Dai geni alle colture: analisi multi-livello con strategie di ‘(Crop) Systems Biology’ per un approccio interdisciplinare alla ricerca in orticoltura

Martina Caramante [Consiglio per la ricerca e la sperimentazione in agricoltura - Centro di Ricerca per l’Orticoltura (CRA-ORT), Pontecagnano (Salerno), Italy]
Nunzio D’Agostino [Consiglio per la ricerca e la sperimentazione in agricoltura - Centro di Ricerca per l’Orticoltura (CRA-ORT), Pontecagnano (Salerno), Italy]
Accursio Venezia [Consiglio per la ricerca e la sperimentazione in agricoltura - Centro di Ricerca per l’Orticoltura (CRA-ORT), Pontecagnano (Salerno), Italy]
Teodoro Cardi [Consiglio per la ricerca e la sperimentazione in agricoltura - Centro di Ricerca per l’Orticoltura (CRA-ORT), Pontecagnano (Salerno), Italy]

Global climate changes, the evolution of eating habits in industrialized and developing countries and the need for a higher economic and environmental sustainability are major challenges to be faced in horticultural research. A large and growing number of scientific and technological innovations can be adopted to influence and improve crop performance in open-field or protected horticulture. Indeed, crop performance depends on the interaction between the genetic potential of the crop and the environment, including management. It is possible to effectively control microclimatic parameters as well as nutrient and water needs using different strategies. Such control achieves maximum efficiency in soilless cultivation systems, where the variability associated to soil is missing. Plants interact also with biotic factors that are present in soil or air environment and may have a positive or negative effects on crop performance. Plant physiology research has been mainly focused on the study of plant-environment interactions, resulting in the characterization of the effect of environmental variables on photosynthetic efficiency, plant growth and organ development, intake and transport of mineral elements and water. Simultaneously, biometrics has used remote sensing technologies, for the acquisition of agronomic and physiological parameters on a large scale, as well as Information Technologies platforms for the planning and management of crop trials. Further, advances in cultivation techniques and agricultural engineering (precision agriculture) have led to an increase in productivity, mainly due to the use of fertilizers, new farming techniques and development of novel plant defense strategies. On the genetic side, the development of ‘-omics’ sciences has allowed new resources for breeding programs to be developed in many vegetable crop species. Sequencing projects have facilitated the construction of high-density linkage maps as well as the development of suitable tools for gene expression analysis. All these resources are being successfully used in the cloning and mapping of genes and QTL as well as in molecular marker-assisted select ion. Proteomics and metabolomics have provided new opportunities in the screening and in the effective use of chemical diversity of plant products, so that the improvement of the nutritional quality of crops has paralleled traditional breeding objectives. Even though present crop productivity gains are the result of the improvement of individual disciplines, it is now necessary to combine and integrate knowledge and methods in order to meet the new challenges. Crop simulation models have been formulated since the ‘60s. More recently, on the basis of findings from genomics and other ‘- omics’ sciences, various cell models have been generated through Systems Biology approaches. However, the exploitation of simulation models has so far been limited due to: i) their inability to consider all the variables that affect crop yield, ii) the limited computational capabilities and iii) scarce/partial knowledge of some biological phenomena. The generation of models where genetic, environmental and agronomic parameters are combined together will provide a better understanding on the relationships between gene functions and physiological processes and will allow the performance of a crop to be predicted as well as the best genotype and agronomic technique for a specific growing environment and/or use to be identified. This holistic vision will give a boost to the improvement of horticultural products and will permit a more efficient exploitation of the available resources.

Keywords: horticultural crops, ‘-omics’ sciences, data network, modeling

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Caramante, M., D’Agostino, N., Venezia, A. and Cardi, T. (2014) 'Dai geni alle colture: analisi multi-livello con strategie di ‘(Crop) Systems Biology’ per un approccio interdisciplinare alla ricerca in orticoltura', Italus Hortus, 21(3), pp. 43-56.