In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R2 for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R2 for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.

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.

Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.

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.

Lupinus mutabilis Sweet (Fabaceae), “tarwi” or “chocho”, is an important grain legume in the Andean region. In Peru, studies on tarwi have been mainly focused on morphological features, however, the have not been molecularly characterized. Currently, it is possible to explore genetic parameters of plants with reliable and modern methods like genotyping-by-sequencing (GBS). We here for the first time used single nucleotide polymorphisms (SNPs) markers to infer the genetic diversity and population structure of 89 accessions of tarwi from nine Andean regions of Peru. A total of 5922 SNPs distributed along all chromosomes of tarwi were identified. STRUCTURE analysis revealed that this crop is grouped into two clusters. A dendrogram was generated using the UPGMA clustering algorithm and, similar to the principal coordinate analysis (PCoA), it showed two groups that correspond to the geographic origin of the tarwi samples. AMOVA showed a reduced variation between clusters (7.59 %) and indicated that variability within populations is 92.41 %. Population divergence (Fst) between clusters 1 and 2 revealed low genetic difference (0.019). We also detected a negative Fis for both populations, demonstrating that, similar to other Lupinus species, tarwi also depends on cross-pollination. SNPs markers were powerful and effective for the genotyping process in this germplasm. We hope that this information is the beginning of the path towards a modern genetic improvement and conservation strategies of this important Andean legume.