1. 16th ICABR Conference - 128th EAAE Seminar
“The Political Economy of the Bioeconomy: Biotechnology and Biofuel”
Ravello - June 24-27, 2012
Farming Systems Study, with the New LSMS Data – Preliminary Results from Uganda
Hans P Binswanger-Mkhize(1), Calogero Carletto (2), Sara Savastano(2), Alberto Zezza(2)
China Agricultural University, Beijing(2) World Bank
(3) CEIS – University of Rome Tor Vergata

2. Objectives
Use the LSMS data for 6-8 African countries to characterize their current agricultural systems across their climatic zones
To apply the Boserup-Ruthenberg framework of the intensification of farming systems and agricultural technology use
To look at the determinants of agricultural production, private investment and growth
agroclimate
population density
public investment into infrastructure, irrigation, and services
output prices, fertilizer prices and wages
access to banks

3. The Boserup-Ruthenberg Framework
Higher population density and market access
Reduce the proportion of land agricultural held under fallow
Leads to the transition from the hand how to the plough via animal draft or tractors
Leads to the introduction of organic fertilizers and manures
Drives investment in drainage and other land improvements, and in irrigation
Increases labor use required to produce annual food supply
Leads to higher outputs per ha
Encourages adoption of yield increasing technologies

4. The LSMS-ISA data sets
6 countries, to have panel data sets of around 3000 households, managed by World Bank.
Ethiopia, Uganda, Tanzania, Nigeria, Niger, and Malawi
Ghana supported by EGC, Yale, and Burkina by USAID
Witch consumption, all incomes, health, education and agricultural data.
Collection of production, cost, land rights, technology, and land quality at individual plot level.
Extensive collection of community data on infrastructure, services, organizations.
Tanzania, Uganda, Ethiopia, Malawi first round are available.
Uganda has a baseline in 2004/05.

5. Econometric issues
Populations move to areas of higher agricultural potential and other location advantages
Governments will put more investments in infrastructure, irrigation, and agricultural services in such areas
Therefore we cannot take either population density or public investments as exogenous to the agroclimate
And we cannot analyze the impacts of the determinants of farming systems in the cross section
And even less the output supply, input and investment decisions of farmers

6. Agroclimate, Population density, and rural infrastructure in India, 1974

7. With Cross section data
We can do a descriptive analysis across the different agroclimate zones and sub-zones covered in the studies
We can estimate the correlations between the different variables characterizing the farming system
R-value: Proportion of agricultural land under fallow
Average length of fallow
Cropping intensity and irrigation intensity
Adoption of organic manure and fertilizers, mechanization and high yielding varieties
Crop yields in value terms
Estimate the causal impact of agro-climate and geographic location on value of farming systems characteristics and value of crop output

8. With Panel data
Estimate the causal impact of population density and market access on farming systems characteristics and value of output
Add other variables whose impact on agricultural output, investment, adoption etc. we would like to know:
Prices and wages
Other infrastructure variables
Agricultural services
Access to banking

9. Analytical Framework for cross section
I = household, j = Enumeration Area, k = kth dependent variable.
(1)
Where D stands for any of the dependent variables
 = the agroclimatic potential: maximum monetary yield per ha at international prices and the current cropping patterns of farmers.
The Dependent variables Dk are
 = Agroclimatie population density: persons per 1000 dollars of agroclimatic potential.
I = Road density or a vector of infrastructure variables
T = High yielding varieties, or a vector of technologies in use
R = Irrigation
Q = Aggregte crop output per ha
 = Farm profits

10. Data and Descriptive Statistics
We use household and community level data from the ALMS Uganda
We transform HH/Community level data into Enumeration area data (203 Eas)
We complement our dataset with GIS data on Agro Ecological Zone, NDVI,
And population density at district level from the Uganda National Livestock Census 2008

11. Agro-Ecological Zones
Source: HarvestChoice

12. Descriptive Statistics
Central
East
North
West
Total
Population Density
401
399
232
237
322
Distance from HH to road in km
2.59
2.77
7.06
6.41
4.58
R-Value
0.64
0.71
0.56
0.58
0.63
Crop Intensity
1.75
1.85
1.66
1.82
1.77
HH has irrigation
1.29%
1.42%
0.83%
1.06%
1.17%
Tropical Livestock Units: total
1.40
1.57
1.63
1.50
1.52
Dummy Purchased Improved Seeds
13%
31%
36% 2%
19%
Value of Crop Production/ha
Share of Sub-Counties by AEZ
Tropic ( warm / subhumid) 1% 3% 9%
Tropic (warm / humid)
51%
74%
79%
15%
53%
Tropic (cool / subhumid) 4%
46%
Tropic (cool / humid)
49%
25%
14%

13. R-Value and Crop-Intensity

14. Land Utilization

15. Average Length of Fallow

16. Correlation Matrix 1 -0.17* 0.12 -0.07 0.16* -0.05 0.11 0.01 -0.03
Pop Density
Distance
Rvalue
Crop Intensity
Irrigation
Improved Seeds 1
-0.17*
0.12
-0.07
0.16*
-0.05
0.11
0.01
-0.03
Improved Seeds
0.00
-0.02
0.02
0.05
Crop Value
0.29*
0.21*
-0.01

17. Impacts of Agro-Climate and Region
VARIABLES
Population Density
Distance to Road in km
R-Value
Crop Intensity
Value of Crop Production/ha
Probit on Improved Seeds
Tropic cool humid
158.71
-1.05
-0.18
0.12
5,249,106*
0.36
Tropic cool sub-humid
99.63
-5.08*
-0.42***
-0.04
5,411,557*
0.38
Tropic warm humid
35.29
-2.35
0.15
2,821,957
0.14
East
28.19
0.47
0.07
0.09**
441,108.98
0.24***
North
4.97***
-0.07
-0.10** **
0.32***
West *
5.08***
0.03
0.16***
2,954,777**
-0.20*
Constant
304.99
4.30*
0.82***
1.62***
1,590,906
Observations
203
R-squared
0.05
0.23
0.27
0.17
0.21
0.18

18. Just at the very beginning Few surprises so far