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Thermal and mechanical properties of PLA-based multiscale cellulosic biocomposites

MIGUEL ANGEL RUZ CRUZ Pedro Jesús Herrera Franco Emmanuel Alejandro Flores Johnson MARIA VERONICA MORENO CHULIM LUCIANO MIGUEL GALERA MANZANO Alex Valadez González (2022, [Artículo])

In this work polylactic acid (PLA) based multiscale cellulosic biocomposites were prepared with the aim to evaluate the effect of the incorporation of cellulose nanocrystals (CNCs) on the PLA biocomposites reinforced with cellulose microfibers (MFCs). For this, PLA composite materials reinforced with both MFCs and with a combination of MFCs and CNCs were prepared, while keeping the content of cellulosic reinforcements constant. The thermal and mechanical properties of these multiscale PLA biocomposites were characterized by thermogravimetry (TGA), differential scanning calorimetry (DSC), flexural mechanical and, dynamic mechanical (DMA) tests. Likewise, they were characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The results show that the replacement of MFCs by CNCs in the 1–5% range appreciably modifies the thermal and mechanical properties of multiscale compounds. For example, they increase the thermal stability of the materials, modify the PLA crystallization process and play the role of adhesion promoters since the mechanical properties in flexure increase in the order of 40% and the storage modulus increases in the order of 35% at room temperature. Also, the addition of CNCs increases the relaxation temperature of the material from 50 to 60 °C, thereby expanding the temperature range for its use. © 2022 The Author(s)

MULTISCALE BIOCOMPOSITES CELLULOSE MICROFIBER CELLULOSE NANOCRYSTALS HIERARCHICAL STRUCTURE PROPERTIES INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS TECNOLOGÍA DE MATERIALES PROPIEDADES DE LOS MATERIALES PROPIEDADES DE LOS MATERIALES

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding

Farmers’ perspectives as determinants for adoption of conservation agriculture practices in Indo-Gangetic Plains of India

Ajay Kumar Mishra ML JAT (2022, [Artículo])

Understanding the farmer's perspective has traditionally been critical to influencing the adoption and out-scaling of CA-based climate-resilient practices. The objective of this study was to investigate the biophysical, socio-economic, and technical constraints in the adoption of CA by farmers in the Western- and Eastern-IGP, i.e., Karnal, Haryana, and Samastipur, Bihar, respectively. A pre-tested structured questionnaire was administered to 50 households practicing CA in Western- and Eastern-IGP. Smallholder farmers (<2 ha of landholding) in Karnal are 10% and Samastipur 66%. About 46% and 8% of households test soil periodically in Karnal and Samastipur, respectively. Results of PCA suggest economic profitability and soil health as core components from the farmer's motivational perspective in Karnal and Samastipur, respectively. Promotion and scaling up of CA technologies should be targeted per site-specific requirements, emphasizing biophysical resource availability, socio-economic constraints, and future impacts of such technology.

Smallholder Farmers Agents of Change Technology Diffusion Climate-Smart Practices CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SMALLHOLDERS SOCIAL STRUCTURE IRRIGATION MANAGEMENT TECHNOLOGY CLIMATE-SMART AGRICULTURE CONSERVATION AGRICULTURE