Correlation and variability analysis of soil physico-chemical properties under different land use systems in Pilibhit District

Introduction

Understanding the interrelationships among soil physico-chemical properties is fundamental for assessing soil fertility, nutrient dynamics, and overall agricultural sustainability. Soil is a complex, dynamic natural resource in which physical, chemical, and biological properties are intricately interconnected (Doran & Safley, 1997) [9] . Any alteration in one property often induces corresponding changes in others, thereby influencing nutrient availability, crop productivity, and ecosystem functioning (Havlin et al., 2010) [15] . In this context, correlation analysis serves as a powerful statistical tool to quantify the degree and direction of associations between different soil parameters, enabling researchers to identify key factors governing soil fertility (Wilding & Lin, 2006) [34] . The Pearson correlation coefficient, developed by Karl Pearson (1896), is widely employed in soil science to evaluate linear relationships between variables such as soil texture, organic carbon, pH, cation exchange capacity, and available nutrients (Srinidhi et al., 2020; Bharne, 2021) [29,1] . Numerous studies have demonstrated significant correlations between soil organic carbon and available nitrogen, confirming the role of organic matter as a primary nitrogen reservoir (Kumar et al., 2022; Prajapat et al., 2023) [8,23] . Similarly, strong positive correlations between clay content and cation exchange capacity highlight the importance of fine-textured soil fractions in nutrient retention (Gopan et al., 2022; Subhash et al., 2025) [11,31] . Negative correlations between bulk density and porosity indicate the inverse relationship between soil compaction and pore space, directly affecting root penetration, water movement, and soil aeration (Haruna et al., 2023; Yeboah et al., 2022) [14,35] . Soil reaction (pH) has been reported to exhibit significant negative correlations with organic carbon and micronutrient availability, as alkaline conditions reduce the solubility of Fe, Zn, and Mn (Dodia et al., 2025; Renuka et al., 2026) [8,25] . Conversely, positive correlations between electrical conductivity and soluble cations such as Ca²⁺, Mg²⁺, and K⁺ reflect the influence of soluble salts on nutrient dynamics (Choudhary et al., 2024; Harshitha & Prakash, 2026) [6,13] . Correlation matrices have also been effectively used to identify limiting nutrients, where weak or negative associations between available nutrients and soil properties indicate fixation, leaching, or antagonistic interactions (Rahaman & Saha, 2025; Sarkar, 2024) [24,27] . In the context of intensively cultivated alluvial landscapes such as the Terai region of Uttar Pradesh, systematic correlation analysis becomes particularly relevant. Continuous cropping, imbalanced fertilization, and land use changes can alter natural soil relationships, accelerating nutrient depletion and soil degradation (Padbhushan et al., 2022; Surjeet et al., 2025) [22,32] . Therefore, evaluating correlation matrices alongside variability analysis provides a scientific basis for identifying nutrient deficiencies, understanding soil behavior, and formulating sitespecific nutrient management strategies (Jena et al., 2022; Saraswat et al., 2023) [17,26] . The present study, therefore, aims to evaluate the correlation matrix of various soil physicochemical properties under different land use systems in Pilibhit District to identify key fertility determinants and support sustainable agricultural planning.

Materials and Methods

Description of the Study Area

The present investigation was conducted in Pilibhit district, located in the sub-Himalayan Terai belt of Uttar Pradesh, India, between 28°06′ to 28°53′ N latitude and 79°57′ to 80°27′ E longitude (Map 1). The district falls under a sub-humid, warm climatic regime, with mean annual temperature ranging from 4.3°C (minimum) to 38.4°C (maximum) and average annual rainfall varying from 1300 to 1400 mm, predominantly received during the southwest monsoon (June-September). The total geographical area of the district is approximately 376,180 ha, of which 71.75% is under agriculture, 18.58% under forest, and the remainder under habitation, water bodies, and other uses (Table 12). Seven development blocks, namely Amaria, Marauri, Lalaurikhera, Barkhera, Bilsanda, Puranpur, and Bisalpur, were selected for the study to represent diverse land use systems and edaphic conditions.

Soil Sampling Design and Collection

A stratified random sampling design was employed to capture spatial variability across the study area. A total of 200 georeferenced surface soil samples (0-15 cm depth) were collected during the pre-monsoon season (May-June 2024) prior to crop sowing. The sampling intensity was approximately one sample per 1,880 ha, ensuring representative coverage of all seven blocks (Table 2). At each sampling location, five subsamples were collected from a 10 m × 10 m area using a screw-type auger and thoroughly mixed to form a composite sample (approximately 500 g). The precise geographic coordinates (latitude and longitude in decimal degrees) of each sampling point were recorded using a handheld GPS receiver (Garmin eTrex 10, ±3 m accuracy) and simultaneously cross-verified using the GPS Map Camera mobile application (Version 1.9.12). Detailed metadata, including farmer name, village, current crop, and fertilizer application history, were documented for each location.

Sample Preparation and Storage

Collected soil samples were spread on clean polyethylene sheets, air-dried at ambient temperature (25-30 °C) for 7-10 days, and gently crushed using a wooden roller to avoid mechanical breakdown of soil particles. The air-dried samples were subsequently sieved through a 2 mm nylon sieve to remove coarse fragments, roots, and organic debris. The processed samples were stored in labeled, airtight polythene bags at room temperature (25±2 °C) until laboratory analysis.

Analytical Methods

All soil analyses were conducted in triplicate at the Soil Science Laboratory, Department of Soil Science and Agricultural Chemistry, R.S.M. (P.G.) College, Dhampur, and the Regional Soil Testing Laboratory, Bareilly. Standard analytical protocols were followed as summarized in Table 1. Analytical-grade reagents and double-distilled water were used throughout the study. Quality assurance was maintained through the analysis of certified reference materials and reagent blanks. [4] Soil colour Munsell colour chart (moist condition) Munsell Color Company (2009) [20] Bulk density (BD)

Core method (core volume: 100 cm³, oven-dry at 105±2°C for 24 h) Black (1965) [3] Porosity Calculated as: Porosity (%) = [1 -(BD/PD)] × 100, assuming particle density (PD) = 2.65 Mg m⁻³

Danielson & Sutherland (1986) [7] Chemical Properties Soil pH (1:2.5 soil:water suspension) Potentiometric using glass electrode (calibrated with pH 4.0, 7.0, and 9.2 buffers) Jackson (1973) Electrical conductivity (EC) (1:2.5 soil:water suspension) Conductometric using conductivity meter (standardized with 0.01 M KCl) Jackson (1973) [16] Organic carbon (OC) Walkley and Black wet digestion method (potassium dichromate oxidation followed by ferrous ammonium sulphate titration)

Walkley & Black (1934) [3] ; Jackson (1973) [16] Cation exchange capacity (CEC) Sodium saturation method (1N sodium acetate, pH 8.2; displaced by 1N ammonium Jackson (1973) [16] acetate; Na⁺ estimated by flame photometer)

Calcium carbonate (CaCO₃) Rapid titration method (neutralization with 0.

Primary Macronutrients

Available nitrogen (N) Alkaline potassium permanganate method (KMnO₄ + NaOH) followed by Kjeldahl distillation and titration

Subbiah & Asija (1956) [30] Available phosphorus (P) Olsen's method (0.5 M NaHCO₃, pH 8.5; colorimetric determination at 882 nm using spectrophotometer) Olsen et al. (1954) [21] Available potassium (K) Neutral 1N ammonium acetate extraction (pH 7.0); determination by flame photometer

Hanway & Heidel (1952) [12] Secondary Nutrients Available sulphur (S) 0.15% CaCl₂ extraction; turbidimetric determination at 420 nm using BaCl₂ Chesnin & Yien (1950) [5] Exchangeable calcium (Ca²⁺) and magnesium (Mg²⁺)

Neutral 1N ammonium acetate extraction (pH 7.0); EDTA complexometric titration (Ca²⁺ with murexide indicator; Ca²⁺+Mg²⁺ with Eriochrome Black T indicator) Sparks (1996) [28] Micronutrients Available zinc (Zn), iron (Fe), copper (Cu), manganese (Mn)

DTPA extraction (0.005 M DTPA + 0.01 M CaCl₂ + 0.1 M triethanolamine, pH 7.3); determination by atomic absorption spectrophotometer (AAS, GBC Avanta, Australia)

Lindsay & Norvell (1978) [19] Available boron (B) Hot water extraction (boiling for 5 min); colorimetric determination using Azomethine-H reagent at 420 nm Bingham (1982) [2] 2.5 Quality Control and Assurance Analytical accuracy was ensured through the analysis of reagent blanks, duplicate samples (10% of total samples), and certified reference materials (CRM, Soil-5 from IAEA). The acceptable relative percent difference (RPD) between duplicate analyses was maintained below 5% for all parameters. Instrument calibration was performed before each analytical batch using standard solutions of appropriate concentrations.

Statistical Analysis: Variability Assessment

Descriptive statistics, including minimum, maximum, mean (x ̅ ), standard deviation (SD), and coefficient of variation (CV), were computed for each soil parameter using IBM SPSS Statistics (Version 26.0, IBM Corp., Armonk, NY, USA). The CV was calculated as:

Spatial variability was classified according to Wilding (1985) [33] as follows: low variability (CV <15%), moderate variability (CV 15-35%), and high variability (CV >35%). Standard error (SE) was also computed to indicate the precision of sample means.

Correlation Analysis

The linear relationships among all measured soil physicochemical properties were evaluated using Pearson's productmoment correlation coefficient (r). The correlation matrix was constructed for 18 soil parameters, including sand, silt, clay, BD, porosity, pH, EC, OC, CEC, available N, P, K, S, Ca²⁺, Mg²⁺, Zn, Fe, Cu, Mn, and B. The Pearson correlation coefficient was calculated using the following formula:

Nutrient Index Calculation

The fertility status of soils was assessed using the Nutrient Index Value (NIV) approach as proposed by Ghosh and Hasan (1980) [10] Where

• N_L, N_M, N_H = number of samples in low, medium, and high categories, respectively • N_T = total number of samples (200) For primary macronutrients (N, P, K), fertility classes were defined as: low (<1.67), medium (1.67-2.33), and high (>2.33). For secondary nutrients and micronutrients, classes were: very low (<1.33), low (1.33-1.66), marginal (1.66-2.00), adequate (2.00-2.33), high (2.33-2.66), and very high (>2.66).

Geospatial Analysis and Thematic Mapping

Spatial distribution maps for soil properties were prepared using ArcGIS Desktop 10.8 (ESRI, Redlands, CA, USA). The point shapefile of 200 sampling locations was created by importing GPS coordinates (WGS 1984 datum, UTM zone 44N projection). The study area boundary was digitized from Survey of India topographic maps (scale 1:50,000; sheet nos. H44T 9 to H44U7). Inverse Distance Weighting (IDW) interpolation with a power parameter of 2 was employed to generate continuous surface maps for pH, EC, OC, CEC, available N, P, K, and micronutrients. The IDW method assumes that points closer to each other are more similar than those farther apart, making it suitable for spatially continuous soil properties.

Results and Discussion

Correlation Matrix Analysis of Soil Properties

The correlation matrix analysis was undertaken to evaluate the degree and direction of association among various physicochemical properties and available macro-and micronutrients of soils under different land use systems in Pilibhit district, Uttar Pradesh. Understanding these interrelationships is essential for interpreting soil behavior, nutrient dynamics, and overall soil fertility status (Wilding & Lin, 2006; Havlin et al., 2010) [34,15] . The analysis revealed a highly complex and interdependent relationship among soil physical and chemical parameters, indicating that soil fertility is regulated by the combined influence of texture, organic matter, and nutrient interactions (Doran & Safley, 1997) [9] .

Interrelationships among Soil Textural Fractions

The relationship among soil textural fractions exhibited strong and highly significant negative correlations. Sand content showed a highly significant negative correlation with silt (r = -0.731*) and clay (r = -0.736*), indicating that an increase in the coarse fraction is associated with a corresponding decrease in finer soil particles. Such relationships are characteristic of soil textural composition and have important implications for soil structure, water retention, aeration, and nutrient-holding capacity. Coarse-textured soils generally possess lower water and nutrient retention compared to fine-textured soils. Similar strong negative correlations between sand and finer fractions have been reported by Bharne (2021) [1] in Ratlam district, Madhya Pradesh, and by Subhash et al. (2025) [31] in the Vertisols of central India.

Bulk Density and Porosity Relationship

Bulk density and porosity exhibited a perfect and highly significant negative correlation (r = -1.000*), confirming the inverse relationship between soil compaction and pore space. As bulk density increases, soil pores decrease, thereby restricting water infiltration, gaseous exchange, and root penetration. This finding is consistent with Haruna et al. (2023) [14] , who reported a very strong negative correlation (r = -0.978) between bulk density and porosity in agricultural farmlands around Zaria, Nigeria, indicating that continuous cultivation and soil compaction significantly degrade soil quality. Similarly, Yeboah et al. (2022) [35] observed strong positive correlations between bulk density and clay content (r = 0.45***) and negative correlations with soil organic carbon in the Guinea Savanna zone of northern Ghana. Bulk density also showed a significant positive correlation with soil pH (r = 0.226**), suggesting that compact soils tend to exhibit relatively higher pH values, possibly due to reduced leaching and greater accumulation of exchangeable basic cations.

Soil pH, Organic Carbon and Electrical Conductivity

Soil reaction (pH) demonstrated a highly significant negative correlation with organic carbon (r = -0.440***), indicating that soils rich in organic matter tend to be comparatively acidic. This may be attributed to the release of organic acids during decomposition and mineralization processes. Similar negative correlations between pH and organic carbon have been reported by Kumar et al. (2022) [8] in Shahjahanpur district (r = -0.757) and by Choudhary et al. (2024) [6] in Tonk district, Rajasthan, where pH showed negative correlations with nitrogen and phosphorus. Electrical conductivity (EC) exhibited a weak but significant positive correlation with organic carbon (r = 0.176*), suggesting that decomposition of organic residues may increase the concentration of soluble salts in soil solution. Comparable observations were made by Harshitha and Prakash (2026) [13] in Tumakuru district, Karnataka, where EC showed strong positive correlations with phosphorus (r = 0.57), calcium (r = 0.43), and sulphur (r = 0.41).

Organic Carbon as a Central Regulator of Nutrient Availability

Organic carbon emerged as one of the most influential parameters affecting nutrient availability and soil fertility. A very strong and highly significant positive correlation was observed between organic carbon and available nitrogen (r = 0.903*), highlighting the role of soil organic matter as a major reservoir of nitrogen. This relationship indicates that nitrogen mineralization is strongly dependent on the organic matter content of soils. This finding corroborates the work of Prajapat et al. (2023) [23] in Indore district, Madhya Pradesh, who reported a highly significant positive correlation between organic carbon and nitrogen (r = 0.927). Similarly, Kumar et al. (2022) [8] observed a strong positive correlation (r = 0.956) between organic carbon and available nitrogen in KVK farm soils of Shahjahanpur. Organic carbon also exhibited strong positive correlations with exchangeable calcium (r = 0.700*) and magnesium (r = 0.368*), suggesting that soils enriched with organic matter possess improved nutrient retention and cation exchange capacity. Gopan et al. (2022) [11] in Kollam district, Kerala, similarly reported positive correlations between organic carbon and CEC (r = 0.451*) and calcium (r = 0.446*). Dodia et al. (2025) [8] also observed positive correlations of organic carbon with phosphorus (r = 0.46), potassium (r = 0.51), calcium (r = 0.33), and magnesium (r = 0.63) in semi-arid soils of Kachchh district, Gujarat.

Cation Exchange Capacity and Texture

Cation exchange capacity (CEC), an important indicator of soil fertility and nutrient retention, showed a highly significant positive correlation with clay content (r = 0.696*), emphasizing the role of fine soil particles in enhancing nutrient-holding capacity. Conversely, CEC exhibited a significant negative correlation with sand content (r = -0.516*), indicating comparatively lower nutrient retention in coarse-textured soils. These findings clearly demonstrate the influence of soil texture on nutrient dynamics and soil fertility. Similar results were reported by Srinidhi et al. (2020) [29] in Chittor district, Andhra Pradesh, where clay content showed positive associations with nutrient retention, and by Rahaman and Saha (2025) [24] in Jalpaiguri district, West Bengal, where CEC was positively correlated with silt content.

Phosphorus and Potassium Dynamics

Available phosphorus showed weak to moderate negative correlations with organic carbon (r = -0.147*) and available nitrogen (r = -0.189**), suggesting possible phosphorus fixation processes or reduced mobility under certain soil conditions. In contrast, available potassium showed mostly weak and nonsignificant correlations with other soil parameters, indicating its relatively independent behavior within the soil system. This finding aligns with Surjeet et al. (2025) [32] , who reported that in the semi-arid Bundelkhand region, organic carbon and nutrients (N, P, K) showed significant spatial variability due to land use and management practices, with phosphorus and potassium exhibiting relatively independent distribution patterns.

Exchangeable Calcium and Magnesium Relationships

Exchangeable calcium exhibited strong positive correlations with organic carbon (r = 0.700*) and available nitrogen (r = 0.695*), indicating that soils rich in organic matter generally possess higher calcium availability.

Magnesium also exhibited a strong positive correlation with calcium (r = 0.653*), reflecting their complementary role in maintaining soil chemical balance and plant nutrition. Similar findings were reported by Choudhary et al. (2024) [6] , who observed a strong positive correlation between magnesium and nitrogen (r = 0.804), and by Renuka et al. (2026) [25] , who noted that exchangeable cations are strongly influenced by soil pH and organic carbon under different land use systems in Rajouri district, Jammu & Kashmir.

Micronutrient Interrelationships

Among micronutrients, zinc showed significant positive correlations with potassium (r = 0.156*) and calcium (r = 0.146*), indicating its association with nutrient-rich soil environments. Iron exhibited a highly significant positive correlation with zinc (r = 0.296***), suggesting their cooccurrence in soil mineral fractions. This is consistent with Dodia et al. (2025) [8] , who reported an extremely high positive correlation between copper and zinc (r = 0.97) in semi-arid soils, indicating a common mineralogical source or similar adsorption behavior. Renuka et al. (2026) [25] also observed that Zn showed positive correlations with sand, clay, organic carbon, and available N, P, and K, but negative correlations with pH and EC. In contrast, manganese showed significant negative correlations with organic carbon (r = -0.203) and nitrogen (r = -0.206), indicating that higher organic matter content may reduce manganese availability through complexation or redox-mediated reactions. Sarkar (2024) [27] in Darjeeling district, West Bengal, also reported seasonal variations in micronutrient availability, with complex interactions between organic matter and metal ions.

Copper exhibited significant positive correlations with organic carbon (r = 0.256*) and nitrogen (r = 0.223), highlighting the role of soil organic matter in improving micronutrient availability through chelation and adsorption mechanisms. This finding suggests that soils with higher organic matter content are more efficient in retaining and supplying micronutrients to plants. Jena et al. (2022) [17] similarly reported that organic carbon and terrain attributes significantly influence micronutrient distribution in the north-eastern Himalayan region. Saraswat et al. (2023) [26] also emphasized that land-use practices such as continuous cropping, fertilization, and irrigation play a significant role in determining micronutrient availability in Firozabad district, Uttar Pradesh. Overall, the correlation analysis clearly demonstrates that soil fertility is governed by a dynamic interaction among physical properties, organic matter, and nutrient elements. Among all the parameters studied, organic carbon emerged as the central factor regulating nutrient availability and soil health. The results emphasize the importance of adopting integrated soil management practices that enhance soil organic matter for sustaining long-term agricultural productivity under different land use systems.

Conclusion

The present study revealed significant spatial variability and interrelationships among soil physico-chemical properties under different land use systems in Pilibhit district, Uttar Pradesh. The soils were predominantly loam and sandy loam in texture, with moderate to high variability observed in organic carbon, cation exchange capacity, and available nutrients. Correlation analysis indicated that organic carbon plays a central role in regulating soil fertility and nutrient availability, showing strong positive relationships with available nitrogen, calcium, and magnesium.

Clay content was positively associated with cation exchange capacity, while sand content negatively influenced nutrient retention. The study also identified nitrogen and zinc as the major limiting nutrients in the region. The findings emphasize the importance of integrated and site-specific nutrient management practices, including balanced fertilization and organic matter addition, for maintaining soil health and sustainable agricultural productivity. GIS-based spatial variability assessment can further support precision farming and efficient soil resource management in the Terai region.

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