Agricultural management is a viable way for recycling animal residues in feedlots. Thesubstances that make up organic residues change the dynamics of the organic matter decompositionin the soil. Information on carbon mineralization curves allows seeking improvements in soil qualityand, consequently, in crop productivity. The Stanford & Smith Nonlinear Model is the most usedto describe C mineralization of organic residues in the soil. This model considers organic residuesare composed of substances that are mineralized exponentially. The Cabrera Model considers twofractions, one composed of substances that are mineralized exponentially and other composed ofmore resistant substances with constant mineralization. The objective of this work was to comparenonlinear models that describe carbon mineralization, considering residues on surface or incorporatedinto the soil. The data evaluated were from an experiment with oat straw, liquid swine manure, andswine litter bedding. The Stanford & Smith and Cabrera Models were used considering structure offirst order autoregressive errors - AR(1), when necessary. The fittings were compared using the AkaikeInformation Criterion (AIC). The Cabrera Model was more adequate to describe C mineralization infour treatments (soil + incorporated liquid swine manure; soil + oat straw on surface + liquid swinemanure on surface; soil + incorporated straw; and soil + straw on surface). The Stanford & SmithModel was better in three treatments (soil + incorporated straw + incorporated liquid swine manure;swine litter bedding on surface; and incorporated swine litter bedding). None of the models describedthe treatment soil + liquid swine manure on surface.Keywords: Decomposition. Half-life. Stanford & Smith Model. Cabrera Model.
The aim of this study was to describe the growth curve of “Aurora 1” peaches using fruit height and diameter data over time through diphasic sigmoidal models constructed from eight combinations of the following models: Brody, Gompertz and Logistic. Data were obtained from an experiment carried out in 2005 in the municipality of Vista Alegre do Alto, São Paulo, Brazil. The parameters of models were adjusted by the least squares method using the Gauss-Newton algorithm implemented in the R software. Assumptions of normality, homogeneity and independence of residues were verified based on Shapiro-Wilk, Breush and Pagan and Durbin-Watson tests, respectively. The goodness of fit of models was verified according to the corrected Akaike information criterion (AICc), residual standard deviation (RSD), asymptote adjustment index (AI) and nonlinearity measures. All models adjusted for both fruit height and diameter variables met the assumptions of normality, independence and homoscedasticity of errors. In addition, all of them present good quality of fit to fruit height and diameter data, since they presented AI values close to one and low RSD values and non-linearity measures. However, the double Gompertz (GG) and the Logistic + Gompertz (LG) models presented, respectively, the best quality of fit to fruit height and diameter data in relation to the other models. It could be concluded that all diphasic sigmoidal models evaluated showed good fit to height and diameter data and can be used to describe the growth curve of “Aurora-1” peaches, according to goodness of fit criteria. However, it is important to highlight that GG and LG models presented the best quality of fit and can be selected to describe the height and diameter growth of “Aurora 1” peach fruits, respectively, with maximum expected growth close to 63 mm in height and 48 mm in diameter.
Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the country from coffee bean export. In coffee plant growth, leaves have a determinant role in higher production; therefore, the leaf count per plant provides relevant information to producers for adequate crop management, such as foliar fertilizer applications. To describe count data, the Poisson model is the most commonly employed model; when count data show overdispersion, the negative binomial model has been determined to be more adequate. The objective of this study was to compare the fitness of the Poisson and negative binomial models to data on the leaf count per plant in coffee seedlings. Data were collected from an experiment with a randomized block design with 30 treatments and three replicates and four plants per plot. Data from only one treatment, in which the number of leaves was counted over time, were employed. The first count was conducted on 8 April 2016, and the other counts were performed 18, 32, 47, 62, 76, 95, 116, 133, and 153 days after the first evaluation, for a total of ten measurements. The fitness of the models was assessed based on deviance values and simulated envelopes for residuals. Results of fitness assessment indicated that the Poisson model was inadequate for describing the data due to overdispersion. The negative binomial model adequately fitted the observations and was indicated to describe the number of leaves of coffee plants. Based on the negative binomial model, the expected relative increase in the number of leaves was 0.9768% per day.
Sugarcane production plays a fundamental role in the Brazilian economy, both for sugar production and renewable energy generation. The development of new cultivars to meet the current needs of the sugarcane industry sector requires efficient phenotyping methods, which should incorporate simplification, speed, accuracy, and consistency. In order to contribute to the development of new phenotyping strategies, this work aimed to develop multivariate regression models using Partial Least Squares (PLS) to classify sugarcane clones based on sugarcane biomass quality parameters, namely fiber (FIB) and apparent sucrose (SC) content. A NIR instrument was used to acquire the reflectance spectra of 196 sugarcane bagasse - collected in two different harvest seasons - and fresh stalk samples. The values predicted by these models allowed the construction of a vector using a confusion matrix that informs whether the clone should be selected or not. PLS models selected to predict each trait under study presented high accuracy and precision, besides small values of false-positive rate and good concordance indication by the Kappa statistic test. The results obtained indicate that the use of fresh stalk samples rather than bagasse samples for the prediction of SC and FIB is recommended as it delivered higher predictive power and is of a more straightforward usage. The utilization of NIR combined with multivariate techniques may help breeding programs in the classification of sugarcane clones based on biomass quality parameters.
Jabuticaba tree is native to the Atlantic Forest in Southern Brazil, and its fruit is widely consumed in the fresh form, but it is highly perishable, requiring conservation techniques. The aim of this study was to describe the drying kinetics of jabuticaba pulp at temperatures of 50 and 60°C, comparing the Henderson, Simple Three-Parameter Exponential, Lewis, Thompson, Fick and Wang and Sing regression models and estimating the Absolute Drying Rate (ADR) for the best model. Parameters were estimated using the SAS software. The evaluation of the quality in the adjustment and selection of models was made based on the adjusted determination coefficient, Residual Standard Deviation and Akaike Information Criterion. Models presented good adjustment to data, and the Lewis model was the most suitable to describe the drying kinetics of jabuticaba pulp at temperatures of 50 and 60°C, with drying rate of 0.000063 and 0.000082 g of water/s respectively. ADR indicated that in one third of the drying time, 70% of moisture loss occurred at both temperatures and after this period, there was a deceleration of moisture loss until stabilization, when equilibrium moisture content is reached.
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