Browsing by Author "Slabbert, Johannes Diederick"
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- ItemContinuation of a pre-breeding program for improving wheat yield(Stellenbosch : Stellenbosch University, 2020-03) Slabbert, Johannes Diederick; Botes, Willem; Visser, Marike; Stellenbosch University. Faculty of AgriSciences. Dept. of Genetics.ENGLISH ABSTRACT: Wheat is a crop that has been cultivated around the globe for centuries and forms a substantial portion of the population’s diet, particularly in third world countries. Food security is under major stress with the human population increasing, thus it is important to increase the amount of wheat produced to meet the demand. Higher yields along with better quality can be reached by an increased breeding efficiency through research and development of breeding techniques. The aim of this study was to identify increased yield-related traits and introducing them into the marker-assisted recurrent selection (MS-MARS) facilitated pre-breeding program of the Stellenbosch University Plant Breeding Lab (SU-PBL) to breed wheat lines higher yields. Germplasm was identified through literature that could benefit the breeding program by having traits related to increased yield. The standard set of the SU-PBLs molecular markers were used to make informed decisions during the selection process. Remote sensing by a Remote Pilot Aircraft System (RPAS) was used to perform detailed observations of wheat and was compared to traditional instruments. Field- and post-harvest phenotyping was done to aid in the selection of lines with high-yielding traits. Lines with high yield-related traits were identified by using the different techniques and were introduced into the MS-MARS scheme. R-squared values of linear regression models displayed that the RPAS data could not predict the phenotypic data in the field, except for yield. Scatter plot matrices shown that there was no correlation between the data captured by the traditional instruments and the RPAS. The field and post-harvest data indicated that a nearest neighbour analysis (NNA) was the best option during this study as there were field trends and the data was used in the selection process. Future studies should include the addition of molecular markers that correlate with increased yield-related traits to make more informed decisions during selection. The development of cameras and software for remote sensing will definitely benefit the tool. Additional vegetative indices can be explored and the model can be improved over time by the addition of data.