While the effects of carbon on soil nitrogen (N) cycle have been extensively studied, it is not clearly understood how co-existing macronutrients, such as phosphorus (P), affect the N cycle in agroecosystems. In this study, P amendment effects on nitrification in a fertile agricultural soil were investigated under a typical N-P amendment rate. In a laboratory incubation study, soils were amended with urea, monopotassium phosphate and a mixture of urea and monopotassium phosphate at the same rate. In soils that received no amendments (control), P only, urea only, and urea plus P amendment, nitrification occurred within the first five days, with an average net nitrification rate of 5.30, 5.77, 16.66 and 9.00 mg N kg⁻¹d⁻¹, respectively. Interestingly, nitrification in urea-treated soils was retarded by P addition where a N:P ratio seemed to be a key factor impeding nitrification. This was also supported by the response of ammonia-oxidizing bacteria (AOB), which was more sensitive to P addition than ammonia-oxidizing archaea (AOA). The outcome of this study showed that application of P fertilizer suppressed the nitrification process in urea amended soil, suggesting that a synergistic aspect of N and P nutrient management should be further explored to retard N losses from agricultural systems.

Fresh sweet and waxy maize (Zea mays) are valuable specialty crops in southern China. Hyperspectral remote sensing offers a powerful tool for detecting maize foliar nutrients non-destructively. This study aims to investigate the capability of leaf spectroscopy (SVC HR-1024i spectrometer, wavelength range: 400–2500 nm) to retrieve maize foliar nutrients. Specifically, we (1) explored the effects of nitrogen application rates (0, 150, 225, 300, and 450 kg·N·ha⁻¹), maize cultivars (GLT-27 and TGN-932), and growth stages (third leaf (vegetation V3), stem elongation stage (vegetation V6), silking stage (reproductive R2), and milk stage (reproductive R3)) on foliar nutrients (nitrogen, phosphorus, and carbon) and leaf spectra; (2) evaluated the transferability of the regression and physical models in retrieving foliar nutrients across maize cultivars. We found that the PLSR (partial least squares regression), SVR (support vector machine regression), and RFR (random forest regression) regression model accuracies were fair within a specific cultivar, with the highest R² of 0.60 and the lowest NRMSE (normalized RMSE = RMSE/(Max − Min)) of 17% for nitrogen, R² of 0.19 and NRMSE of 21% for phosphorous, and R² of 0.45 and NRMSE of 19% for carbon. However, when these cultivar-specific models were used to predict foliar nitrogen across cultivars, lower R² and higher NRMSE values were observed. For the physical model, which does not rely on the dataset, the R² and NRMSE for foliar chlorophyll-a and -b (C_ab), carotenoid (C_xc), and equivalent water thickness (EWT) were 0.76 and 15%, 0.67 and 34%, and 0.47 and 21%, respectively. However, the prediction accuracy for foliar nitrogen, expressed as foliar protein in PROSPECT-PRO, was lower, with an R² of 0.22 and NRMSE of 27%, which was comparable to that of the regression models. The primary reasons for this limited transferability were attributed to (1) the insufficient number of samples and (2) the lack of strong absorption features for foliar nutrients within the 400–2500 nm wavelength range and the confounding effects of other foliar biochemicals with strong absorption features. Future efforts are needed to investigate the physical mechanisms underlying hyperspectral remote sensing of foliar nutrients and incorporate transfer learning techniques into foliar nutrient models.

Little attention has been paid to the impact of vertical canopy position on the leaf spectral properties of tall trees, and few studies have explored the ability of leaf spectra to characterize the variation of leaf traits across different canopy positions. Using a tower crane, we collected leaf samples from three canopy layers (lower, middle, and upper) and measured eight leaf traits (equivalent water thickness, specific leaf area, leaf carbon content, leaf nitrogen content, leaf phosphorus content, leaf chlorophyll content, flavonoid, and nitrogen balance index) in a subtropical evergreen broadleaved forest. We evaluated the variability of leaf traits and leaf spectral properties, as well as the ability of leaf spectra to track the variation of leaf traits among three canopy layers for six species within the entire reflectance spectrum. The results showed that the eight leaf traits that were moderately or highly correlated with each other showed significant differences along the vertical canopy profile. The three canopy layers of leaf spectra showed contrasting patterns for light-demanding (Castanopsis chinensis, Castanopsis fissa, Schima superba, and Machilus chinensis) and shade-tolerant species (Cryptocarya chinensis and Cryptocarya concinna) along the vertical canopy profile. The spectra at the lower and upper canopy layers were more sensitive than the middle layer for tracking the variation of leaf chlorophyll and flavonoid content. Our results revealed that it is important to choose an appropriate canopy layer for the field sampling of tall trees, and we suggest that flavonoid is an important leaf trait that can be used for mapping and monitoring plant growth with hyperspectral remote sensing.

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R² = 0.58–0.84) than indirect approaches (R² = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction.