No description available.

No description available.

The purpose of this study was to assess the adoption response of cotton growers to Integrated Pest Management (IPM) programs, identify the extension strategies used to promote IPM, and provide descriptive comparison of IPM programs between the foremost eleven cotton-producing states in the United States. Integrated pest management is presented as an alternative agricultural production strategy referred to as sustainable agriculture. IPM programs for cotton were evaluated by use of a survey instrument and telephone interviews with the IPM coordinators for the eleven cotton-producing states. The most significant findings were that programs vary considerably between states; the most widely employed IPM techniques were selective use of chemicals, use of economic thresholds and sampling for pests; the Cooperative Extension Service worked closely with private consultants and grower associations; and adoption of a wide variety of IPM techniques has been extensive for cotton producers in the eleven foremost cotton-producing states.

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No description available.

No description available.

No description available.

Field and laboratory investigations were conducted in Burkina Faso to diagnose the causes of severe outbreaks of Bemisia tabaci populations and gênerai failures of chemical control against this pest in cotton. / Field and laboratory investigations were conducted in Burkina Faso to diagnose the causes of severe outbreaks of Bemisia tabaci populations and general failures of chemical control against this pest in cotton. The research efforts were oriented in the following three areas: I) the susceptibility of B. tabaci populations and its parasitoids to cotton insecticides; I) the susceptibility of B. tabaci populations and its parasitoids to cotton insecticides, a) conventional insecticides, b) novel insecticides; II) the relative abundance of B. tabaci and its parasitoids in the field; III) the biological activities of some botanical extracts as alternative insecticides against the pest.

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Orientador: Aledir Silveira Pereira / Banca: Adilson Gonzaga / Banca: Rodrigo Capobianco Guido / Resumo: A manifestação de patógenos nas plantações é a maior causadora de prejuízos nos mais diversos cultivares, podendo ocasionar o aumento dos preços e a perda da qualidade dos produtos cultivados. O quanto antes for identificada a doença, mais cedo é realizado o seu controle através de agrotóxicos, tendendo, dessa forma, a diminuir a sua proliferação e, consequentemente, evitando maiores danos. No entanto, muitas vezes, a inspeção visual dos sintomas apresentados pela ocorrência de patógenos em uma folha de planta, não é considerada o suficiente para avaliar e identificar o tipo de doença que está causando a lesão, sendo necessário, para o diagnóstico da doença, uma análise mais detalhada por um agrônomo ou por meio de um exame laboratorial a partir da amostra da doença. Neste contexto, este trabalho realizou a classificação automática de doenças do algodoeiro, a partir da extração de características dos sintomas foliares apresentados em imagens digitais. Utilizou-se para este processo, a energia da transformada wavelet para extração de características das imagens e para realizar a classificação foi utilizado a Máquina de Vetor de Suporte (MVS). Ao percorrer o sistema final de classificação, uma imagem pode ser classificada entre cinco tipos de diagnósticos diferentes, como sendo "Sadia" (SA), lesionada com as doenças Ramulária (RA), Mancha Angular (MA) e Mancha de Ascochyta (AS), ou identificada pertencente a nenhuma das classes / Abstract: The expression of pathogens in the plantations is the leading cause of damage in several cultivars, which may cause higher prices and loss of quality of crops. The sooner the disease is identified, the sooner it is done through its control of pesticides, tending thus to reduce their proliferation and thus avoiding further damage. However, often the visual inspection of the symptoms presented by the occurrence of pathogens in a leaf, is not considered enough to assess and identify the type of disease that is causing the lesion, being necessary for the diagnosis of disease, a more detailed analysis by an agronomist or through a laboratory test sample from the disease. In this context, this paper carried the automatic classification of diseases of cotton, from the feature extraction of leaf symptoms presented in digital images. It was used for this process, the energy of wavelet transform to extract features from images and to perform the classification, we used the Support Vector Machine (SVM). By walking through the final system of classification, an image can be classified in five types of different diagnoses, as being "Healthy" (SA), with the injured Ramularia diseases (RA), Bacterial Blight (MA) and Ascochyta Blight (AS) or belonging to any of the identified classes / Mestre