Improved Data Analysis for Evaluating Effects of Integrated Nutrient Management Programs.

Integrated nutrient management (INM) practices are becoming common under intensive agricultural systems in Chile. Practices include, the use of organic matter, in different sources, soil microbial inoculants, and the application of biostimulants, of different origin. Compared to the application of macronutrients, for example, the effects of these products are rather modest; besides, trials made at the field level, many times do not have true replications, and not real randomization exists on the treatment assignment. Because of these reasons, most commonly treatments effects cannot be proven. To deal with this reality, statistical techniques, usually used in econometrics, to simulate ceteris paribus have been used. To compare different treatments, we have used regression with binary variables, controlling for ancillary variables such vigor and geographical position, and time, when this is relevant for the experiment. Besides we have corrected for spatial (and temporal) autocorrelation, using spatial lag or spatial autoregressive models. In all our experiments, field data was collected using systematic grid designs, with n>20. Plant vigor was estimated by NDVI using the active sensor OptRx (AgLeader Technologies). In the present work, results of trials in table and wine grapes, as well as in walnut, are presented. In all trials, plant biostimulants were applied and crop yield and quality were the response variable. Results have shown that the proposed methodology is useful to count with better evaluations of field trials for INM practices.