In Latin America, the cultivation of Arabica coffee (Coffea arabica) plays a critical role in rural livelihoods, biodiversity conservation, and sustainable development. Over the last 20 years, coffee farms and landscapes across the region have undergone rapid and profound biophysical changes in response to low coffee prices, changing climatic conditions, severe plant pathogen outbreaks, and other drivers. Although these biophysical transformations are pervasive and affect millions of rural livelihoods, there is limited information on the types, location, and extent of landscape changes and their socioeconomic and ecological consequences. Here we review the state of knowledge on the ongoing biophysical changes in coffee-growing regions, explore the potential socioeconomic and ecological impacts of these changes, and highlight key research gaps. We identify seven major land-use trends which are affecting the sustainability of coffee-growing regions across Latin America in different ways. These trends include (1) the widespread shift to disease-resistant cultivars, (2) the conventional intensification of coffee management with greater planting densities, greater use of agrochemicals and less shade, (3) the conversion of coffee to other agricultural land uses, (4) the introduction of Robusta coffee (Coffea canephora) into areas not previously cultivated with coffee, (5) the expansion of coffee into forested areas, (6) the urbanization of coffee landscapes, and (7) the increase in the area of coffee produced under voluntary sustainability standards. Our review highlights the incomplete and scattered information on the drivers, patterns, and outcomes of biophysical changes in coffee landscapes, and lays out a detailed research agenda to address these research gaps and elucidate the effects of different landscape trajectories on rural livelihoods, biodiversity conservation, and other aspects of sustainable development. A better understanding of the drivers, patterns, and consequences of changes in coffee landscapes is vital for informing the design of policies, programs, and incentives for sustainable coffee production.

Cocoa is the economic backbone of Côte d’Ivoire and Ghana, making them the leading cocoa-producing countries in the world. However, cocoa farming has been a major driver of deforestation and landscape degradation in West Africa. Various stakeholders are striving for a zero-deforestation cocoa sector by implementing sustainable farming strategies and a more transparent supply chain. In the context of tracking cocoa sources and contributing to cocoa-driven deforestation monitoring, the demand for accurate and up-to-date maps of cocoa plantations is increasing. Yet, access to limited reference data and imperfect data quality can impose challenges in producing reliable maps. This study classified full-sun-cocoa-growing areas using limited reference data relative to the large and heterogeneous study areas in Côte d’Ivoire and Ghana. A Sentinel-2 composite image of 2021 was generated to train a random forest model. We undertook reference data refinement, selection of the most important handcrafted features and data sampling to ensure spatial independence. After refining the quality of the reference data and despite their size reduction, the random forest performance was improved, achieving an overall accuracy of 85.1 ± 2.0% and an F1 score of 84.6 ± 2.4% (mean ± one standard deviation from ten bootstrapping iterations). Emphasis was given to the qualitative visual assessment of the map using very high-resolution images, which revealed cases of strong and weak generalisation capacity of the random forest. Further insight was gained from the comparative analysis of our map with two previous cocoa classification studies. Implications of the use of cocoa maps for reporting were discussed.

Over the past three decades, the Vietnamese Mekong Delta has experienced a significant increase in agricultural productivity, partly achieved through increased agrochemical use. To abate negative effects on human and environmental health, several national programs were launched to enhance safer pesticide use. This study aimed to assess the patterns and relationships of official sustainable agriculture educational programs, pesticide safety knowledge, and practices of smallholder farmers in the Mekong Delta. A cross-sectional survey was conducted with 400 smallholder farmers from three communes in Thoi Lai district (Can Tho province) from March to May 2020. Twenty-four questions on pesticide safety knowledge and practices were used to identify traits using latent class analysis. Adjusted generalized linear regression was used to assess determinants of pesticide safety knowledge and estimate associations of pesticide safety knowledge with pesticide practices. 96.2% of participants have used at least one WHO class II pesticide during the past year while the use of specific personal protective equipment was limited mainly due to unavailability (37.0%) or discomfort (83.0%). High education (Odds Ratio (OR), 95% Confidence Interval; 3.84, 1.70–9.45), exposure to official educational programs (1.87, 1.13–3.12), peer-to-peer knowledge exchange (3.58, 2.18–6.00), and learning from governmental extension services (2.31, 1.14–4.98) were positively associated with increased pesticide safety knowledge. Compared to poor practices, pesticide safety knowledge was increasingly positively associated with intermediate (1.65, 1.02–2.66) and good pesticide practices (8.96, 2.58–31.12). These findings highlight the importance of school education and educational programs, access to PPE, and addressing discomforts of PPE to improve the protection of farmers from pesticide exposures. Simultaneously, pesticide market authorization processes should be reconsidered to promote the authorization of less toxic products. Further in-depth studies on the nature of pesticides used, nonuse of personal protective equipment, and effectiveness of educational programs will further define leverage points for safer pesticide use.

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 R² of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R² of 0.71. The overall performance in both countries reached an R² 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.

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.