Sustainable Income Optimization Model Through Food and Livestock Commodity Trade Integration in the Indonesia–Timor Leste Border Area

Infrastructural development in the Indonesia–Timor Leste border region is intended to enhance public accessibility and stimulate economic growth. Empirical evidence showed that infrastructure investment contributed to long-term economic development and reduced income inequality (Nugraha et al., 2020; Taena et al., 2023b; Zhang & Cheng, 2023). The local economy in this region is predominantly supported by traditional food crop farming and cattle breeding, characterized by low levels of education, productivity, and a high poverty rate among farmers (Sipayung et al., 2023; Taena et al., 2023a). The border region functions as a key production center for rice, corn, and beef cattle. Production of rice increased from 131,000 tons in 2020 to 164,000 tons in 2022, while corn production rose from 167,000 tons to 193,000 tons over the same period. Similarly, the cattle population increased from 575,000 heads in 2020 to 631,000 heads in 2022 (Badan Pusat Statistik, 2023).

Development of infrastructure and increased production directly increase trade flows, which impact parties involved in the supply chain. Although rising agricultural output and infrastructure contribute to higher household income, it also generates adverse environmental impacts, including soil degradation, drought, and increased carbon emissions (Waithaka, 2023). These environmental effects reflect the inverse relationship between production growth and sustainability (Agovino et al., 2019; Raihan, 2023), especially when production is driven by traditional farming practices and limited environmental awareness (Waseem et al., 2020). Increased production also tends to intensify trade activities, further elevating carbon emissions (Chen et al., 2021). Sustainable development should address social, economic, and ecological aspects in a balanced manner (Fauzi, 2019; Purvis et al., 2019).

This study aims to optimize both income and carbon emission levels associated with the trade of rice, corn, and cattle in the Indonesia–Timor Leste border region, and to evaluate the sustainability performance of these commodity trade systems.

This study was conducted in Indonesia’s border region with Timor Leste which can be seen in Figure 1, encompassing four regencies: Kupang, North Central Timor, Belu, and Malaka. The research was carried out from July 2024 to December 2024.

This study examined both primary and secondary data. Primary data were collected through interviews with key marketing institutions in the border area, including food crop farmers who also raise cattle, food traders, livestock traders, processing industry actors, and consumers. The total sample consisted of 500 respondents: 100 food crop farmers with cattle, 100 food traders, 100 livestock traders, 100 processing industry representatives, and 100 consumers. Secondary data were obtained from the East Nusa Tenggara Provincial Agriculture Office, the Central Statistics Agency, and the East Nusa Tenggara Provincial Directorate of Land Transportation.

The supply chain system consists of three production nodes (rice, corn, and livestock farmers), intermediate distribution warehouses, and consumers. Product flow can be direct from farmers to consumers or indirect through warehouses, depending on production capacity, storage, transportation, and environmental constraints. The agricultural-livestock trade system in the Indonesia-Timor Leste border region reflects an inherent trade-off between economic objectives, namely increasing income and cost efficiency, and environmental objectives, namely controlling carbon emissions. Therefore, a Multi-Objective Linear Programming (MOLP) approach was adopted to explicitly model the existing trade-offs and generate Pareto-efficient solutions, which allowed policymakers to evaluate various scenarios of alternative development while considering sustainability constraints. Incomes and carbon emissions related to food crop and cattle production were also analyzed by a MOLP model, which incorporated cost efficiency, income maximization, and objectives of environmental sustainability. The model was adapted and developed based on previous approaches recommended by Bacchetti et al. (2021), Wangsa et al. (2023), and Yuniarti et al. (2023). The finalized model aims to (1) increase production and income from rice, corn, and cattle commodities; (2) achieve total cost efficiency in trade activities; and (3) reduce carbon emissions associated with the commodity trade system.

The MOLP model is formulated as follows:

Objective Function:

Constraint Function:

Descriptions:

: index of rice farmers

: index of corn farmers

: index of cattle farmers

: index of rice distribution warehouses

: index of corn distribution warehouses

: index of cattle distribution warehouses

: index of consumers

: index of transportation modes

: objective function for the income of food crop and cattle farmers

: objective function for the total cost in the supply chain system

: objective function for carbon emissions released into the air from supply chain activities

: carbon productivity level from rice farming

: carbon productivity level from corn farming

: carbon productivity level from cattle farming

: carbon productivity level resulting from the use of transportation t

: rice produced by farmer distributed to consumer using transportation

: corn produced by farmer distributed to consumer using transportation

: cattle produced by farmer distributed to consumer using transportation

: rice in distribution warehouse distributed to consumer using transportation

: corn in distribution warehouse distributed to consumer using transportation

: cattle in distribution warehouse distributed to consumer using transportation

Yqrt: cattle in distribution warehouse q distributed to consumer r using transportation t

: profit obtained by farmer if rice is distributed to consumer using transportation

: profit obtained by farmer if corn is distributed to consumer using transportation

: profit obtained by farmer if cattle is distributed to consumer using transportation

: transportation cost incurred to farmer if rice is distributed to consumer using transportation

: transportation cost incurred to farmer if corn is distributed to consumer using transportation

: transportation cost incurred to farmer if cattle is distributed to consumer using transportation

: transportation cost incurred to distribution warehouse if rice is distributed to consumer using transportation

: transportation cost incurred to distribution warehouse if corn is distributed to consumer using transportation

: transportation cost incurred by cattle distribution warehouse when cattle are distributed to consumer using transportation mode

: carbon production level from rice farming

: carbon production level from corn farming

: carbon production level from cattle farming

: carbon emission coefficient associated with the mode of transportation 𝑡

: production capacity of rice farming from farmer

: production capacity of corn farming from farmer

: production capacity of cattle farming from farmer

: storage capacity of rice distribution warehouse -

: storage capacity of corn distribution warehouse -

: storage capacity of cattle distribution warehouse -

D_1r: demand for rice by consumer r

D_2r: demand for corn by consumer r

D_3r: demand for cattle by consumer r

: transport capacity of transportation mode

The α and β parameters were calibrated using region-specific emission factors derived from Intergovernmental Panel on Climate Change (IPCC) guidelines and field survey data. Production-related emissions (α) account for the use of fertilizer, land preparation machinery, and livestock management, while transport emissions (β) are estimated based on vehicle type, carrying capacity, and distance travelled.

To analyze the performance of sustainable integrated trade between food crops (rice and corn) and cattle in the Indonesia–Timor Leste border region, the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was applied due to its predictive capability (Chin, 2010; Hair et al., 2019a; Hair et al., 2021). The conceptual framework of this study, including all latent variables and their corresponding observed indicators, is presented in Figure 2. Farmers’ production is reflected by cultivated land area (Pr11), amount of seeds (Pr12), amount of fertilizer (Pr13), labor input (Pr14), and fixed costs (Pr15), while farmers’ marketing is represented by marketing distance (Pe11), marketing costs (Pe12), selling price (Pe13), quantity sold (Pe14), and sales frequency (Pe15). The social dimension is indicated by motivation (S1), farmer group participation (S2), cooperation (S3), social control (S4), and the ability to access information (S5). Farmers’ sustainability performance is suggested by CO₂ emissions (P1), CH₄ emissions (P2), N₂O emissions (P3), farm income (P4), and investment growth (P5). Livestock traders’ production is mirrored by the number of cattle purchased (Pr21), pen capacity (Pr22), production costs (Pr23), and labor costs (Pr24), while their marketing performance is shown by transportation costs (Pe21), travel distance (Pe22), transport capacity (Pe23), and vehicle age (Pe24). Moreover, traders’ sustainability performance is reflected by CO emissions (Pt1), HC levels (Pt2), CO₂ levels (Pt3), and revenue (Pt4). Food crop traders’ production is exhibited by the amount of harvest purchased (Pr31), warehouse capacity (Pr32), depreciation costs (Pr33), and labor costs (Pr34), while their marketing is reflected by transportation costs (Pe31), travel distance (Pe32), transport capacity (Pe33), and vehicle age (Pe34). Apart from these, their sustainability performance is manifested by CO emissions (Pp1), HC levels (Pp2), CO₂ levels (Pp3), and revenue (Pp4). The processing industry is assessed by travel distance (IP1), transport capacity (IP2), CO emissions (IP3), and HC levels (IP4), while consumer performance is reflected by consumer satisfaction (K1), travel distance (K2), CO emissions (K3), and HC levels (K4).

The results indicated that farmers’ income could increase by approximately IDR 5,779,300,000 per planting season or livestock production cycle, equivalent to an average of around IDR 1,200,000 per month, with a production cost of IDR 64,000 per acre. Production growth can be promoted through both intensification and extensification of rice and corn cultivation. For traders, income is expected to increase by approximately IDR 8,526,000,000, with maximum carbon emissions of 2,443,241 tons CO₂e and average transport costs of IDR 4,600 per km. The optimization results for farmers’ and traders’ income based on considerations of carbon emissions are presented in Table 1.

a. Convergent validity

The convergent validity test indicated that each indicator adequately represented its corresponding variable. All indicators used in the model had outer loading values greater than 0.7, which confirmed that they appropriately reflected their latent variables. Indicators with outer loading values below 0.7 were removed from the model, as this study employed a reflective measurement model that emphasized predictive capability (Ghozali, 2021; Hair et al., 2021). The outer loading values of the indicators used in this study are presented in Table 2.

b. Composite reliability

Composite reliability is used to measure the consistency of indicators in representing their respective variables. A variable is considered to have acceptable reliability if its composite reliability value is greater than 0.7 (Ghozali, 2021; Hair et al., 2019b; Monecke & Leisch, 2012). The composite reliability values for each variable are presented in Table 3.

The goodness-of-fit of the PLS-SEM model was assessed using the R², Q², and f² values. The R² value indicates the extent to which the exogenous constructs explain variance in the endogenous latent variables (Ghozali, 2021; Guenther et al., 2023). The consumer variable had an R² value of 0.24, meaning that 24% of its variance was explained by the model. The farmer variable had an R² value of 0.462, indicating 46.2% explanatory power. The food trader variable had an R² value of 0.962, while the livestock trader variable had an R² value of 0.978, suggesting that both were strongly explained by their respective construct variables. The processing industry variable showed an R² value of 0.22, indicating 22% explanatory capability.

Predictive relevance was evaluated using the Q² value. A Q² value greater than zero signifies that the model has predictive relevance and is therefore suitable for prediction purposes (Hair et al., 2011; Leguina., 2015). The Q² values for all latent variables in this study exceeded zero, confirming the predictive adequacy of the model. The R² and Q² results are presented in Table 4.

The f² value measures the effect size of exogenous variables on endogenous variables within the model. An f² value of 0.02 indicates a weak effect, 0.15 indicates a moderate effect, and 0.35 indicates a strong effect. Values below 0.02 are considered to have no meaningful influence. The f² values for each construct in this study are presented in Table 5.

The farmer variable had a weak influence on the variables of food traders, livestock traders, and consumers, but it had a moderate influence on the processing industry variable. Among the determinants of the farmer variable, farmers’ marketing showed a weak effect, farmers’ production had a strong effect, and social factors had a moderate effect on farmer outcomes.

Similarly, both the livestock trader and food trader variables exerted only weak influences on the processing industry and consumer variables. For both trader types, the marketing construct showed a strong influence on the trader variable, while the production construct showed only a weak influence.

The PLS-SEM analysis produced both direct and indirect effect estimates. The direct effects are presented in Table 6, while the indirect effects are presented in Table 7.

Food crop farmers in the Indonesia–Timor Leste border region cultivate an average of 41 acre of land and maintain 1–2 head of cattle. Their average monthly income from food crop farming is IDR 475,000. Income from cattle is not calculated regularly because cattle are generally treated as a form of savings to meet urgent needs such as education or social obligations. Livestock are raised under a free-range system, and production inputs are seldom recorded. On average, farming and livestock activities generate approximately 32.21 kg CO₂e emissions per farmer. Annual carbon emissions in the border region remain below the optimized emission threshold, indicating that increased agricultural activity can still take place within sustainable limits. Higher incomes may influence changes in farmers’ socioeconomic practices. However, carbon-intensive practices such as land clearing through burning and limited integration between crop and livestock systems pose challenges to sustainability (Taena et al., 2025).

Farmers in the border region are predominantly conventional farmers with relatively low-income levels. Farmers’ income is affected by limited access to agricultural tools and technology, as well as low land productivity for rice and corn. Inappropriate use of fertilizer and limited purchasing power reduce yields (Haneloy et al., 2021; Painneon et al., 2022). Besides, improper fertilizer dosage, suboptimal land management, and waste burning contribute to increased greenhouse gas emissions (Park et al., 2023; Song et al., 2023). Farmers typically sell their harvest in several batches to avoid decline in prices during peak harvest periods and to maintain financial reserves for unexpected expenses. However, repeated transport of small quantities contributes to additional emissions due to increased transportation frequency (Yang et al., 2022; Yang, 2023). Agricultural waste can be converted into fertilizer and complete feed, which can be used to stop the burning process and is expected to have an impact on reducing carbon emissions (Ngadisih et al., 2024). Agricultural contracts can be implemented to maintain price fluctuations at the farm level and reduce the use of repeated transportation. This kind of contracts maintain price stability and simplify farmers’ work (Hoang, 2021; Miyata et al., 2009).

Farmers raise beef cattle, which are mostly free-range in forests and pastures. Cattle farming in border areas contributes significantly to carbon emissions (Pazla et al., 2022). In addition to the increasing livestock population, carbon emissions are generated from livestock manure released from feed obtained from agricultural waste, which is generally high in fiber. Changing livestock farming practices from free-range to intensive systems can reduce carbon emissions by modifying the feed used and utilizing methane for biogas production (Bayat et al., 2017; Giamouri et al., 2023).

Livestock and food traders operate with an average transport capacity of 1.5–2 tons and vehicle ages averaging 8.6 years. Traders travel an average distance of 533.35 km per purchasing cycle. Livestock traders typically have a pen capacity of 16 heads, while food traders have warehouse capacities averaging 2.7 tons. When combined, their trade activities generate approximately 425 kg CO₂e emissions and longer travel distances per purchase cycle. This shows that market demand for food and livestock products remains strong. Enhancing trade efficiency is essential to maintain trade sustainability and support continued income growth among traders (Balcom et al., 2023).

Traders in the border region procure commodities by travelling extensively across the Indonesia–Timor Leste border area. Limited availability of commodities and increased competitions encourage traders to make more frequent purchasing trips and engage more assertively in negotiations. Traders often travel long distances, stay overnight during procurement trips, and conduct vehicle repairs independently. Low awareness and limited enforcement of testing related to routine vehicle emissions contribute to higher greenhouse gas emissions among traders (Hussain et al., 2022; Porter et al., 2013).

Processing industries in the Indonesia–Timor Leste border region source limited raw materials from local food and livestock traders. Instead, inputs are primarily obtained directly from farmers or imported from other regions or islands. Raw materials are delivered either directly by farmers or collected by operators in the processing industry. Transportation activities associated with this supply chain, along with the use of firewood and other biomass fuels, are major contributors to carbon emissions in the processing sector. The reliance on locally available natural fuels reflects the broader energy conditions in rural border areas (Su et al., 2023).

Trader behavior, both in food and livestock markets, influences carbon emissions in the processing industry. Increased trading activity can lead to shortages of raw materials for processors, hence disrupting production. Such disruptions may temporarily reduce emissions but also risk lowering processing industry income due to reduced output (Nayaka & Kartika, 2018; Suryani et al., 2022). Farmers in the border region act as price takers (Lanamana, 2019; Mulyana et al., 2022). Although processing industries offer better prices, their limited capacity restricts the volume of produce they can absorb (Kanza & Vitale, 2015).

The interactions among farmers, traders, and processing industries do not substantially affect consumer behavior. Consumers generally purchase from the nearest market or processing facility and have little direct engagement with farmers. Consequently, consumer choices have limited influence on income distribution or carbon emissions within the agricultural-livestock trade network.

Trade activity in the Indonesia–Timor Leste border region has the potential to substantially increase economic gains while maintaining carbon emissions within manageable limits. Farmer income could rise by IDR 5.7793 billion per planting season, with improved production efficiency at an average cost of IDR 64,000 per acre and a maximum carbon emissions of 356.561 tons CO₂e. Trader income could increase by IDR 8.526 billion, with a maximum carbon emissions of 2,443.241 tons CO₂e and average transportation costs of IDR 4,600 per kilometer.

At the farm level, carbon emissions primarily stem from inefficient use of fertilizer, land management, and waste burning. For traders, emissions are driven by transport distance and limited vehicle capacity. Although processing industries have direct relationships with some farmers, most farmers remain dependent on traders for access to the market. Strengthening and expanding the capacity of the processing industry in the border region is therefore essential to increasing farmers’ income and improving the sustainability of agricultural-livestock trade.

Despite its contributions, this study has several limitations. The analysis was limited to four districts in the Indonesia–Timor Leste border region and three commodities, i.e., rice, corn, and livestock, which may limit the generalizability of the findings. Communities in the Indonesia–Timor Leste border region are predominantly traditional farmers dependent on rainfall. Livestock farming is practiced in open pasture. Furthermore, the MOLP framework relies on linear assumptions, and carbon emission estimates do not fully capture indirect or life-cycle emissions. PLS-SEM results also depend on selected reflective indicators and self-reported survey data. Future research should expand the geographic and commodity coverage, incorporate additional supply chain actors, and adopt dynamic or non-linear approaches to better capture the complexities of sustainable border trade systems.