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Global Crop-Specific Fertilization Dataset from 1961-2019.

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This study developed precise fertilizer application rate predictions using machine learning. The resulting dataset aids in understanding fertilization trends and their drivers for food security and climate change mitigation.

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • Increasing global fertilizer use necessitates high-quality data for informed decision-making.
  • Existing datasets have gaps, hindering comprehensive analysis of fertilizer application trends.

Purpose of the Study:

  • To fill data gaps by predicting nitrogen (N), phosphorus pentoxide (P2O5), and potassium oxide (K2O) application rates.
  • To create a high-resolution dataset of fertilizer application rates for 13 major crop groups from 1961 to 2019.
  • To identify socioeconomic, agricultural, and environmental drivers of fertilizer use.

Main Methods:

  • Employed eXtreme Gradient Boosting and HistGradientBoosting machine learning models.
  • Generated 5-arcmin resolution maps of fertilizer application rates.
  • Validated predictions against existing databases and used SHapley Additive exPlanations (SHAP) to assess drivers.

Main Results:

  • Produced precise country-level predictions for N, P2O5, and K2O application rates.
  • Created a comprehensive historical dataset (1961-2019) for major crop groups.
  • Identified key drivers influencing fertilizer application rates.

Conclusions:

  • The generated dataset is a valuable resource for assessing fertilization trends.
  • Enables analysis of socioeconomic, agricultural, and environmental drivers of fertilizer use.
  • Supports applications in environmental modeling, causal analysis, and forecasting.