Salau, Sheu

Takeshima, Hiroyuki

English

Research Papers in Economics

Assessing Complementarities Among Farm Machineries Through Farmers' 
Investment Behaviors Under An External Capital Injection – Implications on 
Agricultural Mechanization and Tractorization In Sub-Saharan Africa
Hi ki T k hi Hiroyuki Takeshima
International Food Policy Research Institute, H.takeshima@cgiar.org
Sheu Salau Sheu Salau
International Food Policy Research Institute
Poster prepared for presentation at the Agricultural & Applied Economics  oste p epa ed fo p ese tatio at t e g icultu al & pplied co o ics
Association’s 2011 AAEA & NAREA Joint Annual Meeting, Pittsburgh, 
Pennsylvania, July 24-26, 2011
Copyright 2011 by Hiroyuki Takeshima. All rights reserved. Readers may make 
verbatim copies of this document for non-commercial purposes by any means, 
provided that this copyright notice appears on all such copies.Key results by agro-ecological zones
Assessing Complementarities Among Farm Machineries Through Farmers' Investment Behaviors Under 
An External Capital Injection – Implications on Agricultural Mechanization and Tractorization In Sub-Saharan Africa
Hiroyuki Takeshima1, Sheu Salau1
1International Food Policy Research Institute (IFPRI), Nigeria office (H.takeshima@cgiar.org)
1.  1. Background issue
• Challenge in supporting sophisticated farm machineries 
(tractors) in developing countries like Nigeria
4.  Empirical results and policy implications 4.  Empirical results and policy implications 2.  2. Descriptive statistics Descriptive statistics
Empirical specification
• First difference panel using data from 2005 and 2006 Hand tools Draft 
il
Milling 
hi
Tractor / 
till All
Farmers who owned these tools in 2005
Hand Draft Milling Tractor / 
• Scale of required financial support, public sector capacity
• Low level of current farm mechanization
• Rare use of not only tractors but also draft animals
• Potentially high demand for hand tools (hoe, cutlass)
• Literature indicating the role of less sophisticated farm tools
• Higher demand for intermediate tools (draft animals, processing 
machines) or hand tools (Mrema et al, 2008)
• General patterns of mechanization (Rijk, 1999):
• Hand tools => draft animals => mechanization of 
tt i t i ( i illi hi )
pg
• GMM => farmers’ self-selection into project participation  
animalsm a c hine power tiller
Dry-savannah
Fadama*H -.640*** -.007 -.032 -.002
Fadama*D -.070 .181 -.018 -.005
Fadama*M -.050 -.120** .015 .013
Fadama*T .025 -.191 -.019 .200***
Moist-savannah
Fadama*H -.504*** -.150*** .091 .025*
Fadama*D .413** .218** .041 -.015
Fadama*M -.245 -.124* -.045 -.009
Definitions
h d m t =1i faf a r m e ri invested in 2006 into hand tools (machete /c u t l a s s/h o e ) (h)
Endogenous due to self-selection (∆Fit) => GMM 
All Hand 
tools
Draft 
animal 
Milling 
machine power 
tiller
Age 42 42 42 40 46
Gender (% female) 29 23 9 26 25
% completed primary education 58 57 49 58 67
% completed secondary education 33 34 29 32 50
Household size 9 9 12 10 10
Household expenditure in 2005 ($) 238 227 190 522 283
% rented in land in 2005 11 13 9 15 33
% received credit in 2005 12 12 13 10 13
% primary activity is cropping 55 68 73 47 88 stationary operation (processing –m illing machine) => 
motive operation (tractors)
=> necessary pre-conditions for the adoptions of tractors: 
• Adoptions of intermediate tools (draft animals, stationary 
operation)
• Potentially complementary roles played by less sophisticated 
tools 
• Access to draft animals or milling machines = process and 
transport large harvest from using tractors for land preparation
• Individual farmers’ ownership of hand tools = complement the
Summary of findings
• Ownership of less sophisticated farm machineries => no positive effect 
on the investment into more sophisticated machineries
Fadama*T -.133 -.019 -.078 -.024
Humid-forest
Fadama*H -.520*** -.004 .024 .003
Fadama*D -.117 .252*** -.074 -.053
Fadama*M .088 -.002 .201*** .014
Fadama*T .024 -.013 -.044 .047
hi, di, mi, ti = 1 if a farmer i invested in 2006 into  hand tools (machete / cutlass / hoe) (hi)
draft animal (ox-plow / work bull) (di)
milling machine  (mi)
tractor / tractor-plow / power tiller  (ti)
Hi, Di, Mi, 
Ti
= 1 if a farmer i owned in 2005  hand tools (machete / cutlass / hoe) (hi)
draft animal (ox-plow / work bull) (di)
milling machine  (mi)
tractor / tractor-plow / power tiller  (ti)
∆Fi = 1 if joined the Fadama II project in 2006 (prior to the investment)
Excluded IVs
∆Ei*Hi Eligibility to Fadama II * owned hand tools in 2005 or not
∆ *O iii i d* d fi i2 0 0
%O w n e d
in 2005
Invested 
in 2006
Fadama II member (2006) Root 
crop 
grower 
in 2005
Vegetable 
grower in 
2005
All Dry 
savannah
Moist 
savannah
Humid 
forest
Hand tools 59 13 18 19 20 14 17 21
Draft 
animal 
11 4 9 17 6 1 2 9
Milling 
machine
4 375 1 1 6 7 2
% primary activity is cropping 55 68 73 47 88
% primary activity is non-farm activity 18 11 9 27 0
Estimation results (all agro-ecological zones combined 
ihd f lil )
• Individual farmers  ownership of hand tools = complement the 
use of draft animals or milling machines
Implications of preliminary  Implications of preliminary results results
1 1 Ff i i i f i f h i i Ff i i i f i f h i i
on the investment into more sophisticated machineries
• Farmers tend to continue investing in the same type of farm 
machineries
• Though we cannot say much about the mechanization patterns, we may 
say;
• Farm mechanization may evolve along 1) hand tools => 2) draft animal 
=> 3) stationary operation => 4) motive operation but not at the 
individual farmer level
3.  Conceptual framework 3.  Conceptual framework
∆Ei*Oi Eligibility to Fadama II * owned draft animal in 2005 or not
∆Ei*Mi Eligibility to Fadama II * owned milling machine in 2005 or not
∆Ei*Ti Eligibility to Fadama II * owned tractors / tractor-plow / power 
tiller in 2005 or not
∆Ei*Root cropi Eligibility to Fadama II * grew rootcrops in 2005 or not
∆Ei*Vegetablei Eligibility to Fadama II * grew vegetables in 2005 or not
∆Ei*Household expenditurei Eligibility to Fadama II * total household expenditure in 2005
∆Ei*Dependency ratioi Eligibility to Fadama II * dependency ratio
Tractor / 
power tiller
1 0.3 1 1 1 1 1 1
Utility maximization under liquidity constraint and production 
risk
Max  E {U[π] }
s.t. π = p ⋅ f  (xk, xj, η) – wk xk –w j xj
wk xk + wj xj + θ ≤ T
Lagrangian:
In perfect market:
μ k= μ j 
In imperfect market: 
μ k > μ jor μ k < μ j
Definitions
π Profit
p Output price
f Production function
xj, xk Quantities of inputs j and k
Prod ction risk with dummy for agro-ecological zones) 1. 1. Farmers prefer to invest in specific farm machineries Farmers prefer to invest in specific farm machineries
• • Their aversion to risk for investing in other complementary farm machineries may  Their aversion to risk for investing in other complementary farm machineries may 
be greater than the potential benefits be greater than the potential benefits
2. 2. Targeting of farmers is more important when supporting adoptions of  Targeting of farmers is more important when supporting adoptions of 
particular farm machineries particular farm machineries
• • Program like Fadama II may be more appropriate as farmers have ranges of farm  Program like Fadama II may be more appropriate as farmers have ranges of farm 
machineries to choose from machineries to choose from
3. 3. Nigerian government’s continued focus on tractorization makes some  Nigerian government’s continued focus on tractorization makes some 
sense  sense 
• • Supporting adoptions of supposedly complementary machineries do not encourage  Supporting adoptions of supposedly complementary machineries do not encourage 
GMM Rare events 
Logit
Hand tools Draft 
animals
Milling 
machine
Tractor / 
power tiller
Tractor / 
power tiller
Fadama II participation (yes =1) .509*** .076 .041 .001
Fadama*H -.535*** -.052 -.033* .011 .011*
Fadama*D -.000 .200*** -.035* -.012 .004
Fadama*M -.136** -.084*** .082* -.006
Fadama*T -.108 -.105 -.047* .100*** .004*
Fadama*Root crop .157*** -.043** .108*** .012
Fadama*Vegetable .204*** .168*** -.014 .014*
Eligible (yes = 1) -.135** -.072** -.023 .003
L = E {U[ p ⋅ f  (xk, xj, ε) – wk xk –w j xj ] } + λ(T – wk xk – wj xj – θ)
Demand for k (xk*) satisfies
wk / p ≤ {E[Uꞌ(π)]·E[∂f/ ∂xk] + cov[Uꞌ(π), ∂f/ ∂xk]}/{E[Uꞌ(π)] + λ} = μk
(*Inequality when liquidity constraint is binding)
μk:  willingness to pay for additional k (standardized by p)
External capital injection ∆T
 λ ↓
 Farmer changes xk and xj
until μ k = μ j
Ownership of j => affects relationship between μ k and μ j
Key factors - Shape of production possibility frontier with respect to j and k
- Common inputs used for j and k
Owning j (more xj) increases xk when there is ∆T if
η Production risk 
wj,wk Prices of inputs j and k
T Total liquidity asset
θ Other cash requirements
λ Lagrange multiplier (liquidity constraint)
Selected reference
Binswanger, H. and Pingali, P. (1988). Technological priorities for farming in Sub-Saharan Africa. The World 
Bank Observer 3(1), 81-98.
Mrema G, Baker D, and Kahan D. (2008). Agricultural mechanization in sub-Saharan Africa: time for a new 
look. FAO, Rome.
Nkonya, E. et al. (2008). From the ground up: Impacts of a pro-poor community-driven development project 
in Nigeria. IFPRI Discussion Paper 00756. Washington, DC.
Pingali, P. (2007). Agricultural mechanization: Adoption patterns and economic impact. Handbook of 
Al l E 3 2779 2805
adoptions of tractors adoptions of tractors
• • Although supporting less sophisticated complementary machineries is more  Although supporting less sophisticated complementary machineries is more 
feasible, direct support for tractor adoptions should remain substantial feasible, direct support for tractor adoptions should remain substantial
Eligible (yes   1) .135 .072 .023 .003
Eligible*rented in land in 2005 .052** -.011 .016 -.006
Eligible*age .000 .001 .001* .000
Eligible*gender -.114*** -.039* .018 .005
Eligible*household size -.005*** -.000 .000 .000
Eligible*primary education .018 .001 .005 .002
Eligible*secondary education -.032 .013 -.002 .003
Eligible*dry savannah -.100** .115*** -.046*** -.004
Eligible*moist savannah .043 .061*** -.026 .006
Eligible*storage space .140 -.016 -.022** .000
Eligible*credit -.052 .035 .033* .006
Eligible*state 3 .077** .021 .055*** .015**
Eligible*state 4 -.056 .012 -.049** -.008
Eligible*state 5 184*** - 112*** 024** - 001
Owning j (more xj) increases xk when there is ∆T if
Explanation Possible example
Low  ∂f/ ∂xj
Large | cov[Uꞌ, ∂f/ ∂xj] |
- No scale of economy in j
- Low depreciation rate of j
-jis risk increasing 
Due to the lack of tractor (k), harvest is small and milling machine (j) is under-
used => additional milling machine provides no return
High ∂f/ ∂xk
Small | cov[Uꞌ, ∂f/ ∂xk] | 
-kand j are complements
- Investing into k is not risky
Tractor (k) is more profitable if owning milling machine (j) raises return from 
larger harvest
Owning j (more xj) decreases xk when there is ∆T if
Explanation Possible example Agricultural Economics 3, 2779-2805
Rijk AG. 1999. Agricultural mechanization strategy. Plant Production Engineering: 536-553. Available at 
http://www.unapcaem.org/publication/CIGR_APCAEM_Website.pdf.
Takeshima, H. and F. Yamauchi. 2010. Market and climatic risks and farmers’ investment in productive assets 
under Second Fadama Development Project in Nigeria. IFPRI Discussion Paper 01033.
Michael Tomz, Gary King, and Langche Zeng. 1999.  RELOGIT: Rare Events Logistic Regression, Version 
1.1  Cambridge, MA: Harvard University, October 1, http://gking.harvard.edu/
Gary King and Langche Zeng. 1999a. "Logistic Regression in Rare Events Data," Department of Government, 
Harvard University, http://GKing.Harvard.Edu.
Gary King and Langche Zeng. 1999b. "Estimating Absolute, Relative, and Attributable Risks in Case-Control 
Studies," Department of Government, Harvard University, available from http://GKing.Harvard.Edu.
Wooldridge JM. 2002. Econometric Analysis of Cross Section and Panel Data.: MIT Press.
Eligible state 5 .184 -.112 .024 -.001
Eligible*state 6 .130*** -.053* .013*** -.001
Eligible*state 7 -.044 -.026 .085*** -.014**
Eligible*state 8 -.074 -.008 -.053*** .019***
Eligible*state 12 -.033 -.016 .022 -.008
Intercept .124*** .007** .011*** .000
p-value (overall fit) .000 .000 .000 .000
p-value (weak identification) .001 .001 .000 .000
p-value (overidentification) .300 .803 .796 .821
Observation 2822 2822 2822 2822 2822
***: 1% **: 5% *: 10% 
Note: Rare events Logit – marginal effect at the mean of all other variables
Explanation Possible example
High ∂f/ ∂xj
Small | cov[Uꞌ, ∂f/ ∂xj] |
- Scale of economy from j
- Depreciation of j
-jis risk decreasing 
Many farmers bring their harvests for milling for fee => additional milling 
machine (j) provides additional return
Owning milling machine mitigates the price risk for unprocessed crops
Low  ∂f/ ∂xk
Large | cov[Uꞌ, ∂f/ ∂xk] | 
-kand j are substitutes 
- Investment into k is risky, requires 
learning, resources for risk 
mitigation
Owning milling machine (j) requires electricity, cash for operation / maintenance.  
Allocating them for milling machine 
=> Less such resources left for tractor (k)
=> Return is lower / riskier from tractorCompeting hypotheses
Farmers who own certain (less sophisticated) tools are 
more, or less, likely to invest in 
more sophisticated tools than other farmers
Invest into
A
Farmers owning
different tools More Less
If
- Tools are more complementary
• Less risk / less  risk-averse to 
adopting new tools
A
B
C
External capital injection 
(Fadama II project)
different tools
If
- Tools are less complementary
• More risk / more risk-averse to 
adopting new tools

Assessing Complementarities Among Farm Machineries Through Farmers' Investment Behaviors Under An External Capital Injection – Implications on Agricultural Mechanization and Tractorization In Sub-Saharan Africa

Assessing Complementarities Among Farm Machineries Through Farmers' Investment Behaviors Under An External Capital Injection – Implications on Agricultural Mechanization and Tractorization In Sub-Saharan Africa

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics