Assessment of Soil Fertility Status in Paderu Mandal, Visakhapatnam

The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx

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The Egyptian Journal of Remote Sensing and Space Sciences

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Research Paper Assessment of soil fertility status in Paderu Mandal, Visakhapatnam district of Andhra Pradesh through Geospatial techniques ⇑ Ramprasad Naik Desavathu a, , Appala Raju Nadipena b, Jagadeeswara Rao Peddada a a Department of Geo-Engineering, Andhra University, Visakhapatnam 530 003, India b Department of Geography, Andhra University, Visakhapatnam 530 003, India article info abstract

Article history: 82 soil samples were collected randomly at different land use/cover locations, which includes agriculture, Received 20 May 2016 forest, built up area, scrubland and plantation at a depth of 0–30 cm, analyzed for soil pH, electrical con- Revised 24 December 2016 ductivity (EC) and presence of nitrogen (N), phosphorous (P) and potassium (K). Inverse Distance Accepted 15 January 2017 Weightage method (IDW) was employed for analyzing the spatial distribution of soil fertility through Available online xxxx geospatial techniques for sustainable agriculture. It is observed that soil pH varies between 4.8 and 7.5; showing nearly 83% of the study area is acidic in nature. The EC varies from 0.04 to 0.87 ds/m with Keywords: a mean of 0.21 ds/m and non saline in condition. Out of 435 km2 of total study area, 99% of area is less in Soil fertility nitrogen followed by potassium (70%) and phosphorus (42%) respectively. Spatial distribution Ó Inverse Distance method 2017 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. Sustainable agriculture This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc- nd/4.0/).

1. Introduction plying capability; moreover fertility of soil is subject to man’s control (Deshmukh, 2012). It may be maintained by scientific crop In any agricultural operations, soil is the utmost importance as rotations, and the application of manure of fertilizers. it is the cradle for all crops and plants. There are non re-renewable The traditional even fertilizing method is not scientifically suit- resources, formed at the rate of 1 in. every 250–1200 years (John able and efficient to apply fertilizer in places with different soil Madeley, 2002). To make agriculturally productive, it may takes nutrients, because soil fertility various between region. Overuse another 3000–12,000 years (Venkata Ramana et al., 2015). This of fertilizers can certainly lead to a waste of fertilizer resources natural resource is finite in nature and also impossible for within and a serious environmental pollution (Clay, 2002; Yang and time span of a human life (Mandal et al., 2009). The top soil having Zhang, 2008). Hence, a comprehensive knowledge of soil fertility an average depth of about 15–30 cm on which plants grow and the provides a better understanding in the current situation and for farming activities flourishes. Now-a-days, it is facing serious prob- identifying soil nutrient distribution and trends (Dafonte et al., lems due to human pressure and utilization incompatible with its 2010). Earlier studies (Isaaks and Srivastava, 1989; Goovaerts, capacity. Hence, it is important to keep healthy and productive soil 1997; Wollenhaypt et al., 1997; Burrough and McDonnell, 1998; to continue our soil to function optimally to increase agriculture Li et al., 2003; Tan et al., 2005; Xu et al., 2013; Liu et al., 2014; production with appropriate soil amendment and crop manage- Behera and Shukla, 2015) proved that geo-statistical analysis ment practices (MacCarthy et al., 2013). In rural areas, the living methods are most useful for obtain the knowledge of characteris- standards of people mainly depend on agriculture, which is often tics, distribution and variability of soil fertility in a timely and determined by the fertility and productivity of soil. Soil fertility accurate manner for precision farming .It is a management practice is one of the primary constraints to agricultural production in for increasing productivity of agriculture for the site-specific man- developing countries like India (Gruhn et al., 2000). It comprises agement (Cahn et al., 1994). These farming operations are vital not only in supply of nutrient, but also indicates their nutrient sup- decision-making process for land use suitability in improving crop productivity (AbdelRahman et al., 2016), where there is a need to ensure efficiency in the management of soil (Mc Cauley et al., 1997). For the purpose of improving soil management and quality Peer review under responsibility of National Authority for Remote Sensing and as well as cost control/benefit results of agricultural producers by Space Sciences. ⇑ adapting to new technologies like Geospatial Technology (Iftikar Corresponding author. et al., 2010; ; Markoski et al., 2015). Using these advance E-mail address: [email protected] (R.N. Desavathu). http://dx.doi.org/10.1016/j.ejrs.2017.01.006 1110-9823/Ó 2017 National Authority for Remote Sensing and Space Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article in press as: Desavathu, R.N., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2017.01.006 2 R.N. Desavathu et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx technologies, so many emeritus scholars and scientists (Weber and 2. Study area Englund, 1992; Gotway et al., 1996; Kravchenko and Bullock, 1999; Robinson and Metternicht, 2006; Sen and Majumdar, 2006; The study area is lies between 18° 180–17° 560 N of latitudes and AbdelRahman et al., 2016; Tunçay et al., 2015) estimated and 82° 320–82° 530 E of longitudes covering an area of 435 km2 (Fig. 1). mapped the soil fertility distribution of un-sampled locations, Nearly, 73% of area is under forest land fallowed by agricultural using Inverse Distance Weightage method (IDW). It is the one of land (20.2%), plantations (2.5%) and built up land covers only the best interpolation method, because of its simplicity, robustness 2.1%.Where soils are mainly red sandy loams and light to medium and used to derive estimates of the soil fertility properties from in texture. They are continuously affected due to severe weather- irregularly spaced samples (Goovaerts, 1997). ing aberration of natural disturbances. The soil erosion is severe Therefore, the objective of this study was to conduct due to its varied and high topography of the land and heavy rainfall geo-statistical analysis for spatial distribution and variability received during the monsoon period and less vegetative cover on (allocation) of observed values and predicted values through IDW its upper parts of the hills. The normal annual rainfall is interpolation techniques, for estimating soil pH, electrical conduc- 1252 mm and mean annual temperature varies from 24 °Cto tivity (EC) and macro nutrients (N, P, K) as well as its status for a 35 °C. May is the hottest month and January is the coolest month. site speciﬁc management approach in the agriculture ﬁelds of Agriculture is the main source of livelihood of the people living in Paderu Mandal, Visakhapatnam district, Andhrapradesh state, this area and the people practice shifting cultivation on hill slopes. India. Shifting cultivation is locally known as the podu cultivation.

Fig. 1. Location map showing soil sample in the study area.

Please cite this article in press as: Desavathu, R.N., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2017.01.006 R.N. Desavathu et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx 3

3. Materials and methods name of the village, coordinates of samples, soil pH, electrical conductivity (EC) and available nutrients in respect of nitrogen (N), Firstly, a ‘‘V” shaped cut of 0–6 in. depth at random locations phosphorus (P) and potassium (K). Further the study analyzed was made, the same were geographically referenced using a hand- geo-statistical of the soils shown in Table 2. This table illustrates held GPS in each sampling site (Fig. 1) and one inch of soil on either variables values, including data size, mean, median, minimum side of the pit was scraped and collected in polythene bags. Quar- value, maximum value, lower quartile, upper quartile, range of tering technique was adopted to reduce the size of the sample of data, variance, standard deviation, standard error, coefficient of the required mass. A total of 82 samples were air-dried and ground variance, skenesss and Kurtosis from observed values of soil sam- to pass a 2 mm sieve for the analysis. The analysis of soil samples ples and predicted values from IDW interpolation method. Finally, has been done by using standard methods, i.e. pH of soil (1:2.5), Table 3 and Fig. 2–6 shows the existing soil fertility distribution electrical conductivity (1:2.5), available nitrogen (Alkaline Per- and its status, which can guide the users on the amount of fertiliz- manganate method), available phosphorus (Bray’s No. 1) and avail- ers to be applied for different crops in different areas for more pro- able potassium (Ammonium acetate method). ductivity of the study area. For evaluation of the soil fertility of the study area, the spatial distribution for each parameter attribute was assessed using spa- 4.1. Soil pH tial descriptive statistics (Iqbal et al., 2005). This data has X and Y coordinates in respect of sampling site location and Z field was It is important estimation for soils, determines the magnitude of used for different nutrients of soil fertility. Further, this data is the acidity and alkalinity and directly influences agriculture pro- overlaid on land use/cover of the study area, which is derived from ductivity. The pH value reflects the integrated effect of the acid IRS P6 of LISS-III data having 23.5 m resolution dated on March base reactions taking place in the soil system (Mokolobate and 2011 through visual interpretation technique shown in Fig. 2. All Haynes, 2002). In the study area, soil pH values ranged from 4.80 sample point made to placed in from different land uses including to 7.50, with a mean of 5.97 and a median of 5.90 having lowest agriculture (60), forest (14), built up area (5), scrubland (2) and coefficient of variance is 10.22%.The range of the predicted values plantation (1). Finally, this data was integrated into a GIS platform of pH is over-estimated the minimum value by 19% and under- for creating and estimating nutrients spatial distribution of soil fer- estimated the maximum value by 8% with respective of observed tility and its status using Inverse Distance Weighted method value (Table 2). The areal extent is about 361.05 km2 under acidic (IDW), which was found in the optimizer parameter tools of the (<6.5), which is accounts for 83% and rest of area (73.95 km2)is geostatistical analyst extension of the Arc GIS 10.1 software. under normal (6.5–8.5) in condition (Table 3 and Fig. 3).

4. Results and discussion 4.2. Electrical conductivity (EC)

Soil fertility parameters of the Paderu Mandal are presented in Soil electrical conductivity (EC) is a measurement that corre- Table 1. Which provides the basic information like sample number, lates with soil properties that effect productivity, including soil

Fig. 2. Land use/cover features derived form IRS P6 imagery of LISS III date (2011).

Please cite this article in press as: Desavathu, R.N., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2017.01.006 4 R.N. Desavathu et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx

Table 1 Soil Fertility parameters of the soils from Paderu mandal.

Sample number Name of the Villages Coordinates PH EC Available nutrients (kg/ha) Latitude Longitude N P K S1 Donela 18.132 82.547 5.0 0.24 91.00 24.00 113.00 S2 Vurugonda 18.138 82.563 5.3 0.27 81.00 24.00 142.00 S3 Vurugonda 18.132 82.548 5.3 0.18 81.00 29.00 160.00 S4 Donela 18.127 82.545 5.5 0.55 81.00 34.00 149.00 S5 Gondili 18.095 82.590 5.9 0.18 107.00 29.00 105.00 S6 Gondili 18.096 82.586 5.5 0.17 102.00 24.00 363.00 S7 Gondili 18.122 82.542 5.6 0.21 97.00 38.00 69.00 S8 Thumpada 18.084 82.685 6.1 0.37 76.00 144.00 363.00 S9 Thumpada 18.088 82.684 6.0 0.17 76.00 101.00 50.00 S10 Thumpada 18.077 82.690 5.8 0.20 107.00 53.00 51.00 S11 Thumpada 18.079 82.689 5.4 0.18 107.00 19.00 69.00 S12 Thumpada 18.078 82.689 5.7 0.12 97.00 5.00 54.00 S13 Panasapalli 18.104 82.631 6.0 0.11 162.00 19.00 116.00 S14 Panasapalli 18.104 82.649 6.0 0.12 162.00 24.00 304.00 S15 Panasapalli 18.106 82.660 5.7 0.11 284.00 38.00 363.00 S16 Gadivalasa 18.100 82.635 5.5 0.19 102.00 43.00 87.00 S17 Gadivalasa 18.101 82.630 5.7 0.19 102.00 34.00 142.00 S18 Dokuluru 18.089 82.622 5.1 0.19 97.00 19.00 369.00 S19 Dokuluru 18.093 82.619 5.9 0.12 86.00 24.00 76.00 S20 Barisingi 18.084 82.640 6.5 0.09 71.00 29.00 22.00 S21 Barisingi 18.083 82.646 6.7 0.08 71.00 10.00 33.00 S22 Chinthalaveedhi 18.105 82.664 5.4 0.22 117.00 38.00 149.00 S23 Chinthalaveedhi 18.114 82.671 6.1 0.14 127.00 29.00 105.00 S24 Ubbediputtu 18.112 82.674 5.7 0.17 107.00 34.00 102.00 S25 Ubbediputtu 18.108 82.674 5.8 0.24 97.00 48.00 363.00 S26 Gonduvuru 18.063 82.634 6.1 0.10 106.00 144.00 83.00 S27 Gonduvuru 18.064 82.636 6.6 0.13 106.00 29.00 330.00 S28 Seribayalu 18.079 82.682 5.7 0.08 213.00 101.00 32.00 S29 Seribayalu 18.076 82.684 5.6 0.08 213.00 33.00 43.00 S30 Seribayalu 18.082 82.682 5.3 0.08 213.00 76.00 76.00 S31 Seribayalu 18.079 82.680 5.9 0.14 162.00 29.00 101.00 S32 Devapuram 17.955 82.814 6.6 0.20 86.00 240.00 163.00 S33 Devapuram 17.964 82.785 6.9 0.19 86.00 125.00 105.00 S34 Devapuram 17.960 82.790 6.8 0.27 157.00 48.00 36.00 S35 Gurramanupanuku 18.062 82.632 6.2 0.11 66.00 29.00 148.00 S36 Gurramanupanuku 18.062 82.617 6.0 0.12 76.00 43.00 108.00 S37 Gurragaruvu 18.048 82.743 5.4 0.24 97.00 10.00 171.00 S38 Gurragaruvu 18.041 82.728 5.2 0.27 132.00 19.00 144.00 S39 Gurragaruvu 18.040 82.717 5.2 0.46 132.00 5.00 76.00 S40 Gurragaruvu 18.032 82.721 5.4 0.28 132.00 10.00 54.00 S41 Vanugupalli 18.030 82.683 5.3 0.11 122.00 5.00 59.00 S42 Bangarumetta 18.021 82.664 6.9 0.24 132.00 10.00 225.00 S43 Jeedipagada 18.017 82.655 5.6 0.04 137.00 10.00 54.00 S44 Jeedipagada 17.961 82.750 5.5 0.11 137.00 14.00 73.00 S45 Birimisala 18.031 82.677 5.4 0.10 132.00 96.00 54.00 S46 Birimisala 18.029 82.670 6.4 0.17 132.00 24.00 323.00 S47 Guttulaputtu 18.129 82.642 6.9 0.52 97.00 5.00 69.00 S48 Guttulaputtu 18.106 82.646 4.8 0.87 97.00 34.00 363.00 S49 Guttulaputtu 18.115 82.647 5.4 0.18 81.00 10.00 58.00 S50 Bantrothuputtu 18.132 82.547 5.1 0.20 91.00 48.00 105.00 S51 Iradapalli 18.072 82.607 5.7 0.11 254.00 10.00 40.00 S52 Iradapalli 18.099 82.614 6.0 0.24 254.00 5.00 22.00 S53 Kavirai 18.128 82.625 6.3 0.12 152.00 87.00 69.00 S54 Lagisipalli 18.058 82.666 6.5 0.18 147.00 19.00 25.00 S55 Gabbangi 18.120 82.657 6.7 0.21 107.00 48.00 51.00 S56 Aluguru 17.995 82.840 7.5 0.28 137.00 53.00 363.00 S57 Kakaraputtu 18.081 82.668 6.1 0.07 66.00 53.00 58.00 S58 Kakaraputtu 18.062 82.636 5.8 0.10 76.00 24.00 36.00 S59 Gonduvuru 18.083 82.577 6.4 0.11 106.00 19.00 163.00 S60 Malakapalem 17.998 82.735 5.0 0.16 117.00 29.00 261.00 S61 Malakapalem 17.965 82.734 5.7 0.18 97.00 14.00 363.00 S62 Iradapalli 18.080 82.608 6.4 0.24 107.00 14.00 224.00 S63 Jeelugupadu 17.961 82.807 6.2 0.11 86.00 33.00 80.00 S64 Lolangipadu 18.011 82.790 6.7 0.46 97.00 38.00 363.00 S65 Dallapalli 18.050 82.743 6.4 0.10 97.00 5.00 44.00 S66 Buruguchetru 18.060 82.741 5.7 0.13 91.00 14.00 33.00 S67 Minumuluru 18.043 82.703 6.4 0.53 107.00 24.00 185.00 S68 Vanthadapalli 18.027 82.704 6.4 0.55 107.00 19.00 91.00 S69 Boradaputtu 18.124 82.545 5.0 0.50 81.00 24.00 113.00 S70 Bongajangi 18.112 82.536 5.6 0.50 81.00 38.00 69.00 S71 Nadimvidi 18.142 82.727 6.1 0.10 107.00 19.00 80.00 S72 Nadimvidi 18.145 82.730 6.3 0.10 91.00 53.00 58.00 S73 Mariyavalasa 18.126 82.733 7.5 0.20 66.00 48.00 51.00

Table 1 (continued)

Sample number Name of the Villages Coordinates PH EC Available nutrients (kg/ha) Latitude Longitude N P K S74 Sirasapali 18.092 82.741 5.6 0.10 132.00 96.00 54.00 S75 Vantalamamidi 17.977 82.737 5.8 0.20 76.00 20.00 25.00 S76 Peddapulam 18.041 82.826 6.4 0.10 106.00 20.00 163.00 S77 Kotnapalle 18.168 82.732 7.5 0.40 81.00 34.00 144.00 S78 Gondurnmdidi 18.004 82.872 6.3 0.10 86.00 33.00 80.00 S79 Madugulla 17.906 82.813 6.4 0.20 97.00 53.00 58.00 S80 Bongajangi 18.112 82.526 7.5 0.40 81.00 34.00 144.00 S81 Paderu 18.072 82.669 6.3 0.57 117.00 19.00 145.00 S82 Paderu 18.075 82.675 6.1 0.54 117.00 14.00 189.00

Table 2 Geo statistical analysis of the soils from Paderu Mandal.

Soil p H Electrical Nitrogen Phosphorous Potassium Conductivity O IDW O IDW O IDW O IDW O IDW Data size 82 82 82 82 82 82 82 82 82 82 Mean 5.97 5.86 0.23 0.21 113.98 115.58 38.08 39.86 131.9 0.23 Median 5.90 5.83 0.18 0.19 104.00 109.75 29.00 34.16 96 0.23 Minimum 4.80 5.03 0.04 0.10 66.00 82.85 5.00 5.76 22 0.21 Maximum 7.50 6.89 1.52 0.63 284.00 208.20 240 170.30 369 0.25 Lower Quartile 5.50 5.66 0.11 0.15 86.00 98.86 19.00 24.72 54 0.22 Upper Quartile 6.40 6.10 0.24 0.24 132.00 126.70 43.00 44.41 163 0.24 Range 2.70 1.86 1.48 0.53 218.00 125.35 235.0 164.54 347 0.04 Variance 0.37 0.13 0.04 0.00 1849.46 564.99 1387.8 662.47 1106.98 0.00 Standard Deviation 0.61 0.37 0.20 0.09 43.00 23.76 37.25 25.73 104.91 0.1 Standard Error 0.06 0.04 0.02 0.01 4.7 2.62 4.11 2.84 11.58 0.00 Skewness 0.55 0.24 3.60 1.83 2.00 1.43 2.88 2.27 1.25 0.18 Kurtosis 0.07 0.49 18.23 4.37 4.52 3.14 11.02 7.70 0.40 0.45 Coefﬁcient of Variance (%) 10.22 6.31 86.96 42.86 37.73 20.56 97.82 64.55 79.54 43.48

O-Obverse values of Samples. IDW-Inverse Distance Weightage method.

Table3 Soil Fertility Status of the Paderu mandal.

Parameter Value Rating Area (km2) Percentage of area pH (1:2.5) Less than 6.5 Acidic 361.05 83 6.5 to 8.5 Normal 73.95 17 Greater than 8.5 Alkaline – – EC (1:2.5) (dS/m) Less than or equal to 1.0 Normal 435 100 Greater than 1.0 Saline – – Available Nitrogen (kg/ha) Less than 280 Low 430.65 99 280 to 560 Medium 4.35 1 Greater than 560 High 0 – Available Phosphorus (kg/ha) Less than 25 Low 182.7 42 25 to 50 Medium 174 40 Greater than 50 High 78.3 18 Available Potassium (kg/ha) Less than 130 Low 304.5 70 130 to 330 Medium 69.6 16 Greater than 330 High 60.90 14

texture, cation exchange capacity (CEC), drainage conditions, 4.3. Available nitrogen organic matter level, salinity, and subsoil characteristics (Corwin and Lesch, 2005). The EC of soils varies depending on the amount Nitrogen is essential for plant growth and thus, causes prob- of moisture held by soil particles. The spatial distribution of EC lems, when it is deﬁcient. The nitrogen-deﬁcient plants are light in the study area indicates that the mean value is 0.23 ds/m and green in color. The lower leaves are turning yellow and some crops ranged from 0.04 to 0.87 ds/m shown in Table 2. Where EC has they quickly start drying up as if suffering from shortage of water >1 indicates that the soils are free from salinity, which account (Carter and Knapp, 2001; Methods Manual Soil Testing in India, for 100% of entire study area. The maximum value (0.87 ds/m) 2011). Available nitrogen in the study area ranges from 66 to obtained in the Guttulaputtu village (S48). 284 kg/ha with mean and median values are 113.98 kg/ha and

Please cite this article in press as: Desavathu, R.N., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2017.01.006 6 R.N. Desavathu et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx

Fig. 3. Soil pH status of the study area.

Fig. 4. Spatial distribution of available Nitrogen.

104.00 kg/ha respectively. The predicted value from IDW is over- observed values of nitrogen (Table 2). This evidence is further con- estimated the minimum value as 20% and under estimated the ﬁrmed by comparing the estimated values of nitrogen with critical maximum value as 27% and standard error is 2.62 comparing with limits for delineation of soil fertility around 99% of study area were

Fig. 5. Available Phosphorus status of Paderu mandal.

Fig. 6. Available Potassium status of study area.

Please cite this article in press as: Desavathu, R.N., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2017.01.006 8 R.N. Desavathu et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx less in available nitrogen and only Panasapalli (S15) having med- References ium value, which is in between 280 and 560 kg/ha (Table 3 and Fig. 4). AbdelRahman, M.A.E., Natarajan, A., Hegde, R., 2016. Assessment of land suitability and capability by integrating remote sensing and GIS for agriculture in Chamarajanagar district Karnataka India Egypt. J. Remote Sensing Space Sci. 4.4. Available phosphorus http://dx.doi.org/10.1016/j.ejrs.2016.02.001. Behera, S.K., Shukla, A.K., 2015. Spatial distribution of surface soil acidity, electrical It is essential for growth, cell division, root growth, fruit devel- conductivity, soil organic carbon content and exchangeable potassium, calcium and magnesium in some cropped acid soils of India. 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