When doing researches on solute dynamics in porous medium, the knowledge of medium characteristics and percolating liquids, as well as of external factors is very important. An important external factor is temperature and, in this sense, our purpose was determining potassium and nitrate transport parameters for different values of temperature, in miscible displacement experiments. Evaluated parameters were retardation factor (R), diffusion/dispersion coefficient (D) and dispersivity, at ambient temperature (25 up to 28 ºC), 40 ºC and 50 ºC. Salts used were potassium nitrate and potassium chlorate, prepared in a solution made up of 5 ppm nitrate and 2.000 ppm potassium, with Red-Yellow Latosol porous medium. Temperature exhibited a positive influence upon porous medium solution and upon dispersion coefficient.KEYWORDS: breakthrough curves, miscible displacement, computational modeling. EFEITO DA TEMPERATURA NO TRANSPORTE DOS ÍONS POTÁSSIO E NITRATO NO SOLO RESUMO:No estudo da dinâmica de solutos num meio poroso, é de suma importância o conhecimento das propriedades do meio e dos líquidos percolantes, bem como de fatores externos. Um fator externo relevante é a temperatura e, nesse sentido, teve-se como objetivo determinar os parâmetros de transporte dos íons potássio e nitrato para diferentes valores de temperatura em experimentos de deslocamento miscível. Os parâmetros avaliados foram o fator de retardamento (R), o coeficiente de difusão/dispersão (D) e a dispersividade ( ), e as temperaturas utilizadas foram a ambiente (25 a 28 ºC), 40 ºC e 50 ºC. Os sais utilizados foram nitrato de potássio e cloreto de potássio, preparados em solução composta de 50 ppm de nitrato e 2.000 ppm de potássio, sendo o meio poroso um Latossolo Vermelho-Amarelo, textura média. A temperatura apresentou influência positiva na velocidade da solução no meio poroso e no coeficiente de dispersão.
Usually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness that we compare models. Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, posthoc tests, such as ANOVA, are not recommended due to assumption unfulfilled regarding residuals independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-α). The CV-α was meant to create several scenarios of validation (replicates x folds), regardless of the number of treatments. Using CV-α, the number of genotypes in the same fold across replicates was much lower than K-fold, indicating higher residual independence. Therefore, based on the CV-α results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV-α method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost nor complexity, it is more reliable and allows the use of non-subjective methods to compare models and factors. Therefore, CV-α can be considered a more precise validation methodology for model selection.
Haploid maize seeds prediction using deep learning and using mock reference genomes for genomic prediction of hybrids Prediction is a key concept for animal and plant breeding. Accurate estimates of phenotypic and genetic values are crucial for the selection of the best genotypes. For this reason, several tools have been used to improve the accuracy of these estimates, from molecular markers, used to access genetic information, to high-throughput phenotyping, used to increase sample size and phenotypic precision. Here, we present two studies involving the use of different approaches and tools in the prediction process. First, we describe a study using deep learning and images for seed phenotyping. We built a convolutional neural network (CNN) model to classify images from putative and true haploid maize seeds based on the R1-nj phenotype. Our results reveal that the CNN model could classify putative haploid maize seeds with high accuracy (97%). However, the CNN model was unable to recognize true haploid seeds. Finally, we provide a highly accurate and trained CNN model to the scientific community to classify haploid maize seeds via R1-nj. In the latter, we studied using mock genomes to discover markers and their effect on estimates of genetic diversity and genomic prediction of hybrids. Moreover, we compared them with SNP markers from SNP-array and genotyping-bysequencing (GBS) scored in the reference genome B73. Our results show that using mock genomes delivers estimates comparable to standard platforms when considering simple traits and additive effects. However, for complex traits and dominance effects, the estimates were slightly worse. We believe that these studies provide relevant knowledge for the phenotypic and genomic prediction applied to plant breeding.
New apps have changed the traditional way of learning and teaching; they are also applied as a quickly executed and effective method in agriculture. Soil-app is a web application with a friendly click-point interface built through packages lodged in R software. The app is an advanced model of an open-source platform to support teaching and learning activities in soil analyses and fertilizer recommendations. Soil-app includes soil test interpretation, soil amendment calculations (lime and gypsum), the fertilizer rate for the most important crops in Brazil, an NPK blend calculator, and NPK blend evaluation. It also includes experimental statistical analysis as applied to soil science. Soil-app is a user-friendly and high-performance tool, garnering fast adoption by both students and professionals. It is available for network use through the following link:
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