Seasonal Autoregressive Integrated Moving Average (SARIMA) for Melon (Cucumis melo) Yield from 2011 to 2020 Based on Planting Date Period

Mohammad Reza Naroui Rad^1*, Somayeh Soltani-Gerdefaramarzi², Hammed Adebayo Akano³ and Ammara Cheema⁴

¹Horticulture Crops Research Department, Sistan Agricultural and Natural Resources Research and Education Center, AREEO, Zabol, Iran.

²Department of Water Science and Engineering, Collage of Agriculture and Natural Resources , Ardakan University, Ardakan, Iran.

³Federal University of Technology Akure, Ondo State, Nigeria.

⁴Department of Mathematics, Air University, Islamabad, Pakistan.

Corresponding Author E-mail: mr.narouirad@areeo.ac.ir

DOI : http://dx.doi.org/10.12944/CARJ.11.3.06

Abstract:

The main goal of time series modelling is to collect and analyze past values to develop appropriate models that describe the inherent structure and characteristics of the series about the planting date of melon. The data used in this study are the melon yield based on twenty-nine days of planting date in March from the first to the last planting date of March 2011 to  2020, to evaluate the prediction performances of the models, two indices: the root mean squared error (RMSE) and mean absolute percentage error (MAPE) were used to compare the forecasting capabilities of the SARIMA models, Excel was used first for preparing as CSV file and then changed to time series data to make the dataset of yield and R version 4.0.2 (the R Development Core Team) was used to perform ETS and SARIMA models For planting date time series based on yield, seasonal autoregressive integral moving average models (SARIMA) were constructed. The Student's t-test was one of the statistical tests used to evaluate the effectiveness and validity of the SARIMA models Through the use of SARIMA models, it is feasible to create synthetic records that maintain the statistical properties of the historical record. Finally, the results can be applied to different planting dates based on yield. Based on the Bayesian Information Criteria (BIC) values and the overall highest R² values of 0.94, the optimal SARIMA (0,1,1) model was chosen. No limitations were found. It is the first time that this mathematical method was used to analyze time series data for 10 years of planting date of melon and it could be useful for agronomists and horticulturists to choose the best model.

Keywords:

Forecasting; Melon; Prediction; Precision agriculture

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