Agricultural Prices Research Articles

Revitalizing agricultural price mechanisms: A review and call for enhancement

Abstract Indian agriculture has long faced criticism for its lack of a strong price policy, which leaves farmers at the mercy of changes in the market. Although it has been implemented for a few commodities, the minimum support price (MSP) system has not been all-encompassing nor has the implementation of market intervention measures yielded much respite. This study makes the case that a thoughtful combination of the private investments and market intervention schemes (MIS) along with existing regimes of MSP could provide greater results. A strategy like this might offer farmers greater all-encompassing assistance while guaranteeing reasonable prices and stable markets. This study has reviewed two key existing frameworks by examining their strengths and flaws. Based on this analysis, it is possible to move forward by incorporating the best aspects of MIS and MSP. In addition to addressing the long-standing issues Indian farmers confront, this strategy could result in a more egalitarian and sustainable agriculture industry.

Wirtschaftszahlen der GJAE-Ausgabe 1/2024 (Stand: 15.02.2024)

Wirtschaftszahlen: This database contains general data on the development of agricultural producer and input prices, the consumer price index and foreign trade in agricultural and food goods on a monthly basis for the previous 24 months. In addition, this database comprises important volume and price developments on national and international markets for selected product groups of agricultural products such as grain, livestock, meat, milk and dairy products as well as vegetables, fruit and sugar. This file is only available in German.

Impact of agricultural price policy on farm harvest and wholesale prices of legumes in Maharashtra

Legumes have been used as an entry point to improve productivity, food security in terms of nutrition and availability aspect and income for smallholder farmers. The findings reveal that the growth in the area of soybean, tur and gram was significantly increased and the production growth in all the selected crops was significantly increased. Productivity growth was significantly increased in gram on the other hand it was stagnant in case of soybean. The high variability in WSPs in almost all the selected crops indicated that all the crops were volatile in terms of prices and also that large variability in MSP were observed. Significantly positive growth rates were observed for FHP and MSP of all the selected crops.

The time-varying dynamic impact of US renewable energy prices on agricultural prices in China: the case of fuel ethanol.

This paper intends to look into the time-varying dynamic impact of US fuel ethanol, one of the renewable energy sources, on the prices of agricultural products (specifically corn, soybeans, rice, and wheat) in China based on monthly price data from January 2000 to January 2023. To achieve this, a time-varying parameter vector autoregressive (TVP-VAR) model is employed, which takes into account structural changes in emergencies through time-varying parameters. The empirical results show that the equal-interval impulse responses of price fluctuations in agricultural commodities are primarily positive to variations in fuel ethanol prices and production. And the intensity and direction of the effects vary at distinct time lags. Additionally, the magnitude of these responses is most pronounced in the short term for all agricultural commodities except for corn, and the duration of the impulse responses at different time points is generally longer for corn prices compared to other commodities. The study also reveals that the influence of US fuel ethanol on Chinese agricultural commodity prices is not substantial on the whole. Therefore, there is a necessity to advance the growth of biofuels and provide policy support and financial subsidies for agricultural products earmarked for food production. These actions could shed insights into the progression of Chinese renewable energy and food policies, ensuring the stability of the market in the long run.

Comparative Study of EMD based Modelling Techniques for Improved Agricultural Price Forecasting

Forecasting agricultural commodity prices is regarded as a challenging task due to its non-linear and non-stationary nature. As agriculture productionis highly reliant on various biological and agro-meteorological factors, traditional smoothing techniques as well as statistical models often fail tomodel such series satisfactorily. To capture such complex patterns effectively, different data-driven and self-adaptive techniques have been developedtime-to-time. Against this backdrop, in this paper, we have assessed the suitability of empirical mode decomposition (EMD)-based neural network andsupport vector regression (SVR) approaches for forecasting wholesale prices of three major potato markets namely, Agra, Bangalore, and Mumbai.As the benchmark models, autoregressive integrated moving average (ARIMA), time delay neural network (TDNN) and SVR models have beenemployed for the comparative evaluation. The experimental results clearly reveal the comparative superiority of the EMD-SVR model for the Agraand Bangalore markets and the EMD-TDNN model for the Mumbai market in terms of root mean squared error values and turning point predictions.Moreover, all the EMD-based models have performed better than the other competing models.

Determinants of Farmers’ Acceptance of the Volumetric Pricing Policy for Irrigation Water: An Empirical Study from China

Volumetric-based pricing for irrigation water was introduced as part of a comprehensive reform of agricultural water prices in China. However, operational deficiencies and farmers’ lack of willingness to adopt the volumetric pricing policy (VPP) hinder the coordinated implementation of the reform. To address these practical challenges, we employed a binary logistic regression model to analyse farmers’ acceptance of the VPP for agricultural irrigation water usage in Suqian City, Jiangsu Province. A variable set was formed by selecting potential variables from four types of influencing factors: the subject (water users), the object (water supply departments), natural condition factors, and social condition factors. Our results revealed seven factors that determine whether farmers accept the VPP: irrigation water measurement at the water inlet of a lateral canal, the irrigation water-saving rewards scale, enforcement efforts of charging by volume, the irrigation water source type, the use of agricultural water-saving for trade, financial investment in water-saving technology, and the level of irrigation water pricing. We determined the degree of influence of the seven determining factors, among which the irrigation water-saving rewards scale and enforcement efforts of charging by volume most influence farmers’ decisions on the VPP for irrigation water. The results of this study can be used as a reference for innovation of the agricultural water-saving system in Suqian City, optimisation of an accurate fiscal subsidy scale, quantification of irrigation water rights, optimisation of the measurement facility layout, and effective implementation of agricultural water rights trading. More broadly, this study provides a valuable reference for solving the difficulties faced in the comprehensive reform of agricultural water pricing in China, which includes irrigation water pricing mechanisms, management systems, subsidy mechanisms, and water-saving incentive measures.

DETERMINANTS OF CONSUMER EGG PRICES IN TURKIYE USING K-NEAREST NEIGHBORS REGRESSION ANALYSIS

Examining the factors impacting egg consumer prices in Turkiye holds significant importance. This research contributes to a deeper understanding of how specific variables influence market prices and illuminates cause-and-effect relationships in price fluctuations. The study analyzed 131 datasets from Turkiye, covering the period from January 2010 to November 2020, including various factors. The primary aim was to predict the relationships and impacts on egg consumer prices by considering variables such as laying hens feed price, the exchange rate of the US dollar, producer price indices, agricultural producer price indices, feed prices for broilers, corn, barley, and soybeans, utilizing K-nearest neighbors (KNN) regression analysis. Within the model, the consumer price of eggs showed the weakest correlation with the corn variable (r = 0.832). In contrast, the consumer price of eggs displayed the highest correlation with the US dollar exchange rate and soybean variables (r = 0.892). Therefore, it is evident that egg consumer prices are significantly influenced by the costs of raw materials for feed and currency exchange rates. Unlike other studies, this study found that the price of broiler feed also affects consumer egg prices. Thus, it is imperative for Turkiye to implement policies promoting the local supply of imported inputs essential for egg production and adopt measures to manage and mitigate exchange rate fluctuations and production expenses. Keywords: Egg, consumer price, laying hens feed price, Dollar exchange rate, Turkiye

Impacts of commodity prices and governance on the expansion of tropical agricultural frontiers

Deforestation in the tropics remains a significant global challenge linked to carbon emissions and biodiversity loss. Agriculture, forestry, wildfires, and urbanization have been repeatedly identified as main drivers of tropical deforestation. Understanding the underlying mechanisms behind these direct causes is crucial to navigate the multiple tradeoffs between competing forest uses, such as food and biomass production (SDG 2), climate action (SDG 13), and life on land (SDG 15). This paper develops and implements a global-scale empirical approach to quantify two key factors affecting land use decisions at tropical forest frontiers: agricultural commodity prices and national governance. It relies on data covering the period 2004–2015 from multiple public sources, aggregated to countries and agro-ecological zones. Our analysis confirms the persistent influence of commodity prices on agricultural land expansion, especially in forest-abundant regions. Economic and environmental governance quality co-determines processes of expansion and contraction of agricultural land in the tropics, yet at much smaller magnitudes than other drivers. We derive land supply elasticities for direct use in standard economic impact assessment models and demonstrate that our results make a difference in a Computable General Equilibrium framework.

Early forecasting of corn yield and price variations using satellite vegetation products

Several approaches based on process-based, statistical or economic models have been developed and are currently used by market analysts and governmental agencies to forecast agricultural yields and commodity prices at the national and global scales. However, these models require input data that are often difficult to obtain before crop harvest and their predictions are not always reliable. Satellite data products provide easily accessible sources of information on vegetation characteristics, potentially relevant for predicting crop yield and commodity prices at a large scale, before harvest. This study presents and evaluates several methods for forecasting variations in US corn yield and global corn prices using regional vegetation satellite products. The spatial dimension of maps of vegetation indices derived from satellite products in the US Corn Belt is first reduced either by their spatial means or by principal components derived using the Empirical Orthogonal Function (EOF) method. Mean values and EOF principal components are then used as explanatory variables in generalized LASSO linear models to forecast corn price and yield changes at different time horizons, before harvest. A cross-validation method was implemented to assess the model performances using Area under the ROC curve (AUC), the Brier Skill Score (BSS) and the Matthew Correlation Coefficient (MCC). The results revealed that EOF principal components derived from satellite-based gross primary production (GPP) in July could be used to predict variations in US corn yields, with an AUC of 0.97 (95% CI 0.92-1), a BSS of 0.75 and an MCC of 0.83. The results also showed that the July spatial averages of satellite GPP data obtained in the US Corn Belt could be used to forecast the forthcoming increase or decrease of global corn prices as evidenced by an AUC reaching up to 0.91, a BSS up to 0.5, and an MCC up to 0.67. Finally, we compared our forecasts of yield and price variations to those derived from the WASDE forecasting method currently implemented in the US. This comparison showed that our approach was at least as accurate and often more accurate than WASDE. This study demonstrates how satellite data can be used to forecast variations in crop yields and corn prices several months before harvest.

Integrating land quality in peri-urban agriculture for sustainability and green revolution in Vietnam

Agricultural Land Use Systems (LUS) contributes to Green Revolution (GR) and sustainable development in Vietnam, however, the GR exerts a dual impact-both positive and negative-on agricultural LUS in suburban regions. Land Quality Indicators (LQI) is integrated into the Pressure-State-Response (PSR) model to assess agricultural LUS across four landscapes in a peri-urban area of Hanoi capital city (Vietnam). The selected 75 indicators are subject to elimination based on the distinct natural and social contexts prevalent in each landscape. The findings indicate that primary pressures impinging upon agricultural LUS are dwindling irrigation water, escalating erosion resulting from cultivation, inadequate agricultural infrastructure, price imbalances, and pollution from industrial parks and craft villages. An amalgamation of traditional and high-tech solutions is recommended to enhance the quality of organic produce and foster stronger connections between suburban and urban. Integrating LQI into the PSR framework supplants conventional assessment methods, paving the way for more effective sustainable land use planning in peri-urban areas in the foreseeable future.

Agricultural price prediction based on data mining and attention-based gated recurrent unit: a case study on China’s hog

Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture.

Various optimized machine learning techniques to predict agricultural commodity prices

AbstractRecent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries.

Growth and Instability in Agriculture Production in Odisha, India

The performance of agriculture sector in any economy is better understood through the analysis of growth and instability in agriculture production. It is evident that the growth of agriculture in Odisha is very erratic in nature and huge instability is there in agricultural production. Instability in agriculture affects the trend and pattern of production which creates risk in farmers’ ability to adopt new technologies. Therefore, this paper aims to study the growth and instability in agriculture production in Odisha where growth and instability on area, yield and production of twenty crops have been calculated. Instability in agriculture production is caused by various agricultural reforms, weather variation and price fluctuation. Therefore, in this study attempt has been made to analyse the contribution of agriculture and allied sector to GSVA in Odisha over the years, sectoral employment, and district wise crop intensity. To examine growth and instability in agriculture of twenty major crops, this study has undertaken decadal analysis by using secondary data. Total four decades i.e. 1970-80, 1980-90, 1990-00, 2000-10 and 2010-20 are considered. The results show that, agriculture sector is leading sector in providing employment and significantly contribute to GSDP in Odisha. However, the area under cultivation of all the crops area gradually decreasing due to the urbanization but the percentage of area under cereal cultivation is decreasing whereas percentage of area under pulses are increasing but the area under total food grains is decreasing showing farmers are switching towards non-food grain products. So far, the growth of production and yield of different crop areas shows erratic growth in agricultural crops, as evident from the high instability rate in the growth of different crops. The cropping intensity shows that it is lower than the national level and therefore the role of technology needs to be identified.

Monetary policy and distributional effects of agricultural commodity price volatility in Sub-Saharan Africa

Monetary policy and distributional effects of agricultural commodity price volatility in Sub-Saharan Africa

Socioeconomic impact of agricultural water reallocation policies in the Upper Litani Basin (Lebanon): a remote sensing and microeconomic ensemble forecasting approach

The integration of remote sensing and socio-economic data is crucial for policy making in regions suffering from water scarcity and climate change. We present a framework that combines remotely sensed estimates of biomass and water use from FAO’s Water Productivity Open-access portal (WaPOR) (V2) with an ensemble of calibrated mathematical programming models to assess the impact of water reallocations on rain-fed and irrigated land use and production in water-stressed basins. We evaluated our model in the Upper Litani Basin of Lebanon using two water reallocation policies: (i) quotas that progressively reduces water allocation for irrigation up to 100%, at 1% intervals and (ii) a pricing policy that increases the agricultural water price up to 0.25 USD/m3, at 0.0025 USD/m3 intervals. Our results reveal that 1) the impacts of water availability reductions due to quotas on profit and employment are less-than-proportional when water allocation is reduced by <50%, but abruptly increase when water allocation is reduced by >50%; 2) irrigators responses to prices are inelastic until a price increase of 0.03 USD/m3, become significantly more elastic afterwards, and again become inelastic above a price increase of 0.06 USD/m3; 3) higher water prices progressively reduce profit and labor, significantly reduce water use over the elastic interval (where irrigators cultivating low value-added crops shift to rainfed agriculture), and increase tariff revenue during the inelastic interval (where irrigators largely stick to their crop portfolios and pay the higher water prices). Our results reveal nontrivial uncertainties and tipping points, thus highlighting the value of combining reliable water use data with multi-model and multi-scenario ensembles in informing robust policies.

COVID-19, the Russia–Ukraine war and the connectedness between the U.S. and Chinese agricultural futures markets

This study focuses on how recent global crises such as the COVID-19 pandemic and the Russia–Ukraine war have affected the relationship between the U.S. and Chinese agricultural futures markets. By applying wavelet coherence analysis (WCA) and time-varying parameter vector autoregression (TVP-VAR), we obtain the following findings. First, both events have changed the correlation and lead–lag comovement between U.S. and Chinese soybean and corn futures returns but have little impact on the comovement between the two cotton futures returns. Second, U.S. agricultural markets transmit more volatility risk to Chinese markets than the risk spillover from the reverse direction. Third, the risk spillover enhancement effect from the war is stronger than that from the pandemic, which is obvious in both the soybean and corn futures markets but not in the cotton market. Our paper has implications for policy makers seeking to stabilize agricultural commodity prices during global crisis episodes and for designing strategies for cross-market hedging of spillover risks among commodity markets for international investors.

Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners.

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

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.

Vegetable and potato growing in Kazakhstan as the main direction of agricultural production

The goal is to assess the development of potato and vegetable growing and their impact on the state of domestic and regional agricultural markets, to formulate proposals for stabilizing and improving the situation in the near future. Methods – comparative analysis, economicstatistical and structural approaches. Results – the authors note that in order to achieve the required level of food security, it is necessary to increase the volume of cultivation of these products in large agricultural enterprises, which will contribute to the growth of commercial production of vegetables and potatoes of proper quality. The state and potential of vegetable and potato farms, their regional characteristics, formation of vegetable market, and state support for vegetable growing industry at the national level are considered. The issues of regulating cultivation and sale of vegetable products are analyzed, the level of its consumption in the constituent entities of the Republic of Kazakhstan and factors determining it are shown. Monitoring of the areas of potatoes grown in open ground, tools and mechanisms for stimulating their expansion, the share of gross potato harvest for all categories of farms in the republic, the dynamics of prices of agricultural producers and consumer prices was carried out. Taking into account the scientific views and opinions of researchers, analysis of the category “sustainable development” is presented. Conclusions – it is necessary to carry out reclamation measures related to the involvement of agricultural land in circulation for vegetable and potato growing, increase in state support for agricultural producers, as well as funds to reimburse part of costs incurred for production of greenhouse vegetables, and technical re-equipment of production facilities. The implementation of investment projects is important, which will make it possible in the medium term to achieve complete food independence for vegetables and potatoes and ensure an acceptable price environment for their producers and consumers.

Peasant Resistance in Times of Economic Affluence: Lessons From Paraguay

Contrary to the established belief that low agricultural prices fuel peasant rebellion, our study investigates the surge in peasant unrest during a period of high agricultural prices. The transition to capital-intensive agriculture, characterized by reduced labor demand and heightened entry barriers, prompts landowners to expand into the agricultural frontier during periods of price increases. In these frontier regions, the soils, while comparatively less suitable, become economically viable for commercial agriculture when crop prices are high. This scenario sets the stage for heightened collective resistance, particularly where organizational capacities and subsistence communities provide peasants with symbolic and material resources to resist land encroachment. We provide evidence of this argument by using unique municipal-level data from Paraguay between 2000 and 2014, a period of rising yet fluctuating prices. Our study shows how the interplay between technological advancements in agriculture and global market forces reshape the geography of peasant rebellion.