Previsão de preços das commodities agrícolas: uma revisão bibliométrica sobre modelos

Objetivo - Identificar a lacuna de pesquisa sobre modelos de previsão aplicados nos preços das commodities agrícolas e mostrar as principais tendências da previsão. Desenho/ metodologia/abordagem - A análise bibliométrica possibilitou identificar a lacuna científica e gerou resultados quantitativos e tendências. Resultados - Os resultados mostraram que as abordagens ARIMA e redes neurais são os modelos mais utilizados na previsão de preços de commodities agrícolas, no entanto, o modelo ARIMA não tem gerado previsões superiores em comparação aos algoritmos de aprendizado de máquina (ML) e modelos híbridos. As redes neurais são mais precisas para prever preços de commodities agrícolas do que os modelos econométricos. Os modelos híbridos de IA geram predições com melhores níveis de acurácia em comparação aos métodos estatísticos tradicionais ARIMA, modelos individuais e redes neurais em que o desempenho de previsão dos modelos híbridos são melhores do que os dos modelos únicos. É uma tendência a abordagem de modelos híbridos para prever preços de commodities agrícolas em pesquisas futuras. Implicações de pesquisa, práticas e sociais - Estes achados permitem discussões sobre modelagem e previsão de preços de commodities agrícolas. Os modelos abordados neste estudo bibliométrico podem fornecer referência para os econometristas do campo da previsão de preços de produtos agrícolas, e a pesquisa aponta as tendências sobre a temática, assim pode fornecer direções de pesquisa para econometristas. Originalidade/Relevância - No estudo bibliométrico realizado nas bases de dados Web of Science e SCOPUS, não foi encontrada uma revisão bibliométrica ou sistemática sobre o tema. Os estudos dedicados à revisão sobre previsão de preços de commodities agrícolas, ainda são poucos como as revisões de literatura (Brandt e Bessler, 1983; Wang, et al., 2020)

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Uso de séries temporais do sensor MODIS para identificar diferentes culturas agrícolas

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2018.A presente pesquisa objetiva identificar culturas de grãos a partir de séries temporais NDVI MODIS. As culturas agrícolas e regiões analisadas foram: (a) soja, milho e algodão no Estado do Mato Grosso na safra de 2013/2014; (b) trigo no Estado do Rio Grande do Sul; (c) e cultura do arroz no Estado de Santa Catarina. A tese está estruturada em 5 (cinco) capítulos, onde os capítulos de desenvolvimento (2, 3 e 4) foram escritos no formato de artigos científicos. No processamento digital de imagem todas as análises consideraram as seguintes etapas: (a) aquisição das imagens MODIS; (b) tratamento dos ruídos usando o filtro Savitzky- Golay; (c) classificação; e (d) análise de acurácia. A principal diferença metodológica foi a etapa de classificação que utilizou duas abordagens: (a) classificação contínua do terreno considerando as diferentes produções agrícolas (soja, milho e algodão) e os tipos de vegetação a partir de dois métodos de aprendizagem de máquina (Support Vector Machines e Redes Neurais de retro-propagação); e (b) detecção de uma única cultura de pequenos agricultores (arroz em Santa Catarina e trigo no Rio Grande do Sul) usando o método do vizinho mais próximo (caso específico do método K-NN). A primeira abordagem usando classificação contínua do terreno considerou as seguintes assinaturas temporais NDVI: formação florestal, cerrado, pastagem, sistema único de cultivo anual (soja, milho e algodão), sistema duplo de cultivo (soja/milho e soja/algodão) e pivô central (sistema triplo de cultivo). Na classificação foram testados 378 modelos de redes neurais com variações dos parâmetros de entrada e 8 modelos SVM usando diferentes funções Kernel. O índice Kappa mostrou que os melhores modelos da Rede Neural (0,77) e SVM (0,75) foram estatisticamente equivalentes pelo teste McNemar. A classificação baseada no vizinho mais próximo foi constituida de duas fases: (a) geração de imagens métricas (distância Euclidiana e similaridade do cosseno); e (b) definição do melhor valor de corte para caracterizar a máscara da cultura agrícola. Os resultados mostraram diferentes perfis temporais tanto no trigo como no arroz devido às variações do calendário agrícola da região. Nas duas classificações (trigo e arroz), os resultados usando as duas métricas foram estatisticamente equivalentes pelo teste McNemar. Na análise do trigo, a distância Euclidiana obteve um índice Kappa de 0,75 e a semelhança do cosseno um índice Kappa de 0,74. Na análise do arroz a distância Euclidiana obteve um índice Kappa de 0,73 e a semelhança do cosseno um índice Kappa de 0,72. As metodologias descritas demonstram uma grande potencial para o cálculo das áreas de produção agrícola, podendo auxiliar os órgãos federais para o planejamento regional e segurança alimentar.The present research aims to identify grain crops from NDVI MODIS time series. The agricultural crops and the analyzed regions were: (a) soybean, corn and cotton in Mato Grosso State at 2013/14 growing season; (b) wheat in the State of Rio Grande do Sul; (c) and rice in the State of Santa Catarina. The thesis is structured in 5 (five) chapters, where the development chapters (2, 3 and 4) were written in the format of scientific articles. In digital image processing, all analyzes considered the following steps: (a) acquisition of MODIS images; (b) noise treatment using the Savitzky-Golay filter; (c) classification; and (d) accuracy analysis. The main methodological difference was the classification stage that used two approaches: (a) continuous land classification considering the different agricultural production (soybean, corn and cotton) and vegetation types from two methods of machine learning ( Support Vector Machines and Retro-propagation Neural Networks); and (b) detection of a single crop of small farmers (rice in the Santa Catarina and wheat in the Rio Grande do Sul) using the nearest neighbor method (specific case of the K-NN method). The first approach using continuous land classification considered the following NDVI temporal signatures: forest formation, cerrado, pasture, single annual cropping system (soybean, corn and cotton), double cropping system (soybean / corn and soybean / cotton) and pivot (triple cropping system). In the classification were tested 378 models of neural networks with different variations in input parameters and 8 SVM models using different Kernel functions. The Kappa index showed that the best models of the Neural Network (0.77) and SVM (0.75) were statistically equivalent by the McNemar test. The classification based on the nearest neighbor was constituted of two phases: (a) elaboration of metric images (Euclidean distance and similarity of the cosine); and (b) definition of the best threshold value to characterize the agricultural crop mask. The results showed different temporal profiles in both wheat and rice due to variations in the region's agricultural calendar. In both classifications (wheat and rice), the results using the two metrics were statistically equivalent by the McNemar test. In wheat analysis, the Euclidean distance obtained a Kappa index of 0.75 and the cosine similarity a Kappa index of 0.74. In rice analysis, the Euclidian distance obtained a Kappa index of 0.73 and the cosine similarity a Kappa index of 0.72. The methodologies showed a promising potential to determine the areas of crop production and could be very useful for federal agencies for regional planning and food security programs

Increase of Simultaneous Soybean Failures Due To Climate Change

While soybeans are among the most consumed crops in the world, most of its production lies in the US, Brazil, and Argentina. The concentration of soybean growing regions in the Americas renders the supply chain vulnerable to regional disruptions. In 2012, anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. In this study, we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find long-term trends in mean climate increase the frequency of 2012 analogs by 11–16 times and the magnitude by 4–15% compared to changes in climate variability only depending on the global climate scenario. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas

Storylines of weather-induced crop failure events under climate change

Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the midwestern US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present-day, pre-industrial +2 and 3 ∘C warming, respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure and construct analogues of these failure conditions in future climate settings. We find that crop failures in the midwestern US are linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. Results suggest soybean failures are likely to increase with climate change. With more frequent warm years due to global warming, the joint hot–dry conditions leading to crop failures become mostly dependent on precipitation levels, reducing the importance of the relative compound contribution. While event analogues of the 2012 season are rare and not expected to increase, impact analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. This has implications for assessment of the drivers of extreme impact events

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Modeling and Simulation in Engineering

The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers