| dc.contributor.author | Stanley, Jordan D. | |
| dc.description.abstract | Seeding rate in hard red spring wheat (HRSW) (Triticum aestivum L.) production impacts input cost and grain yield. Predicting the optimal seeding rate (OSR) for HRSW cultivars can aid growers and eliminate the need for costly seeding rate research. Research was conducted to determine the OSR of newer HRSW cultivars (released in 2013 or later) in diverse environments. Nine cultivars with diverse genetic and phenotypic characteristics were evaluated at four seeding rates in 11 environments throughout the northern Great Plains region in 2017-2018. Results from ANOVA indicated environment and cultivar were more important than seeding rate in determining grain yield. Though there was no environment x seeding rate interaction (P=0.37), OSR varied among cultivar within each environment. Cultivar x environment interactions were further explored with the objective of developing a decision support system (DSS) to aid growers in determining the OSR for the cultivar they select, and for the environment in which it is sown. Data from seeding rate trials conducted in ND and MN from 2013-2015 were also used. A novel method for characterizing cultivar for tillering capacity was developed and proposed as a source for information on tillering to be used in statistical modelling. A 10-fold repeated cross-validation of the seeding rate data was analyzed by 10 statistical learning algorithms to determine a model for predicting OSR of newer cultivars. Models were similar in prediction accuracy (P=0.10). The decision tree model was considered the most reliable as bias was minimized by pruning methods, and model variance was acceptable for OSR predictions (RMSE=1.24). Findings from this model were used to develop the grower DSS for determining OSR dependent on cultivar straw strength, tillering capacity, and yield of the environment. Recommendations for OSR ranged from 3.1 to 4.5 million seeds ha-1. Growers can benefit from using this DSS by sowing at OSR relative to their average yields; especially when seeding new HRSW cultivars. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU policy 190.6.2 | |
| dc.title | Optimal Seeding Rates for New Hard Red Spring Wheat Cultivars in Diverse Environments | en_US |
| dc.type | Dissertation | en_US |
| dc.type | Video | en_US |
| dc.date.accessioned | 2019-08-23T20:41:49Z | |
| dc.date.available | 2019-08-23T20:41:49Z | |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/30667 | |
| dc.subject | machine learning | en_US |
| dc.subject | modelling | en_US |
| dc.subject | optimal | en_US |
| dc.subject | predict | en_US |
| dc.subject | seeding rates | en_US |
| dc.subject | wheat | en_US |
| dc.identifier.orcid | 0000-0001-7883-6869 | |
| dc.identifier.orcid | 0000-0001-7883-6869 | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Doctor of Philosophy (PhD) | en_US |
| ndsu.college | Agriculture, Food Systems and Natural Resources | en_US |
| ndsu.department | Plant Sciences | en_US |
| ndsu.program | Plant Sciences | en_US |
| ndsu.advisor | Ransom, Joel K. | |
