Implications of coffee obituary notices on tree abundance and richness on coffee farms of mt kenya
•Transferir como PPTX, PDF•
1 gostou•791 visualizações
Recomendados
Mais conteúdo relacionado
Semelhante a Implications of coffee obituary notices on tree abundance and richness on coffee farms of mt kenya
Semelhante a Implications of coffee obituary notices on tree abundance and richness on coffee farms of mt kenya (20)
Mais de World Agroforestry (ICRAF)
Mais de World Agroforestry (ICRAF) (20)
Último
Último (20)
Implications of coffee obituary notices on tree abundance and richness on coffee farms of mt kenya
- 1. Implications of coffee obituary notices on tree abundance and richness on coffee farms of Mt Kenya. Sammy Carsan Collaborators': Stroebel A*, Munyi A, Kindt R. Pinard F. and Jamnadass R. *University of the Free State, South Africa ICRAF Seminar 11th November 2011 November 29, 2011
- 2. Presentation outline 1) Background: Evolution of Kenya’s coffee smallholder sector 2) Study Context: Why coffee agro-forests? 3) Statement of objectives & hypotheses 4) Methods: sampling approach a) Coffee farms typologies b) Whole farm tree inventories c) Tree diversity analysis 6) Results & Discussions Conclusions November 29, 2011
- 3. Evolution of Kenya’s smallholder coffee • Kenya’s coffee previousely estimated to offer livelihood support to over 5 million people directly and indirectly • Developments summed during and after the ICA market regime • Living standards, incomes, food security in coffee growing negatively affected post ICA • Uncertainties in international market and loss of coffee productivity have affected overall coffee profitability • Are smallholders ready for incentives to shift from traditional cash crop systems? Is AF tree cultivation a good incentive? • Smallholder enterprise choice will be driven by resource availability and market value for cash crops November 29, 2011
- 4. Compared to robustas the arabica price differential (premium) is about US$ C 60 (s.d 34.63, n = 129) per pound in the last ten years. Source: ICO Statistics, 2010 arabica robusta Mean price for Columbian Milds, New York Composite and Robusta coffee from 2000 to 2010 November 29, 2011
- 5. Kenya’s coffee exports fell by over 50% between 2000 and September, 2010; world market share declined from 3.1% in 1986 to 0.6% by 2006 (ICO, 2010). Source: ICO statistics, 2010 Columbia Tanzania Kenya Columbian mild coffee exports by: Columbia, Kenya and Tanzania November 29, 2011
- 6. Coffee production in Kenya year 2001-2007 (Source: CBK, 2008) 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 Area in Hectares (Ha) ‘000 Cooperatives 128 128 128 128 128 120.7 Estates 42 42 42 42 42 42 Total 170 170 170 170 170 162.7 Production (tonnes) ‘000 Cooperatives 28.8 34 30 25.5 27 28.4 Estates 23.1 21.4 18.5 19.7 21.3 25. Total 51.9 55.4 48.4 45.2 48.3 53.4 Average yield (kg/Ha) Cooperatives 198.8 265.8 234 199.2 211.3 235 Estates 537 509.9 439.8 469 506 595 November 29, 2011
- 7. Why coffee agro-forests? • ‘Shaded’ coffee as opposed to open ‘sun’ coffee’-a more sustainable production approach (Mas and Diestch, 2004). Coffee AF systems act as reservoirs of indigenous tree species (Perfecto et al., 2005). Trees yield complimentary products e.g. fruits, timber and firewood which diversify, diet and stabilize farmer incomes • Peeters (2003) :coffee shaded with any density of Cordia alliodora has better benefit-cost ratio than un-shaded estates although yields were lower. Simplifying these systems was disadvantageous even if coffee production increases • Structurally complex habitats support more diverse fauna (Garcia et al., 2009). • Coffee AF seen as an approaches to build alliances between ecologically sustainable agriculture and conservation efforts in protected areas. Trees contribute ecological services similar to those provided by forest e.g. soil protection, nutrient cycling, water retention and carbon capture (Chazdon et al., 2009) • Farmers benefit culturally by maintaining biological diversity that ensure productivity (Lengkeek et al. 2005) • Genetic diversity helps farmers to manage their inputs in more efficient ways- e.g. a mix of fast growing and slow growing timber grown for different markets; fruit species with different fruiting phenology to contribute to HH food security (Dawson et al., 2009) November 29, 2011
- 8. Research challenges… • Not clear the extent to which farmers are willing to conserve tree diversity given constraints e.g. land (Lengkeek & Carsan, 2004) • It is challenging to study all the factors affecting biodiversity simultaneously • Impact of converting natural forests to different AF have not been directly compared for many agricultural landscapes (Fitzerbert et al., 2008; Asase and Tetteh, 2010) Characterizing farm tree demographics is useful for identifying shortcomings that may underlie tree based systems- An understanding of the structure and densities of tree population on could help determining the viability of trees on agricultural landscape even for genetic resource supply and conservation. November 29, 2011
- 9. Plant diversity can be related to productivity at: global, regional and plot level (I) Globally, plant diversity in large areas is positively related to increasing productivity (II) Regionally, diversity in small plots is negatively related to increasing productivity (III) Evaluation of large or small scale effects differ according to species and given field conditions. Source: Purvis and Hector (2000) November 29, 2011
- 10. Study objectives • To investigate agroforestry tree species richness and abundances on smallholder coffee farms showing differences coffee production behaviour such as increasing, decreasing or constant yield trends • To determine tree diversity assemblages maintained under different coffee agro-ecological zones around Mount Kenya Hypothesis: H0 :Farms with decreasing coffee production (yields, density of bushes) support higher levels of tree abundances and richness on farm H1 :Farms with increasing coffee production (yields, density of bushes) have decreased tree abundances and richness on farm November 29, 2011
- 11. Research Methods • Cross-sectional survey in three coffee districts of Mt Kenya (Meru , Embu & Kirinyaga) • The zones are comparable on coffee and other crops production practices and largely representative of smallholder coffee systems in Kenya • The regions have strong farmer organization by cooperatives and societies November 29, 2011
- 12. Sampling strategy • Stratified random sampling used to obtain a representative sampling frame (Stern et al., 2005) • 10 Farmer Cooperative Societies (FCS) selected through key informant discussions to cover upper and lower coffee zone in 3 target coffee districts • Farmers selected per society based on cherry deliveries in the last 8 years (2000- 2008) • Farmer produce records was used to cluster sample farmers in three categories: “increasing”, “decreasing” and “constant” • 5 farmers most fitting each prescribed category were picked selecting 15 farmers per society /factory level • Later 2 farmers selected per category for HH interviews and farm assessments (due to survey logistics, resources) November 29, 2011
- 13. Farms sampling strategy November 29, 2011
- 14. Functional coffee farm typology constant November 29, 2011
- 15. Field methods • Ground based methods used to enumerate tree species presence on coffee farms • Trees were defined as all woody perennials growing to over 1.5 m tall, including exotics (Beentje, 1994; Brown, 1997). • Tree basal area (tree cross-sectional area measured at breast height) undertaken • All trees ≥5 cm DBH measured • Local/common names of trees recorded from local farmer consultations • All trees were identified to species level according to Beentje (1994) or Maundu and Tengnäs (2005). November 29, 2011
- 16. Tree diversity analysis Increasing richness • Diversity refers to the number of species that can be differentiated, and to the proportions (or relative abundances) of the number of trees in each species. - diversity refers to both richness and evenness Increasing evenness Source: Kindt and Coe (2005) November 29, 2011
- 17. Farm tree diversity analysis • Farm tree assemblages analyzed following BiodiversityR (Kindt & Coe, 2005; R Dev’t Team, 2010) • Tree abundances and basal area distributions calculated to assess structural composition of current farm tree population (Jongman et al., 1995) • Diversity indices, species accumulation and rank abundance curves used to compare species richness and evenness • Rènyi diversity ordering techniques used to rank tree communities from low to high diversity (Legendre & Legendre, 1998; Kindt et al., 2006) • Renyi profile values (Hα) are calculated from the frequencies of each component species (proportional abundances (Pi ) = abundance of species i/total abundance) and a scale parameter (α) ranging from zero to infinity (Tothmeresz, 1995; Legendre and Legendre, 1998): • Simple linear regressions were used to regress tree diversity measurements against coffee farm categories & agro-ecological zones November 29, 2011
- 18. Results Smallholder farms and households characteristics Farm Categories Increasing Decreasing Constant All Farms HH variables (s.d; n) (s.d; n) (s.d; n) (s.d; n) Farmers age 57 (10.99;61) 58(13.90;57) 60(15.02; 60) 58(13.38;178) Family size 5.4(2.17;61) 4.8(2.20;59 4.9 (2.47;62) 5.02(2.29;182) Farm size (Ha) 1.4(0.97;61) 1.1( 0.84;59) 1.3 (1.27;62) 1.2(1.05;182) AFT Ha-1 182(99;61) 202(152;59) 227 (222;62) 204(166;182) TBA Ha-1 2.76(1.69;61) 2.93(3.00;59) 2.81(1.71;62) 2.83(2.19;182) Coffee bushes Ha-1 596(495;60) 578(697;59) 496 (377;61) 556(536;180) 2544.6 1682.8 2185.8 2140.5 Cherry kg Ha-1 (1943.9;60) (1743.6;59) (2054.8;61) (1941.7;180) 71331.1 47960.8 62295.2 60667.89 Cherry val. Ha-1 (55717.38;61) (49693.76;59) (58560.30;61) (55371.4;181) 29675.7 58416.6 53498.6 47072.9 Banana val. Ks Ha-1 (34682.5;61) (106229.7;59) (80646.6;61) (79773.8;181) 15746.8 21248.3 19933.6 18951.1 Maize val. Ks Ha-1 (13196;.62) (18096.5;59) (16999.3;61) (16292.2;181) TLU Ha-1 3.9(2.9;60) 4.3(4.9;59) 5.4(5.5; 61) 4.5(4.6;180 TLU dairy -1 3.84(2.08;53) 3.36(1.68;46) 3.68 (4.23;53) 3.64(2.92;152) Milk val day-1 211.5(178.3;48) 147.8(109.1;37) 152.2(114.1;47) 172.5(141.87;132) TLU=tropical livestock units; AFT= agroforestry trees; TBA=tree basal area; s.d=standard deviation; Val in Ks, 1 US$ = Ks 80 29, 2011 November
- 19. Just how much coffee and trees is present on farms…? • 75% (156) farms cultivate 250-750 bushes ha-1 • 61%(110) produce 1000-2000 kg cherry ha-1 yr-1 • 41% (75) farms, tree density : 100-200 trees ha-1 • 30% (54) farms: TBA class of 1.1-1.9 m2 ha-1 • 22% (40) farms: TBA class of 2.0-2.9 m2 ha-1 • 35% (66) farms : TBA class of 3-5 m2 ha-1 • Average tree volume : 36.31 (31.1-41.5 ) m3 ha-1 2011 November 29,
- 20. Tree species richness and abundance by farm typology Farm category (n) Farm size Total Total Shannon Inverse – Ha. (s.d) richness abundance index Simpson index (mean) (mean) Constant (60) 1.18 (1.2) 110 (14.4) 10,079 (168) 2.59 4.93 Decreasing (60) 1.12 (0.9) 145 (17.9) 11,149 (186) 2.79 5.49 Increasing (60) 1.37 (0.9) 141 (18.4) 14,592 (243) 2.72 5.51 All farms (180) 1.22 (1.0) 190 (16.9) 35,820 (199) 2.76 5.4 • Tree richness by farm categories: Decreasing>Increasing>Constant (corresponds to Shannon Index) • Richness within farm in category: Increasing>Decreasing>Constant (corresponds to Inverse Simpson Index) • Tree abundance: Increasing>Decreasing>Constant November 29, 2011
- 21. Species accumulation curves (i) and Rènyi diversity profiles (ii) by farm typology constant constant • Sample based accumulation curves and Rènyi diversity profiles for the 3 coffee farm types showed overlapping richness patterns for the coffee increasing and decreasing farms • The constant farm types have smaller species richness, and slightly higher species un-evenness • Rènyi diversity indices: similar proportions (40%) of the most abundant species for the increasing and decreasing farms. The constant category had higher proportions (43%) of most abundant sp November 29, 2011
- 22. Tree diversity analysis by coffee agro-ecological zones Species accumulation curves : UM 3>UM2>UM1 Intersecting Rènyi profiles AEZ Species Shannon Inverse Proportion (%) of most richness (H0) index Simpson dominant species (H∞) (H1) (H2) UM1 (n=70) 98 2.56 4.23 0.38 UM2 (n=68) 110 2.78 4.17 0.40 UM3 (n=42) 129 2.57 5.69 0.46 All farms (n=180) 190 2.76 5.40 0.41 November 29, 2011
- 23. Linear Regression Analysis • Tree richness and abundance significantly different by AEZ (P< 0.001) • Strong evidence that UM3 tree density is different from those in UM1. Abundance in UM2 is however not significantly different from UM1 (P = 0.593) • Indigenous trees abundance in UM3 significantly different (P<0.001) from UM1 There was weak evidence on differences between UM1 & UM2 (P = 0.096) • Exotic trees abundance regressed on coffee AEZ returned a weak model (P = 0.048). There was not strong evidence (P = 0.027) that exotic tree abundance in UM3 was different from UM1 -no evidence that on average exotic tree population in the zone UM2 are different from UM1 or UM3 November 29, 2011
- 24. Tree abundance and basal area distribution (excluding coffee) (I) (II) Abundance Species rank Species rank Rank abundance by tree counts (Relative density) Rank abundance by tree basal area (relative dominance) Rank Species Abundance Proportion Rank Species Total basal Proportion (%) area (m2) (%) 1 Grevillea robusta 14923 41 1 Grevillea robusta 223.3 41.9 2 Eucalyptus sp. 2877 7.9 2 P. americana 40.9 7.7 3 Macadamia sp. 2445 6.7 3 Mangifera indica 37.7 7.1 4 Mangifera indica 1402 3.9 4 Cordia africana* 37.5 7.1 5 Cordia africana* 1086 3 5 Eucalyptus sp. 28.8 5.4 6 Carica papaya 1059 2.9 6 Macadamia sp. 26 4.9 7 P. americana 969 2.7 7 C. macrostachyus* 12.7 2.4 8 Catha edulis* 921 2.5 8 B. micrantha* 9.1 1.7 9 C. lusitanica 920 2.5 9 C. lusitanica 9.1 1.7 10 B. micrantha* 722 2 10 Vitex keniensis* 8.4 November 29, 2011 1.6
- 25. Farm tree population structure… Tree species richness by girth class for the three farm categories surveyed Species richness (tree individuals) Girth class (cm) Constant Decreasing Increasing All classes < 10 99 (7,000) 121 (6,194) 116 (7,374) 160 (20,568) 10.1 – 20 70 (2,864) 79 (2,249) 80 (3,466) 117 (8,579) 20.1 – 30 59 (1,435) 58 (1,325) 70 (1,735) 103 (4,495) 30.1 – 35 32 (417) 23 (267) 34 (471) 49 (1,155) > 35 40 (471) 42 (492) 49 (635) 64 (1,598) All categories 121 (12,187) 142 (10,527) 143 (13,681) 190 (36,395) November 29, 2011
- 26. Simple linear regressions against coffee farm types… Independent variables P-value Decreasing Increasing measured per farm Coffee bushes 0.0004 NS SF*** Cherry kg yr-1 0.0002 NS NS Tree richness 0.295 NS NS Tree abundance 0.3939 NS NS Trees in coffee plot 0.0555 NS SF* Exotic trees abundance 0.0857 NS SF* Indigenous tree abundance 0.3993 NS NS Farm size (Ha) 0.367 NS NS Manure kg 1 yr-1 0.0345 NS SF* Fertilizer kg yr-1 0.0003 NS SF*** November 29, 2011
- 27. Discussion points • Like in natural ecosystems the type of ecological zones have a major influence on species richness since abiotic factors such as precipitation, temperature, altitude play a key role • Data is consistent on rarity of indigenous species compared to exotics for all coffee AEZ. Exotics richness is not different between zones • Significantly different farm types in a same coffee ecological zone showed no evidence of difference in species richness and only a slight difference in tree abundance • The marginal coffee zone (UM3) on average has higher species richness but is also most uneven; main coffee zone (UM2) do not contain significantly different species from the UM1 and UM3 indicating likelihood of species sharing • The ‘coffee increasing farms’ are on average of lower species richness, but higher abundance than the decreasing farms-implies that increased productivity results in competition that displace some species • Tree size diversity is uneven among the surveyed tree population; the ten most highly ranked species are mainly exotics. Current data have showed likely displacement of some exotics e.g. avocado and indigenous species e.g. cordia, vitex and croton) November 29, 2011
- 28. Conclusions • Structuring patterns of species diversity to most important factor structuring tree assemblages can help reveal patterns of variations • Results suggest limited differences exist in tree richness and (abundance) among coffee farm typology • Low densities of indigenous species on farm could pose a challenge to genetic resources provisioning within highland coffee farming landscapes -implications on tree reproductive ecology • Stagnated/ reduced coffee production has potential to reduce tree abundance and richness on farm; increased coffee production does not necessarily lead to more richness but certainly results in higher tree abundance on farm November 29, 2011
Notas do Editor
- Smallholder coffee growing by smallholders officially begun at independenceIn 1933 coffee growing by African smallholders was piloted in small areas of Kisii, Embu and Meru under strict supervision (Barnes, 1979)The Native Grown Coffee Rules of 1934 stipulated coffee growing regulations. African coffee production was in fact considered experimental. Areas in which coffee cultivation was permitted were clearly defined by the director of agriculture. The gazettement of production areas was meant to ensure quality and to some extent quantity of coffee produce (Akiyama, 1987). By 1952 there were about 11,864 farmers cultivating around 3,000 acres of coffee. Smallholder coffee cultivation accelerated after Kenya’s independence in 1963. Production increased at a rapid rate of 6% in the early 1960’s as some of the large estates were given up for sub-divisions to smallholders and un-favourable laws were lifted (Akiyama, 1987). Nonetheless, coffee cultivation by smallholders was by law restricted within cooperatives with government as a significant stakeholder so as to secure foreign exchange earnings and meet obligations entered under the International Coffee Agreement that were politically negotiatedCoffee growing became the backbone of Kenya’s rural highlands economy. Until recently the subsector was claimed to supports over five million Kenyans both directly and indirectly as a result of forward and backward linkages. Coffee remained the nation’s top foreign exchange earner from independence in 1963 until 1989 when it was surpassed by tourism (Karanja, 2002). By 1978, the coffee sector accounted for 9.5 percent of GDP ($500 million in exports). By 2005, the revenues were only $75 million—a mere 0.6 percent of GDP (The World Bank, 2005). Coffee is presently ranked as the fifth foreign currency earner, after remittances from Kenyans abroad, tea, tourism and horticultureUnlike Ethiopia and Uganda, which are Africa's top coffee producers, Kenyan coffee output is under one percent of global production, but its beans are popular for blends and buyers have specific volume requirements (Ponte, 2002). On average, Kenya’s coffee fetches a 10% premium over standard arabica coffees from Central America and Colombia. Presently, about 170,000 hectares is cultivated under coffee by over 700,000 smallholder farmers organized in 569 co-operatives. Small-scale farmers have farms of less than 5 acres. There are also 3270 estates with farms of between 5 to 50 acres. Coffee production has been on a constant decline over the past years. At independence (1963) coffee production was at 43,778 metric tonnes and rose up to 140,000 metric tonnes in 1987/88. Production has declined and stagnated at about 50,000 metric tonnes in the last few years (KNBS, 2010). The smallholder sector, which used to produce 2/3 of the quantity, is producing slightly over 50% of the current low production (Table 1). Kenyan coffee is regarded as one of the best coffees in the World, traded under the ‘Colombian mild’s category. Coffee is mainly traded on the New York and London futures markets, which exert a strong influence on world coffee prices. Coffee prices are very volatile varying daily, hourly and even by the second, depending on factors such as the size of coffee stocks worldwide, weather forecast, insecure political conditions and speculation on the futures markets (ICO, 2010)The coffee value chain power shifts has significantly been influenced in two phases- during the International Coffee Agreement (ICA) regime (1962 to 1989) and secondly in the post ICA regime from 1989 to present. During the ICA era coffee markets were producer driven, while in the post ICA era, markets became buyer driven (Ponte, 2002). Producers no longer have much say in the present value chain (ICO, 2005). Previously, the International Coffee Agreement ensured high coffee prices between 1975 and 1989 but collapsed in 1989 leading to a decline in world coffee prices (Gilbert, 1996). When the agreements were in force, coffee market was regulated through systems of export quotas which were triggered when prices fell to significant levels. Gilbert and Brunette (1998) reported that the ICA may have raised producer prices by about 50-60%. Karanja and Nyoro (2002), report that Kenyan farmers benefited by 30% higher prices under the ICA trade regime.While coffee growers used to capture about 30% of the value of the final retail price of coffee in 1975, by 2000, they captured just 10% as downstream players became increasingly consolidated (Talbot, 1997). During the 1990’s, there were supply increases in the world coffee market, due to expansion of plantations in Brazil and Vietnam's entry into the market in 1994. As a result, by 2001, the world price of arabica coffee fell to below 60 cents a pound from highs of over $2 a pound precipitating a near market collapse (Akiyama et al., 2003; ICO, 2005).The present free market environment and liberalization have nonetheless enhanced price volatility (Karanja, 2002). Higher international coffee prices do not readily translate to increased productivity. For instance, in Uganda when coffee production declined due to low market prices in early 2000, efforts to increase production have not been fruitful despite increased market prices (Baffes, 2006). Market liberalization is also blamed for exposing smallholders to higher price risks. Liberalization meant a significant reduction in public expenditure on agriculture which severely constrained the provision of essential services needed to promote the productivity of smallholder farms. The subsector has evolved in two phases
- Columbian Milds trades at higher price levels than the New York composite prices for all other coffee traded. Price indicators confirm that coffee market rewards quality.
- Figure 8 shows trends in export volume in thousands, 60 kilo-bags of mild arabica coffee exported by Columbia, Kenya, and Tanzania.However internal domestic factors such as production levels, quality and exchange rates play a crucial role in final price determination. These factors influence smallholder coffee productivity and farmer intensification strategies to cope with uncertain coffee markets, and ensure sustainability of other farm based enterprises including food production.
- For human modified systems trade offs between productivity and biodiversity maintenance is keyNumber of species will correlate to size, spatial heterogeneity and competitive exclusions as productivity increasesExperimental manipulations of plant diversity within habitats have showed that although relationship vary, productivity tends to increase with diversity owing to increasing complementarities or positive interactions between species and the likelihood f diverse communities containing highly productive species.There is a strong inverse correlation in many groups between species richness and latitude: the farther from the equator, the fewer species can be found, even when compensating for the reduced surface area in higher latitudes due to the spherical geometry of the earth. Equally, as altitude increases, species richness decreases, indicating an effect of area, available energy, isolation and/or zonation (intermediate elevations can receive species from higher and lower).The Millennium Ecosystem Assessment:"In most ecosystems, changes in the number of species are the consequences of changes in major abiotic and disturbance factors, so that the ecosystem effects of species richness (number of species) per se is expected to be both comparatively small and very difficult to isolate. For example, variation in primary productivity depends strongly on temperature and precipitation at the global scale and on soil resources and disturbance regime at the region-to-landscape-to-local scales. Factors that increase productivity, such as nutrient addition, often lead to lower species richness because more productive species outcompete less productive ones. In nature, therefore, high species diversity and high productivity are often not positively correlated."
- Finally, available literature provides useful perspectives for interpreting species richness data in natural systems. The relation of species richness to species succession stage, abundance and composition are shown as important in biodiversity analysis (see Grime, 1983; Jongman et al., 1995; Humphries et al., 2005).Large changes in species richness at a certain stages of the succession does not necessarily mean large compositional turnover, since compositional turnover mean a change in species abundances which may be uncorrelated with species presences and absences.Plant species diversity is mostly influenced by human impacts and natural disturbances.Clear differences in stand structure and species diversity among undisturbed and disturbed sites of evergreen, semi-evergreen, deciduous and littoral forests werediscernible in this studyDiversity entails richness (or the number of species) and evenness (or equality in the number of individuals for every species).
- The larger Mount Kenya forest biosphere is separated from the coffee zone by a belt of tea. This biome is a world heritage site exhibiting rare fauna and flora. Some 882 plant species, belonging to 479 genera and 146 families have been identified in this ecosystem. Eighty one high altitude plants are endemic to only this system (UNEP, 2005)Three coffee agro-ecological zones namely the upper midland one (UM) 1, 2 and 3, constitute the main coffee agro-ecosystems in Kenya (Jaetzold and Schmidt, 2007). Altitude for coffee growing ranges from 1200 to 1750 m above sea level. Mean temperature range is 18.9 to 20.7 oC. A small change in diurnal temperature range is anticipated due to prevalent cloud cover and minimum temperatures compensating for any rises (Jaetzold and Schimdt, 2007).The soils are mainly ando-humicnitisols and humicandosols, developed on tertiary basic igneous rocks. They are characterised by dark reddish brown, to dark brown colours, with good drainage and extremely deep profile (FAO, 1978). Soil fertility is however on the decline due to recurrent permanent use over the years with little recycling of nutrientsMount Kenya coffee zones are of high agricultural potential characterized by high human settlement. Population density exceeding 400 persons per square kilometer in parts of Embu and Meru central have been recorded (GOK, 2010). Possible population pressure is driving significant land-use change possibly influencing farmer land access, availability, and intensification practicesRecent population census has showed high population densities for Embu and Kirinyaga at 409 and 357 persons per km2 respectively (GOK, 2010). Meru central has a density of 194 persons per km2 despite a similar population size with Kirinyaga; however in the main coffee zone, densities of up to 400 persons per km2 were reported in the latest census (GOK, 2010).Smallholder coffee production records from the year 2004 to 2008 define trends per farm categoryCalculated rates of change in coffee yields showed that the increasing farm category produce more than three times the average rate of the coffee decreasing farms. The constant farms types showed insignificant rate of change in production, but a higher production rate than the decreasing farms
- A total of 29 farmer cooperative societies (FCS) effectively covering the coffee production zone of North-Eastern and Southern Mount Kenya was used to select farmers. Random samples of 182 farm plots were surveyed during the months of June to August 2009. Farmer coffee cherry production records were collated at the FCS level and used to categorize sample farm plots into three groups according to their ‘coffee production trends’ as either ‘increasing’, ‘decreasing’ or `constant’
- The three types of farm plots were compared and contrasted with respects to levels of tree diversity maintained. Coffee, banana and maize farming were assumed to be the most dominant agricultural cropping patterns present in the Mount Kenya coffee system. Household interviews, using close and open ended questionnaires were further used to collect additional farm plot data such as on tree planting practices, farmers’ age, family size, land size, and size of the coffee enterprise.The study used ground based methods to enumerate tree species present in smallholder coffee farms. Tree basal area mensuration (tree cross-sectional area measured at breast height) was also undertaken. All trees greater than or equal to 5 cm diameter at breast height (DBH) were in fact enumerated. DBH’s were readily measured using calibrated tree diameter tapes. Trees were defined as all woody perennials growing to over 1.5 m tall, including exotics (Beentje, 1994; Brown, 1997). Local names of tallied trees were recorded from farmer interviews. All enumerated trees were identified to the species level according to Beentje (1994) or Maundu and Tengnäs (2005). Two persons were required to undertake tree inventories by farm walks; simultaneously recording species presence counts and DBH readings. Farmers’ were requested to facilitate marking-out of plots boundaries and in species identification in local dialects.
- .
- Imagine that you have 2 sites: site A has 3 tree species, whereas site B has 5 tree species. In this situation, site B has the largest species richness.Imagine another situation where both site C and site D contain 3 species. However, site C is dominated by one species that has 4 trees out of the total number of 6 trees on the entire site (or a proportion of 4/6). The other two species have proportions of 1/6. In site D, each species has the same number of trees (or proportions of 2/6). In this situation, site D has the largest evenness, which means that the proportions of the individual species are more similar. In this situation, the proportions are actually all the same for site D, so evenness is maximum for this site. Sites A and D have the same proportions. Since both sites have the same proportions and the same number of species, they have the same diversity. Diversity does not depend on density or total abundance.Sites of maximum evenness will have proportions of 1/S for each species, where S is the number of species (the species richness). For example, a plot with 5 species of maximum evenness will have proportions of 1/5 for each species, whereas a farm with 10 species of maximum evenness will have proportions of 1/10 for each species. If 100 trees were recorded in total, then 5 species will be most evenly distributed when each species has 100/5 = 20 trees.On the other hand, a site of minimum evenness will have only 1 tree for the S-1 less frequent species and Tot - (S-1) trees for the dominant category, if Tot indicates the total number of trees. If a site contains 6 trees and 3 species, the minimum evenness will be where 2 species contain 1 tree and the remaining species contains 6-2=4 trees. If a site contains 100 trees and 5 species, then with minimum evenness the dominant species will contain 100-4=96 trees.In most situations, the evenness will be in between the maximum and minimum evenness.Since diversity refers to richness and evenness, both these facets need to be considered when comparing diversity. If evenness is the same for the sites (sites, farms, sample plots) that you are comparing, then differences in richness will correspond to differences in diversity. If the richness is the same, then differences in evennesswill correspond to differences in diversity. There will be situations, however, where one site has larger richness but lower evenness than another site. Inthese situations, it is not always possible to rank one site as higher in diversity than the other site.
- Species patterns are used here to refer to the spatial dispersion of a species within a given farm and the relationships among many species between farms (Ludwig and Reynolds, 1988). To investigate species aggregation and levels of dominance, rank abundance curves and Rènyi diversity profiles were analyzed.Tree abundances (number of individuals) and basal area (stems area) distributions were calculated to assess structural complexity and heterogeneity of present farm tree populations (Jongman et al., 1995). All calculations were based on species frequencies. Tree diversity was assessed by calculating Shannon and inverse-Simpson diversity indices.In order to assess tree diversity among farm categories, districts and the different coffee agro-ecological settings, Rènyi profiles were used to rank sites from low to high diversity (Kindt and Coe, 2005; Kindt et al., 2006). The profile is calculated from the species proportion and alpha parameter as follows: Rènyi species diversity ordering for all enumerated species was plotted at scale values of: 0, 0.25, 0.5, 1, 2, 4, 8 and ∞ as prescribed in BiodiversityR (Kindt and Coe 2005; Jongman et al., 1995). The values are based on parameter ‘alpha’. The profile value, alpha = 0 provide information on species richness- it equals the logarithm of species richness H0= ln(S). While the profile value, alpha (∞) = infinity provides information on the proportion of the most dominant species- it is calculated as the logarithm of 1/proportion of a given species. In summary, Rènyi diversity profiles calculation includes:H0= Ln (Simpson diversity index)H1= Shannon diversity indexH2= Ln (Simpson-1)H∞= ln (Prop Max-1)Finally, to compare tree richness and evenness in coffee agro-ecological zones, coffee farm categories and coffee regions (districts) surveyed, sample based species accumulation curves were generated. Simple linear regressions were used to regress tree diversity measurements on coffee farm categories and agro-ecologcal zonesThe Renyi diversity profile is one of the techniques for diversity ordering that were specifically designed to rank communities from low to high diversity.Renyi diversity profile values (Ha) are calculated from the frequencies of each component species (proportional abundances pi = abundance of species i/total abundance) and a scale parameter (a) ranging from zero to infinity (Tothmeresz 1995; Legendre and Legendre 1998) It can be demonst rated that values of the Re´nyi profile at the respectivescales of 0, 1, 2 and ¥ are related to species richness S, the Shannon diversity index H, the Simpson diversity index D1 and the Berger–Parker diversity index d1 (Magurran 1988; Legendre and Legendre 1998; Shaw 2003):
- while 40 (22%) farms produce between 2000-5000 kg cherry ha-1 yr-1
- Increasing or decreasing trends in smallholder coffee production was hypothesized to influence tree species richness maintained on coffee farms. Data analysis showed that species richness was largest for the coffee decreasing farms followed by the increasing and the constant ones, however on per farm basis the coffee increasing farms was (Table 14). The Shannon diversity index measure supports this trend. Within farms, species richness was highest for the coffee increasing farm category followed by the decreasing and the constant ones. The inverse-Simpson index was more consistent with this observation (Table 14).
- Species accumulation curves show species richness for combinations of sites (in this case farms). These curves portray the average pooled species richness for all sites together. Average pooled species richness is calculated because different sites combinations have different species richness (Kindt and Coe, 2005). Rényi diversity profiles are curves used to provide information on richness and evenness, they are essentially one of the diversity ordering techniques (Jongman et al.,1995; Kindt and Coe; 2005)Plotting sample based accumulation curves and Rènyi diversity profiles for the ‘increasing’, ʻdecreasing’ and ʻconstant’ coffeefarm categories showed overlapping species richness patterns for the increasing and decreasing farms (Figure 15). Respective sites could not be ordered from low to high diversity. Rènyi profiles however showed that coffee decreasing farms have a slightly higher levels of tree diversity compared to the coffee increasing farmsThe profile values for alpha = 0 (H0) is the logarithm of the species richness. The value H1 is calculated directly as Shannon index and the alpha value = 2 is the logarithm of the reciprocal Simpson diversity index. The alpha value = infinity (H∞) provides information on the proportion of the most abundant species.Rènyi profile calculations show that proportions of the most abundant species for the increasing and decreasing farm categories were similar at 40%. The constant farm category had slightly higher proportions of the most dominant species at 43% (Table 16). High alpha values indicate higher species richness, conversely; low infinity values indicate a higher proportion of the dominant species.
- agro ecological zones; upper midland (UM) 1, 2 and 3. hypothesized to influence coffee production and tree species diversity given their biophysical gradientSpecies accumulation curves showed the size of species richness was as follows; UM 3>UM2>UM1 (Figure 16iThe profile value for alpha = ∞showed that UM3 had the largest proportion (45%) of the most abundant species and was most un-even. UM1 had the smallest proportion of the abundant species (38%) and therefore relatively more even (Table 17). Rènyi profiles on species evenness show that systems with high dominance are less even.Tree richness significantly different by AEZ (P < 0.001)Tree abundance significantly different by coffee (AEZ) (P = 0.0037)Strong evidence that UM3 tree population size is different from those in UM1. Abundance in UM2 is however not significantly different from UM1 (P=0.593)Indigenous trees abundance in UM3 are significantly different (P<0.001) from UM1. There was weak evidence on differences between UM1 and UM2 (P=0.096)Exotic trees abundance regressed on coffee AEZ returned a weak model (P=0.048). There was not strong evidence (P=0.027) that exotic tree abundance in UM3 was different from UM1. Infact there was no evidence that on average exotic tree population in the zone UM2 are different from UM1 or UM 3
- rank-abundance curves showed a wide curve on species abundance counts suggesting a certain degree of species evenness and density for selected tree species On the other hand, analysis showed that tree basal area distribution had a steep curve suggesting greater uneven tree size distribution among the surveyed tree species populationThe 10 most abundant species by ranking account for 75% of all available trees on farm. Invariably, the 10 biggest tree species by basal area ranking accounted for 81% of all the tree basal area for all species inventoried on farms. There is high farm dominance by a few tree species. As mentioned, the rank abundance curve provide a means for visually representing species richness and species evenness. Species richness can be viewed as the number of different species on the chart i.e., how many species were ranked. Species evenness is derived from the slope of the line that fits the graph. A steep gradient indicates low evenness as the high ranking species have much higher abundances than the low ranking species. A shallow gradient indicates high evenness as the abundances of different species are similarTree size class distribution can be used asindicators of changes in population structure andspecies composition (Newbery & Gartlan 1996).
- Tree abundance and richness was greatest in smaller girth class of less than 10 for all farm categoriesClear differences in tree individuals structure and spp diversity among farm categoriesTree size class distribution can be used as indicators of changes in population structure and species composition(newbery & Gartlan 1996)
- to assess, how tree diversity in smallholder coffee farms is influenced by factors associated with coffee productivity such as the number of coffee bushes per farm, yields attained per farm, farm size, tree abundance on the whole farms and within coffee plots; and amounts of fertilizer and manure inputs applied were regressed on the three coffee farm categories studied.Coffee bushes and cherry yields per farm were confirmed to be significantly different by coffee farm types (P < 0.001).Tree richness and abundance per farm was not significantly different by coffee farm types. Tree abundance regression on the coffee decreasing farms returned a negative parameter suggesting possible tree population reduction with a decline in coffee cultivation. Tree individuals maintained in coffee plots among the coffee increasing farms, were significantly higher than those in the decreasing farms (P = 0.05). The number of exotic trees per farm was significantly higher (P = 0.08) among the coffee increasing farms; (143; 14-545) than in the decreasing ones (106; (11-374).