Open Access. Powered by Scholars. Published by Universities.®
- Keyword
- Publication
Articles 1 - 4 of 4
Full-Text Articles in Statistical Models
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
John E. Sawyer
Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset …
Stability Of Single-Parent Gene Expression Complementation In Maize Hybrids Upon Water Deficit Stress, Caroline Marcon, Anja Paschold, Waqas Ahmed Malik, Andrew Lithio, Jutta A. Baldauf, Lena Altrogge, Nina Opitz, Christa Lanz, Heiko Schoof, Dan Nettleton, Hans-Peter Piepho, Frank Hochholdinger
Stability Of Single-Parent Gene Expression Complementation In Maize Hybrids Upon Water Deficit Stress, Caroline Marcon, Anja Paschold, Waqas Ahmed Malik, Andrew Lithio, Jutta A. Baldauf, Lena Altrogge, Nina Opitz, Christa Lanz, Heiko Schoof, Dan Nettleton, Hans-Peter Piepho, Frank Hochholdinger
Dan Nettleton
Heterosis is the superior performance of F1 hybrids compared with their homozygous, genetically distinct parents. In this study, we monitored the transcriptomic divergence of the maize (Zea mays) inbred lines B73 and Mo17 and their reciprocal F1 hybrid progeny in primary roots under control and water deficit conditions simulated by polyethylene glycol treatment. Single-parent expression (SPE) of genes is an extreme instance of gene expression complementation, in which genes are active in only one of two parents but are expressed in both reciprocal hybrids. In this study, 1,997 genes only expressed in B73 and 2,024 genes …
Estimation And Testing Of Gene Expression Heterosis, Tieming Ji, Peng Liu, Dan Nettleton
Estimation And Testing Of Gene Expression Heterosis, Tieming Ji, Peng Liu, Dan Nettleton
Dan Nettleton
Heterosis, also known as the hybrid vigor, occurs when the mean phenotype of hybrid offspring is superior to that of its two inbred parents. The heterosis phenomenon is extensively utilized in agriculture though the molecular basis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers have begun to compare expression levels of thousands of genes between parental inbred lines and their hybrid offspring to search for evidence of gene expression heterosis. Standard statistical approaches for separately analyzing expression data for each gene can produce biased and highly variable estimates and unreliable tests of heterosis. …
Combining Survey And Non-Survey Data For Improved Sub-Area Prediction Using A Multi-Level Model, Jae Kwang Kim, Zhonglei Wang, Zhengyuan Zhu, Nathan B. Cruze
Combining Survey And Non-Survey Data For Improved Sub-Area Prediction Using A Multi-Level Model, Jae Kwang Kim, Zhonglei Wang, Zhengyuan Zhu, Nathan B. Cruze
Zhengyuan Zhu
Combining information from different sources is an important practical problem in survey sampling. Using a hierarchical area-level model, we establish a framework to integrate auxiliary information to improve state-level area estimates. The best predictors are obtained by the conditional expectations of latent variables given observations, and an estimate of the mean squared prediction error is discussed. Sponsored by the National Agricultural Statistics Service of the US Department of Agriculture, the proposed model is applied to the planted crop acreage estimation problem by combining information from three sources, including the June Area Survey obtained by a probability-based sampling of lands, administrative …