Eco-physiological basis of drought stress in coffee (Coffea arabica, L.) in Ethiopia

Melke, Abayneh; Fetene, Masresha

doi:10.1007/s40626-014-0022-2

Cited by 18 publications

(9 citation statements)

References 27 publications

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“…RC, total rural credit by municipality (real); RCA, rural credit of the municipality's agriculture (real); VP, value of the coffee production (real); T, air temperature (°C); R, rainfall (mm per year); ETo, potential evapotranspiration (mm per year); WD, water deficit (mm per year); and SUR water surplus (mm per year). harvests, which is due to the physiology of the crop (Rodrigues et al, 2013;Melke & Fetene, 2014). The calibrated models for the coffee regions followed the variability of coffee supply.…”

Section: Resultsmentioning

confidence: 99%

Economic and climatic models for estimating coffee supply

Moraes-Oliveira

Aparecido

Figueira

2017

Pesq. agropec. bras.

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-The objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000-2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz.Index terms: Coffea arabica, climate, econometrics, modelling, rural credit. Modelos econômicos e climáticos para estimativa da oferta cafeeiraResumo -O objetivo deste trabalho foi estimar a oferta cafeeira por meio da calibração de modelos estatísticos, com variáveis econômicas e climáticas, das principais regiões produtoras do Estado de São Paulo. As regiões estudadas foram Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa e Osvaldo Cruz. Foram utilizados dados de oferta cafeeira, variáveis econômicas (crédito rural, crédito rural na agricultura e valor da produção) e variáveis climáticas (temperatura do ar, precipitação pluvial, evapotranspiração potencial, deficiência e excedente hídrico) de cada região, para o período de 2000-2014. Os modelos foram calibrados com uso de técnicas de regressão linear múltipla, e todas as combinações possíveis foram testadas para a seleção das variáveis. A oferta cafeeira foi a variável dependente, e as demais, as independentes. A acurácia e a precisão dos modelos foram analisadas pelo erro percentual médio e pelo coeficiente de determinação ajustado, respectivamente. As variáveis que mais influenciam a oferta cafeeira são o valor de produção e a temperatura do ar. É possível estimar a oferta cafeeira com regressões lineares múltiplas por meio de variáveis econômicas e elementos climáticos. Os modelos mais acurados são os calibrados para estimar a oferta cafeeira das regiões de Cássia dos Coqueiros e Osvaldo Cruz.

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Section: Resultsmentioning

confidence: 99%

Economic and climatic models for estimating coffee supply

Moraes-Oliveira

Aparecido

Figueira

2017

Pesq. agropec. bras.

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“…However, if other agroecological factors like soils, landforms, or land use are not considered, moving coffee areas to climatically more suitable areas may either not be possible due to land-use conflicts or not advisable due to unsuitable soil or landform conditions. Considering all relevant factors is necessary for actual agricultural planning (FAO 1976;McRae et al 1981;Mighty 2015;Nzeyimana et al 2014).…”

Section: Communicated By Jasper Van Vlietmentioning

confidence: 99%

Land in Central America will become less suitable for coffee cultivation under climate change

2021

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Coffee cultivation in Central America provides goods and services at local, national, and international levels. Climate change is likely to affect the magnitude and continuity of these benefits by reducing the land suitability for coffee cultivation. To quantify the impacts of climate change on land suitability, we use the Bayesian network model Agroecological Land Evaluation for Coffea arabica L. (ALECA) and estimate the land suitability for coffee production in 2000, 2050, and 2080 under three climate change scenarios based on relative concentration pathways (RCPs) 2.6, 4.5, and 8.5. Results indicate that even under the less severe climate scenarios, over half of the current coffee area in Central America will experience a decline in its land suitability for coffee production, from excellent or good to moderate and marginal, and that the change will not happen in the more distant future of 2080, but by 2050. Under RCP 8.5, most coffee areas become of marginal and moderate suitability. The findings show that the continuity of coffee cultivation in a large portion of coffee areas in Central America is under threat and that farmers and policy-makers should develop adaptation portfolios for their farms and regions in a timely manner.

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“…The physiological cause of bienniality of coffee is complex (Valadares et al, 2013;Melke & Fetene, 2014). When coffee develops plagiotropic branches, photoassimilates are allocated to this growth, promoting a large vegetative flux in the plant.…”

Section: Developed Amentioning

confidence: 99%

“…In the first year, coffee will thus show a high-vegetative growth and a low yield. In the following year, the crop will produce a high yield due to the high-bud formation in the previous year, but with low-vegetative growth (Melke & Fetene, 2014). Photoassimilates in years with high yields are allocated to bean filling, so vegetative growth is reduced, providing a lower yield again in the following year (Rodrigues et al, 2013).…”

Section: Developed Amentioning

confidence: 99%

Forecasting of the annual yield of Arabic coffee using water deficiency

Aparecido

Rolim

2018

Pesq. agropec. bras.

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The objective of this work was to develop agrometeorological models for the forecasting of the annual yields of Arabic coffee (Coffea arabica), using monthly water deficits (DEFs) during the coffee cycle, in important locations in the state of Minas Gerais, Brazil. For the construction of the models, a meteorological data set spanning of 18 years and multiple linear regressions were used. The models were calibrated in high- and low-yield seasons due to the high-biennial yields in Brazil. All calibrated models for high- and low-yield seasons were accurate and significant at 5% probability, with mean absolute percentage errors ≤2.9%. The minimum forecasting period for yield is six months for southern Minas Gerais and Cerrado Mineiro. In high-yield seasons, water deficits affect more the reproductive stage of coffee and, in low-yield seasons, they affect more the vegetative stage of the crop.

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Eco-physiological basis of drought stress in coffee (Coffea arabica, L.) in Ethiopia

Cited by 18 publications

References 27 publications

Economic and climatic models for estimating coffee supply

Economic and climatic models for estimating coffee supply

Land in Central America will become less suitable for coffee cultivation under climate change

Forecasting of the annual yield of Arabic coffee using water deficiency

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