No. 2 (2019)
Articles

Biomass production estimation of processing tomato using AquaCrop under different irrigation treatments

Published December 15, 2019
Sándor Takács
Institute of Horticulture, Faculty of Agriculture and Environmental Sciences, Szent István University, Páter K. út 1., H-2100 Gödöllő
Istvánné Rácz
Faculty of Agricultural Sciences and Economics, Szent István University, Szabadság út 1-3., H-5540 Szarvas
Erzsébet Csengeri
Faculty of Agricultural Sciences and Economics, Szent István University, Szabadság út 1-3., H-5540 Szarvas
Tibor Bíró
Institute of Hydraulic Engineering and Water Management, Faculty of Water Sciences, National University of Public Service, Bajcsy-Zsilinszky utca 12-14. H-6500 Baja
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APA

Takács, S., Rácz, I., Csengeri, E., & Bíró, T. (2019). Biomass production estimation of processing tomato using AquaCrop under different irrigation treatments. Acta Agraria Debreceniensis, (2), 131-136. https://doi.org/10.34101/actaagrar/2/3691

The wiser usage of irrigation water is inevitable in the future. Irrigation has very high input cost; therefore, farmers must carry out irrigation with care. Also, the effect of irrigation on crops has a big role in decision making. Modeling provides a possibility to evaluate this effect. AquaCrop, as a crop production simulation model has great potential in this field. The accuracy of tomato biomass yield prediction of the model was tested in this research. For collecting the necessary data, a field experiment was conducted at Szarvas on processing tomato with different water supplies, such as 100% (I100), 75% (I75), 50% (I50) of potential evapotranspiration and a control with basic water supply (C). The relation of the simulation and actual biomass yields was evaluated during the season. Very good correlation was found between the modelled and the actually harvested data. The data for the control and I100 treatments showed higher correlation than the I75 and I50. The relationship for all of the data was moderately strong. Miscalculations occur mostly when the dry biomass yield reaches
7 t ha-1. The accuracy of the model was evaluated with the use of mean absolute error (MAE) and root mean squared error (RMSE) values. The least error was found in the C treatment, which means 0.34 MAE and 0.45 t ha-1 RMSE. The simulation resulted in higher errors in the I75 and I50 treatments.

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