Transcript Econometric-Process Simulation Models for Semi-Subsistence Agricultural Systems:

Econometric-Process Simulation Models for
Semi-Subsistence Agricultural Systems:
Application of the NUTMON Data for Machakos
Various features of semi-subsistence ag pose
challenges to modeling:
• low specialization, high diversification, many crops
and livestock
• complex intercropping
• high rates of crop failure
• limited use of purchased inputs
• small parcels, changes in parcel configuration
between seasons
• family and hired labor
Various features of semi-subsistence ag pose
challenges to modeling (cont.):
• partially observed prices (incomplete input and
output markets)
• transaction costs high and difficult to measure
(transport, information)
• weak institutions (legal, property rights, financial)
Implication: conventional models and statistical analysis may
lead to faulty inferences and predictions.
Integrated Assessment Approach to
Modeling Ag. Production Systems
Soils & Climate Data
Crop/Livestock Models
Environmental
Process Models
Environmental
Outcomes
Economic Data
Yield
Economic Model
Land Use &
Management
Economic
Outcomes
Econometric-Process Simulation
Models
• Use econometric production models to
parameterize statistical simulation model of land
use (extensive margin) and input use (intensive
margin) decisions of farmers
• Use crop models to estimate spatial variation in
productivity and simulate scenarios
• Use spatially referenced data to characterize
spatial distributions of land use and
management
New Developments in E-P
Simulation Models
• Earlier models focused on ag-environment
interactions at the field scale
• For analysis of semi-subsistence systems
need whole farm analysis
– Crop-livestock interactions
– Poverty and food security indicators
Farm Definition
(Location, Size, Fam Labor, TLU)
Livestock Management
(Grazing, Feed)
Crop Output & Failure
Livestock Output
(Grain, Residues)
(Milk, Manure, Monthly time step)
Crop Production Decisions
Crop System Choice (Seasonal
time step)
•Irrigation
•Mixed
•Organic Fert (Manure & Compost
•Maize mono
•H Labor
•Veges
•Mineral Fert
•Grass
•Pesticides
•Maize--Bean
Output Variables
(Crop output, livestock output,
land use, input use, farm income )
Design of the Machakos EP Simulation Model
Machakos Variable Means
V1
V2
V3
V4
V5
V6
0.35
3996
33
39
57
1.69
0.23
231
5
3
54
0.01
0.56
582
9
24
54
0.27
0.98
347
6
0
21
0.12
0.10
828
2
19
57
6.47
0.55
185
65
20
15
2.70
0.48
0.36
0.28
0.43
0.38
0.18
0.09
0.00
0.17
0.55
0.23
0.16
0.05
0.16
0.41
0.16
0.20
0.00
0.10
0.54
0.38
0.22
0.16
0.03
0.21
0.53
0.08
0.26
0.09
0.04
2.68
1.30
175
100
46
8.77
5255
2.26
1.94
75
77
50
8.27
2770
2.77
1.65
113
84
52
6.84
15078
7.04
2.22
154
61
64
7.24
7873
1.50
2.15
169
93
82
8.50
2405
3.48
1.08
152
88
76
7.35
8814
Inputs
Parcel Size (ha)
Manure (kg/ha)
Hired Labor (md/sea)
Mineral Fertilizer (kg/ha)
Seed (kg/ha)
Pesticides (kg/ha)
Systems
Mixed Intercrop
Maize
Vegetables
Grass
Maize-Bean
Farm Characteristics
Size (ha)
Livestock Units
Familly Labor (md/sea)
Literacy (%)
Famer Occupation (%)
Family Size
Off-Farm Income (ks/yr)
Machakos Parcel Size Distribution
35
30
Frequency (%)
25
20
15
10
5
0
0.05
0.1
0.3
0.5
0.7
0.9
1.1
1.3
Parcel Size (ha)
1.5
2
3
4
5
Machakos Mineral Fertilizer Distribution, Maize
90
80
70
60
50
40
30
20
10
0
0
25
50
75
Mineral Fertilizer (kg/ha)
100
125
150
Machakos Maize Yield Distribution
35
30
25
20
15
10
5
0
0
100
500
1000
2000
3000
4000
Maize Yield (kg/ha)
5000
6000
7000
8000
How well do the models work?
Simulated and Actual Land Use for Montana Sub-MLRAs
0.45
0.40
35
0.35
30
Actual
0.30
y = 0.9431x + 0.0081
2
R 25
= 0.7784
0.25
0.20
20
0.15
15
0.10
10
0.05
0.00
0.00
5
0.05
0.10
0.600000
0.15
0.20
0.25
0.30
Simulated
0.35
0.40
0.45
0
Mixed
Maize
Veges
Simulated
Observed
0.500000
0.400000
y = 0.4752x + 0.1015
R2 = 0.3798
0.300000
0.200000
0.100000
0.000000
0.000000
0.100000
0.200000
0.300000
0.400000
0.500000
0.600000
Grass
Maize-Bean