Increasing Paddy Production Through Integrated Farming System Using

System Dynamics Modeling

Haris Raﬁ, Erma Suryani and Amalia Utamima

Information Systems Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

ﬁ

Keywords:

Rice Production, Integrated Farming Systems, Simulation, System Dynamics.

Abstract:

Paddy is a strategic commodity that plays an important role in the economy and national food security. Fulﬁll-

ment of rice for people’s needs is very important for Indonesia because the population continues to increase

every year with a wide and spread geographical coverage. This increase in population has an impact on the

demand for rice, which also increases. Until mid-2021, Indonesia is still importing rice from various countries

to meet rice needs. Therefore, strategies and efforts are needed to increase paddy production so that it does not

depend on imported activities which can drain the country’s foreign exchange. Agricultural activities around

the world can be produced relatively well and sustainably only if there is a large energy input. In a sustain-

able context, integrated farming systems are considered as a breakthrough for sustainable agricultural systems.

Previous research has tackled this problem by focusing on solutions to monoculture farming systems. This

study aims to develop a simulation model to increase paddy production by implementing an integrated farming

system as a scenario solution using a system dynamics approach. System dynamics was chosen because of its

uniqueness which can simulate a complex and dynamic system. The results showed that the system dynamics

approach can be used to model the current paddy production system well. Furthermore, through a predeter-

mined improvement scenario, namely the application of an integrated farming system, paddy production and

rice production can be increased by 15.26% and 15% respectively for the next 17 years.

1 INTRODUCTION

Paddy is a rice-producing plant, one of the main food

needs for most Indonesian people. In recent years,

paddy production in Indonesia has experienced in-

stability, where a drastic decline occurred in 2018

(30.54%) caused by weather factors (dry season)

which caused agricultural land to become dry and

cause crop failure (Suryani et al., 2022). Malang

Regency in East Java Province is an area that has

great potential in the agricultural sector which pro-

duces food crops such as paddy, corn, peanuts, green

beans, soybeans, cassava, sweet potatoes and many

more. In 2020, Malang Regency produces 481,001

tons of paddy, which is a decrease from the previ-

ous year, which was able to produce 498,586 tons,

while the level of rice consumption per capita is 114.8

kg/year (B.P.S., 2020). On the other hand, population

growth is still faster than the rate of food production

in various places due to limited land and water for

agriculture and the impact of global climate change

(Agus, 2018). The higher the population, the higher

the demand for rice food. In addition, an increase in

population will also lead to an increase in demand

for land for residence, which causes the conversion

of land from agricultural land to non-agricultural land

which often occurs. With the decreasing agricultural

land, of course this will threaten the stability of paddy

production. The lack of rice supply has the poten-

tial to cause social, economic, and political instability

in the country. Therefore, a large supply of paddy is

needed to meet the increasing demand for food.

The current agricultural system is dominated by

conventional farming which leads to the cultivation of

similar crops (monoculture) which forces the contin-

uous use of chemical (inorganic) fertilizers and pesti-

cides. This has the potential to cause ecosystem dam-

age which can cause land degradation and contam-

inate surface and ground water (Musa et al., 2018).

Agricultural businesses around the world can produce

optimally and sustainably only if there is a large en-

ergy input. In a sustainable context, an integrated

farming system is a breakthrough for a sustainable

agricultural system. The application of an integrated

farming system is an intensiﬁcation of the agricul-

tural system through integrated resource management

Raﬁ, H., Suryani, E. and Utamima, A.

Increasing Paddy Production Through Integrated Farming System Using System Dynamics Modeling.

DOI: 10.5220/0012443800003848

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Advanced Information Scientiﬁc Development (ICAISD 2023), pages 65-71

ISBN: 978-989-758-678-1

between crops and livestock. The application of an

integrated farming system is highly recommended to

increase agricultural production, farmer income, opti-

mal use of agricultural waste to produce environmen-

tally friendly agriculture (Mukhlis et al., 2018).

Previous research stated that increased paddy pro-

duction could be increased by implementing massive

post-harvest harvesting mechanisms together with

implementation of GAP (Good Agricultural Practice)

according to standards (Aprillya et al., 2019). Mean-

while (Findiastuti et al., 2018) states that expanding

irrigation areas and increasing agricultural investment

is a sustainable food security solution. This study

aims to increase paddy production for food security

by implementing an integrated farming system sce-

nario.

The agricultural production system is a complex

and dynamic system over time because it can be in-

ﬂuenced by many interrelated factors. All types of

dynamic contextual factors from time to time can be

modeled through system dynamics modeling. System

dynamics modeling is a method that can be used to

represent the relationship between variables and com-

ponents in a system such as a paddy production sys-

tem. This study uses a system dynamics approach

to understand the real conditions of the paddy pro-

duction system. The system dynamics approach is

very effective in helping to understand the behavior

of complex components. Therefore, this study aims

to develop a simulation model for paddy production

systems and analyze the simulation results based on

the scenarios applied.

2 RELATED WORKS

Conducted an analysis of the factors driving in-

creased paddy production from the cultivation and

post-harvest side as a basis for developing policy

strategies (Aprillya et al., 2019). There are two sce-

narios proposed for the strategy to increase the qual-

ity of paddy production, namely (1) increasing agri-

cultural equipment and machinery, and (2) increas-

ing seed varieties. The scenario of increasing agri-

cultural equipment and machinery is sufﬁcient to pro-

vide a more signiﬁcant effect. Harvest and post-

harvest mechanisms can be improved by replacing

traditional tools with modern agricultural tools ac-

companied by the implementation of Good Agricul-

tural Practice (GAP). Findiastuti et al. (Findiastuti

et al., 2018) presents a simulation of an assessment

of Indonesian Sustainable Food-Availability (ISFA)

to support policy adjustments in achieving sustain-

ability. The simulation was carried out in three stages

for seven policy scenario options and six policy sce-

nario combinations to analyze how they affect food

availability and agricultural emissions. The ISFA ra-

tio and food availability score were deﬁned as ob-

jective performance. Scenarios that mediate govern-

ment policies carried out during the 2015–2019 pe-

riod which are then simulated until 2025. Scenar-

ios were developed to represent Indonesian govern-

ment policies to achieve sustainable food availability

in 2015–2019. The result is a combination scenario of

expanding irrigation areas and increasing investment

policies is the best policy for ISFA. Sekaran et al.

(Sekaran et al., 2021) explores the beneﬁcial proper-

ties and contribution of integrated crop-livestock sys-

tems (ICLS) to food security, along with their so-

cial and economic beneﬁts, and proposed strategies

for adopting ICLS in low income countries, medium,

and high. This study assesses the production of more

plant residues under ICLS that can be used as ani-

mal feed. ICLS can inﬂuence household dietary di-

versity and support subsistence farmer incomes and

reduce economic risk. Reinventing crops and live-

stock systems could have many potential beneﬁts such

as higher socio-economic returns and better environ-

mental conditions. In low, middle, and high-income

countries, success in implementing ICLS requires or-

ganizational and/or institutional support to form new

marketing opportunities and the application of ICLS

can be increased if government policies can provide

markets, capital, and educational service assistance

for subsistence farmers. However, adopting ICLS re-

quires deep commitment and knowledge of crops and

livestock.

3 RESEARCH METHODS

To overcome the problems in this study, a system dy-

namics approach was used because of its ability to

deal with various complex problems. System dynam-

ics is a continuous simulation technique introduced

by Jay Forrester from MIT around the 1960s. Sys-

tem dynamics focuses on the structure and behavior

of the system consisting of interactions between vari-

ables and feedback loops (Suryani, 2006). System dy-

namics is an approach to study the dynamics of the

behavior of the observed system. System dynamics

modeling is considered suitable to be applied in this

study because it is based on feedback on each struc-

ture of the system that affects other structures. There

are 5 stages that need to be carried out in developing

a system dynamics model (Sterman, 2000), namely

problem articulation, dynamic hypothesis, formula-

tion, testing, and policy formulation.

ICAISD 2023 - International Conference on Advanced Information Scientiﬁc Development

3.1 Problem Articulation

In this process observations are made of systems in

the real world that are the object of observation. Af-

ter that, the key variable is determined. Deﬁning the

problem is also done through literature study, by dig-

ging up information through the literature as a source

of basic concepts or previous research that has been

done regarding the object of the problem in the form

of journals, books, theses, or online references. The

data obtained from the literature is used as a material

consideration in determining the signiﬁcant variables

and additional variables that inﬂuence each other in

the system. In this study, the data used was obtained

from several related government agency sites such as

the Central Statistics Agency (BPS) (B.P.S., 2020;

B.P.S., 2019; B.P.S., 2018); the Ministry of Agri-

culture (Balitbangtan, 2019; RI, 2019); the Ofﬁce of

Agriculture and Food Security of East Java Province,

as well as news portal sites to understand the latest de-

velopments regarding the situation in paddy farming.

Historical data on paddy and rice production were

used in this study from 2008 to 2020 for simulation

purposes and their validation.

3.2 Dynamic Hypothesis

After the process of problem articulation, the next

step is to develop a dynamic hypothesis to account for

the problem behavior. Several internal/endogenous

and external/endogenous variables that affect rice

production are obtained based on the results of the

previous stage. In addition, at this stage a casual

structure description is also carried out based on a

literature study adapted to the system object. Mod-

eling begins by creating a Causal Loop Diagram

(CLD). This diagram contains a series of variables

(components) that each represent a process or sta-

tus and form a series of processes with an empha-

sis on causal aspects (Prahasta, 2018). Each rela-

tionship describes the causality between these vari-

ables. Each relationship also has a positive (+) or neg-

ative (-) polarity to describe how the relationships be-

tween these variables inﬂuence one another. CLD has

two types, namely reinforcing loop (R) which shows

the strengthening of a cycle, and balancing loop (B)

which shows the stability/balance of a cycle.

3.3 Model Formulation

The next step in developing a system dynamics model

is to build a simulation model formulation through a

Stock Flow Diagram (SFD). This stage tests the dy-

namic hypothesis of CLD. The development of SFD

is CLD oriented, but in SFD new components and

information ﬂow (cause-effect relationships) can be

added, without changing the big picture of the system

that has been described in the CLD structure (Soesilo

and Karuniasa, 2014). This stage also determines the

formulation of the equations of each variable as well

as the estimation of the initial values and parameter

values.

3.4 Testing

According to (Barlas, 1989) the system statistical val-

idation process can be carried out in two ways of

testing, namely statistical model validation by test-

ing the average error rate (E1) and model validation

by testing the comparison of amplitude variations or

error variances (E2). The model is said to be valid

if E1 ≤ 5%andE2 ≤ 30%. The equations for model

validation are shown in Equations 1 and 2.

E1 =



S − A



x100 (1)

Where E1 is the average comparison, S is the av-

erage value of the simulated data, and A is the average

value of the actual data.

E2 =



Ss − Sa



100 (2)

Where E2 is the variance error, Ss is the standard

deviation of the simulated data, and Sa is the standard

deviation of the actual data.

3.5 Policy Formulation

Policy design involves creating entirely new strate-

gies, structures, and decision rules. Because the feed-

back structure of a system determines its dynamics,

most of the time policy will involve feedback loops

effecting by reshaping the stock and ﬂow structure,

release the time delay, change the ﬂow and quality of

information available at the primary decision point, or

substantially reinvent the decision-making process of

key players in the system. Based on the objectives of

this study, the scenario that will be applied is the ap-

plication of an integrated farming system to lowland

rice farming to increase paddy production, by chang-

ing the SFD structure that has been developed and de-

clared valid.

4 RESULTS

In this section, a case study of a paddy production sys-

tem simulation with a system dynamics approach will

Increasing Paddy Production Through Integrated Farming System Using System Dynamics Modeling

be presented. This study begins with the articulation

of the problem. In this research, the object of the sys-

tem to be observed is the paddy production system in

Malang Regency, East Java, Indonesia. Malang Re-

gency in 2020 produces 481,001 tons of rice, a de-

crease from the previous year, which was able to pro-

duce 498,586 tons, while the level of rice consump-

tion per capita is 114.8 kg/year (B.P.S., 2020). Simu-

lations were performed using Vensim PLE 9.1.1 x64

simulation software. According to a number of stud-

ies, increased paddy production can be inﬂuenced by

several important factors, such as the use of fertilizers;

the use of superior seeds (Aprillya et al., 2019; Ishaq

et al., 2016); availability of irrigation water (Suryani

et al., 2022; Bashir and Yuliana, 2019); and cropping

index (Suryani et al., 2022; Aprillya et al., 2019). In

this study, the focus of increasing attention is paddy

production and rice production.

4.1 Base Model

Based on the results of the problem articulation, a dy-

namic hypothesis was developed through CLD which

represents the system according to the case in this

study, namely the paddy production system. Mod-

eling in CLD also simulta neously displays the pro-

posed scenario, where in this study the proposed sce-

nario is the application of an integrated farming sys-

tem (green arrow line) to increase paddy production.

CLD that has been developed is shown in Figure 1.

Paddy production yields are inﬂuenced by the har-

vested area and the level of productivity per hectare.

The harvested area is inﬂuenced by the area of paddy

ﬁelds which has a reinforcing loop (R1) for land ex-

pansion and a balancing loop (B1) for land expan-

sion. While productivity is inﬂuenced by several fac-

tors such as seed varieties, fertilization, availability of

irrigation water, and rainfall which have a positive ef-

fect on paddy productivity. On the other hand, yield

loss from harvesting and threshing, as well as pest and

disease attacks on paddy reduces productivity.

Figure 1: CLD of Paddy Production Systems and Scenarios

of Integrated Farming Systems.

The rate of paddy productivity will affect the re-

sulting paddy production. In general, paddy produc-

tion experienced an increasing trend. Meanwhile,

Rice production is the result of converting the pro-

ductivity of harvested dry grain into dry milled paddy

multiplied by the harvested area. Paddy that has been

harvested still has to go through several stages to be

consumed into rice. Milling is the process required

to process harvested paddy/dry milled grain into rice.

During the milling process shrinkage will occur. The

ratio of the weight of rice to the weight of paddy due

to shrinkage is called milling yield. Figure 2 displays

a graph of paddy and rice production.

From agricultural activities, agricultural waste can

be obtained in the form of rice straw. Straw, which is

a waste of paddy plants, is a potential material and is

easy to obtain so that it can be reused as an alterna-

tive animal feed. Meanwhile, from animal husbandry

activities, the resulting livestock manure can be pro-

cessed into organic fertilizer which is useful for in-

creasing rice productivity and production.

After conceptualizing the system through CLD,

SFD development is carried out for model formula-

tion. Figure 2 is the SFD for the base model of a

paddy production system.

Figure 2: SFD of Paddy Production System.

In general, paddy production in Malang Regency

has an increasing trend with an average production of

448224 tons or 1.39% per year. However, in 2019

and 2020 paddy production decreased by 0.28% and

3.53%. Figure 3 shows a graph of current paddy pro-

duction. Likewise with rice production, the average

rice production is 285088 tons or 1.32%. Figure 4

shows a graph of current rice production.

The model that has been developed in the form of

SFD will be validated based on Equations 1 and 2.

Model validation was carried out in the 2008–2020

period for a better understanding of the system based

on available data. Here is some variable validation:

• Paddy Production

E1 = 0.60%

E2 = 18.62%

ICAISD 2023 - International Conference on Advanced Information Scientiﬁc Development

Figure 3: Current Paddy Production.

Figure 4: Current Rice Production.

• Rice Production

E1 = 1.95%

E2 = 23.58%

Based on the results of validation calculations, the er-

ror rate (E1) is ≤ 5% and the error variance (E2) is ≤

30%, which means that the model can be said to be

valid.

4.2 Scenario Model

After the model is said to be valid, then policy sce-

narios are formulated to optimize the running system.

In this study, the scenario that will be applied is the

application of an integrated crop and livestock farm-

ing system. An integrated farming system is an in-

tensiﬁcation of the agricultural system through inte-

grated resource management between crops and live-

stock. Integrated farming systems are assessed as be-

ing able to maintain organic elements in the soil that

are always available to create sustainable agriculture

(Musa et al., 2018). The scenario model is projected

during the 2008-2040 period.

Figure 5 is a model scenario that represents an in-

tegrated farming system scenario. Rice farming will

produce rice straw. The weight of rice straw is 1.4

times that of dry grain paddy yields (Kim and Dale,

2004). Paddy straw is used as the main ﬁbrous feed

for cattle, given as much as 6-8 kg per day. This

means that every year it takes 2190-2920 kg or 2.19-

2.92 tons of straw for animal feed. After that, fe-

ces will be produced from the livestock which can

be processed into organic fertilizer which is produced

through the formation of biogas. One cow can pro-

duce 10-15 kg of manure, where 1 kg of cow ma-

nure has the potential to produce 0.03 m3 of biogas

(Jimmy and Hudha, 2011). From the processing of

livestock manure to producing biogas through the di-

gester, biogas residue will be produced. The biogas

residue is used to produce organic fertilizer to re-

turn to the ﬁeld to increase agricultural productivity

(Paulus et al., 2022).

Based on the research objectives, this research

will focus on the analysis of paddy production and

rice production. The simulation results show, ﬁrst,

the scenario results from 2020 to 2040 paddy produc-

tion are stable with an average production of 567337

tons per year. Whereas in the basic model during this

period, paddy production was stable with an average

production of 492241 tons per year. A comparative

graph of the simulation results of the basic model

and scenarios for paddy production is shown in Fig-

ure 6. Overall, the scenario of implementing an inte-

grated farming system can increase paddy production

by 15.26%.

Second, it can be seen in Figure 7, the scenario

results from 2020 to 2040, rice production is stable

with an average production of 355947 tons per year.

A signiﬁcant increase occurred in 2016 of 13.88%.

Meanwhile, in the base model during this period rice

production was stable with an average production of

308832 tons per year. Overall, the scenario of im-

plementing an integrated farming system can increase

rice production by 15% until 2040.

Figure 5: Current Paddy Production.

Figure 6: Current Rice Production.

Increasing Paddy Production Through Integrated Farming System Using System Dynamics Modeling

Figure 7: Current Rice Production.

5 CONCLUSIONS

The paddy production system is a system with com-

plex problems with various variables that are interre-

lated to each other in it. In this study, paddy produc-

tion was inﬂuenced by harvested area and paddy pro-

ductivity per hectare. The system dynamics approach

is used in this study to ﬁnd solutions to increase paddy

production. Causal Loop Diagram (CLD) was devel-

oped to visualize the relationship between variables in

the paddy production system, which was then devel-

oped with a Stock and Flow Diagram (SFD) to sim-

ulate the current paddy production system. Several

validations were also carried out to prove that the sim-

ulation represented the current system based on the

available data. The simulation results on the basic

model are considered valid because the validation re-

sults obtained are E1 ≤ 5% and E2 ≤ 30%. Through

the scenario of the integrated farming system imple-

mented, paddy production (15.26%) and rice produc-

tion (15%) can be increased for the next 17 years.

Overall, from 2008 to 2040 the average of paddy and

rice production increased by 15%.

The system dynamics approach is useful for pro-

viding insight for stakeholders to ﬁnd solutions in in-

creasing the production of paddy or other agricultural

commodities. This study focuses on the analysis of

the integration of agriculture and livestock on paddy

and rice production behavior, but other aspects of in-

tegrated farming systems are not considered. Another

limitation of this study is the increase in paddy pro-

duction from fertilization intensiﬁcation, while post-

harvest handling was not considered. Further research

can be carried out by focusing on the quality of post-

harvest handling or it can also focus on other aspects

of integrated farming systems such as the energy pro-

duced for community energy efﬁciency.

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Increasing Paddy Production Through Integrated Farming System Using System Dynamics Modeling