<p>Various non-destructive evaluation (NDE) technologies increasingly used to assess log stiffness and support sorting, segregation and processing decisions. However, comprehensive comparative evaluations of various NDE tools across the wood value chain remain limited. Therefore, this study compared several NDE methods for estimating modulus of elasticity (MOE) in southern pine grown in Queensland, Australia, at three processing stages: standing trees pre-harvest, felled logs post-harvest, and boards produced from those logs. Standing trees measurement included ultrasound MOE assessment on increment cores (USMOE) and Director ST300 (ST300_MOE) and IML-Resi PD-400 resistance drilling (Resi_MOE). Felled log measurements were obtained using Beam Identification by Non-destructive Grading (BING_MOE) and the resonance acoustic tool Hitman HM200 (HM200_MOE). These measurements were then related to log MOE, log density, and the average MOE and modulus of rupture of boards cut from the same trees. Finally, log stiffness measurement tools were compared in terms of deployability, efficiency, and predictive power. NDE tools applied to standing trees pre-harvest generally exhibited lower predictive power in estimating log and board MOE than tools applied to felled logs. For log MOE, HM200_MOE showed the strongest relationship with BING_MOE (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({R}^{2}=\)</EquationSource> </InlineEquation>0.78) followed by USMOE (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\({R}^{2}=0.67\)</EquationSource> </InlineEquation>), ST300_MOE (<InlineEquation ID="IEq3"> <EquationSource Format="TEX">\({R}^{2}=0.61\)</EquationSource> </InlineEquation>), and Resi_MOE (<InlineEquation ID="IEq4"> <EquationSource Format="TEX">\({R}^{2}=0.57\)</EquationSource> </InlineEquation>). BING_MOE explained the highest variability in the average board MOE (<InlineEquation ID="IEq5"> <EquationSource Format="TEX">\({R}^{2}=0.84\)</EquationSource> </InlineEquation>), followed by USMOE (<InlineEquation ID="IEq6"> <EquationSource Format="TEX">\({R}^{2}=0.71\)</EquationSource> </InlineEquation>), HM200_MOE (<InlineEquation ID="IEq7"> <EquationSource Format="TEX">\({R}^{2}=0.69\)</EquationSource> </InlineEquation>), ST300_MOE (<InlineEquation ID="IEq8"> <EquationSource Format="TEX">\({R}^{2}=0.64\)</EquationSource> </InlineEquation>), and Resi_MOE (<InlineEquation ID="IEq9"> <EquationSource Format="TEX">\({R}^{2}=0.43\)</EquationSource> </InlineEquation>). These results highlight a clear trade-off between predictive performance and operational practicality. BING and HM200 provided better accuracy and precision, but unsuitable for standing tree assessment. Among the standing trees methods, USMOE provided best balance between precision and accuracy and potential field use but are slower and more expensive. ST300 and Resi were faster and more practical for pre-harvest sorting their lower predictive performance underscores the need for improved calibration or modelling. Overall, tool selection depends on the required balance between predictive performance, operational efficiency, sampling requirement and cost.</p>