Carlos I. Arbizu

The origin and genealogy of "Bicentenaria", its development, agronomic characteristics, and performance within different environments are described. "Bicentenaria" was developed by Universidad Nacional Jose Faustino Sanchez Carrion (Peru) from a cross between three commercial cultivars for processing of tetraploid Solanum tuberosum cultivars: Reiche (Peru), Diacol Capiro (Colombia) and Atlantic (USA). The potato cultivar "Bicentenaria" (Register No. 001-2021-DELYC-SENASA) exhibits early maturity, competitive productivity, and adaptation capacity throughout the central coast of Peru with an average yield of 44.8 t ha-1, compared to the cultivars Canchan INIA (37.2 t ha-1) and Diacol Capiro (32.5 t ha-1). Its short vegetative period, high yields and solids content, combined with an appropriate tuber type make it a suitable potato for processing into products like French fries and chips.

Ceiba spp. are native Amazonian forest trees of ecological and economic importance; however, genetic information on this genus remains limited. Genomic approaches such as genotyping-by-sequencing (GBS) require high-quality DNA, which can be challenging to obtain from leaf tissue. This study aimed to develop and evaluate a protocol for extracting high-quality genomic DNA from silica gel–preserved leaves of Ceiba spp. collected in the Loreto, Ucayali, and Madre de Dios regions of the Peruvian Amazon. Genomic DNA was extracted from 20 samples using a modified cetyltrimethylammonium bromide (CTAB) protocol incorporating a sorbitol-buffer prewash. DNA quantity and quality were assessed by microvolume spectrophotometry and Qubit fluorometry. A260/A280 ratios ranged from 1.70 to 1.99 (mean, 1.84), while spectrophotometric DNA concentrations ranged from 220.8 to 1294.1 ng/µL (mean, 696.5 ng/µL). Qubit measurements indicated detectable double-stranded DNA in all samples, although half of the readings (10/20) exceeded the upper limit of the assay range used. DNA suitability for polymerase chain reaction (PCR) was assessed by amplifying the rbcL gene with universal primers, yielding amplification products in all 20 samples. The protocol yielded genomic DNA with quality characteristics compatible with downstream molecular applications and may support future GBS studies, subject to additional validation.

Maize (Zea mays L.) in Peru exhibits extensive phenotypic diversity traditionally classified into races based on morphological characteristics, but quantitative relationships among races remain insufficiently known. In the present analysis, morphological data from 43 Peruvian maize races were analyzed using principal component analysis and hierarchical clustering to identify discriminant descriptors and phenotypic groups. From 68 quantitative descriptors retained after data completeness filtering, nine non-redundant variables associated with leaf, tassel, and ear morphology captured the main patterns of phenotypic variation. The first two principal dimensions explained 77.50% of the total variation, with leaf and tassel traits predominating in the first dimension and ear characteristics in the second. Hierarchical clustering separated the races into four phenotypically differentiated groups characterized by distinct combinations of leaf, tassel, ear, and cob–rachis traits. These groups showed only partial correspondence with the historical racial classification, indicating that quantitative phenotypic similarity represents an additional dimension of maize diversity. The results provide a parsimonious quantitative basis for characterizing Peruvian maize germplasm and support the complementary use of multivariate phenotyping and historical racial classification for conservation and breeding.

Ceiba pentandra is a forest species of high commercial value in Peru due to the characteristics of its wood. Determining the current and future potential distribution areas of C. pentandra provides a scientific basis for the sustainability of the species. In this study, we used spatial modeling to determine the dynamics of the current and future potential distribution of C. pentandra with the MIROC6 model in two SSPs (245 and 585), using a record of 335 data points on the presence of the species and 12 edaphoclimatic variables, which were integrated into the RandomForest algorithm. The results showed that the prediction is statistically consistent (AUC-ROC = 0.94), based on which the model predicted that C. pentandra is mainly distributed in the northern part of the Amazonas department, specifically in the province of Condorcanqui. In the future, in both SSPs (245 and 585), the geographical distribution area will remain the same, but the areas will decrease compared to those currently predicted. The findings of this research will help the competent authorities to map the optimal areas for reintroducing the species or establishing C. pentandra forest plantations.

Climate change poses an increasing threat to crop suitability and food security, particularly for varieties of great cultural and economic importance, such as Peruvian starchy maize (Zea mays L.), whose optimal growing areas remain poorly characterized at the national level. This study presents the first comprehensive geospatial assessment of current and future land suitability for starchy corn in Peru, integrating bioclimatic, edaphic, and topographic variables using a machine learning approach. A dataset of georeferenced accessions, combined with pseudo-absence data, was used to train and validate a model using the Random Forest algorithm based on 29 bioclimatic, edaphic, and topographic predictors. The model demonstrated high statistical reliability (AUC-training = 0.98; AUC-validation = 0.99; precision = 0.971; recall = 0.962; F1 score = 0.966; accuracy = 0.966), with temperature-related variables (BIO09, BIO05, BIO15, and annual mean temperature) identified as the most important predictors. Projections made using the MIROC6 general circulation model under four shared socio-economic scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8. 5) for the periods 2061–2080 and 2081–2100 indicate a net expansion of highly suitable areas ranging from 20.76% to 45.46%, depending on the scenario. The most optimistic scenario (SSP1-2.6) projects the greatest gain (approximately 45% by 2070), while the most pessimistic (SSP3-7.0) shows an initial contraction followed by a marked recovery by 2100. These findings provide a robust geospatial framework for climate-smart agricultural planning, breeding programs, and sustainable land-use policies for Peruvian starchy maize in the context of future climate change.

Manilkara (Sapotaceae) includes tropical tree species of high ecological and socio-economic value, yet genetic and phylogenetic evidence remains uneven across taxa and eco-geographic regions. Here, we synthesize studies conducted between 1999 and 2025 which summarize the use of molecular markers to infer genetic diversity, connectivity, population structure, and evolutionary relationships within this genus. The studies are dominated by PCR-based marker systems, including dominant markers (like RAPD and SCoT) and microsatellites from the nuclear genome and plastid genome. Other studies rely on PCR-amplified sequence loci, such as ITS and chloroplast regions, while others use high-throughput technologies, including NGS-assisted SSR development, sequences of complete plastomes, and targeted nuclear sequencing. Overall, studies using SSRs provide the most informative estimates for within-species diversity and fine-scale structure, whereas plastid datasets (cpSSR/cpDNA) mainly support inference on maternal lineages and plastid-based relationships but can be constrained by uniparental inheritance and limited variation, especially under small sampling. Some limitations found include heterogeneous sampling, inconsistency in reporting the methodological parameters, and limited connection with ecological or phenotypic parameters which restricts chances of inferences on demography and adaptation. Based on this review, future research in Manilkara would benefit from setting up a broader taxonomic and geographic coverage, incorporating genome-wide technology where feasible to strengthen conservation management, and breeding opportunities in Manilkara.