1. ANALYSIS OF EFFECTS OF TSETSE CONTROL ON LIVESTOCK PRODUCTIVITY AND HEALTH Nicholas N. Ndiwa, Woudyalew Mulatu and John Rowlands International Livestock Research Institute

2. BACKGROUND Trypanosomosis is a serious disease affecting livestock in many parts of sub-Saharan Africa including Ethiopia. The parasite that causes the disease is carried by the tsetse fly. Persistently high levels of trypanosomosis found in cattle at Ghibe in southwest Ethiopia, where ILRI works, occur because of drug resistance. Thus, drug therapy on its own at Ghibe does not work. The alternative is to reduce the numbers of tsetse flies. Two interventions to control tsetse numbers have been implemented 1. - with insecticide impregnated targets 2. - with insecticide pour-on applied to the backs of cattle.

3. Time-line of treatments and tsetse control interventions Drug treatments for disease cases Targets Invasion of third tsetse species Drug treatment for all cattle Pour-on Drug treatment for all cattle Theft of the targets Mar-86 Nov-86 Jul-87 Mar-88 Nov-88 Jul-89 Mar-90 Nov-90 Jul-91 Mar-92 Nov-92 Jul-93 Mar-94 Nov-94 Jul-95 Mar-96 Nov-96 Jul-97 Mar-98

4. Measurements were made monthly on the following:  Packed cell volume (PCV)  Trypanosome prevalence  Body weight Calves were ear-tagged at birth and their details recorded. Disposal (deaths, disappearance or sales) were also recorded.

5.  Tsetse density  Mean body weight, PCV, trypanosome prevalence, no. of treatments - separately for males and females  Calf growth rate and 12-month body weight  Mortality rate in males, females and calves  Abortion rate and calf/cow ratio to reflect fertility level  Herd size Productivity and health variables calculated

6. Possible time units for analysis One month? Problems? - handling seasonal variation - handling increasing ages of cattle - handling pregnancy and lactation - positive serial correlations from month to month - other confounding random variables (e.g rainfall) Three months? Problems? - handling seasonal variation - other confounding random variables - also age, pregnancy, lactation Six months? - now possible to match with season (wet and dry) - other factors not so important Twelve months? - best for matching with agronomic (planting and harvesting) and livestock production / management - matches annual rain cycle

7. Data set structure

9. Statistical model y ijk =  +s i +p j +c k +(pc) jk +e ijk where s=season, p=period and c=control Interaction not significant for any variable. Hence dropped for final model

10. estimates.e. t(18) t pr. Constant 217.24 5.53 39.28 <.001 SEASON 2 6.48 5.07 1.28 0.217 PERIOD 2 11.06 5.10 2.17 0.044 CONTROL 2 11.40 5.28 2.16 <.045 Change d.f. s.s. m.s. v.r. F pr. + SEASON 1 231.11 231.1 1.64 0.217 + PERIOD 1 762.3 762.3 5.40 0.032 + CONTROL 1658.2 658.2 4.66 <.045 Residual 18 2543.3 141.3 Total 21 4194.8 199.8 Least square means Control Body weights.e. 0 226.52 4.21 1 237.91 3.18 Genstat output for analysis of body weights for bulls Accumulated analysis of variance Estimates of parameters

11. Effect of tsetse control on selected variables VariableTsetse control without with SED PChange (%) Bulls Body weight (kg)226.52 237.915.28<0.001 8 PCV (%)22.823.80.64<0.01 7 Trypanasome prevalence (%)0.360.310.042<0.05 24 Annual mortality (%)0.200.110.039<0.001 62 Calves Growth rate – wet season (kg/month)0.220.230.0250. 4 Body weight at 12 months (kg)68762.2<0.01 12

12. TargetsPour-on

13. TargetsPour-on

14. TargetsPour-on

15. TargetsPour-on

16. Conclusions The general health of cattle improved with increased body weights and reduced mortality. This corresponded to decreased trypanosome prevalence, although the average trypanosome prevalence still remained comparatively high. Insecticidal pour-on has an effect, not only on tsetse, but also on other nuisance flies. This may also have helped towards improved cattle health over this period. The analytical approach we adopted provided an analysis that simplified the difficulties in dealing with confounding factors and serial correlations between successive measurements.

17. Conclusions (continued ) We lagged the effect of tsetse control by 6-months based on the knowledge that the intervention of tsetse control has a delayed effect. The data appeared to show this. Our method resulted in 13 observational units when tsetse control was applied and 9 when not; this was more than adequate for the statistical analysis. The length of the study demonstrated that application of tsetse control can be sustainable.