Academic Journal
Environmental and geostatistical modelling of soil properties toward precision agriculture.
| Title: | Environmental and geostatistical modelling of soil properties toward precision agriculture. |
|---|---|
| Authors: | Anthony, Tobore, Nkwunonwo, Ugonna, Emmanuel, Anoke, Ganiyu, Oyerinde |
| Source: | Discover Soil; Dec2025, Vol. 2 Issue 1, p1-16, 16p |
| Subject Terms: | Soils, Precision farming, Statistical models, Spatial arrangement, Normalized difference vegetation index, Geological statistics, Fragmented landscapes, Remote sensing |
| Geographic Terms: | Abeokuta (Nigeria) |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Discover Soil is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 191838797 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1040.81262207031 |
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| Items | – Name: Title Label: Title Group: Ti Data: Environmental and geostatistical modelling of soil properties toward precision agriculture. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Anthony%2C+Tobore%22">Anthony, Tobore</searchLink><br /><searchLink fieldCode="AR" term="%22Nkwunonwo%2C+Ugonna%22">Nkwunonwo, Ugonna</searchLink><br /><searchLink fieldCode="AR" term="%22Emmanuel%2C+Anoke%22">Emmanuel, Anoke</searchLink><br /><searchLink fieldCode="AR" term="%22Ganiyu%2C+Oyerinde%22">Ganiyu, Oyerinde</searchLink> – Name: TitleSource Label: Source Group: Src Data: Discover Soil; Dec2025, Vol. 2 Issue 1, p1-16, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Soils%22">Soils</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+arrangement%22">Spatial arrangement</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Geological+statistics%22">Geological statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Fragmented+landscapes%22">Fragmented landscapes</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Abeokuta+%28Nigeria%29%22">Abeokuta (Nigeria)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Discover Soil is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=191838797 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s44378-025-00083-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Abeokuta (Nigeria) Type: general – SubjectFull: Soils Type: general – SubjectFull: Precision farming Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Spatial arrangement Type: general – SubjectFull: Normalized difference vegetation index Type: general – SubjectFull: Geological statistics Type: general – SubjectFull: Fragmented landscapes Type: general – SubjectFull: Remote sensing Type: general Titles: – TitleFull: Environmental and geostatistical modelling of soil properties toward precision agriculture. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Anthony, Tobore – PersonEntity: Name: NameFull: Nkwunonwo, Ugonna – PersonEntity: Name: NameFull: Emmanuel, Anoke – PersonEntity: Name: NameFull: Ganiyu, Oyerinde IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Numbering: – Type: volume Value: 2 – Type: issue Value: 1 Titles: – TitleFull: Discover Soil Type: main |
| ResultId | 1 |