Data structures and programming of agricultural technological process models

Abstract

This study examines the application of digitalization and IT services in agriculture to develop data structures that enable mathematical description of changing agrometeorological conditions over specific calendar periods. Based on this approach, information services are adapted to support decision-making in managing technological processes, particularly during crop harvesting. The role of information and analytical systems, computer modeling, and digital platforms in data collection, climate and crop growth forecasting, process optimization, resource management, and productivity enhancement under climatic risks and complex production environments is emphasized. IT in agriculture facilitates monitoring, planning, process modeling, and management decision support to improve efficiency. The prerequisites for using IT in the development of analytical tools for decision support in agro-industrial projects are outlined. Existing approaches to studying technological systems under variable external conditions using IT-based modeling methods are analyzed. Key components of technological system projects are identified, and the influence of the external design environment is characterized. Factors causing variability in external conditions during technological processes, exemplified by harvesting, are determined, justifying the use of statistical simulation modeling. The study emphasizes the creation of models representing individual components of harvesting technological systems. Components of the external design environment that determine the probabilistic formation of the work time fund are identified, forming the basis for simulation modeling. A methodology enabling statistical simulation to account for the influence of agrometeorological conditions on work duration during autumn sugar beet harvesting is presented. Modeling results are summarized, showing the use of correlation-regression analysis to identify patterns in the naturally allowed time fund relative to the planned harvesting start. Patterns of time fund variation and their statistical characteristics are established for different start dates. Differential distribution functions for the duration of the naturally allowed time fund during autumn beet harvesting are provided. The proposed approach demonstrates the effectiveness of IT-driven modeling and analytics in optimizing agricultural processes, supporting management decisions, and mitigating the impact of environmental variability on crop harvesting.