Environmental and geostatistical modelling of soil properties toward precision agriculture
Abstract Understanding the spatial distribution of soil properties is critical for achieving precision agriculture. The study aims to model soil property heterogeneity in the context of food sustainability using remote sensing (RS) and geostatistical techniques at Federal University of Agriculture,...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Discover Soil |
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| Online Access: | https://doi.org/10.1007/s44378-025-00083-y |
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| author | Tobore Anthony Ugonna Nkwunonwo Anoke Emmanuel Oyerinde Ganiyu |
| author_facet | Tobore Anthony Ugonna Nkwunonwo Anoke Emmanuel Oyerinde Ganiyu |
| author_sort | Tobore Anthony |
| collection | DOAJ |
| description | Abstract Understanding the spatial distribution of soil properties is critical for achieving precision agriculture. The study aims to model soil property heterogeneity in the context of food sustainability using remote sensing (RS) and geostatistical techniques at Federal University of Agriculture, Abeokuta, Nigeria. We combined RS metrics like Number patches (NP), Largest-path (LP), and effective MESH alongside Normalized difference vegetation (NDVI), and Enhanced vegetation (EVI) indices from 2014 and 2024, with a particular focus on built-up, vegetation, farmlands, and wetlands in the area. We collected and analyzed 70 geocoded composite soil sample (0 to 30 cm) for their physical, chemical, and biological conditions, interpolated by kriging and added to the exponential, spherical and gaussian to model the soil properties. NP, LP, and MESH showed substantial discontinuity and landscape fragmentation, especially in the built-up areas. At the same time, NDVI, and EVI highlight a significant decrease in vegetation cover, respectively. The modelling of soil properties based on cross-validation showed that soil properties in the studied area ranged between strong (< 0.25) and weak (0.25 to 0.75) spatial autocorrelations. The findings could aid in mitigating anthropogenic climate shocks on soil properties and thus ensuring landscape sustainability and precision agriculture. |
| format | Article |
| id | doaj-art-bb01c1dde3cc4f41807939668d943546 |
| institution | Kabale University |
| issn | 3005-1223 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Soil |
| spelling | doaj-art-bb01c1dde3cc4f41807939668d9435462025-08-20T03:45:44ZengSpringerDiscover Soil3005-12232025-07-012111610.1007/s44378-025-00083-yEnvironmental and geostatistical modelling of soil properties toward precision agricultureTobore Anthony0Ugonna Nkwunonwo1Anoke Emmanuel2Oyerinde Ganiyu3Department of Soil Science and Land Management, Federal University of AgricultureDepartment of Geo-Informatics and Surveying, Faculty of Environmental Studies, University of NigeriaDepartment of Soil Science and Land Management, Federal University of AgricultureDepartment of Soil Science, Faculty of Agriculture, University of AbujaAbstract Understanding the spatial distribution of soil properties is critical for achieving precision agriculture. The study aims to model soil property heterogeneity in the context of food sustainability using remote sensing (RS) and geostatistical techniques at Federal University of Agriculture, Abeokuta, Nigeria. We combined RS metrics like Number patches (NP), Largest-path (LP), and effective MESH alongside Normalized difference vegetation (NDVI), and Enhanced vegetation (EVI) indices from 2014 and 2024, with a particular focus on built-up, vegetation, farmlands, and wetlands in the area. We collected and analyzed 70 geocoded composite soil sample (0 to 30 cm) for their physical, chemical, and biological conditions, interpolated by kriging and added to the exponential, spherical and gaussian to model the soil properties. NP, LP, and MESH showed substantial discontinuity and landscape fragmentation, especially in the built-up areas. At the same time, NDVI, and EVI highlight a significant decrease in vegetation cover, respectively. The modelling of soil properties based on cross-validation showed that soil properties in the studied area ranged between strong (< 0.25) and weak (0.25 to 0.75) spatial autocorrelations. The findings could aid in mitigating anthropogenic climate shocks on soil properties and thus ensuring landscape sustainability and precision agriculture.https://doi.org/10.1007/s44378-025-00083-yEcological sustainabilityFUNAAB LandscapeRemote sensing indexSoil conditionSpatial dependences |
| spellingShingle | Tobore Anthony Ugonna Nkwunonwo Anoke Emmanuel Oyerinde Ganiyu Environmental and geostatistical modelling of soil properties toward precision agriculture Discover Soil Ecological sustainability FUNAAB Landscape Remote sensing index Soil condition Spatial dependences |
| title | Environmental and geostatistical modelling of soil properties toward precision agriculture |
| title_full | Environmental and geostatistical modelling of soil properties toward precision agriculture |
| title_fullStr | Environmental and geostatistical modelling of soil properties toward precision agriculture |
| title_full_unstemmed | Environmental and geostatistical modelling of soil properties toward precision agriculture |
| title_short | Environmental and geostatistical modelling of soil properties toward precision agriculture |
| title_sort | environmental and geostatistical modelling of soil properties toward precision agriculture |
| topic | Ecological sustainability FUNAAB Landscape Remote sensing index Soil condition Spatial dependences |
| url | https://doi.org/10.1007/s44378-025-00083-y |
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