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Radiation use efficiency can be predicted with ~70% accuracy. Canopy water content, greenness, and gas exchange spectral indices are the best predictors for RUE, biomass accumulation, and light interception., Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.

To achieve food security, it is necessary to increase crop radiation use efficiency (RUE) and yield through the enhancement of canopy photosynthesis to increase the availability of assimilates for the grain, but its study in the field is constrained by low throughput and the lack of integrative measurements at canopy level. In this study, partial least squares regression (PLSR) was used with high-throughput phenotyping (HTP) data in spring wheat to build predictive models of photosynthetic, biophysical, and biochemical traits for the top, middle, and bottom layers of wheat canopies. The combined layer model predictions performed better than individual layer predictions with a significance as follows for photosynthesis R2 = 0.48, RMSE = 5.24 μmol m–2 s–1 and stomatal conductance: R2 = 0.36, RMSE = 0.14 mol m–2 s–1. The predictions of these traits from PLSR models upscaled to canopy level compared to field observations were statistically significant at initiation of booting (R2 = 0.3, p < 0.05; R2 = 0.29, p < 0.05) and at 7 days after anthesis (R2 = 0.15, p < 0.05; R2 = 0.65, p < 0.001). Using HTP allowed us to increase phenotyping capacity 30-fold compared to conventional phenotyping methods. This approach can be adapted to screen breeding progeny and genetic resources for RUE and to improve our understanding of wheat physiology by adding different layers of the canopy to physiological modeling.

La productividad neta del ecosistema y evapotranspiración (ET) de los ecosistemas áridos y subtropicales es poco entendida por la escasez de mediciones de flujos de CO2 y vapor de agua. Todavía las contribuciones relativas a los flujos en los diferentes estratos (p. ej., sotobosque) han sido pobremente analizadas. Este estudio estima ET y los flujos de CO2 en un estrato arbustivo con presencia de Mimosa distachya de un matorral subtropical, en relación con estimaciones netas de la ET y el flujo de CO2 del ecosistema, determinado con la técnica de covarianza de vórtices. Se tomaron lecturas instantáneas de ET y del intercambio de CO2 en cuatro parcelas durante distintos periodos durante el día (9, 11, 14 y 18 horas), usando una cámara estática (16.4 m3), equipada con un analizador de gases infrarrojo de rápida respuesta. La variación de flujos durante los periodos de medición se usó para integrar la magnitud del flujo durante el día. Se presentó gran variación en los flujos durante julio y septiembre, donde hubo más ganancia de CO2 de la atmósfera hacia el ecosistema, con un intercambio neto de CO2 de -1.35 ± 1.93 g C m-2 d-1 y -1.15 ± 0.74 g C m-2 d-1, para cada mes, respectivamente, lo cual indica que la fotosíntesis fue más alta que la respiración en este estrato. En un periodo seco durante agosto, el flujo de CO2 fue de -0.85 ± 0.73 g C m-2 d-1. Durante estos periodos, el ET fue de 3.63 ± 0.15 mm d-1 en julio; 2.71 ± 0.08 mm d-1 en agosto, y 1.59 ± 0.5 mm d-1 en septiembre. Comparando estos resultados con los flujos netos de vapor de agua y CO2 del ecosistema, se encontró que el estrato arbustivo aporta entre 17 y 21% a los flujos de CO2 y de 25 a 39% en los flujos de vapor de agua durante el monzón de Norteamérica.

Stomata are integral to plant performance, enabling the exchange of gases between the atmosphere and the plant. The anatomy of stomata influences conductance properties with the maximal conductance rate, gsmax, calculated from density and size. However, current calculations of stomatal dimensions are performed manually, which are time-consuming and error prone. Here, we show how automated morphometry from leaf impressions can predict a functional property: the anatomical gsmax. A deep learning network was derived to preserve stomatal morphometry via semantic segmentation. This forms part of an automated pipeline to measure stomata traits for the estimation of anatomical gsmax. The proposed pipeline achieves accuracy of 100% for the distinction (wheat vs. poplar) and detection of stomata in both datasets. The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively. Semantic segmentation provides a rapid and repeatable method for the estimation of anatomical gsmax from microscopic images of leaf impressions. This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry.

Introduction: Mangroves are the largest carbon sinks and contribute to mitigate the effects of global climate change. Objective: To estimate the above-ground biomass (AB) of El Sargento estuary and Bahia del Tobari, to compare the carbon stocks between both places. Materials and methods: Measurements were taken from May 2014 to November 2015. The species were identified, and tree diameter, height and canopy cover were measured in 16 plots of 10 x 10 m. The AB was estimated with allometric equations and was related to the carbon content by the factor 0.5. The statistically significant differences between the carbon contents of both study sites were detected with the T test for independent samples. Results and discussion: The AB in El Sargento estuary was estimated between 108.1 and 316.78 Mg∙ha-1 with predominance of Laguncularia racemosa (L.) Gaertn (60 %); in Bahia del Tobari, the AB varied between 72.12 and 130 Mg∙ha-1, prevailing Avicennia germinans (L.) L. (83.4 %). In both sites Rhizophora mangle L. was found marginally. Total carbon storage was greater in El Sargento (with a range of 54.1 to 158.4 Mg C∙ha-1) compared to Bahia del Tobari (36.1 to 65.5 Mg C∙ha-1); the difference was statistically significant (F = 0.01; P = 0.02). Conclusion: The difference in the carbon reserves of the study sites is related to the good species development and diversity of a pristine environment (El Sargento), compared to another severely impacted environment (Bahia del Tobari).