Introduction

This page evaluates the economic feasibility of the RNA pesticide “MOVE”. Specifically, we calculate the production costs of MOVE in detail and compare them with existing pesticide market prices and the increase in profits due to disease control in major crops. Through this, we will verify whether MOVE is an economically viable solution in agricultural settings.

Evaluation of Economic Effects of Pesticide Introduction

The RNA pesticide “MOVE” targets fungal diseases, that is, diseases to which RNAi can be applied. We will clarify whether farmers can profit from the introduction of the pesticide, and what level of pesticide performance would be reasonable for farmers to gain profit. For this purpose, we will calculate the farmers’ profit rate using MOVE’s control rate as a variable and examine it using visually comprehensible graphs.

Calculation of MOVE’s Drug Price

1. Bioreactor Specifications and Production Capacity

Bioreactor Specifications

For the industrial production of MOVE, we use a device that connects six 100-liter bioreactors in series. Continuous cultivation is carried out by constantly flowing liquid medium into this at a rate of 1L per hour. It is known that the MOVE production rate of this device is 2.13×10^-6 mol per hour per set. This derivation can be confirmed from the Bioreactor page in the model.

Calculation of Annual Production

We calculate the total amount of MOVE that one reactor can produce in a year. Assuming 24 hours in a day and 365.25 days in a year, the production amount when operated continuously for one year can be calculated by computing the MOVE production for 8,766 hours. The annual production is $2.13× 10^-6 [\text{mol/h/set}] \times 8,766 [\text{h/year}] \approx 0.0187 [\text{mol/year/set}]$.

2. Method for Calculating Drug Price

① Product Manufacturing Cost
② Sales and Research Expenses, etc.
③ Operating Profit
④ Distribution Expenses
⑤ Consumption Tax

The drug price consists of the above elements. ① Product manufacturing cost is the total amount of raw material costs, labor costs, and manufacturing expenses, and the sum of ① to ④ is the drug price without considering consumption tax. Based on the drug price calculation formula for pharmaceuticals, we assume that these elements for pesticides also satisfy ②/(①+②+③)≦0.506, ③/(①+②+③)=0.155, ④/(①+②+③+④)=0.075 [1] . In this case, we estimate using ②/(①+②+③)=0.506 to ensure the drug price is not lower than it would be in reality.

3. Determining the Product Manufacturing Cost

As mentioned above, the product manufacturing cost is the sum of raw material costs, labor costs, and manufacturing expenses. Therefore, by evaluating these three elements, we can determine ①.

Raw Material Costs

The cost of the liquid medium itself is about 3,000 yen per liter. Since one reactor uses 1L of medium per hour, to calculate the annual medium cost, we need to calculate the cost of medium used over 8,766 hours. The annual medium cost per reactor is calculated as $3,000 [\text{yen/L}] \times 1[\text{L/h}] \times 8,766 [\text{h}] = 26,298,000 [\text{yen}]$.

In addition to the medium, we need to consider the cost of glucose, which is the main nutrient source. In this system, 1,500 yen worth of glucose is added to one reactor per hour. Similarly, the annual glucose cost per reactor is 13,149,000 yen .

Other raw material costs are negligibly small, so the sum of these, 39,447,000 yen/year/reactor , can be considered as the raw material cost for MOVE.

Labor Costs

Labor cost is estimated at 6,000,000 yen per person per year. The more reactors, the more employees need to be hired. For simplicity, assuming one employee is hired per reactor, the labor cost is estimated at 6,000,000 yen/year/reactor .

Manufacturing Expenses

Manufacturing expenses can be broadly divided into costs associated with the equipment itself (equipment depreciation costs, maintenance costs) and energy costs.

Costs Associated with the Equipment Itself

The equipment cost for one 1L bioreactor is about 3,000,000 yen. Assuming a depreciation period of 10 years, the annual depreciation cost is 300,000 yen. Also, estimating the maintenance cost for one 1L bioreactor at 50,000 yen, the total cost associated with the equipment itself amounts to 350,000 yen per tank per year.

In this case, since one reactor consists of six 100L reactors connected in series, considering the relationship between the reactor volume and its associated costs (∝ surface area), the actual manufacturing expense per reactor is as follows:

Energy Costs

The maximum power consumption of one 100L bioreactor is about 2,300W, which means it consumes 2.3kWh of electricity per hour. [2] Calculating with an electricity rate of 25 yen/kWh, the power cost per hour is determined to be $2.3 [\text{kWh}] \times 25 [\text{yen/kWh}] = 57.5 [\text{yen}]$. This means the annual energy cost is 504,045 yen , and the annual energy cost for one bioreactor (6 tanks) is $504,045 [\text{yen/tank}] \times 6 [\text{tanks}] = 3,024,270 [\text{yen}]$.

Calculation of Product Manufacturing Cost and Unit Optimization

Adding all of the above, the product manufacturing cost is calculated to be 9.37 × 10^8 yen/year/reactor . However, this is just the cost of RNA pesticide produced by one reactor in a year, so we first need to determine the cost per unit quantity. Using the amount of MOVE that one reactor can produce in a year, we can calculate that the cost of 1 mol of MOVE is 5.02 × 10^9 yen .

Furthermore, we need to convert the quantity-based cost to an area-based cost that is easier to evaluate as a pesticide. The amount of MOVE used when spraying RNA pesticide once on 1 ha is 4.00 × 10^-7~-6 mol. Please check the m-MV on the model page for this calculation. 4.00 × 10^-7 mol/ha is the surface density that inhibits up to 90% of protein synthesis when all MOVE attached to the fungal body is taken up, and considering the three-dimensional structure of plants, we have set a tenfold range for the spray amount. As a result, the cost of RNA pesticide for one spray on 1 ha is 2.01 × 10^3~4 yen/ha .

Estimation of Drug Price

The aforementioned relationships ②/(①+②+③)=0.506, ③/(①+②+③)=0.155, ④/(①+②+③+④)=0.075 are equations where ① is a known constant, so we can solve the simultaneous equations to find ②, ③, and ④. Substituting 2.01 × 10^3~4 yen/ha for ① and solving, we get ② as 3.00 × 10^3~4 yen/ha , ③ as 9.18 × 10^2~3 yen/ha , and ④ as 4.80 × 10^2~3 yen/ha . Adding ① to ④, we can derive the tax-exclusive drug price of 6.40 × 10^3~4 yen/ha .

Cost of Pesticide Application

When using pesticides, farmers must consider not only the drug price but also the cost of application itself. This cost is about 20,000 yen per spray per hectare when using drones [3] , so combined with the drug price, one application costs about 2.64~8.40 × 10^4 yen per hectare. In the following, we will use the upper limit of the cost per application of 8.40 × 10^4 yen/ha, or 8.40 × 10^3 yen/10a, to evaluate the economic feasibility of RNA pesticides.

Increase in Farmers’ Profits from Pesticide Introduction

We will calculate the profit increase, excluding the drug price, obtained by introducing MOVE from yield data.

Since the increase in farmers’ revenue depends on the scale of the farmland, we define the profit increase as the increase in revenue per 10a of farmland and calculate this. The profit increase is defined as follows:

Here, the crop yield per 10a in kg refers to the net harvest excluding crops that cannot be marketed due to disease infection. Therefore, to calculate the yield when all crops grown by farmers on 10a are healthy from the crop yield per 10a in kg:

This can be expressed as above. We define the control rate as the proportion of crops that were not infected when MOVE was introduced among those that would have been infected without pesticides.

If we set MOVE’s disease control rate as 100X%, then

This can be expressed as above. However, as mentioned earlier, the increase in crop yield per 10a due to pesticide introduction in kg excludes the drug price of MOVE.

Crop-specific Disease Losses and Effects of MOVE Implementation

The following dollar conversion is 110 yen per dollar from the 2016 data. [4]

1. Tomato

   • Average yield [6] : 6,140 g/10a/year
   • Trading price [7] : 320 yen/kg
   • Disease targeted by MOVE [5] : Loss rate due to gray mold : 16.7%
   • Other diseases [5] : Leaf mold (6.9), Brown leaf spot (5.3), Late blight (2.9), Yellow leaf curl (1.9), Powdery mildew (1.3), Bacterial wilt (1.0)
   • **Profit increase=**6140×100/64×0.167×320×X=512,690X yen/10a/year

2. Carrot

   • Average yield [6] : 3,180 kg/10a/year
   • Trading price : 500 yen/kg [7]
   • Disease targeted by MOVE [5] : Loss rate due to black leaf blight : 5.8%
   • Other diseases [5] : None particularly noteworthy
   • **Profit increase=**3180×100/(100-5.8)×0.058×500X=97,898X yen/10a/year

3. Cabbage

   • Average yield : 4,180 kg/10a/year [6]
   • Trading price : 200 yen/kg [7]
   • Disease targeted by MOVE [5] : Loss rate due to black rot : 11.5%
   • Other diseases [5] : Sclerotinia rot (7.8), Fusarium yellows (4.7), Bacterial soft rot (3.5), Clubroot (1.4)
   • Profit increase =4180×1/(100-11.5-7.8-4.7-3.5-1.4)×0.115×200×X=135,218X yen/10a/year

4. Lettuce

   • Average yield : 2,710 kg/10a/year [6]
   • Trading price : 250 yen/kg [7]
   • Disease targeted by MOVE [5] : Loss rate due to Sclerotinia rot : 10.9%
   • Other diseases [5] : Sclerotinia rot (7.8), Fusarium yellows (4.7), Bacterial soft rot (3.5), Clubroot (1.4)
   • Profit increase =2710×100/(100-10.9-10.2-3.0-2.9-2.8-1.4)×0.109×250×X=107,336X yen/10a/year

5. Asparagus

   • Average yield : 561 kg/10a/year [6]
   • Trading price : 1,437 yen/kg [7]
   • Disease targeted by MOVE [5] : Loss rate due to purple spot : 14.5%
   • Other diseases [5] : Stem blight (13.6), Brown spot (3.2)
   • Profit increase =561×100/(100-14.5-13.6-3.2)×0.145×1437×X=170,149X yen/10a/year

Evaluation of Economic Feasibility

We define economic feasibility at a given control rate as when the profit increase per unit area exceeds $8.40×10^3 [\text{yen/10a}] \times \text{(number of annual applications)}$. Assuming that RNA pesticides are used for 3 months in a year, we can determine the required duration of effectiveness to meet the conditions for the number of annual applications.

The straight lines passing through the origin represent the profit increase for tomatoes, asparagus, cabbage, lettuce, and carrots in order of decreasing slope, while the horizontal lines represent the annual farmer’s cost for 3, 6, 9, and 12 applications per year from bottom to top. Based on the above assumptions, the required duration of effectiveness to achieve these while maintaining efficacy is 1 month, 2 weeks, 10 days, and 1 week, respectively.

From the graph, we can see that for high-value crops like tomatoes, even with an effectiveness duration of 1 week, profits of up to 5 times the cost can be expected. On the other hand, for carrots, it’s not economically viable even if the control rate reaches 100%. However, if MOVE’s control rate is 80% or higher with a 10-day effectiveness duration, or 60% or higher with about 2 weeks, all 5 cases mentioned become economically viable. If future improvements to MOVE can achieve a 1-month effectiveness duration, significant profit increases exceeding farmer costs can be expected even for crops with lower profit margins like carrots and lettuce.

Conclusion

The economic analysis shows that if MOVE can achieve a certain level of effectiveness duration based on its actual efficacy, it can become an economically advantageous product for farmers. In particular, the profit increase from disease control in high-value crops is significant, making MOVE a powerful solution for agricultural producers struggling with fungal diseases.

Moreover, from the perspective of reducing environmental impact and flexibility against resistant strains, contributing to sustainable agriculture, MOVE is a product with high social value. In the future, further cost reductions can be expected through optimization of production processes and refinement of market strategies.

References

[1] 中央社会保険医療協議会, 参考資料 ,“現行の薬価基準制度について”,

[2] Sartorius, Biostat® B Multi-talented bioreactor

[3] ドローン産業株式会社

[4] VALUTA FX , “US Dollar (USD) to Japanese Yen (JPY) 2016 Historical Exchange Rates”

[5] 寺見文宏, 野菜主要病害の発生生態と防除 , 植物防疫 72(8)

[6] e-stat, 作物統計調査 作況調査（野菜） 確報 平成28年産野菜生産出荷統計

[7] 独立行政法人 農畜産業振興機構, 2016年版 野菜統計要覧