can conservation agriculture save tropical forests? the case of minimum tillage in zambia

Can conservation agriculture save tropical forests?The case of minimum tillage in Zambia

Hambulo Ngoma, Arild AngelsenSchool of Economics and Business

Norwegian University of Life Sciences& Center for International Forestry Research (CIFOR), Bogor, Indonesia.

[email protected]; [email protected]

Agri4D Young Researchers MeetingGothenburg, Sweden

June 7-8,2016

Hambulo Ngoma (HH-NMBU) Min till deforestation effects 6/7/16 1 / 15

Outline

1 Motivations

2 Research question

3 MethodsTheoretical modelEmpirical strategy

4 Data and resultsData sources and contextWhere and why do smallholders expand cropland?Does minimum tillage reduce cropland expansion?

5 Discussion and conclusion


Motivations

Motivations

Sub-Saharan Africa (SSA) requires substantial agriculturalproductivity growth [Hallegatte et al., 2016].

I to meet food demand, livelihoods needs, and reduce poverty.

Market imperfections make area expansion de facto choice.I although expansion can rise yield and production

[Ehui and Hertel, 1989, De Janvry and Sadoulet, 2010] - it isunsustainable, may lead to immiseration.

I it is main cause of deforestation in tropics[Angelsen and Kaimowitz, 1999], linked to rainfall shocks and poverty[Hallegatte et al., 2016].

Intensification and climate resilience are key for smallholders in SSA.

Climate smart agriculture (CSA) is an option for tripple wins1

I reduced tillage (soil C sequestration), input-use and policy reforms.

Does it?1Conservation agriculture (CA) principles like MT are central


Motivations

Conservation agriculture components

CA principles include minimum tillage (MT), residue retention andcrop rotation (diversification)We focus on MT practices - the core CA principle

1

Planting basins Ox-drawn ripping

Mechanized ripping

Min. Till= planting basins & ripping

in the context of this study

Source: CFU

Zero Tillage


Research question

Research question

Does adopting minimum tillage (MT) reduce cropland expansion?

Agricultural tech solutions to spare nature not new.I results are mixed, ’Jevons paradox of deforestation’ arguements, but 2

Available studies have data and methods shortcomings, see[Villoria et al., 2014].

I most are global or national and weak on identification strategies.I others lack explicit theoretical models to guide empirical analysis.

Two potential links between MT and mitigation (deforestation),I soil C sequestration - most studied, see [Powlson et al., 2016].I yield (intensification) effects - our focus.

Two features of CA stand out, it is;I labor-intensive (short term) and land-sparing?I yield augmenting and expansionary?

Empirical evidence is thin on direct CA-deforestation nexus.

Despite 66% forest cover, deforestation is on the rise in Zambia.2Productivity growth makes agriculture profitable


Methods Theoretical model

Theoretical model

To understand expansion decions at household levelI we develop a Chayanov type model with limited off farm workI assumes abundant land, and usual utility and production functionsI All income is used for consumption

A household maximizes utility by trading off consumption and leisure,

MaxUc,l,A

= U(c, l : h) (1)

Subject toc = pyf (l

a,M,A)− vM − pxX− t(A−Ao)r + wlo + E (2)

LT = l + lc + la + lo (3)

One main result is Ll = pyfl − z, where z = Ul (c, l) /Uc (c, l).

Comparative statics lead to one corollary:I The effects of MT on cropland expansion are indeterminate a priori.3

3It has positive substitution effects and an indeterminate income effectHambulo Ngoma (HH-NMBU) Min till deforestation effects 6/7/16 6 / 15

Methods Empirical strategy

Empirical strategy

For empirics, consider the reduced form solution

LnA = A (M,y, z; py, px,X, Ao,h) , (4)

where MT (M), yield (y) and shadow wage (z)4 are endogenousI Other exog. variables: value of assets, subsidies, tenure, distance to

protected forest and demographics

Eq. 4 estimated as system of equations using[Zellner and Theil, 1962]’s 3SLS

MT = α1 + zξ + yγ +Xβ + ε1, (5)

z = α2 + yγ +Xβ + ε2, (6)

y = α3 +MTψ +Xβ + ε3, (7)

LnA = α4 + ξz + yγ +MTψ +Xβ + ε4. (8)

X is a vector of all exogenous variables, and E(εε

′)6= 0.

We follow Baum’s overid test for model specification.4computed from an production function


Data and results Data sources and context

Data and context

Survey data from a random sample of 368 households from 30 villagesin 3 rural districts in Zambia in 2014.

Sampled hosueholdsLakes and swampsProtected ares (GMAs, forests, wetlands)District boundaries

Legend

Data sources: Own survey, various GIS files

Figure 1: Spatial location of sampled households.


Data and results Where and why do smallholders expand cropland?

Where do smallholders expand cropland?

19% expanded cropland, clearing 0.14 ha on average.

No major differences by MT adoption status.5

80.98

19.02

0 20 40 60 80

Full sample, N=368

82.28

17.72

0 20 40 60 80percent

MT users, N=79

expanded did not expand

Figure 2: Cropland expansion by smallholder farmers in the full sample and amongthose using minimum tillage

524% if we include expansion into fallows < 15 years.Hambulo Ngoma (HH-NMBU) Min till deforestation effects 6/7/16 9 / 15


Why do smallholders expand cropland?

1.15

1.15

1.15

3.45

4.60

5.75

12.64

70.11

0 20 40 60 80percent

to increase production to increase marketed surplusreduced soil fertility new settlementaccess to FISP access to inputsland available to secure tenure

Figure 3: Reasons farmers expanded cropland into the 2013/2014 season

For those who did not expand;I 68% did not expanded due to lack of meansI 21% cited lack of land, others had no need



Predicted area expansion by selected variables

-.50

.51

pred

icte

d ex

pans

ion

(ha)

0 2 4 6Average area under MT (ha)

95% CI Fitted values

A

-.4-.2

0.2

.4pr

edic

ted

expa

nsio

n (h

a)

5 6 7 8 9Log yield


B

-.50

.51

1.5

pred

icte

d ex

pans

ion

(ha)

0 1 2 3 4 5log farm size


C

0.5

11.

52

pred

icte

d ex

pans

ion

(ha)

2 4 6 8 10 12Log asset value


D

Figure 4: Quadratic predictions of the effects of minimum tillage adoptionintensity (A) yield (B), farm size (C) and value of household assets (D) oncropland expansion.


Data and results Does minimum tillage reduce cropland expansion?

Selected 3SLS results, preliminary results

Table 1: Selected 3SLS regression results

Model 1 Model 26

log expansion log expansionMT area (ha) (4.6) -1.1***(-6.1) -0.97***(-5.7)Shadow wage - 0.75*(1.9)Dist. fores reserve 1.0E-03(0.6) 1.0E-03(0.8)Yield 0.03***(4.0) 0.03***(3.8)Farm size 0.03***(5.0) 0.3(1.9)Asset value -1.0E-03***(-4.0) -1.0E-03***(-3.6)Tenure (yes=1) -0.33***(-2.9) -0.33***(-3.0)Improved inputs (yes=1) -0.15*(-1.9) -0.16**(-2.1)Subsidy (yes=1) 0.04(1.2) 0.02(0.6)Output price - 0.75(1.9)District FE yes yes

Hansen-Sargan stat 11.55 (p=0.71) 35.48 (p=0.127)Observations 350 350

T-stats in parenthesis, ***,**,* significant at 1%, 5% and 10%.

6Models 1 and 2 use observed village wages and shadow wages computed from CBHambulo Ngoma (HH-NMBU) Min till deforestation effects 6/7/16 12 / 15

Discussion and conclusion


MT could potentially reduce cropland expansion,

I 1 ha increase in adoption intensity could reduce expansion by over 60%or 0.09ha. 7

However, this is only 0.01 ha per average household. 8

Higher yield, farm size and out price stimulate expansion.

Land on title and improved inputs reduce expansion.

This suggests complementarity between MT and improved inputs.

And that intensification is likely the cause of yield growth in MT.9

I this makes plausible different effects of yield in general and MT.

Given expansionary factors, MT alone maybe a risk strategy.

Unless combined with measures like direct control of expansion.7using sample mean expansion of 0.14 ha8since the mean MT adoption intensity is 0.13 ha9yield growth may occur independent of technical progress through factor substitution



Acknowledgements

We acknowledge funding from the Center for International ForestryResearch (CIFOR) through the REDD+ project at the School ofEconomics and Business at NMBU.

We thank CIFOR in Zambia for hosting field work and the fieldassistants for a job well done.


References

References

Angelsen, A. and Kaimowitz, D. (1999).

Rethinking the causes of deforestation: lessons from economic models.The world bank research observer, 14(1):73–98.

De Janvry, A. and Sadoulet, E. (2010).

Agriculture for development in sub-saharan africa: An update.African Journal of Agricultural and Resource Economics, 5(1):194–204.

Ehui, S. K. and Hertel, T. W. (1989).

Deforestation and agricultural productivity in the Cote d’Ivoire.American Journal of Agricultural Economics, 71(3):703–711.

Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., and

Vogt-Schilb, A. (2016).Shock waves: Managing the impacts of climate change on poverty.Report, World Bank.

Powlson, D. S., Stirling, C. M., Thierfelder, C., White, R. P., and Jat, M. L. (2016).

Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropicalagro-ecosystems?Agriculture, Ecosystems and Environment, 220:164–174.

Villoria, N. B., Byerlee, D., and Stevenson, J. (2014).

The effects of agricultural technological progress on deforestation: What do we really know?Applied Economic Perspectives and Policy, 36(2):211–237.

Zellner, A. and Theil, H. (1962).

Three-stage least squares: Simultaneous estimation of simultaneous equations.Econometrica, 30(1):54–78.


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can conservation agriculture save tropical forests? the case of minimum tillage in zambia