The economics of sustainable land management practices in the Ethiopian highlands

Menale Kassie, University of Gothenburg; Precious Zikhali, Centre for World Food Studies (SOW-VU); John Pender, United States Department of Agriculture (USDA); Gunnar Köhlin, University of Gothenburg

ABSTRACT: This paper uses data from household and plot-level surveys conducted in the highlands of Ethiopia. Weexamine the contribution of sustainable land management practices to net value of agricultural production in areas withlow versus high agricultural potential. A combination of parametric and non-parametric estimation techniques is used tocheck result robustness. Both techniques consistently predict that minimum tillage is superior to commercial fertilisers,as are farmers’ traditional practices without commercial fertilisers, in enhancing crop productivity in the low agriculturalpotential areas. In the high agricultural potential areas, by contrast, use of commercial fertilisers is superior to bothminimum tillage and farmers’ traditional practices without commercial fertilisers. The results are found to be insensitiveto hidden bias. Our findings imply a need for careful agro-ecological targeting when developing, promoting, and scalingup sustainable land management practices.

***DISCUSSION AFTER THE PRESENTATION: The presentation was followed by a question regarding strategies on howto use the study to fill the gap between research and policy processes. It was replied that the study had been presentedto the Ethiopian Ministry of Agriculture and that workshops have been organised over the three years at the regionallevel to discuss the results together with local and international researchers and policy makers. Discussions have alsotaken place with the World Bank on how to bring these kinds of studies together and synthesise the results in order todevelop a tool to guide the promotion of land management strategies in various agro-ecological areas.

There was also another comment suggesting that it is not only relevant to carry out research on which land managementstrategies work where, but also to look at the approaches in order to promote local participation and farmers’ ownresearch and innovations. A final question concerned the Ethiopian extension system, which is highly politically driven.

Rationale• Ethiopian economy highly dependent on agriculture

• Severe land degradation

• Low agricultural productivity

• High dependency on food aid

• Response from Government, NGOs and donors:– massive programs of natural resource management to

reduce environmental degradation, reduce poverty, andincrease agricultural productivity and food security

However…

• …Success has been limited!

• Low adoption, dis-adoption or reduced use of technologies

– e.g., 16 kg of nutrients per hectare (EEA/EEPRI 2006)

• Continued low productivity!

Why limited success?

• Blanket recommendation: Technology packages are not site or household specific and are disseminated through a ‘quota’ system, eg:

-Commercial fertilizer: 100 kg of Di-AmmoniumPhosphate (DAP) and 100 kg of urea per hectare ispromoted all over Ethiopia

-Uniform SWC technologies released and promoteddisregarding local agro-ecological and socio-economicvariations

Realize!

• Economic returns to different farm technologies varyby agro-ecology:– e.g. physical soil and water conservation investments (e.g. stone

terrace) impacts on productivity are greater in low moisture and lowagricultural potential areas than in high moisture and high agriculturalpotential areas (Gebremedhin et al. 1999; Benin, 2006; Kassie et al.2008)

• Need rigorous empirical research on where particular SLMinterventions are likely to be successful, to ensure sustainableadoption of technologies and beneficial impacts onproductivity and other outcomes

Three comparisons:

Impacts on net value of production in high and low rainfall areas:

1. Commercial Fertilizer (CF) versus Farmers’ Traditional Practices (FTP) (i.e. traditional tillage without CF)

2. Minimum Tillage without commercial fertilizer (MTWOCF) versus FTP and,

3. Minimum Tillage (MTWOCF) versus Commercial Fertilizer (CF)

Data-1• Household-and plot-level data conducted in 1998 and 2001 in the

highlands (above an altitude of 1,500 m.a.s.l) of the Tigray andAmhara regions of Ethiopia.

• A stratified random sample of 99 Peasant Associations wasselected from highland areas of the two regions.

• The Tigray region is typically low moisture and generally low agricultural potential region (Benin, 2006).

• The Amhara region has greater variation in agro-ecological zonesthat have been classified in ”high potential” and ”low potential”areas, primarily based on rainfall patterns.

Descriptive statisticsVariables Amhara region Tigray region

Sampled household 396 357

Sampled villages 98 100

Sampled plots 1365 1113

Rainfall 1981 mm 641 mm

Population density 144 person/km2 141 person/km2

Minimum Tillage plots 15% 13%

Fertilized plots 30% 35%

Extension system Same Same

Rural credit service Same Same

Seed and fertilizer markets and distribution systems

Same Same

Net value of production 2140 ETB 1730 ETB

Estimation methods• Semi-parametric method:

– Propensity score matching (PSM) method: construction of the counterfactual and reduce problems arising from selection biases. Find a group of non-adopters plots similar to the adopters

• Parametric method:

– Switching regression framework: to differentiate each coefficient for adopters and non-adopters

• The parametric analysis is based on matched samples ofadopters and non-adopters obtained from the propensityscore matching (PSM) process.

PSM matching qualityCommon support/overlap region for comparisons

Effect of CF compared to FTP in high potential areas of Amhara region

Effect of CF compared to FTP in low potential areas of Amhara region

Effect of MT compared to FTP in high potential areas of Amhara region

Effect of MT compared to FTP in low potential areas of Amhara region

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

Common support/ overlap region

Effect of CF compared to FTP in Tigray region Effect of MT compared to FTP in Tigray

region

Effect of MT compared to CF in Amhara region Effect of MT compared to CF in Tigray

region

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

0 .2 .4 .6 .8 1Propensity Score

Untreated Treated: On supportTreated: Off support

Reminder: Three comparisons of net value of agricultural production

Three comparisons undertaken to assess Minimum Tillage (MT) and Commercial Fertilizer impacts on productivity.

1. Commercial Fertilizer (CF) versus Farmers’ Traditional Practices (FTP) (i.e. traditional tillage without CF)

2. Minimum Tillage without commercial fertilizer (MTWOCF) versus Traditional Practices and,

3. Minimum Tillage versus Commercial Fertilizer

1. Commercial Fertilizer (CF) vs Farmers’ Traditional Practices (FTP) (Average adoption effects - Semi-parametric method)

High potential areas Low potential areas

Amhara Amhara Tigray

NNM KBM NNM KBM NNM KBM

Average adoptioneffect 1377A 1083A 118 279 56 142

Standard error 349 257 488 399 234 186

Number of observations within common support

Number of treated 313 313 46 45 356 356

Number of control 447 447 331 331 607 607

A significant at 1%; B significant at 5%.Notes: NNM = nearest neighbor matching; KBM = kernel based matching;

2. Minimum tillage (MTWOCF) vs FTP(Average adoption effects (ATT)-Semi-parametric method)

High potential areas Low potential areas

Amhara Amhara Tigray

NNM KBM NNM KBM NNM KBM

Average adoptioneffect

19 253 510B 277 715A 694A

Standard error 994 446 246 219 313 316

Number of observations within common support

Number of treated 19 21 131 131 109 109

Number of control 391 391 349 349 606 606

A significant at 1%; B significant at 5%.Notes: NNM = nearest neighbor matching; KBM = kernel based matching;

3. Minimum tillage (MTWOCF) vs Commercial Fertilizer(Average adoption effects (ATT)-Semi-parametric method)

Amhara TigrayNNM KBM NNM KBM

Average adoptioneffect

-1240A -935A 949A 303

Standard error 519 412 372 465

Number of observations within common support

Number of treated 370 370 92 92

Number of control 112 112 357 357

A significant at 1%; B significant at 5%.Notes: NNM = nearest neighbor matching; KBM = kernel based matching;

Results from switching regressions(Average adoption effect (ATT)-parametric method)

AMHARA REGION TIGRAY REGIONHigh potential

areasLow potential

areasEntire

sampleEntire

sampleEntire

sampleCF vs.FTP

MTWOCF vs. FTP

CF vs.FTP

MTWOCF vs.FTP

MTWOCF vs.CF

Average adoption effect 1051A 293B 173 650B 785A

Standard error 229 149 145 245 302Number of matched observations

Number of treated 313 131 356 109 92Number of control 127 74 115 73 58A significant at 1%; B significant at 5%.

Notes: CF = commercial fertilizer; FTP = farmers’ traditional practices; MTWOCF = minimum tillage without commercial fertilizer.

Source: Own calculation

Conclusions-1• Minimum tillage gives higher productivity gains compared to

commercial fertilizer in the low agricultural potential areas

• Commercial fertilizer gives higher productivity gains compared to minimum tillage in high agricultural potential areas

• A one-size-fits-all approach in developing and promoting technologies not recommended: different strategies are needed for different environments

Conclusions-2

• Relying on external inputs (such as chemical fertilizers) in low-potential areas, which has been the strategy in the past, is not likely to be beneficial unless moisture availability issues are addressed.

• Future research should investigate the combined effects of minimum tillage or other moisture conservation practices and commercial fertilizer.

Thank you!

Extra stuff

Results-3

• Covariate balancing indictors before and after matching.

Amhara region Tigray region High

potential Low potential

High potential

Low potential

Entire sample

Entire sample

Entire sample

Entire sample

CF vs FTP

CF vs. FTP

MTWOCF vs. FTP

MTWOCF vs. FTP

MT VS CF

MTWOCF vs. FTP

CF vs FTP

MT Vs. CF

Before matching Mean standardized difference

19.37 20.47 23.05 22.46 37.96 16.40 14.33 23.89

Pseudo 2R 0.295 0.374 0.285 0.287 0.580 0.249 0.122 0.358

P-value of LR 2χ 0.000 0.000 0.031 0.000 0.000 0.000 0.000 0.000 After matching Mean standardized difference

6.03 11.68 12.80 9.79 11.94 7.67 3.83 10.13

Pseudo 2R 0.055 0.029 0.112 0.090 0.139 0.086 0.015 0.106

P-value of LR 2χ 0.111 0.815 1.000 0.650 0.208 0.973 0.998 0.997

Results-6• Rosenbaum bounds sensitivity test to hidden bias

Critical value of hidden bias( )Γ

CF vs. FTP

MTWOCF vs. FTP

MT Vs. FPT

MTWOCF vs. FTP

MT VS CF

High potential areas

Low potential areas

Entire sample Entire

sample

Entire sample

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