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Not so robust: Robusta coffee production is highly sensitive to temperature
Abstract
Coffea canephora (Robusta coffee) is the most heat tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of production coffee. However, the optimum production range of Robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22‐30 °C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data we used hierarchical Bayesian modelling to quantify Robusta’s optimal temperature range for production. Our climate based models explained yield variation well across the study area with a cross‐validated mean R2 = 0.51. We demonstrate that Robusta has an optimal temperature below 20.5 °C (or a mean minimum / maximum of ≤ 16.2/24.1 °C), which is markedly lower, by 1.5 – 9 °C than current estimates. In the middle of Robusta’s currently assumed optimal range (mean annual temperatures over 25.1 °C), coffee yields are 50% lower compared to the optimal mean of ≤ 20.5 °C found here. During the growing season every 1 °C increase in mean minimum/maximum temperatures above 16.2/24.1 °C corresponded to yield declines of ~14% or 350‐460 kg/ha (95% credible interval). Our results suggest that Robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on Robusta having an optimal temperature range over 22 °C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world’s coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers.
... The other way that climate affects coffee cultivation is through the impacts of inter-annual variability on the annual production cycle and on yields (as opposed to suitability studies, which are based on the presence and/or absence of coffee farms). During any given year, climate hazards such as heatwaves, droughts, frosts and floods can each affect coffee yield [8][9][10][11][12][13][14]. Sub-optimal temperatures and precipitation deficits have negative effects on yield and bean quality, and climate acts as a control on pests and diseases [5]. The timing is also important, as the vulnerability of coffee to climate variables changes depending on the stage of the plant's life cycle [8,15]. ...
... These failures are characterised by large-scale yield deficits, and can arise as a result of widespread, spatially compounding climate anomalies [16][17][18][19][20]. For coffee productivity, however, the impacts of climate variability are typically analysed on national or regional spatial scales [5,8,10,13,14]. On a global scale, the historical variability and changes in the frequency of spatially compounding events that affect coffee production is unknown. ...
... We estimated the flowering and growing (i.e. cherry and fruit development) seasons for each country based on the literature [13,[40][41][42][43]. ...
Global coffee production is at risk from synchronous crop failures, characterised by widespread concurrent reductions in yield occurring in multiple countries at the same time. For other crops, previous studies have shown that synchronous failures can be forced by spatially compounding climate anomalies, which in turn may be driven by large-scale climate modes such as the El Niño Southern Oscillation (ENSO). We provide a systematic analysis of spatially compounding climate hazards relevant to global coffee production. We identify 12 climate hazards from the literature, and assess the extent to which these hazards occur and co-occur for the top 12 coffee producing regions globally. We find that the number of climate hazards and compound events has increased in every region between 1980 and 2020. Furthermore, a clear climate change signature is evident, as the type of hazard has shifted from overly cool conditions to overly warm. Spatially compounding hazards have become particularly common in the past decade, with only one of the six most hazardous years occurring before 2010. Our results suggest that ENSO is the primary mode in explaining annual compound event variability, both globally and regionally. El Niño-like sea-surface temperatures in the Pacific Ocean are associated with decreased precipitation and increased temperatures in most coffee regions, and with spatially compounding warm and dry events. This relationship is reversed for La Niña-like signatures. The Madden Julian Oscillation also shows a strong association with climate hazards to coffee, with increased activity in the Maritime Continent related to a global increase in the number of cold or wet hazards and a decrease in the number of warm or dry hazards. With climate change projections showing a continued rise in temperatures in the tropics is likely, we suggest that coffee production can expect ongoing systemic shocks in response to spatially compounding climate hazards.
... While bioclimatic suitability for robusta production is projected to decline altogether by some global studies, there is a general lack of large-scale research on the climatesensitive flowering and growth phases of robusta. Future research is required to determine its optimal temperature ranges more precisely to enhance yields [27,42]. These results may be attributable to the fact that most of the reviewed manuscripts were conducted on the American continent, where Arabica has the most extensive diffusion. ...
... While bioclimatic suitability for robusta production is projected to decline altogether by some global studies, there is a general lack of large-scale research on the climate-sensitive flowering and growth phases of robusta. Future research is required to determine its optimal temperature ranges more precisely to enhance yields [27,42]. ...
... Intriguingly, one manuscript done in the provinces of Indonesia and Vietnam (Southeast Asia) quantified robusta's optimal temperature range for production and showed it might present losses against climate change. The data indicated a decline in the production potential of Coffea canephora, placing a multibillion-dollar coffee industry and the livelihoods of millions of farmers at risk [42]. ...
Coffee production is fragile, and the Intergovernmental Panel on Climate Change (IPCC) reports indicate that climate change (CC) will reduce worldwide yields on average and decrease coffee-suitable land by 2050. This article adopted the systematic review approach to provide an update of the literature available on the impacts of climate change on coffee production and other ecosystem services following the framework proposed by the Millenium Ecosystem Assessment. The review identified 148 records from literature considering the effects of climate change and climate variability on coffee production, covering countries mostly from three continents (America, Africa, and Asia). The current literature evaluates and analyses various climate change impacts on single services using qualitative and quantitative methodologies. Impacts have been classified and described according to different impact groups. However, available research products lacked important analytical functions on the precise relationships between the potential risks of CC on coffee farming systems and associated ecosystem services. Consequently, the manuscript recommends further work on ecosystem services and their interrelation to assess the impacts of climate change on coffee following the ecosystem services framework.
... The annual climatic seasonality of a region is mainly characterized by variations in the incidence of solar radiation, temperature, rainfall, wind, and air relative humidity [1,2]. These variations can affect morphological, physiological, and biochemical processes, including C-assimilation and partitioning, respiration, nutrient uptake, translocation, and whole metabolism, increasing or reducing crop productive potential of the Coffea canephora Pierre ex Floehner [3][4][5][6]. Approximately 15% of the Brazilian production of C. canephora is from the southwestern Brazilian Amazon, mainly from the state of Rondônia. This region is characterized by two well-defined seasons throughout the year: a rainy season (October to May), and a dry season (June to September). ...
... These assessments allow us to understand how variable and stress conditions can affect different stages of photosynthesis in the plant. However, environmental conditions with a low impact on photosynthesis can result in significant reductions in coffee productivity [4]. Therefore, in some situations, photosynthesis-related assessments, such as gas exchange, may not have a direct relationship with yield [4]. ...
... However, environmental conditions with a low impact on photosynthesis can result in significant reductions in coffee productivity [4]. Therefore, in some situations, photosynthesis-related assessments, such as gas exchange, may not have a direct relationship with yield [4]. Therefore, more studies are needed to associate the common assessments made on the leaves with the yield of coffee trees. ...
Climate variation throughout the year affects photosynthesis and other physiological processes correlated with plant development and yield. This study aimed to evaluate the changes in the physiological attributes of Coffea canephora genotypes over the year in the Brazilian Amazon and assess their relationship with crop yield. The experiment was carried out in three cultivation systems with three genotypes. The evaluations were carried out in four periods: the peak of the dry season (S1); the beginning of the rainy season (S2); the peak of the rainy season (S3); and the beginning of the dry season (S4). A dataset of gas exchange, pigment indices, chlorophyll fluorescence, branch growth, and coffee yield was obtained. The group of gas exchange variables was the main contributor to treatment discrimination and was most affected by seasons. As expected, the values of gs, E, and A were significantly lower in S1, while the values of VPDLeaf-ar, TLeaf, and IWUE were significantly higher. Our results demonstrate that climatic seasonality affects the photosynthesis of Amazonian Robustas coffee, even under irrigated conditions, particularly in response to increased VPD. The physiological variables analyzed at the leaf level, even in different periods, did not explain the differences in the yield of C. canephora.
... Coffee is highly sensitive to climatic variability and, as such, is illustrative of the challenges that climate change poses to the world's plant species and to agricultural production more generally (Chemura et al., 2021;Kath et al., 2020;Moat et al., 2019;Moat et al., 2017). Sixty percent or more of wild coffee species are threatened with extinctionwith climate a key contributing risk . ...
... The climate of Vietnam's coffee growing areas is characterised by a humid tropical climate with mean annual temperatures of around 24°C and a total rainfall of c. 1833 mm per annum (Supplementary Material, Fig. S1). For further details on the climate and management of coffee farms in the study area please see Byrareddy et al. (2020), Byrareddy et al. (2019) and Kath et al. (2020). ...
... Tree age is calculated based on planting year. For further details on the dataset and its collection please see Byrareddy et al. (2020), Byrareddy et al. (2019) and Kath et al. (2020). ...
A shift towards earlier flowering is a widely noted consequence of climate change for the world's plants. However, whether early flowering changes the way in which plants respond to climate stress, and in turn plant yield, remains largely unexplored. Using 10 years of flowering time and yield observations (Total N = 5580) from 558 robusta coffee (Coffea canephora) farms across Vietnam we used structural equation modelling (SEM) to examine the drivers of flowering day anomalies and the consequent effects of this on coffee climate stress sensitivity and management responses (i.e. irrigation and fertilization). SEM allowed us to model the cascading and interacting effects of differences in flowering time, growing season length and climate stress. Warm nights were the main driver of early flowering (i.e. flowering day anomalies <0), which in turn corresponded to longer growing seasons. Early flowering was linked to greater sensitivity of yield to temperature during flowering (i.e. early in the season). In contrast, when late flowering occurred yield was most sensitive to temperature and rainfall later in the growing season, after flowering and fruit development. The positive effects of tree age and fertilizer on yield, apparent under late flowering conditions, were absent when flowering occurred early. Late flowering models predicted yields under early flowering conditions poorly (a 50 % reduction in cross-validated R² of 0.54 to 0.27). Likewise, models based on early flowering were unable to predict yields well under late flowering conditions (a 75 % reduction in cross-validated R², from 0.58 to 0.14). Our results show that early flowering changes the sensitivity of coffee production to climate stress and management and in turn our ability to predict yield. Our results indicate that changes in plant phenology need to be taken into account in order to more accurately assess climate risk and management impacts on plant performance and crop yield.
... Several studies have indicated high sensitivity of coffee to weather (Craparo et al., 2015;Kath et al., 2020Kath et al., , 2021 and climate (Davis et al., 2012;Bunn et al., 2015b;Moat et al., 2017). The interactive effects of precipitation and temperature define where coffee can be grown as an economically profitable crop as well as year-to-year variability in coffee yield and quality. ...
... Furthermore, for robusta coffee in Vietnam and Indonesia, every 1°C increase in mean minimum/maximum temperatures (above 16.2/24.1°C) during the growing season was suggested to lead to a 14 %-yield reduction (Kath et al., 2020). Another study indicates that precipitation-sensitivity is more important for robusta coffee than arabica coffee, the latter being more sensitive to temperature (Bunn et al., 2015b). ...
... Aggregated data on different administrative levels are publicly available from several countries; yet again, most countries have a limited time record and/or report yields only for large spatial areas. Thus, the few studies that assessed weather impact on coffee yield have used yield datasets over some specific locations (e.g., few Brazilian municipalities or farm-level data) with limited time series (the maximum of 10 years) (de Oliveira Aparecido et al., 2017;Valeriano et al., 2018;Kath et al., 2020); while previous global studies made use of indirect coffee information such as the occurrence of coffee-production without yield data (Bunn et al., 2015a, Bunn et al., 2015b. This study focuses on robusta coffee of the Central Highlands of Vietnam, the biggest robusta coffee producing country, using 19 years of data. ...
Weather and climate strongly impact coffee; however, few studies have measured this impact on robusta coffee yield. This is because the yield record is not long enough, and/or the data are only available at a local farm level. A data-driven approach is developed here to 1) identify how sensitive Vietnamese robusta coffee is to weather on district and provincial levels, 2) during which key moments weather is most influential for yield, and 3) how long before harvest, yield could potentially be forecasted. Robusta coffee yield time series were available from 2000 to 2018 for the Central Highlands, where 40% of global robusta coffee is produced. Multiple linear regression has been used to assess the effect of weather on coffee yield, with regularization techniques such as PCA and leave-one-out to avoid over-fitting the regression models. The data suggest that robusta coffee in Vietnam is most sensitive to two key moments: a prolonged rainy season of the previous year favoring vegetative growth, thereby increasing the potential yield (i.e., number of fruiting nodes), while low rainfall during bean formation decreases yield. Depending on location, these moments could be used to forecast the yield anomaly with 3–6 months’ anticipation. The sensitivity of yield anomalies to weather varied substantially between provinces and even districts. In Dak Lak and some Lam Dong districts, weather explained up to 36% of the robusta coffee yield anomalies variation, while low sensitivities were identified in Dak Nong and Gia Lai districts. Our statistical model can be used as a seasonal forecasting tool for the management of coffee production. It can also be applied to climate change studies, i.e., using this statistical model in climate simulations to see the tendency of coffee in the following decades.
... 8 ) or perhaps even higher to 30 °C (ref. 9 ). It is also resistant to the prevalent strains of coffee leaf rust (Hemileia vastatrix Berk. ...
... with these observed data, with a mean annual temperature of 24.9 °C and mean total annual rainfall of 2,288 mm per year (Fig. 2). The mean annual temperature and mean total annual rainfall of stenophylla is slightly and considerably higher (respectively) than wild and cultivated robusta 8,9 and modelled robusta and Liberica, although the ranges for these values are similar ( Fig. 2 and Supplementary Table 6). The mean annual temperatures reported and modelled for Arabica are 19.0 °C (18-20 °C) 5,6 and 18.7 °C (Fig. 2), respectively; and for stenophylla are 25.8 °C (25.5 °C/26 °C) 1,28 and 24.9 °C, respectively (Fig. 2). ...
... 41 ) and ggpubr packages 42 . For validation purposes, our modelled temperatures and rainfall for Arabica and robusta (Fig. 2) were compared against published data for cultivated coffee, and were found to fall within reported ranges 5,6,8,9 . We agree that temperature ranges given for the native range of coffees is often reported as too high 9 , especially when comparing wild and farmed coffee but did not find any marked discrepancies in our analysis and observations. ...
There are numerous factors to consider when developing climate-resilient coffee crops, including the ability to tolerate altered climatic conditions, meet agronomic and value chain criteria, and satisfy consumer preferences for flavour (aroma and taste). We evaluated the sensory characteristics and key environmental requirements for the enigmatic narrow-leaved coffee (Coffea stenophylla), a wild species from Upper West Africa1. We confirm historical reports of a superior flavour1–3 and uniquely, and remarkably, reveal a sensory profile analogous to high-quality Arabica coffee. We demonstrate that this species grows and crops under the same range of key climatic conditions as (sensorially inferior) robusta and Liberica coffee4–9 and at a mean annual temperature 6.2–6.8 °C higher than Arabica coffee, even under equivalent rainfall conditions. This species substantially broadens the climate envelope for high-quality coffee and could provide an important resource for the development of climate-resilient coffee crop plants. Coffea stenophylla is a recently rediscovered, narrow-leaved wild coffee from Upper West Africa. Rigorous sensory evaluation (tasting) rates its flavour profile as analogous to high-quality Arabica coffee, but it can grow at much higher temperatures.
... Specifically, global warming will significantly affect coffee crop production worldwide, with a reduction in 2050 of up to 60% in southern Brazil [30], 90% in Nicaragua [19], and 30-60% in Kenya [31]. Both Robusta and Arabica will be negatively affected by increasing temperature: a 1 • C increase in minimum/maximum temperature (16.2/24 • C) could result in ≈14% or 350-460 kg ha −1 Robusta yield reduction [32], even though the Arabica favourable environment could be relocated to 300 m up the altitude gradient in Nicaragua [19]. In addition, high temperatures would make coffee farming susceptible to fungal attacks, such as coffee rust, at lower altitudes and borer damage at high elevations [33,34]. ...
... The available literature on coffee presents extensive insights and recommendations for using models and other analytical tools to study climate change impacts and adaptations in coffee production in different regions, such as [35] in Central America, [32] in Vietnam, [36] in Brazil, [37] in Colombia, [38] in Uganda, [39] in Ethiopia, and many more. While the impacts of climate change on coffee have been systematically studied [40], modelling tools still have not received enough attention in terms of systematic review and classification. ...
Several modelling tools reported the climate change impact on the coffee agrosystems. This article has adopted a systematic approach to searching out information from the literature about different modelling approaches to assess climate change impacts or/and adaptation on coffee crops worldwide. The review included all scientific publications from the date of the first relevant article until the end of 2022 and screened 60 relevant articles. Most results report research conducted in America, followed by Africa. The models assessed in the literature generally incorporate Intergovernmental Panel on Climate Change (IPCC) emission scenarios (80% of manuscripts), particularly Representative Concentration Pathways (RCP) and Special Report on Emission Scenarios (SRES), with the most common projection periods until 2050 (50% of documents). The selected manuscripts contain qualitative and quantitative modelling tools to simulate climate impact on crop suitability (55% of results), crop productivity (25% of studies), and pests and diseases (20% of the results). According to the analysed literature, MaxEnt is the leading machine learning model to assess the climate suitability of coffee agrosystems. The most authentic and reliable model in pest distribution is the Insect Life Cycle Modelling Software (ILCYM) (version 4.0). Scientific evidence shows a lack of adaptation modelling, especially in shading and irrigation practices, which crop models can assess. Therefore, it is recommended to fill this scientific gap by generating modelling tools to understand better coffee crop phenology and its adaptation under different climate scenarios to support adaptation strategies in coffee-producing countries, especially for the Robusta coffee species, where a lack of studies is reported (6% of the results), even though this species represents 40% of the total coffee production.
... La productividad del café (kg/ha) se evaluó con base en los rendimientos promedio de los dos años anteriores. Finalmente, el rendimiento en los cultivos de café fue obtenido también a través de la correlación de imágenes satelitales con datos in situ de productividad (40), se usaron los índices de vegetación NDVI y EVI para identificar zona sensible al estrés hídrico. Se determinó que un alto índice foliar en épocas lluviosas, hacia más probable un incremento en la productividad, reflejado en una mayor cobertura de área foliar, en contraste a lo sucedido, en las épocas secas donde se redujo. ...
... ;(40) se percibe la influencia de la temperatura (°C) en el rendimiento del café Robusta, determinando que cada aumento de 1°C en las temperaturas medias, mínimas o máximas por encima de 16,2/24,1 °C corresponde a una disminución del rendimiento de aproximadamente 14% o 350-460 kg/ha. la temperatura se agrega generalmente como atributo al conjunto de datos de entrenamiento para los diferentes modelos, se mide con temporalidad trimestral y se incluye con valores promedio, mínimo y máximo. ...
Resumen Coffee is one of the most traded agricultural products internationally; in Colombia, it is the first non-mining-energy export product. In this context, the prediction of coffee crop yields is vital for the sector since it allows coffee growers to establish crop management strategies, maximizing their profits or reducing possible losses. This paper addresses crucial aspects of coffee crop yield prediction through a systematic literature review of documents consulted in Scopus, ACM, Taylor & Francis, and Nature. These documents were subjected to a filtering and evaluation process to answer five key questions: predictor variables used, target variable, techniques and algorithms employed, metrics to evaluate the quality of the prediction, and species of coffee reported. The results reveal some groups of predictor variables, including atmospheric, chemical, satellite-derived, fertilizer-related, soil, crop management, and shadow factors. The most recurrent target variable is yield, measured in bean weight per hectare or other measures, with one case considering leaf area. Predominant techniques for yield forecasting include linear regression, random forests, principal component analysis, cluster regression, neural networks, classification and regression trees, and extreme learning machines. The most common metrics to evaluate the quality of predictive models include root mean squared error, coefficient of determination (R²), mean absolute error, error deviation, Pearson's correlation coefficient, and standard deviation. Finally, robusta, arabica, racemosa, and zanguebariae are the most studied coffee varieties.
... The findigd in line with recent studies that have reached conclusions about coffee's vulnerability to increasing temperatures. Some suggest the widespread loss, in excess of 50%, of suitable growing areas [43,44]. The often cited optimal mean annual temperature range of Robusta is estimated to be between 22 and 26 or 22 and 30℃ [45][46][47]. ...
... Our study also higlight that rainfall influence climate sustainability of Robusta Coffee. Rainfall had a notable negative effect on yields in the flowering season [43]. Excessive rain and cool conditions during the quiescent growth phase can repress flowering and this has been linked to lower yields [45]. ...
This study aims to analyze the impact of climate change on the climate suitability of Robusta coffee in five main Indonesian coffee production centers namely Aceh, North Sumatera, South Sumatera, Bengkulu and Lampung using the Maxent approach. The study used climate data, and climate projections from Worldclim and coffee location data from maps of Indonesia's main agricultural commodities. The results showed that Maxent had good performance in modeling the climatic suitability of Robusta coffee at the provincial level, and the corresponding production areas shifted with different patterns between provinces. The areas with suitable and highly suitable climates for Robusta coffee were projected to decrease in all provinces except for Bengkulu. The findings suggest a future challenge for Robusta coffee sustainability in Indonesia. Aceh, North Sumatera, South Sumatera, and Lampung need to develop adaptation strategies to anticipate the increasingly unsuitable environment. On the other hand, Bengkulu can be considered a new area for coffee plantation. The projection of the suitability of the coffee climate is crucial in determining the future coffee development areas and for the rejuvenation of the existing coffee plantations, highlighting the significance of the study's findings for policymakers, farmers, and other stakeholders.
... Additionally, higher temperatures in general have resulted in higher pest and disease incidence, negatively impacting coffee yields 70 . Particularly C. arabica (the dominant coffee cultivar) is sensitive towards heat stress, leading to higher yield variability 71 , and could potentially need to be replaced by more heat stress tolerant (and less commercially interesting) variants 37 . Extreme temperatures are detrimental to tea growth, and can result in significant decreases in yields 62 , or less predictable yields 72 . ...
... We could not fully address this, as our spatial resolution was coarser, and our location data on producers' locations were approximate, therefore not allowing for identification of local terrain and soil conditions. Similarly, we could not distinguish between different varieties, as some could be more (or less) vulnerable to climate change 43,63,71,94 . We also did not differentiate between various agricultural practices that could be more resilient to climate change, such as shade-grown crops or agroforestry 92,[94][95][96][97][98] . ...
Supply chains of agricultural commodities, such as banana, cocoa, coffee, and tea, are vulnerable to climate change. Their ability to adapt depends on assessments of climate change impacts on producing regions. Such assessments are, however, missing despite available climate projections. Here, we analyze how drought, heat stress, and heavy precipitation could affect over 1.6 million producers within the Fairtrade supply chain by 2050 by using projections from general circulation models. Globally, Fairtrade producers will mainly be subject to increased heat stress. Drought might present particular pressures on Brazilian and Central American coffee producers and on tea producers in southeastern Africa. Heavy precipitation might become more common for producers of cocoa and coffee in the Andes, coffee producers in East Africa, and tea producers in South Asia. Our approach enables the identification of how sensitive different regions are to climate change, allowing for timely adaptation. This is crucial for continued commodity supply and sustainability of farmers’ livelihoods.
... These include standard, peaberry, triage, and elephant shapes [3,4]. Their size mainly, length and width and thickness as described by Bikila [4] can vary depending on environmental conditions, planting conditions [9,10], and the geographical and climate factors [3,[11][12][13]. Thus, temperature and altitude are the main criteria for selecting a parcel of land for growing coffee as determined [14]. ...
... Shape Index (SI)= √(W+T) (13) or SI=De/√(Dp*T) ...
Coffee is an important source of income for African smallholder farmers. The world is predominated by three coffee varieties, namely Arabica, Robusta, and Liberica originated in the African countries. However, the low coffee quality attributes can affect the coffee beans‟ price due to the improper post-harvesting processes. Therefore, this work is aimed to review the physical and mechanical properties of the African coffee cherries and beans on the country-wise level, and review the methods used to measure quality parameters and compare the key coffee quality parameters. This work was qualitatively conducted using secondary data on African Arabica and Robusta coffee from journals, conference proceedings, and reports. Methods used for determining the coffee beans‟ properties were explored country wise. The coffee attributes of the two coffee varieties were compared through statistical analysis using ANOVA. The review found that the coffee quality depends on geographical characteristics, agronomic factors, and post-harvesting processes. The coffee cherries de-pulping process is linked to the coffee quality to avoid the damage of parchment coffee beans based particularly on the size and shape of coffee cherries and beans. The analysed quality parameters showed that the Ethiopian Arabica coffee beans were larger than the Ghanaian Robusta coffee beans. While the size of Robusta coffee in Eastern African countries, particularly Uganda, is bigger than those in Western countries, especially Ghana. Therefore, the information on the coffee quality attributes can help to improve the performance parameters of the coffee de-pulping machine and enhance the price of African coffee.
... Major change in climate under the various climate scenarios and RCPs is likely to affect the suitability of Arabica and Robusta coffee. Robusta coffee's likelihood of remaining marginal under various climate scenarios aligns with observations by Kath et al. (2020Kath et al. ( , 2021 who reported that Robusta coffee majorly depends on the interaction of rainfall and temperature, although may respond better to increasing temperatures than Arabica coffee (Jayakumar et al. 2017). Studies in East African highlands (Bunn et al. 2015b(Bunn et al. , a, 2019Ovalle-Rivera et al. 2015) and Indonesia ) report a shift to higher elevations for optimal coffee production zones within the near-term future. ...
... The no change of crop suitability in western Uganda is attributed to the quasi constancy in rainfall. The expected shifts in climatic suitability suggest that the projected climate may further adversely affect the phenological stages of crops in the CBFS (Kath et al. 2020). Further assessment will perhaps necessitate for relatively higher resolution climate dataset and crop diagnostic parameters at village and farm levels, in order to provide insight on temperature and rainfall effects on crop growth stages and their adaptability to climate change. ...
Coffee-based farming systems (CBFS) support smallholder farmers through mainly coffee growing with integration of other food crops and livestock. Climate change is expected to ravage crop suitability in several agroecological zones, posing a threat to national earnings and livelihoods. However, previous studies have mainly considered crop-specific analyses rather than the major crops in a farming system. This study illustrates variations in climatic suitability of major crops grown in Uganda’s Arabica and Robusta CBFS at disaggregated altitudes. Climate data (1980–2009) was projected for 2010–2039 (near-term future) for five climate scenarios under Representative Concentration Pathways—RCP 8.5 and 4.5 using twenty-nine global climate models (GCMs) based on the delta method. Climatic suitability of coffee, banana, maize, and beans was assessed using EcoCrop model. Rainfall and temperature changes are expected during long rains and second-dry seasons, with higher rainfall increments during short rains. Minimum temperatures are likely to increase in low altitudes under ensemble-mean, hot-wet, and hot-dry scenarios. Crop suitability improvements (> 5% area) are expected in mid to high altitudes under cool-wet and hot-wet, mainly for RCP 4.5 while western Uganda Arabica CBFS are unlikely to experience crop suitability changes. Suitable area for East African banana and beans is likely to increase utmost 44.7%, and expected to decline to marginal utmost 64% (coffee and banana) and 21.2% (maize) in central Robusta and eastern Arabica CBFS under ensemble-mean, cool-dry, and hot-dry scenarios. Plantain and dessert banana are likely to become unsuitable within Robusta and high-altitude Arabica CBFS. This study recommends identification and use of system appropriate climate-smart adaptation strategies to mitigate future crop-climate vulnerabilities within CBFS.
... The coffee market is characterized by low and volatile market prices, and production suffers from labour shortages, low wages, and lack of investment in productivity-raising technology and knowledge (e.g., through farmer outreach and training), reducing the economic sustainability of coffee farming. Social problems include, inter alia, poverty, inequality, and occurrences of child or forced labour in coffee production (Dietz et al., 2018b;Kath et al., 2020;Panhuysen and Pierrot, 2014). Coffee's environmental challenges relate to direct ecosystems impacts, primarily through conversion of natural vegetation to coffee plantations (Ango et al., 2020;Meyfroidt et al., 2013;Pendrill et al., 2019), and resulting losses of ecosystem services and pollution (Cerda et al., 2017;De Leijster et al., 2021;Pico-Mendoza et al., 2020). ...
... Coffee's environmental challenges relate to direct ecosystems impacts, primarily through conversion of natural vegetation to coffee plantations (Ango et al., 2020;Meyfroidt et al., 2013;Pendrill et al., 2019), and resulting losses of ecosystem services and pollution (Cerda et al., 2017;De Leijster et al., 2021;Pico-Mendoza et al., 2020). The use of non-renewable resources (e.g., fertilizers and fossil fuel use) and the combined pressure of climate change and pests and diseases further add to the coffee sectors' environmental impact Kath et al., 2020;Ovalle-Rivera et al., 2015;Pham et al., 2019). ...
Information sharing lies at the core of most governance interventions within agro-food commodity supply-chains, such as certification standards or direct trade relationships. However, actors have little information available to guide sustainable consumption decisions beyond simple labels. Blockchain technology can potentially alleviate the numerous sustainability problems related to agro-food commodity supply-chains by fostering traceability and transparency. Despite significant research on blockchain, there is limited understanding of the concrete barriers and benefits and potential applications of blockchain in real-world settings. Here, we present a case study of blockchain implementation in a coffee supply-chain. Our aim is to assess the potential of blockchain technology to promote sustainability in coffee supply chains through increased traceability and transparency and to identify barriers and opportunities for this. While our pilot implementation clearly illustrates certain benefits of blockchain, it also suggests that blockchain is no silver bullet for delivering agro-food supply chain sustainability. Knowledge on provenance and transparency of information on quality and sustainability can help trigger transformation of consumer behaviour, but the actual value lies in digitising the supply chain to increase efficiency and reduce costs, disputes, and fraud, while providing more insight end-to-end through product provenance and chain-of-custody information. We identify a need to understand and minimize supply chain barriers before we can reap the full benefits of digitalization and decentralization provided by blockchain technology.
... In addition, intensive agricultural practices are used (e.g. fertilization, irrigation, shade management, and pruning) in these coffee farms (Amarasinghe et al., 2015;Kath et al., 2020). The Central Highlands region includes four main coffee-producing provinces, and each province is divided into several districts. ...
... It requires about 8 months (May to December) for the vegetative stage and about 9-11 months (January to September/November) from flowering until fruit ripening for robusta coffee. The weather during the last few months before harvest (i.e. the productive stage) is decisive for the yield (Craparo et al., 2015b;Kath et al., 2020); however, it has been shown that the weather during the previous year's growing season (i.e. the vegetative stage) has a big impact. A prolonged rainy season (14-19 months before harvest) favours vegetative growth and thus increases the potential coffee yield (Kath et al., 2021). ...
The use of statistical models to study the impact of weather on crop yield has not ceased to increase. Unfortunately, this type of application is characterized by datasets with a very limited number of samples (typically one sample per year). In general, statistical inference uses three datasets: the training dataset to optimize the model parameters, the validation dataset to select the best model, and the testing dataset to evaluate the model generalization ability. Splitting the overall database into three datasets is often impossible in crop yield modelling due to the limited number of samples. The leave-one-out cross-validation method, or simply leave one out (LOO), is often used to assess model performance or to select among competing models when the sample size is small. However, the model choice is typically made using only the testing dataset, which can be misleading by favouring unnecessarily complex models. The nested cross-validation approach was introduced in machine learning to avoid this problem by truly utilizing three datasets even with limited databases. In this study, we propose one particular implementation of the nested cross-validation, called the nested leave-two-out cross-validation method or simply the leave two out (LTO), to choose the best model with an optimal model selection (using the validation dataset) and estimate the true model quality (using the testing dataset). Two applications are considered: robusta coffee in Cu M'gar (Dak Lak, Vietnam) and grain maize over 96 French departments. In both cases, LOO is misleading by choosing models that are too complex; LTO indicates that simpler models actually perform better when a reliable generalization test is considered. The simple models obtained using the LTO approach have improved yield anomaly forecasting skills in both study crops. This LTO approach can also be used in seasonal forecasting applications. We suggest that the LTO method should become a standard procedure for statistical crop modelling.
... Coffea canephora has previously been predicted to respond better to climate change than Arabica (Jayakumar et al., 2017;DaMatta et al., 2018). However, recent studies suggest that the alleged "heat tolerance" boasted by C. canephora (Robusta) plants could have been overestimated (Kath et al., 2020). Others point to elite Arabica genotypes which exhibit a heat and drought tolerance when simulated under climate change scenarios such as elevated air CO 2 (eCO 2 ) (Martins et al., 2014;Rodrigues et al., 2016;Ramalho et al., 2018;DaMatta et al., 2019;Avila et al., 2020a,b;Semedo et al., 2021). ...
... Changes in temperatures and intra-and inter-annual rainfall patterns will negatively impact the suitability of large areas traditionally suitable for coffee production (Bunn et al., 2015a;Semedo et al., 2018;de Sousa et al., 2019;Gomes et al., 2020). Although some areas might benefit from new climatic conditions (especially areas at high elevations which will see an increase in temperatures and a shift from sub-optimal to optimal conditions) (Ceballos-Sierra and Dall'Erba, 2021) overall coffee production is expected to decline due to global warming (Kath et al., 2020). In addition, climate change is expected to increase the frequency and severity of extreme temperature events, both for heatwaves or cold spells, which will further impact coffee production. ...
Coffee is deemed to be a high-risk crop in light of upcoming climate changes. Agroforestry practices have been proposed as a nature-based strategy for coffee farmers to mitigate and adapt to future climates. However, with agroforestry systems comes shade, a highly contentious factor for coffee production in terms of potential yield reduction, as well as additional management needs and interactions between shade trees and pest and disease. In this review, we summarize recent research relating to the effects of shade on (i) farmers' use and perceptions, (ii) the coffee microenvironment, (iii) pest and disease incidence, (iv) carbon assimilation and phenology of coffee plants, (v) coffee quality attributes (evaluated by coffee bean size, biochemical compounds, and cup quality tests), (vi) breeding of new Arabica coffee F1 hybrids and Robusta clones for future agroforestry systems, and (vii) coffee production under climate change. Through this work, we begin to decipher whether shaded systems are a feasible strategy to improve the coffee crop sustainability in anticipation of challenging climate conditions. Further research is proposed for developing new coffee varieties adapted to agroforestry systems (exhibiting traits suitable for climate stressors), refining extension tools by selecting locally-adapted shade trees species and developing policy and economic incentives enabling the adoption of sustainable agroforestry practices.
... canephora) still provide ample coffee to supply the global value chain, but narrative from farmers across the world's coffee belt, and ongoing shortfalls in production during weather perturbations and cyclic climatic phenomena, tell of ever-increasing climate-related issues. This is a result of the specificity of the climate envelopes for Arabica (Moat et al., 2017Davis et al., 2018 and robusta (Kath et al., 2020), the perennial nature of the crop, and the fact that coffee farming has been extended into suboptimal climatic space for these two species and their cultivars. ...
... Scatter and density plots were plotted using R (R Core Team, 2016), using the ggplot2 (Wickham, 2016) and ggpubr packages (Kassambara, 2020). For validation purposes, our modelled annual temperatures (from Bio 1), annual precipitation (Bio12) and precipitation seasonality (Bio15) used to produce Figures 1, 2 were compared against publicly available monthly mean temperature precipitation charts for East Africa and published data (Laíns E Silva, 1954; Alègre, 1959;DaMatta, 2004;DaMatta and Ramalho, 2006;Kath et al., 2020). Our modelling methods have been shown to provide climate metrics that are similar to those provided for coffee species in cultivation, produced by direct measurement and other means . ...
Climate change poses a considerable challenge for coffee farming, due to increasing temperatures, worsening weather perturbations, and shifts in the quantity and timing of precipitation. Of the actions required for ensuring climate resilience for coffee, changing the crop itself is paramount, and this may have to include using alternative coffee crop species. In this study we use a multidisciplinary approach to elucidate the identity, distribution, and attributes, of two minor coffee crop species from East Africa: Coffea racemosa and C. zanguebariae . Using DNA sequencing and morphology, we elucidate their phylogenetic relationships and confirm that they represent two distinct but closely related species. Climate profiling is used to understand their basic climatic requirements, which are compared to those of Arabica ( C. arabica ) and robusta ( C. canephora ) coffee. Basic agronomic data (including yield) and sensory information are provided and evaluated. Coffea racemosa and C. zanguebariae possess useful traits for coffee crop plant development, particularly heat tolerance, low precipitation requirement, high precipitation seasonality (dry season tolerance) and rapid fruit development (c. 4 months flowering to mature fruit). These attributes would be best accessed via breeding programs, although these species also have niche-market potential, particularly after further pre-farm selection and post-harvest optimization.
... dw, dry weight. C. arabica is 18-23 °C, and this species is therefore considered to be more heat-sensitive than its lowland relative C. canephora, whose habitat's mean temperature ranges from 22 °C to 30 °C (Kath et al., 2020). Considering the current scenario of global warming, the heat-resistant nature of C. canephora is of particular value as coffee farmers may switch to C. canephora if the local temperature is rising. ...
... Considering the current scenario of global warming, the heat-resistant nature of C. canephora is of particular value as coffee farmers may switch to C. canephora if the local temperature is rising. However, a recent modeling analysis has suggested that C. canephora has an optimal temperature below 20.5 °C for maximum yields, which is markedly lower than the temperature estimated based on the leaf thermotolerance (Rodrigues et al., 2016;Kath et al., 2020). To respond to heat stress, plants employ a large number of adaptation or tolerance mechanisms (Hasanuzzaman et al., 2013). ...
The coffee beverage is the second most consumed drink worldwide after water. In coffee beans, cell wall storage polysaccharides (CWSPs) represent around 50 per cent of the seed dry mass, mainly consisting of galactomannans and arabinogalactans. These highly abundant structural components largely influence the organoleptic properties of the coffee beverage, mainly due to the complex changes they undergo during the roasting process. From a nutritional point of view, coffee CWSPs are soluble dietary fibers shown to provide numerous health benefits in reducing the risk of human diseases. Due to their influence on coffee quality and their health-promoting benefits, CWSPs have been attracting significant research attention. The importance of cell walls to the coffee industry is not restricted to beans used for beverage production, as several coffee by-products also present high concentrations of cell wall components. These by-products include cherry husks, cherry pulps, parchment skin, silver skin, and spent coffee grounds, which are currently used or have the potential to be utilized either as food ingredients or additives, or for the generation of downstream products such as enzymes, pharmaceuticals, and bioethanol. In addition to their functions during plant development, cell walls also play a role in the plant's resistance to stresses. Here, we review several aspects of coffee cell walls, including chemical composition, biosynthesis, their function in coffee's responses to stresses, and their influence on coffee quality. We also propose some potential cell wall-related biotechnological strategies envisaged for coffee improvements.
... dw, dry weight. C. arabica is 18-23 °C, and this species is therefore considered to be more heat-sensitive than its lowland relative C. canephora, whose habitat's mean temperature ranges from 22 °C to 30 °C (Kath et al., 2020). Considering the current scenario of global warming, the heat-resistant nature of C. canephora is of particular value as coffee farmers may switch to C. canephora if the local temperature is rising. ...
... Considering the current scenario of global warming, the heat-resistant nature of C. canephora is of particular value as coffee farmers may switch to C. canephora if the local temperature is rising. However, a recent modeling analysis has suggested that C. canephora has an optimal temperature below 20.5 °C for maximum yields, which is markedly lower than the temperature estimated based on the leaf thermotolerance (Rodrigues et al., 2016;Kath et al., 2020). To respond to heat stress, plants employ a large number of adaptation or tolerance mechanisms (Hasanuzzaman et al., 2013). ...
The coffee beverage is the second most consumed drink worldwide after water. In coffee beans, cell wall storage polysaccharides (CWSPs) represent around 50 per cent of the seed dry mass, mainly consisting of galactomannans and arabinogalactans. These highly abundant structural components largely influence the organoleptic properties of the coffee beverage, mainly due to the complex changes they undergo during the roasting process. From a nutritional point of view, coffee CWSPs are soluble dietary fibers shown to provide numerous health benefits in reducing the risk of human diseases. Due to their influence on coffee quality and their health-promoting benefits, CWSPs have been attracting significant research attention. The importance of cell walls to the coffee industry is not restricted to beans used for beverage production, as several coffee by-products also present high concentrations of cell wall components. These by-products include cherry husks, cherry pulps, parchment skin, silver skin, and spent coffee grounds, which are currently used or have the potential to be utilized either as food ingredients or additives, or for the generation of downstream products such as enzymes, pharmaceuticals, and bioethanol. In addition to their functions during plant development, cell walls also play a role in the plant’s resistance to stresses. Here, we review several aspects of coffee cell walls, including chemical composition, biosynthesis, their function in coffee’s responses to stresses, and their influence on coffee quality. We also propose some potential cell wall–related biotechnological strategies envisaged for coffee improvements.
... Climate change poses serious threats to coffee production and quality Kath et al., 2020;Craparo et al., 2021) and seasonal norms are reported to be increasingly unpredictable. Even though total annual rainfall is reported to slightly increase, rainfall patterns are expected to shift with more rain in the wet season and less rain in the dry season (Baker et al., 2017) with adverse impacts on flowering and fruit setting (Koh et al., 2020). ...
The Central Highlands of Vietnam is an important Robusta coffee growing region. However, the region is facing climate change impacts from rising temperatures and irregular rainfall, while Vietnamese coffee farmers predominantly rely on irrigation from heavily depleted aquifers. To continue productive and sustainable growth, this system requires an innovative approach to meet this hydrological challenge. Here we propose a user-friendly tool, which aims to support coffee farmers’ irrigation decisions, through the Targeted Irrigation Support Tool or ThIRST. ThIRST combines seasonal forecasts, on-farm metrics, and farmer’s expertise. The research comprises baseline ( n = 400) and endline ( n = 237) surveys of coffee farmers in Đắk Lắk and Lâm Đồng Provinces. Through the surveys, farmers’ irrigation needs and the applicability of the tool are evaluated. Despite low smartphone usage for farming advisory, the results show the tool allows coffee farmers to continually achieve water-use efficiency and adapt to climate variability. Involving farmers in the design, production and evaluation of climate services can improve the trust and uptake of agro-advisories and the way this information is communicated.
... J. G. da Silva et al., 2015). However, drought is the main environmental stress affecting coffee production in most growing areas , which together with high air temperatures -above 31.5 °C (Partelli et al., 2010) -can drastically reduce coffee growth due to physiological and biochemical changes (DaMatta & Ramalho, 2006;Kath et al., 2020) related to a decrease in photosynthetic activity and respiratory capacity and increased respiratory rate (Vara Prasad et al., 2005;Dubberstein et al., 2018;Venancio et al., 2020). ...
Neste estudo objetivou-se avaliar o crescimento vegetativo da espécie Coffea canephora, a partir dos ramos ortotrópicos e plagiotrópicos dos cafeeiros das variedades botânicas Conilon e Robusta, em condições irrigada e não irrigadas, durante as estações de chuva e estiagem. O experimento foi conduzido no município de Ouro Preto do Oeste, Rondônia, Brasil, durante dois períodos definidos entre os meses de outubro de 2019 a outubro de 2021. As taxas de crescimentos dos ramos (mm dia-1) foram obtidas a cada quatorze dias e o crescimento sazonal foi plotado em gráficos em série. As médias das taxas de crescimento para cada tipo de ramo foram comparadas pelo teste de Tukey (p ≤ 0,05). O crescimento vegetativo foi sazonal durante os períodos de avaliação e estações do ano e, variou conforme o material genético e uso da irrigação. As taxas de crescimento foram superiores no período chuvoso, independentemente do manejo hídrico e da variedade botânica. A irrigação de cafeeiros realizada durante as épocas de altas temperaturas e forte déficit hídrico proporcionou maior crescimento em relação a plantas não irrigadas. Além disso, o crescimento dos cafeeiros não irrigados ficou represado durante o período da estiagem e foi compensado pelas altas taxas de crescimento no período das chuvas. As plantas da variedade botânica Robusta, em condições de disponibilidade hídrica, mediante chuva ou irrigação, tenderam a crescer mais do que as da variedade Conilon, considerando as condições climáticas da Amazônia Sul-Ocidental.
... Many studies have shown that coffee is highly sensitive to changes in temperature and precipitation levels (Craparo et al 2015, Kath et al 2020, Venancio et al 2020, Dinh et al 2022. According to Venancio et al 2020, droughts and high temperatures have a negative impact on coffee production, with rising temperatures having a more significant effect than decreases in annual rainfall. ...
This research aimed to identify sensitive areas for Robusta coffee trees in Dak Lak province, Vietnam, where frequent droughts caused fluctuations in productivity. To improve yield forecasting, a mask was developed to extract potential predictive variables from satellite-derived vegetation indices (VIs). Correlation coefficients between VIs and coffee yield were analyzed to determine sensitive areas, and grid cells with high multiple correlation coefficients and a variable over time were used to build the mask for extracting VIs as predictor variables. The study found that sensitive areas had more challenging farming conditions than long-term crops, and the Vegetation Health Index was the most appropriate index for predicting coffee yield. The forecast quality for 6-8 months in advance was relatively high, with a "Willmott's index of agreement" ranging from 0.85 to 0.97 and the Mean Absolute Percentage Error ranging from 4.9% to 7.5%. Compared to previous research, the forecast quality has significantly improved. This study provides valuable insights for predicting coffee yield in Dak Lak and highlights the importance of considering sensitive areas and VIs for accurate forecasting.
... The viability of the economic aspects of the coffee agro-food industry is threatened by several factors, such as fluctuating and low prices and the lack of investment in technology. Several other factors, such as the use of child labour, forced labour, labour shortages, and lack of knowledge, are common social problems in the coffee industry (Dietz et al. 2018;Kath et al. 2020). In addition, the coffee agro-food industry also impacts the ecosystem and causes environmental problems (Ango et al. 2020;Pendrill et al. 2019). ...
The coffee agro-food industry has great potential for economic development in tropical countries. However, the issue of agro-food sustainability in the coffee industry is a primary focus, so this industry must conduct a sustainable performance assessment (SPA). This research aims to propose a new SPA framework that incorporates the fuzzy analytical hierarchy process (AHP), rating assessment, and traffic light system (TLS) procedures. The fuzzy AHP is used to weight the indicators, and rating assessment is used to evaluate the performance of each indicator. The rating assessment results are used to evaluate the efficiency of the indicators used to calculate the sustainability score. Furthermore, the sustainability score is classified using the TLS to assess sustainability performance. A case study of the agro-food coffee industry in Indonesia is also presented, with six indicators of the economic dimension, eight of the social dimension, and four of the environmental dimension. The results show that economic factors produce good performance (green). In contrast, social factors produce performance that needs to be improved (yellow). Environmental aspects have poor performance (red). The overall sustainability score assessment results show that the agro-food coffee industry in Indonesia scored 85.71%, which is categorized as needing improvement (yellow). This study also makes strategic recommendations to improve the performance of the coffee agro-food industry.
... Recent data from Kath et al. (2020) suggests Robusta has a lower optimal temperature range than previously thought, with every 1°C increase in the mean maximum temperature above about 24°C associated with a yield reduction of ~ 14% (350-460 kg ha −1 ). Hence, there will be limits to the resilience of yields to increased temperatures when transitioning from Arabica to Robusta production. ...
Climate change is adversely affecting coffee production, impact-ing both yields and quality. Coffee production is dominated by the cultivation of Arabica and Robusta coffee, species that represent 99% of production, but both will be affected by climate change. Sustainable management practices that can enhance the resilience of production and livelihoods to climate change are urgently needed as production supports the livelihoods of over 25 million people globally, the majority of whom are smallholder farmers located in the coffee belt spanning the tropics. These communities are already experiencing the impacts of climate change. We conducted a systematic review, identifying 80 studies that describe the direct and indirect impacts of climate change on coffee agroecosystems, or that identify agroecological practices with the potential to enhance climate resilience. Adverse environmental impacts include a reduction in area suitable for production, lower yields, increased intensity and frequency of extreme climate events, and greater incidence of pests and diseases. Potential environmental solutions include altitudinal shifts, new, resilient culti-vars, altering agrochemical inputs, and agroforestry. However, financial, environmental and technical constraints limit the availability of many of these approaches to farmers, particularly smallholder producers. There is therefore an urgent need to address these barriers through policy and market mechanisms, and stakeholder engagement to continue meeting the growing demand for coffee.
... Cool climates (higher elevations, with at least intermediate canopy cover) had a high potential to produce coffee beans possessing superior total preliminary quality, higher caffeine, total chlorogenic acid (CGA) contents, and trigonelline concentrations (Worku et al. 2018;Tolessa et al. 2017;paper in-press). Water deficits, on the other hand, during the coffee fruit expansion and filling period caused appreciable productivity loss and decreased bean quality (Kath et al. 2020;Semedo et al. 2018). In terms of soil characteristics, is especially soil pH associated with the acidity of coffee, body and cup cleanness. ...
The biophysical drivers that affect coffee quality vary within and among farms. Quantifying their relative importance is crucial for making informed decisions concerning farm management, marketability and profit for coffee farmers. The present study was designed to quantify the relative importance of biophysical variables affecting coffee bean quality within and among coffee farms and to evaluate a near infrared spectroscopy-based model to predict coffee quality. Twelve coffee plants growing under low, intermediate and dense shade were studied in twelve coffee farms across an elevational gradient (1470–2325 m asl) in Ethiopia. We found large within farm variability, demonstrating that conditions varying at the coffee plant-level are of large importance for physical attributes and cupping scores of green coffee beans. Overall, elevation appeared to be the key biophysical variable influencing all the measured coffee bean quality attributes at the farm level while canopy cover appeared to be the most important biophysical variable driving the above-mentioned coffee bean quality attributes at the coffee plant level. The biophysical variables driving coffee quality (total preliminary and specialty quality) were the same as those driving variations in the near-infrared spectroscopy data, which supports future use of this technology to assess green bean coffee quality. Most importantly, our findings show that random forest is computationally fast and robust to noise, besides having comparable prediction accuracy. Hence, it is a useful machine learning tool for regression studies and has potential for modeling linear and nonlinear multivariate calibrations. The study also confirmed that near-infrared spectroscopic-based predictions can be applied as a supplementary approach for coffee cup quality evaluations.
... These modelling methods have been shown to provide climate metrics that are similar to those provided for coffee species in cultivation (including farmed conditions) and in the wild, produced by direct measurement and other means (Davis et al., 2021b). For validation purposes, our modelled mean annual temperatures (from Bio1), total annual precipitation (Bio12) and precipitation seasonality (Bio15), were compared against publicly available monthly mean temperature precipitation charts for Uganda and published data for cultivated C. canephora (DaMatta and Ramalho, 2006;Kath et al., 2020;Venancio et al., 2020); published data are not available for the three other species studied here. ...
Uganda is a major global coffee exporter and home to key indigenous (wild) coffee resources. A comprehensive survey of Uganda’s wild coffee species was undertaken more than 80 years ago (in 1938) and thus a contemporary evaluation is required, which is provided here. We enumerate four indigenous coffee species for Uganda: Coffea canephora, C. eugenioides, C. liberica (var. dewevrei) and C. neoleroyi. Based on ground point data from various sources, survey of natural forests, and literature reviews we summarise taxonomy, geographical distribution, ecology, conservation, and basic climate characteristics, for each species. Using literature review and farm survey we also provide information on the prior and exiting uses of Uganda’s wild coffee resources for coffee production. Three of the indigenous species (excluding C. neoleroyi) represent useful genetic resources for coffee crop development (e.g. via breeding, or selection), including: adaptation to a changing climate, pest and disease resistance, improved agronomic performance, and market differentiation. Indigenous C. canephora has already been pivotal in the establishment and sustainability of the robusta coffee sector in Uganda and worldwide, and has further potential for the development of this crop species. Coffea liberica var. dewevrei (excelsa coffee) is emerging as a commercially viable coffee crop plant in its own right, and may offer substantial potential for lowland coffee farmers, i.e. in robusta coffee growing areas. It may also provide useful stock material for the grafting of robusta and Arabica coffee, and possibly other species. Preliminary conservation assessments indicate that C. liberica var. dewevrei and C. neoleroyi are at risk of extinction at the country-level (Uganda). Adequate protection of Uganda’s humid forests, and thus its coffee natural capital, is identified as a conservation priority for Uganda and the coffee sector in general.
... The amount and timing of rainfall can impact coffee bean quality. Too little rainfall during the growing season stresses plants, causing branch death and defoliation, reducing resources for fruiting, and leading to small and damaged coffee beans (DaMatta et al. 2018;Kath et al. 2020). Too much rainfall can dislodge flowers and fruits. ...
While knowledge on the influence of shade trees on overall coffee quality
remains scarce, there is evidence that agroecosystem simplification is negatively correlated with coffee quality. Given global
concerns about biodiversity and habitat loss, we recommend that the overall definition of coffee quality include measures of
ecological quality, although these aspects are not always detectable in certain coffee quality characteristics or the final cup.
... However, several researchers around the world have adopted TerraClimate in their studies (e.g. Baquero and Machado 2018;Xu et al. 2019;Zhao et al. 2019;Wang et al. 2020;Wu et al. 2020;Kath et al. 2020). On the other hand, the use of SPI has several disadvantages and advantages (Hayes et al. 1999). ...
Islands are highly vulnerable to natural disasters and extreme weather events due to their physical size, remoteness, and limited resources. This paper measured village-level drought risk in Marinduque, Philippines using Principal Component Analysis (PCA) and fuzzy logic. Standard Precipitation Index was used to measure drought hazards in the province utilizing publicly available rainfall. Principal component analysis was used to derive the drought hazard index from SPI-calculated drought magnitude and total drought event at different time scales. The fuzzy logic approach was used to delineate the physical vulnerability of the province to drought. The social vulnerability index was also derived from the socioeconomic and demographic data of Marinduque using PCA. Based on the results, villages with high drought risk were found in the northwest and eastern portion of the province. The results showed that topography and climate influence the hazard and physical vulnerability to drought in the area. Villages in high mountainous regions, and areas with low rainfall have higher drought hazard and physical vulnerability scores. Meanwhile, villages with high social vulnerability are also those with a large population of women, the elderly, and households engage in agriculture.
... Outside the limits from 17 to 31 °C its development and production are reduced or impaired [4,5]. Although the limits are wide, it has been observed that coffee is highly responsive to small variations in temperature [6]. Breeding programs have developed new coffee genotypes, providing not only desirable agronomic characteristics, but also greater adaptability to the environment [7]. ...
This work aimed to use the Bayesian approach to discriminate 43 genotypes of Coffea canephora cv. Conilon, which were cultivated in two producing regions to identify the most stable and productive genotypes. The experiment was a randomized block design with three replications and seven plants per plot, carried out in the south of Bahia and the north of Espírito Santo, environments with different climatic conditions, and evaluated during four harvests. The proposed Bayesian methodology was implemented in R language, using the MCMCglmm package. This approach made it possible to find great genetic divergence between the materials, and detect significant effects for both genotype, environment, and year, but the hyper-parametrized models (block effect) presented problems of singularity and convergence. It was also possible to detect a few differences between crops within the same environment. With a model with lower residual, it was possible to recommend the most productive genotypes for both environments: LB1, AD1, Peneirão, Z21, and P2.
... Despite the importance of coffee production to the economies of coffee-growing countries, there have been no analyses of the key climate variables most affecting coffee yields at a global scale, nor whether they could trigger threshold responses. Although work does exist exploring how climate affects coffee suitability 6,7 , these almost exclusively focus on precipitation and temperature 2,7,8 . Recent work also highlights the importance of the combined and seasonal effect of rainfall and temperature 6,7 . ...
Our understanding of the impact of climate change on global coffee production is largely based on studies focusing on temperature and precipitation, but other climate indicators could trigger critical threshold changes in productivity. Here, using generalized additive models and threshold regression, we investigate temperature, precipitation, soil moisture and vapour pressure deficit (VPD) effects on global Arabica coffee productivity. We show that VPD during fruit development is a key indicator of global coffee productivity, with yield declining rapidly above 0.82 kPa. The risk of exceeding this threshold rises sharply for most countries we assess, if global warming exceeds 2 °C. At 2.9 °C, countries making up 90% of global supply are more likely than not to exceed the VPD threshold. The inclusion of VPD and the identification of thresholds appear critical for understanding climate change impacts on coffee and for the design of adaptation strategies.
... Arabica coffee has a sour taste with a more fragrant aroma, while robusta coffee has a stronger and bitter taste because it contains more caffeine [7]. Robusta coffee is more tolerant to heat and more resistant to climate change, so it is easy to grow at wide range of elevation and temperature [8] which makes it cheaper than Arabica coffee [9]. However, Arabica coffee contributes more than 74% of the coffee world production [10]. ...
Visible-Near Infrared (Vis-NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was used to classify Arabica and Robusta roasted coffee beans. The number of coffee beans analyzed was 200 samples consisting of 5 origins (Flores, Temanggung, Aceh Gayo, Jawa, and Toraja). Reflectance spectra with a wavelength of 450-950 nm were used to build two types of models, namely single-origin and general models. Single-origin Flores, Temanggung, Aceh Gayo, and Toraja models performed very well to classify coffee beans samples from the same origin with Sen, Spe, Acc, and Rel of 1, as well as TFN and TFP of 0. General PLS-DA model with baseline correction pretreatment yields Sen, Spe, Acc, and Rel of 0.97, as well as TFN and TFP of 0.04. Based on this paper, it was concluded that Vis-NIR combined with PLS-DA perform well in classifying roasted coffee beans based on the variety.
... Equation (8) is proposed as a base point for our adaptive algorithm as the variance function and with two constants to find (8) is a base equation where the unknown variables are α and β subject to a frequency range equivalent to a sampling period between 1 min and 30 min [45]. The period range is established based on consultation of interested experts to relate changes in humidity and temperature with studies related to production estimation, disease control, storage, transportation, which require a precise estimate of daily average maximum and minimum the variables selected values in this study [46][47][48]. ...
The Internet of Things (IoT) opens opportunities to monitor, optimize, and automate processes into the Agricultural Value Chains (AVC). However, challenges remain in terms of energy consumption. In this paper, we assessed the impact of environmental variables in AVC based on the most influential variables. We developed an adaptive sampling period method to save IoT device energy and to maintain the ideal sensing quality based on these variables, particularly for temperature and humidity monitoring. The evaluation on real scenarios (Coffee Crop) shows that the suggested adaptive algorithm can reduce the current consumption up to 11% compared with a traditional fixed-rate approach, while preserving the accuracy of the data.
... The state is the fourth largest coffee producer in Brazil, with a production of 25,000 tons (Ibdgee 2020). However, the state of Paraná has coffee productivity below the national average due to factors such as thermal stresses, the occurrence of diseases, and, mainly, the attack of pests (de Camargo 2010;Kath et al. 2020). ...
This study aimed to estimate the number of generations and cycle duration of the southern red mite, coffee berry borer, and coffee leaf miner using the thermal index to assist in controlling these main coffee pests in the state of Paraná, Brazil. The data of maximum and minimum air temperature (°C) and precipitation (mm) of all municipalities in the state from 1984 to 2018 were collected from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER). The reference evapotranspiration was estimated using the Camargo (1971) method and the water balance was calculated using the method of Thornthwaite and Mather (1955). The basal temperature of each pest minus the average temperature of the years was used to calculate the degrees-day, the duration of the pest cycle, and the number of generations per year. The influence of altitude on the development of coffee pests was measured using Pearson correlation. The thermal index is able to estimate the damage caused by coffee pests in the state of Pará, Brazil. Coffee pests show greater severity in the north of Paraná, in the regions with the highest temperatures. The same region that concentrates most of the coffee production of the state. The results of the life cycle and number of generations were interpolated for the entire state using the kriging method. Coffee pests showed the highest severity in the north region of the state of Paraná, more specifically in the Northwest, North Central, and West Central mesoregions. These regions have concentrated most of the state’s coffee production. Mesoregions with the highest coffee production in the state showed higher susceptibility to coffee pests. Altitude showed a high correlation (r > 0.6) with the cycle variability and number of generations of coffee pests. The average cycles of the coffee berry borer, coffee leaf miner, and southern red mite are 24.13 (±8.34), 45.64 (±18.61), and 21.51 (±3.51) days, respectively. The average annual generation was 16.67 (±4.77), 9.02 (±2.75), and 17.32 (±2.63) generations, for the coffee berry borer, the coffee red mite, and the southern red mite, respectively.
... yam and cassava) as well as a range of economically important beverage and spice crops, such as tea, pepper and coffee are of great socio-economic importance for millions of farmers globally, yet we find no evidence of agricultural climate IBI research for these crops. The risks of adverse climatic events on these crops are well documented (coffee, Kath et al. (2020); tea, Nowogrodzki (2019) cassava, Brown et al. (2016), root crops and banana, Adhikari et al. (2015)). Our results suggest there is a clear need for agricultural climate IBI to be carried out for these and other overlooked crops. ...
Agricultural climate index-based-insurance (IBI) compensates farmers for losses from adverse climatic conditions. Using a systemic review, we show that research related to agricultural climate index-based-insurance efficacy and application is lacking in many climate and food security vulnerable countries. We concluded that there are countries with high climate and food insecurity risk based on several climate and food security indicators that lack agricultural climate index-based-insurance research that could help farmers in these countries. Research to date has also largely focused on cereal crops and drought, which only represent a fraction of the crops and climate risks that agricultural climate index-based-insurance could be beneficial in managing. Our paper provides evidence-based recommendations for countries that should be focused on to redress the current disparities in agricultural climate index-based-insurance research.
... From TerraClimate, we also embed downward shortwave radiation and vapor pressure deficit (VPD). TerraClimate data are widely used (e.g., [64,65]) and accuracy measures are available in Abatzoglou et al. [55]. Next, to analyze surface water dynamics, we incorporate the Global Flood Awareness System (GloFAS)-ERA5 river discharge data at a spatial resolution of 0.1 • (10 km) [66]. ...
The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.
... Currently, there is evidence that C. canephora has some tolerance to drought through enrichment of secondary compound metabolic genes, namely antioxidant genes, which play an essential role in coffee drought response [29]. Recent studies underline that at least some genotypes of C. canephora could be far more sensitive to thermal stress than previously thought [30,31]. Other genotypes of C. arabica and C. canephora were found to have the ability to endure harsh temperatures [23,24] and water deficit [25] to a greater extent than usually assumed. ...
Water scarcity is the most significant factor limiting coffee production, although some cultivars can still have important drought tolerance. This study analyzed leaf transcriptomes of two coffee cultivars with contrasting physiological responses, Coffea canephora cv. CL153 and Coffea arabica cv. Icatu, subjected to moderate (MWD) or severe water deficits (SWD). We found that MWD had a low impact compared with SWD, where 10% of all genes in Icatu and 17% in CL153 reacted to drought, being mainly down‐regulated upon stress. Drought triggered a genotype‐specific response involving the up‐regulation of reticuline oxidase genes in CL153 and heat shock proteins in Icatu. Responsiveness to drought also included desiccation protectant genes, but primarily,
aspartic proteases, especially in CL153. A total of 83 Transcription Factors were found engaged in response to drought, mainly up‐regulated, especially under SWD. Together with the enrollment of 49 phosphatases and 272 protein kinases, results suggest the involvement of ABA‐signaling processes in drought acclimation. The integration of these findings with complementing physiological and biochemical studies reveals that both genotypes are more resilient to moderate drought than previously thought and suggests the existence of post‐transcriptional mechanisms modulating the response to drought.
... robusta) growing region in Dak Lak, as there is a significantly less research on robusta compared to arabica (C. arabica), worldwide (Hunt et al., 2020;Kath et al., 2020;Pham et al., 2019). ...
Perennial commodity crops, such as coffee, often play a large role globally in agricultural markets and supply chains and locally in livelihoods, poverty reduction, and biodiversity. Yet, the production of spatial information on these crops are often overlooked in favor of annual food crops. Remote sensing detection of coffee faces a particular set of challenges due to persistent cloud cover in the tropical “coffee belt,” hilly topography in coffee growing regions, diversity of coffee growing systems, and spectral similarity to other tree crops and agricultural land. Looking at the major coffee growing region in Dak Lak, Vietnam, we integrate multi-temporal 10 m optical Sentinel-2 and Sentinel-1 SAR data in order to map three coffee production systems: i) open-canopy sun coffee, ii) intercropped and other shaded coffee and iii) newly planted or young coffee.
Leveraging Google Earth Engine (GEE), we compute five sets of features in order to best enhance separability between coffee and other land cover and within coffee production systems. The features include Sentinel-2 dry and wet season composites, Sentinel-1 texture features, Sentinel-1 spatiotemporal metrics, and topographic features. Using a random forest classification algorithm, we produce a 9-class land cover map including our three coffee production classes and a binary coffee/non-coffee map. The binary map has an overall accuracy of 89% and the three coffee production systems have user accuracies of 65, 56, 71% for sun coffee, intercropped coffee and newly planted coffee, respectively. This is a first effort at large-scale distinction of within-crop production styles and has implications across many applications. The binary coffee map can be used as a high-resolution crop mask, whereas the detailed land cover map can inform monitoring of deforestation dynamics, biodiversity, sustainability certification and implementation of climate adaptation strategies. This work offers a scalable approach to integrating optical and radar Sentinel data for production of spatially explicit agricultural information and contributes particularly to tree crop and agroforestry mapping, which often is overlooked in between agricultural and forestry sciences.
... The intensification of extreme events under global warming scenarios threatens the sustainability of agricultural production on a global scale, with consequences on the amount and quality of harvestable crops for the current production areas, with coffee being no exception [7]. Coffee is considered a crop that is sensitive to climate change [8,9] and particularly to the impact of temperatures (as its optimum temperature range is 18-25 • C) [10]. The impact of climate change on coffee production has been analyzed for the main producing regions [11,12]. ...
Coffee is one of the most commonly traded agricultural commodities globally. It is important for the livelihoods of over 25 million families worldwide, but it is also a crop sensitive to climate change, which has forced producers to implement management practices with effects on carbon balance and water use efficiency (WUE) that are not well understood due to data scarcity. From this perspective, we propose crop canopy coupling to the atmosphere (Ώ) as an index of resilience and stability and we undertook an integrated observational approach for the scaling-up of measurements along the soil–plant–atmosphere continuum at different stages of the coffee crop phenological cycle. Additionally, we develop this perspective under pronounced climatic seasonality and variability, in order to assess carbon balance, WUE, and agroecosystem resilience in a sun-grown coffee field. Further, we devised a field layout that facilitates the measurement of intrinsic, instantaneous, and actual water use efficiency and the assessment of whether coffee fields differ in canopy structure, complexity, and agronomic management and whether they are carbon sources or sinks. Partitioning soil and canopy energy balances and fluxes in a sun-grown coffee field using eco-physiological techniques at the leaf and whole plant levels (i.e., sap flow and gas exchange), as proposed here, will allow the scaling-up to whole fields in the future. Eddy covariance was used to assess real-time surface fluxes of carbon, gross primary productivity (GPP), and evapotranspiration, as well as components of the energy balance and WUE. The preliminary results support the approach used here and suggested that coffee fields are CO2 sinks throughout the year, especially during fruit development, and that the influence of seasonality drives the surface–atmosphere coupling, which is dominant prior to and during the first half of the rainy season. The estimated WUE showed consistency with independent studies in coffee crops and a marked seasonality driven by the features of the rainy season. A plan for the arborization of the coffee agroecosystem is suggested and the implications for WUE are described. Future comparison of sun- and shade-grown coffee fields and incorporation of other variables (i.e., crop coefficient-KC for different leaf area index (LAI) values) will allow us to better understand the factors controlling WUE in coffee agroecosystems.
... The effect of climate change on microbial diversity and the resilience of toxigenic fungi through culture-independent technology has yet to be explored in the coffee crop. Drought is the main environmental restriction affecting coffee growth and production [79], not only for arabica coffee but also for robusta, which until recently was considered resistant to temperature increases, having been recently demystified by research conducted in Southeast Asia [80]. Some studies provided evidence that potential mycotoxigenic fungi may not be affected by the CO 2 treatments [81], nonetheless, these studies need to be better designed in order to include other climatic factors linked to the natural microbiome associated with coffee production/productivity [82]. ...
Brazil holds a series of favorable climatic conditions for agricultural production including the hours and intensity of sunlight, the availability of agricultural land and water resources, as well as diverse climates, soils and biomes. Amidst such diversity, Brazilian coffee producers have obtained various standards of qualities and aromas, between the arabica and robusta species, which each present a wide variety of lineages. However, temperatures in coffee producing municipalities in Brazil have increased by about 0.25 °C per decade and annual precipitation has decreased. Therefore, the agricultural sector may face serious challenges in the upcoming decades due to crop sensitivity to water shortages and thermal stress. Furthermore, higher temperatures may reduce the quality of the culture and increase pressure from pests and diseases, reducing worldwide agricultural production. The impacts of climate change directly affect the coffee microbiota. Within the climate change scenario, aflatoxins, which are more toxic than OTA, may become dominant, promoting greater food insecurity surrounding coffee production. Thus, closer attention on the part of authorities is fundamental to stimulate replacement of areas that are apt for coffee production, in line with changes in climate zoning, in order to avoid scarcity of coffee in the world market.
... Meanwhile, several studies across the globe have used TerraClimate dataset (e.g. [13,[49][50][51][52][53][54]). ...
Drought is a natural hazard with a severe and long-lasting impact on both human and natural systems. Among different drought categories, assessment of meteorological drought is imperative because it is the root cause of other types of drought. In the Philippines, there are limitations (access, availability, and spatial coverage) of long-term climate records to assess drought on the national scale. This paper aims to use the free high spatial resolution and long-term monthly climate data (i.e. rainfall and temperature) from TerraClimate to characterize meteorological drought hazard in the Philippines. The study used two commonly used drought hazard index such as the Standard Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI) to derive different drought characteristics. Based on the results, drought characteristics vary spatially across the country. There are areas in the country where drought duration can last up to 11 months. The magnitude of drought Philippines ranges from 47mm to 677 mm with strength of 60 to 800 mm/month. Lastly, this study showed similar results compared to previous drought records and similar studies in the Philippines.
Jamaica produces one of the most expensive coffees on the global market. The local specialty coffee industry plays a significant role in the island’s economy and also contributes to the livelihood of smallholders—the majority of whom operate the industry’s coffee farms. While climate model projections suggest that Jamaica will continue to experience a warming and drying trend, no study has assessed the future impacts of changing climatic patterns on local coffee-growing areas. This research developed a number of geospatial processing models within the ArcMap software platform to model current coffee suitability and future crop suitability across three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and three future time periods (2021–2040, 2041–2060, and 2081–2100). The results validated current locations of coffee production and revealed that there was an observable decrease in coffee suitability across the island, across all SSP scenarios and time periods under study. Most growing regions were projected to experience declines in production suitability of at least 10%, with the most severe changes occurring in non-Blue Mountain regions under the SSP5-8.5 scenario. Implications of this projected suitability change range from decreased production volumes, increased price volatility, and disruption to market operations and livelihood incomes. The paper’s findings offer stakeholders within Jamaica’s coffee industry the opportunity to develop targeted adaptation planning initiatives, and point to the need for concrete decisions concerning future investment pathways for the industry. It also provides insight into other tropical coffee-growing regions around the world that are facing the challenges associated with climate change.
The use of spatially referenced data in agricultural systems modelling has grown in recent decades, however, the use of spatial modelling techniques in agricultural science is limited. In this paper, we test an effective and efficient technique for spatially modelling and analysing agricultural data using Bayesian hierarchical spatial models (BHSM). These models utilise analytical approximations and numerical integration called Integrated Nested Laplace Approximations (INLA). We critically analyse and compare the performance of the INLA and INLA-SPDE (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation) approaches against the more commonly used generalised linear model (glm), by modelling binary geostatistical species presence/absence data for several agro-ecologically significant Australian grassland species. The INLA-SPDE approach showed excellent predictive performance (ROCAUC 0.9271–0.9623) for all species. Further, the glm approach not accounting for spatial autocorrelation had inconsistent parameter estimates (switching between significantly positive and negative) when the dataset was subsetted and modelled at different scales. In contrast, the INLA-SPDE approach which accounted for spatial autocorrelation had stable parameter estimates. Using approaches which explicitly account for spatial autocorrelation, such as INLA-SPDE, improves model predictive performance and may provide a significant advantage for researchers by reducing the potential for Type I or false-positive errors in inferences about the significance of predictors.
The use of spatially referenced data in agricultural systems modelling has grown in recent decades, however, the use of spatial modelling techniques in agricultural science is limited. In this paper, we test an effective and efficient technique for spatially modelling and analysing agricultural data using Bayesian hierarchical spatial models (BHSM). These models utilise analytical approximations and numerical integration called Integrated Nested Laplace Approximations (INLA). We critically analyse and compare the performance of the INLA and INLA-SPDE (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation) approaches against the more commonly used generalised linear model (glm), by modelling binary geostatistical species presence/absence data for several agro-ecologically significant Australian grassland species. The INLA-SPDE approach showed excellent predictive performance (ROCAUC 0.9271–0.9623) for all species. Further, the glm approach not accounting for spatial autocorrelation had inconsistent parameter estimates (switching between significantly positive and negative) when the dataset was subsetted and modelled at different scales. In contrast, the INLA-SPDE approach which accounted for spatial autocorrelation had stable parameter estimates. Using approaches which explicitly account for spatial autocorrelation, such as INLA-SPDE, improves model predictive performance and may provide a significant advantage for researchers by reducing the potential for Type I or false-positive errors in inferences about the significance of predictors.
Evaporation (E) and transpiration (Tr) are the key terrestrial water fluxes to the atmosphere and are highly sensitive to land cover change. These ecohydrological fluxes can be measured directly only at small scales, such as individual plants or under laboratory experiments. Modelling is needed to upscale E and Tr estimates to plot, hillslope and catchment scales. However, model-derived ecohydrological water partitioning of E and Tr can be ambiguous, particularly when models are trained using hydrometric data and soil moisture. To test the influence of different types of data (i.e., sap flux-derived Tr, Eddy Covariance-derived actual evapotranspiration (AET) and measured soil water content (SWC)) on model calibration and subsequent water partitioning, we developed the low-parameter plot scale ecohydrology model EcoHydroPlot applied to a data-rich experimental agroforestry plot in humid tropical Costa Rica. The model was able to simulate SWC well when calibrated with any data type, but large differences emerged in the E and Tr flux partitioning. Using only hydrometric data for calibration resulted in parameter configurations that produced greater E over Tr fluxes (Tr/AET < 0.5). The opposite was seen for model calibration using Tr data, resulting in Tr/AET ratios close to the observed ~0.9. Further, using all measurements simultaneously (including AET, SWC and Tr) did not improve simulated water partitioning. We only found minimal differences between sun and shade locations with slightly greater average shaded coffee transpiration at the expense of lower upper SWC, higher deeper SWC and less groundwater recharge compared to sun exposed coffee. This work can inform measurement priorities for applications with relatively simple conceptual ecohydrology models and emphasizes the importance of transpiration estimates beyond tropical environments.
Research on the impact of climate change on our society is becoming more important in the current situation. Crops are not only particularly susceptible to climate change, but also they are the basis of people's lives, and from the perspective of food security, they are also important issues that can be said to be one of the most important elements in society. On the other hand, the studies on the relationship between climate change and crop productivity are insufficient despite the importance of analyzing it from a more elaborate statistical perspective. This paper aims to promote and help researchers with statistical and informatics backgrounds participate in studying the impact of climate change on crop productivity. First, we introduce the particularity of the study of climate change, and then point out that the current statistical analysis on crop productivity is inadequate in dealing with factors other than meteorology. We also briefly discuss the application of machine learning analyses and the relationship between process-based crop models and statistical models.
Given the present climate change context, accurate and timely coffee yield prediction is
critical to all farmers who work in the coffee industry worldwide. The aim of this study is to develop
and assess a coffee yield forecasting method at the regional scale in Dak Lak province in the central
highlands of Vietnam using the Crop Growth Monitoring System Statistical Tool (CGMSstatTool—
CST) software and vegetation biophysical variables (NDVI, LAI, and FAPAR) derived from satellite
remote sensing (SPOT-VEGETATION and PROBA-V). There has been no research to date applying
this approach to this specific crop, which is the main contribution of this study. The findings of this
research reveal that the elaboration of multiple linear regression models based on a combination
of information from satellite-derived vegetation biophysical variables (LAI, NDVI, and FAPAR)
corresponding to the first six months of the years 2000–2019 resulted in coffee yield forecast models
presenting satisfactory accuracy (Adj.R2 = 64 to 69%, RMSEp = 0.155 to 0.158 ton/ha and MAPE = 3.9
to 4.7%). These results demonstrate that the CST may efficiently predict coffee yields on a regional
scale by using only satellite-derived vegetation biophysical variables. This study findings are likely
to aid local governments and decision makers in precisely forecasting coffee production early and
promptly, as well as in recommending relevant local agricultural policies.
Comprehensive regionalization of fine farming could provide scientific evidence for the programming and distribution of coffee farming industry. Comprehensively considering three factors influencing coffee cultivation such as climate, topography, and soil, 11 evaluation indexes for ecological adaptability were chosen. Ecological factors were transformed to 0–1 by fuzzy membership function. The weighing coefficients of different factors were confirmed by Analytic hierarchy process. Ordinary Kriging interpolation and inverse distance-weighted interpolation in ArcGIS were used. Raster calculator was used for overlay analysis. Thus, the evaluation model of the comprehensive regionalization of coffee farming was established. There were four grades of coffee farming area in Yunnan Province, China by natural discontinuity point method breaks, including the most suitable, the suitable, the less suitable, and the not suitable. Results showed that the lowest average temperature in the coldest month and the altitude had the largest influence on coffee adaptability. Areas of four grades of Coffea arabica accounted for 16.28%, 23.19%, 28.71%, and 31.82% of the total area, respectively, wherein the most suitable area and the suitable area were mainly distributed in areas of Baoshan, Dehong, Puer, Lincang, Yuxi, Xishuangbanna, Honghe, Wenshan, etc. Areas of four grades of Coffea canephora accounted for 2.21%, 21.70%, 36.79%, and 39.30% of the total area, respectively, wherein the most suitable area and the suitable area were mainly distributed in areas of Xishuangbanna, Honghe, Puer, Dehong, Lincang, Wenshan, etc. Results could provide theoretical evidence of spatial planning and risk management of coffee production in Yunnan Province, China.
This report examines how climate change is impacting agriculture and threatening national and global food systems, particularly in climate hotspots, and how these trends are projected to intensify over the coming decades. The report defines and details transformative adaptation for agriculture and why such longer-term, systemic approaches are needed to protect the lives and livelihoods of millions of small-scale farmers and herders. Transformative adaptation in agriculture promotes long-term resilience by continually shifting the geographical locations where specific types of crops and livestock are produced, aligning agricultural production with changing landscapes and ecosystems, and/or introducing resilience-building production methods and technologies across value chains. The report presents evidence to support a call for urgent action by: Agricultural research organizations, to build and share knowledge regarding transformative approaches; Governments, to integrate this knowledge into plans and policies by establishing and implementing transformative pathways; and Funding entities, to increase financial support for agricultural adaptation and design sustainable financing mechanisms with the right incentives and disincentives to support transformative adaptation. Strategic investments in resilient food systems are crucial to manage intensifying climate change impacts and feed a global population expected to reach 9.7 billion by 2050. Planning for transformative adaptation should center on inclusive, participatory processes that engage a diverse range of stakeholders who may otherwise be marginalized in decision-making, such as women, youth and Indigenous peoples.
An improved understanding of the benefits and uptake of drought mitigation strategies under a changing climate is critical to ensure effective strategies are developed. Here, using 10 years (2008–2017) of farm data from 558 farmers distributed across the major robusta coffee-producing provinces in Vietnam, we analysed coffee farmers’ perceptions on drought and its impacts; we then quantified the impacts of drought on yield and farm profit, and finally, assessed the effectiveness of mitigation strategies. While drought reduced robusta coffee yield by 6.5% on average across all provinces, the impacts on gross margins were noticeable, with an average 22% decline from levels achieved in average-rainfall-condition years. Yield reductions from drought were consistent with farmers’ perceptions, being on average − 9.6%. With irrigation being typical in coffee farming in Vietnam, the majority of surveyed farmers (58%) adopted mulching in drought years and had a 10.2% increase in economic benefits compared to their counterparts who did not. Furthermore, the chances of adopting mulching as an adaptation strategy decreased generally for every one unit increase in perceived drought impact or when shifting from surface water to groundwater in drought years. Although coffee farming remained profitable in drought years, our findings have potential relevance for the design of policies to address drought risks and encourage more resilient adaptation strategies for Vietnam and other coffee-producing countries experiencing similar climatic conditions.
Timely and reliable coffee yield forecasts using agroclimatic information are pivotal to the success of agricultural climate risk management throughout the coffee value chain. The capability of statistical models to forecast coffee yields at different lead times during the growing season at the farm scale was assessed. Using data collected during a 10-year period (2008-2017) from 558 farmers across the four major coffee-producing provinces in Vietnam (Dak Lak, Dak Nong, Gia Lai, and Lam Dong), the models were built through a robust statistical modelling approach involving Bayesian and machine learning methods. Overall, coffee yields were estimated with reasonable accuracies across the four study provinces based on agroclimate variables, satellite-derived actual evapotranspiration, and crop and farm management information. Median values of prediction mean absolute percentage error (MAPE) ranged generally from 8% to 13%, and median root mean square errors (RMSE) between 295 kg ha⁻¹ and 429 kg ha⁻¹. For forecasts at four to one month before harvest, errors did not vary markedly when comparing the median MAPE and RMSE values. For farms in Dak Lak, Dak Nong, and Lam Dong, the median forecasting MAPE and RMSE varied between 13% and 16% and between 420 kg ha⁻¹ and 456 kg ha⁻¹, respectively. Using readily and freely available data, the modelling approach explored in this study appears flexible for an application to a larger number of coffee farms across the Vietnamese coffee-producing regions. Moreover, the study can serve as basis for developing a coffee yield predicting forecasting system that will offer substantial benefits to the entire coffee industry through better supply chain management in coffee-producing countries worldwide.
Assessing and prescribing fertilizer use is critical to profitable and sustainable coffee production, and this is becoming a priority concern for the Robusta coffee industry. In this study, annual survey data of 798 farms across selected Robusta coffee-producing provinces in Vietnam and Indonesia between 2008 and 2017 were used to comparatively assess the fertilizer management strategies in these countries. Specifically, we aimed to characterize fertilizer use patterns in the key coffee-growing provinces and discuss the potential for improving nutrient management practices. Four types of chemical (NPK, super phosphate, potassium chloride and urea) and two of natural (compost and lime) fertilizers were routinely used in Vietnam. In Indonesia, NPK and urea were supplemented only with compost. Farmers in Vietnam applied unbalanced quantities of chemical fertilizers (i.e., higher rates than recommended) and at a constant rate between years whereas Indonesian farmers applied well below the recommended rates because of poor accessibility and financial support. The overuse of chemical fertilizers in Vietnam threatens the sustainability of Robusta coffee farming. Nevertheless, there is a potential for improvement in both countries in terms of nutrient management and sustainability of Robusta coffee production by adopting the best local fertilizer management practices.
Species distribution models (SDM) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI; –6.9%, 0.6%) when one fire occurs and that increasing fires from zero to at least one fire would decrease cover by 44.7% (95% CI; –47.9%, –41.3%). Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.
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Wild coffee species are critical for coffee crop development and, thus, for sustainability of global coffee production. Despite this fact, the extinction risk and conservation priority status of the world’s coffee species are poorly known. Applying IUCN Red List of Threatened Species criteria to all (124) wild coffee species, we undertook a gap analysis for germplasm collections and protected areas and devised a crop wild relative (CWR) priority system. We found that at least 60% of all coffee species are threatened with extinction, 45% are not held in any germplasm collection, and 28% are not known to occur in any protected area. Existing conservation measures, including those for key coffee CWRs, are inadequate. We propose that wild coffee species are extinction sensitive, especially in an era of accelerated climatic change.
Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios, and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO), and population numbers for wild Arabica coffee. Under climate change our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present‐day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria.
The expanding human global footprint and growing demand for fresh water have placed tremendous stress on inland aquatic ecosystems. Aichi Target 10 of the Convention on Biological Diversity aims to minimize anthropogenic pressures affecting vulnerable ecosystems, and pressure interactions are increasingly being incorporated into environmental management and climate change adaptation strategies. In this study, we explore how climate change, overfishing, forest disturbance, and invasive species pressures interact to affect inland lake walleye (Sander vitreus) populations. Walleye support subsistence, recreational, and commercial fisheries and are one of most sought‐after freshwater fish species in North America. Using data from 444 lakes situated across an area of 475 000 km² in Ontario, Canada, we apply a novel statistical tool, R‐INLA, to determine how walleye biomass deficit (carrying capacity – observed biomass) is impacted by multiple pressures. Individually, angling activity and the presence of invasive zebra mussels (Dreissena polymorpha) were positively related to biomass deficits. In combination, zebra mussel presence interacted negatively and antagonistically with angling activity and percentage decrease in watershed mature forest cover. Velocity of climate change in growing degree days above 5°C and decrease in mature forest cover interacted to negatively affect walleye populations. Our study demonstrates how multiple pressure evaluations can be conducted for hundreds of populations to identify influential pressures and vulnerable ecosystems. Understanding pressure interactions is necessary to guide management and climate change adaptation strategies, and achieve global biodiversity targets.
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Coffee, one of the most heavily globally traded agricultural commodities, has been categorized as a highly sensitive plant species to progressive climatic change. Here, we summarize recent insights on the coffee plant’s physiological performance at elevated atmospheric carbon dioxide concentration [CO2]. We specifically (i) provide new data of crop yields obtained under free-air CO2 enrichment conditions, (ii) discuss predictions on the future of the coffee crop as based on rising temperature and (iii) emphasize the role of [CO2] as a key player for mitigating harmful effects of supra-optimal temperatures on coffee physiology and bean quality. We conclude that the effects of global warming on the climatic suitability of coffee may be lower than previously assumed. We highlight perspectives and priorities for further research to improve our understanding on how the coffee plant will respond to present and progressive climate change.
The use of linear mixed effects models (LMMs) is increasingly common in the analysis of biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data types, ecological data are often complex and require complex model structures, and the fitting and interpretation of such models is not always straightforward. The ability to achieve robust biological inference requires that practitioners know how and when to apply these tools. Here, we provide a general overview of current methods for the application of LMMs to biological data, and highlight the typical pitfalls that can be encountered in the statistical modelling process. We tackle several issues regarding methods of model selection, with particular reference to the use of information theory and multi-model inference in ecology. We offer practical solutions and direct the reader to key references that provide further technical detail for those seeking a deeper understanding. This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.
Study on variability in area, production, and productivity of coffee in India during last decade indicates that area and production of coffee is increasing whereas yield of coffee is decreasing trend during the period 1990–1991 to 2015–2016. There was increasing trend of Robusta coffee and decreasing trend of Arabica coffee yields in India with three distinct periods due to climate change. Micro-level study was conducted on variability in yield of Arabica and Robusta coffee vis-à-vis climate change, variability of parameters like rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), and mean relative humidity (RH) was undertaken with data recorded at Regional Coffee Research Station, Chundale, Wayanad, Kerala State. The yield data for 35 years (1980 to 2014) revealed that the yield of Robusta coffee was higher than that of Arabica coffee in most of the years due to favorable climate requirements in Kerala. There was increasing trend of yield of Robusta coffee in Kerala and decreasing trend of Arabica coffee. Blossom showers had significance influence in increasing the yield of coffee rather than total annual rainfall in Robusta coffee. El Niño events have little effect on coffee production in India in general, and out of 11 El Niño years, only 3 years coffee productivity was adversely affected. However, with respect to Kerala, Arabica yield was adversely affected in strong El Niño years, which was again confirmed with NDVI anomaly too.
Coffee farming provides livelihoods for around 15 million farmers in Ethiopia and generates a quarter of the country's export earnings. Against a backdrop of rapidly increasing temperatures and decreasing rainfall, there is an urgent need to understand the influence of climate change on coffee production. Using a modelling approach in combination with remote sensing, supported by rigorous ground-truthing, we project changes in suitability for coffee farming under various climate change scenarios, specifically by assessing the exposure of coffee farming to future climatic shifts. We show that 39–59% of the current growing area could experience climatic changes that are large enough to render them unsuitable for coffee farming, in the absence of significant interventions or major influencing factors. Conversely, relocation of coffee areas, in combination with forest conservation or re-establishment, could see at least a fourfold (>400%) increase in suitable coffee farming area. We identify key coffee-growing areas that are susceptible to climate change, as well as those that are climatically resilient.
Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non-causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross-validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non-random and blocked cross-validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross-validation is nearly universally more appropriate than random cross-validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross-validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations. This article is protected by copyright. All rights reserved.
Background
The coffee species Coffea canephora is commercially identified as “Conilon” when produced in Brazil, or “Robusta” when produced elsewhere in the world. It represents approximately 40 % of coffee production worldwide. While the genetic diversity of wild C. canephora has been well studied in the past, only few studies have addressed the genetic diversity of currently cultivated varieties around the globe. Vietnam is the largest Robusta producer in the world, while Mexico is the only Latin American country, besides Brazil, that has a significant Robusta production. Knowledge of the genetic origin of Robusta cultivated varieties in countries as important as Vietnam and Mexico is therefore of high interest. ResultsThrough the use of Sequencing-based diversity array technology-DArTseq method-on a collection of C. canephora composed of known accessions and accessions cultivated in Vietnam and Mexico, 4,021 polymorphic SNPs were identified. We used a multivariate analysis using SNP data from reference accessions in order to confirm and further fine-tune the genetic diversity of C. canephora. Also, by interpolating the data obtained for the varieties from Vietnam and Mexico, we determined that they are closely related to each other, and identified that their genetic origin is the Robusta Congo – Uganda group. Conclusions
The genetic characterization based on SNP markers of the varieties grown throughout the world, increased our knowledge on the genetic diversity of C. canephora, and contributed to the understanding of the genetic background of varieties from very important coffee producers. Given the common genetic origin of the Robusta varieties cultivated in Vietnam, Mexico and Uganda, and the similar characteristics of climatic areas and relatively high altitude where they are grown, we can state that the Vietnamese and the Mexican Robusta have the same genetic potential to produce good cup quality.
Coffee is grown in more than 60 tropical countries on over 11 million ha by an estimated 25 million farmers, most of whom are smallholders. Several regional studies demonstrate the climate sensitivity of coffee (Coffea arabica) and the likely impact of climate change on coffee suitability, yield, increased pest and disease pressure and farmers’ livelihoods. The objectives of this paper are (i) to quantify the impact of progressive climate change to grow coffee and to produce high quality coffee in Nicaragua and (ii) to develop an adaptation framework across time and space to guide adaptation planning. We used coffee location and cup quality data from Nicaragua in combination with the Maxent and CaNaSTA crop suitability models, the WorldClim historical data and the CMIP3 global circulation models to predict the likely impact of climate change on coffee suitability and quality. We distinguished four different impact scenarios: Very high (coffee disappears), high (large negative changes), medium (little negative changes) and increase (positive changes) in climate suitability. During the Nicaraguan coffee roundtable, most promising adaptation strategies were identified, which we then used to develop a two-dimensional adaptation framework for coffee in time and space. Our analysis indicates that incremental adaptation may occur over short-term horizons at lower altitudes, whereas the same areas may undergo transformative adaptation in the longer term. At higher elevations incremental adaptation may be needed in the long term. The same principle and framework is applicable across coffee growing regions around the world.
Modelling studies have predicted that coffee crop will be endangered by future global warming, but recent reports highlighted that high [CO2] can mitigate heat impacts on coffee. This work aimed at identifying heat protective mechanisms promoted by CO2 in Coffea arabica (cv. Icatu and IPR108) and C. canephora cv. Conilon CL153. Plants were grown at 25/20 ºC (day/night), under 380 or 700 μL CO2 L-1, and then gradually submitted to 31/25, 37/30 and 42/34 ºC. Relevant heat tolerance up to 37/30 ºC for both [CO2] and all coffee genotypes was observed, likely supported by the maintenance or increase of the pools of several protective molecules (neoxanthin, lutein, carotenes, α-tocopherol, HSP70, raffinose), activities of antioxidant enzymes, such as superoxide dismutase (SOD), ascorbate peroxidase (APX), glutathione reductase (GR), catalase (CAT), and the upregulated expression of some genes (ELIP, Chaperonin 20). However, at 42/34 ºC a tolerance threshold was reached, mostly in the 380-plants and Icatu. Adjustments in raffinose, lutein, β-carotene, α-tocopherol and HSP70 pools, and the upregulated expression of genes related to protective (ELIPS, HSP70, Chape 20 and 60) and antioxidant (CAT, CuSOD2, APX Cyt, APX Chl) proteins were largely driven by temperature. However, enhanced [CO2] maintained higher activities of GR (Icatu) and CAT (Icatu and IPR108), kept (or even increased) the Cu,Zn-SOD, APX and CAT activities, and promoted a greater upregulation of those enzyme genes, as well as those related to HSP70, ELIPs, Chaperonins in CL153 and Icatu.. These changes likely favoured the maintenance of reactive oxygen species at controlled levels and contributed to mitigate of photosystem II photoinhibition at the highest temperature. Overall, our results highlighted the important role of enhanced [CO2] on the coffee crop acclimation and sustainability under predicted future global warming scenarios.
Despite the importance of coffee as a globally traded commodity and increasing concerns about risks associated with climate change, there is virtually no information about the effects of rising atmospheric [CO2] on field-grown coffee trees. This study shows the results of the first 2 years of an innovative experiment. Two commercial coffee cultivars (Catuaí and Obatã) were grown using the first free-air CO2 enrichment (FACE) facility in Latin America (ClimapestFACE). Plants of both cultivars maintained relatively high photosynthetic rates, water-use efficiency, increased growth and yield under elevated [CO2]. Harvestable crop yields increased 14.6 % for Catuaí and 12.0 % for Obatã. Leaf N content was lower in Obatã (5.2%) grown under elevated [CO2] than under ambient [CO2]; N content was unresponsive to elevated [CO2] in Catuaí. Under elevated [CO2] reduced incidence of leaf miners (Leucoptera coffeella) occurred on both coffee cultivars during periods of high infestation. The percentage of leaves with parasitized and predated mines increased when leaf miner infestation was high, but there was no effect of elevated [CO2] on the incidence of natural
enemies. The incidence of rust (Hemileia vastatrix) and Cercospora leaf spot (Cercospora coffeicola) was low during the trial, with maximum values of 5.8 and 1 %, respectively, and there was no significant effect of [CO2] treatments on disease incidence. The fungal community associated with mycotoxins was not affected by the treatments.
Coffee is the world’s most valuable tropical export crop. Recent studies predict severe climate change impacts on Coffea arabica (C. arabica) production. However, quantitative production figures are necessary to provide coffee stakeholders and policy makers with evidence to justify immediate action. Using data from the northern Tanzanian highlands, we demonstrate for the first time that increasing night time (Tmin) temperature is the most significant climatic variable responsible for diminishing C. arabica yields between 1961 and 2012. Projecting this forward, every 1 °C rise in Tmin will result in annual yield losses of 137 ± 16.87 kg ha−1 (P = 1.80e-10). According to our ARIMA model, average coffee production will drop to 145 ± 41 kg ha−1 (P = 8.45e-09) by 2060. Consequently, without adequate adaptation strategies and/or substantial external inputs, coffee production will be severely reduced in the Tanzanian highlands in the near future. Attention should also be drawn to the arabica growing regions of Brazil, Colombia, Costa Rica, Ethiopia and Kenya, as substantiated time series evidence shows these areas have followed strikingly similar minimum temperature trends. This is the first study on coffee, globally, providing essential time series evidence that climate change has already had a negative impact on C. arabica yields.
Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for similar to 13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, >60% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (similar to 32-39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability.
Knowledge about the seasonality of different genotypes of Coffea canephora is an important tool for this crop management, particularly with regard to irrigation and fertilisation issues. This study was conducted in Espírito Santo, Brazil and aimed at to evaluate the seasonal vegetative growth in genotypes of C. canephora, as related to climatic factors, based on the growth of groups of orthotropic and plagiotropic branches with different ages. Three groups of plagiotropic branches and one group of orthotropic branches of 14 genotypes (Ipiranga and 13 that belonged to the variety Vitória) were selected and marked to followed along the one-year experiment. Three-year-old plants were cultivated under full-sun conditions, with a spacing of 3 m between rows and 1 m between plants. The growth rates of the orthotropic and plagiotropic branches differed among the genotypes and underwent seasonal variation during the entire year, with high correlations to the air temperature. Under the natural experimental conditions, the growth rate of the branches decreased when the minimum air temperatures were below 17.2ºC for most of the genotypes studied. The plagiotropic branches presented lower vegetative growth, mainly for the coffee berries, compared to the younger branches. Presumably, the genotypes of C. canephora demanded more nutrients for growth between mid-September and the second week of May.