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Soil temperature plays a key role in crop water requirement and crop yield. The accurate field estimation of soil temperature is difficult and expensive. Therefore the present study focuses on the estimation of soil temperature in Mohanpur using Artificial Neural Network with input weather data such as maximum temperature, minimum temperature, wind speed, sunshine hours and the results shows that a good correlation exists between the maximum and minimum temperature with the soil temperature. The results statistics shows that with all the given input data condition model shows good results (R2 = 0.95, RMSE = 1.54, MAE = 1.21) and also the model behaves well for sparse data condition i.e. only when maximum and minimum temperatures are available results (R2 = 0.91, RMSE = 1.86, MAE = 1.46). | EXCELLENT PUBLISHERS International Journal of Current Microbiology and Applied Sciences ISSN 2319-7706 Volume 8 Number 07 2019 Journal homepage http www.ijcmas.com Original Research Article https doi.org 10.20546 ijcmas.2019.807.014 Soil Temperature Prediction under Limited Data Condition Debaditya Gupta Alivia Chowdhury and Md. Shamimur Rahaman Soil and Water Engineering Department Bidhan Chandra Krishi Viswavidyalaya Mohanpur - 741252 Nadia West Bengal India Corresponding author ABSTRACT Keywords Soil temperature Data condition Crop Article Info Accepted 04 June 2019 Available Online 10 July 2019 Soil temperature plays a key role in crop water requirement and crop yield. The accurate field estimation of soil temperature is difficult and expensive. Therefore the present study focuses on the estimation of soil temperature in Mohanpur using Artificial Neural Network with input weather data such as maximum temperature minimum temperature wind speed sunshine hours and the results shows that a good correlation exists between the maximum and minimum temperature with the soil temperature. The results statistics shows that with all the given input data condition model shows good results R2 0.95 RMSE 1.54 MAE 1.21 and also the model behaves well for sparse data condition i.e. only when maximum and minimum temperatures are available results R2 0.91 RMSE 1.86 MAE 1.46 . Introduction Soil temperature is very important for various agricultural processes Sattari et al. 2017 . The daily soil temperature in sub-tropical regions particularly in India shows temporal fluctuations. Soil temperature fluctuations affect various processes within the soil such as microbial decomposition p and k absorption soil-moisture content Elshorbagy and Parasuraman 2008 and soil respiration Gaumont-Guay et al. 2006 . Soil temperature measurements help agriculturists partially to decide and optimize crop water requirement therefore optimal design for the planning of irrigation and drainage systems. .
