Peter R. Thomison
Ohio State University
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Featured researches published by Peter R. Thomison.
Plant Genetic Resources | 2003
Peter R. Thomison; D. J. Barker; Allen B. Geyer; L. D. Lotz; Howard J. Siegrist; T. L. Dobbels
Increased amino acid content in high-oil maize (Zea mays L.) grain may add further value to its use in livestock rations, especially if this enhanced amino acid content is consistent across varying growing conditions. Most high-oil maize (HOM) grown in the USA utilizes the TopCross system which involves planting a blend (TC Blend) of two types of maize. Field experiments and on-farm studies were conducted in 1997 and 1998 to compare the amino acid profile of grain from HOM TC Blends with that of their normal-oil maize (NOM) counterparts across a range of production environments in Ohio. In 1997, the composition of four amino acids (lysine, methionine, glycine and arginine) was significantly higher in HOM compared to NOM grain. In 1998, nine amino acids (lysine, methionine, glycine, arginine, asparagine, threonine, serine, cysteine and tryptophan) were greater in HOM than in NOM grain. Lysine and methionine content in HOM grain averaged 12 and 13% higher than in NOM grain in both years. The number of amino acids significantly affected by the grain parent was greater than that for maize type each year. A significant maize type × grain parent interaction for a limited number of amino acids suggest that TC Blend grain parents may affect the consistency of amino acid composition in HOM grain. Results of this study demonstrate that the levels of several amino acids, including economically important lysine and methionine, were consistently greater in HOM than in NOM grain across a range of production environments. Modelling with livestock ration balancing software showed that the additional amino acids and oil in HOM added 12–20% to its value as livestock feed.
BMC Research Notes | 2018
Naser Alkhalifah; Darwin A. Campbell; Celeste M. Falcon; Jack M. Gardiner; Nathan D. Miller; Maria C. Romay; Ramona L. Walls; Renee Walton; Cheng-Ting Yeh; M. Bohn; Jessica Bubert; Edward S. Buckler; Ignacio A. Ciampitti; Sherry Flint-Garcia; Michael A. Gore; Christopher Graham; Candice N. Hirsch; James B. Holland; David C. Hooker; Shawn M. Kaeppler; Joseph E. Knoll; Nick Lauter; Elizabeth C. Lee; Aaron J. Lorenz; Jonathan P. Lynch; Stephen P. Moose; Seth C. Murray; Rebecca J. Nelson; Torbert Rocheford; Oscar Rodriguez
ObjectivesCrop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available.Data descriptionDatasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.
Crop Management | 2014
Ramarao Venkatesh; Peter R. Thomison; Colette K. Gabriel; Mark A. Bennett; Elaine M. Grassbaugh; Matthew D. Kleinhenz; Scott A. Shearer; Santosh K. Pitla
Seed tape has recently received attention as an alternative planting system for smallholder farmers in underdeveloped regions of South America, Africa, China, and India (Mateus, 2014). Seed companies are also developing seed-tape planting systems for germplasm evaluations (Deppermann et al., 2013). Although seed tape has been promoted as a method for ensuring uniform seed spacing and plant density of smallseeded flowers, herbs, and vegetables (Chancellor, 1969), little or no information is available on the use of seed tape for largerseeded row crops and its effect on crop emergence. The objective of this study was to compare the emergence of corn seed embedded in tape to seeds planted by hand and to determine seed tape effects on rate of corn emergence. Experiments were conducted in 2013 in greenhouses at Ohio State University and consisted of two treatments. Corn seed embedded in tape made of biodegradable cellulose, which is the material most widely used by seed tape manufacturers, was compared with seeds planted by hand. Two corn hybrids were used in the study—Pioneer brand 37Y14 treated with fludioxonil, mefenoxam, azoxystrobin, thiabendaz, and thiamethoxam and DeKalb DKC 65-63 treated with difenoconazole, fludioxonil, mefenoxam, and thiamethoxam. Seed tape and seeds were hand planted 2 inches deep in flats with commercial top soil (Fig. 1). Greenhouse temperature was maintained at 70 to 75°F, and metal halide lamps provided approximately 220 mmol–1 m–2 s–1 supplemental photosynthetic photon flux for a 16-h daily photoperiod. Corn emergence was recorded at the first appearance of coleoptile and monitored for approximately 2 weeks. Mean emergence time (MET) and emergence rate index (ERI) were used to measure how quickly and uniformly the corn emerged after planting. Multiple emergence counts were taken and used to calculate MET and ERI (Karayel and Ozmerzi, 2002). Treatments were arranged in a randomized complete block design replicated three times for each run. The experiment was repeated eight times (total of 24 replications), and a total of 240 seeds was used for each treatment (120 Published in Crop Management DOI 10.2134/CM-2014-0051-BR
Agronomy Journal | 2002
Robert L. Nielsen; Peter R. Thomison; Gregory A. Brown; Anthony L. Halter; Jason Wells; Kirby L. Wuethrich
Agronomy Journal | 2014
Saratha Kumudini; Fernando H. Andrade; Kenneth J. Boote; G. A. Brown; K.A. Dzotsi; G. O. Edmeades; Tom Gocken; M. Goodwin; A. L. Halter; Graeme L. Hammer; Jerry L. Hatfield; James W. Jones; Armen R. Kemanian; Soo-Hyung Kim; Jim R. Kiniry; Jon I. Lizaso; Claas Nendel; R. L. Nielsen; B. Parent; Claudio O. Stöckle; François Tardieu; Peter R. Thomison; Dennis Timlin; Tony J. Vyn; Daniel Wallach; Haishun Yang; Matthijs Tollenaar
Agronomy Journal | 2003
Peter R. Thomison; Allen B. Geyer; L. D. Lotz; Howard J. Siegrist; T. L. Dobbels
Field Crops Research | 2016
Francisco J. Morell; Haishun Yang; Kenneth G. Cassman; Justin van Wart; Roger W. Elmore; Mark A. Licht; Jeffrey A. Coulter; Ignacio A. Ciampitti; Cameron M. Pittelkow; Sylvie M. Brouder; Peter R. Thomison; Joseph G. Lauer; Christopher Graham; Raymond E. Massey; Patricio Grassini
Crop Management | 2004
Peter R. Thomison; Allen B. Geyer; Bert L. Bishop; John R. Young; Edwin Lentz
Agronomy Journal | 2011
Peter R. Thomison; Robert W. Mullen; Patrick E. Lipps; Tom Doerge; Allen B. Geyer
Agronomy Journal | 2005
T. F. Mangen; Peter R. Thomison; Stephen D. Strachan