Linking remote sensing with crop modeling for yield and nitrate leaching predictions in Minnesota

Upscaling crop yield and nitrate-N leaching loss from experimental sites to large areas under alternative crop rotations is crucial for assessing strategies and setting goals to protect groundwater quality at a regional scale. Nitrogen (N) rate field trials were used to calibrate the Environmental Policy Integrated Climate (EPIC) model for continuous-corn (Zea mays L.) (C-C), corn-soybean (Glycine max L.) (C-Sb), and alfalfa (Medicago sativa L.)-corn (A-C), with or without rye (Secale cereale L.) cover crop. Satellite estimates of crop evapotranspiration (ET<sub>c</sub>) were used to upscale the EPIC model for crop yield and nitrate-N leaching, using the irrigation-water permitting data from 2010 to 2017 for 13,375 ha of sandy soils in Bonanza Valley, central Minnesota. Four alternative management scenarios were evaluated with EPIC: (1) reducing N fertilizer rate from the maximum return to N value (MRTN) (of 0.05 to a value of 0.1 (for the N price/crop value ratio), (2) adding rye cover crop at MRTN of 0.1, (3) irrigating with EPIC auto-trigger in scenario 2, and (4) converting 50% of C-C acreage in scenario 3 to A-C. Nash-Sutcliffe coefficients, normalized root-mean-square error, and R<sup>2</sup> values based on ET<sub>c</sub>/crop yield for calibration and validation of the EPIC model ranged 0.95-0.54, 4.67-19.4, and 0.96-0.74; and 0.74-0.41, 7.99-23.4, and 0.88-0.55, respectively. Results indicate that corn yield at MRTN of 0.05 averaged 12.5, 13.2, and 13.4 t ha<sup>-1</sup> under C-C, C-Sb, and A-C rotations, while yields at MRTN of 0.1 were reduced by 4.1%, 3.5%, and 3.3%, respectively. The baseline scenario of C-C, C-Sb, and A-C rotations at MRTN of 0.05 had annual nitrate-N leaching losses of 51.8, 45.5, and 31.4 kg ha<sup>-1</sup>, while MRTN of 0.1 reduced these losses by 9.1%, 5.0%, and 3.8%, respectively. Rye after corn and soybean reduced nitrate-N leaching losses in the MRTN of 0.1 scenario by 5.8% and 13.6%, respectively. EPIC auto-irrigation of corn, soybean, and alfalfa at MRTN of 0.1 reduced nitrate-N leaching losses with rye (relative to conventional irrigation) by 9.6%, 9.1%, and 8.5%, respectively. Further, replacing half of the C-C acreage with A-C rotation would provide a 6.1% reduction, resulting in a total reduction of 27.4% in nitrate-N leaching to groundwater when all alternative practices are combined. Overall, augmenting EPIC model with field-observed ancillary data and remote sensing successfully predicted the yield and NO<sub>3</sub>-N leaching losses under different crop rotations, indicating opportunities to upscale field-scale agroecosystem simulations, particularly if used to calculate NO<sub>3</sub>-N leaching on a long-term basis at the regional scales.

Authors

Institutions

Publication Details

Journal
Journal of Environmental Quality
Published
2026-01-01
DOI
https://doi.org/10.1002/jeq2.70137
Citations
1
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
36.62
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Linking remote sensing with crop modeling for yield and nitrate leaching predictions in Minnesota

David J. MULLA, Muhammad Tahir
1 citations
Journal of Environmental Quality
Plant Water Relations and Carbon Dynamics
36.62
article

Linking remote sensing with crop modeling for yield and nitrate leaching predictions in Minnesota

David J. MULLA, Muhammad Tahir
article en
1 citations

Abstract

Upscaling crop yield and nitrate-N leaching loss from experimental sites to large areas under alternative crop rotations is crucial for assessing strategies and setting goals to protect groundwater quality at a regional scale. Nitrogen (N) rate field trials were used to calibrate the Environmental Policy Integrated Climate (EPIC) model for continuous-corn (Zea mays L.) (C-C), corn-soybean (Glycine max L.) (C-Sb), and alfalfa (Medicago sativa L.)-corn (A-C), with or without rye (Secale cereale L.) cover crop. Satellite estimates of crop evapotranspiration (ET<sub>c</sub>) were used to upscale the EPIC model for crop yield and nitrate-N leaching, using the irrigation-water permitting data from 2010 to 2017 for 13,375 ha of sandy soils in Bonanza Valley, central Minnesota. Four alternative management scenarios were evaluated with EPIC: (1) reducing N fertilizer rate from the maximum return to N value (MRTN) (of 0.05 to a value of 0.1 (for the N price/crop value ratio), (2) adding rye cover crop at MRTN of 0.1, (3) irrigating with EPIC auto-trigger in scenario 2, and (4) converting 50% of C-C acreage in scenario 3 to A-C. Nash-Sutcliffe coefficients, normalized root-mean-square error, and R<sup>2</sup> values based on ET<sub>c</sub>/crop yield for calibration and validation of the EPIC model ranged 0.95-0.54, 4.67-19.4, and 0.96-0.74; and 0.74-0.41, 7.99-23.4, and 0.88-0.55, respectively. Results indicate that corn yield at MRTN of 0.05 averaged 12.5, 13.2, and 13.4 t ha<sup>-1</sup> under C-C, C-Sb, and A-C rotations, while yields at MRTN of 0.1 were reduced by 4.1%, 3.5%, and 3.3%, respectively. The baseline scenario of C-C, C-Sb, and A-C rotations at MRTN of 0.05 had annual nitrate-N leaching losses of 51.8, 45.5, and 31.4 kg ha<sup>-1</sup>, while MRTN of 0.1 reduced these losses by 9.1%, 5.0%, and 3.8%, respectively. Rye after corn and soybean reduced nitrate-N leaching losses in the MRTN of 0.1 scenario by 5.8% and 13.6%, respectively. EPIC auto-irrigation of corn, soybean, and alfalfa at MRTN of 0.1 reduced nitrate-N leaching losses with rye (relative to conventional irrigation) by 9.6%, 9.1%, and 8.5%, respectively. Further, replacing half of the C-C acreage with A-C rotation would provide a 6.1% reduction, resulting in a total reduction of 27.4% in nitrate-N leaching to groundwater when all alternative practices are combined. Overall, augmenting EPIC model with field-observed ancillary data and remote sensing successfully predicted the yield and NO<sub>3</sub>-N leaching losses under different crop rotations, indicating opportunities to upscale field-scale agroecosystem simulations, particularly if used to calculate NO<sub>3</sub>-N leaching on a long-term basis at the regional scales.

Journal of Environmental QualityVol. 55(1)
University of Minnesota (US)
Climate action
Openalex Percentile: Top 2%
Plant Water Relations and Carbon Dynamics
36.62
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.