Ciat capacity gcqri


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Ciat capacity gcqri

  1. 1. W W / YZ/ d d : / Z D D W dK , Z D d Y D t t W Z , Z D t / • t /W / ^ / / Z Z Z Z D W K Z Z ^ Z ^ ^ ^ D ^ W W W ^ D D W D t ^ • E ^d D •D • ^ / s Z ZZ • W• W • ^ / / • s• • / s ^ /W• •
  2. 2. d / W ^ Z D • W D – W W D / –, –, D/ E • ^ ^ D • h D • – • E W – d/ h • E W W D – K D s : : , s // W Z • Identifying potential (regional) – Geographic information systems – Models • Realizing the potential (site specific) – Niche management – Information management – Sustainable access to market Laure Collet, June 2011 / E ^d Probability map Farms sample Standardazied post-harvest process GPS georeferenced fields P( H , E ) P( H E ) = Standard methodology of cupping P( E ) Empirical data Evidence Field value
  3. 3. d What are the variables influencing coffee quality?Geographical databases: DEM → Topography WorldClim → Annual precipitation, dry months, annual average temperature, diurnal temperature range, dew point temperature, solar radiation d K / E ^d Z W Probability map P( H , E ) P( H E ) = P( E ) Empirical data Evidence Field value
  4. 4. Z W Z D , Identifies places climatically and pedologically similar to a known individual location. Concept: Depending on the degree with which climate and soils influence product quality, places with similar climates and soils can have similar qualities. Provides means to identify places with potential for the introduction of a promesing variety / technology. Z W D / Y/ Z/ s ^ , ^ , , s , , W ^ D D ^ D D D D K , , , D , , D / Y/ Z/ s
  5. 5. D W Mycena citricolor attack intensity index 4 behaviours : 1. Low scores with high and low shade cover: environment unfavourable for disease development Sun points Sun points 2. Similar scores with high and low shade cover: no effect of shade 3. Higher scores with low W shade cover : sun exposure is favourable to disease development W 4. Higher scores with high high shade (15 - 65%) and low shade (0 -15 %) cover shade cover : shade is favourable to disease development Group 3 Group 4 Rainfall June to August 1034 986 (mm) The objective of the study was to Rainfall August to 1209 1154 identify the causal but regionally- December (mm) changing relationships between Elevation (m) 1154 1109 quality characteristics of the coffee Slope inclination (%) 9.4 9.5 product and the characteristics of the W Slope aspect (% of environment where it is grown points with East or 63 3 South orientation) W Significant differences, P 0.05 Environmental differences Variety influence 3. Higher scores with low shade cover : sun exposure is favourable to disease Product quality differences development Interactions shade-environment for Spatial structures of the differences Mycena citricolor development 4. Higher scores with high shade cover : shade is favourable to disease development In the study area, shade is especially favourable for Mycena development on West and North oriented slopes, and unfavourable on East and South oriented slopes • Are the growing environments different between the departments? Descriptive statistics, Anova, Cluster analyses, Graphical analyses Comparing Cauca and Nariño all environmental • Are the bean (green, roasted) characteristics different between characteristics except altitude, aspect and dew point departments? are significantly different Descriptive statistics, Anova, Bonferoni multivariate test, Graphical analyses The South of Cauca is environmentally more similar • Are there relationships between environment and bean (green, roasted) to Nariño characteristics? Correlation analyses, Best Linear Unbiased Prediction Within the departments coherent environmental • Are the non-random spatial distribution patterns? clusters can be identified Principal component analyses, Bayesian probability analyses, GWR, semivariograms • How unique are the environments globally? Markov Chain analyses “Homologue Screening”
  6. 6. Z • There are spatial differences for bean characteristics • These differences are (a) variety specific and (b) not equal for the quality descriptors • There are strong relationships DOMAIN I II III IV V VI VI VIII between bean characteristics and Physical characteristics Screen size 18 B1 B B B B B A A environmental factors Screen size 17 A A B A B B A A Biochemical characteristics Caffeine A BC D B BC CD E F • These relationships are highly site Trigonelline A A A B A B B CD and variety specific, i.e. clear G*E Chlorogenic. acid C A AB BC AB AB D D Sensory characteristics effects Fragrance and aroma D C C BC B BC A BC Flavor C ABC BC ABC AB ABC A BC Aftertaste B A B AB AB AB A B Acidity C BC C C AB ABC A C Body C ABC BC ABC AB ABC BC A Clean cup BC A BC A A AB AB C Overall B A AB AB AB AB AB B Uniformity D A CD AB A AB BC BD Balance B A AB AB A AB A AB Sweetness B A A AB A AB A B Z h Positive influence Factors Range Importance Final score Solar radiation (MJ m-2 d-1) 19 –20 2.09 Annual average cloud frequency (%) 87 –90 2.04 Negative influence Factors Range Importance Positive influence Final scoreFactors Range Importance Annual average cloud frequency (%) 75 –78 3.82 Final score Annual total evaporation (mm yr-1) 1321 –1470 2.59Altitude (m) 1575 – 1800 2.08 Diurnal temperature range (°C) 9.1 –9.4 2.18Annual rainfall (mm) 1550 – 1750 2.00 Negative influenceFactors Range Importance Final scoreAverage temperature (°C) 23.6 – 25.05 3.15Altitude (m) 675 – 900 2.59
  7. 7. h K • Identify the most appropriate spatial analyses domain for which the relationships between coffee quality on one side, and environmental and production system characteristics on the other side are analyzed. Such domains reduce as much as possible the environment by genotype interactions, in order to permit the generalization of a single quality profile for each identified domain. • Understand the spatial relationships between coffee quality on one side, and environmental and production system characteristics on the other side for each identified domain. • Identify the most important environmental factors that impact on key coffee quality characteristics. • Provide recommendation as to how unique the identified spatial domains are if compared to other coffee growing regions. / d Intervention Impact / d D Z Time : Eco-Efficient Agriculture for the Poor • s W /• h E •• h s• h D • ^ W• h • D Z W W D W • D Z h Z W • ^ • : d E d Z / • /WZ/ • /ZZ/ Z • /W / • /Z
  8. 8. Z Z Z ^ ^ ^ ^ K W• d –• – d d W d / /
  9. 9. D D , D W : , Z d K Dd,KKKz d Kd/KE E E z^/^ Y D / D D d d Z^hd^ Z^hd^ W W D , W E K t K K t E KK W d W W ^ D h D
  10. 10. KEd d^ D s APECAFE K^WZK ^ z E D/,/ , Z OXFAM K^D CRS ^ d ^K DWK Gimme Coffee! Square Mile t Coffee Roasters d E CIAT D : D KZK TCHO K E E W / d hE^zZ D Intelligentsia Coffee W KZD COMUS WZKKKWd Y Z W W W s W EZ/Yhd ,ZZE W Ed ^D :/EKd E ^ ^ W d ^ s Z
  11. 11. K E • d K /WE/ ^ W K E K E E Z / Z / EKE/ /Z Z h^ E K h EWZK ^ Z ^ h^WZKD / /, , d/ Z/ Z / h^KE E h^ ^ / W W / K K E ^/ WD h^E , K D h^/ / t/ h^ ^ t W d , t^ h^
  12. 12. d W d