USE of ORGANIC FERTILIZER as AN Alternative for Sandy SOIL Amelioration

The effect of agricultural activities on degradation of natural resources (soil erosion, agrochemical use, etc.) is evident in several regions of the country and should be avoided or at least mitigated. The use of biological inoculants (biofertilizers), incorporation of organic amendments, and other agricultural practices such as crop rotation and use of leguminous cover crops, may, in the long term, contribute to the recovery of the soil microbial populations and thereby improve the quality of this resource. An important option to counter such problems is the use of organic fertilizers to provide nutrients to the soil gradually and also improve their physical, chemical and biochemical characteristics that contribute to increase crop production. The objectives of this work were to study the effect of applying three organic fertilizers on physical-chemical properties of sandy soil, also, evaluate the effect of organic fertilizers on the development and production of chili. The experiment was conducted in the Experimental School of Biological and Agricultural Sciences, University of Colima, in Mexico, under field conditions. Organic fertilizers used were Vermicompost, Bocashi, and Cachaza (waste of the sugar cane industry). Doses applied to each product were 5 tons/Ha. Crop growing in all treatments was chili plants (<i>Cucumis annuum</i>). Black plastic mulching was used. Variables measured in soil were: pH, Electrical Conductivity, soluble anions and cations. Data were analyzed using a lineal regression and square model. Also, height plant was measured. Results showed that in Bocashi and Cahchaza (waste of sugar cane industry) treatments, plants grown more than in any other. Electrical Conductivity and pH were increased, respecting to control. Soluble anions and cations, also were increased. In the regression analysis shows, where X represent the height chili plant and is the weight of chili plant, The regression equation  y = -70.82 + 4.22x, and r² = 0.45, variable is highly significant (p <font > 0.001). Using the quadratic model, regression equation is y = -56.9 +3.34 +0,013 x². Regression analysis for this equation was statistically significant for the regression and the linear term explains the relationship between variables y and x. We consider that, is necessary to study should be done for a longer time, to obtain data more consistent.