Supplementary material
Journal: Climatic Change
Paper Title: “Climate change and coffee: Assessing vulnerability by modeling future climate
suitability in the Caribbean island of Puerto Rico”
Authors: Stephen J. Fain, Maya Quiñones, Nora L. Álvarez-Berríos, Isabel K. Parés-Ramos, William
A. Gould
Corresponding Author: Stephen Fain
Email Address: [email protected]
Appendix I: Climate Projections for Puerto Rico
Discussion of Models and IPCC Emission Scenarios
Puerto Rico is projected to experience increasing mean annual temperatures over the course of
the 21st century. The range of increase projected varies according to the emission scenario and
particular climate model used. Emission scenarios are built on a range of assumptions regarding
global economic and technological development, the continued use of fossil fuels, and GHG
mitigation policies1. The scenarios do not take into account implementation of the United Nations
Framework Convention on Climate Change (UNFCCC) or the emissions targets of the Kyoto
Protocol. The Intergovernmental Panel on Climate Change (IPCC) issued a Special Report on
Emission Scenarios (SRES) in 2000 that resulted in four different narratives based on scientific
literature that consistently describe the relationships between emission driving forces and their
evolution and add context for the scenario quantification2. For each narrative, several different
scenarios were developed using different modeling approaches to examine the range of outcomes
arising from a range of models that use similar assumptions about driving forces. The resultant 40
SRES scenarios together encompassed the best understanding of future GHG emission
uncertainties as of 2000. The range of temperature projections for Puerto Rico are the result of
recent modeling efforts by scientists at the US Forest Service International Institute of Tropical
Forestry (IITF) using the SRES A1B, A2, and B1 emissions scenarios3. The A2 scenario is
representative of a heterogeneous world in which economic development is primarily regionally
oriented and per capita economic growth and technological change are more fragmented and
slower which results in greater temperature increases compared to other scenarios modeled for
the region.
Karmalkar et al., (2013) used general circulation model (GCM) data included in the Coupled Model
Intercomparison Project phase 3 (CMIP3) and the UK Hadley Centre regional climate model (RCM)
data to provide both present-day and scenario-based future information on precipitation and
temperature for individual island states within the Caribbean (Fig. 1 & 2) (Karmalkar et al. 2013).
IPCC Special Report on Emission Scenarios (2000) available at: https://www.ipcc.ch/pdf/special-reports/spm/sresen.pdf
2 Ibid
3 Henareh et al., 2016- For a discussion of A1B, A2, and B1 emission scenarios see the IPCC SRES (2000) cited above
1
1
They found that models used in the Caribbean to date have struggled to accurately reproduce
observed rainfall patterns due to the complex interactions between sea surface temperatures, sea
level pressure, and predominant wind patterns. These difficulties have resulted in a generally dry
bias when comparing observed and modeled precipitation and should be noted when studying
precipitation projections. Modeled temperature patterns are more closely aligned with
observations and thus projections are considered more certain although some uncertainty still
exists. The end of century picture of Caribbean climate, as deduced from the CMIP3 models is one
characterized by a decrease in wet season precipitation. The dry season is largely unaltered except
for a small increase in precipitation in November in the Bahamas. The RCM simulations similarly
project a drying (though more intense) for the wet season but the agreement between the two
RCM projections is generally poor with respect to which portion (early or late) will be driest. Both
the GCMs and the RCM project higher warming of surface air temperatures over the northwestern
Caribbean and relatively lower warming in the southeastern Caribbean. The RCMs however have
higher warming values in general, especially over the eastern Caribbean where the landmasses
are better resolved.
2
Figure 1: Projected change in precipitation (percent) in the Caribbean region by the 2080s under the SRES A2 scenario
based on the CMIP3 multi-model ensemble for the following seasons (a) MJJ (early wet season), (b) ASON (late wet
season). Projected change is relative to the mean from 1970-89 (Taken from Kalmalkar et. al 2013).
3
Figure 2: Projected change in mean annual surface air temperature (°C) by the 2080s under the SRES A2 scenario in (a)
CMIP3 multi-model ensemble, (b) RCM-H and (c) RCM-E. Projected change by the 2080s is relative to the mean climate of
1970-1989. In (a) for every grid box, the color indicates ensemble mean projection, the value at the center indicates
ensemble median projection and values at the bottom-left and top-right corners indicate ensemble minimum and
maximum projections, respectively. (Taken from Kalmalkar et. al 2013)
Surface Air Temperatures
Puerto Rico is projected to experience increasing mean annual temperatures over the course of
the 21st century. The range of increase projected varies according to the emission scenario and
climate model. Karmalkar et al., (2013) projected a 2°- 5°C (3.6°-9°F) increase4 for the Latin
American Caribbean based the SRES A2 emission scenario (see Fig. 2). Hayhoe (2012) explored
the potential effects of climate change in various Caribbean islands by utilizing 32 different global
climate models to simulate observed temperature and rainfall variability over the Caribbean and
to generate future projections of temperature and precipitation for Puerto Rico. These future
projections were analyzed in terms of model performance and changes projected for a range of
global mean temperature targets, from +1 to +3°C relative to 1971-2000. For Puerto Rico, the
projected changes were divided into two regions for temperature (hot coastal and more
4
Increase for years 2080-2089 vs. 1970-1989- Puerto Rico and Cuba are projected to increase by <3°C
4
temperate inland) and three regions for precipitation (dry northern coast, dry southern coast, and
wet inland locations)5.
Summary of Temperature Projections from Hayhoe (2013),
 Puerto Rico is expected to warm faster than the global average, with increases in both
mean and extreme temperatures, including days per year over 95°F (35°C) and nights
warmer than 85°F (~29°C).
 With just one degree increase in global temperature, 60% of the wet seasons are projected
to be warmer than the historical maximum and, on average, there would be 100 more days
over 85°F (~29°C), 150 more days over 90°F (~32°C) and 35 more days over 95°F (35°C)
each year.
 With a two-degree increase in global temperature, every day would be warmer than the
historical median, 350 days per year will be warmer than the historical 1-in-4 warmest
days and 300 days per year will be warmer than the historical 1-in-10 warmest days.
 For a global mean temperature increase of three degrees, Puerto Rico’s average daytime
maximum temperature is projected to increase by up to +7°C in the dry season and +6°C in
wet season.
 Increases are projected to be greater for inland locations as compared to coastal and for
nighttime temperatures (over +8°C) compared to daytime.
 Per degree global mean temperature change, temperature on the warmest day of the year
is projected to increase by +3°C while cooling degree-days (a measure of air conditioning
demand) are projected to increase by +600. The range of daily temperature is expected to
increase, particularly in the wet season.
 Projected temperature changes are large enough to affect temperature sensitive crops,
species, and ecosystems, while the combined effects of changes in temperature and
precipitation are likely to increase the demand for energy, the risk of water stress and
drought, and the risk of impacts from heavy rainfall events.
Following up on this work, Henareh et al. (2016) used Puerto Rico as a test case for studying the
potential ecological and economic effects of climate change in tropical islands. Their analysis used
outputs from 12 statistically downscaled general circulation models (GCMs) provided by Hayhoe
(2013). Two strategies were used in selecting models for the study, first, the average of all
available GCMs, then the average of the models that were able to reproduce the observed largescale dynamics that control precipitation over the Caribbean. The projected means from the allmodel ensemble showed increases of 7.5°–9°, 6.4°–7.6°, and 4.6°–5.4°C under the A2, A1B, and B1
scenarios respectively (Fig. 3).
Discussion of Impacts
Any scenario presented by Henareh et al. (2016) and Hayhoe (2013) presents acute challenges to
the agricultural sector of Puerto Rico. Under high GHG emission scenario projections, climate
controlled environments would almost assuredly be required for any viable agricultural
5
See supplemental materials for summary of Hayhoe (2013)
5
operation. Such operations would necessitate massive investments in energy and water
management infrastructure. If global efforts to limit average global warming to 1.5°C recently
ratified at the Paris UNFCC COP are successful, Hayhoe (2013) indicates Puerto Rico may still
experience a dramatic increase in total dry days and days that exceed historical temperature
maximums (particularly in the w et season) (Hayhoe 2013). Prolonged dry periods are expected to
become more frequent with even 1°C of average global warming (Hayhoe 2013). Modeling
indicates much of the mean temperature increase in Puerto Rico are due to increases in mean
minimum temperatures indicating a narrower range of temperature variation (both annual and
diurnal) and sustained higher temperatures(Karmalkar et al. 2013; Hayhoe 2013).
Increasing temperature trends can affect crops in a variety ways. An important aspect of
increasing minimum temperatures is the affect such increases can have on the pollination stage of
crops and other plants. The pollination stage is a critical period in which exposure to high
temperatures can be particularly damaging to crops. Pollen release is related to development of
fruit, grain, and/or fiber. Exposure to high temperatures during this period can greatly reduce
crop yields and increase the risk of total crop failure. Plants exposed to high nighttime
temperatures during the grain, fiber, or fruit production period experience lower productivity and
reduced overall quality (Walthall et al. 2012).
Rising temperatures will also likely lead to increasing pressure from pests and disease, heat stress
in animals, and unpredictable changes in the phenology of many plants. Heat stress among
livestock and water shortages have already been voiced as a growing concern among many
farmers in the US Caribbean and throughout the Latin American and the Caribbean countries
(Gould et al. 2015). Higher temperatures can result in increased respiration rates in plants that in
turn require greater soil moisture to maintain vitality and yields. Maintaining necessary moisture
levels in light of increased temperatures and evapotranspiration rates will likely require a range of
adaptation strategies including investment in water management infrastructure (irrigation, water
storage, etc.) as well as a shift toward agroforestry and other agroecological practices that work to
maintain soil moisture by increasing shade and decreasing ambient air temperatures (Gould et al.
2015). Rising temperatures are also likely to impact forest ecosystems throughout the Caribbean.
Rising temperatures and other changes in climate are also expected to alter the distribution,
population, and effects of existing forest pests and diseases and may lead to additional
vulnerabilities to new pests and diseases or introduced invasive species.
6
Figure 3: Projected increase in mean temperature for Puerto Rico. From: Henareh et al., (2016).
Precipitation
Increasing mean surface air temperatures and sea-surface temperatures are tied to changes in
larger global circulation patterns that correlate to rainfall patterns within the Caribbean
(Karmalkar et al. 2013). As discussed previously, models exhibit greater uncertainty in projecting
future precipitation trends than temperature. That aside, regional models show a drying trend
characterized by a decrease in wet season precipitation (Fig. 4) (Karmalkar et al. 2013). The
7
decrease is generally higher for the early wet season (May, June, July) than the late wet season
(August, September, October, November) and for the western Caribbean (Cuba) than the eastern
Caribbean (Puerto Rico) (Karmalkar et al. 2013). Those trends stand in contrast to study findings
published in 2009 for Puerto Rico showing a decrease in dry season precipitation but an increase
during wet season (Harmsen et al. 2009). Recent efforts to interpolate downscaled climate data
for Puerto Rico show greater variability in precipitation trends over time, but also a linear
increase in important indicators such as total dry days (TDD) and maximum consecutive dry days
(MCDD- an important drought indicator) over the next century (Henareh et al. 2016).
The impact of drying trends and reduced water availability on agricultural systems in Puerto Rico
could be profound. The island has experienced critical water shortages in recent years associated
with prolonged droughts. Increasing temperatures and reduced rainfall could require significant
investments in irrigation and water management infrastructure to insure ample future
agricultural water supply (Harmsen et al. 2009).
Summary Precipitation Projections from Hayhoe (2013):
 Rainfall is projected to decrease, particularly in the wet season, with more frequent dry
days. Precipitation in Puerto Rico and the central Caribbean is characterized by a summer
wet season ranging from May to November, punctuated by a mid-summer drought (MSD).
 The frequency of ‘moderate extreme’ precipitation (e.g., > 1 inch of rain/24hrs) is projected
to decrease, while more extreme precipitation (e.g., > 3 inches of rain/24hrs) is expected to
become more common.
8
Figure 4: Projected changes in annual precipitation for Puerto Rico. From: Henareh et al., (2016).
Summary of Precipitation Projections from Henareh et al. (2016) results:
 The mean pixel decline in rainfall was 510.67, 354.60, and 312.57mm for A2, A1B, and B1
scenarios respectively from the first to the last time interval (1960-1990, 2071- 2099)
based on the multimodel average of all 12 models, whereas the corresponding declines
were 916.30, 842.62, and 619.58mm using the bimodal models (Fig. 4).
 Precipitation was projected to decrease faster in the wetter regions of the island such as
the Luquillo and Central mountain ranges.
 The inter-annual variability in precipitation increased and dominated from the wet to dry
stations with the highest fluctuations in the south coast.
9
 Dramatic changes were projected in the life zone distributions in Puerto Rico in this
century.
 Generally, decreasing trends were observed in the areas of wet and moist zones while
increasing trends were observed in the areas of dry zones in all three scenarios.
Discussion of Impacts
The climate changes implicated by these global and regional climate projections may have
profound impacts for coffee production in Puerto Rico and throughout the LAC. Coffee is a
notoriously climate sensitive crop. Deviations from historical patterns of rainfall and temperature
affect not only plant development and flowering, but also the ultimate quality of the end product.
Prolonged exposure to excessive precipitation and humidity can leave crops vulnerable to
outbreaks of the fungoid coffee rust (roya- Hemileia vastatrix) as seen throughout much of Central
America in 2012-2013 Increases in mean annual temperatures can lead to a different set of
challenges such as accelerated flowering, loss of cup quality and the proliferation of pest such as
the Coffee Berry Borer (Hypothenemus hampei) (Jaramillo et. al 2009).
References
Gould WA, Fain SJ, Pares IK, et al. (2015) Caribbean Regional Climate Sub Hub Assessment of
Climate Change Vulnerability and Adaptation and Mitigation Strategies. United States Department
of Agriculture
Harmsen EW, Miller NL, Schlegel NJ, Gonzalez JE (2009) Seasonal climate change impacts on
evapotranspiration, precipitation deficit and crop yield in Puerto Rico. Agric Water Manag
96:1085–1095.
Hayhoe K (2012) Quantifying key drivers of climate variability and change for Puerto Rico and the
Caribbean: Final report, Agreement.
Henareh A, Gould WA, Harmsen E, et al. (2016) Climate change implications for tropical islands:
Interpolating and interpreting statistically downscaled GCM projections for management and
planning. J Appl Meteorol Climatol 55:265–282.
Jaramillo J, Chabi-Olaye A, Kamonjo C, et al. (2009) Thermal tolerance of the coffee berry borer
Hypothenemus hampei: Predictions of climate change impact on a tropical insect pest.
Karmalkar A V., Taylor MA, Campbell J, et al. (2013) A review of observed and projected changes in
climate for the islands in the Caribbean. Atmósfera 26:283–309.
Walthall, CL, Hatfield, J, Backlund, P, Lengnick, L, Marshall, E, Walsh, M, Adkins, S, Aillery, M,
Ainsworth, EA, Ammann, C, Anderson, CJ, Bartomeus, I, Baumgard, LH, Booker, F, Bradley, B,
Blumenthal, DM, Bunce, J, Burkey, K, Dabney, SM, Delgado, JA, Dukes, J, Funk, A, Garrett, K,
Glenn, M, Grantz, DA, Goodrich, D, Hu, S, Izaurralde, RC, Jones, RAC, Kim, S-H, Leaky, ADB,
Lewers, K, Mader, TL, McClung, A, Morgan, J, Muth, DJ, Nearing, M, Oosterhuis, DM, Ort, D,
Parmesan, C, Pettigrew, WT, Polley, HW, Rader, R, Rice, C, Rivington, M, Rosskopf, E, Salas,
10
WA, Sollenberger, LE, Srygley, R, Stöckle, C, Takle, ES, Timlin, D, White, JW, Winfree, R,
Wright-Morton, L, & Ziska, LH. (2012). Climate Change and Agriculture in the United States:
Effects and Adaptation. USDA Technical Bulletin 1935. (Technical Bulletin 1935). Washington,
DC.
11
Appendix II: Coffee Harvest Statistics
Table 1: Coffee Supplies, Disposition and Consumption 1990/91-2013/2013 (Hundredweight). Source: Puerto Rico
Planning Board, Agricultural Statistics Division, August 2015
12
Appendix III: Bioclimatic Parameters for Coffee Growth
Table 1. Bioclimatic parameters for Coffee Growth
Source
Parameters (annual mean temperature, annual
precipitation, soils, and relief)
DaMatta & Ramalho, 2006
17°C–22°C (59°–72°F)
Davis et al., 2006
18°–21°C (~64°–70°F)
Teketay, 1999
15°–24°C (59°–75°F); 762 mm–1000 mm (min), 1800 mm
(optimal); soil depth: 15–20 cm (min w/high soil
moisture)
Purseglove, 1968
1524–2286 mm (60in–90in) (optimal)
Illy & Viani, 2005
18°–22°C; 1200–1500 mm (min); soil depth: 2m
Coffee Research Institute, 2006
15°–24°C (59–75°F); 1500-2500 mm (min-irrigation may
be required w/less rainfall); pH: 4.5–6 to 20 cm, 80–90%
of feeder roots in 20 cm, greatest concentration of roots
b/w 30–60 cm (Nutman)
Pohlan & Janssens, 2010
18°–22°C; 800–4200 mm (acceptable), 1400–2400 mm
(optimal)
DaMatta et al., 2007
18°–21°C (optimal for C. arabica), 22°–26° (optimal for C.
canephora); C. arabica prefers lower humidity levels than
C. canephora —more prone to coffee leaf rust at higher RH
levels, 1200-1800mm (optimal); main root concentration
generally around 30cm—varies with soil moisture.
Bunn et al., 2015
C. arabica: 14°C–26.4°C; C. canephora 19.2°C–27.8°C
Muñiz et al., 1999
67°–80° F (19.4–26.7°C) annual mean= 75°; 75–100in
(1905–2540mm); pH= 4.0–6.5 Humatas (Ultisol), Alonso
(Inceptisol), Catalina (Oxisol), Daguey (Oxisol) y Los
Guineos (Oxisol);
<= 50% slope between 600–3000ft (acceptable)
1500–3000ft (optimal)
Table 2. Parameters used in the coffee suitability model
Source
Parameters (annual mean temperature, annual
precipitation, soils, and relief)
Natural Resources and Conservation
Service (2016)
Soil series: Ultisol (Humatas), Inceptisol (Alonso), Oxisol
(Catalina, Daguey, Los Guineos)
National Elevation Dataset, USGS (Gesch et
al. 2002)
Elevation range: 182.99–914.4 mm (600–3000 feet)
13
Statistically downscaled climate data
annual means (time blocks 1991–2010,
2001–2040, 2041–2070, 2071–2099), all
model projections ensemble under three
emissions scenarios (Henareh et al. 2016)
Precipitation (1991–2010 average): 1000–1905 mm,
1905–2540 mm
Temperature (1991–2010 average): 18°–27° C
Discussion
Annual precipitation of 1000 mm (~40in) is generally considered a minimum for C. arabica
cultivation, although certain varieties have been document to grow in areas with annual rainfall
totals as low as 762mm (30in) (Teketay 1999). Conditions considered optimal by some are
provided by annual rainfall totals ranging from 1524–2286 mm (60in–90in) (Purseglove 1968);
however, for most of the best coffee growing regions in Africa, Latin America and South-east Asia,
rainfall totals are significantly above 1,800 mm (70in) annually. Precipitation should be well
distributed throughout the year with a drier period (<70mm/month)(Illy & Viani 2005) of two to
four months when surface feeding roots dry, growth slows, young wood hardens and flower buds
develop (Van Hilten et. al 1992). Periods of rainfall after a dry spell can help synchronize
flowering and therefore promote clearly defined harvesting seasons. Coffee producing countries
with more than one wet and dry season will have correlating multiple harvesting seasons (CRI
2016).
As with temperature and precipitation, C. arabica has more particular soil requirements than that
of C. canephora. Arabica prefers deep, well-drained soils that are slightly acidic. Soil profiles and
drainage conditions are a critical factors in regional precipitation requirements as coffee is an
evergreen and requires sub-soil water availability at all times (Teketay 1999). The degree to
which water capacity and depth of soils are important are required correlates to rainfall, length of
dry season, and potential evapotranspiration (PET) of a given area. Flowering usually coincides
with the onset of rains following a dry period, and subsequent fruiting and flowering is very
dependent on available soil moisture. Heavy clay soils can be difficult for deeper root penetration
and lend themselves to flooding and waterlogging. Coffee will not tolerate long periods of
inundation that create anaerobic soil conditions or limit oxygen intake by roots (Teketay 1999).
In regions where coffee is grown at the margins of its climatic niche (high temperature, low
rainfall, longer dry season, etc.) deeper soils are necessary to maintain water supplies in times of
high evapotranspiration, although any direct correlation has yet to be quantified, perhaps due to
the dynamic nature of local factors that contribute to evapotranspiration. In the absence of deep
soils, managing soil moisture can be accomplished through the use of cover crops, shade trees, or
irrigation. Such management actions must be carefully monitored to prevent unnecessary
competition with coffee crops.
Conversely, areas of abundant rainfall, high humidity and cloud cover, as well as a short dry
season, have been documented to successfully grow Arabica in clay soils as shallow as 15 to 20cm
(Teketay 1999). However, as with other crops, coffee grown in marginal soil conditions is more
vulnerable to excessive rainfall or prolonged droughts, which can lead to reduced yields and
ultimately, increased mortality (Teketay 1999). These types of weather patterns are projected to
14
increase in frequency in many coffee-producing regions of the Latin American and Caribbean
countries and specifically Puerto Rico (Hayhoe 2012, Henareh 2016).
References:
Bunn C, Läderach P, Ovalle Rivera O, Kirschke D (2015) A bitter cup: climate change profile of
global production of Arabica and Robusta coffee. Clim Change 129:89–101.
CRI-Coffee Research Institute Arabica and Robusta Coffee Plant.
http://www.coffeeresearch.org/agriculture/coffeeplant.htm. Accessed 27 Jun 2016
DaMatta FM, Cochicho Ramalho JD (2006) Impacts of drought and temperature stress on coffee
physiology and production: A review. Brazilian J. Plant Physiol. 18:55–81.
DaMatta FM, Ronchi CP, Maestri M, Barros RS (2007) Ecophysiology of coffee growth and
production. Brazilian J. Plant Physiol. 19:485–510.
Davis AP, Gole TW, Baena S, Moat J (2012) The Impact of Climate Change on Indigenous Arabica
Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities.
Gesch D, Oimoen M, Greenlee S, et al. (2002) The National Elevation Dataset.
Hayhoe K (2012) Quantifying key drivers of climate variability and change for Puerto Rico and the
Caribbean: Final report, Agreement.
Henareh A, Gould WA, Harmsen E, et al. (2016) Climate change implications for tropical islands:
Interpolating and interpreting statistically downscaled GCM projections for management and
planning. J Appl Meteorol Climatol 55:265–282.
Malavolta E Nutrição Mineral, Calagem, Gessagem e Adubação do Careeiro. Summary in English.
Muñiz WG, Acin N, Hernández E, et al. (1999) CONJUNTO TECNOLOCICO PARA LA PRODUCCION
DE CAFÉ. Universidad de Puerto Rico, Recinto Universitario de Mayagü ez, Colegio de Ciencias
Agrícolas, ESTACION EXPERIMENTAL AGRICOLA, San Juan, Puerto Rico
NRCS-Soil Survey Staff Web Soil Survey [Database]. In: Web Soil Surv. [Database].
http://websoilsurvey.nrcs.usda.gov/. Accessed 20 Jun 2007
Pohlan H, Janssens M (2010) Growth and production of coffee.
Purseglove JW (1968) Tropical Crops: Dicotyledons. Volumes 1 and 2.
Teketay D (1999) History, botany and ecological requirements of coffee. Walia 20:28–50.
15
Appendix VI: Change in area suitable for coffee growth in the top ten
municipalities (Area in square kilometers)
Scenario
AM_1960_1990
0
Municipalities
Adjuntas
Ciales
Jayuya
Lares
Las Marías
Maricao
Orocovis
San Sebastián Utuado
Yauco
Grand Total
15838.10
21937.48
21312.21
7678.15
2602.58
12634.54
14897.52
15635.57
16400.55
34768.50
163705.21
0.2
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
14776.24
14776.24
0.4
0.00
14064.28
0.00
62.13
27.52
0.00
58.00
10.58
6270.85
32710.90
53204.26
0.6
18645.37
38584.75
24527.73
33753.60
9954.03
3461.28
14770.71
60410.01
65406.79
54445.86
323960.12
0.8
65608.28
66551.02
37665.19
89390.42
66306.38
41099.81
106489.78
92255.80
168333.98
37677.20
771377.84
1
73683.79
31630.24
31837.20
28754.54
41556.09
37667.69
28958.81
16141.92
41612.08
3622.43
335464.79
173775.54
172767.77
115342.32
159638.84
120446.60
94863.32
165174.82
184453.88
298024.25
178001.13
1662488.47
Grand Total
AM_A2_1991_2010
0
Adjuntas
Ciales
Jayuya
Lares
Las Marías
Maricao
Orocovis
San Sebastián Utuado
Yauco
Grand Total
15838.10
21937.48
21312.21
7678.15
2602.59
12634.54
14897.52
15635.57
16400.55
34770.96
163707.67
0.2
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
17502.23
17502.23
0.4
0.00
16469.64
0.00
173.55
537.25
0.00
58.00
244.93
11381.97
30035.10
58900.44
0.6
29218.25
44593.59
28563.85
42018.64
11918.04
5868.44
22413.94
65465.53
91906.85
57323.44
399290.56
0.8
66041.09
58708.34
35418.78
82177.74
65717.54
42372.36
99623.56
87229.06
143288.38
34753.25
715330.10
1
62678.11
31058.71
30047.48
27590.75
39671.18
33987.97
28181.80
15878.79
35046.51
3616.16
307757.47
173775.54
172767.77
115342.32
159638.84
120446.60
94863.32
165174.82
184453.88
298024.25
178001.13
1662488.47
Grand Total
AM_A2_2011_2040
0
Adjuntas
Ciales
15838095.58
0.2
Jayuya
Lajas
Las Marías
Maricao
21937482.46
21312206.58 122455505.69
2602584.97 12634544.37
6677437.55
303920.72
4755946.85
103296.41
7602865.84
670167.83
Orocovis
San Sebastián Utuado
14897519.34
15635574.35
16400549.24
21607221.15
53945853.47
Grand Total
63117476.11
306831538.70
15517479.89
81373006.44
14554192.54
8659212.86
104226416.99
0.4
793997.03
14423260.06
1265989.32
3106044.64
0.6
67932019.18
97168618.10
55271705.72
77929612.08
50682935.24 17581121.32 113993736.64
64241180.89 151790194.05
84240483.59
703165114.57
0.8
85565426.01
32548373.21
37038947.22
46701642.34
47742698.61 45666793.95
30686082.66
19408752.62 105180569.97
6392153.89
410229798.13
3646001.16
12603.48
453466.80
23919467.09
7059572.03 18207391.98
4534729.30
74326.29
53702139.83
1
1062748.74
Yauco
9615300.49
10098748.29
Grand Total
173775538.96 172767774.86 115342315.65 156678379.42 120446603.55 94863315.86 165174816.68 184453882.96 298024254.08 178001132.63 1659528014.66
AM_A2_2041_2070
Adjuntas
0
Ciales
Jayuya
Lares
Las Marías
Maricao
Orocovis
San Sebastián Utuado
Yauco
Grand Total
15838.10
21937.48
21312.21
7678.15
2602.58
12634.54
14897.52
15635.57
16400.55
70086.91
199023.62
0.2
0.00
19581.14
0.00
369.74
9276.44
637.04
58.00
60420.59
14501.34
12497.68
117341.96
0.4
15292.68
95025.80
15019.40
108970.02
66376.99
32081.01
38828.33
92255.80
186681.23
71321.39
721852.64
0.6
66465.74
27784.12
57987.02
42591.78
42190.59
36123.23
85039.14
16141.92
65807.84
23162.54
463293.93
0.8
76179.02
8439.23
21023.69
29.15
0.00
13387.49
26351.83
0.00
14633.29
932.61
160976.32
173775.54
172767.77
115342.32
159638.84
120446.60
94863.32
165174.82
184453.88
298024.25
178001.13
1662488.47
Grand Total
AM_A2_2071_2099
0
Adjuntas
Ciales
Jayuya
Lares
15838095.59
21937482.48
21312206.77
7678148.78
0.2
1245970.68
21072641.01
3769414.02
369735.43
0.4
73062913.34
97425434.79
56990473.91 114890669.28
0.6
83628559.36
32332216.61
33270220.94
Grand Total
36700282.14
Las Marías
Maricao
2602584.97 12634544.37
Orocovis
San Sebastián Utuado
Yauco
Grand Total
14897519.34
15635574.36
16400549.24
79207326.05
208144031.93
1627087.86
60420591.38
14554192.54
14645397.84
127683608.63
66387815.41 40316226.90 119321415.98
92255797.21 203581248.42
78961800.17
943193795.41
42179766.47 41210403.41
16141920.01
5186608.58
383467034.91
9276436.69
702141.18
29328793.50
63488263.89
173775538.97 172767774.88 115342315.64 159638835.63 120446603.55 94863315.86 165174816.68 184453882.96 298024254.09 178001132.64 1662488470.88
AM_A1B_1991_2010 Adjuntas
Ciales
Jayuya
Lares
Las Marías
Maricao
Orocovis
San Sebastián Utuado
Yauco
Grand Total
0
173775538.93 172767774.84 115342315.65 159638835.68 120446603.57 94863315.95 165174816.67 184453882.78 298024254.22 178001132.64 1662488470.92
Grand Total
173775538.93 172767774.84 115342315.65 159638835.68 120446603.57 94863315.95 165174816.67 184453882.78 298024254.22 178001132.64 1662488470.92
AM_A1B_2011_2040 Adjuntas
0
15838095.59
0.2
Ciales
21937482.46
Jayuya
21312206.57
8086963.62
Lares
7678148.78
Las Marías
Maricao
2602584.97 12634544.37
Orocovis
14897519.34
369035.90
2574364.86
30895.06
9151972.78
640946.93
62595.31
San Sebastián Utuado
Yauco
Grand Total
15635574.35
16400549.23
55432102.03
2999196.58
1503168.30
24839172.41
184368807.68
40402796.74
69975043.63
14749527.43
5897266.91
117797719.23
0.4
1717.10
11788106.63
57854.81
5472687.71
0.6
44528630.97
71363792.76
38137282.11
48164918.59
17716315.75 11185458.70
59426528.96
58062619.92 118690578.30
64580985.72
531857111.78
0.8
76393942.01
35688787.25
39788662.26
71989695.76
56095079.08 45307452.28
65691556.37
34377947.23 117734654.01
24477741.75
567545518.00
1
37013153.30
23902642.12
16046309.90
25964348.88
32306286.14 25064018.52
25096616.71
2773863.83
220516517.47
Grand Total
Ciales
Jayuya
Lares
15838095.60
21937482.47
21312206.73
7678148.78
0.2
739532.48
19627717.57
1112448.03
369735.43
0.4
69554211.86
97818357.40
55710789.12 114890669.28
0.6
86709920.97
33157025.22
36054509.85
0.8
933778.05
227192.21
1152361.91
Grand Total
28945776.81
173775538.97 172767774.85 115342315.65 159638835.62 120446603.57 94863315.86 165174816.69 184453882.97 298024254.08 178001132.64 1662488470.89
AM_A1B_2071_2099 Adjuntas
0
3403501.26
36700282.14
Las Marías
Maricao
2602584.97 12634544.34
Orocovis
San Sebastián Utuado
Yauco
Grand Total
14897519.35
15635574.36
16400549.24
69506039.36
198442745.21
963049.87
60420591.38
14554192.54
17313445.53
125079290.70
66386176.77 37316534.18 116419110.35
92201889.71 203441427.51
85906849.91
939646016.08
42179552.94 40637988.88
31448670.17
15642502.05
62953441.73
5274797.84
390758691.79
1446466.93
553325.47
674643.07
9276436.69
1852.17
702141.18
3572107.28
8561727.11
173775538.96 172767774.87 115342315.65 159638835.63 120446603.54 94863315.86 165174816.68 184453882.96 298024254.09 178001132.64 1662488470.88
16
AM_B1_1991_2010
0
Adjuntas
Ciales
15838095.29
Jayuya
21937482.32
Lares
21312206.65
Las Marías
7678148.79
Maricao
Orocovis
2602585.25 12634544.16
San Sebastián Utuado
14897519.33
15635574.30
Yauco
16400549.34
0.2
0.4
16883678.85
1641.65
219376.59
667796.48
917.55
62595.31
432419.08
Grand Total
34801744.49
163738449.93
18059141.17
18059141.17
12593593.72
29511627.69
60373646.92
0.6
32250976.65
50522934.20
31793066.87
45135405.86
13925968.60
7819965.64
30334632.09
68235799.44 100341658.71
60250954.96
440611363.02
0.8
68107775.51
53524641.67
36973024.72
79275352.43
65011689.37 44329506.11
92081221.30
84277289.48 136070081.40
31913634.88
691564216.88
1
57578691.46
29899037.85
25262375.75
27330551.94
38238563.89 30078382.47
27798848.66
15872800.56
3464029.46
288141653.06
32618371.02
Grand Total
173775538.92 172767774.89 115342315.64 159638835.61 120446603.59 94863315.94 165174816.69 184453882.86 298024254.19 178001132.65 1662488470.98
AM_B1_2011_2040
Adjuntas
0
Ciales
Jayuya
Lares
Las Marías
Maricao
Orocovis
San Sebastián Utuado
Yauco
Grand Total
15838.10
21937.48
21312.21
7678.15
2602.58
12634.54
14897.52
15635.57
16400.55
56345.18
0.2
0.00
3788.04
0.00
223.42
2191.60
0.00
0.00
3730.69
0.00
20799.30
30733.03
0.4
77.20
15814.49
78.10
2394.30
4735.79
364.46
63.03
58197.90
14472.34
6881.46
103079.08
0.6
49461.92
84153.15
41004.11
51078.22
18940.85
10984.03
70647.93
47890.53
123533.08
70853.81
568547.63
0.8
78005.19
27909.56
39038.69
72483.23
59765.78
46885.58
56494.95
44919.68
116263.65
20365.45
562131.78
1
30393.14
19165.05
13909.21
25781.51
32210.00
23994.70
23071.38
14079.51
27354.63
2755.93
212715.07
173775.54
172767.77
115342.32
159638.84
120446.60
94863.32
165174.82
184453.88
298024.25
178001.13
1662488.47
Grand Total
AM_B1_2041_2070
0
Adjuntas
Ciales
Jayuya
Lares
Las Marías
Maricao
Orocovis
San Sebastián Utuado
Yauco
185281.89
Grand Total
15838.10
21937.48
21312.21
7678.15
2602.58
12634.54
14897.52
15635.57
16400.55
61778.86
0.2
0.00
19581.14
0.00
369.74
9276.44
637.04
58.00
16670.52
14501.34
20620.57
81714.78
0.4
564.75
78064.69
701.89
31854.45
41295.18
2475.82
5471.93
87085.80
68754.84
45458.03
361727.37
0.6
65617.96
27718.08
53878.62
86703.75
50644.13
26258.37
98971.55
51200.44
128987.97
42927.12
632908.01
0.8
85613.23
25415.61
37062.58
28522.96
16067.23
41888.11
34229.89
13861.55
57685.09
6225.48
346571.73
6141.50
50.77
2387.02
4509.80
561.05
10969.43
11545.93
0.00
11694.46
991.07
48851.03
173775.54
172767.77
115342.32
159638.84
120446.60
94863.32
165174.82
184453.88
298024.25
178001.13
1662488.47
1
Grand Total
AM_B1_2071_2099
0
Adjuntas
Ciales
Jayuya
Lares
Las Marías
Maricao
Orocovis
San Sebastián Utuado
Yauco
190715.56
Grand Total
15838.10
21937.48
21312.21
7678.15
2602.58
12634.54
14897.52
15635.57
16400.55
66256.34
195193.04
0.2
0.00
19581.14
0.00
369.74
9276.44
637.04
58.00
52510.03
14501.34
16288.80
113222.53
0.4
14746.62
95024.32
13272.55
105957.77
64970.20
23987.15
34424.61
86541.92
184020.11
70645.88
693591.13
0.6
64180.24
26581.85
57840.19
40322.39
42464.62
35082.89
88885.10
22073.09
63402.03
23774.34
464606.74
0.8
78523.66
9642.98
22898.52
5310.79
1132.76
22521.69
26909.60
7693.27
19700.22
1035.77
195369.25
486.92
0.00
18.85
0.00
0.00
0.00
0.00
0.00
0.00
0.00
505.77
173775.54
172767.77
115342.32
159638.84
120446.60
94863.32
165174.82
184453.88
298024.25
178001.13
1662488.47
1
Grand Total
17
Appendix V: Change in area suitable for coffee growth in Puerto Rico
The following tables indicate the projected change in area within which mean annual
temperatures are desirable for coffee growth. The tables indicate area values for the entirety of
Puerto Rico.
Table 1: Change in area of suitable mean annual temperature for C. arabica along A2, A1B, and B1 IPCC SRES emissions
scenarios.
Area of Temperature Suitability, C. arabica (18°-22°C)
Emission Scenario
A2
A1B
B1
19601990
(km2)
901
901
901
19912010
(km2)
362
336
355
20112040
(km2)
34
23
36
20412070
(km2)
0
0
1
20712099
(km2)
0
0
0
Table 2: Change in area of suitable mean annual temperature for C. canephora along A2, A1B, and B1 IPCC SRES emissions
scenarios
Area of Temperature Suitability, C. canephora (22°-27°C)
Emission Scenario
19601991201120411990
2010
2040
2070
(km2)
(km2)
(km2)
(km2)
A2
8714
8438
4485
947
A1B
8714
8350
4098
895
B1
8714
8405
4752
2089
20712099
(km2)
7
64
992
18