Monoculture caused a gradual decline of soil conditions, while nematodes and salt accumulation stimulated the growers to choose alternative practices, such as soilless cultures, which proved their value in Western Europe. Exact statistics are lacking, but estimates deal with approximately 300-400 hectares of vegetable on rock wool, whereas othe
...r substrates of soilless culture may multiply this number. Real perspectives are attributed to the forced production of pepper, tomato and cucumber. Vegetable production in greenhouses may impair the ecological balance of the environment substantially as far as being uncontrolled. Soilless cultures especially should be handled thoughtfully. A fraction of the nutrients administered, more than 25-30%, is doomed to be lost in an open system, and the resulting ecological risk is accompanied with increasing costs of the production. In Hungary, the quantity of nutrient elements in drainage water is unknown, et all. Connecting the production results with chemical analysis, we gain more information about it. You can see a mathematical method for evaluation of nutrient and water conditions in tomato hydroponics production.
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 grea
...t 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.