Kota Minegishi

**** **** *** *, ***********, MN 55406

240-***-****: adb0dw@r.postjobfree.com

KEY SKILLS

Soft Skills: Grant and manuscript writing; Project management; Application development; Conference presentation

Hard Skills: R, Stata, Matlab, Mathematica, Knowledge of Python, JavaScript, SQL; Econometrics, predictive modeling; Exploratory data analysis, data visualization; Knowledge of Machine Learning methods

WORK EXPERIENCE

University of Minnesota, Twin Cities

August 2015 – Present, Assistant Professor of Dairy Data Analytics

-Developed online decision-support tools for dairy farmers using R-shiny and mongoDB.

-Designed the concept and led the development of a farmer peer-to-peer communication app.

-Wrote grant proposals and published results.

-Analyzed national and regional farm datasets.

-Designed and directed public surveys.

World Bank, Washington D.C.

Consultant, November 2014 - June 2015

-Analyzed rural household survey data.

-Assessed methods and executed data analyses.

-Drafted literature reviews and research reports.

University of Maryland, College Park

Research and Teaching Assistant, August 2007 – May 2011

-Taught lab sessions, designed and graded assignments, held office hours.

-Reviewed literature, analyzed data, drafted manuscripts, presented at a conference.

EDUCATION

Ph.D. Agricultural and Resource Economics, University of Maryland, College Park, 2014.

M.S. Agricultural and Resource Economics, University of Maryland, College Park, 2012.

B.A. Mathematics and Economics, California State University, Chico, 2007, Summa Cum Laude.

AWARDS/HONORS

Dr. and Mrs. Bill V. Lessley Dissertation Excellence Award 2014.

Saitama Prefectural Government Scholarship for Studying Abroad, Year 2011-2012.

Teaching and Research Assistantship, AREC, University of Maryland 2007-2011.

SOFTWARE DEVELOPMENT

Robotic Milking System Investment Decision-support Tool: https://z.umn.edu/robotparlor

ComMoonity: Peer-to-Peer Q&A Platform App for Farming Communities: https://z.umn.edu/ComMoonity

RESEARCH & PROJECT GRANTS

ComMoonity: Network-based Q&A App for Robotic Milking Farmers, PI, North Central Extension Risk Management and Education Grant, Apr. 2019 – Sep. 2020, $49,549

Evaluating milking center decisions: Automatic milking systems vs. parlors, Co-PI, North Central Extension Risk Management and Education Grant, Apr. 2016 – Sep. 2017. $49,550

PEER-REVIEWED PUBLICATIONS

1.Minegishi, K., Heins, B. J., and Pereira, G. M (2019), “Peri-estrus activity and rumination time and its application to estrus prediction: Evidence from dairy herds under organic grazing and low-input conventional production,” Journal of Livestock Science, 221:144-154.

2.Minegishi, K. and D. Johnson (2017), “Dairy productivity and technical change: an analysis of confinement and management intensive grazing dairies in Maryland for 1995–2009,” Agricultural and Resource Economics Review, 1-24.

3.Salfer, J., Minegishi, K., Lazarus, W., Berning, E, and Endres, M. (2017), “Finances for robotic dairies,” Journal of Dairy Science, Vol. 100 (9): 7739-7749.

4.Minegishi, K. (2016), “Comparison of production risks in the state-contingent framework: application to balanced panel data,” Journal of Productivity Analysis, Vol 46: 121-138.

5.Hanson, J, Johnson, D., Lichtenberg, E., and Minegishi, K ( 2013), “Competitiveness of management-intensive grazing dairies in the mid-Atlantic region from 1995 to 2009,” Journal of Dairy Science, 96:1894-1904.

WORKS UNDER REVIEW

1.Boaitey, A and Minegishi, K., “Who are animal welfare conscious consumers?” (Revise and Resubmit, British Food Journal)

2.Minegishi, K and Mieno, T. “Gold in Them Tha-R Hills: A Review of R Packages for Exploratory Data Analysis.”

3.Minegishi, K., “Mitigating Potential Endogeneity in Data Envelopment Analysis: A Weighted DEA Approach.”

4.Jette-Nantel, S, Minegishi, K, Lim, S. “Data show efficient small- and medium-scale dairies can compete with large- and mega-scale dairies.”

5.Minegishi, K. and Jette-Nantel, S., “Note on the Curvature of Distance Functions”

CONFERENCE PRESENTATIONS

1.Minegishi, K., Jette-Nantel, S, and Lim, S. Economic Optimality of Income Over Feed Cost: An Analysis of Wisconsin Dairy Farms (Poster presentation at the 2019 American Agricultural Economics Association (AAEA) annual meetings, July 21-23, Atlanta, GA)

2.Minegishi, K. and Boaitey, A. Farm Animal Welfare Perceptions and Parent-Child Linkage in Dairy Consumption: Evidence from a Field Survey (Lighting talk at the 2019 AAEA annual meetings, July 21-23, Atlanta, GA)

3.Minegishi, K. and Boaitey, A. Farm Animal Welfare Perceptions for Dairy Calves among U.S. Adults and Youths and Parent-Child Linkage (Presented at the 2019 Canadian Agricultura Economics Society (CAES) annual meetings, July 9-12, Ottawa, Canada)

4.Minegishi, K. and Jette-Nantel, S. Robotic milking investment decision tool and simulated profitability (Poster presentation at the 2019 Precision Dairy Farming Conference, June 18-20, Rochester, MN)

5.Minegishi, K. and Jette-Nantel, S. Farmer-to-farmer Q&A Problem-solving App:

A Pilot Case Study of Robotic Milking Dairy Farmers (Presented at the 2019 Western Extension Education and Activities Committee on Agribusiness (WERA-72) annual meetings, June 11-12, Fargo, ND)

6.Minegishi, K. and Jette-Nantel, S. Productivity Decomposition with Parametric and Nonparametric Frontiers: Application to Wisconsin Dairy Production (Presented at the 2019 Southern Agricultural Economics Association (SAEA) annual meetings, February 2-5, Birmingham, AL)

7.Minegishi, K. and Jette-Nantel, S. Productivity Trends of Wisconsin Dairy Farms: An application of DEA-based Total Factor Decomposition. WERA-72 Annual Meetings, June 19-20, 2018, Manhattan, Kansas.

8.Minegishi, K. Converting your spreadsheet decision-making tools into online applications via R-Shiny. WERA-72 Annual Meetings, June 19-20, 2018, Manhattan, Kansas.

9.Minegishi, K., K. Ueda, and S. Pieralli. Technology, Resources, and Knowledge: Structural Change in the US Dairy Industry. Agricultural & Applied Economics Annual Meetings, 2017.

10.Minegishi, K. Integrating Efficiency Concepts in Technology Approximation: A Weighted DEA Approach. Agricultural &Applied Economic Association Annual Meetings, Minneapolis, MN., August 2014.

11.Minegishi, K. A Difference in Distance-Functions (DDF) Approach to Production Heterogeneity: Application to Technical Change Measurement. Economic Research Service, US Department of Agriculture. Washington, D.C., July 2014.

12.Minegishi, K. Comparison of Production Risks in the State-Contingent Framework: Application to Balanced Panel Data Set. Agricultural & Applied Economic Association Annual Meetings, Washington, D.C., August 2013 (Poster Presentation).

13.Minegishi, K. Explaining Production Heterogeneity By Contextual Environments: Two-Stage DEA Application to Technical Change Measurement. Agricultural &Applied Economic Association Annual Meetings, Washington, D.C., August 2013.

14.Minegishi, K., Lichtenberg, E., and Hanson, J. Economics of Intensive Grazing In Dairy Production In the Mid-Atlantic. Agricultural & Applied Economic Association Annual Meetings, Pittsburg, PA, July 2011.

SELECTED EXTENSION PUBLICATIONS

1.Minegishi, K. “A case for small-scale dairies” Dairy Star, Nov. 23, 2019: P. 28

2.Minegishi, K. “Do robots increase production?” Dairy Star, May 25, 2019: P. 29

3.Minegishi, K. “Dairy farm management is an art”, Dairy Star, Mar. 9, 2019: P. 29

4.Minegishi, K. “Can small- and medium-scale dairies compete?” Dairy Star 28 May. 2018: P. 29

5.Minegishi, K. “Getting the most of activity monitoring systems” Dairy Star 10 Mar. 2018: P. 28

JOURNAL REFEREE

American Journal of Agricultural Economics, Journal of Agricultural and Resource Economics, Agricultural Economics, Environmental Science and Pollution Research, International Food and Agribusiness Management Association, Cogent Food & Agriculture, Cogent Social Science

REVIEW PANELS OF EXTERNAL FUNDING AGENCIES

National Institute of Food and Agriculture (NIFA) Exploratory Research program of the Agriculture and Food Research Initiative (AFRI), reviewer

LANGUAGES

English (fluent), Japanese (native), Spanish (basic)

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