Commodities

21 pages, 1601 KB

Open AccessArticle

Macroeconomic Drivers of Poultry Price Volatility in Nigeria: A Study of Inflation and Exchange Rate Dynamics

by Prosper E. Edoja, Rosemary N. Okoh, Emmanuella O. Udueni and Goodness C. Aye

Commodities 2026, 5(1), 3; https://doi.org/10.3390/commodities5010003 - 15 Jan 2026

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Poultry price instability remains a critical challenge for food security in Nigeria. This study examines the relationship between poultry price volatility (PPV), exchange rate (LEXR), and inflation (LCPI) from 1991 to 2024 using the Autoregressive Distributed Lag (ARDL) model. Descriptive results show that PPV had the highest variability (mean 0.65; standard deviation 1.07), while LEXR and LCPI were relatively more stable. Trend analysis indicates that poultry price volatility was high in the early 1990s but declined steadily after 2005, coinciding with persistent inflation and cycles of exchange rate depreciation and appreciation.Unit root and bounds tests confirm that the variables werecointegrated, with an F-statistic of 4.50 exceeding the upper bound at 5 percent significance. The long-run estimates reveal that inflation hada negative effect on poultry price volatility (−0.109), while the exchange rate exerteda positive effect (0.2702). The errorcorrection term (−0.336) indicates a 33.6 percent adjustment to equilibrium each period. In the short run, changes in inflation (0.942) and lagged exchange rate variations significantly influenced poultry price volatility. These findings underscore the importance of stabilizing exchange rates and controlling inflation to reduce price volatility in Nigeria’s poultry sector. Full article

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32 pages, 1806 KB

Open AccessArticle

Mapping the Supply Chain of Lithium-Ion Battery Metals from Mine to Primary Processing by Country and Corporation

by Ramsha Akhter, Sisira Reddy Palli, Mithilesh Walanjuwani and Erick C. Jones, Jr.

Commodities 2026, 5(1), 2; https://doi.org/10.3390/commodities5010002 - 13 Jan 2026

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Abstract

Global critical mineral production patterns differ markedly across the metals needed for advanced energy technologies. This study examines the extraction and processing landscape, in the year 2024, of six key commodities—lithium, cobalt, aluminum, nickel, manganese, and copper—to identify who the major players (countries and corporations) are in the critical mineral space and to understand what they are mining, where they are mining, and where are they sending their ore to be processed. This study aims to provide a snapshot of the critical mineral supply chain that serves as a useful resource for researchers and policymakers seeking to understand and improve the critical mineral supply chain. We analyze company financial filings, government datasets, and other public and proprietary sources for the year 2024. Then, we calculate production volumes and identify geographic and corporate concentration. The results show that copper and aluminum production and processing are relatively diverse, while lithium and cobalt extraction and processing are highly concentrated among a few countries and dominant firms. Nickel and manganese occupy an intermediate position, displaying moderate diversity with emerging signs of consolidation. Full article

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20 pages, 2802 KB

Open AccessArticle

Revisiting Boi Gordo Index Futures: Long-Run Daily Data, Structural Breaks, and a Comparative Evaluation of Classical and Machine Learning Time-Series Models

by Renata G. Alcoforado, Hudo L. S. G. Alcoforado, Alfredo D. Egídio dos Reis and Pedro A. d. L. Tenório

Commodities 2026, 5(1), 1; https://doi.org/10.3390/commodities5010001 - 22 Dec 2025

Viewed by 559

Abstract

We study one of the world’s largest cattle markets by revisiting and extending previous work on the forecasting of Brazil’s Boi Gordo Index (BGI). Using an updated daily dataset (July 2006–September 2025, inflation-adjusted), we evaluate classical and machine learning (ML) approaches for price prediction. Methods include Exponential Smoothing (Simple, Holt, and Holt–Winters), ARMA/ARIMA/SARIMA, GARMA variants, GARCH, Theta, Prophet, and XGBoost; models are compared under a strictly chronological 90/10 holdout (~476 test days) using RMSE, MAE, and MSE, with the AIC guiding within-family selection. Results show that, for the full out-of-sample window, GARMA delivers the best overall accuracy, with ARMA and Holt–Winters close behind, while Prophet and XGBoost perform comparatively worse in this volatile setting. Performance is horizon-dependent: in the first 180 test days, prior to the late-2024 level shift, Holt attains the lowest RMSE/MSE, and XGBoost achieves the lowest MAE. No method anticipates the October–November 2024 exogenous jump and subsequent correction, highlighting the difficulty of structural breaks and the need for timely re-specification. We conclude that GARMA is a robust default for long, turbulent windows, whereas smoothing and ML methods can be competitive on shorter horizons. These findings inform risk measurement and risk mitigation strategies in Brazil’s cattle futures market. Full article

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Commodities, Volume 5, Issue 1 (March 2026) – 3 articles

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