The importance of understanding turbulent scalar exchange over agricultural landscapes motivated this study of the surface renewal (SR) method for deployment in place of or alongside eddy covariance (EC) instrumentation. High-frequency (20 Hz) scalar data were used with turbulence and similarity parameters for SR measurements of turbulent sensible heat (H), latent heat (λE), and CO2 (Fc) flux. The eddy covariance method was used to provide a reference data set to compare the performance of SR to EC flux measurements when the SR input requirements were determined with either: (1) fast-response (SR1) or (2) slow-response (SR2) wind velocities. We test SR1 and SR2 over two agricultural crops, cotton and rice, that are suitable for adaptive (“climate-smart”) management solutions which rely on decisions informed by extensive micrometeorological measurements. One advantage that the SR method provides is its simplicity of deployment and maintenance, requiring less energy and resources than typical EC networks. Regardless of the crop and scalar eddy flux, both SR flux methods agreed well with the EC flux measurements; coefficients of determination (R2) and slopes (s) of linear regression analysis ranges were [0.88, 0.98] and [1.01, 1.22], respectively. It was concluded that when EC measurements are unavailable, SR may be used as an alternative for additional climate-smart agriculture research efforts and this method may allow for higher spatial monitoring resolution because of the possibility to avoid the use of sonic anemometry.