About

Fabian Fernandez is a Professor and Co-Director of Graduate Studies in the Department of Soil, Water, and Climate at the University of Minnesota Twin Cities. His educational background includes a Bachelor of Science and a Master of Science from Brigham Young University, completed in 2000 and 2002 respectively, and a PhD from Purdue University in 2006. His research and extension education programs are focused on soil nutrient management and plant mineral nutrition, with a primary concentration on environmental issues related to nutrient management in corn cropping systems. His work aims to identify and implement nitrogen management practices that are sustainable, with an emphasis on minimizing environmental impacts such as water quality degradation while also improving crop yields. His research encompasses contaminant hydrology, water quality, and nutrient management, contributing to the development of strategies for sustainable agriculture and environmental protection.

Research topics

• Artificial Intelligence
• Computer Science
• Environmental science
• Mathematics
• Chemistry
• Agronomy
• Biology
• Engineering
• Geography
• Statistics
• Remote sensing
• Environmental engineering
• Thermodynamics
• Waste management
• Environmental chemistry
• Physics
• Soil science
• Animal science
• Ecology
• Agricultural engineering
• Computer vision

Selected publications

• Stacking and intersecting nitrogen 4Rs on potato: Nitrogen use efficiency

  Soil Science Society of America Journal · 2026-03-01

  articleOpen access

  Abstract The 4Rs of nutrient management are agronomic guidelines with an aim to balance the economic, social, and environmental outcomes of major cropping systems. The objective of this study was to evaluate individual and stacked 4R management practices and their effect on nitrogen use efficiency (NUE) in “Russet Burbank” potato ( Solanum tuberosum L.) at a field trial near Grace, Idaho, in 2020 and 2023. Nitrogen (N) fertilizer treatments included all combinations of two sources (urea or polymer‐coated urea [PCU]), two rates (82% or 100% of the recommended rate), and/or two timing/placements (all applied at emergence or 84% at emergence + 16% fertigation simulation) compared to an untreated control. Overall, potato was responsive to N for plant biomass and N concentration. There were efficiency increases in four NUE metrics for the reduced rate compared to the full rate of urea with a single application timing. There were no observed differences between any of the 4R treatments for any of the 16 NUE metrics determined. Treatments with PCU had a significant increase in internal efficiency over uncoated urea. The split application timing showed no increase in any NUE metric compared to the single application timing. These data reinforce the 4R principles and suggest that stacking some methods may not be necessary to increase overall NUE.

  PublisherDOI

• Developing an improved remote sensing and calibration strip-based nitrogen management strategy for corn

  Research Square · 2026-04-30

  preprintOpen accessSenior author
  PublisherDOI

• Developing an innovative stacking ensemble machine learning and multi-source data fusion-based precision nitrogen management strategy for corn

  Precision Agriculture · 2026-03-18 · 1 citations

  articleOpen access

  Abstract Purpose The primary goal of this research was to develop an innovative in-season nitrogen (N) recommendation strategy for corn ( Zea mays L.) using stacking ensemble machine learning (ML) and multi-source data fusion. Methods Forty-nine site-years of N rate experiments conducted across the U.S. Corn Belt were used to evaluate the performance of five individual ML algorithms (Random Forest Regressor (RFR), Support Vector Regressor (SVR), Extreme Gradient Boosting Regressor (XGBR), CatBoost Regressor (CBR), and Multi-Layer Perceptron (MLP)) and stacking regression (STR) for in-season corn yield prediction under different preplant and split N application conditions using the active canopy sensor data along with genetics, environmental and management information. These models were further evaluated for their prediction of yield responses to sidedress N application rates and in-season estimation of site-specific economic optimal N rate (EONR) across the U.S. Corn Belt. Results The results indicated that the stacking model performed consistently well across all datasets for corn yield prediction, demonstrating robustness (R 2 > 0.85 for validation dataset). Preplant N rate, Sidedress N rate and normalized difference red edge (NDRE) were identified as key variables for predicting corn yield. For EONR estimation, the stacking regression 2 model (STR2) using RFR, SVR, XGBR, CBR, and MLP as base estimators and linear regression as the meta estimator performed the best for the full dataset (R 2 = 0.82 and root mean square of error (RMSE) = 27.50 kg N ha − 1 ). Conclusion It is concluded that the stacking regression and multi-source data fusion framework is a promising strategy for in-season site-specific corn yield prediction and sidedress N recommendation.

  PublisherOA PDFDOI

• Automatic calibration of DSSAT and APEX models for maize yield simulation and economic optimum nitrogen rate determination

  European Journal of Agronomy · 2025-10-05

  article
  PublisherDOI

• Nitrate, Nitrous Oxide, and Ammonia Loss Mitigation with Optimum Rate of Enhanced Efficiency Nitrogen Fertilizer and Application Timing in Corn

  Progress in soil science · 2025-01-01 · 1 citations

  book-chapter
  PublisherDOI

• Crop metrics for nutrient management research in corn-based cropping systems v1

  2025-01-30 · 1 citations

  preprintOpen access

  This protocol was used by a collaborative research team referred to as the “NutriNet Project” in corn-based cropping systems of the U.S. Midwest and Canada. Plant measurements were collected from field-based experiments to determine aboveground biomass and nutrient concentration at different crop development stages. Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) are the cash crops and cereal rye (Secale cereale L.) is the cover crop described in this protocol. The total amount of nutrient uptake is calculated based on the plant biomass and nutrient concentration measurements. Nutrient analysis was conducted using dried, ground plant samples, separated into grain and non-grain components when applicable. Ancillary measurements, such as chlorophyll meter readings and corn stalk nitrate concentrations, are also described as these provide important context for informing nutrient management decisions. Corn was the dominant crop and focus of the project team when collecting measurements; this is evident by the detail describing corn in this protocol.

  PublisherOA PDFDOI

• Is an economically optimal corn nitrogen rate also environmentally optimal?

  Soil Science Society of America Journal · 2025-07-01

  articleOpen access

  Abstract The economically optimal nitrogen rate (EONR), while an accepted standard as the “right rate” for corn ( Zea mays L.) fertilization, does not directly account for environmental impacts. This study evaluated the effects of nitrogen (N) fertilizer application rate and timing on crop N use and N loss potential, using residual soil nitrate‐N (RSN; 0‐ to 0.9‐m depth) relative to EONR. The evaluation was conducted using 49 N response trials from eight US Midwest states from 2014 to 2016. Nitrogen rates were applied as ammonium nitrate, either all at planting or split between at planting (45 kg N ha −1 ) and the remainder at the ∼V9 growth stage. At EONR, RSN was 42 kg N ha −1 for at‐plant applications and 62 kg N ha −1 for split applications. However, unaccounted for N at the end of the growing season was greater for at‐plant (46 kg N ha −1 ) than for split applications (21 kg N ha −1 ). This suggests a higher susceptibility of N loss during the early season for at‐planting applications and after the season for split applications. Differences in RSN at the EONR between N timings were not explained by differences in total aboveground N uptake at R6. Residual soil nitrate did not substantially increase until N application rates exceeded the EONR by 30 kg N ha −1 . These findings support using EONR, at an N:corn price ratio of 5.6, as an N application sustainability standard that balances profitability and environmental concerns.

  PublisherDOI

• Precipitation influences pre‐sidedress soil nitrate thresholds for corn production

  Soil Science Society of America Journal · 2025-05-01

  articleOpen accessCorresponding

  Abstract Minnesota is a leading corn ( Zea mays L.) producer in the United States, requiring substantial nitrogen (N) inputs for optimal yields. Using an in‐season critical soil nitrate (NO 3 − ‐N) concentration threshold to adjust fertilization rates can improve N management and reduce environmental impacts. This study assessed corn grain yield response to in‐season (i.e., V4–V6 corn development stage) soil NO 3 − ‐N concentration to establish a critical pre‐sidedress soil NO 3 − ‐N test (PSNT) under Minnesota conditions. Data included were obtained from 34 field experiments conducted from 2012 to 2019 across the major corn production regions of Minnesota. Relationships between PSNT and relative corn grain yield were analyzed using a quadratic‐plateau regression model. Across the entire dataset, a PSNT of 20 ± 2.5 mg NO 3 − ‐N kg −1 soil was the critical level to reach 97% of maximum corn grain yield. To increase suboptimum PSNT concentrations up to the critical threshold, application of 13.8 ± 2.4 kg N ha −1 is needed per 1 mg kg −1 increase in soil NO 3 − ‐N concentration based on pre‐/at planting N application, but validation is needed for actual sidedress applications. When precipitation was lower or greater than the 30‐year mean, the critical PSNT value was 21.5 or 17.4 mg kg⁻¹, respectively. Nonetheless, the 20 ± 2.5 mg NO 3 − ‐N kg −1 PSNT critical value is applicable across the state as limited model improvements were achieved when the data were segregated according to soil characteristics, location, corn material, and/or previous crop.

  PublisherOA PDFDOI

• EPIC model prediction of winter rye cover crop effects on crop yield and nitrate‐N leaching in Minnesota

  Agronomy Journal · 2025-04-25 · 5 citations

  articleOpen access

  Abstract Coarse‐textured soils in central Minnesota cultivated with corn ( Zea mays L.) and soybean ( Glycine max L.) exhibit good productivity, however, are vulnerable to nitrate‐N leaching losses. In such circumstances, winter rye ( Secale cereale L.) as a cover crop may reduce nitrate‐N leaching by scavenging soil nitrogen (N) in late‐fall and early‐spring fallow period. The Environmental Policy Integrated Climate (EPIC) model was used for decadal‐scale (2010–2020) simulation of yield/biomass and nitrate‐N leaching in corn– (C–C) and corn–soybean/soybean–corn (C–Sb/Sb–C) rotations, with and without winter rye, under different fertilizer N rates applied to corn (0, 100, 200, 250, and 300 kg ha −1 ) on irrigated coarse‐textured soils in central Minnesota. Model efficiency calculated based on Nash–Sutcliffe coefficient, relative root mean square error, and R 2 statistics indicate that EPIC assessment for calibration and validation treatments was excellent‐good for corn/soybean yield, and good‐satisfactory for rye biomass and NO 3 ‐N leaching losses. Results indicate that N fertilizer rates up to 250 kg N ha −1 applied to corn had a positive impact on rye biomass; however, large crop‐rotation and climate‐induced variations were observed. Annual nitrate‐N leaching losses at maximum return to nitrogen rates at a 0.05 N price to crop value ratio for corn under C–C (250 kg N ha −1 ) and C–Sb/Sb–C (200 kg N ha −1 ) with no‐rye averaged 61.5, 47.4, and 41.8 kg ha −1 , while grain yield averaged 12.5, 12.3, and 4.0 t ha −1 for corn (C–C), corn (C–Sb/Sb–C), and soybean (C–Sb/Sb–C), respectively. Planting rye under these rotations gave annual average reductions in nitrate‐N losses relative to corresponding no‐rye treatments of 2.9 (4.7%), 3.4 (7.3%), and 6.5 kg ha −1 (15.6%), with rye N uptake of 10.3, 12.1, and 33.5 kg ha −1 ; and rye biomass production of 0.61, 0.74, and 2.0 t ha −1 , respectively. EPIC assessment indicates that winter rye as cover crop did not negatively impact the subsequent corn/soybean yield and proved to be an effective strategy for reducing nitrate‐N losses, particularly following the soybean crop.

  PublisherOA PDFDOI

• Corn response to sulfur fertilizer rate and source in Illinois

  Agronomy Journal · 2025-09-01

  articleOpen accessSenior author

  Abstract Sulfur (S) is an essential nutrient for optimizing corn ( Zea mays L.) growth and yield. While S deficiency has increased in recent years, corn response to S fertilizer application remains challenging to predict owing to complex interactions among soil, crop, and weather conditions. Forty field trials were conducted between 2009 and 2011 over a range of soil types and environments to evaluate corn grain yield response to S fertilizer and assess the ability of soil and leaf S concentration to predict yield response to S fertilizer. Rate trials included two (0 and 34 kg S ha −1 ) or five rates (0 to 52 kg S ha −1 , in 13 kg ha −1 increments), whereas S sources were evaluated at 26 kg S ha −1 (ammonium sulfate [21‐0‐0‐24S], elemental S [0‐0‐0‐90S], gypsum [0‐0‐0‐21Ca‐17S], monoammonium phosphate [MAP] MAP‐10S [12‐40‐0‐10S], MAP‐10S+Zn [12‐40‐0‐10S‐1 Zn], and MAP‐15S [13‐33‐0‐15S]). Over the 3‐year study period, we found minimal yield response to S fertilizer application with an overall response rate of 5% (two of 40 trials). In addition, neither S fertilizer evaluated increased corn grain yield relative to no S at any site; however, elemental S significantly reduced yield in one of 18 sites. While S application generally increased soil and earleaf S concentration, this did not translate into yield increases; hence, the lack of relationship between relative yield and soil and earleaf S. Under the study's conditions, these results indicate that S fertilization is unlikely to increase corn yields, and standard diagnostic tests such as soil S and earleaf S concentration are unreliable in predicting yield response in the upper US Midwest. Future research should incorporate other organic and inorganic soil S fractions to improve understanding and prediction of crop response to S fertilization.

  PublisherOA PDFDOI

Frequent coauthors

• Emerson D. Nafziger

  49 shared

• Newell R. Kitchen

  Quality Research

  24 shared

• John E. Sawyer

  Iowa State University

  23 shared

• Yo Toma

  Hokkaido University

  23 shared

• James J. Camberato

  Purdue University West Lafayette

  22 shared

• Carrie A. M. Laboski

  Agricultural Research Service

  21 shared

• David W. Franzen

  21 shared

• Richard B. Ferguson

  University of Nebraska–Lincoln

  21 shared

Labs

• Department of Soil, Water, and ClimatePI

Education

• Ph.D., Agronomy

  Purdue University

  2006

• BS and MS, Agronomy and Horticulture

  Brigham Young University

  2002