Tahir et al. (2026) Linking remote sensing with crop modeling for yield and nitrate leaching predictions in Minnesota

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Identification

• Journal: Journal of Environmental Quality
• Year: 2026
• Date: 2026-01-01
• Authors: Muhammad Tahir, D. J. Mulla
• DOI: 10.1002/jeq2.70137

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study calibrated and upscaled the EPIC model using field trials and satellite data to assess crop yield and nitrate-N leaching losses across 13,375 hectares in central Minnesota under various crop rotations and management scenarios. It found that combining reduced nitrogen fertilizer rates, rye cover crops, auto-irrigation, and converting continuous corn to alfalfa-corn rotations can significantly reduce nitrate-N leaching to groundwater by up to 27.4%.

Objective

• To upscale crop yield and nitrate-N leaching loss from experimental sites to large areas under alternative crop rotations to assess strategies and set goals for protecting groundwater quality at a regional scale.

Study Configuration

• Spatial Scale: 13,375 hectares of sandy soils in Bonanza Valley, central Minnesota.
• Temporal Scale: 2010 to 2017 (8 years).

Methodology and Data

• Models used: Environmental Policy Integrated Climate (EPIC) model.
• Data sources: Nitrogen (N) rate field trials, satellite estimates of crop evapotranspiration (ETc), irrigation-water permitting data.

Main Results

• Corn yield at the maximum return to N (MRTN) value of 0.05 averaged 12.5 tonnes per hectare (t ha⁻¹) for continuous-corn (C-C), 13.2 t ha⁻¹ for corn-soybean (C-Sb), and 13.4 t ha⁻¹ for alfalfa-corn (A-C) rotations.
• Yields at an MRTN value of 0.1 were reduced by 4.1% (C-C), 3.5% (C-Sb), and 3.3% (A-C).
• Baseline annual nitrate-N leaching losses at MRTN 0.05 were 51.8 kg ha⁻¹ (C-C), 45.5 kg ha⁻¹ (C-Sb), and 31.4 kg ha⁻¹ (A-C).
• Reducing N fertilizer to MRTN 0.1 decreased these leaching losses by 9.1% (C-C), 5.0% (C-Sb), and 3.8% (A-C).
• Adding rye cover crop in the MRTN 0.1 scenario further reduced nitrate-N leaching losses by 5.8% after corn and 13.6% after soybean.
• EPIC auto-irrigation of corn, soybean, and alfalfa at MRTN 0.1 with rye reduced nitrate-N leaching losses (relative to conventional irrigation) by 9.6%, 9.1%, and 8.5%, respectively.
• Converting 50% of C-C acreage to A-C rotation provided an additional 6.1% reduction in nitrate-N leaching.
• The combination of all alternative practices resulted in a total reduction of 27.4% in nitrate-N leaching to groundwater.

Contributions

• Successfully demonstrated the upscaling of field-scale agroecosystem simulations for crop yield and nitrate-N leaching losses to regional scales by augmenting the EPIC model with field-observed ancillary data and remote sensing.
• Provided a framework for assessing and quantifying the impact of alternative agricultural management practices on groundwater quality at a large regional scale, particularly for long-term nitrate-N leaching calculations.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Tahir2026Linking,
  author = {Tahir, Muhammad and Mulla, D. J.},
  title = {Linking remote sensing with crop modeling for yield and nitrate leaching predictions in Minnesota},
  journal = {Journal of Environmental Quality},
  year = {2026},
  doi = {10.1002/jeq2.70137},
  url = {https://doi.org/10.1002/jeq2.70137}
}