Evapotranspiration is a complex and non-linear phenomenon because it depends on several interacting climatological factors, such as temperature, humidity, wind speed, solar radiation. In the past decade, ANN intensively used in system modelling, rainfall-runoff modelling, reservoir operation; land drainage design, aquifer parameter estimation etc. shown that ANN is more accurate than conventional methods. Artificial Neural Networks (ANN) is effective tools to model nonlinear systems which may be difficult to present by conventional mathematical equations. The ANN models are well suited under relationships between the input and output variables are not explicit. The ANN models need less data than conventional methods when properly validated and numbers of variables remain constant from one simulation to another. The ANN approach estimates evapotranspiration
The experiment was conducted during year 2008-09 at Hi-Tech Project, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, Ratnagiri under shed net condition to study the response of coloured capsicum for different irrigation and fertilizer levels in terms of growth and yield. The study concluded that the coloured capsicum showed positive response to the different irrigation and fertilizer levels under shed net house over open field condition. The alternate day irrigation and split application of WSF fertilizers with different levels through drip irrigation system responded well by coloured capsicum in terms of growth and yield. The higher levels of irrigation and fertilizer were also boost the yield of coloured capsicum under shed net house. The study suggested that treatment I 3 F 3 (1.0 PE, 120 % RD) gave maximum gross monetary returns (Rs. 239.50/m 2 ) and B:C ratio. (8.60).
Contribution/Originality: The present work on trend analysis using non-parametric test such as MK test was carried out for Mulde Maharashtra. The work was based upon the historical data about 25 years. Very few attempts were made earlier regarding the trend analysis of climatic parameters using MK test. The study will be very useful for water resouces planing, crop planning and for precision agriculture.
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