An economic and environmentally feasible way to recycle sewage sludge is its use in agriculture.Information on carbon mineralization curves allows us to seek improvements in soil quality andcrop productivity. The objective of this work was to evaluate the nonlinear models that describecarbon mineralization in the soil. The experiment was conducted in laboratory and the design wascompletely randomized, with four replicates and three treatments. The following treatments wereevaluated: sewage sludge, black oat straw and sewage sludge + oat straw, incorporated into the soil.Pots with soil and the applied treatment were incubated for 110 days. The Stanford and Smith andCabrera models were used, considering structure of autoregressive errors AR (1) when necessary.The fittings were compared using the Akaike Information Criterion (AIC). The evaluated nonlinearmodels described the carbon decomposition dynamics of the treatments satisfactorily. The Stanfordand Smith model is suitable for describing the carbon decomposition in the soil + sludge and soil +oat straw treatments. The Cabrera model is suitable to describe the carbon decomposition of the soil+ sludge + straw treatment.Keywords: Mineralization. Stanford and Smith model. Cabrera model. Half-life.

Zinc uptake is essential for crop development; thus, knowledge about soil zinc availability is fundamental for fertilization in periods of higher crop demand. A nonlinear first-order kinetic model has been employed to evaluate zinc availability. Studies usually employ few observations; however, inference in nonlinear models is only valid for sufficiently large samples. An alternative is the Bayesian method, where inferences are made in terms of probability, which is effective even with small samples. The aim of this study was to use Bayesian methodology to evaluate the fitness of a nonlinear first-order kinetic model to describe zinc extraction from soil with sewage sludge using seven different extraction solutions. The analysed data were obtained from an experiment using a completely randomized design and three replicates. Fifteen zinc extractions were evaluated for each extraction solution. Posterior distributions of a study that evaluated the nonlinear first-order kinetic model were used as prior distributions in the present study. Using the full conditionals, samples of posterior marginal distributions were generated using the Gibbs sampler and Metropolis-Hastings algorithms and implemented in R. The Bayesian method allowed the use of posterior distributions of another study that evaluated the model used as prior distributions for parameters  in the present study. The posterior full conditional distributions for the parameters  were normal distributions and gamma distributions, respectively. The Bayesian method was efficient for the study of the first-order kinetic model to describe zinc extraction from soil with sewage sludge using seven extraction solutions.

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.

One of the strategies to reduce environmental impacts caused by pig slurry is its application to soils for agricultural productions. Carbon mineralization curves can be used to determine the best periods for the use of organic matter for an adequate management of soils and growing plants. The objective of this study was to evaluate the fit of nonlinear models for soil carbon mineralization. The experiment was conducted using a randomized block design with four replications and four treatments. The treatments consisted of monthly applications of pig slurry at rates of 0, 7.5, 15.0, and 30.0 m3 ha-1 ofpig slurry. Soil samples were collected and incubated for 26 days; then, seven observations of mineralized carbon volume were made over time. The description of the carbon mineralization followed the Stanford and Smith, Cabrera, and Juma models, considering the structure of autoregressive errors AR (1), when necessary; the fits were compared using the Akaike Information Criterion (AIC). The description of carbon mineralization in the treatments by nonlinear models was, in general, adequate. Juma was the most adequate model to describe the treatment with rate of 0. Stanford and Smith was the most adequate model to describe the treatments with rates of 7.5 and 15.0 m3  ha-1. Cabrera was the most adequate model to describe the treatment with rate of 30.0 m3 ha-1.

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.