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 soilcapital. Explaining soil capital facilitates a better understanding of constraints and opportunities for increasedagricultural production and reduced land degradation. This study builds on an unusually rich data set thatcombines data on soil capital (represented by chemical and physical properties) and economic data onhousehold characteristics, labor supply, crop allocation, and conservation investments. The study yields bothmethodological and policy-relevant results.

On methodology, the analysis shows that soil capital is heterogeneous with soil properties widely distributedacross 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. Onthe other hand, soil science research, when limited to biological, physical, and chemical characteristics, fails torecognize that soil is capital that is owned and managed by farmers. Such research runs the risk of omittingimportant socio-economic determinants of soil capital. It also excludes the opportunity to explain some of thedynamics that are determined by soil’s stock character.

For policy, this study shows that farmers’ soil conservation investments, allocation of labor, manure and fertilizerinputs, and crop choice do indeed determine variation in farmers’ soil capital. Particularly strong positive effectson key soil nutrients (nitrogen, phosphorus, and potassium) were observed for certain conservationtechnologies. Extension advice unexpectedly showed no significant effects on soil capital. The wide distributionof soil properties across farms reinforces the need to tailor technical extension advice to the specificcircumstances in each farm, and to enhance the integration of farmers’ knowledge and experiences, expertjudgment, and scientific soil analysis at the farm level.

Rationale1. 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 choiceE = soil erosion severity

Field Study Area

Data (1): Soil sample statisticsSoil 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 260Households

m.e

q./1

00 g

.

CECCaMgK

Data (2): Household StatisticsVariable 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 capitalWide 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