APPLICATIONS OF STATISTICS TO RESEARCH IN AGRICULTURE

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
901	7310	2090	100%

Progress 11/20/56 to 12/31/13

Outputs
Target Audience: Scientists and students doing agricultural research Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Closing state project/Statistics faculty provided statistical research expertise for IANR. How have the results been disseminated to communities of interest? Closing state project/Statistics faculty provided statistical research expertise for IANR. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Closing state project/Statistics faculty provided statistical research expertise for IANR.

Publications

Progress 10/01/11 to 09/30/12

Outputs
OUTPUTS: Publication in refereed journals. Peer-reviewed presentations and proceedings from meetings with Agriculture-oriented statistical content. Invitied workshops on UNL campus, at regional meetings, and other Agricultural-research-oriented sites on statstical applications (e.g. generalized linear mixed models, nonlinear models, Bayesian methods, recent developments in statistical design, state-of-the-art methods for computational biology and genomic analysis). Research includes methodological development in statistics, innovative applications in response to changing needs of agricultural research, comparative assessment of alternative statistical approaches to current topics. In addition, participants in the project actively collaborate in research of mutual interest and provide statistical assistance to researchers in agriculture and allied disciplines throughout the University of Nebraska system. PARTICIPANTS: Participants in Department of Statistics, UNL: Erin Blankenship, Professor of Statistics; Kent Eskridge, Professor of Statistics; Steve Kachman, Professor of Statistics and Interim Chair; Steve Ladunga, Professor of Statistics, David Marx, Professor of Statistics; Anne Parkhurst, Professor of Statistics; Walt Stroup, Professor of Statistics; Dong Wang, Associate Professor of Statistics TARGET AUDIENCES: 1. Agricultural researchers: use of statistical design and analysis is fundamental to their work. They collaborate with statistics faculty when existing statistical methods are inadequate to fully address research problem posed. Statistician-researcher interaction results in more careful & refined approach to research: questions come up that would not have arisen without these conversations. Even in cases where "standard methods" are used, the collaboration insures the quality of the work. 2. Statistical consultants. As new methods are developed, they are shared with colleagues at other universities, research facilities, and in industry & government. 3. Graduate students in statistics. These are the next generation of statisticians who will work with agricultural researchers. Many are from developing countries, so the impact is world-wide. 4. Grad students in agricultural disciplines. Through our courses and through having statistics faculty co-supervise their research, they learn state-of-the-art approaches to doing their research. 5. The public. When questions of interpretation of statistical results arise, we help sort though what can & cannot be said legitimately. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
As the focus of agricultural research evolves to meet new challenges, the statistical issues (what data to collect, what assumptions are reasonable, how to design, model, analyze & test) continue to be in flux. As ag researchers move into uncharted territory, existing statistical methods are often not adequate or suitable and new theory and methodology must be developed. The methodological & innovative application research done under this project is a prerequisite for ag research to go forward. Journal articles and conference presentations of such research serve an essential enabling role. Collaboration has two impacts: 1) immediate and direct: stimulates new areas of research and many new grant proposals to fund such research -- many of which have been funded; 2) long-term and indirect: collaborations stimulate the formation of long-standing interdisciplinary research teams able to pose problems unlikely to be posed by individual researchers working alone and to address problems individual researchers would be ill-equipped to handle alone. In statistical design, innovative developments (e.g. optimal designs for mixed effects models) and more realistic and accurate planning tools (e.g. power assessment for generalized linear mixed models) allow more efficient use of increasingly limited resources (e.g. tight budgets, dwindling faculty due to hiring slow-downs) and more accurate inference to be drawn from data that are available. Innovations have been incorporated into the curriculum in service courses on statistical methods for agricultural researchers and core curriculum in stat graduate degree program, which trains future ag-oriented statistical consultants & researchers.

Publications

Schwasinger-Schmidt, T. E., S. D. Kachman, and L. G. Harshman. 2012. Evolution of starvation resistance in Drosophila melanogaster: measurement of direct and correlated responses to artificial selection. J Evol Biol 25: 378-387.
Stroup, W. W. 2012. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.
Bbur, Stroup, McCarter, Durham, Young, Christman, West and Kramer. 2012. Analysis of Generalized Linear Mixed Models in the Plant and Natural Resource Science. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.
J.E. Gilley, J.R. Vogel, R.A. Eigenberg, D.B. Marx, and B.L. Woodbury. Nutrient losses in runoff from feedlot surfaces as affected by unconsolidated surface materials stJournal of Soil and Water Conservation 2012 67(3):211-217; doi:10.2489va/jswc.67.3.211
J.C. Nickel, Y.M. Gonzalez, W.D. McCall, R. Ohrbach, D.B. Marx, H. Liu, and L.R. Iwasaki. Muscle Organization in Individuals with and without Pain and Joint Dysfunction J DENT RES June 2012 91: 568-573
Wang, J., Green, J. R., Samal, A., Marx, D.B. (2011). Quantifying articulatory distinctiveness of vowels, Interspeech, 277-280.
K. Lora, N. Lewis, K.M. Eskridge, K. Stanek-Krogstrand, and D. Travnicek. 2011. Correlation of omega-3 fatty acids intakes with acculturation and socioeconomic status in Midwestern Latinas. Journal of Immigrant and Minority Health. 13(1):111-118. E. Nowick, D. A. Travnicek and K. M. Eskridge. 2010. A comparison of term clusters for tokenized words collected from controlled vocabularies, user keyword searches, and online documents. Library Philosophy and Practice. November. ISSN 1522-0222. http://0-www.webpages.uidaho.edu.library.unl.edu/~mbolin/lpp.htm
Baenziger, P. S., Dweikat, I., Gill, K., Eskridge, K. M., et al., 2011. Understanding Grain Yield: It Is a Journey, Not a Destination. Czech Journal of Genetics and Plant Breeding. 47(SI):S77-S84.
Wang, Yi, Eskridge, Kent M. and Nadarajah, Saralees. Optimal Design of Mixed-Effects PK/PD Models Based on Differential Equations. 2012. Journal of Biopharmaceutical Statistics. 22(1):180-205.
Serba, D.S., O. Gulsen, B.G. Abeyo, K.L. Amundsen, D.J.Lee, P.S. Baenziger, T. M. Heng-Moss, K.M. Eskridge and R. C. Shearman. 2012. Turfgrass Performance of Diploid Buffalograss (Buchloe dactyloides (Nutt.) Englem) Half-sib Populations. HortScience 47(2):185-188.
Brueggeman AJ, Gangadharaiah DS, Cserhati MF, Casero D, Weeks DP, Ladunga I. (2012) Activation of the carbon concentrating mechanism by CO2 deprivation coincides with massive transcriptional restructuring in Chlamydomonas reinhardtii. Plant Cell. 2012 May;24(5):1860-75. Epub 2012 May 25. PMID: 22634764. Impact factor: 10.224. Highlighted article.
Fang W, Si Y, Douglass S, Casero D, Merchant SS, Pellegrini M, Ladunga I, Liu P, Spalding MH. (2012) Transcriptome-wide changes in Chlamydomonas reinhardtii gene expression regulated by carbon dioxide and the CO2-concentrating mechanism regulator CIA5/CCM1. Plant Cell. 2012 May;24(5):1876-93. Epub 2012 May 25. PMID: 22634760. Impact factor: 10.224. Highlighted article.
Blanc G, Agarkova I, Grimwood J, Kuo A, Brueggeman A, Dunigan DD, Gurnon J, Ladunga I, Lindquist E, Lucas S, Pangilinan J, Proschold T, Salamov A, Schmutz J, Weeks D, Yamada T, Lomsadze A, Borodovsky M, Claverie JM, Grigoriev IV, Van Etten JL. (2012) The genome of the polar eukaryotic microalga Coccomyxa subellipsoidea reveals traits of cold adaptation. Genome Biol. 2012 May 25;13(5):R39. PMID: 22630137. Impact factor: 9.04.
Panda D, Das A, Dinh PX, Subramaniam S, Nayak D, Barrows NJ, Pearson JL, Thompson J, Kelly DL, Ladunga I, Pattnaik AK. (2011) RNAi screening reveals requirement for host cell secretory pathway in infection by diverse families of negative-strand RNA viruses. Proc Natl Acad Sci U S A. 2011 Nov 22;108(47):19036-41. Epub 2011 Nov 7. PMID: 22065774. Impact factor: 9.681.
Friedow, A., Blankenship, E. E., Green, J. L., & Stroup, W. W. (2012). Am I comfortable there: Engaging difference, learning interdisciplinary pedagogies. Pedagogy, 12(3), 403-422.
Ma, J., A. K. Benson, S. D. Kachman, Z. Hu, and L. G. Harshman. 2012. Drosophila melanogaster Selection for Survival of Bacillus cereus Infection: Life History Trait Indirect Responses. International Journal of Evolutionary Biology 2012: 12.
McKnite, A. M., M. E. Perez-Munoz, L. Lu, E. G. Williams, S. Brewer, P. A. Andreux, J. W. M. Bastiaansen, X. Wang, S. D. Kachman, J. Auwerx, R. W. Williams, A. K. Benson, D. A. Peterson, and D. C. Ciobanu. 2012. Murine Gut Microbiota Is Defined by Host Genetics and Modulates Variation of Metabolic Traits. Plos One 7: e39191.

Progress 10/01/10 to 09/30/11

Outputs
OUTPUTS: Publication in refereed journals. Peer-reviewed presentations and proceedings from meetings with Agriculture-oriented statistical content. Invitied workshops on UNL campus, at regional meetings, and other Agricultural-research-oriented sites on statstical applications (e.g. generalized linear mixed models, nonlinear models, Bayesian methods, recent developments in statistical design, state-of-the-art methods for computational biology and genomic analysis). Research includes methodological development in statistics, innovative applications in response to changing needs of agricultural research, comparative assessment of alternative statistical approaches to current topics. In addition, participants in the project actively collaborate in research of mutual interest and provide statistical assistance to researchers in agriculture and allied disciplines throughout the University of Nebraska system. PARTICIPANTS: Participants in Department of Statistics, UNL: Erin Blankenship, Associate Professor of Statistics; Kent Eskridge, Professor of Statistics; Steve Kachman, Professor of Statistics and Interim Chair; David Marx, Professor of Statistics; Anne Parkhurst, Professor of Statistics; Walt Stroup, Professor of Statistics; Dong Wang, Associate Professor of Statistics TARGET AUDIENCES: 1. Agricultural researchers: use of statistical design and analysis is fundamental to their work. They collaborate with statistics faculty when existing statistical methods are inadequate to fully address research problem posed. Statistician-researcher interaction results in more careful & refined approach to research: questions come up that would not have arisen without these conversations. Even in cases where "standard methods" are used, the collaboration insures the quality of the work. 2. Statistical consultants. As new methods are developed, they are shared with colleagues at other universities, research facilities, and in industry & government. 3. Graduate students in statistics. These are the next generation of statisticians who will work with agricultural researchers. Many are from developing countries, so the impact is world-wide. 4. Grad students in agricultural disciplines. Through our courses and through having statistics faculty co-supervise their research, they learn state-of-the-art approaches to doing their research. 5. The public. When questions of interpretation of statistical results arise, we help sort though what can & cannot be said legitimately. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
As the focus of agricultural research evolves to meet new challenges, the statistical issues (what data to collect, what assumptions are reasonable, how to design, model, analyze & test) continue to be in flux. As ag researchers move into uncharted territory, existing statistical methods are often not adequate or suitable and new theory and methodology must be developed. The methodological & innovative application research done under this project is a prerequisite for ag research to go forward. Journal articles and conference presentations of such research serve an essential enabling role. Collaboration has two impacts: 1) immediate and direct: stimulates new areas of research and many new grant proposals to fund such research -- many of which have been funded; 2) long-term and indirect: collaborations stimulate the formation of long-standing interdisciplinary research teams able to pose problems unlikely to be posed by individual researchers working alone and to address problems individual researchers would be ill-equipped to handle alone. In statistical design, innovative developments (e.g. in supersaturated designs) and more realistic and accurate planning tools (e.g. power assessment for generalized linear mixed models) allow more efficient use of increasingly limited resources (e.g. tight budgets, dwindling faculty due to hiring slow-downs) and more accurate inference to be drawn from data that are available. Innovations have been incorporated into the curriculum in service courses on statistical methods for agricultural researchers and core curriculum in stat graduate degree program, which trains future ag-oriented statistical consultants & researchers.

Publications

Abendroth, J. A., Blankenship, E. E., Martin, A. R. and Roeth, F. W. (2011) "Joint Action Analysis Utilizing Concentration Addition and Independent Action Models," Weed Technology, 25: 436-446.
Ali, M. L., P. S. Baenziger, Z. Al Ajlouni, B. T. Campbell, K. S. Gill, K. M. Eskridge, A. Mujeeb-Kazi and I. Dweikat. 2011. Mapping QTLs for Agronomic Traits on Wheat Chromosome 3A and a Comparison of Recombinant Inbred Chromosome Line Populations. Crop Science. 51:553-566.
Mi, X., K. M. Eskridge, V. George and D. Wang. 2011. Structural Equation Modeling of Gene-Environment Interactions in CHD . Annals of Human Genetics. 75:255-265.
Mi, X., K. M. Eskridge D. Wang P. S. Baenziger B. T. Campbell K. S. Gill I. Dweikat and J. Bovaird. 2010. Regression based Multi-trait QTL mapping using a structural equation model. 2010. Statistical Applications in Genetics and Molecular Biology. 9(1):article 38:1-21. DOI: 10.2202/1544-6115.1552 . http://www.bepress.com/sagmb/vol9/iss1/art38
Benson, A. K., S. A. Kelly, R. Legge, F. R. Ma, S. J. Low, J. Kim, M. Zhang, P. L. Oh, D. Nehrenberg, K. J. Hua, S. D. Kachman, E. N. Moriyama, J. Walter, D. A. Peterson, and D. Pomp. 2010. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proceedings of the National Academy of Sciences of the United States of America 107: 18933-18938.
Harshman, L. G., K. D. Song, J. Casas, A. Schuurmans, E. Kuwano, S. D. Kachman, L. M. Riddiford, and B. D. Hammock. 2010. Bioassays of compounds with potential juvenoid activity on Drosophila melanogaster: juvenile hormone III, bisepoxide juvenile hormone III and methyl farnesoates. J Insect Physiol 56: 1465-1470.
Kocher, M. F., J. M. Coleman, J. A. Smith, and S. D. Kachman. 2011. Corn Seed Spacing Uniformity as Affected by Seed Tube Condition. Appl. Eng. Agric. 27: 177-183.
Lora, K., N. Lewis, K.M. Eskridge, K. Stanek-Krogstrand, P. Ritter-Gooder. 2010. Validity and Reliability of an Omega-3 Fatty Acid Food Frequency Questionnaire for First Generation Midwestern Latinas. Nutrition Research. 30(8):550 - 557.
Mamo, M., Ippolito, J., Kettler, T., Reuter, R., McCallister, D., Morner, P., Hussmann, D. and Blankenship, E. (2011) "Learning Gains and Responses to Digital Lessons on Soil Genesis and Development," Journal of Geoscience Education, 59: 194-204.
Montesinos-Lopez, O. A., A. Montesinos-Lopez Abelardo, J. Crossa, K. M. Eskridge, R. A. Saenz. 2011. Optimal sample size for estimating the proportion of transgenic plants using the Dorfman model with a random confidence interval. . Seed Science Research. 21(3):235-245
Nowick, E., D. A. Travnicek and K. M. Eskridge. 2010. A comparison of term clusters for tokenized words collected from controlled vocabularies, user keyword searches, and online documents. Library Philosophy and Practice. November. ISSN 1522-0222. http://www.webpages.uidaho.edu.library.unl.edu/~mbolin/lpp.htm
Okalebo, J., Yuen, G. Y., Drijber, R. A., Blankenship, E. E., Eken, C. and Lindquist, J. L. (2011) "Biological Suppression of Velvetleaf (Abutilon theophrasti ) in an Eastern Nebraska Soil," Weed Science, 59: 155-161.
Otto-Hanson, Lindsey , James R. Steadman, Rebecca Higgins and Kent M. Eskridge. 2011. Variation in Sclerotinia sclerotiorum Bean Isolates from Multi-site Resistance Screening Locations. Plant Disease. 95(11):1370-1377.
Parkhurst, A.M. 2010. Model for understanding thermal hysteresis during heat stress: A matter of direction. Int J Biometeorol 54:637-645 DOI 10.1007/s00484-009-0299-z
Ritter-Gooder, P. , N.M. Lewis, K. Lora and K. M. Eskridge. 2011. Content Validation of a Standardized Language Diagnosis by Certified Specialists in Gerontological Nutrition. J. Am. Diet. Assoc. 111(4):561-566.
Schmidt, J. J., Blankenship, E. E. and Lindquist J. L. (2011) "Corn and Velvetleaf (Abutilon theophrasti ) Transpiration in Response to Drying Soil," Weed Science, 59: 50-54.
Stamm, M. D., Baxendale, F. P., Heng-Moss, T. M., Siegfried, B. D., Blankenship, E. E. and Gaussoin, R. (2011) "Dose-Response Relationships of Clothianidin, Imidacloprid, and Thiamethoxam to Blissus occiduus (Hemiptera: Blissidae)," Journal of Economic Entomology, 104: 205-210.
Tan, S. Y., Cayabyab, B. F., Alcantara, E. P., Ibrahim, Y. B., Huang, F., Blankenship, E. E. and Siegfried. B. D. (2011) "Comparative susceptibility of Ostrinia furnacalis, Ostrinia nubilalis and Diatraea saccharalis (Lepidoptera: Crambidae) to Bacillus thuringiensis Cry1 toxins," Crop Protection, 30: 1184-1189.
Wang D, Eskridge KM and Crossa J. 2011. Identifying QTLs and Epistasis in Structured Plant Populations Using Adaptive Mixed LASSO. Journal of Agricultural, Biological, and Environmental Statistics. 16(2):170-184.
Volesky, J. D., Schacht, W. H., Koehler, A. E., Blankenship, E. and Reece, P. E. (2011) "Defoliation Effects on Herbage Production and Root Growth of Wet Meadow Forage Species,"Range and Ecology and Management, 64: 506-513.
Wegulo, Stephen N., William W. Bockus, John Hernandez Nopsa, Erick D. De Wolf , Kent M. Eskridge, K.H.S. Peiris, and Floyd E. Dowell. 2011. Effects of Integrating Cultivar Resistance and Fungicide Application 1 on Fusarium Head Blight and Deoxynivalenol in Winter Wheat . Plant Disease 95(5):554-560.
Yang, F., A. M. Parkhurst, T.M. Brown-Brandl, R.A. Eigenberg, and J.A. Nienaber, 2010. Evaluating pen-day interactions in body temperature bilogistic mixed model for handling of feedlot heifers during heat stress, Proceedings of Twenty-Second Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 198-211 http://www.k-state.edu/stats/agstat.conference/2010
Yang, F., A. M. Parkhurst, D. A. Spiers, J. B. Gaughan, T. L. Mader and G. L. Hahn, 2010. Characterizing thermal hysteresis in body temperature of heat stressed steers, Proceedings of Twenty-Second Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 198-211 http://www.k-state.edu/stats/agstat.conference/2010

Progress 10/01/09 to 09/30/10

Outputs
OUTPUTS: Publication in refereed journals. Peer-reviewed presentations and proceedings from meetings with Agriculture-oriented statistical content. Invitied workshops on UNL campus, at regional meetings, and other Agricultural-research-oriented sites on statstical applications (e.g. generalized linear mixed models, nonlinear models, Bayesian methods, recent developments in statistical design, state-of-the-art methods for computational biology and genomic analysis). Research includes methodological development in statistics, innovative applications in response to changing needs of agricultural research, comparative assessment of alternative statistical approaches to current topics. In addition, participants in the project actively collaborate in research of mutual interest and provide statistical assistance to researchers in agriculture and allied disciplines throughout the University of Nebraska system. PARTICIPANTS: Participants in Department of Statistics, UNL: Erin Blankenship, Associate Professor of Statistics; Kent Eskridge, Professor of Statistics; Steve Kachman, Professor of Statistics and Interim Chair; David Marx, Professor of Statistics; Anne Parkhurst, Professor of Statistics; Walt Stroup, Professor of Statistics; Dong Wang, Assistant Professor of Statistics TARGET AUDIENCES: 1. Agricultural researchers: use of statistical design and analysis is fundamental to their work. They collaborate with statistics faculty when existing statistical methods are inadequate to fully address research problem posed. Statistician-researcher interaction results in more careful & refined approach to research: questions come up that would not have arisen without these conversations. Even in cases where "standard methods" are used, the collaboration insures the quality of the work. 2. Statistical consultants. As new methods are developed, they are shared with colleagues at other universities, research facilities, and in industry & government. 3. Graduate students in statistics. These are the next generation of statisticians who will work with agricultural researchers. Many are from developing countries, so the impact is world-wide. 4. Grad students in agricultural disciplines. Through our courses and through having statistics faculty co-supervise their research, they learn state-of-the-art approaches to doing their research. 5. The public. When questions of interpretation of statistical results arise, we help sort though what can & cannot be said legitimately. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
As the focus of agricultural research evolves to meet new challenges, the statistical issues (what data to collect, what assumptions are reasonable, how to design, model, analyze & test) continue to be in flux. As ag researchers move into uncharted territory, existing statistical methods are often not adequate or suitable and new theory and methodology must be developed. The methodological & innovative application research done under this project is a prerequisite for ag research to go forward. Journal articles and conference presentations of such research serve an essential enabling role. Collaboration has two impacts: 1) immediate and direct: stimulates new areas of research and many new grant proposals to fund such research -- many of which have been funded; 2) long-term and indirect: collaborations stimulate the formation of long-standing interdisciplinary research teams able to pose problems unlikely to be posed by individual researchers working alone and to address problems individual researchers would be ill-equipped to handle alone. In statistical design, innovative developments (e.g. in supersaturated designs) and more realistic and accurate planning tools (e.g. power assessment for generalized linear mixed models) allow more efficient use of increasingly limited resources (e.g. tight budgets, dwindling faculty due to hiring slow-downs) and more accurate inference to be drawn from data that are available. Innovations have been incorporated into the curriculum in service courses on statistical methods for agricultural researchers and core curriculum in stat graduate degree program, which trains future ag-oriented statistical consultants & researchers.

Publications

Henry C.G., D. D Schulte, S.J. Hoff, L.D. Jacobson, A.M. Parkhurst. 2010. Comparison of Ambient Odor Assessment Techniques in a Controlled Environment. Proceedings of International Symposium on Air Quality and Manure Management for Agriculture. 13-16 September, Dallas, TX
K. J. Hanford, R. M. Thallman, S. D. Kachman, L. A. Kuehn, R. L. Quaas, R. J. Tempelman, R. L. Fernando, E. J. Pollak. 2010. Estimation of The Proportion of Variation Accounted For By DNA Tests. I. Genetic Variance. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany, August 1 - 6. Electronic 822.
Korus, K. A., M. E. Conley, E. E. Blankenship and Ellen T. Paparozzi. 2010. Storage and breakdown of starch aid P. parviflorus in leaf re-greening after nitrogen deficiency. Review of Undergraduate Research in Agriculture and the Life Sciences 5(1):1-16.
Laura Rei Iwasaki, Michael Crosby, Yoly Gonzalez, Willard D. McCall, David B. Marx, Richard Ohrbach, Jeffrey Charles Nickel, (2009) Temporomandibular joint loads in subjects with and without disc displacement, Orthopedic Reviews, Vol 1, No 2.
Li, X. M. Parkhurst, T.L. MadeR, 2009. Comparing experimental designs for a bi-logistical model used to estimate heat stress when moving feedlot cattle, Proceedings of Twenty-First Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 182-197
Liang. B., A.M. Parkhurst, K.G. Gebremedhin, C.N. Lee, R.J. Collier, P.E. Hillman, 2009. Using time series to study dynamics of sweat rates of Holstein cows exposed to initial and prolonged solar heat stress, Proceedings of Twenty-First Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 171-181 http://www .k-state.edu/stats/agstat.conference/2009
Lora, Karina R., Lewis, Nancy M., Eskridge, Kent M. Stanek-Krogstrand, Kaye, Travnicek, Daryl A. Correlation of omega-3 fatty acids intakes with acculturation and socioeconomic status in Midwestern Latinas. J. Immigrant Minority Health. DOI 10.1007/s10903-009-9314-z. January, 23, 2010.
Mengistu, N. P. S. Baenziger, L.A. Nelson, K.M. Eskridge, R. N. Klein, D. D. Baltensperger, and R. W. Elmore. 2010. Grain yield performance and stability of cultivar blends vs. component cultivars of hard winter wheat in Nebraska. Crop Sci. 50: 617-623.
X. Mi K. M. Eskridge D. Wang P. S. Baenziger B. T. Campbell K. S. Gill I. Dweikat. 2010. Bayesian mixture structural equation modelling in multiple-trait QTL mapping. Genetics Research Cambridge. 92:239-250. Y.Wang, K.M. Eskridge, S. Nadarajah, A.T. Galecki. 2009. Bayesian and non-Bayesian analysis of mixed-effects PK/PD models based on differential equations. Monte Carol Methods and Applications 15(2):145-167.
Yanai-Balser GM, Duncan GA, Eudy JD, Wang D, Li X, Agarkova IV, Dunigan DD, Van Etten1 JL. 2010. Microarray analysis of chlorella virus PBCV-1 transcription. Journal of Virology, 84:532-542.
Yong, C.K., Kent M. Eskridge, Chris R. Calkins and Wendy J. Umberger. 2010. Assessing consumer preferences for rib-eye steak characteristics using confounded factorial conjoint choice experiments. J. of Muscle Foods. 21(2):224-242.
Zeng, Y, A.M. Parkhurst, J. Pantoja. 2009. Using time series to study effect of air temperature and humidity on body temperature of cows in Puerto Rico, Proceedings of Twenty-First Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 198-211 http://www .k-state.edu/stats/agstat.conference/2009
Alexandridis, T.K., Katagis, T., Gitas, I.Z., Silleos, N., Eskridge, K.M., and Gritzas, G., 2010. Investigation of aggregation effects in vegetation condition monitoring at a national scale. International Journal of Geographical Information Science. 24(4):507-521.
Coffman, B.A., M.F. Kocher, V.I. Adamchuk, R.M. Hoy, and E.E. Blankenship. 2010. Testing fuel efficiency of a tractor with a continuously variable transmission. Applied Engineering in Agriculture 26(1):31-36.
Harshman, L. G., K. Song, J. Casas, A. Schuurmans, E. Kuwano, S. D. Kachman, L. M. Riddiford, B. D. Hammock, 2009 Bioassays of compounds with potential juvenoid activity on Drosophila melanogaster: Juvenile hormone III, bisepoxide juvenile hormone III and methyl farnesoates, Journal of Insect Physiology, Volume 56, 1465-1470.
Pathak, M, A. M. Parkhurst, R.A. Arias, T.L. Mader, 2009. Comparative Study of Time Series and Multiple Regression for Modeling Dependence of Cattle Body Temperature on Environmental Variables during Heat Stress, Proceedings of Twenty-First Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 85-106 http://www .k-state.edu/stats/agstat.conference/2009
Osval Antonio Montesinos Lopez, Abelardo Montesinos Lopez, Jose Crossa, Kent Eskridge and Carlos Moises Hernandez Suarez. 2010. Sample size for detecting and estimating the proportion of transgenic plants with narrow confidence intervals. Seed Science Research. 20:123-136
Papadopoulos, F., C. Prochaska, A. Papadopoulos, K. M. Eskridge and I. Kalavrouziotis. 2009. Mn and Zn micronutrients in acidic soils and source identification using multivariate methods. Communications in Soil Science and Plant Analysis. 40:2358-2371.
Parkhurst, A.M.. 2010. Model for Understanding Thermal Hysteresis during Heat Stress: A Matter of Direction, International Journal of Biometeorology. Springer. Published online: http://www.springerlink.com/content/gp2x266nx7879503/ and selected for publication in Int J Biometeorol (2010) 54:637-645
Stilwell, A. R., R. J. Wright, T. E. Hunt, and E. E. Blankenship. 2010. Degree-day requirements for alfalfa weevil (Coleoptera: Curculionidae) development in eastern Nebraska. Environ. Entomol. 39: 202-209.
Thallman, R.M., K. J. Hanford, S. D. Kachman, L. A. Kuehn, R. L. Quaas, R. J. Tempelman, R. L. Fernando, E. J. Pollak. 2010. Estimation of The Proportion of Variation Accounted For By DNA Tests. II. Phenotypic Variance. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production, Leipzig, Germany, August 1 - 6. Electronic 918.
Wang D. 2010. Modeling Epigenetic Modifications under Multiple Treatment Conditions. Computational Statistics and Data Analysis, 54: 1179-1189.
Wang D. 2009. Association analysis in structured plant populations, an adaptive mixed LASSO approach. Wang D. Biotechnology and Bioinformatics Symposium, Lincoln, NE, October 2009.

Progress 10/01/08 to 09/30/09

Outputs
OUTPUTS: Publication in refeered journals. Peer-reviewed presentations and proceedings from meetings with Agriculture-oriented statistical content. Invitied workshops on UNL campus, at regional meetings, and other Agricultural-research-oriented sites on sttstical applications (e.g. generalized linear mixed models, nonlinear models, Bayesian methods, recent developments in statistical design, state-of-the-art methods for bionformatics and genomic analysis). Research includes methodological development in statistics, innovative applications in response to changing needs of agricultural research, comparitive assessment of alternative statistical approaches to current topics. In addition, pariticpants in the project actively collaborate in research of mutual interest and provide statistical assistance to researchers in agriculture and allied disciplines thoughout the University of Nebraska system. PARTICIPANTS: Participants in Department of Statistics, UNL: Erin Blankenship, Associated Professor of Statistics; Kent Eskridge, Professor of Statistics; Steve Kachman, Professor of Statistics; David Marx, Professor of Statistics; Anne Parkhurst, Professor of Statistics; Walt Stroup, Dept Chair & Professor of Statistics; Dong Wang, Assistant Professor of Statistics TARGET AUDIENCES: Target audiences: 1. Agricultural researchers: use of statistical design and analysis is fundamental to their work. They collaborate with statistics faculty when existing statistical methods are inadequate to fully address research problem posed. Statistician-researcher interaction results in more careful & refined approach to research: questions come up that would not have arisen without these conversations. Even in cases where "standard methods" are used, the collaboration insures the quality of the work. 2. Statistical consultants. As new methods are developed, they are shared with colleagues at other universities, research facilities, and in industry & government. 3. Graduate students in statistics. These are the next generation of statisticians who will work with agricultural researchers. Many are from developing countries, so the impact is world-wide. 4. Grad students in agricultural disciplines. Through our courses and through having statistics faculty co-supervise their research, they learn state-of-the-art approaches to doing their research. 5. The public. When questions of interpretation of statistical results arise, we help sort though what can & cannot be said legitimately. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
As the focus of agricultural research evolves to meet new challenges, the statistical issues (what data to collect, what assumptions are reasonable, how to design, model, analyze & test) are in flux. As ag researchers move into uncharted territory, exisiting statistical methods are often not adequate or suitable and new theory and methodology must be developed. The methodological & innovative application research done under this project is a prerequisite for ag research to go forward. Journal articles and conference presentations of such research serve an essential enabling role. Collaboration has two impacts: 1) immediate and direct: stimulates new areas of research and many new grant proposals to fund such research -- many of which have been funded; 2) long-term and indirect: collaborations stimulate the formation of long-standing interdisciplinary research teams able to pose problems unlikely to be posed by individual researchers working alone and to address problems individual researchers would be ill-equipped to handle alone. In statistical design, innovative developments (e.g. in supersaturated designs) and more realistic and accurate planning tools (e.g. power assessment for generalized linear mixed models) allow more efficient use of increasingly limited resources (e.g. tight budgets, dwindling faculty due to hiring slow-downs) and more accurate inference to be drawn from data that are available. Innovations have been incorporated into the curriculum in service courses on statistical methods for agricultural researchers and core curriulum in stat graduate degree program, which trains future ag-oriented statistical consultants & researchers.

Publications

S.Y. Lee, K. M. Eskridge, Woonyuen Koh and M. A. Hanna. 2009. Preparation and functional properties of starch-based packaging foams as affected by ingredients and speed using a supersaturated split-plot design. Industrial Crops and Products. 29:427-36
T.W. Crawford, Jr, K.M. Eskridge, C.G. Wang, and J.W. Maranville. 2009. Multi-compartmental modeling of nitrogen translocation in sorghums (Sorghum bicolor (L.) Moench) differing in nitrogen use efficiency. J. of Plant Nutrition. 32(2):335-349.
Yang, Z., Kim, J., Zhang, C., Zhang, M., Nietfeldt, J., Southward, C.M., Surette, M.G., Kachman, S.D. & Benson, A.K. Genomic instability in regions adjacent to a highly conserved pch prophage in Escherichia coli O157:H7 generates diversity in expression patterns of the LEE pathogenicity island. J Bacteriol, Department of Food Science and Technology, University of Nebraska, Lincoln, NE 68583-0919, USA., 2009, Vol. 191(11), pp. 3553-3568
Prokupek, A.M., Kachman, S.D., Ladunga, I. & Harshman, L.G. Transcriptional profiling of the sperm storage organs of Drosophila melanogaster. Insect Mol Biol, School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, USA., 2009, Vol. 18(4), pp. 465-475
T. Hooks, D. B. Marx, S. D. Kachman, and J. F. Pedersen, Optimality Criteria for Models with Random Effects, Revista Colombiana de Estadistica, 2009
Mello, A., N. A. Streck, E. E. Blankenship, and Ellen T. Paparozzi. 2009. Gibberellic Acid Promotes Seed Germination in Penstemon digitalis cv. Husker Red. HortScience 44: 870 - 873.
Puckett, H.L, J.R. Brandle, R.J. Johnson, and E.E. Blankenship (2009) Avian foraging patterns in crop field edges adjacent to woody habitat. Agriculture Ecosystems and Environment 131:9-15.
Pushpadass, H.A., Marx, D.B., R.L. Wehling and Hanna, M.A. 2009. Extrusion and characterization of starch films. Cereal Chem 86(1):44-51.

Progress 10/01/07 to 09/30/08

Outputs
OUTPUTS: Refereed publications - journal articles and proceedings of Agricultural-oriented statistics conferences - of methodological developments, original applications, and comparitive assessment of statistical design and analysis for agricultural research. Specific areas include: new models for mixed (fixed and random effect) model analysis for microarray & related genomics applications; nonlinear mixed models & chaos models to detect or monitor environmental impact on ecosystems, animal and plant physiology, etc.; use of structural equation modeling for a variety of applications including genotype-by-environment interaction; innovative use of experimental design to improve the efficiency & accuracy of agricultural research (supersaturated designs, extensions of response surface designs, designs for spatial data). Project memebers have conducted wokshops for agricultural researchers and for statisticians who consult with agricultural researchers. These workshops have been conducted at UNL, by invitation at regional and national meetings both in statistics and agricultural disciplines. Major workshop at national American Statistical Association meetings on generalized linear mixed models, including new section on use of GLMM theory to assess power & hence use it as a tool for planning efficient experiments. PARTICIPANTS: Participants in Department of Statistics, UNL: Erin Blankenship, Associated Professor of Statistics Kent Eskridge, Professor of Statistics Steve Kachman, Professor of Statistics David Marx, Professor of Statistics Anne Parkhurst, Professor of Statistics Walt Stroup, Dept Chair & Professor of Statistics Dong Wang, Assistant Professor of Statistics TARGET AUDIENCES: Target audiences: 1. Agricultural researchers: use of statistical design and analysis is fundamental to their work. They collaborate with statistics faculty when existing statistical methods are inadequate to fully address research problem posed. Statistician-researcher interaction results in more careful & refined approach to research: questions come up that would not have arisen without these conversations. Even in cases where "standard methods" are used, the collaboration insures the quality of the work. 2. Statistical consultants. As new methods are developed, they are shared with colleagues at other universities, research facilities, and in industry & government. 3. Graduate students in statistics. These are the next generation of statisticians who will work with agricultural researchers. Many are from developing countries, so the impact is world-wide. 4. Grad students in agricultural disciplines. Through our courses and through having statistics faculty co-supervise their research, they learn state-of-the-art approaches to doing their research. 5. The public. When questions of interpretation of statistical results arise, we help sort though what can & cannot be said legitimately. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Numerous publications of both stat-methodlogy articles presenting new theory & methods and subject matter articles using the new methods to enable the research. Collaboration has stimulated new areas or research and many new grant proposals to fund such research -- many of which have been funded. Innovations have been incorporated into curriculum in statistical methods for agricultural researchers service courses and core curriulum in stat graduate degree program, which trains future ag-oriented statistical consultants & researchers. Design & analysis innovation both responds to needs of agricultural researcers who pose problems for which existing statistical methods are inadequate AND stimulates new areas of inquiry. Indirect impact is that these collaborations lead to the formation of long-standing interdisciplinary research teams able to address problems individual researchers would be ill-equipped to handle alone.

Publications

8. Chen, C. Y., Kachman, S. D., Johnson, R. K., Newman, S., Van Vleck, L. D. Estimation of genetic parameters for average daily gain using models with competition effects J. Anim Sci. 2008 86: 2525-2530
1. Combining classical trait and microarray data to dissect transcriptional regulation: a case study. Dong Wang and Dan Nettleton (2008). Theoretical and Applied Genetics, 116: 683-690.
2. Wang, Y., Eskridge, K. M., and Zhang, S. (2008). Semiparametric mixed analysis on PK/PD Models using differential equations. Journal of Pharmacokinetics and Pharmacodynamics. 35(4): 443-63.
3. Searle, C.L., M.F. Kocher, J.A. Smith, and E.E. Blankenship. 2008. Field slope effects on uniformity of corn seed spacing for three precision planter metering systems. Applied Engineering in Agriculture 24(5):581-586.
4. Zhou, M., A..M. Parkhurst, B.C. Pollard, R.J. Collier, 2007. Using a nonlinear crossed random effects model with three-way treatment structure for detecting circadian patterns of hormones in heat stressed Holsteins. Proceedings of Nineteenth Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 126-143
5. Tu,C., A. M. Parkhurst, L. M. Durso, R. W. Hutkins, 2007. Using nonlilnear fixed and mixed model with switch functions to allow for hormesis in growth of Escherichia. Proceedings of Nineteenth Annual Kansas State University Conference on Applied Statistics in Agriculture Proceedings. 180-195
6. Sethuramasamyraja, B., V.I. Adamchuk, A. Dobermann, D.B. Marx, D.D. Jones, and G.E. Meyer. 2008. Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents. Computers and Electronics in Agriculture 60(2): 212-225.
7. Hay, C.H., T.G. Franti, D.B. Marx, E.J. Peters and L.W. Hesse. 2008. Macroinvertebrate Drift Density in Relation to Abiotic Factors in the Missouri River. Hydrobiologia, 598:175-189.

Progress 10/01/06 to 09/30/07

Outputs
Research developing, extending, and evaluating methodology in spatial statistics, innovations in design of experiments, statistical modeling (structural equation, generalized and mixed, linear and nonlinear), genomic and bioinformatics has been published both by statistics faculty and in collaboration with researchers in many agricultural disciplines. These methods have been used in plant selection, evaluation of farm management systems (e.g. for animal health, waste disposal), natural resources, etc. Workshops on spatial statistics, generalized mixed models (including using new SAS Proc GLIMMIX in agricultural research), and statistical genomics have been presented to agriculturally-oriented user groups on UNL campus, at Nebraska regional research centers, and nationally by invitation.

Impacts
Collaboration has resulted in numerous publications in refereed journals and has stimulated new grant proposals, many of which have been funded. Design innovations have helped researchers address increasingly complex agricultural problems to maximize information obtained in the face of often limited resources. SAS for Mixed Models text, published last year (2006) and co-authored by UNL statistics faculty member Stroup, has become an internationally recognized standard reference for the statistical analysis of research in the agricultural & life sciences. This text owes much of its relevance to this project.

Publications

6. P. Dhungana, K. M. Eskridge, P. S. Baenziger, B. T. Campbell, K. S. Gill, I. Dweikat. 2007. Analysis of genotype-by-environment interaction in wheat using chromosome substitution lines and a structural equation model. Crop Science. 47(2):477-484.
7. M. Vargas, J. Crossa, M. Reynolds, P. Dhungana, and K. M. Eskridge. 2007. Structural equation modeling for studying genotype-by-environment interaction of physiological traits affecting yield in wheat. Journal of Agricultural Science.145:151-161
1. Hooks, T., J. F. Pedersen, D. B. Marx, and R, E. Gaussoin 2007. Changing the Support of a Spatial Covariate: A Simulation Study. Crop Sci. 47: 622-626.
2. Schmitz JA, Vogt RJ, Rupp GP, Brodersen BW, Abel JM, Wohlers AR, Marx DB. Factors Associated with Practice Decisions of Nebraska Veterinarians Regarding Type of Practice and Community Size. J Vet Med Educ, Summer 2007; 34: 340 - 349.
3. Petry, D. B., Lunney, J., Boyd, P., Kuhar, D., Blankenship, E. and Johnson, R.K. Differential immunity in pigs with high and low responses to porcine reproductive and respiratory syndrome virus infection. J. Anim Sci. 2007 85: 2075-2092
4. Ananth Parampalli, K.M. .Eskridge, L. Smith, M. M. Meagher, M. C. Mowry and A. Subramanian. 2007. Development of serum-free media in CHO-DG44 cells using a central composite statistical design. Cytotechnology 54 (1): 57-68. Genre: Refereed Journal Article
5. C. A. Prohaska, AnastasiosI. Zouboulis and K. M. Eskridge. 2007. Performance of pilot-scale vertical-flow constructed wetlands as affected by season, substrate, hydraulic load and frequency of application of simulated urban sewage. Ecological Engineering 31:51-66.

Progress 10/01/05 to 09/30/06

Outputs
Personal research and collaboration was done with faculty from other departments and extension centers to improve natural resources management and enhance environmental quality; work in the area of bioinformatics; and study models for use in genetic evaluation in farm animals and plant selection. Statistical research and consultation has been done with extension centers over the course of the year with compilation and interpretation of data for their research as well as how to best gather the statistical information needed to further the agricultural research being done at the centers.

Impacts
Data collected from research and collaboration of grants dealing with farm animals and plant selection was presented in a number of papers, journals, and presentations by faculty from the Statistics department. Collaboration with faculty from other departments also resulted in a number of new grants.

Publications

Guan, J., Eskridge, K. and Hanna, M.A. 2005. Acetylated startch-polylactic acid loose-fill packaging materials. Indust Crops and Prod. 22:2,109-123.
Fufa, H., Baenziger, P.S., Beecher, B.S., Dweikat, I., Graybosch, R.A. and Eskridge, K. 2005. Comparison of phenotypic and molecular marker-based classifications of hard red winter wheat cultivars. Euphytica. 145:133-146.
Martinez, G.E., Koch, R.M., Cundiff, L.V., Gregory, K.E., Kachman, S.D. and Van Vleck, L.D. 2005. Genetic parameters for stayability, stayability at calving and stayability at weaning to specified ages for Hereford cows. Journ of An. Sci. 83: 9,2033-2042.
Sawalha, R.M., Keown, J.F., Kachman, S.D. and Van Vleck, L.D. 2005. Genetic evaluation of dairy cattle with test-day models with autoregressive covariance structures and with a 305-d model. Journ of Dairy Sci. 88:9,3346-3353. Adamchuk, V.I., Wang, C., Marx, D.B., Perrin, R.K. and Dobermann, A. 2005. Assessment of soil mapping value: Part II. Potential profitability. In: Proceedings of the Seventh International Conference on Precision Agriculture, D.J. Mulla, ed. (CD publication). 819-833.
Paparozzi, E.T. and Stroup, W.W. 2005. How to investigate four-way interactions in plants: a new look at response surface methods. HortScience. 130:3, 459-468.

Progress 10/01/04 to 09/30/05

Outputs
Continued work with Agricultural Research Centers in Mead and Clay Center, NE by consulting (Dave Marx and Anne Parkhurst)to continue work already begun on optimization of designs for non-replicated germplasm screening nurseries, soil mapping and applications for developing measurable criteria useful in predicting acceptable well-being of farm animals.

Impacts
By working with researchers in the agricultural research centers, accurate data will be correctly interpreted for information given to the agricultural industry.

Publications

Tyess, D.L., Shea, P.J., and Parkhurst, A.M. 2005 (07/01). Mineralization potential of atrazine and degradation intermediates from clustered characteristics in inoculated soils. Soil and Sediment Contamination.
Sebolai, B., Pedersen, J., Marx, D., Boykin, D. 2004 (11/13). Effect of grid size, control plot density, control plot arrangement, and assumption of random or fixed effects on non-replicated experiments for germplasm screening. Crop Science.
Adamchuk, V.I., Wang., C., Marx, D.B., Perrin, R.K., and Dobermann, A. 2005 (09/01). Assessment of soil mapping value: Part II. Potential profitability. In: Proceedings of the Seventh International Conference on Precision Agriculture. D.J. Mulla, ed. (CD publication) 819-833.
Sethuramasamyraja, B., Adamchuk, V.I., Marx, D.B., and Dobermann, A. 2005 (08/01). Evaluation of ion-selective electrode mthodoloogy for integrated on-the-go mapping of soil chemical properites (pH, K & NO3). ASABE Paper No. 05-1036.
Adamchuk, V.I., Lund, E., Sethuramasamyraja, B., Morgan, M.T., and Dobermann, A. 2005 (06/01). Direct measurement of soil chemical properties on-the-go using ion selective electrodes. Computers and Electronics in Agriculture 48:3,272-294

Progress 10/01/03 to 09/30/04

Outputs
Designs for unreplicated trails were analyzed with remarkable results. We found that best linear unbiased predictors (BLUP's) were very useful indetermining which lines were superior in an unreplicated field experiment. A simulation study with varying numbers and arrangements of check plots showed that a researcher may even use no check plots, but only spatial structure, to evaluate experimental lines of a crop. In addition, one investigator worked closely with dental scientists looking at bone loss and tunrover in human subjects.

Impacts
The designs for unreplicated trials will allow agronomists and plant breeders to effectively evaluate many varieties and adjust their yields or other important characteristics for spatial location. This will allow a more efficient allocation of replicataed trials in the future.

Publications

E. Barbuto, S. Fritz, G. Matkin, D. Marx, 2004 Are Leaders Made or Born: Gender, Education and Age and Leaders use of Influence Tactics and Full Range Leadership Behaviors Psychological Reports
M. Burton, D. Mortensen, D. Marx, J. Lindquist, 2004 Factors affecting the realized niche of common sunflower (Helianthus annuus) in ridge-tillage corn Weed Science
R. Reinhardt, V. Sanderfer, T Meinberg, P. Nummikoske, H. Lee, D. Marx, 2004 Local biochenical markers of bone turnover: relationship to sebsequent density of healing alveolar bone defects Journal of Clinical Periodontology
Iwasaki LR, Crouch LD, Tutor A, Gibson S, Hukmani N, Marx, DB, Nickel JC, 2004 Relationship of tooth movement and cytokines in GCF and whole blood in growing and nongrowing humans American Journal of Orthodontic Dentofacial Orthop
J. Nickel, L. Iwasaki, M. Beatty, D. Marx, 2004 Laboratory stresses and tractional forces on the TMJ disc surface Journal of Dental Research

Progress 10/01/02 to 09/30/03

Outputs
A continuing collaboration with scientists in all disciplines is the focus of this project. I havw aslo continued work with scientists in the dental school. One doctoral student will fisnih this December and work on the design of unreplicated agronomic trials continues. I have published papers in a variety of venues.

Impacts
The designs for unreplicated trials will allow agronomists and plant breeders to effectively evaluate many varieties and adjust their yields or other important characteristics for spatial location. This will allow a more efficient allocation of replicataed trials in the future.

Publications

R. Hensberry, D. Marx, S. Kachman, D. Travnicek, Simulation Study of Spatial Poisson Data Assessing the Inclusion of Spatial Correlation and Non-normality in the Analysis, Conference on Applied Statistics in Agriculture, 2002.
R. Landes, K. Eskridge, S. Baenziger, D. Marx, Are spatial models needed with adequately blocked field trails?, Conference on applied Statistics in Agriculture, 2002.
M. Burton, D. Mortensen, D. Marx, J. Lindquist. Niche effects on wild Helianthus annuus L. in maize (Zea mays L.) nonoculture: Seed germination, emergence and survival. Journal of Weed Science. Accepted and to appear 2002.
E. Bull, D. Marx. Influence of Fish and Habitat Characteristics on Amphibian Communities in High Elevation Lakes in Northeastern Oregon. Northwest Science, 2002, Vol 76, No. 3, 240-248.
B. Yu, R. Perrin, D. Marx. Estimating the Contribution of Sepcific Wheat Varieties to State-level Production. 2003, Conference on Applied Statistics in Agriculture
B. Brayfield, S. Phiri, C. Kankasa, J. Muyanga, H. Mantina, G. Kwenda, J. West, D. Marx, W. Klaskala, C. Mitchell, C. Wood. Postnatal Human Herpesvirus-8 and Human Immunodeficiency Virus-1 Infection In Mothers and Infants from Zambia. Journal of Infectious Diseases. 2003. accepted.
A. Wilson, M. Schmid, D. Marx, R. Reinhardt. Bone Turnover Markers in Serum And Periodontal Microenvironments. Journal of Periodontal Research, to appear in 2002.
M. Thylin, J. McConnell, M Schmid, R. Reckling, J. Ojha, I. Bhattacharyya, D. Marx, R. Reinhardt. Effects of Statin Gels on Murine Calvarial Bone. Journal of Periodontology, to appear in 2002.

Progress 10/01/01 to 09/30/02

Outputs
Designs and analyses of unreplicated field designs were studied. When doing varietial testing often the quantity of seed available is only sufficient for one reasonably sized plot. The proportion and spacing of "check" plots is then important to provide information on the spatial variability of the field. These "check" plots can be used to adjust the experimental entries based upon their location. Simulation studies indicate that as the proportion of "check" plots increases the ability to identify the best experimental entries increases up to about 30 to 35 percent checks.

Impacts
The ability to increase the correct identification of entries in unreplicated field trials will provide researchers with another tool to determine which entries should be grown in a follow-up replicated experiment.

Publications

R. Hensberry, D. Marx, S. Kachman, D. Travnicek, Simulation Study of Spatial Poisson Data Assessing the Inclusion of Spatial Correlation and Non-normality in the Analysis, Conference on Applied Statistics in Agriculture, 2002.
R. Landes, K. Eskridge, S. Baenziger, D. Marx, Are spatial models needed with adequately blocked field trails?, Conference on applied Statistics in Agriculture, 2002.
T. Maas, D. Marx, J. Pedersen. Unreplicated Spatial Designs Compared Using Optimality Criteria. Conference on applied Statistics in Agriculture, 2003.

Progress 10/01/00 to 09/30/01

Outputs
Efforts have continued along the lines of statistical modeling of heat stress in animals, super saturated experimental designs, and spatial modeling of designed experiments. Several papers have been presented as well as published in referred journals.

Impacts
Researchers will be able to design experiments more efficiently and they will be able to analyze their spatial data with greater accuracy using the techniques described by the authors.

Publications

John Bowley, Choi, D. B. Marx, Reliability of an arbitrary ear and face-bow transfer instrument, The Journal of Prosthetic Dentistry, 2001.
B. Walline, J. Wagner, D. Marx, R. Reinhardt, Comparison of methods from measuring root and mucogingival sensitivity, Oral Surgery, Oral Medicine, Oral Pathology Radio Endod, 2000, 90:641-646.
T. Johnson, M. Froeschle, B. Lange, D. Marx, Management of Patients needing Antibiotic Prophylaxis in a Dental Education Setting, Journal of Dental Education, 64 (4), 2000, 276-282.
E. Fung, N. Ewoldsen, H. St. Germain, D. Marx, C. Miaw, C. Siew, H. Chou, S. Gruninger, D. Meyer, Pharmacokinetics of Bisphenol as released from a dental sealant. JADA, 131, 2000, 51-58.
K. Rockwell, J. Schauer, S. Fritz, D. Marx, Incentives and Obstacles Influencing Higher Education Faculty and Administrators to Teach Via Distance, Journal of Distance Learning Administration, 2000.
K. Rockwell, J. Schauer, S. Fritz, D. Marx, Faculty Education, Assistance and Support Needed to Deliver Education via Distance, Journal of Distance Learning Administration, Summer, 2000.
K. Rockwell, D. Marx, J. Fergason, Research and Evaluation Needs foe Distance Education: A Delphi Study, Journal of Distance Learning Administration, Fall, 2000.
S. Fritz, S. Burrow, A. Etling, J. Barbuto, D. Marx, Motivation and Recognition Preferences of 4-H Volunteers, submitted to Journal of Agricultural Education,2001.
R. Hensberry, D. Marx, S. Kachman, D. Travnicek, Simulation Study of Spatial Poisson Data Assessing the Inclusion of Spatial Correlation and Non-normality in the Analysis, Conference on Applied Statistics in Agriculture, 2001.
R. Landes, K. Eskridge, S. Baenziger, D. Marx, Are spatial models needed with adequately blocked field trails?, Conference on applied Statistics in Agriculture, 2001.

Progress 10/01/99 to 09/30/00

Outputs
Designs which allow for screening of many factors in biological experiments have been developed and are in the process of being published. These super saturated designs are appropriate for researchers who need to eliminate possible influencing factors in a project before "zeroing in" on specific ones in futher experimentation. Analysis of spatially correlated categorical data has been investigated. Many experiments involve "count" data which is not normally distributed. Since these data are from field experiments they may be spatially correlated. How well do the state of the art statistical packages analyze this type of data? This is the question answered by the investigators.

Impacts
The super saturated designs will allow for more efficient elimination of possible inflluencing factors in multi-factor experiments. This will reduce cost of experimentation as well as increase efficiency. The analysis of spatially correlated count data will provide a more efficient way to determine treat differences and their significance levels.

Publications

Rockwell, K., Schauer, J., Fritz, S., Marx, D., 2000. Faculty Education, Assistance and Support Needed to Deliver Education via Distance. The Journal of Distance Learning Administration, Summer, 2000.
Barbuto, J., Fritz, S., Marx, D., 2000. A field study of two measures of work motivaiton for predicting leader's transformational behaviors'. Psychological Reports, 2000, 86, 295-300.
Inan, M., Chiruvolu, V. Eskridge, K., Dickerson, K., Brown, S., Meagher,M., 1999. Optimization of temperature-glycerol-pH combinations for a fed-batch fermentation process for recombinant hookwork (Ancylostoma canium) anticoagulant peptide (AcAP-5) production by Pichia pastoris. Enzyme and Microbial Technology 24(7):429-445.
Lochte-Watson, K., Weller, C. Eskridge,K., 2000. Fractional compostion of grain sorghum (Sorghum bicolor) after wet-peeling in a centrifugal pump. Applied Engeering in Agriculture 16(3): 253-258
Eskridge, K., Shah, M., Baenziger, S., Travnicek, D., 2000. Correcting for classification errors when estimating the number of genes using recombinant inbred chromosome lines. jCrop Science 40:398-403.
Klocke, N. L., D. G. Watts, J.P.Schneekloth, D.R.Davison, R. W. Todd, and A. M. Parkhurst. 2000. Nitrate leaching in irrigated corn and soybean in a semi-arid climate, Transaction of the ASAE 42(6):1621-1630. Journal Series No. 12462, Neb. Agric. Res. Div.
Kessavalou, A. and A.M.Parkhurst. 1999. Modeling the fate of toxic chemicals in soils, Proceedings of the Eleventh Annual Kansas State University Conference on Applied Statistics in Agriculture, April 25-27, 1999 p31-42.
Cuppett, S.L, M.McVey McCluskey, E.T. Paparozzi and A. Parkhurst. 1999. Nitrogen and sulfur effects on leaf lettuce quality. Journal of Food Quality 22:363-373.
Drijber, RA.,J.W.Doran,A.M.Parkhurst and D.J.Lyon. 2000. Changes in soil microbial community structure with tillage under long-term wheat-fallow management, Soil Biology and Biochemistry

Progress 10/01/98 to 09/30/99

Outputs
The investigators have worked on new methods of analyzing experimental data and designing experiments in the fields of agriculture and natural resources. There have been developments in mixed model methodology, spatial statistics, and nonlinear models. New mehtods of estimating a sequence in a genomic library have been developed.

Impacts
The ability to design experiments is important and the advances in designing experiments in the presence of spatial correlation allow for more accurate analyses and designs which have the same efficiency as designs with more replications leading to finicial savings.

Publications

Estimating the probability that a genomic library contains a specific DNA sequence. J. of Agri, Biological and Enviornemental Statistics 4(1):1-8, C. Gardner K. Eskridge, H. Zhang, and D. Weeks, 1999
Pharmacokinets of Bisphenola from a Dental Sealant in Humans. to be published by the Journal of the American Dental Association, Y. Fung, N. Ewoldsen, D. Marx, etal. 1999

Progress 10/01/96 to 09/30/97

Outputs
Work continues in the statistical properties of two measures of competition that are being evaluated so a test to determine whether competition is present can be developed. An alternative approach to explaining apparently random variation using non-linear systems-popularly known as "chaos" theory has emerged. A model has been developed using chaos theory to help explain animal response to heat stress. Changes in fractal dimensions have been used to signal heat stress in steers. Work is proceeding in describing how estimation of fractal dimension is influenced by correlated data. Such applications promise to enhance our understanding of animal & plant physiology. Distribution of weeds in NE's soybean & corn fields is being evaluated. Knowledge of these distributions is being used to develop more precise sampling programs for use in field management. Work is proceeding on formulating risk efficient crop models for the particular perspective of the decision maker extending these models to multiple-trait selection. These models are more repeatable & easier to use than current methods. For multiple-trait models computational costs associated with the analysis of textile fabrics. Developed statistical methods for design and analysis of consumer evaluation of plant quality for horticulture. Commercial growers find these methods extremely useful to improve quality. UNL researchers are better able to address producers' "bottom line.".

Impacts
(N/A)

Publications

Brown-Brandl, T.M., Beck, M.M., Schulte, D.D., Parkhurst, A. M., DeShazer, J.A. 1996. Physiological responses of tom turkey's to temperature and humidity change with age. Journal of Thermal Biology
Brown-Brandl, T.M., Beck, M.M., Schulte, D.D., Parkhurst, A.M., DeShazer, J.A. 1996. Evaluation of temperature and humidify effects on physiological responses of tom turkeys from 6 to 21 weeks.
deLeon, A., Cuppett, S., Parkhurst, A., Hodges, L. 1996. Factors affecting asparagus sensory evaluation" Journal of Food Quality
J.E. McNeil, French, R., Hein, G.L., Baneziger, P.S., Eskridge, K.M. 1996. Characterization of genetic variability among natural populations of wheat streak mosaic virus. Pythopathology
E.T. Schreiber, Hallmon, D.F., Eskridge, K.M., Marten, G.G. 1996. Effects of Mesocyclops longisetus (Copepoda: Cyclopidae) on mosquitoes which inhabit tires. Journal of the American Mosquito
M. Bagayoko, Mason, S.C., Traore, K., Eskridge, K.M. 1996. Pearl millet/cowpea cropping system yields and soil nutrient levels. African Journal of Crop Science 4 (4):453-462.
M.T. Mmbaga, Steadman, J.R., Eskridge, K. M. 1996. Virulence patterns of Uromyces appendiculatus from different geographical areas & implications for finding durable resistance to rust of common bean. Journal of Phytopathology 144:533-541.

Progress 10/01/95 to 09/30/96

Outputs
The statistical properties of two measures of competition are being evaluated sothat a test to determine whether competition is present can be developed. An alternative approach to explaining apparently random variation using non-linear systems-popularly known as "chaos" theory has emerged. A model has been developed using chaos theory to help explain animal response to heat stress. Such applications promise to enhance our understanding of animal & plant physiology. The distribution of weeds in Nebraska soybean & corn fields is being evaluated. Knowledge of these distributions is being used to develop more precise sampling programs for use in field management. Work is proceeding on formulating risk efficient crop models for the particular perspective of the decision maker extending these models to multiple-trait selection. These models are more repeatable and easier to use than current methods. For multiple-trait models computational costs associated with the analysis of textile fabrics. Developed stat methods for design & analysis of consumer evaluation of plant quality for horticulture. Commercial growers find these methods extremely useful to improve quality. UNL researchers are better able to address producers' "bottom line". Research in agr. & natural resources increasingly.

Impacts
(N/A)

Publications

ESKRIDGE, K. M., 1996. Analysis of multiple-environment trials using the probability of outperforming a check. New Prspectives on Genotype-by-EnvironmentInteraction., 274-307.
ESKRIDGE, K. M., 1996. Analysis of bird repellent experiments in the presence of treatment interference. Proceedings of the American Statistical Association, Biometrics Section., 246-249.
CUOMO, G.J., ANDERSON, B.E., YOUNG, L.J., WILHEIM, W. W., 1996. Harvest frequencyand burning effects on monocultures of 3 warm-season grasses. Journal of Range Management, 49:157-162.
CREWS, P., WENDELIN, R., KACHMAN, S. D., 1995. Soil Repellency:Effect of fiber content, fabric construction and cleaning on the performance of fluorochemically-finished fabrics. Textile Chemist and Colorist., 27:21.
SKOPP, J., KACHMAN, S.D., HERGERT, G. W., 1995. Comparison of procedures for estimating sample numbers. Communications in soil science and plant analysis., 26:2559.
JOHNSON, J.A., MORTENSEN, D.A., YOUNG, L.J., MARTIN, A. R., 1996. Parametric sequential sampling based on multistage estimation of the negative binomial parameter 1c. Weed Science, 44:555-559.
LITTELL, R.C., MILLIKEN, G.A., STROUP, W.W., WOLFINGER, R. D., 1996. SAS System for Mixed Models. SAS Institute Inc., 633 pages.

Progress 01/01/95 to 12/30/95

Outputs
The statistical properties of two measures of competition are being evaluated sothat a test to determine whether competition is present can be developed. An alternative approach to explaining apparently random variation using non-linear systems-popularly known as "chaos" theory has emerged. A model has been developed using chaos theory to help explain animal response to heat stress. Such applications promise to enhance our understanding of animal & plant physiology. The distribution of weeds in Nebraska soybean & corn fields is being evaluated. Knowledge of these distributions is being used to develop more precise sampling programs for use in field management. Work is proceeding on formulating risk efficient crop models for the particular perspective of the decision maker extending these models to multiple-trait selection. These models are more repeatable and easier to use than current methods. For multiple-trait models computational costs associated with the analysis of original data with generalized linear mixed models is very high. An alternative approach based on estimating equations with much smaller computational costs has been developed. Applications of mixed model techniques to the analysis of textile fabrics. Developed stat methods for design & analysis of consumer evaluation of plant quality for horticulture. Commercial growers find these methods extremely useful to improve quality. UNL researchers are better able to address producers' "bottom line". Research in agr. & natural resour.

Impacts
(N/A)

Publications

Progress 01/01/94 to 12/30/94

Outputs
Developed stat methods for design & analysis of consumer evaluation of plant quality for horticulture. Commercial growers find these methods extremely useful to improve quality. UNL researchers are better able to address producers' "bottom line." For multiple-trait models computational costs associated with the analysis of ordinal data with generalized linear mixed models is very high. An alternative approach based on estimating equations with much smaller computational costs has been developed. Work is proceeding on formulating risk efficient crop models for the particular perspective of the decision maker extending these models to multiple-trait selection. These models are more repeatable and easier to use than current methods. The distribution of weeds in Nebraska soybean & corn fields is being evaluated. Knowledge of these distributions is being used to develop more precise sampling programs for use in field management. The statistical properties of two measures of competition are being evaluated so that a test to determine whether competition is present can be developed. An alternative approach to explaining apparently random variation using non-linear systems-popularly known as "chaos" theory-has emerged. A model has been developed using chaos theory to help explain animal response to heat stress. Such applications promise to enhance our understanding of animal & plant physiology. Research in agr.& natural resources increasingly requires on-site, multi-location studies on-farm trials.

Impacts
(N/A)

Publications

Progress 01/01/93 to 12/30/93

Outputs
Reliability engineering concepts are being used to develop decision models for the selection of risk efficient crop cultivars and technologies. Work is proceeding on formulating these models for the particular perspective of the decision maker (eg. plant scientist, grower or processor), extending these models to multiple-trait selection using nonparametric approaches and assessing the robustness of the models. Initial results indicate these models are more repeatable and easier to use than currently used methods. The distribution of weeds in Nebraska soybean and corn fields is being evaluated. Knowledge of these distributions is being used to develop more precise sampling programs for use in field management. The statistical properties of two measures of competition are being evaluated so that a test to determine whether competition is present can be developed. Mixed models techniques have been expanded to allow for the analysis of traits such as pregnancy rates and calving difficulty which do not follow a Normal distribution. However, each extension has been treated as a special case. Generalized mixed model techniques have now been developed which bring the various special cases under a larger class models and provides estimators which can be obtained without resorting to first principles. Recently, an alternative approach to explaining apparently random variation using non-linear systems - popularly known as "chaos" theory - has emerged. A model has been developed using chaos theory to help.

Impacts
(N/A)

Publications

Progress 01/01/92 to 12/30/92

Outputs
In the application of statistics to research in agriculture, a variety of approaches have been developed to modelling water resource availability and usage in Nebraska. A time series model having repeated measures sampling design has been developed to model Lincoln Water System usage. Spatial statistical theory has been used to model water resource distribution. Water resources are affected by various agricultural interventions. Decision models have been developed specifically to model risk in herbicide applications - such models can be applied to evaluate other such interventions. Classical statistical theory is based on probability models. Recently, an alternative approach to explaining apparently random variation using non-linear systems - popularly known as "chaos" theory - has emerged. A model has been developed using chaos theory to help explain animal response to heat stress. Such applications promise to greatly enhance our understanding of animal and plant physiology. Research in agriculture and natural resources increasingly requires on-site, multi-location studies e.g. on-farm trials. Statistical methods for design and analysis of on-farm trials are being developed. A decision model to evaluate the reliability of bean cultivars under different environments has been developed. Mixed linear model theory has been applied to obtain accurate predictions of treatment performance at particular sites in multi-site studies.

Impacts
(N/A)

Publications

Progress 01/01/91 to 12/30/91

Outputs
In the application of statistics to research in agriculture reliability engineering concepts are being used to develop decision models for the selection of risk efficient crop cultivars and technologies. Formulation of models from the perspective of the decision maker (eg. plant scientist, grower or processor), is proceeding. Initial results indicate these models are more repeatable and easier to use than currently used stability methods. In the animal science field, when comparing animals across environments, it is important to adjust for differences between environments. Not only are some environments more favorable, but the amount of variability is needed. Marginal posterior mode estimators have been developed for estimating the unknown parameters. Conventions of statistical design and analysis in use today were established in the 1930's, well before the invention of the computer. An ever-wider range of applications of the theory to problems encountered by agricultural researchers needs to be developed. Mixed linear model theory and linear prediction were developed for certain specific applications. It has become increasingly clear that the mixed linear model is really THE general linear model within the limits of the current state-of-the-art, and that such a perspective opens up a wide range of possibilities for accommodating data with non-standard variation and for considering the more cost-efficient experimental designs over classical designs.

Impacts
(N/A)

Publications

Progress 01/01/90 to 12/30/90

Outputs
In the application of statistics to research in agriculture, studies are being conducted on the use of decision analysis with regard to decision modeling in genetic and technology selection. Models of arthropod immigration, movement, and emigration within an agroecosystem are being developed. These provide insight into efficient sampling methods. Sequential methods of determining whether an arthropod population is above or below the economic threshold with specified errors of making an incorrect decision have been derived. These differ from the traditional methods in that the average sample size is minized over all possible sizes of arthropod populations. Several factors contribute to inadequate stands in sugarbeets. One of the factors is erratic spacing of plants within a row. The ability of a planter to accurately place sugarbeet seeds has been difficult to quantify. A model has been developed to help quantify the various components associated with the spacing of sugarbeet plants within a row. Three major components of the model are the ability to drop a single seed at a time, the ability to drop the seeds a fixed distance apart, and the ability of the seed to emerge. A maximum likelihood estimation procedure based on the model has been developed. The estimates can then be used as a basis for comparing the ability of different planters to accurately place sugarbeet seeds. Work remains to be done in examining the ability of the estimation procedure to quantify spacing accuracy.

Impacts
(N/A)

Publications

Progress 01/01/89 to 12/30/89

Outputs
There are several criteria used by farmers when evaluating new crop varieties,including grain yield, the ability to stand up until harvested, rate of grain dry down, and others. There is some difficulty in determining the amount of weight given to each trait when selecting among a set of potential new varieties. A mathematical optimization procedure was used to develop a model which can easily be applied in selection programs aimed at developing new crop varieties. Studies continue in the area of development of a method to extract inbred lines from elite maize germ plasm. It is basically a single cycle of recurrent selection using the two first generation hybrids used to produce an elite double cross. The extracted lines are currently being crossed to testers to evaluate their performance.Multivariate statistical techniques is an area under study where discriminant analysis is used as an aid in the exploration of differences between groups. Previously, the focus of discriminant analysis has been on predicting group membership. This new focus on interpretation has uncovered some valuable biological insights. The application of a new tool, Data Dependent Systems, to the biological systems studied in agriculture identifies an adequate mathematical model from a series of observations recorded over time. The models take the form of differential/difference equations that can be used to characterize, forecast & control.

Impacts
(N/A)

Publications

Progress 01/01/88 to 12/30/88

Outputs
MODELING: Chaos - its relevance to modeling plant and animal systems with cyclicfluctuations thought to be circadian is being investigated. Studies are being conducted on the usefulness of second-order non-linear oscillators such as the Rayleigh-Van der Pol equation. Investigations have shown the data dependent system approach is potentially robust in terms of identifying the dimensionality of the model and differentiating among environmental stressors. Growth - Model to characterize pathogenic bacterial populations in dry beans and to evaluate cultivar effects on the parameters is being developed. Potential applications to other areas of plant epidemiology are also being investigated. Spatial Variability - Nearest-neighbor models are used to evaluate experiments with spatial correlation or local treatment-related competition effects. Another approach involves the development of a treatment overlap effects model for wildlife sampling experiments. DECISION RULES of crop genotypes selection. Applications focus on portfolio and safety-first models to aid the plant breeder with making selections in the presence of genotype-environment interaction. Quality control - The primary focus is on applications of acceptance sampling and control charts to food processing. There is potential for extensions to risk assessment in biotechnology, and water quality, i.e., ground-water contamination studies. QUANTITATIVE GENETICS: Work continues on the extraction of maize inbred lines from elite material.

Impacts
(N/A)

Publications

Progress 01/01/87 to 12/30/87

Outputs
More than 1000 conferences were held with Research Division staff and graduate students to assist in design, analysis, and interpretation of agricultural experiments. Increased use of computer-aided design planning tools and graphics has been stressed with favorable results. Research is in progress to address the problem of correlated errors in agricultural research data. One approach focuses on the use of Data Dependent Systems to model or analyze biological time series. A second approach involves the use of mixed model procedures in conjunction with nearest-neighbor models and neighboring-effects designs in order to evalute experiments with spatial correlation or local treatment-related competition effects. Preliminary results indicate a variety of potential applications in modeling physiological processes in animals and plants and in evaluating complex field data such as ground-water contamination studies or non-ideal experimental conditions such as in developing countries. Methods have been developed to apply decision theory and economic risk analysis to plant breeding and crop production. Potential extensions in the animal science are being investigated. A probability model has been developed to describe growth of disease-producing bacteria on dry beans and evaluate treatment effects on parameters of this model. Potential extensions to other areas of plant epidemiology, including risk assessment in biotechnology, are being explored.

Impacts
(N/A)

Publications

Progress 01/01/86 to 12/30/86

Outputs
More than 1000 conferences were held with Research Division staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Work has been completed on the use of a time series approach to the characterization of animal feed data. This work includes the analysis of such data and implications for the design of animal feeding experiments. Progress has been made on statistical aspects of forestry data. One aspect concerns modeling the distribution of wood tissue using data from a digital scanning device. This will lead to improved ability to assess resistance of trees to storm damage. Another aspect is an adaptation of correlated plot methodology to the design and analysis of tree breeding experiments. Multivariate methods are being extended to characterize experiments with repeated measures and complex responses (e.g. botanical composition). Current directions include using mixed model estimation procedures to characterize certain processes as they develop over time. Research is in progress which addresses the problem of correlated errors in agricultural systems. Currently, the focus is on the application of a new technique in time series called Data Dependent Systems (DDS), which can be used for modeling and analysis. In animal processes, preliminary studies suggest DDS have potential for dynamic modeling and quantification of certain digestive and metabolic processes.

Impacts
(N/A)

Publications

Progress 01/01/85 to 12/30/85

Outputs
About 1180 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM System 34 in the Biometrics Center, the IBM 3081 in the University Computing Center and the IBM 3033 in the Statehouse. Published results from the experiments are listed under various Experiment Station projects. Biometrics Center research staff provide program documentations, training, and programming assistance to researchers. The Statistical Analysis System (SAS) and SAS GRAPH is widely used for data analysis. Staff at out-state research centers utilize SAS through terminals and modem-connected microcomputers. Research has been initiated to investigate the following questions about the use of selections indices in plant breeding: 1.) What components are to be included and how should they be weighed and/or restricted? 2.) Are scaling problems introduced when analyzing the index? 3.) As parameters change through cycles of selections, what effect does this have on the selection index and how might it be adjusted accordingly? Computer simulation work is underway to investigate the power of different test procedures for analyzing rating data in agronomy experiments when the assumptions of normality and independence are not satisfied. The tests being evaluated are the non-parametric rank transformation, the Funcat log. linear, and analysis of variance.

Impacts
(N/A)

Publications

Progress 01/01/84 to 12/30/84

Outputs
About 1200 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM System 34 in the Biometrics Center, the IBM 3081 in the University Computing Center and the IBM 3033 in the Statehouse. Published results from the experiments are listed under various Experiment Station projects. Biometrics Center research staff provide program documentation, training, and programming assistance to researchers. The Statistical Analysis System (SAS) and SAS GRAPH is widely used for data analysis. Staff at out-state research centers utilize SAS through terminals and modem-connected microcomputers. Research continued on variance component estimation in genetic research, the application of repeated measures theory and techniques to animal experiments, and the development of efficient sampling techniques for studying wildlife populations. The repeated measures research provides for more precise analysis of repeated measures experiments; improved design of animal experiments; and better understanding of animal group behavior. Research was completed on the use of discriminant analysis to descriminate between enzyme genotypes at a particular locus in maize, using selected morphological traits as discriminating variables.

Impacts
(N/A)

Publications

Progress 01/01/83 to 12/30/83

Outputs
About 1450 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM System 34 in the Biometrics Center, the IBM 370/58 in the University Computing Center and the IBM 3033 in the Statehouse. Published results from the experiments are listed under various Experiment Station projects. Biometrics Center staff continue to make program documentation and training available to users of the computing systems. The Statistical Analysis System (SAS) and SAS GRAPH is available on the IBM 3033 and IBM 370/158 and user training on the use of the system was provided to both staff and graduate students. Staff at out-state research centers utilize SAS through terminals and recently installed microcomputers. Research continued on the use of multivariate statistical methods for the analysis of range vegetation, the use of SAS to analyze experiments conducted over time with multivariate responses and autocorrelated errors, and the use of multivariate methods to analyze split-plot experiments when data do not satisfy the usual analysis of variance assumption. Research was completed on a study of the effect of heterosis, cytoplasm type, and seed source on seedling cold tolerance in grain sorghum. Twenty-eight parents and F(1) hybrids were used to quantify heterosis and compare sterility-inducing and fertility-inducing cytoplasms.

Impacts
(N/A)

Publications

Progress 01/01/82 to 12/30/82

Outputs
About 1480 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected for these experiments were processed on the IBM System 34 Computer in the Biometrics Center and the IBM 370/58 in the Computing Center. Published results from the experiments are listed under various Experiment Station projects. Computer program documentation was updated and made available to users in the form of loose leaf handouts and Biometrics Center Reports. The Statistical Analysis System (SAS) and SAS GRAPH has been implemented on the IBM 3033 and the IBM 370/58 and user-orientation for the use of the system was provided to both staff and students. An outstate computer network has been implemented at nearly eighty locations to provide access to computer terminals for both research and extension applications of SAS. Research is underway on the use of multivariate statistical methods for the analysis of changes in botanical composition of range vegetation and the use of SAS to analyze experiments conducted over time with multivariate responses and autocorrelated errors. The first phase of an experiment to estimate heterosis for cold tolerance in grain sorghum was completed. Inbred lines of grain sorghum and their hybrids were studied to determine the magnitude of heterosis for emergence and growth under cool conditions at two locations in Nebraska.

Impacts
(N/A)

Publications

Progress 01/01/81 to 12/30/81

Outputs
About 1500 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM System 34 Computer in the Biometrics Center and the IBM 360/65 and the IBM 370/58 in the Computing Center. Published results from the experiments are listed under various Experiment Station projects. Comptuer program documentation was updated and made available to users in the form of loose leaf handouts and biometrics Center Reports. The Statistical Analysis System (SAS) and SAS GRAPH has been implemented on the IBM 3033 and the IBM 370/58 and user-orientation for the use of the system was provided to both staff and students. An outstate computer network has been implemented at nearly eighty locations to provide access to computer terminals for both research and extension applications of SAS. Research is in progress utilizing multivariate techniques to study the inheritance of the fatty acid constituents of soybean oil and the botanical composition of range vegetation. Research was initiated on the design of experiments needed to utilize new feed collection technology to analyze the time-series aspect of feed data analysis and the use of time-series concepts to characterize intra-pen competition among animals.

Impacts
(N/A)

Publications

Progress 01/01/80 to 12/30/80

Outputs
About 1485 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Biometrics Center and the IBM 360/65 and the IBM 370/58 in the Computing Center. Published results from the experiments are listed under various Experiment Station projects. Several major computer programs and many routine programs were developed, tested and used to analyze data for individual research projects. Computer program documentation was updated and made vailable to users in the form of loose leaf handouts and biometrics Center Reports. The Statistical Analysis System (SAS) developed at N.C. State Univeristy has been implemented on the IBM 370/58 and user-orientation for the use of the system was provided to both staff and students. An outstate computer network has been implemented at nearly eighty locations to provide access to computer terminals for both research and extension applications of SAS. Research is in progress utilizing multivariate techniques to study the inheritance of the fatty acid constituents of soybean oil and the botanical composition of range vegetation.

Impacts
(N/A)

Publications

Progress 01/01/79 to 12/30/79

Outputs
About 1470 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Biometrics Center and the IBM 360/65 and the IBM 370/58 in the Computing Center. Published results from the experiments are listed under various Experiment Station projects. Several major computer programs and many routine programs were developed, tested and used to analyze data for individual research projects. Computer program documentation was updated and made available to users in the form of loose leaf handouts and Biometrics Center Reports. Data processing was also provided for the Nebraska Electronic Farm Record Project (NEBFARM). The Statistical Analysis System (SAS) developed at N.C. State University has been implemented on the IBM 360/65 and user-orientation for the use of the system was provided to both staff and students. An outstate computer network has been implemented at nearly seventy locations to provide access to computer terminals for both research and extension applications of SAS. Research is in progress utilizing multivariate techniques to study the inheritance of the fatty acid constituents of soybean oil.

Impacts
(N/A)

Publications

Progress 01/01/78 to 12/30/78

Outputs
About 1480 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Biometrics Center and the IBM 360/65 in the Computing Center. Published results from the experiments are listed under various Experiment Station projects. Several major computer programs and many routine programs were developed, tested and used to analyze data for individual research projects. Computer program documentation was updated and made available to users in the form of loose leaf handouts and Biometrics Center Reports. Data processing was also provided for the Nebraska Electronic Farm Record Project (NEBFARM). An information retrieval system that includes data on the research, teaching, and extension personnel in the Institute of Agriculture and Natural Resources and the College of Home Economics received extensive use . The Statistical Analysis System (SAS) developed at N.C. State University has been implemented on the IBM 360/65 and user-orientation for the use of the system was provided to both staff and students. An outstate computer network has been implemented at nearly seventy locations to provide access to computer terminals for both research and extension applications. Research was completed on a study utilizing cluster analysis techniques to classify environments used in soybean yield tests.

Impacts
(N/A)

Publications

Progress 01/01/77 to 12/30/77

Outputs
About 1500 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Biometrics Center and the IBM 360/65 in the Computing Center. Published results from the experiments are listed under various Experiment Station projects. Several major computer programs and many routine programs were developed, tested and used to analyze data for individual research projects. Computer program documentation was updated and made available to users in the form of loose leaf handouts and Biometrics Center Reports. Data processing was also provided for the Nebraska Electronic Farm Record Project (NEBFARM). An information retrieval system that includes data on the research, teaching, and extension personnel in the Institute of Agriculture and Natural Resources and the College of Home Economics received extensive use. The Statistical Analysis System (SAS) developed at N.C. State University has been implemented on the IBM 360/65 and user-orientation for the use of the system was provided to both staff and students. An outstate computer network has been implemented at nearly sixty locations to provide access to computer terminals for both research and extension applications.

Impacts
(N/A)

Publications

Progress 01/01/75 to 12/30/75

Outputs
About 1040 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Statistical Laboratory and the IBM 360/65 in theComputing Center. Published results from the experiments are listed under various Experiment Station projects. Several major computer programs and many routine programs were developed, tested and used to analyze data for individual research projects. Computer program documentation was updated and made availableto users in the form of loose leaf handouts and Statistical Laboratory Reports. Data processing was also provided for the Nebraska Electronic Farm Record Project (NEBFARM). An information retrieval system that includes data on the research, teaching, and extension personnel in the Colleges of Agriculture and Home Economics recieved extensive use. The Statistical Analysis System (SAS) developed at N.C. State University has been implemented on the IBM 360/65 and user-orientation for the use of the system was provided to both staff and students. The first stage of an outstate computer network was implemented at three locations to provide access to computer terminals for both research & extension applications.

Impacts
(N/A)

Publications

Progress 01/01/74 to 12/30/74

Outputs
About 1000 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Statistical Laboratory and the IBM 360/65 in theComputing Center. Published results from the experiments are listed under various Experiment Station projects. Several major computer programs and many routine programs were developed, tested and used to analyze data for individual research projects. Computer program documentation was updated and made available to users in the form of loose leaf handouts and Statistical LaboratoryReports. Data processing was also provided for the Nebraska Electronic Farm Record Project (NEBFARM) and the Specific Pathogen Free (SPF) Swine Project. Aninformation retrieval system that includes extensive data on the research, teaching, and extension personnel in the Colleges of Agriculture and Home Economics received extensive use. The Satistical Analysis System (SAS) developed at N.C. State University has been implemented on the IBM 360/65 and user-orientation for the use of the system was provided to both staff and students.

Impacts
(N/A)

Publications

Progress 01/01/73 to 12/30/73

Outputs
About 940 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretatiion of agricultural experients. Data collected from these experiments were processed on the IBM 1130 computer in the Statistical Laboratory and the IBM 360/65 in theComputing Center. Approximately 2300 data processing jobs were handled during the year. Published results from the experiments are listed under various Experiment Station projects. Seven major computer programs and about 340 routine programs were developed, tested and used to analyze data for individual research projects. Documentation for several major computer programs was updated and made available to users in the form of Statistical Laboratory Reports. Data processing was also provided for the Nebraska Electronic Farm Record Project (NEBFARM) and the Specific Pathogen Free (SPF) Swine Project. Aninformation retrieval system that includes extensive data on the research, teaching, and extension personnel in the Colleges of Agriculture and Home Economics received extensive use. The system provides instantaneous informationto the college administrators for budgetary and other planning purposes. The Statistical Analysis System (SAS) developed at N.C. State University was implemented on the IBM 360/65 in mid-year and user-orientation for the use of the system was provided to both staff and students.

Impacts
(N/A)

Publications

Progress 01/01/71 to 12/30/71

Outputs
About 900 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Statistical Laboratory and the IBM 360/65 in theComputing Center. Approximately 2000 data processing jobs were handled during the year. Published results from the experiments are listed under various Experiment Station projects. Seventeen major computer programs and about 350 routine programs were developed, tested and used to analyze data for individual research projects. Documentation for several major computer programs was developed and made available to users in the form of Statistical Laboratory Reports. Data processing was also provided for the Nebraska Electronic Farm Record Project (NEBFARM) and the Specific Pathogen Free (SPF) Swine Project. Aninformation retrieval system was developed that includes extensive data on the research, teaching, and extension personnel in the Colleges of Agriculture and Home Economics. The system provides instantaneous information to the college administrators for budgetary and other planning purposes.

Impacts
(N/A)

Publications

Progress 01/01/70 to 12/30/70

Outputs
About 800 conferences were held with Experiment Station staff members and graduate students to assist in the design, analysis and interpretation of agricultural experiments. Data collected from these experiments were processed on the IBM 1130 computer in the Statistical Laboratory and the IBM 360/65 in theComputing Center. Approximately 1700 data processing jobs were handled during the year. Published results from the experiments are listed under various Experiment Station projects. Fifteen major computer programs and about 300 routine programs were developed, tested and used to analyze data for individual research projects. Documentation for programs on optimum plot size and variances, standard deviations, and coefficients of variation were developed andmade available to users as Statistical Laboratory Reports 4 and 5. Research wasinitiated on statistical analyses useful in characterizing the relationship between genotype x environment interactions in plant populations and intra-genotypic competition. The results indicate that interactions measured inconventional analyses of variance are affected in a specific way by certain types of inter-plant competition.

Impacts
(N/A)

Publications

Progress 01/01/69 to 12/30/69

Outputs
Data were analyzed on the IBM 1130 computer and the IBM 360/65. The results were published by these Experiment Station workers and are listed under their projects. Approximately 1400 data processing jobs were handled during the year.Many computer programs to perform specific tasks were developed, tested, and used to analyze data for individual research projects. In addition, Version 2 of Duncan's Multiple Range Test and Version 2 of the means, standard deviations and coefficients of variation programs were produced as well as programs and techniques for estimating optimum plot size in maize yield trials. Linear models were developed to describe the competitive interactions among genotypes in populations of plant species. The models were shown to be very reliable in predicting the response of varietal blends of soybeans. Computer simulation of the effects of proposed models indicated that competitive feedback mechanisms may be a powerful force in the establishment of stable equilibria in evolving populations. These theoretical results suggest that competitive interactions may be responsible for the significant amounts of heterozygosity often observed in self-pollinated plant populations. Linear models were also developed for theanalysis of feeding experiments in dairy cattle. The use of these models has materially increased the researcher's ability to detect significant differences in such experiments.

Impacts
(N/A)

Publications

Progress 01/01/68 to 12/30/68

Outputs
Numerous conferences were held with Experiment Station staff members and graudate students to assist in the design, analysis, and interpretation of agricultural experiments. Data collected from these experiments are analyzed onthe IBM 1130 computer in the Statistical Laboratory. The results are published by these Experiment Station workers and are listed under their projects. Approximately 1250 data processing jobs were handled during the year. Many computer programs to perform specific tasks were developed, tested, and used to analyze data for individual research projects. In addition, a statistical analysis system for polynomial regression, step-wise multiple regression, and factor analysis was converted for use on the IBM 1130. Research was conducted on linear models to describe the competitive interactions among genotypes that occur in populations of plant species. Computer simulation of the effects of proposed models indicated that competitive feedback mechanisms may be a powerfulforce in the establishment of stable equilibria in evolving populations.

Impacts
(N/A)

Publications

Progress 01/01/67 to 12/30/67

Outputs
Numerous conference with Experiment Station Staff members were held to assist in the design of an experiment or in the analysis and interpretation of results obtained. Much of the research data being collected by Station workers is now processed and analyzed on the computer in the Statistical laboratory. Computer programs to perform multiple regressions, least-squares analysis of variance, Duncan's Multiple Range Test, randomized complete blocks analysis of variance, frequency distributions and analysis of variance of partially balanced incomplete block designs have been successfully converted from the IBM 1620 to the IBM 1130. In most cases, both the capacity and capabilities of these programs were measurably increased. Many programs to perform specific tasks were developed, tested and used to produce results for individual research projects. A new general-purpose factorial analysis of variance program for the IBM 1130 was completed. The development of a very comprehensive gneral regression system for the IBM 360 is nearing completion. Research on simulationof genetic and breeding systems using the IBM 1130 and 360 computers indicates that negative dominance estimates in Design I mating systems used by geneticists, should be expected in reasonably high frequency. Assortative mating and genetic correlation between yield and days to flow need not be assumed to explain results in the literature.

Impacts
(N/A)

Publications