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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

40104 - Soil science

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Modern Applied Science

ISSN

1913-1844

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

18

Strana od-do

60-77

Kód UT WoS článku

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EID výsledku v databázi Scopus

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