## 2 Primary Factors
In areas where the sum of precipitation and its distribution do not exceed the limit norms, plants produce a higher yield compared with plants grown in areas with a water deficiency [6; 90]. A similar case occurs with excess water. A significant increase in rainfall intensity may deteriorate soil fertility due to blurring of soil colloids, negatively affecting plant production. The sum of rainfall exceeding the capacity of the soil to retain water leads to leaching of nitrogen beyond the root area of plants, thus disrupting its proper development [91]. However, for agriculture, information on the distribution of rainfall from the start of the growing season throughout flowering tofull maturity is important. The water demand of many plant species in the period from sowing to harvesting is often much higher than the sum of precipitation; therefore, both rainfall and its distribution determine the level of plant productivity [92; 93]. Given the above, the distribution of precipitation should be considered while conducting research on forecasting models. [PERSON] [69] predicted rapeseed yield from fields located in Poland, in the central and south-western part of the Greater Poland vovideship. The author, using a multilayer perceptron (MLP) model, showed that the independent variable (the sum of precipitation in the period from 1 September to 31 December) obtained the highest rank for the two models, showing that the indicator had the stronger impact on the yield. Rape is a plant that is sensitive to water stress during key phases such as flowering and seeding. During the flowering period, rapeseed reacts to water stress with a significant delay in reaching maturity. However, subjecting plants to water stress during maturing of grains may result in their earlier ripening [91]. In another study, [PERSON] and [PERSON] [94], using the same yield forecasting method (MLP), obtained models forecasting the yield of winter wheat with a low forecast error (MAPE). This error ranged from 8.85% to 9.07% for all three models. In this study, one of the independent variables was precipitation occurring in the period from September to June of the following year. In the next case study [95], the average monthly values of daily precipitation were used as explanatory variables for projecting almond yield. The research was conducted in Central Valley, California, USA, and covered the years 2009 to 2018. The applied random forest model accounted for 82.0% of the almond nut yield variation on average and had an RMSE of 480 \(\pm\) 9 lbs-acre\({}^{-1}\). The inclusion of weekly precipitation data as an independent variable in predicting the yield of soybeans and maize grown in Maryland, USA was reported by [PERSON] et al. [96]. According to these authors, monthly data may be insufficient to obtain an early and reliable forecast of crop yield. More frequent extreme weather conditions, especially in terms of changes in precipitation and ambient temperatures, may cause disturbances in the physiological processes of plants [97]. Therefore, in agronomic research, it is important to know the Sielianinov value of the hydrothermal coefficient (HTC). Considering this parameter as an independent variable in the work on predictive models is an interesting and non-standard approach. This coefficient considers the sum of precipitation in a given period (month, quarter) and the sum of the temperatures of this period [98; 99]. For this reason, it can be used as a predictor replacing the explanatory variable of precipitation during the growing season of plants. The HTC determines the water relations in the environment. An HTC value <1 indicates the occurrence of drought, whereas HTC \(\geq\) 1 means sufficient humidity [100]. Reliable results of this coefficient were obtained when the average value of daily temperatures exceeds 10 \({}^{\circ}\)C [101]. Strong temporal variability in rainfall and potential evapotranspiration at the intra-annual scale significantly affects plant growth and development [102].