Economic feasibility of glasshouse tomato production in China — A bio-economic stochastic modelling approach

Figures & data

Figure 1. Schematic description of the modelling framework

Table 1. Initial investment costs for a 1.4-hectare Venlo-type glasshouse.

Items	Cost € m^−2	Cost € unit^−1	Maintenance % year^−1	Detail
Structure	47.5 ^b		0.5%^c	Steel structure, aluminium roof, 38% ventilation system, outside and inner walls
Cover	10.0 ^a		0.5%^c	Roof and wall glass, insect net
Roof washer		100,000^a	5% ^c
Screens	20.5^a		5% ^c	Outdoor shade screen, indoor thermal screen
Heating pipes	5.5^b		0.5% ^c	rail heating pipes and growing heating pipes
Boiler		106,700^a	1% ^c
Growing gutters	14 ^a		2% ^c	Hanging gutter system
Irrigation system	5.8 ^b		5% ^c	Drip irrigation, fertilizer dosing system, water collection tank
CO2 distribution system	0.5^a		5% ^c
Fogging system	3.1^a		5% ^c	High-pressure fogging
Pad and fan	3.7^a
Electrical power system	3.3^a		5% ^c	Power distribution system, including wires, circuits, electric panels, etc.
Climate computer		200,000 ^a	8% ^c	Including outdoor climate monitor system and indoor climate control system
Internal transport		9,333 ^a	5% ^c	Harvest trolly (x3)
Remaining costs	6.8 ^a			Costs related to technical room, ground foil, etc.
Installation and transportation	12 ^a			Labour and transportation costs incurred for equipment installation
Total	158.7

a. Source: Investment budget of a commercial tomato glasshouse in Jinshan, Shanghai

b. Source: Vanthoor et al. (2012)

c. Source: Raaphorst et al. (2019)

Table 2. Simulated mean tomato yield and gas use under different temperature management strategies for four regions.

TempShift	Jinshan		Langfang		Weifang		Pingliang
	Yield kg m^−2	Gas use m^3 m^−2	Yield kg m^−2	Gas use m^3 m^−2	Yield kg m^−2	Gas use m^3 m^−2	Yield kg m^−2	Gas use m^3 m^−2
0°C (reference)	18.01	19.54	20.37	27.24	20.57	25.60	25.07	22.45
+1°C	18.39	22.56	20.85	29.77	21.00	28.14	25.59	25.64
−1°C	17.57	17.08	19.78	24.88	20.06	23.46	24.33	20.28
−2°C	17.10	14.42	19.16	22.69	19.46	21.08	23.41	17.67

Table 3. Monthly cherry tomato wholesale prices (¥ kg⁻¹) for 2021.

Month	Jinshan^1	Langfang	Weifang	Pingliang^2
Jan	12.00	15.23	10.86	14.01
Feb	11.18	14.60	12.99	13.43
Mar	9.62	16.91	12.66	15.56
Apr	11.03	15.93	11.12	14.66
May	13.55	14.43	9.05	13.28
Jun	11.63	12.78	6.75	11.76
Jul	11.81	11.58	8.58	10.65
Aug	12.84	11.43	9.12	10.52
Sep	14.19	11.79	9.93	10.85
Oct	15.09	12.35	11.60	11.36
Nov	17.82	17.18	12.84	15.80
Dec	18.71	17.96	13.68	16.52

Source: National commercial information platform of agricultural products (nc.mofcom.gov.cn).

Table 4. Parameters (min, mode, max) for natural gas prices (¥ m⁻³) simulation using triangular distributions.

Month	Jinshan^3	Langfang	Weifang	Pingliang
Jan	2.74, 5.12, 7.01	2.36, 3.23, 8.44	2.88, 4.01, 7.01	1.76, 2.61, 9.63
Feb	3.36, 3.56, 8.33	3.04, 3.36, 6.66	3.24, 3.32, 7.25	2.68, 2.68, 7.68
Mar	3.24, 3.25, 5.14	3.06, 3.25, 4.83	3.15, 3.56, 5.37	2.90, 2.92, 4.44
Apr	2.54, 3.61, 4.20	3.05, 3.10, 4.17	2.95, 3.73, 4.39	2.70, 2.94, 4.01
Oct	2.12, 3.30, 7.95	2.68,2.94, 7.77	2.36, 3.54, 7.79	2.45, 3.35, 7.62
Nov	3.44, 3.78, 8.85	3.66, 3.75, 8.58	3.62, 3.79, 8.73	3.62, 3.73, 8.70
Dec	3.36, 3.44, 8.68	2.65, 4.35, 7.54	3.21, 3.35, 8.80	1.79, 4.25, 9.24

Note: Prices for LNG given by SHPGX are in the unit ¥ ton⁻¹. One ton of LNG was converted to 1380 m³ of natural gas in this study.

Table 5. Simulated net cash flow and NPV of tomato glasshouse under different temperature management strategies for four regions.

TempShift	Indicator	Unit	Jinshan	Langfang	Weifang	Pingliang
0°C (reference)	Mean net cash flow	¥ m^−2 year^−1	48.9	102.0	17.1	143.3
	Mean NPV	¥ m^−2	−593.7	29.4	−957.8	477.0
	5% NPV	¥ m^−2	−829.4	−301.2	−1231.0	182.0
	95%NPV	¥ m^−2	−355.5	347.8	−705.0	749.3
+1°C	Mean net cash flow	¥ m^−2 year^−1	44.5	102.9	15.9	140.5
	Mean NPV	¥ m^−2	−654.1	10.8	−1018.3	439.2
	5% NPV	¥ m^−2	−902.5	−334.8	−1302.5	123.7
	95% NPV	¥ m^−2	−398.2	343.9	−749.2	728.6
−1°C	Mean net cash flow	¥ m^−2 year^−1	54.0	104.3	23.8	138.3
	Mean NPV	¥ m^−2	−546.1	26.5	−888.8	472.3
	5% NPV	¥ m^−2	−768.6	−284.5	−1147.1	194.2
	95% NPV	¥ m^−2	−327.0	331.0	−647.5	727.0
−2°C	Mean net cash flow	¥ m^−2 year^−1	57.9	103.7	24.4	139.8
	Mean NPV	¥ m^−2	−501.1	19.8	−875.1	430.7
	5% NPV	¥ m^−2	−707.6	−267.4	−1112.7	174.2
	95% NPV	¥ m^−2	−300.7	310.6	−644.8	671.8

Table 6. Standardized regression coefficients between the ranked net cash flow and the ranked tomato and natural gas prices.

Variable	Jinshan	Langfang	Weifang	Pingliang
Tomato prices
Jan	0.15	0.11	0.09	n/a
Feb	0.13	0.09	0.10	n/a
Mar	0.09	0.12	0.11	0.07
Apr	0.14	0.15	0.12	0.17
May	0.22	0.16	0.11	0.12
Jun	0.19	0.15	0.09	0.09
Jul	n/a	(0. 02)	(0.01)	0.13
Aug	n/a	n/a	n/a	0.13
Sep	n/a	n/a	n/a	0.11
Oct	n/a	n/a	n/a	0.11
Nov	n/a	(0.01)	(0.02)	0.15
Dec	0.14	0.13	0.11	0.06
Natural gas prices
Jan	−0.48	−0.66	−0.47	−0.29
Feb	−0.50	−0.28	−0.36	−0.28
Mar	−0.17	−0.10	−0.14	−0.12
Apr	−0.07	−0.02	−0.03	−0.06
Oct	n/a	−0.02	n/a	−0.29
Nov	n/a	−0.32	−0.30	−0.21
Dec	−0.56	−0.48	−0.65	−0.45

Notes: statistically insignificant coefficients (p > 0.05) are reported in parenthesis. n/a indicates a month without yield or natural gas use.

Figure 2. Mean NPV of a 1.4-hectare tomato glasshouse under different tomato and gas price changes for Pingliang, without and with 50% subsidy on the initial investment costs

Table A1. Actual information of the glasshouse in Jinshan and the parameters used for model calibration.

Item	Actual information	Description
Area	8000 m^−2	✓
Gutter height	6.5 m	✓
Orientation	EW, 0 degree	✓
Cover	Normal glass, no whitewash	✓
Heating	Boiler (liquid natural gas), no heat storage; Two heating pipes	maximum supply temperature of heating pipes: 60°C for pipe1, 50°C for pipe 2.
Screens	Thermal screen (Svensson Luxous 1347 FR) + interior shading	The thermal screens may be used when the outside temperature is below 12°C. When the outside temperature goes below 0°C, the thermal screen will be kept deployed until the radiation is above 200 w m^−2. When the outside temperature is above 10°C, the thermal screen is only closed when the radiation is below 5 w m^−2. The shading screen will be half closed when the radiation exceeds 600 w m^−2 and fully closed when the radiation exceeds 800 w m^−2.
Cooling	Pad & fan, no fogging. No dehumidification	The maximum amount of outside air sucked in is 100 m3 m^−2 h^−1. The fans will run on the maximum capacity when the difference between the air-temperature and ventilation setpoint excesses 2°C. The air can be cooled down to 0.85 of the wet-bulb temperature. The outlet temperature is 15°C above the average greenhouse temperature.
Illumination	No	✓
Heating period	11/29/2019–04/23/2020	✓
Heating setpoints	No data (climate computer gives abnormal data)	Inferred from realized indoor temperature
Ventilation setpoints	No data	The maximum opening of vents is 100. When the outside temperature is below 5°C, the p-band is 15°C; when the outside temperature is higher than 20°C, the p-band is 4°C. p-band is a key parameter that controls how large the temperature excess has to be before the leeward vents are fully opened. The windward vents only open when the leeward vents are opened above 50%.
CO2	Censor data, mean CO2 = 550 ppm	✓
Transplant date	10/01/2019	✓
Stem density	2.06 (10/01/2019) 2.58 (since 10/22/2019)	✓
Topping date	05/20/2020	✓
Last harvest date	03/06/2020	✓
Nr. fruit per truss	Initially 8, reduced to 6 and increased to 8 eventually	Fruit number per truss was calibrated accordingly
Dry matter content	5%–7%, depend on the development stage. Eventually stabilized around 6%	Dry matter content was set at 6%

Remark: ✓ means that the underlying parameter reflects the exact glasshouse specification, climate or crop management strategies in the actual production.

Table A2. Summary of simulated and actual harvest.

Yield kg m^−2 year^−1	Actual	Simulation
Dec	1.727	1.933
Jan	2.121	2.859
Feb	1.209	1.908
Mar	1.433	2.005
Apr	3.106	2.989
May	3.321	4.160
Jun	0.634^2	0.325
Total	13.55	16.18

Table A3. Summary of the actual and simulated energy use for the Jinshan glasshouse.

Item	Value per year
Actual gas use	12.2 m^3 m^−2
Adjusted actual gas use	12.82 m^3 m^−2
Simulated gas use	15.5 m^3 m^−2
Actual electricity use (everything included)	7.3 kWh m^−2
Actual electricity use (only for glasshouses)	5.67 kWh (estimation, 70%*8.1)
Simulated electricity use (only for heat pumps, air-blower of boiler, pad & fan)	4.3 kWh

Table B1. Calculation of weighted average cost of capital.

Cost of debt	Rate	Source
Cost of debt (Rd)	5.5%	The People’s Bank of China (2022), with the assumption of 20% floating rate on the 5-year Loan Prime Rate of 2022 (4.6%)
Marginal tax rate (T)	0.0%	Enterprise Income Tax Law of the People’s Republic of China, article 27 (2007)
Cost of debt after tax shield	5.5%
Cost of equity
Risk free rate ($Log{X}_{ads}=LogK+nlogc$)	2.79%	China 10-year government bond yield (Ministry of Finance of the People’s Republic of China, 2022)
Market risk premium of China	4.94%	(Damodaran, 2022a)
Beta for farming sector in China	0.79	(Damodaran, 2022b)
Cost of equity	6.69%	Calculated
Capital structure	Ratio
Debt (D)	50%	Authors’ assumption
Equity (E)	50%	Authors’ assumption
WACC	6.10%	Calculated

Table C1. Climate characteristics, cropping and heating schedules of four regions.

	Jinshan	Langfang	Weifang	Pingliang
Longitude	30°49ʹ51.96” N	39°30ʹ34.99” N	36° 42ʹ24.39” N	35°32ʹ21.01” N
Latitude	121°20ʹ38.40” E	116°41ʹ40.99” E	119° 9ʹ 42.33” E	106°41ʹ10.00” E
Gross radiation of the year (MJ m^−2)	5383.1	5728.5	5734.5	5586.4
Gross radiation in Dec, Jan, Feb (MJ m^−2)	843.2	867.0	879.2	933.6
Average temperature in Jan (°C)	6.03	−3.05	−1.24	−2.94
Average temperature in Jul (°C)	28.05	27.63	26.64	22.63
Average humidity in Jul (%)	83.59	70.18	75.54	67.67
Transplanting date	Oct 1	Sep 15	Sep 15	Jan 1
Heating start date	Dec 1	Oct 25	Nov 5	Oct 10
Heating end date	Apr 20, next year	Apr 13, next year	Apr 20, next year	Apr 30
Final harvest date	Jul 1, next year	Jul 10, next year	Jul 10, next year	Dec 15
Weeks of production	39	43	43	50

Table D1. Parameters related to other production costs.

Parameter	Unit	Values
Natural gas use ^a	m^3 m^−2 month^−1	see Table 5
Electricity use ^a	kWh m^−2 month^−1	Jinshan: 7.24 Langfang: 7.43 Weifang: 7.56 Pingliang: 6.23
Wage ^b	¥ hour^−1	Jinshan: 16.9 Langfang: 13.3 Weifang: 11.5 Pingliang: 9.35
Harvest labor hour ^c	hour kg^−1 year^−1	0.032
Non-harvest labor hour ^c	hour m^−2 year^−1	0.64
Land rent ^b	¥ m^−2 year^−1	Jinshan: 1.06 Langfang: 0.63 Weifang: 0.58 Pingliang: 0.50
Water ^c	¥ m^−2	3.33
Electricity ^c	¥ kWh^−1	0.682
Fertilizers ^c	¥ m^−2 year^−1	7.15
Rockwool ^c	¥ m^−2 year^−1	11.88
Seedlings ^c	¥ m^−2 year^−1	1.38
Other material ^c	¥ m^−2 year^−1	3.26

^asource: Simulation outputs of INTKAM-KASPRO model.

^bsource: National Agricultural Products Cost-benefit Data Compilation-2020 (2020).

^csource: Accountancy data from a cherry tomato glasshouse in Jinshan.

Figure E1. Mean NPV of a 1.4-hectare tomato glasshouse under different tomato and gas price changes for Jinshan, without and with 50% subsidy on the initial investment costs.

Figure E2. Mean NPV of a 1.4-hectare tomato glasshouse under different tomato and gas price changes for Langfang, without and with 50% subsidy on the initial investment costs.

Figure E3. Mean NPV of a 1.4-hectare tomato glasshouse under different tomato and gas price changes for Weifang, without and with 50% subsidy on the initial investment costs.

Table E1. Break-even tomato and natural gas price levels of the four regions.

Break-even prices	Without subsidy ssusubsidsubsidy	50% subsidy
Break-even tomato price level
Jinshan	23.7%	0.9%
Langfang	−0.6%	−17.2%
Weifang	39.6%	15.8%
Pingliang	−13.1%	−29.2%
Break-even natural gas price level
Jinshan	−58.1%	−2.2%
Langfang	1.43%	42.4%
Weifang	−70.6%	−28.2%
Pingliang	41.9%	93.4%

Break-even tomato (natural gas) price level refers to the percent change in the current tomato (natural gas) prices to make the glasshouse investment break even (mean NPV = 0), keeping the current natural gas (tomato) prices level unchanged.

Vanthoor, B. H. E., Stigter, J. D., van Henten, E. J., Stanghellini, C., De Visser, P. H. B., & Hemming, S. (2012). A methodology for model-based greenhouse design: Part 5, greenhouse design optimisation for southern-Spanish and Dutch conditions. Biosystems Engineering, 111(4), 350–368. https://doi.org/10.1016/j.biosystemseng.2012.01.005

 Web of Science ®Google Scholar

Raaphorst, M.G., Benninga, J., & Eveleens, B. A. (2019). Quantitative information on Dutch greenhouse horticulture 2019. Report WPR-898 (26th). Wageningen: Wageningen Plant Research, Business Unit Greenhouse Horticulture.

 Google Scholar

National Development and Reform Commission (Ed.). (2020) . National agricultural products cost-benefit data compilation-2020. China Statistics Press.

 Google Scholar