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Background and aims The use of root-associated microorganisms emerge as a sustainable tool to enhance crop tolerance and productivity under climate change, particularly in drought-affected areas. Here, the impact of an inoculum based on arbuscular mycorrhizal fungi (AMF) was evaluated on pepper (Capsicum annuum L.) cultivation at varying water irrigation treatments (well-watered, reduced irrigation and rain-fed) under open-field conditions. Methods Agronomic and ecophysiological parameters, as well as biochemical analyses on stress markers and phytohormones in leaves and on fruit quality traits, were evaluated, along with the shifts in soil- and root-associated microbial communities. Results Rain-fed water treatment caused reduced fruit sizes, while no differences were detected among well-watered and reduced irrigation. Reduced irrigation did not cause a reduction in stomatal conductance. The highest AM fungal colonization rates were observed under reduced irrigation, and the enhanced flavonoid content and reduced oxidative stress markers in AMF-inoculated plants suggested a synergistic effect of AM fungal inoculation in boosting plant tolerance against stress. A shift in microbial community composition in the different irrigation treatments, associated with different enzymatic activity, highlighted the potential role of microbial dynamics in plant stress response under water-limited conditions. Conclusion The study suggests that a reduced irrigation comes along with beneficial impacts on pepper root associated microbes, while not impairing crop performance and yields, indicating a potential of saving water. All together, our results imply that optimization of irrigation and beneficial plant-microbe interactions, such as AM fungal symbiosis, can improve pepper physiological and productivity features under climate change.

Current food crop production systems are heavily dependent on the use of chemical fertilizers, of which those containing phosphorus (P), essential for plant growth and development, represent a non-renewable mineral resource whose reserves are estimated to decline within 100-200 years. The microbiota associated with plant roots, mainly represented by arbuscular mycorrhizal fungi (AMF) can be exploited for reducing external P input into agro-ecosystems. In this study, plant and fungal variables involved in P acquisition were investigated in four maize inbred lines (Oh40B, Mo17, Oh43 and B73), differing for mycorrhizal responsiveness and low-P tolerance, when inoculated with the symbiont Rhizoglomus irregulare. The expression patterns of genes encoding phosphate transporters (PTs) in extraradical and intraradical mycelium (ERM and IRM) and in maize roots were assessed along with plant growth responses, P uptake and ERM extent and structure. The four maize lines differed in expression levels of PT genes in both plant and fungal tissues, in ERM phenotypic traits and plant performance. Expression of PT genes in roots and ERM of the low-P tolerant maize line Mo17 was higher than that detected in the low-P susceptible line B73, which revealed larger ERM hyphal densities and interconnectedness. Significant correlations were found between ERM structural traits, and both expression levels of PT genes and mycorrhizal host benefit data. Further studies aimed at improving our knowledge of the genetic mechanisms regulating the functioning of AMF symbiosis may lead to the development of new strategies of targeted molecular breeding to obtain more sustainable low-P tolerant crops.

Evaluating a three-dimensional lake model requires large datasets of many variables, including velocity fields, that are seldom available. Here we discuss how to assess the performance of a model at multiple scales (in time and space) with data from standard monitoring systems, i.e., mostly limited to water temperature. The modeling chain consists of a lake hydrodynamic model (Delft3D-Flow) forced by an atmospheric model (WRF, Weather Research and Forecasting). The two models are tested on the case study of Lake Garda (Italy), where a comprehensive dataset of atmospheric and water temperature observations is available. Results show that a consistent picture of the inherent dynamics can be reproduced from a heterogeneous set of water temperature data, by distilling information across diverse spatial and temporal scales. The choice of the performance metrics and their limitations are discussed, with a focus on the procedures adopted to manage and homogenize data, visualize results and identify sources of error.

This paper describes and evaluates physical parameterizations accounting for the effect of rooftop mitigation strategies (RMSs) on the urban environment, in the context of the mesoscale model Weather Research and Forecasting (WRF). Through the new implementation, the sensitivity of near-surface air temperature and building energy consumption to different RMSs is evaluated by means of numerical simulations in idealized urban areas, for typical summer and winter conditions. Rooftop mitigation strategies considered include cool roofs, green roofs, and rooftop photovoltaic panels. The reference case simulations are performed assuming buildings made by bricks, with roof composed of clay tiles. Results indicate that near-surface air temperature is reduced by cool and green roofs during summer: cool roofs are the most efficient in decreasing air temperature, followed by irrigated green roofs. Photovoltaic panels, instead, induce a temperature increase during daytime and a small decrease during nighttime. Cool roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems. During wintertime, green roofs maintain a higher near-surface air temperature than clay tile roofs and largely decrease energy consumption. Even PVPs increase air temperature, as in the summer case. On the other hand, cool roofs reduce near-surface air temperature during daytime, inducing an increase in energy consumption. The results presented here show that the parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment. © 2021. The Authors.