About

Charles White is an Associate Professor and Extension Specialist in the Department of Plant Science at Pennsylvania State University. His research focuses on soil fertility and nutrient management, contributing to the understanding and advancement of sustainable soil and plant health practices. As an extension specialist, he plays a key role in translating research findings into practical applications for agriculture and horticulture, supporting the community with expertise in soil-related issues.

Research topics

• Environmental science
• Agronomy
• Biology

Selected publications

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

  Agronomy Journal · 2025-09-01

  articleOpen accessCorresponding

  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

• Legacy effects of cover crops on weed biomass and yield of the subsequent corn crop

  European Journal of Agronomy · 2025-11-01

  articleOpen access

  Cover crops (CC) can exert legacy effects on subsequent crops and weeds by modulating nutrient dynamics and weed pressure. These effects can influence crop performance and weed–crop competition, yet their combined impacts remain poorly understood. We investigated CC legacy effects over six seasons in Pennsylvania (USA), where silage (2012–2015) and grain (2015–2018) corn ( Zea mays ) followed either no CC or 11 CC treatments, including legumes, grasses, brassicas, and mixtures. Each year, CC biomass, C:N ratio, weed biomass and community composition, peak soil N, and corn yield were measured. Path analysis revealed that weed biomass in the preceding CC increased weed biomass in corn and reduced yield. High soil N and C:N ratio also promoted weed biomass in grain corn, while high CC C:N ratio reduced yields in both periods. Legacy effects varied among CC functional groups: in the silage corn period, yields after legumes were 43 % higher than after grasses, while weed biomass was twice as high after legumes compared to grasses. Mixtures containing multiple functional groups achieved both effective weed suppression and high yields. These findings highlight the role of CC functional group selection in shaping cascading effects from cover crops to weeds and crop yield, offering opportunities to improve weed management while maintaining productivity. • Weedy cover crops resulted in an increased weed biomass in the following corn crop. • High C:N ratio cover crops decreased the yield of the following corn. • Corn grown after legume cover crops was more resilient to weed competition than when following grass cover crops. • Corn following cover crop mixtures showed a good compromise of weed control and high yields.

  PublisherDOI

• 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

• Managing cover crop mixtures over a decade via species replacement and seeding rate adjustment

  Agricultural & Environmental Letters · 2025-08-06 · 1 citations

  articleOpen access

  Abstract Cover crop mixtures provide ecosystem services, but species’ relative abundance in mixtures is challenging to manage. We report on an 11‐year experiment where our main objective was to use species selection and seeding rate adjustments over time to increase the evenness of mixtures. Replacing rye with triticale and red clover with crimson clover while adjusting seeding rates resulted in mixtures that were more even and closer to the desired composition (greater legume biomass) than the original communities. For example, the first version of a six‐species mixture produced biomass composed of 81% grass, 5% brassica, and 14% legume, but after adjustments, subsequent versions contained 25% grass, 10% brassica, and 65% legume biomass. Substituting a less aggressive grass for a dominant grass and a more aggressive legume for a weaker legume better balanced the mixture to meet farmers’ ecosystem service goals, as did reducing the proportion of grass seed in the mixtures.

  PublisherOA PDFDOI

• U.S. cereal rye winter cover crop growth database

  Scientific Data · 2024-02-13 · 14 citations

  articleOpen access

  Abstract Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (S ecale cereale ) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001–2022 and encompasses a wide range of agronomic, soils, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha −1 , a median of 2,458 kg ha −1 , and a standard deviation of 3,163 kg ha −1 . The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.

  PublisherOA PDFDOI

• Improving a nitrogen mineralization model for predicting unfertilized corn yield

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

  articleOpen accessSenior author

  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

• The Cycles agroecosystem model: Fundamentals, testing, and applications

  Computers and Electronics in Agriculture · 2024-10-04 · 8 citations

  articleOpen access

  • This paper introduces and applies the Cycles Agroecosystem Model. • Tests show excellent simulation of ET, plant growth, and soil moisture. • Among its innovations is modeling soil carbon and nitrogen saturation explicitly. • An autonomous crop sequence builder illustrates its power in data-model workflows. Models of the soil–plant-atmosphere continuum are repositories of knowledge and gears in analytical and decision support tools applied to agroecosystems. In this paper, we present the Cycles agroecosystem model theory along with test cases and applications. Cycles combines innovations for simulating soil hydrology and biogeochemistry, including carbon and nitrogen saturation theory, with a modular software architecture. These elements enable simulating monoculture or polyculture crop sequences and associated management practices, containerizing for applications that require high-performance computing, and data assimilation at runtime. A comparison of simulated and measured daily evapotranspiration (ET) obtained with the eddy covariance method for maize ( Zea mays L.) and shrub willow ( Salix spp.) shows that Cycles represents well meteorological and vegetation controls of ET (root mean square error or RMSE = 0.75 mm d -1 ). Cycles accurately simulated differences of 150 mm in growing season ET between these two plant communities. Comparisons of modeled versus measured soil water content under soybean ( Glycine max [L.] Merr.) in southeastern Pennsylvania for six soil layers at 0.1-m increments show accurate representation of water depletion and recharge (RMSE of 0.027–0.011 m 3 m −3 ). Simulations of growth and nitrogen uptake of wheat ( Triticum aestivum L.) in eastern Washington also highlight the model’s skill simulating processes that affect water and nutrient fluxes simultaneously. To highlight Cycles’ suitability for incorporation in high performance computing applications, we present a coupling of Cycles with an autonomous crop sequence builder (Cycles-A) in the Chesapeake Bay watershed. This system automatically identified areas for double cropping and selected the optimum combination of annual crops across the watershed. The Cycles model innovations and agroecosystem framing continue advancing the premise of making models not only dynamic knowledge repositories but useful tools for research and landscape management.

  PublisherDOI

• Can nitrogen recommendations for corn production be improved through spatially explicit crediting of cover crops and soil organic matter?

  Smart Agricultural Technology · 2023-10-09 · 2 citations

  articleOpen accessSenior author

  Managing N supply to corn (Zea mays L.) properly is a major challenge, with consequences of mismanagement ranging from N-deficient, low yielding crops to pollution of surface and groundwater. For much of the 20th and 21st centuries, N fertilizer recommendations for corn have been calculated by multiplying the yield goal by a constant equal to the amount of N needed per mass of corn grain. While this approach is often sufficient to support the growing crop without tremendous environmental consequences, it does not explicitly consider N mineralized from soil organic matter (OM) or the dynamics of N mineralization or immobilization caused by previous cover crop residues. While typical N management practices used by farmers have led to relatively low N use efficiency, precision agriculture technologies developed in the late 20th century held the promise of improving N management by considering spatial variability in crop fields and adjusting fertilizer rates accordingly. To date, however, few farms use precision agriculture technologies for managing N. In this experiment, we tested a new N recommendation system based on a biogeochemical model, the soil and cover crop available nitrogen (SCAN) model, which explicitly consider OM and cover crop residue pools of N. We coupled this new N recommendation system with variable rate precision agriculture technology to generate spatially explicit N recommendations in corn fields. We hypothesized that this approach to N management would: i) reduce the amount of N recommended for a corn crop compared to a yield goal approach while, ii) maintaining corn yield at a level similar to that in a traditional yield goal approach. We found that the SCAN model reduced N rates at four of the six sites compared to the yield goal approach but recommended greater N application at two of the six sites. At one of the sites where the SCAN model recommended higher N rates, we also observed increased grain yields. However, when the SCAN model recommended lower N rates than the yield goal approach, we typically observed reduced yields. Additional research is needed to identify the proper agronomic nitrogen efficiency to use in the SCAN model.

  PublisherDOI

• Water quality benefits of weather-based manure application timing and manure placement strategies

  Journal of Environmental Management · 2023-02-08 · 14 citations

  article
  PublisherDOI

• Recalibrating the Pennsylvania pre‐sidedress soil nitrate test recommendations for modern corn production

  Agronomy Journal · 2023-07-19 · 7 citations

  articleOpen access1st authorCorresponding

  Abstract The pre‐sidedress soil nitrate test (PSNT) was developed over 30 years ago to determine sidedress nitrogen (N) fertilizer recommendations for corn ( Zea mays L.). Since the original PSNT calibrations were developed, changes in production practices such as no‐till and cover cropping and increases in corn yields and N use efficiency could affect the accuracy of PSNT recommendations. To update the PSNT recommendations in Pennsylvania and demonstrate the test's efficacy, we compiled a dataset of 32 calibration sites and 13 demonstration sites where the PSNT was conducted, and the economically optimum N rate (EONR) for corn was determined from an N fertilizer yield response curve. We recalibrated the PSNT recommendation algorithm and compared its accuracy to the original calibration. The new calibration resulted in a single long‐term manure history factor that interacted with the PSNT result to adjust the sidedress N recommendation (−5.7 kg N ha −1 [mg NO 3 ‐N kg −1 ] −1 ). The new calibration also included a term for a mixed species cover crop, which increased the sidedress N recommendation 56.2 kg N ha −1 . Finally, the coefficient that scales the N fertilizer recommendation based on yield goal decreased by 29% from the original calibration to 12.9 kg N Mg −1 grain. The new algorithm for predicting EONR reduced the error of the sidedress N recommendation by one‐half compared to the original calibration. The new PSNT calibration will allow users to accurately determine sidedress N recommendations in sites with a long‐term manure history and underscores the importance of updating soil fertility recommendation algorithms with modern data.

  PublisherOA PDFDOI

Frequent coauthors

• Jason P. Kaye

  15 shared

• Denise M. Finney

  Ursinus College

  12 shared

• Armen R. Kemanian

  Pennsylvania State University

  8 shared

• Mitchell C. Hunter

  University of Minnesota System

  6 shared

• Bàrbara Baraibar

  Universitat de Lleida

  6 shared

• Tamie L. Veith

  6 shared

• David A. Mortensen

  University of New Hampshire

  5 shared

• Brosi A. Bradley

  Pennsylvania State University

  5 shared

Education

• B.A.

  Dartmouth College

  2003

• M.S.

  University of Maryland

  2009

• Ph.D.

  Pennsylvania State University

  2016