About

Jerome Henry Cherney is the E.V. Baker Professor of Agriculture at the School of Integrative Plant Science, Soil and Crop Sciences Section. He was born and raised on a dairy farm in central Wisconsin and holds a B.S. degree in Plant Pathology from the University of Wisconsin, an M.S. degree in agronomy from the University of Wisconsin, and a Ph.D. in agronomy from the University of Minnesota. His professional background includes directing a multi-college grass tetany program at Louisiana State University as a post-doctoral Fellow, followed by a faculty position at Purdue University focusing on forage crop research and teaching. In 1990, he became the New York State forage specialist, concentrating on perennial forage management and quality, and was appointed E.V. Baker Professor of Agriculture in 1997. His research primarily involves applied studies in forage quality, management, and utilization aimed at profitable forage crop management while considering environmental concerns. His extension efforts focus on improving the profitability of forage and livestock operations in New York State, emphasizing sustainable practices and the integration of bioenergy crops. Cherney's work emphasizes environmentally responsible land stewardship, forage crop quality, and the economic viability of forage-based systems.

Research topics

• Agronomy
• Computer Science
• Mathematics
• Biology
• Environmental science
• Statistics
• Remote sensing
• Geography
• Ecology
• Botany
• Food science
• Materials science
• Optics
• Chemistry
• Engineering
• Physics
• Animal science

Selected publications

• AMGAN: A multimodal generative adversarial network for near-daily alfalfa multispectral image reconstruction

  Computers and Electronics in Agriculture · 2026-01-23

  article
  PublisherDOI

• AlfAdvisor: A web-based cyber-platform to estimate alfalfa yield and quality to support harvest scheduling

  2025-12-10

  articleOpen access

  Alfalfa is a highly valuable and widely cultivated perennial forage crop in the United States, playing a crucial role in the food supply chain as a primary feedstock for livestock. For alfalfa producers, both yield and quality are key concerns, and the timing of harvest is a critical management practice to maximize yield, quality, and profitability. To address these needs, this project delivers AlfAdvisor, a free, publicly available cyber-platform that integrates satellite remote sensing, machine learning, and economic modeling. AlfAdvisor provides: (i) timely, field-scale estimations of alfalfa yield and quality, and (ii) optimized harvest scheduling by accounting for essential factors such as the yield-quality trade-off, drying rate, and weather risks (e.g., rainfall on cut forage). Through an intuitive web interface, users can visualize yield and quality maps for their fields, view near-term yield and quality forecasts, and explore estimated yield and net revenue outcomes for different cutting dates. AlfAdvisor is poised to have an immediate impact on alfalfa producers, forage and livestock industry professionals, and researchers by enabling improved production and quality through data-driven management, thereby increasing economic opportunities for stakeholders. It is also expected to help improve alfalfa quality and availability in the livestock supply chain to increase animal and milk production, which in turn, will also benefit consumers by maintenance of low-cost animal products.

  PublisherOA PDFDOI

• Moisture relationships among conventional and brown‐midrib corn hybrids for silage

  Crop Forage & Turfgrass Management · 2024-03-19

  articleOpen access1st authorCorresponding

  Abstract Much of the corn acreage in New York state is harvested as corn silage and moisture assessment in the field is necessary for predicting harvest timing, but moisture estimation visually is very problematic, particularly for brown‐midrib (BMR) hybrids. Our goal was to assess plant moisture relationships between BMR and conventional (CONV) corn hybrids, and to identify metadata that may assist in the prediction of whole plant moisture based on ear moisture estimations. In 2023, 202 corn fields were sampled in central New York from August 18 to September 27. A total of 41 different corn hybrids were sampled, with relative maturity (RM) ranging from 84 to 112 days, and 29% of the fields sampled were planted to BMR hybrids. Five representative plants per field were evaluated for plant height, ear length and width, and ear, stover, and whole plant moisture. Estimation of dry ear:stover ratio would be helpful in estimating whole plant moisture based on ear moisture. Ear length was not related to ear:stover ratio, while plant height and ear width were weakly but significantly correlated with ear:stover ratio. Ear moisture was highly correlated with ear:stover ratio (BMR, r = −0.95; CONV, r = −0.90), and highly correlated with whole plant moisture (BMR, r = 0.97; CONV, r = 0.98). Ear moisture averaged 1 to 2% units lower throughout the sampling season for BMR compared to CONV hybrids, while stover moisture averaged 1 to 2% units higher for BMR compared to CONV hybrids prior to optimum harvest moisture. Whole plant moisture declined about 0.6%units/day and was relatively similar across RM groups.

  PublisherOA PDFDOI

• Optimal Integration of Optical and SAR Data for Improving Alfalfa Yield and Quality Traits Prediction: New Insights into Satellite-Based Forage Crop Monitoring

  Remote Sensing · 2024-02-20 · 25 citations

  articleOpen access

  Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger and malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring and management. Alfalfa, a global widely distributed forage crop, requires more attention to predict its yield and quality traits from satellite data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa remote sensing from optical and synthetic aperture radar (SAR) data. Using Sentinel-1 and Sentinel-2 satellite data, this study developed, compared, and further integrated new optical- and SAR-based satellite models for improving alfalfa yield and quality traits prediction, i.e., crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and neutral detergent fiber digestibility (NDFD). Meanwhile, to better understand the physical mechanism of alfalfa optical remote sensing, a unified hybrid leaf area index (LAI) retrieval scheme was developed by coupling the PROSAIL radiative transfer model, spectral response function of the desired optical satellite, and a random forest (RF) model, denoted as a scalable optical satellite-based LAI retrieval framework. Compared to optical vegetation indices (VIs) that only capture canopy information, the results indicate that LAI had the highest correlation (r = 0.701) with alfalfa yield due to its capacity in delivering the vegetation structure characteristics. For alfalfa quality traits, optical chlorophyll VIs presented higher correlations than LAI. On the other hand, LAI did not provide a significant additional contribution for predicting alfalfa parameters in the RF developed optical prediction model using VIs as inputs. In addition, the optical-based model outperformed the SAR-based model for predicting alfalfa yield, CP, and NDFD, while the SAR-based model showed better performance for predicting ADF and NDF. The integration of optical and SAR data contributed to higher accuracy than either optical or SAR data separately. Compared to a traditional embedded integration approach, the combination of multisource heterogeneous optical and SAR satellites was optimized by multiple linear regression (yield: R2 = 0.846 and RMSE = 0.0354 kg/m2; CP: R2 = 0.636 and RMSE = 1.57%; ADF: R2 = 0.559 and RMSE = 1.926%; NDF: R2 = 0.58 and RMSE = 2.097%; NDFD: R2 = 0.679 and RMSE = 2.426%). Overall, this study provides new insights into forage crop yield prediction for large-scale fields using multisource heterogeneous satellites.

  PublisherOA PDFDOI

• Predicting grass proportion in fresh alfalfa: Grass mixtures using a hand‐held near‐infrared spectrometer

  Crop Science · 2024-05-06 · 2 citations

  articleOpen access

  Abstract Technological advancements have made hand‐held near infrared (NIR) spectrometers more affordable and more accurate, creating interest in on‐farm application for forage management. The objective of this study was to evaluate the ability of a hand‐held NIR spectrometer to predict grass percentage within fresh alfalfa ( Medicago sativa L.):grass mixtures. Forage samples were collected at a range of maturities and varieties during the 2021 and 2022 growing seasons from multiple locations in New York. Fresh forage samples were chopped, and pure species were combined into known proportions on a dry matter basis, resulting in 534 samples. Analysis was carried out on NIR spectra collected from a hand‐held NeoSpectra spectrometer using stationary and sliding scanning techniques. Development of calibration models was completed using partial least squares regression with cross validation. The best performing calibration model using absorbance was from the sliding scanning technique with preprocessing consisting of mean‐centering ( R 2 = 0.89, root mean square error of prediction [RMSEP] = 13.7%, and ratio of prediction to deviation = 2.53). A total of 84% of the samples were correctly classified when the grass component was lower than 40%. For samples with the grass component above 40%, a total of 94% of the samples were correctly classified. Correct sample classification is critical considering that the extension recommendation in New York is to reseed alfalfa fields when the grass component exceeds 40% of the sward on a botanical composition basis. This research demonstrates that NIR technology has potential to provide the agricultural industry with rapid, non‐destructive, and affordable information to allow farmers and consultants to predict grass proportion within alfalfa:grass fresh forage mixtures in real time.

  PublisherOA PDFDOI

• Evaluation of meadow fescue grass cultivars seeded with alfalfa in New York state

  Crop Forage & Turfgrass Management · 2024-07-08

  articleOpen access

  Abstract Alfalfa–grass mixtures sown in the northeastern United States provide high‐quality dairy forage, and meadow fescue ( Festuca pratensis Huds.) may improve the quality of these mixtures. Our objectives were to evaluate competitiveness and nutritive value of nine meadow fescue (MF) cultivars in New York State at spring harvest. Three farms, two in central New York State and one in northern New York state, were used. Conventional alfalfa ( Medicago sativa L .) was sown (15 lb acre −1 ) to nine MF cultivars (three tetraploid and six diploid) and one tall fescue Lolium arundinaceum (Schreb.) ‘Darbysh’ cultivar in a randomized complete block design with four field replicates at each field site at three seeding rates (1, 2, and 3 lb acre −1 ). Grass proportion in mixtures was estimated visually. Grass samples were collected shortly before first harvest and analyzed for neutral detergent fiber, neutral detergent fiber digestibility (NDFD), acid detergent fiber, in vitro digestibility, and crude protein. Most meadow fescue cultivars maintained a grass proportion between 20%–45% across farms and growing seasons when seeded at 1lb acre −1 . Seeding rates above 1lb acre −1 resulted in grass proportions above the recommended 20–30% grass proportion rate. Drought in early 2022 resulted in an average drop in grass percentage of 16.9% units for meadow fescue in mixtures, compared to 2021. Nutritive value of cultivars varied among farms and over growing seasons. Meadow fescue cultivars averaged 2.7% units higher NDFD than tall fescue, and cultivars with consistently high NDFD were Hidden Valley, SW Revansch, SW Minto, and Schwetra. Tetraploid cultivars averaged 4.0% units lower NDF compared to diploid cultivars, which is very advantageous for grass in alfalfa–grass mixtures.

  PublisherOA PDFDOI

• Winter cereal species, cultivar, and harvest timing affect trade-offs between forage quality and yield

  Frontiers in Sustainable Food Systems · 2023-03-21 · 17 citations

  articleOpen access

  Volatile feed costs and extreme weather events are contributing to greater economic risk and precarity throughout much of the United States dairy industry. These challenges have prompted dairy farmers to seek ways to reduce feed imports without compromising milk production. For organic dairy farmers, the need to produce more homegrown forage is exacerbated by the high cost and limited supply of organic feed. Integrating winter cereals for forage as part of a double-cropping system is a potential solution, but increasing the amount of forage in dairy cow rations can reduce milk production if the forages are not managed for optimal quality. Organically managed field experiments in Maryland (MD) and New York (NY) were conducted to address two primary objectives: (1) determine the yield and quality of winter cereals—four cultivars each for barley ( Hordeum vulgare L.), cereal rye ( Secale cereale L.), and triticale (× Triticosecale Wittm. ex A. Camus.)—grown as forage and harvested at different crop growth stages, and (2) evaluate the trade-offs between yield and quality in relation to winter cereal phenology and harvest date. Mean yield at a commonly harvested growth stage, swollen boot (Zadoks 45), was 1.3, 2.2, and 2.2 Mg ha −1 in MD and 1.8, 2.5, and 2.9 Mg ha −1 in NY for barley, cereal rye, and triticale, respectively. Mean relative forage quality (RFQ) at the same growth stage was 180, 158, and 163 in MD and 179, 156, and 157 in NY for the three species. Overall, cereal rye reached swollen boot stage the earliest, barley produced the highest RFQ and retained high quality the longest, and cereal rye and triticale produced the highest yields. Based on these results, farmers should consider barley cultivars if quality is the priority and winter-hardiness is not a concern; cereal rye cultivars if an early harvest is most important; and triticale cultivars if greater harvest schedule flexibility would be most valuable. These findings can be used to better meet the needs of dairy farmers, enhance double-cropping system performance, and improve the synchronization of harvest timing with the specific needs of lactating dairy cows, dry cows, heifers, and calves.

  PublisherOA PDFDOI

• Quantifying the roles of intraspecific and interspecific diversification strategies in forage cropping systems

  Field Crops Research · 2023-07-31 · 11 citations

  articleOpen access

  Increases in extreme weather events from climate change are likely to hinder forage crop production. Cropping system diversification may be a strategy for improving productivity and enhancing yield stability of forage production systems in the face of climate change, yet farmers are often constrained by their operations as to what strategies they can readily adopt. Moreover, the type of diversity necessary to provide desired outcomes and the potential tradeoffs from such outcomes are largely unknown. We compared three strategies to increase cropping system diversity via intercropping in winter annuals, summer annuals, and perennials in a three-year forage crop experiment conducted in New York, New Hampshire, and Vermont, USA. For each crop type, three diversification strategies, (1) cultivar diversity (Intraspecific), (2) crop species diversity (Interspecific), and (3) both cultivar and species diversities (Intra+Inter), were compared against a control of a single cultivar of a single species (Baseline). Measured responses included yield, weed biomass, forage nutritive value, and yield stability. Across all crop types, yields in Interspecific and Intra+Inter treatments were 15% and 14% greater than the Baseline treatment, respectively. Species diversity had the greatest effect on performance in perennials, which we partially attribute to temporal diversity indicated by differences in biomass composition across harvests within a season. Perennial diversity treatments that contained species diversity had lower weed biomass, lower crude protein, and greater fiber content than the Baseline or Intraspecific treatments. Stability analysis of yields across growing conditions showed that perennial crop yields were consistently greater in the Inter and Intra+Inter treatments than the Baseline treatment. In the summer annuals, yields of the Intra+Inter treatment were greater than yields of the Baseline treatment in sub-optimal environments (20th percentile of the environmental index), whereas, in winter annuals yields in the Intra+Inter treatment were greater than yields of the Baseline treatment in optimal environments (80th percentile of the environmental index). Across crops, strategies of species diversity had a greater impact on responses than cultivar diversity and no additional benefits were detected in strategies that also incorporated cultivar diversity. Benefits of mixtures could be further enhanced by selecting specific traits that maximize both functional and response diversity. Future research should aim to develop guidelines for species and trait compatibility in mixtures and appropriate seeding rates to optimize performance.

  PublisherDOI

• Evaluation of a handheld NIRS instrument for determining haylage dry matter

  Crop Forage & Turfgrass Management · 2023-06-24 · 1 citations

  articleOpen access1st authorCorresponding

  Abstract Accurate forage dry matter (DM) concentration estimation is essential for maximizing animal performance and minimizing feed costs. One possible method of estimating DM for rebalancing rations daily involves the use of hand‐held near infrared reflectance spectrometer instruments. The SCiO Cup is one of the hand‐held instruments that could be used to estimate forage DM, but a thorough evaluation of its effectiveness has not been conducted. Haylage samples ( n = 600) from 143 bunker silos were collected across New York State over three years, and vacuum packed for eventual analysis using a SCiO Cup. Samples ranged from pure alfalfa ( Medicago L.) to pure grass but were mostly from mixed species. All but one sample received a DM value estimated from several available calibrations pre‐loaded in the device. Sixty samples (representing 10% of the sample population) were too wet or dry to generate a result using the mixed silage calibration. For the remaining 90% of samples, SCiO Cup DM estimates were within 3.22%units of oven DM 80% of the time. Precision of the instrument evaluated with multiple scanning of samples using the mixed silage calibration was very good, with the average standard deviation of three values of 0.40 ( n = 200). The mixed silage calibration was more effective for predicting DM of this set of haylages than either legume or grass silage calibrations.

  PublisherOA PDFDOI

• Remote sensing to estimate yield of field crops

  eCommons (Cornell University) · 2022-03-01

  article

  Because of greater accessibility and technological advancements in remote sensing and imagery processing, we can now tap into high-resolution and high-frequency imagery. Remotely sensed data will become more useful for farmers over time as we explore the data and learn to estimate and predict yield and forage quality at farm, field, and within-field scales. Stay tuned for advances in yield and quality prediction models for your farm as science develops.

  Publisher

Frequent coauthors

• D. J. R. Cherney

  Cornell University

  76 shared

• Quirine M. Ketterings

  Cornell University

  35 shared

• W. J. Cox

  Cornell University

  29 shared

• Michael D. Casler

  University of Wisconsin–Madison

  23 shared

• Tom Kilcer

  20 shared

• David Parsons

  Swedish University of Agricultural Sciences

  14 shared

• K. D. Johnson

  Purdue University West Lafayette

  14 shared

• Jeffrey J. Volenec

  12 shared

Education

• B.S., Plant Pathology

  University of Wisconsin

• M.S., Agronomy

  University of Wisconsin

• Ph.D., Agronomy

  University of Minnesota

Awards & honors

• Agronomic Extension Education Award (2012)
• American Society of Agronomy Crop Science Extension Educatio…
• Crop Science Society of America Research Award (2003)
• Northeast American Society of Agronomy and Soil Science Soci…
• American Society of Agronomy Fellow (1997)