Prediction of Spatial Distribution of Organic Carbon in Lower Brahmaputra Active Floodplain Soils of Bangladesh

Soil acts as a large reservoir of Organic Carbon (OC) but the amount varies significantly with space and time. Thus, soil analysis and interpretation of spatial variability of Soil Organic Carbon (SOC) are keys to site-specific management. The study aimed to characterize the spatial variability of SOC in an active floodplain. Soil samples were collected in three major landform categories (natural levee, back slope, marsh land) from the lower Brahmaputra River floodplain and then analyzed for SOC measurement in the laboratory. The measured data were then analyzed for spatial variability interpretation using descriptive statistics and geo-statistical analysis. The study found that the amount of SOC varies with landform variation, soil texture and distance between sample points. The topsoil of marsh land has the highest (1.41%), back slope holds a moderate amount (1.15%) and the natural levee has the lowest (0.75%) amount of SOC. The amount of clay particles at the top layer was found to be positively correlated to the SOC whereas in the same layer of sand and silt showed a negative correlation. The geo-statistical analysis illustrated the nugget effect. Low (<1%) SOC is commonly found in the agricultural soils of Bangladesh which was corroborated in this study; moderate (1.1%) SOC was found in the floodplain. This study aimed to provide an insight into spatial variability to assist in predicting SOC in the active floodplain; consequently, the interpretation of spatial variability analysis can be implemented for site specific management strategies and to calculate carbon stock in floodplain soils. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 8(2), 2019, P 33-40

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Geomorphic regulation of floodplain soil organic carbon concentration in watersheds of the Rocky and Cascade Mountains, USA

Earth Surface Dynamics ◽

10.5194/esurf-6-1101-2018 ◽

2018 ◽

Vol 6 (4) ◽

pp. 1101-1114 ◽

Cited By ~ 11

Author(s):

Daniel N. Scott ◽

Ellen E. Wohl

Keyword(s):

Organic Carbon ◽

Spatial Scales ◽

Dominant Role ◽

Mountain River ◽

Cascade Mountains ◽

Valley Bottom ◽

Floodplain Soil ◽

Local Factors ◽

Floodplain Soils ◽

Organic Carbon Concentration

Abstract. Mountain rivers have the potential to retain OC-rich soil and store large quantities of organic carbon (OC) in floodplain soils. We characterize valley bottom morphology, floodplain soil, and vegetation in two disparate mountain river basins: the Middle Fork Snoqualmie in the Cascade Mountains and the Big Sandy in the Wind River Range of the Rocky Mountains. We use this dataset to examine variability in OC concentration between these basins as well as within them at multiple spatial scales. We find that although there are some differences between basins, much of the variability in OC concentration is due to local factors, such as soil moisture and valley bottom geometry. From this, we conclude that local factors likely play a dominant role in regulating OC concentration in valley bottoms and that interbasin differences in climate or vegetation characteristics may not translate directly into differences in OC storage. We also use an analysis of OC concentration and soil texture by depth to infer that OC is input to floodplain soils mainly by decaying vegetation, not overbank deposition of fine, OC-bearing sediment. Geomorphology and hydrology play strong roles in determining the spatial distribution of soil OC in mountain river corridors.

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Delineating site-specific management zones for precision agriculture

The Journal of Agricultural Science ◽

10.1017/s0021859615000143 ◽

2015 ◽

Vol 154 (2) ◽

pp. 273-286 ◽

Cited By ~ 9

Author(s):

H. U. FARID ◽

A. BAKHSH ◽

N. AHMAD ◽

A. AHMAD ◽

Z. MAHMOOD-KHAN

Keyword(s):

Spatial Variability ◽

Grain Yield ◽

Precision Farming ◽

Farming Practices ◽

Wheat Grain ◽

Site Specific ◽

Management Zones ◽

Management Zone ◽

Specific Management ◽

Landscape Attributes

SUMMARYDelineating site-specific management zones within fields can be helpful in addressing spatial variability effects for adopting precision farming practices. A 3-year (2008/09 to 2010/11) field study was conducted at the Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the most important soil and landscape attributes influencing wheat grain yield, which can be used for delineating management zones. A total of 48 soil samples were collected from the top 300 mm of soil in 8-ha experimental field divided into regular grids of 24 × 67 m prior to sowing wheat. Soil and landscape attributes such as elevation, % of sand, silt and clay by volume, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus (P) were included in the analysis. Artificial neural network (ANN) analysis showed that % sand, % clay, elevation, soil N and soil EC were important variables for delineating management zones. Different management zone schemes ranging from three to six were developed and evaluated based on performance indicators using Management Zone Analyst (MZA V0·1) software. The fuzziness performance index (FPI) and normalized classification entropy NCE indices showed minimum values for a four management zone scheme, indicating its appropriateness for the experimental field. The coefficient of variation values of soil and landscape attributes decreased for each management zone within the four management zone scheme compared to the entire field, which showed improved homogeneity. The evaluation of the four management zone scheme using normalized wheat grain yield data showed distinct means for each management zone, verifying spatial variability effects and the need for its management. The results indicated that the approach based on ANN and MZA software analysis can be helpful in delineating management zones within the field, to promote precision farming practices effectively.

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Multi-species weed spatial variability and site-specific management maps in cultivated sunflower

Weed Science ◽

10.1614/0043-1745(2003)051[0319:mwsvas]2.0.co;2 ◽

2003 ◽

Vol 51 (3) ◽

pp. 319-328 ◽

Cited By ~ 37

Author(s):

Montserrat Jurado-Expósito ◽

Francisca López-Granados ◽

Luis García-Torres ◽

Alfonso García-Ferrer ◽

Manuel Sánchez de la Orden ◽

...

Keyword(s):

Spatial Variability ◽

Site Specific ◽

Specific Management

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Spatial variability of Southeastern U.S. Coastal Plain soil physical properties: Implications for site-specific management

Geoderma ◽

10.1016/j.geoderma.2006.08.018 ◽

2007 ◽

Vol 137 (3-4) ◽

pp. 327-339 ◽

Cited By ~ 87

Author(s):

Miressa Duffera ◽

Jeffrey G. White ◽

Randy Weisz

Keyword(s):

Spatial Variability ◽

Physical Properties ◽

Coastal Plain ◽

Soil Physical Properties ◽

Site Specific ◽

Specific Management ◽

Coastal Plain Soil

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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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Identificación de zonas de manejo sitio-específico a partir de la combinación de variables de suelo

Ciencia y Tecnología Agropecuaria ◽

10.21930/rcta.vol13_num1_art:239 ◽

2012 ◽

Vol 13 (1) ◽

pp. 47 ◽

Cited By ~ 2

Author(s):

Mariano Córdoba ◽

Mónica Balzarini ◽

Cecilia Bruno ◽

José Luis Costa

Keyword(s):

Spatial Variability ◽

Principal Component ◽

Production Potential ◽

Site Specific ◽

Management Zones ◽

Soil Variables ◽

Regionalized Variables ◽

Several Variables ◽

Specific Management

El manejo sitio-específico demanda la identificación de sub-regiones homogéneas, o zonas de manejo (ZM), dentro del espacio productivo. Sin embargo, definir ZM suele ser complejo debido a que la variabilidad espacial del suelo puede depender de varias variables. La zonificación o delimitación de ZM puede realizarse utilizando una variable de suelo a la vez o considerando varias variables simultáneamente. Entre los métodos de análisis multivariado, difundido para la zonificación, se encuentra el análisis de conglomerados fuzzy k-means (KM) y el análisis de componentes principales (PCA). No obstante, como otros métodos multivariados, éstos no han sido desarrollados específicamente para datos georreferenciados. Una nueva versión del PCA, conocido como MULTISPATI-PCA (PCAe), permite contemplar la autocorrelación espacial entre datos de variables regionalizadas. El objetivo de este estudio fue proponer una nueva estrategia de análisis para la identificación de ZM, combinando la aplicación KM y PCAe sobre datos de múltiples variables de suelo. La capacidad del método propuesto se evaluó en base a la comparación de los rendimientos promedios alcanzados en cada zona delimitada, tanto para la combinación de KM con PCA, la aplicación tradicional de KM sobre las variables originales y la nueva propuesta KM-PCAe. Los resultados mostraron que KM-PCAe fue el único método que permitió distinguir zonas estadísticamente diferentes en cuanto al potencial productivo. Se concluye que la combinación propuesta constituye una herramienta importante para el mapeo de la variabilidad espacial y la identificación de ZM a partir de datos georreferenciados. Identification of site-specific management zones from combination of soil variablesSite-specific management demands the identification of homogeneous subfield regions within the field or management zones (MZ). However, due to the spatial variability of soil variables, determination of MZ from several variables, is often complex. Although the zonification or delimitation of MZ may be univariate, it is more appropriate to consider all variables simultaneously. Fuzzy k-means clustering (KM) and principal component analysis (PCA) are multivariate analyses that have been used for zonification. Nevertheless, PCA and KM have not been explicitly developed for georeferenced data. Novel versions of PCA, known as MULTISPATI-PCA (PCAe), incorporate spatial autocorrelation among data of neighbor sites of regionalized variables. The objective of this study was to propose a new analytical tool to identify homogeneous zones from the combination of KM and PCAe on multiple soil variable data. The performance of proposed method was assessed through comparison of the average yields obtained in each zone delimited by combination of KM with PCA, as well as KM on the original variables and the new proposed method KM-PCAe. The results showed that KM-PCAe was the only method able to identify zones statistically different in terms of production potential. PCAe and its combination with KM are useful tools to map spatial variability and to identify MZ within fields from georeferenced data.

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