Roberto Confalonieri

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.

A deeper understanding of the mechanisms underlying the impacts of multiple stresses in crops is direly needed given the climate change-induced risks to achieving food security for a growing world population. Global warming has already led to a higher frequency of multiple stresses occurring concurrently or subsequently and will continue to do so for the next decades. Plant-stress interactions are commonly subdivided into abiotic and biotic stresses and studied separately. Under field conditions, these stress interactions are usually multiple and interactive in character.To date, the mechanisms determining interactions between abiotic and biotic stresses and their effects on crop performance are unknown for most crops and stress combinations. Field data are particularly scarce as most studies have focused on laboratory model systems using few environmental parameters in controlled conditions, which cannot reflect the dynamics in the field. Adequate modelling approaches capable of describing basic crop growth processes and simultaneously capturing response to abiotic and biotic stress interactions and their impacts on crop yield and quality do not exist so far.The aim of this paper is to present the design of a joint experimental and modelling platform (MultiStress) capable of creating a deeper understanding of the overall impact of combined (abiotic+biotic) stresses on crop physiology and productivity (grain yield, biomass, grain and stover quality, nutrient/water use efficiency, etc.) using the cereal maize as one of the most important crops globally as a model.The empirical knowledge gained from the experimental set-up and formalized in an associated modelling platform is utilized to define traits for stress tolerant breeding to be considered in ideotyping cereal cultivars for future target environments. In our example, in a research Pillar I, we describe a field experimental platform (with rainout shelters) applicable under temperate and tropical climate conditions to investigate the interactions of drought and nitrogen deficiency with the foliar disease Northern Corn Leaf Blight caused by Setosphaeria turcica on the one hand, and stem borer caterpillars on the other. Pillar II is an associated process-based modelling platform enabling integration of new genetic and ecophysiological knowledge and extrapolate the findings in time and space.Applying a systems approach in conjunction with this platform we can test the following hypotheses: (i) the impact of combined abiotic and biotic stress interactions on crop growth and yield formation and quality is non-additive and thus differs from the sum of individual stress impacts; (ii) while the mechanisms underlying the abiotic and biotic stress interactions are of universal validity, their impacts are modulated by certain environmental conditions (such as temperature, light conditions and soil properties).Realization and evaluation of such platform will allow consideration of interactions between abiotic and biotic stresses and hence improve the predictive skill of crop growth models.

Simulating the dynamics of plant species or types in grassland communities remains an open area of research for which the Community Simulation Model (CoSMo) offers novel approaches. The grassland model ModVege was first parameterised based on a functional vegetation typology, in which types “A” and “B” include fast-growing grass species with a phenology-dependent nutrient-capture strategy inherent to fertile grasslands, while the nutrient conservation strategy and late flowering characterise the other types as “b”. ModVege was then coupled to the CoSMo rule set to dynamically simulate the relative abundance of plant functional types or individual species, assessed across fertilised and unfertilised, abandoned and mown conditions in a grassland site of the Massif Central of France. While for the simulation of aboveground biomass, model performance is not unambiguously linked to explicit consideration of plant diversity, the simulation of relative abundance for the whole community is satisfactory (relative root mean square error of ~13–25% when simulating functional types and ~28–52% when simulating species). This study extends previous studies by coupling CoSMo, for the first time, to a grassland-specific model and applying it to conditions (long-term observations, extended number of plant species, absence of fertilisation, frequent mowing and abandonment) never investigated before.

Italy is the most important European country in terms of rice production. Nevertheless, rice farming in Italy is one of the crop systems with the highest impact on the environment. The main peculiarity of the rice cultivation practice is field water management: in fact, rice is cultivated keeping the soil submerged and in an anaerobic state. Under these conditions, organic matter degradation causes large methane emissions into the atmosphere. Previous studies show that methane emissions causes about 50% of climate change of rice production. This study reports the biennial results of the BESTsomRICE project in which the environmental performance of an alternative water management, characterized by an aeration period of the field during the cultivation, was evaluated. For this purpose, field trials were carried out for two consecutive years in northern Italy, a life cycle assessment approach was applied with a from-cradle-to-gate perspective, and 1 ton of rice grain at commercial moisture was chosen as the functional unit. The results confirm that methane emissions are responsible for 50% of global warming. Furthermore, alternative water management reduced global warming by 12% and 11% without affecting yield or the other impact categories analyzed.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait‐based multi‐model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi‐model simulations targeting enhanced productivity, and aggregated results into model‐ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait‐parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP‐Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid‐century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context‐specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait‐based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.

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.