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Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage. [ABSTRACT FROM AUTHOR]

We estimate the implicit market price of soil erosion using quarterly data of 2824 agricultural farms traded in Uruguay between 2000 and 2014. A unique feature of our estimation is that we allow for possible spatial spillovers. We find evidence of a negative and statistically significant association between erosion and land values. On average, an additional loss of 1% of the original topsoil due to erosion is associated with a direct (own) decrease of.24% in the per‐hectare price of agricultural land (P‐value:.012, 95% CI: −.0042, −.0005). In 2023 dollars, this is equivalent to a decrease of USD 8.7 in the average price per hectare, or USD 1130 in the price of the average farm. In terms of tons of soil, the average value is $.24 a ton. Finally, considering the 50 km radius of our spatial model, the value of losing 1% of topsoil is $15.8 million. The value of our estimates is sensitive to our measure of erosion and our specification of the spatial‐temporal weighting matrix, but the statistical association is robust. [ABSTRACT FROM AUTHOR]

The agricultural issue is a focal point of concern for each country, and e-commerce assistance to farmers, as an emerging model, is gaining increasing attention. Considering this trend, this paper bases on e-commerce assistance to farmers and considers four types of government subsidies: no subsidy, price subsidy model with the farmer as the subsidy target, price subsidy model with the e-commerce platform as the subsidy target, and area subsidy model. Based on this, a game model of the contract farming supply chain involving a farmer and an e-commerce platform was established to explore the optimal decision-making in the contract farming supply chain considering various government subsidies under the background of e-commerce assistance to farmers. The results indicate that: (1) Consumer preference for supporting farmers and consumer premiums can stimulate the farmer to expand farm size, increase agricultural production, increase the purchase price of agricultural products and promote the growth of profits for the farmer and the e-commerce platform, contributing to the growth of social welfare. Output volatility can lead to a decrease in farm size and a reduction in the purchase price of agricultural products. (2) Government subsidies can support the farmer in expanding farm size and contribute to increased profits for the farmer and the e-commerce platform. However, government subsidies do not universally have a positive impact on every variable. The influence of government subsidies on the purchase price of agricultural products is contingent upon the method of subsidy distribution. Similarly, the effects of government subsidies on social welfare are influenced by factors such as price elasticity and agricultural productions per unit area. (3) From the perspectives of farm size, the purchase price of agricultural products, and profits of the farmer and the e-commerce platform, the government will have different and more targeted subsidy models, and the corresponding subsidy models are influenced by the agricultural productions per unit area. The research conclusion can provide references for optimal decision-making in contract farming under the model of e-commerce assistance to farmers. [ABSTRACT FROM AUTHOR]

Copyright of Panorama Económico is the property of Universidad de Cartagena and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present paper tests the use of an agricultural futures minimum tracking error portfolio to replicate the price of the Mexican Hass avocado (a non-commodity). The motivation is that this portfolio could be used to balance the basis risk that the avocado price hedge issuer could face. By performing a backtest of a theoretical avocado producer from January 2000 to September 2023, the results show that the avocado producer could hedge the avocado price by 94%, with the hedge offered by a theoretical financial or government institution. Also, this issuer could balance the risk of such a hedge by buying a coffee–sugar futures portfolio. The cointegrated or long-term relationship shows that using such a futures portfolio is useful for Mexican Hass avocado price hedging. This paper stands as one of the first in testing futures portfolios to offer a synthetic hedge of non-commodities through a commodities' futures portfolio. [ABSTRACT FROM AUTHOR]

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty. [ABSTRACT FROM AUTHOR]

Existing studies claim that the Internet of Things (IoTs) raises agricultural producer prices while others claim the contrary. Meanwhile, no studies have been conducted to investigate the impact of IoTs at the macro level, as represented by information globalization. The main objective of this study is to determine the impact of information globalization on agricultural producer prices in developing and developed countries. This study used time series data from 1991 to 2020 and cross-section data from 66 developing and 26 developed countries. The data was analyzed using two-stage least squares. The first stage of analysis shows that pesticides and farm machinery increase agricultural production in developing and developed countries, while employment in agriculture, forestry, and fishing has the opposite effect. Meanwhile, nutrient nitrogen, manure, and irrigation have differing effects on agricultural production in the two areas. The second stage of analysis shows that agricultural producer prices in developing and developed countries will rise when agricultural production, agricultural import, and human capital increase. Agricultural value-added, food consumer price inflation and population growth have varying impacts on agricultural producer prices. Meanwhile, the main variable investigated in this study, information globalization, has been proven to increase agricultural producer prices in both developing and developed countries. [ABSTRACT FROM AUTHOR]

Accurate and reliable price forecasting of agricultural products is significant for promoting the production and distribution of agricultural products, optimizing resource allocation and improving market efficiency. Owing to the nonstationary feature in agricultural price, based on decomposition integration and kernel density estimation (KDE), this paper proposes a hybrid model for agricultural price forecasting that can quantify the uncertainty of potential forecasts by converting traditional point forecasts into interval forecasts. Firstly, the price sequence is decomposed through variational modal decomposition (VMD) determined by energy entropy (EE); secondly, K-means clustering is used to reconstruct the intrinsic modal functions (IMFs) into low-frequency components and high-frequency components, forecasted by different methods. In addition, adaptive kernel density estimation (AKDE) is established through dynamic window and whale optimization algorithm (WOA), which is used to construct prediction interval with the residual signal obtained by VMD. Finally, to validate the superiority of the proposed model, comparative experiments with three different datasets are conducted. The results show that prediction performance of the proposed model is better than other models in both point forecasts and interval forecasts, and it can provide more accurate uncertainty information to agricultural participants. [ABSTRACT FROM AUTHOR]

This research focuses on applying the XGBoost algorithm to analyze and predict cayenne pepper prices. The main aim is to exploit XGBoost's exceptional capability to manage large datasets and discern intricate patterns for precise price forecasting. The dataset comprises historical cayenne pepper price data, along with pertinent economic and climatic factors. The XGBoost model was developed and validated on this dataset, with its performance assessed using metrics. The results indicated a high level of accuracy, achieving an R² score of 99% on the training set and 92% on the test set, reflecting a strong alignment between predicted and actual prices. Moreover, the model attained an average cross-validation score of 96%, reinforcing its robustness and reliability. These findings highlight XGBoost's efficacy in agricultural price prediction, offering stakeholders a potent tool for data-driven decision-making. This study enriches the literature on machine learning applications in agriculture and emphasizes XGBoost's potential to enhance predictive accuracy and operational efficiency. [ABSTRACT FROM AUTHOR]

This paper assesses the response of agricultural commodity markets to Russia's withdrawal from the Black Sea Grain Initiative (BSGI). Employing daily commodity‐level data and event study methods, we analyse the impact on seven agricultural commodities and four key market metrics, including futures prices, historical and implied volatility, and speculative pressure. Our findings show a statistically insignificant increase of 1.1% in agricultural futures prices within the first seven trading days following the BSGI termination. In the following days, futures prices began to decline, eventually returning to levels below those observed before the withdrawal, a pattern further underscored by our implied volatility analysis. While there is no evidence of heightened speculation, we find some evidence for treatment differences across agricultural commodities. These findings suggest that traders did not believe in the likelihood of a blockade of Black Sea grain shipments. [ABSTRACT FROM AUTHOR]

Problem definition: Leading specialty coffee roasters rely on direct trade to source premium coffee beans. We examine a roaster who sells two basic types of roasts: (1) a single-origin roast sourced from a specific locale and (2) a blend roast that uses a mix of beans from sources that vary over the course of a year. The prices of blend roasts are lower than those of single-origin roasts and appeal to a larger market. We study how characteristics of the operating and market environment affect the optimal sourcing strategy for single-origin beans. Methodology/results: We develop a two-stage stochastic program with recourse that reflects these characteristics. A roaster has the option to allocate some of the single-origin beans for sale under a blend label, known as downward substitution. We identify three distinct optimal sourcing strategies—specialized (no downward substitution), diversified (consistent downward substitution), and mixed (between these extremes)—and show that they are robust under different definitions of yield and demand. Managerial implications: We identify four main insights: (1) Two factors determine which strategy is optimal: the mean price of the inferior product (blend label) and the marginal cost of the superior product (single-origin label). (2) When compared with the newsvendor model, we find distinct structural differences across strategies. For example, whereas the effects of increasing uncertainty on optimal quantity align with the newsvendor model under a mixed strategy, the effects are distinctly different under specialized and diversified strategies (e.g., monotonic decreasing behavior for specialized, no change in quantity under diversified). (3) The weighted average price of an agricultural product is decreasing in negative yield–price correlation. We coin this as the "farmer's curse," which carries lessons for direct trade sourcing (e.g., advocating against paying the grower at postharvest market prices). (4) We find evidence of a virtuous feedback loop wherein the grower–roaster relationship becomes stronger over time. Our findings also point to a simple signal that policymakers may use to identify coffee growing locales where targeted interventions can improve grower welfare. Funding: B. Kazaz is thankful for summer support provided by the Whitman School of Management, Syracuse University. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0586. [ABSTRACT FROM AUTHOR]

This paper explores the cointegration between agricultural commodity prices, crude oil prices and exchange rates. Vector autoregressions (VAR) or vector error correction models (VECMs) are applied to price data for French maize and milling wheat, U.S. corn prices and spot and future oil prices. The series are split between 2000-2007 and 2009–2019 omitting the "Great Recession" allowing for analysis of structural market changes. While oil prices influence futures pricing, the link is neither stable nor widespread. Modeling of time series in search of cointegration must consider structural market changes. Results from a "one-model-fits-all" approach are unlikely to be satisfactory. [ABSTRACT FROM AUTHOR]

We analyze the out‐of‐sample predictive power of sentiment for the realized volatility of agricultural commodity price returns. We use high‐frequency intra‐day data covering the period from 2009 to 2020 to estimate realized volatility. Our baseline forecasting model is a heterogeneous autoregressive (HAR) model, which we extend to include sentiment. We further enhance this model by incorporating various key realized moments such as leverage, realized skewness, realized kurtosis, realized upside ("good") volatility, realized downside ("bad") volatility, realized jumps, realized upside tail risk, and realized downside tail risk. In order to setup a forecasting model, we use (i) forward and backward stepwise predictor selection and (ii) a model‐based averaging algorithm. The forecasting models constructed through these algorithms outperform both the baseline HAR‐RV model and the HAR‐RV‐sentiment model. We conclude that, for the agricultural commodities studied in our research, realized moments play a more significant role in forecasting realized volatility compared to sentiment. [ABSTRACT FROM AUTHOR]

The accurate forecasting of agricultural futures prices is critical for ensuring national food security. Therefore, this study proposes a text‐based deep learning forecasting model. This model first uses the ChineseBERT + a text convolution neural network to classify Weibo text and obtain a raw sentiment index. Then, complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, correlation coefficient, and sample entropy are combined to decompose and reconstruct the raw sentiment index and obtain a denoised sentiment index. Subsequently, the neural basis expansion analysis with exogenous variables is improved by designing a weight coefficient and Optuna is used to optimize the designed weight coefficient and the hyperparameters. Finally, the SHapley Additive exPlanations value is used to increase the interpretability of prediction results. Corn futures prices for the Dalian Exchange are used in forecasting to validate the accuracy and stability of the proposed model. Experimental results show that the proposed denoising sentiment index contributes more to the improvement of predictive model performance than the raw sentiment index. The proposed text‐based deep predictive model demonstrates strong predictive ability for prediction horizons of 30 and 60 days. SHapley Additive exPlanations value analysis shows that the three features with greater effects on corn futures prices are as follows: "Corn Spot Price of Zhengzhou market," "CBOT_corn_futures_price," and "Pork futures price." [ABSTRACT FROM AUTHOR]

Most agriculture-based economies in Africa are working on increasing their agricultural productivity and production following the African Union (AU) recommendations. However, it has been shown that increasing agricultural production without ensuring value-chain development may end up in welfare losses for farmers due to falling farm-gate prices. Little research has been conducted so far on the economy-wide effects of food value-chain development. This article contributes to filling this gap by analysing the effects of food-processing sector development on welfare in an agriculture-based economy, taking the example of Benin. We calibrate a Computable General Equilibrium (CGE) model to a comprehensive 2019 Social Accounting Matrix (SAM) for Benin. We introduce a novel welfare measure, accounting for changes in savings. Results show that enhancing the food-processing sector increases demand for agricultural raw materials and hence agricultural production and prices. Accordingly, the price of low-skilled labour increases. This primarily benefits low-income households, who derive most of their income from this factor. The overall income and welfare effects are pro-poor. Accordingly, income disparity as well as poverty incidence and gap decrease. We conclude, that the development of food-processing has the potential to make agricultural development in agriculture-based economies more pro-poor. [ABSTRACT FROM AUTHOR]

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers' planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of "high in winter and low in summer"; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products. [ABSTRACT FROM AUTHOR]

This research delves into the dynamics between the market value of the product of paddy and the associated costs of its cultivation across prominent paddy producing regions in India. Drawing upon secondary data from the period 2004-05 to 2021-22, generously provided by the Commission for Agricultural Costs and Prices (CACP), this study employs growth models and profitability analyses to shed light on emerging patterns within these key states. A detailed examination reveals Punjab as leading in terms of value growth, closely followed by Andhra Pradesh and West Bengal. In contrast, the escalation in cultivation costs is most pronounced in Andhra Pradesh, with Punjab, West Bengal, and Uttar Pradesh also showing significant figures. Through an insightful profitability assessment, the study demonstrates that paddy cultivation is most lucrative in Punjab, with Andhra Pradesh, Uttar Pradesh, and West Bengal following in descending order of profit margins. This hierarchy of profitability signals an imperative for regions like West Bengal and Uttar Pradesh to elevate the quality and market value of their paddy crops to match the standards seen in Punjab and Andhra Pradesh. The comparative advantage enjoyed by farmers in Punjab and Andhra Pradesh in terms of profit margins underscores the critical importance of enhancing productivity, efficiency, and market connectivity in the lesser-performing states of West Bengal and Uttar Pradesh. Such improvements are essential for boosting profitability in paddy cultivation, thereby contributing to the overall growth and sustainability of the agricultural sector in these regions. [ABSTRACT FROM AUTHOR]

Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve fitting algorithm and Long Short-Term Memory (LSTM) network, this study selects corn futures prices listed on the Dalian Commodity Exchange as the research subject to predict and validate their price trends. Firstly, corn futures prices are fitted using segmented Bézier curves. Subsequently, the fitted price sequence is employed as a feature and input into an LSTM network for training to obtain a price prediction model. Finally, the prediction results of the Bézier curve-based LSTM model are compared and analyzed with traditional LSTM, ARIMA (Autoregressive Integrated Moving Average Model), VMD-LSTM, and SVR (Support Vector Regression) models. The research findings indicate that the proposed Bézier curve-based LSTM model demonstrates significant predictive advantages in corn futures price prediction. Through comparison with traditional models, the effectiveness of this model is affirmed. Consequently, the Bézier curve-based LSTM model proposed in this paper can serve as a crucial reference for agricultural product price prediction, providing effective guidance for agricultural production planning and industry development. [ABSTRACT FROM AUTHOR]

The surge in global agricultural input prices, particularly for nitrogen (N) fertilizers, has presented challenges for farmers facing stagnant crop prices and rising production costs. This study explored a system designed to sustain or enhance farmers' income while reducing production costs, with a specific focus on implementing practices that involve cultivating crops with lower rates of fertilizer than conventional rates, particularly in smaller land settings, through on-farm experimentation (OFE). OFE was conducted over three years across 11 fields in Gifu, Japan, utilizing a strip trial design to investigate the effect of fertilizer input rates on rice yield. To collect yield data, unmanned aerial vehicle (UAV)-mounted multispectral cameras were deployed. A Gaussian process model was used to address the impact of fertilizer input rates on yield while accounting for spatial yield variation. The study further assessed the number of field trials achieving positive profits at certain probability levels by integrating outcomes from multiple OFEs. The results highlighted little rice yield response to fertilizer input in most of the fields, while several fields showed significant positive yield responses. Notably, the use of lower fertilizer input rates in certain fields proved to be more profitable whereas higher fertilizer input rates were less profitable in most fields. The results further emphasized the economic viability of lower rates, especially when fertilizer prices are high, thus contributing valuable insights for sustainable and profitable rice production in Japanese cultivation. [ABSTRACT FROM AUTHOR]

This research examines the global implications of agricultural production and price fluctuations via Global Bayesian Vector Autoregression (GBVAR) model. We develop a novel Paasche agriculture price index, based on the relative time-varying contribution of four dominant food commodities shifting contributions of major food commodities (rice, maize, soybeans, wheat), pivotal for global sustenance. The analysis reveals that while agricultural production shocks are important, they exert a comparatively lesser impact than agriculture price shocks. Higher agriculture inflation can be destabilizing via lower output and higher aggregate inflation. [ABSTRACT FROM AUTHOR]

Equity investment in agricultural cooperatives (co-ops) is typically limited to farmer-members; yet farmers are usually cash-constrained. In addition to the common stock that is held by farmer-members, many co-ops are changing their financial structure by raising equity from external investors. This helps co-ops to collect capital, but also brings to the fore the conflicting benefits of farmers and external investors. In this paper, we develop a two-stage game-theoretic model to examine a start-up co-op's farm-gate pricing and financing strategies, considering two types of external fund: preferred stock that bears a fixed return rate and outside stock that shares the net profit (in proportion to equity) with common stock. We characterize the co-op's strategies in different scenarios and generate the following insights. First, while both types of external equity outperform the case with common stock only, the preferred stock generally outperforms outside stock due to its lower financial cost, higher tolerance for fund size limits, and flexibility in setting farm-gate prices. However, outside stock can outperform preferred stock if it allows a higher fund size limit. Second, the co-op's financial strategy exhibits a similar structure in equilibrium regardless of whether it is preferred stock or outside stock, despite their distinct financial terms. Finally, farm-gate pricing has a unique role in co-ops affecting the returns to farmers and external investors, which also highlights the conflicting roles of farmers as both patrons and investors when external equity is used. [ABSTRACT FROM AUTHOR]

Accurately predicting agricultural commodity prices is crucial for India's economy. Traditional parametric models struggle with stringent assumptions, while machine learning (ML) approaches, though data-driven, lack automatic feature extraction. Deep learning (DL) models, with advanced feature extraction and predictive abilities, offer a promising solution. However, their application to agricultural price data ignored the exogenous factors. Hence, the study explored advanced versions of the well-known univariate models, NBEATSX and TransformerX. The research employed price data for essential crops like Tomato, Onion, and Potato (TOP) from major Indian markets and complemented it with corresponding weather data (precipitation and temperature). To provide a comprehensive analysis, the study also evaluated traditional statistical methods (ARIMAX and MLR) and a suite of ML algorithms (ANN, SVR, RFR, and XGBoost). The performance of these models was rigorously evaluated using error metrics like RMSE, MAE, sMAPE, MASE and QL. The findings were significant indicating DL models, particularly when augmented with exogenous variables, consistently outshone other methods with NBEATSX and TransformerX showing an average RMSE of 110.33 and 135.33, MAE of 60.08 and 74.92, sMAPE of 22.14 and 24.00, MASE of 1.02 and 1.32 and QL of 30.04 and 34.07, respectively. They exhibited lower error metrics, as compare to the statistical and ML models underscoring their effectiveness and potential in agricultural crop price forecasting. This study not only bridged a crucial research gap but also highlighted the robust potential of DL models in enhancing the accuracy of agricultural commodity price predictions in India. [ABSTRACT FROM AUTHOR]

Although for decades, the Ministry of Agriculture, Forestry, and Fishery of Japan has formed agricultural policies based on food self-sufficiency as a vital factor in securing the domestic food supply, the impacts of agricultural self-sufficiency on the relationship between international and domestic markets are not well understood. Most previous literature on agricultural price transmissions applies conventional error correction and vector autoregression models. However, we employ the state-of-the-art technique, namely the time-varying parameter vector autoregression (TVP-VAR) connectedness model, as well as the dynamic conditional correlations based on the asymmetric Baba – Engle–Kraft – Kroner method to investigate the effectiveness of beef self-sufficiency and its inventory in stabilizing local beef prices in Japan. Our primary results indicate that the global beef price is the net transmitter of shocks, while the home-grown beef price in Japan is the net receiver of shocks. Furthermore, we find that high self-sufficiency in beef does not significantly protect local beef markets from global beef markets, but the imported beef inventory effectively alleviates the wholesale price volatility of imported beef. One policy implication is that the government should not take protectionist policies to enhance beef self-sufficiency, and import beef stocks could be accumulated to stabilize imported beef markets. [ABSTRACT FROM AUTHOR]

The comprehensive reform of agricultural water prices in China has entered a critical stage, and it is inevitable that the innovation of water-saving incentive mechanisms involving various prices meets the challenge of direct responses of farmers to the actual cost of irrigation water, so the issue of price elasticity of irrigation water demand must be addressed. Based on the 237 estimates on price elasticity of irrigation water demand from 59 studies worldwide between 1963 and 2022, this study quantified the key factors contributing to variations in price elasticity through a metaregression analysis. Subsequently, we explored the potential intervention space for agricultural water pricing policies by considering the heterogeneous effects of both price levels and elasticity intervals. The following results were obtained: ① Agricultural water prices had a positive impact on price elasticity, and increasing water prices remained an effective and robust means of agricultural water saving. Moreover, as price elasticity intervals increased, the impact on price elasticity due to price rises became greater, leading to more significant water-saving effects. ② The polyculture structure had higher price elasticity than the monoculture structure, and the price elasticity of high-value crops was lower than that of low-value crops. This indicated that the inhibitory effect of increasing water prices on irrigation water demand was more achievable when practicing polyculture or planting low-value crops. Furthermore, the water-saving effects of increasing water prices became more pronounced with wider price elasticity intervals in polyculture systems, whereas for farmers growing low-value crops, the watersaving effects of increasing water prices were more noticeable when the initial water price level was already high. ③ Compared to irrigation system reliant on a single water source, irrigation systems utilizing multiple water sources showed greater sensitivity to water price increases. This highlighted the optimal timing of water price adjustments based on the elasticity interval, which could effectively encourage the implementation of new irrigation water source substitution projects. The empirical results provide policy implications for further deepening the comprehensive reform of agricultural water prices and building a water-saving society in China. More emphasis should be placed on the management of irrigation water demand, and the strategy of raising agricultural water prices should be determined by the response degree of irrigation water demand to water prices. Moreover, differentiated agricultural water pricing schemes should be formulated according to the regional cropping structure. Simultaneously, scientific water use prediction based on the price elasticity of irrigation water demand should be included in the demonstration stage of irrigation water source expansion projects. [ABSTRACT FROM AUTHOR]

Recent 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. [ABSTRACT FROM AUTHOR]

This research discusses the impact of climate change on inflation, as well as the limitations of existing econometric models in incorporating multiple climate-related variables in determining inflation. To address this limitation, the study introduces the climate condition index (CCI) for 153 countries covering the 1901–2021 period to investigate the relationship between climate and agricultural inflation. This index represents a weighted average of various climate variables, including cloud coverage, temperature, precipitation, vapor pressure, and wet day frequency. The weights are the long-term cointegration coefficients of the climate variables concerning the Agricultural Producer Price Index (APPI), which were obtained from separate vector error correction models (VECMs) constructed for each country. The study shows that climate change has a significant impact on agricultural prices globally, as evidenced by a median Granger causality p-value of 0.036 across 153 countries (CCI Granger causing APPI in 127 countries with a 90% confidence interval). The results align with panel vector autoregression and country-specific VECMs impulse responses, which indicate that climate conditions negatively impact agriculture in 142 countries, while 11 countries gain from climate change. The study's overall results suggest that climate change substantially affects agriculture globally. In light of the significant economic reliance on agricultural production, it is advisable for governments to consider the issue from the perspective of sustainable growth, while central banks should tackle it with a focus on the inflationary implications through green quantitative easing. [ABSTRACT FROM AUTHOR]

We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market has potential leading information in price discovery. The results of an Impulse Response Function (IRF) analysis also showed that the spot prices react to shocks from the future market and have a lasting impact. This confirms our findings reported for the causality test and information share analysis. [ABSTRACT FROM AUTHOR]

Copyright of Business Administration Quarterly / Kwartalnik Nauk o Przedsiebiorstwie is the property of SGH Warsaw School of Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agriculture plays a leading role in the economic growth and development process. Ensuring the continuity of production depends on the purchasing power of the producers. The sustainability of production and the development of the rural welfare index in favor of the producers will increase the driving role of the agricultural sector, which is also a resource for other sectors. This study determined the variables affecting the Domestic Terms of Trade of the greenhouse tomato producers in Antalya, Türkiye. The product price has been determined as the major factor affecting the Domestic Terms of Trade. In addition, it was concluded that a one-unit increase in the product price increases the Domestic Terms of Trade by 34.99 units. Interviews revealed that the continuity of consumption needs is more important than the continuity of production. It has been determined that the high and variable input costs cause erosion in the incomes of the producers. Agricultural policies will be more effective both in the short and long term when the aim is to improve the welfare and purchasing power of producers, rather than intervening in agricultural prices or reducing prices. [ABSTRACT FROM AUTHOR]

Geographical indication (GI) products serve as one of the significant instruments for increasing farmers' income. While most studies affirmatively indicate that GI products contribute to boosting farmers' income growth, it is noteworthy that their relationship does not consistently demonstrate a positive correlation. The academic discourse on this issue remains inconclusive. This study employs a meta-analysis method to reanalyze 140 effect sizes from 32 independent research samples across diverse global contexts. The findings reveal that the development of GI products significantly promotes farmer income growth, showing a high positive correlation (r = 0.348, CI = [0.104, 0.540]). Specifically, there exists a high positive correlation between GI products and per capita disposable income (r = 0.389) and a moderate positive correlation between GI products and agricultural product prices (r = 0.255). Further analysis indicates that factors at the sample level, literature level, and methodological level all exert moderating effects on the relationship between GI products and farmers' income. This study not only provides a scientific response to the debate surrounding the relationship between GI products and farmers' income but also delves into the underlying mechanisms. It holds significant importance for advancing the rational optimization of agricultural resources and enhancing agricultural competitiveness. [ABSTRACT FROM AUTHOR]

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Therefore, this study takes scallions as an example and extracts the influencing factors of their prices based on the LDA-NLP-TVP-VAR model from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sustainable forest management is important to maintain the ability of forests to grow and provide a range of products and environmental services while retaining their natural features. We find that income, radiata pine prices, trade, agricultural expansion, and environmental policies have a significant impact on timber harvesting in New Zealand. Increase in income, radiata pine prices, number of farms, horticulture plantation and bank loans lead to an increase in timber harvesting. On the other hand, environmental policies, especially those to mitigate climate change, decrease timber harvesting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hundreds of millions of people are now facing food insecurity as challenges from climate change, the aftereffects of the COVID‐19 pandemic, and strife and conflicts make availability of food at reasonable prices challenging. International trade has enabled the reallocation of agricultural products, essential for nutrition, from countries with supply markets to other points of demand and has been the subject of an increasing number of policy interventions by governments. In this paper, a multicommodity international trade network equilibrium model is constructed with the inclusion of nutritional minimal standards to support food security, accompanied by consumer subsidies, for which explicit formulae are provided. The theoretical and computational framework is based on variational inequalities. Numerical examples focusing on Ukraine and MENA (Middle Eastern and North African) countries and a staple commodity of wheat demonstrate the modeling and policy framework. [ABSTRACT FROM AUTHOR]

Free‐on‐board (FOB) export prices for identical products from the same origin often differ across destinations, even when accounting for the trade costs and attributes of the destination country. One explanation for this observed price difference is per‐unit trade costs, and the ability of exporters to vary their markups and/or product quality. Using a novel dataset that details trade flows between countries by mode of transport, we estimate the transport mode‐specific effect of a per‐unit trade cost, specifically specific tariffs, on the FOB export prices of agricultural products. We find an elasticity of specific tariffs to export prices of 1.8%. However, the estimates are heterogeneous across modes of transport. The elasticity of specific tariffs to export prices is 2% for air transport, 5% for road transport, and.3% for sea cargo. Since the observed positive export price effect can reflect product quality differences or markups, we account for the quality element and find that for a given product quality, markups increase with increasing specific tariffs. This form of price discrimination is less pronounced for higher‐quality products that are predominantly shipped by air. [ABSTRACT FROM AUTHOR]

This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The potential factors are categorized into four subgroups: governmental effects, agricultural inputs, macroeconomic indicators, and climatic conditions. The selected agricultural goods for price change measurement include wheat and maize representing subsistence goods, and olive oil and cotton as marketing goods. The autoregressive distributed lag (ARDL) model is applied to observe both the short- and long-term impacts of the variables on price developments. The results suggest that government effectiveness, regulatory quality, nitrogen use, water price, money supply, exchange rate, and GDP under the related categories are the most effective factors in price changes. Among the variables under the category of climatic conditions, significant values are obtained only in the analysis of the temperature impact on olive oil. The analysis also reveals the variable impact of crises on the prices of the chosen products, depending on the goods involved. The maize and wheat analyses yield particularly noteworthy results. In the long run, nitrogen use demonstrates a substantial positive impact, registering at 29% for wheat and 19.47% for maize, respectively. Conversely, GDP exhibits a significant negative impact, with 26.15% and 20.08%. Short-term observations reveal that a unit increase in the governmental effect leads to a reduction in inflation for these products by 17.01% and 21.42%. However, changes in regulatory quality result in an increase in inflation by 25.45% and 20.77% for these products, respectively. [ABSTRACT FROM AUTHOR]

Motivated by the increasing interest in sustainable agriculture and the potential benefits associated with organic certification, this study employs a multidimensional fixed-effects model to analyze data derived from onsite surveys conducted among 681 apple farmers in the Loess Plateau region of China to explore the influence of organic certification on absolute and relative agricultural product prices given online market access. The findings indicated a significant increase in apple prices among farmers who held organic certifications and engaged in online market sales, with prices rising by CNY1.60 per half kilogram. Additionally, this study highlights that the amalgamation of organic certification with online market access significantly enhances agricultural product prices by facilitating better dissemination of market information among farmers. Furthermore, this research addresses a critical gap in the existing literature by clarifying the differential impact of organic certification across distinct farmer demographics and geographical regions. The more pronounced positive impact of organic certification on prices observed among cooperative members and farmers in the low-altitude areas is particularly noteworthy. These results underscore the crucial role of online market access in achieving premium effects and price stability for organically certified products. [ABSTRACT FROM AUTHOR]

This study assesses the potential macro-economic effects of climate change affecting operations in three maritime chokepoints, i.e., the Panama Canal, the Suez Canal, and the Turkish Straits. The analysis focuses on agricultural commodities. It couples a "logistics" model of maritime trade flows with a Computable General Equilibrium model considering three modelling alternatives: (1) increase of "iceberg trade costs", (2) shadow import tariffs, and (3) shadow export tariffs. Methodologically, we found a comforting qualitative agreement across methodologies in predicting the direction of changes in the main economic variables under scrutiny. However, negative GDP performances are more frequent and larger using the first method that also tends to predict lower import contractions than the other two methods. The impact assessment, examining storylines of climate-change-induced events delaying chokepoints operations, highlights that climate change impacts on chokepoints' operations can convey detectable effects on production and prices of agricultural commodities associated with negative GDP impacts worldwide. In addition, although trade re-composition generates winners and losers, total losses tend to prevail. The combined GDP losses of the three chokepoints can reach $34 billion (2014 prices) in 2030. It shows that weather events in remote locations, such as the Panama Canal, can have cascading effects on the EU, with potential losses of USD 2 billion $ in GDP. North Africa, Middle East and Sub-Saharan Africa are particularly vulnerable. They suffer from a drop in imports of agricultural commodities and GDP losses in all the three cases. This impact assessment emphasizes another mechanism at play that could increase the asymmetry and the adverse distributional impacts of climate change on agriculture. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Environmental issues, in particular climate change, have assumed increasing urgency, and have become even more acute with the Covid-19 pandemic and the globally rise of hunger and inequality. Despite this urgency, international demands and prices for agricultural and mineral commodities have been a driving force behind the expansion of the agricultural frontier (changes in land use), particularly with the increase in monocultures, extensive livestock farming and mineral extraction for export. This has generated debates, studies and reflections in Political Economy, Political Ecology, among other areas of knowledge, but has this urgency raised reflections and research on the importance of land and the agrarian issue? Based on some political (dis)agreements and measures, and in dialogue with international literature, this text seeks to contribute to the so-called Critical Agrarian Studies, formulating, conceptually and politically, a notion of "eco-agrarian issue", analyzing and critically interpreting agrarian extractivism in the Brazilian agricultural frontier. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The article discusses the Monetary Policy Committee's (MPC) plan to start reducing interest rates in response to changing economic conditions. Inflation is lower than expected, and the labor market is normalizing, reducing the risk of persistent inflation. The tight monetary conditions and high interest rates are affecting households and hindering investment. The MPC has reinterpreted its previous statement about keeping rates restrictive for an extended period, indicating that rates could be lowered while still maintaining a restrictive stance. The article predicts that the MPC will begin cutting rates in the next few months, gradually returning to a neutral stance over the next 12-24 months. [Extracted from the article]

The article explores the concept of "immaculate disinflation," where inflation returns to target without a significant increase in unemployment. While the Federal Reserve and European Central Bank believe this is possible, the Bank of England predicts a rise in unemployment alongside inflation. However, the article argues that the Bank of England's view is overly pessimistic and that UK inflation will likely return to target without a major increase in unemployment. Factors influencing pay growth and inflation expectations in the UK, US, and eurozone are also discussed. The article concludes that disinflation in the UK is likely to be less severe than predicted by the Bank of England, unless there are adverse supply shocks. The decline in the labor share of GDP and the rise in profit share in the US and eurozone since 2019 is also examined, suggesting that a similar situation could occur in the UK. The article suggests that a sustained return of inflation to target and consistent pay growth is possible in the UK without a significant rise in unemployment. [Extracted from the article]