Digitized by Kansas Correctional Industries

Call number: LD2668 .T4 1964 H14 / Master of Science

Call number: LD2668 .T4 1978 K55 / Master of Science

Digitized by Kansas Correctional Industries

O controle de doenças em plantas geralmente é realizado com o uso de agroquímicos, que podem desencadear efeitos maléficos ao ser humano ou ao ambiente como um todo. Diversos trabalhos mostram o potencial de plantas medicinais no controle de fitopatógenos, tanto por sua ação fungitóxica direta, inibindo o crescimento micelial e a germinação de esporos, quanto pela capacidade de ativar o metabolismo de defesa das plantas, como o acúmulo de fitoalexinas, indicando a presença de moléculas com características elicitoras. Foram desenvolvidos dois experimentos na Universidade Tecnológica Federal do Paraná – Câmpus de Dois Vizinhos, com objetivos de avaliar o pontencial de preparados de Cavalinha (Equisetum sp.) na síntese de metabólitos de defesa em cotilédones de soja (Glycine max L.) e o efeito sobre o crescimento de Rhizoctonia solani, in vitro. O delineamento experimental utilizado para os experimentos foi inteiramente casualizado em esquema fatorial 3x5 resultante da combinação de três formas de extração (extrato alcoólico, infusão e maceração) e cinco concentrações (zero; 1; 10, 20 e 40%), com 4 repetições. No primeiro experimento foi avaliada a indução de fitoalexinas em cotilédones de soja em resposta aos derivados a base de cavalinha. Sementes de soja foram semeadas em areia autoclavada e mantidas em temperatura ambiente por 10 dias. Em seguida os cotilédones das plântulas foram removidos e na face abaxial destes foram aplicados os tratamentos. Após seguir os procedimentos metodológicos da técnica de extração, obteve-se via espectofotometria a quantificação da fitoalexina gliceolina, sendo então utilizados os cotilédones para quantificação da atividade da enzima fenilalanina amônia-liase (FAL) e o teor de fenóis totais. No segundo experimento foi avaliado o potencial fungistático dos preparados sobre Rhizoctonia solani. Em placas de Petri foram adicionados no meio de cultura BDA os preparados nas diferentes concentrações. Após a solidificação foi transferido para o centro de cada placa, um disco de micélio com Rhizoctonia solani. As placas foram incubadas sob alternância de fotoperíodo de 12 horas, em temperatura de 25oC. Os preparados de extrato alcoólico, infusão e maceração de cavalinha apresentam capacidade de indução das fitoalexinas gliceolinas em cotilédones de soja, bem como, ativam o metabolismo de compostos fenólicos. Entre os preparados, o extrato alcoólico e a maceração, se sobressaem sobre a infusão. Os preparados de extrato alcoólico, infusão e maceração de cavalinha em todas as suas concentrações inibem o crescimento do fungo Rhizoctonia solani, in vitro, sendo que o extrato alcoólico apresenta maior capacidade de supressão do crescimento micelial em comparação as demais formas de obtenção dos preparados. / Usually, the plant diseases control is accomplished with the use of chemicals products that can trigger harmful effects for the people or the environment. Several studies showed the potential of medicinal plants in the control of pathogens, either by their direct fungitoxic action, inhibiting the mycelial growth and spore germination, and by ability to activate the metabolism of plant defense, such as phytoalexin accumulation, indicating the presence of molecules with characteristics elicitoras. Two experiments were carried out in the Universidade Tecnológica Federal do Paraná - Câmpus Dois Vizinhos. The aims of these works were to evaluate the potential of "cavalinha" (Equisetum sp.) prepared in the defense metabolites synthesis of soybean cotyledons (Glycine max L.) and the effect on Rhizoctonia solani growth in vitro. The experimental design used for the experiments was completely randomized in factorial 3 x 5 (extract form x concentration), with four replications. The extract forms were alcoholic extract, infusion and maceration and the concentrations tested were zero, 1, 10, 20 and 40%. In the first experiment, the phytoalexins induction in the soybean cotyledons the according with "cavalinha" prepared was evaluated. Soybean seeds were sown in autoclaved sand and kept at room temperature for 10 days. After, the plantlets cotyledons were removed and on the surface of these the treatments were applied. Then, it followed the methodological procedures of extraction technique and it was obtained via spectrophotometry the phytoalexin glyceollin quantification. The cotyledons were then used to quantify of the enzyme phenylalanine ammonia lyase (PAL) activity and total phenols contend. In the second experiment was evaluated the potential fungistatic preparations on Rhizoctonia solani. The prepared with different concentrations was added in Petri dishes with PDA culture medium. After solidification was transferred to the center of each plate, a disc with Rhizoctonia solani mycelia. The plates were incubated alternating the photoperiod in 12 hour, at 25 °C temperature. The alcoholic extract, infusion and maceration "cavalinha" prepared present glyceolin induction of phytoalexins in soybean cotyledons, as well as it to activate the metabolism phenolic compounds. Among the prepared, the alcoholic extract and maceration forms were superior in relation the infusion form. The alcoholic extract, infusion and maceration "cavalinha" prepared using all over the concentrations inhibit Rhizoctonia solani growth in vitro. The alcoholic extract had a higher capacity to suppress mycelial growth compared to other forms of production preparations.

Fusarium virguliforme is a soil-borne pathogen that is the causal agent of sudden death syndrome (SDS). This disease is one of the top contributors to major yield losses in soybean across the United States. Characteristic symptoms of the disease include interveinal chlorosis and/or necrosis of trifoliate leaves and defoliation. In some cases, the foliar symptoms may not be present, but yield loss still occurs. This disease is evaluated using an incidence rating, the percent of plants in the plot that are expressing symptoms, and a severity rating, using a one to nine scale based on varying levels of chlorosis, necrosis, and defoliation. Using remote sensing provides an alternate approach to identify and evaluate plant diseases. It provides a non-destructive method to assess the severity of foliar symptoms and their distribution across production fields. SDS was chosen as the disease to use for this system due to the unique disease symptomology and yield loss. In 2019 and 2020, SDS trials were established in a production field location that has a history of SDS in Valmeyer, IL. This seed treatment study had different chemicals with varying levels of efficacy against SDS. Disease ratings were collected at the first sign of symptoms, and aerial imagery was collected on the same day. There were multiple dates across both years when this data was collected. ArcGIS was used to analyze the multispectral imagery and do a plot by plot analysis for each of the plots. A regression analysis was performed to test the relationship between the foliar disease ratings and the plot data collected from the multispectral imagery. Multiple vegetation indices were tested, and the results showed that overall, in 2019, GNDVI had the strongest relationship with foliar ratings. In 2020, NDRE had the strongest overall relationship with foliar ratings. The relationship between NDVI and the ratings was the most consistent at the last rating of the season.

Call number: LD2668 .T4 1984 C87 / Master of Science

Meloidogyne javanica is the most widely spread nematode pest on soybean in South Africa. Only a few registered cultivars have some resistance to this nematode and there is an urgent need for an efficient breeding programme for resistant cultivars of all maturity groups. However, breeding is hampered by laborious screening procedures for selection of resistant lines. The objective of this study was to develop an economically viable molecular marker system for application in selection procedures. Three techniques of marker identification were investigated, i.e. RAPD, RFLP and AFLP analysis. The RAPD technique proved to be applicable in fingerprinting soybean varieties, but was too sensitive for interplant variation to be used as an efficient system for identification of molecular markers linked to nematode resistance. Both RFLP and AFLP screening identified markers linked to gall index variation in a segregating population of 60 F₂ progeny from across between a resistant cultivar, Gazelle, and a highly susceptible variety, Prima. A codominant RFLP marker( B212) was linked significantly to resistance and explained 62% of the variation in gall index. Seven AFLP markers were linked significantly to the resistance trait, of which four were linked in repulsion phase and three in coupling phase. All seven AFLP markers mapped to LG-F on the public soybean molecular map. The QTL for resistance mapped between markers E-ACC/M-CTC2 linked in coupling phase, 8212 and E-AAC/M-CAT1, linked in repulsion phase. These two AFLP markers bracketing the major resistance QTL were successfully converted to SCARs. Marker E-ACC/M-CTC2 was converted to a codominant SCAR marker SOJA6, which acounted for 41% of variation in gall index in the mapping population. Marker E-AAC/M-CAT1 was converted to a dominant SCAR marker (SOJA7) and explained 42% of gall index variation in the mapping population. These two markers mapped approximately 3.8 cM and 2.4 cM respectively from the resistance QTL. This study represents the first report of the development of PCR-based sequence specific markers linked to resistance to M. javanica in soybean. / Thesis (Ph.D.)-University of Natal, Pietermaritzburg, 2000.

Damage to the crops occurs due to insects eating the leaves, environmental changes, irregular fertilization, and improper use of pesticides decreasing yield. It is important to identify the percentage of damages in crop that will help in selecting the quantity of pesticide used to treat the damage, and in predicting the change in the yield of the crop. So far, the research efforts handled this problem by collecting datasets from near-field and far-field images of damaged crops and then training Deep Learning Model to differentiate healthy leaf and Unhealthy leaf. To the best of our knowledge, there is no deep learning model has been trained to predict and classify the level of damage in the soybean leaves. Therefore, the main aim of this thesis is to propose a deep learning model that predicts the classes of damage from scale of one to five and also identifies healthy leaf from unhealthy leaf. The proposed model analyses dataset containing non-healthy leaves and healthy leaves and estimates the performance of classification methods. This analysis allows the model to predict different damage caused by insects and natural calamities to leaves, therefore aiding the agricultural professional to corrective actions based on specific class of damage.

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