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Determinants of Soil Capital
- an Application to Kenya’s Central Highlands
Anders Ekbom, Dpt of Economics, Environmental Economics Unit, University of Gothenburg
www.focali.se
ABSTRACT
This paper combines knowledge from soil science and economics to estimate economic determinants of soil
capital. Explaining soil capital facilitates a better understanding of constraints and opportunities for increased
agricultural production and reduced land degradation. This study builds on an unusually rich data set that
combines data on soil capital (represented by chemical and physical properties) and economic data on
household characteristics, labor supply, crop allocation, and conservation investments. The study yields both
methodological and policy-relevant results.
On methodology, the analysis shows that soil capital is heterogeneous with soil properties widely distributed
across the farms. Likewise, farmers’ investment decisions and soil management vary widely across farms.
Hence simplifications of soil capital, which are common in the economics literature, may have limited validity. On
the other hand, soil science research, when limited to biological, physical, and chemical characteristics, fails to
recognize that soil is capital that is owned and managed by farmers. Such research runs the risk of omitting
important socio-economic determinants of soil capital. It also excludes the opportunity to explain some of the
dynamics that are determined by soil’s stock character.
For policy, this study shows that farmers’ soil conservation investments, allocation of labor, manure and fertilizer
inputs, and crop choice do indeed determine variation in farmers’ soil capital. Particularly strong positive effects
on key soil nutrients (nitrogen, phosphorus, and potassium) were observed for certain conservation
technologies. Extension advice unexpectedly showed no significant effects on soil capital. The wide distribution
of soil properties across farms reinforces the need to tailor technical extension advice to the specific
circumstances in each farm, and to enhance the integration of farmers’ knowledge and experiences, expert
judgment, and scientific soil analysis at the farm level.
Rationale
1. Soil science, agronomy: Economic factors
(L, I, H etc.) rarely included in the analysis
of soil (S); S not treated as capital.
2. Economics: limited integration & analysis
of soil capital in economic research on
agric. prodn (Barrett, 1991; 2004)
=> Options for more integrated analysis, to
enhance understanding of soil
Depreciation of Soil capital
Appreciation of Soil capital
Model
(1) S = f(H, I, X, PF, R, E)
S = Soil capital (soil properties, Si)
H = household characteristics (age, sex, ed.)
I = soil cons. investments (I1-I11)
X = techn. extension advice
PF = production factors (LQ, F, M)
R = crop choice
E = soil erosion severity
Field Study Area
Data (1): Soil sample statistics
Soil property Unit Mean Min. Max. Std.dev
Soil pH -log H+
5.63 4.1 8.2 0.66
Carbon (C) % 1.51 0.16 2.81 0.45
Organic matter % 2.59 0.28 4.83 0.78
Nitrogen (N) % 0.18 0.08 0.6 0.06
Potassium (K) m.eq./100 g. 2.36 0.15 11 1.73
Sodium (Na) m.eq./100 g. 0.14 0 0.6 0.19
Calcium (Ca) m.eq./100 g. 6.48 1.45 20 3.29
Magnesium (Mg) m.eq./100 g. 5.26 0.02 17.42 2.81
Cation exch.cap. m.eq./100 g. 15.69 0 36.8 5.49
Phosphorus (P) ppm 17.84 1 195 24.67
Sand texture % 16.4 5 50 6.85
Clay texture % 63.16 28 82 10.59
Soil property distribution (pH):
0
1
2
3
4
5
6
7
8
9
0 20 40 60 80 100 120 140 160 180 200 220 240 260
Households
pH
pH (H2O)
pH (CaCl)
Soil property distribution (2)
0
5
10
15
20
25
30
35
40
0 20 40 60 80 100 120 140 160 180 200 220 240 260
Households
m.eq./100g.
CEC
Ca
Mg
K
Data (2): Household Statistics
Variable Variable definition Mean Min. Max. Std. dev.
Q Output (KSh) 38313 2050 304450 43252
LQ Ag. Labor supply: (hrs/yr) 1407 90 6060 980
F Chem. fertilizer (KSh) 3504 0 14400 2543.8
M Manure (KSh) 6343 0 40000 7428
K Ag. land area (acres) 2.4 0.2 8.0 1.3
H1 Sex HH head (1=M;0=F) 0.7 0 1 0.5
H2 Age HH head (years) 55.1 20 96 13.9
H3 Education HH head (yrs) 5.7 0 20 4.4
H4 Livestock capital (KSh) 23778 0 150250 20729
H5 Age of coffee trees (years) 22.4 0 54 11.6
H6 Family members 4.2 1 13 2.2
Regression results: Dependent variables
Carbon Nitrogen Phosphorus Potassium
Variable Coeff.
t-
value
Coeff.
t-
value
Coeff.
t-
value
Coeff.
t-
value
Intercept -0.517 -0.65 -1.893 -2.38 -0.429 -0.51 0.669 2.19
Sex (1=M; 0=F) 0.134 2.80 0.028 0.60 0.040 0.83 0.002 0.11
Education (years) -0.284 -0.95 0.307 1.07 0.411 1.35 -0.232 -2.12
Cut-off drains 0.112 1.18 -0.007 -0.07 -0.082 -0.84 0.164 4.48
Crop cover 0.123 1.76 0.129 1.92 0.028 0.39 0.223 7.31
Tillage practices 0.400 3.74 -0.036 -0.35 0.183 1.68 0.129 3.43
Manure conservation 0.263 2.06 0.441 3.61 0.570 4.37 0.185 4.01
Mulching 0.025 0.60 0.123 3.13 0.023 0.55 0.032 2.58
Green manure 0.144 2.12 -0.008 -0.12 -0.021 -0.30 0.061 2.72
Agro-forestry -0.041 -0.61 0.310 4.85 -0.060 -0.87 -0.003 -0.21
Grazing managem. 0.194 0.65 0.762 2.69 0.371 1.23 0.047 0.42
Terrace quality 0.095 0.99 0.355 3.86 -0.002 -0.02 -0.013 -0.42
Coffee trees (years) 0.093 1.59 0.180 3.22 0.063 1.06 0.046 2.16
Manure/acre (KSh) 0.051 0.32 0.461 3.03 0.146 0.90 0.011 0.29
Coffee area share -0.204 -1.94 -0.196 -1.96 0.003 0.03 0.035 0.99
Maize area share -0.124 -1.88 -0.102 -1.62 -0.026 -0.38 -0.013 -0.61
Concl. & Policy implications (1)
 Soils are complex & heterogenous
(spatially, vertically)
 Soil is capital:
- formed by natural & economic factors
- depreciation may be deliberate/rational
- appreciation requires investment, skills
- evaluate the incentives forming soil
capital
Concl. & Policy implications (2)
 Carefully selected soil cons. technology
(effectively) sustains soil capital
 Wide distribution of Si :
- Adjust technical extension advice to
local farm conditions
- need for detailed soil mapping based on
local data
- greater use of soil sample analysis
combined with local knowledge & field
assessment, and economic analysis

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Determinants of Soil Capital

  • 1. Determinants of Soil Capital - an Application to Kenya’s Central Highlands Anders Ekbom, Dpt of Economics, Environmental Economics Unit, University of Gothenburg www.focali.se ABSTRACT This paper combines knowledge from soil science and economics to estimate economic determinants of soil capital. Explaining soil capital facilitates a better understanding of constraints and opportunities for increased agricultural production and reduced land degradation. This study builds on an unusually rich data set that combines data on soil capital (represented by chemical and physical properties) and economic data on household characteristics, labor supply, crop allocation, and conservation investments. The study yields both methodological and policy-relevant results. On methodology, the analysis shows that soil capital is heterogeneous with soil properties widely distributed across the farms. Likewise, farmers’ investment decisions and soil management vary widely across farms. Hence simplifications of soil capital, which are common in the economics literature, may have limited validity. On the other hand, soil science research, when limited to biological, physical, and chemical characteristics, fails to recognize that soil is capital that is owned and managed by farmers. Such research runs the risk of omitting important socio-economic determinants of soil capital. It also excludes the opportunity to explain some of the dynamics that are determined by soil’s stock character. For policy, this study shows that farmers’ soil conservation investments, allocation of labor, manure and fertilizer inputs, and crop choice do indeed determine variation in farmers’ soil capital. Particularly strong positive effects on key soil nutrients (nitrogen, phosphorus, and potassium) were observed for certain conservation technologies. Extension advice unexpectedly showed no significant effects on soil capital. The wide distribution of soil properties across farms reinforces the need to tailor technical extension advice to the specific circumstances in each farm, and to enhance the integration of farmers’ knowledge and experiences, expert judgment, and scientific soil analysis at the farm level.
  • 2. Rationale 1. Soil science, agronomy: Economic factors (L, I, H etc.) rarely included in the analysis of soil (S); S not treated as capital. 2. Economics: limited integration & analysis of soil capital in economic research on agric. prodn (Barrett, 1991; 2004) => Options for more integrated analysis, to enhance understanding of soil
  • 5. Model (1) S = f(H, I, X, PF, R, E) S = Soil capital (soil properties, Si) H = household characteristics (age, sex, ed.) I = soil cons. investments (I1-I11) X = techn. extension advice PF = production factors (LQ, F, M) R = crop choice E = soil erosion severity
  • 7. Data (1): Soil sample statistics Soil property Unit Mean Min. Max. Std.dev Soil pH -log H+ 5.63 4.1 8.2 0.66 Carbon (C) % 1.51 0.16 2.81 0.45 Organic matter % 2.59 0.28 4.83 0.78 Nitrogen (N) % 0.18 0.08 0.6 0.06 Potassium (K) m.eq./100 g. 2.36 0.15 11 1.73 Sodium (Na) m.eq./100 g. 0.14 0 0.6 0.19 Calcium (Ca) m.eq./100 g. 6.48 1.45 20 3.29 Magnesium (Mg) m.eq./100 g. 5.26 0.02 17.42 2.81 Cation exch.cap. m.eq./100 g. 15.69 0 36.8 5.49 Phosphorus (P) ppm 17.84 1 195 24.67 Sand texture % 16.4 5 50 6.85 Clay texture % 63.16 28 82 10.59
  • 8. Soil property distribution (pH): 0 1 2 3 4 5 6 7 8 9 0 20 40 60 80 100 120 140 160 180 200 220 240 260 Households pH pH (H2O) pH (CaCl)
  • 9. Soil property distribution (2) 0 5 10 15 20 25 30 35 40 0 20 40 60 80 100 120 140 160 180 200 220 240 260 Households m.eq./100g. CEC Ca Mg K
  • 10. Data (2): Household Statistics Variable Variable definition Mean Min. Max. Std. dev. Q Output (KSh) 38313 2050 304450 43252 LQ Ag. Labor supply: (hrs/yr) 1407 90 6060 980 F Chem. fertilizer (KSh) 3504 0 14400 2543.8 M Manure (KSh) 6343 0 40000 7428 K Ag. land area (acres) 2.4 0.2 8.0 1.3 H1 Sex HH head (1=M;0=F) 0.7 0 1 0.5 H2 Age HH head (years) 55.1 20 96 13.9 H3 Education HH head (yrs) 5.7 0 20 4.4 H4 Livestock capital (KSh) 23778 0 150250 20729 H5 Age of coffee trees (years) 22.4 0 54 11.6 H6 Family members 4.2 1 13 2.2
  • 11. Regression results: Dependent variables Carbon Nitrogen Phosphorus Potassium Variable Coeff. t- value Coeff. t- value Coeff. t- value Coeff. t- value Intercept -0.517 -0.65 -1.893 -2.38 -0.429 -0.51 0.669 2.19 Sex (1=M; 0=F) 0.134 2.80 0.028 0.60 0.040 0.83 0.002 0.11 Education (years) -0.284 -0.95 0.307 1.07 0.411 1.35 -0.232 -2.12 Cut-off drains 0.112 1.18 -0.007 -0.07 -0.082 -0.84 0.164 4.48 Crop cover 0.123 1.76 0.129 1.92 0.028 0.39 0.223 7.31 Tillage practices 0.400 3.74 -0.036 -0.35 0.183 1.68 0.129 3.43 Manure conservation 0.263 2.06 0.441 3.61 0.570 4.37 0.185 4.01 Mulching 0.025 0.60 0.123 3.13 0.023 0.55 0.032 2.58 Green manure 0.144 2.12 -0.008 -0.12 -0.021 -0.30 0.061 2.72 Agro-forestry -0.041 -0.61 0.310 4.85 -0.060 -0.87 -0.003 -0.21 Grazing managem. 0.194 0.65 0.762 2.69 0.371 1.23 0.047 0.42 Terrace quality 0.095 0.99 0.355 3.86 -0.002 -0.02 -0.013 -0.42 Coffee trees (years) 0.093 1.59 0.180 3.22 0.063 1.06 0.046 2.16 Manure/acre (KSh) 0.051 0.32 0.461 3.03 0.146 0.90 0.011 0.29 Coffee area share -0.204 -1.94 -0.196 -1.96 0.003 0.03 0.035 0.99 Maize area share -0.124 -1.88 -0.102 -1.62 -0.026 -0.38 -0.013 -0.61
  • 12. Concl. & Policy implications (1)  Soils are complex & heterogenous (spatially, vertically)  Soil is capital: - formed by natural & economic factors - depreciation may be deliberate/rational - appreciation requires investment, skills - evaluate the incentives forming soil capital
  • 13. Concl. & Policy implications (2)  Carefully selected soil cons. technology (effectively) sustains soil capital  Wide distribution of Si : - Adjust technical extension advice to local farm conditions - need for detailed soil mapping based on local data - greater use of soil sample analysis combined with local knowledge & field assessment, and economic analysis