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Carbon Footprint of Rice Production in Indonesia: An Analysis of National Statistics To cite this article: Mufidah Afiyanti and Rose Novita Sari Handoko 2019 IOP Conf. Ser.: Earth Environ. Sci. 239 012015

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IISS 2018 IOP Conf. Series: Earth and Environmental Science 239 (2019) 012015

IOP Publishing doi:10.1088/1755-1315/239/1/012015

Carbon Footprint of Rice Production in Indonesia: An Analysis of National Statistics Mufidah Afiyanti1 and Rose Novita Sari Handoko1 1

Master Program of Natural Resources Management. Postgraduate School of Multidisclipinary Studies. Universitas Brawijaya. 65145 Malang East Java, Indonesia

*

Corresponding author: [email protected]

Abstract. Estimating carbon footprint (CF) of rice production could administer an insight into the input of rice production to climate change and analysing possible greenhouse gas (GHG) alleviation alternatives. In this study, data for the rice production of 34 provinces in Indonesia at year of 2015 were gathered from the Indonesian statistical data on area cultivation, productivity, fertilizer application, diesel, and irrigated water. The CF of indirect and direct carbon emissions which were correlated with those agricultural intakes was calculated with reported emission factors. The result demonstrated the CFs mean in 34 provinces during dry and rainy season in Indonesia was 1,900,341.48 kg CO eq/ha and 1,892,825.68 kg CO eq/ha, respectively. The data also showed that the highest of CF production during dry and rainy seasons obtained by Belitung Islands and East Nusa Tenggara province, respectively. Meanwhile the lowest of CF production for both dry and rainy season belonged to Yogyakarta province. Result on the quantified agricultural intakes demonstrated the irrigation water usage, fertilizer usage, direct methane and diesel for agricultural tools, had biggest to lowest contribution to CF production, respectively. Based on the result, we concluded that a rice cultivation practices which has an efficient irrigation water usage may become an option to reduce CF that leads to climate change mitigation. 2

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1. Introduction A rapid increase in emissions of greenhouse gas (GHG) is having important contribution to the global climate change. According to previous report [1], there was an increase of comprehensive mean temperature from 1906 until 2005. Thus, concern in reducing the emission of GHG has increased assessments on carbon footprint (CF) from various activities and also products. Carbon footprint is the total amount of carbon emissions released in a particular activity, such as agricultural activities, transportation, and even daily activities. Basically the value of carbon footprint will show how much the contribution of certain activities have on carbon emissions and GHG emissions in the atmosphere. Knowledge of the amount of carbon footprint produced in an activity, especially in a massive area of rice farming in Indonesia is very important considering the concentration of GHG in the atmosphere is increasing from year to year. The wetland rice plantation in Indonesia is common and its plantation area is a massive one, that is 4,755,054.10 ha in 2015[2], that is why estimating carbon footprint (CF) of rice production could administer an insight into the input of rice production to climate change and analysing possible greenhouse gas (GHG) alleviation alternatives. Thus, the aim of this paper was analysing the amount of carbon footprint produced from rice farming activities in Indonesia, as well as analysing the factors

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IISS 2018 IOP Conf. Series: Earth and Environmental Science 239 (2019) 012015

IOP Publishing doi:10.1088/1755-1315/239/1/012015

in rice farming techniques in Indonesia that contribute to the production of carbon footprint in rice production in Indonesia 2. Materials and Method 2.1. Data sources In this study, data for the rice production of 34 provinces in Indonesia at year of 2015 were gathered from the Indonesian statistical data on area cultivation, productivity, fertilizer application, diesel, and irrigated water. The data were taken from available published statistic data (Table 1). The CF of indirect and direct carbon emissions which were correlated with those agricultural intakes was calculated with reported emission factors. Table 1. Statistic Data Sources Data Irrigated of rice field area (ha) Rice productivity (kg / ha) Rice production (tons) Need for NPK and urea fertilizer (tons) Water needs (m3) Agricultural equipment and machinery (unit) NPK Emissions CH4 Emissions N2O Emissions Irrigation water emissions Diesel oil emissions Agricultural machine emissions Rice field emissions

Emission Factor -

References [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [13] [14]

1.61 kg CO2 eq / kg product ECH4 = LT x HT x effield 298 kg CO2 eq 81.87 kg CO2 eq 20.2 t C TJ-1 12.8 kg CO2 eq.kg-1 1.30 ton CH4 / ha

2.2. Data calculation The first calculation of GHG is to calculate the manufacture of each input used in production, including fertilizer and the use of diesel fuel for agricultural equipment. Calculation of manufacturing emissions (EM, kg CO eq / ha) [15], namely: 2

E = ∑A x E M

l

(1)

F

Al is agricultural inputs amounts including fertilizer needs (kg) and diesel (kg), the E value (kg CO2 eq) was emission factor from manufacturing as unit of agricultural input. The second calculation is N O emissions (E , kg CO eq/ha). This is obtained from N fertilizer (kg N/ha): F

2

E =NxE N20

FN2O

xT xT xT 0

1

N2O

2

(2)

2

N was the amount of needed fertilizer N (kg); E was factor emission from N O; T was value of 44/28 was the weight molecule of N associated with N O; T was value of 298, the net of global warming potential (GWP) of N O in 100 years and T value of 12/44 was the molecular weight of CO in relation to CE. Third calculation was the emissions from CH : FN2O

2

2

2

2

0

1

2

2

4

E =L xH xe CH4

T

T

(3)

ffields

2

IISS 2018 IOP Conf. Series: Earth and Environmental Science 239 (2019) 012015

IOP Publishing doi:10.1088/1755-1315/239/1/012015

where L was the area of rice planting (hectares), H was the length of planting (days), e factor of rice emissions [10]. The fourth calculation was emissions from irrigation (E , kg CO eq) [16]: T

T

ffields

IRRI

2

E =I xE IRRI

Rij

was the

(4)

Fj

where I was water needed for irrigation water (m ) and factor emissions for irrigation (kg CO eq / m ). All inputs from agricultural activity were added to the value CFA (kg CO eq / ha) [15] with the formulation of CFA: 3

Rij

3

2

2

CFA = E + E + E + E M

N2O

CH4

(5)

IRRI

From CFA value then calculated for CFY (kg CO eq / kg product) [15] which was a carbon footprint production, by using the formula: 2

CFY = CFA / Y

(6)

Y was the value of rice productivity (kg/ha). Then for GHG, used the formula: GHG = needs of fertilizer + CO equivalent CH + CO equivalent N O + E + fuel requirements of each agricultural tool (7) 2

4

2

2

IRRI

3. Result and Discussion 3.1. Total amount of carbon footprint on each provinces in Indonesia Based on the results (Table 2), it demonstrated that during the dry season, the highest CFA, CFY, GHG in East Nusa Tenggara (4,174,623.42 kg CO eq / ha), Bangka Belitung Islands (1,250.43 kg CO eq / kg product), East Nusa Tenggara (4.22 Kton CO eq). The lowest value of CFA, CFY, GHG in West Kalimantan (335,951.03 kg CO eq / ha), Yogyakarta (58.33 kg CO2 eq / kg product), West Kalimantan (0.40 Kton CO eq). And rainy season, the highest value of CFA, CFY, GHG in East Nusa Tenggara (4,448,141.72 kg CO eq / ha), East Nusa Tenggara (1,249.13 kg CO eq / kg product), East Nusa Tenggara (0.49 Kton CO eq). The lowest value of CFA, CFY, GHG in West Kalimantan (335,906.87 kg CO eq / ha), Yogyakarta (60.93 kg CO eq / kg product), West Kalimantan (0.40 Kton CO eq). The result also demonstrated the CFs mean in 34 provinces during dry and rainy season in Indonesia was 1,900,341.48 kg CO eq/ha and 1,892,825.68 kg CO eq/ha, respectively. Previous report in East Nusa Tenggara showed the rice paddy cultivation in this province need more water since the area of topography there is dry [17]. For Bangka Belitung island, since year 2011, the government of this province were intensively opening the rice paddy fields in several area. At year 2012, there were 170 ha of opened rice paddy field in Bangka Belitung island and then grew to 3.128 ha of opened rice paddy at 2015. This has been caused a massive water need in this area (4 fold higher compared to other area) [18] Meanwhile in Yogyakarta, there were several agricultural practices which were more water-efficient including SRI (system of rice intensification) and alternate wetting and dry (AWD) practices. The SRI is a rice cultivation method that intensively control and manage macro and micro nutrients as well as irrigation. This system has been practiced in Sleman, Kulonprogo, and Bantul districts at Yogyakarta provinces since 2011 [19]. The AWD practices is an efficient water usage practices, that is not inundate the land continuously until saturated, but keeping the water into certain height [20]. In order to understand which agricultural inputs having more contributions on total amount of CF and GHG, we then measured the agricultural input proportion on the total amount of CF and GHG. 2

2

2

2

2

2

2

2

2

2

2

2

2

Table 2. Comparison of carbon footprint among provinces in Indonesia

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IISS 2018 IOP Conf. Series: Earth and Environmental Science 239 (2019) 012015

Province

ACEH NORTH SUMATERA WEST SUMATERA

Irrigated Rice Field Area (ha)

Rice Producti vity (kg / ha)

CFA (Dry Season) (kg CO2 eq / ha)

CFA (Rainy Season) (kg CO2 eq / ha)

IOP Publishing doi:10.1088/1755-1315/239/1/012015

CFY(Dry Season) (kg CO2 eq / kg product)

CFY (Rainy Season) (kg CO2 eq / kg product)

GHG (Dry Season) (Kton CO2 eq)

GHG (Rainy Season) (Kton CO2 eq)

191,263.00

5,056.00

2,043,799.89

1,853,150.84

404.23

366.53

2.10

1.91

263,943.00

5,174.00

1,463,560.29

1,381,155.76

282.87

266.94

1.55

1.47

183,374.00

5,025.00

499,258.37

429,334.31

99.35

85.44

0.56

0.49

RIAU

10,382.00

3,663.00

1,555,223.52

1,429,539.62

424.58

390.26

1.97

1.85

JAMBI SOUTH SUMATERA

35,222.00

4,431.00

1,430,037.37

1,354,374.59

322.73

305.66

1.53

1.45

115,687.00

4,867.00

2,125,271.96

2,077,295.74

436.67

426.81

2.30

2.25

BENGKULU

62,419.70

4,492.00

936,100.09

897,451.74

208.39

199.79

0.99

0.95

LAMPUNG BANGKA BELITUNG ISLANDS RIAU ISLANDS

191,932.00

5,149.00

1,784,133.94

1,786,175.59

346.50

346.90

1.95

1.95

3,128.00

2,285.00

2,857,233.79

2,806,257.19

1,250.43

1,228.12

3.53

3.47

126.00

3,646.00

1,664,353.68

1,551,974.42

456.49

425.66

1.77

1.66

529.00

5,595.00

1,557,579.37

1,619,719.98

278.39

289.49

1.64

1.70

WEST JAVA CENTRAL JAVA YOGYAKART A

736,635.00

6,122.00

1,534,823.87

1,593,374.74

250.71

260.27

1.64

1.70

682,236.00

6,025.00

3,826,194.94

3,946,966.14

635.05

655.10

3.98

4.10

44,694.00

6,065.00

353,789.86

369,533.67

58.33

60.93

0.47

0.49

EAST JAVA

851,123.00

6,113.00

3,734,905.50

3,842,887.39

610.98

628.64

3.89

4.00

BANTEN

102,944.00

5,661.00

1,391,541.28

1,430,864.51

245.81

252.76

1.48

1.52

75,360.00

6,214.00

3,301,687.71

3,478,238.45

531.33

559.74

3.39

3.56

209,622.00

5,171.00

2,919,370.73

3,079,913.25

564.57

595.61

3.01

3.18

103,901.60

3,561.00

4,174,623.42

4,448,141.72

1,172.32

1,249.13

4.22

4.49

80,389.00

2,940.00

335,951.03

335,906.87

114.27

114.25

0.40

0.40

17,185.00

3,507.00

828,513.33

885,225.92

236.25

252.42

0.96

1.02

47,877.00

4,187.00

881,205.26

935,848.75

210.46

223.51

0.99

1.05

13,863.00

4,120.00

1,124,671.48

1,124,464.56

272.98

272.93

1.31

1.31

6,051.00

2,727.00

1,031,406.09

931,300.70

378.22

341.51

1.09

0.99

45,771.40

4,905.00

2,499,163.35

2,373,471.13

509.51

483.89

2.56

2.44

114,281.40

4,857.00

3,664,266.06

3,538,672.60

754.43

728.57

3.71

3.59

383,507.00

5,241.00

1,530,609.53

1,541,865.98

292.05

294.19

1.62

1.63

85,701.00

4,707.00

1,229,110.55

1,247,970.43

261.12

265.13

1.28

1.30

GORONTALO WEST SULAWESI

27,066.00

5,551.00

2,485,054.72

2,357,318.00

447.68

424.67

2.58

2.45

35,282.00

4,941.00

2,421,966.91

2,371,246.14

490.18

479.91

2.53

2.47

MALUKU NORTH MALUKU

12,359.00

5,572.00

1,342,119.85

1,351,290.63

240.87

242.51

1.39

1.40

JAKARTA

BALI WEST NUSA TENGGARA EAST NUSA TENGGARA WEST KALIMANTAN CENTRAL KALIMANTAN SOUTH KALIMANTAN EAST KALIMANTAN NORTH KALIMANTAN NORTH SULAWESI CENTRAL SULAWESI SOUTH SULAWESI SOUTHEAST SULAWESI

9,212.00

3,511.00

2,961,786.10

2,961,189.98

843.57

843.40

2.99

2.99

WEST PAPUA

6,883.00

4,212.00

462,327.55

412,747.57

109.76

97.99

0.50

0.45

PAPUA

5,105.00

4,395.00

2,668,561.59

2,619,796.81

607.18

596.09

2.82

2.78

4,755,054.10

5,341.00

64,529,587.57

64,277,242.45

12,081.93

12,034.68

64.57

64.32

INDONESIA

3.2. Agricultural Inputs contribution to GHG

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IISS 2018 IOP Conf. Series: Earth and Environmental Science 239 (2019) 012015

IOP Publishing doi:10.1088/1755-1315/239/1/012015

In order to understand which agricultural inputs have highest to lowest contribution, we compared each agricultural input to GHG production in East Nusa Tenggara province as the highest GHG amount in Indonesia. The result showed the highest contribution was made from irrigation water continued with CH emission, NPK fertilizer applications, N O emission and diesel emission used for agricultural tools. However, there was no significant different between dry and rainy seasons in cope with the total amount of CF production (Figure 1). These results also in agreement with previous reports, that is water irrigation plays important role in increasing CF amount. Previous research also showed that SRI could reduce the emission of greenhouse gases for up to 40% by with saving water for 25-65%. This system was reported as the relevant technology used in mitigating the climate change [21]. Other than SRI, an alternate wetting and dry (AWD) practices has also been reported to reduce the emission of greenhouse gas for up to 26% in paddy field at central Vietnam [22]. Other report demonstrated that a mild AWD practices could reduce the water usage for 23.4 % without any disruption in rice yield production [23]. 4

A

2

B

Dry Season in East Nusa Tenggara NN2O / ha) Emissions(kg (kgCO CO2 / ha) 2 O Emissions 2 eqeq

0.00212%

ECH ECH4 Emissions(kg (kgCO CO2 eq) 4 Emissions 2 eq) Diesel DieselOil OilEmissions Emissions(kg (kgCO CO2 eq) 2 eq)

NN2O / ha) Emissions(kg (kgCO CO2 / ha) 2 O Emissions 2 eqeq

0.00199%

0.01677%

ECH4 Emissions(kg (kgCO CO2 eq) ECH 4 Emissions 2 eq)

0.01574%

0.00182%

Diesel DieselOil OilEmissions Emissions(kg (kgCO CO2 eq) 2 eq)

0.00064%

99.97521%

3 IrrigationWater WaterEmissions Emissions(kg (kgCO CO2 eq / m3) Irrigation 2 eq) / m )

NPKEmissions Emissions(kg (kgCO CO2 eq) NPK 2 eq)

Rainy Season in East Nusa Tenggara

99.97780%

IrrigationWater WaterEmissions Emissions(kg (kgCO CO2 eq / m3) 3 Irrigation 2 eq) / m )

0.00408%

NPKEmissions Emissions(kg (kgCO CO2 eq) NPK 2 eq)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

0.00383% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

Figure 1. dry season (A), rainy season (B) in East Nusa Tenggara 4. Conclusion Our study provides a statistical analysis data which shows water and N fertilizer usage on rice plantation in Indonesia has high impact on its contribution to carbon footprint and greenhouse gas emission. This research also provides an insight to Indonesian Agriculture to promote more water efficient agriculture practices such as SRI and AWD and lower N fertilizer usage, thus it may reduce the carbon footprint and greenhouse gas emission which leads to mitigation of climate change. References [1] IPCC. 2007. Climate Change 2007. Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Eds Core Writing Team, R.K. Pachauri & A. Reisinger). Geneva, Switzerland: IPCC. [2] Indonesian Statistic Data. 2017. Agricultural Land Statistics for 2012-2016. Agricultural Data and Information System Center, Secretariat General of the Ministry of Agriculture in 2017 [3] Central Statistics Agency. 2018. https://www.bps.go.id/subject/53/tanamanpangan.html#subjekViewTab3 (online). Accessed August 03 2018 [4] Central Statistics Agency. 2018. https://www.bps.go.id/dynamictable/2015/09/09/865/produksipadi-menurut-provinsi-ton-1993-2015.html. Accessed August 29 2018. [5] Ministry of Agriculture. 2015. Agricultural Statistics 2015. Agricultural Data and Information Systems Center of the Ministry of Agriculture of the Republic of Indonesia. Jakarta [6] Ministry of Agriculture. 2016. Agricultural Statistics 2016. Agricultural Data and Information Systems Center of the Ministry of Agriculture of the Republic of Indonesia. Jakarta [7] Center for Rice Research. 2018. http://bbpadi.litbang.pertanian.go.id/index.php/berita/infoteknologi/content/234-teknik-irigasi-hemat-air (Online). Accessed August 03 2018. [8] Ministry of Agriculture. 2017. Statistics, Infrastructure and Agricultural Facilities in 2012-2016. Setditjen of Agriculture Infrastructure and Facilities. Jakarta. [9] IPCC. 2007. Carbon Footprint Reference Values

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IOP Publishing doi:10.1088/1755-1315/239/1/012015

[10] Murniyanto, E., Wicaksono, K. P., Muhsono, F. 2011. Analysis of CH Emissions and CO Uptake Agricultural Activities in East Java. Agrovigor, Vol. 4, No. 1 [11] CO2 Equivalents. 2018. https://climatechangeconnection.org/emissions/co2-equivalents/ (Online). Accessed August 09 2018. [12] Water, Agri-Environmental Indicators (FAOSTAT). 2018. http://ref.data.fao.org/dataset-datafilter?entryId=8e39c692-2d1a-47bb-b283-445066af4f65&tab=data (Online). Accessed August 03 2018. [13] Bautista, E. G., Saito, M. 2015. Greenhouse gas emissions from rice production in the Philippines based on life-cycle inventory analysis. Journal of Food, Agriculture & Environment, Vol 13 (1), No. 139-144. [14] Murniyanto, E. Wicaksono, K. P., Muhson, F. 2011. Analisis Emisi CH4 dan Serapan CO2 Aktivitas Pertanian di Jawa Timur. Agrovigor, Vol. 4, No. 1 [15] Cheng K, Yan M, Nayak D, Pan GX, Smith P., Zheng JF and Zheng W. Climate change and agriculture research paper. Carbon footprint of crop production in China: an analysis of national statistic data. J. Agr. Sci. 153. pp. 422-431 [16] Wang, J., Rothausen, S.G.S.A., Conway, D., Zhang, L., Xiong, W., Holman, I.P. & Li, Y. 2012. China’s water–energy nexus: greenhouse-gas emissions from groundwater use for agriculture. Environmental Research Letters 7. [17] Sukarman, Subiksa, IGM and Ritung S. 2012. Identifikasi lahan kering potensial untuk pengembangan tanaman. [18] babel.bps.go.id [19] Purwantana, B. 2011. Studies on energy input in system of rice intensification method of rice cultivation. Agritech. Vol 31 (1) [20] Balai Pengkajian Teknologi Pertanian, 2015. [21] Mboyerwa PA. 2018. Potentials of system of rice intensification (SRI) in climate change adaptation and mitigation. A review. IJAPR. Vol 6 (9), pp. 160-168. [22] Tran DH, Hoang TN, Tokida T, Tirol-Padre A and Minamikawa K. 2018. Impacts of alternate wetting and drying on greenhouse gas emission from paddy field in Central Vietnam. J. Soil Sci. Plant Nutr. Vol 64. No 1. Pp. 14-22 [23] Carrijo DR, Lundy ME and Linquist BA. 2017. Rice yield and water use under alternate wetting and drying irrigation: A meta analysis. Field Crop Research. Pp 173-180 4

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