Search Results (20)

Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. Subsequently, the induced ordered weighted averaging (IOWA) operator is applied to determine the weight vectors for the SSA-SVR and SSA-ELM models, reducing the fluctuating prediction accuracies of individual models at different times. Finally, the residuals of the generalized regression neural network (GRNN) model are forecasted using a combined residual correction method that integrates SSA-SVR and SSA-ELM based on the IOWA operator, refining the GRNN’s forecast outcomes. An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. The findings indicate that the SSA-SVR, SSA-GRNN, and SSA-ELM models outperformed the SVR, GRNN, and ELM models in terms of forecasting accuracy, respectively. This improvement is attributed to the parameter optimization of the SVR, GRNN, and ELM models through the SSA. The proposed model also showed superior forecasting accuracy compared to the nine individual models. The results confirm that the proposed model is an effective tool for predicting agricultural product prices and can be applied to forecast prices of other agricultural products with similar characteristics. Full article

Peanut is Kenya’s second most important legume after beans, primarily grown in the Nyanza and Western regions. This study maps the peanut value chain in Kenya, aiming to identify key actors, quantify costs and value addition, and outline constraints and opportunities, with a view to upgrading the chain. A cross-sectional survey was conducted among value chain actors in Karachuonyo and Nyakach sub-counties, complemented by secondary data sources. Descriptive statistics were used to analyze socio-economic characteristics, production volumes, pricing, demand trends, and policy-related factors. The findings indicate a predominance of female farmers (68%) in peanut production, though few use improved technologies; only 26% were aware of improved seed varieties, and just 1.5% reported using them. Fertilizer usage was absent, attributed to high costs, soil conditions, and limited knowledge. The wholesale and processing segments are male-dominated, largely due to capital intensity and travel requirements, while female traders dominate the retail sector. Strengths Weaknesses Opportunity and Threats (SWOT) analysis highlighted the significant potential of favorable production ecologies, processing options, and robust demand in local and international markets. Key constraints identified include limited seed availability, high fertilizer costs, pest issues, and declining soil fertility. Policy implications include increasing access to affordable inputs, promoting gender-inclusive programs, investing in agricultural research and infrastructure, supporting sustainable farming practices, and fostering public-private partnerships to expand processing and market access. Full article

The rise of rural e-commerce, accelerated by the COVID-19 pandemic, has driven agricultural development, resulting in a dual-channel model that combines offline supermarket docking with online e-commerce direct sales. Furthermore, given the perishable nature of most agricultural products, consumers exhibit a preference for high-quality logistics services. This paper analyses the dynamics of the agricultural supply chain, beginning with the “leading enterprises + farmers” production mode, and establishes two optimal decision-making models for online and offline dual-channel agricultural supply chains, incorporating logistics service levels. The first model considers centralised decision-making, where leading agricultural producers and community superstores make decisions simultaneously, aiming to maximise the overall profit of the supply chain. The second model addresses decentralised decision-making in which the producer, as the dominant player in a Stackelberg game, anticipates the reactions of retailers and sets the wholesale price, online sales price, and online logistics service level accordingly. Retailers, as followers, then determine the offline sales price and offline logistics service level based on the producer’s decisions. Finally, we conduct a sensitivity analysis of the proposed models. Our findings reveal that as consumer focus on logistics service levels increases, the overall profit of the supply chain improves, and as the unit logistics cost corresponding to the level of logistics service increases, the marginal benefits of decreasing profit brought about by the increase in unit logistics cost also decrease. Full article

Due to the uncertainty of world economic development, market demands are stochastic and the supply quantities of suppliers in the supply chain are always flexible, so a mixed wholesale-option-contract (abbreviated as a mixed contract) is one of the good ways for commodity distributors to cope with flexible supply. For a contract farming supply chain composed of a distributor and two suppliers under random demand and yield, we propose the new mixed contracts with flexible supply for the players to make better procurement and inventory decisions. Therefore, with decentralized decision making with a wholesale price contract and centralized decision making as benchmarks for comparison, the advantages of mixed contracts were demonstrated in this paper. The expected profit function under each transaction mode was proved to be concave and the optimal orders or production quantities were obtained and compared. Theoretical derivation and numerical examples were carried out and the main conclusions are as follows. First, the distributor’s total order quantities are the largest under centralized decision making, then the second largest under mixed contracts, then the least under wholesale price contracts. Second, for the dealer under mixed contracts, within the feasible range, the smaller the option price (or option exercise price) is, the greater the dealer’s profit is. Third, with increasing initial order quantity, the gap between the dealer’s profits under different option prices (or option exercise prices) narrows, and eventually tends to the same point. For both the suppliers as a whole, a mixed contract is better than the wholesale price one. Fourth, when the prices of the option contract change within a reasonable range (they may not be too small or too large), the profits of both the dealer and suppliers under a mixed contract are not only higher than those under the wholesale price contract, but also higher than those under centralized decision making. Finally, policies and suggestions (such as full investigation, explicitly defining the process of contracts, establishing real-time supervision and information sharing mechanisms, and so on) were put forward to improve the accuracy of supply and demand forecasting, better implement mixed contracts under flexible supply, and strengthen reforms about agricultural supply side. Full article

Vegetables are important for both nutritional and economic stability and contribute significantly to the agricultural landscape of India. The demand for vegetables is rising, driven by population growth and increased awareness of their benefits. This empirical study highlights the dynamics of agricultural production supply chain networks of capsicum crops in the northwestern Himalayan region, specifically Himachal Pradesh, India. The study employs the Acharya approach to analyse the various marketing channels utilized by farmers in the capsicum supply chain. This methodology sheds light on the economic nuances at each stage and examines marketing channels, costs, margins, price spread and marketing efficiency. Simultaneously, the Garrett ranking method is applied to discern and prioritize constraints faced by farmers. This comprehensive approach ensures a nuanced understanding of the economic and logistical intricacies of capsicum marketing. The analysis of marketing channels reveals five distinct pathways employed by farmers, with Channel-C (Producer–Commission Agent–Retailer–Consumer) standing out as the most dominant, representing 47.25% of the total quantity. Moreover, Channel-A (Producer–Consumer) proves to be the most cost-effective for producers and boasts the highest producer price, while Channel-C, involving commission agents, incurs higher costs. This suggests a preference for intermediaries, emphasizing factors like market access and negotiation skills, whereas Channel-D (Producer–Local Trader–Wholesaler–Retailer–Consumer) has the highest gross marketing margin, emphasizing the trade-offs between efficiency and transaction volume. The results indicate that while Channel-A is the most efficient, it is not the preferred choice due to the lower transaction quantity. Further, the absence of market consultation services, inadequate road infrastructure, high commission charges, nonremunerative prices and untimely availability of vehicles are the major constraints in marketing. The findings of the study call for targeted interventions to create a more robust and farmer-friendly marketing environment for capsicum crops in the region. The study proposes targeted recommendations, emphasizing collaborative efforts between stakeholders, government bodies and farmers. This research contributes to the academic discourse and also offers actionable insights for researchers and policymakers, fostering sustainability, profitability and equity within the capsicum supply chain. Full article

In order to address the significant prediction errors resulting from the substantial fluctuations in agricultural product prices and the non-linear features, this paper proposes a hybrid forecasting model based on variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short-term memory networks (LSTM). This combined model is referred to as the VMD–EEMD–LSTM model. Initially, the original time series of agricultural product prices undergoes decomposition using VMD to obtain a series of variational mode functions (VMFs) and a residual component with higher complexity. Subsequently, the residual component undergoes a secondary decomposition using EEMD. All components are then fed into an LSTM model for training to obtain predictions for each component. Finally, the predictions for each component are linearly combined to generate the ultimate price forecast. To validate the effectiveness of the VMD–EEMD–LSTM model, empirical analyses were conducted for one-step and multi-step forecasts using weekly price data for pork, Chinese chives, shiitake mushrooms, and cauliflower from China’s wholesale agricultural markets. The results indicate that the composite model developed in this study provides enhanced forecasting accuracy. Full article

The energy crisis in Europe and Poland caused by the conflict in Ukraine has renewed the debate in some countries about the wisdom of abandoning the use of local fossil fuels. The rise in gas and oil prices with little change in the cost of extracting lignite from open pit mines has led to a renewed consideration of lignite as the cheapest source of energy. This is not entirely true, as the level of costs at power plants ignores many external costs that are not included in the costs of energy producers, but are borne by the general public or other parties. One such cost is the external costs incurred by agriculture as a result of open pit lignite mining and the associated depression funnels. The Bełchatów lignite open pit is the deepest open pit in Europe and is expected to be in operation by 2038. The aim of the study was to assess the external costs that farmers will incur as a result of further open pit mining of brown call from the Bełchatów deposit, i.e., between 2023 and 2038 as well as in the 16-year extended period of restoration of water relations around the open pit. The decrease in crop yields and animal numbers was estimated in a comparative analysis, which compared changes in the yields of selected plants and animals in the area affected by the open pit to those in neighboring areas that were not affected. The analysis showed that the external costs to be borne by agriculture as a result of the further exploitation of the Bełchatów deposit will amount to an average of EUR 2.90 billion, and, depending on the calculation variant, from EUR 2.51 billion to EUR 3.14 billion. Including this amount in the cost of electricity production would result in an increase of EUR 9.11·MWh⁻¹, which is 18.8% of the average wholesale price of electricity in Poland in 2017–2021. On the one hand, the increased consumption of lignite in electricity production, which is currently observed and may last for several years, may shorten the life of the open pit by up to 2 years. Shortening the mining period by one year, assuming that all the coal in the deposit is mined, will reduce the external costs for agriculture by about EUR 185 million, i.e., EUR 0.58·MWh⁻¹. On the other hand, the increase in European Emissions Trading System (ETS) prices, the decrease in gas prices and the increase in energy production from renewable energy source (RES) will make lignite power generation profitable only during the periods with the highest prices, so that by 2038 lignite will not be mined in its entirety. Every 10 Gg of coal that will not be mined by 2038 will result in an increase in external costs in agriculture per MWh of EUR 0.23·MWh⁻¹. Full article

The aim of this paper was to provide a more scientific and effective analysis of the fluctuation pattern of the Chinese potato market by extracting the characteristics of the price fluctuation cycle to effectively grasp the characteristics of price changes in the potato market, thus promoting the stable and healthy development of the Chinese potato industry, and to expand the application scenarios of the EEMD model to provide a reference for the study of price fluctuation patterns in other agricultural markets. This study used an ensemble empirical modal decomposition (EEMD) model to examine time-series data on Chinese wholesale potato market prices from January 2005 to December 2021. The results showed that (1) Chinese wholesale potato market prices are characterized by some rigidity, with sharp changes in growth rates; (2) Chinese wholesale potato market prices are dominated by short- and medium-term fluctuations, and the decomposed components can better reflect the characteristics of the original series fluctuations; (3) Chinese wholesale potato market monthly prices have long- and short-term fluctuations with a 6- and 19-month cycle, and are dominated by short-term high-frequency fluctuations; (4) monthly price fluctuations in the Chinese wholesale potato market are more intense in high-frequency than low-frequency fluctuations, and there is a strong correlation between high- and low-frequency fluctuations in precipitation, temperature and potato prices. Finally, suggestions were made for creating and improving a national potato price information platform and strengthening the information early warning mechanism; improving the potato production interest linkage mechanism and enhancing potato farmers’ ability to cope with market and natural risks; and improving the potato reserve system and potato storage facilities. Full article

To solve freshness-keeping problems and analyse a retailer’s information sharing strategies in the fresh agricultural product supply chain (FAPSC), often confronted with challenges in keeping agri-products fresh in an uncertain market, we study an FAPSC via a decentralized mode in which the supplier or retailer exerts the freshness-keeping effort while the retailer decides its information sharing strategies regarding private demand forecasting. We consider a contract coordination mode including three incentive contracts, cost-sharing (cs), revenue-sharing (re) and revenue-and-cost-sharing (rc), to facilitate supply chain coordination. The results show that, as opposed to the case where the supplier takes on the freshness-keeping effort, the optimal freshness-keeping effort level, wholesale price and retail price are not only affected by the retailer’s information sharing strategy but also the freshness-keeping efficiency as the retailer exerts the freshness-keeping effort. Regarding the information sharing strategy, when the freshness-keeping effort is undertaken by the retailer, sharing information sometimes benefits the supplier; however, information sharing is never preferable for the retailer. Consequently, it is necessary to explore the supply chain coordination mode via effective incentive contracts which can improve the supplier and retailer’s profit. We also numerically analyze the effects of freshness-keeping efficiency on equilibrium decisions and expected profits in the decentralized mode, and the effects of the three contract parameters on the expected profits in equilibrium in the coordination mode. Full article

Based on the Stackelberg game theory, this paper explores the incentive effects of five government subsidy strategies on agricultural products in e-commerce. A two-tier e-commerce supply chain of one farmer and one e-commerce platform is constructed to examine the impact of five different government subsidy strategies on the greenness of an agricultural product, the wholesale price, the selling price, and the profit of the supply chain. The results show that the effect of offering government subsidies is significant. Also, the direct subsidization from the government to a farmer has the maximum effect on the sales and greenness of the agricultural product. The results of this study provide policy implications for governments in establishing a sustainable mechanism through direct subsidization. Full article

Uncertain price movement in staple food commodities puts agrarian economies at risk if not monitored and managed consistently. Hence, an attempt has been made to analyze the price behavior and integration across major wholesale and retail markets for rice and wheat in India. Monthly data (July 2000 to June 2022) on prices viz. wholesale and retail were sourced from the Food and Agriculture Organization and analyzed using growth rate, instability index, seasonal price index, Bai-Perron’s test for structural breaks, Johansen’s test on cointegration, Granger causality test, and impulse response function. Findings indicated strong evidence of price dynamics in the selected markets in terms of spatial and temporal variation, clear-cut seasonality linking to production, and price divergence between wholesale and retail markets. Johansen’s test indicated a strong cointegration between wholesale and retail prices after accounting for structural breaks, exhibiting unidirectional-, bidirectional- and no causality. Impulse response analysis revealed that the selected wheat and rice markets are efficient in terms of ‘price discovery’ which takes place initially in the wholesale market, and is then transmitted to the retail market. The study advocates decision-making information to the producers, traders, and consumers who are interested in taking advantage of the price movement. It is concluded that strengthening the market intelligence and reducing the distortion in markets will improve the existing overall performance. Full article

To solve the problems of the impact of carbon emission reduction and low-carbon advertising on the supply chain of fresh agricultural products, a three-level low-carbon supply chain system composed of a manufacturer, a retailer and a third-party logistics service provider is taken as the research object. The profit functions of each party under the three contracts of the manufacturer bearing, the retailer bearing and the two parties jointly bearing the advertising cost are, respectively, established to solve the optimal pricing, advertising level preservation efforts, service levels and carbon emission reduction decisions. The numerical analysis shows that, with the increase in wholesale price and the decrease in fresh-keeping price coefficient and low-carbon cost coefficient, manufacturers will choose better fresh-keeping effort level and low-carbon service level. When the proportion of advertising cost borne by the supplier increases, the benefits of all parties in the supply chain will decrease; however, when the retailer bears the advertising cost alone, the profit of the supply chain system is the largest. Full article

In order to enrich the research on the influence of weather factors on agricultural economy and provide practical decision-making references for the relevant market entities, this study took pointed pepper, loofah, Chinese chives and tomato as examples, using weekly wholesale prices and corresponding weather factors data from one of the main production areas in China based on the vector autoregressive (VAR) and the time-varying parameter vector autoregressive (TVP-VAR) models to explore the dynamic impacts of weather changes on vegetable price fluctuations. It was found that price fluctuations of specific vegetable varieties were affected by changes in specific weather factors. The shock intensity of weather factor changes in the same lag period on the current vegetable price fluctuations was time-varying, which could describe the historical dynamics of the impacts of weather changes on vegetable price fluctuations. The dissipation characteristics of the dynamic impacts of weather factors changes occurring at selected time points on vegetable price fluctuations had strong consistency, and the obtained rules could be used to analyze the life cycle and dynamic characteristics of extreme weather impacts on vegetable price fluctuations. In addition, vegetable price fluctuations were affected mostly by their own lag periods, and the shock intensity of vegetable price fluctuations with equal lag periods on their own current price fluctuations were relatively stable. The dynamic impacts of vegetable price fluctuations occurring at chosen time points on its own later fluctuations were highly consistent in the variation from the beginning of the strongest positive shock to the final dissipation. Full article

Facing the changes in China’s agricultural products marketing channel, smallholder farmers with different characteristics choose various strategies to obtain more benefits. To analyze factors affecting smallholder farmers’ marketing channel choice, we classify four types of channels—Broker Channel, Farmers’ Retailing Channel, Wholesale Market Channel, and Cooperative Channel—and inspect 14 variables based on the survey data of 317 households from four provinces. We use a principal components analysis (PCA) to simplify these 14 variables into seven common factors and a multivariate logit model to study how the factors influence smallholder farmers’ choices. We find that compared with the Broker Channel, the Farmers’ Retailing Channel is mainly affected by the logistics factor, skill factor, risk factor, and size factor; the Wholesale Market Channel is influenced by the logistics factor and age factor; and the Cooperative Channel is mainly influenced by the age factor, logistics factor, and price factor. In conclusion, the logistics factor has a significant positive effect on each channel choice, and the improvement of the market and transportation conditions has a general promoting effect. Full article

Globally, multiple efforts are being made to develop active atmospheric water generation (AWG) or atmospheric water extraction (AWE) systems, particularly using direct air-cooling technology to produce water from ambient air. However, this legacy technique is highly energy-intensive; it can only be operated when the local dew point is above the freezing point of water, and does not scale to create enough water to offer solutions for most industries, services, or agriculture. Liquid-desiccant-based AWG methods show promising performance advantages, and offer a versatile approach to help address the thermodynamics, health risks, and geographic constraints currently encountered by conventional active AWG systems. In this study, we performed a techno-economic analysis of a liquid-desiccant-based AWG system with a continuous operating style. An energy balance was performed on a single design point of the AWG system configuration while using a LiCl liquid desiccant loaded with multiwalled carbon nanotubes (MWCNTs). We showed that the MWCNTs can be doped in LiCl for effective heat transfer during water desorption, resulting in lowering of the sensible heat load by ≈49% on the AWG system. We demonstrated that the specific energy consumption (SEC) can currently be obtained as low as 0.67 kWh per US gallon, while changing the inlet desiccant stream concentration of MWCNT-doped LiCl under the given conditions. While the production cost of water (COW) showed a significant regional dependency, economic analysis revealed that water can be produced at a minimum selling price of USD 0.085 per US gallon, based on the 2021 annual average wholesale electricity cost of USD 0.125 per kWh in the U.S.A., thereby providing a strong foundation for future research to meet desirable and competitive water costs by 2026, but before 2031. Full article