Spatial variability of bulk soil electrical conductivity in a Malaysian paddy field: key to soil management

Wayayok, Aimrun; Amin, M. S. M.; Ahmad, Desa; Hanafi, Mohamed; Chan, C. S.

doi:10.1007/s10333-007-0072-z

Cited by 33 publications

(19 citation statements)

References 7 publications

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“…Similar low variance in soil solution pH has been reported elsewhere for instance Aimrun, Amin, Ahmad, Hanafi, and Chan (2007) in Malaysia (2.6%); Abu and Malgwi (2011) in Nigeria (10.3%) and Addis, Klik, and Strohmeier (2015) in Ethiopia (6.5%). This is because pH values are indicated on log scale of proton concentration in soil solution; otherwise there would be a much higher variability if soil acidity is expressed in terms of proton concentration directly (Sun, Zhou, & Zhao, 2003).…”

Section: Soil Phsupporting

confidence: 88%

An Assessement of Paddy Production System in Central Kenya with Special Reference to Micronutrients

Kundu¹,

Ishii²,

Sato³

et al. 2017

JAS

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Soil degradation reduces agricultural productivity and poses a great threat on food security status of households. In Kenya, farmers have for a long time been using only nitrogen and phosphorous based fertilizers oblivious of the soil fertility status. In most cases, there has been lack of plant response to these fertilizers, which could be due to a limitation of nutrients other than nitrogen and phosphorous. Soils are considered as variable natural bodies because of combined intrinsic and extrinsic factors of different intensities at a field or a larger region scale therefore an understanding of such variability is imperative to provide insights needed in their management. This study was thus initiated to assess the availability of soil micronutrients from rice growing Vertisols in the Mwea irrigation scheme. Top (0-15 cm) soil samples were collected across paddy fields in the irrigation scheme and analyzed for iron (Fe), zinc (Zn), copper (Cu) and manganese (Mn) by the 0.1 N HCl extraction method. Soil pH (water and 1 M KCl) was also measured using the glass electrode pH meter. Soil solution pH ranged from 4.56 to 8.05 and 3.33 to 6.63 for water and 1 M KCl respectively. Soil Fe, Zn, Cu and Mn concentration varied greatly and ranged from undetected to 1360.6, 0.12 to 8.00, undetected to 9.29 and 1.50 to 849.2 mg/kg respectively. Coefficient of variations (CVs) for soil micronutrients ranged from 64% to 154% indicating very high variability. Soil pH was least variable with CVs 12% and 15% for water and KCl respectively. These results imply that the paddy soils in Mwea region are highly heterogeneous and soil micronutrients are enriched in some areas and depleted in others due to farm management practices and soil properties. Averagely, soil available Zn was deficient across the scheme and as such Zn fertilization can be effective in increasing soil Zn concentration and availability in the soil-root interface further enhancing soil productivity and yield quality. Attention should also be paid to appropriate farm management practices to avoid accumulation or depletion of nutrients.

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Section: Soil Phsupporting

confidence: 88%

An Assessement of Paddy Production System in Central Kenya with Special Reference to Micronutrients

Kundu¹,

Ishii²,

Sato³

et al. 2017

JAS

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“…These values are relatively low when compared to the apparent electrical conductivity , as shown by Yan et al (2007c) and Yan et al (2008). In non-saline soils, Machado et al (2006) and Aimrun et al (2007) have obtained average apparent soil electrical conductivity values of 5.2 and 5.67 mS m -1 , respectively. These mean values of electrical conductivity are still higher than those found in this study.…”

Section: Resultsmentioning

confidence: 99%

“…This amplitude is less than that obtained by Machado et al (2006), who recorded minimum and maximum conductivities of 1.90 and 13.70 mS m -1 , respectively. Aimrun et al (2007) . These differences in amplitude may not relate to soil moisture conditions.…”

Section: Resultsmentioning

confidence: 99%

The relationship between apparent soil electrical conductivity and soil properties

Valente¹,

Queiroz²,

Pinto³

et al. 2012

Rev. Ciênc. Agron.

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-Precision agriculture that is based on the physical and chemical properties of soil requires a dense sampling to evaluate spatial variability in the field. This dense sampling is often expensive and time consuming. One technique to reduce the number of samples is to define management zones based on information that is collected in the field. Some researchers have demonstrated the importance of the electrical properties of soil in defining management zones. Thus, the objective of this study was to evaluate the relationship between the apparent soil electrical conductivity and soil properties in mountainous areas of coffee production. The electrical conductivity of soil was evaluated at soil depths ranging from 0.00-0.20 m (EC20) and 0.00-0.40 m (EC40) using a portable meter. The mean values of EC20 and EC40 were 1.80 mS m -1 and 1.22 mS m -1 , respectively. Both EC20 and EC40 exhibited comparatively low correlations with the soil properties, whereas higher correlations were obtained for measurements of remaining phosphorus, wherein values of 0.427 and 0.465, respectively, were obtained. Key words: Precision agriculture. Management zone. Sensors.RESUMO -A agricultura de precisão baseada nas propriedades físicas e químicas do solo exige uma amostragem densa para se determinar a variabilidade espacial no campo. Essa amostragem densa, muitas vezes apresenta custo e tempo consumido elevado. Uma das técnicas para reduzir o número de amostras é definir zonas de manejo em função de informações coletadas no campo. Alguns pesquisadores têm demonstrado a importância desempenhada pelas variáveis elétricas do solo para definir zonas de manejo. Dessa forma, este trabalho teve como objetivo avaliar a relação entre a variabilidade espacial da condutividade elétrica aparente e propriedades do solo em regiões de produção de cafés de montanha. A condutividade elétrica foi medida no perfil de solo de 0,00-0,20 m (CE20) e 0,00-0,40 m (CE40) usando um sensor portátil. Os valores de CE20 e CE40 foram de 1,80 mS m -1 e 1,22 mS m -1 , respectivamente. Ambas CE20 e CE40 apresentaram baixa correlação com as propriedades químicas e a textura do solo, sendo que, a correlação mais elevada foi obtida para o fósforo remanescente com valores de 0,427 e 0,465, respectivamente.Palavras-chave: Agricultura de precisão. Zonas de manejo. Sensores. *Autor para correspondência 1 Recebido para publicação em 02/08/2011; aprovado em 01/05/2012 Parte da Tese de Doutorado do primeiro autor apresentada ao Programa

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“…Ordinary kriging have been applied to extrapolate the values of unsampled field parts. To build kriged surface maps ArcGIS 9.2 was used (Blackmore, 1999;Aimrun et al, 2007). Thylen and Murphy (1996); Blackmore and Marshall (1996) and Blackmore (1999) applied this technique in crop properties mapping.…”

Section: Soilmentioning

confidence: 99%

Sugar Beet Performance Affected by Uniformity of N Fertigation

Nouri¹

2010

American Journal of Applied Sciences

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Abstract:In common with the majority of crop species, in sugar beet nutrition nitrogen is an important element due to its role in sucrose utilization and plant growth. Application of precision agriculture, in order to apply the right amount of input at the right time in the right area, is appropriate to decrease chemical use and increase plant growth. Problem statement: More often farmers apply fertilizers with low efficiency, low uniformity and high price such as spinner spreader, boom spreader or application with planting machines simultaneously. Fertigation can reduce labor cost and may improve uniformity, effectiveness and timeliness of application. Approach: The main objective of this study was to determine spatial variability of sugar beet performance affected by urea fertigation as well as established a management strategy based on spatial variability of tubers and soil total N. This study was conducted in Fesaran village, Isfahan Province of Iran and limited to sugar beet (monogerm seed). Urea applied through sprinklers. Soil samples were taken to determine soil total N as well as sugar beet tubers samples to specify yield. Results of soil and crop analysis were used to produce spatial variability maps through GS + and ArcGIS 9.2 software. Semivariogram results were used to perform an ordinary kriging to obtain interpolated values of selected variables from the sample points through and across the study area. Results: It was found that there was a low spatial variability of soil total N and yield which indicates that the soil has a homogenous total N as well as homogenous yield through and across the field. Yield variability map of sugar beet properties demonstrated that the higher yield was seen in the southern part of the study area where laterals were closer to pump. Conclusion: The results of urea fertigation through sprinklers showed pipeline layout and pump station position impact on variability of sugar beet properties.

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Spatial variability of bulk soil electrical conductivity in a Malaysian paddy field: key to soil management

Cited by 33 publications

References 7 publications

An Assessement of Paddy Production System in Central Kenya with Special Reference to Micronutrients

An Assessement of Paddy Production System in Central Kenya with Special Reference to Micronutrients

The relationship between apparent soil electrical conductivity and soil properties

Sugar Beet Performance Affected by Uniformity of N Fertigation

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