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How do inputs and weather drive wheat yield volatility? The example of Germany

Author

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  • Albers, Hakon
  • Gornott, Christoph
  • Hüttel, Silke

Abstract

Increases in cereals production risk are commonly related to increases in weather risk. We analyze weather-induced changes in wheat yield volatility as a systemic weather risk in Germany. We disentangle, however, the relative impacts of inputs and weather on regional yield volatility. For this purpose we augment a production function with phenologically aggregated weather variables. Increasing volatility can be traced back to weather changes only in some regions. On average, inputs explain 49% of the total actual wheat yield volatility, while weather explains 43%. Models with only weather variables deliver biased but reasonable approximations for climate impact research.

Suggested Citation

  • Albers, Hakon & Gornott, Christoph & Hüttel, Silke, 2017. "How do inputs and weather drive wheat yield volatility? The example of Germany," Food Policy, Elsevier, vol. 70(C), pages 50-61.
    • Handle: RePEc:eee:jfpoli:v:70:y:2017:i:c:p:50-61
    DOI: 10.1016/j.foodpol.2017.05.001
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    Cited by:

    1. Bucheli, Janic & Dalhaus, Tobias & Finger, Robert, 2022. "Temperature effects on crop yields in heat index insurance," Food Policy, Elsevier, vol. 107(C).
    2. Tang, Kai & Hailu, Atakelty, 2020. "Smallholder farms’ adaptation to the impacts of climate change: Evidence from China’s Loess Plateau," Land Use Policy, Elsevier, vol. 91(C).
    3. Balsher Singh Sidhu & Zia Mehrabi & Milind Kandlikar & Navin Ramankutty, 2022. "On the relative importance of climatic and non-climatic factors in crop yield models," Climatic Change, Springer, vol. 173(1), pages 1-21, July.
    4. Van Passel, S. & Vanschoenwinkel, J. & Moretti, M., 2018. "The effect of policy leveraging climate change adaptive capacity in agriculture," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277059, International Association of Agricultural Economists.
    5. Duden, C. & Offermann, F., 2019. "Farmers' risk exposition and its drivers," 171st Seminar, September 5-6, 2019, Zürich, Switzerland 333722, European Association of Agricultural Economists.
    6. Schmidt, Lorenz & Odening, Martin & Ritter, Matthias, 2021. "Estimation of the weather-yield nexus with Artificial Neural Networks," Land, Farm & Agribusiness Management Department 316598, Harper Adams University, Land, Farm & Agribusiness Management Department.
    7. Dan Liu & Jia You & Rongbo Wang & Haiyan Deng, 2022. "Agricultural Production Optimization and Marginal Product Response to Climate Change," Agriculture, MDPI, vol. 12(9), pages 1-13, September.
    8. Schmitt, Jonas & Offermann, Frank & Söder, Mareike & Frühauf, Cathleen & Finger, Robert, 2022. "Extreme weather events cause significant crop yield losses at the farm level in German agriculture," Food Policy, Elsevier, vol. 112(C).
    9. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).

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