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Food Productivity Trends from Hybrid Corn: Statistical Analysis of Patents and Field-test data

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  • Mariam Barry
  • Giorgio Triulzi
  • Christopher L. Magee

Abstract

In this research we study productivity trends of hybrid corn - an important subdomain of food production. We estimate the yearly rate of yield improvement of hybrid corn (measured as bushel per acre) by using both information on yields contained in US patent documents for patented hybrid corn varieties and on field-test data of several hybrid corn varieties performed at US State level. We have used a generalization of Moore's law to fit productivity trends and obtain the performance improvement rate by analyzing time series of hybrid corn performance for a period covering the last thirty years. The linear regressions results obtained from different data sources indicate that the estimated improvement rates per year are between 1.2 and 2.4 percent. In particular, using yields reported in a sample of patents filed between 1985 and 2010, we estimated an improvement rate of 0.015 (R2 = 0.74, Pvalue = 1.37 x 10^-8). Moreover, we apply two predicting models developed by Benson and Magee (2015) and Triulzi and Magee (2016) that only use patent metadata to estimate the rate of improvement. We compare these predicted values to the rate estimated using US States field-test data. We find that, due to a turning point in patenting practices which begun in 2008, only the predicted rate (rate = 0.015) using patents filed before 2008 is consistent with the empirical rate. Finally, we also investigate at the micro level - on the basis of 70 patents (granted between 1986 and 2015) - whether the number of citations received by a patent is correlated with performance achieved by the patented variety. We find that the relative performance (yield ratio) of the patented seed is positively correlated with the total number of citations received by the patent (until December 2015) but not the citations received within 3 years after the granted year, with the patent application year used as control variable.

Suggested Citation

  • Mariam Barry & Giorgio Triulzi & Christopher L. Magee, 2017. "Food Productivity Trends from Hybrid Corn: Statistical Analysis of Patents and Field-test data," Papers 1706.05911, arXiv.org.
  • Handle: RePEc:arx:papers:1706.05911
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    References listed on IDEAS

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    1. Christopher L Benson & Christopher L Magee, 2015. "Quantitative Determination of Technological Improvement from Patent Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-23, April.
    2. Béla Nagy & J Doyne Farmer & Quan M Bui & Jessika E Trancik, 2013. "Statistical Basis for Predicting Technological Progress," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    3. Petra Moser & Joerg Ohmstedt & Paul W. Rhode, 2015. "Patent Citations and the Size of the Inventive Step - Evidence from Hybrid Corn," NBER Working Papers 21443, National Bureau of Economic Research, Inc.
    4. Christopher L. Benson & Christopher L. Magee, 2013. "A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 69-82, July.
    5. Christopher L. Benson & Christopher L. Magee, 2013. "Erratum to: A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 83-83, July.
    6. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
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    Cited by:

    1. Triulzi, Giorgio & Alstott, Jeff & Magee, Christopher L., 2020. "Estimating technology performance improvement rates by mining patent data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).

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