Growth Curve of Brazilian Creole Chickens (Canela-Preta Breed) Raised in Two Different Rearing Systems under Tropical Climate

The objective of this study was to describe the growth curve of Brazilian Creole chickens of the Canela-Preta breed raised in two different rearing systems using non-linear growth models. A total of 400 birds were divided into two groups of 200 animals (of both genders), which were kept in confined or semi-confined systems. The confined birds were housed in an experimental masonry shed and the semi-confined animals were housed in another shed with access to pasture from 29 days of age. Birds were individually weighed every seven days during six months for determination of the growth curves of body weight using 10 non-linear models. The parameters of the models were estimated using the Gauss Newton method. The performance of the models was assessed using mean squared error (MSE), coefficient of determination (R2), percentage of convergence, and residual mean absolute deviation (MAD). With the exception of the Inverse Polynomial, all the other models had R2 values close to one. Therefore, the best models were chosen based on the lowest MSE and MAD values, with the Richards model ranking first followed by the Von Bertalanffy model. Gender and rearing system effects significantly influenced (p<0.05) some parameters of the Richards model. In conclusion, the Richards model was the most adequate to describe the growth of Canela-Preta chickens. Gender and rearing system significantly influenced the growth of the birds. The growth rates observed indicated that management strategies can be performed to increase the production efficiency of Canela-Preta chickens.(AU)

The Growth Pattern of Brazilian Canela-Preta Chickens with Different Plumages Reared in Two Rearing Systems

Growth pattern is essential for economically efficient poultry production. In this study, we aimed to describe the growth curve of chickens of the Canela-Preta breed reared in two different rearing systems, considering their different plumage colors. Initially, 204 one-day-old male and female chicks were randomly distributed in confinement and semi-confinement (102 animals in each system) without separation by gender. The animals were individually identified by wing and foot plastic brands and were weighted every seven days. The body weight and age records were used to estimate the growth curves of the following factors using the Richards model: plumage color, gender, and rearing system. The likelihood ratio test was used to verify the equality of parameters and identify nonlinear models to compare the growth patterns of the evaluated groups. The growth pattern of Canela-Preta chickens changed as a function of gender, plumage color, and rearing system. Females with black plumage, black and gold hens, and males with black and white plumage showed greater sensitivity to changes in rearing systems. Within-breed selection strategies for specific colors can improve the use of growth pattern differences, improving production efficiency. Semi-confinement is suitable for rearing Canela-Preta chickens with any plumage color, as these animals meet the free-range poultry niche market requirements.(AU)

Growth curves of broilers fed different nutritional relationships using the Gompertz model

The study aimed to evaluate performance and growth curves of broilers fed different nutritional relations. A total of 1,440 Cobb-500 male day-old chicks were assigned to eight treatments in a 2 x 2 x 2 factorial arrangement with six replicates of 30 birds each. The main factors were nutritional density (control and high), lysine source (HCl and sulfate), and calcium pidolate (presence and absence). Analyses were made for body weight gain (BWG), and feed conversion rate (FCR) at 21, and 42 days of age. The growth curves were adjusted by weighing a bird per plot every three days. Data for BWG were tested by ANOVA to evaluatethe effects of treatments and their interactions at 5% significance, and the Gompertz model was adjusted by NLS. Birds fed a high nutritional density had higher BWG and lower FCR. Calcium pidolate and different sources of lysine did not influence the FCR of broilers, however a triple interaction was evidenced for BWG at 1 to 42 days of age. The day with maximum gain adjusted by Gompertz of all treatments was at the 32ndday of age and the maximum weight (A) was around 5.85 kg.(AU)

AgroReg: main regression models in agricultural sciences implemented as an R Package

Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental results, mainly because much of the existing software perform this analysis and a lack of knowledge of other models. On the other hand, many of the natural phenomena do not present such behavior; nevertheless, the use of non-linear models is costly and requires advanced knowledge of language programming such as R. Thus, this work presents several regression models found in scientific studies, implementing them in the form of an R package called AgroReg. The package comprises 44 analysis functions with 66 regression models such as polynomial, non-parametric (loess), segmented, logistic, exponential, and logarithmic, among others. The functions provide the coefficient of determination (R2), model coefficients and the respective p-values from the t-test, root mean square error (RMSE), Akaike's information criterion (AIC), Bayesian information criterion (BIC), maximum and minimum predicted values, and the regression plot. Furthermore, other measures of model quality and graphical analysis of residuals are also included. The package can be downloaded from the CRAN repository using the command: install.packages("AgroReg"). AgroReg is a promising analysis tool in agricultural research on account of its user-friendly and straightforward functions that allow for fast and efficient data processing with greater reliability and relevant information.

Genome-enabled prediction through quantile random forest for complex traits / Predição genômica por meio do random forest quantílitico para características complexas

ABSTRACT: Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.

RESUMO: Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.

Evaluating tree survival and modeling initial growth for Atlantic Forest restoration / Avaliação da sobrevivência e modelagem do desenvolvimento inicial de árvores para restauração da Mata Atlântica

ABSTRACT: Ecological restoration has become an important complementary practice to protect natural resources and preserve biodiversity. However, native species may be used in restoration programs in ways that do not optimize their performance. This research evaluated the survival and to model the initial growth of 15 native tree species planted in "filling" and "diversity" lines in the post-planting phase of a restoration experiment in the subtropics of the Brazilian Atlantic Forest. We measured survival rate (%) one year after planting and collar diameter (mm), total height (m), crown projection area (m²) and crown volume (m³) in the first 48 months after planting. Growth modeling for each variable and species was based on the non-linear mathematical Logistic, Gompertz, and Chapman-Richards models. Model selection for each variable/species was supported by the Akaike Information Criterion, standard error of the estimate, and coefficient of determination. The highest survival rates were reported for Cordia americana, Gochnatia polymorpha, Inga uruguensis, Peltophorum dubium, Prunus sellowii e Zanthoxylum rhoifolium (91.7%) and for Solanum mauritianum (90.3%). The species with faster growth were, by increasing order, Mimosa scabrella, Trema micrantha, Solanum mauritianum and Croton urucurana. With a better understanding of the initial developmental potential of tree species, it is possible to increase the species and functional diversity of the filling group. There was no single model capable of describing the variables analyzed and different models were needed to describe different characteristics and species.

RESUMO: A restauração ecológica tornou-se uma importante atividade complementar para proteger os recursos naturais e conservar a biodiversidade. No entanto, as espécies nativas podem estar a ser utilizadas em programas de restauração de formas que não otimizam as suas características. O objetivo deste trabalho foi avaliar a sobrevivência e modelar o desenvolvimento inicial de 15 espécies arbóreas nativas plantadas em linhas de "preenchimento" e "diversidade" na fase de pós-plantio numa experiência de restauração nos subtrópicos da Mata Atlântica Brasileira. Avaliou-se a taxa de sobrevivência (%) um ano após o plantio e o diâmetro do colo (mm), a altura total (m), a área de projeção de copa (m²) e o volume de copa (m³) nos primeiros 48 meses após o plantio. A modelagem de crescimento para cada variável e espécie foi baseada nos modelos matemáticos não lineares: Logístico, Gompertz e Chapman-Richards. A seleção do modelo para cada variável/espécie teve como base o Critério de Informação de Akaike, erro padrão da estimativa e coeficiente de determinação. Os percentuais de sobrevivência mais altos foram para Cordia americana, Gochnatia polymorpha, Inga uruguensis, Peltophorum dubium, Prunus sellowii e Zanthoxylum rhoifolium (91,7%) e para Solanum mauritianum (90,3%). As espécies de crescimento mais rápido, por ordem crescente, foram: Mimosa scabrella, Trema micrantha, Solanum mauritianum e Croton urucurana. Com o conhecimento do potencial de desenvolvimento inicial das espécies, é possível aumentar a diversidade de espécies e funcional do grupo de preenchimento. Não houve um modelo único capaz de descrever todas as variáveis de desenvolvimento analisadas. Foram necessários diferentes modelos para descrever as diferentes características e as diferentes espécies.

Genome-enabled prediction through quantile random forest for complex traits / Predição genômica por meio do random forest quantílitico para características complexas

Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.

Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.

Nonlinear models in the height description of the Rhino sunflower cultivar / Modelos não lineares na descrição de altura da cultivar de girassol Rhino

Sunflower produces achenes and oil of good quality, besides serving for production of silage, forage and biodiesel. Growth modeling allows knowing the growth pattern of the crop and optimizing the management. The research characterized the growth of the Rhino sunflower cultivar using the Logistic and Gompertz models and to make considerations regarding management based on critical points. The data used come from three uniformity trials with the Rhino confectionery sunflower cultivar carried out in the experimental area of the Federal University of Santa Maria - Campus Frederico Westphalen in the 2019/2020 agricultural harvest. In the first, second and third trials 14, 12 and 10 weekly height evaluations were performed on 10 plants, respectively. The data were adjusted for the thermal time accumulated. The parameters were estimated by ordinary least square's method using the Gauss-Newton algorithm. The fitting quality of the models to the data was measured by the adjusted coefficient of determination, Akaike information criterion, Bayesian information criterion, and through intrinsic and parametric nonlinearity. The inflection points (IP), maximum acceleration (MAP), maximum deceleration (MDP) and asymptotic deceleration (ADP) were determined. Statistical analyses were performed with Microsoft Office Excel® and R software. The models satisfactorily described the height growth curve of sunflower, providing parameters with practical interpretations. The Logistics model has the best fitting quality, being the most suitable for characterizing the growth curve. The estimated critical points provide important information for crop management. Weeds must be controlled until the MAP. Covered fertilizer applications must be carried out between the MAP and IP range. ADP is an indicator of maturity, after reaching this point, the plants can be harvested for the production of silage without loss of volume and quality.

O girassol produz aquênios e óleo de qualidade, além de servir para produção de silagem, forragem e biodiesel. A modelagem de crescimento permite conhecer o padrão de crescimento da cultura e otimizar o manejo. O objetivo deste trabalho foi caracterizar o crescimento da cultivar de girassol Rhino por meio dos modelos Logístico e Gompertz e fazer considerações a respeito do manejo com base em pontos críticos. Os dados utilizados são oriundos de três ensaios de uniformidade com a cultivar de girassol confeiteiro Rhino, conduzidos na área experimental da Universidade Federal de Santa Maria, Campus Frederico Westphalen, na safra 2019/2020. Foram realizadas 14, 12 e 10 avaliações semanais de altura em 10 plantas, respectivamente, no primeiro, segundo e terceiro ensaio. Os dados foram ajustados em função da soma térmica acumulada. Os parâmetros foram estimados por meio do método dos mínimos quadrados ordinários, usando o algoritmo de Gauss-Newton. A qualidade de ajuste dos modelos aos dados foi medida pelo coeficiente de determinação ajustado, critério de determinação de Akaike, critério bayesiano de informação, e por meio da não linearidade intrínseca e paramétrica. Foram determinados os pontos de inflexão (IP), máxima aceleração (MAP), máxima desaceleração (MDP) e desaceleração assintótica (ADP). As análises estatísticas foram realizadas com Microsoft Office Excel® e o software R. Os modelos descreveram de forma satisfatória a curva de crescimento da altura do girassol, fornecendo parâmetros com interpretações práticas. O modelo Logístico apresenta melhor qualidade de ajuste, sendo o mais adequado para caracterização da curva de crescimento. Os pontos críticos estimados fornecem informações importantes para o manejo da cultura. As plantas daninhas devem ser controladas até o MAP. As aplicações de fertilizantes em cobertura devem ser realizadas entre MAP e IP. O ADP é um indicador de maturidade, após atingir este ponto, as plantas podem ser colhidas para a produção de silagem sem perda de volume e qualidade.

Bayesian modeling of the coffee tree growth curve / Modelagem bayesiana da curva de crescimento do cafeeiro

When modeling growth curves, it should be considered that longitudinal data may show residual autocorrelation, and, if this characteristic is not considered, the results and inferences may be compromised. The Bayesian approach, which considers priori information about studied phenomenon has been shown to be efficient in estimating parameters. However, as it is generally not possible to obtain marginal distributions analytically, it is necessary to use some method, such as the weighted resampling method, to generate samples of these distributions and thus obtain an approximation. Among the advantages of this method, stand out the generation of independent samples and the fact that it is not necessary to evaluate convergence. In this context, the objective of this work research was: to present the Bayesian nonlinear modeling of the coffee tree height growth, irrigated and non-irrigated (NI), considering the residual autocorrelation and the nonlinear Logistic, Brody, von Bertalanffy and Richard models. Among the results, it was found that, for NI plants, the Deviance Information Criterion (DIC) and the Criterion of density Predictive Ordered (CPO), indicated that, among the evaluated models, the Logistic model is the one that best describes the height growth of the coffee tree over time. For irrigated plants, these same criteria indicated the Brody model. Thus, the growth of the non-irrigated and irrigated coffee tree followed different growth patterns, the height of the non-irrigated coffee tree showed sigmoidal growth with maximum growth rate at 726 days after planting and the irrigated coffee tree starts its development with high growth rates that gradually decrease over time.

Na modelagem de curvas de crescimento deve-se considerar que dados longitudinais podem apresentar autocorrelação residual, sendo que, se tal característica não é considerada, os resultados e inferências podem ser comprometidos. A abordagem bayesiana, que considera informações à priori sobre o fenômeno em estudo tem se mostrado eficiente na estimação de parâmetros. No entanto, como geralmente não é possível obter as distribuições marginais de forma analítica, faz-se necessário a utilização de algum método, como o método de reamostragem ponderada, para gerar amostras dessas distribuições e assim obter uma aproximação para as mesmas. Dentre as vantagens desse método, destaca-se a geração de amostras independentes e o fato de não ser necessário avaliar convergência. Diante desse contexto, o objetivo deste trabalho foi apresentar a modelagem não linear bayesiana do crescimento em altura de plantas do cafeeiro, irrigadas e não irrigadas (NI), considerando a autocorrelação residual e os modelos não lineares Logístico, Brody, von Bertalanffy e Richards. Em vista dos resultados, verificou-se que, para as plantas NI, o DIC e CPOc, indicaram que, dentre os modelos avaliados, o modelo Logístico é o que melhor descreve o crescimento em altura do cafeeiro ao longo do tempo. E, para as plantas irrigadas, esses mesmos critérios indicaram o modelo Brody. Assim, o crescimento da planta do cafeeiro não irrigado e irrigado seguiram padrões de crescimento distintos, a altura do cafeeiro não irrigado apresentou crescimento sigmoidal com taxa máxima de crescimento aos 726 dias após o plantio, já o cafeeiro irrigado inicia seu desenvolvimento com altas taxas de crescimento que vão diminuindo aos poucos com o tempo.

Growth predictability models in Mangalarga marchador horses / Modelos de previsibilidade de crescimento em cavalos Mangalarga marchador

This study evaluated the suitability of the Brody, logistic, and quadratic response plateau models to describe chest and cannon girth data obtained cross-sectionally in Mangalarga Marchador horses, in order to select the best model and predict the growth and maturity of males and females of this breed. Data were collected from 230 horses aged 6 to 176 months, divided by sex and age (16 age classes). The studied models were compared according to each quality evaluator by computing the adjusted coefficient of determination (R²adj) and residual standard deviation (RSD) with R statistical software. The chest girths obtained by the models ranged from 172.06 (males) to 181.50 cm (females) (Brody), 172.51 (males) to 181.89 cm (females) (logistic), and 177.67 (males) to 183.09 cm (females) (plateau). For cannon girth, the values were 18.18 (females) to 19.33 cm (males) (Brody), 18.11 (females) to 19.41 cm (males) (logistic), and 18.70 (females) to 19.40 cm (males) (plateau). The logistic model was best suited to describe the growth in chest girth of male and female Mangalarga Marchador horses. For cannon girth growth, the model best suited for males was the logistic model, and the one best suited for females was the Brody model.

RESUMO: O objetivo deste estudo foi avaliar o ajuste dos modelos Brody, Logístico e Platô de resposta quadrática aos dados de perímetros torácico e de canela em equinos Mangalarga Marchador obtidos pelo método transversal, a fim de selecionar o melhor modelo e predizer sobre o crescimento e a maturidade de machos e fêmeas desta raça. Foram utilizados dados de 230 equinos de seis a 176 meses de idade que foram divididos por sexo e em 16 classes de idade. Os modelos estudados foram comparados segundo os avaliadores de qualidade: coeficiente de determinação ajustado (R²adj) e desvio padrão residual (DPR), utilizando-se o software estatístico R. Os perímetros torácicos obtidos pelos modelos variaram de 172,06 a 181,50 cm (Brody), 172,51 a 181,89 cm (Logístico) e 183,09 a 177,67 cm (Platô) para fêmeas e machos, respectivamente. Para o perímetro de canela os valores variaram de 18,18 a 19,33 cm (Brody), 18,11 a 19,41 cm (Logístico) e 18,70 a 19,40 cm (Platô) para fêmeas e machos, respectivamente. O modelo logístico é mais indicado para expressar o crescimento em perímetro torácico de machos e fêmeas da raça Mangalarga Marchador. Já para a variável perímetro de canela, o modelo mais indicado para os machos foi o modelo Logístico e para as fêmeas o modelo de Brody.

Fit and genetic parameters of the growth curve of Polled Nellore cattle / Ajuste e parâmetros genéticos da curva de crescimento em bovinos da raça Nelore Mocho

This study was undertaken to compare different non-linear models for fitting growth curves of Polled Nellore animals as well as to estimate genetic parameters for the components of the growth curve. The study involved body weight-age data of 6,717 Polled Nellore cattle from birth to 650 days of age, which belonged to the Brazilian Association of Zebu Breeders (ABCZ), corresponding to the period from 1980 to 2011. Four non-linear models (Brody, Bertalanffy, Logistic, and Gompertz) were fitted and compared by the adjusted coefficient of determination (R2adj), mean absolute deviation of residuals (MAD), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). To estimate the genetic parameters and genetic values of asymptotic weight (A), integration constant (B), and maturation rate (K), the Bayesian inference method was adopted. The Brody model showed the lowest values of MAD, RMSE, AIC, and BIC and the highest R2adj. Heritability estimates for parameters A, B, and K were 0.11, 0.16, and 0.30, respectively, whereas genetic correlations were 0.01 (A-B), -0.91 (A-K), and 0.24 (B-K). The Brody model provided the best fit. The K parameter shows enough genetic variability for selection in the herd. Heavier animals in adulthood tend to exhibit lower growth rates. Despite the low heritability estimate of parameter A, there were genetic gains, indicating that selection is being efficient on asymptotic weight.(AU)

O objetivo deste estudo foi comparar diferentes modelos não lineares para o ajuste das curvas de crescimento de animais da raça Nelore Mocho e estimar os parâmetros genéticos para os componentes da curva de crescimento. Foram utilizados dados de peso corporal-idade do nascimento aos 650 dias de idades de 6.717 bovinos da raça Nelore Mocho, pertencentes à Associação Brasileira de Criadores de Zebu (ABCZ), referentes ao período de 1980 e 2011. Quatro modelos não lineares (Brody, Bertalanffy, Logístico e Gompertz) foram ajustados e comparados pelo coeficiente de determinação ajustado (R2adj), desvio médio absoluto dos resíduos (DMA), raiz quadrada do quadrado médio do resíduo (RMSE), critério de informação de Akaike (AIC) e o critério de informação bayesiano (BIC). Para estimativas dos parâmetros genéticos e valores genéticos do peso assintótico (A), constante de integração (B) e taxa de maturação (K), utilizou-se o método de inferência Bayesiana. O modelo Brody apresentou os menores valores de DMA, RMSE, AIC e BIC e o maior R2adj. As estimativas de herdabilidade foram 0,11; 0,16 e 0,30 para os parâmetros A, B e K, respectivamente, enquanto as correlações genéticas foram de 0,01 (A-B), -0,91 (A-K) e 0,24 (B-K). Constatou-se que o modelo Brody forneceu o melhor ajuste. O parâmetro K apresenta variabilidade genética suficiente para seleção no rebanho. Animais com maior peso na idade adulta tendem a apresentar menores taxas de crescimento. Apesar da baixa estimativa de herdabilidade do parâmetro A, observou-se ganhos genéticos, indicando que a seleção está sendo eficiente sobre o peso assintótico.(AU)

Nonlinear models for soil moisture sensor calibration in tropical mountainous soils

Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m³ m-³). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m³ m-³, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.

Prediction of live weight in beef heifers using a body volume formula / Predição do peso vivo em novilhas de corte usando a fórmula do volume corporal

The objective of this study was to develop and evaluate linear, quadratic, and allometric models to predict live weight (LW) using the body volume formula (BV) in crossbred heifers raised in southeastern Mexico. The LW (426.25±117.49kg) and BV (338.05±95.38 dm³) were measured in 360 heifers aged between 3 and 30 months. Linear and non-linear regression were used to construct prediction models. The goodness-of-fit of the models was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R²), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through cross-validation (k-folds). The ability of the fitted models to predict the observed values was evaluated based on the RMSEP, R² and mean absolute error (MAE). The quadratic model had the lowest values of AIC (2688.39) and BIC (2700.05). On the other hand, the linear model showed the lowest values of MSE (7954.74) and RMSE (89.19), and the highest values of AIC (2709.70) and BIC (2717.51). Despite this, all models presented the same R² value (0.87). The cross-validation (k-folds) evaluation of fit showed that the quadratic model had better values of MSEP (41.49), R2 (0.85), and MAE (31.95). We recommend the quadratic model to predictive of the crossbred beef heifers' live weight using the body volume as the predictor.

O objetivo deste estudo foi desenvolver e avaliar os modelos linear, quadrático e alométrico para predizer o peso vivo (PV), usando-se a fórmula do volume corporal (VC) em novilhas mestiças criadas no sudeste do México. O PV (426,25+117,49kg) e o VC (338,05±95,38dm³) foram medidos em 360 novilhas, com idade entre três e 30 meses. Regressões lineares e não lineares foram utilizadas para construir os modelos de predição. A adequação dos modelos foi avaliada utilizando-se o critério de informação de Akaike (AIC), o critério de informação bayesiano (BIC), o coeficiente de determinação (R), o quadrado médio do erro (QME) e a raiz do QME (ROME). Além disso, os modelos desenvolvidos foram avaliados por meio de validação cruzada (k-folds). A capacidade dos modelos ajustados em prever os valores observados foi avaliada com base no ROME, no R² e no erro médio absoluto (EMA). O modelo quadrático apresentou os menores valores de AIC (2688,39) e de BIC (2700,05). Por outro lado, o modelo linear apresentou os menores valores de QME (7954,74) e de ROME (89,19); esse modelo apresentou os maiores valores de AIC (2709,70) e de BIC (2717,51). Apesar disso, todos os modelos apresentaram o mesmo valor para o R (0,87). A avaliação de ajuste por validação cruzada (k-folds) mostrou que o modelo quadrático teve melhores valores de ROME (41,49), R² (0,85) e EMA (31,95). Recomenda-se o modelo quadrático para predição do peso vivo de novilhas de corte mestiças utilizando-se o volume corporal como preditor.

Agrometeorological models for predicting seedlings development of two native forest species

Models of development are tools that connect the effects of development on the environment, allowing their applications in several studies. Nevertheless, studies are scarce on models of development for native forest species in Brazil. This study aimed to predict the development of two native forest species - Citharexylum myrianthum Cham. and Bixa orellana L. - with two agrometeorological models, being one linear (Phyllochron) and another nonlinear (Wang and Engel, 1998). Both models predict the cumulative leaf number (CLN) on a daily basis, which generates the seedling phase duration (SPD) when integrated to time. Data were used from two years of experiments conducted during 2015 and 2016 growing seasons and 12 sowing dates in Itajubá, Minas Gerais State, Brazil. These species × sowing dates × years experiments provided a rich dataset for calibrating and evaluating both models. Although both models used in the study allowed predicting the dynamics of leaf development, CLN, and SPD in two native forest species, the Wang and Engel model provided a more accurate prediction of CLN and SPD for C. myrianthum species, with an overall root mean square error (RMSE) of 1.82 leaves (CLN) and 5.9 days (SPD). For B. orellana, the Phyllochron model was slightly better, with an overall RMSE of 1.48 leaves (CLN) and seven days (SDP).

Developing a hybrid forecasting system for agricultural commodity prices (case study: Thailand rice free on board price) / Desenvolvimento de um sistema de previsão híbrido para preços de commodities agrícolas: estudo de caso para arroz tailandês

Forecast the price of agricultural goods is a beneficial action for farmers, marketing agents, consumers, and policymakers. Today, managing this product security requires price forecasting models that are both efficient and reliable for a country's import and export. In the last few decades, the Autoregressive Integrated Moving Average (ARIMA) model has been widely used in economics time series forecasting. Recently, many of the time series observations presented in economics have been clearly shown to be nonlinear, Machine learning (ML) modelling, conversely, offers a potential price forecasting technique that is more flexible given the limited data available in most countries' economies. In this research, a hybrid price forecasting model has been used, through a novel clustering technique, a new cluster selection algorithm and a multilayer perceptron neural network (MLPNN), which had many advantages and using monthly time series of Thai rice FOB price form November 1987 to October 2017. The empirical results of this study showed that the value of root mean square error (RMSE) equals 14.37 and the Mean absolute percentage error (MAPE) equals 4.09% for the hybrid model. The evaluation results of proposed method and comparison its performance with four benchmark models, by monthly time series of Thailand rice FOB price from November 1987 to October 2017 showed the outperform of proposed method.

Prever o preço dos produtos agrícolas é uma ação benéfica para agricultores, agentes de marketing, consumidores e legisladores. Hoje, o gerenciamento da segurança desse produto requer modelos de previsão de preços eficientes e confiáveis para a importação e exportação de um país. Nas últimas décadas, o modelo Autoregressive Integrated Moving Average (ARIMA) tem sido amplamente utilizado na previsão de séries temporais da economia. Recentemente, muitas das observações de séries temporais apresentadas em economia têm se mostrado claramente não lineares. A modelagem de aprendizado de máquina (ML), por outro lado, oferece uma técnica de previsão de preços potencial que é mais flexível, apresentados os dados limitados disponíveis na maioria dos países. Nesta pesquisa, um modelo híbrido de previsão de preços foi usado, por meio de uma nova técnica de agrupamento, um novo algoritmo de seleção de agrupamento e uma rede neural perceptron multicamadas (MLPNN), que teve muitas vantagens, e usando séries temporais mensais de preços FOB do arroz tailandês de novembro 1987 a outubro de 2017. Os resultados empíricos deste estudo mostraram que o valor da raiz do erro quadrático médio (RMSE) é igual a 14,37 e o erro percentual absoluto médio (MAPE) é igual a 4,09% para o modelo híbrido. Os resultados da avaliação do método proposto e a comparação de seu desempenho com quatro modelos de benchmark, por séries temporais mensais de preço FOB do arroz tailandês de novembro de 1987 a outubro de 2017, mostram o desempenho superior do método proposto.

Multivariate analysis of sources of polyunsaturated fatty acids, selenium, and chromium on the productive performance of second-cycle laying hens

An experiment was conducted to evaluate the effect of the intake of a mixture of fish and sacha inchi oils (iOM), organic selenium (iSe), and organic chromium (iCr) on egg production (EP) and feed conversion ratio (FCR) of Isa Brown second-cycle laying hens (SCLH) for 16 weeks (91-106 weeks old). Egg production and FCR were evaluated using multivariate models that included conventional equations and artificial neural networks (ANN) to study multiple nutritional interactions as alternatives to univariate dose-response models. Based on the best models, iOM, iSe, and iCr levels were optimized, and a global sensitivity analysis was implemented to quantify their influence on EP and FCR. The modified logistic model was selected as the best strategy to represent EP. In the case of FCR, an ANN model with a feed-forward architecture and softmax transfer function was selected as the best alternative. One of the scenarios to simultaneously optimize EP (89.1%) and FCR (1.94 kg feed/kg egg) at 16 weeks of production was established with 3.3 g/hen·day of iOM, 0.132 mg/ hen·day of iSe, and 0.176 mg/hen·day of iCr. However, optimization considering only FCR results in much lower optimal iCr levels (between 0.083 and 0.105 mg/hen·day) with a slight decrease in EP (87.9%). The global sensitivity analysis showed that iSe is an essential factor associated with the increase in EP, and iCr is the most influential factor for the decrease in FCR. When both criteria were taken into account simultaneously from a desirability function, iSe was the most critical factor.(AU)

Polymorphism in the BIEC2-808543 locus and its association with growth curve in Brasileiro de Hipismo horse breed

Our objective was to evaluate whether the single nucleotide polymorphism (SNP) BIEC2-808543, identified in some horse breeds, also occurs in the Brasileiro de Hipismo (BH) breed. In addition, we verified if this SNP is related to the growth curve profile of these animals for the variables body mass, height at withers, and height at croup, using nonlinear mixed models. For the DNA isolation, we collected blood samples from 167 young BH horses. We obtained the genotypes of these animals using the polymerase chain reaction-restriction fragment length polymorphism technique. For the association studies of this polymorphism with the growth curve in foals, we selected three traits: body mass, height at withers, and height at croup. Polymorphism C/T exists in BH horses and is significantly associated with the evaluated traits. Animals that presented the TT genotype were smaller and lighter when compared with animals of the CT and CC genotypes. By the Akaike information criterion, the model that best described the growth curve for the body mass variable is the Brody model associated with the power of the mean variance function. For the height at withers variable, the best-fit model was von Bertalanffy, adjusted without polymorphism effect in parameter b, associated with the asymptotic variance. For the height at croup trait, the model that best described the growth curve was Brody model, associated with asymptotic variance. This polymorphism represents a good molecular marker. Nonlinear models are promising for describing growth curves in horses, particularly by the possibility of associating SNP effects to model parameters.

Multivariate modeling strategies to predict nutritional requirements of essential amino acids in semiheavy second-cycle hens

An experiment with 23 diets was performed to evaluate the effect of digestible lysine (Lys), digestible methionine + cysteine (Met+Cys), and digestible threonine (Thr) on egg production of H&N Brown second-cycle laying hens (SCLH) for 20 weeks (92-111 weeks of age) in cages under environmental conditions. Body weight (BW), feed intake (FI), feed conversion ratio (FCR), egg weight (EW), number of hen-housed eggs, and livability were also evaluated during the experiment. Diets were formulated from a central composite design that combined five levels of Lys, Met+Cys, and Thr ranging from 727 to 1159, 662 to 1055, and 552 to 882 mg/kg, respectively. Egg production (EP) data were evaluated through three different modeling strategies: egg production models, multivariate polynomial models, and artificial neural networks (ANN). A cascade-forward neural network with logsigmoid transfer function was selected as the best model according to goodness-offit statistics in both identification and validation data. One of the best scenarios for EP of H&N Brown SCLH under specific outdoor conditions was established at Lys, Met+Cys, and Thr levels of 1138, 1031, and 717 mg/hen·day, respectively. The ANN model may be an appropriate tool to study and predict EP of H&N Brown SCLH based on the combination of three different levels of essential digestible amino acids. The strategies included in this work may contribute to improving poultry performance based on modeling techniques to study other production parameters in terms of different nutritional requirements and productive conditions.

Choice of non-linear models to determine the growth curve of meat-type quail / Escolha de modelos não-lineares para determinar a curva de crescimento de codornas de corte

The objective of this study was to compare non-linear models fitted to the growth curves of quail to determine which model best describes their growth and check the similarity between models by analyzing parameter estimates.Weight and age data of meat-type European quail (Coturnix coturnix coturnix) of three lines were used, from an experiment in a 2 × 4 factorial arrangement in a completely randomized design, consisting of two metabolizable energy levels, four crude protein levels and six replicates. The non-linear Brody, Von Bertalanffy, Richards, Logistic and Gompertz models were used. To choose the best model, the Adjusted Coefficient of Determination, Convergence Rate, Residual Mean Square, Durbin-Watson Test, Akaike Information Criterion and Bayesian Information Criterion were applied as goodness-of-fit indicators. Cluster analysis was performed to check the similarity between models based on the mean parameter estimates. Among the studied models, Richards was the most suitable to describe the growth curves. The Logistic and Richards models were considered similar in the analysis with no distinction of lines as well as in the analyses of Lines 1, 2 and 3.(AU)

Objetivou-se, neste estudo, comparar modelos não lineares ajustados às curvas de crescimento de codornas para determinar qual modelo que melhor descreve o crescimento de codornas e verificar a similaridade dos modelos analisando as estimativas dos parâmetros. Para as análises foram utilizados os dados peso e idade de codornas européias de corte (Coturnix coturnix coturnix) proveniente de três linhagens, em um esquema fatorial 2x4, instalado em um delineamento inteiramente casualizado, com dois níveis de energia metabolizável e quatro níveis de proteína bruta, com seis repetições. Os modelos não lineares utilizados foram: Brody, Von Bertalanffy, Richards, Logístico e Gompertz. Para a escolha do melhor modelo utilizou-se o Coeficiente de Determinação Ajustado, o Percentual de Convergência, o Quadrado Médio do Resíduo, o Teste de Durbin-Watson, o Critério de informação Akaike e o Critério de informação Bayesiano como avaliadores da qualidade do ajuste. Utilizou-se a análise de agrupamento para verificar, baseado nas estimativas médias dos parâmetros, a similaridades entre os modelos. Entre os modelos estudados, o Richard foi o mais adequado para descrever as curvas de crescimento. Os modelos Logístico e Richards foram considerados similares nas análises sem distinção de linhagem, bem como nas análises das Linhagem 1, 2 e 3.(AU)

Relationships of conformation traits with somatic cell score in Nubian goats

Records of 3716 Nubian goats from the United States (US) were analyzed to estimate relationships between fourteen conformation traits (CT) with lactation average somatic cell score (ASCS). To analyze ASCS, a mixed model was implemented. Linear and quadratic effects of CT traits, days in milk (DIM), and kidding age in months (KA) were considered as fixed covariates, and herd-year (HY) of kidding as a random effect. Correlation coefficients between CT traits and ASCS adjusted for HY and linear and quadratic KA effects were also obtained. The average ± standard deviations for ASCS, DIM, and milk yield were 5.17±0.54 Log2, equivalent to 451.3 cells × 103/mL, 266.3±52.1 days, and 776.3±280.4 kg per lactation, respectively. Significant non-linear relationships with an intermediate maximum were found between ASCS with teat diameter and medial suspensory ligament, while linear relationships were observed with stature, strength, rump width, fore udder attachment, udder depth, teat diameter, teat placement, and medial suspensory ligament. The model explained 53.7% of the ASCS variability, but the contribution of each type variable to increase the coefficient of determination was low (<0.52%). Herd-year explained a large proportion of the variation of ASCS (38.4%). All estimated correlations between CT and ASCS had low values, from −0.04 to 0.11, but most were significant. The results of this study show that conformation traits have few opportunities to contribute phenotypically to assess somatic cell score in Nubian goats.