Fusarium oxysporum f. sp. cubense (Foc) poses a significant threat to global banana production. This systematic review updates current knowledge on the efficacy of various antagonistic microorganisms in controlling Foc, considering the recent spread of this disease to new regions. The studies were systematically analyzed, focusing on methodologies, results, and conclusions to provide a comprehensive overview of current research and its practical implications. A total of 118 studies were reviewed, covering the use of antagonistic microorganisms such as Trichoderma spp., Bacillus spp., Streptomyces spp., and Pseudomonas spp., both in pure cultures and in consortia. Most studies focused on controlling Foc TR4 in Cavendish subgroup bananas and originated from Asia. Microbial consortia demonstrated a higher control percentage with lower variability, particularly in genera such as Pseudomonas. In contrast, pure cultures were more commonly used for Streptomyces. The choice between consortia and pure cultures depends on the genus and the experimental context, as each approach has distinct advantages. Although the reviewed studies were generally of high quality, long-term research is still lacking. Antagonistic microorganisms represent a promising alternative for Foc control, although their efficacy depends on the specific strain and environmental conditions. It has been observed that inoculating these microorganisms onto seedlings before transplantation or in combination with organic matter enhances their effectiveness. Localized testing and formulation optimization are recommended to improve their application as preventive and suppressive tools in soil against infections. The review highlights a vast diversity of microbial agents with high efficacy rates, various modes of action, and additional benefits for plant development beyond Foc biocontrol. Furthermore, some studies achieved 100% control at the plant level under controlled conditions. These findings demonstrate that biological control is a viable alternative for integrated Foc management. Future research should prioritize new approaches that facilitate the widespread adoption of these methodologies, including microbial formulation, field application, and integration with other control methods.

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.

Goat farming in Peru is a husbandry activity that, although it is considered secondary in the country, has a great economic and social impact on the rural population, that is why government efforts to develop is so important. The objective of this study was to characterize dairy goat rearing systems in the coastal valleys of the Lima region to identify gaps and opportunities for improvement. This cross-sectional research was conducted in four provinces located in the Lima region, Peru. A total of 62 goat farmers participated in the trial. For data collection, a standard survey was prepared with open and closed questions distributed across two components (socioeconomic and productive). The surveys were processed for qualitative variables using a multiple correspondence analysis (MCA) followed by a hierarchical cluster analysis (HCA) to differentiate the types of farming systems prevalent based on the survey population. The hierarchical cluster analysis resulted in the formation of three separate groups of goat farmers, which can be classified as extensive systems differentiated by management practices and their production and marketing objectives. The test showed a significant difference; therefore, it can be affirmed that they are associated with the groups or clusters formed. These results will allow actors related to goat farming, such as state and regional entities, to focus efforts on addressing specific demands of the different types of goat farmers found in this study.

Background and Aim: Characterizing local animal production systems is crucial for sustainable livestock development. This study aimed to characterize the diversity of goat production systems in the Highlands of Chincha province, Ica, Peru. Materials and Methods: A structured questionnaire was used to gather data from 82 goat breeders in three districts: San Juan de Yanac, San Pedro de Huacarpana, and Chavín. Factor analysis of mixed data and hierarchical classification analysis were conducted to identify typologies of goat production systems using R version 4.4.2. Results: Four distinct goat production types were identified, primarily differentiated by feeding location and deworming frequency. Type 2 (41.5%) was the most prevalent, characterized by grazing on breeders own land, minimal milk production (<1 liter/day, 91.2%), and a focus on cheese and goat kid sales (70.6%). Breeders were predominantly women, with limited resources and extensive management systems. Across all types, mixed breeding was common, and economic reliance on livestock and agriculture prevailed. Conclusion: Despite their diversity, all goat production systems shared extensive management practices and resource constraints, resulting in low productivity. These findings highlight the need for targeted public policies to improve productivity and sustainability in goat farming within the Ica region. Keywords: Chincha Highlands, extensive farming, goat production systems, multivariate analysis, Peru, typology.

Goat production in the dry forest of northern Peru is essential for rural livelihoods but remains poorly characterized regarding its productivity and sustainability. This study used multivariate techniques—a multiple correspondence analysis (MCA), principal component analysis (PCA), factor analysis of mixed data (FAMD), and hierarchical cluster analysis (HCA)—to analyze data from 284 producers in Tumbes, Piura, and Lambayeque. Surveys captured 48 variables (41 qualitative, seven quantitative) on productivity, socioeconomics, and management. The MCA explained 22.07% of the variability in two dimensions, while the PCA accounted for 63.9%, focusing on productivity and diversification. The FAMD integrated these variables, explaining 51.12% of variability across five dimensions, emphasizing socioeconomic and management differences. The HCA identified three clusters: cluster 1 featured intensive systems with advanced management and commercial focus, cluster 2 included extensive systems limited by water scarcity, and cluster 3 reflected semi-intensive systems with irrigation and diversified production. These findings provide a detailed understanding of goat systems in northern Peru, identifying opportunities to improve resource use and tailor strategies to enhance sustainability. The multivariate analysis proved effective in capturing the complexity of these systems, supporting productivity and improving livelihoods in rural areas.