Decisions and coordination of retailer-led fresh produce supply chains under two-period dynamic pricing and portfolio contracts

Fresh agricultural produce is almost the staple food and necessity of people's daily diet all over the world. However, natural perishability and freshness affect the demand for fresh agricultural produce. Due to the change of freshness, the retailer has to adopt a multi-period dynamic pricing strategy to deal with unsold products. The research object of this paper is the retailer's two-echelon supply chain of fresh agricultural produce, and the aim is to achieve the optimal two-period coordination and ordering through options and wholesale contracts in the supply chain. In the case of two-period pricing, we find that the optimal wholesale order quantity increases with the decline of the price in the first period and tends to be stable with the decline of the price in the second period. In contrast, the price change in the first period has a greater impact on the retailer's optimal order quantity. The profits of both the retailer and the supplier increase significantly with the increase of the price in the first period, while the impact of the change of the price in the second period is not obvious. Meanwhile, decentralized decision-making can only be coordinated in the supply chain through the original option contract at the first-period price. In the second period, the cost-sharing contract is introduced to coordinate the supply chain, increase orders, and increase the profits of both the retailer and the supplier. These findings are of great significance for both the retailer and the supplier in the multi-period dynamic pricing of fresh produce under the option contract.

Blockchain for Agri-Food Supply Chain and Logistics Management

Today, the globalization of the supply chain in the food industry has surged remarkably; hence, food safety and quality certification have become critical. Blockchain is recognized as a promising technology in the agri-foods industry where it can act as a systematic and robust mechanism for increasing the food traceability and provide a transparent and efficient way to assure quality, safety, and sustainability of agri-foods. By lowering the cost and increasing value, this digital technology has the potential to increase profitability of agricultural produce along the value chain. This chapter aims to investigate the potential utilization of blockchain technology in the agri-food industry, where it can be used to address issues of trust and transparency and to facilitate sharing of information sharing among stakeholders. The technology is still in a preliminary stage; thus, this chapter is written to examine its implication in the agri-food supply chain, existing initiatives, challenges, and potential.

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.

Marketing: Farmers Promulgates Cash Crops, Itinerary to Support Their Livelihood and Enhance Their Living Standard

This paper is a briefing on the marketing and emergence of cash crops in the Indo-Bangladesh border, South West Khasi Hills District Meghalaya. It is solely aimed at understanding the inborn entrepreneurship skills of the particular sub-tribe of the Khasis called "War". They live in steep and sloppy mountains with moderate temperatures and receive sufficient precipitation throughout the year, which enables them to sustain their farming. Marketing is the heart core of every individual, regardless of any background and professionals. Marketing plays a very important role to the farmers, and everyone could enhance their standard of living due to the technique of commercialization. The Non-farmers can buy the food crops from the farmers through the role of business administration. It is pointless to have money without having a food supply. Thus, the commercialization of agricultural produce is highly required. Cash crops cultivation promotes economic growth and social growth; economically, people can generate income, put savings, and purchase physical capital. Socially they bridged with each other, helping one another, exchanging work, advising the younger ones, and imparting knowledge to one another, providing seeds and saplings to the have not. There is an evolution from practicing traditional crops, which can be consumed directly, to Cash crops, which need to be exported outside of the State through a marketing system with the intention to manufacture further for finished products.

The Coordination of Multi-Stage Discounts in a Dual Channel Fresh Agricultural Pro-duce Supply Chain: Minimizing the Loss of Quantity and Quality

This paper explores the coordination of the agricultural cooperative to supermarket or E-commerce sup-ply chain, under the condition of quantity loss with a mixed decay function of exponential and logistical distribution. The nature of this process is analyzed, and the corresponding demand and supply functions with single- and multi-stage discount strategies are constructed respectively to create a working model. The optimal discount ratios for supermarkets and agricultural cooperatives in decentralized and central-ized decision-making modes coupled with single- and multi-stage discounts are calculated respectively. Finally, a universal optimal strategy is designed, which can be applied to various quantity decay scenarios and makes the discount strategy more generalized. The results show that discounts can coordinate supply chains more effectively; not only is fresh agricultural produce sold before it starts to rot, but the benefit conflicts arising from both supermarkets vs. cooperatives and traditional vs. E-commerce channels are equilibrated. Further, multi-stage discounts are more effective than single-stage ones, but optimal discount ratios rely on the initial quantity of fresh agricultural produce in the supply chain; its market share in the traditional distribution channel; the potential market size; retail price; the price sensitivity coefficient of the channel; the cross-elasticity coefficient of prices between different channels; and the properties of the quantity loss.

Livelihood Challenges and Mitigation Strategies Adopted During COVID-19 Lockdown Period

Over the past century, apart from COVID-19, human civilization has seen five other significant pandemics such as the H1N1 outbreak in 2009, Ebola outbreak in West Africa in 2014, and subsequent outbreak in Congo in 2019, Zika outbreak in 2016, etc. However, of all these outbreaks, perhaps the COVID-19 pandemic is unparalleled due to the global proportions that it has assumed. The severity of the epidemic can be seen in terms of the number of lives lost and the multi-dimensional impact that COVID-19 has had upon the economies of nations and lives of people. Beyond the physical sphere of human life, COVID-19 has also impacted human life's social, mental, and economic aspects. This study was conducted to understand the livelihood challenges faced by the residents of 5 villages in Mulshi taluka during the lockdown period. In-depth interviews were conducted with three respondents from each village (15 respondents). The study drew upon the insights given by key opinion leaders in the towns such as Sarpanch and elected members of the gram panchayat, ASHA workers, ration shop owners etc. Identify the livelihood challenges faced by the people during the lockdown imposed due to COVID-19. Describe the strategies adopted by the people to overcome the challenges to livelihood faced by the people. The residents of the village's studies faced various challenges related to agriculture such as lack of manpower to harvest produce, lack of transportation facilities to transport produce to markets, lack of storage facilities to store agricultural produce etc, loss of employment faced by daily wage laborers due to non-operational status of small businesses during the lockdown period and challenges due to reverse migration.

Multi-mycotoxins and Fungal Contaminants of Wheat from Anambra State, Nigeria

This study was carried out to determine the fungal and mycotoxins contamination of 36 Wheat (Triticum aestivium) samples purchased randomly from the seller of the agricultural produce in local markets of Anambra State, Nigeria. Results from the studies showed that two hundred and three fungal isolates consisting of 18 species of moulds and 5 species of yeasts contaminated the wheat samples at varying degrees. For moulds, Aspergillus species contaminated the samples mostly with (28) isolates followed by Penicillum species (19) isolates while Verticillium species and Cladosporium species had equal least contaminations with (3) isolates each. Among the yeast species, Candida rugosahad the highest number of contamination with (37) isolates followed by Cryptococcus laurentii (31) isolates while Candida stellatoides (9) isolates had the least contamination. Twenty-four fungal metabolites were also recovered. The concentration of trichothecene mycotoxin Deoxynivalenol (2067µg/kg), a protein synthesis and cell proliferation inhibitor in animals exceeded the maximum acceptable limits for human consumption. It can be deduced therefore that wheat circulating in Anambra State, Nigeria are variously contaminated with different xerophilic moulds and mycotoxins which can exert adverse health problems to consumers. Keyword: Wheat samples, fungal contaminants, multi-mycotoxins and market zones

Online Platform for Agricultural Produce Livestock Marketing

The main livelihood of the majoritarian population here is through farming, who dwell in villages and feed the whole country. Food is one of the necessities of a human being, which the framers fulfill. However, they fail to get a proper price of the stock they sell in the market. Hence, they are deprived of getting profits for their stock. APMLOP helps them in getting a proper price for their stock and even get profit for their efforts. This paper aims to increase farm income in an efficient marketing system that controls the number of mediators in the marketing process and ensures maximum income for farmers. In this approach, the farmers directly deal with concerned retailers not efficient also well not at marketing system. Definitely selling the farm crops across the country and even outside the country this well indirectly help to increase the demand of the product and provide higher income to farmers also the growth of agro-based industries. By adopting new technology, develop online agriculture market web application. This web application acts as a platform for moving farmer products from the farms directly to the industry or wholesale retailers.

Overstocked Agricultural Produce and Emergency Supply System in the COVID-19 Pandemic: Responses from China

The spread of COVID-19 has affected not only public health but also agriculture, raising global concerns regarding the food system. As an immediate impact of COVID-19, farmers around the globe have had difficulties with sales, resulting in large amounts of overstocked agricultural products and food loss. This further threatens the livelihood of rural, poor farmers and impacts sustainable production. To provide a better understanding of the overstocking situation after the outbreak of the pandemic, this study depicts the distribution characteristics of overstocked agricultural products in China. After analyzing a nationwide data set collected from 3482 individuals/organizations by the Chinese Agri-products Marketing Association after the outbreak of the pandemic, we found that some of the initial prevention and control measures disrupted sales channels, and in turn, caused the farmers to suffer losses. The impact was more severe in perishable products and their production areas, as well as in poverty-stricken regions. Then, we identified China’s quick and effective actions to match the supply and demand. These findings suggest that emergency responses should coordinate the relationship between emergency actions and the necessary logistics of agricultural production. To prepare for the possibility of such shock in the future, the government should take actions to clear logistics obstacles for necessary transportation, keep enhancing the fundamental infrastructure and effective mechanism of the food supply chain, and actively include innovative techniques to build a more resilient food system.