جستجوی مقالات مرتبط با کلیدواژه « nemoro quality index » در نشریات گروه « آب و خاک »

Soil has various tasks or functions, including the ability to produce crops, store carbon, store water, cycle nutrients and purify water. Soil functions have a high correlation with soil quality. Soil quality indices are often regional and to determine it, it is not possible to consistently use a set of idicators and indices in all regions. In this research, Additive Soil Quality Index (SQIa), the Weighted Additive Soil Quality Index (SQIw) and the Nemero Soil Quality Index (NQI) and the effect of effective characteristics on soil quality in surface soil (0-30 cm) and in the yield of 206 wheat field of East Azarbaijan Province (Tabriz Plain) in 2017 was investigated by measuring physical and chemical properties of soils and wheat grain yield. Organic carbon, bulk density, aveilble potassium and aveilble phosphorus and electrical conductivity were selected as The minimum data set (MDS) using principal component analysis (PCA). Then SQIa, SQIw and NQI were determined using TDS and MDS. The results showed that there is a correlation between wheat yield and soil quality indices using MDS (r=0.60-0.63) and using TDS (r=0.56-0.60) also there was a significant correlation (p <0.01) between the use of TDS and MDS in NQI (r=0.81), SQIw(r=0.84) and SQIa (r=0.88) (p<0.01). Therefore, all three investigated indices are suitable for the study area and using MDS is more suitable than the method of using TDS due to relatively higher correlation and number of features and lower cost.

Soil quality is an essential indicator for sustainable land management that generally depends on soil physical, chemical and biological properties. Due to the multiplicity of soil properties, the number of variables is usually reduced to a minimum set by statistical methods, which reduces study time, decreases monitoring cost for sustainable use of agricultural lands. The aim of this study was to introduce the most effective soil characteristics of agricultural lands in Mohammadshahr plain, Karaj, to prevent the descending trend of soil quality.
In this study, four farms and orchards which were different in terms of crop type and irrigation system were selected and evaluated with Integrated Quality Index (IQI) and Nemero Quality Index (NQI). In both indicators, the characteristics affecting soil quality are combined in the form of a mathematical model and presented as a numerical quantity. For this purpose, first 12 soil profiles were described, followed by sampling from topsoil (surface layer) and sublayers (weighting average for the depths) and testing 17 soil characteristics affecting its quality. In the next step, both indicators were calculated using two different sets of soil properties. The first category, the Total Data Set (TDS), included all measured soil characteristics, and the second group, the Minimum Data Set (MDS), included the most important properties affecting soil quality. The Principle Component Analysis was implemented to select the MDS. Soil properties were scored to calculate IQI and NQI. For this purpose, a function was defined for each soil feature to standardize all scores between zero and one. Weighting various soil quality properties was also performed by calculating the common variance of the variables, which was obtained by factor analysis method.
Calculation of IQI and NQI indices showed that the topsoil samples were in grade III and sublayer samples belonged to grade IV with major limitations due to lack of profile development, organic carbon deficiency, salinity and high gravel. Four and six items out of 16 variables were identified effective for topsoil and sublayers, respectively. The IQI index based on TDS was more accurate and sensitive than the NQI index for soil quality assessment, as more features are considered for TDS. In the IQI index, both the weight of attributes and their scores are effective, while in the NQI index, only the attribute score is considered. On the other hand, the coefficient of determination between the TDS and MDS for topsoil and sublayer samples was 0.55 and 0.56% for IQI model, respectively, and 0.48 and 0.16% for NQI model, respectively. In other words, the determination coefficients showed the reliability of using the MDS instead of TDS in both IQI and NQI models. In the MDS, mean weight diameter (MWD) showed the highest effect on the surface layer and percentage of gravel had the greatest impact on the soil quality of the sublayer.
Although TDS took into account all soil properties and showed a slightly higher coefficient of determinations with both soil quality indicators, the MDS obtained similar results to the TDS with only about half of the properties. In the MDS, the features with an internal correlation is eliminated rendering it more cost effective. The results of this study assist decision-makers to choose better quality management and soil sustainability strategies while decreasing the monitoring cost.

Developing an appropriate methodology to evaluate soil quality is urgent and has great implications in sustainable agricultural production. This study was carried out with the aim of determining the minimum data set (MDS) for soil quality evaluation and the comparison of soil quality evaluation methodsin tea cultivation with different productivities in west Guilan province. Sixty-six soil samples were collected from 0 to 30 cm depth. The green tea leaves were harvested at a 2 m2 plot at each site. In this research, using the principal component analysis (PCA) method, among 15 physical, chemical and biological soil indicators as total data set (TDS), available potassium, organic carbon, pH, available phosphorus, available zinc and nematode were determined as the MDS. Then, the soil quality of tea cultivation with different productivities was evaluated by Integrated Quality Index(IQI) and Nemoro Quality Index (NQI) using two linear and non-linear scoring methods (LS and NLS) and two soil indicator selection approaches, a Total Data Set (TDS) and a Minimum Data Set (MDS). The results showed that all mean soil quality indices of the high productivity tea cultivationexcept IQI-NLSMDSand NQI-NLSMDS were significantly higher than low productivity tea cultivation. It was found that linear scoring methods are superior to non-linear. So that, the correlations between soil quality indices and crop yields for the IQI-LS and NQI-LS methods were Stronger than in the other methods in both TDS (R2=0.55 and 0.54, respectively) and MDS (R2=0.45 and 0.46, respectively). The correlation between TDS and MDS evaluated by the IQI–LS (R2=0.80) were higher than that by NQI–LS (R2=0.59).

Soil quality, as a factor, is changing due to variation of soil inherent characteristics and the type of management practices on the soil. Integrated Quality Index and Nemero Quality Index were applied to evaluate the influence of land use types, different irrigation managements, and types of cultivated products on the soil quality in Semnan region.  After describing 13 soil profiles in five selected crop fields and orchards, soil samples were taken from different horizons and 14 properties efective on the soil quality were determined and the proposed indecs were calculated. The results showed that the quality of top layers of agricultural lands are classified as II and III degree, while the top layer and sublayer of garden lands are classified as IV degree due to lack of profile evolution, low organic carbon and high salinity. The highest average soil quality indices were assigned to alfalfa (flood irrigation) with 0.67; followed by barley (sprinkler irrigation) with 0.59, olive orchard (drip irrigation) with 0.39 and olive orchard (flood irrigation) with 0.32. This finding indicates the positive effect of cover crops on land reclamation of Semnan region. The effect of irrigation system on soil quality indices did not show any significant difference among them, as the flood irrigation in alfalfa and olive farms showed the highest and the lowest soil quality indecs, respectively. Also, there was no significant difference in soil quality of olive orchards under drip irrigation vs. flood irrigation. Soil quality indecs make opportunity to agricultural managers to choose appropriate management strategies and monitoring the changes in the soil quality.

Knowledge of the soils capacity to utilize them in the form of different major uses in agricultural ecosystems and natural resources requires the accurate recognition of the physico-chemical and biological properties of the soil, ensuring the sustainable use of the lands. Therefore, this research was conducted to evaluate the soil quality using quantitative models in the Kane Sorkh lands of Mehran as a part of arid and semi-arid lands of Ilam province (Iran). Soil samples were taken from the depth of 0-30 cm and weighted average for the depths of 30-100 cm of the soil profile. Physicochemical properties were measured based on the standard methods. Using principal component analysis (PCA), among a total 16 soil characteristics (TDS), eight properties were selected based on the eigen values and the correlation coefficient as the least properties affecting soil quality (MDS). Then, soil quality assessment was performed using two models of integrated quality index (IQI), Nemoro Quality Index (NQI), and each in two sets of TDS and MDS. The results showed that the top layer (0-30 cm) have a moderate quality class and the sub layer (30-100 cm of the profiles) have poor quality with severe constraints. According to the results, the most important factors limiting soil quality were organic matter deficiency, high percentage of calcium carbonate, gypsum, gravel and soil salinity. Determination coefficients (R2) for the top and sub-layer samples between the two TDS and MDS sets in both IQI and NQI models were 0.79 and 0.79, 0.95 and 0.94, respectively, indicating the reliability of using MDS set instead of TDS in both models. Therefore, the use of most effective soil properties in the soil quality assessment studies is not only time consuming but also it is cost-effective for sustainable monitoring of agricultural land use.

Assessment of soil quality helps to make a balance between soil function and soil resources, improving soil quality and achieving the sustainable agriculture. For the quantitative evaluation of soil quality in the Shahrekord plain, Chaharmahal va Bakhtiari province, 106 compound surficial soil samples (0-25 cm) were collected. After the pre-treatments of soil samples, 11 physico-chemical soil characteristics/indicators as the total data set (TDS) were measured using the standard methods. Statistical analysis showed the usefulness of Principle Component Analysis (PCA) transformation. The minimum data set (MDS) was selected using PCA. Analytical Hierarchy Process (AHP) was carried out for the quantitative determination of indicator priorities and weights. Soil quality of the samples was calculated by introducing TDS and MDS into Integrate Quality Index (IQI) and Nemero Quality Index (NQI). The results showed the soil quality of the land uses was as Rangelands> Drylands<Irrigated croplands. The correlation coefficient between IQI-TDS and IQI-MDS was 0.97, while this value for NQI-TDS and NQI-MDS was 0.98. The correlation coefficient between IQI-TDS and NQI-TDS was 0.87 and that between IQI-MDS and NQI-MDS was 0.91. Classification of the resulted soil quality map IQI-TDS revealed that 12.5 % and 15.5 % of the plain were in very high and low quality conditions, respectively.

Comprehensive evaluation of cultivated soil quality helps to achieve sustainable agriculture. Providing suitable methods for soil quality assessment is essential for management strategies to improve agricultural practices. In this regard، various models of soil quality and feature selection methods were compared to assess the soil chemical and biological quality. Twelve chemical and biological variables were considered as Total Data Set (TDS) and Minimum Data Set (MDS) were chosen by using the principal component analysis (PCA). Four combinations of methods and models for calculating quality and also the most effective indicators for assessment of soil quality and features were chosen. The linear relationships between IQITDS and IQIMDS (R2=0. 883) and between NQITDS and NQIMDS (R2=0. 818) indicated that IQI had better performance for soil quality assessment in the study area. IQITDS index provides more comprehensive and more accurate assessment of soil quality than other indices. However، the use of Minimum Data Set (MDS) reduces workload and costs associated with the care that is appropriate.