Predicting tomato yield in central Europe through integration of field experiments, DSSAT-Cropgro-Tomato simulations and machine learning

POTOPOVÁ, Vera; TRIFAN, Tudor; TRNKA, Miroslav; ZAHRADNÍČEK, Pavel; ŠTĚPÁNEK, Petr; SOUKUP, Josef; HAMETI, Anxhela

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dc.contributor.author	POTOPOVÁ, Vera
dc.contributor.author	TRIFAN, Tudor
dc.contributor.author	TRNKA, Miroslav
dc.contributor.author	ZAHRADNÍČEK, Pavel
dc.contributor.author	ŠTĚPÁNEK, Petr
dc.contributor.author	SOUKUP, Josef
dc.contributor.author	HAMETI, Anxhela
dc.date.accessioned	2026-05-19T07:36:34Z
dc.date.available	2026-05-19T07:36:34Z
dc.date.issued	2026
dc.identifier.citation	POTOPOVÁ, Vera; Tudor TRIFAN; Miroslav TRNKA; Pavel ZAHRADNÍČEK; Petr ŠTĚPÁNEK; Josef SOUKUP and Anxhela HAMETI. Predicting tomato yield in central Europe through integration of field experiments, DSSAT-Cropgro-Tomato simulations and machine learning. In: Biotehnologiile şi dezvoltarea durabilă = Biotechnologies and Sustainable Development: Simpozion Ştiinţific Naţional cu Participare Internaţională, Chişinău, 12 mai 2026. Universitatea Tehnică a Moldovei, Institutul de Microbiologie şi Biotehnologie. Chişinău, 2026, p. 122. ISBN 978-9975-3711-6-2, ISBN 978-9975-3711-7-9 (PDF).	en_US
dc.identifier.isbn	978-9975-3711-6-2
dc.identifier.isbn	978-9975-3711-7-9
dc.identifier.uri	https://doi.org/10.52757/bsd26.55
dc.identifier.uri	https://repository.utm.md/handle/5014/36226
dc.description	This work was supported by the PERUN project (Prediction, Evaluation and Research for Understanding National Sensitivity and Impacts of Drought and Climate Change for Czechia), co-financed with state support from the Technology Agency of the Czech Republic under the Environment for Life Programme.	en_US
dc.description.abstract	Tomato (Solanum lycopersicum L.) is a major vegetable crop worldwide, valued for its nutritional quality, rich in antioxidants such as lycopene, and its economic significance. The aim of this study was to test three high-yielding tomato varieties (drought-tolerant Cocktail Crush F1, heat-sensitive Nagina F1, and the stable line Momini Salzi) at the Ostra site (2023–2025) to develop models for climate adaptation, disease risk management, and production efficiency.	en_US
dc.language.iso	en	en_US
dc.publisher	Technical University of Moldova	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	short-term memory	en_US
dc.subject	machine learning	en_US
dc.subject	climrisk	en_US
dc.title	Predicting tomato yield in central Europe through integration of field experiments, DSSAT-Cropgro-Tomato simulations and machine learning	en_US
dc.type	Article	en_US