Supply-demand Dynamics Research Articles

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Cold supply chains (CSCs) are critical for preserving the quality and safety of perishable products like milk, which plays a vital role in the daily lives of a vast population, especially in countries like India. This research centers on sustainable milk production in Northern India, with priorities of ensuring efficiency and waste reduction within the cold supply chain. Leveraging data from a prominent North India-based dairy company, Company ‘X’, an ARIMA model is applied for predicting monthly milk production trends. Utilizing the Statistical Package for the Social Sciences (IBM SPSS STATISTICS 20) software, the study forecasts Company ‘X’s monthly milk production and identifies four distinct ARIMA models based on the autocorrelation function (ACF) and the partial autocorrelation function (PACF). By comparing predicted and actual milk production values (April–October 2021), sustainability metrics are integrated into ARIMA forecasts. Implications for the dairy sector’s sustainability and alignment with the Sustainable Development Goals (SDGs) are assessed through error terms such as R squared (R2) and mean absolute percentage error (MAPE). The study promotes sustainable milk production practices in Northern India’s dairy sector, resonating with the SDGs to optimize demand–supply dynamics and foster a more environmentally conscious dairy industry.

Dynamic regime differences in the market behavior of primary natural resources in response to geopolitical risk and economic policy uncertainty

The study explores the dynamic effects of geopolitical risks and economic policy uncertainties on oil and natural gas prices in the US market's less volatile and highly volatile regimes, employing Markov regime-switching dynamic regression models (MS-DR) with monthly data from January 2000 to November 2022. Our empirical results demonstrate that a positive shock in the geopolitical risk of the USA (GPR_US) causes an increase in the growth rate of the prices of WTI at the high volatile periods (regime 1), and its one-period lagged effects are negative at time t in the less volatile periods (regime 0), and it cumulatively causes an increase in WTI price. GPR_US and one lagged effect negatively and positively natural gas prices (NGAS) at both regimes. respectively, where the dynamic cumulative impact of GPR_US causes NGAS to increase. World geopolitical risks (GPR) lead to an increase in WTI and natural gas prices. Furthermore, US economic policy exerts a reduction in WTI price at both less and highly volatile regimes, and its lag influences are positive at both regimes, where it cumulatively causes a decrease in WTI price. In a similar manner, global economic policy uncertainty also reduces WTI. US economic policy and its lag effects exhibit an increase and decrease in NGAS prices in the high volatile regime but exhibited a cumulatively positive response to shock in economic policy uncertainty. Global economic policy uncertainty causes nature gas prices to go up. Hence, geopolitical risks and economic policy uncertainty might have a complex impact on the prices of natural resources. It depends on the specific circumstances and the supply-demand dynamics in the market. Our findings offer insightful implications for economic agents.

Multi-criteria analysis of cross-regional railways interconnection under the post COVID-19 pandemic crisis: A hybrid BWM-FAISM-DFS evaluation framework

The long COVID-19 has had devastating and continuous effects on cross-regional railways (CRRs), yet the relation between public health management and public transport efficiency is still not fully understood. To address this issue, this study incorporates the complex interconnected environment into demand–supply dynamics and develops a three-phase multi-criteria evaluation framework to establish the policy option library for promoting CRRs interconnection performance. This novel framework integrated the Best-Worst Method, Fuzzy Adversarial Interpretive Structure Modeling, and Depth First Search into a novel methodology under fuzzy linguistic terms. Besides, the framework was validated with the Dongguan-Yantian-Hong Kong sea-rail line case; structural hierarchy exploration was conducted as well. The findings show that it is the shocks from demand side (instead of shocks from supply side) that have the greatest impact on interconnection in the post-epidemic era. The optimal management path may lead to spillovers of production factors, requiring decision-makers to adjust input strategies with limited resources. This should be done according to the formation order of the demand–supply, demand-demand, and interconnected environment-interconnected environment components. This is the most significant insight of the proposed framework, which optimizes resource efficiency and provides extensive insights for decision-makers to further explore the reformation of public transportation development.

Returns to digital skills use, temporary employment, and trade unions in European labour markets

This paper investigates the moderating role of institutional factors on returns to ICT skill usage among different groups of workers in eight European labour markets. Using PIAAC data, the paper leverages the segmentation of contractual status, allowing for heterogeneous wage effects among workers holding permanent and temporary contracts. Furthermore, this study considers how gaps in ICT wage premiums mirror the compositional differences in national-specific trade union densities among contractual groups, demonstrating that wage premiums associated with ICT usage are not univocally defined by task content or demand-supply dynamics for specific sectors and occupations. The results highlight different returns between labour market segments according to national-specific trade union densities of temporary and permanent workers, revealing how the consequences of technological change are shaped by institutional cleavages.

Cross-Region Courier Displacement for On-Demand Delivery With Multi-Agent Reinforcement Learning

On-demand delivery has become prevailing for people to order meals and groceries online, especially during the pandemic. It is essential to dispatch massive orders to limited couriers to satisfy on-demand delivery users, especially during peak hours. Existing studies mainly focus on order dispatching within a region, and they are challenging to be applied to the cross-region courier displacement problem due to (i) unique practical factors, including regional spatial-temporal demand-supply dynamics and strict delivery time constraints, and (ii) the large-scale setting and high-dimensional decision space given massive couriers in on-demand delivery. To address these challenges, in this work, we propose an efficient cross-region courier displacement framework, i.e., Courier Displacement Reinforcement Learning (short for CDRL) based on centralized multi-agent actor-critic, which first design the actor-critic network with a time-varying displacement intensity control module to capture demand-supply dynamics and utilize the centralized training and decentralized execution multi-agent framework to address the large-scale coordination. One-month real-world order records collected from one of the biggest on-demand delivery services in the world are utilized to show the performance of our design. The extensive results show that our method offers a 47.97% of increase in balancing supply and demand and reduces idle ride time by 24.62% simultaneously.

The Impact of Urbanization on the Supply–Demand Relationship of Ecosystem Services in the Yangtze River Middle Reaches Urban Agglomeration

The urbanization process can alter the structure of urban land use and result in variations in urban ecosystem services (ESs). Researching the driving mechanism of multi-level indicators of urbanization on the supply and demand of ESs can enhance our understanding of the ecological and environmental impacts of urbanization. This study investigates the driving mechanisms underlying the relationship between urbanization and the supply–demand dynamics of ecosystem services (ESs) in the Yangtze River Middle Reaches Urban Agglomeration (YRMRUA). First, we assessed the variation in the key ESs (food production, carbon storage, and culture service) from 2000 to 2019 at both city and provincial levels. Second, ES demand and the supply–demand index (SDI) were calculated utilizing socioeconomic indicators. The Geographical Detector model was applied to analyze the individual and combined effects of urbanization on the supply and SDI of ESs. The results showed that an increase in areas of supply and demand was unbalanced in the YRMRUA from 2000 to 2019, with a predominant concentration observed in the provincial capital cities. Scale urbanization exhibits the most substantial influence on the SDI, with a q-value of 0.6, while land urbanization exerts the most pronounced effect on ES supply, with a q-value of 0.7. Furthermore, it is noteworthy that the combined effect of urbanization on ESs surpasses the individual effect, with q-values exceeding 0.5. The interaction between scale urbanization and other indicators has the greatest impact on the SDI of carbon storage. Population and economic urbanization exhibit a more substantial impact on food production and cultural service compared to other primary indicators. Simultaneously, the joint effects of secondary indicators between per capita living area and per capita road area have a greater impact on ES supply than other secondary indicators. These findings illustrate that urbanization indicators are not independent of each other, but have a combined effect. Furthermore, the urbanization process in the YRMRUA has exhibited a gradual deceleration, leading to a diminishing influence on ESs. This study can contribute to the comprehension of urbanization and ESs when dealing with the conflict between urban development and ecological sustainability.

Developing integrated direct air capture and bioenergy with carbon capture and storage systems: progress towards 2 °C and 1.5 °C climate goals

Negative emissions technologies are gaining widespread acceptance as crucial tools in achieving climate goals, such as keeping global temperatures below 2 °C of pre-industrial levels by 2100. Two technologies central to carbon dioxide removal efforts are direct air capture and Bioenergy with carbon capture and storage. While both technologies have undergone extensive study, only a few studies have explored the potential of using biomass as an energy source for direct air capture technology. This is despite bioenergy with carbon capture having the ability to provide carbon-negative heat and power, as well as its potential impact on the climate mitigation goals of the century. This study aims to investigate the feasibility of meeting the energy requirements of a direct air capture unit using bioenergy. Combining these units will result in compounded negative emissions for the integrated system. The objective is to examine the thermal and electrical requirements of the two primary approaches used in direct air capture design: the liquid solvent and solid sorbent direct air capture units, and to calculate the compounded negative emissions achieved by integrating them with bioenergy. The results of this study demonstrate that for a direct air capture plant capturing 1 mega ton of carbon dioxide per year, approximately 1200 and 2400 tons of biomass per day would be sufficient to meet the energy needs of the solid sorbent and liquid solvent direct air capture systems, respectively. The combined capture efficiency of both types of direct air capture systems integrated with bioenergy stands at 91.19% to 93.9% with overall carbon captured up to 1.51 mega tons of carbon dioxide per year. Over the century, integrating bioenergy into direct air capture units can remove gigaton levels of carbon from the atmosphere without disrupting the demand–supply dynamics of existing and future energy systems.

Identifying the Factors Influencing the Performance of the Farmer Producer Organizations Dealing with Value Addition

Aims: Identifying the factors influencing the performance of the Farmer Producer Organizations dealing with value addition.
 Study Design: Exploratory research design.
 Place and Duration of Study: The research was carried out in the state of Tamil Nadu. Primary data was collected from a random sample of respondents.
 Methodology: The study was conducted in the state of Tamil Nadu which examines the factors that influencing the performance of the FPOs dealing with the value-added products. The data was collected from 60 FPOs through random selection process, the sample respondents were CEOs and Board of Directors of the FPOs. Personal interview was taken to gather primary data. Exploratory factor analysis was conducted to find the factors that influence the performance of the FPOs dealing with value added products in Tamil Nadu.
 Results: This study identifies that the cumulative variance accounted for by the study amounted to 56.606 percent, the factors exerting substantial influence encompass Innovative Product Development, Integration of E-Commerce, Strategic Product Pricing, Establishment of Collaborative Partnerships, Emphasis on Distinctive Branding & Packaging, Advancements in Processing Facilities, and Assurance of Product Quality. Furthermore, factors like Augmented Market Access, Effective Management of Demand-Supply Dynamics, Optimization of Inventory Holding, Accomplishments in Certification, Efficient Handling of Working Capital, and Access to Credit and Financial Support were determined to have a moderate level of influence. Lastly, factors contributing to the enhancement of Turnover Rates, Incorporation of Valuable Customer Feedback, and Pioneering Product Development were identified as having a lower degree of influence
 Conclusion: From the study it can be concluded that operational factors are the most influencing factors for the performance of the FPO that contributes to the 36 percent of the contribution of the performance.

Coupling PCM wallboard utilization with night Ventilation: Energy efficiency and overheating risk in office buildings under climate change impact

The rising population and increasing thermal comfort expectations are expected to exacerbate the already high HVAC use in offices, which unavoidably places a strain on energy supply systems. Storing thermal energy by incorporating phase change materials (PCMs) is an effective method for improving the buildings’ energy efficiency. Understanding how a PCM wallboard responds to climate change (CC) is crucial to maintaining its viability and effectiveness in achieving energy efficiency and sustainability goals during a building’s lifetime. This study presents a methodological framework to assess the performance of a building utilizing PCM wallboards in terms of cooling/heating energy demand (and corresponding GWP and operation cost) and occupant thermal comfort considering the changing climate. As a case study, a hypothetical office building located in a cold semi-arid climate with high diurnal fluctuations was selected, and the performance of offices orienting in different directions was evaluated when PCM wallboards with different melting ranges were utilized. PCM19 with a melting range close to heating set point reduced the heating demand by 5.0–7.4% for 2020 and 7.8–9.2% for 2050; while PCM25 with a melting range close to cooling set point yielded cooling reductions of 1.9–4.3% for 2020 and 0.7–2.4% 2050. However, PCM19 and PCM25 were ineffective in cooling and heating, respectively, as they were not compatible with the chosen set points. Coupling PCM25 with night-ventilation (NV) significantly enhanced the cooling savings (NV: 23.2–25.7% for 2020; 13.9–15.7% for 2050; NV + PCM25: 29.4–30.6% for 2020; 16.6–17.5% for 2050); though, the effectiveness of NV was impaired with rising night temperatures due to CC. Hourly energy analysis demonstrated varying performance of wallboards based on the time of day, suggesting potential benefits in supply–demand dynamics and time-of-use energy pricing. In naturally-ventilated scenarios, PCM utilization was ineffective in reducing indoor overheating risk, even when night-ventilation was introduced. Results highlight that to effectively mitigate indoor overheating risk in office buildings, PCM wallboards should be coupled with air-conditioning and night-ventilation. Furthermore, for a comprehensive evaluation of the energy/GWP/cost saving potential of a PCM wallboard during a building's lifetime, not only the current but also the projected local climate should be considered given the shifting energy demand towards cooling due to climate change. Overall, this study provides valuable insights into effective PCM wallboard utilization and lays the groundwork for enhancing the resilience of built environments in the face of climate change challenges.

Incentivized user-based relocation strategies for moderating supply–demand dynamics in one-way car-sharing services

One-way car-sharing services have drawn much attention in recent years in transportation research and practice. The flexible operating scheme stimulates patronage but causes unbalanced distributions of the shared cars and consequently low utilization of the services. In light of the empirical evidence that users are willing to cooperate with the operators for relocations, this paper formulates the supply–demand dynamics of shared cars under four user-based relocation strategies, including a naive one without incentive and three alternatives with incentives. We formulate the triggering conditions and travel disutilities responding to the incentive-based strategies based on the concept of load-space–time paths. To systematically assess the effects of the strategies, we propose an incentive-based boundedly rational dynamic user equilibrium (IBR-DUE) model in a multimodal transportation system. A cluster-based column generation algorithm is developed to solve the IBR-DUE model without path enumeration in a space–time supernetwork. Numerical examples demonstrate that the alternative drop-off incentive outperforms other strategies and can improve the utilization and service rates about 2 times on average.

Exploring the effects of physical and social networks on urban water system’s supply-demand dynamics through a hybrid agent-based modeling framework

The urban water system involves complex interactions between physical and social factors, such as physical infrastructures and information communications. Among these, the water supply network (WSN) and water consumers network (WCN) are critical determinants of water supply and demand, which are featured with prominent structural heterogeneity and interrelationships. The structures of WSN and WCN have been traditionally modeled in isolation under the assumption of structural homogeneity. This work proposes a new agent-based modeling (ABM) framework with a local-world fitness network to explore the effects of structural heterogeneity and WSN-WCN interrelationship on water supply–demand dynamics. A case study in Lhasa reveals that, the traditionally assumed fully-connected water supply system and structurally homogeneous water user network may result in simultaneous overestimation of water demand and underestimation of inter-region variances in water demand and groundwater table. Our analysis provides new insights into the disentanglement of the complex interdependencies of WSN and WCN and their associated structural heterogeneities, which could be extended to the modeling of other complex and interrelated networks.

Modeling the Demand-Supply Mismatch and Price Optimization of Natural Gas in Nigeria

Natural gas usage in Nigeria is increasing and capturing a higher percentage of the country’s energy mix. However, the demand is ever-increasing while supply is not able to feed domestic demand. The federal government has devised several strategies, policies, and regulations to ensure the market runs smoothly, but the lack of gas supply infrastructure and low gas prices among other factors remained a major issue. This study analyzes the demand-supply dynamics and price optimization of natural gas in Nigeria using the 2SLS. The study utilizes historical data and mathematical modeling techniques to identify the factors influencing the market, quantify the supply-demand mismatch, and develop an optimized pricing strategy. The research provides insights into the Nigerian natural gas market, offering policy recommendations to address the imbalance and optimize prices. The findings of this study contribute to a better understanding of the challenges and opportunities in the natural gas sector in Nigeria.

Challenges of existing grid codes and the call for enhanced standards

<abstract> <p>The escalating penetration of renewable energy, notably wind and solar, introduces unique complexities into power systems, particularly in frequency and voltage regulation. Current grid codes are inadequate for these emerging dynamics, necessitating significant enhancements to cope with the evolving energy landscape. The paper highlights several pivotal areas of improvement, including increased flexibility within power systems, integration of energy storage systems, expansion of ancillary services, the inclusion of grid-forming inverters, and the international harmonization of grid codes. The research underscores that conventional power system methodologies, primarily reliant on traditional power plants, fail to manage the fluctuating supply-demand dynamics of renewable energy effectively. By proposing improvements in grid codes, the research contributes towards resolving this issue. Furthermore, the paper underlines the paramount importance of international harmonization of grid codes for system interoperability, efficient operation, and exchange of best practices across diverse regions. Through its exhaustive exploration and recommendations, the study empowers policymakers, grid operators, and energy producers to advance grid code frameworks. Consequently, this facilitates renewable energy integration, ensures grid stability, and paves the way for a more sustainable energy future.</p> </abstract>

Sustainability of Residential Investment in Klang Valley, Malaysia

Since the late 1980s, Malaysia's rapid urbanisation and economic growth have resulted in a major increase in housing demand in urban areas. Due to a developing market and active supply-demand dynamics, the Malaysian housing industry has prospered. As a result, many investors are attracted to invest in property as it is one of the stable options to consider as compared to other types of investment. To date, there is no proper guideline as to which area is best to invest the money in as most investors are solely driven by profit but do not consider other factors that can sustain the income in the long term. Since investments are made with the goal of maximising wealth, investors must make reasonable choices based on the facts available and make a judgement that is devoid of emotion. By applying the COPRAS method to property investment, this research focuses on residential properties in Klang Valley, Malaysia. The main objective of this research is to provide an idea to investors on which area in Klang Valley can ensure sustainable investment, particularly residential properties. The final findings show that Petaling best conforms to sustainable investment with the highest degree of utility as compared to other alternatives. It is followed by Klang and the Federal Territory of Kuala Lumpur. This research provides additional literature that may help investors during their investment decision on which area is the most sustainable to invest in Klang Valley.

Navigating nature’s benefits to people: an examination of asymmetrical stakeholder preferences for local forest ecosystem services in South Korea

AbstractRecent social and environmental changes complicate the management of the ecosystem services (ES) provided by local forests. Local ES management requires an understanding of the supply–demand dynamics of ES preferences among stakeholders. To examine the supply and demand characteristics of local forest ES in South Korea, we compared the preferences for ES between the ES suppliers and ES users of two local forests (Gariwangsan and Yeoninsan) that differ in jurisdiction, size, ecological properties and management challenges. We surveyed the forest administrators (n = 20) as ES suppliers and local community residents (n = 47) as well as the general public (n = 1000) as ES users. We applied a fuzzy analytic hierarchy process to examine the priority rankings of 10 types of forest ES in order of importance in management perceived by forest administrators and local residents. The general public’s preferences were measured using the weighted sums of each type of ES ranked based on its perceived importance. We also interviewed the forest administrators and local residents to further explore how the local context of the examined forests might have affected their ES evaluation. The results show that forest administrators and the general public prioritize regulating services while the local community opts for cultural services. Moreover, local residents in close proximity to the forests prefer provisioning services while those farther away prefer regulating services. Considering these differences in stakeholder preferences for local forest ES, local forest management policies can benefit from inclusive decision-making processes to navigate the supply–demand asymmetry.

Capturing features of hourly-resolution energy models through statistical annual indicators

Long term-energy planning has gradually moved towards finer temporal and spatial resolutions of the energy system to design the decarbonization of the society. However, integrated assessment models (IAMs), focusing on a broader concept of sustainability transition, are typically yearly-resolution models which complicates capturing the specific supply-demand dynamics, relevant in the transition towards renewable energy sources (RES). Different methods for introducing sub-annual information are being used in IAMs, but the hourly representation of variable RES remains challenging.This article presents a method to translate the main dynamics of an hourly-resolution energy model into a yearly-resolution model. Here we test our method with the current European Union region (EU-27) by configuring and applying the hourly-resolution EnergyPLAN. Multiple linear regression analysis is applied to 174960 simulations (set by varying 39 inputs by clusters and reaching 100% renewable systems), relating the adjusted capacity factors of the technologies as well as the variation of electricity demand and natural gas consumption as a function of the options installed to manage the variable RES. The obtained results allow validation of the developed approach, which shows to be flexible and easily generalizable enough to be applied to any couple of hourly and annual-resolution models and/or country.

Mapping basin-scale supply-demand dynamics of flood regulation service – A case study in the Baiyangdian Lake Basin, China

Recognizing changes in the supply–demand relationship of Flood Regulation Service (FRS) is significant for the rational planning of urban patterns and the prevention of flooding risks in river basins. Most existing studies have explored its spatial pattern but pay less attention to its spatiotemporal changes. This study explored the dynamic changes in the supply–demand relationship of FRS in rapidly urbanizing basins. We proposed the Supply-Demand Ratio (SDR) method, which used the Soil Conservation Service Curve Number (SCS-CN) model and the flood vulnerability index to quantify the supply and demand of FRS, and then, we calculated the SDR. Additionally, we calculated the rate of change in SDR from 1990 to 2018 in the basin and its subbasins. The case study in the Baiyangdian Lake Basin showed that the SDR method can effectively measure the changes in the relationship between the supply and demand of FRS in the river basin. The SDR was consistent with the supply–demand bivariate mapping method, with Kappa coefficients of 0.610 and 0.835 in 1990 and in 2018, respectively. Furthermore, we found that from 1990 to 2018, the imbalance of FRS in supply and demand intensified. The SDR for the whole basin decreased by 0.088 from 1990 to 2018, with a change rate of −27.8%. Rapid urbanization is an important reason for intensification of the FRS supply–demand imbalance. The proportion of urban area is the most relevant factor for the supply–demand relationship of FRS, with a Pearson correlation coefficient of −0.845 (p < 0.01). Therefore, we suggest that proactive policies be adopted to defend against flood disaster risks in the future construction of the Xiong'an New Area, which is located downstream from the Baiyangdian Lake Basin.

Hydrotreated vegetable oil production from palm oil mill effluents: Status, opportunities and challenges

AbstractThe production of hydro‐treated vegetable oil (HVO) from palm oil mill effluents (POME) can be particularly interesting for Malaysia and Indonesia, the two major palm oil producers. Potentially, about 0.4 and 0.9 billion liters of HVO may be produced from POME in Malaysia and Indonesia, respectively. Large amounts of POME oil were exported to China for biofuel production in recent years, but the supply–demand dynamics remain under‐studied. The current policy trends in the EU are not in favor of POME‐based HVO. Exports to the EU are shadowed by uncertainty regarding its eligibility for double counting owing to its links with the oil palm industry, despite it being claimed to be a waste‐based biofuel. Domestically, Malaysia is still lagging behind in the policy for advanced biofuels. However, the country has shown its ambition in becoming a bio‐jet fuel producer in the recently announced 12th Malaysia Plan (RMK‐12). Converting POME oil into sustainable aviation fuel with HVO technology may be a promising option given the global commitment including Malaysia to reducing emissions in the aviation sector. © 2022 Society of Chemical Industry and John Wiley &amp; Sons, Ltd

Development of a model for the evaluation of compost supply–demand dynamics: theory and case study in an area of Rabat region, Morocco

Development of a model for the evaluation of compost supply–demand dynamics: theory and case study in an area of Rabat region, Morocco

Modeling the Impact of Covid-19 on the Farm Produce Availability and Pricing in India

Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.