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GEOLOGY, ECOLOGY, AND LANDSCAPES 2019, VOL. 3, NO. 1, 1–13 INWASCON https://doi.org/10.1080/24749508.2018.1481633 Estimation of inﬁltration rate from soil properties using regression model for cultivated land a a b c Ghanshyam Tikaram Patle , Tatung Taka Sikar , Kishan Singh Rawat and Sudhir Kumar Singh Department of Soil and Water Engineering, College of Agricultural Engineering and Post Harvest Technology, Gangtok, Sikkim, India; b c Centre for Remote Sensing and Geo-Informatics, Sathyabama University, Chennai, Tamilnadu, India; K. Banerjee Centre for Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Allahabad, India ABSTRACT ARTICLE HISTORY Received 22 November 2017 The study was conducted on cultivated land at College of Agricultural Engineering and Post Accepted 22 May 2018 Harvest Technology (CAEPHT campus), Ranipool, Gangtok, Sikkim, India. Twenty ﬁve points were identiﬁed at 10 m grid interval and ﬁeld measurements were performed using double KEYWORDS ring inﬁltrometer method. Result of soil analysis suggests sandy loam and loamy sand Inﬁltration; bulk density; texture and the bulk density and particle density have varied from 1.412–1.716 g/cm particle density; organic and 2–3.03 g/cm , respectively. The basic inﬁltration rate has varied from 0.3 cm/h to carbon; multiple linear 6.8 cm/h. Result show that sand, particle density and organic carbon content have a regression model; Sikkim positive correlation with inﬁltration rate by 0.75, 0.18 and 0.22, respectively, whereas silt, clay, bulk density and moisture content, have a negative correlation with inﬁltration rate by −0.41, −0.73, −0.33 and −0.22, respectively. The analysis performed for ﬁve classes con- sidering the combination of soil properties and subjected to regression analysis. Result shows that in order to predict soil inﬁltration rate based on few properties of soil with seven independent variables, multi-linear regression model E = -30,578.81–305.56(sand%)-306.16 IR (silt%)-0.306.33(clay%)-5.18(BD%)+.34(MC%)+4.18(PD)+16.85(OC%) with R (0.80), mean RMSE (1.52) and standard error (2.39) is the best model for the estimation of inﬁltration rate and recommended for the study area. 1. Introduction of the important parameter which governs the rate of inﬁltration. Design, operation, management, and Soil and water are the vital natural resources used in hydraulic evaluation of on-farm water applications the crop production system. Eﬃcient management of have also rely on the inﬁltration properties of the soil water will be required a greater control of inﬁltration because inﬁltration behavior of the soil directly deter- in the soil. Increased inﬁltration control would help to mines the essential variables such as inﬂow rate, length solve such wide ranging problems as upland ﬂooding, of run, application time, depth of percolation, and tail- pollution of surface and groundwaters, declining water water run-oﬀ in irrigation systems (Adeniji et al., 2013; tables, ineﬃcient irrigation of agricultural lands, and Sarmadian and Taaghizadeh-Mehrjardi 2014). Martens wastage of useful water (Rashidi, Ahmadbeyki, & and Frankenberger (1992) have carried out the work Hajiaghaei, 2014). Soil inﬁltration rate is the most on the modiﬁcation of inﬁltration rates in an organic- essential process that aﬀects the surface irrigation uni- amended irrigated soil. Soils have been amended using formity and eﬃciency because of its mechanism of three loadings such as poultry manure, sewage sludge, transfer and distributes water from surface to soil barley straw (Hordeum vulgare L.), and alfalfa proﬁle (Rashidi et al., 2014). The measurement of (Medicago sativa L.) to an Arlington soil (coarse inﬁltration of water into the soil is an important indi- loamy, mixed, thermic Haplic Durixeralf) for 2 years cation concerning the eﬃciency of irrigation and drai- and found that water inﬁltration rates in the organic- nage, optimizing the availability of water for plants amended soils have initially increased by stimulation growth and metabolism, improving the yield of crops of microbial activity, which has increased the stability and minimizing erosion (Adeniji, Umara, Dibal, & of soil aggregates. Cerda (1996) studied the inﬁltration Amali, 2013). Adequate knowledge of inﬁltration rate rates for contrasting slope in south Spain using simu- of a soil data is essential for reliable prediction and lated rainfall and ponding method and suggested that control of soil and water related environmental the aspect, slope and vegetation cover governs the hazards. Prediction of cumulative inﬁltration is impor- steady state inﬁltration rates, whereas, seasonal change tant for estimation of the amount of water entering plays an important role in varying inﬁltration rates. and its distribution in the soil. Soil properties are one Fox, Bryan, and Price (1997)studied inﬂuence of slope CONTACT Kishan Singh Rawat email@example.com Supplementary data for this article can be accessed here © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society (INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 2 G. T. PATLE ET AL. angle on ﬁnal inﬁltration rate for inter-rill conditions. inﬁltrometer was better than single ring inﬁltrometer. They found that inﬁltration rate decreased with They also reported the inﬁltration rate was aﬀected increase in slope angle. Diamond and Shanley (1998) due to the cracks of plants root, movement of earth, measured the rate of inﬁltration using double-ring and clay desiccation. Hence the knowledge about inﬁl- inﬁltrometer for freely drained, imperfectly drained tration of water into soils is an important indication and poorly drained sites of Irish during summer and concerning the eﬃciency of irrigation and drainage, winter seasons and reported that 3.5 times higher optimizing the availability of water for plants, improv- inﬁltration rate for summer compared to winter sea- ing the yield of crops, minimizing erosion, and son. Chen-Wuing Liu, Cheng, Wen-Sheng, and Chen wastage of water. A very often, double-ring inﬁltrom- (2003) studied the water inﬁltration rate in cracked eter test is used for the measurement of inﬁltration paddy soil surfaces of paddy ﬁelds and found that a rate which is time consuming and laborious and prac- cracked paddy ﬁeld has signiﬁcantly increased rate of tically diﬃcult, particurally in the hilly terrain. This inﬁltration. Lake, Akbarzadeh, and Mehrjardi (2009) can be accomplished by through the development of developed the various pedo-transfer functions (PTFs) models based on the easily measurable soil properties for Guilan Province of Iran to predict soil physico- because soil properties inﬂuences the inﬁltration char- chemical and hydrological characteristics using multi- acteristics. In view of above, an attempt was made to layer perceptron (MLP), a feed forward artiﬁcial neural predict the inﬁltration rate of an agricultural land network (ANN) method. They found that ANN located at the College of Agricultural Engineering & method was more accurate than multiple linear regres- PHT (CAEPHT) using the PTF developed by the sion (MLR) method for the estimation of inﬁltration Multiple linear Regression Analysis (MLR) to deter- rate. Osuji, Okon, Chukwuma, and Nwarie (2010) mine the optimum soil inﬁltration rate model based studied the inﬁltration characteristics of soil under on some physical properties of soil and to verify the various land use practices in Owerri, Southastern model by comparing the predicted rate with the ﬁeld Nigeria. Joshi and Tambe (2010) measured the eﬀect measured rate of inﬁltration with the following objec- of slope and grass-cover on inﬁltration rate, runoﬀ and tives: (i) to measure the diﬀerent soil properties and sediment yield under simulated rainfall condition in inﬁltration rate of a cultivated ﬁeld; (ii) to develop the upper Pravara Basin in western India. They found the soil inﬁltration rate model based on soil properties and highest inﬁltration for grass covered area with gentle to verify the model by comparing the predicted and slope, and minimum for bare land surface with steep the ﬁeld test inﬁltration results. slope. They also reported that grass cover was the main factor that induced inﬁltration with minimum runoﬀ, resulting to less sedimentation. Ahaneku 2. Materials and methods (2011) conducted study on inﬁltration rate under two 2.1. Study area major soils in North Central Nigeria using inﬁltrom- eter. Dagadu and Nimbalkar (2012) carried out the Inﬁltration rates of an agricultural land located at the inﬁltration studies of diﬀerent soils under diﬀerent College of Agricultural Engineering & PHT soil conditions and compared the inﬁltration models (CAEPHT) were measured using double-ring inﬁlt- with ﬁeld data measured by double-ring inﬁltrometer. rometer test. The study area is located in CAEPHT They reported that the Horton’s model, and Green- campus, situated between 27°17.454ʹ to 27°17.508ʹ N Ampt model were the best ﬁtting to the observed ﬁeld latitude and 88°35.595ʹ to 88°35.635ʹ E longitude data to estimate inﬁltration rates at any given time (Figure 1). Study area is diﬀerentiated in two parts with high degree of correlation coeﬃcient and mini- considering the elevation diﬀerence. One part of mum degree of standard error. Hajiaghaei et al. (2014) study area is located at an altitude ranging from 861 estimated the inﬁltration rate using double-ring inﬁlt- to 865 m above MSL and away from the Ranikhola rometer and predicted soil inﬁltration rate based on river and other part is near to Ranikhola river at an silt and clay content of soil. They developed a relation altitude range of 842–848 m above MSL.The upper 2 2 between soil inﬁltration rate and soil properties (silt area is 400 m (40 x 10 m) and lower area is 1200 m and clay content). Rashidi et al. (2014)carriedouta (40 x 30 m). Double-ring inﬁltrometer tests were ﬁeld experiments at the agricultural ﬁelds of Karaj carried out at 20 locations within the study area. (Iran) and developed a relation between soil inﬁltra- Location of each inﬁltration stations were marked tion rate and physical properties of soil. They pre- using global positioning system (GPS) device. dicted the inﬁltration rate using silt content and clay Details of each station such as latitude, longitude, content, bulk density (BD), organic matter (OM), and and altitude were also recorded. The stations were moisture content (MC) of soil. Champatiray, Balmuri, marked in such a way that each station had distance Patra, and Sahoo (2015) measured inﬁltration rate of of 10 m apart from each other. Table 1 Location of 25 soil using diﬀerent size of single and double-ring stations used in the ﬁeld measurement of inﬁltration inﬁltrometer. They found that double-ring rate. GEOLOGY, ECOLOGY, AND LANDSCAPES 3 Figure 1. Study area map (marked). gauge. The diameter of inner and outer ring was Table 1. Location of 25 stations used in the ﬁeld measure- ment of inﬁltration rate. 25 cm and 35 cm, respectively, and both have equal Station Latitude °N Longitude °E Elevation (m) height of 25 cm each. Both the rings were placed P1 27°17.456’ 88°35.614’ 861 concentric on the soil surface and was hammered P2 27°17.454’ 88°35.608’ 862 into the soil uniformly using the rammer at the P4 27°17.459’ 88°35.606’ 865 P5 27°17.465’ 88°35.611’ 864 depth of 12 cm each. P8 27°17.468’ 88°35.599’ 865 A soil sample for estimating MC was collected P9 27°17.474’ 88°35.594’ 864 P10 27°17.472’ 88°35.595’ 865 nearby prior to inﬁltration from that station using A1 27°17.481’ 88°35.626’ 848 hand screw auger at the depth of 30 cm and MC was A2 27°17.486’ 88°35.625’ 846 A4 27°17.499’ 88°35.635’ 843 determined using oven drying method, keeping soil B1 27°17.485 88°35.623’ 846 samples at 150°C for 24 h. The MC was calculated by B2 27°17.499’ 88°35.622’ 847 B3 27°17.493’ 88°35.625 846 ðÞ M MðÞ M M B4 27°17.496’ 88°35.629 845 2 1 3 1 MC ¼ 100; C1 27°17.491’ 88°35.616’ 846 M M 2 1 C2 27°17.492’ 88°35.620’ 846 C3 27°17.495’ 88°35.622’ 845 where, MC: moisture content (%); M : weight of dish (g); C4 27°17.503’ 88°35.623’ 844 D1 27°17.491’ 88°35.610’ 845 M : weight of wet soil sample with dish (g); M :weightof 2 3 D2 27°17.489’ 88°35.610’ 844 dried soil sample with dish (g). D3 27°17.495’ 88°35.614’ 843 D4 27°17.508’ 88°35.619’ 842 E1 27°17.493’ 88°35.606’ 845 E2 27°17.495’ 88°35.609’ 844 2.2.2 Bulk density(BD) E4 27°17.506’ 88°35.618’ 843 BD of soil samples has been measured using a cylind- rical core cutter of 10 cm diameter and 13 cm length. The volume and weight of core cutter was deter- 2.2. In-situ and laboratory analysis mined. Core cutter was hammered down into the 2.2.1 Measurement of inﬁltration and soil with rammer. The weight of soil with core cutter MC (Moisture content) was determined again. It was calculated by: Inﬁltration rates were measured by using double-ring inﬁltrometer which consist of two concentric metal W W 2 1 BD ¼ ; cylindrical ring, a metal rammer and measuring V 4 G. T. PATLE ET AL. where, BD: bulk density (g/cm ); W₁: weight of core where, Y = Dependent variable, for example soil cutter (g); W₂: weight of core cutter and soil (g); inﬁltration rate (cm/h) V: volume of core cutter (cm ). X=X , ..., X : Independent variables, for example 1 n sand content (%), silt content (%), clay content (%), BD (g/cm), organic content (%), and MC of soil (%); 2.2.3. Particle density (PD) k=k ,k , ..., k : Regression coeﬃcients. 1 2 n PD of soil samples has been determined by using In order to predict soil inﬁltration rate, sand con- density bottle. The oven dried soil sample was tent, silt content, clay content, BD, porosity, OC, and screened through 200 µm sieve. Soil samples of 10 g MC of soil were suggested as independent variables were collected. It was determined by: and all the data were subjected to regression analysis using the Microsoft Excel 2010. M M 2 1 PD ¼ ρ; ðÞ M MðÞ M M 2 1 3 4 3.2. Root mean square error (RMSE) where, PD: particle density (g/cm ); M₁: weight of den- sity bottle (g); M₂: weight of soil and density bottle (g); The RMSE is frequently used to measure the diﬀerence M₃: weight of water, soil, and density bottle (g); between predicted value by a model or an estimator M₄: weight of water and density bottle (g); ρ:density and the value actually observed. RMSE is a good of water (g/cm ). measure of precision. These individual diﬀerences are called residuals, and the RMSE serves to aggregate them into a single measure of predictive power. 2.2.4. Texture and organic carbon content RMSE was calculated as: Texture of soil samples has been analyzed by hydro- vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ u ð meter apparatus. Putting the hydrometer and tem- 2 uP perature reading in the texture analysis work sheet, ðÞ xi yi sand, silt, and clay percentage content was deter- RMSE ¼ ; mined. The textural class of the soil sample was determined by using soil texture triangle (showing where, xi: measured value; yi: estimated value; the 12 major textural classes and particle size scales n = number of values as deﬁned by the USDA). The dried soil samples were screened through 3.3. Standard deviation (SD) and coeﬃcient of 200 µm sieve and 0.48 g was taken for further analy- variation (CV) sis. Organic carbon content of soil samples was deter- mined by using STFR PUSA device. SD and CV are types of measures of dispersion. SD is an absolute measure and CV is a relative measure. SD and CV is calculated by: 3. Statistical analysis sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 3.1. Multiple linear regression (MLR) analysis SD ¼ ðÞ xi x ; i¼1 Regression analysis is a statistical tool of investigation of relationships between variables. When there are SD CV ¼ 100; more than one independent variables then multiple regression analysis has been required to perform. In where, x : measured value; x’: mean of measured MLR analysis, dependent variable and independent value; n: number of measured value. variables are related linearly. The dependent variable is basic inﬁltration rate and the independent variables are Sand (SA), Silt (SI), and Sandy loam (SL), BD, 4. Results and discusions PD, porosity, and organic carbon. Using all these 4.1. Soil physical properties parameters basic inﬁltration rate prediction model was developed using Microsoft Excel data analysis The soil properties were determined for each station tool. The coeﬃcient of determination was also deter- marked at Table 2. These were considered as the mined to check reliability of the model. independent variables which were used in the MLR for the prediction of inﬁltration rate and considered as the main key for analysis and development of the 3.1.1 Prediction model using MLR analysis prediction model. The textural classes of the study A typical multiple-variable linear regression model is area are sandy loam and loamy sand. Sandy loam expressed as follows: texture was observed at 19 stations and loamy sand Y= k+k ×X +k ×X + ...+ k ×X , texture was observed at 6 stations. 1 1 2 2 n n GEOLOGY, ECOLOGY, AND LANDSCAPES 5 Table 2. Physical properties collected soil samples. Station Sand Silt Clay BD PD MC OC Textural class P1 77.00 9.66 13.34 1.56 2.32 28.22 0.301 Sandy loam P2 74.00 11.25 14.75 1.569 2.2 28.93 0.2936 Sandy loam P4 72.40 9.60 18.00 1.674 2.318 28.17 0.2802 Sandy loam P5 80.10 11.26 8.64 1.489 2.372 24.67 0.333 Loamy sand P8 76.88 13.97 9.15 1.62 2.217 26.91 0.3184 Sandy loam P9 68.76 16.21 15.03 1.46 2.428 21.99 0.3328 Sandy loam P10 71.16 16.22 12.62 1.677 2.5 27.83 0.3433 Sandy loam A1 68.63 20.22 11.15 1.716 2.318 24.58 0.3318 Sandy loam A2 82.27 11.33 6.40 1.616 2 21.96 0.3439 Loamy sand A4 82.28 7.36 10.36 1.657 2.702 14.56 0.3432 Loamy sand B1 62.93 18.00 19.07 1.59 2.564 28.4 0.3599 Sandy loam B2 79.88 14.03 6.09 1.412 2.564 22.34 0.3606 Sandy loam B3 70.40 12.56 17.04 1.648 2.702 22.65 0.3606 Sandy loam B4 79.21 9.64 11.15 1.648 2.702 13.96 0.3574 Sandy loam C1 72.00 17.67 10.33 1.716 2.564 27.33 0.3623 Sandy loam C2 69.93 18.33 11.74 1.613 2.857 25.7 0.3512 Sandy loam C3 69.35 20.25 10.40 1.508 2.778 22.77 0.3498 Sandy loam C4 80.60 10.67 8.73 1.432 2.702 16.67 0.3555 Loamy sand D1 84.00 10.14 5.86 1.677 3.03 28.46 0.3525 Loamy sand D2 71.19 19.96 8.85 1.579 2.702 27.48 0.3587 Sandy loam D3 67.19 19.14 13.67 1.465 2.78 24.99 0.361 Sandy loam D4 74.50 13.60 11.90 1.445 2.702 18.96 0.358 Sandy loam E1 70.21 17.17 12.62 1.584 2.857 26.03 0.3257 Sandy loam E2 71.16 15.89 12.95 1.479 2.631 27.36 0.3619 Sandy loam E4 82.23 12.44 5.33 1.503 2.63 20.28 0.3523 Loamy sand Note: Sand, Silt, Clay, MC and OC are in % and BD, PD, are in g/cm Table 3. Descriptive statistics of measured soil properties. of agricultural soils and reported that the inﬁltration Soil property Max Min Mean SD CV rate is greatly reduced by the loss of organic content, Sand (%) 84 62.93 74.33 4.90 6.60 compaction due to movement of heavy machine, and Silt (%) 20.25 7.36 14.26 3.37 23.65 excessive grazing. They have also suggested some Clay (%) 19.07 5.33 11.41 2.87 25.14 BD (g/cm ) 1.716 1.412 1.57 0.08 5.01 management strategies like increase in the amount PD (g/cm ) 3.03 2 2.57 0.19 7.55 of plant cover, especially of plants that have positive MC (%) 28.93 13.96 24.05 3.55 14.75 OC (%) 0.36 0.28 0.34 0.018 5.12 eﬀects on inﬁltration, decrease the extent of compac- Note: Maximum value (Max); Minimum value (Min); Mean value (Mean); tion by avoiding intensive grazing and the use of Standard deviation (SD); Coeﬃcient of variation (CV) machinery when the soils are wet, decrease the for- mation of physical crusts by maintaining or improv- ing the cover of plants or litter and thus reducing the Table 3 presented descriptive statistics of measured impact of raindrops. Adeniji et al. (2013) estimated soil properties. The percentage of sand, silt, and clay the soil inﬁltration rate using soil texture at the uni- varies from 62.93 to 84, 7.36 to 20.25 and 5.33 to versity of Maiduguri. Azuka, Mbagwu, and Oyerinde 19.07, respectively. The percent mean value of sand, (2013) evaluated the soil inﬁltration characteristics in silt, and clay is 74.33, 14.26, and 11.41, respectively. South-Eastern Nigeria and prediction were done BD and PD varies from 1.412 to 1.716 g/cm and 2.0 using the eﬀect OM content, microporosity, BD, 3 3 to 3.03 g/cm , respectively, with mean BD 1.57 g/cm initial MC, coarse sand, silt, and clay contents of and mean PD 2.57 g/cm . The MC varies from soil. They reported that these soil properties have 13.96% to 28.93% with mean value of 24.05%. The great inﬂuence on the inﬁltration characteristics of organic carbon content varies from 0.28% to 0.36% the soils. It is reported that inﬁltration is inﬂuenced with mean value of 0.34%. The standard deviation for by soil OM, PD, BD, MC, sand, silt, clay, porosity, sand, silt, clay, BD, PD, MC, and organic carbon were and speciﬁc gravity (Ayu, Soemarno, and Java 2013; 4.90, 3.37, 2.87, 0.08, 0.19, 3.55, and 0.018, respec- Osuji et al., 2013). The degree of soil OM stratiﬁca- tively. The coeﬃcient of variation for the sand, silt, tion with depth has been suggested as an indicator of clay, BD, PD, MC, and organic carbon are as follows: soil quality, because surface OM is essential to control 6.60, 23.65, 25.14, 5.01, 7.55, 14.75, and 5.12, respec- erosion, water inﬁltration, and conservation of nutri- tively. Franzluebbers (2002) evaluated the water ents (Franzluebbers, 2002). Inherent factors such as inﬁltration and soil structure relation to OM and its soil texture which cannot be changed also aﬀects the stratiﬁcation with depth and found that short-term soil inﬁltration. soil disturbance of previously stratiﬁed soil led to uniform distribution of soil organic carbon (SOC), 4.2. Prediction model using MLR reduced soil BD, and increased water retention. Haghnazari, Shahgholi, and Feizi (2015) evaluated For prediction of basic inﬁltration rate, the analysis the diﬀerent factors aﬀecting the rate of inﬁltration was categorized into ﬁve classes. The ﬁrst class has 6 G. T. PATLE ET AL. Table 4. RMSE values between observed IR and estimated IR based no number (n = 3, 4, 5, 6, 7) of parameters. Station BIR Est RMSE Est RMSE Est RMSE Est RMSE Est RMSE 3P 3P 4P 4P 5P 5P 6P 6P 7P 7P P1 4.80 6.36 1.56 6.75 1.95 7.81 3.01 7.22 2.48 7.07 2.27 P2 4.41 4.69 0.28 5.01 0.60 6.97 2.56 5.94 2.21 5.80 1.39 P4 4.20 2.48 1.72 1.76 2.44 1.29 2.91 0.88 1.88 0.62 3.58 P5 11.70 9.80 1.90 10.75 0.95 11.15 0.55 10.52 9.33 10.51 1.19 P8 8.10 8.54 0.44 7.88 0.22 7.95 0.15 6.78 5.88 6.70 1.40 P9 2.40 3.89 1.49 5.36 2.96 3.32 0.92 2.81 0.03 2.69 0.29 P10 3.30 3.22 0.08 2.11 1.19 2.62 0.68 2.49 0.80 2.63 0.67 A1 3.00 4.92 1.92 3.08 0.08 3.07 0.07 2.10 0.68 2.05 0.95 A2 6.30 11.65 5.35 10.96 4.66 10.17 3.87 8.33 4.30 8.54 2.24 A4 2.40 9.57 7.17 8.69 6.29 6.18 3.78 6.97 0.30 6.98 4.58 B1 0.30 −1.01 1.31 −0.86 1.16 −1.15 1.45 −1.10 2.26 −0.89 1.19 B2 13.80 11.08 2.72 12.75 1.05 12.97 0.83 12.78 11.24 12.83 0.97 B3 3.00 2.37 0.63 1.84 1.16 1.45 1.55 2.17 0.30 2.39 0.61 B4 10.32 8.19 2.13 7.41 2.91 7.42 2.90 8.11 7.62 8.27 2.05 C1 3.30 6.83 3.53 4.97 1.67 4.43 1.13 4.35 0.74 4.45 1.15 C2 3.00 5.02 2.02 4.49 1.49 3.20 0.20 4.07 0.14 3.95 0.95 C3 2.70 2.79 0.09 3.01 0.31 2.75 0.05 2.77 0.08 2.74 0.04 C4 10.80 9.91 0.89 11.56 0.76 8.89 1.91 9.39 8.10 9.38 1.42 D1 16.80 12.93 3.87 11.47 5.33 13.71 3.09 15.33 13.77 15.25 1.55 D2 7.20 6.86 0.34 6.53 0.67 6.84 0.36 7.01 4.50 6.98 0.22 D3 4.80 3.15 1.65 4.48 0.32 3.78 1.02 4.31 2.02 4.31 0.49 D4 8.67 6.35 2.32 7.95 0.72 9.43 0.76 9.86 5.97 9.96 1.29 E1 3.00 4.57 1.57 4.44 1.44 5.36 2.36 6.24 0.14 5.98 2.98 E2 7.50 4.75 2.75 5.95 1.55 8.73 1.23 8.86 4.87 9.03 1.53 E4 15.30 12.21 3.09 12.78 2.52 12.78 2.52 12.90 12.67 12.88 2.42 Note: Basic IR (cm)/Observed IR, BIR;Estimated n Parameter, Est where n = 3,4,5,6,7 np three independent variables such as sand, silt, and to 20.25% with a mean value of 14.26% and clay clay (soil texture). The second class has soil texture content varies from 5.33% to 19.07% with a mean of and BD as independent variables. The third class had 11.41%. The developed prediction equation for the soil texture, BD, and PD as independent variables. E is given below. IR The fourth class had soil texture, BD, PD, and MC as Prediction equation 1, independent variables. The ﬁfth class had soil texture, E = 14,195.35−141.75 (sand%) −142.10 (silt%) IR BD, PD, MC, and organic content as an independent −142.56 (clay%) variable. From Table 4,itwas foundthatthe estimatedinﬁltra- tion rate varies from 1.35 to 12.09 cm/h with an average 4.3. Analysis of ﬁrst class rate of 6.18 cm/h and measured average inﬁltration rate was 6.44cm/h.TheRMSEvariedfrom0.01to7.02with The inﬁltration rate was estimated using sand, silt, an average value of 2.07. The RMSE was lowest at P8 and clay, and the results of analysis are shown in stationand highestatA4station.Thecoeﬃcient of Table 4 and Figure 2, respectively. It was observed determination (R ) was 0.63 and the coeﬃcient of corre- that sand content varies from 62.93% to 84% with a lation, R was 0.79. The standard error (e) was 2.89. mean value of 74.33%, silt content varies from 7.36% y = 0.623x + 2.165 Using Equation 1 R² = 0.63 -2 3 8 13 18 -2 -4 IR Figure 2. Measured inﬁltration rate versus estimated inﬁltration rate. B (cm) IR GEOLOGY, ECOLOGY, AND LANDSCAPES 7 y = 0.694x + 1.645 16 Using Equation 2 R² = 0.69 -2 3 8 13 18 -2 -4 IR Figure 3. Measured inﬁltration rate versus estimated inﬁltration rate. 4.4. Analysis of second class 4.5. Analysis of third class The inﬁltration rate was estimated using sand, silt, The inﬁltration was estimated using sand, silt, clay, clay, and BD. The results of analysis are presented in BD, and PD. The results of analysis are shown in Table 4 and Figure 3, respectively. It was observed Table 4 and Figure 4, respectively. It was found that 3 3 that BD varies from 1.412 to 1.716 g/cm with a mean PD varies from 2 to 3.03 g/cm with a mean value of 3 3 value of 1.57 g/cm . The developed prediction equa- 2.57 g/cm . The developed prediction model is repre- tion for the E is given below. sented by equation 3. IR Prediction equation 2, Prediction equation 3, E = 22,041.03−220.01 (sand%)−220.38 (silt%) IR E = -26,642.18−266.11(sand%)−266.54(silt%) IR −220.77 (clay%)−12.71 (BD%) −266.84(clay%)−12.12(BD%)+3.46(PD%) Estimated inﬁltration rate varies from 1.33 to From Table 4, it was found that the estimated 12.27 cm/h with an average value of 6.12 cm/h. inﬁltration rate varies from 0.88 to 12.82 cm/h with The RMSE varies from 0.03 to 6.29 with a mean an average rate of 6.57 cm/h which is bit higher than value of 1.80. The RMSE was lowest for D2 station average measured inﬁltration rate (IR). The RMSE and found to be highest at station D1 (Table 4). was varied from 0.02 to 7.32 with a mean value of The observed R , R, and e were 0.70, 0.84, and 1.67. RMSE was lowest at station P10 and highest at 2.68, respectively. It is worth to mention that the 2 station A4. The R , R, and e values were 0.73, 0.86, predictability of the model improved with the con- and 2.6, respectively. It was observed that the predict- sideration of BD, which is depicted by the ability of the model improved compared to the pre- increased R and R values compared to the pre- vious developed models which can be seen from vious analyzed ﬁrst class. 2 higher R and R. Using Equation 3 y = 0.794x + 1.327 R² = 0.73 -1 0 2 4 6 8 10 12 14 16 18 IR -3 Figure 4. Measured inﬁltration rate versus estimated inﬁltration rate. B (cm) IR B (cm) IR 8 G. T. PATLE ET AL. 15 Using Equation 4 y = 0.7933x + 1.2567 R² = 0.79 -10 2 4 6 8 1012141618 -3 IR Figure 5. Measured inﬁltration rate versus estimated inﬁltration rate using equation 4. 4.6. Analysis of fourth class 4.7. Analysis of ﬁfth class The inﬁltration was estimated using sand, silt, clay, The inﬁltration was estimated using sand, silt, clay, BD, PD, and MC. The results of analysis are shown in BD, PD, MC, and organic carbon content (OC). The Table 4 and Figure 5, respectively. It was found that results of analysis are shown in Table 4 and Figure 6, MC varies from 13.96% to 28.93% with a mean value respectively. It was found that OC varies from 0.28% of 24.05%. The developed prediction model is repre- to 0.36% with a mean value of 0.34%. The developed sented by equation 4. prediction model is represented by equation 5. Prediction equation 4, Prediction equation 5, E = −30,578.81–305.56 (sand%)−306.16 (silt%) E = −34,344.80−343.17(sand%)−343.74(silt%) IR IR −0.306.33 (clay%)−15.18 (BD%)+0.34 (MC%)+4.18 −343.96(clay%)−15.44(BD%)+0.31(MC%)+4.87(PD%) (PD)+16.85 (OC%) From Table 4, it was found that the estimated From Table 4, it was found that estimated inﬁltra- inﬁltration rate varies from 0.78 to 15.08 cm/h with tion rate varies from 0.37 to 15.04 cm/h with a mean an average rate of 6.37 cm/h which is almost same as value of 6.30 cm/h. The RMSE varies from 0.04 to measured average inﬁltration rate (6.44 cm/h). The 4.89 with a mean value of 1.52. Station D3 has the RMSE was varied from 0.01 to 5.11 with a mean value lowest RMSE and station A4 has the highest RMSE. of 1.57. RMSE was lowest at station C2 and highest at The R , R, and e values are 0.80, 0.89, and 2.39. station A4. The R , R, and e values were 0.79, 0.89, From all the analysis, it was observed that increase and 2.34, respectively. It was also observed that the in independent variable increases the reliability of the predictability of the model has greatly improved com- prediction as R and R increases with increase in pared to the previous developed models which can be number of independent variables. The prediction seen from higher R and R. equation 1 had lowest value of R and R and highest Using Equation 5 y = 0.792x + 1.187 R² = 0.80 -102468 10 12 14 16 18 -3 IR Figure 6. Measured inﬁltration rate versus estimated inﬁltration rate. B (cm) IR B (cm) IR GEOLOGY, ECOLOGY, AND LANDSCAPES 9 Sand (%) vs. B IR y = 0.956x + 68.16 R² = 0.56 0 2 4 6 8 10 12 14 16 18 B (cm) IR Figure 7. Relation between sand and measured inﬁltration rate. value of RMSE and standard error (e) whereas the for the prediction of soil inﬁltration rate based on prediction equation 5 had highest value of R and R sand content of soil in Iran. They developed a rela- and lowest value of RMSE and standard (e) error. tion between soil inﬁltration rate and sand content of This implies that equation 5 is the best amongst all of soil and suggested one linear regression model for the the equations. prediction of inﬁltration rate. From Figure 8, it can be said that increase in silt content will decrease IR but with less signiﬁcant as R is 0.16. From Figure 9 it 4.8. Scatter plot of measured inﬁltration rate can be seen that clay has a strong negative relation- versus soil physical properties ship with IR. Increase in clay will decrease IR signiﬁ- cantly as R is 0.53. From Figure 10 it can be seen that The relationship between inﬁltration rate and each BD has a negative relationship with inﬁltration rate. soil properties were analyzed and are shown in term Increase in BD will decrease inﬁltration rate as R is of scatter plot. The scatter plot of percent sand versus 0.107. Figure 11 shows that MC has negative relation inﬁltration rate, percent silt versus inﬁltration rate, with inﬁltration rate. This means that higher the percent clay against inﬁltration rate, BD against inﬁl- antecedent MC in the soil lesser will be the inﬁltra- tration rate, percent MC against inﬁltration rate, and tion rate of soil. Figure 12 show PD has positive percent organic carbon (OC) against inﬁltration rate relation with inﬁltration rate which means higher are shown in Figure 7, 8, 9, 10, 11, 12, and 13, the PD of soil higher will be the inﬁltration rate but respectively. From these ﬁgures it is depicted that with lesser signiﬁcant eﬀect as R is only 0.032. From inﬁltration rate is either inversely or directly propor- the Figure 13 it is found that organic carbon has tional to the measured soil properties. positive relation with inﬁltration rate but with mini- From the Figure 7 it is observed that sand content mum R is 0.048. But researcher like Franzluebbers has strong positive relation with inﬁltration rate as R (2001) found that presence of higher SOC will reduce is 0.56 which means increasing in sand content in the BD which intern improves the inﬁltration rate with soil will increase the inﬁltration rate signiﬁcantly. greater signiﬁcant value. Rashidi et al. (2014) used the MLR analysis method Silt (%) vs. B IR y = -0.356x + 16.56 R² = 0.17 0 2 4 6 8 10 12 14 16 18 B (cm) IR Figure 8. Relation between silt and measured inﬁltration rate. Silt (%) Sand (%) 10 G. T. PATLE ET AL. Clay (%) vs. B IR y = -0.599x + 15.27 R² = 0.53 02468 10 12 14 16 18 B (cm) IR Figure 9. Relation between clay and measured inﬁltration rate. BD (g/cm³) vs. B IR 1.8 1.6 1.4 1.2 y = -0.006x + 1.617 R² = 0.11 0.8 0.6 0.4 0.2 048 12 16 B (cm) IR Figure 10. Relation between bulk density and measured inﬁltration rate. 35 y = -0.215x + 25.43 MC (%) vs. B IR R² = 0.05 0 2 4 6 8 10 12 14 16 18 B (cm) IR Figure 11. Relation between moisture content and measured inﬁltration rate. 4.9. Correlation between dependent and Among all, the sand had the most positive correlation independent variables followed by clay as negative correlation which causes signiﬁcant impact on inﬁltration rate of any soil type. Table 4 shows the correlation between measured inﬁltra- Genachte et al. (1996) estimated inﬁltration para- tion rate and soil properties. This table shows that sand, meters from basic soil properties in tropical rain forest PD, and organic content had positive correlation with of Guyana using Philip, Green-Ampt, Kostiakov, observed inﬁltration rate by 0.75, 0.18, and 0.22, respec- Horton, multiple regression, and principal components tively, which means increase in sand, PD, and OC will analysis techniques. They found pedotransfer functions increase the inﬁltration rate. Silt, clay, BD, and MC had a negative correlation with inﬁltration rate by −0.41, −0.73, with a R value ranging from 0.599 to 0.76 for the Ferralsol ﬁeld plot and 0.38 to 0.68 for the Arenosol −0.33, and −0.22, respectively. It means that increasing silt, clay, BD, and MC will decrease inﬁltration rate. ﬁeld plot. BD (%) MC (%) Clay (%) GEOLOGY, ECOLOGY, AND LANDSCAPES 11 3.5 PD (g/cm³) vs. B IR 2.5 y = 0.009x + 2.503 R² = 0.03 1.5 0.5 0 2 4 6 8 1012141618 B (cm) IR Figure 12. Relation between particle density and measured inﬁltration rate. 0.38 OC (%) vs. B IR 0.36 0.34 0.32 y = 0.001x + 0.334 0.3 R² = 0.05 0.28 0.26 0.24 0.22 0.2 0 2 4 6 8 1012141618 B (cm) IR Figure 13. Relation between organic carbon and measured inﬁltration rate. Table 5. Correlation between dependent and independent variables. IR (cm/h) Sand Silt Clay BD PD MC OC IR (cm/h) 1.00 Sand (%) 0.75 1.00 Silt (%) −0.41 −0.77 1.00 Clay (%) −0.73 −0.74 0.14 1.00 BD (g/cm ) −0.33 −0.06 −0.05 0.15 1.00 PD (g/cm ) 0.18 −0.08 0.21 −0.09 −0.11 1.00 MC (%) −0.22 −0.44 0.37 0.29 0.24 −0.19 1.00 OC (%) 0.22 −0.03 0.34 −0.32 −0.21 0.58 −0.36 1.00 Note: Sand, Silt, Clay, MC and OC are in % and BD, PD, are in g/cm prediction of inﬁltration rate. The plot of observed vs. 4.10. Statistical performance evaluation estimated inﬁltration rate shows good correlation Results of inﬁltration rate estimation were statistically (Figure 14). Table 6 present the statistical validation validated. The estimated inﬁltration rate results were of observed versus estimated inﬁltration rate. compared with observed inﬁltration rate through seven parameters, R (0.89) shows singnifcant good correlation and it was also supported by other test 5. Conclusions RMSE = 1.103, R-RMSE = 0.244, MAE = 0.342, Inﬁltration rate plays very important role in con- NRMSE = 0.231, MBE = − 0.342 (≈ 0, almost zero), cerning the eﬃciency of irrigation and drainage, MAPE = 0.477, d = 0.993 (~1), RMSE% = 1.538, optimizing the availability of water for plants, NSE = 26.332, RSE = 0.419, IR = 1.072, PE% = 7.154, improving the yield of crops, minimizing erosion, S = 1.136, P (at 95%) = 0.0995, and SD = 0.87), result and wastage of water. The soil physical properties, have positive responses with respect to observed inﬁl- land use, vegetation coverage, and seasons also play tration rate (Table 5). It can be used successfully for OC (%) PD (%) 12 G. T. PATLE ET AL. Table 6. The statistical validation of observed vs. estimated inﬁltration rate. Basic IR (cm) 5.8 2.91 4.2 7.52 7.1 3.1 3.8 3.08 6.8 2.5 0.3 10.5 3 7.32 3.8 Estimated IR 7.07 4.8 3.92 9.75 5.70 2.69 2.63 2.55 7.54 2.98 0.29 11.83 2.39 8.27 4.45 Statistical test Value RMSE 1.103 R-RMSE 0.244 MAE 0.342 NRMSE 0.231 MBE −0.342 MAPE 0.477 D 0.9993 RMSE% 1.538 NSE 26.332 RSE = RMSE/SD in observd 0.419 IR (average index ratio) 1.072 PE (% Error) 7.154 S 1.136 R 0.89 p (at 95%) 0.099 Multi R 0.95 Adj. R 0.890 Standard error 0.870 Figure 14. Plot of observed vs. estimated inﬁltration rate. a very important role in rate of inﬁltration. varies from 0.3 to 16.8 cm/h with a mean value of Inﬁltration models can be developed through PTFs 6.444 cm/h. The basic inﬁltration rate was found to be using diﬀerent soil properties and will be useful for higher in sandy loam soil with minimum value of the prediction of inﬁltration rate in the hilly region 2.4cm/hatstation A4andmaximum valueas of Sikkim where the direct/ﬁeld measurement of soil 16.8 cm/h at station D1 compared to loamy sand inﬁltration rate is very diﬃcult due to one or more soil which has minimum and maximum value as 0.30 and 13.80 cm/h, respectively. Sand, PD, and OC reasons. Therefore, the identiﬁed models will help in the estimation of inﬁltration rate just by using soil have a positive correlation with IR by 0.75, 0.18, and physical properties without much wastage of time 0.22, respectively, whereas silt, clay, BD, and MC have a negative correlation with IR by −0.41, −0.73, −0.33, and energy. The main objective of this study was to develop a model for the prediction of inﬁltration and −0.22, respectively. 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Geology Ecology and Landscapes – Taylor & Francis
Published: Jan 2, 2019
Keywords: Infiltration; bulk density; particle density; organic carbon; multiple linear regression model; Sikkim
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