1. Session: CS2: Efficient Water Use in Agriculture IMPROVING WATER USE EFFICIENCY BY SOIL MOISTURE MEASUREMENT AND MODELLING Ms. Neethu. A. Dr. Santosh G Thampi Dr. Pramada. S. K. DEPARTMENT OF CIVIL ENGINEERING NATIONAL INSTITUTE OF TECHNOLOGY, CALICUT

2. INTRODUCTION Water scarcity is a major concern for water and agricultural authorities around the world It is the need of the hour to save water for irrigation by proper control and management, thereby enhancing water and nutrient use efficiency, maximizing the income of farmers and minimizing pollution An efficient drip irrigation system helps to meet growing irrigation water requirements by exercising proper control over the application of water, fertilizers and pesticides In this study, computation of soil moisture profiles has been performed using HYDRUS-2D HYDRUS-2D (Simunek et al., 1998) is a Windows based computer software package and has been widely used for simulating water, heat, and solute movement in two-dimensional, variably-saturated porous media OBJECTIVE To study the moisture distribution pattern under a drip irrigated field, to compare results of HYDRUS-2D simulations of water infiltration and redistribution with field data, and to assess the utility of using simulation results in the design of drip management practices To improve water use efficiency by soil moisture measurement and modelling 2

3. WATER FLOW MODELLING The study involves simulation of water flow and water infiltration using the HYDRUS 2D model (Simunek et al., 1998) Soil is assumed to be homogeneous and isotropic The governing equation for water flow is the 2D Richards’ equation. The axi-symmetric form is used in this study to simulate water flow under surface drip emitters This equation is solved with the HYDRUS-2D model – Galerkin finite element method The soil hydraulic model describing unsaturated retention and hydraulic conductivity functions is the van Genuchten-Mualem model (van Genuchten, 1980) where, = volumetric water content (L 3 L -3 ) = time (T) = soil water pressure head (L) = radial(horizontal)coordinate (L) = vertical co-ordinate : positive upward (L) - = unsaturated hydraulic conductivity (LT -1 ) - - = sink term representing root water uptake expressed as volume of water removed from a unit volume of soil per unit time (L 3 L -3 T -1 ) 3

4. van GANUCHTEN CONSTITUENT RELATIONSHIPS = volumetric water content (L 3 L -3 ) = unsaturated hydraulic conductivity at the soil water (LT -1 ) = pressure head (L) = residual water content (L 3 L -3 ) = saturated water content (L 3 L -3 ) = effective water content (L 3 L -3 ) = saturated hydraulic conductivity (LT -1 ) = inverse of the air entry pressure head (L -1 ) = pore size distribution index (dimensionless) = pore connectivity parameter = 0.5 (Mualem,1976) 4

5. FIELD STUDY Field experiments were carried during 2015-2016 in the experimental plots at the Kelappaji College of Agricultural Engineering & Technology, Tavanur, Malappuram, Kerala Consisted of design and installation of drip irrigation system, field observations and sampling and analysis of soil samples Drip laterals were placed in the middle of rows over a leveled field and drippers were fixed at 60cm along the lateral line Bed width and emitter spacing = 60cm There were 5 laterals, each with 40 emitters of 2lph capacity each and the flow in each lateral is controlled by a tap valve Soil of the experimental plot - sandy loam texture and is assumed to be homogenous in nature Irrigation water is applied daily at the rate of 2lph for a duration of 1 to 1.5h, depending on the crop water requirement Soil samples were collected from different depths 0.1, 0.2, 0.3, 0.4, 0.5 and 0.6m at horizontal distances of 0, 15, and 30cm from the emitter 5 Layout of drip irrigated plot

6. DETAILS OF CROP The crop is tomato of Anagha variety - crop period of 90 days Crop evapotranspiration was calculated using CROPWAT – Penman-Monteith equation CROPWAT requires daily weather parameters - minimum and maximum temperature humidity, wind speed, sunshine hours for the calculation of reference evapotranspiration (Source: Regional Agricultural Research Station, Pattambi) is calculated : where : crop coefficient HYDRUS requires separate values of potential evaporation and potential transpiration as inputs (Gardens et al., 2005) Potential evapotranspiration is partitioned into potential evaporation and potential transpiration COMPUTATION OF CROP EVAPOTRANSPIRATION ` 6 Stages (mm/day) Initial0.602.880.1442.74 Development1.04.800.2404.56 Late season0.803.850.1933.66

7. CROP WATER REQUIREMENT Crop water requirement of tomato is calculated: (Source: Neelam Patel & T B S Rajput, 2006) SYSTEM GEOMETRY where - crop water requirement, l/day - lateral spacing, cm - emitter spacing, cm - irrigation efficiency (90%) 7 CROP WATER REQUIREMENT FOR TOMATO StagesCrop water requirement (l/day) Early1.7 Development3.0 Late 2.3 Each emitter is assumed to discharge water at the same flow rate Because of the system symmetry, the entire field is subdivided into identical volume elements with an emitter placed at one end Initial condition - initial water content in different soil layers within the flow domain, as observed in the experimental field

8. INPUT PARAMETERS Various parameters namely saturated water content, residual water content and empirical factors for sandy loam soils were taken from the Neural Network prediction model - Rosetta Lite Rosetta implements pedotransfer functions which predict van Ganuchten’s (1980) water retention parameters and saturated hydraulic conductivity in a hierarchal manner from soil textural class, soil textural distribution, bulk density and one or two water retention points as input Rosetta provides soil hydraulic parameters for analytical functions of van Ganuchten for twelve textural classes of USDA textural triangle HYDRUS model was calibrated using the observed values of moisture content at various points in the domain at 4h, 1day, 2days, and 3days after the first irrigation During validation, the simulation period was kept as 90days equal to the growing period of tomato 8

9. VALIDATION It can be inferred from the plots that at emitter as the root zone depth increases moisture content decreases At a distance of 15 cm radially from the emitter, root density is less during early stages of crop growth so moisture content is more During the development stage the root density increases and hence the moisture content decreases Comparison of observed and simulated moisture content at different depths observed near the emitter Comparison of observed and simulated moisture content at different depths observed at 15 cm from the emitter 9

10. STATISTICAL ANALYSIS To evaluate the performance of the HYDRUS 2D model predictions, the root mean square error ( ) and the coefficient of determination ( ) values were evaluated values (based on values of moisture content near the emitter) values (based on values of moisture content 15cm away from emitter) Higher values showed that simulated and observed values of soil moisture content are close enough Based on the results, it can be confirmed that the HYDRUS model is a suitable tool and guide for modelling soil moisture distribution in the vadose zone 10 5 days15 days30 days60 days 0.0060.0080.0040.003 5 days15 days30 days60 days 0.9670.9930.9630.867

11. CONCLUSIONS Simulation studies and experimental work have led to the conclusion that adequate water content is maintained in the active root zone during the crop growth period Properly designed and installed drip irrigation system will facilitate uniform distribution of water in the radial direction - can enhance the overall irrigation efficiency - reduce the possibility of leaching of nutrients and consequent contamination of groundwater Understanding how soil properties affect water storage can help in optimizing water application and thereby water use by plants In this study, it is observed that the HYDRUS-2D predictions of moisture content fairly matched with the measured values in the experimental plot located in sandy loam soil Results of this study justify the use of HYDRUS-2D as a tool for prediction of soil moisture profiles and as an aid in designing drip irrigation systems By predicting the moisture content in the soil, the model enables estimation of how much more water is to be applied and when and this can help to significantly improve the efficiency of water use 11

12. REFERENCES Ajdary, K., Singh, D. K. and M. Khanna (2007), Modelling of nitrogen leaching from experimental onion field under drip fertigation, Agricultural Water Management, 89: 15-28 Gardenas, A., Hopmans J. W., Hanson B. R., and J. Simunek (2005), Two dimensional modelling of nitrate leaching for different fertigation strategies under micro irrigation, Agricultural Water Management., 74: 219-242 Giuseppe Provenzano (2007), Using HYDRUS-2D Simulation Model to Evaluate Wetted Soil Volume in Subsurface Drip Irrigation Systems, Journal of Irrigation Drainage Engineering, 133:342-349 Mualem Y. (1976), A new model for predicting the hydrualic conductivity of unsaturated porous media, Water Resource Research,pp: 12 :593-622 Neelam Patel and T B S Rajput, (2006), Simulation of modeling of moisture distribution in drip irrigated onion, Journal of Agricultural Engineering, 43:2, pp: 22-27 Skaggs, T.H., Trout, T. J., Simunek, J. and Shouse, P. J. (2004): Comparison of HYDRUS-2D simulations of drip irrigation with experimental observations, Journal of Irrigation and Drainage Engineering, 130, pp:304-310 Simunek, J., Sejna, M. and van Genuchten, M.Th. (1998): The HYDRUS 1D software package for simulating water flow and solute transport in two dimensionally variably saturated media. Version 2.0, International Ground Water Modelling Center-TPS-70 van Genuchten M.Th. (1980): A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Science Society Of America Journal, 44, pp:892-898 12

13. THANK YOU 13