Tailoring parameter distributions to specific germplasm : impact on crop model-based ideotyping

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

Trait-based model development to support breeding programs : A case study for salt tolerance and rice

Eco-physiological models are increasingly used to analyze G 7 E 7 M interactions to support breeding programs via the design of ideotypes for specific contexts. However, available crop models are only partly suitable for this purpose, since they often lack clear relationships between parameters and traits breeders are working on. Taking salt stress tolerance and rice as a case study, we propose a paradigm shift towards the building of ideotyping-specific models explicitly around traits involved in breeding programs. Salt tolerance is a complex trait relying on different physiological processes that can be alternatively selected to improve the overall crop tolerance. We developed a new model explicitly accounting for these traits and we evaluated its performance using data from growth chamber experiments (e.g., R2 ranged from 0.74 to 0.94 for the biomass of different plant organs). Using the model, we were able to show how an increase in the overall tolerance can derive from completely different physiological mechanisms according to soil/water salinity dynamics. The study demonstrated that a trait-based approach can increase the usefulness of mathematical models for supporting breeding programs

Analysis and modeling of processes involved with salt tolerance and rice

Salinity is a worldwide problem for rice (Oryza sativa L.) cultivation, and a number of breeding programs targeting increased salt tolerance are ongoing. A new trait-based mathematical model for salt stress on rice was recently proposed, characterized by a high level of detail in the description of physiological mechanisms dealing with crop response to salinity. In this study, dedicated growth chamber experiments were performed where three rice cultivars with different degrees of tolerance were grown under different salinity levels. The aim was to improve the understanding of physiological mechanisms like Na+ uptake and sequestration in structural tissues, and to validate the model using new datasets where temporal dynamics in plant response to salt stress were analyzed. Model evaluation demonstrated strong agreement between measured and simulated dry weights of plant organs (e.g., R2 = 0.88-0.97 for aboveground biomass), [Na+] in plant tissues (R2 = 0.73-0.88), and green leaf area index (R2 = 0.71-0.99). These results demonstrate the reliability of the model and support its adoption within studies aimed at analyzing or predicting the response of different cultivars to temporal dynamics of Na+ concentration in soil and water

Quantifying the accuracy of digital hemispherical photography for LAI estimates on broad-leaved tree species

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical imageswith dedicated tools, several steps are still manual and thus easily affected by user\u2019s experience and sensibility. The purpose of this study was to quantify the impact of user\u2019s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of themethod, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentationmethod was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained formediumand low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory resultswere achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentationmethod on LAI estimates was significant. Despite a non-negligible user effect, the accuracymetrics for DHP are consistent with those determined for other indirectmethods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies

ISIde : A rice modelling platform for in silico ideotyping

Ecophysiological models can be successfully used to analyze genotype by environment interactions, thus supporting breeders in identifying key traits for specific growing conditions. This is especially true for traits involved with resistance/tolerance to biotic and abiotic stressors, which occurrence can vary greatly both in time and space. However, no modelling tools are available to be used directly by breeders, and this is one of the reasons that prevents an effective integration of modelling activities within breeding programs. ISIde is a software platform specifically designed for district-specific rice ideotyping targeting (i) resistance/tolerance traits and (ii) breeders as final users. Platform usability is guaranteed by a highly intuitive user interface and by exposing to users only settings involved with genetic improvement. Other information needed to run simulations (i.e., data on soil, climate, management) is automatically provided by the platform once the study area, the variety to improve and the climate scenario are selected. Ideotypes indeed can be defined and tested under current and climate change scenario, thus supporting the definition of strategies for breeding in the medium-long term. Comparing the performance of current and improved genotype, the platform provides an evaluation of the yield benefits exclusively due to the genetic improvement introduced. An example of the application of the ISIde platform in terms of functionalities and results that can be achieved is reported by means of a case study concerning the improvement of tolerance to heat stress around flowering in the Oristanese rice district (Italy). The platform is currently available for the six Italian rice districts. However, the software architecture allows its extension to other growing areas \u2013 or to additional genotypes \u2013 via dedicated tools available at the application page

Surfing parameter hyperspaces under climate change scenarios to design future rice ideotypes

Growing food crops to meet global demand and the search for more sustainable cropping systems are increasing the need for new cultivars in key production areas. This study presents the identification of rice traits putatively producing the largest yield benefits in five areas that markedly differ in terms of environmental conditions in the Philippines, India, China, Japan and Italy. The ecophysiological model WARM and sensitivity analysis techniques were used to evaluate phenotypic traits involved with light interception, photosynthetic efficiency, tolerance to abiotic stressors, resistance to fungal pathogens and grain quality. The analysis involved only model parameters that have a close relationship with phenotypic traits breeders are working on, to increase the in vivo feasibility of selected ideotypes. Current climate and future projections were considered, in the light of the resources required by breeding programs and of the role of weather variables in the identification of promising traits. Results suggest that breeding for traits involved with disease resistance, and tolerance to cold- and heat-induced spikelet sterility could provide benefits similar to those obtained from the improvement of traits involved with canopy structure and photosynthetic efficiency. In contrast, potential benefits deriving from improved grain quality traits are restricted by weather variability and markedly affected by G 7 E interactions. For this reason, district-specific ideotypes were identified using a new index accounting for both their productivity and feasibility

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

PocketLAI: una smart-app per la determinazione in vigneto dei valori di LAI

The Leaf Area Index (LAI) measure in vineyard involves some critical issues, due to its non-homogeneous and discontinuous canopy that lead to a reduction of the measurements accuracy with the conventional tools. The study goal was to evaluate the suitability of an innovative smart-app, PocketLAI, in measuring the vineyard LAI (LAIv), and carry out the comparison with the performance of hemispherical photography (HP). The estimated LAIv values were compared with those from destructive and direct measurements. Six surveys were carried out on three sampling areas of different vigor in a vineyard. The results show good agreement between PocketLAI data and the direct measurements, especially for LAIv ranging between 0.13-1.41 (R2 = 0.94, RRMSE = 17.27%), while an accuracy decrease, due to a saturation effect, occurred including an outlier LAIv value (R2 = 0.77, RRMSE = 43.00%). The HP showed good agreement with direct measurement (R2 = 0.94), but a large over-estimation (RRMSE = 99.46%). PocketLAI has proved to be a valuable tool to monitor the spatial-temporal variability of vine vigour, as alternative to the traditional methods

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