Environmental factors have a greater influence on photosynthetic capacity in C plants than biochemical subtypes or growth forms.

Our understanding of how photosynthetic capacity varies among C species and across growth and measurement conditions remains limited. We collated 1696 CO response curves of net CO assimilation rate (A/C curves) from C species grown and measured at various environmental conditions and used these data to estimate the apparent maximum carboxylation activity of phosphoenolpyruvate carboxylase (V) and CO-saturated net photosynthetic rate (A), two key parameters describing photosynthetic capacity. We examined how V and A vary with species-specific traits, growth and measurement conditions.

Drought does not mitigate reductions in soybean photosynthesis and yield caused by elevated ozone.

The co-occurrence of elevated tropospheric ozone concentrations and drought in agricultural regions is anticipated to increase with climate change. Both stressors negatively impact leaf photosynthetic capacity and stomatal conductance, contributing to reductions in biomass and yield. The interaction of ozone and drought stress is complex and under-researched, particularly in field settings.

Phylogenetic diversity of light dependent phosphorylation of Thr78 in Rubisco activase.

Rubisco activase is an ATP-dependent chaperone that facilitates dissociation of inhibitory sugar phosphates from the catalytic sites of ribulose-1,5-bisphosphate carboxylase/oxygenase during photosynthesis. In Arabidopsis, Rubisco activase is negatively regulated by dark-dependent phosphorylation of threonine 78. The prevalence of threonine 78 in Rubisco activase was investigated across sequences from 91 plant species, finding 29 (∼32%) species shared a threonine in the same position.

Soybean (Glycine max L.) canopy response to simulated dicamba vapor drift using unmanned aerial sensing.

Background: Concerns about off-target dicamba exposure to sensitive vegetation have escalated following the commercialization of dicamba-tolerant (DT) soybean [Glycine max (L.) Merr.] and cotton (Gossypium hirsutum L.

Constitutive down-regulation of liguleless alleles in sorghum drives increased productivity and water use efficiency.

Plant architecture influences the microenvironment throughout the canopy layer. Plants with a more erect leaf architecture allow for an increase in planting densities and allow more light to reach lower canopy leaves. This is predicted to increase crop carbon assimilation.

Light-Stable, Ultrastretchable Wearable Strain Sensors for Versatile Plant Growth Monitoring.

Wearable electronics have been applied to plants for various applications, including microclimate detection, health diagnosis, and growth rate measurement. However, previously reported plant growth strain sensors have limitations in the strain sensing range, optical transparency, and uncertain stability and reproducibility. Our recent work reported a transparent, conjugated polymer-based strain sensor that achieved above 400% operating strain in measurements of growth in a grass.

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery.

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming.

C photosynthesis, trait spectra, and the fast-efficient phenotype.

It has been 60 years since the discovery of C photosynthesis, an event that rewrote our understanding of plant adaptation, ecosystem responses to global change, and global food security. Despite six decades of research, one aspect of C photosynthesis that remains poorly understood is how the pathway fits into the broader context of adaptive trait spectra, which form our modern view of functional trait ecology. The C CO-concentrating mechanism supports a general C plant phenotype capable of fast growth and high resource-use efficiencies.

Machine learning-enabled computer vision for plant phenotyping: a primer on AI/ML and a case study on stomatal patterning.

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs.

Reducing stomatal density by expression of a synthetic epidermal patterning factor increases leaf intrinsic water use efficiency and reduces plant water use in a C4 crop.

Enhancing crop water use efficiency (WUE) is a key target trait for climatic resilience and expanding cultivation on marginal lands. Engineering lower stomatal density to reduce stomatal conductance (gs) has improved WUE in multiple C3 crop species. However, reducing gs in C3 species often reduces photosynthetic carbon gain.

Greater aperture counteracts effects of reduced stomatal density on water use efficiency: a case study on sugarcane and meta-analysis.

Stomata regulate CO2 and water vapor exchange between leaves and the atmosphere. Stomata are a target for engineering to improve crop intrinsic water use efficiency (iWUE). One example is by expressing genes that lower stomatal density (SD) and reduce stomatal conductance (gsw).

A guide to photosynthetic gas exchange measurements: Fundamental principles, best practice and potential pitfalls.

Gas exchange measurements enable mechanistic insights into the processes that underpin carbon and water fluxes in plant leaves which in turn inform understanding of related processes at a range of scales from individual cells to entire ecosytems. Given the importance of photosynthesis for the global climate discussion it is important to (a) foster a basic understanding of the fundamental principles underpinning the experimental methods used by the broad community, and (b) ensure best practice and correct data interpretation within the research community. In this review, we outline the biochemical and biophysical parameters of photosynthesis that can be investigated with gas exchange measurements and we provide step-by-step guidance on how to reliably measure them.

Similar photosynthetic but different yield responses of C and C crops to elevated O.

The deleterious effects of ozone (O) pollution on crop physiology, yield, and productivity are widely acknowledged. It has also been assumed that C crops with a carbon concentrating mechanism and greater water use efficiency are less sensitive to O pollution than C crops. This assumption has not been widely tested.

Two decades of fumigation data from the Soybean Free Air Concentration Enrichment facility.

The Soybean Free Air Concentration Enrichment (SoyFACE) facility is the longest running open-air carbon dioxide and ozone enrichment facility in the world. For over two decades, soybean, maize, and other crops have been exposed to the elevated carbon dioxide and ozone concentrations anticipated for late this century. The facility, located in East Central Illinois, USA, exposes crops to different atmospheric concentrations in replicated octagonal ~280 m Free Air Concentration Enrichment (FACE) treatment plots.

Climate change challenges, plant science solutions.

Climate change is a defining challenge of the 21st century, and this decade is a critical time for action to mitigate the worst effects on human populations and ecosystems. Plant science can play an important role in developing crops with enhanced resilience to harsh conditions (e.g.

Stronger fertilization effects on aboveground versus belowground plant properties across nine U.S. grasslands.

Increased nutrient inputs due to anthropogenic activity are expected to increase primary productivity across terrestrial ecosystems, but changes in allocation aboveground versus belowground with nutrient addition have different implications for soil carbon (C) storage. Thus, given that roots are major contributors to soil C storage, understanding belowground net primary productivity (BNPP) and biomass responses to changes in nutrient availability is essential to predicting carbon-climate feedbacks in the context of interacting global environmental changes. To address this knowledge gap, we tested whether a decade of nitrogen (N) and phosphorus (P) fertilization consistently influenced aboveground and belowground biomass and productivity at nine grassland sites spanning a wide range of climatic and edaphic conditions in the continental United States.

The leaf economics spectrum of triploid and tetraploid C grass Miscanthus x giganteus.

The leaf economics spectrum (LES) describes multivariate correlations in leaf structural, physiological and chemical traits, originally based on diverse C species grown under natural ecosystems. However, the specific contribution of C species to the global LES is studied less widely. C species have a CO concentrating mechanism which drives high rates of photosynthesis and improves resource use efficiency, thus potentially pushing them towards the edge of the LES.

Plasticity in stomatal behaviour across a gradient of water supply is consistent among field-grown maize inbred lines with varying stomatal patterning.

Stomata regulate leaf CO assimilation (A) and water loss. The Ball-Berry and Medlyn models predict stomatal conductance (g ) with a slope parameter (m or g ) that reflects the sensitivity of g to A, atmospheric CO  and humidity, and is inversely related to water use efficiency (WUE). This study addressed knowledge gaps about what the values of m and g are in C crops under field conditions, as well as how they vary among genotypes and with drought stress.

Variation in leaf transcriptome responses to elevated ozone corresponds with physiological sensitivity to ozone across maize inbred lines.

We examine the impact of sustained elevated ozone concentration on the leaf transcriptome of 5 diverse maize inbred genotypes, which vary in physiological sensitivity to ozone (B73, Mo17, Hp301, C123, and NC338), using long reads to assemble transcripts and short reads to quantify expression of these transcripts. More than 99% of the long reads, 99% of the assembled transcripts, and 97% of the short reads map to both B73 and Mo17 reference genomes. Approximately 95% of the genes with assembled transcripts belong to known B73-Mo17 syntenic loci and 94% of genes with assembled transcripts are present in all temperate lines in the nested association mapping pan-genome.

An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets.

Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO between the leaf and the atmosphere. Representation of stomatal conductance (g ) is therefore an essential component of models that seek to simulate water and CO exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between g and photosynthetic CO assimilation (A).

Soil carbon stocks in temperate grasslands differ strongly across sites but are insensitive to decade-long fertilization.

Enhancing soil carbon (C) storage has the potential to offset human-caused increases in atmospheric CO . Rising CO has occurred concurrently with increasing supply rates of biologically limiting nutrients such as nitrogen (N) and phosphorus (P). However, it is unclear how increased supplies of N and P will alter soil C sequestration, particularly in grasslands, which make up nearly a third of non-agricultural land worldwide.

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes.

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops.

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions.

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production.