Manuel Oliva-Cruz’s research while affiliated with National University Toribio Rodríguez de Mendoza and other places

In this research, species distribution prediction models (i.e., MaxEnt) were applied to analyze the suitability of the ecological environment among the clades of the genus Gynoxys in Peru. Bioclimatic, edaphic, and topographic variables were integrated to predict the areas with the most significant potential for optimal development of this genus. These data were combined to generate potential distribution maps, taking into account the most relevant variables for each clade. The validation of the MaxEnt model showed an outstanding performance, reaching AUC indices above 0.9, reflecting the high accuracy of the predictions. The results reveal that the key variables influencing the selection of the clade occurrence areas are: mintempwarmest (47.70% contribution) in the Discoide clade, topowet (33.20%) in the Gynoxys clade, and monthcountbytemp10 (33.30%) in the Praegynoxys clade. The potential distribution areas of these clades were 132,594 km² for Discoide, 168,574 km² for Gynoxys, and 37,392 km² for Praegynoxys. The areas with the highest probability of presence of the genus were found in the Andean regions of northern and central Peru. However, a significant proportion of these areas were threatened by habitat fragmentation and land degradation. In terms of conservation, it was found that 32.05, 35.46, and 61.02% of the potential distribution areas of the discoid, Gynoxys, and Praegynoxys clades, respectively, are conserved, which could be a relevant factor for the preservation of this genus. These findings underscore the relevance of safeguarding key areas for conserving Gynoxys and montane ecosystems in Peru, emphasizing the need for protection strategies that guarantee the long-term sustainability of these species and their associated habitats.

Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil’s physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (−0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices.

The increasing demand for food and energy presents challenges for agricultural and energy sustainability, especially in regions with limited arable land. This study analyzed the productivity and morphological adaptations of Phaseolus vulgaris L. in agrivoltaic systems using monofacial, bifacial, and semi-transparent photovoltaic technologies under the high Andean climatic conditions of Chachapoyas, Amazonas, Peru. The evaluated varieties, Panamito and Chaucha, were cultivated with planting distances of 25 cm and 35 cm. The analyzed variables included plant height, number of trifoliate leaves, number of flowers, number and weight of pods, grain weight, and yield. The experiment was designed with plots under agrivoltaic systems and a conventional system as a control. Environmental parameters such as photosynthetically active radiation (PAR), irradiance, precipitation, leaf moisture, soil moisture, and ambient temperature were monitored. Results showed that the bifacial system with a planting density of 25 cm was the most efficient, recording a plant height of 139.38 cm, an average grain weight of 67.97 g, and a yield of 700.5 kg/ha, significantly surpassing the conventional system. These findings shows the potential of agrivoltaic systems to enhance agricultural production by efficiently utilizing solar radiation and land, providing an innovative solution for integrating agriculture and energy generation, as well as increasing productivity in scenarios with land-use competition and climatic challenges.

Pitahaya (Hylocereus megalanthus), commonly known as dragon fruit, is grown in tropical areas and has a promising future in the world market. At present, it is a crop developed by small-scale farmers. However, finding optimal areas for installing this crop is a major challenge. In this study, we evaluated the suitability of land for pitahaya cultivation in the department of Amazonas using integrated multi-criteria techniques such as geographic information systems (GISs) and remote sensing (RS). The analytic hierarchy process (AHP) method was used to select and rank the suitability criteria. The fuzzy-AHP (F-AHP) method was then applied to perform pairwise comparisons and determine the linguistic scaling of the requirements, and, using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), the requirements with the highest preference for land suitability were selected. The results reported that for pitahaya cultivation, the most important criterion was mean annual temperature (20.70%), followed by soil organic matter (11.8%), mean annual rainfall (9.50%), and proximity to roads (9.0%). The final suitability map indicated that 0.006% (2.39 km²) was very suitable, 4.60% (1661.97 km²) moderately suitable, 0.10% (34.65 km²) marginally suitable, and 95.30% (34,459.31 km²) of the study area was not suitable.

The genetic improvement of Physalis peruviana L. faces important challenges because it is a recalcitrant species, which limits its use in breeding and conservation programs. In the present research, the objective was to develop an effective regeneration protocol using cotyledon cultures. For this purpose, the effects of zeatin (ZT) and meta-topoline (mT) at concentrations of 0, 0.5, 1.0 and 2.0 mg/L and the effects of several auxins, including indol-butyric acid (IBA), indolacetic acid (IAA), naphthaleneacetic acid (NAA) and 2.4-dichlorophenoxyacetic acid (2.4-D), on morphogenetic responses were evaluated. A maximum regeneration of 62.5 % was achieved with the combination of 2 mg/L ZT, 2 mg/L mT, and 0.5-1 mg/L IBA. For root formation, the best results were obtained with the combination of 2 mg/L ZT, 2 mg/L mT, and 1 mg/L NAA, reaching a maximum rate of 87.5 %. In conclusion, specific combinations of cytokinins and auxins can overcome the resistance of P. peruviana to regeneration and provide a solid basis for biotechnological applications such as genetic transformation and germplasm conservation.