تعیین شاخص کیفیت فیزیکی خاک‌های با بافت متوسط و سبک در استان خراسان رضوی

نوع مقاله : مقالات پژوهشی

نویسندگان

1 مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی خراسان رضوی

2 دانشگاه تهران

چکیده

انتخاب مهم­ترین ویژگی­های فیزیکی کیفیت خاک و تجمیع آنها در قالب یک شاخص، می­تواند به اخذ تصمیمات صحیح مدیریتی منابع و اراضی کمک شایانی نماید. این پژوهش با هدف تعیین شاخص کیفیت فیزیکی خاک در پنج کلاس بافتی لوم، لوم شنی، لوم سیلتی، لوم رسی و لوم رسی سیلتی در ایستگاه تحقیقات کشاورزی و منابع طبیعی طرق در استان خراسان­رضوی انجام شد. پس از نمونه­برداری­های لازم از خاک 30 نقطه و انجام اندازه­گیری­های صحرایی و آزمایشگاهی، تعداد 35 ویژگی فیزیکی خاک تعیین و با استفاده از روش آماری تجزیه مؤلفه­های اصلی، مهم­ترین ویژگی­های فیزیکی کیفیت خاک تعیین گردید. سپس با وزن­دهی و استفاده از دو روش امتیازدهی خطی به ویژگی­های منتخب، شاخص کیفیت فیزیکی خاک در قالب یک مقدار عددی محاسبه شد. نتایج نشان داد که تنها شش ویژگی فیزیکی خاک شامل میانگین قطر منافذ، آب قابل استفاده (ظرفیت مزرعه در مکش 100 سانتی­متر)، تخلخل کل، انرژی انتگرالی دامنه رطوبتی با حداقل محدودیت (ظرفیت مزرعه در مکش 330 سانتی­متر)، شاخص پایداری خاک­دانه و آب قابل استفاده (ظرفیت مزرعه در مکش 330 سانتی­متر) می­توانند حدود 90 درصد تفاوت خاک­های مورد مطالعه را توجیه نمایند. مقایسه روش­های مختلف محاسبه شاخص عددی کیفیت فیزیکی خاک نشان داد که بیش­ترین مقدار ضریب حساسیت مربوط به روش انتخاب متغیرها با استفاده از تجزیه مؤلفه­های اصلی و وزن­دهی آنها و استفاده از روش امتیازدهی در محدوده بین صفر و یک بوده و شاخص محاسبه شده با این روش به‌عنوان مناسب­ترین معیار جهت طبقه­بندی خاک­های مطالعه­ شده در قالب چهار کلاس کیفیت خاک می­باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Determination of Soil Physical Quality Index in Medium to Coarse-textured Soils of Khorasan-Razavi Province

نویسندگان [English]

  • M. Zangiabadi 1
  • M. Gorji 2
  • P. Keshavarz 1
1 Khorasan-Razavi Agricultural and Natural Resources Research and Education Center
2 University of Tehran
چکیده [English]

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.   
 

کلیدواژه‌ها [English]

  • Minimum data set
  • Principal component analysis
  • Soil quality index
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