About

Debbie Jeannine Cherney is a Professor of Animal Nutrition in the Department of Animal Science. Her research focuses on forage utilization and quality, with an emphasis on identifying appropriate forage management practices for perennial grasses and mixed alfalfa-grasses to enable high animal production while maintaining environmental and economic sustainability. Her lab has concentrated on managing perennial grasses for integration with dairies in New York state and on developing methods to improve forage quality and land management in systems involving small landholders in lesser developed countries. Cherney's work includes investigating the use of hand-held near-infrared reflectance spectroscopy instruments to make real-time management decisions in ration formulation and field management. She has also contributed to understanding how forage systems impact animal health and growth. In addition to her research, Cherney is actively involved in outreach and extension activities aimed at educating young people and the broader community about animal agriculture. She has developed programs to introduce youth to ruminant animals, emphasizing grass quality and sustainable animal production systems, and has conducted numerous demonstrations reaching over a thousand young people. Her teaching philosophy centers on engaging students through active learning, critical analysis, and group projects, including innovative approaches such as students writing their own textbook. Cherney also teaches courses on animal welfare and ethics, employing case studies, role-playing, and discussion to foster critical thinking and ethical awareness among students. Her educational efforts aim to develop independent, responsible learners equipped with the social and intellectual skills necessary for productive contributions to society and the field of animal science.

Research topics

• Political Science
• Geography
• Agronomy
• Computer Science
• Biology
• Economic growth
• Materials science
• Chemistry
• Food science
• Statistics
• Economics
• Business
• Agricultural science
• Mathematics
• Agricultural economics
• Environmental science
• Botany
• Medicine
• Forestry
• Remote sensing

Selected publications

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

  Crop Forage & Turfgrass Management · 2024-03-19

  articleOpen access

  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

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

  Crop Science · 2024-05-06 · 2 citations

  articleOpen accessSenior authorCorresponding

  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

• Handheld Near-Infrared Spectroscopy for Undried Forage Quality Estimation

  Sensors · 2024-08-08 · 4 citations

  articleOpen access

  This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments-NeoSpectra, TrinamiX, and AgroCares-when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar R2 values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower R2 values. This study underscores the importance of understanding the water effects on undried forage, as water's high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices.

  PublisherOA PDFDOI

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

  Crop Forage & Turfgrass Management · 2024-07-08

  articleOpen accessSenior authorCorresponding

  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

• Harvest Timing of Standing Corn Using Near-Infrared Reflectance Spectroscopy

  Sensors · 2024-02-21

  articleOpen accessSenior author

  Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R2P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region.

  PublisherOA PDFDOI

• Practical Considerations for Using the NeoSpectra-Scanner Handheld Near-Infrared Reflectance Spectrometer to Predict the Nutritive Value of Undried Ensiled Forage

  Sensors · 2023-02-04 · 10 citations

  articleOpen access

  Prediction models of different types of forage were developed using a dataset of near-infrared reflectance spectra collected by three handheld NeoSpectra-Scanners and laboratory reference values for neutral detergent fiber (NDF), in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), Ash, and moisture content (MO) from a total of 555 undried ensiled corn, grass, and alfalfa samples. Data analyses and results of models developed in this study indicated that the scanning method significantly impacted the accuracy of the prediction of forage constituents, and using the NEO instrument with the sliding method improved calibration model performance (p < 0.05) for nearly all constituents. In general, poorer-performing models were more impacted by instrument-to-instrument variability. The exception, however, was moisture content (p = 0.02), where the validation set with an independent instrument resulted in an RMSEP of 2.39 compared to 1.44 where the same instruments were used for both calibration and validation. Validation model performance for NDF, IVTD, NDFD, ADL, ADF, Ash, CP, and moisture content were 4.18, 3.86, 6.14, 1.10, 2.75, 1.42, 2.71, and 1.67 for alfalfa-grass silage samples and 3.22, 2.21, 4.55, 0.38, 2.07, 0.50, 0.51, and 1.62 for corn silage, respectively. Based on the results of this study, the handheld spectrometer would be useful for predicting moisture content in undried and unground alfalfa-grass (R2 = 0.97) and corn (R2 = 0.93) forage samples.

  PublisherOA PDFDOI

• Effects of implementing a semi-stall-feeding system on goat kid survival and farmer adoption in western Odisha

  Indian Journal of Small Ruminants (The) · 2023-01-01

  articleOpen accessSenior author

  A study was conducted over an eight-month period in 2016 with the objective of implementing and evaluating a semi-stall-feeding system in comparison to the traditional grazing system on goat farms in Kandhamal district, Odisha. Sixteen households across two villages were randomly selected from a sampling frame of farmers that owned at least four adult female goats. Four households from each of the two villages were randomly assigned to the control group (C) that engaged in traditional grazing of goats and four households to the supplemented group (S) which applied semi-stall-feeding (concentrate feeding at 2% of the herd's total body weight of goats). The potential outcomes of a transition to intensive goat production system were assessed by monitoring growth, survival, and milk quality on goat farms. Farmers' input concerning technology adoption was documented. Survivability was 4.26 times greater for kids in the supplemented than control group. Adult and kid weights did not differ among the groups. Sixty-three per cent of participating farmers were interested in supplementing their goats after the project and willing to pay between 1.5 to 15.0 USD/ household per month on goat feed. Results from this study will help policymakers about the potential impact of reducing goat dependence on grazing land and tribal farmer receptivity to goat system intensification.

  PublisherDOI

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

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

  articleOpen access

  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

• The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage Nutritive Value

  Sensors · 2022-01-15 · 12 citations

  articleOpen accessSenior author

  Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002.

  PublisherOA PDFDOI

• Dry Matter Estimation of Standing Corn with Near-Infrared Reflectance Spectroscopy

  Applied Engineering in Agriculture · 2021-01-01 · 7 citations

  articleOpen accessSenior author

  HIGHLIGHTS Quadratic relationships were established to relate ear moisture or stover moisture to whole plant moisture, and they explained 90% and 84% of whole plant moisture, respectively. Based on our observations, the moisture content of a corn field can be estimated within + 1% w.b. in 19 out of 20 fields by sampling 5-10 plants. The calibration offered by SCiO was successful at predicting oven-dried moisture content based on traditional NIRS metrics of R 2 = 0.92, RMSE = 3.6, RPD = 3.2, and RER = 15. However, the 95% prediction bands were + 6.9% w.b., which would indicate little utility in estimating ear moisture content. Based on a prediction model that was developed using the data collected for this study, a significant instrument-to-instrument bias was observed, indicating the necessity of including multiple SCiO devices in calibration spectra collection. ABSTRACT. Determining the appropriate time to harvest whole-plant corn is an essential factor driving the successful preservation via anaerobic fermentation (ensiling). The current options for timely on-farm monitoring of corn moisture in the field include selecting a set of representative plants, chopping and drying a subsample, or harvesting a portion of the field using a harvester equipped with an on-board moisture sensing system. Both methods are time-consuming and expensive, limiting their practicality for harvest decision-making. This work’s objective was to develop a practical solution that utilizes the moisture content of the ear to estimate whole-plant moisture. An improvement of this method was also considered that utilized a hand-held near-infrared reflectance spectroscopy (NIRS) device to predict ear moisture in situ. Based on the data collected during this work, a quadratic relationship was developed where ear moisture explained 90% of the variability in whole-plant corn moisture. However, based on our observations, the hand-held NIRS evaluated would have little utility in predicting whole-plant corn moisture with either the calibration developed here or provided by the manufacturer. The manufacturer’s prediction model yielded the best result with an R 2 of 0.92, and a ratio of performance to deviation of 3.19. However, the 95% prediction band was + 6.85% w.b. Finally, we determined that for a corn field uniform in appearance, sampling five to ten plants is likely to provide a reasonable estimate of field moisture. Keywords: Corn silage, Forage analysis, Harvest timing, Moisture content, NIRS.

  PublisherOA PDFDOI

Frequent coauthors

• J. H. Cherney

  Cornell University

  76 shared

• Quirine M. Ketterings

  Cornell University

  14 shared

• W. J. Cox

  Cornell University

  13 shared

• Keenan C. McRoberts

  10 shared

• David Parsons

  Swedish University of Agricultural Sciences

  10 shared

• L.E. Chase

  Cornell University

  9 shared

• Matthew F. Digman

  8 shared

• D.R. Mertens

  8 shared

Education

• PhD, Animal Science

  University of Florida

  1989

Awards & honors

• Best Western Plus, Hôtel Universel, Drummondville, Quebec, C…