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Cultivation of Coffea arabica is highly sensitive to and has been shown to be negatively impacted by progressive climatic changes. Previous research contributed little to support forward-looking adaptation. Agro-ecological zoning is a common tool to identify homologous environments and prioritize research. We demonstrate here a pragmatic approach to describe spatial changes in agro-climatic zones suitable for coffee under current and future climates. We defined agro-ecological zones suitable to produce arabica coffee by clustering geo-referenced coffee occurrence locations based on bio-climatic variables. We used random forest classification of climate data layers to model the spatial distribution of these agro-ecological zones. We used these zones to identify spatially explicit impact scenarios and to choose locations for the long-term evaluation of adaptation measures as climate changes. We found that in zones currently classified as hot and dry, climate change will impact arabica more than those that are better suited to it. Research in these zones should therefore focus on expanding arabica's environmental limits. Zones that currently have climates better suited for arabica will migrate upwards by about 500m in elevation. In these zones the up-slope migration will be gradual, but will likely have negative ecosystem impacts. Additionally, we identified locations that with high probability will not change their climatic characteristics and are suitable to evaluate C. arabica germplasm in the face of climate change. These locations should be used to investigate long term adaptation strategies to production systems. [ABSTRACT FROM AUTHOR]

Regional studies have shown that climate change will affect climatic suitability for Arabica coffee (Coffea arabica) within current regions of production. Increases in temperature and changes in precipitation patterns will decrease yield, reduce quality and increase pest and disease pressure. This is the first global study on the impact of climate change on suitability to grow Arabica coffee. We modeled the global distribution of Arabica coffee under changes in climatic suitability by 2050s as projected by 21 global circulation models. The results suggest decreased areas suitable for Arabica coffee in Mesoamerica at lower altitudes. In South America close to the equator higher elevations could benefit, but higher latitudes lose suitability. Coffee regions in Ethiopia and Kenya are projected to become more suitable but those in India and Vietnam to become less suitable. Globally, we predict decreases in climatic suitability at lower altitudes and high latitudes, which may shift production among the major regions that produce Arabica coffee. [ABSTRACT FROM AUTHOR]

Social cohesion plays a key role in processes of peacebuilding and sustainable development. Fostering social cohesion might present a potential to enhance the connection of natural resource management and peacebuilding and better functioning of sustainable land use systems. This contribution explores the nexus between social cohesion, natural resource management, and peacebuilding. We do so by (1) reviewing literature on the three concepts and (2) studying four different key action areas in the context of sustainable cocoa production for their potential to enhance social cohesion, namely (a) agroforestry; (b) cooperatives; (c) certification schemes; and (d) trade policies. Research is based on experience from cocoa production in two post-conflict countries, Côte d'Ivoire and Colombia. Our findings show that by fostering environmentally sustainable agricultural practices, these key action areas have a clear potential to foster social cohesion among cocoa producers and thus provide a valuable contribution to post-conflict peacebuilding in both countries. However, the actual effects strongly depend on a multitude of local factors which need to be carefully taken into consideration. Further, the focus in implementation of some of these approaches tends to be on increasing agricultural productivity and not directly on fostering cocoa farmers' wellbeing and societal relations, and hence a shift toward social objectives is needed in order to strengthen these approaches as a part of overall peacebuilding strategies. [ABSTRACT FROM AUTHOR]

Abstract Climate-smart agriculture (CSA) is widely promoted as an approach for reorienting agricultural development under the realities of climate change. Prioritising research-for-development activities is crucial, given the need to utilise scarce resources as effectively as possible. However, no framework exists for assessing and comparing different CSA research investments. Several aspects make it challenging to prioritise CSA research, including its multi-dimensional nature (productivity, adaptation and mitigation), the uncertainty surrounding many climate impacts, and the scale and temporal dependencies that may affect the benefits and costs of CSA adoption. Here we propose a framework for prioritising agricultural research investments across scales and review different approaches to setting priorities among agricultural research projects. Many priority-setting case studies address the short- to medium-term and at relatively local scales. We suggest that a mix of actions that span spatial and temporal time scales is needed to be adaptive to a changing climate, address immediate problems and create enabling conditions for enduring change. Highlights • Prioritising CSA research activities is challenging, primarily because it is multi-dimensional. • We propose and illustrate a simple six-element framework as a "map" to guide prioritisation. • Most priority setting activities need a mix of different quantitative and qualitative methods. • This mix needs to address actions that span spatial and temporal time scales to ensure CSA research effectiveness. [ABSTRACT FROM AUTHOR]

The West African cocoa belt, reaching from Sierra Leone to southern Cameroon, is the origin of about 70% of the world's cocoa ( Theobroma cacao ), which in turn is the basis of the livelihoods of about two million farmers. We analyze cocoa's vulnerability to climate change in the West African cocoa belt, based on climate projections for the 2050s of 19 Global Circulation Models under the Intergovernmental Panel on Climate Change intermediate emissions scenario RCP 6.0. We use a combination of a statistical model of climatic suitability (Maxent) and the analysis of individual, potentially limiting climate variables. We find that: 1) contrary to expectation, maximum dry season temperatures are projected to become as or more limiting for cocoa as dry season water availability; 2) to reduce the vulnerability of cocoa to excessive dry season temperatures, the systematic use of adaptation strategies like shade trees in cocoa farms will be necessary, in reversal of the current trend of shade reduction; 3) there is a strong differentiation of climate vulnerability within the cocoa belt, with the most vulnerable areas near the forest-savanna transition in Nigeria and eastern Côte d'Ivoire, and the least vulnerable areas in the southern parts of Cameroon, Ghana, Côte d'Ivoire and Liberia; 4) this spatial differentiation of climate vulnerability may lead to future shifts in cocoa production within the region, with the opportunity of partially compensating losses and gains, but also the risk of local production expansion leading to new deforestation. We conclude that adaptation strategies for cocoa in West Africa need to focus at several levels, from the consideration of tolerance to high temperatures in cocoa breeding programs, the promotion of shade trees in cocoa farms, to policies incentivizing the intensification of cocoa production on existing farms where future climate conditions permit and the establishment of new farms in already deforested areas. [ABSTRACT FROM AUTHOR]

Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach. The approach uses basic smartphones to take a few pictures of coffee trees; 2,968 trees were investigated with 8,904 pictures in Junín and Piura (Peru), Cauca, and Quindío (Colombia) in 2022, with the help of nearly 1,000 smallholder coffee farmers. Then, we trained and validated YOLO (You Only Look Once) v8 for detecting cherries in the dataset in Peru. An average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per tree. The model's performance in Peru showed an R 2 of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R 2 of 0.71. The overall performance in both countries reached an R 2 of 0.72. The results suggest that the method can be applied to much broader scales and is transferable to other varieties, countries, and regions. To our knowledge, this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale, multiyear, photo-based phenotypic monitoring for coffee crops in low-income countries worldwide., Competing Interests: Competing interests: The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2024 Juan Camilo Rivera Palacio et al.)