Climate change studies involve complex processes translating coarse climate change projections in locally meaningful terms. We analysed the behaviour of weather generators while downscaling precipitation and air temperature data. With multiple climate indices and alternative weather generators, we directly quantified the uncertainty associated with using weather generators when site specific downscaling is performed. We extracted the influence of weather generators on climate variability at local scale and the uncertainty that could affect impact assessment. For that, we first designed the downscaling experiments with three weather generators (CLIMAK, LARS-WG, WeaGETS) to interpret future projections. Then we assessed the impacts of estimated changes of precipitation and air temperature for a sample of 15 sites worldwide using a rice yield model and an extended set of climate metrics. We demonstrated that the choice of a weather generator in the downscaling process may have a higher impact on crop yield estimates than the climate scenario adopted. Should they be confirmed, these results would indicate that widely accepted outcomes of climate change studies using this downscaling technique need reconsideration.

Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system’s capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties—needed to extend the system to new contexts—was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions.

Cultivar recommendation is a key factor in cropping system management. Classical approaches based on comparative multi-environmental trials can hardly explore the agro-climatic and management heterogeneity farmers may have to face. Moreover, they struggle to keep up with the number of genotypes commercially released each year. We propose a new approach based on the integration of in silico ideotyping and functional trait profiling, with the common bean (Phaseoulus vulgaris L.) in Northern Italy as a case study. Statistical distributions for six functional traits (light extinction coefficient, radiation use efficiency, thermal time to first pod and maturity, seed weight, plant height) were derived for 24 bean varieties. The analysis of soil, climate and management in the study area led us to define 21 homogeneous contexts, for which ideotypes were identified using the crop model STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard), the E-FAST (Extended Fourier Amplitude Sensitivity Test) sensitivity analysis method, and the distributions of functional traits. For each context, the 24 cultivars were ranked according to the similarity (weighted Euclidean distance) with the ideotype. Context-specific ideotypes mainly differed for phenological adaptation to specific combinations of climate and management (sowing time) factors, and this reflected in the cultivar recommendation for the different contexts. Feedbacks from bean technicians in the study area confirmed the reliability of the results and, in turn, of the proposed methodology.

Climate change impact on pasture floristic composition needs to be carefully assessed, given its key role for the resilience of pastoral systems and related ecosystem services. Nevertheless, variations in floristic composition are rarely taken into account in climate change impact studies. Here, we used the plant community model CoSMo to simulate future dynamics of biomass accumulation and floristic composition for high-altitude semi-natural alpine pastures. Dedicated multi-site field activities were conducted to collect data for model calibration. Simulations were run for four 20-year climate scenarios centered on 2040, resulting from the combination of two general circulation models (GISS-ES and HadGEM2) and two representative concentration pathways (RCP4.5 and RCP8.5). Results highlighted the capability of CoSMo to successfully reproduce the productivity and floristic composition of semi-natural pastures, modeling efficiency and R² being higher than 0.90 for aboveground biomass accumulation and relative abundance of species. CoSMo simulated an overall positive effect of increasing temperatures on pasture productivity (+ 10.7% on average), due to higher biomass accumulation rates and longer growing seasons. However, these benefits were highly heterogeneous among the monitored pastures (ranging from − 2.5 to + 16.2%), because of differences in floristic composition and in species-specific thermal requirements that led to complex, non-linear reactions to climate variations. A negative impact of climate change was simulated for grazing value (− 11.1% on average), due to the higher suitability to future conditions of Nardus stricta, which has low grazing value compared to other species. Our results highlight that floristic composition should be explicitly considered while assessing climate change impacts on semi-natural pasture productivity and connected ecosystem services.

Crop models are increasingly used to identify promising ideotypes for given environmental and management conditions. However, uncertainty must be properly managed to maximize the in vivo realizability of ideotypes. We focused on the impact of adopting germplasm-specific distributions while exploring potential combinations of traits. A field experiment was conducted on 43 Italian rice varieties representative of the Italian rice germplasm, where the following traits were measured: light extinction coefficient, radiation use efficiency, specific leaf area at emergence and tillering. Data were used to derive germplasm-specific distributions, which were used to re-run a previous modelling experiment aimed at identifying optimal combinations of plant trait values. The analysis, performed using the rice model WARM and sensitivity analysis techniques, was conducted under current conditions and climate change scenarios. Results revealed that the adoption of germplasm-specific distributions may markedly affect ideotyping, especially for the identification of most promising traits. A re-ranking of some of the most relevant parameters was observed (radiation use efficiency shifted from 4th to 1st), without clear relationships between changes in rankings and differences in distributions for single traits. Ideotype profiles (i.e., values of the ideotype traits) were instead more consistent, although differences in trait values were found.