Visible and near-infrared reflectance spectroscopy (vis–NIR) may offer a rapid, low-cost, and reproducible alternative to traditional analytical techniques used to characterize total carbon (Ct), total nitrogen (Nt), and total phosphorus (Pt) concentrations in field crops. However, it remains unclear whether a single global predictive model can be effectively applied to all samples derived from heterogeneous crop species. Based on a wide range of crop samples (n = 624), predictive models were developed and compared for all combined datasets (global calibrations) and for separated datasets (local calibrations) using partial least square regression. The predictive abilities of vis–NIR models were evaluated using R2P, root means square error (RMSE)P, ratio of standard deviation to RMSE (RDP)P, and ratio of performances to interquartile distance (RPIQ)P in the validation datasets. The results showed that the local models more effectively predicted the crop constituents (Ct: R2P = 0.89–0.91, RMSEP = 4.47–10.93, RPDP = 3.11–3.42, RPIQP = 2.31–4.08; Nt: R2P = 0.89–0.92, RMSEP = 0.65–0.97, RPDP = 2.97–3.68, RPIQP = 3.69–4.43; Pt: R2P = 0.60–0.78, RMSEP = 0.12–0.31, RPDP = 1.60–2.15, RPIQP = 1.27–2.15) compared with the global model predictions (Ct: R2P = 0.84, RMSEP = 14.02, RPDP = 2.53, RPIQP = 2.62; Nt: R2P = 0.81, RMSEP = 1.49, RPDP = 2.32, RPIQP = 2.67; Pt: R2P = 0.58, RMSEP = 0.46, RPDP = 1.55, RPIQP = 2.04). vis–NIR was equally applicable to Ct and Nt measurements, whereas the application was limited concerning Pt measurements. These results illustrate the potential of combining the vis–NIR technique with partial least square regression as a routine approach for estimating crop constituents in ecological studies and monitoring programs.