Assessing the Sensitivity of Global Maize Price to Regional Productions Using Statistical and Machine Learning Methods

Zelingher, Rotem; Makowski, David; Brunelle, Thierry

doi:10.3389/fsufs.2021.655206

Cited by 23 publications

(16 citation statements)

References 32 publications

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“…It is based on the use of annual regional yields and productions to enable the user to interpret the results, principally challenging the transparency of each model. The chosen models were those which had been previously tested in relation with the global maize market and regional production (Zelingher et al, 2021), i.e., CART (Breiman et al, 1984), RF (Hastie et al, 2009), and GBM (Friedman, 2001). To those are added two econometric models, each having some advantages: VAR (Sims, 1980), which can detect inter-and intra-effects of local productions shocks, and TBATS (De Livera et al, 2011), as a time-series based approach that has proved to achieve low forecasting errors (Lima and Laporta, 2020).…”

Section: Discussionmentioning

confidence: 99%

Forecasting Global Maize Prices From Regional Productions

Zelingher¹,

Makowski²

2022

Front. Sustain. Food Syst.

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This study analyses the quality of six regression algorithms in forecasting the monthly price of maize in its primary international trading market, using publicly available data of agricultural production at a regional scale. The forecasting process is done between one and twelve months ahead, using six different forecasting techniques. Three (CART, RF, and GBM) are tree-based machine learning techniques that capture the relative influence of maize-producing regions on global maize price variations. Additionally, we consider two types of linear models—standard multiple linear regression and vector autoregressive (VAR) model. Finally, TBATS serves as an advanced time-series model that holds the advantages of several commonly used time-series algorithms. The predictive capabilities of these six methods are compared by cross-validation. We find RF and GBM have superior forecasting abilities relative to the linear models. At the same time, TBATS is more accurate for short time forecasts when the time horizon is shorter than three months. On top of that, all models are trained to assess the marginal contribution of each producing region to the most extreme price shocks that occurred through the past 60 years of data in both positive and negative directions, using Shapley decompositions. Our results reveal a strong influence of North-American yield variation on the global price, except for the last months preceding the new-crop season.

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Section: Discussionmentioning

confidence: 99%

Forecasting Global Maize Prices From Regional Productions

Zelingher¹,

Makowski²

2022

Front. Sustain. Food Syst.

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“…Random Forest and Gradient boosting models have been used to predict the impact on the price of Maize based on the quantity of Maize production in North America (Zelingher et al, 2021). Using a Random forest with 500 trees and a gradient boosting model, authors could predict that an 8% increase in North American output of maize results in a 7% drop in the global price of Maize.…”

Section: Machine Learning Models Used In Agriculture Pricingmentioning

confidence: 99%

Modeling the Impact of Covid-19 on the Farm Produce Availability and Pricing in India

Kumar¹

2022

IJIKM

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Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.

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“…Recently, machine learning techniques have revealed their great potential for price and yield forecasts of a wide spectrum of agricultural commodities (Yuan et al 2020;Rl and Mishra 2021;Storm et al 2020;Kouadio et al 2018;Abreham 2019;Huy et al 2019;Degife and Sinamo 2019;Naveena et al 2017;Lopes 2018;Mayabi 2019;Moreno and Salazar 2018;Zelingher et al 2021;Shahhosseini et al 2021Shahhosseini et al , 2020dos Reis Filho 2020;Zelingher et al 2020;Ribeiro et al 2019;Surjandari et al 2015;Ayankoya et al 2016;Ali et al 2018;Fang et al 2020;Harris 2017;Li et al 2020a;Yoosefzadeh-Najafabadi et al 2021;Ribeiro and dos Santos 2020;Zhao 2021;Jiang et al 2019;Handoyo and Chen 2020;Silalahi 2013;Li et al 2020b;Ribeiro and Oliveira 2011;Zhang et al 2021;Melo et al 2007;de Melo et al 2004;Kohzadi et al 1996;Zou et al 2007;Rasheed et al 2021;Khamis and Abdullah 2014;Dias (Vajda and Santosh 2016;Elliott et al 2020). For example, a fast method has been p...…”

Section: Introductionmentioning

confidence: 99%

“…and Rocha 2019; Gómez et al 2021;Silva et al 2019;Deina et al 2011;Filippi et al 2019;Wen et al 2021), such as soybeans (dos Reis Filho 2020; Li et al 2020a;Yoosefzadeh-Najafabadi et al 2021;Ribeiro and dos Santos 2020;Zhao 2021;Jiang et al 2019;Handoyo and Chen 2020), soybean oil (Silalahi 2013;Li et al 2020b), sugar (Surjandari et al 2015;Ribeiro and Oliveira 2011;Zhang et al 2021;Melo et al 2007;de Melo et al 2004;Silva et al 2019), corn (Xu and Zhang 2021f;Mayabi 2019;Moreno and Salazar 2018;Zelingher et al 2021;Shahhosseini et al 2021Shahhosseini et al , 2020dos Reis Filho 2020;Zelingher et al 2020;Ribeiro et al 2019;Surjandari et al 2015;Ayankoya et al 2016), wheat (Fang et al 2020;Ribeiro and dos Santos 2020;Kohzadi et al 1996;Zou et al 2007;Rasheed et al 2021;Khamis and Abdullah 2014;Dias and Rocha 2019;Gómez et al 2021), coffee (Kouadio et al 2018;…”

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confidence: 99%

Canola and soybean oil price forecasts via neural networks

Zhang

2022

Adv. in Comp. Int.

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Forecasts of commodity prices are vital issues to market participants and policy-makers. Those of cooking section oil are of no exception, considering its importance as one of main food resources. In the present study, we assess the forecast problem using weekly wholesale price indices of canola and soybean oil in China during January 1, 2010-January 3, 2020, by employing the non-linear auto-regressive neural network as the forecast tool. We evaluate forecast performance of different model settings over algorithms, delays, hidden neurons, and data splitting ratios in arriving at the final models for the two commodities, which are relatively simple and lead to accurate and stable results. Particularly, the model for the price index of canola oil generates relative root mean square errors of 2.66, 1.46, and 2.17% for training, validation, and testing, respectively, and the model for the price index of soybean oil generates relative root mean square errors of 2.33, 1.96, and 1.98% for training, validation, and testing, respectively. Through the analysis, we show usefulness of the neural network technique for commodity price forecasts. Our results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis. KeywordsCanola oil • Soybean oil • Price forecast • Time series data • Neural network technique 0123456789().: V,-vol

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Assessing the Sensitivity of Global Maize Price to Regional Productions Using Statistical and Machine Learning Methods

Cited by 23 publications

References 32 publications

Forecasting Global Maize Prices From Regional Productions

Forecasting Global Maize Prices From Regional Productions

Modeling the Impact of Covid-19 on the Farm Produce Availability and Pricing in India

Canola and soybean oil price forecasts via neural networks

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