Spatial distribution of soil physical properties of alluvial soils: a geostatistical approach

Reza, S. K.; Nayak, D.C.; Chattopadhyay, T. K.; Mukhopadhyay, S.; Singh, Surendra; Srinivasan, R.

doi:10.1080/03650340.2015.1107678

Cited by 63 publications

(30 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There is so much variation in the clay, available phosphorus, and total nitrogen parameters and little variation in soil pH and sand content. The result obtained in this study is similar to the report by Denton et al, 2016& Reza et al, 2015. But contradicts the Reza et al, 2015 in the sand content CV.…”

Section: Discussionsupporting

confidence: 84%

“…The result obtained in this study is similar to the report by Denton et al, 2016& Reza et al, 2015. But contradicts the Reza et al, 2015 in the sand content CV. Similarly, the high mean value obtained in the sand content may have been contributed by a large number of point data with high sand content, this also raised the standard deviation.…”

Section: Discussionsupporting

confidence: 84%

“…The soil properties were normally distributed except for soil carbon which was log transformed to removed outliers. This was carried out to yield a more reliable output (Reza et al, 2015).…”

Section: Descriptive Statistics Of the Soil Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

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

John¹,

Lawani²,

Esther³

et al. 2019

MAS

View full text Add to dashboard Cite

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.   

show abstract

Section: Discussionsupporting

confidence: 84%

Section: Discussionsupporting

confidence: 84%

See 1 more Smart Citation

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

John¹,

Lawani²,

Esther³

et al. 2019

MAS

View full text Add to dashboard Cite

show abstract

“…In recent years, geostatistics has been widely used by many researchers for preparation of spatial variability maps of soil properties like soil texture (Safari et al 2013), total soil nitrogen and phosphorus (Wang et al 2009), soil available phosphorus (Reza et al 2012b), soil organic carbon (Lui et al 2006), soil quality (Sun et al 2003), and soil physical properties (Santra et al 2008;Reza et al 2015). Thus, the objective of this study was to determine the spatial variability of selected soil properties, such as pH, organic carbon content (OC), available N (AN), available P (AP), and available K (AK), with the classical statistics and geostatistical analysis for Bongaigoan district of Assam, India.…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

et al. 2016

View full text Add to dashboard Cite

Soil properties like pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) vary spatially from a field to a larger region scale and determine the soil fertility. This study addressed the spatial variability of soil properties in Brahmaputra plains, northeastern India using geostatistical method. For this, a total of 767 soil samples from a depth of 0-25 cm at an approximate interval of 1 km were collected over the entire Bongaigaon district of Assam. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Soil properties showed large variability with greatest variation was observed in AP (86 %) where as the smallest variation was in pH (19 %). The semivariogram for all soil properties were best fitted by exponential models and showed a highest (2.7 km) range for OC and lowest (1.2 km) for AP. The nugget/sill ratio indicates a strong dependence for pH (12 %), moderate spatial dependence for available nutrients (53-72 %) and a weak spatial dependence for OC (77 %). Evaluation of spatial maps indicated that except for AN due to high root mean square error (61.8), kriging could successfully interpolate other soil properties. Soil pH highly negatively correlated with OC (−0.330**) and AN (−0.228**) and highly positive correlated with AP (0.334**) and AK (0.164**). A highly significant correlation was also found between OC and AN (0.490 ** ).

show abstract

“…Además, en las zonas tropicales, donde se cultiva principalmente la palma aceitera, abundan los suelos aluviales en los que la capa superficial (<30cm) puede presentar variaciones significativas de la textura en distancias menores a 400m (Owens, Whisler, Jenkins, Thomasson, & Iqbal, 2005). Esta variabilidad cambia con las inundaciones y el tiempo de manera que gestionar con precisión la administración de fertilizante necesita de un análisis frecuente del suelo y las plantas (Owens et al, 2005;Reza et al, 2016;Hoffmann et al, 2017).…”

Section: Resumen Introducciónunclassified

Monitoreo de nitratos en los drenajes de palma aceitera (Elaeis guineensis): una herramienta para la sostenibilidad del cultivo

Alpizar

Molina

Herrera

2020

URJ

View full text Add to dashboard Cite

Introducción: La pérdida del nitrógeno adicionado en fertilizantes a las plantaciones de palma aceitera (Elaeis guineensis), ocurre principalmente por arrastre pluvial o lixiviación de nitratos, los cuales impactan cuerpos de agua receptores y ecosistemas asociados. Las pérdidas pueden afectar el desarrollo sostenible de la actividad. Objetivo: Evaluamos el monitoreo de nitratos en drenajes agrícolas de un cultivo de palma aceitera, como herramienta para la toma de decisiones de aplicación del fertilizante y reducción de pérdidas. Metodología: Analizamos la concentración de nitratos en cuatro drenajes ubicados en suelo con cultivo de palma de distinta clase textural, con condiciones homogéneas de edad de la plantación, clima y tipo de fertilización, antes y después de eventos de fertilización. Estimamos la carga de nitratos, QNO3- (mgs-1), las diferencias estadísticas y de tendencia en los drenajes. Resultados: QNO3- aumenta después de la fertilización, pero existe una diferencia significativa en la varianza de los grupos (0,000033, alfa=0.05) determinada por la variación de QNO3- del drenaje ubicado en suelo de clase textural arcillosa (Q4). La QNO3- exhibe una velocidad de crecimiento más positiva y una disminución posterior más negativa en Q4 que en el drenaje de suelo de clase textural arenosa. Discusión: Las diferencias de QNO3- en los drenajes se relaciona con la clase textural del suelo drenado, el desarrollo de las raíces y la profundidad a la que suceden los procesos de lavado, asimilación, lixiviación y procesamiento de nitratos. Conclusión: El monitoreo de nitratos en los drenajes de este cultivo permite estimar la tendencia de QNO3- relacionada con la clase textural del suelo, y visualizar la velocidad de pérdidas por lixiviación o lavado pluvial. Recomendamos el monitoreo de QNO3- en los drenajes de palma aceitera para tomar decisiones sobre la aplicación reducida o retrasada de fertilizante con el fin de reducir pérdidas.

show abstract

Spatial distribution of soil physical properties of alluvial soils: a geostatistical approach

Cited by 63 publications

References 23 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 of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Monitoreo de nitratos en los drenajes de palma aceitera (Elaeis guineensis): una herramienta para la sostenibilidad del cultivo

Contact Info

Product

Resources

About