Soil science
F. Maghami Moghim; A.R. Karimi; M. Bagheri Bodaghabadi; H. Emami
Abstract
Introduction The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the ...
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Introduction The type of management operations and land use systems are the key parameters affecting the soil quality and sustainable land use. The exploitation systems by efficient use of soil and water recourse can decrease productions costs and increase the yield as well as conserve the natural resources. However, farmers and stakeholders need to be aware that through their management practices, they affect soil quality and, with the short-term goal of production and greater profitability, lead to soil degradation. They can both use the land economically and improve and maintain soil quality by balancing production inputs and refining their management approaches. There are different management systems of productivity in agricultural lands in Neyshabour plain in northeastern Iran. In addition to the water and soil limitations in the study area, the prevalence of the smallholder system and the unwillingness of farmers to integrate smallholder, has further increased the destruction of soils in the study area. The objective of this study was to assess the changes in soil quality index in surface soil and profile (0-100 cm) and calculate the correlation between soil quality index and alfalfa and rapeseed yield in rangeland and agricultural areas managed by smallholders, total owners, and Binalood Company in the study area.Materials and Methods A total of 21 soil profiles were described in the total owner, smallholder and Binalood company management system and sampled from the alfalfa and rapeseed lands. Questionnaires were prepared with the help of farmers and experts in the study area based on Analytic Hierarchical analysis (AHP) method. The physical and chemical characteristics of the soil samples were determined. The important soil characteristics affecting plant growth were determined by interviewing farmers and experts study area. Soil quality index in the minimum data set (MDS) was calculated by two methods of principal component analysis (PCA) and expert opinion (EO), by additive and weighted methods in surface soil and profile. To achieve a single value for each soil properties in the soil profile, two methods of weighted mean and weighted factor were used. To evaluate the accuracy of the assessment, the correlation between soil quality index and alfalfa and rapeseed yield was investigated of the various management system.Result and DiscussionThe results showed that the highest additive and weighted soil quality index at both surface and soil profile in both PCA and EO methods were in rangeland. It was due to lack of cultivation and maintaining organic matter comparing to agricultural land. The total owner management system due to its economic power and the use of appropriate and scientific methods comparing to smallholder management system, showed the highest additive and weighted soil quality index. In all management system, the EO-calculated weight index by weighted factor method had the highest value due to assigning the suitable weight for soil characteristics. The correlation analyses soil quality indices with canola and alfalfa indicated that the EO soil quality calculated by weighted factor for the soil profile were more correlated than surface soil in total owner system and the Binalood company. Weight coefficient method due to the application of different weights to each layer based on their importance, showed a higher soil quality index in both EO and PCA sets than the weighted average method. The reason for better EO performance probably is that the PCA is a reducing the dimensions, meanwhile, the minimum data selection in the EO method is based on regional experts which are familiar with cause-and-effect relationship of the soil properties. Due to the relatively good correlation of the yield of the studied products, with the soil quality index, an appropriate management needs to maintain and improve soil quality, especially in the smallholder system, as well as meeting the nutritional needs of these products.Conclusion Soil quality assessment in this study indicated that calculation of the soil quality index only considering the surface soil properties may not provide complete information for the farmers and land managers. Then inclusion of both surface and profile soil properties with farmers' knowledge and study area experts are essential for sustainable soil management. On the other hand, the differences in the management system also affected the soil quality index. Although the smallholder management system due to low input, especially chemical fertilizers, water and agricultural implements, had a high potential concerning environmental issues, but in terms of production, total owner and Binalood company management systems because of their high economic strength had the higher soil quality index. The farmers and stakeholders of the total owner management systems should be considered despite the proper management, however due to high inputs of fertilizer and water, especially in the Binalood company, the production may not be sustainable. Therefore, for further studies, calculating the water consumption in the desired management systems is recommended.
Soil science
M. Zangiabadi; manoochehr gorji; P. Keshavarz
Abstract
Introduction: Soil quality can be considered as a comprehensive index for sustainable land management assessment. Studying the most important soil physical properties and combining them as an index of soil physical quality (SPQI) could be used as an appropriate criteria for evaluating and monitoring ...
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Introduction: Soil quality can be considered as a comprehensive index for sustainable land management assessment. Studying the most important soil physical properties and combining them as an index of soil physical quality (SPQI) could be used as an appropriate criteria for evaluating and monitoring soil physical changes. In this regard, this study was conducted to determine the most important soil physical properties and calculate the SPQI of medium to coarse-textured soils of Khorasan-Razavi province.
Materials and Methods: Torogh Agricultural and Natural Resources Research and Education Station of Khorasan-Razavi province is located in south-east of Mashhad city (59° 37' 33"-59° 39' 10" E, 36° 12' 31"-36° 13' 56" N). Soil texture variability in this research station is one of its outstanding features. The soil textures are classified into loam, silt loam, silty clay loam, clay loam, and sandy loam. More than 90% of agricultural soils in Khorasan-Razavi province are classified in these five texture classes. Using the available data, 30 points with different soil textures and OC contents were selected. The soil samples were collected from 0-30 cm soil depth at each point. Intact soil cores (5 cm diameter by 5.3 cm length) were used for sandbox measurements, and disturbed soil samples were used to determine other properties. Required laboratory analysis and field measurements were conducted using standard methods. In this research, 35 soil physical properties as total data set (TDS) including: soil moisture release curve (SMRC) parameters, particle size distribution and five size classes of sand particles, soil bulk and particle density, dry aggregates mean weight diameter (MWD) and stability index (SI), S-index, soil porosity and air capacity, location and shape parameters of soil pore size distribution (SPSD) curves, relative field capacity (RFC), plant available water measured in matric pressure heads of 100 and 330 hPa for the field capacity (PAW100 and PAW330), least limiting water range measured in matric pressure heads of 100 and 330 hPa for the field capacity (LLWR100 and LLWR330), integral water capacity (IWC) and integral energy (EI) of different soil water ranges were measured and calculated for 30 soil samples. The most important soil physical properties were selected using principal component analysis (PCA) method by JMP (9.02) software. Selected physical properties as minimum data set (MDS) were weighted and scored using PCA results and scoring functions, respectively. In this study, three types of linear scoring functions were used. The soil physical quality index (SPQI) was calculated by two scoring and two weighting methods for each soil sample and the differences between these four SPQIs were tested by sensitivity index.
Results and Discussion: Principal component analysis results showed that among 35 soil physical properties (TDS) which were studied at this research, six properties of mean pore diameter (dmean), PAW100, total porosity (PORT), EI LLWR330, SI and PAW330 accounted for about 90% of the variance between soil samples. Weight of the selected properties (MDS) was calculated by the ratio of variation in the data set explained by the PC that contributed the selected property to the total percentage of variation explained by all PCs with eigenvalue ˃ 1. In this research, the parameters of PAW100, total porosity (PORT), SI and PAW330 were scored using scoring function of more is better, EI LLWR330 was scored using scoring function of less is better and dmean was scored using scoring function of optimum by two scoring methods with score ranges of 0.1-1 and 0-1. Considering unweighted and weighted MDS and two ranges of scores, four SPQIs were calculated for each soil sample. The results showed that SPQIs which were calculated by the MDS derived from PCA method and scoring weighted MDS at the range of 0-1, had the highest sensitivity index and could represent the differences between the studied soil samples better than other SPQIs. By this method, maximum and minimum SPQI values for the studied soils were 0.82 and 0.12, respectively. SPQI is a relative comparison criterion to quantify the soil physical quality which could be applied only for the studied soils with specific characteristics.
Conclusion: The results of this research showed that minimum data set (MDS) explained about 90% of the variance between soil samples. Combining MDS into a numerical value called soil physical quality index (SPQI) could be used as a physical comparison criterion for the studied soils. From the SPQI based on the MDS indicator method, soil quality was evaluated quantitatively. Soil samples with grade I, II, III, and IV accounted for 10%, 36.7%, 30%, and 23.3% of the soil samples, respectively.
Sona Azarneshan; farhad khormali; fereydoon sarmadian; farshad kiani; kamran Eftekhari
Abstract
Introduction: Assessing the soil quality of agricultural land is essential for the economic success and sustainability of the environment in developing countries. Recently, there are many types of methods for assessing soil quality, each of them uses different criteria. Considering that Qazvin plain ...
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Introduction: Assessing the soil quality of agricultural land is essential for the economic success and sustainability of the environment in developing countries. Recently, there are many types of methods for assessing soil quality, each of them uses different criteria. Considering that Qazvin plain is one of the most important regions of agricultural products in Iran as well as Middle East, so the assessment of the soil status using quantitative models of soil quality can be used as an indicator of the status of soils in relation to sustainable agriculture, optimal utilization of resources Natural and better land management. Among the quantitative models of soil quality index, cumulative model integrated quality index (IQI) and Nomero (NQI) index can be mentioned. Therefore, this study intends to evaluate the best quantitative and quality index model by examining and comparing two methods of selecting the appropriate criteria, Total data set (TDS) and Minimum (MDS) and the second order soil quality index, integrated quality index(IQI) and Nomero (NQI) index in Qazvin plain lands.
Material and Methods: The study area with 25220 hectares is located in east of Qazvin Province. The average annual precipitation is 275 mm and the soil moisture and temperature regimes are Thermic, Dry xeric and Weak Aridic, respectively. A total of 76 samples from the depth of 0-20 cm of the soil surface were studied and based on uniformity, soil type and land use. In this study, four types of criteria that affect the quality of soil in terms of their performance, including: upper limit, lower limit, optimal limit and descriptive function were selected. To qualify (normalize), the upper limit, lower limit and peak limit were selected. In the following, the Total Data Set (TDS) and the Minimum Data Set (MDS) set of data were used. In the TDS method, all of the measured characteristics (a total of 19 physicals, chemical and biological properties of the soil) were considered. Then, the degree of soil quality indices was determined based on the combination of TDS and MDS criteria and the final NQI and IQI quality indices.
Result and Dissection: Comparison of soil types in the region showed that the Aridisols had good, moderate and poor quality (19.35% of soil with good quality, 67.76% with moderate quality and 12.94% with poor quality), Entisols have good and medium quality (53.21% of the soil with good quality and 46.79% with moderate quality) and Inceptisols have very good, good, moderate and poor quality (96.9% Soils with very good quality, 66.73% with good quality, 15.85% with moderate quality and 13.44% with poor quality).
According to the TDS standard and the NQI model, the soils with qualities I, II and III were 30.67%, 66.86%, 47.2% of the total soils of the area (lands with poor quality soil quality were not observed in TDSNQI method). Therefore, according to this method, Aridisols has a very good, good and medium quality (13.26% of the soil with a very good quality rating, 73.88% with a good quality and 12.84% with a moderate quality grade), Entisols with The good quality (100% of the soil with good quality degree) and Inceptisols have a very good and good quality (28.11% of the soil with a very good quality grade, 71.88% with a good quality grade). The results of quantitative soil quality by using the MDS standard method and IQI model were showed, soils with very good, good, moderate and poor degree are 2.45, 16.45, 48.93 and 46.3 percent of total land area respectively.
The results of the combination of the MDS and the NQI model also showed that the soils with a very good, good and average grade are 30.67%, 66.86% and 47.2% of the total land, respectively. Also, the results of the combination of the MDS and NQI model showed that the soils with very good, good and average quality are 30.67%, 66.86% and 47.2% of the total land area respectively. The results of the evaluation based on 4 indicators showed that good quality (II) was prevalent in the studied soils and accounted for about 47% of the total area studied in Qazvin plain lands. The map of distribution of soil quality degrees, the distribution of soil degrees is relatively similar to all of four combination methods, the choice of criteria and model. By examining the linear relationship between the indices obtained from TDS and MDS criteria and the IQI and NQI indexes, it is observed that the correlation coefficient is more and more reliable than the NQI model when used in the IQI model (R2 = 0.77). So the highest correlation coefficient we observed two methods for selecting the TDS and MDS criteria when using the IQI model. In general, the results of this study indicate a better performance of the MDS criteria than TDS.
Conclusions: Therefore, the main results of this study suggest using the IQI model with the MDS selection method as the starting point in the global standard path for future studies. Special attention should be paid to the criteria chosen by the MDS. In addition, conducting a series of research into the future in order to modify the MDSIQI model can make it more relevant to international standards.
