## 1 Introduction
This approach is most often generalized because it allows the use of many different methods of linear and nonlinear modeling. However, there are cases in which this approach is insufficient, then the following qualitative or quantitative-qualitative data are applied. Many researchers show that combining different types of predictions leads to the better results than using only one method [26; 27]. Combining the prediction model is also the subject of literature using various innovative hybrid methods that show the superiority of combining yield forecasts in relation to particular methods [28]. Similar models are presented in the paper [10], but they concern the forecasting of yield of another species, i.e., winter rape. The same forecast dates were proposed in this paper, but the forecasting modelswere based solely on quantitative data. Other works with models: Quantitative data: Measurement of NDVI and SAVI (Soil-adjusted vegetation index) vegetation indices. [PERSON] et al. [29] applied the linear regression method to create a mathematical description of potato yielding and to show the relationship between actual and predicted yields based on remote sensing measurements. In the work [PERSON] and [PERSON] [30] artificial neural networks were used to forecast potato yields on the basis of energy inputs incurred in Saudi Arabia. Input variables: Human labor energy, machine energy, diesel oil, fertilizers, pesticides, seeds, and irrigation were typically quantitative variables. Potato yield was assumed to be a dependent variable. All values were calculated per one hectare of crop and transformed into energy forms enabling the analysis. There are no studies in which both quantitative and qualitative data are used to construct predictive models. This is mainly due to the fact that most of the models are based on classical methods, which significantly limit the full use of linguistic (qualitative) variables. Wheat is one of the main grain species as well as one of the most important cultivated crops is the basic food product of people and livestock. It is grown mainly in Europe, Canada, Russia, and the United States. Global cereal production in 2017 amounted to 2,980,174,798 tons, with the share of wheat production 771,718,579 tons, which is more than 25% of the global cereal production. In the European Union, cereal production in 2017 was 307,061,987 tons, with the share of wheat production 150,224,838 tons, which accounts for almost 49% of EU production. In contrast, the volume of Polish wheat production amounted to 11,665,702 tons and accounts for almost 8% of EU production with an average yield of 48.7 dt per hectare of growing area. The total wheat cultivation area in Poland in 2017 was 2,391,853 ha [31]. Bearing in mind the above, the authors of the following work have undertaken a task to develop new multi-criteria (on the basis of prediction date), hybrid (on the basis of data type) winter wheat yield models that will use quantitative and qualitative data by means of artificial neural networks. The purpose of this work is to build three independent models built on the basic data obtained from each farm, i.e., on weather and agrotechnical information. It is assumed that each model will be based on 19 basic independent features, while subsequent models will be created based on additional data from subsequent prediction dates, i.e., 15 April, 31 May, and 30 June. All data was obtained from cultivated fields of winter wheat and mobile meteorological stations. It has been assumed that the generated models will have a MAPE (mean absolute percentage error) value below 7%.