Premium
Not a load of rubbish: simulated field trials in large‐scale containers
Author(s) -
Hohmann M.,
Stahl A.,
Rudloff J.,
Wittkop B.,
Snowdon R. J.
Publication year - 2016
Publication title -
plant, cell and environment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.646
H-Index - 200
eISSN - 1365-3040
pISSN - 0140-7791
DOI - 10.1111/pce.12737
Subject(s) - greenhouse , container (type theory) , transferability , abiotic component , agricultural engineering , field trial , crop , environmental science , agronomy , cultivar , biology , mathematics , engineering , ecology , statistics , mechanical engineering , logit
Assessment of yield performance under fluctuating environmental conditions is a major aim of crop breeders. Unfortunately, results from controlled‐environment evaluations of complex agronomic traits rarely translate to field performance. A major cause is that crops grown over their complete lifecycle in a greenhouse or growth chamber are generally constricted in their root growth, which influences their response to important abiotic constraints like water or nutrient availability. To overcome this poor transferability, we established a plant growth system comprising large refuse containers (120 L ‘wheelie bins’) that allow detailed phenotyping of small field‐crop populations under semi‐controlled growth conditions. Diverse winter oilseed rape cultivars were grown at field densities throughout the crop lifecycle, in different experiments over 2 years, to compare seed yields from individual containers to plot yields from multi‐environment field trials. We found that we were able to predict yields in the field with high accuracy from container‐grown plants. The container system proved suitable for detailed studies of stress response physiology and performance in pre‐breeding populations. Investment in automated large‐container systems may help breeders improve field transferability of greenhouse experiments, enabling screening of pre‐breeding materials for abiotic stress response traits with a positive influence on yield.