The domestication process of crops may have placed restrictions on the genetic basis of certain traits. Within cereals, the domestication process targeted a core set of traits collectively known as the ‘domestication syndrome’. These traits include dormancy, seed shattering, branching angle and patterning, internode elongation and biotic and abiotic resistance (Doust, 2007; Abbo et al., 2014; Zhang and Yuan, 2014; Teichmann and Muhr, 2015; Zohary et al., 2015). In comparison to wild relatives, domesticated cereals have a lower amount of vegetative branching, which in turn influenced the structure of the vegetative and influorescent components (Doust, 2007). Whilst the cereals were domesticated independently, molecular techniques indicate that selection was performed upon similar genes or gene pathways (Paterson et al., 1995; Doust, 2007; Meyer and Purugganan, 2013). These changes often resulted in a genome-wide reduction in genetic diversity (Hyten et al., 2006; Haudry et al., 2007; Yang et al., 2019). However, within certain species, such as carrot, einkorn wheat, apples and chicory, genetic diversity has been maintained (Kilian et al., 2007; Cornille et al., 2012; Iorizzo et al., 2013).

The primary goal of most breeding programmes is to achieve an increase in crop yield. However, this does not always occur with conscious selection of traits, but rather their quantitative performance during field trials. Therefore, varieties that feature on recommended lists are not necessarily chosen based on structural or ‘ideotype’ traits but rather their overall performance. In the case of wheat, structural fearures such as reduced height (e.g. Figure 1 ) may comply with the ideotype as a result of breeding and selection against lodging (Berry et al., 2007; Piñera-Chavez et al., 2016). Historically, reduced plant height was achieved during the green revolution through the introduction of dwarfing genes, including the Reduced height (Rht) genes of wheat and semidwarf 1 (SD1) genes of rice (Hedden, 2003; Pearce et al., 2011). These genes target the production and action of the plant hormone gibberellin (Asano et al., 2011).

Breeding programmes also occur with additional selective pressures in the form of breeder and consumer preference. In most instances, cultural preferences for traits will have evolved under conscious selection (Knüpffer et al., 2003; Grobman et al., 2012). For example, it is a common misconception that a deep green colour is the sign of a productive, or healthy plant. Therefore farmers often prefer plants that look very green, which is an indication of high chlorophyll content. However, recent studies indicate that a reduction in chlorophyll content permits increased radiation use efficiency and photosynthetic productivity (Walker et al., 2018). Similar to greenness, farmers in the UK have continuously selected against the presence of awns in wheat despite the potential benefits associated with water use efficiency, radiation interception, and an increase in grain size (Bort et al., 1994; Motzo and Giunta, 2002; Rebetzke et al., 2016; Sanchez-Bragado et al., 2023). This may partly be a result of the promotion of fungal diseases by awns in wetter environments (Sanchez-Bragado et al., 2023). Therefore, in the UK, historically awns may have been selected against to prevent yield loss associated with fungal diseases. However, as the climate continues to change, most countries in Europe, including the UK, are expected to have dryer environments (Christidis and Stott, 2021). Therefore, awns may no longer represent an undesired characteristic, but their future presentation will depend upon farmer acceptance.

The traits expressed by a plant depend on both underlying genetics and the environment (abiotic and biotic) to which they are exposed. Variation in the local environment may lead to a plant structure that differs from a proposed ideotype. Many crop species are sensitive to environmental conditions including changes in weather patterns and extreme weather events (Porter and Semenov, 2005; Semenov et al., 2014; Senapati et al., 2019). For example, exposure to wind can lead to changes in structure including dwarfing, tissue thickening and, under high wind speeds, breakages (de Langre, 2008). Crops that are cultivated close to the equator are likely to experience similar environmental conditions year round, whereas those further away will have to adapt to seasonal variation. For areas with large variation in environmental variables, yield stability may be of greater importance than yield maximisation (Braun et al., 1992).

Whilst the ideotype concept generally refers to a process to assist with breeding, it can also account for the best crop phenotype to grow in a given environment, under a specific cropping system or for a desired end product (Martre et al., 2015). The original ideotype traits proposed by Donald (Donald, 1968) were targeted at zero- to low- stress environments. Therefore these traits are unlikely to be suitable under moderate- to severe- stress. For example, during water limitation, the impact and extent to which the plant is affected will depend upon the timing, severity and length of the stress period as well as any other interacting stressors. Under these conditions, it is therefore unlikely that a single trait will be of benefit although this is likely to require changes in both the shoot and root system, requiring adaptation of the ideotype to the given environmental conditions (Tardieu, 2012; Greenland et al., 2017; Rezzouk et al., 2022).

Similarly, biotic factors such as the presence of herbivores, weeds, pest and disease, or the risk of promoting it, may also impact the traits exhibited by elite varieties in a given area (Arraiano et al., 2006). Depending upon the type and severity of the biotic stress, this may have structural implications such as leaf curling, discolouration or stunted growth. With intense herbivory pressure, plant architecture may adapt to protect the most vulnerable parts of the plant resulting in ‘cage’ structure (Cadenasso and Pickett, 2000; Charles-Dominique et al., 2017). In regions where biotic stressors are a high risk, certain traits will provide a greater benefit than others. For example, early canopy closure can increase competitiveness against weeds (Kaur et al., 2021), however this may come at the expense of light interception (Burgess et al., 2017; Burgess et al., 2021), or provide a more humid microenvironment. Microclimate features including humidity and reduced air flow as well as increased senescence are known to facilitate the spread of bacterial and fungal disease (Chang et al., 2007; Richard et al., 2012; Tivoli et al., 2013). Therefore the selection of beneficial traits will depend upon the tradeoff between multiple stressors. The impact of biotic pressure is also dependent on the life cycle of the crop species. For example, irrigated rice has a short growth cycle and so are less impacted by weed presence and so a more erect canopy structure is suitable (Ong and Monteith, 1992).

Another conflicting factor that will impact the presence of ideotype traits is cropping system and management of a given area. Agronomic practices including soil bed preparation, planting density and the timing of external resource applications will impact the features of the crop. In some instance, this may provide an additional control point to achieve ideotype traits. For example, application of plant growth regulators (PGRs) is common to restrict plant height and achieve uniform growth (Nickell and Louis, 1982). However, in other cases, the combination of management practices used will lead to variation in traits.

Intercropping (sometimes termed mixed cropping, or polyculture) refers to an alternative cropping system in which two or more plants are grown in close proximity during at least part of their lifecycle. Intercropping systems are common to small-holder farming, but due to their potential higher yields and reduced environmental impact, they may provide a sustainable solution for wider-scale agricultural production (Lithourgidis et al., 2011; Maitra et al., 2021; Burgess et al., 2022). However, within such systems, certain negative interactions must be overcome, such as potential interspecific competition.

Most crop varieties were bred for monoculture, and thus are rarely suited to mixed species systems. Within an intercrop, the optimal combination of traits is likely to differ, and thus the corresponding ideotype will also differ (Isaacs et al., 2016; Louarn et al., 2020). Within these systems, the ideotype concept has been extended and the term ‘ideomix’ has been proposed (Litrico and Violle, 2015). To account for multi-species interactions, ideomix selection can be facilitated by participatory plant breeding (PPB), in which farmer knowledge and preferences are integrated within the variety selection process. The PPB approach was applied to an intercrop of maize and common bean (Phaseolus vulgaris L.) for cultivation in Rwanda (Isaacs et al., 2016). Through this approach, Isaacs et al. (2016) identified traits of the bean component, which constitute the ideotype for intercropping systems. These traits included restricted height, columnar plant structure, even distribution of pods, fewer leaves, and earlier maturity.

PPB programs are not only exclusive to intercrop systems but have also been applied to many monocrop species. Through client-orientated focus, it more likely that farmer criteria for genotypes are met, thus improving chance of uptake. This can therefore be used to overcome the social and cultural preferences that can impact expression of the ideotype.

Improvements to plant phenotyping approaches have led to methods to rapidly capture detailed descriptions for a variety of plant traits. These often rely on the use of non-invasive and/or digital technologies (Costa et al., 2019). A number of recent reviews give in depth details to the advances in phenotyping approaches (e.g. (Mishra et al., 2016; Lobos et al., 2017; Ubbens and Stavness, 2017; Li et al., 2020; Pieruschka and Schurr, 2023), however a few key approaches will be given here in relation to ideotype breeding.

This desire for the creation of complex, geometrically accurate three-dimensional models of plants has led to the development of a number of different techniques in order to capture plant structure (e.g. Watanabe et al., 2005; Quan et al., 2006; Song et al., 2013; Pound et al., 2014; Gibbs et al., 2019). Applications of such models are diverse, including the study of photosynthesis for both single plants and whole canopy structures (e.g. Song et al., 2013; Burgess et al., 2017; Wang et al., 2020; Burgess et al., 2021). Image-based models, or those originating from other remote-sensing based techniques, are highly desirable (Houle et al., 2010; Santos and Oliviera, 2012; White et al., 2012; Pound et al., 2014; Gibbs et al., 2019), with the information needed to calculate a number of plant traits including leaf areas and angles, geometry or plant height. Therefore, these models are of particular importance for ideotype design and assessment, with the ability to rapidly and accurately quantify variation in structural traits.

The throughput of field data collection can be improved further through the use of gantry systems, robotics or airborne data collection (For reviews see Mogili and Deepak, 2018; Maes and Steppe, 2019; Atefi et al., 2021; Li et al., 2021; Xu and Li, 2023). In recent years, Unmanned Aerial Vehicles (UAVs), Remotely Piloted Aircrafts (RPAs) and drones have increased in popularity as a result of increases in hardware and software capabilities combined with a reduction in price. Incorporating multiple sensors within the same system can permit high spatial and temporal data collection for a wide range of plant parameters simultaneously (Mogili and Deepak, 2018; Barbedo, 2019). Together, these enable high-resolution spatial and temporal information to be collected, permitting the quantification of a large range of plant structures throughout the growth season.

Generating new ideotypes is confounded by the high level of uncertainty associated with rapidly changing environmental conditions. Whilst the improvements in phenotyping approaches discussed above provide one approach to capturing plant structural traits, limitations arise as a result of access to equipment, knowledge, sufficient variation and time. To overcome limitations associated with the time and resource intensive breeding of ideotypes, tools are being continually developed to narrow down target traits. For example, designing and testing potential ideotypes in silico using simulation models enables a wider range of traits and environmental conditions to be tested. Process-based crop models have been developed to simulate interactions between genotype, environment, and management approaches. Thus, they can also be used to support plant breeding through ideotype design. Model-assisted phenotyping provides a phenotypic fingerprint that can link trait correlations to genetic information (Martre et al., 2015). Through the incorporation of global climate models, these models can also be used to predict both current and future desirable traits. Therefore whilst traditionally, breeding has focused on developing crops that are suitable for a broad variety of environments, model-assisted approaches allow varieties to be adapted to a specific set of environmental conditions. For reviews on the application of crop modelling to aid ideotype design see (Martre et al., 2015; Rötter et al., 2015; Christensen et al., 2018).

Simulation models have been applied to ideotype design for a range of crop species. The first model-aided ideotype design was accomplished within irrigated rice, leading to the production of the super hybrid rice variety ‘Lianyoupeijuu’ (Khush, 1995). Within wheat, Semenov and Stratonovitch (2013) applied the Sirius simulation model to design high-yielding ideotypes for two contrasting European sites, UK and Spain, for the 2050 A1B climate scenario (Meehl et al., 2007). This includes a year-round increase in temperature of 2 – 5°C and variation in monthly precipitation for both sites (Semenov and Stratonovitch, 2013). The authors identified that target traits predicted to achieve the greatest yield increase include improvement to the efficiency light energy conversion, optimal phenology and increased length of the grain filling period.

3-dimensional (3D) visual modelling provides an alternative route in model-assisted ideotype breeding to identify both phenotypic features and agronomic practices which can contribute to yield (Wang et al., 2019). This is facilitated by recent emphasis on the importance of crop canopy architecture as well as advances in the ways that canopies can be captured and modelled in 3D (Murchie and Burgess, 2022). Within rice (O. sativa), phenotypic plasticity was assessed to re-optimisation of physiology and yield forming processes of so called ‘super rice’ (Wang et al., 2019). Under optimal growing conditions, the super rice ideotype was predicted to have a high cumulative leaf area; erect leaf orientations, particularly within upper canopy levels; improved leaf area duration; and overall higher photosynthetic and resource use efficiency. Further potential beneficial traits include promoting early vigour and incorporating stay green phenotypes (Hoang and Kobata, 2009; Thomas and Ougham, 2014; Rebolledo et al., 2015; Zhao et al., 2019).

For ideotype breeding of multi-species cropping systems, plant growth modelling can help predict plant traits that affect both inter- and intraspecific interactions (Bourke et al., 2021). Successful intercrops arise from stronger interactions between the component crops, and thus identifying these interactions are key to ideotype breeding success. One particularly beneficial type of computer modelling approach is functional structural plant modelling (FSPM). In FSPMs, plant growth, and thus structure, is simulated in 3D over time based on the competition for growth resources (Evers et al., 2019). Therefore FSPMs are ideally suited to determining the impact of interspecies interactions under different spatial arrangements. However, for full assessment of plant performance during FSPMs, below-ground interactions must also be accounted for, thus requiring information on root systems (Evers et al., 2019).

The potential limitations associated with the use of process- based models for ideotype design are linked to model accuracy (Rötter et al., 2015). The detail and resolution at which the underlying physiological processes are presented within the model will determine their suitability for estimating beneficial traits. The resolution at which genetic information is incorporated with physiological and environmental controls is also key to progress (Bertin et al., 2010; Hammer et al., 2010; Martre et al., 2015). Incorporation of high-quality, long-term information on environmental variables and plant productivity under different scenarios will be imperative to increase the accuracy of the models going forwards (Rötter et al., 2011; Li et al., 2015). This includes the accurate representations of future weather events, including elevated carbon dioxide concentrations and the impact of elevated temperature on leaf senescence, floret abortion and grain filling (Asseng et al., 2011; Moriondo et al., 2011; Nendel et al., 2011; Rötter et al., 2011).

Many desirable plant traits are quantitative and affected by multiple genes. Therefore, quantitative genetics provides a powerful framework for crop improvement. For ideotype design, parental lines could be selected for breeding programmes. Following trait recombination, genomic selection could be used as a tool to help avoid lengthy phenotypic evaluation of the progeny, while potentially achieving greater genetic gains.

When defining a genetic ideotype, the first stage is to accurately define the target environment and the corresponding impact on the phenotype (Hodson and White, 2007; Trethowan, 2014). Following this, known genetic information, gene x environment interactions and physiological response to stressors can be integrated to predict the optimal plant type. For areas of the genome where unknown genes may be located, association genetic analysis can be performed via random genotyping (Crossa et al., 2007). Once the genetic ideotype has been defined, it can then be bred for. This can be aided by methods such as marker assisted recombinant selection (Charmet et al., 1999) and through software to predict the probability of obtaining the desired genotype.

Whilst computer modelling, genetic selection and new breeding approaches have all resulted in improvements to crop traits, greater improvements could be achieved by combining approaches. For example, combining simulation studies with new breeding approaches provides the platform for targeted ideotype production, significantly accelerating the generation of new cultivars.

Potential drawbacks of each individual approach may be overcome. For example, computer simulation approaches often do not consider the genetic basis of a trait of interest, not do they account for the available genetic variation in traits (Gu, 2022). In comparison, genetic- based models may be reduced in accuracy due to the complex gene x gene and gene x environment interactions. Therefore, quantitative trait loci (QTL) are often only detected under a limited set of environmental conditions (Zhou et al., 2007). Combining both approaches has therefore been proposed to overcome these issues (Letort et al., 2008; Bourke et al., 2021; Gu, 2022). When combining both approaches together, a number of factors must be addressed. For example, using phenotypic data for quantitative genetic analysis requires the growth and measurement of plants under the same environment, at the same growth stage. Conversely, accurate quantitative genetic analyses require screening of large populations to obtain sufficient genetic resolution.

The power of the combined approach comes from the application of crop simulation models in dissecting complex traits. Once dissected, it is then possible to assess genetic variation for each component and evaluate relative importance. Additionally, simulating plant growth and development using FSPMs, for example, permits the visualisation of the change in phenotype over time, and therefore provides direct information about the action of gene. This can be verified using field based phenotyping systems such as those discussed above. Due to these benefits, a combined approach has been suggested for ideotype design for both monocrops and intercrops (Bourke et al., 2021). For maximised success, several suggestions as to requirements of simulation models have been proposed, central to which is high predictability under varied environmental conditions (Hammer et al., 2002; Tardieu, 2003; Hammer et al., 2006). Some limitations may occur when using the combined approach, such as artificial pleiotropic effects, which may be a result of impact on similar primary mechanisms, but not through linked genetic bases (Yin et al., 2003).

Whilst improvements to both simulation and genetic methods will aid to increase success for ideotype breeding, combined approaches have nevertheless been applied to a number of crop species. In rice, combining ecophysiological crop models with genetic markers has been shown to provide a greater number of markers overall, whilst also allowing for prioritisation to a given environment (Gu et al., 2014). Similarly, Xu et al. (2011) were the first to combine an FSPM with a quantitative genetic model to link QTL with whole plant physiological function in rice. Whilst successful in predicting plant height, mismatch between measured and modelled dry matter production indicate further improvements are required (Xu et al., 2011). In apple trees, a FSPM was combined with genome wide information to predict tree structural information (Migault et al., 2017). Whilst the approach was sufficient at predicting phenotype for one growth season, it was less successful for another, indicating that further improvements are required to determine environmental interactions.