The impact of operating leverage and financial elasticity on the budgeting of variable costs in grain production

The sustainability of financial strategies in agricultural enterprises depended on accurate forecasting of production parameters and associated credit risks under volatile market conditions. This study aimed to assess the technological response of grain production to changes in resource supply and to integrate the results into liquidity forecasting and negative interest rate risk assessment. The estimated quadratic production function adequately captured the nonlinear response of wheat yield to variable inputs, confirming diminishing marginal returns. The model demonstrated satisfactory statistical performance (R² = 0.606, adjusted R² = 0.409) and was statistically significant (F = 3.073, p = 0.027). The identified technological optimum corresponded to a maximum predicted yield of 55.1 c/ha, achieved at fertiliser and seed expenditures of approximately 5.3 and 0.85 thousand UAH/ha, respectively. When value-based indicators were applied, the optimum shifted toward profit maximisation. The maximum marginal profit reached 9.32 thousand UAH/ha at slightly lower input levels, with a corresponding yield of 53.8 c/ha, while the maximum net profit equalled 5.46 thousand UAH/ha after accounting for fixed costs. The operating leverage analysis revealed pronounced nonlinearity of financial sensitivity. Extremely high DOL values (up to 9.99) occurred in underfunded production regimes, where net profit approaches zero, indicating critical operational instability, whereas a stable DOL range of 1.1-1.6 corresponded to moderate input levels. Scenario analysis of credit conditions (±20% interest rate variation) indicated asymmetric interest rate risk. The highest financial elasticity of net profit (E ≈ 0.10) was observed in low-input, loss-making regimes, while near the technological optimum elasticity approaches zero, indicating relative financial resilience. The results confirmed that integrating production modelling with financial sensitivity indicators improved liquidity forecasting and credit planning in grain production