There is a number of various approaches to the development of yield predictive models in agriculture. One of the most popular is based on the yield modeling from parameters of crop cultivation technology. However, there is another view on the yield prediction models, particularly, based on the use of life factors as yielding parameters. Our study is devoted to the comparison of a conventional technological approach to the yield prediction with a less prevalent approach of life factor based yield modeling. Testing of two approaches was performed by using the yielding data of sweet corn cultivated in the field trials in the drip-irrigated conditions of the South of Ukraine under the different technological treatments, viz. plowing depth, nutrition, and crop density. We developed two multiple linear regression models to compare their efficiency in the yielding predictions. One of the models used cultivation technology parameters as the inputs while another used life factors as the inputs. Life factors were expressed in numeric values by using such a converter: total water consumption of the crop was used as the factor of water, the total sum of positive temperatures was used as the factor of heat, and the total sum of the main nutrients (NPK) available in the soil was used as the factor of nutrition. The results of the study proved an equal accuracy and reliability of the studied models of sweet corn yields, which is obvious from the values of RSQ. RSQ of the both studied regression models was 0.897. However, additional check of the modeling approaches applied in the feed-forward artificial neural network showed that the life factor based model with the RSQ value of 0.953 provided better yield predictions than the technologically based model with the RSQ value of 0.913. So, we concluded that the life factor approach should be preferred to the technological approach in the development of yield predictive models for agriculture.