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20320140503016
20320140503016
20320140503016
20320140503016
20320140503016
20320140503016
20320140503016
20320140503016
20320140503016
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20320140503016

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  • 1. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 151 A STUDY ON VARIATIONS IN WATER PRODUCTIVITY BY USING GIS BASED EPIC MODEL Santhosh Ram(1) (1) Assistant Professor , M.E, Department of Civil Engineering, SRM University, Ramapuram, Chennai. ABSTRACT Rapid growth of human population increases demand for the agricultural products, in order to ensure food security over coming decades the total food production should be increased with available limited water resources. The gross production will increase with improving Crop water productivity by studying and understanding the major driving factors that greatly influence on it. The lower Bhavani system (LBP) is taken as study area. The high variability in rainfall, hot climate and change in irrigation water quality leads to challenge for agriculture. Among the distributaries in the LBP Kugalur and Mangalapatti distributary were selected from the head and tail reaches of this system. The analysis of irrigation water quality and simulation of crop water productivity (CWP) are the main core of this study. The simulation of CWP done by usage of (GEPIC) GIS based Environmental Policy Integrated Climate Model. GEPIC is the agro-ecosystem simulation model to evaluate spatial and temporal dynamics of crop water productivity and yield in daily time step. The water quality analysis is done by collecting the samples in the study area and analyzing the quality parameters in the laboratory. The productivity can be increased by identifying various driving factors which are solely responsible for the optimum crop yield such as soil parameters, climatic factors, land use pattern, cropping pattern, quality of irrigation water and management factors like amount of irrigation and fertilizer usage. The influence of variation of these driving factors on the CWP was analyzed. Through this work, high water productivity was obtained for Mangalapatti distributary compared to Kugalur distributary. The sugarcane has higher water productivity of 3.742 kg/m3 in Tail-Head reach. It shows that water productivity variations are based on the variations in the influencing factor mainly the management activities like crop selection, amount of irrigation water applied, fertilizer application and farm management. The water quality results show that irrigation water in the selected distributaries is highly suitable for agricultural crops. Keywords: Water Productivity, GIS Based EPIC Model, Water Quality Analysis. INTERNATIONAL JOURNAL OF CIVIL ENGINEERING AND TECHNOLOGY (IJCIET) ISSN 0976 – 6308 (Print) ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME: www.iaeme.com/ijciet.asp Journal Impact Factor (2014): 7.9290 (Calculated by GISI) www.jifactor.com IJCIET ©IAEME
  • 2. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 152 1. INTRODUCTION Rapid growth of population and limited quality water resources, there is a need to increase better management and some effective strategies of water resources in worldwide. Formulations for maintaining or improving the environment are based on an effective strategy of productivity. Therefore, wastes and nonproductive uses must be carefully scrutinized to identify potential savings. The detailed study on effective concepts and better analysis are helps to improve the production. Over the past few years, the concept of water productivity become more important in the agriculture production, and turns the focus to irrigation water from land as an effective factor of agricultural production. The main purpose of this study is to shows the conceptual framework for calculating crop yield, crop water productivity and impacts of variations in some major driving factors of productivity like irrigation water quality, climatic soil and crop parameters. The term water productivity refers to the magnitude of output or benefit resulting from the input quantum of water as applied on a unit base. The concept is based on “more crop per drop” or “producing more food from the same water resources” or “producing the same amount of food from less water resources”. In the domain of agriculture, it is expressed as the net consumptive use efficiency in terms of yield per unit depth of water consumed per unit area of cultivation. 1.2 Crop water productivity Agricultural water productivity can be expressed as a physical productivity in terms of yield over unit quantity of water consumed (Kg per unit volume of water) in accordance with the scale of reference that includes or excludes the losses of water or an economic productivity replacing the yield term by the gross or net present value of the crop yield for the same water consumption (Rupees per unit volume of water). The irrigation water productivity is a ratio between yields of irrigated crop to the amount of irrigation water applied. The variability in quality of irrigation water is directly based on both types and amount of dissolved salts in that water. The domestic and industrial discharge is main sources for this salt and they follow the flow path of the water when it’s introduced in that water. The salt content in soil increasing with increases of total salt content of the irrigation water. The evaporation and consumptive use of salts and minerals in the irrigation water leads to ultimate sink of irrigated soil and crop grown on that soil. So, the quality of irrigation water consider as an important driving factors for sustainable management of water productivity and soil resources. Irrigated agriculture is dependent on an adequate water supply of usable quality. The evaluation of quality of water based on the chemical and physical characteristics of that water and only rarely is any other factors considered important. Here attempt has been made to assess the irrigation water quality of Lower Bhavani Project distributaries. The quality characteristics is analyzed in the present investigations are as follows: Negative logarithm of hydrogen ion concentration (pH), Electrical Conductivity (EC), Chloride (Cl), Sodium (Na) and Potassium (K), Calcium (Ca), Magnesium (Mg), Carbonates (CO3) and bicarbonates ratio (HCO3). 1.3 Objectives of this Study • To estimate the water productivity for several important crops. • To perform water quality analysis at specific locations. • To compare the variations of water quality and other important factors
  • 3. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 153 2 MATERIALS AND METHOD 2.1 Study Area The Bhavani is an important tributary of the Cauvery River in its mid-reaches in Tamil Nadu. It originates in the Silent Valley forest in Kerala and flows in a south-easterly direction for 217 km until it joins the Cauvery at a town named Bhavani. A major portion (87 per cent) of this area is situated in Tamil Nadu. The Lower Bhavani Reservoir was constructed across the confluence of Bhavani and Moyar providing storage of 906 M m3. The Bhavanisagar dam was executed during 1948-1955 and the canal system was constructed in 1956. The LBP Canal was originally designed for the ayacut area of 83,975 hectare, and the ayacut is spread over in Sathyamangalam, Gobi, Bhavani, Erode, Perundurai and Kangayam taluks. In addition to the above ayacut, an extent of 1,012 hectare is also benefited in Karur district. The catchment area for this system is 4200 km2. The Lower Bhavani Basin lies between 110 15‟ N and 110 45‟ N latitudes and 770 00‟ E and 770 40‟ E longitudes. The area is comprised of hilly regions and plain terrain with maximum and minimum altitudes of 1487 m and 215 m above mean sea level (MSL) respectively. In this system most of the command area localized was heavily porous, red soil, gravelly mixed with pebbles its leads to a heavy seepage losses. An allowance for transmission losses of 33.33% was made in the design. The Fig 2.1 shows the Bhavani basin map. (Sources: IWMI Research report 129, 2009) Fig 2.1: Bhavani Basin Map The climate of the study area is dry, except during the monsoon season. The first two months of the year are pleasant. The north-east monsoon gets vigorous only during October or November. The average rain fall of the basin is 715 mm. This basin has a well developed dendritic to sub dendritic drainage system, which indicates the presence of rock in uniform resistance. The area has a steep gradient with the drainage towards the river and also the rocky substratum depth with an overburden of 72.97 m of porous gravelly soil,
  • 4. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 154 the seepage is much and finds its way into local drainage. The topography of this region mainly controls the occurrence of groundwater, land use and drainage pattern. Scattered hillocks of moderate elevation occur within the uplands. The plains area is characterized by gentle undulations with a general gradient east and south-east. The terrain slopes towards south-east. The basin area includes reserve forest; build up lands, agricultural field and barren lands. The Lower Bhavani Study Area Map shown in Fig 2.2. (Sources: Hand Book of Junior Engineer (PWD)) Fig 2.2: Lower Bhavani Study Area Map The main canal covers a total command area of 83,772 ha (2, 07,000 acres).The main canal has three major distributaries taking off at 53 km, 101km and 119km, 69 distributaries ,196 minor distributaries and 118 sluices. Below the distributaries the water courses carry the water to the field channels, which directly irrigate lands. Up to watercourse, the maintenance responsibilities lie with PWD, the field channels are maintained by the farmers themselves. The canal will be thrown open for Irrigation from August 15th and the water will be allowed for Irrigation up to 15th of December (Turn 1) to raise wet crops and after December 15th and up to March 15th (Turn 2) water will be allowed for dry crops in rotation method. In first turn, allowing the supply of 24 TMC for wet crops is found to be optimal period for wet crops. But it is felt , in second turn for dry crops, with a total permissible quantity of 12 TMC is found to be inadequate due to the prevailing of hot summer and it is recommended to raise the quantity from 12 TMC to 14 TMC for the turn. The major crops are paddy, banana, groundnut and sugarcane. Wet season cropping pattern (August - December) is mostly influenced by paddy crop. Dry season is normally meant to grow irrigated dry crops like millets, pulses, cotton, oilseeds etc., however sugarcane and banana are annual crops and hence grown only by farmers with assured ground water facilities. For this study the distributaries are selected from the head and tail reaches of the main system respectively.
  • 5. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 155 Table 2.1 shows the details of selected distributaries. Table 2.1: Details of Selected Distributaries S.NO Name Of The Distributary Reach Length(km) Ayacut area (ha) Turn I Turn II 1 Kugalur Head 15.054 2012.978 1948.226 2 Mangalapatti Tail 6.207 700.463 595.200 2.2 GIS Based EPIC Model GEPIC is a GIS-based agroecosytem model integrating a bio-physical EPIC model (Environmental Policy Integrated Climate) with a Geographic Information System (GIS). The GEPIC model can be used to simulate the spatial and temporal dynamics of the major processes of the soil–crop–atmosphere-management system. The GEPIC version 0.1(2009) is used for this study to simulate the crop water productivity. Model needs set of input data to assess the water productivity for the number of individual crops selected in the head and tail reach of the distributary command area. The all input data should be in the raster data format. It can be done by using ArcGIS software. Following data are needed to simulate the crop water productivity in GEPIC model, • Soil physical parameters • Crop parameter • Land use • Climatic data • Information about location DEM Slope • Management data Irrigation Fertilizer application This model generates the results in daily, monthly production for the corresponding crop. It can simulate the yield, crop water productivity, evapotranspiration, irrigation requirement, harvest index. The validation of the model is done by comparing the model output with field data. The model is assured if the validation gives good result. The model output is in the form of raster image format and text format. The model can simulate the various output parameters in daily time steps as follows, • Yield (kg/ha) • Crop water productivity (kg/m3) • Biomass (kg/ha) • Evapotranspiration (mm) • Irrigation water requirement (mm)
  • 6. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 156 2.3 Methodology To performance the water quality analysis and simulation of crop water productivity would be the main task of this study. Previous work carried out on important driving factors of water productivity were analyzed in the context of improving the productivity. Hence this study has been planned to analyze the response of crop water productivity through variations in some important driving forces of it. This chapter is organized into four sections. The first section: data collection. The second section: analyze the crop water productivity for varies crops. It contains crop selection and usage of GEPIC model. The third section: performance the irrigation water quality analysis and comparing the existing quality with standards. The fourth section: analyze the variations in various driving factors of crop water productivity. This study need various data related to the factors of water productivity. For easy collection and working it grouped into two categories as primary data and secondary data. The approach used for this data collection is primary surveys. Two distributaries in Lower Bhavani project would be selected for the study. There are Kugalur and Mangalapatti distributary respectively from head and tail reach of the main canal. The primary data such as crop details, land use, fertilizer and irrigation management are can be acquire by conducting questionnaire survey. The farmers consider for the survey in each distributary based on the random sampling method. The information would be obtained through detailed questionnaires in systematic manner. This data would be a source to compare and evaluate the productivity with help of secondary data. Some of the main features consider for questionnaire survey giving below: • Background of the farmer • Crop details • Irrigation management activities • Problems facing • Water and land resources The secondary data such as required soil, climate (rainfall, maximum and minimum temperature), and site location data would be collected from Public Works Department (PWD) and Agricultural Engineering Department. The DEM data collect from SRTM model for the required boundary. Flow data will collect from the LBP flow measurement relevant sources. The suitable major crops are selected crops based on the soil type, irrigation water quality, crop varieties and fertilizer usage. Data regarding primary and secondary data (soil, climate) are verified and given as input for GEPIC model. The irrigation water (canal water, well water) quality assess by analyzing the quality parameters as per Irrigation Water Quality. The water samples from the selected distributaries will be collect and test in the laboratory. The main reason for analyzing the water quality is as follows, • To know the existing quality of Water • To compare it with the standards • To evaluate the impact of water quality on crop water productivity The collected samples will be analyzed for the level of water quality parameters and compare with general guide lines for irrigation water provided by Irrigation Water Quality index. The impact of the quality of water in crop water productivity will be evaluated. The standard guide lines for intercept the quality of irrigation water is shown in table 2.2,
  • 7. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 157 The following water quality parameters is used for analyze the irrigation water quality, • pH • Electrical conductivity(EC) • Total Dissolved Solids(TDS) • Sodium and Potassium • Calcium • Magnesium From above quality parameters the sodium absorption ratio (SAR) will be calculated by ratio of sodium [Na+] to calcium [Ca++] and magnesium [Mg++]. SAR 3. RESULTS AND DISCUSSION 3.1 Assessment of Irrigation Water Quality The values of selected water quality parameters in collected samples were assessed in laboratory by using appropriate procedures and the results are compared with Indian standards for irrigation water. The EC and TDS values represent the salinity hazard, one can conclude that the agricultural fields around the selected two distributaries have the best quality of irrigation water when salinity hazards are considered. . The SAR and EC result shows that quality of irrigation water in selected distributaries is good when infiltration and permeability problems are considered. Based on sodium, potassium and pH results are also shows that the water in that area is suitable for irrigation. From this assessment in those areas the CWP is not varied based on the quality of irrigation water. 3.2 Assessment of Water productivity by using model The simulated water productivity results show that the productivity of water varies with respect to variations in the driving factors. Based on the cropping pattern the water productivity values are differed. The sugarcane has the higher water productivity value in the all different reaches of Kugalur and Mangalapatti distributaries compared to the other selected crops. The average water productivity values of sugarcane in all reaches is 3.478 kg/m3 and its range varies from 3.742 kg/m3 in the Tail-Head reach to 3.172 kg/m3 in the Head-Head reach of the selected distributaries. The physical water productivity value for sugarcane is high in all reaches due to higher yield than other two selected crops. The lower water productivity value is obtained for groundnut in different reaches of two selected distributaries. The average physical water productivity of groundnut is 0.385 kg/m3 and its value varies from 0.362 kg/m3 in Tail-Tail reach to 0.402 kg/m3 in the Head-Tail reach of selected distributaries. The groundnut productivity value is lower due to low yield obtained compared to other two crops in all reaches of Kugalur and Mangalapatti distributaries. The average water productivity of paddy is 0.91 kg/m3 and its range varies from 0.824 kg/m3 in Head-Head reach to 1.021 kg/m3 in the Tail-Head reach of two selected distributaries. In the order of higher to lower water productivity values, the crops are listed as sugarcane, paddy and groundnut. The Fig 5.4.1 shows the variations in the simulated water productivity based on crop selection in the different reaches of Kugalur distributary and Mangalapatti distributary.
  • 8. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 158 4. CONCLUSION The water productivity is mainly dependant on the amount of yield from the crop and amount of water applied to the crop. These two factors are mostly responsible for the variations in the water productivity. Other than these factors the climatic conditions, soil characteristics, timing of irrigation and management activities like fertilizer applications were influenced on water productivity. The amount of fertilizer application and the timing of fertilizer application were also considered important for crop yield. The following driving factors are responsible for the variation in water productivity: • Soil type, climatic conditions at the location, elevation of the field and quality of irrigation water supplied to the field are responsible for the variations in water productivity. • Management activities like fertilizer application, tillage practices, crop selection, method of irrigation water application, weeds and pest control in the field. • Farmer’s knowledge on new techniques in agriculture, economic status of the farmer and number of labours used. 4. REFERENCES 1. Berka., Schreier., Hall. (2001), “Linking Water Quality with Agricultural Intensification in A Rural Watershed”, J. of Water, Air, and Soil Pollution, Vol. 127, pp. 389-401. 2. Dinesh Kumar M, Singh, Madar Samad O, Chaitali Purohit and Malkit Singh Didyala. (2004) “Water Productivity of Irrigated Agriculture in India: Potential areas for improvement, J. of Ethiopia agricultural, Vol.21, pp. 142-156 3. Hongjun Li., Li Zheng., Yuping Lei., Chunqiang Li., Zhijun Liu.(2008), “Estimation Of Water Consumption And Crop Water Productivity Of Winter Wheat In North China Plain Using Remote Sensing Technology”, J. of Agricultural Water Management, Vol. 95, pp. 1271-1278. 4. Jalali M. (2002), “Composition of Irrigation Waters in West of Iran”, proceedings of 17 world congress of soil science, Thailand, pp. 2184-1 to 2184-4. 5. Joshi D.M., Kumar A. and Agrawal N. (2009), “assessment of the irrigation water quality of river ganga in hadridwar district”, Vol.2, pp. 285-292 6. Junguo Liu., Jimmy R. Williams., Alexander J.B. Zehnder. (2007), “GEPIC – Modeling Wheat Yield and Crop Water Productivity with High Resolution on A Global Scale”, J. of Agricultural Systems, Vol. 94, pp. 478-493. 7. Junguo Liu., Jimmy R. Williams., Alexander J.B. Zehnder. (2007), “Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China”, J. of Irrigation Science, Vol. 26, pp. 21-33. 8. Leven and Garcia. (2008), “Simulation Yield Response to Water of Quieno with AQUACROP”, Deficit Irrigation Strategies via Crop Water Productivity Modeling, Chapter 10, pp. 121-146. 9. Mintesinot Jirua and Eric Van Ranst. (2010), “Increasing Water Productivity on Vertisols: Implications for Environmental Sustainability”, J. of Science Food Agriculture, Research Article, and Vol. 90, pp. 2276-2281. 10. Palanisami k., Senthilvel S., .Ranganathan, Ramesh T. (2009), “Water Productivity At Different Scales Under Canal, Tank And Well Irrigation Systems”, Centre for Agricultural and Rural Development Studies (CARDS),Tamil Nadu Agricultural University, Coimbatore.
  • 9. International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308 (Print), ISSN 0976 – 6316(Online) Volume 5, Issue 3, March (2014), pp. 151-159 © IAEME 159 11. Samiha A., Mouhamed A., Rashad Abou Elenin. (2010), “Increasing Water Productivity of Faba Bean Grown Under Deficit Irrigation at Middle Egypt”, Fourteenth International Water Technology Conference, IWTC 14 2010, Cairo, Egypt. 12. Turral H., Cook s., Gichuki F. (2004), “Water Productivity Assessment: Measuring and Mapping Methodologies”, CGIAR Challenge Program on WATER & FOOD, Basin Focal Project, working paper No.2. 13. Timsina., Godwin., Humphreys., Yadvinder-Singh. (2008), “Evaluation of Options for Increasing Yield and Water Productivity of Wheat in Punjab, India Using The DSSAT-CSM- CERES-Wheat Model”, J. of Agricultural Water Management, Vol. 95, pp. 1099-1110. 14. Nangia., Fraiture., Turral. (2008), “Water Quality Implications Of Raising Crop Water Productivity”, J. of Agricultural Water Management, Vol. 95, pp. 825-835. 15. Vinay Nangia., Hugh Turral., David Molden. (2008), “Increasing Water Productivity with Improved N Fertilizer Management”, J. of Irrigation Drainage System, Vol. 94, pp. 478-493. 16. R Radhakrishanan and A Praveen, “Sustainability Perceptions on Wastewater Treatment Operations in Urban Areas of Developing World”, International Journal of Civil Engineering & Technology (IJCIET), Volume 3, Issue 1, 2012, pp. 45 - 61, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. 17. Rumman Mowla Chowdhury, Sardar Yafee Muntasir, Md. Niamul Naser and Sardar Rafee Musabbir, “Water Quality Analysis of Surface Water Bodies Along the Dhaka-Mawa- Bhanga Road Based on Pre-Monsoon Water Quality Parameters for Aquaculture”, International Journal of Civil Engineering & Technology (IJCIET), Volume 3, Issue 2, 2012, pp. 154 - 168, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. 18. Mohammed Hashim Ameen and Dr. R. K. Pandey, “Delineation of Irrigation Infrastructural, Potential and Land Use/ Land Cover of Muzaffarnagar by Using Remote Sensing and GIS”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 3, 2013, pp. 1 - 11, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.

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