Examinando por Materia "Agroforestry systems"

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Economic profitability of carbon sequestration of fine-aroma cacao agroforestry systems in Amazonas, Peru
(MDPI, 2024-03-08) Goñas Goñas, Malluri; Rojas Briceño, Nilton B.; Gómez Fernández, Darwin; Iliquín Trigoso, Daniel; Atalaya Marin, Nilton; Cajas Bravo, Verónica; Díaz Valderrama, Jorge R.; Maicelo Quintana, Jorge L.; Oliva Cruz, Manuel
Currently, the economic profitability of cocoa is being affected by the increasing incidence of pests, low selling prices, high production costs, and the presence of cadmium in cocoa farms, posing a potential risk of crop abandonment. Therefore, the objective of the present research was to evaluate the economic profitability of carbon sequestration of fine-aroma cacao agroforestry systems in Amazonas, Peru, using the economic indicators of NPV, EIRR, and the benefit–cost ratio. For this purpose, 53 small cocoa producers of the APROCAM cooperative were involved, from which data were obtained on the general characteristics of the production system, production and maintenance costs, indirect costs, and administrative costs; in addition, the costs of implementation and maintenance of an environmental services project were calculated to finally make a cash flow projected over 5 years. As part of the results, the economic analysis was carried out on 104.25 hectares of cocoa belonging to the total number of farmers evaluated, who reported an average yield of 957.32 kg of dry cocoa per he. In addition, it was found that the production cost is PEN 3.91/kg of dry cocoa, and the average selling price is PEN 7.38/kg of dry cocoa. After the economic analysis, it was found that the implementation of an environmental services project is profitable (NPV = PEN 1,454,547.8; EIRR = 44% and B/C = 1.86). These results open up an opportunity for cocoa farmers to diversify and increase their income by contributing to climate change mitigation.
Estimation of aboveground biomass and carbon sequestration in a cocoa agroforestry system using UAV-LiDAR in northwestern Peru
(Elsevier B.V., 2025-10-08) Atalaya Marin, Nilton; Sanchez Fuentes, Teiser; Goñas Goñas, Malluri; Tineo Flores, Daniel; Taboada Mitma, Víctor Hugo; Cabrera Hoyos, Héctor Antonio; Cruz Luis, Juancarlos Alejandro; Ganoza Roncal, Jorge Juan; Gómez Fernández, Darwin
Accurate estimation of biomass and carbon in agroforestry systems is essential to assess their contribution to climate change mitigation and to improve their management. In this context, UAV-mounted LiDAR technology emerges as a fast, accurate, and non-destructive alternative for the structural characterization of cocoa agroforestry systems. This study aimed to estimate and analyze structural parameters, mainly tree height and diameter at breast height (DBH), as well as to calculate aboveground biomass and carbon sequestration in a cocoa agroforestry system, using LiDAR data obtained with a DJI Matrice 350 RTK UAV equipped with a Zenmuse L2 sensor, complemented by automatic tree segmentation in LiDAR360 and the application of species-specific allometric equations. The results showed a 93 % segmentation efficiency, with accuracies of 0.93 and 0.99 for DBH and height estimations, respectively. The evaluated plot, located at the Yanayacu Experimental Center (Jaén, Peru) and covering an area of 0.58 ha, had stored 15,492.5 kg of aboveground biomass and 7746.25 kg of aboveground carbon, with Mangifera indica and Cocos nucifera contributing more than 80 %. Consequently, this approach demonstrates the potential of UAV-based LiDAR to generate accurate and detailed information on system structure, enabling optimized management of high-biomass species and the development of more efficient and sustainable management strategies.
Integrating agroecological suitability of cacao (Theobroma cacao L.) with biodiversity and land-use constraints in Peru
(Elsevier Ltd., 2026-01-29) Cotrina Sanchez, Alexander; Guzman Valque, Betty Karina; Barboza, Elgar; Oliva, Manuel; Huaman Pilco, Angel Fernando; Rojas Briceño, Nilton B.
CONTEXT: Cacao cultivation is vital for rural economies in Peru, but its expansion often overlaps with sensitive ecosystems, raising concerns for biodiversity conservation. Despite international commitments to deforestation-free supply chains, integrated analyses combining agroecological suitability with land-use constraints remain scarce in Peru. OBJECTIVES: This study aims to identify suitable areas for cacao cultivation under multiple exclusion scenarios, evaluate conflicts with biodiversity and conservation areas, and quantify degraded lands that could provide opportunities for agroforestry-based restoration. METHODS: Cacao suitability was modelled with an ensemble of nine machine-learning algorithms using bioclimatic, edaphic, and topographic predictors. Outputs were filtered to exclude biophysical barriers and overlaid with national-scale layers of species richness, protected areas, forest cover, and degraded lands through GIS-based spatial analysis to evaluate exclusion scenarios and trade-offs. RESULTS AND CONCLUSIONS: The ensemble achieved high predictive power, with Random Forest (AUC = 0.997) and XGBoost (AUC = 0.972) performing best. Highly suitable areas were concentrated in the Andean-Amazon transition, especially in San Martín, Cusco, Huánuco, and Junín departments, where they overlapped with biodiversity hotspots and legally protected areas. Degraded yet suitable lands highlighted opportunities to expand cacao through agroforestry systems, reducing forest pressure and enhancing ecological restoration. SIGNIFICANCE: By integrating suitability modelling with national-scale geospatial layers, this study delivers a framework linking crop suitability with land-use constraints. The findings support national-scale planning while remaining adaptable to local contexts. They also align with international policy frameworks such as the European Deforestation Regulation (EUDR), promoting sustainable cacao production, biodiversity conservation, and long-term rural development in Peru.
Mapping of coffee cultivation based on land use/land cover in the tropics of Cajamarca (Peru): An integration of Sentinel data, machine learning, and Google Earth Engine
(Elsevier B.V., 2026-08-12) Barboza, Elgar; Aragon, Amanda; Pizarro, Samuel; Roman Peña, Alcides; Adams, Brandon; Delgado, Ellen; Cotrina Sanchez, Alexander; Tariq, Aqil; Medina Medina, Angel; Tuesta Trauco, Katerin; Rivera Fernandez, Abner; Zabaleta Santisteban, Jhon; Ocaña, Candy; Madden, Marguerite
Coffee cultivation is a strategic economic activity in the Andean-Amazonian region of Peru, characterized by its diversity of production systems and complex landscape. In this study, a methodological approach based on multitemporal mapping of land cover and land use (LULC) was developed to identify coffee crops by integrating optical satellite imagery (Sentinel-2) and radar data (Sentinel-1), as well as topographic variables, processed on the Google Earth Engine (GEE) platform, with supervised classification based on the Random Forest (RF) algorithm. The study area covered the provinces of Jaen and San Ignacio (Cajamarca), the main coffee-growing areas in the country's north. A time series of satellite data from 2019 to 2024 was used, considering two climatic seasons (dry and wet). The 69 spectral, textural, and topographic variables were evaluated but subsequently reduced to 22 using the Variance Inflation Factor (VIF). The classification distinguished 11 LULC classes, including shade-grown and non-shade-grown coffee, achieving an overall accuracy of over 84% and a Kappa index of 0.90. The binary coffee maps made it possible to quantify and analyze the spatial dynamics of the crop in both provinces, showing a growing trend toward shade-grown coffee, especially in San Ignacio. The proposed approach proved to be operational for mountainous tropical areas with high cloud cover.
Portable NIR spectroscopy for soil property prediction and fertility assessment in coffee and cocoa systems
(Elsevier Ltd., 2026-09-01) Grandez Alberca, Marlen A.; Puscan Rojas, Julio; Silva Melendez, Teodoro B.; Juarez Contreras, Lily del Pilar; Medina Medina, Angel J.; Zabaleta Santisteban, Jhon A.; Rivera Fernandez, Abner S.; Tuesta Trauco, Katerin M.; Coronel Alcalde, Mirella N.; Roman Peña, Alcides; Pizarro, Samuel; Barboza, Elgar
The study evaluated the potential of portable Near-Infrared (NIR) spectroscopy to predict soil physicochemical properties in coffee and cocoa production systems in Amazonas, Peru. A total of 126 topsoil samples were initially collected using a stratified sampling design. A total of 125 surface soil samples were analyzed using a stratified sampling design. Soil properties were determined using standard laboratory reference methods, and spectral data were acquired with a NeoSpectra portable spectrometer operating in the 1350–2550 nm (FT-NIR) range. Predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), and Partial Least Squares Regression (PLSR), and their performance was evaluated using nested five-fold cross-validation (CV). The predictive performance of portable NIR spectroscopy varied according to the soil property, production system, preprocessing technique, and machine learning algorithm. Coffee soils showed higher predictive performance than cocoa soils. The best results were obtained for clay (R²CV = 0.775; RPDCV = 2.276) using RF with first-derivative preprocessing, followed by phosphorus (R²CV = 0.772; RPDCV = 2.037) using PLSR with band-depth preprocessing. Soil pH and silt also showed satisfactory predictive performance in coffee soils. In cocoa soils, potassium exhibited the highest predictive capability (R²CV = 0.454; RPDCV = 1.378) using SVM with second-derivative preprocessing, although the overall model performance was lower than that observed for coffee soils. Portable NIR spectroscopy provided potentially useful estimates for selected soil properties, particularly clay, phosphorus, pH, and silt in coffee soils. However, its predictive performance was strongly dependent on the soil property, preprocessing strategy, machine learning algorithm, and production system.
Sistemas de producción de musáceas en Perú
(Bioversity International, 2022-12-31) Rojas Llanque, Juan Carlos; Arévalo Quinde, Cinthya Geraldine; Marín Horna, Julio Ernesto; Dita Rodriguez, Miguel Ángel
Este documento se elaboró como parte de las actividades del proyecto “Fortaleciendo conocimientos, capacidad de diagnóstico y buenas prácticas frente a la amenaza de Fusarium y otros problemas fitosanitarios para las familias productoras de plátanos del Perú”, implementado por la Alianza de Bioversity International y el CIAT, el Instituto Nacional de Innovación Agraria (INIA), el Servicio Nacional de Sanidad Agraria (SENASA) y la Secretaría Técnica de CGIAR en Perú. Está orientado a productores, estudiantes, técnicos y profesionales del rubro musáceas, que buscan ampliar sus conocimientos y fortalecer sus capacidades técnicas para mejorar la toma de decisiones en los sistemas productivos de estos cultivos.