Quantification of soil quality under semi-arid agriculture in the northwest of Iran

Nehrani, Somayeh Hamidi; Askari, Mohammad Sadegh; Saadat, Saeed; Delavar, Mohammad Amir; Taheri, Mehdi; Holden, Nicholas M.

doi:10.1016/j.ecolind.2019.105770

Cited by 39 publications

(12 citation statements)

References 52 publications

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“…These results are logical, as the soil pH is affected by increasing the percentage of calcium carbonate of soils [69]. The obtained factor loading illustrates the acceptable grouping of soil properties and confirm the ability of PCA for group soil properties in different clusters [70]. The first PCs showed an accumulation of 36.9% of the soil characteristics where the ESP, OM, CEC, N, P and K were allocated in these PCs due to the association of the natural conditions and soil formation processes in the study area [71,72].…”

Section: Multivariate Statistical For Land Evaluationsupporting

confidence: 67%

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

et al. 2020

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The development of the agricultural sector is considered the backbone of sustainable development in Egypt. While the developing countries of the world face many challenges regarding food security due to rapid population growth and limited agricultural resources, this study aimed to assess the soils of Sidi Barrani and Salloum using multivariate analysis to determine the land capability and crop suitability for potential alternative crop uses, based on using principal component analysis (PCA), agglomerative hierarchical cluster analysis (AHC) and the Almagra model of MicroLEIS. In total, 24 soil profiles were dug, to represent the geomorphic units of the study area, and the soil physicochemical parameters were analyzed in laboratory. The land capability assessment was classified into five significant classes (C1 to C5) based on AHC and PCA analyses. The class C1 represents the highest capable class while C5 is assigned to lowest class. The results indicated that about 7% of the total area was classified as highly capable land (C1), which is area characterized by high concentrations of macronutrients (N, P, K) and low soil salinity value. However, about 52% of the total area was assigned to moderately high class (C2), and 29% was allocated in moderate class (C3), whilst the remaining area (12%) was classified as the low (C4) and not capable (C5) classes, due to soil limitations such as shallow soil depth, high salinity, and increased erosion susceptibility. Moreover, the results of the Almagra soil suitability model for ten crops were described into four suitability classes, while about 37% of the study area was allocated in the highly suitable class (S2) for wheat, olive, alfalfa, sugar beet and fig. Furthermore, 13% of the area was categorized as highly suitable soil (S2) for citrus and peach. On the other hand, about 50% of the total area was assigned to the marginal class (S4) for most of the selected crops. Hence, the use of multivariate analysis, mapping land capability and modeling the soil suitability for diverse crops help the decision makers with regard to potential agricultural development.

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Section: Multivariate Statistical For Land Evaluationsupporting

confidence: 67%

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

et al. 2020

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“…Increasing amounts of insoluble compounds, including Cr6 + , are deposited in soil and undergo limited vertical migration, resulting in heavy metal accumulation in surface soils 61 . In the semi-arid area of Iran, industrial and agricultural activities have led to the excessive accumulation of heavy metals in the local soil 62 .…”

Section: Current Status Of Heavy Metal Pollution Inmentioning

confidence: 99%

Comprehensive evaluation of soil quality in a desert steppe influenced by industrial activities in northern China

Wen-bao

et al. 2021

Sci Rep

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Desert steppe soil security issues have been the focus of attention. Therefore, to understand the impact of industrial activities on the soil quality of desert grasslands, this experiment investigated the Gaoshawo Industrial Concentration Zone in Yanchi County. Based on the distance and direction from the industrial park, sample plots were established at intervals of 1–2 km. A total of 82 surface soil samples (0–20 cm) representing different pollution sources were collected. The samples were analysed for pH, total nitrogen, total phosphorus, available phosphorus, available potassium, organic matter, copper (Cu), cadmium (Cd), chromium (Cr), lead (Pb), and zinc (Zn). The desert steppe soil quality was analysed based on the integrated fertility index (IFI) and the Nemerow pollution index (PN), followed by the calculation of the comprehensive soil quality index (SQI), which considers the most suitable soil quality indicators through a geostatistical model. The results showed that the IFI was 0.393, indicating that the soil fertility was relatively poor. Excluding the available potassium, the nugget coefficients of the fertility indicators were less than 25% and showed strong spatial autocorrelation. The average values of Cu, Cd, Cr, Pb and Zn were 21.64 ± 3.26, 0.18 ± 0.02, 44.99 ± 21.23, 87.18 ± 25.84, and 86.63 ± 24.98 mg·kg−1, respectively; the nugget coefficients of Cr, Pb and Zn were 30.79–47.35%. Pb was the main element causing heavy metal pollution in the study area. Higher PN values were concentrated north of the highway in the study area, resulting in lower soil quality in the northern region and a trend of decreasing soil quality from south to north. The results of this research showed that the average SQI was 0.351 and the soil quality was extremely low. Thus, industrial activities and transportation activities in the Gaoshawo Industrial Zone significantly impact the desert steppe soil quality index.

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“…The analysis is used software (Minitab19). Principal Component Analysis (PCA) is method to reduce the dimensions of the data set, so that the determinants of soil fertility could be identified (Nehrani et al, 2020;Zhan et al, 2020) The results of classifying parameters on each PC, then selected to become MDS. Furthermore, the selected MSFIs were assessed based on the assessment criteria (shown in Table 2).…”

Section: Discussionmentioning

confidence: 99%

The distribution of soil fertility index and its interaction with earthworms density under organic, semi-organic, and inorganic rice fields

Suntoro,

Hikmah,

Mujiyo

et al. 2024

Soil Sci. Ann.

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Earthworms are soil macrofauna that play a role in maintaining the sustainability of soil use through increasing organic matter and soil fertility. Soil fertility assessment is important to suggest management to create an optimum conditions for plant growth, so it can be maintained the quality and quantity of rice productivity. The purpose of this research was to identify soil fertility index (SFI), the density of earthworms and the relationship between the two under different rice fi eld systems, namely organic, semi-organic, and inorganic. This research also identifi es indicators that determine SFI to be able to provide appropriate and effi cient strategies in subsequent land management. This method used an exploratory descriptive with a survey approach. Sampling points was taken based on the farming systems (organic, semi-organic, and inorganic rice fi elds) on fl at (0-8%) and sloping (8-15%) slopes. The SFI was determined used the Minimum Soil Fertility Indicator (MSFI). The results showed that differences of rice fi eld farming systems is signifi cantly effects on SFI. The index of soil fertility in the research area is moderate, with the highest SFI in the organic farming system. Soil fertility and earthworms density had a signifi cant positive correlation, that the higher earthworms density, the higher soil fertility. Key indicators were found including organic C, available P, Al saturation, and exchangeable bases which determine SFI. The organic rice fi eld system has the best SFI as well as the highest earthworm population, so we recommend increasing the use of organic materials in the tillages process and expanding the organic rice fi eld system to maintain fertility and the sustainability of soil used for rice production.

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Quantification of soil quality under semi-arid agriculture in the northwest of Iran

Cited by 39 publications

References 52 publications

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

On the Use of Multivariate Analysis and Land Evaluation for Potential Agricultural Development of the Northwestern Coast of Egypt

Comprehensive evaluation of soil quality in a desert steppe influenced by industrial activities in northern China

The distribution of soil fertility index and its interaction with earthworms density under organic, semi-organic, and inorganic rice fields

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