Business expectations as indicators of production constraints in agriculture

Purpose. The purpose of the study is to identify latent indicators derived from business expectations of agricultural enterprises regarding production barriers (obstacles) and to assess their impact on agricultural output dynamics. Methodology / approach. The principal components analysis (PCA) method was applied to reduce dimensionality and eliminate multicollinearity among independent variables and to aggregate subjective assessments of production barriers reported by agricultural producers. Regression and correlation analysis are used to examine the direction and strength of relationships between the principal components and production volumes. The data are from the State Statistics Service of Ukraine for 2015–2024. Results. Two latent components (PC1, PC2) summarising the structure of production barriers were identified. The principal components reflect the opposite poles of farmers’ expectations: on the one hand, financial and material constraints, and on the other, the absence of constraints. This confirms that business expectations can be reduced to integrated latent dimensions that summarise the presence or absence of barriers to production. PC1 and PC2 revealed a strong positive statistically significant relationship with actual production volumes, which indicates their significance as aggregate indicators of business assessments. At the same time, the negative insignificant correlation of PC1 with production in constant prices affects the ambivalence of its interpretation, since its structure simultaneously contains the influence of both favourable and restrictive factors. Additional components (such as PC7) demonstrated a higher significant correlation with production volumes in constant prices than the leading components. This means that secondary, less dispersion-significant latent factors may be more informative for explaining the dynamics of production volume in constant prices. The business expectation system in agriculture has a multidimensional nature, where key barriers and incentives do not always coincide with the most variable factors, but may have greater predictive value for assessing future production. Originality / scientific novelty. The study presents a novel combination of PCA and regression analyse for interpreting business expectations as a latent indicator of production conditions. The scientific contribution lies in identifying latent indicators of subjective evaluations that are statistically linked to real production outcomes, even in the absence of direct objective measurements. Practical value / implications. The results can be used to develop a risk monitoring system in agriculture based on aggregated indicators. This approach improves the accuracy of assessing sectoral conditions and contributes to the development of more targeted agricultural policies, aimed both at overcoming critical barriers and enhancing adaptive capacity of producers.