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.
mehdi zangiabadi; manoochehr gorji; Mehdi Shorafa; Payman Keshavarz; Saeed Saadat
Abstract
Introduction: Soil physical quality isone of the most important factors affects plants water use efficiency in agricultural land uses. In the literature, some soil physical properties and indices such as S-index, Pore Size Distribution (PSD), porosity, Air Capacity (AC), Plant Available Water (PAW) content, ...
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Introduction: Soil physical quality isone of the most important factors affects plants water use efficiency in agricultural land uses. In the literature, some soil physical properties and indices such as S-index, Pore Size Distribution (PSD), porosity, Air Capacity (AC), Plant Available Water (PAW) content, Least Limiting Water Range (LLWR) and Integral Water Capacity (IWC) were mentioned as the soil physical quality indicators. It has been reported that the soils with the same PAW, LLWR and IWC may have different physical qualities. The index of Integral Energy (EI) of the soil moistureranges may differ between the soils with equal soilmoistureover a defined water content range. This index is defined as the required energy to uptake the unit mass of soil moistureby plants. According to this definition, the soils with low EI would have better physical quality for plants roots growth. In this research, we hypothesized that EI of different soil moistureranges were negatively related to S-index which means the plants required energy to uptake the soil water in the soils with high S-index, is lower than the soils with poor physical quality (less S-index). So we examined our hypothesis in medium to coarse-textured soils of Khorasan-Razavi province (Iran).
Materials and Methods:This research was conducted in Torogh Agricultural and Natural Resources Research and Education Station in Khorasan-Razavi province, north-eastern Iran (59° 37' 33"-59° 39' 10" E, 36° 12' 31"-36° 13' 56" N). Soil textures of this research station, are classified into loam, silt loam, silty clay loam, clay loam, and sandy loam which is as the same in more than 90% of agricultural soils in Khorasan-Razavi province. Thirty points with different soil textures and organic carbon contents were selected. In order to measure different properties of the soil, two soil samples (5 cm diameter × 5.3 cm length core sample and a disturbed soil sample) were collected from 0-30 cm depth of each point. After conducting required laboratory and field measurements using standard methods, the Soil Moisture Release Curve (SMRC) parameters (RETC program), S-index, PAW and LLWR (measured in matric heads of 100 and 330 cm for the field capacity), IWC and EI of mentioned soil moisture ranges were calculated. In this regard, integration calculations were done by Mathcad Prime 3 software. Finally, the relationships between the measured properties and EI values (for PAW100, PAW330, LLWR100, LLWR330 and IWC) were analyzed using Pearson correlation coefficient and stepwise multivariate linear regression by JMP (9.02) statistical software.
Results and Discussion: The S-index of 30 soil samples were between 0.029-0.070 with average of 0.046. These results showed that 90% of studied soil samples had good and very good and 10% had poor physical quality. The lowest average EI values of different soil moisture ranges were observed in sandy loam and silt loam and the highest was observed in silty clay loam soil textures. The EI(IWC) mean value was lower than EI(PAW) and EI(LLWR) mean values which indicated that calculating the EI values based on continuous effects of water uptake physical limitations, resulted in lower required energy for plants to uptake the unit mass of soil moisture . Statistical analysis resulted in significantly negative relations between S-index and two indices of EI(PAW100) and EI(IWC). Multivariate regression analysis showed that EI(PAW100) and EI(LLWR100) could be estimated by shape parameter (n) of SMRC by regression coefficients of 0.95 and 0.22, respectively and the value of EI(IWC) could be estimated by S-index and organic carbon content by regression coefficient of 0.57. The parameter of saturated volumetric water content (θvs) of SMRC and sand percentage were determining factors of EI(PAW330). In this research, it was not obtained the significant relationship between EI(LLWR330) values and measured soil physical properties. According to the results, increment of the shape parameter (n) of SMRC and S-index led to reducing the plants required energy to uptake the unit mass of soil moisture in PAW100 and IWC ranges. We found that EI of different soil moisture ranges could be used to evaluate the soil physical quality between the soils with equal soilmoisture contents.
Conclusion: This Research investigated the relationship of PAW, LLWR and IWC EI values with soil physical properties and S-Index in medium to coarse-textured soils. The results indicated that increment of S-index led to decreasing EI(PAW100) and EI(IWC) indices. According to the results, the shape parameter of SMRC and S-index could be accounted for determining factors of EI(PAW100) and EI(IWC) indices values.
Mehdi Zangiabadi; manoochehr gorji; Mehdi Shorafa; Saeed Khavari Khorasani; Saeed Saadat
Abstract
Introduction: Soil is the main source of water retention and availability for plant uptake. The supplement of water is completely dependent on soil physical properties. The soils with higher values of available water are generally more productive because they can supply adequate moisture to plants during ...
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Introduction: Soil is the main source of water retention and availability for plant uptake. The supplement of water is completely dependent on soil physical properties. The soils with higher values of available water are generally more productive because they can supply adequate moisture to plants during the intervals between irrigation or rainfall events. Generally according tothe spatial and temporal distribution of precipitation, Iran has an arid climate in which most of the relatively low annual precipitation falls from October through April. Thus, water deficiency along with the lack of organic carbon in the soil justifies the necessity of studying the soil, water and plant relationships that may improve the efficiency of water consumption in agricultural practices. For that reason, this research was conducted to investigate the relationship between some soil physical properties and Integral Water Capacity (IWC) index as one of the soil physical quality indices.
Materials and Methods: This study was conducted in Torogh Agricultural and Natural Resources Research Station in Khorasan-Razavi province, north-eastern Iran during 2013-2014. This station is located in south-east of Mashhad city with a semi-arid climate, annual precipitation of 260 mm and mean air temperature of 13.5 °C. The soil was classified in Entisols and Aridisols with a physiographic unit of alluvial plain that generally had medium to coarse textures in topsoil. Thirty points with different soil textures and organic carbon contents were selected as experimental plots. In order to measure different properties of the soil, two soil cores (8 cm diameter × 4 cm length cylinder for bulk density and 5 cm diameter × 5.3 cm length cylinder for sandbox measurements) and one disturbed soil sample (for other measurements) were collected from 0-30 cm depth of each plot. After conducting required laboratory analysis and field measurements using standard methods, the soil moisture curve parameters (RETC program), Porosity (POR), Air Capacity (AC), Relative Field Content (RFC) and Integral Water Capacity (IWC) index, were calculated. In this regard, integration calculations were done by Mathcad Prime 3 software. Finally, the relationship between the measured properties and IWC index were analyzed using Pearson correlation coefficient and stepwise multiple linear regression by SAS (9.1) statistical software.
Results and Discussion: Laboratory analysis results showed that the soil texture classes of samples were loam (40%), silt loam (23%), silty clay loam (17%), clay loam (13%), and sandy loam (7%). On average, very fine sand particles were dominant between five size classes of sand and the lowest values were devoted to very coarse sand particles. Soil porosity and air capacity calculation results indicated that on average bulk soil porosity (PORt) and bulk soil air capacity (ACt) were 0.46 and 0.20 (cm3cm-3), respectively. According to the results, RFC of 60% of studied soil samples were lower than 0.6, 7% were higher than 0.7 and only 33% were between 0.6-0.7 (optimal range). IWC index calculations were resulted in 0.13-0.25 (cm3cm-3) in different soil textures. The highest IWC were related to Loam and Clay Loam textures, respectively. Statistical analyses indicated that there were no significant relationship between soil particles (sand, silt and clay) and organic carbon content with IWC index. The factors of soil bulk density and RFC were negatively correlated with IWC index that means decreasing the soil bulk density and RFC would lead to the reduction of the effects of water uptake limitation factors by increasing the values of weighting functions (IWC calculations), and improvement of soil physical quality. High significant (P < 0.001) positive correlation coefficients were observed between IWC index and the factors of soil PORt, ACt and soil matrix air capacity (ACf) in this study. Multiple regression analysis results showed that IWC index could be estimated by the factors of ACt and PORt with the determination coefficient of 0.63. The partial determination coefficients indicated that ACt factor accounted for 50% and PORt accounted for 13% of IWC index variations.
Conclusion: The results indicated that in medium to coarse-textured soils, IWC index could be estimated using the bulk soil air capacity (ACt) and bulk soil porosity (PORt) factors that are derived from soil volumetric water content at saturation and field capacity points.
F. Asadzadeh; manoochehr gorji; A. Vaezi; S. Mirzaee
Abstract
Introduction: Field plots are widely used in studies related to the measurements of soil loss and modeling of erosion processes. Research efforts are needed to investigate factors affecting the data quality of plots. Spatial scale or size of plots is one of these factors which directly affects measuring ...
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Introduction: Field plots are widely used in studies related to the measurements of soil loss and modeling of erosion processes. Research efforts are needed to investigate factors affecting the data quality of plots. Spatial scale or size of plots is one of these factors which directly affects measuring runoff and soil loss by means of field plots. The effect of plot size on measured runoff or soil loss from natural plots is known as plot scale effect. On the other hand, variability of runoff and sediment yield from replicated filed plots is a main source of uncertainty in measurement of erosion from plots which should be considered in plot data interpretation processes. Therefore, there is a demand for knowledge of soil erosion processes occurring in plots of different sizes and of factors that determine natural variability, as a basis for obtaining soil loss data of good quality. This study was carried out to investigate the combined effects of these two factors by measurement of runoff and soil loss from replicated plots with different sizes.
Materials and Methods: In order to evaluate the variability of runoff and soil loss data seven plots, differing in width and length, were constructed in a uniform slope of 9% at three replicates at Koohin Research Station in Qazvin province. The plots were ploughed up to down slope in September 2011. Each plot was isolated using soil beds with a height of 30 cm, to direct generated surface runoff to the lower part of the plots. Runoff collecting systems composed of gutters, pipes and tankswere installed at the end of each plot. During the two-year study period of 2011-2012, plots were maintained in bare conditions and runoff and soil loss were measured for each single event. Precipitation amounts and characteristics were directly measured by an automatic recording tipping-bucket rain gauge located about 200 m from the experimental plots. The entire runoff volume including eroded sediment was measured on storm basis using the collection tanks. The collected runoff from each plot was then mixed thoroughly and a sample was taken for determining sediment concentration by weight. The per-storm soil loss was then obtained.
Results and Discussion: A wide range of rainfall characteristics were observed during the study period.The results indicated that the maximum amount of coefficients of variation (CVs) for runoff and soil loss from replicated plots were 60 and 80 percent, respectively, which were considerably higher than the variability of soil characteristics from these plots. CV of runoff and soil loss data among the replicates decreased as a power function of mean runoff (R2= 0.661, P
J. Kakeh; manoochehr gorji; A. A. Pourbabaei; A. Tavili; M. Sohrabi
Abstract
Introduction: Physical and biological soil crusts are the principal types of soil crusts. Physical and biological soil crusts are distributed in arid, semi-arid and sub-humid regions which constitute over 40% of the earth terrestrial surface. Biological soil crusts (BSCs) result from an intimate association ...
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Introduction: Physical and biological soil crusts are the principal types of soil crusts. Physical and biological soil crusts are distributed in arid, semi-arid and sub-humid regions which constitute over 40% of the earth terrestrial surface. Biological soil crusts (BSCs) result from an intimate association between soil particles and cyanobacteria, algae, fungi, lichens and mosses in different proportions which live on the surface, or in the immediately uppermost millimeters of soil. Some of the functions that BSCs influences include: water absorption and retention, nutrient retention, Carbon and nitrogen fixation, biological activate and hydrologic Status. BSCs are important from the ecological view point and their effects on the environment, especially in rangeland, and desert ecosystems and this caused which researchers have a special attention to this component of the ecosystems more than before.
Materials and Methods: This study carried out in the Qara Qir rangelands of Golestan province, northeast of Iran (37º15′ - 37º23′ N &54º33′ -54º39′ E), to investigate the effects of BSCs on some of soil biological properties. Four sites including with and without BSCs cover were selected. Soil biological properties such as microbial populations, soil respiration, microbial biomass carbon and nitrogen, as well as, other effective properties such asorganic carbon percent, total nitrogen, electrical conductivity, and available water content were measured in depths of 0-5 and 5-15 cm of soil with four replications. The gathered data were analyzed by nested plot, and the mean values were compared by Duncan test.
Results and Discussion: The results showed that organic carbon and water content were higher at the surface under BSCs, followed by 5-15 cm soils under BSCs. Both soil depths of uncrusted soils showed substantially lower organic carbon and water content than the BSC-covered soils. Total nitrogen was far higher in BSC-encrusted surface soils than uncrusted surface soils or BSC sub-surface soils. All Electrical conductivities were lower in surface soils covered with BSCs than sub-surface soils. The values for non-BSC covered soils were far higher than values for soils covered with BSCs. The values of soil biological properties such as microbial populations, soil respiration, microbial biomass carbon and nitrogen were higher at the surface under BSCs, followed by 5-15 cm soils under BSCs. The values for non-BSC covered soils were far lower than values for soils covered with BSCs at 0-5 cm depth but these properties in the uncrusted soils did not differ with BSCs covered surface at 5-15 cm depth. The amount of organic carbon was higher in BSC-covered surface soils at both measured depths, likely due to the ability of BSCs to fix atmospheric carbon. This leads to enhanced BSCs biomass and thus organic carbon especially in the soil surface layer (0-5 cm). An extensive cover of even a thin layer of photosynthetically active organisms can be an important basis for carbon input into the soil. BSCs also produce and secrete extracellular polysaccharides into surrounding soils, increasing the soil carbon and nitrogen pool. In general, there is a positive correlation between C and N fixation by BSCs. Also distribution of soil microbial population is positively correlated with the distribution of organic carbon and nitrogen. Microbial population is reduced following increase at depth, which is proportional to reduce of the concentration of nutrient and suitable conditions such as water content for growing them. Therefore proportionate to Microbial population, the properties such as soil respiration and microbial biomass carbon and nitrogen were reduced following increase at depth, because it did not provide the conditions for living organisms. These conditions were more inappropriate for non-BSC covered soils due to lower water content, organic carbon, total nitrogen and much higher electrical conductivity at both depths especially at 5-15 cm depth.
Conclusion: Biological soil crusts can play a key role in the biological properties of soil. Our data showed that organic carbon percent, total nitrogen, and available water content and biological properties such as microbial populations, soil respiration and microbial biomass carbon and nitrogen were increased significantly in two mentioned depths especially in 0-5 cm depth on sites covered with BSCs, relative to without BSCs. Electrical Conductivity had a reverse trend. In general, it can be concluded that BSCs improve soil conditions and provide suitable habitats for heterotrophic microorganisms and increase soil microbial activity. As the presence of BSCs generally increased the positive qualities of the soil, it is suggested that they can be used as a qualitative indicator of soil quality in rangelands.
jalil kakeh; manoochehr gorji
Abstract
Biological soil crusts (BSCs( result from an intimate association between soil particles and cyanobacteria, algae, fungi, lichens and mosses in different proportions, which live on the surface, or immediately in the uppermost millimeters of soil. Biological soil crusts, are important from the ecological ...
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Biological soil crusts (BSCs( result from an intimate association between soil particles and cyanobacteria, algae, fungi, lichens and mosses in different proportions, which live on the surface, or immediately in the uppermost millimeters of soil. Biological soil crusts, are important from the ecological view point and their effects on the environment, especially in rangeland, and desert ecosystems. These effects have encouraged researchers to have a special attention to this components of the ecosystems. The present study carried out in Qara Qir rangeland of Golestan province, Iran, to investigate the effects of BSCs on Soil saline-sodic properties. In the study area, four sites were selected which included sections with and without BSCs. Soil sampling was carried out in each section for depths of 0-5 and 5-15 cm, with four replication. The gathered data from soil samples were analyzed by nested plot. Results showed that BSCs than non-BSCs, significantly decrease the amount of soil acidity, calcium carbonate and soil saline-sodic properties such as electrical conductivity, sodium, calcium and magnesium concentration, sodium adsorption ratio, and exchangeable sodium percentage at both depths. In general, it can be concluded that BSCs enhance soil infiltration rate and available water content, that together their bioaccumulation properties, leads to decreasing soil saline-sodic properties. Potassium concentration did not differ among areas covered by BSCs and without BSCs. But infiltration rate and available water content were increased significantly in two mentioned depths on sites covered with BSCs than without BSCs. In general, it can be concluded that BSCs enhance soil infiltration rate and available water content, that together their bioaccumulation properties, leads to decreasing soil saline-sodic properties.
A.A. Zolfaghari; Mehdi Shorafa; M.H. Mohammadi; A. Liaghat; A. Hoorfar; manoochehr gorji
Abstract
Quantitative knowledge of soil hydraulic properties such as the soil moisture characteristics curve (SMC) is crucial for flow and transport modeling supporting hydrologic and agricultural engineering. However, many laboratory and field methods are currently available for direct measurement of the soil ...
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Quantitative knowledge of soil hydraulic properties such as the soil moisture characteristics curve (SMC) is crucial for flow and transport modeling supporting hydrologic and agricultural engineering. However, many laboratory and field methods are currently available for direct measurement of the soil hydraulic properties but, most or all of direct methods are too time consuming and costly. Thus developing of physically-based methods for predicting SMC is essential. In this study, an analytical method was developed to estimate Brooks-Cory model parameters using horizontal infiltration data. The new method was compared with Wang et al (2002) method. Sixteen soils with wide range of hydraulic properties were used to test the new method. The results showed that the new method estimates n and hd parameters smaller than those experimental values. Although, results showed that the new method properly predicts the measured SMC data. High coefficient of determination (R2=0.93) and low root mean square error (RMSE =0.03) confirmed the accurate predictability of new method. Mean RMSE of Wang et al (2002) method was 0.049. Therefore, results indicated that the new method is more accurate than Wang et al (2002) method for predicting soil moisture characteristics curve. The sensitivity analysis indicated that, for a given soil, the accurately estimation of SMC depends mainly on sorptivity parameter.
Y. Parvizi; M. Gorji; M.H. Mahdian; M. Omid
Abstract
چکیده
کربن آلی مهمترین مشخصه کیفی خاک بوده که حفاظت از آن محور اصلی کشاورزی پایدار و حفظ زیست بوم خاک است. پراکندگی کربن آلی خاک بیش از هر متغیر دیگری وابسته به وضعیت مدیریتی ...
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چکیده
کربن آلی مهمترین مشخصه کیفی خاک بوده که حفاظت از آن محور اصلی کشاورزی پایدار و حفظ زیست بوم خاک است. پراکندگی کربن آلی خاک بیش از هر متغیر دیگری وابسته به وضعیت مدیریتی خاک می باشد. تحقیق حاضر با هدف بررسی اثرات متغیرهای فیزیکی و مدیریتی در تغییرپذیری کربن آلی خاک و مقایسه کمی نقش این متغیرها در توزیع کربن آلی خاک و همچنین انتخاب متغیرها بر حسب اولویت تاثیرگذاری در یک حوزه با کاربری دیم انجام شد. برای این هدف از تکنیک آنالیز چند متغیّره تحلیل تفکیک متعارف (CDA) به دو روش متعارف و گام به گام استفاده شد. در این رابطه، مقادیر کربن آلی خاک در نقاط نمونه برداری در چهار کلاس کیفی خیلی کم ، کم، متوسط و زیاد دسته بندی شد. سپس اثرات 30 متغیر پیش بینی کننده فیزیکی و مدیریتی در پیش بینی سطوح کربن آلی خاک مورد تحلیل قرار گرفت. نتایج نشان داد که از میان متغیرهای فیزیکی، فقط مدلی حاوی متغیرهای خصوصیات خاک شامل درصد آهک، درصد اشباع، و مقادیر رس، شن و درصد حجمی سنگریزه بود، توانست به شکل معنیداری در پیش بینی کلاس های بهینه کربن آلی موفق عمل نماید. این در حالی است که کلیه مدلهای دربردارنده متغیرهای پیش بینی کننده مدیریتی با استفاده از اولین بردار توابع اعتباری خود در سطح معنی داری0001/0>α در پیش بینی کلاس کربن آلی موفق بودند. مدل M5 بالاترین همبستگی اعتباری را برای اولین محور خود نشان داد. همه ترکیبات متغیرهای مدلهای معنی دار، کلاس یک و دو کربن را با دقت مطلوبی پیش بینی کرد. ولی فقط مدل M5 بالاترین توان را در تشخیص کلاس چهار یا زیاد کربن آلی خاک نشان داد. در میان متغیرهای مدیریتی، سناریوی سامانه خاکورزی و اجزای آن به بهترین شکلی تغییرات کربن آلی خاک را در این حوزه با کاربری دیمزار توجیه می نمودند. اعمال تحلیل گام به گام در تحلیل تشخیص، توانست اثر سامانه سنتی آیش زمستانه را در بهبود کربن آلی خاک و توجیه تغییرپذیری آن آشکار سازد.
واژههای کلیدی: کربن آلی خاک، تحلیل تفکیک متعارف، تحلیل تفکیک گام به گام، کلاس بندی
