Abstract Noncoding regions mediate transcriptional adaptation to stress in plants, yet the genomic determinants driving gene expression responses remain poorly understood. Here, we evaluate the ability of two deep learning approaches, a convolutional neural network (CNN) and a transformer-based genomic language model pre-trained on plant genomes to predict changes in gene expression under various abiotic and biotic stress conditions. Using RNA-seq time-series data from Arabidopsis thaliana, we explored different strategies for summarising expression dynamics to capture treatment-specific transcriptional changes relative to control conditions. Both models achieved low to moderate predictive performance, with pattern-triggered-immunity related treatments showing the strongest sequence-based predictability. While extending promoter regions upstream had a limited impact, including coding sequences significantly improved performance. Model interpretation revealed that the CNN recovered sequence features comparable to those identified by simple 6-mer based linear models, suggesting limited gains in regulatory insight from increased model complexity. These findings underscore both the promise and limitations of sequence-based models in uncovering the regulatory logic of induced plant stress responses.

Abstract Decision Support Systems (DSS) in crop protection provide valuable support for pest risk prognosis and recommendations for pest control, enabling farmers to make better-informed decisions. As a part of the European Union’s strategy for the sustainable use of plant protection products, the “IPM Decisions” project developed an online platform that gives farmers and advisors access to a wide range of DSS for major pests, weeds, and diseases in a variety of crops across Europe. Multiple DSS models relevant for different crops and geographical regions of Europe were selected for integration in the platform. Information on the models is compiled into a model catalogue, which serves as a core component of the IPM Decisions platform. To facilitate the use of these models, two Application Programming Interfaces (APIs) were developed. In line with the FAIR (Findable, Accessible, Interoperable, Reusable) principles, the DSS API provides access to models and their metadata, including descriptions of input and output parameters. The Weather API enables access to European online weather data sources and adapts this data to meet the requirements of DSS models. While these APIs are integrated into the IPM Decisions Platform, they are also open source, allowing other crop protection and farm management software to inspect, download, modify, install, run and use them. In this article, we describe the development of the DSS and Weather APIs, outline their structure and definitions, and present the services that DSS API and Weather API provide. Finally, we demonstrate their application through three practical use cases.

Abstract Process-based crop modelling platforms such as DSSAT are potentially valuable tools for crop breeding programmes, with the capacity to predict genotype-by-environment-by-management interactions. However, their application for breeding is challenged by the need to calibrate large numbers of genotypes within populations. In wheat (Triticum aestivum L.), using pre-existing DSSAT-CERES wheat ecotypes can introduce unrealistic parameter compensation during cultivar calibration. To address this, we developed a two-phase sequential calibration framework. This workflow uses phenotypic clustering to first define representative ecotypes using experiment-specific data before proceeding with cultivar-level parameter estimation. We demonstrate the utility of this framework to integrate direct measurements from proximal and remote sensing data collected on 14 genotypes grown under well-watered, drought, and heat stress field conditions. Incorporating experiment-derived ecotypes reduced compensatory adjustments in cultivar coefficients and improved simulation accuracy compared with default or non-representative ecotypes. Time-series data enhanced calibration, although the effect of different data combinations varied with environmental scenario and trait. Model simulations under stress conditions generally captured drought effects on biomass but underestimated heat stress impacts. This framework provides a systematic and scalable approach for integrating high-throughput phenotyping and process-based crop modelling.

Abstract Crop yields in intercropping systems are the result of a combination of factors dominated by plastic responses in plant traits due to the heterogeneity associated with the intercrop design and row configuration. Disentangling their relative influence is infeasible in situ but crucial for cultivar selection and intercrop design. Using functional-structural plant (FSP) modelling, these effects can be separated in silico. Here, a mechanistic FSP model was developed, including three-dimensional aboveground plant architecture of maize and soybean, radiation distribution, and assimilate allocation using published data. The model was used to explore the potential to improve yields in a simultaneous intercrop by disentangling the contribution of three plastic traits related to photosynthesis, leaf thickness and plant height. The improved phenotypes were then simulated in two intercrop configurations for potential increases in land-use efficiency. The study revealed that for maize, photosynthesis had the greatest contribution (+78%), followed by plant height (+31%) and leaf thickness (+6%), where the total maize monoculture phenotype produced the greatest maize yield without affecting the yield of intercropped soybean. However, soybean trait plasticity had a negligible effect on soybean productivity, but its monoculture phenotype with a low light-saturated photosynthetic rate resulted in the greatest intercropped maize yield. These improved phenotypes may increase land-use efficiency by 1%–3% relative to the standard monoculture systems of the Midwest, USA, which is also a ∼22% increase from published empirical data. Together, these results could aid the selection of crop germplasm to improve the productivity of a simultaneous intercrop.

Abstract Dwarf tomatoes are well suited for vertical farming due to their compact architecture and determinate growth, but their current genotypes are not optimized for high-density indoor systems. We developed the first functional–structural plant (FSP) model tailored to dwarf tomatoes in vertical farming to simulate light interception, photosynthesis, and biomass allocation at the organ level. The model integrates both static and dynamic modes using multiscale tree graph (MTG) formalism to encode plant architecture and was implemented in GroIMP. The model was parameterized and validated using experimental data obtained at multiple planting densities under controlled environmental conditions. The model accurately predicted key plant traits such as fruit dry mass, total dry mass, and leaf area index. In follow-up scenario analyses, we explored model responses to temperature perturbations (±2∘C) and photosynthetically active radiation changes (±20%) at selected planting densities, quantifying how these factors modulate the same traits. The model was executed both in dynamic and static mode to assess architectural ideotypes with focus on leaflet morphology, revealing that leaflet curvature and shape influence light distribution and carbon gain, with effects differing across densities and simulation modes. The model highlights the importance of dynamic feedbacks in high-density vertical farming and supports ideotype design through in silico evaluation of morphological traits. This work establishes a validated modelling framework for guiding breeding and cultivation strategies aimed at enhancing productivity and light use efficiency in vertical farming systems.

Abstract Pest damage exhibits considerable spatial heterogeneity among individual plots in the field. Such spatial heterogeneity has often been treated as a nuisance in crop breeding trials; however, a part of among-plot variation may be explained by genetic factors such as neighbouring genotypes. To test whether neighbouring genotypes accounted for spatial variation in disease infection, we applied two methods, Spatial Analysis of Field Trials with Splines (SpATS) and Neighbor Genome-Wide Association Study (Neighbor GWAS), to barley cultivars. Having compiled the CIMMYT Australia ICARDA Germplasm Evaluation (CAIGE) data, we first applied SpATS to three disease phenotypes such as the net form net blotch, spot form net blotch, and scald damage. This SpATS analysis showed extraneous phenotypic variation unexplained by smooth spatial trends, thereby leading us to focus on neighbouring genotypes as an extraneous biological factor. We then applied the Neighbor GWAS model and found that neighbour genotypic identity explained 0.1–0.3 fractional variation in the three disease phenotypes. The Neighbor GWAS method also detected two significant variants on the barley 7H chromosome, which were associated with neighbour genotypic influence on the net form net blotch and scald damage. These variants were estimated to have beneficial effects that could reduce disease damage by their allelic mixtures. Our findings suggest that neighbour genotypic identity can account for spatial variation in disease infection and its genetic architecture may provide a key to multiline cultivation for pest management.

Abstract Digital-twin technology is a promising decision-support tool in controlled-environment agriculture, that can be applied to optimise agronomic production, improve management decisions, and formulate data-driven breeding strategies. We developed a concept for a digital twin of a tomato crop in a high-tech greenhouse with the aim to help increase resource-use efficiency of greenhouse tomatoes. At the core of the digital twin is a functional-structural plant (FSP) model that can simulate 3D architectural development and growth of young tomato plants and can be used to predict plant responses to environmental factors and management interventions. In this study, we present the newly developed tomato FSP model and procedures to use data from climate sensors and image data of single plants from high-throughput phenotyping, to feed the FSP model to ensure synchronisation with the real-world crop development. A greenhouse climate model is used to calculate the indoor climate above the crop based on outdoor weather data, greenhouse properties, and climate control settings. Synchronisation of plant architecture between the real and the virtual crop is demonstrated for plant height adjustments, by performing model calibration based on Bayesian optimisation. The tomato FSP model is designed to study the role of individual organ traits and assess the effects of architectural manipulations, such as leaf pruning, and lighting strategies. The presented framework addresses a vital component of a digital twin representing the flow of information from the real crop to the virtual crop.

Abstract Understanding plant temperature responses at a molecular level can speed up the development of climate-resilient crops. However, the threats due to anthropogenic warming necessitate imminent solutions to ensure food security. Modeling plant thermal responses at a molecular level offers a feasible strategy to identify gene targets that can mitigate the negative effects of projected temperature increases. Implementation of this strategy in practice requires the development and usage of temperature-aware models that incorporate a systems-level description of the existing knowledge on plant metabolism. Here, we detail the assumptions and building blocks of the recently assembled ecAraCore, an enzyme-constrained temperature-aware model of central metabolism for the model plant Arabidopsis thaliana. The goal of this study is to provide a primer for building a temperature-aware model of plant metabolism and to offer step-by-step instructions for using the ecAraCore model to simulate metabolic traits, including: relative growth rate, reaction fluxes, and enzyme abundances for specified environmental inputs. In addition, we provide another case study of how the ecAraCore model can be used to simulate temperature-sensitive knockouts. The primer sets the basis for the development of more involved metabolic engineering strategies aimed at the mitigation of the negative effects of temperature increases—the hallmark of future climate scenarios.

Abstract axiomFP.py is an open-source software developed to diagnose ploidy level and call quality for samples genotyped on Affymetrix Axiom SNP arrays by making frequency plots of normalized SNP call positions among SNPs meeting specific clustering parameters. This research outlines the methods employed in the development of the software, and presents the results obtained through its application on a dataset of mixed ploidy apple (Malus spp.) cultivars and germplasm accessions. The tools required to prepare the input files and operate the software are also described. The frequency plots generated by the software require a visual inspection to assess ploidy and call quality. The results have been validated using the available ploidy data, as well as flow cytometry, and have shown complete accuracy. The software is available on GitHub at https://github.com/allmiraria/axiomFP.

Abstract The Farquhar-von-Caemmerer-Berry (FvCB) model is the most widely-used mechanistic model of C3 net CO2 assimilation, and it plays a significant role in plant physiology, ecology, climate science, and Earth system modeling. As use of the model has grown, multiple variants have appeared across publications. Although many of these are commonly used, there has not been a detailed investigation of existing variants and their impacts on results and interpretations. Here we summarize the types of variants and their prevalence in the literature, and we present a comprehensive comparison of differences between them. A key finding is that a common variant that uses the minimum of assimilation rates rather than the minimum of carboxylation rates, which we call the “min-A variant,” makes different predictions than the original “min-W variant,” yet appears in approximately half of highly-cited publications and software tools that use the FvCB model. Another concern is that although leaf biochemistry restricts the range of CO2 partial pressures where limitations due to triose phosphate utilization (TPU) can occur, this restriction is commonly omitted from the model’s equations. Among other potential issues, these variations can introduce errors exceeding 20% when estimating photosynthetic parameter values from CO2 response curves. It is therefore important to be aware of this source of error when fitting the model, to avoid using the min-A variant, and to include the biochemically-derived CO2 threshold for TPU limitations.