This illustrated pocket guide is intended for cotton growers who need to identify common pests and diseases rapidly in the field. The pests and diseases are grouped in order of their occurrence in the cotton development cycle and include seedling and leaf pests, sucking insects, mites and bollworms.

A study was conducted to determine the farmers' ability to recognize insectpests and diseases and to select the appropriate pesticides. Farmers' averagescores regarding identifying insect pests and diseases were 4.03 and 4.12 out of10, respectively. While farmers' average score regarding selecting properinsecticides was 3.25 out of 10, the average score regarding selecting properpesticides was 3.38 out of 10. Of all the farmers' average scores weresignificantly less than five. The study suggests that framers need more educationregarding identifying insect pests and diseases and selecting proper pesticides tosolve the insect pest and disease problem in cotton.

Gift

Linear regression was applied to social science data to study the significant factors contribute to enhancing the farmers' knowledge regarding identifying insect pests and diseases and selecting proper pesticides. Assumptions of the each model were tested to make the model more appropriate for the collected data. To find the Significant Independent Variables, Regression was applied taking farmers knowledge of identifying insect pests, and diseases, and selecting, proper insecticides, pesticides as dependent variable while demographic characteristics, and different sources of information contributing to enhancing the farmers' knowledge as independent variables. Four models were developed each represents the relationship between farmers' knowledge and significant independent variables. To Test the Assumptions of Linear Regression for Likert Type Scale Data. To check the assumptions of linear regression model tested were errors are normally distributed, errors are independently distributed and constant variances of errors. The Residual plot and Non Constant Variance Plot were constructed. Moreover, P-values were also reported for each model to test the null hypothesis. weather there was need of transformation or not. If assumptions were not fulfilled. Therefore, Box and Cox method was applied to get appropriate transformation. The Box and Cox method suggested 0.5, square root transformation. After transformation the model obtained was "QN33=0.397+ 0.016d4 + 0.0007d5 + 0.0188q 332þ. In the model, variable d 4 was not significant because P-value of its slope was greater than 0.05. A regression model was sought having significant independent variables and that fulfills the assumptions of the linear regression. When the model was applied on the actual values, it did not satisfy the assumption of Non-constant Variance. While the model was applied on the transformed values, one independent variables was not significant. To remove this ambiguity, a regression was applied on the actual values of the dependent variable taking two independent variables, ds and 8332 while the third one, d4, was dropped. Stepwise regression suggested the model is given as under: Q33 = 2.093+ 0.006182 d5 + 0.234 q332. The above model reveals that cultivated area under cotton crop, and help from Pesticide Agents had significantly positive effect on the farmers' knowledge regarding selecting the proper insecticides.

Initial accomplishments of the project includes the following: establishment of fibercrops collection garden as possible sources of breeding materials; development of a sound fertilization program of salago; disease identification and development of control strategies against some diseases of salago; and post-harvest processing. For germplasm collection and evaluation activities, the following were maintained in the collection garden: pineapple, maguey, sansiviera, pandan, salago, abaca, jute, ramie, mulberry, coconut, cotton, and kapok. For the fertilizer requirement of salago, initial results showed that fiber yield and agronomic characters of three-year-old salago plants were significantly affected by nitrogen level. Fiber quality on the other hand, was not significantly affected. For disease identification and management, it was observed in salago areas of Manambulan, Davao City and those at the PICRI experimental areas were infected with Rhizoctonia solani disease. Moreover, leaf spot and leaf blight were noted at the PICRI experimental areas. R. solani was identified as the causal pathogen as evidenced by the pathogenicity test. Bioassay test was likewise conducted to determine control measures against salago disease. Initial observations showed that of the 17 fungicides tested, 12 were found effective. The gaining popularity of handmade paper and the revival of the fibercraft industry and the movement to conserve the forests as well as the desire to conserve the environment from the problems posed by non-biodegradable materials like plastics has contributed to the growing demand for natural fibers. These are the areas the project sought to address in the coming years