Simulating the effects of water limitation on plant biomass using a 3D functional-structural plant model of shoot and root driven by soil hydraulics

Two-dimensional modeling of CO2 mineral trapping through the oxalate‑carbonate pathway: Influence of the root system model

Two-dimensional reactive transport models, one with a simplified root system and the other accounting for dynamically evolving root architecture, were constructed to examine the influence of model complexity on capturing the effect of soil-root dynamics relating to the Oxalate Carbonate Pathway (OCP) of the Iroko tree over 170 years. Oxidation of oxalate from fallen tree tissue by soil bacteria enables local soil pH increase, leading to the sequestration of atmospheric carbon in carbonate minerals (calcite) in the shallow soil surrounding the tree. Simulations of both root models corroborate previous one-dimensional models of the OCP focused on Ca and C mass balance, where high weathering rates of Ca-containing silicate minerals in bedrock, along with contributions from groundwater, provided sufficient Ca for precipitation of observed quantities of calcite. Both simulations demonstrate the development of a distinct high pH zone where oxalate is oxidized, Ca accumulates, and calcite precipitates (OCP zone); and a low pH zone where roots collect Ca, later returned to the top soil as calcium oxalate (Total Root Extent/TRE zone) via litterfall. While the extent of OCP zone development near the ground surface was very similar between simulations, differences in localized root water uptake between the two approaches resulted in variation in water and solute transport and influenced the geometry of the OCP zone at depth, with implications for calcite precipitation in the soil. Trends in CO2 and O2 partial pressures in the OCP zone were mirrored in the TRE zone, suggesting linkage between the two zones with regard to gas transport. Near the end of the tree's lifespan, results indicate that soil permeability decreases due to calcite precipitation may limit O2 ingress and availability in the shallow soil, while trapping CO2 released from the oxidation of organics in the shallow soil, with implications for the long-term sustainability of the OCP itself.

Climate factors affect forest biomass allocation by altering soil nutrient availability and leaf traits

Biomass in forests sequesters substantial amounts of carbon; although the contribution of aboveground biomass has been extensively studied, the contribution of belowground biomass remains understudied. Investigating the forest biomass allocation is crucial for understanding the impacts of global change on carbon allocation and cycling. Moreover, the question of how climate factors affect biomass allocation in natural and planted forests remains unresolved. Here, we addressed this question by collecting data from 384 planted forests and 541 natural forests in China. We evaluated the direct and indirect effects of climate factors on the belowground biomass proportion (BGBP). The average BGBP was 31.09% in natural forests and was significantly higher (38.75%) in planted forests. Furthermore, we observed a significant decrease in BGBP with increasing temperature and precipitation. Climate factors, particularly those affecting soil factors, such as pH, strongly affected the BGBP in natural and planted forests. Based on our results, we propose that future studies should consider the effects of forest type (natural or planted) and soil factors on BGBP.

Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery

Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.

Vegetation clumping modulates global photosynthesis through adjusting canopy light environment

The spatial dispersion of photoelements within a vegetation canopy, quantified by the clumping index (CI), directly regulates the within-canopy light environment and photosynthesis rate, but is not commonly implemented in terrestrial biosphere models to estimate the ecosystem carbon cycle. A few global CI products have been developed recently with remote sensing measurements, making it possible to examine the global impacts of CI. This study deployed CI in the radiative transfer scheme of the Community Land Model version 5 (CLM5) and used the revised CLM5 to quantitatively evaluate the extent to which CI can affect canopy absorbed radiation and gross primary production (GPP), and for the first time, considering the uncertainty and seasonal variation of CI with multiple remote sensing products. Compared to the results without considering the CI impact, the revised CLM5 estimated that sunlit canopy absorbed up to 9%-15% and 23%-34% less direct and diffuse radiation, respectively, while shaded canopy absorbed 3%-18% more diffuse radiation across different biome types. The CI impacts on canopy light conditions included changes in canopy light absorption, and sunlit-shaded leaf area fraction related to nitrogen distribution and thus the maximum rate of Rubisco carboxylase activity (V_cmax ), which together decreased photosynthesis in sunlit canopy by 5.9-7.2 PgC year^-1 while enhanced photosynthesis by 6.9-8.2 PgC year^-1 in shaded canopy. With higher light use efficiency of shaded leaves, shaded canopy increased photosynthesis compensated and exceeded the lost photosynthesis in sunlit canopy, resulting in 1.0 ± 0.12 PgC year^-1 net increase in GPP. The uncertainty of GPP due to the different input CI datasets was much larger than that caused by CI seasonal variations, and was up to 50% of the magnitude of GPP interannual variations in the tropical regions. This study highlights the necessity of considering the impacts of CI and its uncertainty in terrestrial biosphere models.

Plant water uptake modelling: added value of cross-disciplinary approaches

In recent years, research interest in plant water uptake strategies has rapidly increased in many disciplines, such as hydrology, plant ecology and ecophysiology. Quantitative modelling approaches to estimate plant water uptake and spatiotemporal dynamics have significantly advanced through different disciplines across scales. Despite this progress, major limitations, for example, predicting plant water uptake under drought or drought impact at large scales, remain. These are less attributed to limitations in process understanding, but rather to a lack of implementation of cross-disciplinary insights into plant water uptake model structure. The main goal of this review is to highlight how the four dominant model approaches, that is, Feddes approach, hydrodynamic approach, optimality and statistical approaches, can be and have been used to create interdisciplinary hybrid models enabling a holistic system understanding that, among other things, embeds plant water uptake plasticity into a broader conceptual view of soil-plant feedbacks of water, nutrient and carbon cycling, or reflects observed drought responses of plant-soil feedbacks and their dynamics under, that is, drought. Specifically, we provide examples of how integration of Bayesian and hydrodynamic approaches might overcome challenges in interpreting plant water uptake related to different travel and residence times of different plant water sources or trade-offs between root system optimization to forage for water and nutrients during different seasons and phenological stages.

Towards smart and sustainable development of modern berry cultivars in Europe

Fresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together, and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies, such as molecular marker arrays, genomic selection and genome-wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future.

Modeling Global Carbon Costs of Plant Nitrogen and Phosphorus Acquisition

Most Earth system models (ESMs) do not explicitly represent the carbon (C) costs of plant nutrient acquisition, which leads to uncertainty in predictions of the current and future constraints to the land C sink. We integrate a plant productivity-optimizing nitrogen (N) and phosphorus (P) acquisition model (fixation & uptake of nutrients, FUN) into the energy exascale Earth system (E3SM) land model (ELM). Global plant N and P uptake are dynamically simulated by ELM-FUN based on the C costs of nutrient acquisition from mycorrhizae, direct root uptake, retranslocation from senescing leaves, and biological N fixation. We benchmarked ELM-FUN with three classes of products: ILAMB, a remotely sensed nutrient limitation product, and CMIP6 models; we found significant improvements in C cycle variables, although the lack of more observed nutrient data prevents a comprehensive level of benchmarking. Overall, we found N and P co-limitation for 80% of land area, with the remaining 20% being either predominantly N or P limited. Globally, the new model predicts that plants invested 4.1 Pg C yr-1 to acquire 841.8 Tg N yr-1 and 48.1 Tg P yr-1 (1994-2005), leading to significant downregulation of global net primary production (NPP). Global NPP is reduced by 20% with C costs of N and 50% with C costs of NP. Modeled and observed nutrient limitation agreement increases when N and P are considered together (r 2 from 0.73 to 0.83).

Two decades of functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology

BACKGROUND

Functional-structural plant models (FSPMs) explore and integrate relationships between a plant's structure and processes that underlie its growth and development. In the last 20 years, scientists interested in functional-structural plant modelling have expanded greatly the range of topics covered and now handle dynamical models of growth and development occurring from the microscopic scale, and involving cell division in plant meristems, to the macroscopic scales of whole plants and plant communities.

SCOPE

The FSPM approach occupies a central position in plant science; it is at the crossroads of fundamental questions in systems biology and predictive ecology. This special issue of Annals of Botany features selected papers on critical areas covered by FSPMs and examples of comprehensive models that are used to solve theoretical and applied questions, ranging from developmental biology to plant phenotyping and management of plants for agronomic purposes. Altogether, they offer an opportunity to assess the progress, gaps and bottlenecks along the research path originally foreseen for FSPMs two decades ago. This review also allows discussion of current challenges of FSPMs regarding (1) integration of multidisciplinary knowledge, (2) methods for handling complex models, (3) standards to achieve interoperability and greater genericity and (4) understanding of plant functioning across scales.

CONCLUSIONS

This approach has demonstrated considerable progress, but has yet to reach its full potential in terms of integration and heuristic knowledge production. The research agenda of functional-structural plant modellers in the coming years should place a greater emphasis on explaining robust emergent patterns, and on the causes of possible deviation from it. Modelling such patterns could indeed fuel both generic integration across scales and transdisciplinary transfer. In particular, it could be beneficial to emergent fields of research such as model-assisted phenotyping and predictive ecology in managed ecosystems.