Atmosphere 2020 , 11, x doi: FOR PEER REVIEW www.mdpi.comjournalatmosphere [624341]

Atmosphere 2020 , 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/atmosphere
Article 1
Greenhouse gas emissions from cultivation of plants 2
used for biofuel production in Poland 3
Paweł Wiśniewski 1,* and Mariusz Kistowski 1 4
1 Department of Landscape Research and Environmental Management, Faculty of Oceanography and 5
Geography, University of Gdansk, Bażynskiego 4, 80- 309 Gdańsk, Poland 6
* Correspondence: [anonimizat] 7
Received: date; Accepted: date; Published: date 8
Abstract: Reduction of greenhouse gas (GHG) emissions as well as increase in the share of 9
renewable energy are the main objectives of EU ener gy policy. In Poland, biofuels play an important 10
role in the structure of obtaining energy from rene wable sources. In the case of biofuels obtained 11
from agricultural raw materials, one of the signifi cant components of emissions generated in their 12
full life cycle is emissions from the cultivation s tage. The aim of the study is to estimate and 13
recognize the structure of GHG emission from biomas s production in selected farms in Poland. For 14
this purpose, the methodology recommended by the Po lish certification system of sustainable 15
biofuels and bioliquids production, approved by the European Commission, was used. Calculated 16
emission values vary between 41.5 kg CO 2eq/t and 147.2 kg CO 2eq/t dry matter. The highest average 17
emissions were obtained for wheat (103.6 kg CO 2eq/t), followed by maize (100.5 kg CO 2eq/t), triticale 18
(95.4 kg CO 2eq/t) and rye (72.5 kg CO 2eq/t). The greatest impact on the total GHG emissio ns from 19
biomass production is caused by field emissions of nitrous oxide and emissions from the production 20
and transport of fertilizers and agrochemicals. Emi ssions generated at the stage of production, 21
storage and transport of seeds and during the use o f fuels in agricultural and forestry machinery 22
have, significantly, smaller share in the total GHG emissions from biomass production. 23
Keywords: greenhouse gases; emission; biofuels; biomass; cult ivation; plants; Poland 24
25
1. Introduction 26
According to the Directive of the European Parliame nt and of the Council of 11 December 2018 27
on the promotion of the use of energy from renewabl e sources [1], the share of energy from renewable 28
sources (RES) in the EU, in gross final consumption of energy by 2030, should be at least 32%. The 29
directive defines, among other things, the criteria of sustainable development and reduction of 30
greenhouse gas (GHG) emission in relation to biofue ls, bioliquids and fuels from biomass. According 31
to its assumptions, this limitation should be at le ast 50% in relation to the fossil equivalent in the case 32
of biofuels and biogas used in the transport sector and bioliquids produced in installations that were 33
put into operation on 5 October 2015 or earlier, an d at least 60% in in the case of biofuels and bioga s 34
used in the transport sector and bioliquids produce d in installations that were put into operation 35
from October 6, 2015 to December 31, 2020. This is expected to increase to 65% from 1st January 2021 36
and from 2026 to 70%. The national target for Polan d, set out in Annex I of the aforementioned 37
Directive, assumes the achievement of 15% share of energy from renewable sources in gross final 38
energy consumption in the year 2020. Based on the d ata of the Central Statistical Office [2], the shar e 39
of energy from renewable sources in the total prima ry energy acquisition in Poland in 2018 amounted 40
to 14.3% (compared to 29.9% in EU-28), which gives an increase of 2.2% compared to 2014 (in EU-28 41
there was a 3.8% increase during the same period). 42
Biofuels and biogas play an important role in the s tructure of energy generation from renewable 43
sources in Poland. Solid biofuels (mainly agricultu ral biomass, firewood and waste from forestry) are 44
the main carrier of renewable energy – their share in obtaining energy from RES in Poland is 69.3%. 45

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From liquid biofuels, 10.2% of energy is obtained f rom renewable sources, while from biogas – 3.3% 46
[2]. Therefore, green technologies in the Polish en ergy sector are primarily based on biomass, and ful l 47
use of its energy potential may be ensured by reach ing the National Indicative Target and fulfilment 48
of EU legal obligations for 2020 [3-5]. However, in order to assess the fulfilment of the criterion 49
concerning the GHG emission reduction capacity in r elation to biofuels, bioliquids and fuels from 50
biomass, it is necessary to know the GHG emission g enerated in their full life cycle, including during 51
the cultivation of raw materials. The aim of the st udy is to estimate GHG emission caused by 52
cultivation of selected plants (wheat, triticale, r ye and maize), used for biofuel production in Polan d, 53
as well as to recognize the structure of emission f rom biomass production. 54
2. Materials and Methods 55
Questionnaires provided by agricultural producers o f raw materials for biofuel production were 56
the source material for the research. The studied f arms are located in the north-western and western 57
part of Poland, in West Pomeranian Voivodeship (12 objects) and Lubusz Voivodeship (3 objects) 58
respectively (Table 1, Figure 1). Most of them are located in Gryfice district (6: three in Płoty 59
commune, two – in Gryfice and one – Trzebiatowo) as well as Gryfino district (4: two each in Banie 60
and Chojna communes). One farm is located in each o f the following districts: Kamieński (Świerzno 61
commune), Stargard (Stara Dąbrowa commune), Sulęcin (Krzeszyce commune), Świebodzin 62
(Świebodzin) and Żagański (Niegosławice commune). 63
64
Table 1. Location and area of study farms 65
No. of
farms Place Farm area
(hectares) Voivodeship District
1 Banie 900 West Pomeranian Gryfice
2 Świerzno 100 West Pomeranian Kamień Pomorski
3 Gryfice Zdrój 300 West Pomeranian Gryfice
4 Trzebiatów 60 West Pomeranian Gryfice
5 Kicko 120 West Pomeranian Stargard
6 Lusowo 65 West Pomeranian Gryfice
7 Chojna 700 West Pomeranian Gryfino
8 Płoty 180 West Pomeranian Gryfice
9 Gryfice 165 West Pomeranian Gryfice
10 Gościejewo 142.5 West Pomeranian Gryfice
11 Swobnica 121 West Pomeranian Gryfino
12 Godków 350 West Pomeranian Gryfino
13 Mycielin 250 Lubusz Żagań
14 Brzozowa 68.5 Lubusz Sulęcin
15 Świebodzin 520 Lubusz Świebodzin
(Source: Authors) 66
67
All the farms are located in the lowland area of Po land, at altitudes not exceeding 100 m above 68
sea level, with the exception of two farms in the c entral and southern part of the Lubusz Voivodeship 69
– located 150-200 m above sea level. Most of the fa rms (9) are located in the natural region of Szczec in 70
Coastland, most of them (7) in the Gryfice Plain, l ocated closest to the Baltic Sea, and one each in t he 71
Plains: Nowogard and Wełtyń. Three farms are locate d within the Western Pomerania Lakeland 72
region in the Myślibórz Lakeland, and one each in t he Gorzów Basin, which is part of the Toruń- 73
Eberswalde Ice Marginal Valley, and in the Łagów La keland – a part of the Lubuskie Lakeland [6]. 74
The southernmost farm in Mycielin is located on the Dalków Hills, which are part of the Trzebnica 75
Range, and is the only one outside the reach of the young glacial areas. The area of seven farms 76
located in the north of the area is drained to the Baltic Sea by the Rega River, Kicko by the Ina Rive r 77
to the Oder, and of the four farms located closest to the border with Germany by smaller watercourses 78

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to the west of the Oder. In the Lubusz Voivodeship, the farm in Brzozowa is drained to the Warta 79
River, in Mycielin to the Odra River, and Świebodzi n is located in the watershed zone of the central 80
Odra and Obra catchment (Figure 1). 81
82
83
Figure 1. Location of study farms (source: Authors) 84
85
The studied farms are characterized by different na tural conditions for agricultural production 86
(Figure 2). On the basis of the analysis of the spa tial distribution of the index of agricultural 87
valorisation of production space, taking into accou nt soil quality, agroclimate, relief and water 88
conditions [7, 8], it was found that the farms in B anie and Swobnica have excellent conditions. The 89
decisive factor is constituted by soils, partially included in the so-called Pyrzyce black earths, whi ch 90
are among the most fertile in Poland [9, 10]. The o ther two farms in the Gryfino district are 91
characterized by very good conditions. The remainin g farms in the West Pomeranian Voivodeship 92
have half as good and moderate conditions for culti vation, whereas in the Lubusz Voivodeship: 93
Mycielin has good conditions and the other two are moderate. 94
The 15 farms under analysis cover a total area of 4 042 ha (Table 1). Their acreage is highly 95
diversified, from 60 to 900 ha. They can be divided into four surface categories. The four smallest 96
ones (up to 100 ha) are located in the northern par t of the area, closest to the Baltic Sea, and in 97

Atmosphere 2020 , 11, x FOR PEER REVIEW 4 of 11
Brzozowa. The next five, occupying from a 101 to 20 0 ha each, are located in the area of Gryfice and 98
Płoty as well as in Kicko and Swobnica. Three large farms (250 – 350 ha each) are located in Zdrój nea r 99
Gryfice, Godków and Mycielin. The three with the la rgest area are located in Świebodzin (520 ha), 100
Chojna (700 ha) and Banie (900 ha) – the last two i n the best agro-ecological conditions. 101
102
103
Figure 2. Location of study farms against the background of agricultural production space (source: 104
Authors based on [7, 8]) 105
106
The estimation of GHG emission caused by the cultiv ation of raw materials for biofuel 107
production in the above mentioned farms was made in accordance with the methodology 108
recommended by the KZR INiG System [11]. This is th e Polish system of certification of sustainable 109
production of biofuels, bioliquids and raw material s, owned by the Oil and Gas Institute – National 110
Research Institute. This system was approved by the European Commission in 2014 in relation to 111
proving compliance with the sustainable development criteria, according to the directives of the 112
European Parliament and the EU Council [12, 13]. In put data (variables) affecting emissions from 113
crops include seeds, fuel, fertilizers, pesticides, yield and field emission of N 2O. On the basis of 114
questionnaires gathered from selected agricultural producers, information was obtained on the area 115
of cultivation, the name and amount of fertilizers used, the name and amount of plant protection 116

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products used, the amount of seed, fuel consumption for driving agricultural machinery and other 117
energy sources used during cultivation in 2019 (Tab le 2). 118
119
Table 2. Agricultural data based on which components of GHG emissions have been determined 120
No. of
farms Crop Seed
(kg/ha) Yield
(t/ha) Mineral fertilizers
(kg/ha pure nutrient) Pesticides
(kg/ha) Fuel
consumption
(l/ha) N P 2O5 K CaO
1 wheat 117 6.8 61.1 21.2 48.0 0 1.0 120
triticale 135 10.0 53.1 21.2 48.0 0 1.0 85
2 wheat 180 5.8 46.0 40.0 60.0 0 0.02 100
triticale 180 5.8 46.0 40.0 60.0 0 0.02 100
3 maize 60 7.0 8.0 6.3 72.0 1000 0.2 80
4 rye 120 4.5 23.8 6.0 12.0 0 0 80
5 triticale 120 6.0 45.6 30.0 140.0 0 0.4 80
6 triticale 200 6.0 51.7 20.0 30.0 33.4 1.1 85
7 wheat 160 7.0 53.8 6.0 25.0 333.3 0.2 105
triticale 150 5.5 46.6 0 0 222 0.2 105
maize 20 9.0 46.2 13.8 24.0 333.3 0.1 105
8 rye 180 5.0 30.1 2.4 3.6 500 1.2 100
9 triticale 200 4.5 30.1 2.4 3.6 500 1.2 100
10 triticale 160 5.0 26.0 14.4 14.4 0 0 105
11 triticale 165 7.2 28.8 13.8 26.4 387.5 1.0 87
12 triticale 110 6.0 39.2 0 40.0 750 0.4 110
13 wheat 140 8.0 41.9 21.2 4..0 0 0.8 140
triticale 150 6.0 39.1 21.2 40.0 0 0.8 80
14 rye 150 5.0 0 0 0 0 0.2 85
15 maize 20 5.0 57.6 32.0 48.0 0 0.06 85
(Source: Authors based on surveys of agricultural p roducers) 121
122
The greenhouse gas emissions from biomass productio n were calculated according to the 123
following formula: 124
eec = e seed + e chem + e irr + e field + e burn + e mm , (1)
gdzie: e ec – GHG emissions from biomass production; e seed – emissions from the use of grain for 125
sowing; e chem – emissions from the production and transport of fe rtilizers and agrochemicals; e irr – 126
emissions from irrigation; e field – emissions from soil cultivation (field emissions) ; e burn – emissions 127
from burning of ground before and after cultivation ; e mm – emissions from agricultural and forestry 128
machinery and other mobile or stationary equipment. 129
The GHG emission from biomass production (e ec ) was expressed in carbon dioxide equivalent 130
(CO 2eq) per dry matter unit [kg CO 2eq/t], adopting the global warming potential (GWP) for N 2O = 131
265, according to the value indicated in the IPCC F ifth Assessment Report [14]. The GHG emission 132
from seeds used for sowing (e seed ) includes the emission generated during the produc tion, storage 133
and transport of seeds. Where the seed was self-pro duced, in order to calculate the net biomass 134
production, the amount of biomass retained as seed was deducted from the total biomass production. 135
Standard coefficients of GHG emission from seeds us ed for sowing were applied according to the 136
BioGrace calculator (v. 4d) recommended by the Euro pean Commission [15]. The GHG emission from 137
the production and transport of fertilizers and pla nt protection products (e chem ) was calculated 138
according to a formula: 139
echem = Q chem * F chem , (2)
140

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The amount of fertilizers or plant protection produ cts (Q chem ) applied per unit area of land (ha) is 141
expressed in mass unit (kg) of the pure component – nitrogen, phosphorus, potassium or active 142
substance of the plant protection product. The GHG emission factor from the production and 143
transport of mineral fertilizers or plant protectio n products (F chem ) is expressed in CO 2 equivalent per 144
unit mass of fertilizer or plant protection product . The emission factors were applied according to th e 145
BioGrace calculator (v. 4d) [15]. GHG emissions fro m field irrigation (e irr ) are those resulting from the 146
use of equipment for pumping, storing and distribut ing water. The associated GHG emissions are 147
calculated as e mm emissions. In order to calculate the field emissio n of nitrous oxide (e field ), the GNOC 148
(Global Nitrous Oxide Calculator) recommended by th e European Commission was used [16]. In 149
accordance with the Communication of the European C ommission [17] as well as the guidelines of 150
the KZR INiG [11], the IPCC methodology, including both direct and indirect emissions, can be 151
applied when taking into account the N 2O emission from soils. All three levels are allowed – from 152
the basic and commonly used Tier 1 level, not even based on national data, to the most detailed Tier 153
3 level. The GNOC calculator used in this work allo ws to estimate the amount of field emission at 154
Tier 2 level. The tool was developed based on of pr oduction data from the Food and Agriculture 155
Organization of the United Nations (FAO) database a nd the input data necessary to perform the 156
calculations include, among others, the place of cu ltivation, grain and straw yields, soil conditions, 157
information on irrigation, the dose of applied fert ilizers, information on post-harvest residues as we ll 158
as basic environmental parameters [18, 19]. The com bustion of plants, dead organic matter and post- 159
harvest residues may cause the emission of CH 4 and N 2O due to incomplete combustion. The CO 2 160
emission from biomass combustion (e burn ) is considered zero [20, 21]. The emission from th e use of 161
fuels in agricultural and forestry machines (e mm ), expressed in CO 2 equivalent per unit of area during 162
the year, was calculated according to the formula: 163
Flmm = Q mmf * E f, (3)
Fuel consumption in the use of agricultural and for estry machinery (Q mmf ) is expressed in volume 164
unit per area unit per year (dm 3/ha/year). The standard coefficient of emissions fr om production and 165
fuel consumption (E f), according to the data of BioGrace calculator (v. 4d) was used [15]. For diesel, 166
it is 87.64 gCO 2eq/MJ. 167
3. Results and Discussion 168
The estimated emissions from biomass production in the farms under study, range from 41.5 kg 169
CO 2eq/t dry matter in the case of rye cultivation (in the farm No. 14) to 147.2 kg CO 2eq/t in the case 170
of maize cultivation (in the farm No. 15) (Table 3) . Distinguishing from the other results, the low 171
value of emission from rye cultivation in the farm No. 14, located in Lubusz Voivodeship, is mainly 172
the result of not using mineral fertilizers at the cultivation stage. On the other hand, the maximum 173
GHG emission caused by maize cultivation in farm No . 15 is mainly the result of the applied dose of 174
fertilizers and relatively high nitrous oxide field emission estimated in the GNOC calculator at 1.4 k g 175
N2O-N/ha. 176
The average GHG emissions from biomass production a re highest for wheat cultivation at 103.6 177
kg CO 2eq/t. The lower average emission value was obtained for maize (100.5 kg CO 2eq/t), followed 178
by triticale (95.4 kg CO 2eq/t) and rye (72.5 kg CO 2eq/t) respectively (Figure 3). 179
The calculations show that field emissions of nitro us oxide (e field ) as well as emissions from the 180
production and transport of fertilizers and agroche micals (e chem ) are the key components of GHG 181
emissions from biomass production (Table 4). The do minant share of these sources in the structure 182
of GHG emission is characteristic for all the exami ned crops and amounts on average to 52.7% 183
(maximum 73.5% for rye in farm No. 14) in the case of field emission and 37.4% (maximum 53.8% for 184
maize in farm No. 3) in the case of emission from t he production and transport of fertilizers and 185
agrochemicals. For this reason, it is important to use an appropriate tool to estimate the emissions 186
from cultivation. The improvement of the accuracy o f field emission estimations is of significant 187
importance for the reduction of agricultural emissi ons in the full life cycle of the biofuel [22]. 188
189

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Table 3. GHG emissions from cultivation of agricultural cro ps for biofuels (e ec ) 190
No. of
farms Crop eec
(kg CO 2eq/t dry matter)
1 wheat 113.4
triticale 74.4
2 wheat 112.4
triticale 104.5
3 maize 77.6
4 rye 78.1
5 triticale 106.8
6 triticale 108.4
7 wheat 105.6
triticale 99.8
maize 76.7
8 rye 98.0
9 triticale 108.5
10 triticale 79.0
11 triticale 76.0
12 triticale 101.4
13 wheat 82.9
triticale 95.2
14 rye 41.5
15 maize 147.2
Total minimum 41.5
maximum 147.2
average 94.4
σ 21.9
(Source: Authors) 191
192
193
Figure 3. Descriptive statistics of the GHG emissions from c ultivation of agricultural crops for 194
biofuels (e ec ) (source: Authors) 195
196

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The studies carried out so far indicate that taking into account local environmental conditions in the 197
GNOC calculator used in this work allows for a more accurate estimation of field emission of N 2O. It 198
is usually lower than the emission estimated with t he use of a simpler, but omitting local climatic 199
conditions, soil and farm management method, IPCC m ethods at Tier 1 level [23-25]. Moreover, the 200
application of this tool enables the development of effective climate change mitigation strategies, 201
including the assessment of the impact of the dose and type of fertilizer, period and manner of its 202
application, as well as the applied cultivation tec hniques [26]. Taking into account the emissions fro m 203
the production and transport of fertilizers and agr ochemicals, it should be noted that not only the 204
dose of applied fertilizers but also their type (ma nufacturer, composition and emission factor) is 205
important for the final GHG emission from biomass p roduction. Therefore, it is necessary to raise 206
farmers' awareness in this respect and to increase the attention of fertilizer manufacturers to the 207
aspect of GHG emission reduction [27]. 208
209
Table 4. The structure of GHG emissions from cultivation of agricultural crops for biofuels 210
No. of
farms Crop Share of individual emission
sources (%)
eseed e chem e field e mm
1 wheat 4.8 40.3 53.5 1.4
triticale 5.7 36.9 56.4 1.0
2 wheat 8.5 39.6 50.6 1.3
triticale 9.1 42.6 46.9 1.4
3 maize 5.5 53.8 39.1 1.5
4 rye 10.9 31.6 55.5 2.0
5 triticale 5.8 47.8 45.3 1.1
6 triticale 9.8 41.1 47.9 1.1
7 wheat 6.7 38.3 53.8 1.2
triticale 8.5 38.8 51.0 1.6
maize 1.3 39.8 57.6 1.4
8 rye 11.2 42.0 45.0 1.7
9 triticale 12.5 42.6 43.2 1.7
10 triticale 12.2 31.4 54.1 2.2
11 triticale 11.3 24.1 63.0 1.6
12 triticale 5.5 48.0 45.0 1.5
13 wheat 6.4 32.6 59.9 1.0
triticale 8.1 36.0 54.7 1.2
14 rye 22.2 0.8 73.5 3.4
15 maize 1.1 39.2 58.7 1.0
Total minimum 1.1 0.8 39.1 1.0
maximum 22.2 53.8 73.5 3.4
average 8.4 37.4 52.7 1.5
σ 4.6 10.8 7.9 0.6
(Source: Authors) 211
212
The total GHG emissions from biomass production are much less affected by emissions 213
generated during the production, storage and transp ort of seeds (e seed ). Its share in the structure of 214
emissions from cultivation ranges from 1.1% for mai ze in farm No. 15 to 22.2% for rye in farm No. 215
14, averaging 8.4%. Of minimum significance is the emission from the use of fuels in agricultural and 216
forestry machines (e mm ) – from 1% for triticale in farm No. 1 and wheat i n farm No. 13, to 3.4% for rye 217
in farm No. 14, with the average for all the examin ed crops at the level of 1.5%. It should also be bo rne 218
in mind that the GHG emission from biomass producti on is also influenced by the yield level of 219
individual plants. Calculations and studies of othe r authors [28] show that lower yields cause an 220

Atmosphere 2020 , 11, x FOR PEER REVIEW 9 of 11
increase in the estimated greenhouse gas emissions as a result of the cultivation of raw materials, 221
expressed per dry matter unit. 222
4. Conclusions 223
Biomass energy generation aims to reduce GHG emissi ons. According to EU directives, all actors 224
involved in the production process of a bio-compone nt or biofuel are required to provide estimates 225
of life cycle emissions (and their limitations) of the biofuel, including those caused by the cultivat ion 226
of raw materials. The estimated emissions from biom ass production in the examined farms vary from 227
41.5 kg CO 2eq/t of dry matter in the case of rye cultivation t o 147.2 kg CO 2eq/t in the case of maize 228
cultivation. The average emissions are highest for wheat (103.6 kg CO 2eq/t), slightly lower for maize 229
(100.5 kg CO 2eq/t) and triticale (95.4 kg CO 2eq/t) and lowest for rye (72.5 kg CO 2eq/t). 230
The field emissions of nitrous oxide and emissions from the production and transport of 231
fertilizers and agrochemicals are the key component s of GHG emissions from biomass production. 232
The dominant share of these sources in the emission structure is characteristic for all the crops 233
examined and amounts to 52.7% (maximum 73.5%) and 3 7.4% (maximum 53.8%), respectively. 234
Taking into account local environmental conditions in the GNOC calculator used in this study 235
allowed for a more accurate estimation of field emi ssion of nitrous oxide, which is important for 236
reducing agricultural emissions in the full life cy cle of biofuel. In the case of emissions connected with 237
the use of fertilizers, not only their dose, but al so their type and composition are important. 238
The emission generated at the stage of seed product ion, storage and transportation (8.4% on 239
average) as well as the emission from the use of fu els in agricultural and forestry machines (1.5% on 240
average) have much lower impact on the total GHG em ission from biomass production. Crop yield 241
also has a noticeable impact on the results obtaine d – its decrease causes an increase in the estimate d 242
GHG emission from biomass production. 243
Author Contributions: Conceptualization, P.W. and M.K.; methodology, P.W. and M.K.; formal 244
analysis, P.W. and M.K.; investigation, P.W. and M. K.; resources, P.W. and M.K.; data curation, P.W. 245
and M.K.; writing—original draft preparation, P.W. and M.K.; writing—review and editing, P.W. and 246
M.K.; visualization, P.W. and M.K. All authors have read and agreed to the published version of the 247
manuscript. 248
Funding: This research was funded by Department of Landscap e Research and Environmental 249
Management, University of Gdansk. 250
Conflicts of Interest: The authors declare no conflict of interest. 251
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