Browsing by Author "de Freitas, Sergio Tonetto"

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    Ammonium Excess Leads to Ca Restrictions, Morphological Changes, and Nutritional Imbalances in Tomato Plants, Which Can Be Monitored by the N/Ca Ratio
    (MDPI, 2021) Bonomelli, Claudia; de Freitas, Sergio Tonetto; Aguilera, Camila; Palma, Carola; Garay, Rebeca; Dides, Maximiliano; Brossard, Natalia; O'Brien, Jose Antonio
    Both nitrogen and calcium fertilization management are vital for crops, where an imbalance of these elements can cause both physiological and yield problems. It has been proposed that nitrogen absorption, particularly ammonium, is in part dependent on calcium supply. Moreover, the balance between these two nutrients could be a key indicator of plant growth in some species. Tomato, one of the most cultivated crops worldwide, can also be widely affected by nutritional imbalance. Using large amounts of N fertilizers could lead to an imbalance with other nutrients and, thus, detrimental effects in terms of plant development and yield. Here we show that ammonium excess has a negative impact on plant development and results in calcium deficiency. Moreover, a deficit in calcium nutrition not only affects calcium concentration but also leads to a restriction in N uptake and reduced N concentration in the plant. These effects were evident at the seedling stage and also during flowering/fruit set. Using PCA analysis, we integrated both phenotypic and nutritional imbalances in seedlings and grown plants. Interestingly, the Ca/N ratio appears to be a key indicator to monitor appropriate N and calcium nutrition and more importantly the balance between both. Maintaining this balance could be an essential element for tomato crop production.
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    NIR spectral models for early detection of bitter pit in asymptomatic 'Fuji' apples
    (2021) Rene Mogollon, Miguel; Contreras, Carolina; de Freitas, Sergio Tonetto; Zoffoli, Juan Pablo
    Bitter pit (BP) is a physiological disorder that develops in apples, mainly during storage. This study sought to develop NIR spectral models for prediction of future BP incidence and severity in 'Fuji' apples using spectral data collected at harvest and during storage. Partial Least Square classification models obtained from spectra reflectance between 950 and 1200 nm were compared, starting at harvest, at 10 days postharvest and every 20 days thereafter over 110 days at 0 degrees C in relation to BP severity (number of pits per fruit) after 150 days at 0 degrees C. The models used data from a total of 3000 fruit, collected over two seasons (2018 and 2019) from two orchards. All models were evaluated for Accuracy, Sensitivity, Specificity, Positive Predicted Value (PPV) and Negative Predicted Value (NPV). In the validation dataset, Accuracy, Specificity and NPV values varied between 60 and 80 % and were independent of the time of evaluation during storage. Sensitivity and PPV values did not exceed 60 % in the same dataset. Here, BP incidences in fruit with severities of <8 pits per fruit, achieved accuracies and NPVs between 60 and 70 % in the calibration and validation datasets using spectral data collected at harvest. For comparison, the detection of high BP severities (8-9 pits per fruit), these same metrics achieved between 80 and 90 % using spectral data collected during the first 10 days of storage.