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The Analysis of Greenhouse Gas Emissions Mitigation: A System Thinking Approach (Case Study: East Java)
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Procedia Computer Science 00 (2019) 000–000 Procedia Computer Science 00 (2019) 000–000
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ScienceDirect Procedia Computer Science 161 (2019) 951–958
The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019
The Analysis of Greenhouse Gas Emissions Mitigation: A System The Analysis of Greenhouse Gas Emissions Mitigation: A System Thinking Approach (Case Study: East Java) Thinking Approach (Case Study: East Java) Andriyan Rizki Jatmiko, Erma Suryani*, Dhyna Octabriyantiningtyas Andriyan Rizki Jatmiko, Erma Suryani*, Dhyna Octabriyantiningtyas Institut Teknologi Sepuluh Nopember, Jalan Raya ITS, Keputih, Sukolilo, Surabaya, 60111 Institut Teknologi Sepuluh Nopember, Jalan Raya ITS, Keputih, Sukolilo, Surabaya, 60111
Abstract Abstract The area of Indonesian rice fields in 2016 reached 8.19 million hectares (ha), an increase of 1.16% from the previous year, this The areacomprising of Indonesian fields reached 8.19 million (ha), an increase ofrice 1.16% from the previous year, this number 4.78rice million hain of2016 irrigated rice fields and 3.4 hectares million ha of non-irrigated fields. In 2017, especially in East number 4.78 ha of irrigatedand ricerice fields and 3.4 million ha of non-irrigated rice fields. In 2017, especially in East Java, thecomprising harvest area is million 2,278,460 hectares, production is 13,633,701 tons. On the other hand, increasing agricultural Java, the can harvest is 2,278,460 hectares, production is 13,633,701 On the other hand, increasing agricultural activities also area contribute to emissions thatand can rice trigger climate change throughtons. the greenhouse gases (GHG) produced. GHG is activities can also to emissions that canand trigger climate change throughSide the greenhouse gases (GHG) GHGcan is a gas contained in contribute the atmosphere, which absorbs re-emits infrared radiation. effects of greenhouse gasproduced. accumulation acause gas contained in the atmosphere, which absorbs re-emits infrared radiation. effects of greenhouse gas accumulation extreme climate change that affects land and productivity. This study aims Side to analyze rice field productivity and buildcana cause extreme affects land study aims to analyze ricetofield productivity andstudy buildarea greenhouse gas climate emissionchange model that that produce fromproductivity. the rice fieldThis productivity system. Emissions be discussed in this greenhouse that produce the rice field productivity system.itEmissions to be discussed this studynonare CO2, CH4, gas and emission N2O. Themodel approach is carriedfrom out with a system dynamics because has the characteristics of aincomplex, CO2, N2O. The approach is carried out with system it has new the characteristics of a the complex, linear CH4, systemand dynamics, changes in system behavior overa time anddynamics feedback because that describes information about state ofnonthe linear system dynamics, in system behavior overdecisions. time and feedback that information about the the rice productivity system, changes which will then produce further The results of describes this studynew are causal loop diagram of state CH4,ofCO2 rice system, whichfrom will then produce further decisions. The results of this studydynamics. are causal This loop diagram of CH4,GHG CO2 and productivity N2O emissions resulting the productivity of rice fields modeled using system model contains and N2O emissions the productivity rice fieldsThis modeled system This model contains GHG information generatedresulting from thefrom agricultural sector andofits impact. modelusing can be used dynamics. as a consideration for the government information generated from thefor agricultural sector and its impact. Thismitigation model cantechnology be used asasa part consideration for the government and stakeholders as a reference making policies for GHG emission of smart agriculture. and stakeholders as a reference for making policies for GHG emission mitigation technology as part of smart agriculture. © 2019 The Authors. Published by Elsevier B.V. © 2019 2019 The The Authors. Published by B.V. © Authors. by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND licenseThe (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of scientific committee Fifth Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Information Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Greenhouse Gas Mitigation; System Dynamics; Decision Support Keywords: Greenhouse Gas Mitigation; System Dynamics; Decision Support
* Corresponding author. Tel.: +62-81-231-352-063; fax: +62-31-596-4965. address:author. [email protected] * E-mail Corresponding Tel.: +62-81-231-352-063; fax: +62-31-596-4965. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019
1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.204
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Andriyan Rizki Jatmiko et al. / Procedia Computer Science 161 (2019) 951–958 Author name / Procedia Computer Science 00 (2019) 000–000
1. Introduction Indonesia is one of the developing countries with as known as an agricultural country because most of its territory is used as agricultural land. One of the most important agricultural sectors is rice because of the high demand for rice in Indonesia. Rice is a basic necessity that used as a daily food and a source of carbohydrates. Therefore, the need for rice will always increase along with the increasing population [1]. On the other hand, according to world bank data, population growth in Indonesia has reached 263 million in 2017. The rice production in Indonesia in 2017 grew by 2.16 percent from the previous year. The four largest rice producing provinces in Indonesia, including East Java, Central Java, West Java, and South Sulawesi. East Java is the largest rice producer in Indonesia with a harvested area of 2,278,460 hectares, and rice production is 13,633,701 tons [2]. In line with this, it can be said that rice is one of the biggest commodities in East Java, followed by maize, sugar cane, coconut, and fruits [3]. It encourages the Indonesian government continues to try to improve the rice farming business through the agricultural service in each of its regions. On the other hand, behind efforts to increase rice production on a large scale, there are problems with the high productivity of land in the rice farming system. Scientists and academics have found that in some activities the productivity of paddy fields is a source of greenhouse gas (GHG) emissions [4,5]. Activities that can produce emissions start from farmland tillage such as straw residue and manure residues. Maintenance activities such as urea or nitrogen fertilization and irrigation. The last is harvesting activity when grinding using a machine so as to produce exhaust gas [6,5,7]. Indonesia is the world's 18th GHG emitter, the sources of GHG emissions are the energy, waste, agriculture and industry sectors with total GHG emissions in 2012 are estimated at 1,454 million metric tons of MtCO2e carbon dioxide equivalent. The two main sectors contribute nearly 83% of total GHG emissions, which are about 48% change in land use and peat recharge and around 35% in the energy sector. However, by 2020 the energy sector is projected to surpass the land-based sector as the most significant source of emissions, accounting for 50% of the total national BAU emissions by 2030 [8]. Therefore, in accordance with Presidential Regulation No. 61 of 2011 Article 2 concerning Action Plan for Reducing Greenhouse Gas Emissions abbreviated as RAN-GRK, then Indonesia as a country that contributes emissions from the agricultural sector targets a reduction in GHG of 0.008 gigatons by 2020 [8]. The mitigation actions listed in the RAN-GRK include various policies and actions for land use. The agricultural sector generates approximately 10-12% of total global greenhouse gas (GHG) emissions, of which 60% is N20 and 40% is CH4and each of these GHGs has its own driving factor, of which the global issue is the addition of organic material into the paddy fields as soil-enhancing material [9]. As with the provision of organic materials into paddy fields, the method of tillage is also believed to have an effect on GHG emissions from paddy fields. Three GHGs that act as forming an infrared ray filter and consequently increasing the temperature of the earth are CO2, CH4, and N2O. Besides reflecting infrared light, these three gases also cause ozone depletion. The amount of carbon (C) in the form of GHG compounds, such as CH4 gas, which escapes into the atmosphere due to biological processes and human activities causes the formation of layers in the stratosphere which results in the reflection of infrared radiation that should be released into the Earth's atmosphere [10]. The biggest threat that can be caused by GHGs is the occurrence of extreme climate change. Agriculture, in the food crop sector, is very vulnerable to changes in rainfall, because food crops are generally seasonal crops that are relatively sensitive to excess and lack water [11,12]. Increasingly frequent flooding has resulted in a significant reduction in harvested area and a significant decrease in rice production. Increased intensity of flooding can affect production due to the increasing attack of plant pest organisms. A shift in the pattern of rain affects agricultural resources and infrastructure which causes shifts in planting time, season and cropping patterns, as well as land degradation. The tendency of shortening the rainy season and increasing rainfall resulted in changes in the initial and duration of the planting season, thus affecting the planting index (IP), planting area, beginning of planting time and cropping pattern [11]. Research carried out by A. López [13] using system dynamics in the agricultural sector in Colombia states the relationship between the causes of GHGs and their impact on climate change. The results of this study are the causal loop diagram in the agricultural industry. The dynamic system method for modeling and simulating GHGs, especially CO2, has also been done by Á. M. Nieto [14]. This study produces a model that shows how the relationship of each variable produces CO2. This study aims to analyse the productivity of rice fields by considering several internal and external factors which can be seen in Table 1. According to the system model based on observations from the real system and literature
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studies obtained. This model will be used as a consideration for the government and policymaker as a reference for making policies for GHG emission mitigation technology which is part of smart agriculture. Therefore, a system dynamics approach is used to support policymakers to understand and assess the complex relationships between parameters as an effort and strategy to reduce the level of emissions growth in rice field productivity. System dynamics has some characteristics such as complex, non-linear, changes in system behavior with respect to time and the presence of feedback that describes new information about the state of the system, which will then produce further decisions. System dynamics frameworks can be used to analyze models and produce scenarios to improve system performance [15,16]. 2. Literature review This section shows the literature study collected by the author to support research on the topic of the issues discussed. 2.1. N2O emission The initial method to increase land productivity is to use chemical fertilizers and pesticides. However, since the 1990s there has been a decrease in soil fertility, and the dependence of chemical fertilizers (inorganic) is increasing. Then developed environmentally friendly organic agriculture; the main characteristic of organic farming is the use of local varieties followed by fertilizing with organic fertilizers and pest control with natural pesticides. However, with the characteristics of rice plants planted with flooded soil and high nitrogen inputs, the release of N2O from the soil through increased denitrification and contributes to global warming [1]. N2O has a strong infrared absorption capacity and traps radiation from the earth's surface. It is estimated that N2O has an infrared absorption capacity of around 300 times more than CO2 [9]. In the upper stratosphere, N2O is oxidized to NO by the action of UV light, and NO destroys the ozone layer which protects living things against UV-sun radiation. Nitrogen fertilization increases both N2O emissions from continuous flooding and maintenance of midseason drainage because it is emitted through flooded rice plants. The application of mineral N increases the availability of substrates for nitrification and denitrification can provide more N available for soil microbes and will lead to higher N2O efflux [1]. 2.2. CH4 emission Rice fields are the largest anthropogenic wetlands on earth and are an important anthropogenic biological source of atmospheric methane (CH4). CH4 is produced by the decomposition of anaerobic organic matter, organic amendments and water regimes are two important factors that control CH4 emissions from rice fields. Plant residues are the main source for the input of organic matter on agricultural land. Burning straw at the site is usually a common approach for easy land preparation. However, large-scale straw burning causes severe air pollution which affects public health. Therefore, burning straw has been banned, and the application of straw is recommended to increase soil fertility. In general, in intensive double rice systems, straw from the initial rice is usually plowed into the soil. However, this practice stimulates CH4 emissions because the anaerobic decomposition of fresh rice straw accelerates the reduction process and releases the organic substrate for CH4 production. In contrast, straw mulch, allowing the straw to rot on the ground, can reduce carbon input into the soil anaerobic zone and reduce the impact on soil redox conditions. Continuous flood irrigation is beneficial for CH4 emissions [7]. 2.3. CO2 emission Fossil fuels began to be known during the first industrial revolution and until now it continues to be used to a very wide scale. However, excessive fossil energy consumption from large-scale carbon dioxide (CO2) emissions. It is known that CO2 is the root of environmental problems such as global warming, sea level, and frequent extreme weather [6,11,5]. Fossil energy is concentrated in solar energy with carbon compounds. When carbon is burned, it
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produces CO2 particles that extend through the entire atmosphere [13]. The use of agricultural equipment that still uses fossil fuels is the driving factor for the production of exhaust gas in the form of CO2. Agricultural equipment used such as plow machines, irrigation machines, and harvest machines. 2.4. System dynamics System dynamics are a computer-assisted approach to analyzing policies and designs. System dynamics simulation is a continuous simulation developed by Jay Forrester (MIT) in the 1960s, focusing on the structure and behavior of a system consisting of interactions between variables and feedback loops. Relationships and interactions between variables are stated in the diagram centered. The feedback process can be grouped into two parts [15,16]: Positive Feedback This type of feedback creates a growth process, where an event can cause consequences that will increase the next occurrence continuously. This feedback can cause instability, imbalance, and continuous growth. Example: population growth system. Negative feedback This type of feedback seeks to create balance by giving corrections so that goals can be achieved. Example: regulator system room temperature. Five steps presented by Sterman of dynamic modeling as a loop feedback process or iterative cycle that is affected mainly by mental models and information collected from the real world, which is later tested virtually through the model. The process performed is built up with the aim of designing new strategies, structures, and decision rules to address complex problems. Likewise, it enables us to create and to implement policies that will lead to new insights to evaluate a situation and to consider further improvements. The first stage is the problem articulation to identify the problems to be discussed. The second stage is the dynamics of the hypothesis to explain the feedback process and flow structure in the system. The third stage is to formulate a simulation model using equations and parameters. The fourth stage is testing which aims to compare the accuracy of models with real systems. The fifth stage is design and evaluation which includes the formulation of policymaking making the strategies and rules for making decisions by the reach of interests in the system. 3. Model construction 3.1. Problem articulation The agricultural sector is one sector that is very vulnerable to climate change which has an impact on agricultural productivity and farmer income. On the other hand, agricultural activities also have an impact on climate change due to warming. The agricultural sector contributes around 14% of the world's total greenhouse gas emissions. Fertilizers are the largest source of emissions (38%) for the agricultural sector. Soil releases N2O in the process of nitrification and denitrification. The use of both organic and inorganic fertilizers increases the levels of N2O released by the soil [9,11,13]. Based on GHG inventory data by the Ministry of Environment and Forestry [17], emissions generated from the agricultural sector continue to increase. Fig. 1 shows the growth of GHG emissions in the agricultural sector which has continued to increase since 2000 with 89.864gg CO2 to reach 122.190gg CO2 in 2017. The literature collection phase is needed by the author to strengthen the theoretical basis to support research. Searches are made in relevant journals or previous research, related to gas emissions resulting from rice productivity, as well as ways to reduce gas emissions due to rice farming, especially in East Java province. Then the authors conducted data collection, which is a continuation of the literature study stage. This stage was carried out in order to obtain valuable data on rice field area, rice production, rice seeds used, rice productivity and the factors that influence it, the availability of land in East Java for rice farming, as well as factors that lead to increased greenhouse gas emissions due to rice farming and how to handle it. From the results of the literature study and data collection, the authors can model the rice land productivity system in the form of a causal loop diagram which can later be used as a model framework in preparing scenarios for GHG mitigation strategies.
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Fig. 1. GHG inventory in Indonesia (MoE&F).
3.2. Dynamic hypothesis The causal loop diagram is a diagram that is used to describe the system in general. From the CLD that has been made, a simulation will be carried out with a system dynamics approach. Making this diagram is based on the results of the stages of data collection and literature review. If the relationship is in the direction of the arrow is positive (+), but vice versa, if the relationship is in the opposite direction, then the arrow is negative. The central diagram to be made will illustrate the system of how the GHG process occurs in the productivity of rice fields. According to [16] in his book, Causal Loop Diagram (CLD) is a form of mapping that shows a causal relationship between variables and arrows from cause to effect. CLD is very good for: (1) Quickly capture a hypothesis about the causes of dynamics. (2) Generate and capture models individually or in groups. (3) Important feedback communication is the responsibility of a problem. 3.3. Model N2O emission N2O emissions consist of direct emissions and indirect emissions. Direct emission of N2O in the soil occurs due to the process of nitrification and denitrification and denitrification chemically which does not involve microbes. Nitrification is the aerobic process of oxidizing ammonium (NH4+) by microbes into nitrite with an intermediate yield of NH2OH, and then turning into nitrate. If the amount of oxygen is limited (the soil moisture content is close to saturation), the ammonium oxidizer can utilize NO-as the electron acceptor and then produce N2O. N2O is also formed in the denitrification process, which is the process of reducing nitrates by microbes in an anaerobic state which produces NO, N2O and N2 gases. In general, an increase in N concentration in the soil will increase nitrification and denitrification which then increases the production of N2O. Available N enhancements can occur due to N fertilization, changes in land use and management of organic matter that causes soil organic N mineralize [5,11]. Fig. 2 shows a causal loop diagram of N2O emissions. On the causal loop diagram model, it can be seen that what affects the production of N2O emissions is the use of fertilizers. GHG Accumulation +
a
N2O Emission
+ Climate Change
B1
+
+
+ Land Productivity +
Rice Cultivation
+ Climate Change
Land Productivity
GHG Accumulation +
c +
B2
N2O Manure
-
GHG Accumulation +
b
CH4 Emission +
B3
+ Climate Change
+ Production
Irrigation Rice Field + CH4 Manure +
+ Land Productivity +
+
Rice Cultivation
Fig. 2. (a) Model N2O; (b) Model CH4; (c) Model CO2 emission.
+
+
Time duration of Agriculture tools utilization + +
Harvesting Land Rice Cultivation
CO2 Emission -
Paddy planting area +
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3.4. Model CH4 emission Rice fields in Indonesia are generally managed in waterlogged conditions. Farmers want the water to inundate rice plants because it can reduce the growth of weeds that often drain the costs and labor of farmers in managing rice fields. Methane is one of the GHGs produced through the anaerobic decomposition of organic matter. To break down organic matter into CH4, the redox potential is needed below -100 mV and pH ranges from 6-7. Irrigation rice fields are ideal conditions for this process [4]. In addition to the decomposition of organic matter, other sources of CH4 release are enteric fermentation from the digestion of livestock, incomplete processes of combustion of organic matter, and the consequences of the exploration process of oil and gas mining [7]. Fig. 2 shows a causal loop diagram of CH4 emissions. In the causal loop diagram model, it can be seen that CH4 emissions produce from the irrigation rice field and manure. 3.5. Model CO2 emission Fig. 2 show a causal loop diagram of CO2 emissions. At the stage of processing, paddy fields begin with land plowing. The use of plow machines that still use fossil fuels will cause exhaust gas in the form of CO2 which will be released into the air. While at the stage of maintenance land, irrigation activities can cause CO2 emissions through the use of water pumping machines. Whereas in harvesting activities, CO2 emissions are produced from harvesting machine that using fossil fuels. Exhaust gas from burning fossil fuels contributes to carbon emissions in the air due to incomplete combustion. On the other hand, planting rice can absorb CO2 from photosynthesis. 3.6. Causal loop diagram GHG Based on the sub-models that have been made, then they are reassembled and developed into a complete CLD. CLD as a whole is made based on references that have been obtained by the author then adjusted to the research needs. The main model is shown in Fig. 3 as follows. Land Productivity + Rice cultivation
- + +
+ Production +
Harvesting land
Irrigation machine
Harvesting machine
Plowing machine +
+ + + Time duration of Agriculture tools + utilization
Paddy planting area
Organic fertilizer
Irrigation rice field
CH4 Manure
+
Rainfall
-
Temperature
+
+
CO2 emission
+ +
inorganic fertilizer
+
Burning cropland
+ Urea
Climate change +++
+ +
+ + N2O Manure
B3 +
+
+ CH4 emission B2 + N2O emission B1
Fig. 3. Causal loop diagram GHG.
GHG +Accumulation +
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3.7. Validation structure Validation aims to identify the suitability of variable structures. The test used for SD model structural validation is boundary adequacy, whether the concepts and important structures for overcoming policy problems are endogenous towards the model [18]. Table 1 shows endogenous variables and exogenous variables as influencing factors in each model. The structural validation process is carried out by using references from each variable. Table 1. Model Boundary GHG in Land Productivity. Submodel
Endogenous Variable
Exogenous Variabel
Reference
N2O emission
a. Inorganic fertilizer b. Organic fertilizer
-
[4,13,1]
CH4 emission
c. CH4 Manure d. Irrigation rice field
-
[4,13,5,19,7]
CO2 emission
e. f. g. h.
-
[6,11,12]
GHG Accumulation
Irrigation Machine Harvesting machine Burning cropland Paddy planting area
-
Land productivity
i. N2O Manure
Paddy production
j. Land productivity k. Harvesting land
a. b.
Global warming Climate change
[9,10,11]
c. d.
Rainfall Temperature
[20,21] [20,21]
4. Conclusion Every year the number of GHG emissions produced by paddy fields in Indonesia is increasing. There are activities that have the potential to produce GHG emissions on the productivity of paddy fields. Most of the GHG emissions generated from rice fields are methane (CH4), nitric oxide (N2O) and carbon dioxide (CO2) gas. In this study, we conducted an analysis of the rice productivity system by considering internal and external factors in the rice productivity system. The author conducted a literature study by collecting related references and previous study to obtain a theory that is in accordance with the research. The next step is to develop the model by considering endogenous and exogenous variables to ensure that the variables used are in accordance with the real system. Structural validation is then carried out using appropriate references to develop linkages and causal relationships for each variable. This study presents the development of a model that contains information about the factors that influence GHG emissions produced from paddy fields and their impacts. In the future, this model can be used as a consideration for the government and stakeholders as a reference for making policies from GHG emission mitigation technology as part of intelligent agriculture. Reference [1] Rahmawati, A., S. De Neve, and B. Heru. (2015) “N 2 O-N Emissions from Organic and Conventional Paddy Fields from Central Java, Indonesia”, in Procedia Environ. Sci. 28: 606–612. [2] BPS-Statistics Indonesia. (2018) “Statistical Yearbook of Indonesia 2018.” Badan Pusat Statistik. [3] Nugroho, I., U. Widyagama, and N. Hanani. (2007) “Studi Investasi untuk Pengembangan Komoditi Pertanian di Propinsi Lampung : Studi Investasi untuk Pengembangan Komoditi Pertanian [Title in English: The Study of Investment to Develop Agriculture Commodity in Lampung Province.”
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[4] Tian, Z., et al.(2017) “Maintaining Rice Production While Mitigating Methane and Nitrous Oxide Emissions from Paddy Fields in China : Evaluating Tradeoffs By Using Coupled Agricultural Systems Models.” Agric. Syst. 0–1. [5] Begum, K., et al. ( 2019) “Geoderma Modelling Greenhouse Gas Emissions and Mitigation Potentials in Fertilized Paddy Rice Fields in Bangladesh.” Geoderma, 341: 206–215. [6] Lin, B., and B. Xu. (2018) “Factors Anffacting CO 2 Emissions in China's Agriculture Sector : A Quantile Regression.” 94: 15–27. [7] Wu, X., W. Wang, K. Xie, C. Yin, H. Hou, and X. Xie. (2019) “Combined Effects of Straw and Water Management on CH 4 Emissions From Rice Fields,=.” 231: 1257–1262. [8] Hidayatno, A., and A. Rahmawan. (2019) “Conceptualizing Carbon Emissions from Energy Utilization in Indonesia ’ s Industrial Sector.” Energy Procedia 156: 139–143. [9] IPCC. (2013) “Climate Change 2013: The Physical Science Basis.” [10] IPCC. (2014) “Climate Change 2014 Synthesis Report.” [11] A. E. C. (2016) “Climate Change Vulnerability Assessment in the Agriculture Sector : Typhoon Santi Experience.” 216: 440–451. [12] Marino, R., A. S. Atzori, M. D. Andrea, G. Iovane, M. Trabalza-marinucci, and L. Rinaldi. (2016) “Climate change : Production Performance, Health Issues, Greenhouse Gas Emissions and Mitigation Strategies in Sheep and Goat Farming.” Small Rumin. Res. 135: 50– 59. [13] López Astudillo, A., L. M. Rodríguez, C. M. Lubo, F. Arenas, B. E. Sierra, and B. E. Sierra. (2014) “Evaluating Carbon Footprint Behavior in the Agriculture and Energy Sectors: A Review.” Sist. y Telemática 12: 35. [14] Nieto, Á. M. (2014) “Application of the System Dynamics Methodology For Modeling And Simulation Of The Greenhouse Gas Emissions (GGE)”, in Cartagena De Indias. [15] Suryani, E. (2006) “Pemodelan dan Simulasi [Title in English: Modelling and Simulation] .” Yogyakarta: Graha Ilmu. [16] Sterman, J. (2000) “Business Dynamics: Systems Thinking and Modeling for a Complex World.” Hill Boston. [17] MoE&F, “Emisi - SIGN SMART,” 2018. [Online]. Available from: http://signsmart.menlhk.go.id/v2.1/menu-emisi/. [18] Qudrat-ullah, H. (2008) “Structural Validation of Simulation Models : An Illustration.” pp. 537–542. [19] Feng et al., (2013) “Agriculture, Ecosystems and Environment Impacts of Cropping Practices on Yield-Scaled Greenhouse Gas Emissions From Rice Fields in China : A Meta-Analysis.” “Agriculture, Ecosyst. Environ. 164: 220–228. [20] Jiang, G., R. Zhang, W. Ma, D. Zhou, and X. Wang. (2017) “Land Use Policy Cultivated Land Productivity Potential Improvement in Land Consolidation Schemes in Shenyang, China : Assessment And Policy Implications.” 68: 80–88. [21] Chen, Q., Y. Liu, Q. Ge, and T. Pan. (2018) “Land Use Policy Impacts Of Historic Climate Variability and Land Use Change on Winter Wheat Climatic Productivity in The North China Plain During 1980 – 2010.” Land Use Policy 76: 1–9.
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ScienceDirect Procedia Computer Science 161 (2019) 951–958
The Fifth Information Systems International Conference 2019 The Fifth Information Systems International Conference 2019
The Analysis of Greenhouse Gas Emissions Mitigation: A System The Analysis of Greenhouse Gas Emissions Mitigation: A System Thinking Approach (Case Study: East Java) Thinking Approach (Case Study: East Java) Andriyan Rizki Jatmiko, Erma Suryani*, Dhyna Octabriyantiningtyas Andriyan Rizki Jatmiko, Erma Suryani*, Dhyna Octabriyantiningtyas Institut Teknologi Sepuluh Nopember, Jalan Raya ITS, Keputih, Sukolilo, Surabaya, 60111 Institut Teknologi Sepuluh Nopember, Jalan Raya ITS, Keputih, Sukolilo, Surabaya, 60111
Abstract Abstract The area of Indonesian rice fields in 2016 reached 8.19 million hectares (ha), an increase of 1.16% from the previous year, this The areacomprising of Indonesian fields reached 8.19 million (ha), an increase ofrice 1.16% from the previous year, this number 4.78rice million hain of2016 irrigated rice fields and 3.4 hectares million ha of non-irrigated fields. In 2017, especially in East number 4.78 ha of irrigatedand ricerice fields and 3.4 million ha of non-irrigated rice fields. In 2017, especially in East Java, thecomprising harvest area is million 2,278,460 hectares, production is 13,633,701 tons. On the other hand, increasing agricultural Java, the can harvest is 2,278,460 hectares, production is 13,633,701 On the other hand, increasing agricultural activities also area contribute to emissions thatand can rice trigger climate change throughtons. the greenhouse gases (GHG) produced. GHG is activities can also to emissions that canand trigger climate change throughSide the greenhouse gases (GHG) GHGcan is a gas contained in contribute the atmosphere, which absorbs re-emits infrared radiation. effects of greenhouse gasproduced. accumulation acause gas contained in the atmosphere, which absorbs re-emits infrared radiation. effects of greenhouse gas accumulation extreme climate change that affects land and productivity. This study aims Side to analyze rice field productivity and buildcana cause extreme affects land study aims to analyze ricetofield productivity andstudy buildarea greenhouse gas climate emissionchange model that that produce fromproductivity. the rice fieldThis productivity system. Emissions be discussed in this greenhouse that produce the rice field productivity system.itEmissions to be discussed this studynonare CO2, CH4, gas and emission N2O. Themodel approach is carriedfrom out with a system dynamics because has the characteristics of aincomplex, CO2, N2O. The approach is carried out with system it has new the characteristics of a the complex, linear CH4, systemand dynamics, changes in system behavior overa time anddynamics feedback because that describes information about state ofnonthe linear system dynamics, in system behavior overdecisions. time and feedback that information about the the rice productivity system, changes which will then produce further The results of describes this studynew are causal loop diagram of state CH4,ofCO2 rice system, whichfrom will then produce further decisions. The results of this studydynamics. are causal This loop diagram of CH4,GHG CO2 and productivity N2O emissions resulting the productivity of rice fields modeled using system model contains and N2O emissions the productivity rice fieldsThis modeled system This model contains GHG information generatedresulting from thefrom agricultural sector andofits impact. modelusing can be used dynamics. as a consideration for the government information generated from thefor agricultural sector and its impact. Thismitigation model cantechnology be used asasa part consideration for the government and stakeholders as a reference making policies for GHG emission of smart agriculture. and stakeholders as a reference for making policies for GHG emission mitigation technology as part of smart agriculture. © 2019 The Authors. Published by Elsevier B.V. © 2019 2019 The The Authors. Published by B.V. © Authors. by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND licenseThe (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of scientific committee Fifth Information Systems International Conference 2019 Peer-review under responsibility of the scientific committee ofofThe Fifth Information Systems International Conference 2019. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 Keywords: Greenhouse Gas Mitigation; System Dynamics; Decision Support Keywords: Greenhouse Gas Mitigation; System Dynamics; Decision Support
* Corresponding author. Tel.: +62-81-231-352-063; fax: +62-31-596-4965. address:author. [email protected] * E-mail Corresponding Tel.: +62-81-231-352-063; fax: +62-31-596-4965. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019 This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019
1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of The Fifth Information Systems International Conference 2019. 10.1016/j.procs.2019.11.204
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Andriyan Rizki Jatmiko et al. / Procedia Computer Science 161 (2019) 951–958 Author name / Procedia Computer Science 00 (2019) 000–000
1. Introduction Indonesia is one of the developing countries with as known as an agricultural country because most of its territory is used as agricultural land. One of the most important agricultural sectors is rice because of the high demand for rice in Indonesia. Rice is a basic necessity that used as a daily food and a source of carbohydrates. Therefore, the need for rice will always increase along with the increasing population [1]. On the other hand, according to world bank data, population growth in Indonesia has reached 263 million in 2017. The rice production in Indonesia in 2017 grew by 2.16 percent from the previous year. The four largest rice producing provinces in Indonesia, including East Java, Central Java, West Java, and South Sulawesi. East Java is the largest rice producer in Indonesia with a harvested area of 2,278,460 hectares, and rice production is 13,633,701 tons [2]. In line with this, it can be said that rice is one of the biggest commodities in East Java, followed by maize, sugar cane, coconut, and fruits [3]. It encourages the Indonesian government continues to try to improve the rice farming business through the agricultural service in each of its regions. On the other hand, behind efforts to increase rice production on a large scale, there are problems with the high productivity of land in the rice farming system. Scientists and academics have found that in some activities the productivity of paddy fields is a source of greenhouse gas (GHG) emissions [4,5]. Activities that can produce emissions start from farmland tillage such as straw residue and manure residues. Maintenance activities such as urea or nitrogen fertilization and irrigation. The last is harvesting activity when grinding using a machine so as to produce exhaust gas [6,5,7]. Indonesia is the world's 18th GHG emitter, the sources of GHG emissions are the energy, waste, agriculture and industry sectors with total GHG emissions in 2012 are estimated at 1,454 million metric tons of MtCO2e carbon dioxide equivalent. The two main sectors contribute nearly 83% of total GHG emissions, which are about 48% change in land use and peat recharge and around 35% in the energy sector. However, by 2020 the energy sector is projected to surpass the land-based sector as the most significant source of emissions, accounting for 50% of the total national BAU emissions by 2030 [8]. Therefore, in accordance with Presidential Regulation No. 61 of 2011 Article 2 concerning Action Plan for Reducing Greenhouse Gas Emissions abbreviated as RAN-GRK, then Indonesia as a country that contributes emissions from the agricultural sector targets a reduction in GHG of 0.008 gigatons by 2020 [8]. The mitigation actions listed in the RAN-GRK include various policies and actions for land use. The agricultural sector generates approximately 10-12% of total global greenhouse gas (GHG) emissions, of which 60% is N20 and 40% is CH4and each of these GHGs has its own driving factor, of which the global issue is the addition of organic material into the paddy fields as soil-enhancing material [9]. As with the provision of organic materials into paddy fields, the method of tillage is also believed to have an effect on GHG emissions from paddy fields. Three GHGs that act as forming an infrared ray filter and consequently increasing the temperature of the earth are CO2, CH4, and N2O. Besides reflecting infrared light, these three gases also cause ozone depletion. The amount of carbon (C) in the form of GHG compounds, such as CH4 gas, which escapes into the atmosphere due to biological processes and human activities causes the formation of layers in the stratosphere which results in the reflection of infrared radiation that should be released into the Earth's atmosphere [10]. The biggest threat that can be caused by GHGs is the occurrence of extreme climate change. Agriculture, in the food crop sector, is very vulnerable to changes in rainfall, because food crops are generally seasonal crops that are relatively sensitive to excess and lack water [11,12]. Increasingly frequent flooding has resulted in a significant reduction in harvested area and a significant decrease in rice production. Increased intensity of flooding can affect production due to the increasing attack of plant pest organisms. A shift in the pattern of rain affects agricultural resources and infrastructure which causes shifts in planting time, season and cropping patterns, as well as land degradation. The tendency of shortening the rainy season and increasing rainfall resulted in changes in the initial and duration of the planting season, thus affecting the planting index (IP), planting area, beginning of planting time and cropping pattern [11]. Research carried out by A. López [13] using system dynamics in the agricultural sector in Colombia states the relationship between the causes of GHGs and their impact on climate change. The results of this study are the causal loop diagram in the agricultural industry. The dynamic system method for modeling and simulating GHGs, especially CO2, has also been done by Á. M. Nieto [14]. This study produces a model that shows how the relationship of each variable produces CO2. This study aims to analyse the productivity of rice fields by considering several internal and external factors which can be seen in Table 1. According to the system model based on observations from the real system and literature
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studies obtained. This model will be used as a consideration for the government and policymaker as a reference for making policies for GHG emission mitigation technology which is part of smart agriculture. Therefore, a system dynamics approach is used to support policymakers to understand and assess the complex relationships between parameters as an effort and strategy to reduce the level of emissions growth in rice field productivity. System dynamics has some characteristics such as complex, non-linear, changes in system behavior with respect to time and the presence of feedback that describes new information about the state of the system, which will then produce further decisions. System dynamics frameworks can be used to analyze models and produce scenarios to improve system performance [15,16]. 2. Literature review This section shows the literature study collected by the author to support research on the topic of the issues discussed. 2.1. N2O emission The initial method to increase land productivity is to use chemical fertilizers and pesticides. However, since the 1990s there has been a decrease in soil fertility, and the dependence of chemical fertilizers (inorganic) is increasing. Then developed environmentally friendly organic agriculture; the main characteristic of organic farming is the use of local varieties followed by fertilizing with organic fertilizers and pest control with natural pesticides. However, with the characteristics of rice plants planted with flooded soil and high nitrogen inputs, the release of N2O from the soil through increased denitrification and contributes to global warming [1]. N2O has a strong infrared absorption capacity and traps radiation from the earth's surface. It is estimated that N2O has an infrared absorption capacity of around 300 times more than CO2 [9]. In the upper stratosphere, N2O is oxidized to NO by the action of UV light, and NO destroys the ozone layer which protects living things against UV-sun radiation. Nitrogen fertilization increases both N2O emissions from continuous flooding and maintenance of midseason drainage because it is emitted through flooded rice plants. The application of mineral N increases the availability of substrates for nitrification and denitrification can provide more N available for soil microbes and will lead to higher N2O efflux [1]. 2.2. CH4 emission Rice fields are the largest anthropogenic wetlands on earth and are an important anthropogenic biological source of atmospheric methane (CH4). CH4 is produced by the decomposition of anaerobic organic matter, organic amendments and water regimes are two important factors that control CH4 emissions from rice fields. Plant residues are the main source for the input of organic matter on agricultural land. Burning straw at the site is usually a common approach for easy land preparation. However, large-scale straw burning causes severe air pollution which affects public health. Therefore, burning straw has been banned, and the application of straw is recommended to increase soil fertility. In general, in intensive double rice systems, straw from the initial rice is usually plowed into the soil. However, this practice stimulates CH4 emissions because the anaerobic decomposition of fresh rice straw accelerates the reduction process and releases the organic substrate for CH4 production. In contrast, straw mulch, allowing the straw to rot on the ground, can reduce carbon input into the soil anaerobic zone and reduce the impact on soil redox conditions. Continuous flood irrigation is beneficial for CH4 emissions [7]. 2.3. CO2 emission Fossil fuels began to be known during the first industrial revolution and until now it continues to be used to a very wide scale. However, excessive fossil energy consumption from large-scale carbon dioxide (CO2) emissions. It is known that CO2 is the root of environmental problems such as global warming, sea level, and frequent extreme weather [6,11,5]. Fossil energy is concentrated in solar energy with carbon compounds. When carbon is burned, it
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produces CO2 particles that extend through the entire atmosphere [13]. The use of agricultural equipment that still uses fossil fuels is the driving factor for the production of exhaust gas in the form of CO2. Agricultural equipment used such as plow machines, irrigation machines, and harvest machines. 2.4. System dynamics System dynamics are a computer-assisted approach to analyzing policies and designs. System dynamics simulation is a continuous simulation developed by Jay Forrester (MIT) in the 1960s, focusing on the structure and behavior of a system consisting of interactions between variables and feedback loops. Relationships and interactions between variables are stated in the diagram centered. The feedback process can be grouped into two parts [15,16]: Positive Feedback This type of feedback creates a growth process, where an event can cause consequences that will increase the next occurrence continuously. This feedback can cause instability, imbalance, and continuous growth. Example: population growth system. Negative feedback This type of feedback seeks to create balance by giving corrections so that goals can be achieved. Example: regulator system room temperature. Five steps presented by Sterman of dynamic modeling as a loop feedback process or iterative cycle that is affected mainly by mental models and information collected from the real world, which is later tested virtually through the model. The process performed is built up with the aim of designing new strategies, structures, and decision rules to address complex problems. Likewise, it enables us to create and to implement policies that will lead to new insights to evaluate a situation and to consider further improvements. The first stage is the problem articulation to identify the problems to be discussed. The second stage is the dynamics of the hypothesis to explain the feedback process and flow structure in the system. The third stage is to formulate a simulation model using equations and parameters. The fourth stage is testing which aims to compare the accuracy of models with real systems. The fifth stage is design and evaluation which includes the formulation of policymaking making the strategies and rules for making decisions by the reach of interests in the system. 3. Model construction 3.1. Problem articulation The agricultural sector is one sector that is very vulnerable to climate change which has an impact on agricultural productivity and farmer income. On the other hand, agricultural activities also have an impact on climate change due to warming. The agricultural sector contributes around 14% of the world's total greenhouse gas emissions. Fertilizers are the largest source of emissions (38%) for the agricultural sector. Soil releases N2O in the process of nitrification and denitrification. The use of both organic and inorganic fertilizers increases the levels of N2O released by the soil [9,11,13]. Based on GHG inventory data by the Ministry of Environment and Forestry [17], emissions generated from the agricultural sector continue to increase. Fig. 1 shows the growth of GHG emissions in the agricultural sector which has continued to increase since 2000 with 89.864gg CO2 to reach 122.190gg CO2 in 2017. The literature collection phase is needed by the author to strengthen the theoretical basis to support research. Searches are made in relevant journals or previous research, related to gas emissions resulting from rice productivity, as well as ways to reduce gas emissions due to rice farming, especially in East Java province. Then the authors conducted data collection, which is a continuation of the literature study stage. This stage was carried out in order to obtain valuable data on rice field area, rice production, rice seeds used, rice productivity and the factors that influence it, the availability of land in East Java for rice farming, as well as factors that lead to increased greenhouse gas emissions due to rice farming and how to handle it. From the results of the literature study and data collection, the authors can model the rice land productivity system in the form of a causal loop diagram which can later be used as a model framework in preparing scenarios for GHG mitigation strategies.
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Fig. 1. GHG inventory in Indonesia (MoE&F).
3.2. Dynamic hypothesis The causal loop diagram is a diagram that is used to describe the system in general. From the CLD that has been made, a simulation will be carried out with a system dynamics approach. Making this diagram is based on the results of the stages of data collection and literature review. If the relationship is in the direction of the arrow is positive (+), but vice versa, if the relationship is in the opposite direction, then the arrow is negative. The central diagram to be made will illustrate the system of how the GHG process occurs in the productivity of rice fields. According to [16] in his book, Causal Loop Diagram (CLD) is a form of mapping that shows a causal relationship between variables and arrows from cause to effect. CLD is very good for: (1) Quickly capture a hypothesis about the causes of dynamics. (2) Generate and capture models individually or in groups. (3) Important feedback communication is the responsibility of a problem. 3.3. Model N2O emission N2O emissions consist of direct emissions and indirect emissions. Direct emission of N2O in the soil occurs due to the process of nitrification and denitrification and denitrification chemically which does not involve microbes. Nitrification is the aerobic process of oxidizing ammonium (NH4+) by microbes into nitrite with an intermediate yield of NH2OH, and then turning into nitrate. If the amount of oxygen is limited (the soil moisture content is close to saturation), the ammonium oxidizer can utilize NO-as the electron acceptor and then produce N2O. N2O is also formed in the denitrification process, which is the process of reducing nitrates by microbes in an anaerobic state which produces NO, N2O and N2 gases. In general, an increase in N concentration in the soil will increase nitrification and denitrification which then increases the production of N2O. Available N enhancements can occur due to N fertilization, changes in land use and management of organic matter that causes soil organic N mineralize [5,11]. Fig. 2 shows a causal loop diagram of N2O emissions. On the causal loop diagram model, it can be seen that what affects the production of N2O emissions is the use of fertilizers. GHG Accumulation +
a
N2O Emission
+ Climate Change
B1
+
+
+ Land Productivity +
Rice Cultivation
+ Climate Change
Land Productivity
GHG Accumulation +
c +
B2
N2O Manure
-
GHG Accumulation +
b
CH4 Emission +
B3
+ Climate Change
+ Production
Irrigation Rice Field + CH4 Manure +
+ Land Productivity +
+
Rice Cultivation
Fig. 2. (a) Model N2O; (b) Model CH4; (c) Model CO2 emission.
+
+
Time duration of Agriculture tools utilization + +
Harvesting Land Rice Cultivation
CO2 Emission -
Paddy planting area +
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3.4. Model CH4 emission Rice fields in Indonesia are generally managed in waterlogged conditions. Farmers want the water to inundate rice plants because it can reduce the growth of weeds that often drain the costs and labor of farmers in managing rice fields. Methane is one of the GHGs produced through the anaerobic decomposition of organic matter. To break down organic matter into CH4, the redox potential is needed below -100 mV and pH ranges from 6-7. Irrigation rice fields are ideal conditions for this process [4]. In addition to the decomposition of organic matter, other sources of CH4 release are enteric fermentation from the digestion of livestock, incomplete processes of combustion of organic matter, and the consequences of the exploration process of oil and gas mining [7]. Fig. 2 shows a causal loop diagram of CH4 emissions. In the causal loop diagram model, it can be seen that CH4 emissions produce from the irrigation rice field and manure. 3.5. Model CO2 emission Fig. 2 show a causal loop diagram of CO2 emissions. At the stage of processing, paddy fields begin with land plowing. The use of plow machines that still use fossil fuels will cause exhaust gas in the form of CO2 which will be released into the air. While at the stage of maintenance land, irrigation activities can cause CO2 emissions through the use of water pumping machines. Whereas in harvesting activities, CO2 emissions are produced from harvesting machine that using fossil fuels. Exhaust gas from burning fossil fuels contributes to carbon emissions in the air due to incomplete combustion. On the other hand, planting rice can absorb CO2 from photosynthesis. 3.6. Causal loop diagram GHG Based on the sub-models that have been made, then they are reassembled and developed into a complete CLD. CLD as a whole is made based on references that have been obtained by the author then adjusted to the research needs. The main model is shown in Fig. 3 as follows. Land Productivity + Rice cultivation
- + +
+ Production +
Harvesting land
Irrigation machine
Harvesting machine
Plowing machine +
+ + + Time duration of Agriculture tools + utilization
Paddy planting area
Organic fertilizer
Irrigation rice field
CH4 Manure
+
Rainfall
-
Temperature
+
+
CO2 emission
+ +
inorganic fertilizer
+
Burning cropland
+ Urea
Climate change +++
+ +
+ + N2O Manure
B3 +
+
+ CH4 emission B2 + N2O emission B1
Fig. 3. Causal loop diagram GHG.
GHG +Accumulation +
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3.7. Validation structure Validation aims to identify the suitability of variable structures. The test used for SD model structural validation is boundary adequacy, whether the concepts and important structures for overcoming policy problems are endogenous towards the model [18]. Table 1 shows endogenous variables and exogenous variables as influencing factors in each model. The structural validation process is carried out by using references from each variable. Table 1. Model Boundary GHG in Land Productivity. Submodel
Endogenous Variable
Exogenous Variabel
Reference
N2O emission
a. Inorganic fertilizer b. Organic fertilizer
-
[4,13,1]
CH4 emission
c. CH4 Manure d. Irrigation rice field
-
[4,13,5,19,7]
CO2 emission
e. f. g. h.
-
[6,11,12]
GHG Accumulation
Irrigation Machine Harvesting machine Burning cropland Paddy planting area
-
Land productivity
i. N2O Manure
Paddy production
j. Land productivity k. Harvesting land
a. b.
Global warming Climate change
[9,10,11]
c. d.
Rainfall Temperature
[20,21] [20,21]
4. Conclusion Every year the number of GHG emissions produced by paddy fields in Indonesia is increasing. There are activities that have the potential to produce GHG emissions on the productivity of paddy fields. Most of the GHG emissions generated from rice fields are methane (CH4), nitric oxide (N2O) and carbon dioxide (CO2) gas. In this study, we conducted an analysis of the rice productivity system by considering internal and external factors in the rice productivity system. The author conducted a literature study by collecting related references and previous study to obtain a theory that is in accordance with the research. The next step is to develop the model by considering endogenous and exogenous variables to ensure that the variables used are in accordance with the real system. Structural validation is then carried out using appropriate references to develop linkages and causal relationships for each variable. This study presents the development of a model that contains information about the factors that influence GHG emissions produced from paddy fields and their impacts. In the future, this model can be used as a consideration for the government and stakeholders as a reference for making policies from GHG emission mitigation technology as part of intelligent agriculture. Reference [1] Rahmawati, A., S. De Neve, and B. Heru. (2015) “N 2 O-N Emissions from Organic and Conventional Paddy Fields from Central Java, Indonesia”, in Procedia Environ. Sci. 28: 606–612. [2] BPS-Statistics Indonesia. (2018) “Statistical Yearbook of Indonesia 2018.” Badan Pusat Statistik. [3] Nugroho, I., U. Widyagama, and N. Hanani. (2007) “Studi Investasi untuk Pengembangan Komoditi Pertanian di Propinsi Lampung : Studi Investasi untuk Pengembangan Komoditi Pertanian [Title in English: The Study of Investment to Develop Agriculture Commodity in Lampung Province.”
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[4] Tian, Z., et al.(2017) “Maintaining Rice Production While Mitigating Methane and Nitrous Oxide Emissions from Paddy Fields in China : Evaluating Tradeoffs By Using Coupled Agricultural Systems Models.” Agric. Syst. 0–1. [5] Begum, K., et al. ( 2019) “Geoderma Modelling Greenhouse Gas Emissions and Mitigation Potentials in Fertilized Paddy Rice Fields in Bangladesh.” Geoderma, 341: 206–215. [6] Lin, B., and B. Xu. (2018) “Factors Anffacting CO 2 Emissions in China's Agriculture Sector : A Quantile Regression.” 94: 15–27. [7] Wu, X., W. Wang, K. Xie, C. Yin, H. Hou, and X. Xie. (2019) “Combined Effects of Straw and Water Management on CH 4 Emissions From Rice Fields,=.” 231: 1257–1262. [8] Hidayatno, A., and A. Rahmawan. (2019) “Conceptualizing Carbon Emissions from Energy Utilization in Indonesia ’ s Industrial Sector.” Energy Procedia 156: 139–143. [9] IPCC. (2013) “Climate Change 2013: The Physical Science Basis.” [10] IPCC. (2014) “Climate Change 2014 Synthesis Report.” [11] A. E. C. (2016) “Climate Change Vulnerability Assessment in the Agriculture Sector : Typhoon Santi Experience.” 216: 440–451. [12] Marino, R., A. S. Atzori, M. D. Andrea, G. Iovane, M. Trabalza-marinucci, and L. Rinaldi. (2016) “Climate change : Production Performance, Health Issues, Greenhouse Gas Emissions and Mitigation Strategies in Sheep and Goat Farming.” Small Rumin. Res. 135: 50– 59. [13] López Astudillo, A., L. M. Rodríguez, C. M. Lubo, F. Arenas, B. E. Sierra, and B. E. Sierra. (2014) “Evaluating Carbon Footprint Behavior in the Agriculture and Energy Sectors: A Review.” Sist. y Telemática 12: 35. [14] Nieto, Á. M. (2014) “Application of the System Dynamics Methodology For Modeling And Simulation Of The Greenhouse Gas Emissions (GGE)”, in Cartagena De Indias. [15] Suryani, E. (2006) “Pemodelan dan Simulasi [Title in English: Modelling and Simulation] .” Yogyakarta: Graha Ilmu. [16] Sterman, J. (2000) “Business Dynamics: Systems Thinking and Modeling for a Complex World.” Hill Boston. [17] MoE&F, “Emisi - SIGN SMART,” 2018. [Online]. Available from: http://signsmart.menlhk.go.id/v2.1/menu-emisi/. [18] Qudrat-ullah, H. (2008) “Structural Validation of Simulation Models : An Illustration.” pp. 537–542. [19] Feng et al., (2013) “Agriculture, Ecosystems and Environment Impacts of Cropping Practices on Yield-Scaled Greenhouse Gas Emissions From Rice Fields in China : A Meta-Analysis.” “Agriculture, Ecosyst. Environ. 164: 220–228. [20] Jiang, G., R. Zhang, W. Ma, D. Zhou, and X. Wang. (2017) “Land Use Policy Cultivated Land Productivity Potential Improvement in Land Consolidation Schemes in Shenyang, China : Assessment And Policy Implications.” 68: 80–88. [21] Chen, Q., Y. Liu, Q. Ge, and T. Pan. (2018) “Land Use Policy Impacts Of Historic Climate Variability and Land Use Change on Winter Wheat Climatic Productivity in The North China Plain During 1980 – 2010.” Land Use Policy 76: 1–9.