Academic Research

The following is the list of my collaborative research and software projects.

A Dynamic Assessment of The Economic Impacts of A Foot-and-Mouth Disease Outbreak on the U.S. Beef Cattle Industry

Dinesh R Poddaturi, Chad E Hart, Lee L Schulz

A dynamic framework integrating the optimizing behavior of a rancher, biology of the cattle, stock replacement, age distribution, and market processes is conceptualized and calibrated to the U.S. beef cattle industry. The framework is demonstrated by first showing the results from the model capture the recent evolution of the U.S. beef cattle industry and then by estimating the economic impacts of a hypothetical Foot-and-Mouth Disease outbreak. The exogenous shocks of the Foot-and-Mouth Disease such as international trade, domestic consumption, and supply shocks are integrated into the data-driven dynamic framework to capture the market response to the disease outbreak. Scenarios are designed to quantify the impact on prices and supplies and to determine the age distribution of beef cattle under varying levels of shocks from the disease. While the specific application in this case is a disease outbreak, the framework can be utilized to capture the market response to a variety of production and policy shifts and quantify the short-run and long-run economic impacts, and the variation of the economic impacts over time. The findings of the study demonstrate the value of the dynamic framework for policy work, including the design, development, and implementation of disease management policies.

This is my Job Market Paper. Click here for a PDF version the paper.

(I presented this paper at the 2022 Western Agricultural Economics Association annual meeting)

A Dynamic Model of U.S. Beef Cattle

Dinesh R Poddaturi, Chad E Hart, Lee L Schulz

The primary purpose of this paper is the development of an economic framework that incorporates the essential dynamic process of the U.S. beef cattle industry. In particular, a dynamic economic model of the U.S. beef cattle industry is conceptualized and developed. The economic model is conceived upon a bottom-up framework, where the behavior of a representative farmer, the biology, age, and gender composition of cattle, evolving changes in the U.S. cattle structure, and micro-foundations are incorporated. The model exclusively depends on the data (measured consistently), and the data are collected and compiled from various USDA organizations. The data-driven economic model is calibrated (under Naïve price expectations and Rational price expectations) to adequately capture the dynamics observed in the U.S. beef cattle industry. The calibrated model is further utilized to project the beef cattle prices and quantities several years into the future. The model fitness is tested by computing an error in unit-free form and by replicating U.S. cattle inventories from the fitted model. Besides studying the beef industry structure and the dynamics of the beef cattle, the dynamic model can also be used to run counterfactual simulations to analyze policy impacts on the beef cattle industry and provide important recommendations, making the model an interest to policymakers, industry stakeholders, and beef cattle farmers.

(I presented this paper at the 2020 Agricultural and Applied Economics Association annual conference)

Implementing a National Animal Identification and Traceability Program: Economic Assessment using a Dynamic Model of U.S. Beef Cattle

Dinesh R Poddaturi, Chad E Hart, Lee L Schulz

This study quantifies the economic impacts of mandatory national animal identification and traceability system on U.S. beef cattle producers. Using a dynamic model of the U.S. beef cattle and rich cattle industry data, we estimate the short-run and long-run impacts and how these impacts change over time. In particular, we quantify the producer surplus losses from traceability systems. We show how the prices and quantities would change with the increased costs associated with the traceability system. In the short-run, the aggregate change in producer surplus is negative. Over time, the losses in producer surplus decline, and in the long run the producer surplus would be positive. We also quantify the changes in producer surplus with different cost-sharing programs under different adoption rates.

(I presented this work as a poster presentation at the 2021 Agricultural and Applied Economics Association annual conference)

Autoregressive Tempered Fractionally Integrated Moving Average Model (2018)

Dinesh R Poddaturi

The objective of this work is to learn the ARTFIMA time series model and use it to fit real-world data and compare the model with Autoregressive Fractionally Integrated Moving Average (ARFIMA) model in terms of goodness of fit and predictions. The ARTFIMA model is a short-range dependent time series that exhibits semi-long range dependency. For small values of tempering parameter λ > 0, the spectral density behaves like a power law at low frequencies, and it remains bounded as frequency reaches zero. ARTFIMA can be extended to ARFIMA when the tempered parameter λ is zero, and to Autoregressive Moving Average (ARMA, which is the most commonly used time series model) when both tempered parameter λ and difference parameter d are zero. We can consider the ARTFIMA model as a generalized time series model, whereas both ARFIMA and ARMA are extensions of ARTFIMA.

Development of a CRUSH Margin Calculator for Beef and Pork Markets (2017)

Dinesh R Poddaturi, Garland Dahlke, Russ Euken, Lee L Schulz

A convenient, web-based application was developed that utilizes Chicago Mercantile Exchange (CME) futures settlement price data to generate an expected margin. Futures price data is available for live cattle, feeder cattle, lean hogs, soybean meal, and corn. The prices of these commodities on the futures market can be hedged in advance of cash purchases or sales to reduce price risk or used to estimate potential margins when producing and selling fed cattle or market hogs.

(This software product is used daily by the beef and pork producers)

Management Minder – A web-based Data Management Application (2016)

Dinesh R Poddaturi, Garland Dahlke, Sandy Johnson

Management Minder is a web-based application developed as a joint project between the Iowa Beef Center and Kansas State University allowing beef cattle producers to manage their day-to-day schedule regarding cattle nutrition, reproduction, health, and herd management. Producers can create a customized calendar and share it with their employees, consultants, and veterinarians over the internet. This application also provides web links containing useful information regarding “best management practices” in regard to these different activities. Softwares used in the development of the application are Java, MySQL, and Apache Tomcat. These open source softwares are used to develop a web application with high performance, less maintenance, low cost, and low overhead/computing.

(This software product is used by beef cattle producers in the United States and around the world)