A Mass Balance Approach to Identify and Compare Differential Routing of \u3csup\u3e13\u3c/sup\u3eC-Labeled Carbohydrates, Lipids, and Proteins In Vivo

All animals route assimilated nutrients to their tissues where they are used to support growth or are oxidized for energy. These nutrients are probably not allocated homogeneously among the various tissue and are more likely to be preferentially routed toward some tissues and away from others. Here we introduce an approach that allows researchers to identify and compare nutrient routing among different organs and tissues. We tested this approach by examining nutrient routing in birds. House sparrows Passer domesticus were fed a meal supplemented with one of seven 13C-labeled metabolic tracers representing three major classes of macronutrients, namely, carbohydrates, amino acids, and fatty acids. While these birds became postabsorptive (2 h after feeding), we quantified the isotopic enrichment of the lean and lipid fractions of several organs and tissues. We then compared the actual 13C enrichment of various tissue fractions with the predictions of our model to identify instances where nutrients were differentially routed and found that different classes of macronutrients are uniquely routed throughout the body. Recently ingested amino acids were preferentially routed to the lean fraction of the liver, whereas exogenous carbohydrates were routed to the brain and the lipid fraction of the liver. Fatty acids were definitively routed to the heart and the liver, although high levels of palmitic acid were also recovered in the adipose tissue. Tracers belonging to the same class of molecules were not always routed identically, illustrating how this technique is also suited to examine differences in nonoxidative fates of closely related molecules. Overall, this general approach allows researchers to test heretofore unexamined predictions about how animals allocate the nutrients they ingest

Soil geochemistry – and not topography – as a major driver of carbon allocation, stocks, and dynamics in forests and soils of African tropical montane ecosystems

The lack of field-based data in the tropics limits our mechanistic understanding of the drivers of net primary productivity (NPP) and allocation. Specifically, the role of local edaphic factors - such as soil parent material and topography controlling soil fertility as well as water and nutrient fluxes - remains unclear and introduces substantial uncertainty in understanding net ecosystem productivity and carbon (C) stocks. Using a combination of vegetation growth monitoring and soil geochemical properties, we found that soil fertility parameters reflecting the local parent material are the main drivers of NPP and C allocation patterns in tropical montane forests, resulting in significant differences in below- to aboveground biomass components across geochemical (soil) regions. Topography did not constrain the variability in C allocation and NPP. Soil organic C stocks showed no relation to C input in tropical forests. Instead, plant C input seemingly exceeded the maximum potential of these soils to stabilize C. We conclude that, even after many millennia of weathering and the presence of deeply developed soils, above- and belowground C allocation in tropical forests, as well as soil C stocks, vary substantially due to the geochemical properties that soils inherit from parent material

The interplay between ozone and urban vegetation – BVOC emissions, ozone deposition, and tree ecophysiology

Tropospheric ozone (O3) is one of the most prominent air pollution problems in Europe and other countries worldwide. Human health is affected by O3 via the respiratory as well the cardiovascular systems. Even though trees are present in relatively low numbers in urban areas, they can be a dominant factor in the regulation of urban O3 concentrations. Trees affect the O3 concentration via emission of biogenic volatile organic compounds (BVOC), which can act as a precursor of O3, and by O3 deposition on leaves. The role of urban trees with regard to O3 will gain further importance as NOx concentrations continue declining and climate warming is progressing—rendering especially the urban ozone chemistry more sensitive to BVOC emissions. However, the role of urban vegetation on the local regulation of tropospheric O3 concentrations is complex and largely influenced by species-specific emission rates of BVOCs and O3 deposition rates, both highly modified by tree physiological status. In this review, we shed light on processes related to trees that affect tropospheric ozone concentrations in metropolitan areas from rural settings to urban centers, and discuss their importance under present and future conditions. After a brief overview on the mechanisms regulating O3 concentrations in urban settings, we focus on effects of tree identity and tree physiological status, as affected by multiple stressors, influencing both BVOC emission and O3 deposition rates. In addition, we highlight differences along the rural-urban gradient affecting tropospheric O3 concentrations and current knowledge gaps with the potential to improve future models on tropospheric O3 formation in metropolitan areas

Faba Bean Cultivation – Revealing Novel Managing Practices for More Sustainable and Competitive European Cropping Systems

Faba beans are highly nutritious because of their high protein content: they are a good source of mineral nutrients, vitamins, and numerous bioactive compounds. Equally important is the contribution of faba bean in maintaining the sustainability of agricultural systems, as it is highly efficient in the symbiotic fixation of atmospheric nitrogen. This article provides an overview of factors influencing faba bean yield and quality, and addresses the main biotic and abiotic constraints. It also reviews the factors relating to the availability of genetic material and the agronomic features of faba bean production that contribute to high yield and the improvement of European cropping systems. Emphasis is to the importance of using new high-yielding cultivars that are characterized by a high protein content, low antinutritional compound content, and resistance to biotic and abiotic stresses. New cultivars should combine several of these characteristics if an increased and more stable production of faba bean in specific agroecological zones is to be achieved. Considering that climate change is also gradually affecting many European regions, it is imperative to breed elite cultivars that feature a higher abiotic–biotic stress resistance and nutritional value than currently used cultivars. Improved agronomical practices for faba bean crops, such as crop establishment and plant density, fertilization and irrigation regime, weed, pest and disease management, harvesting time, and harvesting practices are also addressed, since they play a crucial role in both the production and quality of faba bean

Organic matter cycling along geochemical, geomorphic and disturbance gradients in forests and cropland of the African Tropics – Project TropSOC Database Version 1.0

The African Tropics are hotspots of modern-day land-use change and are, at the same time, of great relevance for the cycling of carbon (C) and nutrients between plants, soils and the atmosphere. However, the consequences of land conversion on biogeochemical cycles are still largely unknown as they are not studied in a landscape context that defines the geomorphic, geochemically and pedological framework in which biological processes take place. Thus, the response of tropical soils to disturbance by erosion and land conversion is one of the great uncertainties in assessing the carrying capacity of tropical landscapes to grow food for future generations and in predicting greenhouse gas fluxes (GHG) from soils to the atmosphere and, hence, future earth system dynamics. Here, we describe version 1.0 of an open access database created as part of the project &ldquo;Tropical soil organic carbon dynamics along erosional disturbance gradients in relation to variability in soil geochemistry and land use&rdquo; (TropSOC). TropSOC v1.0 contains spatial and temporal explicit data on soil, vegetation, environmental properties and land management collected from 136 pristine tropical forest and cropland plots between 2017 and 2020 as part of several monitoring and sampling campaigns in the Eastern Congo Basin and the East African Rift Valley System. The results of several laboratory experiments focusing on soil microbial activity, C cycling and C stabilization in soils complement the dataset to deliver one of the first landscape scale datasets to study the linkages and feedbacks between geology, geomorphology and pedogenesis as controls on biogeochemical cycles in a variety of natural and managed systems in the African Tropics. The hierarchical and interdisciplinary structure of the TropSOC database allows for linking a wide range of parameters and observations on soil and vegetation dynamics along with other supporting information that may also be measured at one or more levels of the hierarchy. TropSOC&rsquo;s data marks a significant contribution to improve our understanding of the fate of biogeochemical cycles in dynamic and diverse tropical African (agro-)ecosystems. TropSOC v1.0 can be accessed through the supplementary material provided as part of this manuscript or as a separate download via the websites of the Congo Biogeochemistry observatory and the GFZ data repository where version updates to the database will be provided as the project develops.</p

Plant roots and spectroscopic methods – analyzing species, biomass and vitality

In order to understand plant functioning, plant community composition, and terrestrial biogeochemistry, it is decisive to study standing root biomass, (fine) root dynamics, and interactions belowground. While most plant taxa can be identified by visual criteria aboveground, roots show less distinctive features. Furthermore, root systems of neighboring plants are rarely spatially segregated; thus, most soil horizons and samples hold roots of more than one species necessitating root sorting according to taxa. In the last decades, various approaches, ranging from anatomical and morphological analyses to differences in chemical composition and DNA sequencing were applied to discern species’ identity and biomass belowground. Among those methods, a variety of spectroscopic methods was used to detect differences in the chemical composition of roots. In this review, spectroscopic methods used to study root systems of herbaceous and woody species in excised samples or in situ will be discussed. In detail, techniques will be reviewed according to their usability to discern root taxa, to determine root vitality, and to quantify root biomass non-destructively or in soil cores holding mixtures of plant roots. In addition, spectroscopic methods which may be able to play an increasing role in future studies on root biomass and related traits are highlighted

It’s Complicated: Intraroot System Variability of Respiration and Morphological Traits in Four Deciduous Tree Species

International audienceWithin branched root systems, a distinct heterogeneity of traits exists. Knowledge about the ecophysiology of different root types is critical to understand root system functioning. Classification schemes have to match functional root types as closely as possible to be used for sampling and modeling. Among ecophysiological root traits, respiration is of particular importance, consuming a great amount of carbon allocated. Root architecture differs between the four deciduous tree seedlings. However, two types of terminal root segments (i.e. first and second orders), white colored and brown colored, can be distinguished in all four species but vary in frequency, their morphology differing widely from each other and higher coarse root orders. Root respiration is related to diameter and tissue density. The use of extended root ordering (i.e. order and color) explains the variance of respiration two times as well as root diameter or root order classes alone. White terminal roots respire significantly more than brown ones; both possess respiration rates that are greater than those of higher orders in regard to dry weight and lower in regard to surface area. The correlation of root tissue density to respiration will allow us to use this continuous parameter (or easier to determine dry matter content) to model the respiration within woody root systems without having to determine nitrogen contents. In addition, this study evidenced that extended root orders are better suited than root diameter classes to picture the differences between root functional types. Together with information on root order class frequencies, these data allow us to calculate realistic, species-specific respiration rates of root branches

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits

Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding—especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants.A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pair-wise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5)—Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0-5cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars

Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters

Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.publishedVersio