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Defining Optimal Soybean Sowing Dates across the US.

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Area of Science:

  • Agricultural Science
  • Climate Science
  • Data Science

Background:

  • Global crop demand is projected to rise significantly by 2050, exacerbating food security concerns.
  • Climate change is increasingly impacting crop yields, necessitating adaptive strategies in agriculture.
  • Understanding weather's influence on crop production is crucial for mitigating risks.

Purpose of the Study:

  • To analyze the sensitivity of US soybean yields to in-season weather variations using machine learning.
  • To identify specific weather conditions limiting soybean production and quantify their economic impact.
  • To evaluate the potential benefits of adjusting soybean planting dates for climate change adaptation.

Main Methods:

  • Utilized machine learning algorithms on a decade of US soybean cultivar trial data (2007-2016) across 27 states.
  • Analyzed the correlation between weather variables, specifically water vapor pressure deficit (drought indicator), and monthly soybean yields.
  • Modeled the impact of altered planting schedules on total yield and monetary gains nationwide.

Main Results:

  • Identified increased water vapor pressure deficit as a key factor negatively affecting soybean yields during specific months.
  • Simulated earlier planting (12 days sooner on average) across most of the US could increase total soybean yield by 10%.
  • Estimated a cumulative monetary gain of approximately US$9 billion from optimizing planting dates, excluding Texas and Mississippi.

Conclusions:

  • Adjusting soybean planting timing presents a significant, region-specific strategy for climate change adaptation in agriculture.
  • Failure to adapt planting strategies to changing weather patterns poses risks to US food security and the agricultural economy.
  • Policy makers and farmers must acknowledge and integrate climate change considerations into agricultural practices for sustained productivity and economic stability.