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Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management

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Title Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management
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Creator Santosha Rathod , Amit Saha , Rahul Patil , Gabrijel Ondrasek , Channappa Gireesh , Madhyavenkatapura Siddaiah Anantha , Dhumannatarao Venkata Krishna Nageswara Rao, Nirmala Bandumula , Ponnuvel Senguttuvel , Arun Kumar Swarnaraj , Shaik N. Meera , Amtul Waris, Ponnuraj Jeyakumar, Brajendra Parmar, Pitchiahpillai Muthuraman and Raman Meenakshi Sundaram
 
Subject spatiotemporal time series; STARMA; ARIMA; TDNN; two-stage STARMA; crop yield prediction
 
Description Not Available
A robust forecast of rice yields is of great importance for medium-to-long-term planning
and decision-making in cereal production, from regional to national level. Incorporation of spatially
correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space
Time Autoregressive Moving Average (STARMA) is the most popular class of model in linear
spatiotemporal time series modelling. However, STARMA cannot process nonlinear spatiotemporal
relationships in datasets. Alternately, Time Delay Neural Network (TDNN) is a most popular
machine learning algorithm to model the nonlinear pattern in data. To overcome these limitations,
two-stage STARMA approach was developed to predict rice yield in some of the most intensive
national rice agroecosystems in India. The Mean Absolute Percentage Errors value of proposed
STARMA-II approach is lower compared to Autoregressive Moving Average (ARIMA) and STARMA
model in all examined districts, while the Diebold-Mariano test confirmed that STARMA-II model is
significantly different from classical approaches. The proposed STARMA-II approach is promising
alternative to classical linear and nonlinear spatiotemporal time series models for estimating mixed
linear and nonlinear patterns and can be advanced tool for mid-to-long-term sustainable planning
and management of crop yields and patterns in agroecosystems, i.e., food supply and demand from
local to regional levels.
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Date 2022-04-05T13:56:28Z
2022-04-05T13:56:28Z
2021-12-09
 
Type Research Paper
 
Identifier Rathod, S.; Saha, A.; Patil, R.; Ondrasek, G.; Gireesh, C.; Anantha, M.S.; Rao, D.V.K.N.; Bandumula, N.; Senguttuvel, P.; Swarnaraj, A.K.; et al. Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management. Agronomy 2021, 11, 2502. https:// doi.org/10.3390/agronomy11122502
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http://krishi.icar.gov.in/jspui/handle/123456789/71197
 
Language English
 
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Publisher Not Available