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BACKGROUND: Quality improvement collaboratives (QIC) are an approach to accelerate the spread and impact of evidence-based interventions across health facilities, which are found to be particularly successful when combined with other interventions such as clinical skills training. We implemented a QIC as part of a quality improvement intervention package designed to improve newborn survival in Kenya and Uganda. We use a multi-method approach to describe how a QIC was used as part of an overall improvement effort and describe specific changes measured and participant perceptions of the QIC. METHODS: We examined QIC-aggregated run charts on three shared indicators related to uptake of evidence-based practices over time and conducted key informant interviews to understand participants perceptions of quality improvement practice. Run charts were evaluated for change from baseline medians. Interviews were analysed using framework analysis. RESULTS: Run charts for all indicators reflected an increase in evidence-based practices across both countries. In Uganda, pre-QIC median gestational age (GA) recording of 44% improved to 86%, while Kangaroo Mother Care (KMC) initiation went from 51% to 96% and appropriate antenatal corticosteroid (ACS) use increased from 17% to 74%. In Kenya, these indicators went from 82% to 96%, 4% to 74% and 4% to 57%, respectively. Qualitative results indicate that participants appreciated the experience of working with data, and the friendly competition of the QIC was motivating. The participants reported integration of the QIC with other interventions of the package as a benefit. CONCLUSIONS: In a QIC that demonstrated increased evidence-based practices, QIC participants point to data use, friendly competition and package integration as the drivers of success, despite challenges common to these settings such as health worker and resource shortages. TRIAL REGISTRATION NUMBER: NCT03112018.

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved ( 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

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. heat, drought, salt stress, flooding, disease outbreaks) and engineering efficient carbon-capturing and carbon-sequestering plants. Here, we present examples of research being conducted in these areas and discuss challenges and open questions as a call to action for the plant science community.

Following a severe drought in the late 1970s and early 1980s, the invasive shrub Prosopis, a kind of mesquite native to South America, was introduced by international organizations to locations across Kenya's drylands, including the Turkana region in the far north. Prosopis, known as etirae in Turkana, was envisaged as a solution to a range of problems, including deforestation, fuelwood shortages and general environmental deterioration. While exacerbated by drought, these problems were perceived to reflect a much more fundamental crisis, with prevalent views at the time envisaging pastoralism as unsustainable, destructive and in need of overhaul--a narrative dating to colonial times that has since been discredited. Since its introduction, etirae has spread relentlessly, invading riparian land, encroaching on cultivation plots and growing to new heights and thicknesses. Investigating its entanglements with Turkana livelihoods and economic relationships is also a process of understanding how it has braided its way through contested processes of social change, and how it has come to be intertwined with conceptions of cascading crisis quite distinct from the narratives that led to its initial introduction. Implications emerge regarding both the complicated biological residues of past development interventions and the totalizing crisis-oriented narratives that shape drylands development in the current era of climate change. Apres une periode de grave secheresse a la fin des annees 1970 et au debut des annees 1980, des organisations internationales ont introduit le Prosopis, un arbuste envahissant de type mesquite, natif d'Amerique du Sud, dans des regions arides du Kenya comme le Turkana, dans l'extreme nord du pays. Le Prosopis, connu sous le nom d'etirae au Turkana, se voulait comme une solution a de nombreux problemes comme la deforestation, le manque de bois de feu et la degradation generale de l'environnement. Bien qu'exacerbes par la secheresse, ces problemes etaient percus comme le reflet d'une crise bien plus fondamentale, les opinions repandues a l'epoque jugeant le pastoralisme non viable, destructeur et a reformer, un discours datant de la periode coloniale et desormais discredite. Depuis son introduction, l'etirae n'a cesse de se propager, envahissant les terres riveraines, empietant sur les terrains cultives et gagnant en hauteur et en epaisseur. L'etude de ses imbrications avec les moyens de subsistance et les relations economiques dans le Turkana permet egalement de comprendre comment il s'est tresse dans des processus contestes de changement social, et comment il en est venu a s'entremeler avec des conceptions de crise en cascade nettement distinctes des discours qui ont mene a son introduction initiale. Des implications emergent concernant les residus biologiques compliques d'anciennes interventions de developpement et les discours totalisants orientes sur la crise qui faconnent le developpement des regions arides a l'ere du changement climatique. Apos uma grave seca no final da decada de 1970 e inicio da decada de 1980, o arbusto invasor Prosopis, uma especie de mesquite nativa da America do Sul, foi introduzido por organizacoes internacionais em locais das terras secas do Quenia, incluindo a regiao de Turkana, no extremo norte. A Prosopis, conhecida como etirae em Turkana, foi encarada como uma solucao para uma serie de problemas, incluindo a desflorestacao, a escassez de lenha e a deterioracao geral do ambiente. Embora exacerbados pela seca, estes problemas eram vistos como reflexo de uma crise muito mais fundamental, com as opinioes prevalecentes na altura a considerarem a pastoricia como insustentavel, destrutiva e a necessitar de uma revisao--uma narrativa que remonta aos tempos coloniais e que, desde entao, tem sido desacreditada. Desde a sua introducao, a etirae espalhou-se implacavelmente, invadindo terras ribeirinhas, invadindo parcelas de cultivo e crescendo a novas alturas e espessuras. Investigar os seus envolvimentos com os meios de subsistencia e as relacoes economicas da Turkana e tambem um processo de compreensao da forma como a etirae se entrelacou com processos contestados de mudanca social e como se entrelacou com concepcoes de crise em cascata bastante distintas das narrativas que levaram a sua introducao inicial. Surgem implicacoes relativas tanto aos complicados residuos biologicos de intervencoes de desenvolvimento passadas como as narrativas totalizantes orientadas para a crise que moldam o desenvolvimento das terras secas na atual era das alteracoes climaticas., Introduction: tangled crises In the late 1970s and early 1980s, drylands across Africa, from the Sahel to the Horn, experienced an acute environmental crisis. Severe and protracted drought provoked large-scale [...]

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. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

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. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

Previous studies have found that maximum quantum yield of CO2 assimilation (Φ CO2,max,app) declines in lower canopies of maize and miscanthus, a maladaptive response to self-shading. These observations were limited to single genotypes, leaving it unclear whether the maladaptive shade response is a general property of this C4 grass tribe, the Andropogoneae. We explored the generality of this maladaptation by testing the hypothesis that erect leaf forms (erectophiles), which allow more light into the lower canopy, suffer less of a decline in photosynthetic efficiency than drooping leaf (planophile) forms. On average, Φ CO2,max,app declined 27% in lower canopy leaves across 35 accessions, but the decline was over twice as great in planophiles than in erectophiles. The loss of photosynthetic efficiency involved a decoupling between electron transport and assimilation. This was not associated with increased bundle sheath leakage, based on 13C measurements. In both planophiles and erectophiles, shaded leaves had greater leaf absorptivity and lower activities of key C4 enzymes than sun leaves. The erectophile form is considered more productive because it allows a more effective distribution of light through the canopy to support photosynthesis. We show that in sorghum, it provides a second benefit, maintenance of higher Φ CO2,max,app to support efficient use of that light resource.

Geometric morphometric analyses are used to examine the maxillary shape of the Kenyanthropus platyops Leakey, Spoor, Brown, Gathogo, Kiarie, Leakey & McDougall, 2001 holotype KNM-WT 40000 and the Australopithecus deyiremeda Haile-Selassie, Gilbert, Melillo, Ryan, Alene, Deino, Levin, Scott & Saylor, 2015 holotype BRT-VP-3/1, expanding on the work of Spoor et al. (2010, 2016) by using more accurate data and a larger comparative sample. The main objective is to assess whether these two specimens differ from the contemporary taxon Australopithecus afarensis Johanson, White & Coppens, 1978 and more broadly from species of Australopithecus Dart, 1925 and Paranthropus Broom, 1938, as well as from each other. Five two-dimensional landmarks recorded on virtual models obtained from computed tomography scans quantify key features of the maxilla used in the differential diagnoses of K. platyops and A. deyiremeda. Principal component analyses were performed to describe shape differences, and the magnitudes of these differences and their statistical significance were assessed using Procrustes and Mahalanobis distances, respectively. The maxillary shapes of both KNM-WT 40000 and BRT-VP-3/1 are significantly different from A. afarensis, the former more so than the latter, and they differ from A. afarensis in dissimilar ways. Where KNM-WT 40000 has a more anterosuperiorly positioned zygomatic process with a longer, more orthognathic, and transversely flat subnasal clivus than A. afarensis, the shape difference of BRT-VP-3/1 is best described as a posterior shift (retraction) of the entire dental arcade. The findings of this study quantitatively support the species status of K. platyops and A. deyiremeda, and corroborate the notion that hominin diversity extended well into the mid-Pliocene of eastern Africa. [ABSTRACT FROM AUTHOR]

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.

Background Quality improvement collaboratives (QIC) are an approach to accelerate the spread and impact of evidence-based interventions across health facilities, which are found to be particularly successful when combined with other interventions such as clinical skills training. We implemented a QIC as part of a quality improvement intervention package designed to improve newborn survival in Kenya and Uganda. We use a multi-method approach to describe how a QIC was used as part of an overall improvement effort and describe specific changes measured and participant perceptions of the QIC.Methods We examined QIC-aggregated run charts on three shared indicators related to uptake of evidence-based practices over time and conducted key informant interviews to understand participants’ perceptions of quality improvement practice. Run charts were evaluated for change from baseline medians. Interviews were analysed using framework analysis.Results Run charts for all indicators reflected an increase in evidence-based practices across both countries. In Uganda, pre-QIC median gestational age (GA) recording of 44% improved to 86%, while Kangaroo Mother Care (KMC) initiation went from 51% to 96% and appropriate antenatal corticosteroid (ACS) use increased from 17% to 74%. In Kenya, these indicators went from 82% to 96%, 4% to 74% and 4% to 57%, respectively. Qualitative results indicate that participants appreciated the experience of working with data, and the friendly competition of the QIC was motivating. The participants reported integration of the QIC with other interventions of the package as a benefit.Conclusions In a QIC that demonstrated increased evidence-based practices, QIC participants point to data use, friendly competition and package integration as the drivers of success, despite challenges common to these settings such as health worker and resource shortages.Trial registration number NCT03112018.

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.

Abstract There are strong links between heritage and the environment yet, heritage is not fully included in existing ecosystem‐based frameworks. Different understandings of heritage values exist, and heritage values are not yet related to key value categories in environmental values research. To address this gap and facilitate a common values‐based approach, we develop a novel framework that links heritage and environmental values. First, we expand the understanding of heritage values by linking heritage to key environmental value categories. We then use the Life Framework of Values to show how heritage features in the different ways in which people relate to the world. The resulting heritage values framework is operationalised by applying it to six case examples drawn from participatory research on the governance of European coastal and maritime heritage. We found that the environment was not only considered to be a setting for heritage but was itself valued as heritage in different ways; that heritage is not extrinsic to the environment but is also a way in which people see meaning in the environment; and that multiple value frames and types were involved in shaping this perspective. The results highlight important discrepancies between stakeholders' perspectives and existing management approaches. Applying the framework shows the ways in which heritage and nature are entwined by providing a structure for elucidating what can be valued as heritage, what values can inform heritage values and how heritage values feature in human–nature relations. Read the free Plain Language Summary for this article on the Journal blog.

Abstract 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 m2 Free Air Concentration Enrichment (FACE) treatment plots. Each FACE plot is paired with an untreated control (ambient) plot. The experiment provides important ground truth data for predicting future crop productivity. Fumigation data from SoyFACE were collected every four seconds throughout each growing season for over two decades. Here, we organize, quality control, and collate 20 years of data to facilitate trend analysis and crop modeling efforts. This paper provides the rationale for and a description of the SoyFACE experiments, along with a summary of the fumigation data and collation process, weather and ambient data collection procedures, and explanations of air pollution metrics and calculations.

Ozone is the most damaging air pollutant to crops, currently reducing Midwest US maize production by up to 10%, yet there has been very little effort to adapt germplasm for ozone tolerance. Ozone enters plants through stomata, reacts to form reactive oxygen species in the apoplast and ultimately decreases photosynthetic C gain. In this study, 10 diverse inbred parents were crossed in a half-diallel design to create 45 F1 hybrids, which were tested for ozone response in the field using free air concentration enrichment (FACE). Ozone stress increased the heritability of photosynthetic traits and altered genetic correlations among traits. Hybrids from parents Hp301 and NC338 showed greater sensitivity to ozone stress, and disrupted relationships among photosynthetic traits. The physiological responses underlying sensitivity to ozone differed in hybrids from the two parents, suggesting multiple mechanisms of response to oxidative stress. FACE technology was essential to this evaluation because genetic variation in photosynthesis under elevated ozone was not predictable based on performance at ambient ozone. These findings suggest that selection under elevated ozone is needed to identify deleterious alleles in the world's largest commodity crop.

Sorghum (Sorghum bicolor L.) is a major food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. The lack of recombination, drift, and the coupling with favorable loci impede the removal of deleterious mutations from the genome by selection. To study how deleterious variants impact phenotypes, we identified putative deleterious mutations among ∼5.5 M segregating variants of 229 diverse biomass sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that ∼33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups. Across racial groups, the mutation burden correlated negatively with biomass, plant height, specific leaf area (SLA), and tissue starch content (TSC), suggesting that deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly one-half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (average of 3.1% across all stages). There is no advantage in total heritable variance for SLA and TSC. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, these results suggest that incorporating putatively deleterious variants into genomic models slightly improves prediction accuracy because of extensive linkage. Knowledge of deleterious variants could be leveraged for sorghum breeding through either genome editing and/or conventional breeding that focuses on the selection of progeny with fewer deleterious alleles.

How animals respond to seasonal resource availability has profound implications for their dietary flexibility and realized ecological niches. We sought to understand seasonal dietary niche partitioning in extant African suids using intra-tooth stable isotope analysis of enamel. We collected enamel samples from canines of red river hogs/bushpigs (Potamochoerus spp.) and third molars of warthogs (Phacochoerus spp.) in 3 different regions of central and eastern Africa. We analyzed multiple samples from each tooth and used variations in stable carbon and oxygen isotope ratios (δ13C and δ18O) and covariances between them to infer seasonal dietary changes. We found that most Phacochoerus display C4-dominated diets, while most Potamochoerus display C3-dominated diets. Phacochoerus and Potamochoerus that co-occur in the same region display no overlap in intra-tooth δ13C, which suggests dietary niche partitioning. They also show diverging δ13C values as the dry seasons progress and converging δ13C values during the peak of the rainy seasons, which suggests a greater dietary niche separation during the dry seasons when resources are scarce than during the rainy season. We found statistically significant cross-correlations between intra-tooth δ13C and δ18O in most specimens. We also observed a temporal lag between δ13C and δ18O in some specimens. This study demonstrates that intra-tooth stable isotope analysis is a promising approach to investigate seasonal dietary niche variation. However, large inter-individual variations in δ18O at certain localities can be challenging to interpret. Future studies that expand the intra-tooth stable isotope surveys or include controlled feeding experiments will improve its application in ecological studies. [ABSTRACT FROM AUTHOR]

The article "Stomata: custodians of leaf gaseous exchange" published in the Journal of Experimental Botany discusses the role of stomata in regulating gaseous exchange in plants. Stomata, surrounded by guard cells, modulate CO2 uptake for photosynthesis while minimizing water loss. The article explores the impact of stomatal conductance on photosynthesis, water use efficiency, and plant performance, emphasizing the potential for crop improvement through enhanced photosynthesis and water use efficiency. It also delves into the manipulation of stomatal anatomical features to improve water use efficiency and plant resilience to climate-induced stresses. [Extracted from the article]

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. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping. [ABSTRACT FROM AUTHOR]

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 (g s) has improved WUE in multiple C3 crop species. However, reducing g s in C3 species often reduces photosynthetic carbon gain. A different response is expected in C4 plants because they possess specialized anatomy and biochemistry which concentrates CO2 at the site of fixation. This modifies the relationship of photosynthesis (A N) with intracellular CO2 concentration (c i), such that photosynthesis is CO2 saturated and reductions in g s are unlikely to limit A N. To test this hypothesis, genetic strategies were investigated to reduce stomatal density in the C4 crop sorghum. Constitutive expression of a synthetic epidermal patterning factor (EPF) transgenic allele in sorghum led to reduced stomatal densities, reduced g s, reduced plant water use, and avoidance of stress during a period of water deprivation. In addition, moderate reduction in stomatal density did not increase stomatal limitation to A N. However, these positive outcomes were associated with negative pleiotropic effects on reproductive development and photosynthetic capacity. Avoiding pleiotropy by targeting expression of the transgene to specific tissues could provide a pathway to improved agronomic outcomes. [ABSTRACT FROM AUTHOR]

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 (g sw). However, the quantitative relationship between reduced SD, g sw, and the mechanisms underlying it is poorly understood. We addressed this knowledge gap using low-SD sugarcane (Saccharum spp. hybrid) as a case study alongside a meta-analysis of data from 10 species. Transgenic expression of EPIDERMAL PATTERNING FACTOR 2 from Sorghum bicolor (SbEPF2) in sugarcane reduced SD by 26–38% but did not affect g sw compared with the wild type. Further, no changes occurred in stomatal complex size or proxies for photosynthetic capacity. Measurements of gas exchange at low CO2 concentrations that promote complete stomatal opening to normalize aperture size between genotypes were combined with modeling of maximum g sw from anatomical data. These data suggest that increased stomatal aperture is the only possible explanation for maintaining g sw when SD is reduced. Meta-analysis across C3 dicots, C3 monocots, and C4 monocots revealed that engineered reductions in SD are strongly correlated with lower g sw (r 2=0.60–0.98), but this response is damped relative to the change in anatomy. [ABSTRACT FROM AUTHOR]

Abstract Background Roots are vital to plant performance because they acquire resources from the soil and provide anchorage. However, it remains difficult to assess root system size and distribution because roots are inaccessible in the soil. Existing methods to phenotype entire root systems range from slow, often destructive, methods applied to relatively small numbers of plants in the field to rapid methods that can be applied to large numbers of plants in controlled environment conditions. Much has been learned recently by extensive sampling of the root crown portion of field-grown plants. But, information on large-scale genetic and environmental variation in the size and distribution of root systems in the field remains a key knowledge gap. Minirhizotrons are the only established, non-destructive technology that can address this need in a standard field trial. Prior experiments have used only modest numbers of minirhizotrons, which has limited testing to small numbers of genotypes or environmental conditions. This study addressed the need for methods to install and collect images from thousands of minirhizotrons and thereby help break the phenotyping bottleneck in the field. Results Over three growing seasons, methods were developed and refined to install and collect images from up to 3038 minirhizotrons per experiment. Modifications were made to four tractors and hydraulic soil corers mounted to them. High quality installation was achieved at an average rate of up to 84.4 minirhizotron tubes per tractor per day. A set of four commercially available minirhizotron camera systems were each transported by wheelbarrow to allow collection of images of mature maize root systems at an average rate of up to 65.3 tubes per day per camera. This resulted in over 300,000 images being collected in as little as 11 days for a single experiment. Conclusion The scale of minirhizotron installation was increased by two orders of magnitude by simultaneously using four tractor-mounted, hydraulic soil corers with modifications to ensure high quality, rapid operation. Image collection can be achieved at the corresponding scale using commercially available minirhizotron camera systems. Along with recent advances in image analysis, these advances will allow use of minirhizotrons at unprecedented scale to address key knowledge gaps regarding genetic and environmental effects on root system size and distribution in the field.

Insufficient water availability for crop production is a mounting barrier to achieving the 70% increase in food production that will be needed by 2050. One solution is to develop crops that require less water per unit mass of production. Water vapor transpires from leaves through stomata, which also facilitate the influx of CO2 during photosynthetic assimilation. Here, we hypothesize that Photosystem II Subunit S (PsbS) expression affects a chloroplast-derived signal for stomatal opening in response to light, which can be used to improve water-use efficiency. Transgenic tobacco plants with a range of PsbS expression, from undetectable to 3.7 times wild-type are generated. Plants with increased PsbS expression show less stomatal opening in response to light, resulting in a 25% reduction in water loss per CO2 assimilated under field conditions. Since the role of PsbS is universal across higher plants, this manipulation should be effective across all crops.

Rising atmospheric concentrations of CO2 and O3 are key features of global environmental change. To investigate changes in the belowground bacterial community composition in response to elevated CO2 and O3 (eCO2 and eO3) the endosphere, rhizosphere and soil were sampled from soybeans under eCO2 and maize under eO3. The maize rhizosphere and endosphere α-diversity was higher than soybean, which may be due to a high relative abundance of Rhizobiales. Only the rhizosphere microbiome composition of the soybeans changed in response to eCO2, associated with an increased abundance of nitrogen fixing microbes. In maize, the microbiome composition was altered by the genotype and linked to differences in root exudate profiles. The eO3 treatment did not change the microbial communities in the rhizosphere, but altered the soil communities where hybrid maize was grown. In contrast to previous studies that focused exclusively on the soil, this study provides new insights into the effects of plant root exudates on the composition of the belowground microbiome in response to changing atmospheric conditions. Our results demonstrate that plant species and plant genotype were key factors driving the changes in the belowground bacterial community composition in agroecosystems that experience rising levels of atmospheric CO2 and O3.