Linking photosynthesis and yield reveals a strategy to improve light use efficiency in a climbing bean breeding population.

Photosynthesis drives plant physiology, biomass accumulation, and yield. Photosynthetic efficiency, specifically the operating efficiency of PSII (Fq'/Fm'), is highly responsive to actual growth conditions, especially to fluctuating photosynthetic photon fluence rate (PPFR). Under field conditions, plants constantly balance energy uptake to optimize growth. The dynamic regulation complicates the quantification of cumulative photochemical energy uptake based on the intercepted solar energy, its transduction into biomass, and the identification of efficient breeding lines. Here, we show significant effects on biomass related to genetic variation in photosynthetic efficiency of 178 climbing bean (Phaseolus vulgaris L.) lines. Under fluctuating conditions, the Fq'/Fm' was monitored throughout the growing period using hand-held and automated chlorophyll fluorescence phenotyping. The seasonal response of Fq'/Fm' to PPFR (ResponseG:PPFR) achieved significant correlations with biomass and yield, ranging from 0.33 to 0.35 and from 0.22 to 0.31 in two glasshouse and three field trials, respectively. Phenomic yield prediction outperformed genomic predictions for new environments in four trials under different growing conditions. Investigating genetic control over photosynthesis, one single nucleotide polymorphism (Chr09_37766289_13052) on chromosome 9 was significantly associated with ResponseG:PPFR in proximity to a candidate gene controlling chloroplast thylakoid formation. In conclusion, photosynthetic screening facilitates and accelerates selection for high yield potential.

Adapting crop production to climate change and air pollution at different scales.

Air pollution and climate change are tightly interconnected and jointly affect field crop production and agroecosystem health. Although our understanding of the individual and combined impacts of air pollution and climate change factors is improving, the adaptation of crop production to concurrent air pollution and climate change remains challenging to resolve. Here we evaluate recent advances in the adaptation of crop production to climate change and air pollution at the plant, field and ecosystem scales. The main approaches at the plant level include the integration of genetic variation, molecular breeding and phenotyping. Field-level techniques include optimizing cultivation practices, promoting mixed cropping and diversification, and applying technologies such as antiozonants, nanotechnology and robot-assisted farming. Plant- and field-level techniques would be further facilitated by enhancing soil resilience, incorporating precision agriculture and modifying the hydrology and microclimate of agricultural landscapes at the ecosystem level. Strategies and opportunities for crop production under climate change and air pollution are discussed.

Water Stress Alters Physiological, Spectral, and Agronomic Indexes of Wheat Genotypes.

Selecting drought-tolerant and more water-efficient wheat genotypes is a research priority, specifically in regions with irregular rainfall or areas where climate change is expected to result in reduced water availability. The objective of this work was to use high-throughput measurements with morphophysiological traits to characterize wheat genotypes in relation to water stress. Field experiments were conducted from May to September 2018 and 2019, using a sprinkler bar irrigation system to control water availability to eighteen wheat genotypes: BRS 254; BRS 264; CPAC 01019; CPAC 01047; CPAC 07258; CPAC 08318; CPAC 9110; BRS 394 (irrigated biotypes), and Aliança; BR 18_Terena; BRS 404; MGS Brilhante; PF 020037; PF 020062; PF 120337; PF 100368; PF 080492; and TBIO Sintonia (rainfed biotypes). The water regimes varied from 22 to 100% of the crop evapotranspiration replacement. Water stress negatively affected gas exchange, vegetation indices, and grain yield. High throughput variables TCARI, NDVI, OSAVI, SAVI, PRI, NDRE, and GNDVI had higher yield and morphophysiological measurement correlations. The drought resistance index indicated that genotypes Aliança, BRS 254, BRS 404, CPAC 01019, PF 020062, and PF 080492 were more drought tolerant.

Foliar Phenotypic Plasticity Reflects Adaptation to Environmental Variability.

Arabidopsis thaliana ecotypes adapted to native habitats with different daylengths, temperatures, and precipitation were grown experimentally under seven combinations of light intensity and leaf temperature to assess their acclimatory phenotypic plasticity in foliar structure and function. There were no differences among ecotypes when plants developed under moderate conditions of 400 µmol photons m-2 s-1 and 25 °C. However, in response to more extreme light or temperature regimes, ecotypes that evolved in habitats with pronounced differences in either the magnitude of changes in daylength or temperature or in precipitation level exhibited pronounced adjustments in photosynthesis and transpiration, as well as anatomical traits supporting these functions. Specifically, when grown under extremes of light intensity (100 versus 1000 µmol photons m-2 s-1) or temperature (8 °C versus 35 °C), ecotypes from sites with the greatest range of daylengths and temperature over the growing season exhibited the greatest differences in functional and structural features related to photosynthesis (light- and CO2-saturated capacity of oxygen evolution, leaf dry mass per area or thickness, phloem cells per minor vein, and water-use efficiency of CO2 uptake). On the other hand, the ecotype from the habitat with the lowest precipitation showed the greatest plasticity in features related to water transport and loss (vein density, ratio of water to sugar conduits in foliar minor veins, and transpiration rate). Despite these differences, common structure-function relationships existed across all ecotypes and growth conditions, with significant positive, linear correlations (i) between photosynthetic capacity (ranging from 10 to 110 µmol O2 m-2 s-1) and leaf dry mass per area (from 10 to 75 g m-2), leaf thickness (from 170 to 500 µm), and carbohydrate-export infrastructure (from 6 to 14 sieve elements per minor vein, from 2.5 to 8 µm2 cross-sectional area per sieve element, and from 16 to 82 µm2 cross-sectional area of sieve elements per minor vein); (ii) between transpiration rate (from 1 to 17 mmol H2O m-2 s-1) and water-transport infrastructure (from 3.5 to 8 tracheary elements per minor vein, from 13.5 to 28 µm2 cross-sectional area per tracheary element, and from 55 to 200 µm2 cross-sectional area of tracheary elements per minor vein); (iii) between the ratio of transpirational water loss to CO2 fixation (from 0.2 to 0.7 mol H2O to mmol-1 CO2) and the ratio of water to sugar conduits in minor veins (from 0.4 to 1.1 tracheary to sieve elements, from 4 to 6 µm2 cross-sectional area of tracheary to sieve elements, and from 2 to 6 µm2 cross-sectional area of tracheary elements to sieve elements per minor vein); (iv) between sugar conduits and sugar-loading cells; and (v) between water conducting and sugar conducting cells. Additionally, the proportion of water conduits to sugar conduits was greater for all ecotypes grown experimentally under warm-to-hot versus cold temperature. Thus, developmental acclimation to the growth environment included ecotype-dependent foliar structural and functional adjustments resulting in multiple common structural and functional relationships.

Effect of organic photovoltaic and red-foil transmittance on yield, growth and photosynthesis of two spinach genotypes under field and greenhouse conditions.

The galloping rise in global population in recent years and the accompanying increase in food and energy demands has created land use crisis between food and energy production, and eventual loss of agricultural lands to the more lucrative photovoltaics (PV) energy production. This experiment was carried out to investigate the effect of organic photovoltaics (OPV) and red-foil (RF) transmittance on growth, yield, photosynthesis and SPAD value of spinach under greenhouse and field conditions. Three OPV levels (P0: control; P1: transmittance peak of 0.11 in blue light (BL) and 0.64 in red light (RL); P2: transmittance peak of 0.09 in BL and 0.11 in RL) and two spinach genotypes (bufflehead, eland) were combined in a 3 × 2 factorial arrangement in a completely randomized design with 4 replications in the greenhouse, while two RF levels (RF0: control; RF1: transmittance peak of 0.01 in BL and 0.89 in RL) and two spinach genotypes were combined in a 2 × 2 factorial in randomized complete block design with four replications in the field. Data were collected on growth, yield, photosynthesis and chlorophyll content. Analysis of variance (ANOVA) showed significant reduction in shoot weight and total biomass of spinach grown under very low light intensities as a function of the transmittance properties of the OPV cell used (P2). P1 competed comparably (p > 0.05) with control in most growth and yield traits measured. In addition, shoot to root distribution was higher in P1 than control. RF reduced shoot and total biomass production of spinach in the field due to its inability to transmit other spectra of light. OPV-RF transmittance did not affect plant height (PH), leaf number (LN), and SPAD value but leaf area (LA) was highest in P2. Photochemical energy conversion was higher in P1, P2 and RF1 in contrast to control due to lower levels of non-photochemical energy losses through the Y(NO) and Y(NPQ) pathways. Photo-irradiance curves showed that plants grown under reduced light (P2) did not efficiently manage excess light when exposed to high light intensities. Bufflehead genotype showed superior growth and yield traits than eland across OPV and RF levels. It is therefore recommended that OPV cells with transmittance properties greater than or equal to 11% in BL and 64% in RL be used in APV systems for improved photochemical and land use efficiency.

Field phenotyping of ten wheat cultivars under elevated CO2 shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices.

In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO2]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO2] (~622 ppm), a Free-Air CO2 Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (Fq'/Fm') and the kinetics of electron transport, i.e. the reoxidation rates Fr1' and Fr2'. Our results suggest that elevated [CO2] significantly increased Fq'/Fm' and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in Fq'/Fm' was observed under elevated [CO2]. This was also reflected in the reoxidation rates Fr1' and Fr2'. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO2]. Our results indicate that the rise in atmospheric [CO2] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.

HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence.

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy's structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun-canopy-sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m-2 sr-1 nm-1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m-2 sr-1 nm-1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m-2 sr-1 nm-1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants' photosynthetic capacity.

Retrieval of Crop Variables from Proximal Multispectral UAV Image Data Using PROSAIL in Maize Canopy.

Mapping crop variables at different growth stages is crucial to inform farmers and plant breeders about the crop status. For mapping purposes, inversion of canopy radiative transfer models (RTMs) is a viable alternative to parametric and non-parametric regression models, which often lack transferability in time and space. Due to the physical nature of RTMs, inversion outputs can be delivered in sound physical units that reflect the underlying processes in the canopy. In this study, we explored the capabilities of the coupled leaf-canopy RTM PROSAIL applied to high-spatial-resolution (0.015 m) multispectral unmanned aerial vehicle (UAV) data to retrieve the leaf chlorophyll content (LCC), leaf area index (LAI) and canopy chlorophyll content (CCC) of sweet and silage maize throughout one growing season. Two different retrieval methods were tested: (i) applying the RTM inversion scheme to mean reflectance data derived from single breeding plots (mean reflectance approach) and (ii) applying the same inversion scheme to an orthomosaic to separately retrieve the target variables for each pixel of the breeding plots (pixel-based approach). For LCC retrieval, soil and shaded pixels were removed by applying simple vegetation index thresholding. Retrieval of LCC from UAV data yielded promising results compared to ground measurements (sweet maize RMSE = 4.92 µg/cm2, silage maize RMSE = 3.74 µg/cm2) when using the mean reflectance approach. LAI retrieval was more challenging due to the blending of sunlit and shaded pixels present in the UAV data, but worked well at the early developmental stages (sweet maize RMSE = 0.70m2/m2, silage RMSE = 0.61m2/m2 across all dates). CCC retrieval significantly benefited from the pixel-based approach compared to the mean reflectance approach (RMSEs decreased from 45.6 to 33.1 µg/m2). We argue that high-resolution UAV imagery is well suited for LCC retrieval, as shadows and background soil can be precisely removed, leaving only green plant pixels for the analysis. As for retrieving LAI, it proved to be challenging for two distinct varieties of maize that were characterized by contrasting canopy geometry.

Does Fluctuating Light Affect Crop Yield? A Focus on the Dynamic Photosynthesis of Two Soybean Varieties.

In natural environments, plants are exposed to variable light conditions, but photosynthesis has been mainly studied at steady state and this might overestimate carbon (C) uptake at the canopy scale. To better elucidate the role of light fluctuations on canopy photosynthesis, we investigated how the chlorophyll content, and therefore the different absorbance of light, would affect the quantum yield in fluctuating light conditions. For this purpose, we grew a commercial variety (Eiko) and a chlorophyll deficient mutant (MinnGold) either in fluctuating (F) or non-fluctuating (NF) light conditions with sinusoidal changes in irradiance. Two different light treatments were also applied: a low light treatment (LL; max 650 µmol m-2 s-1) and a high light treatment (HL; max 1,000 µmol m-2 s-1). Canopy gas exchanges were continuously measured throughout the experiment. We found no differences in C uptake in LL treatment, either under F or NF. Light fluctuations were instead detrimental for the chlorophyll deficient mutant in HL conditions only, while the green variety seemed to be well-adapted to them. Varieties adapted to fluctuating light might be identified to target the molecular mechanisms responsible for such adaptations.

Toward predicting photosynthetic efficiency and biomass gain in crop genotypes over a field season.

Photosynthesis acclimates quickly to the fluctuating environment in order to optimize the absorption of sunlight energy, specifically the photosynthetic photon fluence rate (PPFR), to fuel plant growth. The conversion efficiency of intercepted PPFR to photochemical energy (ɛe) and to biomass (ɛc) are critical parameters to describe plant productivity over time. However, they mask the link of instantaneous photochemical energy uptake under specific conditions, that is, the operating efficiency of photosystem II (Fq'/Fm'), and biomass accumulation. Therefore, the identification of energy- and thus resource-efficient genotypes under changing environmental conditions is impeded. We long-term monitored Fq'/Fm' at the canopy level for 21 soybean (Glycine max (L.) Merr.) and maize (Zea mays) genotypes under greenhouse and field conditions using automated chlorophyll fluorescence and spectral scans. Fq'/Fm' derived under incident sunlight during the entire growing season was modeled based on genotypic interactions with different environmental variables. This allowed us to cumulate the photochemical energy uptake and thus estimate ɛe noninvasively. ɛe ranged from 48% to 62%, depending on the genotype, and up to 9% of photochemical energy was transduced into biomass in the most efficient C4 maize genotype. Most strikingly, ɛe correlated with shoot biomass in seven independent experiments under varying conditions with up to r = 0.68. Thus, we estimated biomass production by integrating photosynthetic response to environmental stresses over the growing season and identified energy-efficient genotypes. This has great potential to improve crop growth models and to estimate the productivity of breeding lines or whole ecosystems at any time point using autonomous measuring systems.

Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant.

Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction ( f esc ) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine f esc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy ( SIF 760 canopy ) to leaf level ( SIF 760 leaf ). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine f esc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating f esc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency ( ε SIF ) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.

A low-cost automated growth chamber system for continuous measurements of gas exchange at canopy scale in dynamic conditions.

BACKGROUND: Obtaining instantaneous gas exchanges data is fundamental to gain information on photosynthesis. Leaf level data are reliable, but their scaling up to canopy scale is difficult as they are acquired in standard and/or controlled conditions, while natural environments are extremely dynamic. Responses to dynamic environmental conditions need to be considered, as measurements at steady state and their related models may overestimate total carbon (C) plant uptake. RESULTS: In this paper, we describe an automatic, low-cost measuring system composed of 12 open chambers (60 × 60 × 150 cm; around 400 euros per chamber) able to measure instantaneous CO2 and H2O gas exchanges, as well as environmental parameters, at canopy level. We tested the system's performance by simulating different CO2 uptake and respiration levels using a tube filled with soda lime or pure CO2, respectively, and quantified its response time and measurement accuracy. We have been also able to evaluate the delayed response due to the dimension of the chambers, proposing a method to correct the data by taking into account the response time ([Formula: see text]) and the residence time (τ). Finally, we tested the system by growing a commercial soybean variety in fluctuating and non-fluctuating light, showing the system to be fast enough to capture fast dynamic conditions. At the end of the experiment, we compared cumulative fluxes with total plant dry biomass. CONCLUSIONS: The system slightly over-estimated (+ 7.6%) the total C uptake, even though not significantly, confirming its ability in measuring the overall CO2 fluxes at canopy scale. Furthermore, the system resulted to be accurate and stable, allowing to estimate the response time and to determine steady state fluxes from unsteady state measured values. Thanks to the flexibility in the software and to the dimensions of the chambers, even if only tested in dynamic light conditions, the system is thought to be used for several applications and with different plant canopies by mimicking different environmental conditions.

High-throughput field phenotyping reveals genetic variation in photosynthetic traits in durum wheat under drought.

Chlorophyll fluorescence (ChlF) is a powerful non-invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high-throughput field phenotyping capabilities. The light-induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( Fq'/Fm' ) and the kinetics of electron transport measured by reoxidation rates ( Fr1' and Fr2' ). Short- and long-term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, Fq'/Fm' and Fr2' were little affected, while Fr1' was consistently accelerated in water-limited compared to well-watered plants, increasingly so with rising vapour pressure deficit. This high-throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.

Genotypic variation in the response of soybean to elevated CO2.

The impact of elevated CO2 (eCO2) on soybean productivity is essential to the global food supply because it is the world's leading source of vegetable proteins. This study aimed to understand the yield responses and nutritional impact under free-air CO2 enrichment (FACE) conditions of soybean genotypes. Here we report that grain yield increased by 46.9% and no reduction in harvest index was observed among soybean genotypes. Elevated CO2 improved the photosynthetic carbon assimilation rate, leaf area, plant height, and aboveground biomass at vegetative and pod filling stages. Besides the positive effects on yield parameters, eCO2 differentially affected the overall grain quality. The levels of calcium (Ca), phosphorous (P), potassium (K), magnesium (Mg), manganese (Mn), iron (Fe), boron (B), and zinc (Zn) grain minerals decreased by 22.9, 9.0, 4.9, 10.1, 21.3, 28.1, 18.5, and 25.9% under eCO2 conditions, respectively. Soluble sugars and starch increased by 9.1 and 16.0%, respectively, phytic acid accumulation increased by 8.1%, but grain protein content significantly decreased by 5.6% across soybean genotypes. Furthermore, the antioxidant activity decreased by 36.9%, but the total phenolic content was not affected by eCO2 conditions. Genotypes, such as Winsconsin Black, Primorskaja, and L-117, were considered the most responsive to eCO2 in terms of yield enhancement and less affected in the nutritional quality. Our results confirm the existence of genetic variability in soybean responses to eCO2, and differences between genotypes in yield improvement and decreased sensitivity to eCO2 in terms of grain quality loss could be included in future soybean selection to enable adaptation to climate change.

Diurnal dynamics of nonphotochemical quenching in Arabidopsis npq mutants assessed by solar-induced fluorescence and reflectance measurements in the field.

Solar-induced fluorescence (SIF) is highly relevant in mapping photosynthesis from remote-sensing platforms. This requires linking SIF to photosynthesis and understanding the role of nonphotochemical quenching (NPQ) mechanisms under field conditions. Hence, active and passive fluorescence were measured in Arabidopsis with altered NPQ in outdoor conditions. Plants with mutations in either violaxanthin de-epoxidase (npq1) or PsbS protein (npq4) exhibited reduced NPQ capacity. Parallel measurements of NPQ, photosystem II efficiency, SIF and spectral reflectance (ρ) were conducted diurnally on one sunny summer day and two consecutive days during a simulated cold spell. Results showed that both npq mutants exhibited higher levels of SIF compared to wild-type plants. Changes in reflectance were related to changes in the violaxanthin-antheraxanthin-zeaxanthin cycle and not to PsbS-mediated conformational changes. When plants were exposed to cold temperatures, rapid onset of photoinhibition strongly quenched SIF in all lines. Using well-characterized Arabidopsis npq mutants, we showed for the first time the quantitative link between SIF, photosynthetic efficiency, NPQ components and leaf reflectance. We discuss the functional potential and limitations of SIF and reflectance measurements for estimating photosynthetic efficiency and NPQ in the field.

The Cassava Source-Sink project: opportunities and challenges for crop improvement by metabolic engineering.

Cassava (Manihot esculenta Crantz) is one of the important staple foods in Sub-Saharan Africa. It produces starchy storage roots that provide food and income for several hundred million people, mainly in tropical agriculture zones. Increasing cassava storage root and starch yield is one of the major breeding targets with respect to securing the future food supply for the growing population of Sub-Saharan Africa. The Cassava Source-Sink (CASS) project aims to increase cassava storage root and starch yield by strategically integrating approaches from different disciplines. We present our perspective and progress on cassava as an applied research organism and provide insight into the CASS strategy, which can serve as a blueprint for the improvement of other root and tuber crops. Extensive profiling of different field-grown cassava genotypes generates information for leaf, phloem, and root metabolic and physiological processes that are relevant for biotechnological improvements. A multi-national pipeline for genetic engineering of cassava plants covers all steps from gene discovery, cloning, transformation, molecular and biochemical characterization, confined field trials, and phenotyping of the seasonal dynamics of shoot traits under field conditions. Together, the CASS project generates comprehensive data to facilitate conventional breeding strategies for high-yielding cassava genotypes. It also builds the foundation for genome-scale metabolic modelling aiming to predict targets and bottlenecks in metabolic pathways. This information is used to engineer cassava genotypes with improved source-sink relations and increased yield potential.

Phenotyping: New Windows into the Plant for Breeders.

Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

Quantification of Leaf Phloem Anatomical Features with Microscopy.

Measurements of vein density and foliar minor vein phloem cell numbers, minor vein phloem cell sizes, and transfer cell wall ingrowths provide quantitative proxies for the leaf's capacities to load and export photosynthates. While overall infrastructural capacity for sugar loading and sugar export correlated positively and closely with photosynthetic capacity, the specific targets of the adjustment of minor vein organization varied with phloem-loading mechanism, plant life-cycle characteristics, and environmental growth conditions. Among apoplastic loaders, for which sugar loading into the phloem depends on cell membrane-spanning transport proteins, variation in minor vein density, phloem cell number, and level of cell wall ingrowth (when present) were consistently associated with photosynthetic capacity. Among active symplastic loaders, for which sugar loading into the phloem depends on cytosolic enzymes, variation in vein density and phloem cell size were consistently associated with photosynthetic capacity. All of these anatomical features were also subject to acclimatory adjustment depending on species and environmental conditions, with increased levels of these features supporting higher rates of photosynthesis. We present a procedure for the preparation of leaf tissue for minor vein analysis, using both light and transmission electron microscopy, that facilitates quantification of not only phloem features but also xylem features that provide proxies for foliar water import capacity.

Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding.

New types of phenotyping tools generate large amounts of data on many aspects of plant physiology and morphology with high spatial and temporal resolution. These new phenotyping data are potentially useful to improve understanding and prediction of complex traits, like yield, that are characterized by strong environmental context dependencies, i.e., genotype by environment interactions. For an evaluation of the utility of new phenotyping information, we will look at how this information can be incorporated in different classes of genotype-to-phenotype (G2P) models. G2P models predict phenotypic traits as functions of genotypic and environmental inputs. In the last decade, access to high-density single nucleotide polymorphism markers (SNPs) and sequence information has boosted the development of a class of G2P models called genomic prediction models that predict phenotypes from genome wide marker profiles. The challenge now is to build G2P models that incorporate simultaneously extensive genomic information alongside with new phenotypic information. Beyond the modification of existing G2P models, new G2P paradigms are required. We present candidate G2P models for the integration of genomic and new phenotyping information and illustrate their use in examples. Special attention will be given to the modelling of genotype by environment interactions. The G2P models provide a framework for model based phenotyping and the evaluation of the utility of phenotyping information in the context of breeding programs.

Maximum fluorescence and electron transport kinetics determined by light-induced fluorescence transients (LIFT) for photosynthesis phenotyping.

Photosynthetic phenotyping requires quick characterization of dynamic traits when measuring large plant numbers in a fluctuating environment. Here, we evaluated the light-induced fluorescence transient (LIFT) method for its capacity to yield rapidly fluorometric parameters from 0.6 m distance. The close approximation of LIFT to conventional chlorophyll fluorescence (ChlF) parameters is shown under controlled conditions in spinach leaves and isolated thylakoids when electron transport was impaired by anoxic conditions or chemical inhibitors. The ChlF rise from minimum fluorescence (Fo) to maximum fluorescence induced by fast repetition rate (Fm-FRR) flashes was dominated by reduction of the primary electron acceptor in photosystem II (QA). The subsequent reoxidation of QA- was quantified using the relaxation of ChlF in 0.65 ms (Fr1) and 120 ms (Fr2) phases. Reoxidation efficiency of QA- (Fr1/Fv, where Fv = Fm-FRR - Fo) decreased when electron transport was impaired, while quantum efficiency of photosystem II (Fv/Fm) showed often no significant effect. ChlF relaxations of the LIFT were similar to an independent other method. Under increasing light intensities, Fr2'/Fq' (where Fr2' and Fq' represent Fr2 and Fv in the light-adapted state, respectively) was hardly affected, whereas the operating efficiency of photosystem II (Fq'/Fm') decreased due to non-photochemical quenching. Fm-FRR was significantly lower than the ChlF maximum induced by multiple turnover (Fm-MT) flashes. However, the resulting Fv/Fm and Fq'/Fm' from both flashes were highly correlated. The LIFT method complements Fv/Fm with information about efficiency of electron transport. Measurements in situ and from a distance facilitate application in high-throughput and automated phenotyping.