Browsing by Author "Oakes, Joseph C."

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    Aerial high-throughput phenotyping of peanut leaf area index and lateral growth
    Sarkar, Sayantan; Cazenave, Alexandre-Brice; Oakes, Joseph C.; McCall, David S.; Thomason, Wade E.; Abbott, A. Lynn; Balota, Maria (Springer Nature, 2021-11-04)
    Leaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models’ suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.
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    The Evaluation of Winter Wheat Response to Nutrient Sources of Sulfur and Application Timing
    Lee, Michelle V.; Oakes, Joseph C.; Reiter, Mark S.; Thomason, Wade E. (Virginia Tech, 2023-09-11)
    Enhanced efficiency fertilizers (EEFs) have gained considerable interest in recent years as human population trends demand greater productivity from cropping systems while minimizing human health and environmental concerns from nutrient loss. Previous research has shown EEFs to be beneficial in diverse cropping systems, but research into their use in winter wheat development has been limited. This study assessed the effects of Sulfur (S) sources derived from three EEF products in comparison to a commonly used commercial product (Ammonium Sulfate) and a control (no S) as well as the effect of application timing of S on the production of tillers and grain yield of winter wheat. Field trials were conducted over a 3-year period in Warsaw, VA and Westmoreland County, VA. During the early growing season of winter wheat, the tissue samples and aerial normalized difference vegetation index (NDVI) values of before and after the mid-winter application indicated that there was some response to application timing of S, but with the exception of the Warsaw 2021 second tissue sampling S percentage analysis, there was no significant response from the sources of S tested. As the growing season progressed, NDVI values measuring tiller density showed no significant difference, which later corresponded with the end of the growing season, as there was no grain yield response to source of S or application timing of S. Overall, the S additives from the EEFs tested did not consistently impact wheat tiller development or grain yield and are therefore cost prohibitive.
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    Identification of quantitative trait loci associated with nitrogen use efficiency in winter wheat
    Brasier, Kyle G.; Ward, Brian P.; Smith, Jared; Seago, John E.; Oakes, Joseph C.; Balota, Maria; Davis, Paul H.; Fountain, Myron O.; Brown-Guedira, Gina L.; Sneller, Clay H.; Thomason, Wade E.; Griffey, Carl A. (2020-02-24)
    Maintaining winter wheat (Triticum aestivum L.) productivity with more efficient nitrogen (N) management will enable growers to increase profitability and reduce the negative environmental impacts associated with nitrogen loss. Wheat breeders would therefore benefit greatly from the identification and application of genetic markers associated with nitrogen use efficiency (NUE). To investigate the genetics underlying N response, two bi-parental mapping populations were developed and grown in four site-seasons under low and high N rates. The populations were derived from a cross between previously identified high NUE parents (VA05W-151 and VA09W-52) and a shared common low NUE parent, 'Yorktown.' The Yorktown x VA05W-151 population was comprised of 136 recombinant inbred lines while the Yorktown x VA09W-52 population was comprised of 138 doubled haploids. Phenotypic data was collected on parental lines and their progeny for 11 N-related traits and genotypes were sequenced using a genotyping-by-sequencing platform to detect more than 3,100 high quality single nucleotide polymorphisms in each population. A total of 130 quantitative trait loci (QTL) were detected on 20 chromosomes, six of which were associated with NUE and N-related traits in multiple testing environments. Two of the six QTL for NUE were associated with known photoperiod (Ppd-D1 on chromosome 2D) and disease resistance (FHB-4A) genes, two were reported in previous investigations, and one QTL, QNue.151-1D, was novel. The NUE QTL on 1D, 6A, 7A, and 7D had LOD scores ranging from 2.63 to 8.33 and explained up to 18.1% of the phenotypic variation. The QTL identified in this study have potential for marker-assisted breeding for NUE traits in soft red winter wheat.
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    Peanut Variety and Quality Evaluation Results, 2015. I, Agronomic and Grade Data
    Balota, Maria; Isleib, Thomas G.; Oakes, Joseph C.; Chapin, Jay W. (Virginia Cooperative Extension, 2016-01-25)
    This 2015 report provides data for peanut varieties planted at test locations in Virginia and the Carolinas. Data include planting dates, weather conditions, cultural practices, soil type, fertilizers, irrigation, use of herbicides and insecticides, harvest dates, and more.
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    Peanut Variety and Quality Evaluation Results, 2015. II, Quality Data
    Balota, Maria; Isleib, Thomas G.; Oakes, Joseph C.; Chapin, Jay W. (Virginia Cooperative Extension, 2016-04-01)
    This report discusses results of new peanut cultivars grown in Virginia, North Carolina and South Carolina in 2015.
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    Peanut Variety and Quality Evaluation Results, 2016. I, Agronomic and Grade Data
    Balota, Maria; Isleib, Thomas G.; Oakes, Joseph C.; Anco, Dan (Virginia Cooperative Extension, 2016-12-20)
    This 2016 report provides data for peanut varieties grown at test locations in Virginia and the Carolinas. Data include planting dates, weather conditions, cultural practices, soil type, fertilizers, irrigation, use of herbicides and insecticides, harvest dates, and more.
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    Peanut Variety and Quality Evaluation Results, 2017. I, Agronomic and Grade Data
    Balota, Maria; Isleib, Thomas G.; Oakes, Joseph C.; Anco, Dan (Virginia Cooperative Extension, 2018-01-26)
    The 2017 report provides data for peanut varieties grown at test locations in Virginia and the Carolinas. Data include planting dates, weather conditions, cultural practices, soil type, fertilizers, irrigation, use of herbicides and insecticides, harvest
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    Peanut Variety and Quality Evaluation Results, 2017. II, Quality Data
    Balota, Maria; Isleib, Thomas G.; Oakes, Joseph C.; Anco, Dan (Virginia Cooperative Extension, 2018-04-24)
    Presents data on Virginia-type peanut cultivars and on new breeding lines of these cultivars. Compares quality of kernels and pods of peanut varieties grown at test plots.
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    Registration of 'SB255' winter barley
    Brooks, Wynse S.; Griffey, Carl A.; Vaughn, Mark E.; Seago, John E.; Thomason, Wade E.; Fitzgerald, Joshua; Christopher, Anthony; Pitman, Robert M.; Dunaway, David W.; Light, Jon; Rucker, Elizabeth; Behl, Harry D.; Beahm, Bruce R.; Browning, Phillip; McMaster, Nicole J.; Schmale, David G. III; Hardiman, Thomas H.; Custis, J. Tommy; Gulick, Steve; Ashburn, S. Bobby; Jones, Ned, Jr.; Marshall, David; Fountain, Myron O.; Tan Tuong; Oakes, Joseph C. (2021-05)
    'SB255' (Reg. no. CV-373, PI 693987) is a six-rowed hulled barley (Hordeum vulgare L.) cultivar with winter growth habit. The cultivar was released by the Virginia Agricultural Experiment Station in May 2019. SB255 is widely adapted, high yielding, high grain volume weight, and medium tall. It has good winterhardiness and good straw strength. The spikes of SB255 are strap and slightly waxy with no overlapping lateral kernels and with long awns. Prior to being named, SB255 was tested under the experimental designation VA11B-141 (LA). It was derived from the cross Spont03-44/VA01B-44 and developed using a modified-bulk breeding method. It was evaluated from 2013 to 2019 in the Virginia Official Variety Trials at five to six locations. SB255's average grain yield (5,214 kg ha(-1)) was similar to the check cultivars 'Secretariat' and 'Thoroughbred' but significantly (P <= .05) higher than 'Atlantic', 'Price', 'Callao', 'Nomini', and 'Wysor'. Average grain volume weight of SB255 (60.8 kg hL(-1)) was similar to Secretariat and Price but exceeded (P <= 0.05) those of Thoroughbred, Atlantic, Callao, Nomini, and Wysor. Head emergence of SB255 was similar to Thoroughbred and 2-5 d later than winter feed barley cultivars Secretariat, Atlantic, Price, Callao, and Nomini. SB255 was developed primarily as a feed barley cultivar. It provides barley producers and end users in the eastern United States with a high-grain-yielding cultivar having good to moderate resistance to all diseases prevalent in the eastern United States, including Fusarium head blight (FHB), and also lower deoxynivalenol (DON) accumulation in the grain.
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    Registration of three soft red winter wheat germplasm lines with exceptional milling and cookie baking performance
    Meier, Nicholas A.; Malla, Subas; Oakes, Joseph C.; Murphy, Joseph Paul; Baik, Byung-Kee; Chao, Shiaoman; Griffey, Carl A. (Wiley, 2020-08-21)
    The release of soft red winter wheat (Triticum aestivum, L.) germplasm lines VA11DH‐P46xTrib‐28 (Reg. no. GP‐1048, PI 691656), VA11DH‐P46xTrib‐99 (Reg. no. GP‐1049, PI 691657), and VA11DH‐P46xTrib‐103 (Reg. no. GP‐1050, PI 691658) is intended to provide breeders with genetic material having exceptional milling and baking quality performance. The quantitative nature of milling and baking performance makes improvement and early generation selection difficult. Marker assisted and genomic selection breeding schemes can be improved by introducing breeding material with superior end‐use quality and use of known predictive DNA markers. These three lines have acceptable agronomic performance with grain yields (4605–5733 kg ha−1) similar to or higher than those of Pioneer ‘26R46’ (4568 kg ha−1). The lines have exceptional milling and baking performance with mean flour yields (733–736 g kg−1), softness equivalence (550–573 g kg−1), flour protein (89–94 g kg−1), solvent retention capacity for lactic acid (1162–1189 g kg−1) and sodium carbonate (672–697 g kg−1), and cookie diameters (19.1–19.5 cm) that are better than or similar to (p < .05) those of Pioneer 26R46 (721 g kg−1, 531 g kg−1, 93 g kg−1, 1221 g kg−1, 703 g kg−1, and 18.9 cm).
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    Small Grains in 2018
    Brooks, Wynse S.; Bee, Khim Chim; Custis, Tom; Griffey, Carl A.; Langston, David B.; Light, Jon; Oakes, Joseph C.; Pitman, Robert M.; Rucker, Elizabeth; Vaughn, Mark; Lael, Brad; Horn, Doug; Jones, Ned (Virginia Cooperative Extension, 2018-07-20)
    Notes the cultivars of barley and wheat recommended in Virginia for 2018. Discusses performance testing for these varieties and summarizes the results.
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    Small Grains in 2019
    Thomason, Wade E.; Rucker, Elizabeth; Custis, Tom; Jones, Karl; Oakes, Joseph C.; Vaughn, Mark; Jones, Ned; Brooks, Wynse S.; Light, Jon; Lillard, Greg; Clark, Bobby; Behl, Harry D.; Griffey, Carl A.; Langston, David B. (Virginia Cooperative Extension, 2019-07-15)
    Notes the cultivars of barley and wheat recommended for Virginia in 2019. Discusses performance testing for these varieties and summarizes the results.
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    Small Grains in 2020
    Thomason, Wade E.; Rucker, Elizabeth; Custis, Tom; Jones, Karl; Oakes, Joseph C.; Vaughn, Mark; Jones, Ned; Brooks, Wynse S.; Light, Jon; Lillard, Greg; Clark, Bobby; Behl, Harry D.; Griffey, Carl A.; Langston, David B. (Virginia Cooperative Extension, 2020-07-18)
    Notes the cultivars of barley and wheat recommended for Virginia in 2020. Discusses performance testing for these varieties and summarizes the results.
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    Soybean Growth and Yield Response to Seeding Rate in Virginia
    Bowers, Lindsey Carolle (Virginia Tech, 2021-06-28)
    Soybean [Glycine max (L.) Merr.] seed cost has increased dramatically with the introduction and adoption of herbicide-resistant cultivars, generating interest from growers to reduce seeding rates to the lowest possible level that does not affect yield. Research indicates that greater seeding rates are needed to maximize yield under low-yielding environments and less seed is needed in high-yielding environments, but this has not been confirmed with recent research in Virginia. The objectives of this research was to 1) determine the yield response of soybean cultivars with differing growth habits and maturities grown in full-season and double-crop systems to seeding rate under different yield environments; and 2) compare two seeding rates in large on-farm strip-plots to determine if the growth environment within the field affects the yield response to seeding rate. For objective 1, small-plot research was conducted on Piedmont and Coastal Plain sites across Virginia from 2017 thru 2020. Maturity group (MG) 4 or 5 cultivars were planted in 46-cm rows at the following seeding rates: full-season soybean – 74,130, 148,260, 222,390, 296,520, 370,650, and 444,780 seed ha-1; and double-crop soybean – 197,680, 296,520, 395,360, 494,200, 543,620, and 593,040 seed ha-1. One cultivar per MG was used in 2017 and 2018, but the experiments were expanded to include two cultivars, differing in canopy structure, within each MG in 2019 and 2020. On-farm research compared a high and low seeding rate with a 100,000 seed ha-1 difference based upon grower current practices. To determine growth influence on the yield response, normal difference vegetative index (NDVI) was measured at 2-week intervals from late-vegetative to late-reproductive stages in small-plot and on-farm experiments. Double-crop soybean required an average of 205,000 more seed ha-1 than full-season soybean. Although yield response varied with site and year, MG 4 cultivars usually yielded more than MG 5 at higher seeding rates, but less at lower seeding rate. No differences between cultivar canopy structure were present in full-season systems; differences were revealed in double-crop systems but were not consistent over sites. To obtain 95% of maximum yield, 170,000 to 390,000 seed ha-1 were required in full-season soybean and 470,000 to 550,000 seed ha-1 were required in double-crop soybean. While the NDVI response to seeding rate generally reflected the yield response at most site-years, relationship between yield and NDVI was weak. In on-farm experiments, higher seeding rates yielded more at 3 of 6 sites, but differences varied within the field. The yield-NDVI relationship was stronger due to greater variability within the field, but these differences due to seeding rate could not be discerned. Growing environment, primarily amount and distribution of rainfall, greatly influenced these results; therefore, more exact site-specific seed rate recommendations will be difficult in Virginia's environment.
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    Specialty Small Grains in 2019
    Thomason, Wade E.; Griffey, Carl; Mehl, Hillary; Behl, Harry D.; Rucker, Elizabeth; Swoish, Michael; Boyd, Luke; Custis, Tom; Langston, David B.; Jones, Karl; Byrd-Masters, Linda; Byrum, Steve; Kaur, Navjot; Oakes, Joseph C.; Vaughn, Mark; Jones, Ned; Griffey, Carl; Brooks, Wynse S.; Light, Jon; Clark, Bobby; Lillard, Gregory (Virginia Cooperative Extension, 2020)
    Discusses test results for several varieties of barley, wheat and triticale grown in Virginia. Presents yield performance data for each variety for the year and also averaged for several years. Also discusses milling and baking qualities of the barley and wheat cultivars, and reports on barley and wheat scab research.
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    Using Aerial Spectral Indices to Determine Fertility Rate and Timing in Winter Wheat
    Oakes, Joseph C.; Balota, Maria; Cazenave, Alexandre-Brice; Thomason, Wade E. (MDPI, 2024-01-03)
    Tiller density is indicative of the overall health of winter wheat (Triticum aestivum L.) and is used to determine in-season nitrogen (N) application. If tiller density exceeds 538 tillers per m2 at GS 25, then an N application at that stage is not needed, only at GS 30. However, it is often difficult to obtain an accurate representation of tiller density across an entire field. Normalized difference vegetative index (NDVI) and normalized difference red edge (NDRE) have been significantly correlated with tiller density when collected from the ground. With the advent of unmanned aerial vehicles (UAVs) and their ability to collect NDVI and NDRE from the air, this study was established to examine the relationship between NDVI, NDRE, and tiller density, and to verify whether N could be applied based on these two indices. From 2018 to 2020, research trials were established across Virginia to develop a model describing the relationship between aerial NDVI, aerial NDRE, and tiller density counted on the ground, in small plots. In 2021, the model was used to apply N in small plots at two locations, where the obtained grain yield was the same whether N was applied based on tiller density, NDVI, or NDRE. From 2022 to 2023, the model was applied at six locations across the state on large scale growers’ fields to compare the amount of GS 25 N recommended by tiller density, NDVI, and NDRE. At three locations, NDVI and NDRE recommended the same N rates as the tiller density method, while at two locations, NDVI and NDRE recommended less N than tiller density. At one location, NDVI and NDRE recommended more N than tiller density. However, across all six locations, there was no difference in grain yield whether N was applied based on tiller density, NDVI, or NDRE. This study indicated that UAV-based NDVI and NDRE are excellent proxies for tiller density determination, and can be used to accurately and economically apply N at GS 25 in winter wheat production.
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    Virginia Grain Sorghum Performance Tests, 2015
    Balota, Maria; Oakes, Joseph C.; Thomason, Wade E.; Pitman, Robert M.; Mehl, H. L. (Virginia Cooperative Extension, 2016-02-12)
    Offers data about the grain sorghum testing program, and evaluations of commercial and experimental varieties of grain sorghum. Statistical analyses are provided, as well as information on relative yield, grain moisture, head mold, and more.
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    Virginia Grain Sorghum Performance Tests, 2016
    Balota, Maria; Oakes, Joseph C.; Mehl, H. L.; Acharya, Bhupendra (Virginia Cooperative Extension, 2017-02-17)
    Offers data about the grain sorghum testing program, and evaluations of commercial and experimental varieties of grain sorghum. Statistical analyses are provided, as well as information on relative yield, grain moisture, head mold, and more.
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    Virginia Grain Sorghum Performance Tests, 2017
    Balota, Maria; Oakes, Joseph C. (Virginia Cooperative Extension, 2018-02-21)
    Offers data about the grain sorghum testing program, and evaluations of commercial and experimental varieties of grain sorghum. Statistical analyses are provided, as well as information on relative yield, grain moisture, head mold, and more.
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    Virginia Peanut Production Guide, 2016
    Balota, Maria; Cahoon, Charlie; Grisso, Robert D.; Herbert, D. Ames Jr.; Mehl, H. L.; Roberts, Mike (Virginia Cooperative Extension, 2016-01-28)
    Offers information on peanut varieties, including varieties to choose, disease and insect resistance, weed control, insect control, disease control, irrigation, fertilizers, and equipment.