Growth temperature alters temperature dependence of the photosynthetic rate (temperature acclimation). In many species, the optimal temperature that maximizes the photosynthetic rate increases with increasing growth temperature. In this minireview, mechanisms involved in changes in the photosynthesis–temperature curve are discussed. Based on the biochemical model of photosynthesis, change in the photosynthesis–temperature curve is attributable to four factors: intercellular CO₂ concentration, activation energy of the maximum rate of RuBP (ribulose-1,5-bisphosphate) carboxylation (V_c max), activation energy of the rate of RuBP regeneration (J_max), and the ratio of J_max to V_c max. In the survey, every species increased the activation energy of V_c max with increasing growth temperature. Other factors changed with growth temperature, but their responses were different among species. Among these factors, activation energy of V_c max may be the most important for the shift of optimal temperature of photosynthesis at ambient CO₂ concentrations. Physiological and biochemical causes for the change in these parameters are discussed.

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.