About

Professor John T Spargo is involved with the Fertilizer Recommendation Support Team (FRST), which aims to increase soil testing transparency by promoting clear and consistent interpretations of fertilizer recommendations. The team focuses on removing political and institutional bias from soil test interpretation and providing the best possible science to enhance end-user adoption of nutrient management recommendations. His work includes supporting various field trials such as nitrogen rate trials, rice fertility trials, and studies on nutrients through the soil profile, collaborating with institutions like South Dakota State University, Oklahoma State University, the University of Arkansas, the University of Delaware, and the University of Kentucky. The overarching goal of his research and efforts is to improve fertilizer knowledge sharing and promote environmental stewardship through creating online databases and fostering better understanding of soil and fertilizer interactions.

Research topics

• Computer Science
• Geography
• Database
• Horticulture
• Chemistry
• Botany
• Agronomy
• Biology
• Environmental science
• Environmental chemistry

Selected publications

• Stakeholder perspectives on soil‐test‐based phosphorus and potassium fertilization in US agriculture

  Agronomy Journal · 2026-03-01

  articleOpen access

  Abstract Most public and private phosphorus (P) and potassium (K) fertilizer recommendations are based on soil testing and developed around a critical soil test value (CSTV) to separate responsive and nonresponsive soils. A survey was developed, as part of the Fertilizer Recommendation Support Tool (FRST) project activities, to improve understanding of stakeholder utilization of soil‐test‐based information for P and K fertilization. The survey consisted of 23 questions and covered topics pertaining to demographics, soil testing goals, the fertilizer decision‐making process, fertilizer recommendation philosophy, and FRST use. Respondents were grouped into four categories for analysis: farmers ( n = 21), independent crop advisors ( n = 84), industry agronomists ( n = 104), and public‐sector professionals ( n = 29). Soil testing was recognized as an important aspect of P and K fertilization decision‐making by 91% of respondents; however, less than 8% of respondents in any occupation category reported making P and K fertilization decisions exclusively on soil test information. Yield goal was a key determinant of P and K fertilizer rate decisions for stakeholders, particularly for farmers and industry agronomists. Less than one‐half of the farmers, independent crop advisors, and industry agronomist respondents indicated CSTV as being beneficial information to inform rate decisions. The survey results emphasize the importance of strengthening soil‐test‐based P and K fertilizer recommendations to better reflect stakeholder expectations and align with the key factors influencing on‐farm, soil‐test‐based fertilizer decisions.

  PublisherDOI

• The Fertilizer Recommendation Support Tool: A relational database and decision interface tool

  Agricultural & Environmental Letters · 2025-04-20 · 3 citations

  articleOpen access

  Abstract The Fertilizer Recommendation Support Tool (FRST) Project is a collaborative effort involving most land grant institutions, USDA branches, nonprofit organizations, and private industry. The FRST objectives are to develop a soil fertility community of practice, preserve soil test correlation and calibration data in a relational database, and develop a decision tool to provide consistent soil test interpretations. Released in April 2024, the interactive tool acts on an evolving database that contained 1455 P trials, 1316 K trials, and 143 S trials from 44 states and Puerto Rico by March 1, 2025. Decision tool outputs include an interactive county‐level map of available data and an estimated critical soil test value. The FRST relational database is a repository for soil‐test‐based P, K, and S data to support data‐driven management recommendations. Continued success of the FRST project and decision tool utility rely on collaboration and support from the soil‐test‐based nutrient management community.

  PublisherOA PDFDOI

• Nitrogen fertilizer rate not timing determines no‐till corn yield following cereal rye cover crop in northeastern United States

  Agricultural & Environmental Letters · 2025-11-20

  articleOpen access

  Abstract There is relatively low adoption of winter cover crops across the United States, despite the many ecosystem service benefits they provide, and there has been much debate about corn yield penalties following cereal cover crops such as cereal rye ( Secale cereale L.). This 12 site‐year, coordinated study across a latitudinal gradient in the northeastern United States sought to determine the interactions between cereal rye biomass and fertilizer nitrogen (N) rate and timing on no‐till corn ( Zea mays L.) yield. Total N rates, not the timing of N fertilization, significantly affected corn yields, and higher cereal rye biomass slightly increased corn yields once sufficient N was added. We conclude that if total fertilizer N rates are sufficient, the split between starter N application at planting and sidedress N fertilization does not affect yield in no‐till corn across a range of cereal rye cover crop biomass levels.

  PublisherOA PDFDOI

• Soil fertility and plant nutrition

  Elsevier eBooks · 2025-01-01

  book-chapter
  PublisherDOI

• Delta yield predicts nitrogen fertilizer requirements for corn in US production systems

  Agronomy Journal · 2025-09-01

  articleOpen access

  Abstract Predicting crop nitrogen (N) fertilizer needs is a major challenge in contemporary agriculture. Despite the success of current N recommendation tools, environmental concerns over N pollution from agriculture, and the adoption of improved corn ( Zea mays L.) technologies with enhanced N efficiencies highlight the need for more accurate N fertilizer recommendation systems. Here, we aimed to develop a methodology to predict corn N requirements based on delta yield (dY = maximum yield−unfertilized yield). To develop this delta yield‐based nitrogen (dY‐based N) tool, we selected 486 quadratic‐plateau corn yield response to N curves (from 732 N rate trials across northern US) to calculate dY and N fertilizer required to reach the yield plateau (N x ). The economic optimum nitrogen rate (EONR) was calculated using different fertilizer:crop price ratios (PR). The response curve outputs were then partitioned into calibration and validation sets. The calibration set was used to select linear models to predict N x based on dY, resulting in nine state, agroecosystem region, and irrigation‐specific sub‐models. These sub‐models predicted N x of the validation set with a mean absolute error (MAE) of 33.0 kg N ha −1 . Predicted values from the site‐year quadratic‐plateau response fits were used to improve further predictions’ outcomes. Predictions of EONR based on dY had a lower MAE than the predictions of N x , ranging between 19.9 and 25.4 kg N ha −1 depending on the PR, highlighting the system's predictive power. The exclusion of non‐responsive and linear‐response trials in our proposed dY‐based approach enables future model refinement to improve EONR prediction accuracy across a broader range of yield responses to fertilizer‐N rates. The proposed dY‐based N system, which integrates both economic and agronomic inputs (including management, environmental effects on soil N supply, and maximum yields), could help to reduce N losses and provide functional benefits for N optimization.

  PublisherOA PDFDOI

• Assessment of soil pH and lime requirement methods and recommended lime rates for six reference soils across US land grant institutions

  Soil Science Society of America Journal · 2025-09-01 · 1 citations

  articleOpen access

  Abstract Soil pH and liming recommendations that address the soils, crops, and liming materials have been developed and adopted by land grant universities since the early 20th century. We inventoried land grant institution soil pH and lime requirement (LR) measurement methods for 1980 and 2020 and examined differences in lime rate recommendations for six reference soils using a survey developed by members of the Fertilizer Recommendation Support Tool initiative. Laboratory analysis for six acidic soils with a range of properties was shared with scientists requesting a lime recommendation for each, assuming a 0‐ to 15‐cm soil depth, 6.5 target pH, and lime material having 100% effective calcium carbonate equivalence. Soil pH methods, LR methods, and lime rate recommendations were documented for 48, 41, and 34 states, respectively. The most widely used pH method was a 1:1 soil–water ratio (34 states, 71%). Thirty‐one states use one or more buffer solutions to determine LR with the most widely used being the Sikora (10 states), Mehlich (10 states), and Shoemaker, McLean, and Pratt (nine states) buffers. Forty lime rate recommendations from 34 states for each soil were summarized with median rates ranging from 2242 to 9079 kg ha −1 and coefficients of variation ranging from 41% to 73%. The reasons for high LR variability are likely due to different calibrations as no strong trends for LR method or region were observed. Efforts are needed to develop and harmonize lime recommendations to provide accurate and transparent guidance, especially for states sharing common soils and boundaries.

  PublisherOA PDFDOI

• Soil test phosphorus predicts field‐level but not subfield‐level corn yield response

  Agronomy Journal · 2025-01-01 · 4 citations

  articleOpen access

  Abstract Soil test‐based fertilizer recommendations traditionally serve to predict average nutrient needs across fields, but their effectiveness for precision agriculture remains uncertain. Our objectives were to evaluate whether soil phosphorus (P) concentrations predicted corn ( Zea mays ,r L.) yield response to P at the sub‐field level, and to determine if soil test critical levels varied within field boundaries. We conducted research over seven growing seasons at two Kentucky sites collecting spatially dense yield response data from over 150 paired plots per field. Mehlich 3 extractable phosphorus (M3P) soil ranged from 0.8 to 63 mg kg −1 , with 96% of sample points falling below the University of Kentucky's fertilizer cutoff of 30 mg kg −1 M3P for corn. Each plot (10 −2 ha) received 0 or 29.5 kg ha −1 P. While M3P effectively predicted average field‐level response, with yield increases in five of seven site‐years, it failed to predict subfield responses, where only 51% of plots showed positive yield response to P application. Linear plateau models revealed that conventional statistical treatments of soil test correlation data mask important subfield variability. The poor relationship between soil test P and yield response at the subfield scale suggests that variable rate P management requires incorporating additional factors beyond soil P concentration or moving away from such deterministic models toward probabilistic models. Our findings demonstrate that while current soil test recommendations provide accurate field‐scale guidance, they lack the precision required for variable rate application.

  PublisherOA PDFDOI

• Models and sufficiency interpretation for estimating critical soil test values for the Fertilizer Recommendation Support Tool

  Soil Science Society of America Journal · 2024-06-08 · 14 citations

  articleOpen access

  Abstract Soil test correlation determines whether a soil test can be used to predict the need for fertilization based on the critical soil test value (CSTV). Our objectives were to compare the CSTV estimated from five combinations of correlation models and yield sufficiency interpretations and to select one method for soil test correlation performed with the Fertilizer Recommendation Support Tool (FRST). Four models were fit to three datasets with strong (Mehlich‐1 K), moderate (Mehlich‐3 K), or weak (Olsen P) correlations between soil test P or K and crop relative yield. We tested the arcsine‐log calibration curve (ALCC), exponential (EXP), linear plateau (LP), and quadratic plateau (QP) models. The CSTV was defined as 95% of the maximum predicted yield for the ALCC and EXP methods, the join point for LP, and both the join point and 95% of the maximum for the QP providing five CSTV predictions. The five CSTVs ranged from 46 to 66 mg kg −1 for the Mehlich‐1 K dataset, 115 to 165 mg kg −1 for the Mehlich‐3 K dataset, and 7 to 16 mg kg −1 for the Olsen P dataset. Ten pairwise comparisons showed the estimated CSTV was numerically and sometimes statistically influenced by the model and sufficiency level interpretation. Despite differences among CSTVs, the frequency of significant yield responses above and below the predicted CSTV was generally comparable among the methods, with false‐negative errors occurring at 0%–18% of sites for a given dataset. The QP model with a CSTV at 95% of the predicted maximum was selected as the modeling approach for FRST.

  PublisherOA PDFDOI

• Accumulation of cadmium in soils, litter and leaves in cacao farms in the North Sierra Nevada de Santa Marta, Colombia

  Geoderma Regional · 2024-01-11 · 9 citations

  article
  PublisherDOI

• Improving a nitrogen mineralization model for predicting unfertilized corn yield

  Soil Science Society of America Journal · 2024-04-11 · 3 citations

  articleOpen access

  Abstract Crop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn ( Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency ( ε ). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of ε (root mean square error [RMSE] = 1.43 Mg ha −1 ; r 2 = 0.69). In the new model, clay has a stronger influence than sand on ε , corresponding to lower predicted mineralization rates on fine‐textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha −1 ; r 2 = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions.

  PublisherOA PDFDOI

Frequent coauthors

• Peter J. A. Kleinman

  Agricultural Research Service

  36 shared

• Robert S. Hedin

  27 shared

• Clinton D. Church

  United States Geological Survey

  26 shared

• Ray B. Bryant

  Agricultural Research Service

  25 shared

• Kyle R. Elkin

  Waters (United States)

  25 shared

• Lou S. Saporito

  Waters (United States)

  25 shared

• Amy G. Wolfe

  Pennsylvania State University

  25 shared

• Steven B. Mirsky

  21 shared

Labs

• Fertilizer Recommendation Support TeamPI

Education

• B.S.

  Texas A&M University

  2002

• M.S., Crop and Soil Environmental Sciences

  Virginia Polytechnic Institute and State University

  2004

• Ph.D., Crop and Soil Environmental Sciences

  Virginia Polytechnic Institute and State University

  2008