Reanalyses of the historical series of UK variety trials to quantify the contributions of genetic and environmental factors to trends and variability in yield over time

Abstract

Historical datasets have much to offer. We analyse data from winter wheat, spring and winter barley, oil seed rape, sugar beet and forage maize from the UK National List and Recommended List trials over the period 1948-2007. We find that since 1982, for the cereal crops and oil seed rape, at least 88% of the improvement in yield is attributable to genetic improvement, with little evidence that changes in agronomy have improved yields. In contrast, in the same time period, plant breeding and changes in agronomy have contributed almost equally to increased yields of forage maize and sugar beet. For the cereals prior to 1982, contributions from plant breeding were 42, 60 and 86% for winter barley, winter wheat and spring barley, respectively. These results demonstrate the overwhelming importance of plant breeding in increasing crop productivity in the UK. Winter wheat data are analysed in more detail to exemplify the use of historical data series to study and detect disease resistance breakdown, sensitivity of varieties to climatic factors, and also to test methods of genomic selection. We show that breakdown of disease resistance can cause biased estimates of variety and year effects, but that comparison of results between fungicide treated and untreated trials over years may be a means to screen for durable resistance. We find the greatest sensitivities of the winter wheat germplasm to seasonal differences in rainfall and temperature are to summer rainfall and winter temperature. Finally, for genomic selection, correlations between observed and predicted yield ranged from 0.17 to 0.83. The high correlation resulted from markers predicting kinship amongst lines rather than tagging multiple QTL. We believe the full value of these data will come from exploiting links with other experiments and experimental populations. However, not to exploit such valuable historical datasets is wasteful.

Yield Stability in Winter Wheat Production: A Survey on German Farmers’ and Advisors’ Views

“…Laidig et al [32] stated that most of the positive yield progress of winter wheat over the last 30 years in Germany was generated mainly by genetic improvements, with the contribution of other agronomic factors having been of lower importance. Based on the re-analysis of official variety trials of the United Kingdom over the period 1948-2007 for winter wheat, at least 88% of the improvement in grain yield can be attributed to genetic improvement, with only 12% ascribed to changes in agronomy [33]. For the period 1983-2014, a large gain in grain yield of winter wheat in official variety trials (24%) and on farms (32%) was achieved in Germany [34].…”

“…Apart from the yield and quality of wheat, the ability to adjust to changing environmental conditions, ability to compete against weeds as well as pest and disease resistance will increase in importance [39]. The climatic factors that the winter wheat cultivars have the greatest sensitivities to are the summer rainfall and winter temperature [33]. Thus, drought tolerance in the case of early summer drought, resistance to lodging in heavy rainfall events and winter hardiness are very important properties that should be considered in cultivar choice in terms of climate change.…”

A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials

“…These experiments, however, are time and resource intensive, and are not suited to the estimation of the phenological parameters of the many new cultivars that are released each year. However, there have been some attempts to utilize existing data available from conventional variety trials for this purpose (e.g., Mavromatis et al, 2001;Anothai et al, 2008;Mackay et al, 2011). Because variety trials are commonly used to evaluate new cultivars, the utilization of the resulting data has great potential to improve the efficiency of parameter estimation.…”

GplusE: beyond genomic selection

“…However, when lines are partitioned into an older set in the RP and modern lines in the test set (case 2), the correlation drops from 0.8 to 0.2, though for lines released within 10 years of the youngest line in the reference set the correlation is 0.4 (Mackay et al. 2011). Case 2 mimics more closely what breeders desire by predicting forward over generations.…”