Application of an agent-based modeling approach to study the postharvest loss of food grains in India

Global food security is vital to sustain a population of 9 billion by 2050. Consequently, greater demand is being placed on water, land, and nutrients to increase food production – quickly surpassing resource availability. Developing regions, which already experience widespread food insecurity and limited resources, are increasingly challenged by extreme weather events. These regions also face economic and population growth and require strategies to produce higher quality food and feed more people. Fortunately, evidence shows agriculture already provides 2720 kcal person-1 day-1— exceeding the suggested daily value. However, ≈30% of harvested food is lost globally in the supply chain. India alone experiences ≈11-15 Mt of food grain postharvest loss (PHL) annually —enough for 1/3 of India’s food insecure population. Reduction of PHL can increase available food and prevent unnecessary waste of resources. Few studies have been conducted to identify the precise source of PHL in India, and there are many discrepancies within existing data. Such discrepancies demonstrate the inherent complex nature of food grain supply chains and suggest the need for further analysis. This project sought to (i) create a model to simulate the production dynamics of a food grain supply chain and (ii) use this model to simulate scenarios, observe production dynamics, and examine the effects of interventions on postharvest loss. India’s grain production was used as the initial case study for this work. An agent-based modeling approach was selected due to the dynamic interactions between farms, traders, storage facilities, and the government; the ability to model individual decision-making processes; and the capacity to represent social interactions. The model was developed in MATLAB using modifications to a previously published agent-based model. Applications of the model observed the production dynamics of two scenarios: (i) the effects of a fixed government minimum support price and (ii) the effects of a dynamic government minimum support price under the introduction of a new processing facility. This work concluded that the introduction of a new processing facilities may able to capture up to 40% of the grain market and reduce PHL, if grain handling efficiency is greater than that of existing government storage facilities. The differences in PHL under static and dynamic government minimum support prices were insignificant. However, dynamic support prices showed potential for stabilizing and improving farmers’ profits. Additional modeling work is necessary to understand the impact of pricing interventions and emerging markets on PHL of grains in India. This work presents a potential agent-based modeling framework, suitable for study of India’s grain supply chains and PHL, for further development and exploration.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Agricultural & Biological Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Paulausky, Patricia K

Contributors dc:contributor

• Gates, Richard S.
• Rausch, Kent D.
• Bhattarai, Rabin

• agent-based model, India, post harvest loss

Statement dc:rights

• Copyright 2018 Patricia Paulausky

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101733

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101733