Induction of gibberellin 20-oxidases and repression of gibberellin 2β-oxidases in unfertilized ovaries of entire tomato mutant, leads to accumulation of active gibberellins and parthenocarpic fruit formation

In tomato (Solanum lycopersicum L.), auxin and gibberellins (GAs) cross-talk plays an important role during fruit-set. The entire tomato mutant has been previously reported to carry a deletion in the coding region of the SlIAA9 gene, a member of the auxin signal repressor family Aux/IAA. In this paper, we examined the role of ENTIRE gene in controlling GAs metabolism and directing spontaneous fruit initiation and early ovary growth. It was shown that, similarly to pollinated fruits, facultative parthenocarpy in entire depends on active GA metabolism, since fruit growth is suppressed when GA biosynthesis is blocked. Analysis of endogenous GAs during the first 10 days after flower emasculation revealed that entire fruits accumulated higher amounts of active GAs (GA1 and GA3) in comparison to wild type pollinated fruits, suggesting that a different GA homeostasis regulation occurs. Transcript analysis of the main GA biosynthesis genes showed that differently from unpollinated and non parthenocarpic wild type ovaries, in entire active GA flux modulation is regulated by the activation of SlGA20ox1 and SlGA20ox2 and also by a marked reduction of GA catabolism (reduced transcription of GA 2β-oxidase genes) during the early fruit expansion phase.Fil: Mignolli, Francesco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; ArgentinaFil: Vidoz, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Botánica del Nordeste. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias. Instituto de Botánica del Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias; ArgentinaFil: Mariotti, Lorenzo. Università degli Studi di Pisa; ItaliaFil: Lombardi, Lara. Università degli Studi di Pisa; ItaliaFil: Picciarelli, Piero. Università degli Studi di Pisa; Itali

Weed Emergence Models

Weed emergence models are practical tools that aim to describe the dynamics of emergence in the field. Such models can be conceptualized from two main perspectives: a reductionist/mechanistic approach and an empirical modelling viewpoint. While the former provides a close description of the basic ecophysiological processes underlying weed emergence (i.e. seed dormancy, germination and pre-emergence growth), they usually require a large amount of difficult to estimate species-specific parameters, as well as sometimes unavailable or missing experimental data for model development/calibration/validation. Conversely, the latter aims to describe the emergence process as a whole by seeking a general mathematical description of field emergence data as a function of field environmental variables, mainly temperature and precipitation. As reviewed in the literature, most emergence models have been developed using nonlinear regression (NLR) techniques. NLR sigmoidal type models which are based on cumulative thermal or hydrothermal time have become the most popular approach as they are easy to develop and use. However, some statistical and bioecological limitations arise, for example, the lack of independence between samplings, censored data, need for threshold thermal/hydric parameter estimation and determination of ‘moment zero’ for thermal/hydrothermal-time accumulation, among other factors, which can lead to inaccurate descriptions of the emergence process. New approaches based on soft computing techniques (SCT) have recently been proposed as alternative models to tackle some of the previously mentioned limitations. In this chapter, we focus on empirical weed emergence models with special emphasis in NLR models, highlighting some of the main advantages, as well as the statistical and biological limitations that could affect their predictive accuracy. We briefly discuss new SCT-based approaches, such as artificial neural networks which have recently been used for weed emergence modelling.Fil: Royo Esnal, Aritz. Universidad de Lleida; EspañaFil: Torra, Joel. Universidad de Lleida; EspañaFil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentin