An assessment of the variation in soil properties within and between landform in the Amol region, Iran

Momtaz, H. R.; Jafarzadeh, Ali Asghar; Torabi, Hossein; Oustan, Shahin; Samadi, Abbas; Davatgar, Naser; Gilkes, Robert

doi:10.1016/j.geoderma.2008.11.016

Cited by 35 publications

(26 citation statements)

References 10 publications

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“…Factors were subjected to varimax rotation with Kaiser normalization in order to generate factor patterns that load highly significant variables into one factor, thereby producing a matrix with a simple structure that is easy to interpret (Ameyan and Ogidiolu, 1989;de Lima et al, 2008;Momtaz et al, 2009). Factors with eigenvalues of less than 1 were ignored.…”

Section: Statistical Analyses and Index Developmentmentioning

confidence: 99%

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

2017

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Abstract. Cocoa agroecosystems are a major land-use type in the tropical rainforest belt of West Africa, reportedly associated with several ecological changes, including soil degradation. This study aims to develop a composite soil degradation assessment index (CSDI) for determining the degradation level of cocoa soils under smallholder agroecosystems of southwestern Nigeria. Plots where natural forests have been converted to cocoa agroecosystems of ages 1-10, 11-40, and 41-80 years, respectively representing young cocoa plantations (YCPs), mature cocoa plantations (MCPs), and senescent cocoa plantations (SCPs), were identified to represent the biological cycle of the cocoa tree. Soil samples were collected at a depth of 0 to 20 cm in each plot and analysed in terms of their physical, chemical, and biological properties. Factor analysis of soil data revealed four major interacting soil degradation processes: decline in soil nutrients, loss of soil organic matter, increase in soil acidity, and the breakdown of soil textural characteristics over time. These processes were represented by eight soil properties (extractable zinc, silt, soil organic matter (SOM), cation exchange capacity (CEC), available phosphorus, total porosity, pH, and clay content). These soil properties were subjected to forward stepwise discriminant analysis (STEPDA), and the result showed that four soil properties (extractable zinc, cation exchange capacity, SOM, and clay content) are the most useful in separating the studied soils into YCP, MCP, and SCP. In this way, we have sufficiently eliminated redundancy in the final selection of soil degradation indicators. Based on these four soil parameters, a CSDI was developed and used to classify selected cocoa soils into three different classes of degradation. The results revealed that 65 % of the selected cocoa farms are moderately degraded, while 18 % have a high degradation status. The numerical value of the CSDI as an objective index of soil degradation under cocoa agroecosystems was statistically validated. The results of this study reveal that soil management should promote activities that help to increase organic matter and reduce Zn deficiency over the cocoa growth cycle. Finally, the newly developed CSDI can provide an early warning of soil degradation processes and help farmers and extension officers to implement rehabilitation practices on degraded cocoa soils.

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Section: Statistical Analyses and Index Developmentmentioning

confidence: 99%

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

2017

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“…Different areas display different natural properties and management practices [48]. Momtaz et al [48] and Seyfried et al [49] observed that the distribution and variability of land properties depend on the scale-the bigger the scale, the C12 C11 C5 C10 C1 C2 C3 C4 C8 C9 C6 CV of SCPDHP % CV of SWMDHP % Owing to the very complicated nature of soil, which depends on its physical, chemical and biological properties and their interactions, the accuracy of soil quality assessment is increased by using as many indicators as possible [47]. This variability may be due to spatial differences in geographical location, as well as land management practices [4].…”

Section: Variation In Lupcs Across the Study Areamentioning

confidence: 99%

“…Different areas display different natural properties and management practices [48]. Momtaz et al [48] and Seyfried et al [49] observed that the distribution and variability of land properties depend on the scale-the bigger the scale, the higher the variability, and vice versa [4]. For all that, the variability provided a better view of the status of the soil across the study area [4].…”

Section: Variation In Lupcs Across the Study Areamentioning

confidence: 99%

An Improved Evaluation Scheme for Performing Quality Assessments of Unconsolidated Cultivated Land

Peng

Yan

et al. 2017

Sustainability

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Socioeconomic factors are extrinsic factors that drive spatial variability. They play an important role in land resource systems and sometimes are more important than that of the natural setting. The study aims to build a comprehensive framework for assessing unconsolidated cultivated land (UCL) in the south-central and southwestern portions of Hubei Province, China, which have not experienced project management and land consolidation, to identify the roles of natural and especially socioeconomic factors. Moreover, the study attempts to identify the attributes and indicators that describe the characteristics of the extrinsic factors affecting land spatial variability. Assessment supplement 12 proposed land use indicators on the basis of natural factors using the method of gradation of agricultural land quality (GALQ). The overall level of cultivated land quality (CLQ) in the two study areas is moderate, and this quantity is significantly correlated with topography. Excellent and high-quality UCL are mainly distributed in the south-central plain division of Hubei Province (SCPDHP), whereas lower grades are mainly distributed in the area of the southwestern mountainous division of Hubei Province (SWMDHP). These results suggest that the pattern of small-scale agricultural development depends strongly on the labor force and is the key land use-related factor that limits the improvement of regional CLQ. Such assessments and their findings are essential for the protection of cultivated land and the adjustment of agricultural structure to promote the sustainable use of UCL.

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“…Interpolation semivariance and the prediction principle of OK have been widely established by many authors (Lark et al 2006;Wang et al 2009). Studies have shown that soil types and land-use patterns both have important impacts on the spatial distribution of SOC (Momtaz et al 2009;Jiao et al 2010;Zhang et al 2010). Samples within the same land-use pattern or soil type usually have similar SOC contents due to similar impact factors while there are greater differences between different soil types or land-use patterns (Liu et al 2006;Wei et al 2008).…”

Section: Soil Sampling and Laboratory Analysismentioning

confidence: 99%

Effects of prediction methods for detecting the temporal evolution of soil organic carbon in the Hilly Red Soil Region, China

et al. 2010

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Though numerous studies have evaluated the effect of prediction method on soil organic carbon (SOC) spatial distribution, the influence of prediction method on detecting the temporal evolution of SOC has rarely been reported. This study was conducted in Yujiang County, Jiangxi Province, China using 174 and 257 samples collected in 1982 and 2007, respectively. The SOC spatial distributions for those 2 years and the associated temporal evolution were predicted via ordinary kriging (OK) and kriging combined with soil types and land-use (KSTLU) patterns. Results showed that KSTLU had better spatial prediction accuracy than OK for both years. The mean absolute errors (MAEs) of OK for 1982 and 2007 were 2.45 and 5.99 g kg -1 , and the root mean square errors (RMSEs) were 3.37 and 7.23 g kg -1 , respectively. Meanwhile, the MAEs of KSTLU for 1982 and 2007 were 1.99 and 3.23 g kg -1 which are 19 and 46% lower than those of OK, respectively. The RMSEs were 2.76 and 3.97 g kg -1 which were 18 and 45% lower than those of OK, respectively. Moreover, the result of temporal evolution from the KSTLU prediction showed that the area of increasing SOC (70%) is remarkably larger than the area of decreasing SOC (30%). The KSTLU prediction was more consistent with the statistical results than the OK prediction. Furthermore, the contours of SOC temporal evolution from KSTLU had a more detailed description for local change of SOC than OK. Thus, the prediction methods greatly affect the detection of SOC temporal evolution. Results of this study indicated that KSTLU is more efficient and rational than OK in studying the SOC temporal evolution in the Hilly Red Soil Region of South China.

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An assessment of the variation in soil properties within and between landform in the Amol region, Iran

Cited by 35 publications

References 10 publications

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

An Improved Evaluation Scheme for Performing Quality Assessments of Unconsolidated Cultivated Land

Effects of prediction methods for detecting the temporal evolution of soil organic carbon in the Hilly Red Soil Region, China

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