Sustainability Of Coffee Production Research Articles

Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning

The sustainability of coffee production is a concern for producers around the world. To be sustainable, it is necessary to achieve satisfactory levels of coffee productivity and quality. Pests and diseases cause reduced productivity and can affect the quality of coffee beans. To ensure sustainability, producers need to monitor pests that can lead to substantial crop losses, such as the coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae), which belongs to the Lepidoptera order and the Lyonetiidae family. This research aimed to use machine learning techniques and vegetation indices to remotely identify infestations of the coffee leaf miner in coffee-growing regions. Field assessments of coffee leaf miner infestation were conducted in September 2023. Aerial images were taken using remotely piloted aircraft to determine 13 vegetative indices with RGB (red, green, blue) images. The vegetation indices were calculated using ArcGis 10.8 software. A comprehensive database encompassing details of coffee leaf miner infestation, vegetation indices, and crop data. The dataset was divided into training and testing subsets. A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. Remarkably, both the SVM and SGD models demonstrated superior performance in estimating coffee leaf miner infestations, with kappa indices of 0.6 and 0.67, respectively. The combined use of vegetation indices and crop data increased the accuracy of coffee leaf miner detection. The RF model performed poorly, while the SVM and SGD models performed better. This situation highlights the challenges of tracking coffee leaf miner infestations in fields with varying ages of coffee plants, different cultivars, and other environmental variables.

APPLICATION OF TWO GROUPS ANALYSIS AND CLUSTER ANALYSIS ON COMPARISON OF CHARACTERISTICS OF PAGARALAM COFFEE FARMERS CATEGORIES

Land care is one of the factors that must be considered in the sustainability of coffee production. A small number of Pagaralam coffee farmers know and apply reductant herbicide in weed control. This study aims to compare the characteristics of Pagaralam coffee farmers based on the use of reductant herbicide by using two groups analysis and cluster analysis. The characteristics of the farmers studied included 17 variables on 165 respondents who were selected by purposive sampling. The variables studied include land productivity and farmers' income. Respondents were divided into 2 categories, namely users and non-users of reductant herbicide. The initial stages of data processing in the form of descriptive statistics, hypothesis testing by mean difference test, correlation between variables, Principal Component Analysis, and biplot analysis. According to the results of the two groups analysis and cluster analysis, the variables dominantly tend to characterize the similarity in comparison between the categories of non-users and users are the variables that join into one cluster, namely land area, number of trees, coffee bean production, estimated yield, total harvest, Gross income, and Net income. While the variables that dominantly characterize the dissimilarity between the two categories are the variables that form 5 separate clusters, namely the clusters of plantation productivity, respondent identity, education, tree age, and length of harvest period.

Genomic-assisted breeding for climate-smart coffee.

Coffee is a universal beverage that drives a multi-industry market on a global basis. Today, the sustainability of coffee production is threatened by accelerated climate changes. In this work, we propose the implementation of genomic-assisted breeding for climate-smart coffee in Coffea canephora. This species is adapted to higher temperatures and is more resilient to biotic and abiotic stresses. After evaluating two populations, over multiple harvests, and under severe drought weather condition, we dissected the genetic architecture of yield, disease resistance, and quality-related traits. By integrating genome-wide association studies and diallel analyses, our contribution is four-fold: (i) we identified a set of molecular markers with major effects associated with disease resistance and post-harvest traits, while yield and plant architecture presented a polygenic background; (ii) we demonstrated the relevance of nonadditive gene actions and projected hybrid vigor when genotypes from different geographically botanical groups are crossed; (iii) we computed medium-to-large heritability values for most of the traits, representing potential for fast genetic progress; and (iv) we provided a first step toward implementing molecular breeding to accelerate improvements in C. canephora. Altogether, this work is a blueprint for how quantitative genetics and genomics can assist coffee breeding and support the supply chain in the face of the current global changes.

Calcium and Potassium Nutrition Increases the Water Use Efficiency in Coffee: A Promising Strategy to Adapt to Climate Change

Coffee (Coffea spp.) represents one of the most important sources of income and goods for the agricultural sector in Central America, Colombia, and the Caribbean region. The sustainability of coffee production at the global and regional scale is under threat by climate change, with a major risk of losing near to 50% of today’s suitable area for coffee by 2050. Rain-fed coffee production dominates in the region, and under increasing climate variability and climate change impacts, these production areas are under threat due to air temperature increase and changes in rainfall patterns and volumes. Identification, evaluation, and implementation of adaptation strategies for growers to cope with climate variability and change impacts are relevant and high priority. Incremental adaptation strategies, including proper soil and water management, contribute to improved water use efficiency (WUE) and should be the first line of action to adapt the coffee crop to the changing growing conditions. This research’s objective was to evaluate at field level over five years the influence of fertilization with calcium (Ca+2) and potassium (K+) on WUE in two coffee arabica varieties: cv. Castillo and cv. Caturra. Castillo has resistance against coffee leaf rust (CLR) (Hemileia vastatrix Verkeley and Brome), while Caturra is not CLR-resistant. WUE was influenced by yield changes during the years by climate variability due to El Niño–ENSO conditions and CLR incidence. Application of Ca+2 and K+ improved the WUE under such variable conditions. The highest WUE values were obtained with an application of 100 kg CaO ha−1 year−1 and between 180 to 230 kg K2O ha−1 year−1. The results indicate that adequate nutrition with Ca+2 and K+ can improve WUE in the long-term, even underwater deficit conditions and after the substantial incidence. Hence, an optimum application of Ca+2 and K+ in rain-fed coffee plantations can be regarded as an effective strategy to adapt to climate variability and climate change.

Resistance of Conilon coffee cultivar Vitoria Incaper 8142 to Meloidogyne paranaensis under field conditions

AbstractMeloidogyne paranaensis is responsible for considerable losses in coffee production. Because of the distribution of this species in the main Coffea arabica producing regions, there is a need for management practices to ensure the sustainability of coffee production. In this work, we evaluated the agronomic performance of resistant clones of the Conilon coffee cultivar Vitoria Incaper 8142 in areas infested by M. paranaensis in the west region of Minas Gerais, Brazil. Clones 2V, 3V, and 6V presented the lowest number of nematodes per gram of roots and were considered resistant to M. paranaensis. All other clones were considered tolerant to this nematode, and one had good vegetative growth but allowed nematode reproduction. Clones of Vitoria Incaper 8142 of C. canephora represent an alternative to coffee production in areas infested by M. paranaensis including areas traditionally cultivated with C. arabica.

Ensuring Economic Viability and Sustainability of Coffee Production

Ensuring Economic Viability and Sustainability of Coffee Production

The utility of farmer ranking of tree attributes for selecting companion trees in coffee production systems

There is increasing interest in the potential of agroforestry to improve the productivity and sustainability of coffee production, but designing management options is knowledge intensive. Tree-crop interactions and the biophysical and socio-economic factors influencing farmers’ decision-making about companion trees are complex and context-specific but fine scale data relating to them are rarely available. A novel method was used to analyse trees ranked by farmers for a range of attributes and evaluate the consistency of farmers’ knowledge underpinning decisions about tree management in coffee production systems in Rwanda. Farmers’ knowledge about tree planting was changing, in line with new shade management recommendations being promoted alongside a limited number of tree species, often freely distributed through eco-certification initiatives. Farmers had detailed knowledge about soil and water conservation processes associated with trees, but they traded these off against perceived competition for light, water and nutrients with coffee. The competitiveness of trees with coffee was influenced by combinations of attributes related to: crown architecture, foliage properties and growth patterns; as well as how trees responded to management, and, their utility. Farmers consistently ranked 20 tree species for 12 attributes (five related to ecology, four to management and three to utility). Given the paucity of data on tree attributes for many species, systematically acquired and consistent local knowledge complements global scientific information and can be useful in bridging knowledge gaps relating to the integration of tree diversity in coffee production systems, which is an increasingly important strategy for smallholder farmers adapting to climate change.

Coffee Silverskin Extract Protects against Accelerated Aging Caused by Oxidative Agents.

Nowadays, coffee beans are almost exclusively used for the preparation of the beverage. The sustainability of coffee production can be achieved introducing new applications for the valorization of coffee by-products. Coffee silverskin is the by-product generated during roasting, and because of its powerful antioxidant capacity, coffee silverskin aqueous extract (CSE) may be used for other applications, such as antiaging cosmetics and dermaceutics. This study aims to contribute to the coffee sector’s sustainability through the application of CSE to preserve skin health. Preclinical data regarding the antiaging properties of CSE employing human keratinocytes and Caenorhabditis elegans are collected during the present study. Accelerated aging was induced by tert-butyl hydroperoxide (t-BOOH) in HaCaT cells and by ultraviolet radiation C (UVC) in C. elegans. Results suggest that the tested concentrations of coffee extracts were not cytotoxic, and CSE 1 mg/mL gave resistance to skin cells when oxidative damage was induced by t-BOOH. On the other hand, nematodes treated with CSE (1 mg/mL) showed a significant increased longevity compared to those cultured on a standard diet. In conclusion, our results support the antiaging properties of the CSE and its great potential for improving skin health due to its antioxidant character associated with phenols among other bioactive compounds present in the botanical material.