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Abstract <p>This study aimed to evaluate the effects of different land use types (Melissa, cotton, pistachio, and uncultivated areas) on the physical, chemical, and biochemical properties of soils developed on the same parent material under semi-arid conditions, and to support these findings through spectral reflectance analysis. A total of 16 soil samples were collected from a depth of 0–30 cm and subjected to laboratory analyses. Depending on land use, the highest organic matter content (1.8%) was observed in Melissa-managed land, whereas the lowest value (1.2%) was recorded in uncultivated land. β-glucosidase and dehydrogenase activities in Melissa-managed land were 19.2 mg p-nitrophenol/g and 16.9 µg TPF/g, respectively, while these values were lower in uncultivated soils (7.1 mg/g and 11.8 µg/g). Electrical conductivity (EC) was highest in cotton-managed land (365.7 µS/cm) and lowest in uncultivated land (292.7 µS/cm). Principal Component Analysis (PCA) applied to soil parameters (OM, EC, pH, clay, sand, enzymatic activities, etc.) revealed that the first two components explained 77.3% of the total variance (PC1: 50.1%; PC2: 27.2%). Melissa and pistachio areas were positively differentiated along the axes associated with organic matter and enzymatic activity, whereas uncultivated areas were located in the negative direction. A separate PCA based on spectral reflectance data showed that the first two components accounted for 92.4% of the total variance (PC1: 70.6%; PC2:&#xa0;21.8%). Notably, Melissa-managed land exhibited lower reflectance values in the 1400–1900 nm range, strongly associated with organic matter and biological activity. In conclusion, this study provides a comprehensive assessment of the effects of land use on soil health and functionality by integrating traditional laboratory analyses with spectral reflectance-based statistical approaches. The findings suggest that spectral data can serve as a valuable complementary tool for soil monitoring, offering crucial insights for the development of sustainable land management strategies.</p>

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Assessment of Enzyme Activities and Other Soil Properties under Different Types of Land Use by Spectral and Laboratory Methods

  • 期刊论文

  • 发表日期：

    2025年12月18日

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  • F. Kaplan,
  • S. B. Rufaioğlu,
  • A. V. Bilgili,
  • Ç. Küçük

摘要

Abstract

This study aimed to evaluate the effects of different land use types (Melissa, cotton, pistachio, and uncultivated areas) on the physical, chemical, and biochemical properties of soils developed on the same parent material under semi-arid conditions, and to support these findings through spectral reflectance analysis. A total of 16 soil samples were collected from a depth of 0–30 cm and subjected to laboratory analyses. Depending on land use, the highest organic matter content (1.8%) was observed in Melissa-managed land, whereas the lowest value (1.2%) was recorded in uncultivated land. β-glucosidase and dehydrogenase activities in Melissa-managed land were 19.2 mg p-nitrophenol/g and 16.9 µg TPF/g, respectively, while these values were lower in uncultivated soils (7.1 mg/g and 11.8 µg/g). Electrical conductivity (EC) was highest in cotton-managed land (365.7 µS/cm) and lowest in uncultivated land (292.7 µS/cm). Principal Component Analysis (PCA) applied to soil parameters (OM, EC, pH, clay, sand, enzymatic activities, etc.) revealed that the first two components explained 77.3% of the total variance (PC1: 50.1%; PC2: 27.2%). Melissa and pistachio areas were positively differentiated along the axes associated with organic matter and enzymatic activity, whereas uncultivated areas were located in the negative direction. A separate PCA based on spectral reflectance data showed that the first two components accounted for 92.4% of the total variance (PC1: 70.6%; PC2: 21.8%). Notably, Melissa-managed land exhibited lower reflectance values in the 1400–1900 nm range, strongly associated with organic matter and biological activity. In conclusion, this study provides a comprehensive assessment of the effects of land use on soil health and functionality by integrating traditional laboratory analyses with spectral reflectance-based statistical approaches. The findings suggest that spectral data can serve as a valuable complementary tool for soil monitoring, offering crucial insights for the development of sustainable land management strategies.