Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-25T06:39:06.741Z Has data issue: false hasContentIssue false

Evaluation of AquaCrop model of cucumber under greenhouse cultivation

Published online by Cambridge University Press:  24 June 2021

H. Khafajeh
Affiliation:
Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran
A. Banakar*
Affiliation:
Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran
S. Minaei
Affiliation:
Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran
M. Delavar
Affiliation:
Department of Water Resources Management, Tarbiat Modares University, Tehran, Iran
*
Author for correspondence: A. Banakar, E-mail: [email protected]

Abstract

Water consumption in agriculture is impossible without considering relations between water, soil and plant. In this regard, there are various models and developed software in order to evaluate relation between soil, water and crop growth stages. These models can be used for irrigation planning if properly optimized and applied. AquaCrop is one of the known crop models, which was developed by the Food and Agriculture Organization of the United Nations. In order to optimize this model for crop production and irrigation management, an experiment was developed in a hydroponic cucumber greenhouse. Various parameters including water consumption volume, crop yield and leaf area index were measured during a season. A fuzzy control system was utilized for controlling temperature, relative humidity, planting bed moisture, light intensity and carbon dioxide values. The main purpose of designing a control system in the greenhouse is to achieve the desired values of temperature and relative humidity. In this model, evapotranspiration, irrigation requirements and crop yield were simulated. The results show that the AquaCrop model can estimate evapotranspiration with the least error in the greenhouse environment, which is controlled by a fuzzy controller. Also the system has estimated the crop yield and biomass of the product with a good degree of precision and it may support crop production in a greenhouse, including crop management and environmental control.

Type
Crops and Soils Research Paper
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdalhi, MAM, Jia, Z, Luo, W, Ali, OO and Chen, C (2020) Simulation of canopy cover, soil water content and yield using FAO-AquaCrop model under deficit irrigation strategies. Russian Agricultural Sciences 46, 279288.CrossRefGoogle Scholar
Abdelkhalik, A, Pascual-Seva, N, Nájera, I, Giner, A, Baixauli, C and Pascual, B (2019) Yield response of seedless watermelon to different drip irrigation strategies under Mediterranean conditions. Agricultural Water Management 212, 99110.CrossRefGoogle Scholar
Aldrich, RA and Bartok, JW (1989) Norteast Regional Agricultural Engineering Service. New York: Coop. Extension, p. 203.Google Scholar
Ali, RB, Bouadila, S and Mami, A (2018) Development of a fuzzy logic controller applied to an agricultural greenhouse experimentally validated. Applied Thermal Engineering 141, 798810.Google Scholar
Anar, MJ, Lin, Z, Hoogenboom, G, Shelia, V, Batchelor, WD, Teboh, JM, Ostlie, M, Schatz, BG and Khan, M (2019) Modeling growth, development and yield of sugarbeet using DSSAT. Agricultural Systems 169, 5870.CrossRefGoogle Scholar
Bacci, L, Battista, P and Rapi, B (2012) Evaluation and adaptation of TOMGRO model to Italian tomato protected crops. New Zealand Journal of Crop and Horticultural Science 40, 115126.CrossRefGoogle Scholar
Biswal, A, Chakraborty, A and Murthy, CS (2021) Spatialization of crop growth simulation model using remote sensing. In Mitran, T, Meena, RS and Chakraborty, A (eds). Geospatial Technologies for Crops and Soils. Singapore: Springer, pp. 153199. https://doi.org/10.1007/978-981-15-6864-0_4.CrossRefGoogle Scholar
Boote, KJ, Jones, JW and Pickering, NB (1996) Potential uses and limitations of crop models. Agronomy Journal 88, 704716.CrossRefGoogle Scholar
Carlson, B, Sommer, R, Paul, BK, Muli, M and Stöckle, C (2016) Enhancing CropSyst for Intercropping Modeling. Berlin: iCrop, MILRI.Google Scholar
Foster, T, Brozović, N, Butler, AP, Neale, CMU, Raes, D, Steduto, P, Fereres, E and Hsiao, TC (2017) AquaCrop-OS: an open source version of FAO's crop water productivity model. Agricultural Water Management 181, 1822.CrossRefGoogle Scholar
García-Gutiérrez, V, Stöckle, C, Gil, PM and Meza, FJ (2021) Evaluation of Penman–Monteith model based on Sentinel-2 data for the estimation of actual evapotranspiration in vineyards. Remote Sensing 13, 478.CrossRefGoogle Scholar
Hoekstra, AY, Mekonnen, MM, Chapagain, AK, Mathews, RE and Richter, BD (2012) Global monthly water scarcity: blue water footprints versus blue water availability. PLoS One 7, e32688.CrossRefGoogle ScholarPubMed
Holzworth, D, Huth, NI, Fainges, J, Brown, H, Zurcher, E, Cichota, R, Verrall, S, Herrmann, NI, Zheng, B and Snow, V (2018) APSIM next generation: overcoming challenges in modernising a farming systems model. Environmental Modelling & Software 103, 4351.CrossRefGoogle Scholar
Hsiao, TC, Heng, L, Steduto, P, Rojas-Lara, B, Raes, D and Fereres, E (2009) AquaCrop – the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal 101, 448459.CrossRefGoogle Scholar
Jadhav, PB, Thokal, RT, Kadam, SA and Gorantiwar, SD (2018) Evaluation of AquaCrop model for irrigation planning in command area under changing climate.CrossRefGoogle Scholar
Janoudi, AK, Widders, IE and Flore, JA (1993) Water deficits and environmental factors affect photosynthesis in leaves of cucumber (Cucumis sativus). American Society for Horticultural Science 118, 366370.CrossRefGoogle Scholar
Jones, JW, Hoogenboom, G, Porter, CH, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, AJ and Ritchie, JT (2003) The DSSAT cropping system model. European Journal of Agronomy 18, 235265.CrossRefGoogle Scholar
Jones, JW, Antle, JM, Basso, B, Boote, KJ, Conant, RT, Foster, I, Godfray, HCJ, Herrero, M, Howitt, RE and Janssen, S (2017) Brief history of agricultural systems modeling. Agricultural Systems 155, 240254.CrossRefGoogle ScholarPubMed
Karbalaee, F (2010) Water crisis in Iran. In 2010 International Conference on Chemistry and Chemical Engineering. IEEE, pp. 398400.CrossRefGoogle Scholar
Karimi, S, Egdernezhad, A and Nakhjavanimoghaddam, MM (2021) Assessing AquaCrop model accuracy for simulation of corn yield and water use efficiency in different plant densities and water amount. Journal of Environment and Water Engineering 7, 5972.Google Scholar
Keating, BA, Carberry, PS, Hammer, GL, Probert, ME, Robertson, MJ, Holzworth, D, Huth, NI, Hargreaves, JNG, Meinke, H and Hochman, Z (2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267288.CrossRefGoogle Scholar
Lafont, F and Balmat, J-F (2002) Optimized fuzzy control of a greenhouse. Fuzzy Sets and Systems 128, 4759.CrossRefGoogle Scholar
Maniruzzaman, M, Talukder, MSU, Khan, MH, Biswas, JC and Nemes, A (2015) Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh. Agricultural Water Management 159, 331340.CrossRefGoogle Scholar
Marcelis, LFM, Elings, A, De Visser, PHB and Heuvelink, E (2008) Simulating growth and development of tomato crop, in: International Symposium on Tomato in the Tropics 821, pp. 101110.Google Scholar
Mekonnen, MM and Hoekstra, AY (2016) Four billion people facing severe water scarcity. Science Advances 2, e1500323.CrossRefGoogle ScholarPubMed
Möller, M, Tanny, J, Li, Y and Cohen, S (2004) Measuring and predicting evapotranspiration in an insect-proof screenhouse. Agricultural and Forest Meteorology 127, 3551.CrossRefGoogle Scholar
Momeni, D, Banakar, A, Ghobadian, B and Minaei, S (2015) Applications of PCMs and solar energy for greenhouse heating. Journal of Energy and Environmental Research and Technology 2, 13.Google Scholar
Nouri, H, Stokvis, B, Galindo, A, Blatchford, M and Hoekstra, AY (2019) Water scarcity alleviation through water footprint reduction in agriculture: the effect of soil mulching and drip irrigation. Science of the Total Environment 653, 241252.CrossRefGoogle ScholarPubMed
Pohanková, E, Hlavinka, P, Orság, M, Takáč, J, Kersebaum, KC, Gobin, A and Trnka, M (2018) Estimating the water use efficiency of spring barley using crop models. Journal of Agricultural Science 156, 628644.CrossRefGoogle Scholar
Raeisi, LG, Morid, S, Delavar, M and Srinivasan, R (2019) Effect and side-effect assessment of different agricultural water saving measures in an integrated framework. Agricultural Water Management 223, 105685.CrossRefGoogle Scholar
Raes, D, Steduto, P, Hsiao, TC and Fereres, E (2009) AquaCrop – the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agronomy Journal 101, 438447.CrossRefGoogle Scholar
Razzaghi, F, Zhou, Z, Andersen, MN and Plauborg, F (2017) Simulation of potato yield in temperate condition by the AquaCrop model. Agricultural Water Management 191, 113123.CrossRefGoogle Scholar
Revathi, S and Sivakumaran, N (2016) Fuzzy based temperature control of greenhouse. IFAC-PapersOnLine 49, 549554.CrossRefGoogle Scholar
Saito, T, Mochizuki, Y, Kawasaki, Y, Ohyama, A and Higashide, T (2020) Estimation of leaf area and light-use efficiency by non-destructive measurements for growth modeling and recommended leaf area Index in greenhouse tomatoes. Journal of Horticulture UTD-171 89, 445453.CrossRefGoogle Scholar
Soomro, KB, Alaghmand, S, Shahid, MR, Andriyas, S and Talei, A (2020) Evaluation of AquaCrop model in simulating bitter gourd water productivity under saline irrigation. Irrigation and Drainage 69, 6373.CrossRefGoogle Scholar
Spitters, CJT, Van Keulen, H and Van Kraalingen, DWG (1989) A simple and universal crop growth simulator: SUCROS87. In Rabbinge, R, Ward, SA and van Laar, HH (eds). Simulation and Systems Management in Crop Protection. Wageningen: Pudoc, pp. 147181 (Simulation monographs).Google Scholar
Stanghellini, C (1987) Transpiration of greenhouse crops: an aid to climate management (Doctoral dissertation, IMAG), Wageningen.Google Scholar
Steduto, P, Hsiao, TC and Fereres, E (2007) On the conservative behavior of biomass water productivity. Irrigation Science 25, 189207.CrossRefGoogle Scholar
Steduto, P, Hsiao, TC, Raes, D and Fereres, E (2009) AquaCrop – the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal 101, 426437.CrossRefGoogle Scholar
Stockle, C, Donatelli, M and Nelson, R (2003) CropSyst, a cropping systems simulation model. European Journal of Agronomy 18, 289307. doi: https://doi.org/10.1016/S1161-0301(02)00109-0.CrossRefGoogle Scholar
Vanuytrecht, E, Raes, D, Steduto, P, Hsiao, TC, Fereres, E, Heng, LK, Vila, MG and Moreno, PM (2014) AquaCrop: FAO's crop water productivity and yield response model. Environmental Modelling & Software 62, 351360.CrossRefGoogle Scholar
Xu, J, Bai, W, Li, Y, Wang, H, Yang, S and Wei, Z (2019) Modeling rice development and field water balance using AquaCrop model under drying-wetting cycle condition in eastern China. Agricultural Water Management 213, 289297.CrossRefGoogle Scholar
Yang, K-W, Chapman, S, Carpenter, N, Hammer, G, McLean, G, Zheng, B, Chen, Y, Delp, E, Masjedi, A and Crawford, M (2021) Integrating crop growth models with remote sensing for predicting biomass yield of sorghum. in silico Plants 3, 119.CrossRefGoogle Scholar
Yau, J, JianWei, J, Wang, H, Eniola, O and Ibitoye, FP (2020) Modelling of ventilation rate and heating rate using multi-module fuzzy control system for a greenhouse. European Journal of Engineering and Technology 5, 800806.CrossRefGoogle Scholar