Agricultural Product Price Forecasting Methods: A Review

Sun, Feihu; Meng, Xianyong; Zhang, Yan; Wang, Yan; Jiang, Hongtao; Liu, Pingzeng

doi:10.3390/agriculture13091671

Cited by 19 publications

(4 citation statements)

References 47 publications

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“…Refs. [23,24] comprehensively reviewed the literature on the price prediction of pork, livestock, and agriculture products. Since the 1920s, scholars have widely researched pork price prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Yu,

Liu,

Lai

2024

Sustainability

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The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry.

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Section: Literature Reviewmentioning

confidence: 99%

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Yu,

Liu,

Lai

2024

Sustainability

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“…An exemplary instance of Bagging is the Random Forest method. In a Random Forest, several decision trees are trained using distinct bootstrap samples of the dataset (Sun et al,2023). During the prediction process, each tree in the forest provides its own prognosis individually.…”

Section: Bagging (Bootstrap Aggregating)mentioning

confidence: 99%

Crop Recommendation Predictive Analysis using Ensembling Techniques

Abdullahi,

Olalere,

Aimufua

et al. 2024

jobasr

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Crop recommendation systems play a crucial role in modern agriculture by aiding farmers in making well-informed choices to optimize crop yield and resource utilization. Ensemble learning approaches can significantly improve the effectiveness of crop recommendation systems. To achieve this, multiple forecasts are combined from various models. In this paper, a complete Machine Learning Pipeline is used to evaluate the performance of ensemble learning models in crop recommendation tasks. A diverse dataset is used to select and train four ensemble learning methods, Bagging, Voting, Stacking, and One-Vs-Rest (OVR), as separate classifiers. The dataset includes various agricultural factors such as soil characteristics, meteorological conditions, and past crop productivity. Various metrics, including accuracy, precision, recall, F1-score, and support, are utilized for each model. Bagging is considered the most effective ensemble learning technique, demonstrating excellent levels of accuracy and overall performance. The bagging algorithm achieves a high level of accuracy, reaching 99.32%. It also achieves perfect precision, recall, and F1-score metrics, with values of 0.99, 1.00, and 1.00 respectively. The support value, which represents the number of instances used for evaluation, is 141. This study provides valuable perspectives on the choice of appropriate ensemble learning models for crop recommendation tasks. Consequently, it enables farmers and other individuals involved in agriculture to make well-informed choices using data, resulting in enhanced agricultural output and sustainability.

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“…In the field of agricultural product price prediction, different experts and scholars have conducted research on agricultural product price prediction from the aspects of prediction methods, price fluctuations, and influencing factors, etc. The agricultural product price prediction method has undergone a development process from qualitative methods to quantitative methods, from single models to combination models, and from traditional econometric prediction methods to intelligent prediction methods [11]. The earliest use of econometric methods to predict agricultural product prices can be traced back to 1917 [12].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Weekly Price Trend of Garlic Based on Classification Algorithm and Combined Features

Sun,

Meng,

Zhang

et al. 2024

Horticulturae

Self Cite

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To promote the sustainable development of the garlic industry and provide a reference for the prediction of agricultural product price trends, this study used the garlic price in Jinxiang, China as the research object. First, the feature combination De was obtained by extracting the sequence obtained using VMD decomposition. Then, the De_Vo combined feature was constructed by combining the volatility feature Vo. Classification algorithms, such as logistic regression, SVM, and XGBoost, were used to classify and predict the garlic price trend. The results showed that the prediction results based on the combined features were better than those based on the single De or Vo features. In the binary classification prediction, the accuracy values for LR, SVM, and XGBoost were 62.6%, 71.4%, and 72.9%, respectively. Among them, the XGBoost algorithm performed better than the LR and SVM algorithms in the three-class, four-class, and five-class predictions.

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Agricultural Product Price Forecasting Methods: A Review

Cited by 19 publications

References 47 publications

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications

Crop Recommendation Predictive Analysis using Ensembling Techniques

Prediction of Weekly Price Trend of Garlic Based on Classification Algorithm and Combined Features

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