Numerical Simulation of Soil Water Dynamics in Automated Drip Irrigated Okra Field Under Plastic Mulch

Author:

Nagaraju Vidya K.1ORCID,Nagarajan Karuppalaki2,Kannan Balaji2ORCID,Ramanathan Subbiah3ORCID,Duraisamy Ramasamy4

Affiliation:

1. PhD Scholar, Department of Soil and Water Conservation Engineering , Tamil Nadu Agricultural University , Coimbatore , . Tamil Nadu , India

2. Faculty of Agricultural Engineering, Department of Soil and Water Conservation Engineering , Tamil Nadu Agricultural University , Coimbatore, 641003. Tamil Nadu, India , India

3. Faculty of Agricultural Meteorology, Department of Agro Climate Research Centre , Tamil Nadu Agricultural University , Coimbatore, 641003. Tamil Nadu , India

4. Faculty of Agricultural Statistics, Department of Physical Sciences and Information technology , Tamil Nadu Agricultural University , Coimbatore, 641003. Tamil Nadu, India , India

Abstract

Abstract In India, drip irrigation with plastic mulch is a common practise for irrigation that conserves water. For the design and administration of irrigation regimes, a thorough understanding of the distribution and flow of soil water in the root zone is required. It has been demonstrated that simulation models are effective tools for this purpose. In this work, an automated drip-irrigated Okra field with seven treatments namely T1- Soil moisture-based drip irrigation to 100% FC, T2- Soil moisture-based drip irrigation to 80% FC, T3- Soil moisture-based drip irrigation to 60% FC, T4- Timer based drip irrigation to 100% CWR, T5- Timer based drip irrigation to 80% CWR, T6- Timer based drip irrigation to 60% CWR and T7- Conventional drip irrigation at 100% CWR were utilised to mimic the temporal fluctuations in soil water content using the numerical model HYDRUS- 2D. With the help of the observed data, the inverse solution was used to optimise the soil hydraulic parameters. The model was used to forecast soil water content for seven field treatments at optimal conditions. Root mean square error (RMSE) and coefficient of determination (R2) were used to assess the congruences between the predictions and data. With RMSE ranging from 0.036 to 0.067 cm3 cm- 3, MAE ranging from 0.020 to 0.059, and R2 ranging from 0.848 to 0.959, the findings showed that the model fairly represented the differences in soil water content at all sites in seven treatments.

Publisher

Walter de Gruyter GmbH

Subject

Agricultural and Biological Sciences (miscellaneous),Environmental Engineering

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