## 1 Introduction  
Maximizing yield while minimizing costs and caring for the environment are the basic goals of agricultural production [1]. Early detection and management of problems limiting plant production can help to increase yields and thus obtain more income for the farm [2]. Some of the techniques used for forecasting the yield of crops are statistical models and machine learning algorithms, which enable yield estimation during the growing season [3, 4, 5]. Knowing the predicted crop size in a specific year can be helpful in making decisions concerning the seasonal planning of cultivation or storage areas [6]. On the basis of the yield forecast, it is possible to improve the profitability of a farm, as well as to balance the amount of production means used, such as fertilizers [7] or plant protection products. The balanced consumption of these products leads to both a reduction in energy inputs on the farm and in human labor inputs. Ultimately, the farming company experiences an increase in income due to the lower production costs [8]. In addition, an early and reliable monitoring of crop yields can help policymakers and grain marketing agencies to plan imports and exports. Yield forecasting increases the profit of processing plants; forecasting tools provide valuable information on the basis of which it is possible to estimate the amount of raw material that these companies will be able to process [9; 10; 11]. Crop prediction for the current growing season is a relatively difficult task due to the complexity of the relationship between the plant growth process and environmental factors such as weather or soil variability [12]. As such, the final yielding result is determined by many independent variables. Additionally, in yield prediction, possible interactions between factors or groups of yield-forming factors should be considered [13; 14]. The application of statistical modeling methods such as multiple linear regression (MLR) does not always produce satisfactory results as they assume a linear relationship between the variables related to plant production, whereas these relationships usually follow a nonlinear course [15; 16; 17]. Failure by regression models to meet the assumption of multiple collinearity of dependent and independent variables results in poor to moderate results in the prediction of crop yield [6; 18]. An essential element in predictive modeling is the accurate assessment of the correctness of the model's functionality. For this purpose, ex post forecast quality indices are used. One of the most frequently used forecast error indicators is the mean absolute percentage error (MAPE), which is calculated according to Formula (1) [19; 20; 21; 22; 23; 24; 25]. MAPE measures the error in percent and specifies the average percentage deviation between the forecast value and the actual implementation [26]. [PERSON] et al. [27] reported that if the MAPE value is below 10%, the degree of model goodness-of-fit is perfect; if it falls within the range of 10-20%, the model goodness-of-fit is good. In the range of 20% to 30%, the error level is acceptable, while error exceeding 30% is considered a poor result and the model should therefore be rejected. According to [PERSON] and [PERSON] [28], when the real value is close to or equal to zero, the MAPE provides infinite or undefined values, which is considered to be its significant disadvantage. Therefore, to verify in detail the assessment of the efficiency of the prediction model, a combination of the MAPE and the root mean square error (RMSE) is used [29]. RMSE indicates the absolute fit of the observed data point to the predicted values. It is determined, according to Formula (2), as the root of the second order from the mean square of all errors [26; 30]. The lower the value of MAPE and RMSE, the higher the accuracy of the obtained predictive model [31]. Other measures to determine error include mean absolute error (MAE), mean squared error (MSE), relative absolute error (RAE), root relative squared error (RRSE), and others [32; 33; 34; 35].