Predicted Impact of Climate Change on Coffee Supply Chains

Läderach, Peter; Lundy, Mark; Jarvis, Andy; Ramírez, Julián; Portilla, Emiliano Pérez; Schepp, Kathleen; Eitzinger, Anton

doi:10.1007/978-3-642-14776-0_42

Cited by 60 publications

(45 citation statements)

References 8 publications

(5 reference statements)

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“…Läderach et al (2010) have outlined the impact of progressive climate change on coffee (Coffea spp. ) supply, farmer's livelihoods and environmental services provided by agroforestry systems, including carbon storage and watershed services.…”

Section: Introductionmentioning

confidence: 99%

Predicting the future climatic suitability for cocoa farming of the world’s leading producer countries, Ghana and Côte d’Ivoire

Läderach¹,

Martinez-Valle²,

et al. 2013

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Ghana and Côte d'Ivoire are the world´s leading cocoa (Thebroma cacao) producing countries; together they produce 53% of the world's cocoa. Cocoa contributes 7.5% of the Gross Domestic Product (GDP) of Côte d'Ivoire and 3.4% of that of Ghana and is an important cash crop for the rural population in the forest zones of these countries. If progressive climate change affected the climatic suitability for cocoa of West Africa, this would have implications for the national economies and farmer livelihoods, with potential repercussions for forests and natural habitat as cocoa growing regions expand, shrink or shift. The objective of this paper is to present future climate scenarios for the main cocoa growing regions of Ghana and Côte d'Ivoire and to predict their impact on the relative suitability of these regions for growing cocoa. These analyses are intended to support the respective countries and supply chain actors in developing strategies for reducing the vulnerability of the cocoa sector to climate change. Based on the current distribution of cocoa growing areas and climate change predictions from 19 Global Circulation Models, we predict changes in relative climatic suitability for cocoa for 2050 using the MAXENT method. According to the model, some current cocoa producing areas will become unsuitable (Lagunes and Sud-Comoe in Côte d'Ivoire) requiring crop change, while other areas will require adaptations in agronomic management, and in yet others the climatic suitability for growing cocoa will increase (Kwahu Plateu in Ghana and southwestern Côte d´Ivoire). We recommend the development of site-specific strategies to reduce the vulnerability of cocoa farmers and the sector to future climate change.

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Section: Introductionmentioning

confidence: 99%

Predicting the future climatic suitability for cocoa farming of the world’s leading producer countries, Ghana and Côte d’Ivoire

Läderach¹,

Martinez-Valle²,

et al. 2013

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“…The climatic conditions are favorable for coffee production, and most countries in the region are recognized for their high-quality coffee and shaded coffee systems [28][29][30][31][32], together producing more than 10% of the total global coffee supply [33,34]. However, projections of climate change show that the region is likely to experience severe alterations in climate in the future, which may negatively impact coffee production [35][36][37].…”

Section: Study Regionmentioning

confidence: 99%

Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Lara-Estrada

Rasche

Sucar

et al. 2018

Land

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Abstract:Climate data availability plays a key role in development processes of policies, services, and planning in the agricultural sector. However, data at the spatial or temporal resolution required is often lacking, or certain values are missing. In this work, we propose to use a Bayesian network approach to generate data for missing variables. As a case study, we use relative humidity, which is an important indicator of land suitability for coffee production. For the model, we first extracted climate data for the variables precipitation, maximum and minimum air temperature, wind speed, solar radiation and relative humidity from the surface reanalysis dataset Climate Forecast System Reanalysis. We then used machine learning algorithms to define the model structure and parameters from the relationships of the variables found in the dataset. Precipitation, maximum and minimum air temperature, wind speed, and solar radiation are then used as proxy variables to infer missing values for monthly relative humidity and relative humidity for the driest month. For this, we used both complete and incomplete initial data. In both scenarios of data availability, the comparison of estimated and measured values of relative humidity shows a high level of agreement. We conclude that using Bayesian Networks is a practical solution to estimate relative humidity for coffee agricultural planning.

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“…Its applications range from studying the impact of the climate change on coffee supply chains [6] to calculations of drug absorption rates [7]. In astronomy it is used in nearly all wavelengths regimes: from radio astronomy (cf., e.g.…”

Section: Maximum Entropy Deconvolutionmentioning

confidence: 99%

Application of Maximum Entropy Deconvolution to $\gamma$-ray skymaps

Raab¹,

Jung-Richardt²

2016

Proceedings of the 34th International Cosmic Ray Conference — PoS(ICRC2015)

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Skymaps measured with imaging atmospheric Cherenkov telescopes (IACTs) represent the real source distribution convolved with the point spread function of the observing instrument. Current IACTs have an angular resolution in the order of 0.1 • which is rather large for the study of morphological structures and for comparing the morphology in γ-rays to measurements in other wavelengths where the instruments have better angular resolutions. Serendipitously it is possible to approximate the underlying true source distribution by applying a deconvolution algorithm to the observed skymap, thus effectively improving the instruments angular resolution. From the multitude of existing deconvolution algorithms several are already used in astronomy, but in the special case of γ-ray astronomy most of these algorithms are challenged due to the high noise level within the measured data. One promising algorithm for the application to γ-ray data is the Maximum Entropy Algorithm. The advantages of this algorithm are the possibility to take a priori knowledge into account and that it is an independent approach to previous work, e.g., [1] who applied the Richardson Lucy Algorithm to γ-ray skymaps. An implementation of the Maximum Entropy Algorithm is provided in the MemSys5 software package [2]. As this algorithm is very sensitive to various input parameters it is essential to understand their influences. We present a study of the influences of these parameters in order to investigate the applicability of the Maximum Entropy Algorithm for the deconvolution of skymaps in γ-ray astronomy.

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Predicted Impact of Climate Change on Coffee Supply Chains

Cited by 60 publications

References 8 publications

Predicting the future climatic suitability for cocoa farming of the world’s leading producer countries, Ghana and Côte d’Ivoire

Predicting the future climatic suitability for cocoa farming of the world’s leading producer countries, Ghana and Côte d’Ivoire

Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks

Application of Maximum Entropy Deconvolution to $\gamma$-ray skymaps

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