The role of Geographic Information Systems (GIS) in sustainable agriculture: Assessing soil fertility and land use changes

Introduction

Sustainable agriculture aims to meet present food and fibre needs without compromising future productivity or ecosystem health. Achieving this requires accurate, detailed and up-to-date information about soil conditions and how land use evolves [1,2] . Traditional soil surveys and field-based monitoring alone fall short in terms of spatial coverage, frequency, and scalability, especially on large, heterogeneous landscapes. GIS, often in combination with remote sensing, offers a data-driven, spatially explicit, and cost-effective alternative. By overlaying multiple layers -soil data, topography, land use, climate, and cadastral boundaries -GIS enables mapping of soil fertility and land suitability, as well as detecting temporal changes in land use. This review explores how GIS contributes to sustainable agriculture via fertility assessment and land-use monitoring, and how it supports informed decision-making for resource-efficient land management [3,4] .

Soil Fertility and Land Use Change: Definitions and Importance

Soil fertility refers to the capacity of soil to supply essential nutrients and water to crops, maintain adequate structure and porosity, and support biological activity for healthy plant growth. Key fertility parameters include nutrient content (macro and micro), soil organic matter, pH, texture, structure, bulk density, and moisture-holding capacity. Fertile soils underpin yield stability, crop quality, and long-term sustainability of agriculture [5,6] . Land use change (LUC) involves the conversion or alteration of land cover -for example, from forest or wasteland to agricultural land, or from agricultural use to urban built-up areas. LUC affects soil fertility and ecosystem services. Unsustainable or unplanned conversions may lead to soil degradation, erosion, nutrient depletion, loss of organic matter, compaction, or decline in water-holding capacity. Monitoring such changes is critical to safeguard soil health, maintain productivity, and balance land-use demand with environmental sustainability. [7,8] . In a rapidly changing socio-economic context, with population growth, urbanisation, changing diets, and climate pressures, reliable data on soil fertility and land-use dynamics are essential for planning, sustainable intensification, land conservation, and policy-making [9,10] .

GIS and Remote Sensing in Soil Fertility Assessment

GIS and remote sensing (RS) together allow spatially explicit mapping of soil fertility parameters over large areas, filling gaps left by traditional point-based sampling [11,12] .

Methodological Principles

 Overlay and layer approach: GIS overlays multiple thematic layers -soil laboratory data, soil type maps, topography (DEM), land use/land cover (LULC), climate, and management history -to spatially represent fertilityrelated attributes [13,14] .  Interpolation and spatial modelling: For unsampled locations, GIS uses interpolation (e.g., kriging) or modelbased spatial prediction to generate continuous fertility maps. For example, researchers applied spatial modelling and ordinary kriging, along with RS-derived variables, to develop soil quality maps in arid lands [14,15] .  Indices-based soil quality modelling: Some studies combine multiple soil attributes (chemical, physical, geomorphologic, and fertility indices) into composite Soil Quality or Soil Fertility Indices (SQI/SFI). GIS integrates these indices to classify land into different fertility categories [16,17] .  Remote sensing data integration: Satellite imagery, spectral indices (e.g., vegetation indexes, spectral reflectance), and terrain data complement soil sampling to infer fertility-related conditions -such as moisture, organic matter content, and degradation or erosion risk [17,18] .

Benefits for Fertility Assessment

 Large-scale coverage and landscape-level insights: GIS enables fertility mapping over entire districts, watersheds or regions, beyond what is feasible through only soil sampling [19, 20] .  Frequent monitoring and updates: With updated soilsample data and RS layers, fertility maps can be revised periodically to reflect changes due to land use, cropping, and soil management [21,22] .  Supporting decision-making: Fertility maps help identify zones of high fertility, degraded zones needing intervention, or variability within farms -enabling targeted soil management, fertiliser application, and sustainable practices [23, 24] .

GIS-Based Land Suitability and Land Use Change Analysis

GIS has proven critical for assessing land suitability for agriculture and tracking land-use/land-cover (LULC) changes core components of sustainable land management [25,26] .

Land Suitability Assessment

Land suitability assessment uses GIS to evaluate multiple criteria -soil properties (texture, depth, pH), topography (slope, aspect), climate, water availability -to classify land into suitability categories for specific crops. Techniques often include multi-criteria decision analysis (MCDA), weighted overlay, and suitability scoring. Recent studies proposed combining analytic frameworks (e.g., AHP) with GIS for rigorous land suitability evaluation [27,28] . Such suitability maps guide farmers and planners in allocating land for agriculture optimally, choosing crops suited to local soil and climate, avoiding cultivation on marginal or degradationprone land, and reducing environmental stress [29,30] .

Land Use / Land Cover Change Analysis

By comparing classified satellite imagery across different time periods, GIS enables the detection of LULC transitions -e.g., agricultural land converted to built-up areas, forest-to-cropland conversion, cropping intensification, and increased fallow land. This provides evidence of land-use pressures, degradation trends, and urbanisation-driven agricultural land loss [3,31] . An Indian study covering 2000-2018 used Landsat imagery and GIS-based classification to quantify the shrinkage of agricultural land and the expansion of non-agricultural uses in the Allahabad district. r Other studies globally and in developing regions apply similar methods for land-use change detection and impact assessment on soil health and ecosystem services [32,33] . Fertility maps guide variable-rate fertiliser application, optimising nutrient use, reducing waste, protecting soil health and the environment. GIS enables resource-efficient farm management tailored to intra-field variability [33,34] .  Land-use planning and policy support: Land suitability maps inform crop planning, land allocation, zoning decisions, and sustainable land-use policy at regional and national levels. They help prevent the overexploitation of fragile soils [35,36] .  Soil degradation detection and conservation prioritisation: LULC change detection reveals zones of deforestation, land degradation, urban encroachment or soil fertility loss -informing conservation, reforestation, or restoration efforts [37,38] .  Supporting resource-efficient and sustainable water, fertiliser, and land management: By correlating soil fertility data with land use, water availability, and cropping patterns, GIS helps optimise irrigation scheduling, cropping intensity, and soil amendment, thereby improving sustainability and reducing environmental footprint [39,40] .  Food security, yield optimisation, and environmental sustainability: Accurate spatial data helps maintain soil health, prevent degradation, and support long-term yield stability, crucial under pressures of growing population, climate change, and limited arable land [41,42] .

Case Studies (with Emphasis on Indian Context)

This section highlights empirical applications of GIS-based fertility assessment and land-use change monitoring, especially in Indian agroecological settings [43,44] .

Soil Fertility and Land Suitability in a Hilly Himalayan Region

Researchers used GIS to integrate soil sampling data, physiography, land use and terrain attributes to generate soil fertility and land suitability maps. The results informed optimal land-use allocation, fertiliser recommendations, and sustainable farming practices in a terrain otherwise rugged to survey manually. The study demonstrated that GIS-based interpolation and overlay analysis reduce time, cost, and labour. Out of different physiographic units, the " intermontane valley" zones were found most suitable for high-value crops, owing to deeper soils, adequate organic carbon, moderate slope, and favourable soil texture. Soils in other units (hills, plateaus) required caution or amelioration before cultivation [45,46] .

Agricultural Land Use Change in Allahabad (Prayagraj), India (2000-2018)

A long-term study tracked the conversion of agricultural land to non-agricultural uses (urbanisation, built-up areas, wastelands) using multi-temporal Landsat satellite imagery and GIS classification. The analysis revealed a steady shrinkage of agricultural land over 18 years, reflecting population pressure and urban sprawl [47,48] . Quantifying such land loss is critical for sustainable agriculture planning. The study provided a spatial baseline, enabling policymakers to assess land-use trends, manage urban encroachment, and protect fertile soils from irreversible conversion [34,35] .

Soil Attribute Mapping and Spatial Variability Assessment in Siliguri, West Bengal, India

In a district-level study, soil samples were collected at multiple points, and GIS was used to map the spatial variability of soil properties (e.g., texture, pH, fertility parameters). The resulting soil attribute maps helped identify zones with high fertility, degraded areas, or regions needing targeted soil management [43,45] . This spatial understanding supported local agricultural planning, guiding fertiliser use, crop selection, and soil amendment, and helped detect potential risks of overuse, soil depletion, or mismanagement [32,33] .

Soil Quality Index Mapping in Semi-arid Region (International Example)

Although conducted outside India, a study in a semi-arid region used a GIS-based model combining chemical, physical, fertility, and geomorphological indices with RS data (satellite imagery, DEM) to classify soil quality across the landscape. Soil quality classes ranged from high to very low, providing a spatial decision-support tool for sustainable land use. This demonstrates that GIS-driven soil quality assessment is viable even under challenging climatic and edaphic conditions a model that can be emulated in arid or semi-arid areas of India [49,50] .

Challenges, Limitations, and Uncertainties

Despite clear advantages, GIS-based soil fertility and land-use analysis face several constraints.  Data quality and ground-truth dependency: GIS-based fertility assessment relies on soil sample data for calibration and validation. In regions with sparse or outdated sampling, fertility maps may misrepresent actual conditions. Sampling depth, frequency, and lab standardisation remain limiting factors [51,52] .  Temporal limitations and dynamics: Soil fertility and land use evolve due to cropping, management, erosion, urbanisation, and climate influences. Unless soil data are updated periodically, GIS maps may become obsolete, limiting their utility for dynamic decision-making [53,54] .  Heterogeneity and scale issues: Soil properties vary at fine spatial scales. Interpolation or suitability analysis at coarse resolution may overlook micro-scale variability or local pockets of poor soil, leading to suboptimal recommendations [55,56] .  Integration complexity: Combining multiple layers, soil, topography, climate, land use, management history, demands computational resources, technical expertise, and institutional capacity. Smallholder farmers or underresourced regions may lack access to such capacity [57,58] .  Uncertainty and classification errors in LULC change detection: Satellite image classification, change detection and interpretation are prone to mistakes, e.g., misclassification between land-use categories, seasonal variation, cloud cover, or image resolution limitations. These errors propagate into analyses and decision tools [49,50] .  Adoption and implementation barriers: Even when GISbased maps exist, actual integration into farm management, policy planning, or extension services remains limited. Institutional inertia, lack of awareness, or insufficient capacity hinder the translation of GIS insights into action [51,52] .

Future Directions and Recommendations

To enhance the role of GIS in sustainable agriculture, the following directions are essential:  Develop periodic and standardised soil-sampling networks: Establishing systematic soil sampling and monitoring across agro-ecological zones, including sample depth, frequency, and standardised lab protocols will strengthen GIS-based fertility mapping accuracy [52,53] .  Integrate GIS with other technologies: Combine GIS with remote sensing, drone imagery, IoT-based soil sensors, and crop modelling tools (e.g., coupling with crop simulation software) to create dynamic, high-resolution decision-support systems [54,55] .  Promote land suitability and agricultural zoning at the policy level: Government and planning agencies should adopt GIS-based land suitability assessments to regulate land use, prevent overexploitation of fragile soils, and align agricultural practices with long-term sustainability [51,52] .

 Capacity building and democratisation of GIS tools:

Provide training and affordable GIS/RS tools (open-source GIS, mobile GIS apps) to local planners, extension workers, and farmers. Encourage participatory GIS (PGIS) to involve stakeholders in land-use planning and soil management [51,52] .  Continuous monitoring and time-series analysis of land use change: Maintain and update land-use classification maps periodically to detect urban encroachment, agricultural land loss, deforestation, or degradationenabling early intervention and conservation planning [48,50] .  Research and model refinement: Develop and validate models (e.g., soil quality indices, land suitability, risk of degradation) adapted to local contexts, cropping systems, and soil-climate conditions. Validate these models with ground data and long-term monitoring [41,50] .

Conclusion

GIS, often integrated with remote sensing and spatial modelling, plays a critical role in advancing sustainable agriculture through detailed, spatially explicit assessments of soil fertility and land-use dynamics. It enables large-scale soil fertility mapping, soil quality classification, land suitability analysis, and land-use change detection, all essential for informed decision-making in agriculture, land management, and policy [53,54] . Empirical studies, including those from India, demonstrate that GIS-based approaches help identify fertile zones, degraded soils, land suitability for specific crops, and trends of shrinking agricultural land. Such insights support precision agriculture, resource-efficient farming, soil conservation, and sustainable land-use planning. However, the full potential of GIS remains underutilised due to constraints around data, sampling, capacity, and institutional adoption. Overcoming these challenges through systematic sampling, technological integration, stakeholder engagement, and policy support can unlock GIS's capacity to support sustainable, resilient, and productive agriculture -crucial in a world facing increasing population, land pressure, and environmental change [55] .

References

1. Anderson MC, Hain CR, Lannom KA. Remote sensing and GIS applications in agricultural water management. Agric Water Manag. 2015;162:1-13.
2. Bian J, Liu X. Mapping soil organic carbon stocks in cropland soils using remote sensing and GIS techniques. Remote Sens Environ. 2018;215:29-38.
3. Bishnoi S, Shukla A. Application of remote sensing and GIS in soil health assessment. Indian J Soil Conserv. 2020;48(2):145-52.
4. Bhattacharyya R, Yadav R. Monitoring soil quality using remote sensing and GIS: A case study of the upper Brahmaputra basin, India. Soil Use Manag. 2019;35(4):55867.
5. Brown JR, Fox J. Soil health assessment using remote sensing technologies. Agronomy. 2020;10(3):345-59.
6. Chen J, Zhang J, Li X. Soil organic carbon estimation using remote sensing data in the semi-arid region of China. Geoderma. 2016;265:57-66.
7. Das R, Chattopadhyay R. GIS-based mapping of soil fertility and its implications for sustainable agriculture in India. J Soil Sci Environ Manag. 2018;9(6):85-92.
8. Feng Q, Zhao Z. GIS and remote sensing integration for precision agriculture: Applications to soil moisture and organic carbon monitoring. Precis Agric. 2021;22(5):110012.
9. Ghosh S, Saha A. Integrating remote sensing and GIS for monitoring soil degradation in the Gangetic plains of India. Remote Sens Appl Soc Environ. 2019;12:10-22.
10. Guo X, Li W. Using remote sensing and GIS to assess soil salinity in arid regions. Land Degrad Dev. 2020;31(3):41223.
11. Jackson ML, Burnell J. Advances in remote sensing techniques for soil health monitoring. Soil Sci Soc Am J. 2017;81(6):1221-34.
12. Jha RK, Kumar A. Soil health monitoring using remote sensing data: A review of techniques and case studies in India. J Environ Manag. 2019;235:159-73.
13. Joshi P, Pandey V. Remote sensing techniques in soil health monitoring and their applications for sustainable agriculture. Int J Environ Sci Technol. 2020;17(4):2531-42.
14. Kharche SR, Singh M. Integration of remote sensing data with GIS for soil erosion assessment in the northern Himalayas. Hydrol Res. 2021;52(2):345-58.
15. Kundu A, Jain R. Precision agriculture in India: Soil health monitoring using remote sensing and GIS. Agric Syst. 2018;166:60-71.
16. Li H, Liu Y. Remote sensing and GIS for sustainable soil management. Environ Monit Assess. 2017;189(10):389402.
17. Liu D, Zhu Y. Soil organic carbon mapping using remote sensing and GIS techniques: A comprehensive review. Agron Sustain Dev. 2020;40(1):16-27.
18. Liu L, Chen Y. Applications of remote sensing and GIS in soil health management: A case study of Indian agriculture. J Environ Eng Sci. 2019;4(2):55-63.
19. Lu S, Zhang H. Soil moisture estimation using remote sensing and GIS: A case study in arid regions. Remote Sens. 2019;11(7):1234-48.
20. Mehdizadeh S, Sadeghi S. The integration of remote sensing and GIS for monitoring soil salinity and sodicity in arid regions. Agric Water Manag. 2018;204:128-38.
21. Mishra SK, Tripathi NK. GIS-based soil health monitoring in the Narmada River Basin, India. Environ Monit Assess. 2018;190(6):328.
22. Panda S, Senapati P. Integration of remote sensing and GIS for monitoring soil erosion and degradation in the western Himalayas. Land Degrad Dev. 2021;32(5):1500-11.
23. Patil M, Sahu N. Remote sensing and GIS applications for soil erosion and fertility mapping in the Indian subcontinent. Environ Earth Sci. 2019;78(6):255-67.
24. Pradhan R, Singh A. Digital soil mapping using remote sensing and GIS: Applications to soil health monitoring. Agric For Meteorol. 2017;238:160-72.
25. Singh M. Sustainable harvest management: Balancing productivity and environmental impact. Agrifrontline. 2025;1(3):11-4.
26. Ranjan R, Sharma A. Integrating remote sensing with GIS for assessing soil erosion in the Indo-Gangetic plains. J Hydrol. 2019;577:1211-25.
27. Singh K, Kumar R. GIS and remote sensing applications for soil quality monitoring in India. Agric Agric Sci Procedia. 2020;18:78-85.
28. Singh R, Mahajan P. Soil health monitoring using remote sensing and GIS: A review of methodologies and challenges in Indian agriculture. Indian J Soil Conserv. 2020;48(3):248-59.
29. Singh S, Tripathi A. Remote sensing and GIS for mapping soil fertility in the Indian subcontinent. Geoderma. 2021;385:114839.
30. Zhang C, Zhang L. GIS-based soil quality monitoring using remote sensing data: A case study in the North China Plain. Field Crops Res. 2020;245:107-15.
31. Zomer RJ, Bossio DA. Remote sensing for monitoring soil organic carbon stocks in agricultural lands: A global perspective. Environ Res Lett. 2021;16(7):074004.
32. Anderson M, Zhao L. Applications of geospatial technology in agriculture: GIS-based solutions for soil health monitoring. Agric Syst. 2016;142:28-39.
33. Kumar R, Singh M. Remote sensing for soil health management in semi-arid regions. J Remote Sens. 2020;35(2):45-56.
34. Thakur K, Raghavendra S. Remote sensing and GIS for soil organic carbon mapping in the Indian Himalayan region. Geocarto Int. 2021;36(4):415-30.
35. Rawat G, Yadav S. GIS-based mapping of soil erosion risk in the river basin using remote sensing data. Land Use Policy. 2020;99:104808.
36. Choudhury D, Mishra R. Mapping soil health indicators using satellite imagery: An assessment from North India. Environ Monit Assess. 2020;192:295-310.
37. Sharma A, Srivastava P. Remote sensing applications for soil degradation and land restoration in India. Soil Use Manag. 2021;37(1):20-9.
38. Singh S, Kumar N. Digital soil mapping using GIS and remote sensing: A case study in Rajasthan, India. Indian J Soil Sci. 2019;67(4):492-501.
39. Bhagat A, Patel K. Use of hyperspectral remote sensing in soil nutrient mapping and monitoring. Remote Sens Appl Soc Environ. 2021;19:1-12.
40. Shukla TD, Nimma D, Pokkuluri KS, Najmusaqib S, Sivakumar KK, Bala BK. Utilizing artificial intelligence for enhancing performance and preventing injuries in sports analytics. In: 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (ICSIT). IEEE; 2024. p. 1-6.
41. Sharma S, Pandey S. Assessment of soil fertility using remote sensing and GIS techniques in agriculture. Environ Sci Pollut Res. 2019;26(10):10084-97.
42. Magrey AH, Belagalla N. Plastic pollution in agriculture: The unsung impact on soil fertility. Agrifrontline. 2025;1(3):24-8.
43. Jha A, Mukherjee R. GIS and remote sensing for soil health assessment in India's agricultural landscapes. Ecol Indic. 2021;125:107314.
44. Gupta R, Sharma K. Integration of satellite imagery and GIS for soil moisture and fertility monitoring in agricultural land. Agric Ecosyst Environ. 2020;292:106829.
45. Bhamini K. Banana plantations under threat: Controlling Panama disease in India. Agrifrontline. 2025;1(4):7-10.
46. Rao SV, Latha P. GIS-based approach for assessment of land use and soil properties in the Krishna River basin. Environ Monit Assess. 2018;190(8):486.
47. Alijoyo FA, Muzafer SS, Bhasin NK, Chattopadhyay D, Das B, Raj II. Optimizing climate impact assessment: Slothinspired optimization with ridge regression for socioeconomic policy insights. In: 2024 IEEE 1st International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST). IEEE; 2024. p. 1-6.
48. Patel R, Soni N. Use of satellite-based remote sensing for mapping soil degradation in the arid regions of India. Int J Remote Sens. 2019;40(18):6950-75.
49. Yadav S, Meena M. Geospatial tools for soil health and productivity mapping: Applications in semi-arid agriculture. Indian J Agric Sci. 2021;91(9):1382-94.
50. Kaur H, Sethi V. Monitoring soil organic matter changes using GIS and remote sensing in a tropical forest ecosystem. Environ Manag. 2020;66(4):507-15.
51. Bhamini K, Kumar A, Magrey AH, Fatima Jafri SK, Najmusaqib S, Raising PL, Ravi, Jan I. The role of GIS and GPS in precision fertilization: Optimizing nutrient management for sustainable cropping. Int J Res Agron. 2025;8(2):260-7.
52. Bhamini K, Meena HL. Advanced soil fertility management: Balancing nutrients for optimal crop yields. Agrifrontline. 2024;1(2):1-5.
53. Nadaf SA, Amara DMK, Patil PL. Distribution of micronutrients in selected pedons of sugarcane-growing Vertisols in Northern Karnataka State of India. Open J Soil Sci. 2022;12:1-12. doi:10.4236/ojss.2022.121001.
54. Amara DMK, Nadaf SA, Saidu DH, Vonu OS, Musa RM, Kamanda PJ, et al. Studies on land resource inventory for agricultural land use planning in Northern Transition Zone of India through remote sensing and GIS techniques. J Geogr Inf Syst. 2021;13:710-728. doi:10.4236/jgis.2021.136039.
55. Nadaf SA, Patil PL, Dasog GS. Identification of micronutrient constraints in sugarcane-growing Vertisols of Northern Transition Zone of Karnataka by GIS technique. Karnataka J Agric Sci. 2015;28(1).