Effects of Seasonality on the Productivity of Pastoral Goat Herds in Northern Kenya Ingo Hary12Chapter 191.1 Background1011 1.2 Problem statement1213141516171.3 Objectives and structure of the thesis18Chapter 2192.1 Introduction 2.2 Materials and Methods20212223 2.3 Results24252627282930313233 2.4 Discussion343536373839402.5 ConclusionsChapter 3413.1 Introduction 3.2 Materials and Methods424344453.3 Results3.3.1 Kid survival4647484950513.3.2 Doe survival525354553.4 Discussion3.4.1 Kid survival56573.4.2 Doe survival583.4.3 Analytical approach596061Chapter 4634.1 Introduction 4.2 Materials and Methods646566674.3 Results 4.3.1 Growth performance of kids 68697071727374757677784.3.2 Doe liveweight798081828384854.4 Discussion4.4.1 Growth performance of kids868788 4.4.2 Body weight development of does89904.5 ConclusionsChapter 5915.1 Introduction925.2 Materials and Methods93945.3 Results95969798991005.4 Discussion1011021031041055.5 ConclusionsChapter 61076.1 Introduction1086.2 Stage-structured matrix population models and their parameterisation109110111112113 6.3 Procedure for estimating feed energy requirements at the herd level1141156.4 Procedure for estimating herd outputs 6.5 Derivation of steady state optimal culling policy and herd structure 116117118116 6.6 Discussion1201211221206.7 ConclusionsChapter 71257.1 Introduction 7.2 Materials and Methods1261271281291301311321341347.3 Results135136137138140141142143144145146147 7.4 Discussion148149150151152153154155156Chapter 8157158159160 References161162163164165166167168169170171172173170171Appendix 1176177174 Eidesstattliche Erklärung177enTable of contentsHelpAus dem Institut für Nutztierwissenschaften der Landwirtschaftlich-Gärtnerischen Fakultät der Humboldt-Universität zu BerlinDissertation <strong>Effects of Seasonality on the Productivity <br/> of Pastoral Goat Herds<br/> in Northern Kenya</strong> Zur Erlangung des akademischen Grades doctor rerum agriculturarum (Dr. rer. agr.) der
Landwirtschaftlich-Gärtnerischen Fakultät der Humboldt-Universität zu Berlin vorgelegt von
Dipl. Ing. (agr.) Ingo Hary geb. am 03.08.1965 in Saarbrücken

Präsident:
Präsident der Humboldt-Universität zu Berlin: Prof. Dr. Dr. H.c. Hans Meyer


Dekan der Landwirtschaftlich-Gärtnerischen Fakultät: Prof. Dr. Dr. H.c. Ernst Lindemann Prof. Dr. H.J. Schwartz Prof. Dr. J.M. King Priv. Doz. Dr. H. Schafft

Weitere Mitglieder der Kommission:
4. Prof. Dr. D. Kirschke
5. Dr. A. Simon

Eingereicht am: 05.07.1999 Datum der Promotion: 08.12.1999 Summary

Under semi-arid rangeland conditions in northern Kenya, the main factor influencing the productivity of small ruminant flocks is climatic seasonality. In pastoral production systems, alternatives to herd mobility as an efficient adaptive management strategy to overcome nutritional deficits are few. One possible intervention is to manipulate the total seasonal nutrient requirements of the herd through controlled seasonal breeding, which commonly is not practised by pastoralists.

The restriction of breeding as a management strategy to match periods of critical nutrient demands with seasonal feed supply in pastoral goat flocks has so far received little attention in research. In order to gain a clearer understanding of the merits and demerits of controlled seasonal breeding, a systematic breeding programme in a herd of Small East African goats was initiated for a period of four years (1984-1988) in Isiolo District, northern Kenya. The study was undertaken to (1) assess the effect of seasonal forage supply on various parameters determining pastoral goat flock performance, and (2), using these baseline data, to test the hypothesis that a seasonally restricted breeding regime can increase flock productivity.

A total of 145 does of the Small East African type were maintained under simulated pastoral management conditions and used for a total of 381 exposures which were distributed among 18 consecutive breeding groups consisting of approximately 18 does each. The experimental design resulted in six different, consecutive mating periods or seasons per year, which were replicated three times during the course of the experiment. A total of 8547 recordings were obtained on survival, liveweight, and milk production of does; 9837 observations were available on survival and liveweight development of youngstock. Detailed statistical analyses were performed on all traits relevant to assessing overall biological herd productivity, including: survival of kids and does; reproductive performance of does; growth performance of kids and body weight development of does; and milk production. A steady-state herd model was developed and used to assess overall flock productivity for each of the six consecutive two-month breeding seasons. The procedure is based on a stage-specific description of population dynamics and uses non-linear programming to derive the steady-state herd structure and culling policy that maximizes overall energetic efficiency of the herding enterprise.

Mating season had no statistically significant effect on reproduction traits, most likely due to the large variability in within-season environmental conditions among the three production cycles. Differences in kid survival among mating seasons were marked. The results demonstrated that restricted breeding can be an effective means to control kid mortality. Similar conclusions apply with respect to milk yield, which was an important risk factor affecting kid survival until weaning. Although growth performance of kids until weaning differed substantially among mating seasons, these had largely disappeared by one year of age. Therefore, seasonal breeding does not seem to confer any major advantage in terms of growth performance of youngstock per se.

Steady-state herd productivity assessments revealed that under the current production conditions reproductive performance traits are far less important as contributors to biological productivity than is often assumed. Sensitivity analyses showed that juvenile survival rate is the most important factor determining overall energetic efficiency. Restricted breeding can be used as a management control to manipulate overall biological herd productivity primarily because of its positive effect on youngstock mortality rates. In contrast, yield levels, i.e., growth and milk performance, are less important as determinants of biological herd productivity, once their effect on youngstock mortality has been accounted for. Joining does at the peak of the long dry season (July and August) proved to be the optimal management strategy in terms of energetic efficiency at the herd level. Whether restricted breeding is biologically superior to an aseasonal breeding management, as is often practised by pastoral producers, remains ambiguous. The results for a simulated aseasonal breeding regime indicated that the potential improvements in biological productivity are probably much smaller than is usually presumed.

With respect to steady-state herd productivity assessment, results of the present work emphasized the importance of utilizing an optimality approach for obtaining a common basis on which management alternatives can be compared in terms of their effect on energetic efficiency at the herd level. The assessment procedure is essentially a device with which standardized comparisons of biological or economic productivity in livestock herds can be carried out, but it can also be a valuable aid in understanding or optimising the production system looked at.

 seasonality herd productivity goats pastoral systems Zusammenfassung

Der wichtigste Bestimmungsfaktor für die Produktivität in der Herdenhaltung kleiner Wiederkäuer unter semi-ariden Weidebedingungen ist die klimatisch bedingte Saisonalität im Futteraufwuchs. In pastoralen Produktionssystemen gibt es nur wenige Alternativen zur Mobilität als effiziente und angepaßte Strategie zur Überwindung von Nährstoffdefiziten. Eine denkbare Intervention bestünde darin,, den saisonalen Nährstoffbedarf der Herde über ein kontrolliertes Anpaarungsmanagement zu steuern. Eine derartige Strategie wird jedoch von pastoralen Produzenten üblicherweise nicht durchgeführt.

Restriktives Anpaarungsmanagement als ein Mittel zur Synchronisation der Nährstoffansprüche pastoraler Ziegenherden mit dem saisonalen Futterangebot hat bisher in der Forschung nur wenig Beachtung gefunden. Um die Vor- und Nachteile einer kontrollierten saisonalen Anpaarung zu untersuchen, wurde über einen Zeitraum von vier Jahren (1984-1988) im Isiolo Distrikt im Norden Kenias ein systematisches Anpaarungsprogramm in einer Herde kleiner Ostafrikanischer Ziegen durchgeführt. Ziel der Studie war es, (1) den Effekt einer saisonalen Anpaarung auf wichtige Leistungsmerkmale von Ziegenherden zu untersuchen, und (2) diese Daten für den Test der Hypothese zu verwenden, daß ein restriktives saisonales Anpaarungsmanagement die Produktivität pastoraler Ziegenherden zu steigern vermag.

Es wurden 145 Muttertiere auf 18 aufeinander folgende Anpaarungsgruppen mit jeweils ca. 18 Tieren verteilt und für insgesamt 381 Anpaarungen verwendet. Jeder dieser 18 Gruppen wurde über einen Zeitraum von 2 Monaten der gleiche Zuchtbock zugeführt. Aus dem experimentellen Design ergaben sich 6 Anpaarungsperioden pro Jahr, die jeweils dreimal im Verlauf des Experiments wiederholt wurden. Es wurden 8547 Messungen bezüglich der Mortalität, der Gewichtsentwicklung und Milchleistung der Muttertiere erhoben; 9337 Messungen wurden zur Mortalität und Wachstumsleistung der Jungtiere erhoben. Statistische Analysen wurden für alle relevanten Leistungsmerkmale durchgeführt. Dazu zählen die Überlebensleistung von Jung- und Muttertieren, die Reproduktionsleistung der Muttertiere, die Wachstumsleistung der Jungtiere, sowie die Gewichtsentwicklung und Milchleistung der Mütter. Für die Ermittlung der Herdenproduktivität in den sechs Anpaarungsperioden wurde ein neues steady-state Herdenmodell entwickelt. Dieses Verfahren basiert auf einer zustandsstruktierten Beschreibung der Populationsdynamik und verwendet einen nicht linearen Optimierungsansatz zur simultanen Bestimmung der steady-state-Herdenstruktur und der Merzpolitik, die die energetische Effizienz auf Herdenebene maximiert.

Die Anpaarungsperiode hatte keinen signifikanten Effekt auf Reproduktionsmerkmale, was höchstwahrscheinlich auf die hohe Variabilität in den Produktionsbedingungen zwischen den drei Wiederholungen einer Periode zurückzuführen ist. Die Unterschiede in der Überlebensleistung der Jungtiere zwischen den Anpaarungsperioden waren stark ausgeprägt. Die Ergebnisse belegen, daß restriktive Anpaarung ein effektives Mittel zur Reduzierung der Jungtiersterblichkeit sein kann. Ähnliche Schlußfolgerungen gelten in Bezug auf die Milchleistung. Die anfänglichen Unterschiede in der Wachstumsleistung zwischen den Anpaarungsperioden verschwanden weitestgehend bis die Tiere das Jährlingsstadium erreicht hatten. Daher kann man davon ausgehen, daß eine saisonale Anpaarung per se keinen nennenswerten Vorteil bezüglich der Wachstumsleistung von Jungtieren verschafft.

Die ermittelten steady-state-Produktivitäten verdeutlichen, daß unter den gegebenen Bedingungen Reproduktionsleistungsmerkmale weitaus weniger bedeutsam für die biologische Herdenproduktivität sind, als dies häufig angenommen wird. Mit Hilfe von Sensitivitätsanalysen wurde gezeigt, daß die Jungtiersterblichkeit mit Abstand der wichtigste Bestimmungsfaktor für die energetische Effizienz auf Herdenebene ist. Restriktive Anpaarung kann aufgrund des positiven Einflusses auf die Überlebensleistung der Lämmer als Strategie zur Steigerung der biologischen Produktivität genutzt werden. Milch- und Wachstumsleistung sind von untergeordneter Bedeutung, nachdem ihre positiven Effekte auf die Überlebensleistung der Jungtiere berücksichtigt wurden. Eine Anpaarung auf dem Höhepunkt der langen Trockenzeit (Juli bis August) stellte die optimale Managementstrategie dar. Ob eine restriktive Anpaarung einer kontinuierlichen Anpaarung überlegen ist, konnte jedoch nicht zweifelsfrei festgestellt werden. Die Ergebnisse eines simulierten asaisonalen Managements deuten darauf hin, daß der durch saisonale Anpaarung erzielbare Effizienzzuwachs deutlich geringer ausfallen dürfte, als bisher angenommen wurde.

Bezüglich der Methode zur Ermittlung der steady-state Herdenproduktivität ist festzuhalten, daß der Verwendung eines Optimalitätsansatzes für die Schaffung einer gemeinsamen Vergleichsbasis eine herausragende Bedeutung zukommt. Der entwickelte Bewertungsansatz erlaubt die Durchführung standardisierter Effizienzvergleiche auf Herdenebene und Kann zugleich ein wertvolles Hilfsmittel für ein besseres Verständnis von Produktionssystemen, bzw. für deren Optimierung sein.

 Saisonalität Herdenproduktivität Ziegen pastorale Systeme Table of contents

  • Chapter 1  General introduction

    • 1.1 Background

    • 1.2 Problem statement

    • 1.3 Objectives and structure of the thesis

  • Chapter 2  Effects of controlled seasonal breeding on reproductive performance traits of pastoral goat herds in northern Kenya

    • 2.1 Introduction

    • 2.2 Materials and Methods

    • 2.3 Results

    • 2.4 Discussion

    • 2.5 Conclusions

  • Chapter 3  An analysis of survival curves in seasonally mated pastoral goat herds in northern Kenya using logistic regression techniques

    • 3.1 Introduction

    • 3.2 Materials and Methods

    • 3.3 Results

      • 3.3.1 Kid survival

      • 3.3.2 Doe survival

    • 3.4 Discussion

      • 3.4.1 Kid survival

      • 3.4.2 Doe survival

      • 3.4.3 Analytical approach

  • Chapter 4  Effects of seasonal breeding on productive performance of pastoral goat herds in northern Kenya: a longitudinal analysis of growth in kids and body weight development of does

    • 4.1 Introduction

    • 4.2 Materials and Methods

    • 4.3 Results

      • 4.3.1 Growth performance of kids

      • 4.3.2 Doe liveweight

    • 4.4 Discussion

      • 4.4.1 Growth performance of kids

      • 4.4.2 Body weight development of does

    • 4.5 Conclusions

  • Chapter 5  The impact of controlled breeding on milk production in pastoral goats in northern Kenya: an application of polynomial growth curve fitting

    • 5.1 Introduction

    • 5.2 Materials and Methods

    • 5.3 Results

    • 5.4 Discussion

    • 5.5 Conclusions

  • Chapter 6  An approach to steady state productivity assessment in livestock herds based on stage-structured matrix population modelsand mathematical programming

    • 6.1 Introduction

    • 6.2 Stage-structured matrix population models and their parameterisation

    • 6.3 Procedure for estimating feed energy requirements at the herd level

    • 6.4 Procedure for estimating herd outputs

    • 6.5 Derivation of steady state optimal culling policy and herd structure

    • 6.6 Discussion

    • 6.7 Conclusions

  • Chapter 7  Assessing the effect of controlled seasonal breeding on steady state productivity of pastoral goat herds in northern Kenya

    • 7.1 Introduction

    • 7.2 Materials and Methods

    • 7.3 Results

    • 7.4 Discussion

  • Chapter 8  General discussion

  • References

  • Appendix 1

  • Eidesstattliche Erklärung
Tables

  • Table 2.1. Summary information on breeding and mating season groups

  • Table 2.2. Classification factors used in statistical analyses.

  • Table 2.3. Analyses of deviance for logit models fitted to conception rates.

  • Table 2.4. Parameter estimates for logit models fitted to conception rates (f denotes the extra dispersion parameter).

  • Table 2.5. Analyses of deviance for logit models fitted to abortion rates.

  • Table 2.6. Parameter estimates for the final model fitted to abortion rates.

  • Table 2.7. Analyses of deviance for logit models fitted to birth rates.

  • Table 2.8. Parameter estimates for final logit models fitted to birth rates.

  • Table 2.9. Analyses of deviance for Poisson regression models fitted to prolificacy rates.

  • Table 2.10. Parameter estimates for final models fitted to prolificacy rates.

  • Table 2.11. Analysis of deviance for models fitted to fecundity rates.

  • Table 2.12. Parameter estimates for final models fitted to fecundity rates.

  • Table 2.13. Analysis of deviance for Poisson regression models fitted to weaning rates.

  • Table 2.14. Parameter estimates for final Poisson regression models fitted to weaning rates.

  • Table 3.1. Definition of predictors included in initial models fitted to survival data.

  • Table 3.2. Causes of age-specific juvenile and kid mortality.

  • Table 3.3. Analyses of deviance for final models fitted to data on kid survival.

  • Table 3.3. (continued)

  • Table 3.4. Estimated mean effects of range condition scores [I] and [II] on the odds of death in kids per two weeks time interval. Approximate relative risks (odds ratios) among score levels are given on the right.

  • Table 3.5. Estimated probabilities and 95 percent bootstrap confidence intervals of kids surviving to selected time points, according to mating season, birth weight, and milk yield until weaning.

  • Table 3.6. Analysis of deviance for the final model fitted to data on doe survival.

  • Table 3.7. Estimated probabilities and 95% bootstrap confidence intervals of does surviving to selected time points by mating season, parity at breeding, and reproductive status.

  • Table 3.8. Effect of different methods of estimation on survival rate estimates. The numerical example was taken from PAN Livestock Services (1991) and shows a population that is censused repeatedly over a one year observation period.

  • Table 4.1. Explanatory variables included in initial models fitted to longitudinal data. For random effects, nesting factors are given in square brackets. (PC=production cycle; MS=mating season; RC=range condition).

  • Table 4.2. Tests of fixed effects and estimates of variance components and covariance parameters for growth curve models fitted to daily weight gains of kids (g×kg-0.75×day-1), accounting for the effects of mating season.

  • Table 4.3. Tests of fixed effects and estimates of variance components and covariance parameters for growth curve models fitted to daily weight gains of kids (g×kg-0.75×day-1) until 50 weeks of age, accounting for the effects of milk yield until weaning, litter size, and a) lagged median range condition score [I] and b)lagged median range condition score [II].

  • Table 4.4. Tests of fixed effects and estimates of variance components and covariance parameters for the model fitted to daily weight gains of kids (g·day-1), accounting for the effects of mating season and sex.

  • Table 4.5. Least-squares means and standard errors (in parentheses) of body weight gains (g×day-1) in kids over six consecutive age intervals of 16 weeks length each, according to mating season and sex.

  • Table 4.6. Tests of fixed effects and estimates of variance components and covariance parameters for growth curve models fitted to kid body weights from birth to two years of age, accounting for the effects ofa) mating season, parity and sex, and b) milk yield until weaning, litter size and sex.

  • Table 4.7. Least-squares means and standard errors (in parentheses) of kid body weights at selected ages, according to mating season, litter size, sex, and milk yield until weaning.

  • Table 4.8. Tests of fixed effects and estimates of variance components and covariance parameters for growth curve models fitted to liveweights of fertile does, accounting for the effects of mating season and parity.

  • Table 4.9. Tests of fixed effects and estimates of variance components and covariance parameters for growth curve models fitted to percent change in liveweights of fertile does, accounting for the effects of mating season and parity.

  • Table 4.10. Tests of fixed effects and estimates of variance components for growth curve models fitted to a) liveweights andb) relative changes in liveweight of fertile does, accounting for the effects of litter size and parity at breeding. A heterogeneous first-order autoregressive covariance structure was used in fitting both models. Within-subject covariance parameter estimates were very similar to those presented in Tables 4.8 and 4.9.

  • Table 4.11. Least-squares means and standard errors (in parentheses) of liveweight of fertile does (kg) at selected time points during a reproductive cycle of one year duration, according to mating season and parity at breeding.

  • Table 4.12. Least-squares means and standard errors (in parentheses) of relative liveweight change (%) in fertile does from date at conception until selected time points during a reproductive cycle of one year duration, according to mating season and parity at breeding.

  • Table 4.13. Least-squares means and standard errors (in parentheses) of liveweights (kg) of fertile does at selected time points during a reproductive cycle of one year duration, according to litter size and parity at breeding.

  • Table 4.14. Least-squares means and standard errors (in parentheses) of relative liveweight change (%) of fertile does at selected time points during a reproductive cycle of one year duration, according to litter size and parity at breeding.

  • Table 5.1. Explanatory variables included in initial models fitted to lactation data. Nesting factors are given in square brackets. (PC=production cycle; MS=mating season).

  • Table 5.2. Tests of fixed effects and estimates of variance components and covariance parameters for lactation curves fitted to daily milk yields (square root-scale) by a) mating season and parity class, and b) litter size, postpartum liveweight, and lagged range condition score [II].

  • Table 5.3. Tests of fixed effects and estimates of variance components and covariance parameters for growth curves fitted to cumulative milk yields (square root-scale) by a) mating season, and b) litter size and postpartum live weight.

  • Table 5.4. Least-squares means and 95 percent confidence limits (in brackets) of cumulative milk yields at selected time points during lactation, according to mating season, litter size and postpartum live weight of does.

  • Table 7.1. Estimated kidding intervals (days) for different values of conception rate and period between successive mating events. Calculations are based on equation (37) of Chapter 6.

  • Table 7.2. Results of herd productivity assessments by mating season group for the baseline scenario. All values are means over three consecutive production cycles (percentage coefficients of variation in parentheses). Note that values for, RPI, FPI, and offtake rates relate to a time unit length of five months. Estimates in a given column without common letters in their superscripts differed at the five percent level of significance.

  • Table 7.3. Optimal steady state stage distributions and culling policies (percentage stage abundances and offtake rates) in each mating season group for the baseline scenario, obtained from runs of "average" models.

  • Table 7.4. Elasticities of herd growth rate to fecundity and survival rates for the six mating season groups and the aseasonal reference herd (BF = breeding female). Calculations were based on “average” projection matrices (baseline scenario).

  • Table 7.5. Elasticities of herd growth rate to probabilities of surviving and moving into pregnant stages for the six mating season groups and the aseasonal reference herd (BF = breeding female). Calculations were based on “average” projection matrices (baseline scenario).

  • Table 7.3. Optimal steady state stage distributions and culling policies (percentage stage abundances and offtake rates) in each mating season group for the baseline scenario, obtained from runs of „average“ models.

  • Table 7.7. Percentage energetic efficiency of some grazing systems, defined as the ratio of gross energy output for human consumption divided by metabolizable forage energy intake.

  • Table 7.8. Productivity indices for various goat production systems in Africa

Images

  • Figure 2.1. Observed median range condition in the herblayer (score [I]) and browse-adjusted range condition (score [II]) by mating season group (MS) over a production cycle of one year duration. Mating starts in week 0; the x-axis label "-4" refers to the time point four weeks prior to mating.

  • Figure 2.2. Box and Whisker plot of doe body weight at mating against parity number. The fitted function is a second-order polynomial.

  • Figure 2.3. Frequency distribution by mating season of the duration between onset of mating and parturition in fertile females.

  • Figure 3.1. Estimated hazard rate curves by mating season (MS) for the data on kid survival.

  • Figure 3.2. Estimated hazard rate curves for the kid survival data, according to a) birth weight, and b) total milk yield of mother until weaning.

  • Figure 3.3. Estimated hazard rate curves according to reproductive status for the data on doe survival.

  • Figure 3.4. Estimated hazard rate curves according to mating season (MS) for the data on doe survival.

  • Figure 4.1. Estimated relationships (least-squares means) between daily weight gains by mating season (MS) and age of kids from a) birth until one year of age, and b) from one to two years of age.

  • Figure 4.2. Relationships (least-squares means) between daily weight gains and age of kids, according to a) milk yield until weaning and b) litter size (estimates were obtained from model a) in Table 4.3).

  • Figure 4.3. Effect of lagged median range condition scores on daily weight gains of kids until 50 weeks of age.

  • Figure 4.4. Estimated growth curves (least squares means) of kids by mating season group (MS).

  • Figure 4.5. Estimated growth curves (least squares means) of does by mating season over a reproductive cycle of one year duration. The time origin corresponds approximately to the date at conception.

  • Figure 4.6. Estimated simple effects of litter size within parity class on relative liveweight change in does over a reproductive cycle of one year duration. The time origin corresponds approximately to the date at conception.

  • Figure 5.1. Estimated lactation curves (least squares means) by mating season (MS).

  • Figure 5.2. Estimated lactation curves (least squares means) by a) litter size, b) postpartum live weight (kg), and c) lagged range condition score [II] in each time interval.

  • Figure 5.3. Estimated cumulative milk yield curves (least squares means) by mating season (MS).

  • Figure 6.1. Life cycle graph of breeding females of a hypothetical livestock species.

  • Figure 6.2. Modified life cycle graph of a hypothetical livestock species taking into account male juveniles (stage 1m) and female and male surplus stages (2f and 2m). The parameter σ denotes the fixed proportion of female immatures that are to be reared as breeding females. A sex ratio of unity was assumed at birth.

  • Figure 7.1. Life cycle graph for breeding does. The reproductive cycles for does in parities 2, 3 and 4 are identical to the one depicted for primiparous does. Surviving breeding does are culled (or die) after completing the fifth lactation.

  • Figure 7.2. Life cycle graph for breeding does, aseasonal breeding regime.

  • Figure 7.3. Life cycle graphs for surplus animals.

  • Figure 7.4. Relationships between degree of maturity and protein and fat contents in empty bodies of goats (data from Viljoen et al., 1988).

  • Figure 7.5. Values of selected production efficiency measures by mating season group for the baseline scenario. (EBWT = Empty body weight).

  • Figure 7.6. Values of λ (in percent increase per 5 months time unit), optimum offtake rate, and maximum proportional offtake rate according to mating season group for the baseline scenario ("average" models).

  • Figure 7.7. Effects of culling age of male surplus animals on energetic efficiency determined at the male herd and total herd level for mating season groups 1 and 4 ("average" models). Note that energetic efficiencies for the newborn stage could not be calculated by considering the male herd only since, at birth, cumulative metabolizable energy requirements were assumed to be equal to zero.

  • Figure 7.8. Effect on asymptotic herd growth rate of proportional changes in conception rate by +3.5 to –30 percent (group 1, corresponding to conception rates of 100 to 67 percent) and +12.5 to –30 percent (group 4, corresponding to conception rates of 100 to 62 percent). Calculations for mating season groups 1 and 4, based on “average” projection matrices (baseline scenario).

  • Figure 7.9. Effect on asymptotic herd growth rate of proportional changes in age at first breeding over the range from 10 (a decrease of 33.3 percent from an initial value 15 months) to 20 months (an increase of 33.3 percent from an initial value 15 months). Calculations for mating season groups 1 and 4, based on “average” projection matrices (baseline scenario).

  • Figure 7.10. Changes in energetic efficiency, RPI, and FPI in the increased milk offtake scenario relative to the baseline scenario.

  • Figure 7.11. Relationship between asymptotic population growth rate (λ) and energetic efficiency (EE), reproductive performance (RPI), and flock performance (FPI). Second-order polynomials in λ were used to fit trendlines. The plots are based on the results from the productivity assessments for all mating seasonxproduction cycle combinations, including results for the aseasonal reference group as well as for the seven “average” model runs.

  • Figure A.1. Forrester-Diagramm of the simulation model used to assess the effect of an aseasonal breeding regime on the distribution of pregnancies in goat herds.