Smallholder Farmers’ Perspectives on Climatic Variability and Adaptation Strategies in East Africa: The Case of Mount Kilimanjaro in Tanzania, Taita and Machakos Hills in Kenya

Climate change is expected to have serious economic and social impacts in East Africa, particularly on rural farmers whose livelihoods largely depend on rain-fed agriculture, hence adaptation is required to offset projected drawbacks of climate change on crop productivity. This paper examines farmers' perceptions and understanding of climatic variability, coping strategies adopted and factors that influence the choice of a particular adaptation. The study uses cross section data collected from 510 farmers in three mountain gradients sites, namely; Mount Kilimanjaro of Tanzania, Taita and Machakos Hills of Kenya. Farmers’ perceptions were compared to actual trend in meteorological records over the last thirty years (1981-2010). The result revealed that farmers in East Africa were partly aware of climate variability, mainly in temperature and rainfall patterns. Many respondents reported that conditions are drier and rainfall timing is becoming less predictable. The perception of farmers on temperature and rainfall were in line with recorded meteorological data, but contrary with that of recorded rainfall in Machakos which was perceived to be decreasing by the farmers. Farmers perceived changes in rainfall and temperature to have negative effects on the production and management of crops. The common adaptation strategies used by farmers include water harvesting, soil conservation techniques and shifting of planting periods. The most important variables affecting farmers choices in regards to adaptation option were, lack of access to credit, farming experience and household size. As a conclusion, there is a need for these factors to be taken into account in the development and implementation of smallholder farmers’ adaptation strategies to climate variability in East Africa. Additionally, dedicated capacity building and extensive outreach initiatives on adaptation through governments, researchers, policy-makers and the farmers groups themselves are needed to achieve large scale success

Stability analysis of competing insect species for a single resource

The models explore the effects of resource and temperature on competition between insect species. A system of differential equations is proposed and analysed qualitatively using stability theory. A local study of the models is performed around axial, planar, and interior equilibrium points to successively estimate the effect of (i) one species interacting with a resource, (ii) two competing species for a single resource, and (iii) three competing species for a single resource. The local stability analysis of the equilibrium is discussed using Routh-Hurwitz criteria. Numerical simulation of the models is performed to investigate the sensitivity of certain key parameters. The models are used to predict population dynamics in the selected cases studied. The results show that when a single species interacts with a resource, the species will be able to establish and sustain a stable population. However, in competing situation, it is observed that the combinations of three parameters (half-saturation, growth rate, and mortality rate) determine which species wins for any given resource. Moreover, our results indicate that each species is the superior competitor for the resource for the range of temperature for which it has the lowest equilibrium resource

Predicting the impact of temperature change on the future distribution of maize stem borers and their natural enemies along east african mountain gradients using phenology models

Lepidopteran stem borers are among the most important pests of maize in East Africa. The objective of the present study was to predict the impact of temperature change on the distribution and abundance of the crambid Chilo partellus, the noctuid Busseola fusca, and their larval parasitoids Cotesia flavipes and Cotesia sesamiae at local scale along Kilimanjaro and Taita Hills gradients in Tanzania and Kenya, respectively. Temperature-dependent phenology models of pests and parasitoids were used in a geographic information system for mapping. The three risk indices namely establishment, generation, and activity indices were computed using current temperature data record from local weather stations and future (i.e., 2055) climatic condition based on downscaled climate change data from the AFRICLIM database. The calculations were carried out using index interpolator, a sub-module of the Insect Life Cycle Modeling (ILCYM) software. Thin plate algorithm was used for interpolation of the indices. Our study confirmed that temperature was a key factor explaining the distribution of stem borers and their natural enemies but other climatic factors and factors related to the top-down regulation of pests by parasitoids (host-parasitoid synchrony) also played a role. Results based on temperature only indicated a worsening of stem borer impact on maize production along the two East African mountain gradients studied. This was attributed to three main changes occurring simultaneously: (1) range expansion of the lowland species C. partellus in areas above 1200 m.a.s.l.; (2) increase of the number of pest generations across all altitudes, thus by 2055 damage by both pests will increase in the most productive maize zones of both transects; (3) disruption of the geographical distribution of pests and their larval parasitoids will cause an improvement of biological control at altitude below 1200 m.a.s.l. and a deterioration above 1200 m.a.s.l. The predicted increase in pest activity will significantly increase maize yield losses in all agro-ecological zones across both transects but to a much greater extent in lower areas

Modelling the Distributions of Maize Stem Borers at Local Scale in East African Mountain Gradients Using Climatic and Edaphic Variables

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Change in the establishment, abundance and activity indices of <i>C</i>. <i>partellus</i> and <i>C</i>. <i>flavipes</i> study along the Taita hills transect; <i>C</i>. <i>partellus</i> current distribution, (A) (ERI), (B) (GI), (C) (AI); <i>C</i>. <i>flavipes</i> current distribution (D) (ERI), (E) (GI), (F) (AI); absolute establishment index change between 2013 and 2055, (G) <i>C</i>. <i>partellus</i>, (H) <i>C</i>. <i>flavipes</i>; (K) <i>C</i>. <i>partellus</i> and <i>C</i>. <i>flavipes</i> synchrony under future climate for the establishment index between 2013 and 2055.

<p>ERI = Establishment Index, GI = Generation Index, AI = Activity Index.</p

Minimum and maximum temperatures curves for current (2013) and future (2055); (A) Miwaleni (764 m.a.s.l); (B) Marua (1683 m.a.s.l) along Mount Kilimanjaro transect; (C) Kipusi (832 m.a.s.l); (D) Vuria (1800 m.a.s.l) along Taita hills transect.

<p>Bars above the x-axis indicate the maize-cropping season and oval indicates the starting of rainy season.</p

Changes in the establishment, abundance and activity indices of <i>B</i>. <i>fusca</i> and <i>C</i>. <i>sesamiae</i> study along the Taita hills transect; <i>B</i>. <i>fusca</i> current distribution, (A) ERI, (B) GI, (C) AI; <i>C</i>. <i>sesamiae</i> current distribution (D) ERI, (E) GI, (F) AI; absolute establishment index change between 2013 and 2055, (G) <i>B</i>. <i>fusca</i>, (H) <i>C</i>. <i>sesamiae</i>; (K) <i>B</i>. <i>fusca</i> and <i>C</i>. <i>sesamiae</i> synchrony under future climate for the establishment index between 2013 and 2055.

<p>ERI = Establishment Index, GI = Generation Index, AI = Activity Index.</p

Change in the establishment, abundance and activity indices of <i>C</i>. <i>partellus</i> and <i>C</i>. <i>flavipes</i> along the Mount Kilimanjaro transect; <i>C</i>. <i>partellus</i> current distribution, (A) (ERI), (B) (GI), (C) (AI); <i>C</i>. <i>flavipes</i> current distribution (D) (ERI), (E) (GI), (F) (AI); absolute establishment index change between 2013 and 2055, (G) <i>C</i>. <i>partellus</i>, (H) <i>C</i>. <i>flavipes</i>; (K) <i>C</i>. <i>partellus</i> and <i>C</i>. <i>flavipes</i> synchrony under future climate for the establishment index between 2013 and 2055.

<p>ERI = Establishment Index, GI = Generation Index, AI = Activity Index.</p