Promoting the Economic Sustainability of Small-Scale Farmers Through Versatile Machinery in the Republic of Korea

Abstract

The increasing use of tractors and implements is replacing manual labor, but adds financial burdens on small-scale farmers due to rising costs. Many farmers have turned to leasing and renting machinery to mitigate these expenses, while repair and maintenance costs remain significant. Government interventions aim to alleviate these burdens, but income disparities between urban and rural areas persist, and the impact of machinery use on climate change and the environment poses further challenges. Strategies like omitting some operation steps and adopting versatile machinery are proposed to cut costs and promote economic sustainability for small-scale farmers. Therefore, this study assessed the economic benefits of using versatile machinery in farming, especially for small-scale rural farmers. Farming processes were divided into field preparation and crop season activities. Field preparation included rotary tillage, ridge formation, and mulching, whereas crop season activities included harvesting and transportation. Annual usage and production cost analyses per hectare, including labor, fuel, and interest, alongside purchasing cost surveys, were conducted. Versatile machinery reduced annual usage costs for field preparation and crop season activities by 63.54% and 71.71%, respectively. This effect was more pronounced for farms under 2 ha, especially those employing manual harvest and transportation. Small-scale farmers, such as those cultivating hot pepper farms, are strongly encouraged to adopt versatile machinery to mitigate expenses and labor costs. The significance of adopting studied methodology will be amplified with the rising cost of labor. Consequently, utilization of versatile machinery in field farming for small-scale farms is projected to increase incomes not through enhanced production, but by significantly reducing the annual usage costs associated with agricultural machinery. This approach not only alleviates financial burdens but also enhances the sustainability of farm management, ensuring long-term viability and environmental stewardship.

1. Introduction

The utilization of tractors as the primary power source, coupled with a range of implements such as tillage tools, plows, planters, and harvesters, has become the dominant agricultural practice, gradually replacing manual labor [1]. However, this transition necessitates careful strategic planning [2,3,4]. The increasing variety of implements [5,6] and the rising costs associated with tractor components have significantly amplified the financial burden on farmers [7]. Consequently, many farmers have adopted alternative strategies, such as leasing or renting machinery instead of outright purchases [8,9], to mitigate these costs. In addition, secondary costs, particularly those related to repair and maintenance, constitute a considerable portion of overall expenditures [10], necessitating efforts to minimize these financial outlays [11]. However, for small-scale farmers, overcoming these challenges is compounded by various external factors, making it difficult to ensure stable income levels [12]. National-level interventions, such as government cooperation in providing socialized agricultural machinery services [13,14] or relieving the economic burden through pricing policies [15], have been implemented. Nevertheless, the income disparity between urban and rural areas remains a contentious issue [16], and land transfer policies have also emerged as a possible solution for the sustainable development of agriculture [17].

In addition to financial concerns, climate change presents another significant risk, negatively impacting productivity and income due to unpredictable precipitation patterns and temperature fluctuations [18,19]. Paradoxically, the use of agricultural machinery itself contributes to gas emissions, which exacerbates global climate change [20,21]. Efforts to introduce electric-powered agricultural machinery have been explored as an alternative to fossil-fuel-dependent equipment [22]; however, the production of electricity often still relies on fossil fuels, and effective strategies for reducing emissions are still needed. Therefore, there is an urgent need to explore strategies that alleviate the exacerbating financial pressures faced by farmers, emphasizing the dual goals of enhancing economic sustainability [23] and mitigating environmental impacts.

One approach to alleviating operational costs involves deviating from conventional farming practices, such as omitting tillage operations to reduce machinery-related expenditures [24]. This adjustment not only lowers fossil fuel and labor costs [25] but also mitigates time investment [26] and alleviates health-related issues associated with manual labor, particularly benefiting farmers in low- and middle-income countries across Asia and Africa [27]. Nonetheless, the decision to bypass certain steps in traditional farming processes remains contentious [28,29]. Moreover, small-scale farmers are disproportionately affected, as their operational expenses often exceed annual income, a phenomenon observed on a global scale [30,31,32]. Given that 72% of farms worldwide are less than 1 hectare in size [33], it is evident that many farmers experience considerable financial strain, reinforcing the necessity of further research into strategies that can alleviate these pressures.

In addition to omitting individual steps within conventional farming processes, adopting a comprehensive mechanization system represents another viable strategy for enhancing agricultural efficiency. For example, integrating tractor implements to perform multiple tasks concurrently [34], or utilizing versatile agricultural machinery designed for multifunctional use—such as plowing, sowing, irrigation, spraying, and cutting—can significantly reduce labor costs [35]. Koo and Kim (2018) introduced a system that combined five field operations—rotary tillage, ridge formation, pest control, mulching, and planting—into a single tractor implement, assessing its field performance and economic savings. They evaluated the efficiency of various agricultural tasks in relation to machinery capacity, which reflects the ability of agricultural machinery to execute specific tasks within a set timeframe, taking into account environmental factors such as weather conditions, operator expertise, and fallow periods. Furthermore, machinery capacity also quantifies the area or amount of work the equipment can complete within a given time frame. Although this approach increased effective field capacity and reduced annual fixed costs, the study found that hourly fixed costs remained comparable to conventional methods, and the slow planting speed limited the overall system performance [32]. Therefore, careful selection of farming operations for integration into a single piece of equipment is critical for optimizing both field efficiency and cost savings. Despite the demonstrated safety benefits of versatile agricultural machinery, its economic implications have not been sufficiently explored [36].

This study addresses this gap by evaluating the economic viability of using versatile agricultural machinery. A range of implements was selected based on prior research, and the parameters for economic analysis were determined through field tests. The field trials were conducted according to the agricultural equipment standards set in the Republic of Korea [37]. The economic benefits of versatile agricultural machinery were then assessed by comparing its performance and cost outcomes with those of conventional farming methods.

2. Literature Review

2.1. Agricultural Machinery in Small-Scale Farming

Despite the significant impact of current agricultural machinery on small-scale farmers [38], there is a continuous effort to integrate advanced technologies into agriculture. Technologies such as UAV applications, ICT technology, and autonomous driving technology are primarily adopted in both unprotected and protected agricultural systems. However, many of these advanced technologies remain impractical for small-scale farms. For instance, an autonomous driving sprayer with an RTK sensor and variable rate control technology is designed for large farms and is not economically or practically feasible for small ones [39]. The primary objective of utilizing such machinery is to reduce labor costs and enhance work convenience. However, small-scale farmers are often hesitant to incur the high expenses and are generally willing to pay less for agricultural machinery than the actual cost [40]. The expenses associated with purchasing agricultural machinery thus emerge as a critical issue for them. Another aspect of utilizing agricultural machinery for small-scale farming is to provide “responsible innovation,” meaning that the framework of mechanization should proceed with consideration of current environmental and social aspects [41]. Advanced agricultural machinery suitable for small-scale farming should not be the autonomous driving tractor but rather machinery that produces fewer gas emissions, reduces expenses, and increases incomes [42]. One trial to achieve this involves integrating two systems to increase profit. Quaicoe et al. (2024) demonstrated increased annual profits for small-scale ranchers by integrating cattle and mushroom production systems. This integrated system showed significant long-term returns with minimal risk, and reduced feed and fertilizer expenses while increasing annual profits [43].

2.2. The Economic Value of Small-Scale Farming

Ricciardi et al. (2018) investigated global small-scale farm food production (under 2 ha) and reported that 28–31% of total crop production, and 30–34% of food worldwide, is supplied by small-scale farming [44]. Considering unidentified farming, the actual crop and food production may be higher than reported. This high proportion of food supply from small-scale farming is not temporary and has been maintained according to recent studies [45]. Moreover, Cui et al. (2018) reported that excessive fertilizer use, soil acidification, water pollution, and increasing greenhouse gases mean that a sustainable food supply cannot be maintained without small-scale farming. Enhancing independence for small-scale farmers is crucial for achieving higher productivity and contributing to a sustainable future. Significant benefits, including improved crop diversification, job security, and self-sufficiency, will enhance social, cultural, and economic outcomes [46]. Despite the value of small-scale farms, numerous challenges threaten their management. Jouzi et al. (2016) emphasized the importance of increasing incomes, reducing expenses, and maintaining the local community to ensure livelihood sustainability [47]. Given the economic burdens on small-scale farms [48], it is evident that alleviating their economic burden is essential.

3. Materials and Methods

3.1. Selection and Integration of Farming Steps

For selecting the appropriate farming steps for integration, the entire farming process was reviewed (Figure 1) according to the general method used to cultivate the most demanded crop in the Republic of Korea, the hot pepper. Taking insights from a previous study, combinations of farming steps were categorized based on their characteristics [31]. Consequently, rotary tillage, ridge formation, and mulching operations were grouped under field preparation and considered operations that could be performed simultaneously using versatile machinery. Conversely, harvesting and transportation were combined as a crop season activity based on the general process of upland field farming, but operated independently using versatile machinery with distinct attachments. Meanwhile, crop planting and field management processes were performed following conventional practices.

3.2. Machinery Capacity

To assess the economic implications of adopting agricultural machinery, the machinery capacity was determined by considering factors such as the coverage area of the machine, environmental constraints, operation season, and available operating time per day [49]. However, it was improved when using integrated machines expressed as a machinery capacity discussed in previous research [31] and the formula of it was shown in Equations (1) and (2) below:

$$A_t={\displaystyle \frac{T}{\sum _{i=0}^n{t}_i}}={\displaystyle \frac{{\epsilon }_u\times {\epsilon }_d\times U\times D}{t_1+t_2+\cdots +t_n}}$$

(1)

$$t_i=S·W·{\epsilon }_f$$

(2)

where $A_t$ is the total machinery capacity for a combined machine (ha); $T$ is the available time during the field operating season (h); $t_i$ is the machinery capacity considering a single machine; $S$ is the operating speed (km/h); $\mathrm{W}$ is the operating width (m); ${\epsilon }_f$ is the coverage efficiency; ${\epsilon }_u$ and ${\epsilon }_d$ are the actual rate of operating time and day, respectively; $U$ is the daily operating time; and $D$ represents the days of the field operating season.

According to Park (2008), the above factors were established, and the practice values defined, via decades of experiment results. For categorizing the field operations in two sections, the field preparation and crop season activities, the adopted factors for each operation are presented in

Table 1. Established factors and specifications of the machine for defining the machinery capacity.

Versatile Machinery
operating speed, km/h (S)	rotary tillage	2.0
	ridge formation	2.0
	mulching	2.0
	harvest	2.16
	transportation	35.0
operating width, m (W)	rotary tillage	1.3
	ridge formation	1.3
	mulching	1.3
	harvest	1.4
	transportation	1.3
coverage efficiency (${\epsilon }_f$)	rotary tillage	0.89
	ridge formation	0.7
	mulching	0.5
	harvest	0.7
	transportation	0.7
Factors
actual rate of operating time (${\epsilon }_u$)	rotary tillage	0.89
	ridge formation	0.8
	mulching	0.7
	harvest	0.75
	transportation	0.8
actual rate of operating day (${\epsilon }_d$)	rotary tillage	0.89
	ridge formation	0.89
	mulching	0.9
	harvest	0.65
	transportation	0.75
daily operating time, h ($U$)	rotary tillage	8
	ridge formation	8
	mulching	8
	harvest	8
	transportation	8
days of the field operating season, day ($D$)	rotary tillage	30
	ridge formation	25
	mulching	20
	harvest	20
	transportation	30

below [50].

3.3. Cost Analysis Framework

3.3.1. Purchasing Cost

To evaluate the economic benefits of using versatile agricultural machinery, the purchasing costs of the selected machines were determined. Given that the versatile machinery is currently unavailable for sale, its purchasing cost was estimated based on a survey conducted in the Republic of Korea on machines with similar power (30 kW). A survey was conducted targeting major agricultural machinery companies in the Republic of Korea that operate on a national scale, and the purchasing costs of the machines were investigated through telephone inquiries. Additionally, the purchasing costs for the harvesting machine, general agricultural transport vehicle, and implements for field preparation were determined through a similar survey. Finally, the purchasing cost of the versatile machinery and its attachments were determined based on the survey results and information provided by the manufacturer (Bulls Co., Ltd., Seongju, Republic of Korea) (

Table 2. Purchasing costs of conventional and versatile agricultural machinery and implements.

Parameter			Cost, Thousand KRW
Conventional method	Driving machine		27,340
	Implement	Rotary tillage	2500
		Ridge formation	4000
		Mulching	3000
	Harvesting machine		67,238
	Transporting vehicle		27,340
Integration	Versatile machine [Rotary tillage + Ridge formation + Mulching]		12,500
	Attachment	Harvest	13,670
		Transportation	4557

).

In many countries, agricultural input subsidies (AIS) offset a portion of the purchasing costs for farmers [51]. Malimi (2023) employed a linear function for estimating the effects of AIS on farm productivity, represented by the equation [52]:

$$y_{it}={\alpha }_1{AIS}_{it}+{\alpha }_3{x}_{it}+{\mu }_i+{ϵ}_{it}$$

(3)

where $\mathrm{y}$ represents the productivity (kg/ha); ${\alpha }_1$ represents parameters representing the effects of AIS; ${\alpha }_3$ represents a coefficient for respective parameters; $i$ represents a plot; $t$ indicates cultivating time; $\mathrm{x}$ represents household and community factors that affect labor productivity; ${\mu }_i$ represents the time-invariant unobserved heterogeneity; and ${ϵ}_{it}$ represents the time-varying unobservable factors.

In this study, the economic analysis of utilizing versatile machinery factored in the amount of subsidy provided for purchasing agricultural machinery. The AIS implemented in the Republic of Korea, where 50% was the maximum portion in 2023 [53], was considered. Therefore, the purchasing cost of the versatile agricultural machinery was determined based on both the survey results and AIS.

3.3.2. Annual Usage Cost

The annual usage cost for operating versatile agricultural machinery was analyzed considering both fixed and variable costs. Fixed costs, which are expenses not dependent on productivity, but rather on factors like annual interest cost, taxes, insurance, and housing, were computed as follows:

$$C_f=D+R+I+X+H+K$$

(4)

where $C_f$ is the fixed cost; $D$ is the depreciation cost; $R$ is the repair cost; $I$ is the interest cost; $X$ represents tax; and $H \mathrm{a}\mathrm{n}\mathrm{d}$ $K$ represent the insurance and housing costs, respectively.

The depreciation cost (D), calculated using the general straight-line method [54], was expressed as follows:

$$D={\displaystyle \frac{P_i-P_s}{L}}$$

(5)

where $P_i$ and $P_s$ are the initial and scrap values of the machine, respectively, and $L$ is the duration of machine use. The value of L was determined based on a survey of durable years for different kinds of agricultural machinery, which is 12 years, and $P_s$was assumed to be 5% of the purchasing price of the machine [55].

The repair cost (R), insurance cost (H), and housing cost (K) were assumed to be 6%, 0.1%, and 1%, respectively, of the purchasing price of the machine [51]. Taxes (X) were exempted based on the tax regulation for agricultural machinery in the Republic of Korea. The interest cost (I) was determined by multiplying the average of P_i and P_s with the annual interest rate (i_rate), 3.5%, and the duration of machine use (L).

$$I=\left({\displaystyle \frac{P_i+P_s}{2}}\right)\times i_{rate}\times L$$

(6)

Variable costs (Cy), encompassing daily energy expenses such as fuel, electricity, and labor, were synthetically considered [56] and calculated as follows [38]:

$$C_y=H\times (F+O+La+T)$$

(7)

where $C_y$ is the variable cost; $H$ is the annual operating time; $F$ and O are fuel and lubrication costs (KRW), respectively; La is the labor cost for the operator (KRW); and $T$ is the cost of machine use per hour.

Data regarding the annual operating time (H) for each agricultural machine were obtained from NIAS (2017) [55]. In the Republic of Korea, the annual operating time (H) for general tractors and harvesting machines is 88–132 h and 25–27 h, respectively. Therefore, the annual operating time (H) for integrated machines for field preparation and crop season activities was assumed to be 110 and 26 h, respectively. The fuel cost was computed based on the previously evaluated engine performance of the selected machine [57] and the price of tax-exempted oil in 2023 [58]. The lubrication cost (O) was assumed to be 15% of the fuel cost [55,59], and the labor cost for the operator was calculated based on the standard daily wage (8 h per day) [58].

3.3.3. Substituted Labor Cost

In cases where crop season activities were performed manually, conventional labor costs for harvesting and transportation were analyzed. This aspect was specifically analyzed for small-scale farmers who do not utilize machinery for these operations. The analysis did not account for factors such as the skill, gender, health, or age of the workers. The substituted labor cost was computed based on the work efficiency and labor cost per hour, and the computed labor costs for harvesting and transportation are presented in

Table 3. Computed labor costs for each operation.

Operation	Required Hour per Unit Area, h/10a	Labor Cost, KRW/h	Reference
Harvest	95.4	13.875	[60]
Transportation	9.3		[61]

.

3.3.4. Crop Production

For economic analysis, the total crop yield after harvest should be considered, as it directly affects the results. Therefore, the yield per unit area was considered in this study. The productivity panel data of hot pepper in the Republic of Korea, provided by the Gyeongsangbuk-do Agricultural Research and Extension Service (GARES), indicated the average crop productivity of 2813.3 kg/ha [62].

3.3.5. Production Cost

For a comparison between using versatile agricultural machinery and conventional method, the fixed and variable costs were analyzed. Also, the labor cost for crop season activities was determined and utilized for economic evaluation. By harvesting, the productivity of hot pepper by unit area (P_u; kg) referred in 3.3.4. and the performance coefficient of the harvesting machine (M_p) were considered for calculating the net harvests. The parameter M_p was adopted from the previous study, and the quality of hot pepper using the harvester was valid when there was no breakage or tears. Finally, the required cost per hectare area (production cost, KRW·ha⁻¹) was expressed by multiplying the crop production (C_p, kg·ha⁻¹) in this study. From the parameters above, the production cost can be calculated as follows:

$$Production cost={\displaystyle \frac{C_f+C_y}{P_u\times M_p}}\times C_p$$

(8)

where $C_f$ is the fixed cost; $C_y$ is the variable cost; $P_u$ is the productivity of hot pepper per area; $M_p$ is the performance coefficient of harvester; and $C_p$ is the crop production.

4. Results

4.1. Annual Usage Costs

Table 4. Comparison of the annual usage costs for field preparation activities performed via conventional and integrated methods (unit: thousand KRW).

		Conventional Method			Integrated_1
		Rotary Tillage (A)	Ridge Formation (B)	Mulching (C)	A + B + C
Fixed costs	Depreciation	1280.12	1398.87	1319.70	989.58
	Repair	970.20	1060.20	1000.20	750.00
	Interest	3565.48	3896.23	3675.73	2756.25
	Tax	-			-
	Insurance	16.17	17.67	16.67	12.50
	Housing	161.70	176.70	166.70	125.00
	Subtotal (1)	5993.67	6549.67	6179.00	4633.33
Variable costs	Annual operating time, h	169	100	50	50
	Fuel	13.51			5.14
	Lubrication	2.02			0.77
	Labor	13.87			13.87
	Cost of machine use per hour	32.53	60.07	113.35	85.00
	Subtotal (2)	10,466.17	8947.00	7137.50	5239.00
Sum of 1 and 2		16,459.84	15,496.67	13,316.50	9872.33
Machinery capacity, ha		43.99	25.91	13.10	11.60
Annual usage cost thousand KRW·ha−1		374.17	943.40	1016.52	851.06
Total annual usage costs		2334.09

presents a comparison of the annual usage costs for field preparation (rotary tillage, ridge formation, and mulching) via conventional and integrated (Integrated_1) methods. The conventional method necessitated purchasing individual implements, resulting in an annual fixed cost of 5993.67, 6549.67, and 6179.00 thousand KRW for rotary tillage, ridge formation, and mulching operations, respectively. Conversely, the integrated approach significantly reduced fixed costs due to the requirement of fewer agricultural machines, resulting in an overall expense of only 4633.33 thousand KRW.

Regarding variable costs, while the cost per hour for using versatile machinery in the integrated method was 85 thousand KRW, a substantial 50.05% expense reduction was achieved due to the decreased annual operating time, resulting in a variable cost of 5239.00 thousand KRW. Consequently, while field preparation following the conventional method incurred a total expense of 2334.09 thousand KRW/ha, the integrated method incurred only 36.46% of that amount. Thus, the reduced annual usage cost per hectare had a greater effect on reducing the total usage cost per hectare compared to the reduced machinery capacity (Table 4). Additionally, the cost savings achieved by integrating field operations aligned with findings from a previous study [31].

For crop season activities, substantial reductions in fixed costs were observed for both the harvesting and transportation steps, with 77% and 84% of fixed costs saved, respectively. Overall, the reduction in fixed costs was more noticeable during the crop season than during field preparation. A comparison of variable costs revealed that reduced purchasing costs and machine maintenance costs considerably influenced the differences between the conventional and integrated (Integrated_2) methods. Notably, using the integrated method resulted in 73% and 58% savings during the harvesting and transportation steps, respectively, despite the duration of machine operation being the same in both methods. Although the machinery capacity for integrating the two operations—harvesting and transportation—was 22.23 ha, representing a substantial reduction for the transport step, the annual usage costs per hectare were 2933.14 and 829.97 thousand KRW/ha for the conventional and integrated methods, respectively, resulting in total savings of 71.71% (

Table 5. Comparison of the annual usage costs for crop season activities performed via conventional and integrated methods (unit: thousand KRW).

		Conventional Method		Integrated_2
		Harvest	Transport	Harvest	Transport
Fixed costs	Depreciation	6387.61	2164.41	1298.65	432.91
	Repair	4034.28	1640.40	820.20	273.42
	Interest	12,354.98	6028.47	2511.86	837.34
	Tax
	Insurance	67.24	27.34	13.67	4.55
	Housing	672.40	273.40	136.70	45.57
	Subtotal (1)	23,516.51	10,134.02	4781.08	1593.79
Variable costs	Annual operating time, h	80	143	80	143
	Fuel	13.51	7.59	11.58
	Lubrication	2.02	1.14	1.73
	Labor	13.87		13.87
	Cost of machine use per hour	285.76	65.00	58.09	9.56
	Subtotal (2)	25,212.80	12,526.80	6821.60	5253.82
Sum of 1 and 2		48,729.31	22,660.82	11,602.68	6847.61
Machinery capacity, ha		16.9	455.45	22.23
Annual usage cost thousand KRW·ha−1		2883.39	49.75	829.97
Total annual usage costs		2933.14

). Moreover, in the case of performing the selected operations manually, especially considering the high proportion of small-scale farmers, the effect of integrating crop season activities is expected to be greater than the results observed in this study. For instance, pest management was not included in this study, and the cost for pest management also can be optimized [63]. They varied the scenarios of risk efficiency by choosing the subsidies from pest management and showed that the farmers apparently preferred not to choose the conventional method. This indicated that the more considerations are included in the economic evaluation, the more hidden cost can be saved.

4.2. Cost Benefits

Based on comparative results of the annual usage costs in Table 4, cost benefits on different scales of farms were analyzed (

Table 6. Comparison of the annual usage costs of field preparation based on cultivation area. (unit: thousand KRW).

	Cultivation Area, ha
Operation Method	0.1	0.5	1	2	3	4	5	6	7	8	9	10
Integrated_1	8891.8	8925.9	8968.6	9053.8	9139.1	9224.4	9309.7	9395.0	9480.2	9565.5	9650.8	9736.1
Conventional_RT *	11,502.7	11,547.9	11,604.4	11,717.4	11,830.4	11,943.4	12,056.4	12,169.4	12,282.4	12,395.4	12,508.4	12,621.4
Conventional_RF *	12,568.8	12,614.2	12,670.9	12,784.4	12,897.9	13,011.4	13,124.9	13,238.4	13,351.9	13,465.3	13,578.8	13,692.3
Conventional_M *	11,858.0	11,902.9	11,959.0	12,071.2	12,183.4	12,295.7	12,407.9	12,520.1	12,632.4	12,744.6	12,856.8	12,969.0
Total cost of conventional method	35,929.6	36,065.1	36,234.4	36,573.1	36,911.9	37,250.6	37,589.3	37,928.0	38,266.7	38,605.4	38,944.1	39,282.9
Saving effect	27,037.7	27,139.1	27,265.8	27,519.3	27,772.7	28,026.1	28,279.6	28,533.0	28,786.4	29,039.9	29,293.3	29,546.7

* RT: Rotary tillage; RF: Ridge formation; M: Mulching.

) and the equation of savings effect was suggested. Among three field operations, creating the ridge step incurred the most expenses, and the least was incurred with the rotary tillage step at all cultivation areas. Assuming the minimum scale of farm as 0.1 ha, using versatile machinery is the highest profiting method with 27,037.7 thousand KRW compared with total cost incurred by conventional methods. Also, considering the savings effect of using versatile machinery increases with the cultivation area, and the high proportion of small-scale farming in the Republic of Korea, it was demonstrated that the evolution of selecting agricultural machinery is necessary in the Republic of Korea.

Based on the results on cost benefits of using versatile machinery during the field preparation, the savings effect of using versatile machinery can be predicted with each expense of operation method, and the regression model was analyzed based on linear regression at a significance level of 5%. Considering the significance level, the expense incurred by Integrated_1, Conventional_RT, Conventional_RF, and Conventional_M did not take account for developing the model. The savings expense (S_e) can be predicted with the cultivation area and total incurred cost for using three conventional methods (Conventional_T) based on the results in Table 5. The standard error of regression was 0.04, and the equation is shown below:

$$S_e=-1.454·Cultivation area+0.752·Conventional\_T$$

(9)

which described that the effect of integrating conventional methods reduced the total incurred cost with a coefficient of 0.752, while it was enhanced with a coefficient of −1.454 as a consequence of increasing scale of field.

Regarding the expenses incurred for cultivating a single hectare of field crop, Figure 2 presents a comparison of the production costs incurred when using two different agricultural machines versus a single versatile machine for performing harvesting and transportation. Both conventional and integrated methods resulted in decreased production costs, which stabilized for areas exceeding 5 ha. However, using individual machines resulted in higher production costs compared with the Integrated_2 method in all cultivation areas, with the greatest savings effect observed in small-scale farms, especially those below 3 ha. The savings in production costs incurred by the integrated method compared to the conventional method for areas of 0.1, 0.5, 1.0, 2.0, and 3.0 ha were 697,467; 139,366; 69,604; 34,723; and 23,096 thousand KRW, respectively (Figure 2a). Despite the continuing decrease in production costs across all cultivation areas, considering the high proportion of small-scale farms in the Republic of Korea, the impact of using versatile machines will be clearly demonstrated in terms of profitability. The relationship between size and unit cost of the production curve aligned with the hyperbolic form of regression model suggested by Soltani (1976) [64] and the ordinary least square estimators proposed by Debrah and Adanu (2022) [65]. This underscores the need for a revolution in small-scale farming practices, guided by effective management policies aimed at elevating farmer incomes to the next scale. A comparison of production costs incurred by farms using versatile machinery instead of manpower for performing crop season activities revealed farms larger than 2 ha would benefit from using versatile machinery, as the differences between production costs for manpower and Integrated_2 turned negative (Figure 2b). While the production cost incurred by Integrated_2 drastically reduced after 0.5 ha of cultivation area, the cost for manpower kept increasing with larger field areas. Considering the accelerated aging in rural areas and the loss of labor in the Republic of Korea, performing field operations using agricultural machinery is a viable option, and greater savings in production cost can be achieved using versatile machinery. Moreover, the worldwide increase in the minimum wage for laborers would enhance the effect of replacing operations that employ manpower, as indicated by Keller et al. (2022) [66].

Moreover, the demonstrations are still valid in other field crops which require similar processes to those considered in this study. For instance, in cultivating one of the most demanded crops in the Republic of Korea, garlic, the classification standard is similar with the described in Figure 1, while specific operations only observed in garlic farm exist. Another aspect is the unpredictable global price of hot pepper and rising labor cost. The price of hot pepper is expected to rise due to the climate crisis in cases of field farming, while the labor cost will be increased globally. Therefore, the effect of varied cost parameters on the results in this study cannot be estimated hawkishly and can only be done by analyzing the most rapidly increasing parameters.

5. Conclusions

This study evaluates the economic advantages of substituting conventional farming methods with an integrated system utilizing versatile machinery. Agricultural operations were divided into two main categories: field preparation and crop season activities. In the integrated system, three operations—rotary tillage, ridge formation, and mulching—were consolidated into what we term “Integrated_1,” while harvesting and transportation were grouped as “Integrated_2.” The findings indicate substantial savings, with the integrated method reducing annual usage costs by 63.54% (1483.03 × 10³ KRW), comprising 27.08% from fixed costs and 36.46% from variable costs, during field preparation. Additionally, crop season activities saw a 71.71% (2103.17 × 10³ KRW) reduction in annual usage costs, particularly during harvesting, where an increase in machinery burden area and a decrease in usage costs were noted. However, contrary to this trend, the transportation process did not exhibit the same cost-efficiency. While this analysis categorizes the entire upland farming process into field preparation and crop season activities, it is important to acknowledge that the economic outcomes could vary if additional processes, such as vinyl mulch removal or soil disinfection—which entail further equipment or material costs—are included, or if alternative classification frameworks are adopted. Consequently, the suggested methodology for promoting the economic sustainability of small-scale farms will enhance the resilience of the aging rural population, reduce essential expenses, increase related incomes, and achieve sustainable food production by improving the farming efficiency of small-scale farmers.