Joseph et al. (2026) Propagation of rainfall dataset differences into crop model outputs and economic indicators

Identification

• Journal: European Journal of Agronomy
• Year: 2026
• Date: 2026-09-24
• Authors: Jacob Emanuel Joseph, Christoph Gornott, Rahel Laudien, Stefan Siebert, Omonlola Nadine Worou, Anthony Michael Whitbread, Reimund Rötter
• DOI: 10.1016/j.eja.2026.128344

Research Groups

• University of Göttingen, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Germany
• International Livestock Research Institute (ILRI), Dakar, Senegal
• International Livestock Research Institute (ILRI), Dar es salaam, Tanzania
• Potsdam Institute for Climate Impact Research (PIK), Leibniz Association, Potsdam, Germany
• University of Kassel, Agroecosystem Analysis and Modelling, Witzenhausen, Germany
• University of Göttingen, Agronomy Group, Göttingen, Germany

Short Summary

This study evaluates the impact of differences in gridded precipitation products (GPPs) on simulated maize yield and economic indicators using the Agricultural Production Systems sIMulator (APSIM). The analysis links rainfall dataset evaluation to crop-model propagation and economic analysis within a single framework.

Objective

• Evaluate how GPPs represent rainfall characteristics critical for rainfed agriculture, including seasonal totals, rainy days, onset timing, and drought occurrence.
• Examine how rainfall dataset differences propagate into APSIM simulations of maize phenology and yield formation by holding non-climatic inputs constant.
• Assess how different GPPs alter the structure of nitrogen × planting-density yield response surfaces and derived economically optimal input levels.

Study Configuration

• Spatial Scale: Semi-arid regions in Senegal and Tanzania with distinct rainfall regimes, temperature patterns, soils, and farming systems.
• Temporal Scale: Long-term (1983–2020) daily weather data were obtained from the Tanzania Meteorological Authority for Kongwa and from the Agence Nationale de l'Aviation Civile et de la Météorologie for Kaffrine.

Methodology and Data

• Models used: Agricultural Production Systems sIMulator (APSIM)
• Data sources: Six widely used gridded rainfall datasets derived from satellite observations, ground-based station data, and reanalysis products—CHIRPS, CPC, ERA5 Ag, MERRA2, MSWEP, and TAMSAT.

Main Results

• Rainfall performance varied across indicators and sites, with higher agreement for seasonal totals than onset.
• Several GPPs over-represented light rainfall and under-represented heavy rainfall, reflecting spatial averaging.
• Differences in rainfall datasets translated into site-specific yield responses driven by early-season rainfall.
• Yield response shapes to nitrogen and planting density remained similar across datasets, but economically optimal input levels varied.

Contributions

• This study offers a more comprehensive basis for assessing GPP suitability than meteorological validation alone.
• The analysis links rainfall dataset evaluation to crop-model propagation and economic analysis within a single framework.
• The results demonstrate the importance of considering both meteorological performance and crop model diagnostics when evaluating GPPs for agronomic applications.

Funding

• This research was funded by the University of Göttingen, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), Germany.

Citation

@article{Joseph2026Propagation,
  author = {Joseph, Jacob Emanuel and Gornott, Christoph and Laudien, Rahel and Siebert, Stefan and Worou, Omonlola Nadine and Whitbread, Anthony Michael and Rötter, Reimund},
  title = {Propagation of rainfall dataset differences into crop model outputs and economic indicators},
  journal = {European Journal of Agronomy},
  year = {2026},
  doi = {10.1016/j.eja.2026.128344},
  url = {https://doi.org/10.1016/j.eja.2026.128344}
}