Pot size matters revisited: does container size affect the response to elevated CO2 and our ability to detect genotypic variability in this response in wheat?

Mathematical Modeling to Estimate Photosynthesis: A State of the Art

Photosynthesis is a process that indicates the productivity of crops. The estimation of this variable can be achieved through methods based on mathematical models. Mathematical models are usually classified as empirical, mechanistic, and hybrid. To mathematically model photosynthesis, it is essential to know: the input/output variables and their units; the modeling to be used based on its classification (empirical, mechanistic, or hybrid); existing measurement methods and their invasiveness; the validation shapes and the plant species required for experimentation. Until now, a collection of such information in a single reference has not been found in the literature, so the objective of this manuscript is to analyze the most relevant mathematical models for the photosynthesis estimation and discuss their formulation, complexity, validation, number of samples, units of the input/output variables, and invasiveness in the estimation method. According to the state of the art reviewed here, 67% of the photosynthesis measurement models are mechanistic, 13% are empirical and 20% hybrid. These models estimate gross photosynthesis, net photosynthesis, photosynthesis rate, biomass, or carbon assimilation. Therefore, this review provides an update on the state of research and mathematical modeling of photosynthesis.

Modification of storage proteins in the barley grain increases endosperm zinc and iron under both normal and elevated atmospheric CO2

Increasing atmospheric CO₂ concentration is expected to enhance the grain yield of C₃ cereal plants, while at the same time reducing the concentrations of minerals and proteins. This will lead to a lower nutritional quality and increase global problems associated with micronutrient malnutrition. Among the barley grain storage proteins, the C-hordein fraction has the lowest abundance of sulfur (S) containing amino acids and is poorest in binding of zinc (Zn). In the present study, C-hordein-suppressed barley lines with reduced C-hordein content, obtained by use of antisense or RNAi technology, were investigated under ambient and elevated atmospheric CO₂ concentration. Grains of the C-hordein-suppressed lines showed 50% increase in the concentrations of Zn and iron (Fe) in the core endosperm relative to the wild-type under both ambient and elevated atmospheric CO₂ . Element distribution images obtained using laser ablation-inductively coupled plasma-mass spectrometry confirmed the enrichment of Fe and Zn in the core endosperm of the lines with modified storage protein composition. We conclude that modification of grain storage proteins may improve the nutritional value of cereal grain with respect to Zn and Fe under both normal and future conditions of elevated atmospheric CO₂ .

DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays)

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.

The case for improving crop carbon sink strength or plasticity for a CO2-rich future

Atmospheric CO₂ concentration [CO₂] has increased from 260 to 280μmolmol^-1 (level during crop domestication up to the industrial revolution) to currently 400 and will reach 550μmolmol^-1 by 2050. C3 crops are expected to benefit from elevated [CO₂] (e-CO₂) thanks to photosynthesis responsiveness to [CO₂] but this may require greater sink capacity. We review recent literature on crop e-CO₂ responses, related source-sink interactions, how abiotic stresses potentially interact, and prospects to improve e-CO₂ response via breeding or genetic engineering. Several lines of evidence suggest that e-CO₂ responsiveness is related either to sink intrinsic capacity or adaptive plasticity, for example, involving enhanced branching. Wild relatives and old cultivars mostly showed lower photosynthetic rates, less downward acclimation of photosynthesis to e-CO₂ and responded strongly to e-CO₂ due to greater phenotypic plasticity. While reverting to such archaic traits would be an inappropriate strategy for breeding, we argue that substantial enhancement of vegetative sink vigor, inflorescence size and/or number and root sinks will be necessary to fully benefit from e-CO₂. Potential ideotype features based on enhanced sinks are discussed. The generic 'feast-famine' sugar signaling pathway may be suited to engineer sink strength tissue-specifically and stage-specifically and help validate ideotype concepts. Finally, we argue that models better accounting for acclimation to e-CO₂ are needed to predict which trait combinations should be targeted by breeders for a CO₂-rich world.

Pot binding as a variable confounding plant phenotype: theoretical derivation and experimental observations

MAIN CONCLUSION

Theoretical derivation predicted growth retardation due to pot water limitations, i.e., pot binding. Experimental observations were consistent with these limitations. Combined, these results indicate a need for caution in high-throughput screening and phenotyping. Pot experiments are a mainstay in many plant studies, including the current emphasis on developing high-throughput, phenotyping systems. Pot studies can be vulnerable to decreased physiological activity of the plants particularly when pot volume is small, i.e., "pot binding". It is necessary to understand the conditions under which pot binding may exist to avoid the confounding influence of pot binding in interpreting experimental results. In this paper, a derivation is offered that gives well-defined conditions for the occurrence of pot binding based on restricted water availability. These results showed that not only are pot volume and plant size important variables, but the potting media is critical. Artificial potting mixtures used in many studies, including many high-throughput phenotyping systems, are particularly susceptible to the confounding influences of pot binding. Experimental studies for several crop species are presented that clearly show the existence of thresholds of plant leaf area at which various pot sizes and potting media result in the induction of pot binding even though there may be no immediate, visual plant symptoms. The derivation and experimental results showed that pot binding can readily occur in plant experiments if care is not given to have sufficiently large pots, suitable potting media, and maintenance of pot water status. Clear guidelines are provided for avoiding the confounding effects of water-limited pot binding in studying plant phenotype.

Plant phenotyping: increasing throughput and precision at multiple scales

In this special issue of Functional Plant Biology, we present a perspective of the current state of the art in plant phenotyping. The applications of automated and detailed recording of plant characteristics using a range of mostly non-invasive techniques are described. Papers range from tissue scale analysis through to aerial surveying of field trials and include model plant species such as Arabidopsis as well as commercial crops such as sugar beet and cereals. The common denominators are high throughput measurements, data rich analyses often utilising image based data capture, requirements for validation when proxy measurement are employed and in many instances a need to fuse datasets. The outputs are detailed descriptions of plant form and function. The papers represent technological advances and important contributions to basic plant biology, and these studies are commonly multidisciplinary, involving engineers, software specialists and plant physiologists. This is a fast moving area producing large datasets and analytical requirements are often common between very diverse platforms.