Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F19%3A79648" target="_blank" >RIV/60460709:41210/19:79648 - isvavai.cz</a>
Result on the web
<a href="http://www.ccsenet.org/journal/index.php/mas/article/view/0/40749" target="_blank" >http://www.ccsenet.org/journal/index.php/mas/article/view/0/40749</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5539/mas.v13n10p60" target="_blank" >10.5539/mas.v13n10p60</a>

Result language
angličtina
Original language name
Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models
Original language description
In precision Agriculture, geostatistical methods as a predictive tool have been extensively utilized. The approach estimates soil properties spatial variability and dependency. This study was carried out in Ovia north east Local Government Area of Edo State of Nigeria in order to map soil properties Sand, Clay, pH, OC, P, N and CEC and redict their spatial variability. Twenty nine 29 soil samples were collected randomly from Typic Kandiudults soil type under three different land use, teak forest plantation, shrub, and arable farm. The soil samples were air dried and passed through a 2 mm sieve before being analyzed for pH(CaCl2), SOC, Sand, Clay, Phosphorus, Nitrogen, and CEC. Generated data were statistically and geostatistically computed to explain the spatial variability of soil properties. The traditional method of soil analysis and interpretation are tedious, time-consuming with escalating budgets thus geostatical approach. Available phosphorus yielded large variability with CV=57,08% followed
Czech name
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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