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COVID-19 has caused severe agriculture and food supply chain disruptions, significantly affecting smallholder farmers who supply most of the world's food, specifically their changes in vulnerability, resilience, and food loss and waste. Therefore, the objective of this study was to understand the complex causal and feedback relationships for this s...
Contexts in source publication
Context 1
... pandemic revealed the financial fragilities of rural farming communities in many regions, mainly where farmers' income usually depends on their short-term -weekly or daily -activities (Ali et al., 2020; IFAD, 2020). The Farm Finance sector in the model consists of financial inflows, outflows, and instruments that affect the survivability of the farmer in pandemic conditions ( Figure 6). ...
Context 2
... that case, the farmer might make a Decision to Seek Additional Credit. This Use of Credit increases Debt, which has two serious risks: 1) Payment of Debt draws from existing Liquid Assets, there is a risk of getting stuck in a vicious cycle in which so much revenue has to go to debt payments that Farmer's Liquidity becomes permanently depleted (see R7 in Figure 6), and 2) When credit runs out, the farmer might need to make a Decision to Sell Capital Assets, which increases Liquid Assets in the "short term" but also decreases Creditworthiness and the farmer's Net Worth over time, potentially creating a vicious cycle that leads, essentially, to selling off the farm (see R8 in Figure 6). To prevent these risks, the farmer must find new sources of revenue when the agriculture and food supply chain is disrupted. ...
Context 3
... that case, the farmer might make a Decision to Seek Additional Credit. This Use of Credit increases Debt, which has two serious risks: 1) Payment of Debt draws from existing Liquid Assets, there is a risk of getting stuck in a vicious cycle in which so much revenue has to go to debt payments that Farmer's Liquidity becomes permanently depleted (see R7 in Figure 6), and 2) When credit runs out, the farmer might need to make a Decision to Sell Capital Assets, which increases Liquid Assets in the "short term" but also decreases Creditworthiness and the farmer's Net Worth over time, potentially creating a vicious cycle that leads, essentially, to selling off the farm (see R8 in Figure 6). To prevent these risks, the farmer must find new sources of revenue when the agriculture and food supply chain is disrupted. ...
Context 4
... pandemic revealed the financial fragilities of rural farming communities in many regions, mainly where farmers' income usually depends on their short-term -weekly or daily -activities (Ali et al., 2020; IFAD, 2020). The Farm Finance sector in the model consists of financial inflows, outflows, and instruments that affect the survivability of the farmer in pandemic conditions ( Figure 6). ...
Context 5
... that case, the farmer might make a Decision to Seek Additional Credit. This Use of Credit increases Debt, which has two serious risks: 1) Payment of Debt draws from existing Liquid Assets, there is a risk of getting stuck in a vicious cycle in which so much revenue has to go to debt payments that Farmer's Liquidity becomes permanently depleted (see R7 in Figure 6), and 2) When credit runs out, the farmer might need to make a Decision to Sell Capital Assets, which increases Liquid Assets in the "short term" but also decreases Creditworthiness and the farmer's Net Worth over time, potentially creating a vicious cycle that leads, essentially, to selling off the farm (see R8 in Figure 6). To prevent these risks, the farmer must find new sources of revenue when the agriculture and food supply chain is disrupted. ...
Context 6
... that case, the farmer might make a Decision to Seek Additional Credit. This Use of Credit increases Debt, which has two serious risks: 1) Payment of Debt draws from existing Liquid Assets, there is a risk of getting stuck in a vicious cycle in which so much revenue has to go to debt payments that Farmer's Liquidity becomes permanently depleted (see R7 in Figure 6), and 2) When credit runs out, the farmer might need to make a Decision to Sell Capital Assets, which increases Liquid Assets in the "short term" but also decreases Creditworthiness and the farmer's Net Worth over time, potentially creating a vicious cycle that leads, essentially, to selling off the farm (see R8 in Figure 6). To prevent these risks, the farmer must find new sources of revenue when the agriculture and food supply chain is disrupted. ...
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The decision process of purchasing fresh fruit that meets export criteria is a complex problem that directly involves the efficient management of the agri-food supply chain. Although this issue has been adequately addressed in the manufacturing and service industries, studies in agri-food chains have been scarce and therefore have gained the intere...
Citations
... The study highlights disruptive technology applications in tackling resilience impediments such as perishability, demand-supply mismatch, unfair prices, and SC nontransparencies. Balkan et al. (2022) attempted to address the impact of COVID-19 on Agriculture and Food Supply Chain using system modelling for the resilience of small farmers. The study shows that medium to longer term impact of pandemic and roadmap for the post-pandemic resilience. ...
Purpose
Agriculture logistics networks are vulnerable to several disruptions. Disruptions impeding agriculture produce logistical flows often result into distorted food supplies, excessive logistics lead times and higher food prices. The strategies for enabling agriculture produce logistics resilience (APLR) are becoming crucial for managing logistical disturbances. The purpose of this study is to devise strategic implementation framework for APLR managing disruptions.
Design/methodology/approach
The factors contributing APLR are identified utilizing literature review and discussions with various stakeholders of agro-logistics sectors. The integrated N-WINGS-ISM approach is developed to explore causal interrelationship among APLR factors. Geographical Indication (GI) tagged Nashik grapes from Nashik, Maharashtra, India have been specifically chosen to demonstrate application of the developed model to devise the strategic framework for managing disruptions.
Findings
The study identifies 16 APLR factors imparting logistics resilience. N-WINGS provide categorical clustering of APLR into – Priority, Contingency, Autonomous and Long-term factors. ISM model structures the hierarchy of the implementation strategies highlighting three levels – Depth, Transition and Surface level factors. N-WINGS-ISM combined results are utilized to devise APLR implementation strategies.
Originality/value
The study argues that the need for developing proactive resilience systems incorporating resilience culture. Moving beyond technology integration, developing “Human-Technology-Systems” ecosystem is the primary imperative for systemic resilience.
... Then, Ashtab and Campbell [6] analysed the impact of consumers' buying decisions in local markets in Canada's food production and distribution systems. As well as Balkan et al. [8] studied the complex causal and feedback relationships for the FSC using the system dynamics methodology. ...
... In the medium term, changes in consumer behaviour, such as a greater preference for non-perishable items, have contributed to fewer customers and less profit, reducing the capacity to invest in future inputs and offer competitive wages to secure labour. This situation may lead to increased unemployment and farm closures in the long term [8]. ...
Disruptions in the food supply chain are events that affect the flow of products and can be caused by extreme weather, natural disasters, conflicts, pandemics, and political situations, among others. These events can significantly impact food products' availability, quality, and cost, creating risks to the well-being of local populations and livelihoods. The specific literature on food supply chains needs to address other approaches to risk categorisation, which allow for establishing reference frameworks focused on the general classification of types of disruption and parameters related to solution methods. In this paper, we present a literature review to analyse the disruptions in the food supply chain. We classified 74 papers according to the types of disruptions, stakeholders, response level, supply chain echelon, solution methods, goals, and related considerations. The review results showed that the most common disruptions in the food supply chain are climatic, biological and environmental, logistics and infrastructure, and supply. The results of this review allow us to suggest some new research directions.
... Model validation is a crucial aspect of system dynamics modeling that ensures the validity of the model's results (Barlas, 1996) and establishes trust in the model's usefulness for its intended purposes (Barlas, 1996;Atamer Balkan et al., 2021). Model validation is an ongoing process that occurs throughout the model conceptualization, construction, and communication stages (Forrester and Senge, 1979). ...
This study aims to simulate the future trends of carbon emissions under different international sanction scenarios in Iran. A System Dynamics (SD) model is developed and several variables that capture multiple levels of economic, social, and environmental concepts are taken into account. Our findings indicate that, despite Iran's sluggish economic growth, fossil fuel use and CO2 emissions will rise in the scenarios with international sanctions. Imposed sanctions on Iran exacerbate the environmental negative externalities through increasing energy intensity of economic sectors and consequently cause more CO2 emissions. Thus, based on our findings, prolonging international sanctions could be a major barrier to improving energy intensity and lowering CO2 emissions. Given the potential unintended environmental consequences of international sanctions, this study suggests that international communities, particularly sanctioning countries, should consider the environmental impacts of sanctions in their policy-making decisions in order to reduce emissions and related environmental damages.
Economic sanctions have the potential to affect environmental factors through various channels, including income reduction and constraints on accessing finance and environmentally friendly technologies. This study aims to simulate the energy and environmental consequences resulting from international sanctions in Iran. To achieve this, we develop a System Dynamics model integrating a spectrum of variables spanning economic, social, and environmental dimensions, for the period from 2008 to 2028. Our simulation outcomes indicate that by 2028, sanctions are anticipated to precipitate a notable upsurge in CO2 emissions, ranging from 12.5% to 30% compared to the baseline scenario. The magnitude of this escalation hinges on a variety of factors, including energy intensity and scenarios related to economic growth. According to our findings, prolonging international sanctions could be a major barrier to improving energy intensity and lowering CO2 emissions. Given the potential environmental consequences of sanctions, international communities, particularly sanctioning countries, should consider the environmental impacts of sanctions in their policy-making decisions in order to reduce emissions damage.
Systems perspectives and system dynamics have been widely used in decision making for agricultural problems. However, their use in dairy farm management remains limited. This work demonstrates the use of systems approaches and feedback thinking in modeling for dairy farm management. The application of feedback thinking was illustrated with causal loop and stock-and-flow diagrams to disentangle the complexity of the relationship among farm elements. The study aimed to identify the dynamic processes of an intensive dairy farm by mapping the animal stocks (e.g., heifers, lactating cows, dry cows) with the final objective of anticipating the expected milk deliveries over a long time period. The project was conducted for a reference dairy farm that was intensively managed with a herd size of > 2 500 cattle heads, which provided monthly farm records from Jan 2016 to Dec 2019. Model development steps included: i) problem articulation with farm interviews and data analysis; ii) the development of a dynamic hypothesis and a causal loop diagram; iii) the development of a stock-and-flow cattle model describing aging chains of heifers and cows and subsequent calibration of the model parameters; iv) the evaluation of the model based on lactating cows and milk deliveries against farm historical records; and v) the analysis of the model results. The model characterized the farm dynamics using three main feedback loops: one balancing loop of culling and two reinforcing loops of heifers’ replacement and cows’ pregnancy, pushing milk delivery. The model reproduced the historical oscillation patterns of lactating cows and milk deliveries with high accuracy (root-mean-square percentage error of 2.8% and 5.2% for the number of lactating cows and milk deliveries, respectively). The model was shown to be valid for its purpose, and applications of this model in dairy farm management can support decision-making practices for herd composition and milk delivery targets.
Digital transformation has unveiled new prospects for increased performance and productivity in the agricultural sector to meet rising food security needs. Continuous industrialization and unexpected disruptions (e.g., workforce mobility restrictions due to the COVID-19 pandemic) call for the adoption of agricultural robots. However, automated solutions could be associated with societal challenges in rural areas; unemployment growth has been perceived as a major threat that jeopardizes societal welfare, potentially hindering the implementation of digital technologies. In this context, human–robot synergistic systems could act as a promising socially viable alternative. Through systems thinking, this research investigates the complex interconnections and key feedback mechanisms of automation diffusion (conventional and human–robot interactive) under the socio-economic perceptions (drivers and barriers) of agribusinesses and rural communities. Overall, this study contributes towards eliciting the mental models that underpin the transition from agricultural robots to human–robot collaboration by transforming automation-related societal risks into opportunities for sustainable rural development.
