Although drought is an increasing problem in agriculture, the contribution of the root-associated bacterial microbiome to plant adaptation to water stress is poorly studied. We investigated if the culturable bacterial microbiome associated with five grapevine rootstocks and the grapevine cultivar Barbera may enhance plant growth under drought stress. Eight isolates, over 510 strains, were tested in vivo for their capacity to support grapevine growth under water stress. The selected strains exhibited a vast array of plant growth promoting (PGP) traits, and confocal microscopy observation of gfp-labelled Acinetobacter and Pseudomonas isolates showed their ability to adhere and colonize both the Arabidopsis and grapevine rhizoplane. Tests on pepper plants fertilized with the selected strains, under both optimal irrigation and drought conditions, showed that PGP activity was a stress-dependent and not a per se feature of the strains. The isolates were capable of increasing shoot and leaf biomass, shoot length, and photosynthetic activity of drought-challenged grapevines, with an enhanced effect in drought-sensitive rootstock. Three isolates were further assayed for PGP capacity under outdoor conditions, exhibiting the ability to increase grapevine root biomass. Overall, the results indicate that PGP bacteria contribute to improve plant adaptation to drought through a water stress-induced promotion ability.

Abstract. Data of soil hydraulic properties forms often a limiting factor in unsaturated zone modelling, especially at the larger scales. Investigations for the hydraulic characterization of soils are time-consuming and costly, and the accuracy of the results obtained by the different methodologies is still debated. However, we may wonder how the uncertainty in soil hydraulic parameters relates to the uncertainty of the selected modelling approach. We performed an intensive monitoring study during the cropping season of a 10 ha maize field in Northern Italy. The data were used to: i) compare different methods for determining soil hydraulic parameters and ii) evaluate the effect of the uncertainty in these parameters on different variables (i.e. evapotranspiration, average water content in the root zone, flux at the bottom boundary of the root zone) simulated by two hydrological models of different complexity: SWAP, a widely used model of soil moisture dynamics in unsaturated soils based on Richards equation, and ALHyMUS, a conceptual model of the same dynamics based on a reservoir cascade scheme. We employed five direct and indirect methods to determine soil hydraulic parameters for each horizon of the experimental profile. Two methods were based on a parameter optimization of: a) laboratory measured retention and hydraulic conductivity data and b) field measured retention and hydraulic conductivity data. The remaining three methods were based on the application of widely used Pedo-Transfer Functions: c) Rawls and Brakensiek, d) HYPRES, and e) ROSETTA. Simulations were performed using meteorological, irrigation and crop data measured at the experimental site during the period June -October 2006. Results showed a wide range of soil hydraulic parameter values generated with the different Correspondence to: G. Baroni (gabriele.baroni@email.it) methods, especially for the saturated hydraulic conductivity K sat and the shape parameter α of the van Genuchten curve. This is reflected in a variability of the modeling results which is, as expected, different for each model and each variable analysed. The variability of the simulated water content in the root zone and of the bottom flux for different soil hydraulic parameter sets is found to be often larger than the difference between modeling results of the two models using the same soil hydraulic parameter set. Also we found that a good agreement in simulated soil moisture patterns may occur even if evapotranspiration and percolation fluxes are significantly different. Therefore multiple output variables should be considered to test the performances of methods and models.

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Exponentially growing water demands and increasingly uncertain hydrologic regimes due to changes in climate and land use are challenging the sustainability of agricultural water systems. Farmers must adapt their management strategies in order to secure food production and avoid crop failures. Investigating the potential for adaptation policies in agricultural systems requires accounting for their natural and human components, along with their reciprocal interactions. Yet this feedback is generally overlooked in the water resources systems literature. In this work, we contribute a novel modeling approach to study the coevolution of irrigated agriculture under changing climate, advancing the representation of the human component within agricultural systems by using normative meta‐models to describe the behaviors of groups of farmers or institutional decisions. These behavioral models, validated against observational data, are then integrated into a coupled human‐natural system simulation model to better represent both systems and their coevolution under future changing climate conditions, assuming the adoption of different policy adaptation options, such as cultivating less water demanding crops. The application to the pilot study of the Adda River basin in northern Italy shows that the dynamic coadaptation of water supply and demand allows farmers to avoid estimated potential losses of more than 10 M€/yr under projected climate changes, while unilateral adaptation of either the water supply or the demand are both demonstrated to be less effective. Results also show that the impact of the different policy options varies as function of drought intensity, with water demand adaptation outperforming water supply adaptation when drought conditions become more severe.