Maize is considered less drought-tolerant than sorghum, but sorghum is commonly grown as a short triple dwarf (3dwarf) type, so difference in plant height confounds the species comparison. The objectives of this study were to experimentally determine effects of species and plant height differences on transpiration efficiency (TE) and transpiration rate per unit green leaf area (TGLA) and use findings to explain input parameters in a simulation study on the comparative adaptation of 3dwarf sorghum and maize in environments with contrasting water availability. Maize, tall double dwarf (2dwarf) and short 3dwarf sorghum genotypes were grown in two lysimeter experiments in 2011 in SE Queensland, Australia. Each plant was harvested after anthesis and total transpiration, shoot and root dry mass were measured to estimate TE. Daily TGLA was used to compare transpiration rates. Species and height had limited effect on TE, but significantly affected TGLA. This was associated with differences in biomass allocation. The similar TE but higher TGLA in maize compared with 3dwarf sorghum meant it potentially produces more biomass, consistent with published differences in biomass accumulation and radiation use efficiency (RUE). The simulation study, which used similar TE for maize and 3dwarf sorghum, but captured differences in TGLA through differences in RUE, predicted crossover interactions for grain yield between species and total water use. The greater TGLA of maize decreased grain yield in water-limited environments, but increased yields in well-watered situations. Results highlight that similarity in TE and differences in TGLA can influence comparative adaptation to water limitation.

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Classical crop models have been developed to predict crop yield and quality, and they are based on physiological and environmental inputs. After molecular discoveries, models should integrate genetic variation to allow predictions that are more genotype-dependent. An interesting approach, Quantitative Trait Locus (QTL)-based ecophysiological modeling, has shown promising results for the design of ideotypes that are adapted to biotic and abiotic stresses, but there are still limitations to attaining a fully integrated model. The aim of this case study is to clarify the impact of choosing different model equations (closely related and with different numbers of parameters) and optimization methods on the detection of QTLs controlling the parameters of crop growth. Different growth equations were parameterized based on a genetic population by following different approaches. The correlations between parameters were analyzed, and two different strategies were adopted to address the correlation issue. QTL analysis was performed on the optimized values of the parameters of the growth equations and on the observed dry mass (DM) data to validate the QTLs detected. Overall, models and strategies resulted in different QTLs being detected. Similar LOD profiles but with peaks of different heights were observed, some of which were significant, resulting in different numbers of QTLs. In some cases, peaks had slightly different positions or were absent. Even closely related growth models led to the detection of different QTLs. The goodness of fit and complexity of the growth models were found to be insufficient to select the best model. Calculating parameters independently of observed data may not be a good strategy, whereas setting parameters independent of the genotype is recommended. Given the large-scale global optimization problem and the strong correlations between parameters, the two algorithms tested showed poor performance. Currently, the lack of effective algorithms is the main obstacle to answering the question posed. The authors therefore suggest testing different model formulations and comparing the QTLs detected before choosing the best formulation to use in an ecophysiological modeling approach based on QTLs.

Abstract. Drought and heat events are becoming more frequent in Europe due to human-induced climate change, affecting many aspects of human well-being and ecosystem functioning. However, the intensity of these drought and heat events is not spatially and temporally uniform. Understanding the spatial variability of drought impacts is important information for decision makers, supporting both planning and preparations to cope with the changing climatic conditions. Currently, data relating to the damage caused by extended drought episodes is scattered across languages and sources such as scientific publications, governmental reports and the media. In this review paper, we compiled data of damages caused by the drought and heat of 2018 until 2022 in forest ecosystems and relate it to large European data sets, providing support for decision making both on the regional and European levels. We partitioned data from 16 European countries to the following regions: Northern, Central, Alpine, and South. We focused on drought and heat damage to forests, and categorized them as (1) physiological (2) pest, and (3) fire damage. We were able to identify the following key trends: (1) Relative defoliation rates of broadleaves is higher than of conifers in every country with the exception of Czech Republic (2) the incidence of wood destroyed by insects is extremely high in Central Europe and Sweden (3) Although forest fires can be related to heat and drought, they are superimposed by other anthropogenic influences (4) In this period (2018–2022), forests in central Europe are particularly affected, while forests in the Northern and Alpine zones are less affected, and adaptations to heat and drought can still be observed in the Southern zone. (5) Although in several regions 2021 was an average year still high levels of damages were observed indicating strong legacy effects of 2018–2020. We note that the inventory should be continuously updated as new data appear.