TOOLKIT FOR THE EVALUATION AND SELECTION OF INNOVATION AND INVESTMENT PROJECTS FOR AGRICULTURAL ENTERPRISES

Abstract

The article theoretically substantiates, develops, and empirically validates a comprehensive methodological toolkit designed for evaluating and selecting innovative investment projects of agricultural enterprises under conditions of macroeconomic instability, severe wartime disruption, and the strategic imperatives of the European Green Deal. The research demonstrates that traditional deterministic decision-making approaches fail to accommodate the complex operational realities of modern agribusiness. In particular, conventional models ignore the stochastic nature of agricultural production, seasonal cash flow asymmetries, high capital intensity, and critical non-financial parameters such as climate resilience and soil preservation. Consequently, the study demonstrates the necessity of transitioning from rigid financial metrics to a multi-dimensional, adaptive evaluation model that integrates qualitative expert judgments and risk profiles.

To address these limitations, a convergent analytical framework is conceptualized. The proposed methodology synthesizes risk-adjusted discount rates that incorporate sector-specific, technological, and military risk premiums, thereby replacing standard weighted-average cost-of-capital calculations. Qualitative and strategic criteria—spanning financial sustainability, technological innovation, eco-circular impact, and operational security—are operationalized through the Fuzzy Analytic Hierarchy Process, using triangular fuzzy numbers to mitigate cognitive bias. Furthermore, the framework integrates Monte Carlo Simulation to generate empirical probability distributions for project viability, alongside Real Options Analysis to capture managerial flexibility regarding project deferral, expansion, or abandonment under dynamic market conditions.

The analytical architecture is structured into a four-stage project selection algorithm known as the Gate System. This workflow incorporates initial fatal flaw screening, multi-criteria evaluation across four fundamental viewpoints, probabilistic risk simulation, and binary zero-one linear programming for capital budget optimization under strict financial constraints. The practical implementation and empirical verification of the toolkit were conducted on a representative agricultural enterprise operating a six-thousand-hectare land bank, evaluating four distinct project alternatives ranging from precision farming technologies to bioenergy and processing facilities. The findings confirm that combining stochastic simulation with fuzzy decision-making significantly enhances the precision of capital allocation, minimizes financial risk, and accelerates technological modernization in alignment with international standards.