Estimate of lysine nutritional requirements for Japanese quail breeders.

Background: Japanese quail breeders are the basis for genetic improvement and multiplication for commercial layers, however, there have been no known studies on the optimal lysine level for these birds. Thus, study the egg output response to the lysine (Lys) supply using different e-functions and evaluate the that best fit, have allowed the partition the lysine requirements for maintenance, both weight and egg output maximum. Methods: The objectives of this study were to identify the responses to various Lys levels, identify the functions related to these responses and determine the ideal Lys intake amount for Japanese quail breeders. A completely randomized design of seven treatments with seven replicated was used. Treatments consisted of diet supplementation by Lys in concentrations of 16.8, 11.8, 8.4, 6.7, 5.0, 3.4, and 1.7 g/kg. Six exponential models were adjusted. Results: The level of Lys was found to affect bird responses (P < 0.001). The birds responded to the levels provided, allowing for the creation of a lysine response curve. A monomolecular function with four parameters was balanced against the statistics of adjustment and selection of models. It was possible to estimate the level of lysine required for maintenance as 133 ± 2 mg/kg BW0.67, and based an average of 41% efficiency, 22 mg Lys produced 1 g of egg output (EO). The daily intake calculated by the monomolecular factorial model was 284 mg Lys for a bird with 0.170 kg body weight and production of 10 g EO/day. The four-parameter monomolecular function proposed in this study is adequate for interpreting the animal response and calculating lysine intake for breeders.

Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil.

The purpose of this study was to estimate the temporal variability of CO2 emission (FCO2) from O2 influx into the soil (FO2) in a reforested area with native vegetation in the Brazilian Cerrado, as well as to understand the dynamics of soil respiration in this ecosystem. The database is composed of soil respiration data, agroclimatic variables, improved vegetation index (EVI), and soil attributes used to train machine learning algorithms: artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The predictive performance was evaluated based on the mean absolute error (MEA), root mean square error (RMSE), mean absolute percentage error (MAPE), agreement index (d), confidence coefficient (c), and coefficient of determination (R2). The best estimation results for validation were FCO2 with multilayer perceptron neural network (MLP) (R2 = 0.53, RMSE = 0.967 µmol m-2 s-1) and radial basis function neural network (RBF) (R2 = 0.54, RMSE = 0.884 µmol m-2 s-1) and FO2 with MLP (R2 = 0.45, RMSE = 0.093 mg m-2 s-1) and RBF (R2 = 0.74, 0.079 mg m-2 s-1). Soil temperature and macroporosity are important predictors of FCO2 and FO2. The best combination of variables for training the ANFIS was selected based on trial and error. The results were as follows: FCO2 (R2 = 16) and FO2 (R2 = 29). In all models, FCO2 outperformed FO2. A primary factor analysis was performed, and FCO2 and FO2 correlated best with the weather and soil attributes, respectively.

Machine learning for prediction of soil CO2 emission in tropical forests in the Brazilian Cerrado.

Soil CO2 emission (FCO2) is a critical component of the global carbon cycle, but it is a source of great uncertainty due to the great spatial and temporal variability. Modeling of soil respiration can strongly contribute to reducing the uncertainties associated with the sources and sinks of carbon in the soil. In this study, we compared five machine learning (ML) models to predict the spatiotemporal variability of FCO2 in three reforested areas: eucalyptus (RE), pine (RP) and native species (RNS). The study also included a generalized scenario (GS) where all the data from RE, RP and RNS were included in one dataset. The ML models include generalized regression neural network (GRNN), radial basis function neural network (RBFNN), multilayer perceptron neural network (MLPNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF). Initially, we had 32 attributes and after pre-processing, including Pearson's correlation, canonical correlation analysis (CCA), and biophysical justification, only 21 variables remained. We used as input variables 19 soil properties and climate variables in reforested areas of eucalyptus, pine and native species. RF was the best model to predict soil respiration to RE [adjusted coefficient of determination (R2 adj): 0.70 and root mean square error (RMSE): 1.02 µmol m-2 s-1], RP (R2 adj: 0.48 and RMSE: 1.07 µmol m-2 s-1) and GS (R2 adj: 0.70 and RMSE: 1.05 µmol m-2 s-1). Our findings support that RF and GRNN are promising for predicting soil respiration of reforested areas which could help to identify and monitor potential sources and sinks of the main additional greenhouse gas over ecosystems.

Modeling the response of Japanese quail to arginine intake.

Background: Understanding how Japanese quails respond to arginine intake has been an objective that previous studies have not fulfilled. The main responses to be quantified include the arginine requirement for maintenance (mg/kg0.67) and egg mass production (mg/g). Quantifying maintenance and production relationships are essential steps for predicting animal response. The current study aimed to describe how quails respond to arginine intake and determine arginine requirements for maintenance and egg production in Japanese quails. Methods: The experiment was carried out in a completely randomized design, with seven treatments and seven replicates with individual birds as experimental units. The arginine levels were: 2.43, 3.64, 4.85, 6.07, 9.07, 12.13, and 14.56 g/kg. The experiment lasted for eight weeks. The variables analyzed were daily arginine intake, daily arginine deposition in eggs, and body weight. The data were analyzed using a mixed model, with the experimental unit being the random effect and the experimental levels of arginine as a fixed effect. When the effect of arginine levels was detected (P ≤ 0.05), saturation kinetics and an exponential model with four parameters (monomolecular) were adopted. ANOVA results indicated that dietary arginine levels significantly affected (P < 0.01) the analyzed variables. The formulation strategy of the experimental diets allowed amplitude in the dietary arginine levels, and according to bird responses, arginine was the limiting nutrient. Results: The arginine requirement for body weight maintenance (BW0.67) was estimated to be 90 mg/kg BW0.67 by the monomolecular function. The requirement for egg mass (EM) production was estimated to be 25 mg/g per egg. A factorial model was parameterized as follows: daily arginine intake mg/bird = 90 × BW0.67 + 25 × EM ± 12 mg. The model was applied to data obtained from literature, and the resultant error was within the expected limit of 12 mg. The recommended daily arginine intake for the daily production of 11 g of egg and 180 g of BW was determined to be 304 mg/bird. The current study provides procedures that researchers can easily adopt.

Factorial models to estimate isoleucine requirements for broilers.

The objective of this work was to determine the efficiency of utilization (EU) and produce factorial models for optimal isoleucine (Ile) intake. Six dose-response trials were carried out, three for males and three for females, with 640 Ross 308 in each studied phase. The initial (1-14 days), grower (15-28 days) and finisher (29-42 days) phases were evaluated to cover the growing phase of the broiler chicken. In total, eight treatments were randomly distributed to four replicates of 20 birds each. The treatments consisted of seven crescent levels of Ile and one counter proof to ensure that Ile was the first limiting amino acid in the diet. Dilution technique was applied to produce the levels of Ile and keep the amino acid ratio with lysine. The EU was determined to account for whole body or partitioned for feather-free body (Bff) and feather. Two distinct factorial models were adjusted, M1 and M2. The M2 model was evaluated for one or two EU, being denominated as M2 and M3. When the efficiency was partitioned, the values of 53% and 69% for feather and Bff were determined. The optimal Ile intake estimated for each model were of 275, 908, 1,412 mg of Ile/bird/day (M1); 258, 829, 1,321 mg of Ile/bird/day (M2); and 284, 835, 1,288 mg of Ile/bird/day (M3) for initial, grower and finisher phases respectively. The EU partitioned for feather-free body and feather reduced the biased of the model M3. Overall, higher values of Ile intake are estimated when model M1 is used, which may be the difference in account for body weight gain (M1) or only protein gain (M2 and M3) to estimate the amount of amino acid required for broiler.

Descrição do potencial de retenção de nitrogênio em frangas de postura por diferentes metodologias: mínima retenção / Description of the potential for nitrogen retention in pullets by different methodologies: minimum retention

Objetivou-se com esta pesquisa descrever o potencial de retenção de nitrogênio por duas técnicas: abate comparativo e balanço de nitrogênio. Também se objetivou comparar os modelos ajustados para cada técnica e estimar as exigências de mantença para frangas de postura. Os ensaios foram realizados no período de 14 a 28, 56 a 70 e 98 a 112 dias de idade, utilizando 168 frangas. Os ensaios tiveram sete tratamentos e oito repetições. Os tratamentos consistiram de níveis de proteína variando de 75 a 435gkg-1 de matéria seca. As variáveis coletadas pelo abate comparativo foram nitrogênio ingerido e depositado e, pela técnica do balanço de nitrogênio, ingestão e excreção de nitrogênio. O intercepto da relação exponencial entre nitrogênio excretado e ingerido foi considerado a exigência de mantença. Os modelos ajustados foram comparados pelo teste da razão de máxima verossimilhança. As técnicas comparadas descreveram de forma diferente a exigência de mantença de nitrogênio. Para o abate comparativo, a exigência de mantença foi estimada em 342, 372 e 543mg/kgPC0,67 e para o balanço de nitrogênio foi de 342, 225 e 284mg/kgPC0,67, para os período de 14 a 28, 56 a 70 e 98 a 112 dias de idade, respectivamente.

The objectives of this study were to describe the potential for nitrogen retention by two techniques: comparative slaughter and nitrogen balance; compare the adjusted models for each technique and estimate the maintenance requirement for pullets. Assays were performed during 14 to 28, 56-70 and 98 to 112 days of age, using 168 pullets. The design was completely randomized with seven treatments and eight replications. Treatments consisted of protein levels ranging from 75 to 435gkg-1 of dry matter. The variables were collected by comparative slaughter and nitrogen ingested and deposited in nitrogen balance technique intake and nitrogen excretion. The intercept of the exponential relationship between ingested and excreted nitrogen was considered a requirement for maintenance. The adjusted models were compared by using the maximum likelihood ratio. The techniques described differently compared the requirement for maintenance of nitrogen. For comparative slaughter requiring maintenance was estimated at 342, 372 and 543mg/kgPC0,67 and for nitrogen balance was 342, 225 and 284mg/kgPC0,67 for the period of 14 to 28, 56-70 and 98 to 112 days of age, respectively.