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Heterogeneous Yield Impacts from Adoption of Genetically Engineered Corn and the Importance of Controlling for Weather

In: Agricultural Productivity and Producer Behavior

Author

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  • Jayson L. Lusk
  • Jesse Tack
  • Nathan P. Hendricks
Abstract
Concern about declining growth in crop yields has renewed debates about the ability of biotechnology to promote food security. While numerous experimental and farm-level studies have found that adoption of genetically engineered crops has been associated with yield gains, aggregate and cross-country comparisons often seem to show little effect, raising questions about the size and generalizability of the effect. This paper attempts to resolve this conundrum using a panel of United States county-level corn yields from 1980 to 2015 in conjunction with data on adoption of genetically engineered crops, weather, and soil characteristics. Our panel data contain just over 28,000 observations spanning roughly 800 counties. We show that changing weather patterns confound simple analyses of trend yield, and only after controlling for weather do we find that genetically engineered crops have increased yields above trend. There is marked heterogeneity in the effect of adoption of genetically engineered crops across location partially explained by differential soil characteristics which may be related to insect pressure. While adoption of genetically engineered crops has the potential to mitigate downside risks from weeds and insects, we find no effects of adoption on yield variability nor do we find that adoption of presently available genetically engineered crops has led to increased resilience to heat or water stress. On average, across all counties, we find adoption of GE corn was associated with a 17 percent increase in corn yield.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Jayson L. Lusk & Jesse Tack & Nathan P. Hendricks, 2018. "Heterogeneous Yield Impacts from Adoption of Genetically Engineered Corn and the Importance of Controlling for Weather," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 11-39, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:13940
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    Cited by:

    1. Wolfram Schlenker, 2018. "Introduction to "Agricultural Productivity and Producer Behavior"," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 1-9, National Bureau of Economic Research, Inc.
    2. Barry K. Goodwin & Nicholas E. Piggott, 2020. "Has Technology Increased Agricultural Yield Risk? Evidence from the Crop Insurance Biotech Endorsement," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(5), pages 1578-1597, October.
    3. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    4. Thomas, Elizabeth & Fan, Linlin & Stevens, Andrew W., 2020. "Consumer Purchasing Response to Genetically Engineered Labeling," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304523, Agricultural and Applied Economics Association.
    5. Gaurav Arora & Hongli Feng & Christopher J. Anderson & David A. Hennessy, 2020. "Evidence of climate change impacts on crop comparative advantage and land use," Agricultural Economics, International Association of Agricultural Economists, vol. 51(2), pages 221-236, March.
    6. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    7. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    8. Monika Verma & Christine Plaisier & Coen P. A. van Wagenberg & Thom Achterbosch, 2019. "A Systems Approach to Food Loss and Solutions: Understanding Practices, Causes, and Indicators," Sustainability, MDPI, vol. 11(3), pages 1-22, January.
    9. Scheitrum, Daniel & Schaefer, K. Aleks & Nes, Kjersti, 2020. "Realized and potential global production effects from genetic engineering," Food Policy, Elsevier, vol. 93(C).
    10. Ortiz-Bobea, Ariel & Tack, Jesse B., 2018. "Another genetic yield revolution is needed to offset climate change effects on U.S. maize," 2018 Annual Meeting, August 5-7, Washington, D.C. 274380, Agricultural and Applied Economics Association.
    11. Liu, Menglin, 2022. "The effect of conservation tillage on corn and soybean yields in the US Corn Belt: a Post-Double-Selection method," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322417, Agricultural and Applied Economics Association.
    12. GwanSeon Kim & Mehdi Nemati & Steven Buck & Nicholas Pates & Tyler Mark, 2020. "Recovering Forecast Distributions of Crop Composition: Method and Application to Kentucky Agriculture," Sustainability, MDPI, vol. 12(7), pages 1-17, April.

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    More about this item

    JEL classification:

    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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