## 4 Discussion  
Plant yield prediction is based on historical empirical data, which most often appear in the form of quantitative data, such as anthropogenic, climatic, and geomorphological factors. Such a large number of variables necessary to generate a properly functioning prognostic model causes problems with choosing an appropriate modeling method. In addition, some data can be obtained only from specific researches [36], however it can only be used for scientific purposes. Therefore, in the following work, the authors created three predictive neural models that give the possibility of their use in the current agotechnical season before harvesting. These models are based on data available for each farmer and are based on two types of data: Quantitative and qualitative. This is a new approach to problems in the prediction of crop yields. The assumption was made that MAPE for each generated predictive model would be lower than 7%. It was assumed that the correct functioning of the created neural models will be verified by comparing the obtained predicted with the observed wheat yields in the last (2015) year of the study. Figure 4: Relation between the observed and predicted yield by model: (**a**) QQWW15_4, (**b**) QQWW31_5, and (**c**) QQWW30_6. The neural modeling method used for performance of nonlinear tasks gives better results compared to classic statistical methods [11; 37]. A common problem in the neural network of plant yield prediction is the selection of the appropriate network topology. From the literature sources [10; 33], the MLP network, also known as multilayer perceptron, is most commonly used in terms of forecasting. In this work, three models based on the above type of network were created: QQWW15_4, QQWW31_5, and QQWW30_6. Each of the models was based on quantitative and qualitative data, which were 19, 23, and 25 independent variables respectively, with each model having five qualitative variables (Table 2). Four ex post error measures were used in the work, i.e., relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). They were used to determine the quality of models and to determine the errors of winter wheat yield prediction. Table 4 presents the error values of the ex post measures for all created neural models. The most commonly used indicators characterizing prediction error values include MAPE [10; 34; 38; 39; 40; 41; 42]. The lowest MAPE values were obtained for the QQWW31_5 neural model based on the MLP network of the 23:38-16-8-1:1 structure, which was 6.63%. Bearing in mind the critical level of MAPE at a complex level of up to 7%, the results obtained for all models were highly satisfactory, as they ranged up to 6.92% (for the QQWW15_4 model). After all the calculations were completed, a network sensitivity analysis was performed for all the generated neural models. The meteorological data (Table 5) obtained the highest rank 1 for all networks. The result was similar to those obtained in earlier studies [35]. The following shows that the weather factors T9-12_PY, T1-4_CY, and R5_CY had the greatest impact on the winter wheat yield. The occurrence of optimal temperatures in the autumn season decided on the proper pullulation and preparation of plants to inhibit vegetation. However, appropriate thermal conditions that prevail during winter guarantee proper overwintering and good preparation of plants into spring vegetation. The appearance of any temperature anomalies during these periods may lead to a reduction in yield [43]. Another significant factor was the sum of rainfall from 1 May to 31 May the actual year. This period was a phase of critical water demand for winter wheat--from the beginning of shooting to the beginning of eating [44]. Water shortage results in a poor increase of assimilation organs and poor ear formation. Maintaining optimal thermal conditions during this period leads to the proper formation of subsequent development phases by plants and good grain yield.