Beggartick was affected by soybean in all living ratios in substitutive experiments. The larger inhibitions of this weed were observed when 75% of beggar tick plants competed with 25% of soybean , at which point the observed productivity was farthest from the expected. In soybean, beggar tick in low densities causes significant losses, but if it provided the fastest development to crop, this weed is properly suppressed by soybean complementarily to chemical control. The Genus Bidens infests several crops, being highlighted Bidens pilosa with worldwide occurrence, being also very aggressive and efficient in the extraction of water from soil . Soybean, regardless of its proportion, accumulated dry mass volumes close to the expected ; this illustrates its potential to suppress beggar tick under field conditions, although with some harm to its own development. With proportion of 75%:25% soybean/beggar tick, as example, dry mass of crop was 61% of the total obtained in the community, compared to the expected ratio of 75% . In the same situation, the dry weight of the weed, which should represent 25% of the system, represented something around 5%. The ecological system was hampered by competition, and the total productivity of the system cannabis vertical farming, which should be 100%, has always been lower than expected, with 55% reduction at proportion 75:25 beggar tick/soybean .
In summary, there were losses in the system when soybean competed with beggar tick, being the crop superior in competitive ability although it suffered due to the need for energy to be applied in competition against the weed instead of growth. In fact, the relative competitiveness and aggressiveness coefficients indicated that soybean is more competitive than beggar tick, which was also reflected by the clustering coefficient However, when considering the density of the crop and the weed under field conditions, one should see the need for associating weed control methods to optimize soybean suppression on this weed. It should be noted that the competition between plants is more drastic when the involved individuals have similar life cycle, germinate and are included in the same botanical family or have similar morpho-physiological characteristics between themselves. Under these conditions, the great determinant of which species will be most affected is the potential for competition with the species with which it competes. If the emergence of one of the competitors is delayed, usually the individual that germinates first takes advantage in the competition. Thus, one should always install crops in a weed-free area, and ensure its rapid establishment. Of course, if there are differences in the density between crop and weeds, or if crop stand is uneven, weeds may have competitive advantage. Table 7 presents a summary of results we obtained in the experiments for both methods, where we tried to create a link between them and find the main differences.
Although distinct in the interpretation of the data, both competition study methods provided information with practical nature and applicable to the field, though obviously limited because they were basic and exploratory studies. Competition results from controlled environment, regardless of the method of study, must be complemented by field trials. The substitutive method, when compared to the additive method has two disadvantages: first, it requires the installation of a pre-test to determine the minimum population for each species, from which occurs stabilization in the dry mass. This pre-test,cannabis drying racks although desirable when choosing the additive method, is not compulsory and density of plants in this study method can be determined per se, at the discretion of an experienced researcher on the subject—since data is interpreted only in the studied range.
The second drawback relates to difficulty in data processing and obtaining the graphics and coefficients inherent to the substitutive method. For the additive method, virtually any statistical software and spreadsheet with basic skills in graphics generation make it possible to analyze experimental data; for the substitutive method, although part of the easiness in installing and conducting the experiment and data collection are similar to the additive method, data analysis software based on programming languages is demanded. While data processing can be accomplished in spreadsheets also for substitutive experiments, this way is neither easy nor indicated.Research has reported that due to substitutive experiments to be installed in fixed densities, they cannot be used for inferences about mixtures where density is not kept constant . Substitutive experiments are widely used, but the results cannot be easily interpreted because they are so restrictive that valid generalizations should not be made beyond that particular situation inherent in the experiment. The findings of most studies using this method require some review .