DOI: 10.26188/28528694.v1
title: Field experimental data for 7 elite Australian wheat cultivars and 5 near-isogenic lines with matched phenology alleles

description: The premise of this study is that the wheat module of the APSIM crop simulation model (www.apsim.info) was originally parameterised with data from field experiments that used Hartog or cultivars of that era. Hartog released in Mexico as Pavon 76 in 1976 and Australia as Hartog in 1982. The aims of the study were to; 1.     Determine if genetic yield gain through crop breeding had increased yield above that of Hartog since APSIM was first parameterised.2.     If yields had increased relative to Hartog, determine which aspects of crop physiology had changed to inform parameter changes within the model.Experiments compared Hartog to what were at the time (2014) elite cultivars with adaptation to different regions of the Australian wheat belt (see Genotypes tab). Cultivar selection was based on performance in National Variety Trials (www.nvt.grdc.com.au), popularity with growers and similar phenology within the Australian Cereal Phenology Classification classes of Quick and Quick-Mid spring (Celestina et al., 2023)To allow for the fact that phenology of the lines may be somewhat different to Hartog, a range of near isogenic lines (NILs) in a Sunstate background (Bloomfield et al., 2019; Bloomfield et al., 2023; Steinfort et al., 2017) that had the same alleles of five major phenology loci (Ppd-B1, Ppd-D1, Vrn-A1, Vrn-B1, Vrn-D1) as the elite cultivars were included in the experiment. Sunstate is backcross 4 to Hartog, and thus Hartog and all the Sunstate NILs are near isogenic. The idea being that the flowering date of at least one of the NILs should be close to concurrent with each of the cultivars to allow comparisons to be made.Field experiments were situated at Gatton in southern QLD, Junee and Temora in southern NSW and Minnipa on the Eyre Peninsula of SA in 2014 and 2015. In 2014 two experiments were conducted at Gatton, one with natural rainfall (dryland) and one irrigated to create different environments.The study was initially funded by CSIRO (2014-2015) following collapse of negotiations for the work to be undertaken as part of a GRDC project continuing the 'Managed NVT' investment led by Dr Zvi Hochman. Retrospective compilation and analysis of the data were revived through GRDC investment UOM2312 commencing in 2024, including MIR analysis of surviving dry matter samples for N, WSC and 13C.<b>References</b>Bloomfield, M. T., Celestina, C., Hunt, J. R., Huth, N., Zheng, B., Brown, H., Zhao, Z., Wang, E., Stefanova, K., Hyles, J., Rathjen, T., & Trevaskis, B. (2023). Vernalisation and photoperiod responses of diverse wheat genotypes. Crop and Pasture Science, 74(5), 405-422. https://doi.org/https://doi.org/10.1071/CP22213 Bloomfield, M. T., Hunt, J. R., Trevaskis, B., Ramm, K., & Hyles, J. (2018). Ability of alleles of PPD1 and VRN1 genes to predict flowering time in diverse Australian wheat (Triticum aestivum) cultivars in controlled environments. Crop and Pasture Science, 69(11), 1061-1075. https://doi.org/https://doi.org/10.1071/CP18102 Celestina, C., Hunt, J., Kuchel, H., Harris, F., Porker, K., Biddulph, B., Bloomfield, M., McCallum, M., Graham, R., Matthews, P., Aisthorpe, D., Al-Yaseri, G., Hyles, J., Trevaskis, B., Wang, E., Zhao, Z., Zheng, B., Huth, N., & Brown, H. (2023). A cultivar phenology classification scheme for wheat and barley. European Journal of Agronomy, 143, 126732. https://doi.org/https://doi.org/10.1016/j.eja.2022.126732 Steinfort, U., Trevaskis, B., Fukai, S., Bell, K. L., & Dreccer, M. F. (2017). Vernalisation and photoperiod sensitivity in wheat: Impact on canopy development and yield components. Field Crops Research, 201(Supplement C), 108-121. https://doi.org/https://doi.org/10.1016/j.fcr.2016.10.012