The increasing need for efficient resource management and sustainable production in the agro-industrial sector necessitates advanced analytical approaches capable of accurately identifying key influencing factors. This study proposes a hybrid algorithmic framework for feature importance analysis in agro-industrial efficiency assessment by integrating Shapley Additive Explanations (SHAP), Gradient Boosting, and Principal Component Analysis (PCA). The proposed methodology combines linear and nonlinear feature evaluation techniques to enhance interpretability and predictive performance. The approach was tested on data collected from agro-industrial enterprises in the North Kazakhstan region, covering production, climatic, and economic indicators from 2020 to 2022. The results revealed that crop area, yield per hectare, and climatic factors are the most significant contributors to key performance indicators, including yield increase, seasonal profit, and risk reduction. The hybrid analysis lowered prediction uncertainty by 28% and increased model accuracy by 15 to 20% compared to single-method approaches. Using SHAP made the model clearer and helped identify key features, which aided decision-making in agro-industrial management. The proposed framework has high potential for implementation in precision agriculture and strategic management and provides an effective tool for maximizing agricultural efficiency under varying environmental conditions.

Machine-learning surrogates predict composite properties but report no calibrated reliability and may violate physics. Bound-constrained conformal prediction confines intervals to physical bounds but assumes them exact. This paper keeps the corridor as a hard constraint and characterizes what a decision-support system guarantees when it is wrong. If a fraction $\eta$ of responses lies outside the corridor, no admissible interval covers more than $1-\eta$, so the nominal level $1-\alpha$ is attainable only if $\eta \le \alpha$. A projection-aware calibration scores out-of-corridor points as infinitely nonconforming. Its marginal coverage is at least $1-\alpha -\eta {\pi }_{\infty }$ at every calibration size (${\pi }_{\infty }$ the fallback probability); its coverage across calibrations follows an exact Beta law. In the limit it reaches the nominal level whenever attainable; calibrate-then-clip loses up to $\eta$. For $\eta <\alpha$ the system thus delivers admissible intervals at the nominal level for a small, quantified width premium, with a feasibility verdict and a Shapley explanation. Across eight datasets it recovers the nominal level on a woven laminate with a misspecified rule-of-mixtures bound (90.3% against 85.6% for naive projection) and tracks the frontier under normative bounds the data contradict. Under exact Voigt–Reuss bounds it removes all violations and narrows intervals thirteen-fold. Width, paid inside the corridor, is the price.