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A model of the lodging process has been successfully adapted for use on spring wheat grown in North-West Mexico (NWM). The lodging model was used to estimate the lodging-associated traits required to enable spring wheat grown in NWM with a typical yield of 6 t ha?1 and plant height of 0.7 m to achieve a lodging return period of 25 years. Target traits included a root plate spread of 51 mm and stem strength of the bottom internode of 268 N mm. These target traits increased to 54.5 mm and 325 N mm, respectively, for a crop yielding 10 t ha?1. Analysis of multiple genotypes across three growing seasons enabled relationships between both stem strength and root plate spread with structural dry matter to be quantified. A NWM lodging resistant ideotype yielding 6 t ha?1 would require 3.93 t ha?1 of structural stem biomass and 1.10 t ha?1 of root biomass in the top 10 cm of soil, which would result in a harvest index (HI) of 0.46 after accounting for chaff and leaf biomass. A crop yielding 10 t ha?1 would achieve a HI of 0.54 for 0.7 m tall plants or 0.41 for more typical 1.0 m tall plants. This study indicates that for plant breeders to achieve both high yields and lodging-proofness they must either breed for greater total biomass or develop high yielding germplasm from shorter crops.

Lodging-related traits were evaluated on the CIMMYT Core spring wheat Germplasm Panel (CIMCOG) in the Yaqui Valley of North-West Mexico during three seasons (2010–2013). Genetic variation was significant for all the lodging-related traits in the cross-year analysis, however, significant G × E interaction due to rank changes or changes in the absolute differences between cultivars were identified. The inconsistences on cultivar performances across seasons particularly reduced the heritability of key characters related to root lodging resistance (anchorage strength). Target characters related to stem lodging resistance (stem strength) showed good heritability values equal or above 0.70. Positive correlations between stem strength and stem diameter and between root plate spread and root strength were found. Selecting for greater stem diameter and wall width, greater root plate spread and shorter plant height could enable breeders to increase lodging resistance by increasing stem strength, root strength and decreasing plant leverage, respectively. Achieving a lodging-proof crop will depend on finding a wider root plate spread and implementing new management strategies. Genetic linkages between lodging traits will not constrain the combination of the key lodging-trait dimensions to achieve a lodging-proof ideotype. However, strong association between stem strength and stem wall width will increase the total biomass cost needed for lodging resistance.

Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles.

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