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The mitochondrial genome of Pumpo (Bos taurus), a prominent breed contributing to livestock farming, was sequenced using the Illumina HiSeq 2500 platform. Assembly and annotation of the mitochondrial genome were achieved through a multifaceted approach employing bioinformatics tools such as Trim Galore, SPAdes, and Geseq, followed by meticulous manual inspection. Additionally, analyses covering tRNA secondary structure and codon usage bias were conducted for comprehensive characterization. The 16,341 base pair mitochondrial genome comprises 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes. Phylogenetic analysis places Pumpo within a clade predominantly composed of European cattle, reflecting its prevalence in Europe. This comprehensive study underscores the importance of mitochondrial genome analysis in understanding cattle evolution and highlights the potential of genetic improvement programs in livestock farming, thus contributing to enhanced livestock practices.

Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.

This study aimed to determine the impact of age on the fecal microbiota in the genetic nucleus of cattle, with a focus on microbial richness, composition, functional diversity, and correlations with blood parameters. Fecal and blood samples from 21 cattle were analyzed using 16S rRNA gene sequencing. Older cattle exhibited greater bacterial diversity and abundance, with significant changes in alpha diversity indices (p < 0.05). Beta diversity analysis revealed significant variations in microbial composition between age groups and the interaction of age and sex (p < 0.05). Correlations between alpha diversity, community composition, and hematological values highlighted the influence of microbiota on bovine health. Beneficial butyrate-producing bacteria, such as Ruminococcaceae, were more abundant in older cattle, suggesting a role in gut health. Functional diversity analysis indicated that younger cattle had significantly more abundant metabolic pathways in fermentation and anaerobic chemoheterotrophy. These findings suggest management strategies including tailored probiotic therapies, dietary adjustments, and targeted health monitoring to enhance livestock health and performance. Further research should include comprehensive metabolic analyses to better correlate microbiota changes with age-related variations, enhancing understanding of the complex interactions between microbiota, age, and reproductive status.

New-generation sequencing technologies, among them SNP chips for massive genotyping, are useful for the effective management of genetic resources. To date, molecular studies in Peruvian cattle are still scarce. For the first time, the genetic diversity and population structure of a reproductive nucleus cattle herd of four commercial breeds from a Peruvian institution were determined. This nucleus comprises Brahman (N = 9), Braunvieh (N = 9), Gyr (N = 5), and Simmental (N = 15) breeds. Additionally, samples from a locally adapted creole cattle, the Arequipa Fighting Bull (AFB, N = 9), were incorporated. Female individuals were genotyped with the GGPBovine100K and males with the BovineHD. Quality control, and the proportion of polymorphic SNPs, minor allele frequency, expected heterozygosity, observed heterozygosity, and inbreeding coefficient were estimated for the five breeds. Admixture, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were performed. Also, a dendrogram was constructed using the Neighbor-Joining clustering algorithm. The genetic diversity indices in all breeds showed a high proportion of polymorphic SNPs, varying from 51.42% in Gyr to 97.58% in AFB. Also, AFB showed the highest expected heterozygosity estimate (0.41 ± 0.01), while Brahman the lowest (0.33 ± 0.01). Besides, Braunvieh possessed the highest observed heterozygosity (0.43 ± 0.01), while Brahman the lowest (0.37 ± 0.02), indicating that Brahman was less diverse. According to the molecular variance analysis, 75.71% of the variance occurs within individuals, whereas 24.29% occurs among populations. The pairwise genetic differentiation estimates (FST) between breeds showed values that ranged from 0.08 (Braunvieh vs. AFB) to 0.37 (Brahman vs. Braunvieh). Similarly, pairwise Reynold’s distance ranged from 0.09 (Braunvieh vs. AFB) to 0.46 (Brahman vs. Braunvieh). The dendrogram, similar to the PCA, identified two groups, showing a clear separation between Bos indicus (Brahman and Gyr) and B. taurus breeds (Braunvieh, Simmental, and AFB). Simmental and Braunvieh grouped closely with the AFB cattle. Similar results were obtained for the population structure analysis with K = 2. The results from this study would contribute to the appropriate management, avoiding loss of genetic variability in these breeds and for future improvements in this nucleus. Additional work is needed to speed up the breeding process in the Peruvian cattle system.

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.

The alpaca population mostly consists of the Huacaya phenotype and is widely distributed in Southern Peru. This study aimed to estimate the genetic diversity and population structure of two Huacaya alpaca populations (Ajoyani and Quimsachata) using fourteen and twelve microsatellite markers for each population, respectively. A total of 168 alpaca biological samples were outsourced to Peruvian laboratories for DNA extraction and genotyping. For genetic diversity, observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC), and fixation indices values were estimated. An admixture analysis was performed for the population structure analysis. Different programs were used for these estimations. In total, 133 (Ajoyani) and 129 (Quimsachata) alleles were found, with a range of 4 to 17 by locus. The mean HO, HE, and PIC per marker for Ajoyani were 0.764 ± 0.112, 0.771 ± 0.1, and 0.736; for Quimsachata, they were 0.783 ± 0.087, 0.773 ± 0.095, and 0.738, respectively. The population structure showed no structure with K = 2. This study provides useful indicators for the creation of appropriate alpaca conservation programs.

The Peruvian creole cattle (PCC) is a neglected breed and an essential livestock resource in the Andean region of Peru. To develop a modern breeding program and conservation strategies for the PCC, a better understanding of the genetics of this breed is needed. We sequenced the whole genome of the PCC using a de novo assembly approach with a paired-end 150 strategy on the Illumina HiSeq 2500 platform, obtaining 320 GB of sequencing data. A reference scaffolding was used to improve the draft genome. The obtained genome size of the PCC was 2.81 Gb with a contig N50 of 108 Mb and 92.59% complete BUSCOs. This genome size is similar to the genome references of Bos taurus and B. indicus. In addition, we identified 40.22% of repetitive DNA of the genome assembly, of which retroelements occupy 32.39% of the total genome. A total of 19,803 protein-coding genes were annotated in the PCC genome. For SSR data mining, we detected similar statistics in comparison with other breeds. The PCC genome will contribute to a better understanding of the genetics of this species and its adaptation to tough conditions in the Andean ecosystem.