This report explores the genetic and phenotypic components of ideotypes, emphasizing their role in bridging the phenotype-genotype gap. Through the lens of quantitative genetics and the integrative approach of Crop Systems Biology (CSB), the report examines the complex interaction between genetic information, environmental factors, and crop physiology, offering insights into improving crop breeding strategies.
1 Definition and components of a plant ideotype
1.1 Definition of the concept ideotype:
1.2 Genetic and phenotypic components of an ideotype:
1.3 Several examples of ideotypes for specific environments:
2 Quantitative genetics methods, useful to understand crop physiology
2.1 Line Development Methods
2.2 Mapping and Association Studies
2.3 Assisting Technologies (physiology and statistics)
2.4 Future of the Methods from the Quantitative Genetics Framework
3 The purpose of plants expressing large genotypic and phenotypic variation
4 Relevance of the phenotype to genotype gap for improving the understanding of crop physiology by different stakeholders
5 The ways crop physiologists can use genetic information for a better understanding of crop physiology
5.1 Bottlenecks
5.2 Additional Strategies
6 Integrate crop models, genetic information, and physiological information for more efficient breeding.
7 Case studies on rice and wheat exemplify the crop systems biology approach.
7.1 Different aspects of CSB
7.2 Opportunities
8 The future potential of the CSB approach to support crop physiology and how this can assist breeders.
9 The biggest challenges crop physiologists and breeders will face in the near future.
10 Conclusion
11 References
An ideotype is a conceptual plant model combining optimal morphological and physiological traits for specific environments. They can be used for guiding long-term breeding to enhance e.g., crop efficiency, adaptability, and yield in targeted agricultural settings1-3. An ideotype can be achieved by combining traits of interest for a specific scenario such as: yield potential or product quality4. In modelling terms, an ideotype represents a set of genetic vectors , optimized to maximize genotype performance across a specified range of environmental conditions5.
Genetic components of an ideotype refer to DNA-encoded traits affecting the phenotype, whereas phenotypic components are the observable attributes, emerging from the genetic components of a crop and are also influenced by the environment.
Genetic components are fixed genetic characteristics (e.g., genes and their alleles, and epigenetic methylation) and their relation among each other (e.g., pleiotropy, and polygenic). When these genetic components are then put in to a physiological representation (proteins, structures and metabolism) they interact with the environment, forming phenotypic components/traits (drought resistance, yield, size, flavor, or disease resistance).
These components (genetic and phenotypic) can not only be a characteristic of individual crops, since single genotypes can also vary across a crop population . This is a characteristic trait by itself: genetic variability of a trait with in a population. Additionally, certain traits, including genetic variation that contributes to maximizing yield stability or sustaining the heterosis effect in hybrid crops, become apparent only when observed at the population level, rather than in individual organisms.
In the development of ideotypes, plant breeding strategies focus on enhancing traits such as drought, heat, and salt tolerance, which are critical for adaptation to specific environmental challenges like arid conditions, climate change, and flooding in coastal regions. Crop Systems Biology (CSB) plays a crucial role in defining these ideotypes by integrating genetic, physiological, and environmental data to identify and select for traits that improve resilience and productivity under abiotic stress. For instance, in the creation of climate-adapted genomes, CSB methodologies enable the identification of genetic markers associated with enhanced drought or heat tolerance, thereby facilitating the breeding of crops better suited to changing environmental conditions (e.g., climate change)1,2. Similarly, the development of bread wheat ideotypes resistant to abiotic stresses leverages marker-assisted selection to pinpoint traits contributing to stress resilience5. The targeting of light interception in oil palm ideotypes exemplifies another application of CSB-methods4, focusing on this trait to increase the overall photosynthetic activity of the crop and, consequently, enhance crop yield, due to photosynthetic activity positively corelating with crop yield (see LINTUL-models). These examples showcase the practical application of CSB in addressing complex challenges in crop production and breeding.
Quantitative genetics is a branch of genetics focused on analyzing and understanding the inheritance of traits governed by multiple genes, each contributing incrementally to the total phenotype. It specifically examines how polygenic traits are transmitted across generations and how genetic variations affect observable characteristics within individuals and populations. By employing a range of statistical methods to assess the genetic contribution to trait variation, quantitative genetics aims to predict the outcomes of genetic combinations on phenotypic traits, facilitating the study of complex traits influenced by both genetic factors and environmental conditions.
In the context of crop physiology, quantitative genetics is instrumental in unravelling the genetic basis of complex traits8, aiding in the selection and breeding of crops with desirable characteristics under diverse environmental conditions. There is a large number of quantitative genetics methods, hereafter there is a focus on the most common once ordered in categories (line development, mapping and association, and assisting methods) and each explained briefly. The methods from the mentioned categories, while distinct in their approaches and objectives, are inherently interlinked and often employed in a combined manner within the framework of quantitative genetics. For instance, line development methods such as creating RILs or NILs provides a controlled genetic foundation that enhances the effectiveness of mapping and association studies like GWAS or QTL mapping, by offering clear, defined genetic variations for association with specific traits. Furthermore, methods from the last category (assisting methods) are then used e.g., to gather even more data with the identification and quantification of metabolites, enzymes, and other physiological indicators related to genetic variations, and statistical tools to correlate the (at this point large amount) of data point, gathered by all of the formally employed tools. At the end of a quantitative genetics study, it is likely that methods from all three categories will have been used.
These methods focus on developing specific types of genetic lines (such as inbred or isogenic lines) to isolate and study the effects of particular genes or chromosomes on traits.
These techniques involve mapping genetic markers or analyzing whole genomes to identify associations between genetic variations and specific traits.
The techniques mentioned above generate extensive amounts of data, and sophisticated statistical tools are necessary to unravel the complex interactions between genes and to accurately identify associations with specific traits. The link between genetics and traits are formed by the physiology of a plant and to inspect this, further methods are needed. Those are focused on detecting metabolites, enzymes and other proteins in a plant, that are the cause for trait-formation of a plant.
Examples where quantitative genetics would have an added value are for example with breeders using them to identify subtle differences among genotypes in a genetic population, aiding in selection towards target phenotypes. However, a framework prioritizing physiological markers, such as proteins or metabolites, may offer more cost-effectiveness due to their simplicity and lower costs of associated assays, like coulometric tests. A practical example for such an approach could be, that breeders focus on ascorbate-glutathione cycle enzymes for enhanced drought tolerance in rice10. However, this framework sacrifices reasoning at the genetic level, making it more practical for breeders focusing on phenotypic traits rather than for scientists seeking detailed genetic explanations.
The future of quantitative genetics is likely for significant advancements, largely driven by the growth in computational power, the application of new processing concepts like machine learning, and high-throughput phenotyping technologies like hyperspectral imaging (HIS) and thermal imaging. As computational capabilities continue to expand, in line with Moore’s Law, methods that rely heavily on computational analyses, such as GWAS and NAM, are expected to become even more refined and powerful, allowing for more precise identification of genetic associations with complex traits.
Simultaneously, the integration of pattern recognition, deep learning, machine learning and other AI techniques is transforming quantitative genetics by improving the quality of mapping and association studies by enabling more sophisticated analyses of genetic data. For example, machine learning can enhance the interpretation of GWAS results, uncovering complex genetic patterns and interactions that were previously undetectable. Additionally, the application of computation heavy methods in line development can streamline the creation of new populations by predicting optimal crossing strategies, analyzing vast amounts of data, that were before inaccessible.
High-throughput phenotyping methods, such as HSI, multispectral imaging (MSI), thermal imaging, and RGB imaging, are revolutionizing the field by providing detailed phenotypic data at unprecedented scales. These technologies, coupled with machine learning tools, allow for the rapid and accurate measurement of traits across large populations, significantly improving the efficiency of selection processes, enhancing the predictive power of crop models and facilitating the breeding of crops with desired characteristics. Especially the methods of PCA, pattern recognition, and multivariate modelling will be elevated by this.
As these technologies continue to evolve, the interaction between computational power, machine learning, and high-throughput phenotyping with the mentioned methods is expected to leverage and amplify the capabilities of almost every method within the quantitative genetics’ framework. As a rule of thumb: Methods that rely on computational power and automated phenotyping are set to become increasingly powerful in the future.
The evolutionary purpose of plants expressing large genotypic and phenotypic variation is primarily for adaptation and survival under diverse environmental conditions. This variation allows at least some plants of the gene pool to respond and adapt to various challenges such as drought, floods, extreme temperatures, and salinity, thereby securing the survival of the species. Similarly, interspecific diversity, the variation among species, is crucial for ecological robustness. It ensures ecosystem resilience by distributing species across diverse niches, thereby maximizing resource utilization. It also acts as a buffer against environmental fluctuations, promoting ecosystem stability by enabling complementary and synergistic interactions among species. Such biodiversity enables complex ecological interactions, enhancing nutrient cycling, ecosystem stability, and ensures, that the ecosystem has diverse options in case of harsh environmental conditions, which collectively reinforces the integrity and functionality of ecosystems.
This diversity is also crucial for crop improvement, as it provides a reservoir of traits that can be harnessed for breeding more resilient and productive crops. Scientists can utilize this variability to correlate phenotypic differences in crops with their genetic variations, directly linking specific traits to genes (e.g., via GWAS, see Chapter 2.2) and advancing the understanding of crop genetics.
Dispensable genes, which are not universal across all varieties in a gene pool, are often linked with local adaptation to abiotic and biotic stressors. These include traits like flowering time, disease resistance, and response to environmental stress, as well as involvement in signal transduction pathways. Their role in such adaptations makes them valuable for breeding climate-resilient crops12-15.
Interestingly, even single metabolic variables like erythritol, amino acids, and MDA (malondialdehyde) demonstrate a significant influence on grain yield stability in rice, especially under drought conditions. This correlation is valuable for breeders, guiding targeted trait selection for improved drought resistance and yield. Thus, metabolic profiling bridges biochemical markers with key agronomic traits, enhancing crop improvement strategies10.
The genotype to phenotype gap is crucial for advancing the understanding of crop physiology as it illuminates the complex relationship between a plant’s genetic makeup and its observable characteristics. This gap highlights how genetic variations translate into diverse phenotypic expressions, such as resistance to environmental stresses, yield, and growth patterns. Bridging this gap allows researchers to identify specific genes responsible for desirable crop traits, facilitating targeted breeding and efforts aimed at enhancing crop resilience and productivity. Understanding the genotype to phenotype gap also enables the prediction of crop behavior under various environmental conditions, improving the management and cultivation strategies to optimize yield and sustainability. By decoding the genetic basis of physiological traits, scientists can develop crops that are better adapted to changing climates and environmental challenges, thereby contributing to food security and agricultural sustainability.
For example, the genotype to phenotype gap is relevant to improve our understanding in crop physiology because it sheds light on how plants utilize genetic information to navigate environmental stresses. For instance, identifying the genetic markers linked to enhanced photosynthetic efficiency under high temperatures can unravel the physiological pathways that some crops employ to maintain productivity in heatwaves or under the aspect of intensifying climate change. Such insights not only enhance our grasp of plant metabolic processes but also illuminate the mechanisms behind stress tolerance, growth rates, water usage, and resource allocation within plants. Understanding these physiological adaptations driven by genetic factors directly contributes to the field of plant physiology by providing a molecular basis for the observable diversity in plant behaviors and responses to their environment, thereby deepening our comprehension of plant survival and productivity strategies.
Understanding the gap between a plant’s observable traits (phenotype) and its genetic code (genotype) is important across various fields in agriculture, each stakeholder is leveraging this knowledge differently based on their specific needs and objectives. From breeders to policy makers, the interpretation and application of the phenotype-genotype gap significantly influence decision-making, breeding strategies, and policy formulation in the agricultural sector.
Breeders focus on practical outcomes, using the phenotype-genotype gap primarily to link observable plant traits (phenotypes) like yield or drought resistance to specific genetic markers (genotypes). This knowledge directly informs their breeding strategies, helping them develop crop varieties that meet specific needs. While breeders value the underlying genetic knowledge, their main interest lies in how this information can be practically applied (in contrast e.g., to scientists) to produce robust, high-yielding crops.
Geneticists and biochemists, although they operate in different domains, share a common interest in the phenotype-genotype gap. Geneticists are keen on deciphering the genetic basis of complex traits, unravelling the DNA sequences that command phenotypic expressions. Biochemists, on the other hand, delve into the molecular pathways and biochemical processes that bridge the gap between genetic information and observable traits. For both, the phenotype-genotype gap is a gateway to understanding the fundamental biological mechanisms that govern crop traits.
Crop physiologists use the knowledge of the phenotype-genotype gap to understand how genetic factors influence a plant’s growth, development, and response to environmental conditions. Their focus is on how the plant’s internal processes and external interactions contribute to the overall phenotype. In contrast e.g., to geneticists and biochemists, physiologists are focused on how these traits manifest and interact within the plant and its environment.
Agricultural economists and policy makers, although not directly involved in the scientific aspects of the phenotype-genotype gap, are interested in the implications of this knowledge for agricultural productivity and sustainability. For them, understanding the phenotype-genotype gap is about translating scientific findings into policies that support efficient, sustainable, and future-proof farming practices. E.g., with the approaching climate change, it is expected that the phenotype to genotype gap will shift, acknowledging and understanding this is ab imported step in the political field, so research, awareness, information campaigns, and congresses regarding this can be supported and funded.
In conclusion, while the relevance of the phenotype-genotype gap spans across various stakeholders, each group utilizes this knowledge differently, based on their specific interests and operational domains. Breeders focus on the practical application of this knowledge, geneticists and biochemists seek a deeper scientific understanding, crop physiologists look at the plant as a whole system, and agricultural economists and policy makers are -hopefully- interested in the broader economic and policy implications.
One way of gaining a better understanding of physiology involves the integration of genetic control into crop modelling. For example, models that incorporate the genetic control of model parameters have been used in beans (Phaseolus vulgaris L.)16, soybeans (Glycine max (L.) Merr.)17-19, wheat (Triticum aestivum L.), and common beans (Phaseolus vulgaris L.)20. This involved using linear regression to estimate model-input traits from allelic information on candidate genes. However, it’s important to note that this method assumes traits are controlled by the pleiotropic effects of a few genes, potentially overlooking additional trait-specific genes.
Unlike gene-based modelling, which focuses on specific genes, QTL-based modelling, examines broader genetic regions influencing key traits like biomass and yield under different environmental conditions. This can be put in to practice, by identifying QTLs for various (GECROS) model input parameters, including plant height, grain weight, time to flowering and time to maturity, under both drought and well-watered conditions21. Virtual ideotypes, that are created by using modelling-derived markers demonstrated a yield increase ranging from 19% up to 36%, compared to virtual ideotypes that were solely reliant on yield-specific markers22, proofing the potential in gene- and QTL-based modelling.
Bottlenecks with these approaches are the limited understandings of the complex interactions between genes and environmental variables, which complicates the prediction accuracy of phenotypic traits from genotypic data. Furthermore, the polygenic nature of most traits presents additional complexity in establishing precise genotype-phenotype correlations. In addition, there is a need for advanced, cost-effective techniques for comprehensive genotyping and phenotyping, which are essential for this approach. As discussed in chapter 2, a framework that relies more on the metabolic aspects of plants for modelling is imaginable, offering a more cost-effective alternative yet with less explanatory power.
In addition to refining models, crop physiologists can utilize genetic information through diverse strategies to deepen their understanding of crop physiology. Breeding methods, such as marker-assisted selection and genome editing, offer paths to directly manipulate genes associated with desired traits. By changing a narrow set of genes and then look how it effects the plants traits and pathways is a direct way to use genetic information to gain a better understanding of the link between plant physiology and genetics. Exploring gene-environment interactions is another way of providing insights into how genetic variants respond to different environmental stresses, making the way for tailored agronomic practices and improved crop management strategies e.g., studying how specific genetic variants influence a crop’s response to drought conditions can inform the development of irrigation strategies tailored to maximize water-use efficiency and yield. Moreover, delving into genetic diversity within crop species unveils genetic variations that can be leveraged to enhance adaptability (see chapter 3 for more information). Integrating genetic data with CSB approaches elucidates complex regulatory networks and metabolic pathways, shedding light on the molecular mechanisms underpinning key agronomic traits. Empowering farmers with genetic information through accessible tools and resources enables informed decision-making, optimizing crop selection, input utilization, and pest management practices tailored to specific agricultural contexts.
The integration of crop models, genetic information, and physiological data into breeding can be conceptualized through the MGB (modelling-genetics-biochemistry) triangle framework. This approach combines insights from these three fields to enhance breeding efficiency and crop performance prediction22.
Regarding the necessity of knowledge on crop physiology, genetics, and crop models for breeders, it’s not imperative for breeders to deeply understand all these scientific details. They often prioritize models that are practical and straightforward (often descriptive), focusing on outputs that guide trait or phenotype selection, rather than the underlying scientific complexities. On the other hand, for scientific research, explanatory models hold more value. These models, that represent real-world variables and their interactions, are crucial for understanding the fundamental principles of crop physiology, genetics, and biochemistry. They aid in deciphering the complex mechanisms underlying crop traits and responses, thus contributing to the broader knowledge base necessary for advancing crop science. Hence, partnerships between breeders and scientists enable the effective utilization of the MGB-triangle, with researchers contributing advanced knowledge and methodologies, and breeders providing practical insights, experimental frameworks, and data. This collaborative effort ensures the continuous improvement and relevance of the models, ultimately benefiting the breeding process.
An example for such a collaboration could be, that scientists develop and train models with experimental data to understand how changing climate conditions affect the needs, genetics, and biochemistry of potatoes. Through these models, significant improvements in potato yield under climate change scenarios are achieved. The collaboration between scientists and breeders is crucial at this stage, as breeders leverage the models to inform their selection processes. However, for the models to be optimally beneficial to breeders, ongoing communication is essential. This dialogue covers critical aspects such as the selection of potato varieties, field conditions, nutrient management, irrigation practices, and geographical considerations. Such comprehensive communication ensures that the models accurately reflect practical breeding scenarios, enabling breeders use models, tailor to their needs and therefor make informed decisions.
The presented case studies5,10,23–25 show different elements in their approach to tackle their respective problem. In wheat, the study23 of root anatomical and histochemical traits under water-deficit stress and their linkage to genetic differences is a clear example of CSB, combining genetics, physiology, and environmental factors. The rice-study10 focused on metabolites and enzymes expressed under drought stress conditions, reflecting the linkage of physiological and biochemical responses to environmental stressors. The GWAS study24 approached the problem of grain yield under drought in rice by providing multiple novel genetic loci thought associating genetic variants in different individuals with variants in traits. The second rice paper25 combined GWAS with physiological markers to identify corresponding markers. At last, the second wheat paper5 is using maker-based crop models to create an ideotype, that avoids abiotic stress. In summary, these cases illustrate CSB’ s holistic view, where genetic, physiological, and environmental data is integrated to enhance our understanding and management of crops.
Following the framework of the MGB-Concept (introduced in chapter 6 and elaborated here), the sections below detail the core aspects of Crop Systems Biology as revealed in the case studies, exploring how Crop Modelling, Genetics/Genomics, Biochemistry/Physiology, and Bioinformatics each play a critical role in advancing our understanding and improving crop performance.
Crop Modelling is a significant aspect of these studies, as seen in the development of ideotypes in wheat and the integration of physiological markers in rice. Crop models help simulate the performance of different cultivars under varying environmental conditions, incorporating genetic and physiological data to predict outcomes and guide breeding strategies.
Genetics/Genomics: The studies on wheat and rice demonstrate a strong emphasis on genetics and genomics. For example, GWAS in rice links genetic variants to traits like grain yield under drought stress. This genomic analysis is crucial in identifying genetic loci that are essential for developing stress-resistant crops.
Biochemistry/Physiology: The physiological and biochemical responses of crops to environmental stressors are central to these studies. In rice10, the focus on metabolites and enzymes expressed under drought conditions, and in wheat, the examination of root traits under water-deficit stress, highlight the role of physiological and biochemical processes in crop adaptation.
Bioinformatics: While not explicitly detailed in the studies, bioinformatics plays a crucial role in analyzing and interpreting the vast amount of data generated from genomics and physiology studies and is therefore a critical part of CSB as a supporting method, closing the gap between observation and result. Tools for genomic data analysis, image processing, and statistical analysis are implicit in managing and understanding the complex relationships in CSB.
Integrated Omics and Environmental Stress Response in Rice and Wheat: Conducting comprehensive omics studies (genomics, transcriptomics, proteomics, metabolomics) in rice and wheat under various environmental stress conditions. This would include analyzing gene expression, protein profiles, and metabolic changes in response to factors like drought, salinity, and temperature extremes.
Advanced Crop Modelling incorporating genetic and environmental data by developing sophisticated crop models that integrate genetic information with environmental data. These models would predict how different genotypes of e.g., rice and wheat would respond to specific environmental conditions, aiding in the selection of varieties best suited for specific environmental conditions.
This collection of case studies illustrates the CSB-approach, integrating the above-mentioned methods to enhance crop resilience and productivity. The studies reveal how each element forms the predictive ability of crop models and the genetic dissection of traids to the biochemical understanding of crop responses and the bioinformatics tools for data interpretation, finally contributing to a holistic understanding of crop performance under e.g., environmental stresses. Future directions, such as integrated omics-studies and advanced crop modeling, promise to further disentangle the complexities of crop-environment interactions, thereby closing the phenotype to genotype gap and enabling the development and breeding of robust, stress-resilient crop varieties.
In light of the case studies reviewed, certain potential remained unexplored, presenting opportunities for further research in the future:
High throughput phenotyping (Image analysis, Remote sensing): Only the one of the rice studies10 used high-throughput methods and those in the context of metabolite detection. Deployed, to capture a crops phenotype on the field (height, LAI, canopy coverage, …), those systems are able to provide large amounts of data points that later can be correlated with other data from the studies (metabolites, GWAS, alleles, …), thereby providing more opportunities to make cross links between the different data-levels (genetics, metabolism and phenotype).
Practical Applications: Bridging the gap between these scientific findings and their practical application in breeding programs. This involves not only identifying traits and their links but also translating this knowledge into applicable breeding strategies for breeders and farmers, using and validating the scientific results in real-life. This goes hand-in-hand with the next opportunity: Cross-Disciplinary Collaborations, encouraging more collaboration between geneticists, physiologists, bioinformaticians, crop modelers and of cause breeders. In the moment all considered case studies stand for their own. A meta-analysis could help to solve this problem, linking the concepts and different data levels together. This will lead to a more holistic understanding and innovative solutions in crop science.
The future potential of the CSB approach in supporting crop physiology is promising due to a holistic perspective on plant functioning by integrating genetic, physiological, and environmental factors. In the future, this will be crucial for understanding even more complex physiological processes in crops, such as stress responses, links between genes, metabolites and traits to the fullest, and also for using this information in the reverse to optimize breeding by choosing the right genetic and/or metabolite markers. Breeders can make use of this information by relating the ideotype for a specific environment or resistance with the markers identified by CSB and selecting for these markers, enhancing the resulting crop in the direction of the chosen ideotype.
This ideotypes can be developed by integrating genetic, physiological, and environmental data within the framework of crop models. These models simulate complex interactions between the named factors to predict crop performance under varying conditions. By analyzing model outputs, it could be possible to pinpoint ideotypes, that are most likely to show the highest expression of desired traits. Breeders can then utilize this information to select for specific markers, guiding the development of crop varieties tailored to meet targeted ideotypes, that were predicted by models to provide the maximum of the desired trait.
Furthermore, in the future, CSB-based refinement of models, that is relying on genes, genetic markers and/or metabolites will help to better understand crop physiology by using the outcome of CSB-research and then model the outcome based on initial conditions (genes, markers, environmental conditions). By then comparing the result from the models with the outcome of real-world experiments, the links between genetics and phenotypes can further be evaluated, discovered and refined, forming a positive feedback loop (see chapter 4 for more information on the phenotype to genotype gap). Thereby, breeders can benefit from phenotyping, genotyping, more stable QTL and ideotypes because those elements enable more precise selection of desirable traits and development of crops, optimized for specific environmental challenges, enhancing breeding efficiency and crop adaptation in the process.
New insights in crop physiology, facilitated by possible future advancements in modeling, genetic insights, and other CSB-methods, hold significant implications for crop and resource management, environmental protection, and policy making. Understanding the intricate relationships between genetic traits, physiological processes, and environmental factors enables more precise crop management strategies tailored to specific conditions. For instance, by analyzing genetic data alongside environmental parameters, farmers can adjust planting schedules to optimize crop growth and yield potential in varying climates and soil conditions. This can lead to more reliant income for the farmers and optimized resource utilization, such as water and fertilizer, reducing input wastage and environmental impact. Additionally, the ability to predict crop responses to stressors like pests, diseases, and climate variability allows for proactive mitigation measures, assisting the farmer with crop management and protect crop yields and food security. Moreover, policies informed by these insights can promote sustainable agricultural practices, endorsing the adoption of technologies that enhance crop resilience while minimizing environmental harm. For instance, government subsidies could be directed towards farmers adopting precision agriculture techniques based on genetic and environmental data, leading to more efficient resource use and reduced environmental footprint.
While the emerging approaches in Crop Systems Biology (CSB) are exciting and hold great promise, there is a need for caution to avoid getting lost in complexity. Balancing comprehensive research with practical applications is key, ensuring that the intricate interplay of genetics, physiology, and environment remains manageable and applicable. This is especially important for breeders, who rely on actionable insights from CSB. A focus on, user-friendly applications of CSB-findings will ensure that breeders can effectively leverage these advances without being overwhelmed by the underlying complexity. Otherwise, there is a realistic possibility that CSB will collapse under its complexity and become an exclusively academic discipline with no possibility of practical application!
In agriculture technology, integrating high-throughput phenotyping with omics approaches (including genomics, proteomics, metabolomics, and phenomics) offers significant potential for enhancing crop selection, breeding for resilience and yield improvement, and understanding plant responses to environmental stresses. This can be achieved by utilizing advanced techniques such as image analysis, hyperspectral imaging (HSI), multispectral imaging (MSI), and sensor networks provides detailed insights into crop performance and health in natural conditions. Despite its promise, this integration poses challenges, notably the high costs associated with omics analyses and the necessity for complex data management. Sophisticated statistical methods, including neural networks for pattern recognition and image analysis, are crucial for correlating high-throughput data with omics findings, underlining the importance of investment in these technologies to drive advancements in crop science and food security.
As climate change accelerates, adapting agricultural practices becomes critical. This adaptation involves calibrating and evaluating crop models to simulate crops in hotter and drier conditions. Preparing crops for these changes through breeding is essential, focusing on developing varieties that are resilient to extreme weather and altered environmental factors. This preparation requires a forward-thinking by training models in already affected regions to anticipate future agricultural needs. Aside from model training and according breeding on the plant side, implementing adapting agricultural practices on the side of environmental conditions is also a column to improve food-security. This includes the adoption of e.g., precision agriculture techniques, which leverage data analytics and technology to optimize water usage, nutrient application, and pest management, thereby reducing environmental stress on crops. Conservation agriculture practices, such as crop rotation, cover cropping, and reduced tillage, can also improve soil health and water retention, making crops more resilient to extremes in weather. In this context, the challenge facing crop physiologists involves conducting extensive research to correlate a vast array of data points with practical, real-world decisions that enhance food security. Collectively, these strategies complement breeding efforts by creating a more robust agricultural system capable of facing the challenges posed by climate change.
The application of these advanced agricultural technologies must strike a balance between scientific complexity and practical usability (see chapter 6 and 9). While genetic studies provide valuable insights, they should not become so intricate that they lose real-world applicability. Incorporating breeders into the scientific process ensures that the research remains grounded and relevant, leading to outcomes that are not only scientifically robust but also practically implementable in everyday agricultural settings.
This report underscores the significance of understanding and manipulating the phenotype-genotype gap in crop physiology and breeding. Integrative approaches like CSB, which combine genetic, physiological, and environmental insights, hold a great potential for advancing crop science and breeding. However, the challenges of complexity, data management, and climate change adaptation necessitate a balanced approach that aligns scientific advancement with practical breeding applications. As these challenges are approached, the fusion of high-throughput phenotyping, ‘omics-technologies, and advanced modelling will be pivotal in shaping resilient and productive crops for the future.