Forecasting Soybean Production in Turkey: A Comparative Analysis of Automated and Traditional Methods

Hakan Duman

Makale detayları

Forecasting Soybean Production in Turkey: A Comparative Analysis of Automated and Traditional Methods

Yazarlar:

Hakan Duman

Yayıncı:

Igdir University

Ana Başlıklar:

Tarım Ekonomisi (Diğer)

Dergi:

Iğdır Üniversitesi Tarım Bilimleri Dergisi Vol.2 Issue 1, pp.19-31

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001319913

pISSN:

3023-7017

Anahtar kelimeler:

Özet:

 Türkiyes climate and soil are well-suited for the cultivation of oilseed crops, which are of vital importance to various industries and human and animal diets. Among oilseeds, soybeans, a legume, possess a distinctive nutritional profile. While existing research covers soybean production in Türkiye, this study aims to: a evaluate production levels using different forecasting algorithms to identify the most accurate model, and b based on the chosen model, forecast future production and assess the current and future entrepreneurial potential of the soybean industry in Türkiye. Soybean production data 1990-2022 from TURKSTAT was divided into training n25 and test n8 sets for cross-validation. By applying univariate time series methods, including ARIMA, SES, NNAR, MN, and Naive to the training dataset, it was found that ARIMA 1,1,1 performed best according to test set RMSE values. The performance ranking in terms of RMSE was as follows: ARIMA 13019  SES 13888  Naive 14240  NNAR 58393  MN 80418. Notably, for this dataset, the performance of automated processes was relatively worse than that of manual methods, suggesting that relying solely on automated methods may lead to suboptimal forecasting results. These findings underscore the importance of human oversight in the use of automated algorithms for time series forecasting and highlight the need for caution when employing automated methods. The ARIMA 1,1,1 model predicts a flat trend in production from 2023 to 2032, with an initial production volume of 154 516 tonnes and a slight decline to 153 607 tonnes. This predicted stagnation implies that, in the context of economic and population growth, soybean production will fall further behind domestic demand, leading to increased import reliance. These findings are of serious importance to farmers and policymakers alike, as they can assist in the formulation of informed decisions pertaining to resource allocation, crop planning, and market strategies. Local producers may potentially benefit from increased production efficiency, improved competitiveness, and potential revenue growth by catering to both domestic and export markets. Furthermore, an understanding of these trade dynamics can assist stakeholders in identifying potential avenues for collaboration or investment within the Turkish soybean industry. Further analysis of these results is ongoing in order to gain deeper insights into the factors influencing soybean production trends in Türkiye.

 Türkiyes climate and soil are well-suited for the cultivation of oilseed crops, which are of vital importance to various industries and human and animal diets. Among oilseeds, soybeans, a legume, possess a distinctive nutritional profile. While existing research covers soybean production in Türkiye, this study aims to: a evaluate production levels using different forecasting algorithms to identify the most accurate model, and b based on the chosen model, forecast future production and assess the current and future entrepreneurial potential of the soybean industry in Türkiye. Soybean production data 1990-2022 from TURKSTAT was divided into training n25 and test n8 sets for cross-validation. By applying univariate time series methods, including ARIMA, SES, NNAR, MN, and Naive to the training dataset, it was found that ARIMA 1,1,1 performed best according to test set RMSE values. The performance ranking in terms of RMSE was as follows: ARIMA 13019  SES 13888  Naive 14240  NNAR 58393  MN 80418. Notably, for this dataset, the performance of automated processes was relatively worse than that of manual methods, suggesting that relying solely on automated methods may lead to suboptimal forecasting results. These findings underscore the importance of human oversight in the use of automated algorithms for time series forecasting and highlight the need for caution when employing automated methods. The ARIMA 1,1,1 model predicts a flat trend in production from 2023 to 2032, with an initial production volume of 154 516 tonnes and a slight decline to 153 607 tonnes. This predicted stagnation implies that, in the context of economic and population growth, soybean production will fall further behind domestic demand, leading to increased import reliance. These findings are of serious importance to farmers and policymakers alike, as they can assist in the formulation of informed decisions pertaining to resource allocation, crop planning, and market strategies. Local producers may potentially benefit from increased production efficiency, improved competitiveness, and potential revenue growth by catering to both domestic and export markets. Furthermore, an understanding of these trade dynamics can assist stakeholders in identifying potential avenues for collaboration or investment within the Turkish soybean industry. Further analysis of these results is ongoing in order to gain deeper insights into the factors influencing soybean production trends in Türkiye.

 Türkiyes climate and soil are well-suited for the cultivation of oilseed crops, which are of vital importance to various industries and human and animal diets. Among oilseeds, soybeans, a legume, possess a distinctive nutritional profile. While existing research covers soybean production in Türkiye, this study aims to: a evaluate production levels using different forecasting algorithms to identify the most accurate model, and b based on the chosen model, forecast future production and assess the current and future entrepreneurial potential of the soybean industry in Türkiye. Soybean production data 1990-2022 from TURKSTAT was divided into training n25 and test n8 sets for cross-validation. By applying univariate time series methods, including ARIMA, SES, NNAR, MN, and Naive to the training dataset, it was found that ARIMA 1,1,1 performed best according to test set RMSE values. The performance ranking in terms of RMSE was as follows: ARIMA 13019  SES 13888  Naive 14240  NNAR 58393  MN 80418. Notably, for this dataset, the performance of automated processes was relatively worse than that of manual methods, suggesting that relying solely on automated methods may lead to suboptimal forecasting results. These findings underscore the importance of human oversight in the use of automated algorithms for time series forecasting and highlight the need for caution when employing automated methods. The ARIMA 1,1,1 model predicts a flat trend in production from 2023 to 2032, with an initial production volume of 154 516 tonnes and a slight decline to 153 607 tonnes. This predicted stagnation implies that, in the context of economic and population growth, soybean production will fall further behind domestic demand, leading to increased import reliance. These findings are of serious importance to farmers and policymakers alike, as they can assist in the formulation of informed decisions pertaining to resource allocation, crop planning, and market strategies. Local producers may potentially benefit from increased production efficiency, improved competitiveness, and potential revenue growth by catering to both domestic and export markets. Furthermore, an understanding of these trade dynamics can assist stakeholders in identifying potential avenues for collaboration or investment within the Turkish soybean industry. Further analysis of these results is ongoing in order to gain deeper insights into the factors influencing soybean production trends in Türkiye.

 Türkiyes climate and soil are well-suited for the cultivation of oilseed crops, which are of vital importance to various industries and human and animal diets. Among oilseeds, soybeans, a legume, possess a distinctive nutritional profile. While existing research covers soybean production in Türkiye, this study aims to: a evaluate production levels using different forecasting algorithms to identify the most accurate model, and b based on the chosen model, forecast future production and assess the current and future entrepreneurial potential of the soybean industry in Türkiye. Soybean production data 1990-2022 from TURKSTAT was divided into training n25 and test n8 sets for cross-validation. By applying univariate time series methods, including ARIMA, SES, NNAR, MN, and Naive to the training dataset, it was found that ARIMA 1,1,1 performed best according to test set RMSE values. The performance ranking in terms of RMSE was as follows: ARIMA 13019  SES 13888  Naive 14240  NNAR 58393  MN 80418. Notably, for this dataset, the performance of automated processes was relatively worse than that of manual methods, suggesting that relying solely on automated methods may lead to suboptimal forecasting results. These findings underscore the importance of human oversight in the use of automated algorithms for time series forecasting and highlight the need for caution when employing automated methods. The ARIMA 1,1,1 model predicts a flat trend in production from 2023 to 2032, with an initial production volume of 154 516 tonnes and a slight decline to 153 607 tonnes. This predicted stagnation implies that, in the context of economic and population growth, soybean production will fall further behind domestic demand, leading to increased import reliance. These findings are of serious importance to farmers and policymakers alike, as they can assist in the formulation of informed decisions pertaining to resource allocation, crop planning, and market strategies. Local producers may potentially benefit from increased production efficiency, improved competitiveness, and potential revenue growth by catering to both domestic and export markets. Furthermore, an understanding of these trade dynamics can assist stakeholders in identifying potential avenues for collaboration or investment within the Turkish soybean industry. Further analysis of these results is ongoing in order to gain deeper insights into the factors influencing soybean production trends in Türkiye.

Makale detayları

Forecasting Soybean Production in Turkey: A Comparative Analysis of Automated and Traditional Methods

Yazarlar:

Hakan Duman

Yayıncı:

Igdir University

Ana Başlıklar:

Tarım Ekonomisi (Diğer)

Dergi:

Iğdır Üniversitesi Tarım Bilimleri Dergisi Vol.2 Issue 1, pp.19-31

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001319913

pISSN:

3023-7017

Soybean production

Türkiye

Time series forecasting

ARIMA algorithm

NNAR

Auto-ARIMA

Sayı

Makale detayları

Forecasting Soybean Production in Turkey: A Comparative Analysis of Automated and Traditional Methods

Yazarlar: Hakan Duman

Yayıncı: Igdir University

Ana Başlıklar: Tarım Ekonomisi (Diğer)

Dergi: Iğdır Üniversitesi Tarım Bilimleri Dergisi Vol.2 Issue 1, pp.19-31

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001319913

pISSN: 3023-7017

Soybean production

Türkiye

Time series forecasting

ARIMA algorithm

NNAR

Auto-ARIMA

Sayı

Yazarlar:

Hakan Duman

Yayıncı:

Igdir University

Ana Başlıklar:

Tarım Ekonomisi (Diğer)

Dergi:

Iğdır Üniversitesi Tarım Bilimleri Dergisi Vol.2 Issue 1, pp.19-31

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001319913

pISSN:

3023-7017