Ideotype definition to adapt legumes to climate change : A case study for field pea in Northern Italy

One of the key strategies to alleviate negative impacts of climate change on crop production is the development of new cultivars better adapted to the conditions expected in the future. Despite the role of legumes as protein sources, medium- and long-term strategies currently debated mainly focus on agricultural policies and on improved management practices, whereas ideotyping studies using climate projections are scarcely reported. The objective of this study was to define pea ideotypes improved for yield and irrigation water productivity targeting current climate and four future projections centred on 2040, resulting from the combination of two General Circulation Models (HadGEM2 and GISS-ES) and two Representative Concentration Pathways (RCP4.5 and RCP8.5). The STICS model was used, with the default pea parameterization refined using data from two years of dedicated field experiments. Ideotypes were defined by combining STICS and the E-FAST sensitivity analysis method focusing on model parameters representing traits on which breeding programs are ongoing. Results showed that climate change is expected to decrease the productivity of current pea cultivars (up to -12.6%), and that increasing irrigation (to cope with the expected less favourable rainfall distribution) would not avoid yield losses. The proposed ideotypes, characterized by a shorter vegetative phase and by increased tolerance to high temperature, performed better than current varieties, providing higher yields (+4.5%) and reduced water consumption (-20%). For the first time, we demonstrated the suitability of STICS for ideotyping purposes and used a simulation model to define pea breeding strategies targeting future climate conditions

Analysis of the Similarity between in Silico Ideotypes and Phenotypic Profiles to Support Cultivar Recommendation: A Case Study on Phaseolus vulgaris L.

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