دانلود مقاله ISI انگلیسی شماره 24864
عنوان فارسی مقاله

ارزیابی هدایت آبی اشباع شده خاک از ارزیابی کارشناس از ویژگی های زمینه با استفاده از مدل رگرسیون لجستیک سفارش داده

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
24864 2011 12 صفحه PDF سفارش دهید 7390 کلمه
خرید مقاله
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عنوان انگلیسی
Soil saturated hydraulic conductivity assessment from expert evaluation of field characteristics using an ordered logistic regression model
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Soil and Tillage Research, Volumes 115–116, October–November 2011, Pages 27–38

کلمات کلیدی
آب شناسی - خاک - هدایت هیدرولیکی خاک - مدل رگرسیون لجستیک سفارش داده شده - تجزیه و تحلیل مکاتبات -
پیش نمایش مقاله
پیش نمایش مقاله ارزیابی هدایت آبی اشباع شده خاک از ارزیابی کارشناس از ویژگی های زمینه با استفاده از مدل رگرسیون لجستیک سفارش داده

چکیده انگلیسی

The knowledge of the soil saturated hydraulic conductivity (Ks) is essential for irrigation management purposes and for hydrological modelling. Several attempts have been done to estimate Ks in base of a number of soil parameters. However, a reliable enough model for qualitative Ks estimation based on the expert assessment of field characteristics had not been developed up to date. Five field characteristics, namely macroporosity (M), stoniness (S), texture (T), compaction (C) and sealing (L), in addition to tillage (G) were carefully assessed according to three classes each, in 202 sites in an agricultural irrigated area in Eastern Mediterranean Spain. After the evaluation of field characteristics, a single ring infiltrometer was used to determine the Ks value as the solution of the infiltration equation when the steady state was reached. The distribution of the Ks was assessed and five classes with 10-fold separations in class limits were defined accordingly. The relationships among site characteristics and Ks were analyzed through a correspondence analysis (CA). Next, an ordered logistic regression model (OLRM) for the prediction of the Ks class was developed. The CA revealed that, though tightly related, the set of six site characteristics should not be simplified into a smaller set, because each characteristic explains a significantly different aspect of Ks. Consequently, the OLRM was based on the six characteristics, which presented the following order of importance: L > M > G > T > C > S. According to the cross-validation of the OLRM the hit probability for the prediction of the Ks class attained an average value of 50%, which increased to 63% for the highest class of Ks. Moreover, wrong estimation of the Ks class exceeded the ±1 range only in 3% of sites. Therefore, a reliable enough assessment of Ks can be based on the expert assessment of field characteristics in combination with an OLRM.

مقدمه انگلیسی

Saturated hydraulic conductivity (Ks), as a measure of the ability of soil to transmit water, is essential in infiltration-related applications such as irrigation and drainage management ( Wu et al., 1999 and Radcliffe and Rasmussen, 2002) and for modelling the hydrology of the landscape. This parameter is obviously related to the hazard of ponding and to the potential of soils for tile drainage, which can affect the production of certain crops ( McKeague et al., 1982). Ring infiltrometers are often used for measuring the water intake rate at the soil surface. The total flow rate into the soil from a single-ring infiltrometer is a combination of both vertical and horizontal flow. Wu et al. (1997) found that the infiltration rate of a single-ring infiltrometer was related to the one-dimensional (1-D) infiltration rate for the same soil. For a relatively small ponded head, the 1-D final infiltration rate of a field soil is approximately equal to the field Ks, which is valuable information for computer modelling and irrigation management. Even with improved equipment, the Ks measurement is time consuming, and thus, models are recommended. Several attempts ( Rawls et al., 1982, Rawls et al., 1998, Tietje and Hennings, 1996 and Dexter and Richard, 2009) have been made to estimate the Ks from readily available analytical soil data such as particle size distribution, bulk density and organic matter content by means of pedotransfer functions or by physical modelling of the pore size distributions. However, all these estimation methods exhibit large differences between predictions and measurements of Ks ( Tietje and Hennings, 1996 and Landini et al., 2007), or the hydraulic conductivity close to water saturation could not be estimated based only on the usually available estimators ( Weynants et al., 2009). Models based on soil characteristics such as bulk density and pore size distribution give better predictions as shown by Mbagwu (1995), who estimated Ks from bulk density, macroporosity, mesoporosity and microporosity. Since these models are generally high data demanding and need cumbersome laboratory determinations, the applicability for farmers in irrigation management is reduced. To avoid this, qualitative models based on the expert assessment of morphological characteristics of soil could be an alternative approach to model the Ks. This idea of qualitatively describing water flow through soils has been credited to the Soil Conservation Survey ( Norton, 1939). Since then, several models for the qualitative classification of soil ease to permit water flow have been developed. Mason et al. (1957) developed such a model based on the expert assessment of 14 soil morphologic characteristics in order to classify 900 soils in an ordinal scale of seven permeability classes, defined as the ease with which pores of a saturated soil permit water movement. They attained a hit probability of 30%, and suggested that 95% probability of making a correct prediction could be achieved by using only three to five permeability classes. McKeague et al. (1982) developed guidelines for estimating the class of saturated hydraulic conductivity of soil horizons from observations of soil morphology in 78 soil horizons ranging in texture from sandy to clayey. The major factors contributing to high Ks values were abundant biopores, textures coarser than loamy fine sand, and strong, fine to medium blocky structure. The lowest values were associated with clayey horizons that had been compressed or puddled by cultivation. The guidelines presented, though incomplete and subjective to some degree, improved the estimates of Ks in limited testing by pedologists. The results also indicated that it was not feasible to assign a unique Ks estimate to near-surface horizons of cultivated soils of a particular series. Tillage practices and current land use have a major effect on soil structure, porosity and density, and hence on Ks. Saturated hydraulic conductivity can also be related to soil morphological criteria based on the expert assessment and the classes of the Factual Key (McKenzie et al., 2000). Lin et al. (2006) presented a vision that advocates hydropedology as an advantageous integration of pedology and hydrology for studying the intimate relationships between soil, landscape, and hydrology. Landscape water flux is suggested as a unifying precept for hydropedology, through which pedologic and hydrologic expertise can be better integrated. The discretization of continuous field measurements such as the Ks, is usually of high practical value to perform this integration. The indication of a class of Ks is, on the one hand, more informative, and on the other hand, more stable in space and time than the indication of an X% confidence interval derived from an ordinary least squares regression model. Given a saturated hydraulic conductivity expressed in an ordinal scale, the datum to predict is not longer the actual value of Ks, but the probability of an observation to belong to a certain class of Ks. This can be adequately performed using logistic regression models (LRM). Logistic regression modelling has been previously used in soil research to assess water erosion from expert evaluation of site characteristics ( Sonneveld and Albersen, 1999). The development of a LRM appears as an adequate methodology for predicting an ordinal variable from other ordinal variables, which to our knowledge has not been carried out up to date for the Ks assessment. The objective of the present study was to develop a methodology for the estimation of the class of soil saturated hydraulic conductivity based on several field characteristics such as tillage, macroporosity, stoniness, texture, compaction and sealing. This main objective was split into two partial objectives: (i) the development of a methodology for the expert evaluation of the soil characteristics, and (ii) the development of an ordered logistic regression model for the Ks prediction on basis the six field characteristics.

نتیجه گیری انگلیسی

A methodology for the qualitative classification of the soil saturated hydraulic conductivity based on the evaluation of field characteristics has been developed. Based on the current knowledge on soil hydraulic conductivity, five field characteristics namely, macroporosity, sealing, texture, compaction and stoniness, in addition to tillage were selected, and a methodology for their field expert classification in three classes each was established. Particularly, a new methodology for compaction assessment was described. According to a correspondence analysis, several remarkable associations among the six field characteristics were revealed. However, the characteristics explained different aspects of soil saturated hydraulic conductivity, i.e., each class acts at different levels of the Ks, and simplification into a smaller set of variables would not have been adequate. Therefore, the six characteristics were subsequently used as a set of six independent variables to develop an ordered logistic regression model for the estimation of five classes of Ks with 10-fold separations in class limits. The cross-validation of the OLR model gave a hit probability of 50% with error estimations seldom outside the ±1 range. Therefore, a reliable enough assessment of Ks can be based on the expert assessment of field characteristics plus the use of an ordered logistic regression model.

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