk for providing information on Canada’s managed land. And we thank L. Baskaran for help in rasterizing these data.

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Contributions

B.B., J.L., K.W.B., M. P.-C., A.C. and S.P. conceptualized and designed the study. K.M. provided atmospheric CO production and OH estimates. G.R.v.d.W extended the GFED4.1s dataset for this experiment. D.W., P.O.W. and C.M.R. provided TCCON data. S.S. provided MERRA-2 reanalysis for the model. B.B. conducted the analysis and wrote the paper, with input from all authors.

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Extended data figures and tables

Extended data fig. 1 schematic diagram of the tropomi xco inversion procedure..

(a) Ensembles of inversions are performed based on three different flux inventories. (b) To quantify the sensitivity to systematic error sources, four inversions are performed that differ in observational error constraints, prior error constraints, and temporal optimization frequency. (c) Bayesian posterior error estimates are estimate for 2023 by following the Monte Carlo approach for 4D-Var of Chevalier et al. 71 . (d) The posterior best estimates are taken as the average maximum a posteriori estimate across inversion configurations while the uncertainty is taken to be the sum-of-squares of the error components estimated in (b) and (c).

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Supplementary sections 1 and 2, Figs. 1–14, Tables 1–3 and references.

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Byrne, B., Liu, J., Bowman, K.W. et al. Carbon emissions from the 2023 Canadian wildfires. Nature (2024). https://doi.org/10.1038/s41586-024-07878-z

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DOI : https://doi.org/10.1038/s41586-024-07878-z

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