Integrating Soil Sample analysis and Conservation Investment Assessment for Increased Food Production in Tropical

Agriculture – a Case Study of Kenya’s Central HighlandsAnders Ekbom and Thomas Sterner, Dpt of Economics, Environmental Economics Unit, University of

Gothenburg www.focali.se

ABSTRACT

This paper integrates economic variables, soil properties, and soil conservation technologiesin order to estimate agricultural output among small-scale farmers in Kenya’s highlands.Increasing our understanding of determinants of agricultural production is essential in view ofthe challenges posed by climate change, land degradation, increasing food needs andcompetition over land resources. We find that integrating economics and soil science isvaluable in this area of research. Omitting soil capital can cause omitted-variables biasbecause farmer’s choice of inputs depend both on the quality and status of the soil and thefarm’s specific economic conditions, such as availability and cost of labor, fertilizers, and otherinputs.

The study shows that key soil properties produce very different – and sometimes evennegative - output effects. Hence, the central policy implication is that while fertilizers aregenerally beneficial, their application is a complex art, and more is not necessarily better. Thelimited local market supply of fertilizers, combined with the different output effects ofconservation technologies, highlight the importance of improving the performance of inputmarkets and strengthening agricultural extension.

Rationale, Objectives

Issue: What determines agricultural production?

Economics: Q = f(K,L,F,P,X); Output (Q), Capital (K), Labour (L), fertilizers (F), pesticides (P), Soil Cons techn. (X)

⇒ limited integration of soil capital, heterogeneity in soil properties (S) across farms => Biased results due to omitted variables

Soil science, agronomy: Limited attention to farmers’ behaviour & response, labour constraints

Empirical approach: Estimate agricultural prod. function which combines data on: soil, socio-economic factors, soil conservation investments

The Kenyan situation

Staggering food production, growing population ⇒ declining yields/cap., declining yields/ha for major crops

Wide-spread soil erosion (5-150 tons/ha/yr) ⇒Negative on-site yield effects (+ downstream effects)

Farmer response: soil & water conservation (terraces, green manure, agro-forestry), differentiated crop & input mixes

Government response: Extension advice, Catchment planning & management support + general public services (roads)

The Model (1)

Agricultural production (general form):

Q = f(Z; X; S)

Q = Output

Z = Labour (L), fertiliz.(F), manure (M), land (K)

X = Soil cons. investments, socio-economic factors (age, education, access to services etc), capital (livestock)

S = Soil capital

The Model (2)

Ag. Production: specific form, excluding soil properties (S):

Ag. Production: specific form, including S:

1 1 1

1(3) ln( ) ln( ) ln( ) ln( )2

n n nA

i i ij i j i ii i j

Q Z Z Z Xα β β γ ε= = =

= + + + +∑ ∑ ∑

1 1 1

1(4) ln( ) ln( ) ln( ) ln( )2

ln( )

n n nB

i i ij i ji i j

i i i i

Q Z Z Z

X S

α β β

γ δ ε= = =

= + +

+ + +

∑ ∑ ∑

The Study Area

Two catchments in Kenya’s Central Highlands, SW Mt Kenya, 1500m.a.s.l., 2.8 acres/farm, steep slopes.

Climate: erosive rains (2 periods), erodible deep soils

Cash crop: coffee (mango, French beans, macadamia nuts)Old food crops: cassava, yams, sorghum, millet, kale, maize”New” crops: hybrid-maize, beans, potatoes, banana, papaya, carrots, cabbage, avocado, arrow root, onion, passion fruit, pumpkin, sugar-cane, tomatoes, sweet potatoes, oranges…

Technology: low (hoe only), family + hired labour, few varieties fertilizer, pesticides

Descriptive 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

Descriptive statistics: soil propertiesSoil 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

Heterogeneity in S across farms:

0

1

2

3

4

5

6

7

8

1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239

pH

0

2

4

6

8

10

12

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241

K

0

20

40

60

80

100

120

140

160

180

200

1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243

P Clay (%)

0

10

20

30

40

50

60

70

80

90

1 15 29 43 57 71 85 99 113 127 141 155 169 183 197 211 225 239

Statistical results: Correlating Predicted Output and Observed Output

lnQ = - 0.12 + 1.02(lnQ RM 1)adj. R2 = 0.45; t-value = 14.3; F-value = 204

7.0

8.0

9.0

10.0

11.0

12.0

13.0

7.0 8.0 9.0 10.0 11.0 12.0

Predicted O utput (lnQ RM 1)

Obs

erve

d O

utpu

t (ln

Q)

Statistical results: Mean Output Elasticities of Explanatory Variables

Output UM RM1 RM2

elasticity Variable Estimate t-value Est. t-value Est. t-valu

ˆˆ

QQLε Labor 0.131 1.23 0.114 1.09 0.000 0.01

Q̂Fε Fertilizer 0.254 3.01 0.272 3.31 0.277 3.39

Q̂Mε Manure 0.141 2.01 0.150 2.30 0.243 3.95

Q̂Kε Land 0.475 3.22 0.464 3.31 0.479 3.59

1Q̂Iε Green manure 0.130 1.20 0.131 1.67

1Q̂Iε Terrace cons. 0.188 1.45 0.204 1.65

2Q̂Iε Access infrastruct. -0.134 -2.11 -0.131 -2.36

3Q̂Iε Tree capital 0.043 1.27 0.064 1.99

1ˆ ˆQSε Nitrogen 0.290 1.70 0.273 1.62

2ˆ ˆQSε Potassium 0.450 1.57 0.352 1.78

3ˆ ˆQSε Phosphorus -0.266 -2.25 -0.220 -2.30

Statistical results

• Integration of soil properties (Si) reduce output effect of labour and other typical economic determinants (eg F)

• Output elasticity of nitrogen, potassium positive

• Closer access to infrastructure positive output effect

• Output elasticity of fertilizer (F), manure (M) moderate

• Investments in Cons Terraces, green manure etc. positive output effects

• Land largest output effect;

Methodology & Policy Conclusions

• Inclusion of soil capital & SC factors in basic model shows: – slightly higher explanatory power– (for economists): individual soil properties important production factors – significant interaction effects across production factors (labour, fertilizer etc) and across soil properties

• Promote research based on integrated fields of analysis• Enhance opportunities for adequate on-farm soil assessment• Improve performance of input markets, e.g. increase supply of specific combinations of fertilizers• Increase access to public goods & services (roads, markets)