Understanding spatial distribution variability of surface soil reaction and electrical conductivity in cultivated soils of India as influenced by some environmental factors

Abstract

Assessing and mapping spatial distribution variability of soil reaction (alkalinity and acidity denoted as soil pH) and electrical conductivity (EC) in cultivated areas is crucial for degradation monitoring, precision soil fertility management, and sustainable crop production. The distribution of soil reaction and EC is affected by several factors together with elevation (E), temperature and rainfall. We assessed spatial distribution variability of soil reaction and EC in cultivated areas of 29 states of India, and evaluated the relationship of soil pH and EC with E, average annual maximum temperature (AAMT), and average annual rainfall (AAR). Altogether, 237,496 georeferenced and representative surface (depth of 0 to15 cm depth) soil samples were collected, and processed. Subsequently, processed soil samples were analyzed for soil pH and EC. Soil pH varied from 2.80 to 10.2 (mean ± SD 6.98 ± 1.16) with coefficient of variation of 16.64%. Whereas, EC values ranged from 0.01 to 7.75 dS m⁻¹ (mean ± SD 0.31 ± 0.30 dS m⁻¹) with CV of 96.30%. Soil pH was negatively and significantly correlated with E, AAMT, and AAR. Whereas, soil EC was positively and significantly correlated with E, and negatively and significantly correlated with AAMT, and AAR. The semivariogram analysis resulted in exponential best-fitted model both for soil pH and EC with the lowest MSE and RMSE values. Soil pH (nugget / sill ratio 0.35) and EC (nugget / sill ratio 0.57) had moderate spatial dependence. The interpolation maps, developed by ordinary kriging technique, revealed diverse distribution shapes for soil pH and EC in different states, and agro-ecological regions of India. About 9.7, 18.8, and 31.2% area had soil pH of > 4.5 to ≤ 5.5, > 5.5 to ≤ 6.5, and > 6.5 to ≤ 7.5, respectively. Whereas, 34.6 and 5.6% area had pH value of > 7.5 to ≤ 8.5, and > 8.5, respectively. About 93.1 and 6.7% study area had soil EC value of ≤ 0.50, and > 0.50 to ≤ 1.00 dS m⁻¹, respectively. The generated soil pH and EC maps could be used as reference for local and regional scale policy making, and adoption of suitable soil-crop management strategies for effective soil acidity and salinity management in the study area in order to achieve improved soil health, land degradation neutrality, and sustainable crop production.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-52844-6.

Introduction

Soils play critical role in obtaining sustainable development goals, and providing important ecosystem services^66,60. Maintaining soil health is important for maintaining better plant, human, animal, and planetary health³⁷. Therefore, it is imperative to have proper understanding of soil properties and processes at different scales. This helps in adoption of sustainable soil management practices by effective implementation of suitable amelioration strategies for improving or maintaining soil health.

Soils of different parts of the world are degrading due to several natural and anthropogenic activities which are unstainable in nature³⁵. According to an estimate, soil degradation will directly impact 90% of global soils including almost all populations and ecosystems by 2050¹⁸. In India, soil degradation is occurring in about 147 million hectares of land⁸. Among the causes of soil degradation, soil acidity and salinity are unique ones adversely affecting agricultural production. Soil pH indicates acidity and alkalinity of soil. Generally, soils with 6.50 to 7.50 value are considered neutral. Soils with pH value of < 6.50 and > 7.50 are considered as acidic and alkaline, respectively. Soil acidity is characterized with higher concentrations of Al⁺³ and H⁺ ions, lower base saturation, increased or decreased nutrient availability⁵. Whereas, the excess of soluble salts in saline soils adversely influence plant growth by creating oxidative and osmotic stress, ionic imbalance and reducing nutrient uptake².

Several natural and anthropogenic factors including elevation, temperature and rainfall influence soil acidity and salinity. In general, soil acidity decreases with increase in elevation due to enhanced rainfall and leaching of basic cations. Gu et al.²⁵ recorded decline in soil pH with increase in elevation in different agricultural systems of China. Similarly, Wang et al.⁶⁸ reported increase in soil pH with increase in elevation in typical paddy terrace wetlands of hilly areas of south-west China. Regasa et al.⁴⁸ recorded soil pH reduction in croplands with enhancement in elevation in Nitisols and Cambisols of western Ethiopia. Zhang and Xu⁷¹ recorded variations in soil nutrients and associated soil properties along with altitudinal slope in agricultural land use of Tibetan Plateau. Elevation influences soil salinity by modifying factors related to evaporation, water movement and salt accumulation. Bakr and Ali³ reported strong reverse relationship between elevation and soil salinity in cultivated lands of desert region of Egypt. Researchers reported influence of prevailing temperature and rainfall of an area on soil pH and salinity in various agricultural soils world-wide⁷⁴.

Around half of world’s arable land is having acid soil^67,12,26, with 30% and 75% ice-free top soil and sub-soil, respectively, are acidic⁴³. About 34% of cropped area covering 49 million ha land of India is having acid soil³⁹. One global meta-analysis revealed 13.7% reduction in crop yield due to soil acidification, with vegetables are being more sensitive to soil acidity compared with leguminous and cereal crops¹⁴. Like soil acidity, soil salinity spreads in one billion ha of land covering more than 100 countries threatening agricultural productivity and sustainable development^29,57. India has about 6.74 million ha area with salt affected soils³⁶, with an estimation of 10% additional area becoming saline every year. Moreover, the distribution pattern of both soil pH and salinity vary spatially^30,72,73. This highlights the need for proper assessment and mapping of cultivated areas affected with soil acidity^6,58 and soil salinity^15,51,53 for implementation of suitable management strategies.

Soil sample collection with geo-coordinates by conducting surveys, and analysis of samples help in preparation of spatial distribution maps of soil parameters including soil pH and salinity. The spatial distribution maps help in explaining process and pattern of spatial variability of soil pH and salinity due to combined influence of soil types, soil processes, and anthropogenic activities occurring at various spatio-temporal scales²², and thereby adoption of precision soil fertility management. Using limited samples from an agricultural area, the maps can be prepared using geostatistical tools⁴⁴. The values for the unsampled locations can be predicted using kriging technique by considering spatial correlation with reduced cost of investigation and error of estimation. Various researchers used kriging technique to map soil properties in different parts of world^31,75. Among different kriging techniques, ordinary kriging is the mostly used one. In view this, we hypothesized that spatial distribution of soil pH and electrical conductivity (EC) (which is effective estimation soil salinity) in agricultural soils of India vary and these parameters are influenced by elevation, prevailing temperature and rainfall. We carried out the present study (i) to estimate the values of soil pH and EC, (ii) to assess relations of soil pH and EC with elevation (E), average annual maximum temperature (AAMT), and average annual rainfall (AAR), and (iii) to prepare spatial distribution maps of soil pH and EC of cultivated soils of India, using semivariogram analysis and ordinary kriging technique.

The novelty of the study is that it emphasizes the use of a harmonized national-scale dataset and the integration of soil properties with environmental drivers at a country-wide level. This advances beyond existing knowledge. The study is relevant to soil health monitoring, land degradation assessment, and precision agriculture, and the dataset used is a significant strength.

Materials and methods

Study area

The area of study spreads 29 states (including undivided Jammu & Kashmir) of India. Surface soil (depth of 0 to15 cm) samples from were collected from the study area (10° 0' N to 40° 0' N latitude, 70° 0' E to 100° 0' E longitude, and 0 to 7276 m elevation) spreading 3,056,164 km² (Fig. 1). The details of sample site characteristics, cultivated crops and important agricultural practices are given in Table 1. The Thar Desert and the Himalayas strongly influence the climate of the study area. The area has mainly four climate groups such as tropical dry, tropical wet, montane and subtropical humid. Major portions of southern and eastern part of the study area experience tropical climate. Whereas, lowlands of northern portion experience subtropical situations which become temperate and continental based on areas. Whereas, starkly different microclimates prevail in certain areas. The study area receives wide ranging rainfall with AAR of 1270 mm. The summers are dry and hot with day and night temperature hovering around 41 °C and 29 °C, respectively. Whereas, the winters are mild with average day and night temperature is around 29 °C and 12 °C, respectively. Soils exhibit diverse textures including clayey, sandy and silty types and belong to the Vertisols, Alfisols, Inceptisols, Aridisols, Ultisols, Mollisols and Entisols orders⁹.

Fig. 1.

Study area with (a) soil sampling points, (b) digital elevation model, (c) distribution maps of average annual maximum temperature, and (d) distribution maps of average annual rainfall.

Table 1.

Sample site characteristics and agricultural practices.

Site characteristics and agricultural practices	Description
Soil orders and types	Study area consists primarily of seven orders: Inceptisols (39.74%), Entisols (28.08%), Alfisols (13.55%), Vertisols (8.52%), Aridisols (4.28%), Ultisols (2.51%), and Mollisols (0.40%). Inceptisols, which are young, developed soils, dominate the landscape. These orders define the agricultural and environmental landscape, covering nearly all major soil types from the vast Indo-Gangetic plains to the black soil regions The major types include Alluvial, Black (Regur), Red & Yellow, Laterite, Forest/Mountain, Arid/Desert, Saline/Alkaline, and Peaty/Marshy soils. These soils vary due to climate, rock types, and geographical location
Climate	The study area has a diverse tropical monsoon climate, strongly influenced by the Himalayan mountains and the Thar Desert, which regulate temperatures and monsoon rains. The climate ranges from alpine in the north to tropical in the south, characterized by four main seasons namely winter (December to February), summer (March to May), southwest monsoon (June to September), and post-monsoon (October to November)
Cultivated crops	The study area has a wide range of cultivated crops, broadly classified into food grains (rice, wheat, maize), pulses, oilseeds, commercial cash crops (sugarcane, cotton, jute), and plantation crops (tea, coffee). Agriculture operates in three main seasons: Kharif (monsoon), Rabi (winter), and Zaid (summer), with major producers including Uttar Pradesh, Punjab, West Bengal, and Maharashtra
Important agricultural practices	The important agricultural practices in the study area combine traditional methods with modern sustainable techniques to ensure food security and improve soil health. The key practices include soil preparation, sowing quality seeds, irrigation, manuring, and harvesting, heavily supported by crop rotation, mixed cropping, and agroforestry to boost biodiversity

Soil sample collection, processing and analysis

Altogether 237,496 soil samples were obtained using stainless steel soil augers during April-June months between 2019 and 2024 from the agricultural farms of 29 states of India. Necessary permissions were obtained from the farm owners for soil sample collection. The samples were collected by stratified random sampling technique²⁰. Soil samples were collected from large, medium and small farms. Eight to ten, 5–6 and 2–3 sub-samples were collected from large, medium and small farms, respectively, to make a composite sample. The collected samples were duly processed. The ground samples were sieved using 2 mm size mesh. Polythene bottles were used for storing processed samples for further analysis. Further, soil pH and EC of processed samples were measured in 1:2.0 soil to solution ratio (w/v) using pH meter and EC meter, respectively³².

Collection of elevation and climatic data

The data pertaining to AAR (average value for 1901–2022), E (SRTM DEM) and AAMT (average value for 1901–2022) of the study area were obtained from high spatial resolution (0.5° × 0.5°) data freely available on web such as https://earthexplorer.usgs.gov/, and https://crudata.uea.ac.uk/cru/data/hrg/#current.These datasets were used for preparation of geospatial maps of E, AAMT, and AAR of the study area (Fig. 1).

Statistical analysis

The descriptive statistics namely mean, minimum, maximum, skewness, kurtosis, coefficient of variation (CV), and standard deviation (SD) of soil pH and EC were estimated utilizing SAS statistical software (SAS Institute 2011). The relationships of soil pH and EC with E, AAMT, and AAR were computed by Pearson’s correlation coefficient analysis through extracting dataset (n = 23,354) from the grids of the study area. The linking of point soil data with coarse environment data was carried out by spatial downscaling. Spatial downscaling is a process that disaggregates low-resolution data into high-resolution maps by leveraging high-resolution environmental covariates. This method has been used by several researchers^11,54,70. This process introduces significant uncertainties, primarily because it involves interpolating or inferring small-scale details that are not explicitly represented in the original data. The uncertainties propagated through downscaling directly affect the accuracy of the final impact analysis. The negative impacts of downscaling on study results was reduced by undertaking validation against ground data. The principal component analysis (PCA) was conducted utilizing inputs from correlation analysis. The bi-plot of principal component (PC) 1 and PC 2 was obtained to understand grouping of soil pH, EC, E, AAMT, and AAR.

Geostatistical analysis

The normality of soil pH and EC data was checked by Kolmogorov–Smirnov test, and the data cleared the test (at 0.05 level of significance) after logarithmic transformation of data pertaining to EC. ArcGIS 10.7.1 software was utilized for evaluating spatial structure of soil pH and EC and defining semivariograms. The semivariogram analysis was carried out because interpolation function is determined by semivariogram model (Eq. 1)²¹ as described below.

1

where, γ (h) and m(h) represent semivariogram value at h distance and number of sample pair values within h distance, respectively. Z(X_i), Z(X_i + h) are sample values separated by h distance. Different semivariogram models were tested to select the best fitted with the data. The best fitted exponential model (Eq. 2) was chosen by cross-validation out of exponential, Gaussian and spherical models. The semivariogram parameters such as nugget (C₀), partial sill (C), sill (C₀ + C), range (m), were estimated. Nugget/sill ratio was derived to describe the spatial correlation¹⁰. The prediction accuracy of semivariogram models was evaluated by mean square error (MSE) (Eq. 3) and root mean square error (RMSE).

2

3

where, n denotes the number of observations of soil pH and EC. z(xi, yi) denote observed and measured SOC value. z*(xi, yi) and (xi, yi) represent sampling coordinates. Ordinary kriging procedure was used for interpolation²¹. The boundary lines of agroecological regions (AERs) were overlapped on kriged map to visualize soil pH and EC distribution pattern in different AERs of the study area.

Results

Variability of soil reaction, electrical conductivity, annual average rainfall, elevation and annual average maximum temperature

Soil pH varied widely (2.80 to 10.2) with the mean value of 6.98 and CV value of 16.64% (Table 2). Whereas, EC varied from 0.01 to 7.75 dS m⁻¹ with CV of 96.5%. About 19.2, 28.4, and 32.7% of samples were having soil pH of > 5.50 to ≤ 6.50, > 6.50 to ≤ 7.50, and > 7.50 to ≤ 8.50, respectively (Fig. 2). Whereas, about 84.3, 13.1 and 1.8% of soil samples had EC of ≤ 0.50, > 0.50 to ≤ 1.00, and > 1.00 to ≤ 1.50 dS m⁻¹, respectively. The AAR values of 23,354 grids were from 10 to 3590 mm with mean of 1227 mm. The mean value of E was 588 m with values ranging from 0 to 7276 m. The AAMT varied from 1.02 to 53.6 °C with mean value of 45.8 °C.

Table 2.

Descriptive statistics of soil pH, EC, elevation, average annual maximum temperature and average annual rainfall in the study area.

Soil parameters	n	Minimum	Maximum	Mean	SD	CV (%)	Skewness	Kurtosis
Soil pH	237,496	2.80	10.20	6.98	1.16	16.64	− 0.43	− 0.61
EC, dS m−1	237,496	0.01	7.75	0.31	0.30	96.30	4.68	59.72
E, m	23,354	0.00	7226.00	587.82	989.55	168.34	3.48	12.07
AAMT, °C	23,354	1.02	53.58	45.79	9.58	20.92	− 3.00	8.71
AAR, mm	23,354	10.00	3590.00	1226.89	998.51	81.39	0.50	− 1.17

EC electrical conductivity, E elevation, AAMT average annual maximum temperature, AAR average annual rainfall, m meter, mm milli meter, n number of observations/ data points, SD standard deviation, CV coefficient of variation.

Fig. 2.

Frequency distribution showing per cent soil samples having different levels (a) soil pH and (b) EC.

Relationships of soil reaction, electrical conductivity, with annual average rainfall, elevation and annual average maximum temperature

Soil pH was negatively and significantly correlated with E (r = − 0.034 p ≤ 0.01), AAMT (r = − 0.019 p ≤ 0.01), and AAR (r = − 0.638 p ≤ 0.01). Whereas, soil EC had positive and significant correlation with E (r = 0.131 p ≤ 0.01), and negatively and significantly correlated with AAMT (r = − 0.124 p ≤ 0.01) and AAR (r = − 0.197, p ≤ 0.01). The PC1 vs PC2 bi-plot highlighted 4 clusters of the study parameters (Fig. 3).

Fig. 3.

Bi-plot (PC1 vs PC2) of soil pH, EC, E, AAMT, and AAR of the study area. PC1 principal component 1, PC2 principal component 2, EC electrical conductivity, E elevation, AAMT average annual maximum temperature, AAR average annual rainfall, m meter, mm milli meter.

Spatial variability of soil reaction and electrical conductivity

The best fitted semivariogarm models with the lowest MSE and RMSE values for soil pH and EC were exponential (Table 4, Fig. 4). The MSE and RMSE are the error components of best fitted semi-variogram model during geostatistical analysis. They measure the difference between empirical data points and the fitted theoretical model, with lower values indicating a more accurate model fit. The nugget value of best fitted semivariogarm models for soil pH and EC were 0.43 and 0.06, respectively. Soil pH and EC had moderate spatial dependence with nugget/sill ratio 0.35, 0.57 respectively. The range values were 705,427 and 50,136 m for soil pH and EC, respectively. The distribution variabilities of soil pH (Fig. 5; Supplementary Fig. 1) and EC (Fig. 6; Supplementary Fig. 2) were different. About 7.2, 17.6, and 33.8% area had soil pH of > 4.5 to ≤ 5.5, > 5.5 to ≤ 6.5, and > 6.5 to ≤ 7.5, respectively. Whereas, 35.7 and 5.7% area had soil pH of > 7.5 to ≤ 8.5, and > 8.5, respectively. About 93.1 and 6.7% area were having soil EC of ≤ 0.50, and > 0.50 to ≤ 1.00 dS m⁻¹, respectively. The distribution variability of soil pH and EC in different agro-ecological regions of the study area were different.

Table 4.

Semi-variogram parameters of soil pH and EC.

Soil parameters	Model	C0 (nugget)	C1 (partial sill)	C0 + C1 (sill)	C0/ C0 + C1 (nugget/sill)	Spatial dependence	Range (m)	MSE	RMSE
Soil pH	Exponential	0.43	0.79	1.22	0.35	Moderate	705,427	0.367	0.606
	Gaussian	0.60	0.58	1.18	0.51	Moderate	456,389	0.379	0.615
	Spherical	0.51	0.67	1.18	0.43	Moderate	542,243	0.372	0.610
EC, dS m−1	Exponential	0.06	0.04	0.10	0.57	Moderate	50,136	0.053	0.230
	Gaussian	0.08	0.03	0.11	0.76	Strong	61,092	0.056	0.237
	Spherical	0.07	0.03	0.10	0.71	Moderate	61,092	0.055	0.234

EC electrical conductivity, m meter, MSE mean square error, RMSE root mean square error.

Fig. 4.

Experimental semivariogarm models of soil pH and EC.

Fig. 5.

Distribution map of soil pH in the study area.

Fig. 6.

Distribution map of soil EC in the study area.

Discussion

Variability of soil reaction, and electrical conductivity

The CV value indicates relative dispersion of dataset⁴⁵. Soil pH (CV 16.64%) and EC (CV 96.30%) had moderate (10 to 100%) variability. The lower CV value for soil pH indicates that the soil acidity or alkalinity is relatively uniform and stable across the area.

Whereas, the higher CV value for soil EC indicates significant spatial or temporal variability in salinity, moisture, or soil texture across the study area. The higher CV value with extreme spatial heterogeneity has been captured by ordinary kriging geostatistical interpolation method which estimates the values at unsampled locations while accounting for high local variation. Soil pH varied widely (2.80 to 10.20). This variation is attributed to parent material, climate, crop cover, topography, and anthropogenic activities⁷³. Similar to the present findings, Belay et al.⁶ reported surface (0 to 30 cm depth) soil pH of 4.2 to 8.82 (with mean value of 5.84) in northwestern highlands of Ethiopia. Zhang et al.⁷⁴ recorded soil pH of 3.94 to 7.74 in A horizon soil of a hilly region of Chongqing, southwestern China. The variation in soil EC values in the study area from 0.01 to 7.75 dS m⁻¹ is ascribed to soil texture, water content, organic matter content, temperature and salinity. Similarly, Shukla et al.⁵⁶ recorded soil EC from 0.01 to 5.98 dS m⁻¹ in surface soils of north-western Indian Himalayan region.

Relationships of soil reaction, and electrical conductivity, with annual average rainfall, elevation and annual average maximum temperature

Soil pH was having negative and significant correlation with E, AAMT and AAR (Table 3). This indicates the decrease in soil pH with increase in E, AAMT and AAR and vice-versa. The contribution of AAR towards soil pH was 41% (R² = 0.4065) (Fig. 7). The decrease in soil pH with increase in E is accredited to leaching of Ca⁺⁺, Mg⁺⁺, and K⁺ with higher rainfall, slower breakdown of accumulated organic matter and nature of parent material^25,40. Soil pH reduction with increase in AAMT is credited to release of acids due to chemical weathering of minerals, and increased microbial decomposition of organic matter. Sun et al.⁵⁹ also recorded the effect of climate change on soil pH in grass land areas of Tibetan plateau. As discussed above, higher rainfall causes leaching of basic cations resulting in decline in soil pH levels. Similarly, Belay et al.⁶ reported inverse relationship between soil pH and rainfall in northwestern region of Ethiopia.

Table 3.

Pearson’s correlation coefficient values showing relationship of soil pH and EC with elevation, average annual maximum temperature, and average annual rainfall (n = 23,354).

	Soil pH	Soil EC (dS m−1)	E(m)	AAMT (°C)	AAR (mm)
Soil pH	1.000
Soil EC, dS m−1	0.148**	1.000
E, m	− 0.034**	0.131**	1.000
AAMT, °C	− 0.019**	− 0.124**	− 0.564**	1.000
AAR, mm	− 0.638**	− 0.197**	− 0.185**	0.118**	1.000

EC electrical conductivity, E elevation, AAMT average annual maximum temperature, AAR average annual rainfall, m meter, mm milli meter, ** = significant at p ≤ 0.01.

Fig. 7.

Relationship of soil pH with average annual rainfall.

The significant and positive correlation of soil EC with E highlights the fact that soil EC increases with rise in E and vice-versa. This is attributed to increased soil organic matter (SOM) and nutrient retention, higher soil moisture content, higher content of finer particles, decreased mineralization and leaching (if temperatures are low enough to inhibit microbial activity (decomposition), nutrients can build up rather than being leached out), higher acidity and accumulation of nutrients such as total nitrogen, organic carbon, and exchangeable Mg at higher E. Several researchers have studied the relation of soil EC with E^27,33,46. Negative and significant correlation of soil EC with AAMT and AAR indicates the fact that soil EC decreases with increase in AAMT and AAR and vice-versa. The inverse relation between soil EC and AAMT found in the present study is in contrary to the general trend of increase in soil EC with enhanced temperature due to increased ion mobility in soil water²⁸. However, the negative and significant correlation of soil EC with AAMT is ascribed to reduced soil moisture (lower conductivity), salt leaching/accumulation patterns and reduced water bridging at higher temperature. Sakaguchi et al.⁴⁹ reported decrease in thermal conductivity with increasing temperature in nearly dry sandy soil. The reduction in soil EC with rise in AAR is attributed to removal of soluble salts by leaching from surface soils⁵³.

Spatial variability of soil reaction and electrical conductivity

The best fitted semivariogram model is dependent on definite dataset and the process which is modelled. In the present study, soil pH and EC had exponential best fitted model (Table 4, Fig. 4). The exponential model indicates that the data either decays or grows at a growing rate, making a curve which rapidly rise or quickly falls and levels off subsequently. Behera et al.⁴ reported exponential best fitted model for soil EC and pH in cultivated areas Haryana state of India. Soil pH had higher nugget value (0.43) than the nugget value of soil EC (0.06). The nugget value represents microvariability, impact of measurement errors, and other variability at distances lower than sampling interval. The nugget/sill ratio is considered for describing level spatial dependence levels of soil variables^10,35. The values ≤ 0.25, 0.25 to 0.75, and > 0.75 indicate low, moderate and strong spatial dependence, respectively. Both soil pH (nugget/sill ratio 0.35) and EC (nugget/sill ratio 0.57) had moderate spatial dependence level, which is attributed to joint effect of inherent soil properties like soil mineralogy and soil-crop management practices. Soil pH had higher range value (705,427 m) than the range value of soil EC (50,136 m). The sample points located at the distance lower than the range value are related spatially¹⁷. Whereas, the sample points located at the distance higher than the range are not related spatially. The higher range highlights the impact of natural and anthropogenic factors on soil parameter for the higher distance and vice-versa. The information regarding semivariogram range of soil pH and EC help in guiding soil sampling design in future for soil pH and EC mapping in comparable areas. It is necessary to have sampling distance half of semivariogram range¹⁹.

Spatial distribution map of soil pH revealed that about 29% of study area was acidic having soil pH of ≤ 6.50 (Fig. 5, Supplementary Fig. 1). These areas were found to be mainly distributed in north-eastern states, Bihar, Madhya Pradesh, West Bengal, Jharkhand, Chhattisgarh, Odisha, Kerala, Karnataka, Uttarakhand, Himachal Pradesh, Jammu & Kashmir. Mandal et al.³⁸ also reported distribution of acid soils mainly in the hilly areas of north-east and north-west Himalayan region, eastern plateau and the western coastal plains of India with more than 1200 mm mean annual rainfall. Soil acidity in these regions is attributed to both natural factors such as parent material, high rainfall and leaching of base cations, and decomposition of organic matter; and anthropogenic influences, including soil-crop management practices and fertilizer application⁷⁷. Parent materials of acid soil regions are primarily acidic rocks and sediments low in base cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and high in silica, which weather slowly to produce acidic residues. The key parent materials include granite, gneiss, quartzite, sandstone, and schist, which are common in high-precipitation and leaching environments. Comparable patterns of spatial variability in soil properties influenced by environmental and land-use factors have been widely documented^62,69,63.

To address soil acidity, the application of liming materials and adoption of integrated soil management practices such as organic amendments and agroforestry systems are essential for improving crop productivity^16,23. Evidence suggests that integrating soil fertility management with modern monitoring approaches enhances soil quality and yield performance⁶⁴. Furthermore, the cultivation of acid-tolerant crop varieties is critical for sustainable production^1,76. The effectiveness of soil amendments such as calcite and basic slag has been demonstrated in rehabilitating acidic soils under cropping systems like mustard–rice⁷, with additional support from broader soil management studies^50,61.

Approximately 31% of study area exhibited neutral soil pH (> 6.50 to ≤ 7.50), requiring minimal intervention. In contrast, about 40% of the area was alkaline (pH > 7.50), distributed across states such as Punjab, Jammu and Kashmir, Haryana, Rajasthan, Uttar Pradesh, Bihar, Gujarat, Madhya Pradesh, Telangana, Maharashtra, Karnataka, Andhra Pradesh, and Tamil Nadu. Soil alkalinity in these regions is primarily caused by arid/semi-arid climates with low rainfall and high evaporation, leading to the accumulation of carbonates and bicarbonates of sodium, calcium, and magnesium. The important drivers include weather-intensive mineral weathering, poor drainage, excessive groundwater extraction for irrigation, and the use of sodic irrigation water. The management strategies including gypsum application, organic amendments, improved irrigation practices, micronutrient supplementation, and agroforestry are necessary for sustainable crop production in these regions^24,41,42,13. According to Minhas and Sharma⁴¹, application of amendments like gypsum, equivalent to 50% gypsum requirement, to the surface 15-cm soil only was enough to start cropping with rice. Light and frequent irrigation are ideal for upland crops. Application of higher doses of nitrogen in splits to compensate for volatilization losses, organic matter additions through green manure to increase reclaiming action, skipping phosphorus application in the initial years and the application of zinc to each crop are some of the recommendations for managing these soils. Application of low-grade pyrites also gave encouraging results in calcareous soils. Feasibility of drainage has been successfully demonstrated in quicker rehabilitation of saline and waterlogged soils. Provision for subsurface drains at 1.5 to 2.0 m depth and 50 to75 m spacing in alluvial soils and 12 to 24 m interval in Vertisol was sufficient for facilitating growing crops within 2 to 3 years on lands lying barren for considerable period. Studies on spatial modelling and soil variability further emphasize the importance of site-specific soil management interventions^63,64.

The spatial distribution of soil EC indicated that about 93% of the area had EC ≤ 0.50 dS m⁻¹ (Fig. 6, Supplementary Fig. 2), reflecting predominantly non-saline soils. However, approximately 7% of the area exhibited moderate salinity (> 0.50 to ≤ 1.50 dS m⁻¹), confined to localized pockets. Appropriate salinity management practices including leaching with good-quality irrigation water, crop rotation, residue mulching, and cultivation of salt-tolerant crops are essential^36,47. Advanced techniques such as spectral reflectance analysis and remote sensing have also proven effective in assessing soil properties and guiding sustainable land management practices⁶⁵.

Conclusions

The study delivers knowledge pertaining over all variability and spatial structure of soil pH and EC, and their relationships with E, AAMT and AAR in order to implement precision soil-crop management practices for managing soil acidity, alkalinity and salinity in cultivated areas of India. Soil pH and EC varied widely with CV of 16.64 and 96.30%, respectively. Geostatistical analysis resulted in exponential best fitted model with moderate spatial dependence level for soil pH and EC. The prepared spatial distribution maps highlighted varied patterns of distribution of soil pH and EC in various states, and AERs. About 29 and 40% area had soil acidity and alkalinity, respectively. The efficient soil-crop management strategies need to be adopted in different states and AERs (especially areas with soil acidity and alkalinity) for sustainable soil management and crop production. The generated soil pH and EC maps will serve as base information for the study area for taking suitable policy decisions. Further, periodic monitoring of these parameters is needed to assess the impact of soil-crop management practices on soil pH and EC status for implementation of corrective measures. Alike studies should be carried in agriculturally important areas of the world in order to undertake efficient soil management for arresting land degradation and improving crop growth and production.

Author contributions

Sanjib Kumar Behera: Investigation, Data analysis, Conceptualization, Validation, Methodology, Investigation, Writing-Reviewing, Arvind Kumar Shukla: Conceptualization, Validation, Methodology, Writing-Reviewing, Rahul Mishra: Investigation, Data curation, Writing-Reviewing, Vimal Shukla: Investigation, Data curation. Writing-Reviewing, Amaresh Kumar Nayak: Conceptualization, Validation, Methodology, Mangi Lal Jat: Conceptualization, Validation, Methodology. All authors have read and agreed to the submitted version of the manuscript.

Funding

The research work was financially supported by Department of Agricultural Education and Research, Indian Council of Agricultural Research, Govt. of India, New Delhi funded the research.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.