Many crop species are cultivated to produce seeds and/or fruits and therefore need reproductive success to occur. Previous studies proved that high temperature on mature pollen at anther dehiscence reduce viability and germinability therefore decreasing crop productivity. We hypothesized that high temperature might affect pollen functionality even if the heat treatment is exerted only during the microsporogenesis. Experimental data on Solanum lycopersicum ‘Micro-Tom’ confirmed our hypothesis. Microsporogenesis successfully occurred at both high (30 °C) and optimal (22 °C) temperature. After the anthesis, viability and germinability of the pollen developed at optimal temperature gradually decreased and the reduction was slightly higher when pollen was incubated at 30 °C. Conversely, temperature effect was eagerly enhanced in pollen developed at high temperature. In this case, a drastic reduction of viability and a drop-off to zero of germinability occurred not only when pollen was incubated at 30 °C but also at 22 °C. Further ontogenetic analyses disclosed that high temperature significantly speeded-up the microsporogenesis and the early microgametogenesis (from vacuolated stage to bi-cellular pollen); therefore, gametophytes result already senescent at flower anthesis. Our work contributes to unravel the effects of heat stress on pollen revealing that high temperature conditions during microsporogenesis prime a fatal shortening of the male gametophyte lifespan.

Olea europaea L. is a crop typical of the Mediterranean area that has an important role in economy, society, and culture of this region. Climate change is expected to have significant impact on this crop, which is typically adapted to certain pedo-climatic characteristics of restricted geographic areas. In this scenario, the aim of this study was to evaluate the time-course response of pollen viability to different combinations of temperature and humidity. The study was performed comparing flowering time and pollen functionality of O. europaea from twelve cultivars growing at the same site belonging to the Campania olive collection in Italy. Pollen was incubated at 12 °C, 22 °C, and 36 °C in combination with 50% RH or 100% RH treatments for 5 days. The results highlighted that a drastic loss of pollen viability occurs when pollen is subjected to a combination of high humidity and high temperature, whereas 50% RH had less impact on pollen thermotolerance, because most cultivars preserved a high pollen viability over time. In the ongoing climate change scenario, it is critical to assess the effect of increasing temperatures on sensitive reproductive traits such as pollen viability to predict possible reduction in crop yield. Moreover, the results highlighted that the effect of temperature increase on pollen thermotolerance should be evaluated in combination with other environmental factors such as humidity conditions. The screening of olive cultivars based on pollen thermotolerance is critical in the ongoing climate change scenario, especially considering that the economic value of this species relies on successful fertilization and embryo development, and also that production cycle of Olea europaea can be longer than a hundred years.

Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.

The Annurca type is an historical Italian apple fruit probably native to the Campania region. These fruits are harvested before a full maturity stage and go through an open-field reddening process. Products based on 1-methylcyclopropene (1-MCP) are widely used in postharvest to improve apple quality and avoid disorders in controlled atmosphere. In this work, we tested the impact of 1-MCP in the traditional reddening system of Annurca type by analyzing three postharvest strategies, namely, fruits exposed for ten days to the traditional reddening and then cold-stored (RDG treatment); fruits treated in postharvest with 1-MCP and then cold-stored (MCP treatment); fruits treated in postharvest with 1-MCP, exposed for ten days to the open-field reddening and then cold-stored (MCP + RDG treatment). Using a full factorial design, we measured main physical and compositional traits and described the properties of the products by consumer testing at two storage times. The MCP + RDG treatment provided the most valuable results, by positively affecting fruit flesh firmness and several sensorial attributes (e.g., hardness, crunchiness, juiciness, sweetness), including a reduction of the mealiness. At the end of the cold-storage, MCP + RDG apples scored a higher consumer’s overall liking. The work demonstrated that the 1-MCP technology can be integrated into a traditional open-field postharvest process to improve fruit quality and, potentially, to extend the shelf life of Annurca apples.

Background: Long-term space missions will necessarily require producing viable seeds to be used for plant cultivation over time under altered gravity conditions. Pollen is known to play a key role in determining seed and fruit production over seed-to-seed cycles, but very few studies have evaluated pollen functionality under altered gravity.Methods: We performed ground-based experiments to test how simulated microgravity can affect the directional growth of pollen tubes as a potential bottleneck in seed and fruit sets. The effect of clinorotation was assessed in the pollen of Solanum lycopersicum L. cv. ‘Micro-Tom’ and Brassica rapa L. var. silvestris, both eligible for cultivation in space. Pollen tube length and tortuosity were compared under 1g and simulated microgravity with a uniaxial clinostat.Results: The main results highlighted that simulated microgravity significantly increased pollen tube length and tortuosity compared to 1g conditions. Further, clinorotation prompted a differential effect on pollen germination between S. lycopersicum and B. rapa. A more in-depth analysis evaluating the effect of gravity on the directional growth of pollen tubes excluded gravitropic responses as responsible for the tube tip position reached after germination.Discussion: This research provides new insights into how altered gravity can interfere with plant reproduction and, in particular, microgametophyte functionality. Our findings represent a basis for further studies aimed at understanding the effect of real microgravity on plant reproduction and developing countermeasures to ensure seed-to-seed cultivation in long-term space missions and achieve self-sufficiency in food supplies from Earth.

Numerous new technologies have been implemented in image analysis methods that help researchers withdraw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Here is the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. Results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures As expected, machine learning methods applied to digital image analysis can overtake the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their great nutritional qualities and biological plasticity.