Capirona (Calycophyllum spruceanum (Benth.) K. Schum.) and Bolaina (Guazuma crinita Lam.) are fast-growing Amazonian trees with increasing demand in timber industry. Therefore, it is necessary to determine the content of cellulose, hemicellulose, holocellulose and lignin in juvenile trees to accelerate forest breeding programs. The aim of this study was to identify chemical differences between apical and basal stem of Capirona and Bolaina to develop models for estimating the chemical composition using Fourier transform infrared (FTIR) spectra. FTIR-ATR spectra were obtained from 150 samples for each species that were 1.8 year-old. The results showed significant differences between the apical and basal stem for each species in terms of cellulose, hemicellulose, holocellulose and lignin content. This variability was useful to build partial least squares (PLS) models from the FTIR spectra and they were evaluated by root mean squared error of predictions (RMSEP) and ratio of performance to deviation (RPD). Lignin content was efficiently predicted in Capirona (RMSEP = 0.48, RPD > 2) and Bolaina (RMSEP = 0.81, RPD > 2). In Capirona, the predictive power of cellulose, hemicellulose and holocellulose models (0.68 < RMSEP < 2.06, 1.60 < RPD < 1.96) were high enough to predict wood chemical composition. In Bolaina, model for cellulose attained an excellent predictive power (RMSEP = 1.82, RPD = 6.14) while models for hemicellulose and holocellulose attained a good predictive power (RPD > 2.0). This study showed that FTIR-ATR together with PLS is a reliable method to determine the wood chemical composition in juvenile trees of Capirona and Bolaina.

Although barley is extensively grown in the Peruvian highlands as a food crop, agricultural and quality performance have historically been poor.

Population growth, climate change and global warming are the great challenges facing agriculture in the 21st century. Therefore, it is necessary to increase the efficiency of selection of new varieties in plant breeding programs. In this regard, flow cytometry has proven to be a very powerful tool to speed-up selection processes in plant breeding because of its versatility and capacity to evaluate large populations.

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Se realizó una estandarización del método de extracción de ADN para cañihua (Chenopodium pallidicaule Aellen) y la caracterización molecular mediante marcadores moleculares ISSR (Inter Simple Sequence Repeat). Se utilizaron hojas de 40 accesiones de cañihua tomadas del banco de germoplasma del Programa de Cereales y Granos Nativos de la UNALM provenientes de dos provincias de Puno. Se realizó la extracción del ADN mediante el método CTAB. La agregación de cloroformo: alcohol isoamílico (24:1) y del etanol 96% permitieron la extracción de ADN de buena calidad. Obtenido el ADN, se realizó la amplificación PCR, PAGE y tinción con nitrato de plata. Se evaluó el porcentaje de loci polimórficos, contenido de información polimórfica y análisis de variancia molecular. Se seleccionaron 7 cebadores que produjeron 52 locus polimórficos de 144 marcadores ISSR, siendo los más informativos 841, 812, 810, 834 y BOR2. Una alta diferenciación genética entre poblaciones fue detectada basada en el AMOVA (Fst = 0.1544). Esto hace suponer que, la posición geográfica marca la diferenciación genética entre poblaciones de diferentes provincias.

Introduction. Coffee (Coffea arabica L.) is an important crop in producing countries like Peru, where approximately two million families depend on its production, distribution, and marketing. But in recent years, climate change has increased the presence of coffee leaf rust - CLR (H. vastatrix), a disease that has decreased Peruvian production by up to 27%. Objective. Monitor the severity of CLR in different genotypes of coffee cv. Typica from April-2017 to March-2018. Material and methods. The experiment was carried out in the coffee germplasm bank at the Development Regional Institute (IRD)-Selva of the Universidad Nacional Agraria La Molina. The severity and the area under the disease progress curve (AUDPC) in the lower, middle, and upper part of coffee trees were quantified. Result. There was high severity and AUDPC in the dry season (low precipitation) compared to the rainy season (high precipitation). Severity and AUDPC gradually decreased from the bottom to the top of the plant (lower>middle>upper). Also, UNACAF-24A, UNACAF-16, UNACAF-158, and UNACAF-162 genotypes stood out by showing lowest severity (0-1.1 %) and AUDPC (0-714) in the experiment. Conclusion. In this experiment, the genotypes in dry season presented high peaks of severity and UDPC of CLR, meanwhile, in rainy season the presence of CLR was lower. However, in both seasons, severity and AUDPC gradually decreased from the lower third to the upper thirf of the plant. Finally, UNACAF-24A, UNACAF-16, UNACAF-158, and UNACAF-162 presented the lowest degrees of severity of CLR.

Fast-growing trees like Capirona, Bolaina, and Pashaco have the potential to reduce forest degradation because of their ecological features, the economic importance in the Amazon Forest, and an industry based on wood-polymer composites. Therefore, a practical method to discriminate specie (to avoid illegal logging) and determine chemical composition (tree breeding programs) is needed. This study aimed to validate a model for the classification of wood species and a universal model for the rapid determination of cellulose, hemicellulose, and lignin using FTIR spectroscopy coupled with chemometrics. Our results showed that PLS-DA models for the classification of wood species (0.84 ≤ R2 ≤ 0.91, 0.12 ≤ RMSEP ≤ 0.20, accuracy, specificity, and sensibility between 95.2 and 100%) were satisfied with the full spectra and the differentiation among these species based on IR peaks related to cellulose, lignin, and hemicellulose. Besides, the full spectra helped build a three-species universal PLS model to quantify the principal wood chemical components. Lignin (RPD = 2.27, $${R}_{c}^{2}$$ R c 2 = 0.84) and hemicellulose (RPD = 2.46, $${R}_{c}^{2}$$ R c 2 = 0.83) models showed a good prediction, while cellulose model (RPD = 3.43, $${R}_{c}^{2}$$ R c 2 = 0.91) classified as efficient. This study showed that FTIR-ATR, together with chemometrics, is a reliable method to discriminate wood species and to determine the wood chemical composition in juvenile trees of Pashaco, Capirona, and Bolaina.