REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS on the status of production expansion of relevant food and feed crops worldwide
Date: 2026-01-20
Institutions: Directorate-General for Energy, European Commission
Subjects: EU energy policy, bioenergy, biofuel, biomass, deforestation, energy crop, environmental impact, land use, reduction of gas emissions, renewable energy

EN 

  EN 

   EUROPEAN COMMISSION  Brussels, 20.1.2026  COM(2026) 36 final  REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS on the status of production expansion of relevant food and feed crops worldwide       
 
 
I. 

INTRODUCTION 

Directive (EU) 2018/20011 (the Renewable Energy Directive) introduces a targeted approach 
to  address  emissions  from  indirect  land-use  change  (ILUC)  associated  with  conventional 
biofuels, bioliquids and biomass fuels. It sets a limit on biofuels, bioliquids, and biomass fuels 
produced from food or feed crops for which a significant expansion on land with high carbon 
stock has been observed (high ILUC-risk fuels). This limit applies to the amount of these fuels 
that can be counted towards the targets for renewable energy set out in the Renewable Energy 
Directive. The limit has to gradually decrease to zero by 2030. Biofuels, bioliquids and biomass 
fuels that are certified as having low ILUC-risk (low ILUC-risk fuels) are exempted from the 
limit. 

Delegated  Regulation  (EU)  2019/8072  (the  ‘ILUC  Delegated  Regulation’)  supplements  the 
Renewable Energy Directive by laying down both criteria to determine when feedstocks for 
the production of biofuels, bioliquids and biomass fuels are high ILUC-risk, as well as rules 
for the certification of low ILUC-risk fuels (see Chapter III).  

Article 3 of the ILUC Delegated Regulation provides that, in order to determine the high ILUC-
risk feedstock, two criteria must apply cumulatively (see the box below). The first criterion is 
related to the average annual expansion of the global production area of the feedstock since 
2008.  For  a  feedstock  to  be  characterised  as  high  ILUC-risk,  the  average  annual  expansion 
must be higher than 1 % and affect more than 100 000 hectares. The second criterion concerns 
the  share  of  such  expansion  into  land  with  high-carbon  stock.  For  a  feedstock  to  be 
characterised as high ILUC-risk this share must be higher than 10 % as calculated according to 
the formula below. 

For  the  purpose  of  determining  the  high  indirect  land-use  change-risk  feedstock  for  which  a 
significant  expansion  of  the  production  area  into  land  with  high-carbon  stock  is  observed,  the 
following cumulative criteria shall apply: 
(a) the average annual expansion of the global production area of the feedstock since 2008 is higher 
than 1 % and affects more than 100 000 hectares;  
(b) the share of such expansion into land with high-carbon stock is higher than 10 %, in accordance 
with the following formula: 

𝑥𝑓 + 2,6 𝑥𝑝
𝑃𝐹

𝑥ℎ𝑐𝑠 =  
where 

of 

the 

use 

from 

energy 

sources, 

1 Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion 
of 
21.12.2018, 
renewable 
ELI: http://data.europa.eu/eli/dir/2018/2001/oj)  as  amended  by  Directive  (EU)  2023/2413  of  the  European 
Parliament  and  of  the  Council  of  18 October  2023  amending  Directive  (EU) 2018/2001,  Regulation 
(EU) 2018/1999  and  Directive 98/70/EC  as  regards  the  promotion  of  energy  from  renewable  sources,  and 
repealing 
31.10.2023, 
ELI: http://data.europa.eu/eli/dir/2023/2413/oj) 
2  Commission Delegated Regulation (EU) 2019/807 of 13 March 2019 supplementing Directive (EU) 2018/2001 
of the European Parliament and of the Council as regards the determination of high indirect land-use change-risk 
feedstock for which a significant expansion of the production area into land with high carbon stock is observed 
and  the  certification  of  low  indirect  land-use  change-risk  biofuels,  bioliquids  and  biomass  fuels,  OJ  L  133, 
21.5.2019, p. 1. 

(EU) 2015/652 

2023/2413, 

Directive 

Council 

328, 

(OJ 

(OJ 

L, 

L 

1 

 
 
 
 
 
 
 
 
 
xhcs = share of expansion into land with high-carbon stock; 
xf = share of expansion into land referred to in Article 29(4)(b) and (c) of Directive (EU) 
2018/2001; 
xp = share of expansion into land referred to in Article 29(4)(a) of Directive (EU) 2018/2001 
including peatland; 
PF = productivity factor.  
PF shall be 1,7 for maize, 2,5 for palm oil, 3,2 for sugar beet, 2,2 for sugar cane and 1 for all other 
crops. 
The application of the criteria in points (a) and (b) above shall be based on the information included 
in the Annex, as revised in accordance with Article 7. 
Article 3 of the ILUC Delegated Regulation, establishing criteria for determining high ILUC-risk feedstock. 

The ILUC Delegated Regulation was accompanied by a Commission report on the status of 
production expansion of relevant food and feed crops worldwide (‘the Commission 2019 ILUC 
report’)3.  According  to  Article  7  of  the  ILUC  Delegated  Regulation,  the  Commission  is 
required to review that report, which is the objective of the present report. Article 26(2), fifth 
subparagraph, of the Renewable Energy Directive further requires the Commission to review 
the criteria laid down in the ILUC Delegated Regulation and to include a trajectory to gradually 
decrease  the  contribution  of  high  ILUC-risk  fuels  to  the  overall  Union  target  and  to  the 
minimum share of 29 % renewable energy or the 14.5% greenhouse gas intensity reduction 
target in the transport sector, as referred to in Article 25(1), first subparagraph, point (a) of the 
Renewable Energy Directive. 

II.  UPDATE AND ASSESSMENT OF THE AVAILABLE SCIENTIFIC DATA  

To  support  the  review  of  the  Commission  2019  ILUC  report,  which  was  based  on  an 
assessment undertaken by the Commission’s Joint Research Centre (JRC),  a study has been 
conducted with the aim to update the data on feedstock expansion, in view of new scientific 
evidence. The study was developed in two phases and was carried out by a consortium led by 
Guidehouse.  A literature review has been  carried out, and the statistics  on global feedstock 
expansion  have  been  updated4.  The  literature  review  confirmed  the  Commission’s  2019 
assessment that most studies focus on specific regions and specific crops rather than providing 
more global results. The literature identified covers the regions of Latin America, South-East 
Asia (mainly Indonesia and Malaysia) and West Africa that are known to have an elevated risk 
of deforestation. The main results of this exercise are summarised below by feedstock.  

For soybean, scientific literature primarily focuses on South American countries. New studies 
assess the link of soy expansion into pastureland and the consequent pastureland expansion 
into  land  with  high-carbon  stock,  as  well  as  the  impact  of  new  policies,  such  as  the  Soy 
Moratorium and the new Brazilian forest code in Brazil. One study5 found that policy initiatives 
led  to  a  reduction  in  deforestation  rates  but  steered  new  soybean  production  into  older 

3 COM/2019/142 final  - Report from the Commission to the  European Parliament,  the Council, the European 
Economic  and  Social  Committee  and  the  Committee  of  the  Regions  on  the  status  of production  expansion  of 
relevant food and feed crops worldwide. 
4 doi:10.2833/7401246 
5 Amaral, D. F., De Souza Ferreira Filho, J. B., Chagas, A. L. S., & Adami, M. (2021). Expansion of soybean 
farming into deforested areas in the amazon biome: the role and impact of the soy moratorium. Sustainability 
Science, 16(4), 1295–1312. https://doi.org/10.1007/s11625-021-00942-x  

2 

 
 
 
 
 
 
 
 
converted areas, such as pastureland. Another study6 similarly analysed the linkage between 
soy  and  pasture  expansion,  finding  that  soy  expansion  commonly  occurred  on  pastureland, 
which in turn drives pasture expansion and hence land-use conversion of high carbon stock 
land. Between 2006 and 2017, the soybean producing areas in Mato Grosso grew from 5.8 to 
9.3Mha, an increase of 59.5%. Additionally, a different study7 found that between 2000 and 
2019, annual soybean expansion in South America grew from 26.4 to 55.1Mha, with significant 
growth along ‘deforestation fronts’, indirectly causing deforestation by displacing pastureland. 
The soybean production in the Brazilian Amazon experienced the fastest expansion, increasing 
from 0.4Mha to 4.6Mha over the period. Another study8 estimated that, on average, 19% of 
soy production expansion involves high ILUC-risk.  

Regarding palm oil, scientific evidence concluded that it continued to expand, in forests and 
in peatland, in Malaysia, Indonesia and Thailand, and is emerging in growing regions in Brazil, 
Peru and Africa. Studies show the complex dynamics of palm oil cultivation, revealing that 
while policy measures like Indonesia's forest moratorium and sustainable production programs 
have attempted to curb deforestation, significant environmental changes persist. These include 
high  rates  of  land  use  conversion  from  forests  and  peatlands  to  plantations,  with  varying 
impacts9 from industrial and smallholder practices. In South-East Asia (Indonesia, Malaysia, 
Thailand),  studies10  found  that  palm  oil  expansion  has  been  significant,  with  plantations 
extending on peatlands and natural forests. In South America, palm oil cultivation in Brazil 
mainly took place on pastureland11, while in Peru industrial plantations largely expanded into 
old-growth forests. A study conducted in Peru12 found that 26% of the expansion of smallholder 
palm oil plantations took place in old-growth forests, while 70% of the expansion driven by 
industrial plantation was happening in old-growth forests. In Africa, palm oil production has 

6 Picoli, M. C. A., Rorato, A. C., Leitão, P. J., Câmara, G., Maciel, A., Hostert,  P., & Sanches, I. D. (2020). 
Impacts of Public and Private Sector Policies on Soybean and Pasture Expansion in Mato Grosso—Brazil from 
2001 to 2017. Land, 9(1), 20. https://doi.org/10.3390/land9010020  
7 Song, X., Hansen, M. C., Potapov, P., Adusei, B., Pickering, J., Adami, M., Lima, A., Zalles, V., Stehman, S. 
V., Di Bella, C. M., Conde, M. C., Copati, E. J., Fernandes, L. B., Hernández-Serna, A., Jantz, S. M., Pickens, A., 
Turubanova,  S.,  &  Tyukavina,  A.  (2021).  Massive  soybean  expansion  in  South  America  since  2000  and 
implications for conservation. Nature Sustainability, 4(9), 784–792. https://doi.org/10.1038/s41893-021-00729-z 
8 Strapasson, A., Falcão, J. P., Rossberg, T., Buss, G., Woods, J., & Peterson, S. (2019). Land Use  Change and 
the European Biofuels Policy: The expansion of oilseed feedstocks on lands with high carbon stocks. Oilseeds 
and Fats, Crops and Lipids, 26, 39. https://doi.org/10.1051/ocl/2019034 
9  Schoneveld,  G.,  Ekowati,  D.,  Andrianto,  A.,  &  Van  Der  Haar,  S.  (2019).  Modeling  peat-  and  forestland 
conversion  by  oil  palm  smallholders  in  Indonesian  Borneo.  Environmental  Research  Letters,  14(1),  014006. 
https://doi.org/10.1088/1748-9326/aaf044  and Glinskis, E. A., & Gutiérrez-Vélez, V. H. (2019). Quantifying and 
understanding land cover changes by large and small oil palm expansion regimes in the Peruvian Amazon. Land 
Use Policy, 80, 95–106. https://doi.org/10.1016/j.landusepol.2018.09.032  
10 Astuti, R., Miller, M. A., McGregor, A., Sukmara, M. D. P., Saputra, W., Sulistyanto, & Taylor, D. (2022). 
Making illegality visible: The governance dilemmas created by visualising illegal palm oil plantations in Central 
Kalimantan, Indonesia. Land Use Policy, 114, 105942. https://doi.org/10.1016/j.landusepol.2021.105942 , Jing, 
Z., Lee, J. S. H., Elmore, A. J., Fatimah, Y. A., Numata, I., Xin, Z., & Cochrane, M. A. (2022). Spatial patterns 
and  drivers  of  smallholder  oil  palm  expansion  within  peat  swamp  forests  of  Riau,  Indonesia.  Environmental 
Research  Letters,  17(4),  044015.  https://doi.org/10.1088/1748-9326/ac4dc6,  and  Schoneveld,  G.,  Ekowati,  D., 
Andrianto, A., & Van Der Haar, S. (2019). Modeling peat- and forestland conversion by oil palm smallholders in 
Indonesian Borneo. Environmental Research Letters, 14(1), 014006. https://doi.org/10.1088/1748-9326/aaf044  
11 Benami, E., Curran, L. M., Cochrane, M. A., Venturieri, A., Franco, R. V., Kneipp, J. M., & Swartos, A. (2018). 
Oil palm land conversion in Pará, Brazil, from 2006–2014: evaluating the 2010 Brazilian Sustainable Palm Oil 
Production Program. Environmental Research Letters, 13(3), 034037. https://doi.org/10.1088/1748-9326/aaa270  
12 Glinskis, E. A., & Gutiérrez-Vélez, V. H. (2019). Quantifying and understanding land cover changes by large 
and  small  oil  palm  expansion  regimes  in  the  Peruvian  Amazon.  Land  Use  Policy,  80,  95–106. 
https://doi.org/10.1016/j.landusepol.2018.09.032 

3 

 
 
 
 
 
 
significantly grown from 2Mha in the 1980s to 5Mha by 2018, largely driven by expansion 
within Nigeria and Côte d'Ivoire13.  

For  sugar  cane  and  maize,  a  few  additional  studies  have  been  identified,  compared  to  the 
Commission 2019 ILUC report. For both feedstocks, the conclusions are confirmed: expansion 
has been identified on pasture or agricultural land. When it comes to sugar cane, studies14 found 
that while sugar cane expansion into forests was not prominent, expansion is increasing, mainly 
in Brazil and mostly onto pastures.  

For other crops, no additional studies have been identified.  

III.  UPDATE ON GLOBAL EXPANSION IN AGRICULTURAL COMMODITIES  

The analysis regarding the trends in the global production expansion of feedstocks that can be 
used  for  producing  fuels  has  been  updated  and  now  contains  the  latest  available  data  from 
FAOstat15 and the USDA16, being based on data from 2014 to 2021. For maize and soybeans 
in Brazil where multi-cropping is prevalent, and for palm oil fruit production in Indonesia and 
Malaysia,  FAOStat  harvested  area  data  has  been  replaced  with  data  on  planted  area  from 
national statistics to better measure amount of land that is used for crop production. FAOstat 
only provides data on harvested areas, not planted areas, which means that practices such as 
multi- or sequential cropping are recorded as twice the amount of cropland, and for palm trees 
the harvested area does not accurately reflect land use because palm trees take several years to 
mature before being harvested. The updated results are included in Table 1.  

Crop 

Total 
production 
2014 (kt) 

Annual net 
increase of 
production 
2014-2021 (%) 

Harvested 
area 2014 
(kha) 

Harvested 
area 2021 
(kha) 

Annual net 
increase of 
harvested 
area 2014-
2021 (kha) 

Annual net 
increase of 
harvested 
area 2014-
2021 (%) 

Total net 
expansion 
(kha) 

Total gross 
expansion 
(kha) 

Wheat 

728,758 

Maize 

1,040,718 

Sugar cane 

1,885,079 

Sugar beet 

270,250 

0.8% 

2.2% 

-0.2% 

0.0% 

219,755 

220,760 

143 

177,675 

191,193 

1,931 

27,069 

26,350 

4,469 

4,399 

-103 

-10 

0.1% 

1.1% 

-0.4% 

-0.2% 

1,004 

11,001 

13,518 

18,096 

-720 

-70 

976 

313 

13 Duguma LA, Muthee K, Minang PA, van Noordwijk M, Duba D, Bah A, Piabuo SM, Wainaina P. 2021. The 
palm oil sector in Africa: the dynamics, challenges and pathways to sustainability. Chapter 9. In: Minang PA, 
Duguma LA, van Noordwijk M, eds. Tree commodities and resilient green economies in Africa. Nairobi, 
Kenya: World Agroforestry (ICRAF)  
14 Guarenghi, M. M., Garofalo, D. F. T., Seabra, J. E. A., Moreira, M. M. R., Novaes, R. M. L., Ramos, N. P., 
Nogueira, S. F., & de Andrade, C. A. (2023). Land use change net removals associated with sugarcane in Brazil. 
Land, 12(3), 584. https://doi.org/10.3390/land12030584, Vera, I., Wicke, B., & van der Hilst, F. (2020). Spatial 
variation in environmental impacts of sugarcane expansion in Brazil. Land, 9(10), 397. 
https://doi.org/10.3390/land9100397 and Picoli, M. C. A., & Machado, P. G. (2021). Land use change: The 
barrier for sugarcane sustainability. Biofuels, Bioproducts and Biorefining, 15(6), 1591–1603. 
https://doi.org/10.1002/bbb.2270 
15 Food and Agriculture Organization of the United Nations - Statistics 
16 United States Department of Agriculture National Agricultural Statistics Service 

4 

 
 
 
 
 
 
 
 
 
Rapeseed 

74,509 

-0.6% 

36,460 

36,774 

Oil palm 

327,489 

Soybeans 

306,301 

Sunflower 
seed 

40,613 

3.5% 

2.8% 

5.3% 

22,971 

29,124 

117,633 

128,886 

1,608 

45 

879 

0.1% 

3.4% 

1.3% 

313 

6,153 

3,494 

7,244 

11,253 

14,486 

24,350 

29,532 

740 

2.8% 

5,182 

5,893 

Table 1: Guidehouse calculations updating the table on Global production expansion of main biofuel feedstock 
based on data from FAOstat, USDA FAS, (CONAB, 2022) for maize and soybeans in Brazil, Statistics Indonesia 
(Statistics Indonesia, 2022) for palm oil fruit Indonesia, MPOB (Malaysian Palm Oil Board, 2022) and Gunarso 
et al. (Gunarso, Hartoyo, Agus, & Killeen, 2013) for palm oil fruit Malaysia. 

Based  on  the  results  included  in  Table  1,  in  the  years  2014-2021,  the  highest  annual  net 
harvested area17 increase has been observed for oil palm (3.4%), followed by sunflower seed 
(2.8%). An increase has also been observed for soybeans (1.3%) and maize (1.1%). While the 
increase for wheat and rapeseed has been minimal (0.1% for each), sugar cane and sugar beet 
are  the  only  crops  for  which  the  results  indicate  a  negative  value  (-0.4%  and  -0.2%, 
respectively).  

IV.  UPDATE OF GLOBAL MAPPING GIS ASSESSMENT AND REGIONAL 

MAPPING ASSESSMENT TO ESTIMATE FEEDSTOCK EXPANSION INTO 
HIGH CARBON STOCK LAND  

Global mapping  

In recent years, global demand for agricultural commodities has increased (for food, feed, fiber 
or energy) and part of it has been met through an expansion of the agricultural land globally. 
Higher demand for biofuels, bioliquids and biomass fuels contributed to this development. If 
this expansion takes place  on land with  high carbon stock,  it results in  a severe increase in 
greenhouse gas emissions and loss of biodiversity.  

To  update  the  data  on  the  deforestation  impact  of  crops  and  to  determine  their  share  of 
expansion into high carbon stock land, a mapping exercise has been conducted, which included 
the eight main crops used for biofuels production: maize, oil palm, rapeseed, soybean, sugar 
beet, sugar cane, sunflower and wheat. The methodology used was similar to the one used in 
the Commission 2019 ILUC report but introduced a number of improvements.  

The main improvements to the methodology focused on refining data sets related (i) to crop 
and  grassland  distribution,  (ii)  drivers  of  deforestation,  and  (iii)  oil  palm  expansion  on 
peatlands. Data sets on crop and grassland were improved with the integration of the updated 
MapSPAM 2010 product for 201018 and a precise global soybean map from 2015, allowing for 
more accurate monitoring. As regards the drivers of deforestation, a tropical drivers of forest 
loss layer (IIASA-TDFL v1) was developed to address commodity-driven deforestation more 
accurately.  Additionally,  the  estimation  of  oil  palm  expansion  on  peatlands  was  refined  by 
comparing maps from 2007 and 2017-2019, providing insights into expansion trends. Updated 
maps  were  provided  by  GRAS  covering  oil  palm  expansion  in  peatlands  in  Indonesia  and 

17 Harvested area includes the area on which crops are produced, excluding planted areas, which are not yet 
producing. 
18 MapSPAM 2010 v2r0 

5 

 
 
 
 
 
 
 
Malaysia for the same years. In addition, the tree loss layer was updated, which included the 
tree loss up to 2021. 

Regional mapping  

The  results  of  the  global  mapping  were  complemented  by  more  precise  regional  mapping, 
which allowed for a more detailed assessment of the expansion of crops in high carbon stock 
in  key  regions  which  have  been  identified  in  literature  and  deforestation  maps  as  being 
particularly relevant or which are key productions regions for crops linked to expansion. For 
the purpose of regional mapping, remote sensing and satellite imagery were used. Based on the 
abovementioned criteria, five regions were chosen:  Indonesia  for oil palm, Malaysia for  oil 
palm, Amazon basin and Cerrado states in Brazil for soybeans, Cerrado and Southern parts in 
Brazil  for  sugar  cane,  and  Gran  the  Chaco  region  in  Paraguay,  Bolivia  and  Argentina  for 
soybeans.  For  the  purpose  of  regional  mapping,  remote  sensing  and  satellite  imagery  were 
used.  

Finally, the different data sources were integrated into the global mapping dataset. The primary 
crop data was sourced from the 10x10 km resolution MapSPAM 2010, augmented by regional 
results at a 30x30 m resolution to accurately pinpoint palm oil areas in Indonesia and Malaysia 
and sugar cane in Brazil. Additionally, the 5x5 km GEOGLAM 2015 soybean layer provided 
comprehensive  global  coverage  with  regional  mapping  incorporated  for  South  American 
countries like Brazil, Argentina, Paraguay and Bolivia. These high-resolution  layers, paired 
with the updated Hansen Global Forest Change layers19 for tree loss and Miettinen's peatland 
extension data20, allowed for a detailed assessment of crop expansion trends.  

V.  DETERMINING ‘SIGNIFICANT EXPANSION’ INTO HIGH CARBON 

STOCK LAND 

Greenhouse gas emissions related to feedstock expansion in high carbon stock land  

In the evaluation of GHG emissions associated with feedstock expansion into high carbon stock 
land, oil palm  was  found to  be the crop with  the highest GHG burden between 2014-2021, 
largely  due  to  the  expansion  of  palm  oil  production  onto  peatlands,  which  accounted  for 
approximately 52% of its emissions. Other crops, such as maize, sugar cane, and sugar beet 
also contributed significant emissions, primarily due to the removal of living biomass and dead 
organic matter, which constituted over 85% of their emissions.  

The weighted average based on area of expansion of GHG emissions for all eight crops is 25 
tCO2/ha/yr,  higher  than  the  19.6  tCO2/ha/yr  which  was  reported  in  the  Commission  2019 
ILUC report. The explanation for this increase is two-fold. First, the calculation used specific 
values of above ground biomass per climatic zone and hectares of expansion per climate zone. 
This results in an on-average higher value of net carbon loss per hectare for all crops. Second, 

19 Hansen Global Forest Change Layers v1.7 was used in the first phase of the Guidehouse study and v1.9 was 
used in the second phase, following the methodology described in Hansen, et al., 2013,  
20 Miettinen, J., Shi, C., & Liew, S. C. (2016). Land cover distribution in the peatlands of Peninsular Malaysia, 
Sumatra and Borneo in 2015 with changes since 1990. Global Ecology and Conservation.  

6 

 
 
 
 
 
 
 
 
emissions from soil carbon, below ground biomass (roots) and dead organic matter, were also 
included.  

Results  on  GHG  emissions  depend  on  whether  crops  are  assumed  to  replace  primary  or 
secondary forest, which determines the above ground biomass carbon stock. To manage this 
variability, an average above ground biomass factor was adopted for Indonesia and Malaysia 
Tropical Rainforests from the Global Forest Resource Assessment21.  

Crop 

GHG Burden [tCO2/yr/ha] 

Share of total expansion area of all 
crops [ha] 

Oil, palm 
Soybeans 
Maize 
Sugar cane 
Wheat 
Sunflower seed 
Rapeseed 
Sugar beet 

32.6 
19.9 
22.5 
20.8 
16.2 
19.1 
15.5 
20.8 

Table 2- GHG emissions per crop per hectare converted 

Threshold for expansion  

39% 
33% 
21% 
3% 
3% 
1% 
1% 
0.01% 

The threshold of expansion (%) is estimated by comparing the default minimum CO2 savings 
(in CO2/MJ) to the calculated indirect GHG emissions (in CO2/MJ) due to the expansion of 
feedstock into high carbon stock land. Previously, a 14% expansion threshold was identified 
based  on  specific  GHG  savings  and  energy  yield  inputs.  Applying  a  30%  precautionary 
discount  factor,  this  was  reduced  to  10%,  as  set  out  in  Article  3  of  the  ILUC  Delegated 
Regulation. This threshold was recalculated using updated inputs, i.e. a higher average GHG 
emission rate of 25 tCO2/ha/yr and an adjusted energy yield of 53.6 GJ/ha/yr, resulting in a 
new threshold of 11.0%, which confirms the choice of the 10% threshold.  

Average energy yield per feedstock  

The average energy yield of each feedstock crop was calculated through an approach consisting 
of four steps. First, the top 10 producing countries per feedstock annually were identified, and 
their contribution percentages were determined. Then, FAOstat yield data provided the basis 
for calculating the average crop yield for these 10 countries each year. As a third step, using 
this yield, the annual singular energy yield was calculated for each crop. Finally, the average 
energy yield for the period 2014-2021 was calculated, as shown in Table 3.  

Period  Wheat  Maize 

Sugar 
cane 

Sugar beet  Rapeseed 

Oil palm 
fruit 

Soybeans 

Sunflower 
seed 

2014-
2021 

32 

62 

144 

133 

32 

132 

19 

30 

Table 3- Average energy yield per feedstock in GJ/ha 

Productivity Factors 

The productivity factors for various crops were calculated by, first, determining the average 
yield  per  hectare  for  each  crop  for  the  period  from  2014  to  2021,  expressed  in  tonnes  per 

21 FaoSTAT, 2021 

7 

 
 
 
 
 
 
 
hectare. Next, the total energy of all allocated materials per unit crop weight was calculated, 
taking into account all traded products, along with any losses, such as those occurring during 
transport. Then, the energy of all allocated materials was calculated for a planted hectare over 
a span of 20 years. Finally, the productivity factor for each crop was derived by indexing the 
calculated energy values calculated in the previous step. The values calculated as part of the 
Guidehouse study closely followed the values provided in the Commission 2019 ILUC report. 
Maize, sugar cane, sugar beet and oil palm were found to have significantly higher yields than 
other crops, which justifies continuing applying higher productivity factors for these crops.  

Crop 

Wheat 
Maize 
Sugar cane 
Sugar beet 
Rapeseed 
Oil, palm 
Soybeans 
Sunflower 

PF from Feedstock Expansion 
report 2008-2017 

PF from this analysis 
2014-2021 

1 
1.7 
2.2 
3.2 
1 
2.5 
1 
1 

0.9 
2.0 
1.9 
3.1 
0.9 
2.2 
1.0 
0.8 

Table 4- Productivity Factors per crop 

Final results 

In the Commission 2019 ILUC report, three factors were deemed crucial in determining the 
‘significance’ of the expansion of the production area of a specific crop into land with high-
carbon stock for the purposes of the Renewable Energy Directive: (a) the absolute and relative 
magnitude  of  the  land  expansion  since  a  specific  reference  year  compared  to  the  total 
production area of the relevant crop; (b) the share of this expansion into land with high-carbon 
stock;  and  (c)  the  type  of  high-carbon  stock  area.  These  factors  as  well  as  the  specific 
productivity  factors  for  each  group  of  crop  were  considered  when  setting  the  criteria  to 
determine high ILUC-risk feedstock in the ILUC Delegated Regulation. 

The results of the updated analysis can be found in the table below:  

Crop  

Share of 
expansion forest  

Share of 
expansion peat  

Average annual 
expansion (kha)  

Average annual 
expansion (%)  

Wheat  
Maize  
Sugar cane  
Sugar beet  
Rapeseed  
Oil, palm  
Soybeans  
Sunflower  
Table 5: Guidehouse calculations - Final Results22  

1.6%  
7.0%  
16.1%  
0.2%  
1.0%  
27.1%  
14.1%  
1.0%  

0.0%  
0.0%  
0.0%  
0.0%  
0.0%  
13.7%  
0.0%  
0.0%  

-183   
2,527   
215   
4   
363   
937   
2,565   
313   

0.0%  
1.4%  
0.7%  
0.4%  
1.6%  
3.8%  
2.2%  
1.1%  

22  The values included in this table have been calculated in accordance with the formula included in the Delegated 
Regulation 2019/807 (see Chapter I). For the calculation, the results from the updated statistics analysis and the 
updated mapping were combined with the productivity factors for each group of crop, as suggested by the JRC 
and as indicated in the Delegated Act. 

8 

 
 
 
 
 
 
 
 
 
 
 
 
 
As explained  in  Chapter I, for  a specific  crop to  be categorised as high  ILUC-risk, the two 
criteria  set  in  Article  3  of  the  ILUC  Delegated  Regulation  must  be  fulfilled  cumulatively. 
Taking into account these two criteria, and according to the updated data and new scientific 
evidence, oil palm remains a feedstock that is to be classified as high ILUC-risk. In addition, 
soybeans should be classified as a high ILUC-risk feedstock, as both criteria of Article 3 of 
the ILUC Delegated Regulation are fulfilled. This means that the expansion of the palm oil and 
soybeans production area into high-carbon stock land is so significant that the greenhouse gas 
emissions that result from land use change offset all greenhouse gas emission savings of fuels 
originating from this feedstock, when compared to the use of fossil fuels. 

VI.  UPDATE ON LOW ILUC-RISK FUELS CERTIFICATION  

Low  ILUC-risk  biofuels,  bioliquids  and  biomass  fuels  are  defined  in  Article  2(37)  of  the 
Renewable  Energy  Directive  as  (a)  those  originating  from  feedstock  for  which  a  yield 
improvement on existing land has been observed - through improved agricultural practices - or 
(b) those cultivated on unused land. These two options are called “additionality measures” in 
the ILUC Delegated Regulation23. Article 4 of the ILUC Delegated Regulation contains general 
criteria  for  the  certification  of  low  ILUC-risk  biofuels,  bioliquids  and  biomass  fuels,  while 
Article 5 further describes the additionality measures. Low ILUC-risk fuels must be produced 
in accordance with the sustainability and GHG emission saving criteria pursuant to Article 29 
of the Renewable Energy Directive. 

Article 5(1) of the ILUC Delegated Regulation describes the conditions that have to be fulfilled 
for  the  feedstock  used  for  the  production  of  biofuels,  bioliquids  and  biomass  fuels  to  be 
classified as additional and thus the fuel produced to be eligible for low ILUC-risk certification. 
There are three different conditions listed in point (a) of Article 5(1), at least one of which has 
to  be  fulfilled.  Financial  attractiveness  is  the  first  condition.  It  means  that  the  additionality 
measure  makes  the  fuel  eligible  for  certification  as  a  low  ILUC-risk  fuel  where  the 
implementation of the measure is made financially attractive because the fuel produced can be 
counted towards the renewable energy targets, or because other barriers that would otherwise 
prevent  its implementation are removed as  a result of being  eligible to  count  towards those 
targets. For the other two conditions, namely cultivation in abandoned or severely degraded 
land and application of the additionality measures by smallholders, additionality is assumed. 
The latter is to ensure that unnecessary administrative burden is avoided. This exemption is 
justified  and  can  be  maintained  because  smallholders  are  facing  barriers  that  hinder  the 
implementation of measures to increase productivity.  

To  allow  economic  operators  to  recuperate  investments  costs  while  ensuring  the  continued 
effectiveness of the framework, point (b) of Article 5(1) of the ILUC Delegated Regulation 
requires that the additionality measures have been taken no longer than 10 years before the 
certification  of  the  biofuels,  bioliquids  and  biomass  fuels  as  low  ILUC-risk  fuels.  This 
condition works well for additionality measures that have an immediate effect. However, to 
better  cover  cases  where  significant  time  passes  until  they  yield  additional  feedstock,  it  is 

23 Article 2(5). 

9 

 
 
 
 
 
 
 
 
justified  to  determine  the  period  of  their  eligibility  based  on  the  point  in  time  when  the 
production of additional feedstock started, rather than the point in time of their implementation.  

Further  guidance  on  the  implementation  of  low  ILUC-risk  certification  is  included  in 
Chapter V of  Implementing  Regulation  (EU) 2022/99624 on certification  rules  for voluntary 
schemes. Its Articles 24 to 27 explain the specific requirements for low ILUC-risk certification 
and  include  rules  for  proving  additionality  and  detailed  guidance  for  complying  with  the 
requirements  for  production  on  unused  or  abandoned  land  and  for  determining  additional 
biomass  for yield increase measures. These technical  rules aim to ensure a harmonised and 
robust  approach  across  certification  bodies.  Specifically  when  it  comes  to  the  additionality 
measures and the eligibility period mentioned above, Article 24(6) of Implementing Regulation 
(EU) 2022/996 introduced the rule that for perennial crops, an economic operator can choose 
to  delay  the  start  of  the  10-year  validity  period  by  up  to  2  years  in  the  case  of  operational 
additionality measures or up to 5 years in the case of replanting. 

VII.  CONCLUSIONS 

The findings of the review of scientific evidence included in this report are consistent with the 
data  included  in  the  2019  feedstock  report  and  confirm  the  approach  taken  in  the  ILUC 
Delegated Regulation. Accordingly, the Commission intends to limit the review of the ILUC 
Delegated Regulation to minor changes of the methodology as well as an update of the data on 
feedstock expansion and the productivity factors. According to the updated data, both palm oil 
and soybeans qualify as high ILUC-risk feedstock.  

24 Commission Implementing Regulation (EU) 2022/996 of 14 June 2022 on rules to verify sustainability and 
greenhouse gas emissions saving criteria and low indirect land-use change-risk criteria, OJ L 168, 27.6.2022, p. 
1. 

10