No abstract

Production systems that promote the accumulation of soil organic matter (SOM) must be implemented to maintain the sustainability of agriculture, livestock, and forestry. Since increases in MOS content contribute to improving the chemical, physical, and biological quality of the soil, as well as helping to reduce carbon emissions to mitigate climate change. Therefore, the objective of this study was to evaluate soil organic carbon (SOC) and nitrogen (N) stocks after the implementation of agrosilvopastoral (ASP) systems in a Cerrado-Caatinga transition zone in Brazil. Native vegetation of Cerrado-Caatinga (NV), regenerating stratum of Cerrado-Caatinga (RS), two arrangements of ASP systems cultivating Cenchrus ciliaris L. intercropped with Eucalyptus camaldulensis Dehnh. × Eucalyptus tereticornis Sm. hybrid (ASP1 and ASP2), and intercropped with Eucalyptus urophylla S.T. Blake × Eucalyptus grandis W. Mill ex Maiden hybrid (ASP3 and ASP4) were evaluated. Soil C and N stocks and the C content in the humic fractions of SOM were evaluated at 0–10, 10–20, and 20–30 cm soil depths. The introduction of ASP2, ASP3, and ASP4 systems in an area previously occupied by low productivity pasture increased and restored SOC stocks to levels found in NV, at a depth of 0–30 cm. N stocks were higher in ASP systems, regardless of the arrangement studied. As a result, the ASP systems provided accumulations that ranged from 1.0 to 4.31 Mg SOC ha−1 yr−1 and from 0.33 to 0.36 Mg N ha−1 yr−1. The carbon contents in humic fractions remained higher in NV. The hierarchical grouping and principal component analysis showed that the implementation of the ASP systems was efficient in increasing soil C and N stocks over time. In conclusion, the present study identified that integrated production systems can support land use intensification strategies based on sustainable and low-carbon agriculture in a transition area between the Cerrado and Caatinga biomes in Brazil.

No abstract

The improvement of sweet potato is a costly job due to the large number of characteristics to be analyzed for the selection of the best genotypes, making it necessary to adopt new technologies, such as the use of images, associated with the phenotyping process. The objective of this research was to develop a methodology for the phenotyping of the root production aiming genetic improvement of half-sib sweet potato progenies through computational analysis of images and to compare its performance to the traditional methodology of evaluation. Sixteen half-sib sweet potato families in a randomized block design with 4 replications were evaluated. At plant level, the weight per root and the total number of roots were evaluated. The images were acquired in a “studio” made of mdf with a digital camera model Canon PowerShotSX400 IS, under artificial lighting. The evaluations were carried out using the R software, where a second-degree polynomial regression model was fitted to predict the root weight (in grams) and the genetic values and expected gains were obtained. It was possible to predict the root weight at plant and plot level, obtaining high coefficients of determination between the predicted and observed weight. Computer vision allowed the prediction of root weight, maintaining the genotype ranking and consequently the similarity between the expected gains with the selection. Thus, the use of images is an efficient tool for sweet potato genetic improvement programs, assisting in the crop phenotyping process.

The selection of superior sweet potato genotypes using Bayesian inference is an important strategy for genetic improvement. Sweet potatoes are of social and economic importance, being the material for ethanol production. The estimation of variance components and genetic parameters using Bayesian inference is more accurate than that using the frequently used statistical methodologies. This is because the former allows for using a priori knowledge from previous research. Therefore, the present study estimated genetic parameters and selection gains, predicted genetic values, and selected sweet potato genotypes using a Bayesian approach with a priori information. Root shape, soil insect resistance, and root and shoot productivity of 24 sweet potato genotypes were measured. Heritability, genotypic variation coefficient, residual variation coefficient, relative variation index, and selection gains direct, indirect and simultaneous were estimated, and the data were analyzed using Bayesian inference. Data from 11 experiments were used to obtain a priori information. Bayesian inference was a useful tool for decision-making, and significant genetic gains could be achieved with the selection of the evaluated genotypes. Root shape, soil insect resistance, commercial root productivity, and total root productivity showed higher heritability values. Clones UFVJM06, UFVJM40, UFVJM54, UFVJM09, and CAMBRAIA can be used as parents in future breeding programs.