Harvester Evaluation Using Real-Time Kinematic GNSS and Hiring Service Model
Harvester Evaluation Using Real-Time Kinematic GNSS and Hiring Service Model
Hasan, Md. Kamrul;Tanaka, Takashi S. T.;Ali, Md. Rostom;Saha, Chayan Kumer;Alam, Md. Monjurul
2021-06-06 00:00:00
AgriEngineering Article Harvester Evaluation Using Real-Time Kinematic GNSS and Hiring Service Model 1 , 2 3 , 2 2 Md. Kamrul Hasan , Takashi S. T. Tanaka * , Md. Rostom Ali , Chayan Kumer Saha and Md. Monjurul Alam The United Graduate School of Agricultural Science, Gifu University, Gifu 5011193, Japan; engrkamrul.bd@gmail.com Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh; rostomfpm@bau.edu.bd (M.R.A.); cksaha@bau.edu.bd (C.K.S.); mmalam.bau@gmail.com (M.M.A.) Faculty of Applied Biological Sciences, Gifu University, Gifu 5011193, Japan * Correspondence: takashit@gifu-u.ac.jp Abstract: To reduce human drudgery and the risk of labor shortages in the Asian developing countries, the appropriate introduction of agricultural machinery, especially combine harvesters, is an urgent task. Custom hiring services (CHSs) are expected to contribute to making paddy harvesters prevalent in developing countries; however, the economic performance has been rarely quantified. The study was carried out to precisely evaluate the machine performance attributes of medium and large combine harvesters using the real-time kinematic (RTK) global navigation satellite system (GNSS) and to estimate the economic performance of CHSs of paddy harvesters in Japan, as a typical case of Asian countries. The financial profitability was evaluated by four major indicators: net present value, benefit–cost ratio, internal rate of return, and payback period. The financial indicators showed Citation: Hasan, M.K.; Tanaka, T.S.T.; that both types of harvester could be considered financially viable. Thus, the investment in combine Ali, M.R.; Saha, C.K.; Alam, M.M. harvesters can be highly profitable for CHS business by a local service provider and custom-hire Harvester Evaluation Using entrepreneur, providing a great opportunity to use a combine harvester without initial investment Real-Time Kinematic GNSS and by general farmers. The findings demonstrated the high feasibility of CHSs of paddy harvesters in Hiring Service Model. Japan, while they highlighted that further study is needed to estimate the feasibility of CHS in the AgriEngineering 2021, 3, 363–382. https://doi.org/10.3390/ other Asian developing countries. agriengineering3020024 Keywords: precision agriculture; paddy; RTK GNSS; ArcGIS; combine harvester; cost analysis; Academic Editor: Mathew G. Pelletier custom hiring service (CHS) Received: 28 April 2021 Accepted: 2 June 2021 Published: 6 June 2021 1. Introduction Agricultural mechanization is a crucial input for profitable crop production but histori- Publisher’s Note: MDPI stays neutral cally has been neglected in the context of developing countries. Mechanization technologies with regard to jurisdictional claims in continue to change with the industrial growth of a country and socioeconomic advance- published maps and institutional affil- ment of farmers [1]. Due to the migration of labor in nonagricultural sectors, shortages iations. of labor and costs for paddy harvesting are serious problems in the peak harvesting sea- son [2]. Currently, timely harvesting of paddies is a major challenge due to the shortage and high cost of labor. Harvests delayed by 5, 7, and 10 days resulted in 3%, 6%, and 11% decreases in paddy yields, respectively [3]. Developed countries worldwide are currently Copyright: © 2021 by the authors. using automatic combine harvesters for harvesting cereal grains, while many developing Licensee MDPI, Basel, Switzerland. countries are still using reapers for harvesting paddies and wheat to minimize production This article is an open access article costs [4]. In comparison to harvesting manually, using mini-combine harvesters can save distributed under the terms and 97.5% of time, 61.5% of costs, and 4.9% of grain losses on average [5]. Adoption of modern conditions of the Creative Commons mechanical harvesting practices, i.e., combine harvesters, is urgently needed to save money, Attribution (CC BY) license (https:// time, and labor by reducing human labor, labor costs, and losses during harvesting and creativecommons.org/licenses/by/ increasing cropping intensity, crop productivity, and economic freedom. The average time, 4.0/). AgriEngineering 2021, 3, 363–382. https://doi.org/10.3390/agriengineering3020024 https://www.mdpi.com/journal/agriengineering AgriEngineering 2021, 3 364 cost, and grain savings obtained by using a combine harvester over manual methods were 97.50%, 35.00%, and 2.75%, respectively [6]. In addition to mechanizations in agricultural sectors, agricultural production systems have benefited from the incorporation of technological advances primarily developed for other industries. Precision agriculture (PA) is one of the top ten revolutions in agriculture [7] that uses information technology, including global navigation satellite system (GNSS), geographic information system (GIS), remote sensing, miniaturized computer components, automatic control, telecommunications, and proximal data gathering, to optimize returns on inputs while potentially reducing environmental impacts [8]. Precision agriculture generally involves better management of farm inputs [9] and is conceptualized by a system approach to reorganize the total system of agriculture towards low-input, high-efficiency, and sustainable agriculture [10]. The invention of the automatic navigation technology of the harvester can effectively reduce the driver ’s work intensity while improving the operating efficiency, which is of great significance [11,12]. Now, agricultural machinery navigation systems based on the real-time kinematic (RTK) GNSS have been adopted and are widespread [13]. The technology of GNSS can be used effectively to determine a harvester ’s speed, operational time, turning time, and idle time throughout field operations. In all cases, the efficiency of farm machinery operations can be affected by three factors: (i) travel speed, (ii) effective swath width, and (iii) field traffic pattern [14]. With the harvesting speeds ranging from 0.8 to 4.5 km h , the mini-combine harvester had a field 1 1 capacity of 0.10 to 0.39 ha h and consumed as much as 11 L ha of fuel while having a track slip of 6% to 9% [15]. Therefore, the speed of a harvester directly affects machine capacity and efficiency. Harvest efficiency showed a stronger relationship with turning time than with field efficiency, and the values of both were negative. Efficiencies decrease with increasing turning time per acre. More than 60% of the variability in harvest efficiency was captured with turning time, which is substantially better than that obtained with unloading time [16]. Considering two harvest patterns, results reveal that field efficiency could be improved by optimizing harvest patterns [17]. Machine idle time is also one of the most important factors in reducing machine efficiency. Machine idling during harvesting can occur for many reasons, such as an operator ’s issue, clogs in the machine, and disturbances in the field. Idling of machines contributes to ineffective field operation, thus reducing field efficiency [18]. The GNSS-based evaluation of heading changes and harvesting tracks can be considered a method for utilizing harvesting machines more efficiently. The automated combine harvester and RTK GNSS, which allows a precise evaluation of machine performances, are available in Japan. Japan’s small agricultural sector is highly mechanized, sophisticated, and automated. It has a strong farm machinery industry with export to Asian countries and other regions of the world. Many machinery designs cur- rently found in Southeast Asian countries for transplanting, harvesting, and milling were developed in Japan [19]. Japan’s machinery research and development have been oriented towards high technology applications, new farm machinery with much higher field ca- pacity, automation of farm machinery, agricultural robots, energy saving and alternative energy development, and biotechnological equipment and devices [20]. Conversely, the knowledge about either the feasibility or economic benefit of farm machinery management is still largely insufficient in the other Asian developing countries. Suitable machinery, especially harvesting machinery is an urgent need to increase production in the developing countries by reducing drudgery, increasing efficiency, and lowering cost [21]. Due to high initial investment, a combine harvester is not suitable for the small, marginal, and low-income farmers. However, there is an opportunity to use it through a custom-hire service (CHS) to avoid the initial investment issue. As a result, even the small- est farm households can usually access relatively affordable machinery services through a CHS [22,23]. Most private equipment owners started providing the CHS of various machines to the farmers at appropriate times and at reasonable rates which ultimately reduce the fixed cost of farm operations and reduce the burden of capital investments or credit from the bank. The cost of farm operations could be reduced to almost half by custom AgriEngineering 2021, 3 FOR PEER REVIEW 3 AgriEngineering 2021, 3 365 from the bank. The cost of farm operations could be reduced to almost half by custom hiring of the machinery services [24]. Local machinery service providers are conducting business in the agricultural field as CHSs [25]. hiring of the machinery services [24]. Local machinery service providers are conducting The main objective of this study was to evaluate the benefit of mechanical harvesting business in the agricultural field as CHSs [25]. in Japan. Considering the research goal, we evaluated the machine performance attributes The main objective of this study was to evaluate the benefit of mechanical harvesting precisely by using georeferenced data recorded by GNSS receivers during field opera- in Japan. Considering the research goal, we evaluated the machine performance attributes tions. In this study, we also conducted a field survey to assess the present mechanization precisely by using georeferenced data recorded by GNSS receivers during field operations. situation, especially during paddy harvesting. The precise information on machinery per- In this study, we also conducted a field survey to assess the present mechanization situation, formance attributes in Japan would be valuable in considering the feasibility of spreading especially during paddy harvesting. The precise information on machinery performance combine harvesters in developing countries because of the similarities in farming scale attributes in Japan would be valuable in considering the feasibility of spreading combine and field capacity. Therefore, we conducted a detailed study to determine the harvester harvesters in developing countries because of the similarities in farming scale and field performances precisely and estimated the economic performance of CHSs of paddy har- capacity. Therefore, we conducted a detailed study to determine the harvester performances vesters in Japan. precisely and estimated the economic performance of CHSs of paddy harvesters in Japan. 2. Methodology 2. Methodology 2.1. Experimental Locations 2.1. Experimental Locations To assess the performance of paddy harvesters, three experiments were carried out in To assess the performance of paddy harvesters, three experiments were carried out two different working locations, as shown in Figure 1. One location was in the research in two different working locations, as shown in Figure 1. One location was in the research field of Gifu University, and the other location was in a farmer ’s field in Kaizu city in Gifu field of Gifu University, and the other location was in a farmer’s field in Kaizu city in Gifu (35.4234 N, 136.7606 E), Japan. (35.4234° N, 136.7606° E), Japan. ii (a) (b) Figure 1. Experimental locations in Japan: (a) Gifu city is shown on a map of Japan; (b) the research field of Gifu University Figure 1. Experimental locations in Japan: (a) Gifu city is shown on a map of Japan; (b) the research field of Gifu University and farmer ’s field of Kaizu city are shown on a map of Gifu. The original maps are available from d-maps [26]. and farmer’s field of Kaizu city are shown on a map of Gifu. The original maps are available from d-maps [26]. 2.2. Selected Harvesting Machines 2.2. Selected Harvesting Machines Two types of Kubota combine harvesters were used for mechanical harvesting of the Two types of Kubota combine harvesters were used for mechanical harvesting of the paddies at the experimental locations in Japan, as shown in Figure 2. One was medium paddies at the experimental locations in Japan, as shown in Figure 2. One was medium (Model: ER329), and the other was large (Model: ER6120). Both harvesters are manufactured (Model: ER329), and the other was large (Model: ER6120). Both harvesters are manufac- by Kubota Corporation (Osaka, Japan). A brief description of the technical specifications of tured by Kubota Corporation (Osaka, Japan). A brief description of the technical specifi- the harvesters is presented in Table 1. cations of the harvesters is presented in Table 1. AgriEngineering 2021, 3 FOR PEER REVIEW 4 AgriEngineering 2021, 3 366 (a) (b) Figure 2. Mechanical harvesting scenario using (a) medium combine (Model: ER329) and (b) large Figure 2. Mechanical harvesting scenario using (a) medium combine (Model: ER329) and (b) large combine (Model: ER6120). combine (Model: ER6120). Table 1. Specifications of the medium and large combine harvesters. Table 1. Specifications of the medium and large combine harvesters. Testing Item Designed Value Testing Item Designed Value Model ER329 ER6120 Model ER329 ER6120 Overall dimension (L × W × H) (mm) 3890 × 1870 × 2090 4850 × 2325 × 2660 Overall dimension (L W H) (mm) 3890 1870 2090 4850 2325 2660 Weight (kg) 1950 4160 Weight (kg) 1950 4160 Header width (mm) 1219 1981 Header width (mm) 1219 1981 0–1.05 0–2.00 Forward speed (during harvesting) (m s ) −1 Forward speed (during harvesting) (m s) 0–1.05 0–2.00 Capacity (ha h ) 0.20–0.40 0.50–0.80 −1 Capacity (ha h) 0.20–0.40 0.50–0.80 Fuel consumption (L h ) 03–06 12–20 −1 Fuel consumption (L h ) 03–06 12–20 Engine type Diesel engine Diesel engine Engine Engine type power (kW) Diese 21.3 l engine Diese88.3 l engine Cutting row 3 6 Engine power (kW) 21.3 88.3 Cutting row 3 6 2.3. In-Field Activities and Performance Indicators 2.3. In-Field Activities and Performance Indicators Before starting the field test, the soil conditions, crop conditions, number of tillers/hills, and Before yield conditions starting thwer e fie eld t recor estded. , the soi Engine l condit fuel ions and , croil op condit levels wer ionse, number o checked befor f till- e operation. To cross-check the RTK GNSS receiver data during mechanical harvesting, each ers/hills, and yield conditions were recorded. Engine fuel and oil levels were checked be- plot was measured using a measuring tape, and the total harvesting time and idle time fore operation. To cross-check the RTK GNSS receiver data during mechanical harvesting, were recorded using a stopwatch. Additionally, after completing the harvesting operation each plot was measured using a measuring tape, and the total harvesting time and idle in each plot, fuel consumption, labor requirements, and grain yields were recorded. Grain time were recorded using a stopwatch. Additionally, after completing the harvesting op- losses were collected in a polythene bag and measured after completion of the harvesting eration in each plot, fuel consumption, labor requirements, and grain yields were rec- operation for further analysis. orded. Grain losses were collected in a polythene bag and measured after completion of To assess the technical performance of the combine harvester during paddy harvesting, the harvesting operation for further analysis. some parameters were analyzed after collecting the GNSS receiver data, i.e., harvesting To assess the technical performance of the combine harvester during paddy harvest- area, harvesting time, harvesting track, harvesting speed, average harvesting speed, speed ing, some parameters were analyzed after collecting the GNSS receiver data, i.e., harvest- variation during harvesting, turning loss time, idle time of harvesting, effective/active ing area, harvesting time, harvesting track, harvesting speed, average harvesting speed, harvesting time, and effective field capacity of the harvester. speed variation during harvesting, turning loss time, idle time of harvesting, effective/ac- tive harvesting time, and effective field capacity of the harvester. 2.4. Data Collection during Mechanical Harvesting Performance analysis is the most important part of developing an appropriate business 2.4. Data Collection during Mechanical Harvesting policy for agricultural machinery; i.e., mechanical harvesting of paddies is more profitable Performance analysis is the most important part of developing an appropriate busi- than traditional manual harvesting systems. For better analysis, original data were collected ness policy for agricultural machinery; i.e., mechanical harvesting of paddies is more prof- through field experiments during paddy harvesting in the selected locations. Experimental itable than traditional manual harvesting systems. For better analysis, original data were data were collected using an RTK GNSS (Model: U-Blox M8T, Switzerland). First, paddy collected through field experiments during paddy harvesting in the selected locations. fields and harvesters were prepared for harvesting the experimental field. The rover station Experimental data were collected using an RTK GNSS (Model: U-Blox M8T, Switzerland). of the GNSS receiver was fixed on top of the harvester, and the base station was kept beside First, paddy fields and harvesters were prepared for harvesting the experimental field. the experimental field. This module can receive satellite signals from the global positioning The rover station of the GNSS receiver was fixed on top of the harvester, and the base system (GPS), Galileo, Beidou, and quasi-zenith satellite system (QZSS) at a one-second station was kept beside the experimental field. This module can receive satellite signals interval. Uses of RTK can easily obtain cm-level accuracy of user positions in real time by AgriEngineering 2021, 3 367 using the measurements of GNSS signals received both at the user receiver and at the base station [27]. 2.5. Data Analyses The data recorded by the GNSS receiver were analyzed with the following steps: (i) Positioning RTKLIB ver. 2.4.3 b33 was used for analysis and reviewing the data quality received by the rover and base station of the GNSS receivers according to the standard protocol. RTKLIB is a compact and portable program library written in C to provide a standard platform for RTK GNSS applications [28]. The position of the base rover was evaluated by static analysis. The GNSS-based control station of the Geospatial Information Authority of Japan was used for the reference to determine the position of the base station. The coordinates of the rover station were determined by kinematic analysis using the reference base station. (ii) Mapping In this study, the harvesting time, harvesting area, harvesting track, harvester speed, and speed variation during harvesting operation were evaluated from georeferenced data and visually represented by using ArcGIS 10.3 (Esri, Inc., Redlands, CA, USA). (iii) Identification of operations R version 3.5.3 (11 March 2019) was used to create a histogram to show the frequency of velocity during mechanical harvesting using a combine harvester. We assumed that bin width may represent the different harvesting conditions (e.g., harvesting, idle, and unloading time), and 5 bins were used for the identification. 2.6. Cost Determination In this study, the economic profitability of the combine harvester was estimated based on cost analysis. The cost analysis was performed considering the fixed and variable costs to determine the operating cost of the harvester. 2.6.1. Fixed Cost Fixed costs are fixed in total but decline per ha as the annual use of a machine increases [29]. Fixed costs consist of those costs that must be borne regardless of the machine used. These costs include (i) depreciation cost; (ii) interest in investment; and (iii) taxes, shelter, and insurances. (i) Depreciation cost: Depreciation is the reduction in the value of a machine as a result of use (wear and tear) and obsolescence (availability of newer and better models). In the calculation of a fixed cost, sinking-fund depreciation is assumed and was calculated by the following equation [30]: " ( ) # " ( ) # L n L n+1 1 + i 1 + i 1 + i 1 + i ( ) ( ) ( ) ( ) D = (P S) + S (P S) + S (1) L L (1 + i) 1 (1 + i) 1 where D = depreciation, USD year ; P = purchase price, USD; S = salvage value (10% of P), USD; L = effective working life of machine, years; n = age of the machine in years at the beginning of the year, years; and I = annual bank interest rate, decimal. (ii) Interest on investment: The interest on investment for a combine harvester is included in the fixed cost estimation. The following equation was used for the calculation of interest on investment [30]: P + S Interest on investment, I = i USD year (2) where P = purchase price, USD; S = resale value, USD; and i = annual interest rate. AgriEngineering 2021, 3 368 (iii) Taxes, Shelter, and Insurance (STI): The shelter, tax, and insurance were considered in calculating the fixed cost of the harvesting machine. The following equation was used for the calculation of STI [30]: STI = 2.5% of P (3) where P = purchase price of the harvester, USD. 2.6.2. Variable Cost The variable cost of a combine harvester is reflected by the cost of fuel, lubrication, daily service, power, and labor cost. These costs increase with increasing machine use and vary to a large extent in direct proportion to hours or days of use per year. The cost of operator/labor was calculated as the labor rate in USD h . The fuel and oil costs were estimated from the consumption rate and multiplied by their respective prices. Fuel cost, oil cost, labor cost, and repair and maintenance cost were determined using the following equations [30]: 1 1 Fuel consumed (L day ) Price (USD L ) Fuel cost, F (USD ha ) = (4) Area covered (ha day ) Oil cost, O (USD ha ) = 15% of Fuel cost, F (5) Sum of wages of labor (USD day ) Labor cost, L (USD ha ) = (6) Area covered (ha day ) Repair and maintenance cost, R&M (USD ha ) = 0.025% of purchase price (7) Total variable cost = (F + O + L + R&M + M ) USD ha (8) M = Miscellaneous cost, USD ha 2.6.3. Operating Cost The operating costs are recurring costs that are necessary to operate and maintain a machine during its useful life [31]. The main operating costs of the combine harvester were divided into fixed costs and variable costs. The following equation was used to calculate the operating cost, considering the sum of the fixed and variable costs. 1 1 1 Operation cost (USD ha ) = Fixed cost (USD ha ) + Variable cost (USD ha ) (9) 2.7. Sinking Fund Annual Payment (SFP) or Payment for Replacement The replacement of a machine by a new one is essential because beyond economic life it is no longer useful for field operation. The performance of a new machine is significantly superior, and it makes the old machine inoperative. Anticipated costs for operating the old machine exceed those of a replaced machine. Uniform annual payments to a fund are of such a size that by the end of the life of the machine the funds and their interest have accumulated to an amount that will purchase another equivalent machine. The following equation was used to find the sinking fund annual payment (SFP) [30]: Sinking fund annual payment, SFP = (P S) 100 (10) (1 + i) 1 where P = purchase price of harvester, USD; S = salvage value, USD; L = life of harvester, years; and i = interest rate, decimal. AgriEngineering 2021, 3 369 2.8. Rent-Out Charge The rent-out charge is determined by the machine operational cost, sinking fund annual payment, and business profit. An entrepreneur can estimate the harvester rent-out charge from the following expression: Rent-out charge = Operating cost + Sinking fund annual payment + Estimated profit (11) The profit of the entrepreneur depends on the socio-economic condition of the har- vester user as well as the country. In this study, the profit of the entrepreneur was estimated on the basis of middle-class family income in Japan. 2.9. Economic Analysis for Custom-Hire Service Business The project appraisal technique has been followed to estimate the profitability of harvesters from the view of the owners. There are four alternative discounting measures that are commonly applied for project appraisal [32,33]. These measures are (a) net present value (NPV), (b) benefit–cost ratio (BCR), (c) internal rate of return (IRR), and (d) payback period (PP). However, this appraisal is based on four assumptions, which are as follows: (i) all the devices are purchased with cash, (ii) operation technology remains unchanged throughout the project life, (iii) prices of all inputs and outputs are given and constant throughout the project life, and (iv) 0.25% interest rate is used for calculating NPV and BCR. 2.9.1. Net Present Value (NPV) The NPV is a scientific method of calculating the present value of cash flows. The NPV is computed by finding the difference between the present worth of benefit stream minus the present worth of cost stream. Both inflows and outflows of an investment proposal, a discount rate, and subtracting the present value of outflows are used to get the NPV. It is simply the present worth of the cash flow stream, since it is a discounted cash flow measure of project worth along with IRR. The NPV is calculated by using the following formula: NPV = PWB PWC (12) å å where PWB = present worth of benefits and PWC = present worth of costs. 2.9.2. Benefit–Cost Ratio (BCR) The BCR is an important factor for measuring the profitability of using a combine harvester. The BCR is the ratio of present worth of benefit stream to present worth of cost stream. If the BCR is greater than unity, then it will be economically viable. The method of benefit–cost analysis is simple in principle. The BCR is calculated by using the following formula: BCR = PWB/ PWC (13) å å where PWB = present worth of benefits and PWC = present worth of costs. 2.9.3. Internal Rate of Return (IRR) The IRR is the value of the discount factor when the NPV is zero. It is considered to be the most useful measure of project worth. The IRR is also a relative measure that may be defined as the average earning power of the money invested in a project over the project life [34]. It represents the average earning power of the money used in the project over the project life. The IRR is not affected by the rate of discount, while the NPV may change as a result of using different discount rates [35,36]. It is the maximum interest that a project can pay for the use of resources if the project is to recover its investment and operating cost AgriEngineering 2021, 3 370 and still break even. At this point, the BCR is equal to unity. IRR is usually found by trial and error, by interpolation and using the following equation: NPV LIR IRR = LIR + (HIR LIR) (14) NPV NPV HIR LIR where LIR = lower interest rate and HIR = higher interest rate. 2.9.4. Payback Period (PP) The PP is the length of time in which the costs of investment can be recovered by revenues. Shorter paybacks mean more attractive investments. Depreciation is not included in the computation of cost to avoid double accounting since the initial capital is included in the computation. The PP can be computed by applying the following formula: PP = Total initial investment (USD)/Annual profit (USD yr ) (15) 2.10. Break-Even Use The break-even analysis is a useful tool to study the relationship between operating costs and returns. It is the intersection point at which neither profit nor loss occurs. Above this point, the machine use can be considered as net gain [32]. The break-even use of a combine for capital recovery depends on its capacity of harvesting, power requirement, labor requirement, and other charges. 1 1 1 Break-even use (ha yr ) = Investment, USD yr /(Return Operating cost) USD ha (16) 3. Results and Discussion 3.1. Harvesting Track and Harvested Area of the Combine Harvester Harvesting tracks for each plot were identified and are presented in Figure 3a–c. In Figure 3, pictures (a–c) represent the harvesting tracks of Plot 1, Plot 2, and Plot 3, respectively. Picture (a) represents the harvesting track of the medium combine harvester (Model: ER329) during harvesting at the research field of Gifu University, and the other two pictures (b and c) represent the harvesting track of the large combine harvester (Model: ER6120) at the farmer ’s field in Kaizu city, Japan. Additionally, some other movement tracks in each plot are visible, which represent the movement path of the harvester during the unloading and return to the previous harvesting point. After each grain tank fill-up, the harvester was moved to a certain place to unload the grain, but it did not follow any certain path to that place or any return path to the previous harvesting point; thus, the GNSS receiver recorded these tracks within the harvesting operational track. The estimated harvesting areas were 0.303, 0.315, and 0.308 ha for Plot 1, Plot 2, and Plot 3, respectively. 3.2. Speed Variation during Harvesting and Turning Loss Measurement Histograms were developed to determine the density of the machine speed during paddy harvesting, as presented in Figure 4. In both cases, the green shows the highest speed density. For the medium combine, the green shows speed values from 2 to 3 km h , and the density was 0.65 when considering a binary width of 1. On the other hand, for the large combine, the green shows speed values from 4 to 6 km h , and the density was 0.55 (2 0.275) when considering a binary width of 2. In fact, this highest density occurred during standing crop harvesting. In both cases, black shows the lowest speed density. 1 1 Black points show speed values from 4 to 5 km h and 8 to 10 km h for the medium and large combine harvesters, respectively. In both cases, machine movement for grain unloading and returning to the harvesting point had the lowest speed density due to its shorter duration than the other operations. AgriEngineering 2021, 3 FOR PEER REVIEW 9 the unloading and return to the previous harvesting point. After each grain tank fill-up, the harvester was moved to a certain place to unload the grain, but it did not follow any certain path to that place or any return path to the previous harvesting point; thus, the GNSS receiver recorded these tracks within the harvesting operational track. The esti- AgriEngineering 2021, 3 371 mated harvesting areas were 0.303, 0.315, and 0.308 ha for Plot 1, Plot 2, and Plot 3, respec- tively. (b) (c) (a) AgriEngineering 2021, 3 FOR PEER REVIEW 10 Figure 3. Harvesting tracks: (a) Plot 1 at Gifu University; (b) Plot 2 and (c) Plot 3 at Kaizu. Figure 3. Harvesting tracks: (a) Plot 1 at Gifu University; (b) Plot 2 and (c) Plot 3 at Kaizu. 3.2. Speed Variation during Harvesting and Turning Loss Measurement Histograms were developed to determine the density of the machine speed during paddy harvesting, as presented in Figure 4. In both cases, the green shows the highest −1 speed density. For the medium combine, the green shows speed values from 2 to 3 km h , and the density was 0.65 when considering a binary width of 1. On the other hand, for the −1 large combine, the green shows speed values from 4 to 6 km h , and the density was 0.55 (2 × 0.275) when considering a binary width of 2. In fact, this highest density occurred during standing crop harvesting. In both cases, black shows the lowest speed density. −1 −1 Black points show speed values from 4 to 5 km h and 8 to 10 km h for the medium and large combine harvesters, respectively. In both cases, machine movement for grain un- loading and returning to the harvesting point had the lowest speed density due to its shorter duration than the other operations. Speed variations in the harvesters were classified from histograms as shown in Fig- ure 5. The five types of speeds were assumed to be (i) turning, (ii) lodging crop harvesting, (iii) standing crop harvesting, (iv) last time of harvesting and movement for grain unload- ing, and (v) movement for grain unloading and returning to the harvesting point. The (b) (a) speed remained almost constant during standing crop harvesting. The operator increased the speed as he finished the operations during the last harvesting and unloading. In com- Figure 4. Histogram of speed by R: (a) medium combine (Model: ER329); (b) large combine (Model: ER6120). parison, there was a low speed during turning and lodging crop harvesting. During turn- Figure 4. Histogram of speed by R: (a) medium combine (Model: ER329); (b) large combine (Model: ER6120). ing, the machine first needs to slow before turning. Additionally, machines need to oper- Speed variations in the harvesters were classified from histograms as shown in a Figur te slowly du e 5. The ri five ng lodgi typesn of g crop ha speeds wer rvesti e assu ng to med minimize harvesting lo to be (i) turning, (ii) sses lodging and hazards crop har- from straw c vesting, (iii) standing logging. crop harvesting, (iv) last time of harvesting and movement for grain unloading, and (v) movement for grain unloading and returning to the harvesting point. The speed remained almost constant during standing crop harvesting. The operator in- creased the speed as he finished the operations during the last harvesting and unloading. In comparison, there was a low speed during turning and lodging crop harvesting. During turning, the machine first needs to slow before turning. Additionally, machines need to operate slowly during lodging crop harvesting to minimize harvesting losses and hazards from straw clogging. AgriEngineering 2021, 3 FOR PEER REVIEW 11 AgriEngineering 2021, 3 372 (b) (a) Figure 5. Harvester speed variations: (a) Plot 1 at Gifu University; (b) Plots 2 and 3 at Kaizu. Speed variations are presented Figure 5. Harvester speed variations: (a) Plot 1 at Gifu University; (b) Plots 2 and 3 at Kaizu. Speed variations are presented by 5 different colors: red, yellow, green, blue, and black. The values of different speed ranges are classified from 0 to 1, 1 by 5 different colors: red, yellow, green, blue, and black. The values of different speed ranges are classified from 0 to 1, 1 to -1 −1 to 2, 2 to 3, 3 to 4, and 4 to 5 km h for the medium combine and from 0 to 2, 2 to 4, 4 to 6, 6 to 8, and 8 to 10 km h for the 1 1 2, 2 to 3, 3 to 4, and 4 to 5 km h for the medium combine and from 0 to 2, 2 to 4, 4 to 6, 6 to 8, and 8 to 10 km h for the large combine and are shown in red, yellow, green, blue, and black, respectively. large combine and are shown in red, yellow, green, blue, and black, respectively. Another analysis was performed to estimate the turning loss during harvesting. Plot 1 and Plot 2 were harvested by following the same harvesting pattern, but a different pattern was followed when Plot 3 was harvested. For this reason, turning loss analysis was performed for Plots 1 and 2, as presented in Table 2. After a comparison with the harvesting area, turning loss was found to be 0.96 and 0.60 h ha for medium and large combine harvesters, respectively. Turning loss was less for the large combine due to its greater cutting width and effective field capacity than the medium combine. However, after a comparison with the active harvesting time, turning loss was found to be 15.99% and 35.03% for the medium and large combines, respectively. The turning loss percentage was less for the medium combine than for the large combine due to the higher active harvesting time of the medium combine. In fact, turning loss varied due to variations in machine size, plot size, operator skill, soil condition, and crop condition. Ultimately, harvesting 1 1 time (h ha ) and turning loss (h ha ) will be less when using a large combine harvester due to its cutting width and effective field capacity being greater than those of a medium combine. 3.3. Estimating Average Harvesting Speed and Idle Time of Harvesting After the analysis of the RTK GNSS receiving data through ArcGIS, we obtained five types of speeds, but we needed the average value for the technical and economic analysis. Linear speed trend lines were drawn to represent the average harvesting speeds. The within-field speed variation in the harvester is shown in Figures 6 and 7. The average estimated harvesting speeds were 2.50 and 5.52 km h for the medium and large combine harvesters, respectively. The maximum harvesting speeds were 4.18 and 9.78 km h for the medium and large combines, respectively. On the other hand, the lowest speed was approximately 0 km h during the still position (e.g., unloading, straw clog removal, and AgriEngineering 2021, 3 373 waiting for the grain transfer pickup after filling the grain collector tank of the harvester). The on-field and off-field speeds of the harvesters varied greatly. Table 2. Turning losses estimation. Average Turning Loss Turning Total Active Total Turns, Turning Harvesting with Active Loss with Machine Plot Turning Harvesting No. Loss, Area, ha Harvesting Harvesting Loss, h Time, h s Turn Time, % Area, h ha Medium combine Plot 1 73 14.33 0.2906 1.8175 0.3029 15.99 0.96 (Model: ER329) AgriEngineering 2021, 3 FOR PEER REVIEW 13 Large combine Plot 2 54 12.67 0.1901 0.5425 0.3150 35.03 0.60 (Model: ER6120) AgriEngineering 2021, 3 FOR PEER REVIEW 14 Figure Figure 6. 6. Dif Differ ferent ent speed speeds s and and id idle le t times imes during during harv harvesting esting at Plo at Plott 1 1 by by m medium edium com combine. bine. Figure 7. Figure 7. Differ Differe ent nt speed speeds s and and id idle le t times imes du during ring harvesting harvesting at Plo at Plots ts 2 2 and and 3 3 by by larg large e com combine. bine. The total machine operation times, idle times, and effective harvesting times are The total machine operation times, idle times, and effective harvesting times are pre- presented in Table 3. Idle times represent not only the still position (speed was 0 km−1 h ) sented in Table 3. Idle times represent not only the still position (speed was 0 km h ) but but also the times for moving to the unloading point and returning to the previous har- also the times for moving to the unloading point and returning to the previous harvesting vesting point (speed was highest). During the harvesting operation, the total number of point (speed was highest). During the harvesting operation, the total number of idle ac- tivities was eight for the medium combine (grain unloading six times and straw clog re- moving two times) and six for the large combine (grain unloading four times and straw clog removing two times). The estimated time loss percentages due to idle time were 23.14% and 41.46% for medium and large combine harvesters, respectively. Time loss per- centages depend on the distances between the harvesting point and the grain storehouse. Additionally, they depend on crop conditions and operator skill. Harvesting field capacity and efficiency can be increased by reducing harvesting time losses. The pick-up operators should be aware of minimizing the grain shifting time from the field to the storehouse, and harvester operators must be skilled enough to operate the harvester properly and quickly implement troubleshooting in the field during harvesting time. AgriEngineering 2021, 3 374 idle activities was eight for the medium combine (grain unloading six times and straw clog removing two times) and six for the large combine (grain unloading four times and straw clog removing two times). The estimated time loss percentages due to idle time were 23.14% and 41.46% for medium and large combine harvesters, respectively. Time loss percentages depend on the distances between the harvesting point and the grain storehouse. Additionally, they depend on crop conditions and operator skill. Harvesting field capacity and efficiency can be increased by reducing harvesting time losses. The pick-up operators should be aware of minimizing the grain shifting time from the field to the storehouse, and harvester operators must be skilled enough to operate the harvester properly and quickly implement troubleshooting in the field during harvesting time. Table 3. Idle time loss of the combine harvester. Total Effective Idle Times Idle Time Total Idle Machines Operational Harvesting Loss % Time, h Nos. Item Names Time, s Time, h Timed, h Grain unloading and 1 shifting to 673 storehouse 2 Grain unloading 271 Medium combine 3 Straw clog removing 133 (Model: ER329) 0.55 2.37 1.82 23.14 4 Grain unloading 194 (Plot 1) 5 Grain unloading 172 6 Grain unloading 179 7 Grain unloading 216 8 Straw clog removing 136 1 Straw clog removing 129 2 Grain unloading 381 3 Grain unloading 301 Large combine 0.80 1.93 1.13 41.46 4 Grain unloading 496 (Model: ER6120) 5 Straw clog removing 632 (Plot 1 + Plot 2) Waiting for pick-up 6 940 and grain unloading Nos. = Consequence of idle activities during harvesting operations. 3.4. Technical Performances of Harvester The technical performances of the harvesters were measured from each paddy plot harvest and are presented in Table 4. The estimated average values of forward speed, fuel 1 1 1 consumption, and effective field capacity were 2.50 km h , 3.18 L h , and 0.17 ha h 1 1 1 and 5.52 km h , 11.93 L h , and 0.55 ha h , using medium and large combine harvesters, respectively. The effective field capacity was greater for the large combine than for the medium combine due to the larger cutting width and engine power of the large com- bine. Similar results were found for a combine harvester (Model: DR150A) by a previous researcher: the average values of forward speed, fuel consumption, and effective field 1 1 1 capacity were 6.71 km h , 10.76 L h , and 0.33 ha h , respectively [37]. The average value of effective field capacity of a combine harvester was found to be 0.64 to 0.81 ha h with the average forward speed value of 2.75 to 3.00 km h [38]. The estimated field performances varied due to variations in machine size, plot size, forward speed, operator skill, soil condition, and crop condition. AgriEngineering 2021, 3 375 Table 4. Technical performance of the combine harvesters. Forward Fuel Con- Fuel Con- Effective Field Effective Field Place and Plots Speed sumption sumption Capacity Capacity Use of Harvester Model 1 1 1 1 1 (km h ) (L h ) (L ha ) (ha h ) (Decimal h ) Gifu University farm in Gifu, Japan Plot 1 2.50 3.18 19.08 0.17 42 Model: ER329 Plot 2 5.84 12.18 20.98 0.58 143 Kaizu city farm in Gifu, Japan Plot 3 5.20 11.68 22.24 0.53 131 Model: ER6120 Average for 5.52 11.93 21.61 0.55 137 Model: ER6120 3.5. Economic Performances 3.5.1. Operating Cost of a Combine Harvesters After field experiment and data analysis, salient features of combine harvester custom- hire entrepreneurship are shown in Table 5. The operating costs (sums of fixed and variable costs) were found to be 903 and 421 USD ha using medium and large combine harvesters, respectively. Fixed cost mainly depends on the purchase price of the harvester, and variable cost depends on the costs of fuel, lubrication, daily service, power, and labor. Fixed costs were found to be 142.71 and 125.97 USD ha and variable costs were found to be 759.87 and 295.51 USD ha using medium and large combine harvesters, respectively. The operating costs of combine harvesters have been mentioned by other researchers: the 1 1 operating cost was 124 USD ha for using the model of DSC-48 [39] and 123 USD ha for using the model of DR150A [37]. Operating costs mainly varied due to the variations in machine purchase price and labor cost. Table 5. Major cost items for a combine operation business in custom-hire services. Items Unit * Amount Medium Combine Large Combine (Model: ER329) (Model: ER6120) Purchase price of combine (P) USD 50,275 143,578 Salvage value (S) (10% of P) USD 5028 14,358 Working life (L) years 10 10 Average working hours per year hr year 240 240 Field capacity of harvester ha h 0.17 0.55 Average working hectare per year ha year 40.80 132.00 Annual fixed cost USD year 5822.51 16,628.15 Fixed cost per hour 24.26 69.28 USD h A. Fixed cost per hectare USD ha 142.71 125.97 Fuel cost per hour USD h 3.27 11.99 Lubricant cost per hour 0.49 1.80 USD h Repair and maintenance cost (0.025% of P) USD h 12.57 35.89 Labor cost USD h 11.01 11.01 Operator cost USD h 13.76 13.76 Straw and paddy bag collection cost per hour USD h 88.07 88.07 Variable cost per hour 129.18 162.53 USD h Annual variable cost USD year 31,002.53 39,007.74 B. Variable cost per hectare USD ha 759.87 295.51 Operating cost of a harvester (A+B) USD ha 903 421 * Approximately USD 1 = JPY 109 (JPY = Japanese Yen); daily working of operator and labor = 8 h; daily effective use of machine = 6 h; yearly use = 40 days; price of diesel = 1.01 USD L AgriEngineering 2021, 3 376 3.5.2. Comparison of Financial Features of Harvesters for Custom-Hire Business The business of medium and large combine harvesters is seasonal. In a year, a medium combine harvester can be used at least 40 days or for 40.80 ha harvesting, and a large combine harvester can be used 40 days or for 132.00 ha harvesting. The harvester machine can be used based on the average working capacity of the machine. Estimated working life of both harvesters is at least 10 years. For using combine harvesters, one operator and one laborer are required for harvesting, preparing the paddy field, and carrying paddy bags to home. Major cost items of a harvester operation business in custom-hire service are presented in Table 6. Table 6. Financial features of harvesters for custom-hire business. Amount (Harvesting to Cleaning) Items Unit Medium Combine Large Combine Model: ER329 Model: ER6120 Purchase price of combine (P) USD 50,275 143,578 Working life (L) years 10 10 Rent out charge USD ha 1835 1835 (Including operating cost, profit, and SFP) Operating cost USD ha 903 421 Profit USD ha 823 1317 Sinking fund payment (SFP) USD ha 109 97 Sinking fund payment (SFP) 4474 12,777 USD year Net present value (NPV) at 10% DF USD 219,225 1,104,962 Benefit–cost ratio (BCR) % 1.91 3.88 Internal rate of return (IRR) - 87% 142% Payback period (PP) years 1.15 0.71 Break-even use ha year 5.42 10.80 PV, IRR, BCR, and PP of Harvesters Economic analysis for CHS was carried out from the viewpoint of the harvester owner as presented in Table 6. The results supported investment in combine harvesters being highly profitable. Considering a 10% discount rate, the NPVs of the medium and large combine harvesters in existing condition were USD 219,225 and USD 1,104,962, respectively. The NPVs of medium and large combine harvester indicate that both harvesters could be considered financially sound and viable because estimated IRRs for medium and large combine harvester were 87% and 142%, respectively, which all are far greater than the bank interest rate. This indicates that investing in a medium and large combine harvester is highly profitable and suitable for the development of custom-hire entrepreneurs. The esti- mated BCRs for medium and large combine harvesters are 1.91 and 3.88, respectively, and are higher than unity. The PPs of medium and large combine harvesters were determined to be 1.15 and 0.71 years with initial investment sizes of USD 50,275 and USD 143,578, respectively, which means the stream of cash proceeds produced by an investment to equal the initial expenditure would be incurred after 1.15 years for a medium combine and 0.71 years for a large combine harvester. Similar results were mentioned by another researcher for a mini-combine harvester: estimated IRR, BCR, and PP were 40%, 1.52, and 2.41 years, respectively [40]. Other corresponding results were found for a reaper: esti- mated IRR, BCR, and PP were 123%, 2.89, and 1.14 years, respectively [41]. The estimated results varied corresponding to the machine purchase price, size of the machine, labor cost, and return from the rent-out charge. Sinking Fund Annual Payment (SFP) of Combine Harvesters Considering the economic life of medium and large combine harvesters, an en- 1 1 trepreneur needs to save or deposit 4,474 USD year and 12,777 USD year in a bank account, for medium and large combine harvesters, respectively, as shown in Table 6, so AgriEngineering 2021, 3 377 that he or she can buy a new harvester when the economic life of the harvester expires due to harvesting operations. Replacement of a medium or large combine harvester with a new one is essential because beyond economic life it will no longer be useful for operating in the field on a profit basis. The performance of a new harvester is significantly superior, and it makes the old harvester obsolete. Anticipated costs for operating the old harvesters exceed those of replacement combine harvesters. Therefore, a combine harvester entrepreneur has to save money to buy the new one. Uniform annual payments to a fund are of such a size that by the end of the economic life of the machine, the funds and their interest will have accumulated to an amount that will purchase another equivalent machine. Rent-Out Charge of Harvester Operation for Custom-Hire Service Business Rent-out charge must be determined to sustain the entrepreneurship or CHS business. Based on the field data, the estimation of cost items with appropriate equations, and the assumptions, the rent-out charge of a combine harvester for the paddy harvesting operation was estimated as 1835 USD ha , as shown in Table 6, in which operating cost, profit, and SFP are included. Rent-out charge may differ based on harvester capacity and quality and may vary from country to country as economic conditions differ. Break-Even Use of Medium and Large Combine Harvesters AgriEngineering 2021, 3 FOR PEER REVIEW 19 The break-even uses of the medium and large combine harvesters were found to be 5.42 and 10.80 ha year , respectively, as shown in Figure 8. The medium and large combine harvesters will run fully on a profit basis if the machines can be used more −1 1835 USD ha for each harvester on the basis of field survey. Total cost was estimated than mentioned areas per year. For determining the break-even use, rent-out charge was from the summation of annual fixed cost and variable cost. Annual fixed cost will not considered 1835 USD ha for each harvester on the basis of field survey. Total cost was vary, but total variable cost will vary depending on the annual area coverage. A similar estimated from the summation of annual fixed cost and variable cost. Annual fixed cost will −1 result was mentioned for a mini-combine harvester: estimated BEU was 9.24 ha year [40]. not vary, but total variable cost will vary depending on the annual area coverage. A similar −1 Another similar result was found for a combine harvester, and it was 22.17 ha year at a result was mentioned for a mini-combine harvester: estimated BEU was 9.24 ha year [40]. −1 harvesting capacity of 0.39 ha h considering break-even point 133 ha of paddy field and 1 Another similar result was found for a combine harvester, and it was 22.17 ha year at harvesting during an economic life 1 of 6 years [15]. In addition, the estimated BEU was a harvesting capacity of 0.39 ha h considering break-even point 133 ha of paddy field −1 14.79 ha year for using a reaper [42]. The results varied corresponding to the machine and harvesting during an economic life of 6 years [15]. In addition, the estimated BEU was size, purchase price, labor cost, and return from CHS business. 14.79 ha year for using a reaper [42]. The results varied corresponding to the machine size, purchase price, labor cost, and return from CHS business. (a) (b) Figure 8. Break-even use analysis for (a) medium combine and (b) large combine. Figure 8. Break-even use analysis for (a) medium combine and (b) large combine. 3.5.3. Project Worth Analysis 3.5.3. Project Worth Analysis Project Project wort worth h ev evaluations aluations ar are e shown shown in in Tables Table 7sand 7 and 8 for 8 for m medium ediu and m and largelar combines, ge com- bines, respectively respect . iDiscounted vely. Discounted project meas project measures ur wer es w e used ere us for ed cash for caflow sh flow an analysis, alysis which , which is is evidently evidentlay little a little more more acc eptable acceptable since sin the ce the use of use undiscounted of undiscoumeasur nted me esasures o of project f project worth worth prevents ta prevents taking into king consideration into considera the tiotiming n the timing of benefits of benefits and costs. and costs. The The NPV,NP BCR, V, BCR, IRR, IRR and, PP and of Pharvesters P of harves with ters wit existing h exist inflation ing inflat conditions ion conditwer ionse wer estimated e estimat ate10% d at discount 10% dis- rates where the minimum percentage of interest rate associated with agricultural loans to count rates where the minimum percentage of interest rate associated with agricultural loans to purchase agricultural machinery was 0.25% in Japan. Project worth evaluations are shown in Tables 7 and 8. The results revealed that investments in medium and large combine harvesters were profitable for an entrepreneur in a CHS business operation. Table 7. NPV, BCR, IRR, and PP calculation for medium combine at DF 10%. Fixed Variable Cost Gross Benefit Cash Flow Present Value of Present Value of Present Value of Balance Year Cost 黄芳(USD −1 (USD year ) (USD) Cash Flow (USD) Cost (USD) Benefit (USD) (USD) −1 (USD) year ) 0 50,275 50,275 −50275 50275 0 −50275 −50275 1 0 31,003 74,862 43,860 28,184 68,057 39,873 −6,415 2 0 31,003 74,862 43,860 25,622 61,870 36,248 37,444 3 0 31,003 74,862 43,860 23,293 56,245 32,953 81,304 4 0 31,003 74,862 43,860 21,175 51,132 29,957 125,164 5 0 31,003 74,862 43,860 19,250 46,484 27,234 169,024 6 0 31,003 74,862 43,860 17,500 42,258 24,758 212,884 7 0 31,003 74,862 43,860 15,909 38,416 22,507 256,744 8 0 31,003 74,862 43,860 14,463 34,924 20,461 300,604 9 0 31,003 74,862 43,860 13,148 31,749 18,601 344,463 10 0 31,003 74,862 43,860 11,953 28,863 16,910 388,323 NPV = USD 219,225; BCR = 1.91; IRR= 87%; PP = 1.15 years Table 8. NPV, BCR, IRR, and PP calculation for large combine at DF 10%. AgriEngineering 2021, 3 378 purchase agricultural machinery was 0.25% in Japan. Project worth evaluations are shown in Tables 7 and 8. The results revealed that investments in medium and large combine harvesters were profitable for an entrepreneur in a CHS business operation. Table 7. NPV, BCR, IRR, and PP calculation for medium combine at DF 10%. Present Present Gross Cash Present Fixed Cost Value of Value of Balance Variable Cost Benefit Year Flow Value of (USD) Cash Flow Benefit (USD) (USD year ) (USD (USD) Cost (USD) (USD) (USD) year ) 0 50,275 50,275 50,275 50,275 0 50,275 50,275 1 0 31,003 74,862 43,860 28,184 68,057 39,873 6415 2 0 31,003 74,862 43,860 25,622 61,870 36,248 37,444 3 0 31,003 74,862 43,860 23,293 56,245 32,953 81,304 4 0 31,003 74,862 43,860 21,175 51,132 29,957 125,164 5 0 31,003 74,862 43,860 19,250 46,484 27,234 169,024 6 0 31,003 74,862 43,860 17,500 42,258 24,758 212,884 7 0 31,003 74,862 43,860 15,909 38,416 22,507 256,744 8 0 31,003 74,862 43,860 14,463 34,924 20,461 300,604 9 0 31,003 74,862 43,860 13,148 31,749 18,601 344,463 10 0 31,003 74,862 43,860 11,953 28,863 16,910 388,323 NPV = USD 219,225; BCR = 1.91; IRR= 87%; PP = 1.15 years Table 8. NPV, BCR, IRR, and PP calculation for large combine at DF 10%. Present Present Gross Cash Present Fixed Cost Value of Value of Balance Variable Cost Benefit Year Flow Value of (USD) Cash Flow Benefit (USD) (USD year ) (USD (USD) Cost (USD) year ) (USD) (USD) 0 143,578 143,578 143,578 143,578 0 143,578 143,578 1 0 39,008 242,202 203,194 35,462 220,183 184,722 59,616 2 0 39,008 242,202 203,194 32,238 200,167 167,929 262,810 3 0 39,008 242,202 203,194 29,307 181,970 152,663 466,004 4 0 39,008 242,202 203,194 26,643 165,427 138,784 669,198 5 0 39,008 242,202 203,194 24,221 150,388 126,168 872,392 6 0 39,008 242,202 203,194 22,019 136,717 114,698 1,075,587 7 0 39,008 242,202 203,194 20,017 124,288 104,271 1,278,781 8 0 39,008 242,202 203,194 18,197 112,989 94,792 1,481,975 9 0 39,008 242,202 203,194 16,543 102,717 86,174 1,685,169 10 0 39,008 242,202 203,194 15,039 93,379 78,340 1,888,363 NPV = USD 1,104,962; BCR = 3.88; IRR= 142%; PP = 0.71 years 3.5.4. BCR, IRR, PP, and BEU of Combine Harvesters for Project Worth Evaluation The results in Tables 9 and 10 reveal that the BCRs of the medium and large combine harvesters are 1.91 and 3.88 and are higher than unity. Custom-hire business of any farm machine will be profitable if the BCR of the machine is higher than unity. The estimated IRRs are 87% and 142% for medium and large combine harvesters, respectively, and are far greater than the bank interest rate. The PPs of medium and large combine harvesters are 1.15 and 0.71 years with a machine working life of 10 years. This means that the machine owner will obtain profit after 1.15 and 0.71 years, respectively, of using medium and large combine harvesters, until 10 years. The BEUs of the medium and large combine harvesters are 5.42 and 10.80 ha year , respectively, with annual machine working capacities of 40.80 and 132.00 ha year , respectively. This means that machine owners will obtain profit after exceeding the use rates of 5.42 and 10.80 ha year , respectively, for medium and large combine harvesters considering 10 years of working life. This indicates that investments in both types of combine harvesters are profitable and suitable for the development of AgriEngineering 2021, 3 379 a custom-hire entrepreneur. Comparatively, a large combine harvester provides more benefit than the medium size combine harvester in terms of harvesting capacity and return. A corresponding result was observed in another study considering a mini-combine harvester: estimated BCR, IRR, PP, and BEU were 1.52, 40%, 2.41 years, and 9.24 ha year , respectively [40]. Another similar result was mentioned for a reaper: estimated BCR, IRR, PP, and BEU were 2.04, 91%, 1.06 years, and 14.79 ha year , respectively [42]. The estimated results varied corresponding to the machine purchase price, size of the machine, labor cost, and return from the rent-out charge. Table 9. Project worth evaluation of medium combine. Items Value Remarks Benefit–cost ratio (BCR) 1.91 If greater than 1.0 (1.91 > 1.0), acceptable as profitable If greater than prevailing interest rate (87% > 9%), Internal rate of return (IRR) 87% acceptable If less than economic life (1.15 years < 10 years), Payback period (PP) 1.15 years acceptable If less than service area Break-even use (BEU) 5.42 ha year 1 1 (5.42 ha year < 40.80 ha year ), acceptable Table 10. Project worth evaluation of large combine. Items Value Remarks Benefit–cost ratio (BCR) 3.88 If greater than 1.0 (3.88 > 1.0), acceptable as profitable If greater than prevailing interest rate Internal rate of return (IRR) 142% (142% > 0.25%), acceptable If less than economic life (0.71 year < 10 years), 0.71 year Payback period (PP) acceptable If less than service area Break-even point (BEU) 10.80 ha year 1 1 (10.80 ha year < 132.00 ha year ), acceptable 4. Conclusions Our study demonstrated that combine harvesters could be a cost-saving technology and that the application of GNSS and GIS in modern agriculture is essential to quantify machinery performance precisely. The application of RTK GNSS and GIS successfully visualized spatial information about machinery performance attributes, such as area cov- erage, operational time, harvesting speed, machine idle times, effective operational time, field capacity, harvesting location with operational track, and turning pattern with loss time. In comparison to the other harvesting methods, the large combine harvester had a greater area coverage rate, and its turning loss time was less. Harvester performance could be increased by reducing the turning loss and idle time during harvesting operation. The operating cost of a combine harvester is an important economic aspects of harvester custom-hire entrepreneurship. The results of PBP, BCR, NPV, and IRR further indicated that investments in both types of combine harvesters were highly profitable and suitable for the development of custom-hire entrepreneurs to support Japanese smallholders. To avoid initial investment, there is a great opportunity to use paddy harvesters through CHSs by local service providers and custom-hire entrepreneurs to avoid the initial investment of farmers. Both sides (service provider/entrepreneur and farmer) could benefit from the CHS business of the harvester. Considering the harvesting capacity and return from investment, the large combine harvester might provide more benefit than the medium size combine harvester. Based on the analyses of the collected data, it can be also recommended that innovative farmers and entrepreneurs in well-organized farmers’ groups can invest their shared capital in providing services of combine harvesters to the members of the group and other neighboring farmers for paddy harvesting. Although the findings were based on the estimation in Japan, combine harvesters for paddy harvesting might be also an appropriate solution in developing countries to meet the labor shortages in the peak harvesting period. Thus, further research is needed to estimate the feasibility of CHSs in developing countries on the assumption that the medium and large combine harvesters are introduced in the future. As discussed earlier, the actual performances of reapers or AgriEngineering 2021, 3 380 mini-combine harvesters have been reported previously. To the best of our knowledge, this is the first study that providing on-farm precise estimates of machinery performance attributes of medium and large paddy harvesters, which would be very informative in evaluating the feasibility of CHSs in the other Asian developing countries. Author Contributions: Conceptualization, M.K.H. and T.S.T.T.; methodology, M.K.H., T.S.T.T., and M.R.A.; validation, T.S.T.T., M.R.A., and C.K.S.; formal analysis, M.K.H., T.S.T.T., and M.M.A.; data curation, M.K.H. and T.S.T.T.; software, M.K.H. and T.S.T.T.; writing—original draft preparation, M.K.H. and T.S.T.T.; writing—review and editing, M.K.H., T.S.T.T., and M.R.A.; supervision, C.K.S. and M.M.A. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by “The United Graduate School of Agricultural Science, Gifu University (UGSAS, GU), Japan” and Feed the Future Innovation (FtF) Lab for Sustainable Intensifi- cation through the United States Agency for International Development (USAID), and University of Illinois at Urbana-Champaign, USA (Subaward Number: 2015-06391-06, Grant code: AB078). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: This research was conducted as part of “The United Graduate School of Agricul- tural Science, Gifu University (UGSAS, GU), Japan”, which provided financial support during the research activities in addition to providing a 6-month sandwich doctoral program at this university. The first author would also like to acknowledge his research in Bangladesh as part of the Appropriate Scale Mechanization Consortium (ASMC) project “Appropriate Scale Mechanization Innovation Hub (ASMIH)—Bangladesh”, which was supported by the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID) and University of Illinois at Urbana-Champaign, USA (Subaward Number: 2015-06391-06, Grant code: AB078). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Conflicts of Interest: The authors declare no conflict of interest. References 1. Singh, G. Estimation of a Mechanisation Index and Its Impact on Production and Economic Factors—A Case Study in India. Biosyst. Eng. 2006, 93, 99–106. [CrossRef] 2. Noby, M.M.; Hasan, M.K.; Ali, M.R.; Saha, C.K.; Alam, M.M.; Hossain, M.M. 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