Soil analysis is fundamental to sustainable agriculture; however, traditional laboratory methods are time-consuming, expensive, and environmentally hazardous. Spectroscopy techniques, particularly Near-Infrared (NIR), have gained considerable attention because they require minimal sample preparation, no chemicals, and predict multiple soil properties with a single scan. However, selecting appropriate equipment remains critical, as previous studies have reported inconsistent performance between conventional Near-Infrared (NIR) spectroscopy and advanced Fourier-Transform Near-Infrared (FT-NIR) spectroscopy. Therefore, this study aimed to compare the predictive performance of conventional NIR and advanced FT-NIR spectroscopy for sixteen soil properties. Soil samples (n = 567) were collected from different land-use types across South Korea at a depth of 0–20 cm and analyzed using laboratory methods and spectroscopy techniques. Five models, including partial least squares regression (PLSR), Cubist, support vector machine (SVM), random forest (RF), and memory-based learning (MBL), were evaluated using 15-fold cross-validation to assess prediction accuracy. Overall, conventional NIR spectroscopy yielded consistently higher accuracy for all soil properties than FT-NIR. Strong predictive accuracy was achieved for EC (R² = 0.84), OM (R² = 0.84), avl. P (R² = 0.77), TN (R² = 0.84), and CEC (R² = 0.76). In contrast, FT-NIR provided good prediction accuracy only for Ex. K (R² = 0.72) and TN (R² = 0.84). The average performance of NIR (R² = 0.67) outperformed FT-NIR spectroscopy (R² = 0.63) across all soil properties. These findings demonstrate that, despite their lower spectral resolution, NIR spectra provide robust predictive capability across a wide range of soil properties, which can significantly reduce the investment cost of advanced equipment such as FT-NIR for routine soil analysis.

Infrared (IR) spectroscopy has emerged as a rapid, cost-effective, and reliable alternative to traditional methods, enabling real-time, indirect monitoring of nutrients. Most reviews have discussed visible-near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy individually for soil analysis. This review highlights the application of IR spectroscopy, particularly Vis-NIR, MIR spectroscopy, and their data fusion, coupled with chemometrics and spectral preprocessing for estimating soil attributes. Additionally, the crucial functions of assessing model accuracy and validating model estimates of soil properties are discussed. Partial least squares regression (PLSR) was used in more than 100 studies in 2022. Based on the literature published from 2020 to 2025, the data fusion method predicts soil properties more accurately. This review also sheds light on recent advances in spectroscopic methods, including improvements in speed (e.g., MIR spectroscopy is up to 12 times faster than traditional methods), instrument miniaturization, and integration with portable devices, which can make field analysis more affordable. However, the sensitivity of IR spectroscopy to soil moisture, sample heterogeneity, vegetation cover, and calibration transfer issues remains a significant challenge in certain studies. Therefore, a discussion on the challenges in implementing this technique is included in this review, and future perspectives, such as integration of various sensors and portable devices for real-time soil assessment, are successively discussed.

This study evaluated whether compost produced by agricultural residues-mixed livestock manure could improve crop productivity and soil quality while reducing inorganic fertilizer use and nitrous oxide (N₂O) emissions in upland cultivation of autumn Chinese cabbage (Brassica rapa L.). Direct incorporation of crop residues often provides limited benefits due to slow decomposition and nitrogen immobilization; therefore, residues were aerobically co-composted with livestock manure and applied in combination with inorganic fertilizers. The results showed that treatments receiving the agricultural residue–manure compost significantly increased cabbage growth and yield. In particular, the treatment combining compost with 50% the recommended inorganic fertilizer rate produced higher yields than the 100% inorganic fertilizer treatment alone. Compost application improved soil chemical properties, including higher pH, organic carbon, available phosphorus, and exchangeable base cations, and enhanced nitrogen uptake and nitrogen use efficiency. Moreover, cumulative N₂O emissions were reduced by about 21% in the 50% rate inorganic fertilizer plus compost treatment compared with inorganic fertilizer alone. Overall, these findings indicate that co-composted agricultural residues and livestock manure can serve as an effective soil amendment, maintaining high crop productivity while lowering greenhouse gas emissions and reducing reliance on inorganic fertilizers, thereby supporting more sustainable nutrient management in upland cropping systems. Acknowledgments This study was supported by (2026) the RDA Fellowship Program of National Institute of Agricultural Sciences, Research Program for Agriculture Science and Technology Development (Project No. RS-2022-RD010368), Rural Development Administration, Republic of Korea.