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This paper describes a novel qualitative dataset regarding coffee pests based on the ancestral knowledge of coffee farmers in the Department of Cauca, Colombia. The dataset has been obtained from a survey applied to coffee growers with 432 records and 41 variables collected weekly from September 2020 to August 2021. The qualitative dataset includes climatic conditions, productive activities, external conditions, and coffee bio-aggressors. This dataset allows researchers to find patterns for coffee crop protection through the ancestral knowledge not detected by real-time agricultural sensors. As far as we are concerned, there are no datasets like the one presented in this paper with similar characteristics of qualitative value that express the empirical knowledge of coffee farmers used to detect triggers of causal behaviors of pests and diseases in coffee crops. Dataset:  https://doi.org/10.5281/zenodo.8275090. Dataset License: Licensed under Creative Commons Attribution 4.0 International (CC-BY-4.0). [ABSTRACT FROM AUTHOR]

Power transformers' reliability is of the highest importance for distribution networks. A possible failure of them can interrupt the supply to consumers, which will cause inconvenience to them and loss of revenue for electricity companies. Additionally, depending on the type of damage, the recovery time can vary and intensify the problems of consumers. This paper estimates the maintenance required for distribution transformers using Artificial Intelligence (AI). This way the condition of the equipment that is currently in use is evaluated and the time that maintenance should be performed is known. Because actions are only carried out when necessary, this strategy promises cost reductions over routine or time-based preventative maintenance. The suggested methodology uses a classification predictive model to identify with high accuracy the number of transformers that are vulnerable to failure. This was confirmed by training, testing, and validating it with actual data in Colombia's Cauca Department. It is clear from this experimental method that Machine Learning (ML) methods for early detection of technical issues can help distribution system operators increase the number of selected transformers for predictive maintenance. Additionally, these methods can also be beneficial for customers' satisfaction with the performance of distribution transformers, which would enhance the highly reliable performance of such transformers. According to the prediction for 2021, 852 transformers will malfunction, 820 of which will be in rural Cauca, which is consistent with previous failure statistics. The 10 kVA transformers will be the most vulnerable, followed by the 5 kVA and 15 kVA transformers. [ABSTRACT FROM AUTHOR]

Crop yield is an important factor for evaluating production processes and determining the profitability of growing coffee. Frequently, the total number of coffee beans per area unit is estimated manually by physically counting the coffee cherries, the branches, or the flowers. However, estimating yield requires an investment in time and work, so it is not usual for small producers. This paper studies a non-intrusive and attainable alternative to predicting coffee crop yield through multispectral aerial images. The proposal is designed for small low-tech producers monitored by capturing aerial photos with a MapIR camera on an unmanned aerial vehicle. This research shows how to predict yields in the early stages of the coffee tree productive cycle, such as at flowering by using aerial imagery. Physical and spectral descriptors were evaluated as predictors for yield prediction models. The results showed correlations between the selected predictors and 370 yield samples of a Colombian Arabica coffee crop. The coffee tree volume, the Normalized Difference Vegetation Index (NDVI), and the Coffee Ripeness Index (CRI) showed the highest values with 71%, 55%, and 63%, respectively. Further, these predictors were used as the inputs for regression models to analyze their precision in predicting coffee crop yield. The validation stage concluded that Linear Regression and Stochastic Descending Gradient Regression were better models with determination coefficient values of 56% and 55%, respectively, which are promising for predicting yield. [ABSTRACT FROM AUTHOR]

Existing models to estimate profitability in small-scale coffee production are based on limited information leading to precision problems in the estimations and, therefore, fail to represent the real economic return. This leads smallholders to make decisions based on inaccurate information with negative consequences on their financial status. This paper introduces a novel hierarchical approach called HMP-Coffee (Hierarchical Model Profitability Coffee) to estimate the profitability level in small-scale coffee productions, supporting smallholders, in decision-making, to improve their income and, consequently, their economic sustainability. HMP-Coffee considers a Contextual Knowledge Phase, based on expert knowledge, to create a conceptual model about the profitability in small-scale coffee productions and a Hierarchical-Multicriteria Phase responsible for translating such a conceptual model into an understandable hierarchical qualitative model able to estimate the level of profitability in small coffee productions precisely. HMP-Coffee was developed by considering the La Sultana farm's operation in Cauca, Colombia and evaluated with independent data from the Costa Rican Coffee Institute. In the evaluation results, HMP-Coffee achieved 81.72% accuracy, 81.33% precision, 92.30% recall, and 83.46% F-Score. From the results obtained, we conclude that HMP-Coffee is a reliable model to estimate the profitability of small-scale coffee production. Its reliability improves the decision-making for obtaining crops with better economic sustainability. [ABSTRACT FROM AUTHOR]

The exploration of natural H2 or white hydrogen has started in various geological settings. Ophiolitic nappes are already recognized as one of the promising contexts. In South America, the only data available so far concerns the Archean iron-rich rocks of the Mina Gerais in Brazil or the subduction context of Bolivia. In Colombia, despite government efforts to promote white hydrogen, data remain limited. This article introduces the initial dataset obtained through soil gas sampling within the Cauca-Patia Valley and Western Cordillera, where the underlying geology comprises accreted oceanic lithosphere. In this valley, promising areas with H2 potential were identified using remote sensing tools, in particular vegetation anomalies. The Atmospherically Resistant Vegetation Index (ARVI) appears to be well adapted for this context and the field data collection confirmed the presence of H2 in the soil in all pre-selected structures. The valley undergoes extensive cultivation, mainly for sugar cane production. While H2 emissions lead to alterations in vegetation, unlike reports from other countries, they do not result in its complete disappearance. Soil gas measurements along the thrusts bordering the Cauca Valley also show high H2 content in the fault zones. In the valley, the presence of sedimentary cover above the ophiolites which are presumably the H2 generating rocks, which addresses the possible presence of reservoirs and seals to define potential plays. Drawing parallels with the Malian case, it could be that the intrusive element could serve as seals. [ABSTRACT FROM AUTHOR]

The phenological patterns of coffee flowering in Colombia have typically been studied in a descriptive way, with knowledge from an inferential perspective being scarce. The present study evaluated the effect of geographic location and accession on the floral patterns and phenological descriptors of Coffea arabica L. Fifteen accessions from the Colombian coffee collection (four tall and eleven short) were planted in the departments of Cesar, Caldas, Quindío and Cauca (Colombia). The number of flower buds per branch per plant per evaluated accession was recorded weekly during four flowering semesters. Subsequently, the phenological flowering descriptors, namely synchrony among individuals, intraindividual temporal variability and number of events were calculated. The data were analyzed descriptively, and then the inferential component was conducted using analysis of variance for a two-factor additive model and randomization restriction. The results showed that there are two flowering patterns according to the expression of flowering in the floral cycles, the "annual" class in the department of Cesar and the "continual" class in the departments of Caldas, Quindío and Cauca. The phenological descriptors show differences between the departments according to the coffee zone to which it belongs (northern, central or southern). In turn, the floral pattern of each area can be linked to the latitudinal change in daily sunshine, as well as to the distribution of rainfall and temperature, in a very broad sense and based on the literature. The data did not provide statistical evidence to suggest differences among the accessions or between the tree sizes evaluated. [ABSTRACT FROM AUTHOR]

Avocado is an important export and consumption product in Colombia, and its economic importance is expected to increase in the coming years. With its vast potential territory for avocado cultivation, the department of Cauca is a crucial area for producing this variety. However, small producers in the region often need more knowledge of the most suitable locations for planting. This study seeks to determine the ideal areas for Hass avocado cultivation in Cauca using geographic information tools and multi-criteria decision analysis, using a set of official data from different governmental entities and the hierarchical analytical process that allows determining the intensity of the interrelation of factors in the cultivation of Hass avocado. The results indicate that the municipalities near the Popayán plateau have the most significant potential for Hass avocado production, using the analytical hierarchy process. Approximately 9.2% of the administrative territory of the region is classified as highly suitable for Hass avocado cultivation, and an additional 14.2% is considered moderately suitable, constituting about 700,000 hectares of arable land. This research provides decision-makers and producers with valuable knowledge to support and improve Hass avocado agriculture in the region by implementing agricultural engineering practices. [ABSTRACT FROM AUTHOR]

In recent years, agriculture has become an essential activity in Colombia, despite the challenges faced by farmers due to low yields and insufficient resources to improve their main activities, such as irrigation systems, agricultural practices, and industrial machinery. This Hass avocado approach has been addressed in previous research considering system dynamics simulation to evaluate farmers' behavior strategies and improve their competitiveness. However, these studies typically examine a single strategy effect and avoid multiple integrated strategies. Other studies focused on the complex interactions between different factors in the production chain and their feedback effects on farmers' productivity and cash flow. For these reasons, this research provides a comprehensively dynamic model and evaluates long-term strategies and their effects on supporting and improving small farmers' productivity and profitability. A system dynamics methodology was used to model complex systems processing Hass avocado farmer association data and explore their effects on competitiveness for long-term sustainable and profitable agriculture. This research proposes optimal scenarios for small farmers, including strategies such as low-interest credit access, logistics practices, and government technical support. The scenarios provide a proactive tool for decision makers and promote rural farmers' development, aligning high-quality fresh product supply and demand. [ABSTRACT FROM AUTHOR]

An increasing frequency of extreme atmospheric events is challenging our basic knowledge about the resilience mechanisms that mediate the response of small mountainous watersheds (SMW) to landslides, including production of water-derived ecosystem services (WES). We hypothesized that the demand for WES increases the connectivity between lowland and upland regions, and decreases the heterogeneity of SMW. Focusing on four watersheds in the Central Andes of Colombia and combining "site-specific knowledge", historic land cover maps (1970s and 1980s), and open, analysis-ready remotely sensed data (GLAD Landsat ARD; 1990–2000), we addressed three questions. Over roughly 120 years, the site-specific data revealed an increasing demand for diverse WES, as well as variation among the watersheds in the supply of WES. At watershed-scales, variation in the water balances—a surrogate for water-derived ES flows—exhibited complex relationships with forest cover. Fractional forest cover (pi) and forest aggregation (AIi) varied between the historic and current data sets, but in general showed non-linear relationships with elevation and slope. In the current data set (1990–2000), differences in the number of significant, linear models explaining variation in pi with time, suggest that slope may play a more important role than elevation in land cover change. We found ample evidence for a combined effect of slope and elevation on the two land cover metrics, which would be consistent with strategies directed to mitigate site-specific landslide-associated risks. Overall, our work shows strong feedbacks between lowland and upland areas, raising questions about the sustainable production of WES. [ABSTRACT FROM AUTHOR]