THE DRIVERS OF LIVESTOCK
PRODUCTIVITY IN THE
NIGERIA
Kelechi G. Odoemena%, Jeffrey P. Walters, Olubunmi. A. Alawode
* Corresponding author: kelechiodoemen@gmail.com
ISDCCONFERENCE, ICELAND AUGUST 2018
Introduction
¢ The livestock system is an open, inter-related, feedback dominated,
non-linear featuring time delays as in gestation periods, processing
and response of price to change in demand.
¢ The livestock system is characterized by incomplete information,
uncertainty and distributed decision-making (FMARD, 2016).
¢ The livestock subsector has been reported to be highly dynamics
(Thornton, 2014) as increasing demand, climate constraints,
environment, health and social cultural issues are factors affecting the
subsector globally.
Research Questions
¢ What are the key drivers of livestock productivity in Nigeria?
* How do these drivers relate with each other and affect livestock
productivity?
¢ What are the future productivity levels based on current trend?
* How will the subsector’s productivity perform in the long run based
on controlled changes in land use and livestock feed?
Objectives of the Study
The general objective of this study is to use system dynamics approach to
model drivers of the Nigerian livestock subsector’s productivity.
The specific objectives of the study are to;
* Identify key drivers of productivity in the livestock subsector
¢ Examine the trend of key drivers of livestock productivity
¢ Predict future productivity levels based on existing trend in the
subsector
* Forecast productivity levels based on scenario changes in land use and
livestock feed in the livestock subsector
Literature Review
Theoretical Framework: Theories of production, productivity, systems
and dynamic systems.
Methodological Review: Agricultural productivity measures,
productivity assessment and herd growth models; system dynamic
models.
Empirical Review: Livestock production and its trends; livestock drivers
and livestock models; non-dynamic models of livestock productivity.
Conceptua
Using livestock
subsector’s
indicators as drivers
Hypothesize
preliminary
model
structure
Producti m
factors
Animal feed
Pasture size
Livestock capital
Livestock labour
Source: Author’s conception, 2017
Examine
drivers of
livestock
subsector
productivity
Economic factors
GDP Per Capita
Livestock input growth
Livestock output
Livestock output growth
Domestic demand
Livestock prices
Supply of animal protein
Production value
Food deficit
| Framework
Build Causal
Loop Diagram
(CLD) model
Perform Assess
polarity subsector’s
analysis trend
ag =
Build Stock and
Flow Diagram |
(SFD) model |
Simulate
SFD model
i
v
Parameterize
model with
variable estimates
Predict future
livestock
subsector’s events
Environmental_and
social factors
Methane emission
from livestock
Herdsmen attack
and insecurity
Population
estock di
Modify SFD
model based
on scenarios
=
Simulate
model with
selected
scenarios
a
:
Changes in
pasture size
Changes in
feed
consumption
Research Methodology
¢ The study covered livestock productivity in Nigeria
* Quantitative data was obtained from sources like the World Bank,
FAO, NBS and USDA
¢ The data set used in the study ranged from 1980 - 2016
* The data was analysed using trend line, regression, Causal Loop
Diagram (CLD), Stock Flow Diagram (SFD) and sensitivity analysis
* Forecast simulation period ranged from 2017 — 2041 (25 years)
Results (Causal Loop Diagram)
Low
Livestock input ate
growth
domestic demand R4
Price & Demand
Population
te)
Supply. Demand 42Pth of food
& Deficit ficit
animal feed
Livestock
1) output
Production ?
Driver
Livestock output
growth
pasture size Supply of animal
rissa mies Price, Demand &
Deficit
+ Livestock
production value
Value added per
livestock worker
ivestock Pric
>} Methane emissions
from livestock
Livestock disease
prevalence
Fulani herdsmen conflict and insecurity in Northern Nigeria
Results (Stock and Flow Diagram)
0.027
Birth rate
Population
Depth of food deficit
Livestock TFP
Feed
Domestic demand
Livestock price
Livestock value
Pasture
GDP per capita
% Ag Input Growth
Livestock
Livestock capital
>
i a
E > So
Livestock ME ee eee Livestock output
GHG emission
Results (Livestock Productivity Forecast)
Current Trend Forecast
230
210
190
170
Results (Productivity Forecast based on
Scenarios)
300
278
256
233
211
189
167
144
122
100
Effect of 40% reduction on TFP
1 4 6 9 12 14 17 20 22 25
Year
— Baseline —Feed (-40%)
—Pasture (-40%)
350
328
306
283
261
239
217
194
172
150
Effect of 40% increment on TFP
4 6 9 12 14
Year
—Pasture (+40%)
—— Feed (+40%)
17
Baseline
Summary and Conclusion
* The system dynamics approach was well suited in analysis and
prediction of livestock productivity
¢ There are 3 reinforcing and 3 balancing feedback loops
¢ The regression models had statistically significant variables
* Livestock productivity is expected to rise to 223% by 2041
* A commensurate increase in pasture expenditure raises livestock
productivity more than feed expenditure
Recommendations
* Increase spending on pasture
* Limit feed expenditure
* Quantitative data gathering
Selected References
Atzori, A. S., Tedeschi, L. O., & Cannas, A. (2011). The dynamics in the dairy cattle sector:
policies on cow milk production can reduce greenhouses gas emissions and land use.
In Proceeding of the 29th International Conference of the System Dynamics Society.
Washington, DC, USA.
O’Donnell, C. J. (2010). Measuring and decomposing agricultural productivity and profitability
change. Australian Journal of Agricultural and Resource Economics, 54(4), 527-560
Parsons, D., & Nicholson, C. F. (2017). Assessing policy options for agricultural livestock
development: A case study of Mexico’s sheep sector. Cogent Food & Agriculture, 3(1),
1313360.
Steinfeld, H., Wassenaar, T., & Jutzi, S. (2006). Livestock production systems in developing
countries: status, drivers, trends. Rev Sci Tech, 25(2), 505-516.
Stephens, E. C., Nicholson, C. F., Brown, D. R., Parsons, D., Barrett, C. B., Lehmann, J., ... &
Riha, S. J. (2012). Modelling the impact of natural resource-based poverty traps on food
security in Kenya: The Crops, Livestock and Soils in Smallholder Economic Systems (CLASSES)
model. Food Security, 4(3), 423-439.
