• Energy Research

An analysis of the condition of implementation of projects in agricultural production is carried out. The disadvantages of existing methods and models of planning of the content and time of execution of works in the projects, which mostly do not take into account the changing components of their project environment, are substantiated. The proposed methodology for justifying the models of a changing project environment for harvesting grain, oilseed and legume crops is based on the analysis of official statistics of agrometeorological stations and involves the implementation of production experiments, which makes it possible to increase the accuracy of the results. It has been established that the dewy periods of time in the projects for the collection of early oilseeds, cereals and legumes are characterized by a probabilistic distribution of the time of occurrence of dew and its duration. The indicated regularity and the established correlation relationship between the occurrence of dew and its duration are the main components of the model. The substantiated model of the pink period of time allows to take into account the changing events of the project environment and to improve the quality of the content management process and the time of performance of the harvesting work. It is established that the deficit of humidity in the air, in which the performance of harvesting is effective, changes over the course of the day by parabolic dependence. Its maximum value depends on the agrometeorologically acceptable duration of the works in the projects of harvesting early oilseeds, grain and legume crops, which is the basis for substantiating the model of the air humidity deficit and taking into account its impact on the implementation of works in these projects. The obtained results of the research are the basis of development of simulation models of projects for the collection of early oilseeds, grain and legume crops to improve the accuracy of determining the use indicators and resource requirements for the implementation of these projects. The obtained models increase the quality of management decision making in the projects of harvesting early oilseeds, grain and legume crops.