Behavior and performance of the integrated farming system model for cassava agribusiness development in Aceh Besar regency

The aim of this research is to analyze the behavior and performance of the integrated farming system model to support the sustainable and competitive development of cassava agribusiness. This study was conducted in Aceh Besar Regency using a system dynamics methodology approach. Data and information were collected through action research, observation, and interviews. The analysis and discussion were carried out using policy structure diagrams and simulations. The model formulation and data analysis were performed using Veneta Simulation software (Vensim PLE). The research findings indicate that the behavior and performance of the integrated farming system model are significantly influenced by three groups of system behaviors, namely: (1) production facilities subsystem, (2) production subsystem, and (3) processing and marketing subsystem. The interaction among these three groups of system behaviors is expected to support the sustainable and competitive development of cassava agribusiness in Aceh Besar Regency.


Introduction
The continuously growing population in Indonesia creates significant pressure on food production, making the development of sustainable and agribusiness-oriented farming systems increasingly important.Cassava (Manihot esculenta Crantz), as one of the primary sources of carbohydrates, has great potential to address this challenge.Furthermore, cassava also holds high economic value and plays a crucial role in improving farmers' livelihoods.As a highly promising food crop, cassava can meet food demands and make a significant contribution to the development of the agribusiness sector in Indonesia, including in Aceh Besar Regency.Aceh Besar Regency boasts geographical and climatic conditions that support cassava growth, making it one of the promising areas for cassava agribusiness development.
Aceh Besar Regency is one of the largest cassava production centers in Aceh Province.The development of cassava production in Aceh Besar Regency during the period from 2013 to 2021 experienced fluctuations.The peak of production increase occurred in 2015, reaching 11,587 tons.However, production declined in 2016 and reached its lowest point in 2020 at 4,110 tons.Subsequently, production increased again in 2021, reaching 5,650 tons [1].The challenges faced by cassava agribusiness stakeholders in Aceh Besar Regency include low technology adoption, limited capital, price fluctuations during harvest, the perishable nature of the product, and a lack of coordination among agribusiness actors.The consequences of these challenges include low cassava productivity, low selling 1297 (2024) 012025 IOP Publishing doi:10.1088/1755-1315/1297/1/012025 2 prices for farmers, and difficulties in meeting consumer demands in terms of quantity, quality, price, timing, and location suitability.
In an effort to enhance cassava production and quality in Aceh Besar Regency, the implementation of the Integrated Farming System Model (IFSM) has become an attractive alternative.IFSM combines agricultural components such as crop farming, livestock, and fisheries into a single integrated system.This model aims to achieve efficient resource utilization, increased farmer income, and reduced negative environmental impacts [2].Pretty [3] noted that Integrated Pest Management (IPM) is one of the key elements within IFSM.This research emphasizes the importance of integrating various pest and disease control techniques in the context of sustainable agriculture.Findings research of Fadlullah [4] indicate that IFSM positively contributes to improving household food security among farmers in Indonesia.Research conducted by Demeke et.al. [5] evaluated the impact of integrated soil fertility management approaches on food security in Ethiopia, highlighting increased productivity that enhances food security.Other studies, such as Rukmana et.al. [6] identified prospects and challenges in cassava agribusiness in Indonesia, one of the world's major cassava producers.The study of Devi et.al. [7] described how cassava agribusiness development has increased farmer income in Kerala, India, creating economic opportunities in the region.Furthermore, the research of Sarker et.al. [8] examined the impact of climate change on agriculture in an Indian state, highlighting the challenges faced by farmers in addressing it.Meanwhile, the research of Uddin et.al. [9] identified socio-economic factors, such as access to agricultural credit, influencing agricultural productivity in Bangladesh.The research of Ariadi [10] developed an integrated farming model for cassava agribusiness development in Trenggalek Regency, East Java Province.Findings from this research demonstrate that the IFSM model comprises components that integrate cassava farming systems, livestock farming, and agro-industry in an integrated manner.
The impact assessment of the implementation of the Integrated Farming System Model (IFSM) in the context of cassava agribusiness includes an analysis of increased productivity, farmers' income, and contributions to food security.Positive impacts on productivity and food security serve as indicators of the success of the IFSM implementation.In the context of Aceh Besar Regency, the development of cassava agribusiness through the IFSM is relevant due to the various potentials and challenges faced by farmers.Factors such as climate change, socio-economic changes, and resource limitations need to be addressed in efforts to develop a sustainable and competitive agricultural system.This research applies system dynamics methodology, which is a systems thinking-based modeling approach that leverages feedback and delay perspectives to understand the complex behavioral dynamics occurring in the cassava agribusiness development system in Aceh Besar Regency.System dynamics is one of the methodologies used in systems approaches, employing computer assistance to analyze and solve complex problems, with a focus on policy analysis and design [11].Considering the complexity and dynamics of these issues, this study aims to examine the behavior and performance of the Integrated Farming System Model in the context of developing a more competitive and sustainable cassava agribusiness in Aceh Besar Regency.

Research methods
This research employs a system dynamics approach, which is a systems thinking-based modeling approach that utilizes perspectives based on information feedback and delays.It aims to comprehend the complex behavioral dynamics within the physical, biological, and social systems.This analysis of behavior and performance of the integrated farming system model supports the development of cassava agribusiness in Aceh Besar Regency, a central cassava production area in Aceh Province and the focus area of this research.
To achieve the research objectives, data, information, and knowledge were collected from both primary and secondary sources.The data collection procedure involved action research, observation, interactive dialogue, as well as interviews with respondents and key informants representing diverse stakeholders, including practitioners and policymakers involved in the stages of input provision, the IOP Publishing doi:10.1088/1755-1315/1297/1/0120253 production process, and the marketing of cassava.Respondent selection was based on criteria ensuring the representativeness and credibility of micro-level information.The required data for this research were classified into three categories: numerical data, written data, and mental models.
The numerical data used in this research encompass a variety of variables within the physical structure and decision-making processes of the integrated cassava farming system under investigation.These variables include input supplies (comprising labor, fertilizers, seeds, and agricultural machinery), cassava production quantities, cassava productivity, cassava cultivation patterns within cassava farming, cassava marketing, cassava processing, cassava market demand, and other pertinent elements.Written data consists of various references employed in the modeling process, such as secondary data sources, research journals, and literature relevant to the context of this study.Meanwhile, mental models encompass the norms and rules that serve as the foundation for decision-making by the actors involved in the integrated cassava farming system at the heart of this research focus.Numerical data and mental models were acquired through respondent interviews and the conduct of Focus Group Discussion (FGD) activities involving relevant stakeholders.
The mental model, literature, and numerical data collected were processed into a model framework using the system dynamics methodology as illustrated in Figure 1 [12].The development of the system dynamics model was carried out using Veneta Simulation software (Vensim PLE), which includes stages such as creating a cause-and-effect diagram, developing flowcharts or sub-model diagrams (level and rate) of the system under study, model system development, model assumption testing, and simulation stages.

Figure 1. Model design of system dynamics
Causal loop diagrams in system dynamics are also known as qualitative modeling because they aim to both describe and explore, as well as describe and explain inductively [13].In general, there are six steps to analyze a problem from a system dynamics perspective, which are problem identification and definition, system conceptualization, model formulation, model simulation and validation, policy analysis and improvement, and model implementation.

Results and discussion
The physical structure and decisions within the integrated farming model for cassava agribusiness development, as previously developed, have been validated and deemed adequate.Subsequently, the system was simulated to observe its behavior under the baseline scenario (base run).The behavior exhibited in the baseline scenario represents the system's performance under normal business conditions, referred to as 'business as usual' (BAU), where it operates in an equilibrium state without any interventions, whether by altering parameter values or modifying the model's structure.The parameter values and model structure in the baseline scenario are based on various sources, including literature, numerical data analysis, and interviews with stakeholders involved in the system.The behavior and performance of the integrated farming system model are significantly influenced by three groups of system behaviors, namely: (1) the production facilities subsystem, (2) the production subsystem, and (3) the processing and marketing subsystem.The interactions among these three groups of system behaviors are expected to support the development of a sustainable and competitive cassava agribusiness in Aceh Besar Regency.

Sub-system of production facilities in integrated farming management
The production facilities required in integrated farming management for cassava encompass several elements, including capital, technology, labor, seedlings, pest and disease control, farming equipment, manure, cultivation land, livestock feed, livestock seedlings, animal pens, and other equipment.These production facilities, in addition to being provided by the farmers themselves, are also obtained from external sources.Farmers provide production inputs such as manure and livestock feed, both in the form of forage crops and concentrated livestock feed, to support sustainable farming practices and integration with livestock management and agro-industrial activities.

Figure 2. Sub-model of cassava integration production with livestock
The integrated production of cassava with livestock is a cultivation process that coordinates the utilization of available resources to produce the main product, cassava, as well as supporting products such as livestock, and manages waste in the form of feces.The production results from this model add value to the cassava production carried out by farmers.Figure 2 illustrates the physical structure and decisions made by farmers in the management of their resources.From the figure, we can see how this structure affects the dynamics of cassava production by farmers.The dynamics of cassava production by farmers have a significant impact on the dynamics of livestock feed supply and organic fertilizer in Aceh Besar Regency.This depends on the integration system between cassava cultivation with livestock management and sustainable agro-industrial practices, which form the basis for cassava production, organic fertilizer, livestock forage, and concentrated livestock feed.The behavior of livestock feed supply in Aceh Besar Regency, as seen in the simulation in Figure 4, indicates that in the first five years, historical data shows a relatively stable trend.A similar pattern is observed in the forecast data for the next five years.However, after passing through the 22nd to the 25th year, a drastic decrease occurs, causing the livestock feed supply to become extremely limited, even unable to meet the demand.
The dynamics of manure utilization in livestock farming, as depicted in Figure 5, show a sustained increasing trend in both historical and forecasted data.This can be explained by the practice of converting livestock waste into manure, where a significant portion of the waste undergoes minimal processing to become compost, and it is left in the barn for 6 months to 1 year.When used, this compost is collected and placed in plastic bags, which are then transported to the fields.However, the average usage of manure currently remains around 150 kg per hectare, which is still below the recommended amount.Behavioral analysis within the subsystem of production facilities, as outlined above, indicates that the integrated farming system has the capability to develop cassava agribusiness.This is evident from the increased cassava production capacity.This increase is related to the growth of agro-industry both upstream and downstream, reflected in market growth, sustainable cassava production, and manure produced through the integration of cassava systems with livestock management.Additionally, products such as cassava flour and cassava chips produced through the integration of cassava systems with agroindustry also contribute to increased production capacity.

Sub-system of production in integrated farming management
Figure 6 illustrates the interaction between cassava supply and demand, where this interaction results in transaction agreements for sales.The dynamics of cassava sales and prices at the farmer level have a direct impact on farmer income.The income obtained by farmers is added to their cash reserves, which are then used to finance cassava production costs.The ratio of farmer cash to cost requirements in this farming enterprise is known as cash liquidity.The value of cash liquidity ensures the continuity of cassava production.Furthermore, within the farmer's cash reserves, profit and revenue-cost ratio are included as financial performance indicators.Farmer profit is calculated as the difference between farmer receipts and expenses.Meanwhile, the revenue-cost ratio (R/C ratio) is obtained by comparing receipts to expenses, determining whether the farming enterprise is viable or not.
The dynamics of farmer cash are determined by the structure of farmer cash inflows and outflows.Cassava farmer cash inflows are based on two main factors: the revenue component from cassava sales and the loans received by farmers.Farmer cassava revenue is measured based on the quantity of cassava sold multiplied by the cassava price at the time of sale.Farmer loans are provided based on their needs for production costs and the available loan fraction.On the other hand, farmer cash outflows consist of financing farming activities and repaying loans received by farmers.Farm financing components include planting and crop maintenance costs, while loan repayments by farmers are also part of these expenditures.

Figure 6. Sub-model for cassava supply and market expansion
Figure 6 illustrates the inventory and cassava market growth sub-model at the farmer level.This submodel has two aspects: one related to the backward linkages with cassava-livestock production or farming activities and the other related to the forward linkages with inventories and the cash of cassava collectors, wholesalers, and agro-industries.The interaction between these aspects is explained through the variable of cassava demand by cassava collectors, wholesalers, and agro-industries.
Production in integrated farming management encompasses various components, such as cassava, organic fertilizer, and livestock feed, consisting of forage and concentrated animal feed.The discussed production subsystem includes several aspects, including cassava production capacity management, the availability of cassava from farmers, farmer cassava prices, sales processes, income, and farmer cash liquidity.Perception of cassava stock availability at the farmer level is constructed based on the availability of cassava stock with farmers at a specific point in time (known as a delay).This perception information holds significant importance as it will affect the determination of cassava prices for farmers, which is based on the average price.The simulation results (see Figure 7) depict the behavior of cassava stock availability perception at the farmer level.In the early years up to year 4, a significant decline occurs, but after that, the trend indicates an increase until reaching year 25.

Figure 7. The perception of cassava availability at the farmer level
The impact of dynamics in cassava stock availability perception on cassava prices among farmers can be observed through the cassava price simulation presented in Figure 8.The simulation results depict historical data from the early years to year 4, where cassava prices experience a sharp increase followed by a relatively slow decline.Furthermore, from year 5 to year 25, cassava prices show a continuous decreasing trend, with a sharp decline occurring from year 16 to year 25 in the forecasted data.The significant spikes in cassava prices correlate with the dynamic behavior of farmers' perceptions of cassava availability.When farmers' perceptions of cassava stock availability sharply decline, this coincides with a significant increase in cassava prices.Conversely, when farmers' perceptions of cassava stock availability increase sharply, cassava prices experience a quite significant decrease.

Figure 8. The pricing behavior of cassava at the farmer level
In the context of farm profitability performance, the dynamic simulation of farmer profit (see Figure 9) reflects that at the beginning of the year, the level of farmer profit experiences a significant decrease, followed by an increase, and tends to continue rising until year 25.The results of the behavioral analysis in the production subsystem, as previously described, indicate that the integrated farming system has the capability to develop the cassava agribusiness.This is evident from the dynamics of cassava inventory perception, changes in cassava prices, and sustainable increases in farmer cassava agribusiness profitability.

Sub-system of processing and marketing in integrated farming management
The cassava supply and collector's cash sub-model depicts aspects related to the supply or inventory of cassava at the collector level, including the addition of cassava stock by collectors, the sales process, and the cash position of collectors (see Figure 10).In this context, it is known that the cassava inventory needs of the collector depend on the cassava sales by farmers, taking into account the fraction of cassava sales to the collector.The fraction of cassava sales to the collector reflects the demand for cassava submitted by the collector in relation to the total demand for cassava from farmers.The demand for cassava is influenced by the quantity of cassava offered by farmers and the liquidity factors held by the collector.Liquidity factors reflect the collector's ability to finance its operational needs, so an increase in cassava supply from farmers accompanied by an increase in collector liquidity will result in increased demand for cassava by the collector.

Figure 10. The cassava supply sub-model at the collector level
The availability of cash for collectors is determined by the balance between receipts and financing received by collectors.Collector receipts are measured based on the volume of sales made by collectors, taking into account the agreed-upon price (price at the collector level).On the other hand, the financing received by collectors is determined by the additional cassava supply received by collectors and the prevailing cassava prices among farmers.The process of receipts and the addition of cassava supply will contribute to the dynamics of available cash for collectors.
Figure 10 illustrates the sub-model of cassava inventory and cash for collectors, which is related to the cassava-livestock production sub-model by farmers in the previous period and will be related to the The dynamics of cassava sales at collectors have a significant impact on the performance of collectors.The simulation results (see Figure 11) illustrate the dynamics of collector income, which experiences a sharp decline in the early years and then increases from the second year to the 25th year.

Figure 11. The income behavior of cassava collectors
The profit obtained by collectors is influenced not only by the value of cassava sales but also by the marketing costs incurred by collectors.The marketing costs incurred by collectors include the cost of purchasing cassava from farmers, sorting activities, loading and unloading processes, as well as transportation from the farmer's production center (i.e., farmland) to the village-level storage and eventually to the end consumers.Findings indicate that the largest cost incurred by collectors is transportation costs, mainly because the farmland of farmers is generally small, scattered, and located far from the storage centers.
Figure 12 depicts aspects related to the supply or inventory of cassava at the wholesale level, including the addition of cassava stock by wholesalers, the sale process of cassava by wholesalers, and the financial condition of wholesalers.In the context of this research, it can be observed that the need to increase cassava inventory at the wholesale level is fulfilled through two sources: sales made by collectors and increased sales made directly by farmers.The increase in cassava stock resulting from direct sales by farmers is obtained through two components, namely direct cassava sales by farmers and the fraction of cassava sales to wholesalers by farmers.The fraction of cassava sales to wholesalers by farmers reflects the cassava demand submitted by farmers and responds to cassava supply from farmers, limited by the liquidity capacity of wholesalers.The value of the fraction of cassava sales to wholesalers becomes a determining factor in increasing cassava stock at wholesalers that originates from farmers.

Figure 12. Sub-model of wholesaler cassava supply
Figure 12 illustrates that the financial condition of wholesalers is influenced by the balance between income and financing.Wholesaler income is measured from the volume of cassava sales made by wholesalers to agro-industries and traders from outside the region at agreed-upon prices.On the other hand, wholesaler financing includes the purchase of cassava from collectors at the prices prevailing at the collector level, as well as the addition of cassava supply obtained directly from farmers at prices corresponding to the farmer-level cassava prices.Based on these conditions, dynamics occur that affect the financial condition of wholesalers.
The performance of wholesalers is in line with the dynamics of cassava sales.The simulation results depicted in Figure 13 indicate that wholesaler income at the beginning of the year experiences a significant increase, but starting from the second year, this income continues to decline until reaching its lowest point in the fourth year.After that period, wholesaler income tends to increase again and remains relatively stable until reaching the 25th year.In addition to being influenced by the value of cassava sales, the profit of wholesalers is also affected by the magnitude of marketing costs incurred.The marketing costs incurred by wholesalers include expenses for the cassava concentration process at the district level, storage, and distribution processes related to sales agents or retailers.The largest marketing expense incurred by wholesalers is storage costs, considering that cassava is a perishable commodity.The simulation depicted in Figure 14 shows that the profit of wholesalers experiences a significant increase at the beginning of the year until the second year, followed by a decline until the 25th year.

Conclusions
The research results indicate that the behavior and performance of the integrated farming system model are greatly influenced by three groups of system behaviors, namely: (1) the production facilities subsystem, (2) the production subsystem, and (3) the processing and marketing subsystem.These conditions affect farmers' intensity in managing their farming activities and have an impact on the performance of various aspects, including cassava production, inventory, cassava sales, maintenance costs (fertilizer usage), farmers' cash liquidity, cassava productivity, as well as the interconnection of farming with other activities such as livestock and agro-industry.Furthermore, these various factors have implications for added value, profitability, and farmers' profits.

Figure 3 .
Figure 3.The cassava production behavior of farmer

Figure 4 .
Figure 4.The behavior of livestock feed supply

6 Figure 5 .
Figure 5.The Behavior of Manure Production

Figure 9 .
Figure 9.The profit behavior of cassava and livestock farmer or Depr eci at i on of cassava i n col l ect or Cassava t i me at t he col l ect or Cassava of f er s at col l ect or s Tot al cassava demand t o col l ect or Demand f or cassava f r om whol esal er t o col l ect or Cassava agr oi ndust r y demand t o col l ect or Cassava at Col l ect or Cassava i ni t he ear l y col l ect or Cassava avai l abi l i t y at col l ect or Del ay i n per cept i on of cassava avai l abi l i t y at col l ect or Per cept i on of cassava avai l abi l i t y among col l ect or The ef f ect of per cept i on on t he avai l abi l i t y of cassava pr i ces at col l ect or Aver age pr i ce of cassava Pr i ces of cassava at col l ect or Pr of i t of cassava col l ect or <Sal es of cassava col l ect or > Li qui di t y of cassava col l ect or Fr act i on of cassava depr eci at i on i n col l ect or The ef f ect of l i qui di t y on spendi ng by cassava col l ect or Demand f or cassava at col l ect or Fr act i on of cassava sal es t o col l ect or <Tot al cassava demand> <Far mer s' cassava of f er s> Col l ect or ' s need f or cassava shoppi ng Cassava col l ect or adequacy t i me <I ncr ease i n cassava at t he col l ect or > Cash needs of cassava col l ect or f or shoppi ng Sal es of cassava f ar mer s <Pr i ce of cassava f or f ar mer > 1297 (2024) 012025 IOP Publishing doi:10.1088/1755-1315/1297/1/01202510 cassava wholesaler inventory and cash sub-model as well as the cassava inventory and growth submodel in the future period through cassava demand variables.

Figure 13 .
Figure 13.The income behavior of cassava wholesalers

Figure 14 .
Figure 14.The profit behavior of cassava wholesalers