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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.

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... Intensified full-sun systems were popularized through rapid production growth in Brazil and Vietnam in the 1990s, but shading and agroforestry have recently gained renewed attention as a potential measure of crop protection against changing climate (Lin 2010), particularly in low-input production systems (van Kanten and Vaast 2006;Rahn et al. 2018a). Studies have shown that rapidly advancing minimum or nocturnal (night-time) temperatures have a detrimental effect on coffee yields (Craparo et al. 2015;Kath et al. 2020) and quality , as is also the case for Coffea canephora (Robusta) and several other tropical crops (Peng et al. 2004;Nagarajan et al. 2010;Luedeling 2012;Bapuji Rao et al. 2014;Kath et al. 2020). A recent pioneering study by Jung et al. (2016) revealed how plant phytochromes (red light receptors) in Arabidopsis function as thermoreceptors at night, providing a molecular explanation as to why nocturnal temperatures are a key driver in plant physiology. ...
... Intensified full-sun systems were popularized through rapid production growth in Brazil and Vietnam in the 1990s, but shading and agroforestry have recently gained renewed attention as a potential measure of crop protection against changing climate (Lin 2010), particularly in low-input production systems (van Kanten and Vaast 2006;Rahn et al. 2018a). Studies have shown that rapidly advancing minimum or nocturnal (night-time) temperatures have a detrimental effect on coffee yields (Craparo et al. 2015;Kath et al. 2020) and quality , as is also the case for Coffea canephora (Robusta) and several other tropical crops (Peng et al. 2004;Nagarajan et al. 2010;Luedeling 2012;Bapuji Rao et al. 2014;Kath et al. 2020). A recent pioneering study by Jung et al. (2016) revealed how plant phytochromes (red light receptors) in Arabidopsis function as thermoreceptors at night, providing a molecular explanation as to why nocturnal temperatures are a key driver in plant physiology. ...
... Given considerable recent work (e.g. Bunn et al. 2014Bunn et al. , 2015Craparo et al. 2015;Läderach et al. 2016;Martins et al. 2016;Moat et al. 2017;DaMatta et al. 2018;Davis et al. 2019;Kath et al. 2020), there is now a much better understanding of how current and future climate change may impact various coffee species physiology and their relative environmental suitability. However, the extent to which climate change affects coffee phenology remains uncertain. ...
Article
Studies have demonstrated that plant phenophases (e.g. budburst, flowering, ripening) are occurring increasingly earlier in the season across diverse ecologies globally. Despite much interest that climate change impacts have on coffee (Coffea arabica), relatively little is known about the driving factors determining its phenophases. Using high-resolution microclimatic data, this study provides initial insights on how climate change is impacting C. arabica phenophases in Tanzania. In particular, we use generalized additive models to show how warming nocturnal temperatures (Tnight), as opposed to day-time or maximum tem- peratures, have a superseding effect on the ripening of coffee and subsequent timing of harvest. A warm night index (WNI), generated from mean nocturnal temperature, permits accurate prediction of the start of the harvest season, which is superior to existing methods using growing degree days (GDD). The non-linear function indicates that a WNI of 15 °C is associated with the latest ripening coffee cherries (adjusted R2 = 0.95). As the WNI increases past the inflection point of ~ 16 °C, ripening occurs earlier and progresses more or less linearly at a rate of ~ 17 ± 1.95 days for every 1 °C increase in WNI. Using the WNI will thus not only allow farmers to more accurately predict their harvest start date, but also assist with identifying the most suitable adaptation strategies which may reduce harvest-related costs and buffer potential losses in quality and production.
... 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. ...
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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.
... With rainfall and temperatures projected to change in many important coffee producing It is not well tested whether robusta coffee bean characteristics respond to temperature and rainfall variation in a similar way as arabica beans. Arabica yields are more sensitive to temperature increases than robusta (Craparo et al., 2015;Martins et al., 2018;Kath et al., 2020), but it is unknown whether this is also the case for coffee bean size and defects. Previous studies in the key robusta growing areas of South East Asia have focused on robusta coffee yields sensitivity to climate variability Byrareddy et al., 2020). ...
... Previous studies in the key robusta growing areas of South East Asia have focused on robusta coffee yields sensitivity to climate variability Byrareddy et al., 2020). Kath et al. (2020) showed strong relationships between temperatures and yields. In the central highlands of Vietnam, Byrareddy et al. (2020) has also examined the impact of irrigation practices and drought on coffee yields. ...
... Within the Central Highlands, coffee bean data was collected from 30 farmers from Bao Loc and Bao Lam districts (total number of farms was 60) in Lam Dong province ( Fig. 1) over 5 seasons from 2012 to 16 (total n = 300). This is a different dataset, covering a smaller spatial and temporal scale, than that examined by Kath et al. (2020). ...
Article
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Robusta coffee is the primary source of income for millions of smallholder farmers throughout the world’s tropics. The price smallholder farmers can get for their coffee is strongly influenced by bean characteristics (i.e. beans are of a sufficient size and have minimal defects). Climate is a key determinant of successful coffee production, but scant research has been undertaken to test and quantify climate impacts on robusta coffee bean physical characteristics. Here we investigate how climate relates to the risk of poor coffee bean characteristics in one of South East Asia’s key coffee producing areas, the central highlands of Vietnam. We use 5 years (2012–2016) of coffee bean characteristic data from 60 farms. Hierarchical modelling was used to investigate how rainfall and temperature related to two indicators of coffee bean characteristics (1) the probability of below average coffee bean size and (2) the probability of above average coffee bean defects. Low rainfall (80% probability) of below average coffee bean size. Conversely, high rainfall (>750 mm) and high mean minimum temperature (>22 °C) during harvest (October-December) increased the risk (>75% probability) of above average coffee bean defects. Various coffee bean characteristic subcomponents (e.g. insect damage and mouldy beans) and different bean sizes were also examined and were affected by a range of rainfall and temperature predictors across the flowering, growing and harvest seasons. With this information targeted risk-management strategies (e.g. targeted irrigation during hot and dry growing seasons, adjusting harvest timing and employing drying techniques during wet and cold harvest periods) could be developed to minimise the effect of climate conditions that increase the risk of coffee bean defects. Successfully managing the impacts identified here, could decrease coffee bean defects and in turn increase the incomes of smallholder coffee farmers.
... 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. ...
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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.
... 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 . ...
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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). ...
Article
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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). ...
Article
Full-text available
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.
... 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. ...
Article
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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.
... 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. ...
Article
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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.
... 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). ...
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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.
... 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). ...
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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.
... 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]. ...
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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 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]. ...
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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.
... Furthermore, it has been shown that arabica coffee has already declined in Tanzania highlands and in India, owing to increasing temperatures (Craparo et al. 2015;Jayakumar et al. 2017). Robusta coffee yield may respond better to increasing temperatures than arabica coffee, although robusta coffee yield depends on the interaction of rainfall, temperature and phenological stage (Jayakumar et al. 2017;Kath et al. 2020). Although water supply is important for coffee production, it is difficult to know how future change in rainfall pattern will impact coffee production as the consolidated models of rainfall changes have a high degree of uncertainty given disparity in projections between individual models (Intergovernmental Panel on Climate Change 2019). ...
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Productivity of coffee plantations is threatened by both climate change and decreasing revenues of coffee growers. Using shade trees might protect against temperature variability, erosion and excessive radiation but there may be trade-offs in productivity and quality. While impacts of shade trees on arabica (Coffea arabica) have been reviewed, a global synthesis on robusta (Coffea canephora) coffee is lacking. We assessed how shade affects robusta growth and productivity, and what are the interactions and trade-offs. We conducted a systematic literature search in Web of Science and CAB Abstracts on 16 December 2019. Thirty papers fulfilled our inclusion criteria of being experimental studies on the impact of overstory trees with approximately half being from Brazil or India. Shade improved robusta tree growth and yield with some contrasting effects on physicochemical and biochemical properties. Shade (> 30%) was associated with reduced beverage quality. Significant interactions between shade and location, rainfall level and robusta clone were found. Among the clones tested, 06V, C153, LB1, GG229 and JM2 showed a higher productivity and growth (from + 17 to + 280%) under moderate shade (41-65%). This is the first meta-analysis of the effects of shade on robusta coffee. By synthesizing data from different studies, we highlight for the first time that the effect of shade on robusta coffee depends on tree age. Shade had positive impacts on older robusta trees (mean of 16 years), while the impact of shade on younger trees was either insignificant or negative. We highlight the importance of both clone type and tree ages. Research gaps included a lack of knowledge on the effects of shade with respect to coffee and shade tree age as well as interactive effects. More in-depth studies are needed to understand the mechanisms of how shade trees affect robusta coffee.
... The general consensus that Robusta's optimal temperature range is between 22 and 28 • C [9] comes from the climatic conditions of its native habitat. This was challenged by Kath et al. [42]. Using temperature and precipitation data and yields across southeast Asia, the authors used hierarchical Bayesian modelling to investigate Robusta's optimal temperature and found that it lies below 20.5 • C, which is significantly lower than previously thought. ...
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Coffee is one of the most traded commodities in the world. It plays a significant role in the global economy, employing over 125 million people. However, it is possible that this vital crop is threatened by changing climate conditions and fungal infections. This paper reviews how suitable areas for coffee cultivation and the toxigenic fungi species of Aspergillus, Penicillium, and Fusarium will be affected due to climate change. By combining climate models with species distribution models, a number of studies have investigated the future distribution of coffee cultivation. Studies predict that suitable coffee cultivation area could drop by ~50% under representation concentration pathway (RCP) 6.0 by 2050 for both Arabica and Robusta. These findings agree with other studies which also see an altitudinal migration of suitable cultivation areas to cooler regions, but limited scope for latitudinal migration owing to coffee’s inability to tolerate seasonal temperature changes. Increased temperatures will see an overall increase in mycotoxin production such as aflatoxins, particularly in mycotoxigenic fungi (e.g., Aspergillus flavus) more suited to higher temperatures. Arabica and Robusta’s limited ability to relocate means both species will be grown in less suitable climates, increasing plant stress and making coffee more susceptible to fungal infection and mycotoxins. Information regarding climate change parameters with respect to mycotoxin concentrations in real coffee samples is provided and how the changed climate affects mycotoxins in non-coffee systems is discussed. In a few areas where relocating farms is possible, mycotoxin contamination may decrease due to the “parasites lost” phenomenon. More research is needed to include the effect of mycotoxins on coffee under various climate change scenarios, as currently there is a significant knowledge gap, and only generalisations can be made. Future modelling of coffee cultivation, which includes the influence of atmospheric carbon dioxide fertilisation and forest management, is also required; however, all indications show that climate change will have an extremely negative effect on future coffee production worldwide in terms of both a loss of suitable cultivation areas and an increase in mycotoxin contamination.
... The general consensus that Robusta's optimal temperature range is between 22 and 28 • C [9] comes from the climatic conditions of its native habitat. This was challenged by Kath et al. [42]. Using temperature and precipitation data and yields across southeast Asia, the authors used hierarchical Bayesian modelling to investigate Robusta's optimal temperature and found that it lies below 20.5 • C, which is significantly lower than previously thought. ...
Article
Full-text available
Coffee is one of the most traded commodities in the world. It plays a significant role in the global economy, employing over 125 million people. However, it is possible that this vital crop is threatened by changing climate conditions and fungal infections. This paper reviews how suitable areas for coffee cultivation and the toxigenic fungi species of Aspergillus, Penicillium, and Fusarium will be affected due to climate change. By combining climate models with species distribution models, a number of studies have investigated the future distribution of coffee cultivation. Studies predict that suitable coffee cultivation area could drop by ~50% under representation concentration pathway (RCP) 6.0 by 2050 for both Arabica and Robusta. These findings agree with other studies which also see an altitudinal migration of suitable cultivation areas to cooler regions, but limited scope for latitudinal migration owing to coffee's inability to tolerate seasonal temperature changes. Increased temperatures will see an overall increase in mycotoxin production such as aflatoxins, particularly in mycotoxigenic fungi (e.g., Aspergillus flavus) more suited to higher temperatures. Arabica and Robusta's limited ability to relocate means both species will be grown in less suitable climates, increasing plant stress and making coffee more susceptible to fungal infection and mycotoxins. Information regarding climate change parameters with respect to mycotoxin concentrations in real coffee samples is provided and how the changed climate affects mycotoxins in non-coffee systems is discussed. In a few areas where relocating farms is possible, mycotoxin contamination may decrease due to the "parasites lost" phenomenon. More research is needed to include the effect of mycotoxins on coffee under various climate change scenarios, as currently there is a significant knowledge gap, and only generalisations can be made. Future modelling of coffee cultivation, which includes the influence of atmospheric carbon dioxide fertilisation and forest management, is also required; however, all indications show that climate change will have an extremely negative effect on future coffee production worldwide in terms of both a loss of suitable cultivation areas and an increase in mycotoxin contamination.
... For the three municipalities the average annual temperature in 2015/2016 was higher than ideal range, considering values used by Eugenio et al. 47 in their study about zoning agroclimatological Coffea canephora for Espírito Santo (22.5-24 °C) or very close to the superior limit, considering data of Matiello 48 (Table 1). In the modeling study by Kath et al. 52 , the authors concluded that an ideal temperature range above 22 °C is probably overestimated. On the other hand, we believe that the conclusions of these authors are specific to the region where the data were collected and cannot be directly applied to the species cultivated in other regions of the world, especially in Brazil. ...
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Droughts are major natural disasters that affect many parts of the world all years and recently affected one of the major conilon coffee-producing regions of the world in state of Espírito Santo, which caused a huge crisis in the sector. Therefore, the objective of this study was to conduct an analysis with technical-scientific basis of the real impact of drought associated with high temperatures and irradiances on the conilon coffee (Coffea canephora Pierre ex Froehner) plantations located in the north, northwest, and northeast regions of the state of Espírito Santo, Brazil. Data from 2010 to 2016 of rainfall, air temperature, production, yield, planted area and surface remote sensing were obtained from different sources, statistically analyzed, and correlated. The 2015/2016 season was the most affected by the drought and high temperatures (mean annual above 26 °C) because, in addition to the adverse weather conditions, coffee plants were already damaged by the climatic conditions of the previous season. The increase in air temperature has higher impact (negative) on production than the decrease in annual precipitation. The average annual air temperatures in the two harvest seasons that stood out for the lowest yields (i.e. 2012/2013 and 2015/2016) were approximately 1 °C higher than in the previous seasons. In addition, in the 2015/2016 season, the average annual air temperature was the highest in the entire series. The spatial and temporal distribution of Enhanced Vegetation Index values enabled the detection and perception of droughts in the conilon coffee-producing regions of Espírito Santo. The rainfall volume accumulated in the periods from September to December and from April to August are the ones that most affect coffee yield. The conilon coffee plantations in these regions are susceptible to new climate extremes, as they continue to be managed under irrigation and full sun. The adoption of agroforestry systems and construction of small reservoirs can be useful to alleviate these climate effects, reducing the risk of coffee production losses and contributing to the sustainability of crops in Espírito Santo.
... Nevertheless, it would be important to analyze the costs and benefits of a transition to robusta or other agroforestry products to generate evidence and better understand in which situations the advantages could outweigh the disadvantages. In addition, new research questions how tolerant robusta actually is to high temperatures (Kath et al. 2020). ...
Article
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This practice note examines how climate change is threatening coffee-growing regions in Costa Rica, specifically the Coto Brus region. By 2050, absent adaptation measures, experts project that climate change will reduce the global areas suitable for growing coffee by about 50% (Bunn et al. 2015). The case study outlines key findings from this region, including main challenges and existing factors that present opportunities to enhance climate resilience, and recommends actions that key sectoral actors can take to improve the sector’s climate resilience and long-term sustainability. Based on a literature review, interviews, a workshop and field visits with coffee farmers, government ministries, funders, and other stakeholders, this case study identified six key recommendations to increase the short-, medium-, and long-term climate resilience of the coffee sector. These are: promote promising adaptation options identified by stakeholders such as diversifying incomes of farmers and replanting farms with climate-resilient coffee varieties, with regular technical follow-up; establish baselines and monitor the impacts of adaptation measures; map out when and where coffee may no longer be viable in the coming decades, and how to support those farmers who may need to shift away from coffee; develop farmer-tailored business education; and expand peer-to-peer learning between farmers. Despite the case study’s local focus, the lessons and experiences shared in this paper are relevant for other coffee-growing regions and countries ̶ Colombia, Mexico, Guatemala, Honduras, Vietnam, Indonesia, Ethiopia, Uganda, and others ̶ where coffee producers are facing the effects of climate change, and hope that it will serve as a tool and inspiration for accelerating adaptation action.
... Meanwhile, several studies across the globe have used TerraClimate dataset (e.g. [13,[49][50][51][52][53][54]). ...
Article
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.
... 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]. ...
Article
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.
... 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). ...
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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.
... 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]. ...
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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.
... 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). ...
Article
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.
... 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. ...
Article
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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.
... 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]. ...
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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.
... 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. ...
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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.
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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.
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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.
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Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world’s protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001–2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate < 5% and in more than half of the analyzed areas. Cross-validated variable selection and importance assessment based on the Boruta algorithm indicated high importance of the reanalysis datasets ERA5-Land and MERRA-2 in both areas but higher differences and variability between the regions with all other products. CHIRPS, GPCC and the bias-corrected version of MERRA-2 were unsuitable and not important in both regions. We provide evidence that reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis whereas gauge- or satellite-based products and their combinations are highly variable and may not be applicable in peripheral areas.
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Global environmental research requires long-term climate data. Yet, meteorological infrastructure is missing in the vast majority of the world’s protected areas. Therefore, gridded products are frequently used as the only available climate data source in peripheral regions. However, associated evaluations are commonly biased towards well observed areas and consequently, station-based datasets. As evaluations on vegetation monitoring abilities are lacking for regions with poor data availability, we analyzed the potential of several state-of-the-art climate datasets (CHIRPS, CRU, ERA5-Land, GPCC-Monitoring-Product, IMERG-GPM, MERRA-2, MODIS-MOD10A1) for assessing NDVI anomalies (MODIS-MOD13Q1) in two particularly suitable remote conservation areas. We calculated anomalies of 156 climate variables and seasonal periods during 2001–2018, correlated these with vegetation anomalies while taking the multiple comparison problem into consideration, and computed their spatial performance to derive suitable parameters. Our results showed that four datasets (MERRA-2, ERA5-Land, MOD10A1, CRU) were suitable for vegetation analysis in both regions, by showing significant correlations controlled at a false discovery rate < 5% and in more than half of the analyzed areas. Cross-validated variable selection and importance assessment based on the Boruta algorithm indicated high importance of the reanalysis datasets ERA5-Land and MERRA-2 in both areas but higher differences and variability between the regions with all other products. CHIRPS, GPCC and the bias-corrected version of MERRA-2 were unsuitable and not important in both regions. We provide evidence that reanalysis datasets are most suitable for spatiotemporally consistent environmental analysis whereas gauge- or satellite-based products and their combinations are highly variable and may not be applicable in peripheral areas.
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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.
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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.
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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.
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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. This article is protected by copyright. All rights reserved.
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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.
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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.
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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. This article is protected by copyright. All rights reserved.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Coffee has proven to be highly sensitive to climate change. Because coffee plantations have a lifespan of about thirty years, the likely effects of future climates are already a concern. Forward-looking research on adaptation is therefore in high demand across the entire supply chain. In this paper we seek to project current and future climate suitability for coffee production (Coffea arabica and Coffea canephora) on a global scale. We used machine learning algorithms to derive functions of climatic suitability from a database of geo-referenced production locations. Use of several parameter combinations enhances the robustness of our analysis. The resulting multi-model ensemble suggests that higher temperatures may reduce yields of C. arabica, while C. canephora could suffer from increasing variability of intra-seasonal temperatures. Climate change will reduce the global area suitable for coffee by about 50 % across emission scenarios. Impacts are highest at low latitudes and low altitudes. Impacts at higher altitudes and higher latitudes are still negative but less pronounced. The world’s dominant production regions in Brazil and Vietnam may experience substantial reductions in area available for coffee. Some regions in East Africa and Asia may become more suitable, but these are partially in forested areas, which could pose a challenge to mitigation efforts.
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Overall, drought and unfavourable temperatures are the major climatic limitations for coffee production. These limitations are expected to become increasingly important in several coffee growing regions due to the recognized changes in global climate, and also because coffee cultivation has spread towards marginal lands, where water shortage and unfavourable temperatures constitute major constraints to coffee yield. In this review, we examine the impacts of such limitations on the physiology, and consequently on the production of mainly Coffea arabica and C. canephora, which account for about 99 % of the world coffee bean production. The first section deals with climatic factors and the coffee plant’s requirements. The importance of controlling oxidative stress for the expression of drought and cold tolerance abilities is emphasized in the second section. In the third section, we examine the impacts of drought on cell-water relations, stomatal behaviour and water use, photosynthesis and crop yield, carbon and nitrogen metabolism, root growth and characteristics, and on drought tolerance. In the fourth section, the impacts of low positive and high temperatures on coffee physiology are discussed; some insights about effects of negative temperatures are also presented. Finally, the last section deals with shading in harsh environments as a mean of buffering climatic fluctuations, as well as of increasing environmental sustainability in coffee exploitation.
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This note is intended to serve primarily as a reference guide to users wishing to make use of the Tropical Rainfall Measuring Mission data. It covers each of the three primary rainfall instruments: the passive microwave radiometer, the precipitation radar, and the Visible and Infrared Radiometer System on board the spacecraft. Radiometric characteristics, scanning geometry, calibration procedures, and data products are described for each of these three sensors. 1. TRMM overview The atmosphere gets three-fourths of its heat energy from the release of latent heat by precipitation, and an estimated two-thirds of this precipitation falls in the Tropics. Differences in large-scale rainfall patterns and their associated energy release in the Tropics, in turn, affect the entire global circulation, as manifested in El Nino events, to name just one example. The most im- portant impact of rain and its variability is on the bio- sphere, including humans. The ''average'' rainfall is rarely observed. Instead, several seasons of drought and starvation are often followed by a year or two of tor- rential downpours and disastrous floods. Quantitative estimates of tropical precipitation, unfortunately, still vary by as much as 100%, depending upon the esti- mates. These differences are due to both the lack of good direct measurements of rainfall, as well as the highly variable nature of the parameters both spatially and temporally. Cloud and rain processes are now simulated fairly well on the scale of cloud ensembles (50-100 km). However, global models for prediction of weather and climate have much coarser resolution, therefore they must ''parameterize'' cloud processes. Most of these
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The climatic variability is the main factor responsible for the oscillations and frustrations of the coffee grain yield in Brazil. The relationships between the climatic parameters and the agricultural production are quite complex, because environmental factors affect the growth and the development of the plants under different forms during the growth stages of the coffee crop. Agrometeorological models related to the growth, development and productivity can supply information for the soil water monitoring and yield forecast, based on the water stress. A soil water balance during different growth stages of the coffee crop, can quantify the effect of the available soil water on the decrease of the final yield. Other climatic factors can reduce the productivity, such as adverse air temperatures happened during different growth stages. Solar radiation and relative humidity influence many physiological processes of the coffee tree but are not generally thought to play an important role as thermal and rainfall conditions in defining potential yield or ecological limitations for this crop. According to the last report of the Intergovernmental Panel on Climate Change (IPCC, 2007), the global temperature is supposed to increase 1.1ºC to 6.4ºC and the rainfall 15% in the tropical areas of Brazil. Some Global warming projections as presented by IPCC will cause a strong decrease in the coffee production in Brazil. According to the literature besides the reduction of suitable areas for coffee production, the crop will tend to move South and uphill regions. This review article analyze the effect that these possible scenarios would have in the agro-climatic coffee zoning in Brazil, and adaptive solutions, such as agronomic mitigations and development of cultivars adapted to high temperatures is considered.
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Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F1 and F2 tests, and in many cases, even worse than F1 alone. Maximal LMEMs should be the ‘gold standard’ for confirmatory hypothesis testing in psycholinguistics and beyond.
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Collinearity refers to the non independence of predictor variables, usually in a regression-type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold-based pre-selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor-response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine-learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold-based pre-selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold-based pre-selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’-thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre-analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
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Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, "latent Gaussian models", where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged. Copyright (c) 2009 Royal Statistical Society.
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While the international coffee trade is concerned with only two coffee species — Coffea arabica and C. canephora — botanists regard as coffee trees all tropical plants of the Rubiaceae family, which produce seed resembling coffee beans. During botanical explorations of the tropical regions, from the sixteenth century onwards, wild coffees also attracted the attention of explorers and botanists. Their specimens are found in the herbaria and the names of the most famous explorers have been commemorated in both specific and generic epithets. Hundreds of species have been described, but the taxonomic classification of the genus Coffea has become very complex and rather confused.
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The actual (A) and potential (Apot) photosynthetic rates of C3 and C4 tropical crop species grown under greenhouse conditions was compared. The following species were investigated: Oryza sativa, Phaseolus vulgaris, Glycine max, Helianthus annuus, Gossypium hirsutum, Manihot esculenta, Theobroma cacao, Coffea arabica, Hevea brasiliensis, and Eucalyptus urophylla × E. grandis, all from the C3 group, and Amaranthus sp., Panicum maximum, Pennisetum purpureum, Zea mays and Saccharum officinarum, from the C4 group. A, determined under non-limiting light at ambient temperature and CO2, was measured with an infrared gas analyser, whilst Apot, determined under saturating light and CO2 at an optimal temperature (35 ºC for all species), was gauged with a gas-phase oxygen electrode. On an area basis, A varied from 5.0 up to 26.3 mumol CO2 m-2 s-1, whilst Apot was very similar in 14 of the 15 species, with an average rate of 35.0 ± 2.4 mumol O2 m-2 s-1. The value of Apot in T. cacao was approximately half the mean of the remaining species. On a mass basis, variations in A were much larger, and differences in Apot, although not large, emerged. The overall mean Apot per unit mass in the four tree species was 28.0 ± 2.2 mumol O2 g-1 min-1 against 44.6 ± 5.8 mumol O2 g-1 min-1 in the remaining species. As a whole, the results evidenced a conservative behaviour of the photosynthetic apparatus to fix CO2 amongst the species investigated, despite the large differences in A among them.
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The tropical coffee crop has been predicted to be threatened by future climate changes and global warming. However, the real biological effects of such changes remain unknown. Therefore, this work aims to link the physiological and biochemical responses of photosynthesis to elevated air [CO2 ] and temperature in cultivated genotypes of Coffea arabica L. (cv. Icatu and IPR108) and C. canephora cv. Conilon CL153. Plants were grown for 1 year at 25/20ºC (day/night) and 380 or 700 μL CO2 L(-1) , then subjected to temperature increase (0.5ºC/day) to 42/34ºC. Leaf impacts related to stomatal traits, gas exchanges, C-isotope composition, fluorescence parameters, thylakoid electron transport and enzyme activities were assessed at 25/20ºC, 31/25ºC, 37/30ºC and 42/34ºC. The results showed that 1) both species were remarkably heat tolerant up to 37/30ºC, but at 42/34ºC a threshold for irreversible non-stomatal deleterious effects was reached. Impairments were greater in C. arabica (especially in Icatu) and under normal [CO2 ]. Photosystems and thylakoid electron transport were shown to be quite heat tolerant, contrasting to the enzymes related to energy metabolism, including RuBisCO, which were the most sensitive components. 2) Significant stomatal trait modifications were promoted almost exclusively by temperature and were species dependent. Elevated [CO2 ] 3) strongly mitigated the impact of temperature on both species, particularly at 42/34ºC, modifying the response to supra-optimal temperatures, 4) promoted higher water use efficiency under moderately higher temperature (31/25 ºC), and 5) did not provoke photosynthetic down-regulation. Instead, enhancements in [CO2 ] strengthened photosynthetic photochemical efficiency, energy use and biochemical functioning at all temperatures.. Our novel findings demonstrate a relevant heat resilience of coffee species and that elevated [CO2 ] remarkably mitigated the impact of heat on coffee physiology, therefore playing a key role in this crop sustainability under future climate change scenarios. This article is protected by copyright. All rights reserved.
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Warm nights are a widespread predicted feature of climate change. This study investigated the impact of high night temperatures during the critical period for grain yield determination in wheat and barley crops under field conditions, assessing the effects on development, growth and partitioning crop-level processes driving grain number per unit area (GN). Experiments combined: (i) two contrasting radiation and temperature environments: late sowing in 2011 and early sowing in 2013, (ii) two well-adapted crops with similar phenology: bread wheat and two-row malting barley, and (iii) two temperature regimes: ambient and high night temperatures. The night temperature increase (ca. 3.9°C in both crops and growing seasons) was achieved using purpose-built heating chambers placed on the crop at 7 pm and removed at 7 am every day from the third detectable stem node to 10 days post-flowering. Across growing seasons and crops, the average minimum temperature during the critical period ranged from 11.2 °C to 17.2 °C. Wheat and barley grain yield were similarly reduced under warm nights (ca. 7% °C(-1) ), due to GN reductions (ca. 6% °C(-1) ) linked to a lower number of spikes m(-2) . An accelerated development under high night temperatures led to a shorter critical period duration, reducing solar radiation capture with negative consequences for biomass production, GN and therefore, grain yield. The information generated could be used as a starting point to design management and/or breeding strategies to improve crop adaptation facing climate change. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
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Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio�-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations. o
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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.
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Under two contrasting light regimes (full sun and 45% shade) and the optimal coffee-growing conditions of the central valley of Costa Rica, production pattern, bean characteristics and beverage quality were assessed over two production cycles on dwarf coffee (Coffea arabica L. cv. Costa Rica 95) trees with varying fruit loads (quarter, half and full loads) imposed by manual fruit thinning. Shade decreased coffee tree productivity by 18% but reduced alternate bearing. Shade positively affected bean size and composition as well as beverage quality by delaying berry flesh ripening by up to 1 month. Higher sucrose, chlorogenic acid and trigonelline contents in sun-grown beans pointed towards incomplete bean maturation and explained the higher bitterness and astringency of the coffee beverage. Higher fruit loads reduced bean size owing to carbohydrate competition among berries during bean filling. These results have important implications in terms of agricultural management (shade, fruit thinning, tree pruning) to help farmers increase coffee plantation sustainability, produce coffee beans of larger size and higher quality and ultimately improve their revenues, especially during times of world overproduction. Copyright © 2005 Society of Chemical Industry
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Environmental constraints disturb plant metabolism and are often associated with photosynthetic impairments and yield reductions. Among them, low positive temperatures are of up most importance in tropical plant species, namely in Coffea spp. in which some acclimation ability has been reported. To further explain cold tolerance, the impacts on photosynthetic functioning and the expression of photosynthetic-related genes were analyzed. The experiments were carried out along a period of slow cold imposition (to allow acclimation), after chilling (4°C) exposure and in the following rewarming period, using 1.5-year-old coffee seedlings of 5 genotypes with different cold sensitivity: Coffea canephora cv. Apoatã, Coffea arabica cv. Catuaí, Coffea dewevrei and 2 hybrids, Icatu (C. arabica×C. canephora) and Piatã (C. dewevrei×C. arabica). All genotypes suffered a significant leaf area loss only after chilling exposure, with Icatu showing the lowest impact, a first indication of a higher cold tolerance, contrasting with Apoatã and C. dewevrei. During cold exposure, net photosynthesis and Chl a fluorescence parameters were strongly affected in all genotypes, but stomatal limitations were not detected. However, the extent of mesophyll limitation, reflecting regulatory mechanisms and/or damage, was genotype dependent. Overnight retention of zeaxanthin was common to Coffea genotypes, but the accumulation of photoprotective pigments was highest in Icatu. That down-regulated photochemical events but efficiently protected the photosynthetic structures, as shown, e.g., by the lowest impacts on A(max) and PSI activity and the strongest reinforcement of PSII activity, the latter possibly reflecting the presence of a photoprotective cycle around PSII in Icatu (and Catuaí). Concomitant to these protection mechanisms, Icatu was the sole genotype to present simultaneous upregulation of caCP22, caPI and caCytf, related to, respectively, PSII, PSI and to the complex Cytb(6)/f, which could promote better repair ability, contributing to the maintenance of efficient thylakoid functioning. We conclude that Icatu showed the best acclimation ability among the studied genotypes, mostly due to a better upregulation of photoprotection and repair mechanisms. We confirmed the presence of important variability in Coffea spp. that could be exploited in breeding programs, which should be assisted by useful markers of cold tolerance, namely the upregulation of antioxidative molecules, the expression of selected genes and PSI sensitivity.
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Higher growing season temperatures can have dramatic impacts on agricultural productivity, farm incomes, and food security. We used observational data and output from 23 global climate models to show a high probability (>90%) that growing season temperatures in the tropics and subtropics by the end of the 21st century will exceed the most extreme seasonal temperatures recorded from 1900 to 2006. In temperate regions, the hottest seasons on record will represent the future norm in many locations. We used historical examples to illustrate the magnitude of damage to food systems caused by extreme seasonal heat and show that these short-run events could become long-term trends without sufficient investments in adaptation.
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In order to evaluate food security, technology potential and the environmental impacts of production in a strategic and regional context, it is critical to have reliable information on the spatial distribution and coincidence of people, agricultural production, and environmental services. This paper proposes a spatial allocation model for generating highly disaggregated, crop-specific production data by a triangulation of any and all relevant background and partial information. This includes national or sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rainfed production systems, cropping intensity, and crop prices. This information is compiled and integrated to generate "prior" estimates of the spatial distribution of individual crops. Priors are then submitted to an optimization model that uses cross-entropy principles and area and production accounting constraints to simultaneously allocate crops into the individual "pixels" of a GIS database. The result for each pixel (notionally of any size, but typically from 25 to 100 square km) is the area and production of each crop produced, split by the shares grown under irrigated, high-input rainfed, low-input rainfed conditions (each with distinct yield levels). Tested in Latin America and sub-Saharan Africa, the spatial allocation model is applied here to generate a global distribution of crop area and production for 20 major crops (wheat, rice, maize, barley, millet, sorghum, potato, sweet potato, cassava and yams, plantain and banana, soyb ean, dry beans, other pulse, sugar cane, sugar beets, coffee, cotton, other fibres, groundnuts, and other oil crops). The detailed spatial datasets represent a truly unique and extremely rich platform for exploring the social, economic and environmental consequences of agricultural production in a strategic policy context.
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Previous studies have found that atmospheric brown clouds partially offset the warming effects of greenhouse gases. This finding suggests a tradeoff between the impacts of reducing emissions of aerosols and greenhouse gases. Results from a statistical model of historical rice harvests in India, coupled with regional climate scenarios from a parallel climate model, indicate that joint reductions in brown clouds and greenhouse gases would in fact have complementary, positive impacts on harvests. The results also imply that adverse climate changes due to brown clouds and greenhouse gases contributed to the slowdown in harvest growth that occurred during the past two decades. • agricultural impact • air pollution • carbon dioxide warming • climate change • South Asia
Café conilon: como plantar, tratar, colher, preparar e vender. MM Produções Gráficas
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