Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation

This study aimed to characterize the spatial variability of pH in soils of two farms in the state of Paraná, Brazil, based on two different sampling methods used in precision agriculture, by means of geostatistical analyzes. The first method of sampling the pH grid consisted in the collection of soil samples by the traditional method (1 point / ha). The second method of pH determination was by on-the-go soil sensor (200 points / ha). The spherical model was better suited to most semivariograms, regardless of the sampling method. After adjusting the semivariograms for soil pH determination methods, thematic maps were made using normal kriging. The best spatial distribution of pH was obtained where the attribute was sampled by the on-the-go sensor. The number of pH samples collected and the sampling method influenced the visual representation of pH variability.

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Spatial variability of soil pH and land use as the main influential factor in redbeds of the Nanxiong Basin, China

10.7287/peerj.preprints.27156v1 ◽

2018 ◽

Author(s):

Ping Yan ◽

Hua Peng ◽

Luobin Yan ◽

Shaoyun Zhang ◽

Aimin Chen

Keyword(s):

Land Use ◽

Spatial Variability ◽

Soil Ph ◽

Nutrient Management ◽

Soil Nutrient ◽

Spherical Model ◽

Influential Factor ◽

Land Use Patterns ◽

Use Patterns ◽

Main Factors

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for soil nutrient management and soil pollution prediction. In order to explore the causes of spatial variability of soil pH in redbed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of the soil pH were analysed by geostatistics and classical statistical methods, and the main factors influencing the spatial variability of soil pH are discussed. The results showed that the coefficient of variation in the redbed areas of Nanxiong Basin was 17.18%, indicating moderate variability. The geostatistics analysis showed that the spherical model is the optimal theoretical model for explaining the soil pH’s variability, which is influenced by both structural and random factors. The spatial distribution and pattern analysis showed that soil pH content in the northeast and southwest is relatively high, and is lower in the northwest. These results indicate that topographic factors and land use patterns are the main factors.

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Optimum size in grid soil sampling for variable rate application in site-specific management

Scientia Agricola ◽

10.1590/s0103-90162011000300017 ◽

2011 ◽

Vol 68 (3) ◽

pp. 386-392 ◽

Cited By ~ 26

Author(s):

Marcos Rafael Nanni ◽

Fabrício Pinheiro Povh ◽

José Alexandre Melo Demattê ◽

Roney Berti de Oliveira ◽

Marcelo Luiz Chicati ◽

...

Keyword(s):

Spatial Variability ◽

Precision Agriculture ◽

Production Efficiency ◽

Sampling Interval ◽

Soil Samples ◽

Soil Sampling ◽

Kappa Statistics ◽

Increase Production Efficiency ◽

Site Specific ◽

Specific Management

The importance of understanding spatial variability of soils is connected to crop management planning. This understanding makes it possible to treat soil not as a uniform, but a variable entity, and it enables site-specific management to increase production efficiency, which is the target of precision agriculture. Questions remain as the optimum soil sampling interval needed to make site-specific fertilizer recommendations in Brazil. The objectives of this study were: i) to evaluate the spatial variability of the main attributes that influence fertilization recommendations, using georeferenced soil samples arranged in grid patterns of different resolutions; ii) to compare the spatial maps generated with those obtained with the standard sampling of 1 sample ha-1, in order to verify the appropriateness of the spatial resolution. The attributes evaluated were phosphorus (P), potassium (K), organic matter (OM), base saturation (V%) and clay. Soil samples were collected in a 100 × 100 m georeferenced grid. Thinning was performed in order to create a grid with one sample every 2.07, 2.88, 3.75 and 7.20 ha. Geostatistical techniques, such as semivariogram and interpolation using kriging, were used to analyze the attributes at the different grid resolutions. This analysis was performed with the Vesper software package. The maps created by this method were compared using the kappa statistics. Additionally, correlation graphs were drawn by plotting the observed values against the estimated values using cross-validation. P, K and V%, a finer sampling resolution than the one using 1 sample ha-1 is required, while for OM and clay coarser resolutions of one sample every two and three hectares, respectively, may be acceptable.

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Geostatistical Analysis on Spatial Variability of Soil Nutrients in Vertisols of Deccan Plateau Region of North Karnataka, India

International Journal of Mathematical Engineering and Management Sciences ◽

10.33889/ijmems.2020.5.2.023 ◽

2020 ◽

Vol 5 (2) ◽

pp. 283-295

Author(s):

Vinod Tamburi ◽

Amba Shetty ◽

S. Shrihari

Keyword(s):

Land Use ◽

Spatial Variability ◽

Soil Nutrients ◽

Soil Ph ◽

Agricultural Land ◽

Microwave Plasma ◽

Spherical Model ◽

Atomic Emission ◽

Deccan Plateau ◽

Land Use And Management

Different methods of land use and management have a significant effect on soil properties distribution. Understanding of variations in soil nutrients in agricultural land use is important. An increase in extraction of nutrients, soil degradation, and management of nutrients is leading to a decline in quality of vertisols across the Deccan plateau of India. Though there are studies on spatial variability of vertisols macronutrients, studies on available calcium (Ca) and available magnesium (Mg) are rare. This study is conducted in Gulbarga taluk, north Karnataka, India, to evaluate the variability of soil pH, Ca, Mg, and Zinc (Zn). A total of 78 samples of soils are collected at 0 to 15 cm depth based on the accessibility and distribution of field patterns. Four subsamples represent a single composite sample. Agilent 4200 MP-AES (Microwave Plasma-Atomic. Emission Spectrometer) was used for determining the concentration of soil nutrients. The soil nutrients represent wide variation in coefficient of variation (CV) with a value of 6 % (for pH) to 70.9 % (for Zn). The soil pH showed a significantly positive correlation to Ca and a negative correlation to Mg. Geostatistical investigation indicates spherical model is the best fit for all nutrients. Except for Ca, all nutrients showed moderate spatial dependence. Ordinary kriging is used to generate spatial variability maps. The maps of spatial variability are highly variable in nutrients content and indicate that site-specific management needs to be taken by local authorities and improve the livelihood of marginal farmers and also for sustainable agriculture.

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Status and Spatial Variability of Trace Elements in the Low Ganges River Floodplain Soils of Bangladesh

Journal of Environmental Science and Natural Resources ◽

10.3329/jesnr.v9i2.32160 ◽

2017 ◽

Vol 9 (2) ◽

pp. 71-78

Author(s):

MD Islam ◽

MM Rahman ◽

MH Kabir ◽

GKMM Rahman ◽

MS Hossain

Keyword(s):

Trace Elements ◽

Organic Carbon ◽

Spatial Variability ◽

Soil Ph ◽

Crop Production ◽

Spatial Diversity ◽

Soil Samples ◽

Ganges River ◽

River Floodplain ◽

Distribution Maps

Soils of the Low Ganges River Floodplain encroaching Faridpur district of Bangladesh have immense contribution to crop production, while little information available focusing the spatial variability of trace elements in the area. Therefore, the study was conducted to quantify the trace elements collecting a total of 122 representative soil samples from rice fields of Faridpur district. Soil samples were analyzed and found that Cu, Fe, Mn, Zn and B were ranged from 0.80-6.80, 24295, 10129, 0.122.20 and 0.5-9.05 ppm, respectively. The pollution indexes are noteworthy features which revealed that only Mn may exhibit a risk for environmental pollution. The concentrations of trace elements, pH and organic carbon in soils displayed a significant spatial diversity because of anthropogenic and geogenic contribution. The distribution maps of soil pH, organic carbon and trace elements might be useful to farmers, researchers and planners in designing and planning agricultural programs in the study area.J. Environ. Sci. & Natural Resources, 9(2): 71-78 2016

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SPACE DISTRIBUTION OF SOYBEAN SEED STORAGE POTENTIAL

Revista Caatinga ◽

10.1590/1983-21252019v32n213rc ◽

2019 ◽

Vol 32 (2) ◽

pp. 399-410

Author(s):

RAFAEL DE OLIVEIRA VERGARA ◽

ALEXANDRE GAZOLLA-NETO ◽

GIZELE INGRID GADOTTI

Keyword(s):

Spatial Distribution ◽

Spatial Variability ◽

Protein Content ◽

Precision Agriculture ◽

Seed Quality ◽

Space Distribution ◽

Soybean Seeds ◽

Bed Bug ◽

Physiological Quality

ABSTRACT The objective of this study was to identify the spatial distribution of the physiological quality of soybean seeds during storage from a production field of 39 hectares using geostatistical techniques in the 2012/2013 harvest. Seeds were sampled at geo-referenced points for the determination of physiological quality and spatial dependence analysis. The results were submitted to analysis of descriptive statistics, Pearson's linear correlation and geostatistics. The grid of one point per hectare and a georeferenced sampling mesh with spacing of 100 meters between points was efficient in the evaluation of the spatial variability. It was verified the existence of a negative correlation between the variable protein content and bed bug attack and a significant correlation between the intensity of bed bug damage and the protein content with the variables related to seed quality. Physiological quality is not uniform, particularly in relation to vigor, providing better diagnosis through interpolation maps. Precision agriculture, coupled with the monitoring of seed quality during storage, indicated spatial variability of quality from harvest to the end of storage. Areas with high rates of bedbug and unit damage presented low quality physiology and reduced protein levels. The geostatistics allows to determine the spatial distribution of the physiological quality of soybean seeds in the area of seed production, facilitating the decision making, regarding the areas to be harvested.

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Spatial variability in soil pH and land use as the main influential factor in the red beds of the Nanxiong Basin, China

PeerJ ◽

10.7717/peerj.6342 ◽

2019 ◽

Vol 7 ◽

pp. e6342 ◽

Cited By ~ 2

Author(s):

Ping Yan ◽

Hua Peng ◽

Luobin Yan ◽

Shaoyun Zhang ◽

Aimin Chen ◽

...

Keyword(s):

Land Use ◽

Spatial Variability ◽

Soil Ph ◽

Soil Nutrient ◽

Spherical Model ◽

Influential Factor ◽

Land Use Patterns ◽

Use Patterns ◽

Factors Analysis ◽

Red Bed

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for the management of soil nutrients and the prediction of soil pollution. In order to explore the causes of spatial variability in soil pH in red-bed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of soil pH were analyzed by geostatistics and classical statistical methods, and the main factors influencing spatial variability in soil pH are discussed. The coefficient of variation in the red-bed areas of Nanxiong Basin was 17.18%, indicating moderate variability. Geostatistical analysis showed that the spherical model is the optimal theoretical model for explaining variability in soil pH, which is influenced by both structural and random factors. Analysis of the spatial distribution and pattern showed that soil pH is relatively high in the northeast and southwest, and is lower in the northwest. These results indicate that land use patterns and topographic factors are the main and secondary influencing factors, respectively.

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Distribuição Espacial da Condutividade Elétrica e Matéria Orgânica em um Solo Neossolo Flúvico (Spatial Distribution of Electrical Conductivity and Organic Matter in a Soil Fluvic Neosol)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v6i4.233067 ◽

2013 ◽

Vol 6 (4) ◽

pp. 764 ◽

Cited By ~ 1

Author(s):

Jucicléia Soares da Silva ◽

Abelardo Antônio de Assunção Montenegro ◽

Ênio Farias de França e Silva ◽

Carolyne Wanessa Lins de Andrade ◽

José Roberto Lopes da Silva

Keyword(s):

Electrical Conductivity ◽

Organic Matter ◽

Spatial Distribution ◽

Organic Carbon ◽

Total Organic Carbon ◽

Precision Agriculture ◽

Spherical Model ◽

Contour Maps ◽

Saturation Extract ◽

Cultivar Selection

A agricultura de precisão permite, pelo uso de delimitação de lavouras por coordenadas georreferenciadas, um planejamento mais racional do manejo de nutrientes, incidência de pragas, umidade do solo, plantas daninhas, além de seleção de cultivares em função de sua adaptabilidade às diferentes condições identificadas nas áreas cultivadas. Com isso, o objetivo do presente trabalho foi avaliar a distribuição espacial da condutividade elétrica do extrato de saturação, carbono orgânico total e matéria orgânica em um solo Neossolo Flúvico. O experimento foi conduzido em uma área com malha regular 4 x 4 m, totalizando com 49 pontos, onde foram coletadas amostras nas camadas de 0- 0,20 m para analisar a condutividade elétrica do extrato de saturação, carbono orgânico total e matéria orgânica. As variáveis foram analisadas por meio da estatística descritiva e de ferramentas de geoestatística. As variáveis apresentaram distribuição normal, os semivariogramas se ajustaram a um modelo esférico, a variabilidade do carbono orgânico total e matéria orgânica apresentaram moderadas, a condutividade elétrica do extrato de saturação apresentou fraca dependência espacial. Os mapas de isolinhas apresentaram homogeneidade e similaridade, os mapas condutividade elétrica do extrato de 0-0,20 m foram inversamente proporcionais aos da matéria orgânica e do carbono orgânico. A B S T R A C T Precision agriculture allows, by the use of delimitation of crops for georeferenced coordinates, more rational planning of the management of nutrients, pests, soil moisture, weeds, and cultivar selection due to its adaptability to different conditions in the areas identified cultured. With it, the objective of this study was to evaluate the spatial distribution of the electrical conductivity of the saturation extract, total organic carbon and organic matter in soil Fluvic Neosol. The experiment was conducted in an area with regular mesh 4 x 4 m, with a total of 49 points, samples were collected in layers from 0 to 0.20 m to analyze the electrical conductivity of the saturation extract, total organic carbon and organic matter. The variables were analyzed using descriptive statistics and geostatistical tools. The variables were normally distributed, the semivariogram adjusted to a spherical model, the variability of total organic caborn and organic matter showed a moderate electrical conductivity of the saturation extract showed weak spatial dependence. The contour maps showed homogeneity and similarity maps the electrical conductivity of the extract of 0-0.20 m was inversely proportional to the organic matter and organic carbon. Key-Words: Geostatistics, salinity, total organic carbon, organic matter

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

Modern Applied Science ◽

10.5539/mas.v13n10p60 ◽

2019 ◽

Vol 13 (10) ◽

pp. 60 ◽

Cited By ~ 5

Author(s):

John Kingsley ◽

Solomon Odafe Lawani ◽

Ayito Okon Esther ◽

Kebonye Michael Ndiye ◽

Ogeh Joseph Sunday ◽

...

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Precision Agriculture ◽

Soil Analysis ◽

Soil Samples ◽

Soil Parameters ◽

Available Phosphorus ◽

Predictive Tool ◽

Large Variability ◽

North East

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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Median Double Ranked Set Sampling Method

JOURNAL OF ADVANCES IN MATHEMATICS ◽

10.24297/jam.v14i1.7173 ◽

2018 ◽

Vol 14 (1) ◽

pp. 7503-7512

Author(s):

Nuran Medhat Al-Mawan ◽

El-Houssainy Rady ◽

Nasr Rashwan

Keyword(s):

Random Sampling ◽

Traditional Method ◽

Sampling Method ◽

Sampling Methods ◽

Cost Effective ◽

Simple Random Sampling ◽

Ranked Set Sampling ◽

Median Ranked Set Sampling ◽

Observational Economy ◽

The Mean

In environmental monitoring and assessment, the main focus is to achieve observational economy and to collect data with unbiased, efficient and cost-effective sampling methods. Ranked set sampling (RSS) is one traditional method that is mostly used for accomplishing observational economy. In this article, we suggested new sampling method called median double ranked set sampling (MDRSS). The newly suggested sampling method MDRSS is compare to the simple random sampling (SRS), RSS, double ranked set sampling (DRSS), median ranked set sampling (MRSS). When the underlying distributions are symmetric and asymmetric, it is shown that, the variance of the mean estimator under MDRSS is always less than the variance of the mean estimator based on SRS and the other methods.

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Terrain forms and spatial variability of soil properties in an area cultivatedwith citrus

Engenharia Agrícola ◽

10.1590/s0100-69162011000400003 ◽

2011 ◽

Vol 31 (4) ◽

pp. 643-651 ◽

Cited By ~ 3

Author(s):

Marisol G. A. de Leão ◽

José Marques Júnior ◽

Zigomar M. de Souza ◽

Diego S. Siqueira ◽

Gener T. Pereira

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Precision Agriculture ◽

Chemical Properties ◽

Clay Content ◽

Soil Samples ◽

Descriptive Statistics ◽

Geostatistical Modeling ◽

Soil Landscape ◽

Kriging Maps

The technique of precision agriculture and soil-landscape allows delimiting areas for localized management, allowing a localized application of agricultural inputs and thereby may contribute to preservation of natural resources. Therefore, the objective of this work was to characterize the spatial variability of chemical properties and clay content in the context of soil-landscape relationship in a Latosol (Oxisol) under cultivation of citrus. Soil samples were collected at a depth of 0.0-0.2 m in an area of 83.5 ha planted with citrus, as a 50-m intervals grid, with 129 points in concave terrain and 206 points in flat terrain, totaling 335 points. Values for the variables that express the chemical characteristics and clay content of soil properties were analyzed with descriptive statistics and geostatistical modeling of semivariograms for making maps of kriging. The values of range and kriging maps indicated higher variability in the shape of concave topography (top segment) compared with the shape of flat topography (slope and hillside segments below). The identification of different forms of terrain proved to be efficient in understanding the spatial variability of chemical properties and clay content of soil under cultivation of citrus.

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