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Climate Change, Drought, and Jamaican Agriculture: Local Knowledgeand the Climate RecordDouglas W. Gamblea; Donovan Campbellb; Theodore L. Allenc; David Barkerb; Scott Curtisd; DuncanMcGregore; Jeff Popked

a Department of Geography and Geology, University of North Carolina Wilmington, b Department ofGeography and Geology, University of West Indies at Mona, c Rosenstiel School of Marine andAtmospheric Science, University of Miami, d Department of Geography, East Carolina University, e

Department of Geography, Royal Holloway University of London,

First published on: 05 August 2010

To cite this Article Gamble, Douglas W. , Campbell, Donovan , Allen, Theodore L. , Barker, David , Curtis, Scott ,McGregor, Duncan and Popke, Jeff(2010) 'Climate Change, Drought, and Jamaican Agriculture: Local Knowledge andthe Climate Record', Annals of the Association of American Geographers,, First published on: 05 August 2010 (iFirst)To link to this Article: DOI: 10.1080/00045608.2010.497122URL: http://dx.doi.org/10.1080/00045608.2010.497122

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Climate Change, Drought, and JamaicanAgriculture: Local Knowledge and the Climate

RecordDouglas W. Gamble,∗ Donovan Campbell,† Theodore L. Allen,‡ David Barker,† Scott Curtis,§

Duncan McGregor,# and Jeff Popke§

∗Department of Geography and Geology, University of North Carolina Wilmington†Department of Geography and Geology, University of West Indies at Mona‡Rosenstiel School of Marine and Atmospheric Science, University of Miami

§Department of Geography, East Carolina University#Department of Geography, Royal Holloway University of London

The purpose of this study is to reach a basic understanding of drought and climate change in southwestern Jamaicathrough an integration of local knowledge and perception of drought and its physical characteristics manifestedin remotely sensed precipitation and vegetation data. Local knowledge and perception are investigated througha survey of sixty farmers in St. Elizabeth Parish and physical characteristics of drought are examined throughstatistical analysis of satellite precipitation and vegetation vigor time series. The survey indicates that mostfarmers are concerned about an increase in drought occurrence. Satellite estimates of rainfall and vegetationvigor for St. Elizabeth Parish support this perception and suggest that severe drought events are becoming morefrequent. The satellite precipitation time series also suggest that the early growing season is becoming drieras compared to the primary growing season, especially since 1991. This recent divergence in growing seasonmoisture conditions might add to farmers’ observations that drought is becoming more prevalent. Consequently,Jamaican farmers perceptions of drought are not driven by magnitude and frequency of dry months alone butrather by the difference between growing seasons. Any development of drought adaption and mitigation plansfor this area must not focus solely on drought; it must also compare moisture conditions between months andseasons to be effective. Key Words: agriculture, climate change, drought, Jamaica, local knowledge.

El proposito de este estudio es lograr una comprension basica de la sequıa y el cambio climatico en el sudoeste deJamaica, mediante la integracion del conocimiento local y la percepcion de la sequıa y sus caracterısticas fısicas,segun se manifiestan en datos sobre precipitacion y vegetacion obtenidos por sensores remotos. El conocimientoy percepcion locales se investigaron mediante el estudio de sesenta cultivadores de la Parroquia de St. Elizabeth,mientras las caracterısticas fısicas de la sequıa se examinan por medio del analisis estadıstico de series detiempo existentes de precipitacion y vegetacion obtenidas con satelites. El estudio indica que la mayorıa de loscultivadores estan preocupados por el aumento en la ocurrencia de sequıas. Los calculos de satelite existentessobre precipitaciones y vegetacion para la Parroquia de St. Elizabeth apoyan esa percepcion y sugieren que loseventos de sequıa severa se estan haciendo mas frecuentes. Las series de tiempo de satelite sobre precipitaciontambien sugieren que la estacion de crecimiento temprana se esta haciendo mas seca, comparada con laestacion de crecimiento primaria, especialmente desde 1991. Esta reciente divergencia en las condiciones dehumedad durante la estacion de crecimiento podrıa anadirse a las percepciones de los cultivadores de una sequıa de

Annals of the Association of American Geographers, 100(4) 2010, pp. 1–14 C© 2010 by Association of American GeographersInitial submission, July 2009; revised submission, November 2009; final acceptance, January 2010

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2 Gamble et al.

prevalencia cada vez mayor. En consecuencia, las percepciones de los cultivadores jamaiquinos sobre sequıa noestan solo determinadas por la magnitud y frecuencia de los meses secos, sino principalmente por la diferenciaentre las estaciones de crecimiento. Cualquier desarrollo de planes sobre adaptacion a la sequıa o mitigacionde la misma en esta area debe centrarse no solo en la sequıa misma; para que esos planes sean efectivos, debencompararse tambien las condiciones de humedad entre meses y estaciones. Palabras clave: agricultura, cambioclimatico, sequıa, Jamaica, conocimiento local.

Global climate change is projected to increasethe vulnerability of small island developingstates (SIDS) to natural hazards. SIDS are

vulnerable to the effects of global climate change andnatural hazards because of their small size; locationin regions prone to natural disasters; high populationdensities; poorly developed infrastructure; limitednatural, human, and economic resources; and influenceof external forces such as terms of trade, economicliberalization, and migration flows (Gable et al. 1990;Pelling and Uitto 2001; Lewsey, Cid, and Kruse2004; Mimura et al. 2007). For the Caribbean region,the deterioration of water resources through climatechange–induced drought and the consequent negativeimpact on agriculture are of particular concern (Watts1995; Intergovernmental Panel on Climate Change[IPCC] 2001). Indeed, the IPCC Fourth AssessmentReport indicates with high confidence that islandsin the Caribbean will experience increased droughtas a result of climate change (Mimura et al. 2007).Most agriculture in the region is dependent on rainfallbecause of a lack of sustainable groundwater resources(Gamble 2004). Recent trends and predictions suggestgreater variability in rainfall and drier summers in theCaribbean (Singh 1997; Peterson et al. 2002; Angeleset al. 2007; Gamble 2009). Thus, effective droughtadaptation and mitigation plans must be developed toincrease the resilience of Caribbean SIDS in the faceof climate change and drought.

There is no universally accepted definition of drought(Wilhite 2000; Keyantash and Dracup 2002). For ex-ample, one study identified more than 150 definitions ofdrought that used a variety of meteorological, hydrologi-cal, agricultural, and socioeconomic variables (Wilhiteand Glantz 1985). Wilhite (2000, 6) therefore statedthat: “realistically, definitions of drought must be re-gion and application (or impact) specific.” Given this,there is a need for measurements and assessments ofdrought at the local or regional scale.

Effective adaption to and mitigation of droughtalso requires an understanding of local perception andknowledge of a hazard. Such a location-specific under-standing of drought is even more important given thebroad, ambiguous definitions surrounding global cli-

mate change. Pettenger (2007, 5) pointed out: “Askten people how to define climate change, its causesand effects, and you will get ten different answers.”Many scholars have noted that discussions and descrip-tions of climate change contain value-laden languagethat might have different meanings for different groupsof people (Palutikof, Agnew, and Hoar 2004; Smith2007). Thus, an understanding of local knowledge andperception of weather and climate is essential for thedevelopment of effective climate change policy at anygiven location, especially if it is to effectively addressdrought.

In regard to agriculture, farmers’ perceptions of cli-mate are inseparable from their accumulated localknowledge, through which they cognize, negotiate, andadapt to environmental and climate change (Osunade1994; Ovuka and Lindqvist 2000; Roncoli, Ingram, andKirshen 2002; Luseno et al. 2003; Meze-Hausken 2004;Vedwan 2006). Studies indicate that, although farm-ers might not understand the science behind climatechange, their continual observation of and interac-tion with the surrounding environment heightens theirawareness of even minor deviations from what is per-ceived to be normal weather and climate conditions.The majority of these studies are from Africa, Asia,and Latin America, with a paucity of studies for theCaribbean (Beckford, Barker, and Bailey 2007). Thefew studies that have been completed in Jamaica in-clude analysis of environmental knowledge and deci-sion making in small-farming systems (Davis-Morrisonand Barker 1997) and the use of indigenous knowledgein yam production (Beckford 2002; Barker and Beck-ford 2006). No studies have been completed to datethat have specifically assessed local knowledge and per-ceptions of drought and climate change and comparedthese to the climate and vegetation data for Jamaica.

Accordingly, the overall purpose of this study isto reach a basic understanding of drought hazardin southwestern Jamaica through an integration oflocal knowledge and perception of drought and itsphysical characteristics manifested in the climaterecord. Specifically, this study examines Jamaicanfarmers’ knowledge and perceptions of drought in lightof trends in local precipitation and remotely sensed


Climate Change, Drought, and Jamaican Agriculture 3

vegetation data. The aim is to highlight the relation-ships between the climate record and the narratedpractical experiences of farmers. As such, the analysispresented here seeks to integrate local and scientificknowledge and to increase our understanding of thenexus between the perception of weather and climateand data analysis within the Jamaican and Caribbean

settings. Such a study provides an opportunity toexamine the impacts of climate change from differentepistemological perspectives, allowing for greaterdiversity in the climate change dialogue and avoidingan inappropriate application of a uniform definitionof global climate change (Demeritt 2001; Smith2007).

Figure 1. (A) A typical St. Elizabethagricultural plot with scallions andguinea grass mulch (Photographer: E. J.Popke). (B) Map of St. Elizabeth Parishand towns included in survey.


4 Gamble et al.

Study Area

St. Elizabeth Parish is known as the “bread basket ofJamaica” and has been the top or second highest pro-ducer of domestic food crops in each of the last twentyyears (Beckford, Barker, and Bailey 2007). Crops pro-duced by local farmers include tomatoes, melons, cab-bage, scallion, yam, beans, and cassava. The majority ofthe farms are small, individually owned plots, rangingfrom 0.1 to thirty acres (Figure 1A). The rainfall patternfor the parish as a whole is highly variable with markeddifferences in annual totals experienced over relativelyshort distances due to a complex interaction amongtopography, proximity to the coast, and the Jamaicanrain shadow. Recent (2000–2008) average annual rain-fall totals range from over 2,000 mm at stations in thenorth of the parish to around 1,000 mm in stations onthe coast and below 800 mm in the dry coastal fringe.

This study focuses on the relatively drier southernportion of the parish, an area that serves as Jamaica’sprincipal vegetable-producing region (Figure 1B).Farmers here have adapted to the dry environmentthrough ingenious use of hand and drip irrigationcombined with grass mulch techniques, offering acommunity for study that has a long tradition of usingaccumulated local knowledge to guide agriculturalinnovation (Beckford, Barker, and Bailey 2007;McGregor, Barker, and Campbell 2009). Recently,the agriculture system is perceived by farmers asbeing under stress through a series of recent earlyseason droughts (2004, 2005, 2008) that were directlyfollowed by stormy primary growing season conditions.

The primary growing season in St. Elizabeth Parishoccurs in August through November due to relativelyreliable rain. An early growing season also exists fromApril through June and has less reliable rain. The twogrowing seasons are separated by a midsummer dry spellthat typically occurs in July (Chen and Brown 1994;Gamble and Curtis 2008; see Figure 2A). Many farmersin St. Elizabeth Parish view the early growing seasonas an opportunity to create capital that can be usedto increase planting in the primary season, resulting ingreater profit for the year.

Methodology

A survey of sixty farmers was conducted in threecommunities (Top Hill, Southfield, and Flagaman) insouthern St. Elizabeth in June 2008, immediately afteran early season drought event (Figure 1B). Twenty

Figure 2. (A) Mean monthly rainfall (mm) for Southfield, St.Elizabeth Parish, Jamaica. (B) Frequency of farmers’ perceptionsof dry and rainy months. (C) Frequency of farmers’ perceptions ofthe most severe dry month in terms of crop damage.

farmers were surveyed in each community to assess theirperceptions of climate and climate change, with partic-ular reference to drought. Findings from this survey arevalidated and complemented by two previous surveys(2006 and 2007), each of sixty farmers, and a randomsample of 282 questionnaire-based interviews. Theprimary purpose of the survey reported here was to assessdomestic food production and drought vulnerability inSt. Elizabeth Parish. The general research questions ad-dressed by the survey include the following: What havebeen the effects of droughts on farming over the lasttwenty years? What have been the adaptive strategies offarmers to these changes? What are the perceptions andattitudes of farmers to these events? For the purposes ofthis study, only results concerning farmers’ knowledge,perceptions, and attitudes of drought are presented.

Precipitation data are available for multiple locationsin St. Elizabeth Parish for the period from 1993 to 2006.This period of record is not long enough to assess cli-matic variability and it is necessary to find data thatoffer a better representation of long-term precipitationpatterns. To develop a time series more appropriatefor assessment of climatic variability, satellite-derivedrainfall data can be used to represent rainfall in St.



Figure 3. Monthly frequency of Standardized Precipitation Indexdefined drought events and dry months for the St. Elizabeth ParishGlobal Precipitation Climatology Project node, 1980–2007.

Elizabeth Parish. The best option for satellite precipita-tion data is the recently released Global PrecipitationClimatology Project (GPCP) Version 2.1 monthly dataset (Adler et al. 2003; Huffman et al. 2009). The GPCPdata extend from 1979 to 2008 and are available at a2.5◦ latitude/longitude grid. Analysis indicates that theGPCP data offer the best satellite estimator of rainfallover the Caribbean with the lowest errors and uncer-tainty over the western Caribbean and Jamaica, despitea potential dry bias (Jury 2009). The grid point closestto St. Elizabeth Parish (centered on 78.75◦W, 18.75◦N)was fairly well correlated (r = 0.67) with the average

of available monthly St. Elizabeth Parish rainfall datafrom 1998 through 2006. However, it should be notedthat these results are most applicable to the southern(coastal) portion of St. Elizabeth Parish, which has abetter correlation (r = 0.66) with the GPCP data ascompared to the northern portion (r = 0.52), which isat a higher elevation.

Drought events and dry months were defined in thisstudy using the Standardized Precipitation Index (SPI;McKee, Doesken, and Kleist 1993). The SPI has beendescribed as the best indicator of climatic drought inprevious studies (Keyantash and Dracup 2002; Akhtariet al. 2008). It is calculated by first creating a three-month moving average from the raw precipitation data,then a gamma distribution is fit to the three-monthmoving average series and z scores are assigned to eachobservation based on the gamma distribution probabil-ity. The end result is a dimensionless unit similar tothe Palmer Drought Index (Palmer 1965) that gives anindication of the magnitude of drought or absence ofprecipitation. A positive value indicates a wet month,whereas a negative value indicates a dry month. TheSPI is a climatological drought index that only assessesfrequency and magnitude of rain and does not take intoconsideration surface conditions or farmer water needs.

One modification of previous SPI methodology wasimplemented here. Typically, the SPI is calculated byfitting the gamma distribution to the entire time series,allowing for comparison of all months within a giventime period. Here, the SPI is calculated by fitting the

Figure 4. Calendar month Standard-ized Precipitation Index for St.Elizabeth Parish Global PrecipitationClimatology Project node, 1980–2007.


6 Gamble et al.

gamma distribution to a time series for each calendarmonth. Thus, a total of twelve gamma distributionswere fitted to twelve monthly time series. The rationalefor this modification of standard SPI methodology isthat farmers were most likely to compare rainfall of anindividual month to that same month in previous years.For example, a farmer is more likely to compare thisMay’s precipitation to the previous May’s precipitationthan to all months in the time series, as called for bytraditional SPI methodology.

Once the SPI was calculated using the GPCP 1979–2008 time series for the St. Elizabeth Parish grid, bothdrought events and dry months were identified. Theyears 1979 and 2008 were dropped from classifica-tion of the monthly SPI values because calculation ofthree-month moving averages reduced the number ofobservations available in 1979 and 2008, creating anunequal number of observations for January and De-cember as compared to the rest of the months. Conse-quently, all analyses of SPI data were completed for theyears 1980 through 2007 to allow for consistent com-parison. For this study, a drought event is defined as aminimum of two consecutive months with a negativeSPI. A dry month is defined as a single month with anegative SPI value, preceded and followed by positiveSPI values. After identification of drought event anddry months, basic descriptive statistics were calculatedand linear trend analysis was completed. The lineartrend analysis is not intended to produce statisticallyrobust predictive models of the time series. Rather, theintention is to use the trend analysis to provide a de-scriptor (the slope) of the time series that can be usedto compare with farmer perception.

Beyond analysis of an SPI monthly time series forthe period of record from 1980 through 2007, two addi-tional SPI time series were constructed, a primary sea-son and an early season SPI time series. The reason forthe inclusion of these two additional SPI time series inthe analysis is that farmers frequently refer to moistureconditions in terms of seasons (Ovuka and Lindqvist2000). Thus, data analysis will include the appropriatetime frame for comparison to farmer perception.

Land surface and vegetation moisture conditions arerepresented in this study through the use of vegetationvigor estimates from satellite images. Specifically, vege-tation vigor is estimated through the Global InventoryModeling and Mapping Studies Normalized DifferenceVegetation Index (NDVI; Pinzon, Brown, and Tucker2005; Tucker et al. 2005). The NDVI is calculated bydividing the difference of the spectral reflectance be-tween the visible channel (0.58–0.68 µm) and near

infrared (NIR) channel (0.73–1.10 µm) by the sum ofthe two spectral ranges (Rouse et al. 1974):

NDVI = (NIR − visible)/(NIR + visible)

The result is a value ranging between −1 and +1,where higher values are more representative of greeneror more vigorous vegetation. NDVI is calculated withthe Advanced Very High Resolution Radiometer satel-lite, which provides 8-km, fifteen-day averages of NDVI

Figure 5. (A) Duration of individual Standardized Precipitation In-dex (SPI) defined drought events for the St. Elizabeth Parish GlobalPrecipitation Climatology Project (GPCP) node, 1980–2007. (B)Average drought event SPI for St. Elizabeth Parish GPCP node,1980–2007. Dashed lines represent linear regressions.



for cloud-free scenes. The NDVI period of record for thestudy, similar to GPCP period, is 1981 to 2003. Monthlyaverages of NDVI were computed for a representativegrid box in the southeastern corner of St. ElizabethParish (77.6◦W, 17.9◦N). Then a monthly z score wascalculated using the average NDVI values.

Results

Crop Calendars, Drought, and Seasonality

Analysis of the survey data and interview contentreveals that farmers in southern St. Elizabeth possessappreciable knowledge of seasonal drought and climatevariability. The farmers emphasize the timing of droughtas opposed to the magnitude and duration of droughtevents as being the most harmful to crop production.Generally speaking, the farmers surveyed and inter-viewed were more interested in when below-normal lev-els of rainfall events are likely to occur as opposed tomagnitude and duration of a drought event. Similarresults of farmers being more concerned with the in-teraction of drought and cropping schedules have beenreported for Kenya (Ovuka and Lindqvist 2000) andBurkina Faso (Roncoli, Ingram, and Kirshen 2002).

In St. Elizabeth Parish, the focus on timing of droughtby farmers is not only a result of the prevailing rainfallregime but it is also a result of a cropping schedules beingadapted to maximize production and profit. In recentyears, farmers indicated that cropping schedules havechanged in response to growth in tourism and the hotelindustry. Farmers are utilizing more “quick crops” thatmature in less than eight weeks and are popular with

Figure 6. Calendar month Normalized Difference Vegetation In-dex z score for southern St. Elizabeth Parish, 1981–2003. Dashedline represents linear regression.

hotel guests (lettuce, zucchini, turnip, string bean, beet-root, cauliflower, broccoli, yellow squash, watermelon,honeydew melon, and cantaloupe). Such quick cropsallow farmers to generate income during the dry seasonand “go out big” with primary crops in the rainy sea-son. Consequently, if a drought occurs during a quickcrop cycle, not only can it reduce income during the

Figure 7. The (A) primary (August–November) and (B) early(April–June) growing season monthly Standardized PrecipitationIndex for the St. Elizabeth Parish Global Precipitation ClimatologyProject node, 1980–2007. Dashed lines represent linear regressions.


8 Gamble et al.

dry season, but it can diminish potential planting andeventual income during the rainy season.

As mentioned previously, the rainfall pattern ofJamaica is bimodal by nature (Figure 2A) with one ini-tial peak that extends from April to June and the otherfrom August to November (Chen and Brown 1994;Gamble and Curtis 2008). Both peaks are separated bya relatively low period of rainfall termed the midsum-

mer drought or midsummer dry spell (Magana, Amador,and Medina 1999; Gamble and Curtis 2008). This cli-matology is consistent with the farmers’ perceptionsdescribed earlier (Figure 2B). Farmers are in agreementthat in a typical year they experience four dry months(January–March and July) and four rainy months (Mayand September–November; see Figure 2B). The monthsof April, June, August, and December are “dry–wet”

Figure 8. (A) August to Novemberand (B) April to June monthly Stan-dardized Precipitation Index for theSt. Elizabeth Parish Global Precipita-tion Climatology Project node, 1980–2007. Dashed lines represent linearregressions.



months representing a transition between wet and dryconditions, as indicated by comparable frequencies offarmers identifying these months as either wet or dry.

For many decades farmers in southern St. Elizabethhave been fine-tuning farming practices to fit withinthis climatic pattern (McGregor, Barker, and Campbell2009). These farmers know which crop to plant when,but extended or unusual periods of low rainfall remaina stress to most farmers, especially those with limited

resources. Climate data analysis shows that such dryperiods are not uncommon; there were a total of thirty-one drought events and thirteen dry months duringthe 1980 to 2007 study period. The range of droughtevent duration was two to twenty-four months withan average duration of five months. The range of to-tal drought event magnitude (mean monthly SPI val-ues in an event) was −0.91 to −0.25 and the averagemean magnitude was −0.67. April and January have

Figure 9. Growing season NormalizedDifference Vegetation Index (NDVI) zscore for southern St. Elizabeth Parish,1981–2003. Dashed line represents lin-ear regression.


10 Gamble et al.

the highest drought event frequency and July, June,and February have the lowest (Figure 3).

For farmers, however, drought during certain monthsmight be much more damaging for agriculture. Whenasked to identify the most critical month in terms ofdrought, farmers displayed an understanding that Julyrepresents the midsummer dry spell and has the largestpotential impact on agricultural yields (Figure 2C).Such a response is an indication of how important thequick crops for tourism have become in farmer pro-duction strategies. Because farmers are growing quickcrops more frequently during the dry period of the year(December–March, July) a drought during this time canhave a significant impact on farmer production. A lowyield from a quick crop diminishes the amount of moneythat can be used on crops in the primary season. Thus,a month like July, which might have a low frequencyof climatological drought as defined by the SPI, has aninordinate impact on agricultural production

Drought and Rainfall Variability

One of the key questions addressed in farmer sur-veys was their perception of long-term climate change.A majority of farmers (67 percent) indicated that therehave been changes in weather patterns in the area. Oneof the most often-noted changes related to drought,with 65 percent of the surveyed farmers indicating thatdroughts are worse than in the past. Farmers reportedthat droughts have had specific impacts on agricul-

tural practices. Sixty percent of the surveyed farmers,for example, reported having to buy water during re-cent drought, and 53 percent of farmers said they lostmore than half of their crops, especially melon, toma-toes, and cucumbers, due to drought. Consequently,some farmers lament that the days of relying on theold mariner’s poem to guide planting schedules (June—too soon, July—stand by, August—look out you must,September—remember, October—all over) has long gone.

Graphical analysis of the St. Elizabeth Parishmonthly SPI time series from 1980 through 2007indicates oscillation back and forth between wet and dryconditions throughout the time series (Figure 4). How-ever, the frequency of longer drought events appears tochange in the early 1990s. Before 1991, positive SPImonths occur more frequently (86 out of 132 monthsor 58 percent) and the mean monthly SPI is above zero(0.31). After 1991, negative SPI months occur morefrequently (119 out of 205 months or 76 percent), andthe mean monthly SPI is less than zero (−0.17). Ananalysis of means test was performed at the 95 per-cent confidence level to test the null hypothesis thatthere is no statistically significant difference in pre-1991mean SPI and post-1991 mean SPI. The test (t = 4.62,p = 0.001) results in the rejection of the null hypoth-esis, indicating a wetter pre-1991 (mean SPI = 0.306)and drier post-1991 (mean SPI = −0.17).

Analysis of a drought event duration time series indi-cates a slight linear increase in the duration of droughtevents (y = 4.11 + 0.0045x) and drought event

Figure 10. The primary (August–November) and early (April–June)growing season cumulative Stan-dardized Precipitation Index (SPI)for the St. Elizabeth Parish GlobalPrecipitation Climatology Projectnode, 1980–2007. Diamonds representopposite signs between primary andearly growing season.



magnitude (y = −0.51 −0.0009x) in St. ElizabethParish between 1980 and 2007 (Figure 5). These trendsare not statistically significant due to the considerablevariability in the data. A clear or significant linear trenddoes not exist in the time series of the NDVI, with alinear trend close to zero (Figure 6).

Primary and Early Growing Season

Farmer perceptions of increasing drought might re-flect relative changes in the early (April–June) andprincipal (August–November) growing seasons. Specif-ically, many farmers commented in interviews thatdrought is becoming more prevalent in the early grow-ing season as compared to the primary growing season.In particular, the farmers contend that the dry seasonbefore the early growing season is getting longer andthe midsummer dry spell is starting sooner, in effectreducing the early growing season to one month, May.

A time series graph of monthly SPI values for theprimary and early growing season indicates a nega-tive trend for both growing seasons from 1979 through2007, although these trends are not statistically sig-nificant (Figure 7). Despite the lack of statistical sig-nificance the trend lines can be compared, suggestinggreater change in the early season time series (−0.016SPImonth

−1) as compared to the primary time series(−0.0036 SPImonth

−1), supporting farmers’ perceptionsthat the early season is becoming drier as comparedto the primary season. In terms of within growing sea-son variability, each monthly SPI within each grow-ing season (primary = August, September, October,November; early = April, May, June) was analyzed forthe existence of a trend. Each of the primary seasonmonths exhibited a negative trend from 1980 through2007 with similar slopes (−0.0067 to −0.24 SPIyr−1;Figure 8). There is greater variability in trends for theearly growing season with April, May, and June eachhaving negative slopes of −0.057, −0.051, and −0.039SPIyr−1, respectively (Figure 8). Also, the slopes for theearly season months are all steeper than the primaryseason months, further suggesting that the entire earlyseason is drying faster than the primary season. Apriland May have the steepest slopes, supporting the farmercontention that the dry season comes before and short-ens the early growing season. Vegetative vigor shows noconsistent trend for either the primary or early growingseason (Figure 9).

The aggregation of the monthly SPI values into a cu-mulative seasonal SPI further supports the contentionthat dry months are more frequent in the latter half

of the early growing season time series in comparisonwith the primary growing season time series (Figure10). The frequency of opposite sign primary and earlyseason SPI has increased since 1991. Since 1991 theprimary and early season have exhibited opposite sign

Figure 11. (A) The difference in the primary (August–November)and early (April–June) growing season cumulative Standardized Pre-cipitation Index (SPI) for the St. Elizabeth Parish Global Precipi-tation Climatology Project node, 1980–2007. (B) The difference inthe primary (August–November) and early (April–June) growingseason Normalized Difference Vegetation Index (NDVI) for south-ern St. Elizabeth Parish, 1980–2007. Dashed lines represent linearregressions.


12 Gamble et al.

SPI values six times (35 percent), whereas this onlyoccurred two times pre-1991 (18 percent). For the sixincidents of opposite sign, the last three represent adrier early season and a wet primary season. Such recentoccurrence of dry early and wet primary season mighthave a strong influence on current farmer perception.Overall, analysis of the difference between the primarySPI and early SPI (Figure 11) indicates a trend to-ward a positive difference or a wetter primary season ascompared to a drier early season. This is also the casewith the normalized NDVI (Figure 11).

Discussion and Conclusions

Farmers in St. Elizabeth Parish are keenly aware ofannual rainfall patterns and have adapted their agri-cultural practices to take advantage of natural moisturesurpluses. The somewhat marginal environment foragriculture in this area means that farmers are particu-larly sensitive to changes in weather and climate, withmany expressing concerns about an increase in drought.In accordance with farmer perceptions of worseningdrought conditions, satellite estimates of rainfallsuggest that severe (high magnitude/long-duration)drought events are becoming more frequent in St.Elizabeth Parish. Such results, even though they arenot statistically significant, are consistent with previousreports of a negative trend in Caribbean rainfall sincethe 1960s (Peterson et al. 2002) and a decrease inrainfall in Jamaica over the past century (McGregor,Barker, and Campbell 2009).

In terms of the primary versus the early growing sea-son, the climate data suggest that the early growingseason is drying faster than the primary growing season,particularly since 1991. Even though the trend for nor-malized NDVI is slightly positive, this same seasonaldifference is depicted. Such a discrepancy between thedata sets is due to the fact vegetation vigor is dependenton more than just precipitation, such as evapotranspira-tion and fires. This recent divergence in growing seasonmoisture conditions might be reflected in farmers’ ob-servations that the early growing season is becomingdrier than the primary, or drought is becoming moreprevalent in the early season. Consequently, Jamaicanfarmer perception of drought may be driven not onlyby magnitude and frequency of dry months alone butalso the difference between seasons. Specifically, farm-ers notice oscillation between a dry early season and wetprimary season just as they do persistent dry conditions.This sensitivity to early season rainfall deficit might in

part be due to regional changes in cultivation patterns,as farmers increase their production of dry season quickcrops to take advantage of an expanding tourist mar-ket. Thus, any development of drought adaption andmitigation plans must not focus solely on the magni-tude and frequency of drought; it must also comparemoisture conditions between seasons in the context ofchanging livelihood strategies among local farmers.

At a more general level, the perception that droughtsare worse now than in the past underscores farmers’feelings of increased vulnerability to changes in theirlocal environmental setting. Beyond climate condi-tions, socioeconomic factors might serve to increasethe sense of insecurity among farmers and thus amplifythe impacts of environmental change. In recent years,Jamaican farmers have been hampered by increasedproduction costs, declining state support, and enhancedcompetition from imports due to trade liberalization(Weis 2004). In such a context, farmers might be moresensitive to minor changes in weather and climatepatterns. For example, the impacts of the increasinglyunpredictability of rainfall in St. Elizabeth Parish cannormally be offset by the increasing use of irrigationsystems or the purchase of water from a vendor. In thecontext of declining rural fortunes and declining statesupport, however, this might no longer be possible, leav-ing many farmers at the mercy of climate variability andchange as well as a dependence on family members andfriends to survive. More than 90 percent of the farmerssaid that they did not receive any assistance fromthe government following the 2008 drought, a higherpercentage than those who reported having receivedno assistance after Hurricane Ivan (McGregor, Barker,and Campbell 2009). This combination of physical andsocioeconomic changes works to increase the stress onagricultural systems, as has been noted in Jamaica, theCaribbean, and other parts of the world (Glantz 1987;Liverman 1990; Barker 1993; Weis 2004).

It is clear from the record of interviews that small-holder farmers need help to adapt to changes in rain-fall patterns. They are doing their best to cope withthe changes by utilizing their accumulated local knowl-edge. However, the implementation of future policiesand programs that complement existing local knowl-edge and perception, particularly by focusing on therange of moisture conditions as opposed to droughtalone, will be essential in alleviating stresses, as wellas protecting the local knowledge base that supportsthe existence of these farming communities. Withoutsuch efforts, adaption and mitigation plans risk the errorof devaluing local expertise and cultural notions about



the nature of knowledge (Roncoli, Ingram, and Kirshen2002).

Ultimately, many researchers and policymakersdesire to increase the capacity of vulnerable farmers toeffectively adapt to climate change through utilizationof a combination of traditional, local knowledge andscientific understanding of climate change (Roncoli,Ingram, and Kirshen 2002). Through such a balancedapproach to combining local knowledge and scientificinformation, efforts can harness a rich history ofadaptive capacity and resilience on small islands whereindividuals, communities, and governments havetraditionally adapted to environmental change overa long period of time (Mimura et al. 2007). Specificfuture actions that can assist in developing balancedapproaches to adapting to climate change and droughtin Jamaica and the Caribbean include a refinement ofdownscaling techniques to appropriately assign climatedatabases to a specific location; a more in-depth analy-sis of farmer experience in forecasting and adaption tospecific drought events; a better understanding of howgovernment policy and socioeconomic forces intersectwith environmental change; and development ofend-user-focused drought management products.Through such efforts, Caribbean SIDS will be able tobuild greater adaptive capacity and resilience to anuncertain future brought on by global climate change.

Acknowledgments

This material is based on work supported in partby the National Science Foundation under Grant No.BCS-0718257. We gratefully acknowledge the willing-ness and enthusiasm of the farmers of St. ElizabethParish for participation in this project.

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Correspondence: Department of Geography and Geology, University of North Carolina Wilmington, Wilmington, NC 28403, e-mail:[email protected] (Gamble); Department of Geography and Geology, University of West Indies at Mona, Mona, Jamaica, e-mail: [email protected] (Campbell); [email protected] (Barker); Rosenstiel School of Marine and Atmospheric Sci-ence, University of Miami, Miami, FL 33149, e-mail: [email protected] (Allen); Department of Geography, East Carolina University,Greenville, NC 27858, e-mail: [email protected] (Curtis); [email protected] (Popke); Department of Geography, Royal Holloway, Universityof London, Egham, Surrey TW20 0EX, UK, e-mail: [email protected] (McGregor).


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