## 2 Primary Factors
In the research devoted to plant yield forecasting with the use of artificial neural networks, the intensity of solar radiation and wind force have also been used as some of the explanatory variables. The dependence of climatic factors, including wind speed and hours of sunshine, on the yield of rice seeds cultivated in Sri Lanka was analyzed. Three ANN algorithms were used: the Levenberg-Marquardt algorithm, the Bayesian regularization algorithm, and the scaled conjugated gradient algorithm. The research found that all analyzed models produced high-accuracy predictions (the MSE value ranged from 0.01 to 0.39 t\(\cdot\)ha\({}^{-1}\)). However, the Levenberg-Marquardt and scaled conjugated gradient algorithms required fewer epochs and a shorter computation time [115]. [PERSON] et al. [18], apart from the sum of precipitation, minimum and maximum temperature, relative humidity, and field location, also used solar radiation (in MJ/m\({}^{2}\)) as an explanatory variable in the prediction of agricultural crops grown in Sinaloa (Western Mexico). The MLP neural network forecasting the yield of snap bean considering the above-mentioned predictors obtained an RMAE ranging from 1.72% to 6.41%. However, the forecast model of the size of maize and tomato was characterized by an error of 8.46% and 24.27%, respectively. The highest error value was obtained for the potato yield forecast model (RMAE was 26.29% on average). In other studies on rice yield forecasting, apart from climatic factors (temperature, precipitation, evaporation, solar radiation, sunshine duration, wind speed, pressure at the station, etc. ), the biological features of plants were also included (effective panicle number, filled grains per panicle, and growth period) and agronomic factors (seed set rate). Two models were used in the following research: feed-forward backpropagation neural network (FFBN) and partial least squares regression (PLSR). The area of analysis was fields located in eastern China. The acquired results showed that after incorporating all the predictive variables, the FFBN model was more accurate. The RMSE values for the training and test sets were 0.41 and 0.44 t\(\cdot\)ha\({}^{-1}\), respectively. The PLSR model showed an error of 0.56 and 0.55 t\(\cdot\)ha\({}^{-1}\) for the training and test sets, respectively [116]. The agotechnical treatments performed in the previous year and/or in the year of forecasting are also important input variables that allow the yield to be estimated with satisfactory accuracy. The sowing date is one of the key agotechnical treatments that is a crucial yield factor. For example, a delay in sowing of maize in the northern part of New Zealand may result in a yield reduction of 10% to 24% depending on the grown cultivar [83]. Using the planting date as one of the inputs enabled [PERSON] et al. [117] to develop a feed-forward neural network that predicted the phenological development of soybean. The mean prediction error for vegetative development was 3.6 days, and for generative growth, it was 4.4 days. The results of the sensitivity analysis of the neural network showed that the sowing date is a core independent variable in forecasting the yield of rapeseed. Moreover, including the fertilization doses of nitrogen, potassium, phosphorus, magnesium, molybdenum, zinc, sulfur, and copper enabled the construction of prognostic models whose MAE ranged from 0.52 to 0.55 t\(\cdot\)ha\({}^{-1}\), which correspond to MAPE values of 6.63-6.92% [118]. Fertilization is one of the agotechnical procedures used to provide plants with digestible forms of nutrients. The natural concentration of the above-mentioned compounds in the growing environment is usually insufficient. The application of mineral and organic fertilizers, in proper doses, affects the correct growth and development of plants. Too-low fertilization leads to deficiencies in micro- and macro-elements in the plant, thus disrupting its physiological processes. It also contributes to soil depletion from absorbable forms of nutrients.