An algorithm was developed for assessing disease risk and improving fungicide timing for control of Sclerotinia blight of peanut, caused by Sclerotinia minor. A 5-day index (FDI) of disease risk was calculated daily by multiplying indices of moisture, soil temperature, vine growth and canopy density and summing the values for the previous 5 days. Spray thresholds of FDI 16, 24, 32, 40, 48 were compared to a 60, 90, 120 DAP (days after planting) schedule and the standard demand program. Field trials in 1994 indicated that fluazinam (0.58 kg a.i./ha) applied at an FDI of 32 performed similarly to the demand program and was more efficient than the DAP schedule. However, the original FDI 32 algorithm triggered sprays 13 days subsequent to disease onset in 1995, indicating the need for improved vine growth and temperature parameters as well as DAP-dependent FDI thresholds. Results from 1996 and 1997 demonstrated that algorithms with new vine growth and temperature parameters coupled with DAP-dependent thresholds performed as well or better than the original FDI 32 algorithm, demand program, or DAP schedule. Protection intervals of 7 and 14 days improved the performance of iprodione (1.12 kg a.i./ha) while fluazinam provided protection for up to 21 days when applied according to the original FDI 32 algorithm. Planting date was evaluated for its effect on disease and fungicide use patterns. Late planting (20-28 May) delayed disease onset and reduced early season disease incidence three of the four years tested. When averaged across planting dates, the original FDI 32 algorithm performed as well or better than the demand program in 1994 and 1995, as did algorithms utilizing new vine growth and temperature parameters with DAP-dependent thresholds in 1996 and 1997. Chemicals for altering plant architecture were compared to defoliation by corn earworm and leaf spot for suppression of Sclerotinia blight. Chlorimuron (8.8 g a.i./ha) and withholding fungicide for leaf spot control demonstrated the most significant disease suppression and yield improvement. Results show the importance of fungicide timing and plant growth and canopy architecture modification for control of Sclerotinia blight of peanut. / Ph. D.

Strawberry production in California is limited by plant diseases such as Macrophomina root rot (caused by Macrophomina phaseolina) and Botrytis fruit rot (BFR) (caused by Botrytis cinerea). Current disease management strategies are compromised due to fumigant regulations or ineffective disease management practices. This thesis investigated methods to potentially improve the management of these two diseases. Host plant resistance evaluations for Macrophomina root rot were conducted for the 2020-2021 and 2021-2022 growing seasons. Fifty-one strawberry genotypes were screened in two field experiments where plants were inoculated artificially with Macrophomina phaseolina in both seasons. A wide range of plant resistance to Macrophomina root rot was observed. The three most resistant genotypes based on final plant mortality were ‘17C721P606’, ‘Yunuen’, and ‘Xareni’ in 2020-2021; ‘UCD Mojo’, ‘Mariposa’, and ‘Dayana’ in 2021-2022. A summary of similar experiments done in the previous four years showed ‘Osceola’ as highly resistant. Disease severity varied among years for specific genotypes as well as the average final mortality for all genotypes in the experiments. Strong positive associations were found for soil temperature during the first month after planting (R2= 0.79, P2= 0.79, P A survey of BFR levels in commercial strawberry fields with and without fungicide applications was conducted in Santa Maria, CA in 2021 and 2022. Weather stations were installed at each field to collect leaf wetness duration and temperature data and calculate the BFR risk factor based on the Strawberry Advisory System (StAS) developed at the University of Florida. There were no statistically significant differences between fungicide and no-fungicide treatments for both in-field and postharvest BFR incidence in 2021 and in-field BFR incidence in 2022, while no-fungicide treatment showed higher postharvest BFR incidence in 2022. BFR levels were low in both years. In 2021, average in-field BFR incidence for fungicide and no-fungicide treatments were 2.6 ± 0.3% and 2.5 ± 0.4%, respectively. Average postharvest BFR incidence for fungicide and no-fungicide treatments were 1.8 ± 0.2% and 2.0 ± 0.3%, respectively. In 2022, average in-field BFR incidence for fungicide and no-fungicide treatments were 3.0 ± 0.4% and 3.7 ± 0.4%, respectively. Average postharvest BFR incidence for fungicide and no-fungicide treatments were 0.6 ± 0.1% and 1.5 ± 0.2%, respectively. Risk factor from StAS was significantly associated with BFR incidence in 2021, but not in 2022. Screening new strawberry genotypes against Macrophomina root rot should be ongoing as part of a standard process for determining the susceptibility of currently grown and potentially new cultivars. Additional research under more diverse weather conditions is necessary to verify the impacts of reducing fungicide use in BFR management and to validate the use of StAS in making fungicide use decisions in California fields.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This study aimed to evaluate the severity of occurrence of septoria leaf spot in sunflower, its association with meteorological variables and its effect on yield of field irrigated sunflower. The experiments were conducted in Santa Maria - RS during the 2008 spring season and during the 2009 fall growing season, with sowings in September and in February, respectively. Observations of severity of septoria leaf spot were made throughout the crop cycle, and with a disease forecasting system different times for fungicide applications have been identified. Results show that a sequence of rainy days, with favorable temperatures to the pathogen, tend to contribute to the development of septoria leaf spot in sunflower. In contrast, when water needed by plants is restored by irrigation, even temperature is favorable to the pathogen, disease development rate is lower than during and within 10 days of rainy periods. There were observed differences in septória leaf spot severity between treatments with and without fungicide application, but were not related to the yield of sunflower achenes. Fungicide application according to the different treatments did not significantly affect achene yield either. Therefore, significant differences in the variables that represent the progression of the disease do not necessarily imply in significant differences in yield. / Neste trabalho objetivou-se verificar a severidade de ocorrência de mancha de septória em girassol, sua associação aos elementos meteorológicos e seu efeito na produtividade do girassol cultivado a campo sob irrigação. Os experimentos foram conduzidos em Santa Maria RS durante a safra de 2008 e safrinha de 2009, com semeaduras em setembro e fevereiro, respectivamente. Foram realizadas observações de severidade de mancha de septória durante todo o ciclo da cultura e, com o auxílio de um sistema de previsão de doenças, foram definidos diferentes momentos para as aplicações de fungicida. Os resultados demonstram que sequencias de dias chuvosos, com temperatura favorável ao patógeno, tendem a contribuir para o desenvolvimento da mancha de septória no girassol. Em compensação, quando a água necessária às plantas é reposta por meio de irrigações, mesmo que a temperatura seja favorável ao patógeno, a taxa de desenvolvimento da doença é menor do que durante e até 10 dias após períodos chuvosos. Foram verificadas diferenças de severidade observada de mancha de septória entre tratamentos com e sem aplicação de fungicida, mas essas não apresentaram relação significativa com a produtividade de aquênios do girassol. A aplicação de fungicida, conforme os diferentes tratamentos, também não apresentou associação significativa com a produção de aquênios. Portanto, diferenças significativas nas variáveis que representam a progressão da doença não implicam necessariamente em diferenças significativas de produtividade.

Orientador: Luiz Henrique Antunes Rodrigues / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-11T10:02:19Z (GMT). No. of bitstreams: 1 Meira_CarlosAlbertoAlves_D.pdf: 2588338 bytes, checksum: 869cc28d2c71dbc901870285cc32d8f9 (MD5) Previous issue date: 2008 / Resumo: Sistemas de alerta de doenças de plantas permitem racionalizar o uso de agrotóxicos, mas são pouco utilizados na prática. Complexidade dos modelos, dificuldade de obtenção dos dados necessários e custos para o agricultor estão entre as razões que inibem o seu uso. Entretanto, o desenvolvimento tecnológico recente - estações meteoro lógicas automáticas, bancos de dados, monitoramento agrometeorológico na Web e técnicas avançadas de análise de dados - permite se pensar em um sistema de acesso simples e gratuito. Uma instância do processo de descoberta de conhecimento em bases de dados foi realizada com o objetivo de avaliar o uso de classificação e de indução de árvores de decisão na análise e no alerta da ferrugem do cafeeiro causada por Hemileia vastatrix. Taxas de infecção calculadas a partir de avaliações mensais de incidência da ferrugem foram agrupadas em três classes: TXl - redução ou estagnação; TX2 - crescimento moderado (até 5 p.p.); e TX3 - crescimento acelerado (acima de 5 p.p.). Dados meteorológicos, carga pendente de frutos do cafeeiro (Coffea arabica) e espaçamento entre plantas foram as variáveis independentes. O conjunto de treinamento totalizou 364 exemplos, preparados a partir de dados coletados em lavouras de café em produção, de outubro de 1998 a outubro de 2006. Uma árvore de decisão foi desenvolvida para analisar a epidemia da ferrugem do cafeeiro. Ela demonstrou seu potencial como modelo simbólico e interpretável, permitindo a identificação das fronteiras de decisão e da lógica contidas nos dados, allf'iliando na compreensão de quais variáveis e como as interações dessas variáveis condicionaram o progresso da doença no campo. As variáveis explicativas mais importantes foram a temperatura média nos períodos de molhamento foliar, a carga pendente de frutos, a média das temperaturas máximas diárias no período de inG:!Jbação e a umidade relativa do ar. Os modelos de alerta foram deserivolvtdos considerando taxas de infecção binárias, segundo os limites de 5 p.p e 10 p.p. (classe- '1' para taxas maiores ou iguais ao limite; classe 'O', caso contrário). Os modelos são específicos para lavouras com alta carga pendente ou para lavouras com baixa carga. Os primeiros tiveram melhor desempenho na avaliação. A estimativa de acurácia, por validação cruzada, foi de até 83%, considerando o alerta a partir de 5 p.p. Houve ainda equilíbrio entre a acurácia e medidas importantes como sensitividade, especificidade e confiabilidade positiva ou negativa. Considerando o alerta a partir de 10 p.p., a acurácia foi de 79%. Para lavouras com baixa carga pendente, os modelos considerando o alerta a partir de 5 p.p. tiveram acurácia de até 72%. Os modelos para a taxa de infecção mais elevada (a partir de 10 p.p.) tiveram desempenho fraco. Os modelos mais bem avaliados mostraram ter potencial para servir como apoio na tomada de decisão referente à adoção de medidas de controle da ferrugem do cafeeiro. O processo de descoberta de conhecimento em bases de dados foi caracterizado, com a intenção de que possa vir a ser útil em aplicações semelhantes para outras culturas agrícolas ou para a própria cultura do café, no caso de outras doenças ou pragas / Abstract: Plant disease warning systems can contribute for diminishing the use of chemicals in agriculture, but they have received limited acceptance in practice. Complexity of models, difficulties in obtaining the required data and costs for the growers are among the reasons that inhibit their use. However, recent technological advance - automatic weather stations, databases, Web based agrometeorological monitoring and advanced techniques of data analysis - allows the development of a system with simple and free access. A process .instance of knowledge discovery in databases has been realized to evaluate the use of classification and decision tree induction in the analysis and warning of coffee rust caused by Hemileia vastatrix. Infection rates calculated from monthly assessments of rust incidence were grouped into three classes: TXl - reduction or stagnation; TX2 - moderate growth (up to 5 pp); and TX3 - accelerated growth (above 5 pp). Meteorological data, expected yield and space between plants were used as independent variables. The training data set contained 364 examples prepared from data collected in coffee-growing areas between October 1998 and October 2006. A decision tree has been developed to analyse the coffee rust epidemics. The decision tree demonstrated its potential as a symbolic and interpretable model. Its mo deI representation identified the existing decision boundaries in the data and the logic underlying them, helping to understand which variables, and interactions between these variables, led to, coffee rust epidemics in the field. The most important explanatory variables were mean temperature during leaf wetness periods, expected yield, mean of maximum temperatures during the incubation period and relative air humidity. The warning models have been developed considering binary infection rates, according to the 5 pp and 10 pp thresholds, (class '1' for rates greater than or equal the threshold; class 'O;, otherwise). These models are specific for growing are as with high expected yield or areas with low expected yield. The former had best performance in the evaluation. The estimated accuracy by cross-validation was up to 83%, considering the waming for 5 pp and higher. There was yet equivalence between accuracy and such important measures like sensitivity, specificity a~d positive or negative reliability. Considering the waming for 10 pp and higher, the accuracy was 79%. For growing areas with low expected yield, the accuracy of the models considering the waming for 5 pp and higher was up to 72%. The models for the higher infection rate (10 pp and higher) had low performance. The best evaluated models showed potential to be used in decision making about coffee rust disease control. The process of knowledge discovery in databases was characterized in such a way it can be employed in similar problems of the application domain with other crops or other coffee diseases or pests / Doutorado / Planejamento e Desenvolvimento Rural Sustentável / Doutor em Engenharia Agrícola

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights. In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into a hierarchy made up of different temporal scales, produces forecasts at each level of the hierarchy, then reconciles those forecasts using optimized weighted forecast combination. While THieF's unique approach has shown substantial accuracy improvements in a diverse range of applications, such as operations management and emergency room admission predictions, this technique had not previously been applied to outbreak forecasting. We generated candidate models formulated using the THieF methodology, which differed by their hierarchy schemes and data transformations, and ensembles of the THieF models, computed as a mean of predictive quantiles. The models were evaluated using weighted interval score (WIS) as a measure of forecast skill, and the top-performing subset was compared to several benchmark models. These models included simple ARIMA and seasonal ARIMA models, a naive baseline model, and an ensemble of operational incident hospitalization models from the US COVID-19 Forecast Hub. The THieF models and THieF ensembles demonstrated improvements in WIS and MAE, as well as competitive prediction interval coverage, over many benchmark models for both the validation and testing phases. The best THieF model generally ranked second out of nine total models during the testing evaluation. These accuracy improvements suggest the THieF methodology may serve as a useful addition to the infectious disease forecasting toolkit.