AUTHOR: Getrude Tausi Leshamta SUPERVISORS: Pr. J. Muthama & Dr. Karanja

Transcription

1 ASSESSING THE RELATIONSHIP BETWEEN TEMPERATURE EXTREMES AND TEA YIELDS IN KISII, LAKE BASIN REGION OF KENYA. AUTHOR: Getrude Tausi Leshamta SUPERVISORS: Pr. J. Muthama & Dr. Karanja

2 INTRODUCTION Climate variability refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all temporal and spatial scales beyond that of individual weather events. (IPCC TAR, 2001). Temperature variability is therefore the short term deviation from the average temperature such as the occurrence of extremes. Extreme cold conditions detrimental to tea production is frost condition which damages tea leaves, roots and reduces efficiency of labourers in charge of tea leaves picking. Extreme hot conditions may add heat stress to tea leaves, increase pest infestation and disease prevalence thus reducing both quality and quantity of tea leaves.

3 Kenya is the largest exporter of black tea in the World with smallholder production under KTDA accounting for about 66% of total tea production (378 million kilograms in 2011), (Kariuki, 2012). Kenya is number four globally in tea production, number one in Africa and number one in tea quality globally. Tea production in Kenya earns the country s highest foreign income of up to 26% of exports and 4% of GDP according to KTDA which is the manager for the small scale tea farmers and a key tea marketer in Kenya. Tea is grown in the highlands east and highlands west of the rift valley regions due to their suitable altitude, soils and climate and therefore deviation from the mean especially temperature may tend to reduce their suitability.

4 As a labour intensive industry, tea sector supports livelihoods of more than three million persons directly and indirectly (about 10% of Kenya s total population) (Tea Board of Kenya, 2008). Tea production goes on year-round, with two flushes, peak seasons of high crop-march to July, and October to December, coinciding with the country s rainy seasons (Kinyili, 2003).

5 PROBLEM STATEMENT Climate hazards such as temperature extremes in the form of frost or heat stress are well known to reduce crop yields. Livelihoods of small holder farmers under Kenya Tea Development Agency (KTDA) which comprise the majority producers of tea in Kenya are greatly affected. The quality of tea is also reduced hence reducing the fame that Kenyan tea has worldwide together with its GDP. As air temperatures get warmer than normal, the land suitable for tea production will be reduced while pests and diseases that never used to bother tea production in Kenya will begin to interfere with its production.

6 JUSTIFICATION Kenya being part of the developing countries with a low financial capacity to sustain planned Adaptation to adjust to climate change impacts is more vulnerable to climate change hazards. As weather extremes become more severe, the level of exposure differs from place to place hence rendering different communities vulnerable differently. Kenya ranking fourth globally in tea production and first in Africa puts tea yields in Kenya especially in Kisii county which is among the three key tea growing areas under much consideration. The tea sector also offers employment all-year-round to about 639,521 growers in the rural areas in addition to proving employment in other parts of the tea value chain.

7 OBJECTIVES This study s overall objective is to show the effect of temperature variations to tea yields in Kisii county and consequently the GDP of Kenya from 1970 to 2014 and determine their future projections. The specific objectives that will lead to the achievement of the main objective are: To determine the spatial and temporal variations of temperature and tea yields in the region of study hence investigate whether the cool/hot months are cooler or warmer. Show the relationship between air temperatures, the national income and tea yields. Determine the future estimates of air temperature, GHGs emissions and tea yields in the region of study hence evaluate the suitability of the region of study by the year 2100.

8 STUDY AREA The study area Kisii is located in highlands west of the rift valley, lake basin region within latitudes of degrees S and within degrees E longitude. (Cali, Managua: April, 2011; Future Climate Scenarios for Kenya s Tea Growing Areas), the most suitable regions for tea growing are: Meru, Embu, Kirinyaga, Nyeri, Murangá, Kiambu, Kisii, Nyamira, Kericho, Bomet, Narok, Migori. Kisii is among the three counties namely: Bomet, Kisii and Nyamira, that will remain suitable for tea production by the year (A. Eitzinger et al, 2011; Future Climate Scenarios for Kenya s Tea Growing Areas). Kisii is also among the Key tea producing regions in Kenya namely: Kericho, Kisii and Nandi hills (ratetea.com), thus progressively contributes significantly to tea yields and consequently the national income.

9 CLIMATOLOGY OF THE AREA OF STUDY The climate of Kisii in the lake basin region of Kenya is mainly controlled by the northward and southward movement of the ITCZ. There exists a bimodal rainfall pattern; the long rains from March to May and the short rains from October to December. The Lake Basin region however experiences off-season rainfall due to local effects of the influence of Lake Victoria, Nandi hills and the Congo air mass. The tea peak seasons are March July and October December coinciding with the long and short rains seasons respectively(kinyili,2003).

10 LITERATURE REVIEW Bilham, J (2011); (Climate Change Impacts upon crop yields in Kenya) found out that temperature had more effect upon yield outputs than precipitation and some evidence that temperature thresholds, which severely limit yields, may already have been reached. Rwigi and Otengi,2009; Influence of Climate on Tea Yields in Mount Kenya Region; the three most important climatic parameters for tea yields in Kiambu are the mean minimum, mean maxi- mum temperature and the terrestrial radiation. The current tea-growing areas in Kenya will decrease quite seriously by The optimum tea-producing zone is currently at an altitude between 1500 and 2100 masl and will by 2050 increase to an altitude between 2000 and 2300 masl; (A.Eitzinger et al,2011; Future Climate Scenarios for Kenya s Tea Growing Areas).

11 Delphine Derying et al,2014; Global crop yield response to extreme heat stress under multiple climate change features used PEGASUS crop model where the tropics and subtropics were registered to face substantial yield declines due to excess heat stress. Projections of climate change suggest that East Africa will experience warmer temperatures by 2050 (Hulme et al; IPCC, 2001.) Temperature affects the development rates of vectors; (Zhou et al., 2004; Craig et al., 2004). Patterns of changing climatic conditions may results in variations in weevil distribution over years as a result to climate change, ( Henrik K. Ruto et al, 2016;The Distribution of Tea Weevils (Curculionid) in Eastern Kenya Region ).

12 Tea requires temperature range of between 19-29degrees Celsius according to Kenya s Tea Research Foundation. Although according to Sofkenya.com, tea farming has increased from 131,000 hectares in 2003 to 188,000 hectares while production has increased from 293,000 metric tonnes to 378,000 metric tonnes ;The Ethical Tea Partnership (ETP) says that climate change is expected to reduce the land suitable for tea production by 40% by the year 2050.

13 DATA AND METHODOLOGY DATA TYPE AND PERIOD Three data sets will be used in this study for the period 1970 to The monthly temperature data for synoptic stations within the neighbourhood of Kisii will be obtained from Kenya Meteorological Department and supplemented with worlclim.org 1km resolution historical data. Tea yield data for the factories within Kisii county will be obtained from KTDA. GDP data will be obtained from Kenya Bureau of Statistics.

14 DATA QUALITY CONTROL The data collected will be subjected to various data quality control procedures and techniques. The data quality control will help to check, minimise and detect errors in the data sets, check homogeneity of data and its predictability. Missing data Since temperature will be drawn from strategic stations, arithmetic mean method will be used to estimate any missing data. Temporal Consistency Check: Mass curve Analysis The single mass curve will be plotted to test for the homogeneity of the data. This will be done by plotting the cumulative frequencies of the temperature against the entire period of study. Significance test Data will be divided into two samples, their Means obtained and plotted against each other in order to establish whether there exists any significant change in the slope so as to approve the trends.

15 METHODOLOGY Methods that will be used to achieve the objectives include both graphical and statistical as follows: Time series trend analysis Time series trend analysis of the temperature, GDP and tea yields for the region of study will be applied using R STATISTIC and GENSTAT software and compared to depict temporal variations. Time series seasonal and inter-annual variability analysis. Monthly Mean + or - 1 Standard deviation for the entire period of study will be used to show the average, above average and below average values hence establish the magnitude and frequency of variability of the variables on both seasonal and inter-annual basis using R STATISTIC and GENSTAT.

16 Regression analysis Multiple Linear Regression analysis between temperature, tea yields and GDP using GENSTAT and R will be carried out with the equation showing the coefficients of each variable as follows: Y=Bo+B1X1+B2X2+E Correlation analysis. Correlation between temperature and tea yields will be done using R statistic and GENSTAT to show their inter-relationship during the period of study.

17 Suitability test Suitability of the region of study will be determined by feeding the worldclim and KMD data into MAXENT MODEL. Projections Crop and climate models such as MAGICCSCENGEN/ PEGASUS, will be used to perform simulations on tea yields, air temperature and GHGs up to the year 2100.

18 EXPECTED RESULTS Trends of tea yields, GDP and air temperature. Seasonal and inter-annual variability of air temperatures, tea yields and GDP. Relationship between temperature, tea yields and GDP in each tea growing zone. Future estimates of temperature, tea yields and GHGs in the region of study. Suitability of the region of study for tea production by 2100.

19 BUDGET AND WORK PLAN WORK PLAN 4.11: WORK PLAN S/N TIME PERIOD ACTIVITY 1 January & February, 2016 Literature review and Proposal writing 2 Conceptual framework & Proposal March, 2016 submission 3 April, 2016 Proposal presentation 4 May, 2016 Data collection and Analysis 5 June, 2016 Progress report presentation /submission 6 Final Report writing July, August, 2016 Presentation of results and submission of final report. 8 Sept, 2016-Nov, 2016 Publishing of report

20 BUDGET 4.12 BUDGET S/N ITEM/ACTIVITY AMOUNT (KSHS) 1 Writing, printing and photocopying of proposal 5, Data collection (travel and communication) 20, Report writing and binding 5, Miscellaneous 5, Computer Acquisition 50, Total 85,000.00

21 REFERENCES A. Eitzinger,et al,2011,ciat; Future Climate Scenarios for Kenya s Tea Growing Areas. B. Cheserek et al, 2015;Links between Climate Variables and Tea Production in the Recent Past in Kenya. C.Managua,2011,CIAT; Future Climate Scenarios for Kenya s Tea Growing Areas. Delphine Derying et al,2014; Global crop yield response to extreme heat stress. De Wit et al, 2012; Quantifying the effects of climate extremes on the future yield gap. Elizbarashville et al, 2013;Annual and seasonal trends of temperature means and extremes in Georgia German. Henrik K. Ruto et al ; The Distribution of Tea Weevils (Curculionid) in Eastern Kenya. IPCC, 2007;Hansen and Indeje; 2004, Birech et al; 2008, Rarieya and Fortun;2009. Climate Change reports a high level of confidence that agriculture production will be severely affected by climate change and variability. IPCC, 2001, (Hulme et al); Projections of climate change suggest that East Africa will experience warmer temperatures by 2050 IPCC 2007 LC Smith et al, 2005; Permafrost thawing. John K. Bore, B.C. Cheserek and P. Ngeno,2014; Long term impact of climate change on tea yields.

22 J. Bilham, (2011); Climate Change Impacts upon crop yields in Kenya. Kinyili, (2003); Tea production goes on all year round. See comment in PubMed Commons belownordhaus, (2006); warmer world. Rwigi and Otengi,2009,journal of meteorology and related sciences; Influence of Climate on Tea Yields in Mount Kenya. Zhou et al., Craig et al., (2004).Temperature affects the development rates of vectors and parasites while rainfall affects the availability of mosquito breeding sites.

23 CONCEPTUAL FRAMEWORK

24 SUITABILITY MAP IN 2011 SUITABILITY MAP FOR TEA GROWING AREA IN KENYA Suitability for tea production within tea-growing districts of Kenya; (Cali, Managua: April, 2011; Future Climate Scenarios for Kenya s Tea Growing Areas) using MAXENT model where the most suitable regions are: Meru, Embu, Kirinyaga, Nyeri, Murangá, Kiambu, Kisii, Nyamira, Kericho, Bomet, Narok, Migori. The remaining districts are in general rather less suitable.

25 SUITABILITY MAP FOR 2020

26 SUITABILITY MAP FOR 2050