Cocoa beans are produced all across the humid tropics under different environmental conditions provided by the region but also by the season and the type of production system. Agroforestry systems compared to monocultures buffer climate extremes and therefore provide a less stressful environment for the understory cocoa, especially under seasonally varying conditions. We measured the element concentration as well as abiotic stress indicators (polyamines and total phenolic content) in beans derived from five different production systems comparing monocultures and agroforestry systems and from two harvesting seasons. Concentrations of N, Mg, S, Fe, Mn, Na, and Zn were higher in beans produced in agroforestry systems with high stem density and leaf area index. In the dry season, the N, Fe, and Cu concentration of the beans increased. The total phenolic content increased with proceeding of the dry season while other abiotic stress indicators like spermine decreased, implying an effect of the water availability on the chemical composition of the beans. Agroforestry systems did not buffer the variability of stress indicators over the seasons compared to monocultures. The effect of environmental growing conditions on bean chemical composition was not strong but can contribute to variations in cocoa bean quality.
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http://dx.doi.org/10.1021/acs.jafc.7b04490 | DOI Listing |
Front Plant Sci
December 2024
Centro de Investigación e Innovación para el Cambio Climático (CiiCC), Universidad Santo Tomás, Valdivia, Chile.
Introduction: Secondary forests and coffee cultivation systems with shade trees might have great potential for carbon sequestration as a means of climate change adaptation and mitigation. This study aimed to measure carbon stocks in coffee plantations under different managements and secondary forest systems in the Peruvian Amazon rainforest (San Martín Region).
Methods: The carbon stock in secondary forest trees was estimated using allometric equations, while carbon stocks in soil, herbaceous biomass, and leaf litter were determined through sampling and laboratory analysis.
The conversion of tropical rainforests to agriculture causes population declines and biodiversity loss across taxa. This impacts ants (Formicidae), a crucial tropical group for ecosystem functioning. While biodiversity loss among ants is well documented, the responses of individual ant taxa and their adjustments in trophic strategies to land-use change are little studied.
View Article and Find Full Text PDFJ Environ Manage
December 2024
CE3c - Centre for Ecology, Evolution and Environmental Changes & CHANGE - Global Change and Sustainability Institute, Departamento de Biologia Animal, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal.
Bats provide important ecosystem services, particularly in agriculture, yet integrating bat management into conservation plans remains challenging. Some landscape features considerably influence bat presence, diversity, and ecosystem service provision. Understanding the relationship between landscape structure, composition, pest suppression, and ecosystem services is crucial.
View Article and Find Full Text PDFPlants (Basel)
November 2024
Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autónoma de México, Antigua Carretera a Pátzcuaro No. 8701, Morelia 58190, Michoacán, Mexico.
Wild edible trees (WETs) play an important role in the diet of many rural communities. Therefore, research on their use and management is important to support both food sovereignty and local conservation of biocultural resources. We evaluated the different uses of WETs by the community of Zacualpan, Colima, in western Mexico, through 32 semi-structured interviews registering the species richness, plant parts consumed, and non-food uses.
View Article and Find Full Text PDFHeliyon
December 2024
Laboratory of Genetics, Biotechnology and Applied Botany (GEBBA), National School of Applied Biosciences and Biotechnologies (ENSBBA) of Dassa Zoumé/ National University of Sciences, Technologies, Engineering and Mathematics (UNSTIM), PO Box 14, Dassa-Zoumé, Benin.
This study examines the performance of machine learning algorithms for identifying importance features for agroecological practices adoption in shea agroforestry systems. Primary data were collected from 272 representative and randomly selected farmers in two regions of northern Benin. Four machine learning algorithms (Naïve Bayes, Neural Network, Support Vector Machine and Bagging Decision Trees) were compared using four statistical performance metrics: accuracy, balanced accuracy, recall, and the area under the receiver operating characteristic curve (AUC), as well as calibration plots.
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