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Plant Architecture and Seeding Rate Responses to Markets, Resources, and Technologies

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  • Che, Yuyuan
  • Feng, Hongli
  • Hennessy, David A.

Abstract

Corn and soybean seeding rates in the United States have moved in opposite directions over recent decades, with the former trending upward and the latter trending downward. Both seed markets have experienced similar market, technological and environmental shocks over that time. This paper aims to better understand how farmers make seeding rate choices and why corn and soybean seeding rates have trended in opposite directions. We develop a model of seeding rate choices by incorporating a resource budget trade-off between more seeds and fewer resources allocated to each seed. With a unique detailed U.S. farm-level market data, consisting of more than 600,000 plot-level choices over 1995-2016 for corn and 1996-2016 for soybean, we assess how farmers’ seeding rate choices respond to markets, resources, and technologies. We find that the soybean seeding rate choice to be more price elastic than that for corn, i.e., seed companies are likely to have less power in the soybean seed market. Furthermore, most inputs that are endowed with the land, and so are shared across all seeds, increase both corn and soybean seeding rates; while inputs that come with the seed increase corn rates but decrease soybean rates. Representative farmers reveal some different ideas and they rely most heavily on their own experience when deciding on seeding rate choices. When joined with an earlier paper on ecological effects, our findings further suggest that targeted tax or price policies on seed or crop will mitigate neonicotinoid-related ecological impacts.

Suggested Citation

  • Che, Yuyuan & Feng, Hongli & Hennessy, David A., 2021. "Plant Architecture and Seeding Rate Responses to Markets, Resources, and Technologies," 2021 Annual Meeting, August 1-3, Austin, Texas 342492, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea21:342492
    DOI: 10.22004/ag.econ.342492
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    References listed on IDEAS

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    1. Barry J. Barnett & Dmitry V. Vedenov, 2007. "Is There a Viable Market for Area-Based Crop Insurance?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 508-519.
    2. Isaias Hazarmabeth Salgado-Ugarte & Makoto Shimizu & Toru Taniuchi, 1994. "Exploring the shape of univariate data using kernel density estimators," Stata Technical Bulletin, StataCorp LP, vol. 3(16).
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