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The ability of CROPGRO-Tomato model to simulate the growth characteristics of Thomas F1 tomato cultivar grown under open field conditions

Published online by Cambridge University Press:  24 November 2021

N. Muntean ,
R. A. Chawdhery ,
V. Potopová Open the ORCID record for V. Potopová [Opens in a new window]  and
L. Tűrkott
N. Muntean
Affiliation:
Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Czech Republic
R. A. Chawdhery
Affiliation:
Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Czech Republic
V. Potopová*
Affiliation:
Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Czech Republic
L. Tűrkott
Affiliation:
Department of Agroecology and Crop Production, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Czech Republic
*
Author for correspondence: V. Potopová, E-mail: [email protected]
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Abstract

There are few studies about the ability of CROPGRO-Tomato model to simulate tomato growth under field conditions as a function of both local weather and soil conditions. The aim of this work was to calibrate the CROPGRO-Tomato model, included in the Decision Support System for Agrotechnology Transfer (DSSAT) software, for the Thomas F1 indeterminate tomato cultivar grown under open field conditions at two locations in the Czech Republic with different soil and climate conditions. Additionally, this paper focuses on modelling the impact of compound weather events (CEs) on the growth characteristics of the hybrid field tomato variety. The genotype file, including the main parameters of crop phenology and plant growth, was adapted to the Thomas F1 indeterminate tomato cultivar. The CROPGRO-Tomato model was calibrated by inputting the soil characteristics, weather data and crop management data and then by adjusting the genetic coefficients to simulate the observed Leaf Area Index (LAI) and Above Ground Biomass (AGB) from transplanting to harvest under the farmers' field conditions. The comparison of the LAI simulated by the model and measured under field conditions showed adequate representation with the root mean square error of 0.86 and 1.11 m2/m2. Although there was a good fit for LAI and AGB between the simulated and measured data during the first part of the growing season, increasing differences were found in the growing season with cool-wet and/or hot-dry thresholds of CEs.

Keywords

Compound weather eventscrop simulation modelDSSATSolanum lycopersicum L
Type
Climate Change and Agriculture Research Paper
Information
The Journal of Agricultural Science , Volume 159 , Issue 7-8 , September 2021 , pp. 473 - 487
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Alam, MS, Sultana, N, Ahmad, S, Hossain, MM and Islam, A (2010) Performance of heat tolerant tomato hybrid lines under hot, humid conditions. Bangladesh Journal of Agricultural Research 35, 367–373.CrossRefGoogle Scholar
Angstrom, A (1924) Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quarterly Journal of the Royal Meteorological Society 50, 121–126.CrossRefGoogle Scholar
Ayankojo, IT and Morgan, KT (2020) Increasing air temperatures and its effects on growth and productivity of tomato in South Florida. Plants 9, 1245.CrossRefGoogle ScholarPubMed
Bailey, KJ and Leegood, RC (2016) Nitrogen recycling from the xylem in rice leaves: dependence upon metabolism and associated changes in xylem hydraulics. Journal of Experimental Botany 67, 2901–2911.CrossRefGoogle ScholarPubMed
Bianculli, ML, Aguirrezábal, LAN, Pereyra Irujo, GA and Echarte, MM (2017) Contribution of incident solar radiation on leaves and pods to soybeanseed weight and composition. European Journal of Agronomy 77, 1–9.CrossRefGoogle Scholar
Boote, KJ (2017) Modelling crop growth and yield in tomato cultivation. In Mattoo, A and Handa, A (eds). Achieving sustainable cultivation of tomatoes. Cambridge: Burleigh Dodds Science Publishing Limited, pp. 3–22.CrossRefGoogle Scholar
Boote, KJ, Rybak, MR, Scholberg, JMS and Jones, JW (2012) Improving the CROPGRO-Tomato model for predicting growth and yield response to temperature. HortScience 47, 1038–1049.CrossRefGoogle Scholar
Deligios, PA, Cossu, M, Murgia, L, Sirigu, A, Urracci, G, Pazzona, A, Pala, T and Ledda, L (2016) Modeling tomato growth and production in a photovoltaic greenhouse in southern Italy. In V International Symposium on Models for Plant Growth, Environment Control and Farming Management in Protected Cultivation, 1182, pp. 203–210.Google Scholar
Feller, C, Bleiholder, H, Buhr, L, Hack, H, Hess, M, Klose, R, Meier, U, Stauss, R, Boom, T van den, and Weber, E (1995) Phenological growth stages of vegetable crops. II. Fruit vegetables and pulses. Coding and description according to the extended BBCH scale – with illustrations. Nachrichtenblatt des Deutschen Pflanzenschutzdienstes 47, 217–232.Google Scholar
Gary, C, Baille, A, Navarrete, M and Espanet, R (1996) TOMPOUSSE, un modèle simplifié de prévision du rendement et du calibre de la tomate. In Séminaire.Google Scholar
Gary, C, Jones, JW and Tchamitchian, M (1998) Crop modelling in horticulture: state of the art. Scientia Horticulturae 74, 3–20.CrossRefGoogle Scholar
Gijzen, H, Heuvelink, E, Challa, H, Marcelis, LFM, Dayan, E, Cohen, S and Fuchs, M (1997) HORTISIM: a model for greenhouse crops and greenhouse climate. II Modelling Plant Growth. Environmental Control and Farm Management in Protected Cultivation 456, 441–450.Google Scholar
Heuvelink, E and Bertin, N (1994) Dry-matter partitioning in a tomato crop: comparison of two simulation models. Journal of Horticultural Science 69, 885–903.CrossRefGoogle Scholar
Heuvelink, E and Dorais, M (2005) Crop growth and yield. Crop Production Science in Horticulture 13, 85–144.Google Scholar
Hlavinka, P, Kersebaum, KC, Dubrovský, M, Fischer, M, Pohanková, E, Balek, J, Žalud, Z and Trnka, M (2015) Water balance, drought stress and yields for rainfed field crop rotations under present and future conditions in the Czech Republic. Climate Research 65, 175–192.CrossRefGoogle Scholar
Hoogenboom, G, Porter, CH, Boote, KJ, Shelia, V, Wilkens, PW, Singh, U, White, JW, Asseng, S, Lizaso, JI, Moreno, LP, Pavan, W, Ogoshi, R, Hunt, LA, Tsuji, GY and Jones, JW (2019) Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.5 (www.DSSAT.net). DSSAT Foundation, Gainesville, Florida, USA.Google Scholar
Jones, JW, Dayan, E, Allen, LH, Keulen, H and van and Challa, H (1991) A dynamic tomato growth and yield model (TOMGRO). Transactions of the ASAE 34, 663–672.CrossRefGoogle Scholar
Jones, JW, Hoogenboom, G, Porter, CH, Boote, KJ, Batchelor, WD, Hunt, LA, Wilkens, PW, Singh, U, Gijsman, AJ and Ritchie, JT (2003) DSSAT cropping system model. European Journal of Agronomy 18, 235–265.CrossRefGoogle Scholar
Khan, TA, Fariduddin, Q and Yusuf, M (2015) Lycopersicon esculentum under low temperature stress: an approach toward enhanced antioxidants and yield. Environmental Science and Pollution Research International 22, 14178–14188.CrossRefGoogle ScholarPubMed
Meena, YK, Khurana, DS, Kaur, N and Singh, K (2018) Towards enhanced low temperature stress tolerance in tomato: an approach. Journal of Environmental Biology 39, 529–535.CrossRefGoogle Scholar
Ogweno, JO, Song, XS, Hu, WH, Shi, K, Zhou, YH and Yu, JQ (2009) Detached leaves of tomato differ in their photosynthetic physiological response to moderate high and low temperature stress. Scientia Horticulturae 123, 17–22.CrossRefGoogle Scholar
Ozores-Hampton, M, Kiran, F and McAvoy, G (2012) Blossom drop, reduced fruit set, and post-pollination disorders in tomato. EDIS, 2012.Google Scholar
Potopová, V and Tűrkott, L (2014) Agronomic evidence as input data for CROPGRO vegetables model. Úroda 62, 405–408.Google Scholar
Potopová, V, Štěpánek, P, Farda, A, Türkott, L, Zahradníček, P and Soukup, J (2016) Drought stress impact on vegetable crop yields in the Elbe River lowland between 1961 and 2014. Cuadernos de Investigación Geográfica 42, 127–143.CrossRefGoogle Scholar
Potopová, V, Zahradníček, P, Štěpánek, P, Türkott, L, Farda, A and Soukup, J (2017 a). The impacts of key adverse weather events on the field-grown vegetable yield variability in the Czech Republic from 1961 to 2014. International Journal of Climatology 37, 1648–1664.CrossRefGoogle Scholar
Potopová, V, Tűrkott, L and Hiřmanová, D (2017 b) Využití modelu CROPGRO-Tomato pro simulaci růstových parametrů rajčete jedlého v polních podmínkách Polabí. In Rožnovský, J., Litschmann, T. (eds): Mrazy a jejich dopady Hrubá Voda 26.–27. 4. 2017, ISBN 978-80-87577-69-1.Google Scholar
Potopová, V, Štěpánek, P, Zahradníček, P, Farda, A, Türkott, L and Soukup, J (2018) Projected changes in the evolution of drought on various timescales over the Czech Republic according to Euro-CORDEX models. International Journal of Climatology 38, 939–954.CrossRefGoogle Scholar
Potopová, V, Lhotka, O, Možný, M and Musiolková, M (2021) Vulnerability of hop-yields due to compound drought and heat events over European key-hop regions. International Journal of Climatology 41, 2136–2158.CrossRefGoogle Scholar
Priestley, CHB and Taylor, RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100, 81–92.2.3.CO;2>CrossRefGoogle Scholar
Sauviller, C, Baets, W, Pien, H and Lemeur, R (2002) SIMULTOM: a diagnostic tool for greenhouse tomato production. In IV International Symposium on Models for Plant Growth and Control in Greenhouses: Modeling for the 21st Century-Agronomic, 593, pp. 219–226.Google Scholar
Scholberg, JMS, Boote, KJ, Jones, JW and McNeal, BL (1997) Adaptation of the CROPGRO model to simulate the growth of field-grown tomato. In Kropff, MJ, Teng, PS, Aggarwal, PK, Bouma, J, Bouman, BAM Jones, JW and van Laar, HH (eds), Applications of Systems Approaches at the Field Level: Volume 2 Proceedings of the Second International Symposium on Systems Approaches for Agricultural Development, Held at IRRI, Los Baños, Philippines, 6–8 December 1995 Systems Approaches for Sustainable Agricultural Development. Dordrecht: Springer Netherlands, pp. 135–151.CrossRefGoogle Scholar
Swann, ALS, Hoffman, FM, Koven, CD and Randerson, JT (2016) Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proceedings of the National Academy of Sciences of the USA 113, 10019–10024.CrossRefGoogle ScholarPubMed
Van der Sande, MT, Claros, MP, Ascarrunz, N, Arets, EJMM, Licona, JC, Toledo, M and Poorter, L (2017) Abiotic and biotic drivers of biomass change in a Neotropical forest. Journal of Ecology 105, 1223–1234.CrossRefGoogle Scholar
Ventrella, D, Charfeddine, M, Giglio, L and Castellini, M (2012) Application of DSSAT models for an agronomic adaptation strategy under climate change in Southern of Italy: optimum sowing and transplanting time for winter durum wheat and tomato. Italian Journal of Agronomy 7, 109–115.CrossRefGoogle Scholar
Young, LW, Wilen, RW and Bonham-Smith, PC (2004) High temperature stress of Brassica napus during flowering reduces micro- and megagametophyte fertility, induces fruit abortion, and disrupts seed production. Journal of Experimental Botany 55, 485–495.CrossRefGoogle ScholarPubMed
Zscheischler, J, Orth, R and Seneviratne, SI (2017) Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields. Biogeosciences 14, 3309–3320.CrossRefGoogle Scholar