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

Abstract

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.

Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

“…By considering the MAPE values, a significant difference between the actual and forecasted values can be obtained by the DM test. The DM test was similarly used to compare the inter-combinational significance comparison between the models [45][46][47][48]. The results of the DM test revealed that in two sets (training and testing set) of data, the extreme learning machine intervention model performed better than all other models (Table 8).…”

Predictions and Estimations in Agricultural Production under a Changing Climate

“…In the second article, the authors predicted yields of rice grown in the most agriculturally intensive regions of India [8]. The two-step STRAMA (autoregressive moving av-erage) approach, referred to as STRAMA-II in this paper, was used to achieve the research objective.…”

Salt Stress in Plants and Mitigation Approaches

“…The AI and ML algorithms have been successfully applied in a wide range of agroenvironmental areas, including: plant-based [124,[129][130][131][132][133], pedological [134][135][136][137], and salinity-based [116,120,138] studies (Table 2). However, as soil salinization is commonly a highly complex and nonlinear variable [12], the data processed by AI and ML techniques could result in better outcomes vs. classical statistical methods in soil salinity classification and prediction.…”

“…However, in some cases, ML (optimization) algorithms are employed to correct the assumptions of classical statistical models to obtain the robust predictions [ 123 , 124 ]. Additionally, for specific situations, neither linear nor nonlinear models provide better fitting; therefore, a combination of two or more algorithms/statistical models is needed, resulting in so-called hybrid or two-stage modeling approaches [ 116 , 125 , 126 , 127 , 128 , 129 ].…”

“…The AI and ML algorithms have been successfully applied in a wide range of agro-environmental areas, including: plant-based [ 124 , 129 , 130 , 131 , 132 , 133 ], pedological [ 134 , 135 , 136 , 137 ], and salinity-based [ 116 , 120 , 138 ] studies ( Table 2 ).…”