Methods for Mid-Term Forecasting of Crop Export and Production

A vast number of studies are devoted to the short-term forecasting of agricultural production and market. However, those results are more helpful for market traders than producers and agricultural policy regulators because any structural change in that field requires a while to be implemented. The m...

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Autores principales:	Dmitry Devyatkin, Yulia Otmakhova
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wheat production and export forecast transformer recurrent network NARX regularization sentiment analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/2f6138cc3e9740d2ae14fdb67d113b96
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spelling	oai:doaj.org-article:2f6138cc3e9740d2ae14fdb67d113b962021-11-25T16:42:22ZMethods for Mid-Term Forecasting of Crop Export and Production10.3390/app1122109732076-3417https://doaj.org/article/2f6138cc3e9740d2ae14fdb67d113b962021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10973https://doaj.org/toc/2076-3417A vast number of studies are devoted to the short-term forecasting of agricultural production and market. However, those results are more helpful for market traders than producers and agricultural policy regulators because any structural change in that field requires a while to be implemented. The mid and long-term predictions (from one year and more) of production and market demand seem more helpful. However, this problem requires considering long-term dependencies between various features. The most natural way of analyzing all those features together is with deep neural networks. The paper presents neural network models for mid-term forecasting of crop production and export, which considers heterogeneous features such as trade flows, production levels, macroeconomic indicators, fuel pricing, and vegetation indexes. They also utilize text-mining to assess changes in the news flow related to the state agricultural policy, sanctions, and the context in the local and international food markets. We collected and combined data from various local and international providers such as UN FAOSTAT, UN Comtrade, social media, the International Monetary Fund for 15 of the world’s top wheat exporters. The experiments show that the proposed models with additive regularization can accurately predict grain export and production levels. We also confirmed that vegetation indexes and fuel prices are crucial for export prediction. Still, the fuel prices seem to be more important for predicting production than the NDVI indexes from past observations.Dmitry DevyatkinYulia OtmakhovaMDPI AGarticlewheat production and export forecasttransformerrecurrent networkNARXregularizationsentiment analysisTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10973, p 10973 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wheat production and export forecast transformer recurrent network NARX regularization sentiment analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	wheat production and export forecast transformer recurrent network NARX regularization sentiment analysis Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Dmitry Devyatkin Yulia Otmakhova Methods for Mid-Term Forecasting of Crop Export and Production
description	A vast number of studies are devoted to the short-term forecasting of agricultural production and market. However, those results are more helpful for market traders than producers and agricultural policy regulators because any structural change in that field requires a while to be implemented. The mid and long-term predictions (from one year and more) of production and market demand seem more helpful. However, this problem requires considering long-term dependencies between various features. The most natural way of analyzing all those features together is with deep neural networks. The paper presents neural network models for mid-term forecasting of crop production and export, which considers heterogeneous features such as trade flows, production levels, macroeconomic indicators, fuel pricing, and vegetation indexes. They also utilize text-mining to assess changes in the news flow related to the state agricultural policy, sanctions, and the context in the local and international food markets. We collected and combined data from various local and international providers such as UN FAOSTAT, UN Comtrade, social media, the International Monetary Fund for 15 of the world’s top wheat exporters. The experiments show that the proposed models with additive regularization can accurately predict grain export and production levels. We also confirmed that vegetation indexes and fuel prices are crucial for export prediction. Still, the fuel prices seem to be more important for predicting production than the NDVI indexes from past observations.
format	article
author	Dmitry Devyatkin Yulia Otmakhova
author_facet	Dmitry Devyatkin Yulia Otmakhova
author_sort	Dmitry Devyatkin
title	Methods for Mid-Term Forecasting of Crop Export and Production
title_short	Methods for Mid-Term Forecasting of Crop Export and Production
title_full	Methods for Mid-Term Forecasting of Crop Export and Production
title_fullStr	Methods for Mid-Term Forecasting of Crop Export and Production
title_full_unstemmed	Methods for Mid-Term Forecasting of Crop Export and Production
title_sort	methods for mid-term forecasting of crop export and production
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2f6138cc3e9740d2ae14fdb67d113b96
work_keys_str_mv	AT dmitrydevyatkin methodsformidtermforecastingofcropexportandproduction AT yuliaotmakhova methodsformidtermforecastingofcropexportandproduction
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Methods for Mid-Term Forecasting of Crop Export and Production

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