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Abstract Wheat (Triticum aestivum L.) as a staple crop is closely interwoven into the development of modern society. Its influence on culture and economic development is global. Recent instability in wheat markets has demonstrated its importance in guaranteeing food security across national borders. Climate change threatens food security as it interacts with a multitude of factors impacting wheat production. The challenge needs to be addressed with a multidisciplinary perspective delivered across research, private, and government sectors. Many experimental studies have identified the major biotic and abiotic stresses impacting wheat production, but fewer have addressed the combinations of stresses that occur simultaneously or sequentially during the wheat growth cycle. Here, we argue that biotic and abiotic stress interactions, and the genetics and genomics underlying them, have been insufficiently addressed by the crop science community. We propose this as a reason for the limited transfer of practical and feasible climate adaptation knowledge from research projects into routine farming practice. To address this gap, we propose that novel methodology integration can align large volumes of data available from crop breeding programs with increasingly cheaper omics tools to predict wheat performance under different climate change scenarios. Underlying this is our proposal that breeders design and deliver future wheat ideotypes based on new or enhanced understanding of the genetic and physiological processes that are triggered when wheat is subjected to combinations of stresses. By defining this to a trait and/or genetic level, new insights can be made for yield improvement under future climate conditions.

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.

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.

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.

Soil respiration (RS) is a CO2 efflux from the soil to the atmosphere defined as the sum of autotrophic (respiration by roots and mycorrhizae), and heterotrophic (respiration of microorganisms that decompose fractions of organic matter and of soil fauna) respiration. Globally, RS is considered to be the second largest flux of C to the atmosphere. From published literature it is clear that its main controls are soil temperature, soil moisture, photosynthesis, organic matter inputs and soil biota composition. Despite its relevance in C cycle science, there have been only twenty eight studies in Mexico in the last decade where direct measurement of gas exchange was conducted in the field. These studies were held mostly in agricultural and forest ecosystems, in Central and Southern Mexico where mild subtropical conditions prevail. However, arid, semi-arid, tropical and wetland ecosystems may have an important role in Mexico’s CO2 emissions because of their extent and extensive land use changes. From the twenty eight studies, only two provided continuous measurements of RS with high temporal resolution, highlighting the need for long-term studies to evaluate the complex biophysical controls of this flux and associated processes over different ecological succession stages. We conclude that Mexico represents an important opportunity to understand its complex dynamics, in national and global context, as ecosystems in the country cover a wide range of climatic conditions. This is particularly important because deforestation and degradation of Mexican ecosystems is rapidly increasing along with expected changes in climate.