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Modelling the Effect of Conservation Measures on Potential Soil Erosion: A USLE and GIS Approach


Affiliations
1 Department of Agricultural Engineering, Navsari Agricultural University, Navsari 396 450, India
2 Department of SWCE, Mahatma Phule Krishi Vidyapeeth, Rahuri 413 722, India
3 Centre for Water Engineering Management, Central University of Jharkhand, Brambe 835 205, India
 

Quantitative assessment of soil loss in Ambika watershed, Gujarat, India was done using universal soil loss equation (USLE) and geographic information system (GIS) to analyse spatial distribution of soil loss. The annual average soil loss for the entire watershed was estimated at 22.41 tonne ha–1 year–1, which substantially contributes to low agricultural productivity of the area. About 80% of the watershed area is affected by moderately high to very high soil erosion (>15 tonne ha–1 year–1) and requires immediate attention for soil conservation measures. The average slope of micro-watersheds in this area varies between 5% and 8%. So, according to recommendations of land capability classification for class IV lands, contour bunds and terraces were selected as soil conservation measures, which resulted in the reduction of annual average soil erosion of the entire watershed to 17 tonne ha–1 year–1. Therefore, these conservation measures can be effectively applied in watersheds with similar geomorphology and average slope up to 8%, to reduce soil erosion. The cumulative effect of soil conservation measures indicated that area affected by soil erosion magnitude under priority class 1 was reduced from 7.5% to 0%; under priority class 2 it reduced from 49.75% to 37.19%, and for priority class 3 the area reduced from 22.41% to 17.63%. Hence, determination and monitoring of soil erosion and subsequent planning of soil conservation practices for improving agricultural productivity can be effectively achieved using USLE and GIS technology.

Keywords

Agricultural Productivity, Conservation Measures, Soil Erosion, Mathematical Modelling, Water-Shed Area.
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  • Ni, S. X., Ma, G. B., Wei, Y. C. and Jiang, H. F., An indicator system for assessing soil erosion in the Loess Plateau gully regions: a case study in the Wangdonggou watershed of China. Pedosphere, 2004, 14, 37–44.

  • FAO and ITPS, Status of the world’s soil resources (SWSR) – main report. Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, Rome, Italy, 2015.

  • Adgo, E., Teshome, A. and Mati, B., Impacts of long-term soil and water conservation on agricultural productivity: the case of Anjenie watershed, Ethiopia. Agric. Water Manage., 2013, 117, 55–61; doi:10.1016/j.agwat.2012.10.026.

  • Jaiswal, R. K., Thomas, T., Galkate, R. V., Ghosh, N. C. and Singh, S., Watershed prioritization using Saaty’s AHP based decision support for soil conservation measures. Water Resour. Manage., 2014, 28, 475–494; doi:10.1007/s11269-013-0494-x.

  • Singh, G. and Panda, R. K., Grid-cell based assessment of soil erosion potential for identification of critical erosion prone areas using USLE, GIS and remote sensing: a case study in the Kapgari watershed, India. Int. Soil Water Conserv. Res., 2017, 5(3), 202– 211.

  • Quine, T. A. and Zhang, Y., An investigation of spatial variation in soil erosion, soil properties, and crop production within an agricultural field in Devon, United Kingdom. J. Soil Water Conserv., 2002, 57, 55–65.

  • Shinde, V. T., Sharma, A., Tiwari, K. N. and Singh, M., Quantitative determination of soil erosion and prioritization of microwatersheds using remote sensing and GIS. J. Indian Soc. Remote Sensing, 2011, 39(2), 181–192; doi:10.1007/s12524-011-0064-8.

  • Welde, K., Identification and prioritization of subwatersheds for land and water management in Tekeze dam watershed, Northern Ethiopia. Int. Soil Water Conserv. Res., 2016, 4(1), 30–38.

  • Pandey, V. K., Panda, S. N., Raghuwanshi, N. S. and Sudhakar, S., Delineation and parameterization of Banikdih watershed using remote sensing and AVSWAT model. J. Indian Soc. Remote Sensing, 2006, 34(2), 143–152.

  • Misra, N., Satyanarayana, T. and Mukherjee, R. K., Effect of top elements on the sediment production rate from sub-watershed in upper Damodar Valley. J. Agric. Eng., 1984, 21(3), 65–70.

  • Bali, Y. P. and Karale, R. L., A Sediment Yield Index for Choosing Priority Basins, IAHS-AISH Publication No. 122, Paris, 1977, pp. 180–188.

  • Wischmeier, W. H. and Smith, D. D., Predicting Rainfall Erosion Losses – A Guide to Conservation Planning, United States Department of Agriculture, Washington, DC, USA, Agricultural Handbook No. 537, 1978.

  • Griffin, M. L., Beasley, D. B., Fletcher, J. J. and Foster, G. R., Estimating soil loss on topographically nonuniform field and farm units. J. Soil Water Conserv., 1988, 43, 326–331.

  • Alewell, C., Borrelli, P., Meusburger, K. and Panagos, P., Using the USLE: chances, challenges and limitations of soil erosion modelling. Int. Soil Water Conserv. Res., 2019, 7(3), 203–225; https://doi.org/10.1016/j.iswcr.2019.05.004.

  • Jain, S. K., Kumar, S. and Varghese, J., Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resour. Manage., 2001, 15, 41–54.

  • Jain, M. K. and Kothyari, U. C., Estimation of soil erosion and sediment yield using GIS. J. Hydrol. Sci., 2000, 45(5), 771–786.

  • Javed, A., Khanday, M. Y. and Ahmed, R., Prioritization of subwatersheds based on morphometric and land use analysis using remote sensing and GIS techniques. J. Indian Soc. Remote Sensing, 2009, 37, 261–274.

  • Mani, P. and Chakravorty, B., Remote sensing based sedimentation study of Maithon reservoir. J. Indian Soc. Remote Sensing, 2007, 35(1), 118–120.

  • Kadam, A. K., Jaweed, T. H., Kale, S. S., Umrikar, B. N. and Sankhua, R. N., Identification of erosion-prone areas using modified morphometric prioritization method and sediment production rate: a remote sensing and GIS approach. Geomat. Nat. Haz. Risk, 2019, 10(1), 986–1006.

  • Terranova, O., Antronico, L., Coscarelli, R. and Iaquinta, P., Soil erosion risk scenarios in the Mediterranean environment using RUSLE and GIS: an application model for Calabria (southern Italy). Geomorphology, 2009, 112, 228–245.

  • Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K. and Yoder, D. C., Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE), United States Department of Agriculture, Washington, DC, USA, USDA Agricultural Handbook No. 703, 1997.

  • Millward, A. A. and Mersey, J. E., Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. Catena, 1999, 38, 109–129.

  • Choudhury, M. K. and Nayak, T., Estimation of soil erosion in Sagar Lake catchment of Central India. In Proceedings of the International Conference on Water and Environment, Bhopal, 2003, pp. 387–392.

  • Wischmeier, W. H., Johnson, C. B. and Cross, B. V., A soil erodibility nomograph from farmland and construction sites. J. Soil Water Conserv., 1971, 26, 189–193.

  • Haan, C. T., Barfield, B. J. and Hayes, J. C., Design Hydrology and Sedimentology for Small Catchments, Academic Press, New York, USA, 1994.

  • USDA, Soil Survey Laboratory Methods Manual, Soil Survey Investigation Report No. 42, Version 4.0, USDA-Natural Resources Conservation Service, Lincoln, NE, USA, 2004.

  • Kayet, N., Pathak, K., Chakrabarty, A. and Sahoo, S., Evaluation of soil loss estimation using the RUSLE model and SCS–CN method in hill slope mining areas. Int. Soil Water Conserv. Res., 2018, 6, 31–42; https://doi.org/10.1016/j.iswcr.2017.11.002.

  • Pancholi, V. H., Lodha, P. P. and Prakash, I., Estimation of runoff and soil erosion for Vishwamitri River watershed, western India using RS and GIS. Am. J. Water Sci. Eng., 2015, 1(2), 7–14.

  • USDA, Handbook No. 282, Washington, DC, USA, 1981.

  • Dutta, D., Das, S., Kundu, A. and Taj, A., Soil erosion risk assessment in Sanjal watershed, Jharkhand (India) using geoinformatics, RUSLE model and TRMM data. Model. Earth Syst. Environ., 2015, 1(4), 1–9; http://dx.doi.org/10.1007/s40808-015-0034-1.

  • Klingbiel, A. A. and Montgomery, P. H., Land Capability Classification – Agriculture Handbook No. 210, Soil Conservation Service, USDA, Washington, DC, USA, 1961.

  • Murthy, V. V. N. and Jha, M. K., Land and Water Management Engineering, Kalyani Publishers, Ludhiana, 2011.

  • Walworth, J. L., Physical Contaminants, Environmental Monitoring and Characterization, Elsevier, USA, 2004.


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  • Modelling the Effect of Conservation Measures on Potential Soil Erosion: A USLE and GIS Approach

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Authors

Vipul Shinde
Department of Agricultural Engineering, Navsari Agricultural University, Navsari 396 450, India
Manjushree Singh
Department of Agricultural Engineering, Navsari Agricultural University, Navsari 396 450, India
Sachin Nandgude
Department of SWCE, Mahatma Phule Krishi Vidyapeeth, Rahuri 413 722, India
Birendra Bharti
Centre for Water Engineering Management, Central University of Jharkhand, Brambe 835 205, India

Abstract


Quantitative assessment of soil loss in Ambika watershed, Gujarat, India was done using universal soil loss equation (USLE) and geographic information system (GIS) to analyse spatial distribution of soil loss. The annual average soil loss for the entire watershed was estimated at 22.41 tonne ha–1 year–1, which substantially contributes to low agricultural productivity of the area. About 80% of the watershed area is affected by moderately high to very high soil erosion (>15 tonne ha–1 year–1) and requires immediate attention for soil conservation measures. The average slope of micro-watersheds in this area varies between 5% and 8%. So, according to recommendations of land capability classification for class IV lands, contour bunds and terraces were selected as soil conservation measures, which resulted in the reduction of annual average soil erosion of the entire watershed to 17 tonne ha–1 year–1. Therefore, these conservation measures can be effectively applied in watersheds with similar geomorphology and average slope up to 8%, to reduce soil erosion. The cumulative effect of soil conservation measures indicated that area affected by soil erosion magnitude under priority class 1 was reduced from 7.5% to 0%; under priority class 2 it reduced from 49.75% to 37.19%, and for priority class 3 the area reduced from 22.41% to 17.63%. Hence, determination and monitoring of soil erosion and subsequent planning of soil conservation practices for improving agricultural productivity can be effectively achieved using USLE and GIS technology.

Keywords


Agricultural Productivity, Conservation Measures, Soil Erosion, Mathematical Modelling, Water-Shed Area.

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DOI: https://doi.org/10.18520/cs%2Fv119%2Fi6%2F984-991