Tapan Pathak

Tapan Pathak

School of Natural Resources, University of Nebraska – Lincoln, USA

Title: In-season updates of the DSSAT CROPGRO-cotton model forecasts using climatology and El-Niño southern oscillation (ENSO) indices


Tapan Pathak has completed his Ph.D. in the field of Agricultural Engineering from the University of Florida in 2010. He is currently a faculty at University of Nebraska-Lincoln with areas of research and extension in Agricultural Climatology. He has published many papers and Extension newsletters on utilizing climate information for agricultural decisions. He has been awarded a FELLOW for the center for Great Plain Studies at University of Nebraska and also recently won Paula Ford Professional Development Proposal of the year. He has been on the review panel for several peer-reviewed journals.


Although cotton is considered as a drought tolerant crop, climate variability may adversely impact cotton production. Especially, cotton produced under rain fed conditions could be severely affected by a variable climate. El Niño Southern Oscillation (ENSO) is a dominant phenomenon of climate variability in southeast US and other locations worldwide. An effective way to reduce agricultural vulnerability to climate variability is through an effective use of climate forecasts. In this research, CROPGRO-Cotton model was used to forecast cotton yield using climatology and ENSO based climate forecast. Specific research questions addressed in this research were; Do the in-season updates of CROPGRO-Cotton model yield forecasts using updated weather data improve accuracy over the forecast obtained before season. Which of the three ENSO indices provides the best cotton yield forecasting accuracy? Do the in-season updates of CROPGRO-Cotton yield forecasts obtained with the ENSO forecasts have better skills than using climatology alone? This study was focused on a single location using 1951-2005 weather data and three ENSO indices. Results showed that the in-season updates of weather forecast improved the forecasting skills of cotton yield. Results also demonstrated that the cotton yield forecasts obtained using ENSO based climate forecasts were better than the cotton yield forecast obtained using climatology in CROPGRO-Cotton model. Overall, these results show that there is a great potential in utilizing the CROPGRO-Cotton model for forecasting cotton yield using in-season updates and using ENSO based climate forecasts.