1,720,965 research outputs found
Democratising Agricultural Commodity Price Forecasting: The AGRICAF Approach
Get PDFEnsuring food security is a global challenge, particularly in low-income countries where food prices affect access to nutritious food. The instability of global agricultural commodity (AC) prices exacerbates food insecurity, with international trade restrictions and market disruptions further complicating the situation. Although online platforms exist for monitoring food prices, there is still a need for accessible, detailed forecasts for non-specialists. This paper proposes the Agricultural Commodity Analysis and Forecasts (AGRICAF) methodology, integrating explainable machine learning (XML) and econometric techniques to analyse and forecast global ACs prices up to one year ahead across different horizons. This innovative integration allows us to model complex interactions while providing clear, interpretable results. We demonstrate the utilization of AGRICAF, applying it to three major commodities and explaining how different factors impact prices across months and forecast horizons. By facilitating access to reliable forecasts of AC prices, AGRICAF can advance a fairer and sustainable food system
Prévision des prix des produits agricoles à l'aide de techniques d'apprentissage automatique
No full textWould it be possible to develop a forecasting tool for agricultural commodity (AC) prices that is both accurate and interpretable and publicly accessible? Such a tool could turn the forecasting and analysis of food prices into an implementable instrument used by whoever is concerned by food security. This PhD explores the feasibility of this idea in three parts: The first part aims to test the ability of several statistical and machine learning (ML) models to simulate changes in maize prices based on annual changes in maize production and yield observed in major producing regions. The second part of the thesis applies the models developed in the first part and adapt them to produce monthly forecasts of maize prices. We compare the performance of these models to that of forecasting techniques often used for time series analysis. Finally, the third part extends the model to consider two other different crops – soybeans and cocoa. We evaluate the forecasting ability of the techniques developed in the previous stages to predict price changes for soybeans and cocoa. Additionally, we test the sensitivity of the results relative to three geographic scales. Also is the application of ML methods to identify which production shocks drive price shocks. Overall, this thesis shows that ML methods are a potential tool for understanding and forecasting the impact of agricultural production on price variations. These approaches can be easily implemented since they rely on publicly available data, accessible via public website. These tools can thus contribute to democratising the analysis and forecasting of variation in AC prices.Serait-il possible de développer un outil de prévision des prix des produits agricoles de base qui soit à la fois précis, interprétable et accessible au plus grand nombre ? Un tel outil permettrait à ceux qui n'ont pas la capacité financière ou le bagage technique appropriés de prévoir les prix des produits agricoles de base, un ou plusieurs mois à l'avance. Ce doctorat explore la faisabilité de cette idée en trois parties : L'objectif de la première partie est de tester la capacité de plusieurs modèles statistiques et d'apprentissage automatique à simuler les variations du prix du maïs en fonction des variations annuelles de production et de rendement du maïs observées dans les principales régions productrices. Dans la deuxième partie de la thèse, les modèles développés dans la première partie sont adaptés pour effectuer des prévisions mensuelles de prix du maïs. Nous comparons les performances de ces modèles à celles de techniques prédictives souvent utilisées pour l'analyse des séries chronologiques. Enfin, dans la troisième partie, nous étendons le travail réalisé sur le maïs à deux autres cultures très différentes - le et le cacao. Nous analysons la capacité des techniques de prévision mises au point dans la partie précédente à prédire les variations de prix du soja et du cacao et nous analysons également l'effet de l'échelle géographique considérée pour calculer les variations de production. Dans cette partie également, nous montrons comment les méthodes d'apprentissage machine peuvent être utilisées pour identifier les chocs de production à l'origine des chocs de prix. Globalement, cette thèse montre que les méthodes d'apprentissage automatique sont des outils potentiellement utiles à la fois pour comprendre l'impact de la production agricole sur les variations de prix et pour prédire ces variations plusieurs mois à l'avance. Ces approches sont assez faciles à appliquer et peuvent être calibrées avec des données de prix et de production publiquement accessibles. Elles peuvent ainsi contribuer à démocratiser l'analyse et la prévision des variations de prix agricole
Prévision des prix des produits agricoles à l'aide de techniques d'apprentissage automatique
No full textWould it be possible to develop a forecasting tool for agricultural commodity (AC) prices that is both accurate and interpretable and publicly accessible? Such a tool could turn the forecasting and analysis of food prices into an implementable instrument used by whoever is concerned by food security. This PhD explores the feasibility of this idea in three parts: The first part aims to test the ability of several statistical and machine learning (ML) models to simulate changes in maize prices based on annual changes in maize production and yield observed in major producing regions. The second part of the thesis applies the models developed in the first part and adapt them to produce monthly forecasts of maize prices. We compare the performance of these models to that of forecasting techniques often used for time series analysis. Finally, the third part extends the model to consider two other different crops – soybeans and cocoa. We evaluate the forecasting ability of the techniques developed in the previous stages to predict price changes for soybeans and cocoa. Additionally, we test the sensitivity of the results relative to three geographic scales. Also is the application of ML methods to identify which production shocks drive price shocks. Overall, this thesis shows that ML methods are a potential tool for understanding and forecasting the impact of agricultural production on price variations. These approaches can be easily implemented since they rely on publicly available data, accessible via public website. These tools can thus contribute to democratising the analysis and forecasting of variation in AC prices.Serait-il possible de développer un outil de prévision des prix des produits agricoles de base qui soit à la fois précis, interprétable et accessible au plus grand nombre ? Un tel outil permettrait à ceux qui n'ont pas la capacité financière ou le bagage technique appropriés de prévoir les prix des produits agricoles de base, un ou plusieurs mois à l'avance. Ce doctorat explore la faisabilité de cette idée en trois parties : L'objectif de la première partie est de tester la capacité de plusieurs modèles statistiques et d'apprentissage automatique à simuler les variations du prix du maïs en fonction des variations annuelles de production et de rendement du maïs observées dans les principales régions productrices. Dans la deuxième partie de la thèse, les modèles développés dans la première partie sont adaptés pour effectuer des prévisions mensuelles de prix du maïs. Nous comparons les performances de ces modèles à celles de techniques prédictives souvent utilisées pour l'analyse des séries chronologiques. Enfin, dans la troisième partie, nous étendons le travail réalisé sur le maïs à deux autres cultures très différentes - le et le cacao. Nous analysons la capacité des techniques de prévision mises au point dans la partie précédente à prédire les variations de prix du soja et du cacao et nous analysons également l'effet de l'échelle géographique considérée pour calculer les variations de production. Dans cette partie également, nous montrons comment les méthodes d'apprentissage machine peuvent être utilisées pour identifier les chocs de production à l'origine des chocs de prix. Globalement, cette thèse montre que les méthodes d'apprentissage automatique sont des outils potentiellement utiles à la fois pour comprendre l'impact de la production agricole sur les variations de prix et pour prédire ces variations plusieurs mois à l'avance. Ces approches sont assez faciles à appliquer et peuvent être calibrées avec des données de prix et de production publiquement accessibles. Elles peuvent ainsi contribuer à démocratiser l'analyse et la prévision des variations de prix agricole
Prévision des prix mondiaux du maïs à partir des productions régionales
No full textInternational audienceThis 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
Economic Impacts of Climate Change on Vegetative Agriculture Markets in Israel
Get PDFWe integrate the combined agricultural production effects of forecasted changes in CO2, temperature and precipitation into a multi-regional, country-wide partial equilibrium positive mathematical programming model. By conducting a meta-analysis of 2103 experimental observations from 259 agronomic studies we estimate production functions relating yields to CO2 concentration and temperature for 55 crops. We apply the model to simulate climate change in Israel based on 15 agricultural production regions. Downscaled projections for CO2 concentration, temperature and precipitation were derived from three general circulation models and four representative concentration pathways, showing temperature increase and precipitation decline throughout most of the county during the future periods 2041–2060 and 2061–2080. Given the constrained regional freshwater and non-freshwater quotas, farmers will adapt by partial abandonment of agriculture lands, increasing focus on crops grown in controlled environments at the expense of open-field and rain-fed crops. Both agricultural production and prices decline, leading to reduced agricultural revenues; nevertheless, production costs reduce at a larger extent such that farming profits increase. As total consumer surplus also augments, overall social welfare rises. We find that this outcome is reversed if the positive fertilization effects of increased CO2 concentrations are overlooked
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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