Understanding the characteristics of the balanced nutrient requirements for peanut to achieve target yields is paramount when formulating fertilizer management strategies to increase yields and avoid fertilizer loss. Nutritional requirement estimation models can provide effective alternatives for the estimation of the optimum crop balanced nutrient requirements under varied agricultural conditions which are less time consuming and expensive. In the present study, the quantitative estimation of the optimum crop balanced nutrient requirements of peanut in China were obtained using quantitative evaluation of fertility of tropical soils (QUEFTS) model. The database covered the main agro-ecological region for peanut crops in China between 1993 and 2018. The predicted results of the QUEFTS model indicated that nutrient uptake requirements increased linearly with increasing pod yields until the yields had reached approximately 60% to 70% of the potential pod yields. It was found that with the increasing pod yields during the nutrient linear absorption stage, the plants had required 38.4 kg N, 4.3 kg P, and 14.0 kg K in total to produce 1000 kg of pods, and the corresponding internal efficiencies were 26.0 kg N/kg, 235.0 kg P/kg, and 71.6 kg K/kg, respectively. In addition, the balance rates of the removal nutrient in the pods were determined to be 29.4 kg N, 2.9 kg P, and 4.9 kg K per 1000 kg of pod yield, or approximately 76.5%, 67.4%, and 34.7% of N, P, and K in the total plants, respectively. This study’s field validation experiments verified the applicability and accuracy of the QUEFTS model. Therefore, it was considered to be an effective alternative for the estimation of the optimal balance N, P, and K uptake requirements for peanut crops. These findings will potentially be helpful when making future decisions regarding fertilizer recommendations for peanut crops in China.

Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was $\left({\mathrm{R}}_{723}-{\mathrm{R}}_{738}\right)/\left({\mathrm{R}}_{723}-{\mathrm{R}}_{722}\right)$ with a cross-validation R²_cv of 0.91 and RMSE_cv of 3.53 μg/cm². The DLARI formula provided the best performing indices, which were $\left({\mathrm{R}}_{735}-{\mathrm{R}}_{753}\right)/\left({\mathrm{R}}_{715}-{\mathrm{R}}_{819}\right)$ for estimating LCC from the adaxial surface (R²_cv = 0.96, RMSE_cv = 2.37 μg/cm²) and $\left({\mathrm{R}}_{732}-{\mathrm{R}}_{754}\right)/\left({\mathrm{R}}_{724}-{\mathrm{R}}_{773}\right)$ for estimating LCC from reflectance of both sides (R²_cv = 0.94, RMSE_cv = 2.81 μg/cm²). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.