2020-08-09
Dias, Santuza Silverio Hermes, Cubas, Anelise Leal Vieira, Soares, Cláudio Roberto Fonsêca Sousa, Giachini, Admir José
Dias, Santuza Silverio Hermes ; Cubas, Anelise Leal Vieira ; Soares, Cláudio Roberto Fonsêca Sousa ; Giachini, Admir José ; ABSTRACT. Sewage sludge generated from wastewater treatment plants is an environmental and economic liability that burdens not only waste management companies, but the society at large, because it causes environmental damage. Therefore, this study aimed to seek an alternative to the destination of this sludge, such as using it as a substrate to produce chrysanthemum (Dendranthema grandiflora) and petúnia (petunia x hybrid) seedlings. The sludge was collected at CASAN, Florianópolis (SC-Brazil), and subjected to solar drying for later application in proportions of 0, 5, 10, and 25%, together with soil collected from the Ressacada Farm Experimental Station, and used as inoculum for arbuscular mycorrhizal fungi. The parameters used in the analyses were plant height (cm), root length (cm), production of aerial part dry matter, production of root dry matter (g), nitrogen content in the aerial part (g kg-1), nitrogen accumulation in the aerial part (mg plant-1), phosphorus content in the aerial part (g kg-1), phosphorus accumulation in the aerial part (mg plant -1), soil arbuscular mycorrhizal fungi spore count (in 50 cm of soil), mycorrhizal colonization (%), and pH of the substrate (pre- and post-treatment). The study showed that solar drying is an efficient method of eliminating pathogens, and the sludge was classified as proper for agricultural application. The results showed that the best dosages of sludge for the growth of chrysanthemum and petunia varied from 5 to 13%, and 13 to 18%, respectively. These concentrations provided the best yield for all variables tested, except mycorrhizal colonization and spore counting that decreased as the sludge concentration increased.
2020-08-09
Souza, Antonio Maricélio Borges de, Santos, Samuel Gonçalves Ferreira dos, Rodrigues, Cleidiane Alves, Silva, Ítallo Jesus, Coelho, Ana Paula de Freitas, Araujo, Eduardo Fontes, Gonzatto, Mateus Pereira
Souza, Antonio Maricélio Borges De ; Santos, Samuel Gonçalves Ferreira Dos ; Rodrigues, Cleidiane Alves ; Silva, Ítallo Jesus ; Coelho, Ana Paula De Freitas ; Araujo, Eduardo Fontes ; Gonzatto, Mateus Pereira ; ABSTRACT. Citrus seedlings are typically propagated through grafting, using rootstocks grown from seeds. However, the coating of citrus rootstock seeds can hinder germination. Therefore, applying sodium hypochlorite (NaClO) provides an alternative to manually removing the seed coat, potentially enhancing seed germination and seedling emergence. This study investigates the use of sodium hypochlorite (NaClO) as a scarification agent on freshly harvested Rangpur lime seeds to improve germination and seedling emergence. The experiment employed a completely randomized design with a 3 × 3 factorial arrangement and an additional treatment across four replications. The variables assessed included three NaClO concentrations (0.0% - distilled water; 2.5%, and 5.0% active chlorine) and three soaking durations (3, 6, and 9 hours), with a control of unsoaked seeds retaining their coats. The evaluated parameters were germination rate, first germination count, seedling emergence, speed index, and mean seedling emergence time. Results showed that NaClO effectively degrades the seed coat, with a 2.5% concentration for 6 hours optimizing germination. While NaClO treatment did not alter emergence rates, it reduced the average time to seedling emergence. Conversely, a 5.0% concentration for 9 hours detrimentally affected germination and vigor. Sodium hypochlorite soaking presents a viable alternative for seed coat removal, accelerating germination and emergence processes in Rangpur lime tree seeds, potentially enhancing seedling production efficiency.
2020-08-09
Simão, Maria Clara Rodrigues, Silva, Francisco Assis da, Santos, Carlos Henrique dos, Almeida, Leandro Luiz de, Artero, Almir Olivette
Simão, Maria Clara Rodrigues ; Silva, Francisco Assis Da ; Santos, Carlos Henrique Dos ; Almeida, Leandro Luiz De ; Artero, Almir Olivette ; ABSTRACT. Nitrogen (N) is among the main nutrients widely used in agriculture worldwide; however, its administration and management can be challenging. Excess nitrogen is harmful to plant health and the environment, requiring effective monitoring of leaf nitrogen concentration (LNC) in field crops. Remote sensing stands out as a valuable tool in this context. This study contributed to the monitoring of LNC by implementing a machine learning algorithm based on the processing of reflectance data from Sentinel-2 (S2) satellites obtained via spectral resampling. For this purpose, five independent datasets containing leaf reflectance measurements collected by spectroradiometers were resampled to the spectral resolution of the sensors onboard the S2 satellites. LNC prediction models were developed from the resampled datasets, using Support Vector Regression (SVR) and Random Forest Regression (RFR), with 75% of the data from each set used to train a model and the remaining 25% for validation. The models demonstrated good predictive power, with the Root Mean Squared Error (RMSE) ranging from 0.39 to 0.94%. Furthermore, this study investigated the transferability of the models' predictive power by using 100% of the data from each set for training and validating predictions on the other sets. To improve transferability, the Transfer Component Analysis (TCA) technique was applied to adapt domains between the sets. This analysis revealed favorable results, especially with the TCA-SVR and TCA-RFR combinations, highlighting a greater capacity to extract transferable spectral features between different leaf reflectance datasets. It was concluded that spectral resampling does not hinder the development of effective LNC prediction models. Aligning this resampling with the resolution of Sentinel-2 sensors, resulted in more efficient monitoring of LNC, eliminating the need to individually reference each sampling point. This approach simplified the monitoring process, reduced both time and costs, and was directly beneficial to producers.
2020-08-09
Santos, Mateus Araújo dos, Lima, Lucas Kennedy Silva, Santos, Idalia Souza dos, Rosa, Raul Castro Carriello, Jesus, Onildo Nunes de
Santos, Mateus Araújo Dos ; Lima, Lucas Kennedy Silva ; Santos, Idalia Souza Dos ; Rosa, Raul Castro Carriello ; Jesus, Onildo Nunes De ; ABSTRACT. In this study, an alternative method was developed to evaluate fruit and pulp color, using the CIE-L*a*b* kernel space and near-infrared reflectance spectroscopy in predicting the chemical characteristics of Passiflora edulis fruits. Five passion fruit hybrids were evaluated, four with purple-skinned (H09-163, H09-164, H09-166, and H09-125) and one with yellow--skinned (H09-165), in addition to BGP418 (control, yellow-skinned). BGP418 stood out for most physical characters, mainly in the weight of the fruits (224.67 g) and the pulp with seeds (112.77 g). However, its pulp yield was 11% lower compared to other genotypes. Cluster analysis based on fruit skin and pulp color using CIE-L*a*b* space, revealed greater consistency of groups compared to using the conventional method with a color palette. A higher soluble solids content was recorded in fruits with light purple-skinned and light-yellow pulp. Based on the skin and pulp color, the other chemical characteristics did not differ between the groups formed. With UV-VIS-NIR spectra, it was possible to distinguish the genotypes in the 350 and 2,500 nm spectra and the separation between the purple and yellow-skinned l genotypes. However, there was no consistent grouping in relation to the skin and pulp color or relationship with the chemical characteristics of the fruits. The breeding program can utilize the information generated to continue the development of cultivars for fresh consumption.