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Purpose <p>Soil properties and soil-crop management practices influence temporal changes in spatial distribution of soil fertility parameters (SFP). Proper understanding of temporal changes in spatial distribution of SFP is crucial for adoption of modified and informed precision nutrient management strategies (PNMS). This is important because of emerging nutrient deficiencies especially available sulphur (AS) and micronutrients in various soils and crops of different areas of world adversely affecting crop production.</p> Methods <p>We carried out the present study to assess the temporal changes (during the year 2016 to 2023) in spatial distribution of soil pH, electrical conductivity (EC), soil organic carbon (SOC), AS, available zinc (AZn), available manganese (AMn), available iron (AFe), available copper (ACu), and available boron (AB) in cultivated soils of Haryana, India, by employing geostatistical technique.</p> Results <p>The values of evaluated SFP varied widely with coefficient of variation of 6.22% (soil pH) to 127% (EC). There was significant reduction in mean value of EC (0.39 dS m<sup>−1</sup>), AS (65.9&#xa0;mg&#xa0;kg<sup>−1</sup>), AZn (1.56&#xa0;mg&#xa0;kg<sup>−1</sup>), AFe (11.0&#xa0;mg&#xa0;kg<sup>−1</sup>), and AMn (9.14&#xa0;mg&#xa0;kg<sup>−1</sup>) in 2023 compared to mean value of EC (0.50 dS m<sup>−1</sup>), AS (83.4&#xa0;mg&#xa0;kg<sup>−1</sup>), AZn (1.99&#xa0;mg&#xa0;kg<sup>−1</sup>), AFe (12.6&#xa0;mg&#xa0;kg<sup>−1</sup>), and AMn (11.6&#xa0;mg&#xa0;kg<sup>−1</sup>) in 2016. In the year 2016 and 2023, soil pH was positively and significantly correlated with AS (r = 0.108**, 0.109**) and AB (r = 0.051**, 0.113**); and negatively and significantly correlated with AZn (r = -0.079**, -0.096**), AFe (r = -0.019*,—0.219**), and AMn (r = -0.082**, -0.330**). Whereas, SOC was positively and significantly correlated with AZn (r = 0.088**, 0.229**), AFe (r = 0.083**, 0.335**), ACu (r = 0.137**, 0.225**), and AMn (r = 0.031*, 0.235**), and; negatively and significantly correlated with AB (r = -0.029*, -0.036*). SFP had weak to strong spatial dependence; exponential, Gaussian and stable best-fitted (with lower mean square error value) models; and different distribution patterns. There were changes in spatial distribution patterns of SFP during the study period. The areas with lower concentration of AS, AZn, AFe, and AMn increased during 7&#xa0;years period.</p> Conclusions <p>This research elucidates occurrence of temporal changes in mean values and spatial distribution patterns of SFP in the study area highlighting the importance of monitoring the changes in SFP in cultivated areas for applying PNMS. The generated spatial distribution maps of AS and available micronutrients could be used to adopt area specific S and micronutrient management strategies in the study area.</p>

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Geostatistics-Based Understanding of Temporal Changes In Spatial Distribution of Soil Fertility Parameters In An Intensively Cultivated Area of North India

  • 期刊论文

  • 发表日期：

    2025年3月24日

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  • Sanjib Kumar Behera,
  • Arvind Kumar Shukla,
  • Rohtas Kumar,
  • Rahul Mishra,
  • Vimal Shukla,
  • Harendra Yadav,
  • Siba Prasad Datta

摘要

Purpose

Soil properties and soil-crop management practices influence temporal changes in spatial distribution of soil fertility parameters (SFP). Proper understanding of temporal changes in spatial distribution of SFP is crucial for adoption of modified and informed precision nutrient management strategies (PNMS). This is important because of emerging nutrient deficiencies especially available sulphur (AS) and micronutrients in various soils and crops of different areas of world adversely affecting crop production.

Methods

We carried out the present study to assess the temporal changes (during the year 2016 to 2023) in spatial distribution of soil pH, electrical conductivity (EC), soil organic carbon (SOC), AS, available zinc (AZn), available manganese (AMn), available iron (AFe), available copper (ACu), and available boron (AB) in cultivated soils of Haryana, India, by employing geostatistical technique.

Results

The values of evaluated SFP varied widely with coefficient of variation of 6.22% (soil pH) to 127% (EC). There was significant reduction in mean value of EC (0.39 dS m−1), AS (65.9 mg kg−1), AZn (1.56 mg kg−1), AFe (11.0 mg kg−1), and AMn (9.14 mg kg−1) in 2023 compared to mean value of EC (0.50 dS m−1), AS (83.4 mg kg−1), AZn (1.99 mg kg−1), AFe (12.6 mg kg−1), and AMn (11.6 mg kg−1) in 2016. In the year 2016 and 2023, soil pH was positively and significantly correlated with AS (r = 0.108**, 0.109**) and AB (r = 0.051**, 0.113**); and negatively and significantly correlated with AZn (r = -0.079**, -0.096**), AFe (r = -0.019*,—0.219**), and AMn (r = -0.082**, -0.330**). Whereas, SOC was positively and significantly correlated with AZn (r = 0.088**, 0.229**), AFe (r = 0.083**, 0.335**), ACu (r = 0.137**, 0.225**), and AMn (r = 0.031*, 0.235**), and; negatively and significantly correlated with AB (r = -0.029*, -0.036*). SFP had weak to strong spatial dependence; exponential, Gaussian and stable best-fitted (with lower mean square error value) models; and different distribution patterns. There were changes in spatial distribution patterns of SFP during the study period. The areas with lower concentration of AS, AZn, AFe, and AMn increased during 7 years period.

Conclusions

This research elucidates occurrence of temporal changes in mean values and spatial distribution patterns of SFP in the study area highlighting the importance of monitoring the changes in SFP in cultivated areas for applying PNMS. The generated spatial distribution maps of AS and available micronutrients could be used to adopt area specific S and micronutrient management strategies in the study area.