Assessment of spatial variability and mapping of soil properties for sustainable agricultural production using geographic information system techniques (GIS)

Denton, O. A.; Aduramigba-Modupe, Vincent; Ojo, A. O.; Adeoyolanu, O. D.; Are, Kayode Steven; Adelana, Ayodele Olumide; Oyedele, A. O.; Adetayo, A.O.; Oke, AO

doi:10.1080/23311932.2017.1279366

Cited by 54 publications

(32 citation statements)

References 17 publications

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“…Clay content and Available phosphorus were strongly spatial autocorrelated (<0.25). But a high nugget effect was obtained in Nitrogen semivariogram plot contradicts the result by Denton et al, 2017. CEC yielded moderate spatial autocorrelation (0.25-0.75).…”

Section: Discussionmentioning

confidence: 58%

“…Soil mapping, on the other hand, is the process of gathering, describing, manipulating, classifying and predicting soil properties (Esu,2005). It also provides up-to-date information in terms of landforms, terraces, and vegetation (Denton et al, 2017;Brown et al, 1978). Importantly, these updated soil inventories are reliable in policy and decision making under precision agriculture (Denton et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In the past 20 years, so much have been achieved as regards to the approaches towards soil mapping (Goovaerts, 1999;Mcbratney et al, 2000Pravat et al, 2016, Denton et al, 2017. Conventional soil mapping which happened to be a tedious method of gathering information about the soil resources by soil mappers have been improved with great advances in spatial science (Esu et al, 2005, Fasina et al, 2012 In traditional soil mapping, a representative soil property from a given location is used to describe a soil unit, which is then vectorized using physiographic and landforms methods in the soil map.…”

Section: Introductionmentioning

confidence: 99%

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

John¹,

Lawani²,

Esther³

et al. 2019

MAS

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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 by clay content with CV=49.03%. Spherical, Gaussian, Hole Effect model, Stable, Exponential and Circular models were fitted for all the soil parameters. The result revealed that soil pH, Sand content, TN and CEC were moderate spatially autocorrelated with nugget/sill value of 0.32, 0.21, 0.49 and 0.30 respectively.  SOC also gave a moderate spatially autocorrelated with nugget/sill value of 0.44. And Clay and Available phosphorus were strong spatially autocorrelated with nugget/sill value of 0.15 and 0.13 respectively. Cross-validation of the output maps using the semivariogram showed that the interpolation models are superior to assuming mean for any unsampled area. The output maps will help soil users within the area to proffer best management technology to improve crop, fiber and water production.   

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Section: Discussionmentioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

John¹,

Lawani²,

Esther³

et al. 2019

MAS

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“…Challenges such as acidity, erosion risk, and input requirements, vary from place to place and an optimal response (for productivity, profitability and environmental impact) should be based on local soil information. Therefore variation of soil fertility properties must be examined to understand the effects of land use and management systems on soil functions [7,8]. It is important to understand this variation and how far it reflects the influence of long-range factors.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe

et al. 2021

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A study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe.

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“…Geostatistical methods are successfully employed to determine such spatial dependency (Öztas 1995). It is quite significant to determine distance-dependent variations in soil physical and chemical characteristics for investigation and mapping of soil characteristics and developing proper management methods accordingly (Denton et al 2017). In spatial analyses, two similar stages are applied for regression analysis and geostatistical analysis.…”

Section: Introductionmentioning

confidence: 99%

Geostatistical Assessments for Characteristics of Soils Around Naip Dam

Sarı

2019

int.jour.sci.res.mana

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Investigation and mapping spatial variations (distance-dependent variations) in soil characteristics with the aid of geostatistical methods will bring about significant savings for labor, time and cost in agricultural practices. From this point forth, this study was conducted around Naipköy dam to determine spatial distribution of physical and chemical soil characteristics. Soil samples were taken from both sides of Tekirdağ Ganos Mountain (the side towards Naip plain and the side towards Marmora Sea). The research site was divided into 1000 x 2000 m grids and disturbed samples were taken from 0-30 cm soil profile of 24 points. Soil samples were subjected to texture, organic matter, pH, EC, calcium, magnesium, potassium and phosphorus analyses. Analyses revealed that the sections towards Naip plain had quite high clay and silt contents because of clayey deposition over these sections. These samples also had high calcium and thusly pH values because of lime layers of these sections. Soil properties were mapped in ArcGIS/ArcMAP 10.6 software with the aid of Inverse Distance Weighting (IDW) method. The maps generated facilitated assessments made for the relationships between land use and physico-chemical soil characteristics. Statistical analyses revealed the least coefficient of variation (8.44%) for pH and the greatest coefficient of variation (73.51%) for phosphorus.

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Assessment of spatial variability and mapping of soil properties for sustainable agricultural production using geographic information system techniques (GIS)

Cited by 54 publications

References 17 publications

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

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

Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe

Geostatistical Assessments for Characteristics of Soils Around Naip Dam

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