AGRICULTURAL

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Agricultural Systems 88 (2006) 75–91

SYSTEMS

Diversity and development domains inthe Ethiopian highlands

Gideon Kruseman a,b,*, Ruerd Ruben b, Girmay Tesfay c

a Institute for Environmental Studies (IVM), Free University, De Boelelaan 1087,

1081 HV Amsterdam, The Netherlandsb Wageningen University, Department of Social Sciences, Development Economics Group.

Hollandseweg 1, 6704 KN Wageningen, The Netherlandsc Mekelle University College, P.O. Box 231, Mekelle, Tigray, Ethiopia

Abstract

In the discussion on targeting of development interventions there is an ongoing debate onthe usefulness of geographic targeting versus targeting of specific household types. In thispaper, we examine the degree to which there is heterogeneity in the diversity of livelihoodstrategies in comparable geographic areas in northern Ethiopia. We use the concept of devel-opment domains to quantify differences amongst specific geographic areas (communities) andestimate the degree to which development domain dimensions account for differences in pro-duction systems indicators and to what extent variability between the overall results and theobserved patterns can be attributed to these development domain dimensions. Our researchshows that development domain dimensions matter both for production indicators (choiceof technology, investment, cropping pattern and livestock use), as well as for the occurrenceof heterogeneity in those indicators. Diversity is especially linked to better market access,homogeneity in agricultural production indicators to poor soils. This offers scope for targetingdevelopment interventions.� 2005 Elsevier Ltd. All rights reserved.

Keywords: Development pathways; Diversity in livelihood strategies; Agricultural intensification; North-ern Ethiopia

0308-521X/$ - see front matter � 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.agsy.2005.06.020

* Corresponding author. Tel.: +31 20 5989555; fax: +31 20 5989553.E-mail address: gideon.kruseman@ivm.vu.nl (G. Kruseman).

76 G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91

1. Introduction

In the process of agricultural development two processes take place, the first is adiversification into new crops and technologies as new knowledge becomes available.The second is a process of agricultural intensification in which more intensive use ismade of external inputs and production factors to attain productivity increases.These two development processes can have both positive and negative externalitieson the environment, e.g., in terms of soil degradation.

The concept of development domains can be used to facilitate the targeting ofdevelopment interventions. Major dimensions of development domains usually in-clude agricultural potential, market access and population density (Pender et al.,1999). The development domain dimensions act together to provide possible devel-opment pathways, unlike the theses of Malthusian or Boserupian development thatlook primarily at population density. Areas with high population density, low agri-cultural potential and low market access can be expected to follow a Malthusiandevelopment path, where land resources typically suffer from soil mining and re-source degradation (Cleaver and Schreiber, 1994). Boserupian development occurswhen there is sufficient market access that enables specialization, leading to a moreefficient use of scarce resources, as is illustrated in the study More people less erosion

(Tiffen et al., 1994). In the Machakos case the overriding effect was market accessand not population density in isolation (Tiffen et al., 1994). In these setting, the prox-imity of markets allows the adoption of more sustainable agricultural practices.However, in many parts of Africa soils are so poor that the maximum carryingcapacity is already reached at rather low population densities (Kruseman, 2000).

The identification of development potentials can be addressed in a quantitativefashion, identifying the possibilities for targeting of interventions (Krusemanet al., in press). This implies the use of precise definitions of the critical dimensionsthat determine different strategic development options. The dimensions commonlyused for distinguishing between development domains are: (i) agricultural potential(biophysical environment), (ii) population density, and (iii) market access (socio-economic environment). These dimensions are exogenous to the households thattry to cope with their biophysical and socio-economic environment. Household deci-sions regarding land use practices and production technologies result in particularlivelihood strategies. In addition to these commonly used development domaindimensions, a fourth set can be distinguished comprising social and institutionalissues.

Identification and quantification of development domains have an importantpractical meaning. It offers a framework for the design of particular developmentinterventions that are appropriate for certain areas. Village stratification is consid-ered useful in order to identify the structural factors that influence the choice ofcertain livelihood strategies. When diversity between villages is more importantcompared to heterogeneity amongst households, targeting can be safely done atthe community level (Bigman and Fofack, 2000). Geographical targeting can thenbe considered as an effective strategy for selectively enhancing a process of agricul-tural intensification. Even if different households within the geographic area pursue

G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91 77

different livelihood strategies, the range of these strategies can be linked to the devel-opment domain dimensions.

In this paper, we analyze to what degree the heterogeneity in major indicators foragricultural production depend on development domain dimensions. For each indi-cator separately, we look at the explanatory power of the development domaindimensions. Then finally we look at the degree to which the heterogeneity itself inotherwise similar settings can be explained by the development domain dimensions.The remainder of the paper is structured as follows. In the next section, the conceptof development domains is discussed within the context of the Ethiopian highlands.Next, we briefly discuss the methodology used in the analysis. Then we present theresults and finally derive some conclusions with respect to the importance of heter-ogeneity amongst similar environments.

2. Development domains in the Ethiopian highlands

The northern Ethiopian highlands face serious problems related to the high pop-ulation density and the limited agro-ecological potential. Regional programs forsoil and water conservation have been launched that intend to increase land andlabor productivity. However, given the modest public resources that are available,some choices have to be made where to target specific activities. Not all activitiesare suitable for each community and different communities are likely to benefitmost from different types of interventions. Under these conditions, we cannot relyon a ‘‘one-size-fits-all’’ strategy, and specific criteria must be developed in order todifferentiate between communities and to select the most appropriate developmentstrategies.

The concept of development domains can be used to identify the critical dimen-sions that influence the adoption of certain resource management practices (Penderet al., 1999; Fitsum Hagos et al., 1999). This approach is based on the notion that itis possible to disentangle the core elements of successful local development strategiesthat can be subsequently addressed through a selective offer of technologies orservices.

One of the main hypotheses of development domains concept is the existence ofdifferences in comparative advantages for adopting alternative livelihood strategies,giving rise to essentially different development pathways. These differences in com-parative advantage can be attributed to three main factors: agricultural potential,market access, and population pressure (Pender et al., 1999, 2001).

Agricultural potential is a complex aggregate of various biophysical and agro-climatic factors that can be decomposed in a number of different underlying factors,including rainfall, soil type and quality, altitude, slope, topography, and the presenceof pests and diseases. These aspects are – to a large extent – exogenous to the farmhouseholds, but are of overriding importance for determining the absolute compar-ative advantage of producing different kinds of agricultural commodities in a specificsetting, because certain crops are suitable for some agro-ecological zones and not forothers (barley for instance is grown in high altitudes and maize at low altitudes).

78 G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91

The role of the agricultural potential varies for different commodities and overtime as a result of human-induced (e.g., soil degradation) and exogenous change(e.g., climate change). The multi-dimensional aspect of agricultural potential,especially the fact that it can change over time should be taken into account in devel-oping medium and long-term strategies.

Market access is equally important for determining the comparative advantage ofa specific locality for producing certain tradable commodities. Market access alsoinvolves various dimensions and encompasses distance to roads, quality of roads,travel time, distance to markets and urban centers, degree of competition, informa-tion costs, transport opportunities, etc. Although many factors play a role, traveltime is usually considered most crucial for market exchange and input purchase deci-sions. Travel time is the result of some of the before-mentioned variables (e.g.,distance, quality of roads and transport opportunities) and determines others (infor-mation costs). It is therefore important to define a measurable proxy for this factor.Market access is closely linked to the concept of transaction costs whereby thepenalty related to lack of market access influences farm household decisions relatedto consumption and production (Goetz, 1992; Omamo, 1998).

Population pressure has long been acknowledged as being a major driving forcewith respect to the labor intensity of agriculture and determines demand for output,creating an environment conducive to innovations in technology, institutions,markets and infrastructure (Boserup, 1965; Ruthenberg, 1980; Hayami and Ruttan,1985; Biswanger and McIntire, 1987). Population pressure is related to both thedensity of population and the locally available purchasing power. Populationpressure thus affects labor utilization decisions and hence agricultural managementpractices as well as the return to different types of investments.

These three main factors obviously interact with each other in complex ways. Ingeneral, population pressure tends to be higher in areas with a greater agriculturalpotential and with better market access or both, allowing the existing populationto attain incomes above the poverty-line while encouraging immigration from lessfavored areas. On the other hand, increased population pressure is considered amajor contributing factor to the severe land degradation found in many parts ofAfrica, thus affecting the agricultural potential. Market access tends to be betterin highly populated areas where the per-capita costs of infrastructural investmentare lower. Availability of infrastructure also tends to be better in high agriculturalpotential areas that guarantee higher returns to investment. In their seminal studyof Machakos, Tiffen et al. (1994) made a case for increased population pressureleading to less soil degradation. In this specific case, better market access permittedthe necessary investments to reverse the process of soil degradation, allowing alter-native employment outside agriculture to reduce the population pressure. Theabsence of such market outlets in other parts of East Africa has led to accelerateddegradation. In areas with low population density and a limited agricultural poten-tial zone where market access is constrained, small changes in population dynamicscan set off a chain of events leading to degradation beyond the point of no return,as illustrated by the case of the Mossi plateau in Burkina Faso (Savenije and VanZutphen, 1991).

G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91 79

In summary, the variables of market access and population pressure are veryoften correlated at the local level. Increasing population pressure may lead to bettermarket access, while improved market access tends to attract immigration and henceincreasing population pressure. Similarly, population pressure may be related toagricultural potential, but this relationship is easily offset under conditions of scar-city of market infrastructure.

In the Northern Ethiopian highlands, we can identify settings with high and lowpopulation density both in remote and accessible regions. In addition, there is noclear correspondence between population density and the available agro-ecologicalpotential. In the following, we will discuss these settings and identify their impor-tance for the selection of typical land use practices.

3. Methodology

To get a handle on the classification of situations in the highlands of Ethiopia amore statistically robust methodology is needed. Development domain dimensionsare quantified using multivariate analysis on community level data sets (Kruseman,et al., in press). In this procedure, a small set of independent factors is derivedfrom a large number of indicator variables that describes the development domaindimensions in terms of agricultural potential, market access and populationdensity.

The methodology makes use of the availability of a village level survey of 100villages in the case study area (the data were collected with group interviews in1999). The goal of the exercise is to identify for each village (kushet) the factors influ-encing development potential. At the same time an analysis of livelihood strategiesderived from the same survey will give an indication of the development opportuni-ties in each village.

For each dimension there are usually a number of different variables availablethat are related to it. To choose a useful proxy variable is not always easy. By usingfactor analysis to reduce the data, single quantitative measures are derived for eachmain factor. This has the advantage of being able to use all the variables in the dataset that are relevant while preventing multicollinearity from occurring. For eachdevelopment domain dimension, relevant indicator variables were used. For instancefor market access travel times and distances to markets, buses, and trunk roads wereused which all loaded onto a single factor. For agricultural potential, variables onsoil quality and land degradation were used which loaded onto two factors. Rainfalland population density loaded on a factor each. For institutional presence, fourfactors came out of the analysis one pertaining to credit, one related to irrigation,one to livestock promotion and one to cooperatives.

Because we are not able to a priori determine if the development domain dimen-sions are completely independent, we test for this independence using seeminglyunrelated regression analysis on the quantified development dimensions (Krusemanet al., in press). We then regress the development domain dimensions (agriculturalpotential, market access, population density and institutional factors) on selected

80 G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91

variables pertaining to livelihood strategies in order to determine the effect of thosedevelopment domain dimensions on livelihood strategies.

We use a third-order polynomial functional form to regress the developmentdomain dimensions on sets of indicator variables observed in the survey data

Y ij ¼Xk

b1jkX ik þ

Xk;l

b2jklX ikX il þ

Xk

b3jkX ikX ikX ik þ lij; ð1Þ

where Yij are the variables related to livelihood strategies (investments in soil andwater conservation structures, cropping patterns, livestock use, and technologychoice) and Xik are the development domain dimensions. In this equation, i pertainsto villages, j to the agricultural systems variables and k to the development domaindimensions. The dependent variables are the unconditional proportion of farmersengaged in an activity. Although it can lead to somewhat biased estimates in somecases because we are dealing with limited dependent variables on a scale of 0–1,we choose to ignore this because for the present analysis a tobit regression wouldnot be suitable to uncover diversity. By using a flexible third-order polynomial weaccount for some of the non-linearities arising from the nature of the limited depen-dent variables.

Since it is plausible that the error terms of the equations are correlated we useseemingly unrelated regression (SUR) to get unbiased estimates.

We use this analysis to determine to what extent indicator variables depend onthese development domain dimensions. We then quantify the observed heterogeneityin indicator variables by

Xj

jlijj ¼Xk

b1kX ik þ

Xk;l

b2klX ikX il þ

Xk

b3kX ikX ikX ik þ ei. ð2Þ

The dependent variable is the sum of the absolute values of the errors. In this way,we measure if the variation is correlated with the development domain dimensions,i.e., do development domain dimensions influence the degree of diversity in predom-inant livelihood strategy indicator variables? Note that this is not a measure ofheteroskedasticity because we aggregate individual underlying variables. (Even ifthe value of the dependent variable would have been determined by heteroskedasticresults alone (which is not the case), the significance of the coefficients would indicateif certain development domain dimensions should be considered responsible forthese deviations across all variables.) The indicator variables we consider arecropping patterns, livestock use, technology choice and investment in soil waterconservation (SWC) structures.

For livestock use and investment in SWC structures this straightforwardapproach can be used, for cropping patterns and technology choice we use a slightlydifferent approach. We do this because it is plausible to assume that cropping pat-terns are partly dependent on past investments and that technology choice is corre-lated with cropping patterns, livestock use and investment. These variablesthemselves are determined by the development domain dimensions and hence wehave endogeneity. The second source of endogeneity is that the same developmentdomain dimensions simultaneously determine the different dependent variables

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within a cluster; the dependent variables are not mutually independent. Croppingpatterns variables are linked. Livestock strategies are linked as is technology choiceand investment in soil and water conservation structures.

To solve this issue, we must derive a system of equations. We write the model inmatrix notation as

AY ¼ BXþU ð3Þwith Y is an (g · n) matrix of endogenous variables, X is an (n · k) matrix of exog-enous variables (development domain dimensions), A denotes a (g · g) matrix ofcoefficients, B denotes (k · g) matrix of coefficients and U denotes an (n · g) matrixof error terms. This model contains too many coefficients to estimate directly.

In our case, we assume that we are only dealing with endogeneity and do not haveto bother with simultaneity, hence matrix A has specific structure. The simultaneityis caused by the development domain dimensions and the non-simultaneous depen-dent variables which means we do not have to account for them in the estimationprocedure. Imposing Aii = 1 as normalization on the equations the relationship forthe other coefficients takes on the form of a nested structure

I 0 0 0

�A21 I 0 0

0 0 I 0

�A41 �A42 �A43 I

26664

37775

with Aij being (gj · gi) blocks of multivariate linkage coefficients.Solving the model with three stage least squares is not a feasible option because

there is a lack of degrees of freedom, since n = 82 (observations),1 k = 38 (constant,development domain dimensions) , g1 = 9 (investment), g2 = 15 (crops), g3 = 9(livestock), g4 = 17 (technology), such that DF = �6. The number of independentvariables is higher than the number of development domain dimensions due tocross-terms and polynomial terms, see Eq. (1).

The nested structure of the model allows us to pursue a different route. For invest-ment and livestock there are no endogenous variables hence we can use a straightfor-ward seemingly unrelated regression, since using SUR ensures that we do not runinto problems with correlated error terms. Each regression equation has 46 degreesof freedom

IYi ¼ BiXþUi ð4Þfor i = 1, 3. For crops the model becomes

IY2 ¼ B2Xþ A21Y1 þU2. ð5ÞFirst, we estimate

IYi ¼ BiaXþUia; ð6Þfor i = 2, where B2a are the direct and indirect effects of X on Y2.

1 Not all 100 villages could be used because data were not available for all variables in all villages.

82 G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91

B2a ¼ B2 þ A21B1

and U2a is the combined effect of error terms

U2a ¼ U2 þ A21U1 ð7Þthat can be estimated using ordinary least squares (OLS).

The final step in the procedure is to redo the regression using the results from Eq.(7) inserting the estimated value of the residuals ðUia ¼ AijUjÞ in Eq. (8), where Ci

denotes a (g) vector of coefficients to be estimated. The constant in the regressionof the error terms will be zero since the mean value of the errors is zero. Ideally,Ci is equal to unity, but is not imposed on the model and thus serves as a test forrobustness. Again we use SUR to account for correlated residuals

IYi ¼ BiaXþ CiUia þUi. ð8ÞFor technology choice, we have a double nested model which can be estimated in asimilar way using Eq. (6) so that B4a are the direct and indirect effects of X on Y4

B4a ¼ B4 þ A41B1 þ A42 B2a � A21B1ð Þ þ A43B3

and U4a is the combined effect of error terms

U4a ¼ U4 þ A41U1 þ A42ðU2a � A21U1Þ þ A43U3 ð9Þwhich can again be estimated using OLS.

In the final step, the results from Eq. (9) are used to estimate Eq. (8). The impli-cation of this approach for the regression on heterogeneity is that the lij in Eq. (2)corresponds to the elements of Ui and not Uia.

4. Results

The final results of the regression models presented above are summarized inTable 1. For the present analysis, we are less interested in the understanding thedirect and indirect effects of development domain dimensions on the explanationof investment decisions, predominant cropping patterns, livestock use and technol-ogy choice. This is discussed elsewhere (Kruseman et al., in press). However, weare interested in the degree in which these indicators of production systems areexplained by the development domain dimensions. We observe the degree to whichthe indicator variables are explained by development domain dimensions as acombined result of direct and indirect effects. For many of the indicators, usingthe flexible third-order polynomial functional form, we are able to explain a largeportion of the variation. There are exceptions, however, especially in those caseswhere it concerns only small proportions of farm households engaging in a certainactivity. With respect to investments this is the case for grass strips and irrigationwells. However, private nurseries, although only important for very few farmers,can be explained by the development domain dimensions. In the case of croppingpatterns sesame is hardly explained by the development domain dimensions. Withtechnology choice green manure application is explained to a large extent by a

Table 1Degree to which production system indicators are explained by direct and indirect effects of development domain dimensions and other (endogenous)production system indicators in their own right

Production system indicator characteristics (%) Variance explained by the model (%)

Kushets with: Farm households engaging inactivity if present (on average)

Developmentdomain dimensions

Investmentdecisions

Investment decisions,cropping pattern, livestock use

Total

Investment decisions

Drainage ditches 30 49 42 42Grass strips 6 21 0 0Gully stabilisation 97 42 44 44Irrigation canals 54 45 20 20Irrigation wells 6 15 0 0Private nurseries 2 6 71 71Soil bunds 57 40 38 38Stone terraces 94 55 18 18Tree planting 78 74 14 14

Cropping pattern

Barley 84 26 75 6 81Teff 92 22 43 2 45Maize 87 15 39 14 53Wheat 64 17 57 2 59Sorghum 51 18 56 13 69Finger millet 64 12 29 12 41Faba beans 63 7 42 12 54Millet 49 6 23 7 30Chick pea 66 4 56 14 70Filed peas 48 5 27 5 32Flax 71 3 8 39 47Lentils 49 4 43 7 50Haricot beans 14 6 53 4 57Sesame 2 15 5 0 5Niger seed 29 3 14 23 37

(continued on next page)

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Table 1 (continued)

Production system indicator characteristics (%) Variance explained by the model (%)

Kushets with: Farm households engaginginactivity if present (on average)

Developmentdomain dimensions

Investmentdecisions

Investment decisions,cropping pattern, livestock use

Total

Livestock use

No oxen 100 24 0 0One ox 100 42 25 25Two oxen 97 26 33 33More than 2 oxen 66 14 34 34Cows 97 39 28 28Goats 86 33 39 39Sheep 84 33 32 32Donkeys 97 50 18 18Beehives 85 20 25 25

Technology choice

Burn crop residues 82 77 43 22 65Compost 60 35 60 26 86Contour plowing 100 96 46 27 73Crop residuesincorporation

16 45 52 25 77

Crop rotation 96 90 67 24 91Fallow 51 36 49 29 78Feed 83 48 49 39 88Fertiliser 99 68 62 23 85Green manure 2 10 19 47 66Herbicide 18 16 10 36 46Improved fallow 5 30 11 36 47Intercropping 77 65 51 31 82Manure application 93 67 60 22 82Mulch 2 30 23 0 23Pestcide use 42 33 57 22 79Improved seed 88 33 56 25 81Vaccinations 94 77 59 28 87

Note. Because of the large number of underlying equations, the complete results cover 18 pages of tables. These are available from the author upon request.

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G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91 85

combination of past investments, cropping patterns and livestock use and to a lesserextent by development domain dimensions, while mulch is not explained by the vari-ables in the model. Sometimes an indicator variable has a high occurrence (manynon-zero values) but the variation in its value is low, i.e., it is a homogenous variable,which leads to low explanatory power, as is the case with no oxen, and to a lesserextent tree planting, stone terraces, and donkey ownership.

However, not all variation is explained. Even though the heterogeneity present inthese indicator variables, graphically presented in Figs. 1–4, cannot be explaineddirectly or indirectly by the development domain dimensions, the occurrence of devi-ations from the pattern might be. As one can observe in the graphical representationsof the residuals (the unexplained parts of the indicator variables) there are quite anumber of both positive and negative outliers. By summing the absolute values ofthe residuals of each of the regressions, we get an indicator for the degree to whichthe indicators for agricultural production deviate from the expected pattern. Whatwe would like to know is whether this index is related to the development domain

-0.4

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Drainage ditches

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Grass strips

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Gully stabilisation

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Irrigation canals

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Irrigation wells

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Private nurseries

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Soil bunds

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Stone terraces

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0.0

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10 20 30 40 50 60 70 80 90

Tree planting

Fig. 1. Residuals from third-order polynomial estimation of proportion of farmers having invested in soiland water conservation measures explained by development domain dimensions (direct and indirecteffects). X-axis: observations, Y-axis: residual value.

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0.0

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Barley

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Teff

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Maize

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Wheat

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Sorghum

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Finger millet

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Faba beans

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10 20 30 40 50 60 70 80 90

Millet

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Chickpea

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Filed pea

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Flax

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Lentils

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Haricot beans

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10 20 30 40 50 60 70 80 90

Sesame

-0.04

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0.00

0.02

0.04

0.06

10 20 30 40 50 60 70 80 90

Niger seed

Fig. 2. Residuals from third-order polynomial estimation of proportion of farmers growing cropsexplained by development domain dimensions (direct and indirect effects). X-axis: observations, Y-axis:residual value.

86 G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91

dimensions. Again we use a third-order polynomial functional form. The results aresummarized in Table 2.

The analysis of third-order polynomial regression functions can be puzzlingbecause of the combined effects, variables are also found as quadratic and cubic form

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Goats

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Donkeys

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Beehives

Fig. 3. Residuals from third-order polynomial estimation of proportion of farmers using livestock explainedby development domain dimensions (direct and indirect effects).X-axis: observations,Y-axis: residual value.

G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91 87

and in cross-terms. Table 2 indicates that with respect to past investments somewhatmore heterogeneity is found at the extremes of elevation levels, but notably in thecenter with respect to rainfall and population density. The most important conclu-sion is that institutional presence leads to a more variety in investments not so muchlinkage between specific institutions and investments. In total 30% of the diversitycan be explained in this way.

With respect to crops more diversity is found especially in high elevation areasand areas closer to markets. High population density areas tend to be less heteroge-neous just as we find less diversity on poor and degraded soils. Finally, diversity incropping patterns across villages with similar development domain dimensions isalso linked to the presence of credit by REST. The presence of NGOs with theirextension activities leads to a greater variety of cropping patterns. However, the typecrop diversification is not the same in otherwise similar communities due to differ-ences in emphases placed by the NGOs. More than 37% of the diversity can be ex-plained this way (referring to the adjusted R2).

Diversity in livestock systems is linked to soil quality but in a complex and nottransparent way. More diversity in livestock systems is found at lower altitudes. Less

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0.2

0.4

10 20 30 40 50 60 70 80 90

Burn crop residue

-0.2

-0.1

0.0

0.1

0.2

-0.2

-0.1

0.0

0.1

0.2

10 20 30 40 50 60 70 80 90

Compost

-0.08

-0.04

0.00

0.04

0.08

10 20 30 40 50 60 70 80 90

Contour plowing

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

10 20 30 40 50 60 70 80 90

Iincorporate crop residue

10 20 30 40 50 60 70 80 90

Crop rotations

10 20 30 40 50 60 70 80 90

Fallow

-0.2

-0.1

0.0

0.1

0.2

0.3

10 20 30 40 50 60 70 80 90

Improved Feed

10 20 30 40 50 60 70 80 90

Fertiliser use

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.015

0.020

10 20 30 40 50 60 70 80 90

Green manure

-0.08

-0.04

0.00

0.04

0.08

10 20 30 40 50 60 70 80 90

Herbicide use

10 20 30 40 50 60 70 80 90

Improved fallow

-0.4

-0.2

0.0

0.2

0.4

10 20 30 40 50 60 70 80 90

Intercropping

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

10 20 30 40 50 60 70 80 90

Manure use

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

10 20 30 40 50 60 70 80 90

Mulch

10 20 30 40 50 60 70 80 90

Pesticide use

-0.3

-0.2

-0.1

0.0

0.1

0.2

10 20 30 40 50 60 70 80 90

Improved seed

10 20 30 40 50 60 70 80 90

Vaccinations

1

Fig. 4. Residuals from third-order polynomial estimation of proportion of farmers using technologyexplained by development domain dimensions (direct and indirect effects). X-axis: observations, Y-axis:residual value.

88 G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91

Table 2Relationship between indicators for heterogeneity for investment, crops, livestock and technology anddevelopment domain dimensions

Investment Crops Livestock Technology

Dependent variableP

|l|Mean 0.98 0.47 0.79 0.89Median 0.94 0.46 0.80 0.81Maximum 2.33 0.78 1.50 1.98Minimum 0.23 0.12 0.25 0.14Standard deviation 0.38 0.15 0.30 0.39Skewness 0.84 �0.15 0.25 0.34Kurtosis 4.03 2.29 2.29 2.71

Jarque-Bera 13.68 2.12 2.58 1.87Probability 0.00 0.35 0.27 0.39Observations 84 84 82 82

Regression coefficients

Constant 1.21*** 0.51*** 1.03*** 1.34***Degree of soil degradation (SD) �0.07 0.00 �0.16** 0.12Inherent good soils (GS) 0.06 0.04* 0.09* �0.09Elevation (EL) �0.22** �0.03 �0.20** �0.06Rainfall (RF) �0.05 �0.01 �0.11 �0.10Population density (PD) �0.03 �0.01 0.02 �0.02Distance to markets (DM) 0.08 �0.07*** 0.01 �0.12*SD · SD �0.03 0.02 �0.03 �0.04GS ·GS �0.01 �0.03 �0.06* �0.07*EL · EL �0.06 �0.09*** �0.02 �0.16**RF · RF �0.13** �0.02 �0.14** �0.07PD · PD �0.16** �0.01 �0.03 �0.13DM · DM �0.03 �0.06** 0.01 �0.19***SD ·GS �0.03 0.03** �0.01 0.02SD · EL �0.01 �0.02 0.06 �0.14***SD · RF �0.09* �0.01 0.03 �0.02SD · PD �0.06 0.00 0.00 �0.01SD ·DM 0.07 0.00 �0.03 0.00GS · EL �0.02 0.00 0.01 �0.07GS · RF 0.04 0.01 0.04 �0.06GS · PD 0.00 0.02 0.01 0.06GS ·DM 0.01 0.02 �0.01 0.01EL · RF �0.16** �0.02 0.01 �0.25***EL · PD 0.22* 0.20*** 0.04 �0.03EL · DM �0.03 �0.09** 0.05 �0.25**RF · PD �0.01 0.03 0.07 �0.02RF ·DM 0.07 �0.05 0.14* �0.25***PD · DM �0.03 0.06* 0.02 0.08SD · SD · SD 0.03 �0.01 0.07*** �0.07**GS ·GS · GS 0.00 0.00 �0.01 �0.01EL · EL · EL 0.08*** 0.03** 0.03 0.04RF · RF · RF 0.00 0.01 0.04 0.06*PD · PD · PD 0.01 �0.01 0.00 0.02DM · DM · DM �0.01 0.01 �0.01 0.05*Presence of cooperatives 0.10*** 0.00 �0.01 �0.09**

(continued on next page)

G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91 89

Table 2 (continued)

Investment Crops Livestock Technology

Absence of irrigation institutions �0.19*** 0.00 �0.02 �0.02Presence of livestock promotion 0.09*** 0.01 0.04 �0.04Presence of REST credit 0.11** 0.04** 0.00 �0.04

Adjusted R2 0.31 0.37 0.19 0.40Observations 84 84 82 82

Note. ***99% significance level, **95% significance level, 90% significance level.

90 G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91

diversity is found with higher rainfall. Only 19% of the heterogeneity is explained.Diversity in technology choice is found in places with an average market access, withsomewhat poorer soils in the higher elevation areas. Some 40% of diversity is ex-plained this way. It should be noted that in the very remote areas and in areas withvery high rainfall less diversity can be observed.

5. Conclusions

The diversity in past investments, current major cropping patterns, livestock useand technology choice observed in Tigray can be explained to some extent, and oftenin a complex way, by the diversity in development domain dimensions. However,part of the diversity remains in similar conditions when viewed from the perspectiveof those development domains.

Homogeneity in development pathways is found in higher elevation areas withpoor soils and poor market access. Heterogeneity in past investments is more com-mon in areas with heavy institutional presence, indicating that there is a variety offeasible options available for development of agricultural production. Rainfall doesnot have an important impact on diversity in agricultural production indicators.

The policy implication of this analysis is that development domains do offer animportant entry point for targeting interventions. We have shown that strong link-ages exist between investments, cropping patterns and technology choice. Institu-tional presence is very instrumental in securing investments that lie at the heart ofchanges in cropping patterns and technology choice. Patterns of crop diversificationand the adoption of alternative technologies, both instrumental in the alleviation ofpoverty and in ensuring sustainable livelihoods, are linked to development domains.Specific policy recommendations related to specific interventions in different domainsare beyond the scope of the present paper, but will be feasible with additionalresearch based on this approach.

Acknowledgements

The authors are grateful to John Pender (IFPRI, Washington DC) and BerhanuGebremedin (ILRI, Addis Ababa) for their support with the data analysis andinterpretation. Data collection took place under the collaborative program between

G. Kruseman et al. / Agricultural Systems 88 (2006) 75–91 91

IFPRI, ILRI and Mekelle University College. We sincerely thank MUC authoritiesfor their willingness to share the data for this purpose. We are grateful to two anon-ymous referees for comments on an earlier version of the paper.

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