Espectroscopia no infravermelho próximo para avaliação dos teores de N, P, K E C em cama de aviário - Bedin, Flavia Chiamulera Borsatti (2017)
The poultry industry has the highest share and the highest growth rates in the world agribusiness, Brazil being one of the main producers in this segment as it is the second largest producer of chicken meat in the world and the largest exporter of this product. However, as chicken production increases, the amount of residues from the activity increases simultaneously, and the need to reach a new level of knowledge, inserting this production niche also as raw material for other activities and income generation. Thus, the use of organic residues of aviaries as agricultural fertilizer presents itself as an excellent option, since, besides allowing a correct destination to this one, it promotes improvements in the vegetal and animal production through introduction and increase of the availability of nutrients in the soil. However, several factors influence the quality of this residue as fertilizer, having high variability in its chemical composition, so the periodic chemical analysis is extremely necessary for the characterization of the compound. However, this is not a recurrent procedure, it is attributed to the inherent difficulties of the methodologies traditionally used that demand greater availability of sample, time, quantity of reagents and generation of residues, in comparison with NIR. Therefore, the objective of this work is to compare the conventional chemical analysis methods recommended by Tedesco et al. (1995) with the NIR method and propose this new method for quantification of N, P, K and C contents in poultry litter. A total of 160 bed samples were analyzed in aviaries from different breeding systems at different stages of production and several production cycles under the same bed in the southwestern regions of Paraná and western Santa Catarina. The verification of the great variability in the chemical composition of the studied materials was made through the descriptive statistical analysis and the multivariate analysis PCA, the latter also performed with the NIR spectral data to choose the pretreatments that best fit the data set, choosing The second derivative was the one with the best dispersion. Therefore, the construction of the multivariate calibration models was done through the acquisition of three-dimensional spectra using the FT-NIR spectrometer model MPA. The pre-treatments used were centering the data on the mean and second derivative. We constructed models following the Cross Validation and Test Set methodologies, analyzing RMSEE, RMSECV and RMSEP, R² and RPD as determinant statistical parameters. Based on the obtained values it is concluded that the models developed for N and C, in the two methodologies, correspond to good models. However, for the P and K contents it is considered that the use of these models allows only a distinction between low and high concentrations, because it presents very high errors. Such an analysis may be economically and environmentally feasible for the characterization of avian bed samples.