CAN PAYMENTS FOR ECOSYSTEM SERVICES REDUCE SOIL EROSION? THE CASE OF EASTERN REGION OF MOROCCO

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International Journal of Advanced Research in Engineering and Technology

(IJARET) Volume 6, Issue 11, Nov 2015, pp. 18-36, Article ID: IJARET_06_11_002

Available online at

http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=6&IType=11

ISSN Print: 0976-6480 and ISSN Online: 0976-6499

© IAEME Publication

___________________________________________________________________________

CAN PAYMENTS FOR ECOSYSTEM

SERVICES REDUCE SOIL EROSION? THE

CASE OF EASTERN REGION OF

MOROCCO

M Boughlala and A Laamari

National Institute of Agricultural Research, CRRA of Settat, 26000, Settat, Morocco,

Faculty of Legal, Economic and Social Sciences / University Hassan 1, 26000 Settat,

Morocco

A Abdouni

Faculty of Legal, Economic and Social Sciences,

University Hassan 1, 26000 Settat, Morocco

M Belmekki

Faculty of Sciences and Techniques / University Hassan 1, 26000 Settat, Morocco

ABSTRACT

The fragile dryland areas in Morocco are seriously threatened by soil and

water degradation. Each year, 100 million tons of soil are removed. Several

soil conservation technologies have been developed in Morocco and are

available for large diffusion but in many cases these technologies have not

been permanently adopted. It seems that a large dissemination of these new

practices requires some financial incentives that must be sufficiently high to

stimulate farmers to adopt the technologies. The objective of this study is to

identify the optimum level of payments for eco-system services (PES) that

allows an acceptable adoption rate. In this study we used the Minimum data

approach (MD) to assess the adoption rates of soil conservation technologies.

Input data needed by this model are in general simple, compared to other

data-intensive models. They are very often available from secondary sources.

The technologies concerned by this study are Alley cropping of Atriplex with

barley, deep de-stoning and improved olive trees. The results show that

farmers in the study area are very aware of soil erosion damages. Model

results show very high adoption rates of the proposed technologies (more than

90%) and without any financial incentives. Compared to field data the model

over-estimates the adoption rates. This is mainly due to the fact that the model

does not consider institutional and political constraints to the adoption.

Can Payments For Ecosystem Services Reduce Soil Erosion? The Case of Eastern Region of

Morocco


Therefore, any improvement of the adoption rates of soil conservation

technologies in the region should consider the problem of credit access and

land tenure as a first step. Furthermore, in complex environment such as the

study region we suggest that this type of approach be coupled with qualitative

analysis in order to better analyze and understand the problem of technology

adoption.

Key words: Soil Erosion, Adoption, PES, MD

Cite this Article: M Boughlala, A Laamari, A Abdouni and M Belmekki. Can

Payments For Ecosystem Services Reduce Soil Erosion? The Case of Eastern

Region of Morocco. International Journal of Advanced Research in

Engineering and Technology, 6(11), 2015, pp. 18-36.

http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=6&IType=11

1. INTRODUCTION

The fragile dryland areas in northern Africa are seriously threatened by soil and water

degradation. Morocco is a good example where soils with relatively low organic

matter contents (<2%) are directly impacted by the serious erosion processes which

can exceed 20 tons ha-1

yr-1

([1], [20]). It is estimated that the total annual cost of

erosion from agriculture in Morocco is about 0.41% of its GDP [34]. Although land

degradation in these environments is partly caused by natural factors, the accelerated

erosion rates are mostly human induced. Overgrazing is the most important cause of

soil degradation in the eastern regions of Morocco, the Souss, the Pre-Sahara and the

Sahara and approximately 8.3 million ha of rangelands are heavily degraded [24]. The

land in these regions is collectively used by local tribes and communities. Land use

conflicts over access to grazing areas and water are major contributors to continual

and often intensive land degradation. In the eastern region, feed subsidies for instance

discourage the reduction of livestock pressure on collective rangeland. Higher prices

encourage farmers to grow cereals, especially bread wheat, in steeply sloped plots.

These government support policies combined to other environmental problems

contributed also to soil erosion.

According to authors in references [2], there are at least three major viewpoints on

soil erosion. First, the population explanation, which argues that growing population

pressure, causes higher pressure on natural resources. Second, past policies such as

price support, subsidized credit and soil conservation subsidies created distortions and

have in some cases exacerbated erosion. Third, poor institutions such as weak

property rights and high transactions costs have prevented the emergence of markets

that would have mitigated soil erosion but has not received adequate attention.

A strong link exists also between land degradation and the resulting desertification

and poverty. The proportion of poor populations is significantly higher in the

drylands, especially among rural populations. Poor communities do not intentionally

degrade their environment. Their fragile and limited resources, their often poorly

defined rights, and their limited access to credit and insurance markets prevent them

from investing as much as they should in environment protection. When they do make

investments, they need quick results [33].

Soil erosion has serious negative on-site effects, such as a decrease in soil

productivity. However, it may also have off-site effects, such as silting of dams. In

Morocco more than 50 million m3 of sediment are deposited each year in dams’

reservoirs. This volume corresponds to a loss equivalent to the volume of water

M Boughlala, A Laamari, A Abdouni and M Belmekki


needed to irrigate 5,000 hectares [2]. To efficiently reduce these off-site effects,

adequate government interventions are needed. This may include incentives to

implement soil and water conservation practices to farmers. Globally there has been a

shift from traditional subsidy and trade policies to policies that provide farmers with

incentives to increase the supply of ecosystem services from agriculture [16]. This is

being referred to as payments for eco-system services [19].

Several soil conservation technologies have been developed in Morocco and are

available for large diffusion. These technologies deal mainly with soil and water

conservation techniques and water harvesting. The Taourirt-Taforalet Rural

Development Project (PRDTT) financed by the International Fund for Agricultural

Development (IFAD) contributed financially to the introduction of these new

practices at some farms in the eastern region in order to encourage large

dissemination. However, only a small percentage of farmers (7%) adopted the new

technologies without the project support [27]. It seems that a large dissemination of

these technologies in this area requires a subsidy that must be sufficiently high to

stimulate farmers to adopt the technologies. The incentive should be greater than the

opportunity cost of shifting from the current practices to the new technologies that

makes farmers adopt the technologies and meets the government target adoption rate.

The objective of this study is to identify the optimum level of payments for eco-

system services (PES) that allows an acceptable adoption rate. Because of the arid

character of the region and the resulting high special heterogeneity in environmental

condition this PES optimum level should vary between different ecosystems. Analysis

based on average farmers or average conditions would not help in identifying the

adequate level of PES needed for a specific ecosystem or a specific farmer’s category.

In order to take in consideration this variability we use the technology adoption model

developed by Antle and Valdivia [6] to model the supply of ecosystem services from

agriculture. The pertinence of this model is that it is based on a minimum-data (MD)

approach. Input data needed by this model are in general simple, compared to other

data-intensive models ([4], [25]), and very often available from secondary sources.

The MD approach is principally motivated by the demand for timely, quantitative

analysis of agriculture-environment interactions that can provide sufficiently accurate

analysis of policy scenarios to support informed policy decision makers [6].

In this study we will first give a summary of some causes of land degradation. In

the second Section we will give a general description of the economic model

developed by Antle and Valdivia [6]. In the next Section we give a description of the

study area and the materials and methods that were used to implement the TOA-MD

model in the area. Next, we discuss the survey data and the results of the model.

Finally, we draw some general conclusions.

2. CAUSES OF LAND DEGRADATION

According to Slegers and Stroosnijder [28], land degradation is defined as the level of

the reduction of the land potential production under arid, semi-arid and dry sub-humid

climates. Climate variation and anthropologic actions are the main driving forces.

They provoke the depletion of vegetative cover and the loss of biophysical and

economic productivity through exposure of the soil surface to wind and water erosion.

Land degradation can also take place through salinization and waterlogging leading to

deterioration of physical, chemical and biological soil properties [9]. Land

degradation in drylands is a very complex process, which is not a single factor


Morocco


outcome, but is rather an emergent property of interacting human and biophysical

factors.

2.1 CLIMATE

The climate is the major determinant of land degradation in arid and semi-arid zones.

Morocco experiences, in addition to highly variable rainfall, recurrent periods of

drought. Morocco has experienced a drought in one year out of every three over the

last century [32]. Rainfall variability and drought spells would definitely have adverse

impacts on Land productivity, enhancing erosion processes, degrading plant cover and

other desertification processes.

Scarcity and fragility of water and arable land resources contribute to their

degradation through deepening the chronic unbalance between increasing population

and available natural resources. In 1994, water availability per capita and per year was

1,100 m3. It is predicted that this quantity will drop to only 689m3/ per capita/ per

year, in 2025. Similarly, arable lands will undergo the same constraints already

affecting the water resources.

2.2 POPULATION PRESSURE

In a study that concerns the causes of land degradation in South America and Africa,

authors in references [29] found that the principal response in these two continents to

demographic and market shocks is extensive agriculture production on fragile lands

rather than intensified production.

The Morocco’s increasing population pressure and the accelerating loss of topsoil

seem to go hand in hand. Overgrazing is the most important cause of soil degradation,

accounting for 49 percent of the area, followed by agricultural activities (24 percent),

deforestation (14 percent), and overexploitation of vegetative cover (13 percent). All

these types of degradation cause a decline in the productive capacity of the land,

reducing potential yields [18]. Continued deforestation for different reasons including

deforestation to convert to cultivated lands, forest fires, cutting trees and shrubs for

fuel, lead to enhanced soil erosion processes with adverse environmental impacts and

a decline of palatable species and domination of non-palatable and invasive species.

Cultivation of marginal lands and forest clearing to increase the cereal production

have lead to destruction of plant cover, loss of valuable biodiversity while achieving

marginal productivity. At present, reforestation efforts are unable to come even close

to offsetting the loss of natural forests.

2.3 MECHANIZATION OF AGRICULTURAL SYSTEMS

The use of extensive tillage with heavy machinery has been accompanied by

damaging effects and pressure on the environment. These negative effects on the

environment created an increasing awareness of the need for an economically and

environmentally sustainable agriculture and consequently a review of much of the soil

management practices. The need for tillage has been questioned, in the last decades,

partly because of the excessive erosion from farmlands [21]. Intensive land cultivation

methods using tractors and plow are a major cause of severe soil loss and land

degradation. Especially in warmer areas, where the topsoil layer is thin, conventional

tillage contributes to soil loss. However, most farmers, researchers and extension

agents still believe that loosening the soil with tillage implements increase rainwater

infiltration.



2.4 LAND TENURE

Poorly defined property rights may lead to resource degradation, as individual

investments in maintenance and enhancement can be captured by others [15]. The

complexity of the land use patterns in Morocco is also compounded by the difficulties

linked to the multiple age-old land tenure systems. Empirical evidence of the open-

access regime can be found in the rangelands situated in Eastern part of Morocco

where land conservation practices are highly discourage as farmers can only

temporarily use rangelands parcels under the current communal tenure regimes.

3. MATERIALS AND METHODS

3.1 ECONOMIC MODEL

Various studies used highly elaborate models based on site-specific data to assess the

adoption of alternative agricultural systems ([5], [25] and [30]). These models use

significant input data that require intensive data collection efforts. It is true that these

models produce a detailed analysis and information with a high level of accuracy. The

question remains whether results at this level are always required? When this

information is mainly destined to support policy decision making process more

general outcomes may be good enough. Antle and Valdivia [6] argued that the

appropriate level of accuracy for policy analysis is lower than standards acceptable for

publication in scientific journals because ex ante modeling involves a large number of

unquantifiable uncertainties, and because the policy process utilizes many sources of

information and must balance competing political interests.

In this study we are dealing with a very specific question: what is the effect of

different level of PES on the adoption rate of water and soil conservation

technologies?

The challenge of this study is to provide timely quantitative policy analysis using

limited resources to support the policy decision process. A number of studies show

that the MD approach gave a good trade-off between timeliness, resource limitation

and accuracy of information produced for policy decision making ([6], [8] and [16]).

Furthermore, unlike cost/benefit approach based on representative farmer, the MD

approach takes into consideration the variability in agricultural systems that

characterize spatially heterogeneous arid and semi-arid environments. For these

reasons we choose to use the MD approach to estimate the rate of adoption of water

and soil conservation practices for varying levels of PES in the Eastern arid region of

Morocco.

The MD model assumes that farmers take land use and management decisions to

maximize their profit. In this context farmers often do not consider negative impacts

or negative externalities (like soil erosion) of their decision. If we want farmers to

change their practices in ways that protect environment, government must provide

them with adequate incentives.

To illustrate this approach, we consider two competing management practices: 1)

a current practice M1, and 2), a soil conservation technology M2. At each agro-

ecological z an ecosystem service of e(z) is produced if practice M2 is adopted and

e(z) equal zero if farmers continue using practice M1.

ω(p,z) = υ(p,z,ma) - υ(p,z,mb) ≤ pe e(z) (1)

where υ is the net return, p is a vector of input and output prices, and z the zone

index. ω(p,z) can be interpreted as the opportunity cost of changing from M1 to M2.


Morocco


This equation implies that farmers are willing to change practices if the

government will provide farmers with a payment of pe for each ton of reduction in soil

erosion. A farmer will decide to adopt M2 if ω/e ≤ pe, i.e. if the opportunity cost per

unit of reduction in soil erosion is less than the price paid for the same unit. If we

order all the zones z for a given p within a region in increasing order of ω(p,z) we can

define the spatial distribution of opportunity cost per unit of ecosystem e, Ø(ω/e). The

proportion of total number of farmers who adopt M2 without payment is then given by

equation:

r(p) =

0

φ(ω/e)d(ω/e) (2)

The baseline equilibrium supply of ecosystem services before farmers are given

payments is then given by

S(p) = r (p)He (3)

where H is the total area of the region. Similarly by integrating Ø(ω/e) between

zero and pe the fraction of total farmers that change from practice M1 to M2 given pe ,

r(p,pe ) is found. The total supply of ecosystem services in that case equals

S(p) = r(p)He+ r(p,pe )He (4)

Figure1 shows how the supply of ecosystem services is derived from the spacial

distribution of opportunity cost. The curve in the left side of the graph represents the

opportunity cost distribution. The price per unit of e, Pe , is shown on the vertical axis

and the density function Ø(ω/e) is shown in the horizontal axis. The area under this

curve in the price range from - ∞ to 0 is the initial equilibrium supply for ecosystem

services. The right side of the graph represents the supply curve. The horizontal axis

shows the supply of ecosystem services as a function of price per unit e on the vertical

axis. The supply curve crosses the horizontal axis at the initial equilibrium where

pe = ω/e = 0. The supply of ecosystem services increases as the Pe increases. The

rate of increase (slope of the supply curve) depends on the shape of the distribution of

the opportunity costs. The supply curve approaches a vertical asymptote equal to the

maximum amount of ecosystem services that can be produced when all farmers

switch to practice M2.

Figure 1 Derivation of the supply of ecosystem services from the spatial distribution of

opportunity cost per unit of ecosystem services.



3.2. STUDY AREA

Several soil conservation techniques have been developed in Morocco or in the region

and available for large diffusion. Some of these technologies have been tested in the

Eastern part of Morocco, especially in Tancherfi community (fig. 2). This community is

characterized by a Mediterranean climate with continental effects. The mean annual

rainfall for the region is less than 250 mm. It is one of the 13 communities concerned by the

PRDTT Project and it covers some 61200 ha and concerns about 1444 rural families. The

land occupation pattern shows that forests represent 66.5%, dry farming 22%,

collective rangeland 6.1%, nonproductive land 4.9% and irrigation less than 0.5%.

For land tenure, about 66.5% of the community area is under forest regime (state

land), around 11% is common tribal land and the remaining 22.5% corresponds to

private land. But the majority of farmers under this last category of tenure have no

appropriation titles which makes it difficult to obtain credit for investments.

Crop-livestock systems are the predominant farm enterprises, with major

household income generated from small ruminant production. Traditionally, livestock

feed on extensive rangeland during winter and spring grazing, and are moved to

cultivated areas for grazing of cereals stubbles and other crop residues in summer and

fall.

The community is divided into four agro-ecological zones. The main

characteristics of each zones are:

Zone 1: Mkhilikha Hassi Sedra

This zone represents only 8 % of total community land, mainly dominated by

common rangelands (50%). The nonproductive land represents 20% of total zone area

which reflect the highly degraded character of this zone. The soils are stony and

relatively shallow. The production system is based on the integration between

livestock and crops, mainly barley. Almond and olive trees represent 6% of total zone

area.

Zone 2: Subzone Igherma-ouene

The second zone represents 9% of total community area and 59% of this area is

destined to cropping activities which are largely dominated by soft wheat (40%). The

fallow practice represents 20% and concerns the rotation bred wheat/Fallow. The area

of forage crops are relatively important (8%) compared to the other zones (maximum

2%). This is due probably to the absence of forest and to the small area of collective

rangeland which represent the main source of animal feeding in this region. Zone 2

exhibits also clear signs of land degradation, since 16% of this zone is considered as

non-productive land. Soils are relatively deep in the North part of this zone.

Zone 3 : subzone Lamgader

The main characteristic of this zone is the existence of a diversified production system

which includes rainfed cereals (49%), mainly barley, forage crops (2%), almond trees

(7%), olive trees (8%) and irrigated crops (6%). The forest and collective rangelands

represent respectively 11% and 7% of the area. Soils are generally sandy and clay.

Zone 4: Erzaine-Matroh

This zone is the largest one for the community (78%). The forest covers almost all the area (84.5%). The cropping area represents less than 13% and is dominated by rainfed

cereals, mainly barley (5%). The topography is very sloppy with relatively shallow


Morocco


soils. Water erosion is important in this zone. Farmer in this location has large herd

size with dominance of mixed sheep and goats and has the right to use the forest for

grazing.

Figure 2 Map of Tancherfi (district of the province of Taourirt)

3.3. EXISTING DATA

Experimental data on the soil conservation techniques was collected at the level of

local extension service at Laayoune city, province of Oujda. The main technologies

dealing with water and soil conservation that were tested in Tancherfi community are:

alley cropping, deep de-stoning and improved olive trees plantation.

3.3.1 ALLEY CROPPING TECHNOLOGY

Alley cropping is a system where annual crops (in the case barley) are grown

simultaneously in alleys formed by hedgerows of shrubs (Atriplex nummularia)

(fig.3). Atriplex, also known as saltbush, thrives in dry alkaline soils and has high

protein content. Alley cropping of Atriplex with barley is a technology that has been

introduced in Morocco. The potential benefits of the introduced technology are: (1) an

increase in barley production due to the important role that Atriplex plays in wind

breaking [10], (2) provide environment benefits and restore soil fertility, consequence

of their drought resistance, organic matter supply, and deep root development [22],

(3) a source of emergency feed during drought seasons, reduced grazing pressure and

reduced feeding costs [27]. The planting density of Atriplix varies from 500 to 700

plants per hectare and the width of the alleys is about 13 m. In average and in an alley

cropping system, Atriplex nummularia produces 670 Fodder Units (FU) per year and

per hectare (1 FU is equivalent to the energy value of 1 kg of barley mature grain).

The lifespan of Atriplex plantation is about 15 years. The PRDTT project estimated

the adoption cost of this technology at 21.78 $ ha-1

yr-1

as an extra cost to the

production cost of one hectare of barley without Atriplex. This technology concerns

zone 2, 3 and 4.

Z

1

Zone 1

Z

2

Zone 1

Z

3

Zone 1

Z

4

Zone 1



Figure 3 Alley cropping system in Eastern part of Morocco

3.3.2 DEEP DE-STONING

This operation concerns mainly good soil situated in valleys, but, the presence of

rocks and stones in the surface reduces water infiltration and causes soil erosion. A

deep plowing to remove rocks is practiced in order to increase water infiltration,

increase soil water storage and limit water soil erosion. The cost of removing the

rocks and cleaning the plots is estimated at 2,365 $ ha-1

. This technology concerns

only zone1.

3.3.3 IMPROVED OLIVE TREES

The aim of this technology is to plant olive trees, which are very adapted to arid

conditions, in the area of land formed at the foot of mountain range (piedmont). This

area is very sloppy and water erosion is very important. Trees plantations will

increase water infiltration, limit soil erosion and improve the productivity of these

areas. Olive trees can live up to 50 years or more and the maximum production is

reached at the age of 30 years. The cost of adoption of this improved olive trees was

estimated by the local extension service at 1,224 $ ha-1

. This technology concerns all

zones.

3.4. SURVEY

A sample of 152 farmers was randomly selected but strata importance was considered

in the distribution of the cases and villages among the sample. The sample represents

more than 25% of total households of Tancherfi community (604 farmers). The

sampling framework was developed in collaboration with local extension service. The

distribution of the sample among zones is represented in table 1.

Table 1 Sample distribution

Zones Total of households Sample (cases) % of households

1 109 28 18

2 193 49 32

3 163 41 27

4 139 34 23

Total 604 152 100


Morocco


The questionnaire was developed in collaboration with researchers from Social,

Economic and Policy Research Program of ICARDA and it is made of three parts.

First, household data on size of the farm, age, gender, level of education of the

farmers and on and off farm income were asked. Second, data on production systems,

crop yields, and crop production costs and livestock activity were collected. The third

section concerns farmers’ perception and evaluation of soil erosion, estimation of soil

erosion cost and main causes of soil erosion at the plots level.

3.5. MODEL DEFINITION

Depending on the case study, the MD approach allows the use of two different

options of payment. If the estimation of the amount of ecosystem services produced is

technically and economically possible, the payment will be based on the quantity of

ecosystem services produced. If these measurements are not possible, either ex ante or

ex post, we can simply use payments for adoption. In our case we will use the second

option, so only farmers that adopt soil conservation technology will receive a

payment.

Thus farmers will be willing to switch to the new practice if the payment per

hectare for adoption (pa) is greater than the opportunity cost (ω). We assume that if

the conservation technology is adopted, one unit of ecosystem services will be

produced. Thus, instead of having an ecosystem services supply curve we will have

an adoption curve.

Similarly to the theoretical model, the special distribution of opportunity cost for

switching from current practice to soil conservation practice is defined as φ(ω). The

proportion of total number of farmers who adopt the new practice without payment is

then given by

r(p) =

0

φ(ω)d(ω) (1)

The baseline area under the new practice before farmers are given payments is

then given by

S(p) = r(p)H (2)

The proportion of total number of farmers who switch to new practice after

receiving pa is given by

r(p,pa) = pa

0 φ(ω)d(ω) (3)

The total area practicing the new technology after providing farmers with pa > 0 is

equal to

S(p,pa) = S(p) + r(p,pa)H (4)

To model the adoption rate per agro-ecological zone we use the MD approach to

parameterize the spatial distribution of opportunity cost by using the yield variability

together with mean estimates of cost and returns of each practice. The opportunity

cost per hectare is given by

ω = p. (Y1 – Y2 ) – c1 + c2 (5)

where p is the local market price for barley, wheat or olives. c1 and c2 are the

average production costs per hectare respectively for current and new practice and Y1

and Y2 are their respective yields.



Antle and Capalbo [3] found that the coefficient of variation in net returns for a

given crop across land units in a region (at a point in time) can be estimated by the

spatial coefficient of variation for its yield. Antle and Valdivia [6] confirmed that use

of the MD approach based on this assumption provides a reasonable approximation

that is well within an order of magnitude. The MD approach implemented in this

study utilizes the same assumption.

The coefficient of variance in yield for current practice (CV1) was calculated

using the survey data. We assumed that coefficient of variance in yield for the new

practice (CV2) will be equal to CV1.

The opportunity cost is assumed to be normally distributed and its variance is

calculated according to the following set of equations:

σω2 = σ1

2 + σ2

2 - 2σ12 (6)

σa2 = CV1

2 . v1

2 (7)

σb2 = CV2

2 . v2

2 (8)

σab = CV1 . v2. CV2 . v2 . ρ12 (9)

where σ12 and σ2

2 are the variances in net returns of practice 1 and 2 respectively,

v1 and v2 are mean yields, and σ12 and ρ12 are the covariance and spatial correlation

coefficient in net returns between practice 1 and practice 2. The MD model estimates

the opportunity cost distribution per agro-ecological zone and by sampling this

distribution at different pa, the adoption curve of the new practice is calculated.

4. RESULTS

4.1. SURVEY RESULTS

Compared to the other zones, zone1 is characterized by a medium slope angle, higher

soil organic matter and higher surface stone rate (table 2). In average, soils in zone2

contain the lowest percentage of clay and has relatively high surface stone rate. Zone3

can be considered as a small plateau where soils are loamy clay and poor in organic

matter. Zone4 lies along the foot of Tancherfi Mountain. This zone is characterized by

an irregular topography. The variability in slope angle is very high compared to the

other zones.

Table 2 Land characterization per zone (in percent)

Z1 Z2 Z3 Z4

Slope angle (deg) 4.20 (5.56)* 5.57 (6.28) 1.93 (2.14) 7.43 (13.71)

OM 2.65 (0.52) 2.05 (0.68) 1.84 (0.55) 2.63 (1.37)

Clay % 30.45 (7.00) 18.96 (10.47) 38.30 (12.82) 31.67 (13.17)

Stone Rate (%) 28.07 (19.65) 14.84 (15.07) 6.00 (5.78) 14.94 (18.29)

Altitude (m) 715.67 (43.34) 725.27 (67.72) 1046.9 (40.61) 1126.06 (115.06)

*Numbers between brackets are Standard deviations

According to the household survey, the average age of farmers is 57 years. More

than 75% of farmers are illiterate and only 7% of them have a high school level. The

average family size is around 8 persons. More than 98% of farmers have agriculture

as first activity, and it is their main source of income (63% surveyed farmers). Off-


Morocco


farm activities generate 37% of household income and concerns different activities.

Livestock is the most income generating activity (57%).

One of the particularities of the agricultural system in this region is that the same

farmer can have plots in different zones. This is due essentially to the prevailing

tenure system in this region. The cropping land was commonly managed in the past

and around the '60s the community decided to share the land and each farmer have

received plots situated in different land categories. That’s why results will be

presented by farm size rather than zones.

Table 3 Summary of survey results per farm size.

Small Medium Large

Number of plots 5.98 (4.92)* 7.87 (6.72) 9.87 (7.75)

Total farm area (ha) 6.25 (2.23) 19.19 (5.49) 58.81 (22.74)

Irrigated area (ha) 1.17 (1.31) 2.27 (3.53) 3.25 (5.13)

Rainfed area (ha) 4.98 (2.58) 17.03 (5.79) 55.56 (22.49)

Wheat 0.86 (0.42) 0.77 (0.39) 0.79 (0.39)

Yield (t/ha) Barley 0.83 (0.41) 0.81 (0.45) 0.94 (0.42)

Olive 1.88 (1.19) 2.23 (1.73) 2.53 (1.45)

Wheat 0.49 0.51 0.49

Coefficient of variation Barley 0.49 0.56 0.45

of observed yields Olive 0.64 0.78 0.57

Wheat 112.53 (89.15) 99.60 (44.13) 103.79 (57.94)

Production cost ($/ha) Barley 86.88 (24.87) 93.07 (32.98) 90.83 (20.52)

Olive 597.77 (184.49) 510.11(193.68) 626.31(157.48)

Wheat 296 (215.60) 292.70 (190.20) 297.90 (92.10)

Product Price ($/t) Barley 204.40 (31.80) 201.80 (19.60) 201.80 (19.60)

Olive 638.30 (56.9) 634.30 (46.20) 631.10 (41.40)

Susceptibility to water erosion (%) Yes 83.60 93.30 100.00

No 16.40 6.70 0.00

Slope 39.60 35.20 68.80

Factors leading to soil erosion (%) Flood 35.40 35.20 18.80

Rain 25.00 29.60 12.40

Loss of land relative to farm size (%) 10.28 (7.73) 9.35 (8.53) 9.65 (8.12)

Erosion cost ($/year) 540.14(730.29) 988.93(1733.19) 2638.60(3137.53)

*Numbers between brackets are Standard deviations

The average farm size for small-scale farmers is 6.25ha (table 3). This category of

farmers represents 38% of total farmers in the region but have control over only 13%

of total area. The average farm size for medium and large farms is respectively equal

to 19.19 and 58.81ha. Medium farm size category controls the largest area (53%) and

represents the majority (51%) of farmers in this region. Large farmers represent only

11% but have control over 34% of land. Rainfed cropping system is dominant in this

region and represents around 81%, 88% and 94%, respectively, for small, medium

and large farm size.

As signaled above, the main inputs for MD model are the observed yields and

their coefficient of variation, production costs and prices. Data show a small positive

correlation between production costs and yields. With 8% increase in wheat

production costs, small-scale farmers obtain an increase of almost 10% in wheat yield.

The importance of this crop for this category of farmers can be explained by the

strategic role of wheat grain for family consumption. The three types of farmers face



almost the same market prices. The small difference in prices can be explained either

by the difference in product quality or the bargaining power of big producers.

When asked about soil susceptibility to water erosion, 100% of large-scale

farmers respond positively and around 16% among small-scale farmers respond that

they never experienced soil erosion. This means that large-scale farmers are more

aware of soil erosion than small-scale farmers. Looking to the factors leading to soil

erosion, it was declared that slope represents the main cause of soil erosion for large

farm size and according to small and medium-scale farmers the slope and flood are

the principal factors leading to soil erosion. It was established also that the three

categories of farmers were aware that soil erosion was damaging their land. The

average annual loss of land relative to farm size was around 10%. This means that if

nothing is done to stop the soil erosion farmers will lose their total land in ten years.

Based on the land prices in the region it was estimated that the cost of soil erosion

represent up to 21% of total net revenue for small and medium-scale farmers and

around 35% for large-scale farmers.

4.2 MODEL SETUP

Arable land in zone1, 2, 3 and 4 is, respectively, equal to 1,500, 4,206, 2,079 and

6,010ha. Alley cropping of Atriplex with barley will be tested in zone2, 3 and 4. This

technology will replace the current barley cultivation technique. The respective

weights of this activity in zone2, 3 and 4 are 38%, 56% and 75%. Deep de-stoning

concerns only zone1. Before de-stoning farmers grow wheat in this area and after de-

stoning they will cultivate irrigated vegetables. The weight of this new activity is

estimated at 32%. Improved olive trees plantation concerns all zones. The proportions

of land concerned by this technology are 16, 3.6, 14 and 1.4%, respectively, for

zone1, 2, 3 and 4. Yield of barley with Atriplex was estimated at 1.5 t ha-1

for zone2

and 1.3 t ha-1

for zone3 and 4. The same level of olive trees production (10 t ha-1

) was

applied to all zones. The production costs per hectare for barley are estimated at 71 $

ha-1

in zone2 and 3, and 74 $ ha-1

for zone4. The production cost for olive trees was

estimated at 1,224 $ ha-1

for all zones. Because of lack of data concerning vegetables

the net return was used in the model. This one was estimated at 2,451$ ha-1

.

The same prices were used for both current and new practices. These prices,

yields and production costs for current technologies are presented in table 3.

4.3 MODEL RESULTS

Figures 4 to 13 show the results of the economic model. Fig.4 shows the adoption

curve for Alley cropping of Atriplex with barely for the three categories of farmers.

Nearly all farmers change from barely to Atriplex with barely. No differences

between small, medium and large size farm were identified. In average 98% of

farmers adopt the technology even if no additional financial incentives are provided

(see aggregated results in Fig. 5). For these levels of adoption the total area cultivated

under the new technology for small, medium and large farm size is, respectively,

equal to 920, 3974 and 2285 hectares. The same tendency is observed in Fig. 6. In

average 92% of farmers adopt the improved olive trees without any payment and no

significant difference in adoption between farm categories was observed. The

adoption rate curves for de-stoning are exactly the same for small, medium and large

farm size category (Fig. 8). 98% of farmers adopt the de-stoning technique without

any incentive and the total de-stoned area is estimated to 4755 hectares. Fig. 10 and

11 show that even though when the proposed technologies are considered as a


Morocco


package, the adoption rate with out payment remains very high (97%). Unlike the

results presented above, Fig. 12 and 13 show that the adoption rate with zero

incentives for the new technological package by agro-ecological zone is relatively low

(61% in average). The lowest adoption rate is observed in zone 4 (24%). This zone is

very sloppy and farmers are more interested in extensive livestock production. A

payment of 5000 MDH (556 USD $) to farmers in this zone improves the adoption

rate to 97%.

This model indicates that a majority of farmers adopts the proposed technologies

without any financial incentives. However, in reality only a small percentage of

farmers (7%) adopted the new technologies without incentives [27]. It is clear that the

model is overestimating the adoption rate for the proposed technologies. In fact, the

adoption of the three proposed technologies causes establishment costs (EC) that are

proportional to the area concerned by the technology. Compared to Moroccan

standard of living, these costs are relatively high. The establishment costs for deep de-

stoning, olive and Atriplex plantation are respectively equal to 2,365, 2,043, and 367$

per hectare. In the model the EC were accounted as annual fixed costs (59, 51 and 22$

per hectare, respectively, for deep de-stoning, olive and Atriplex plantation).

However, in reality farmer must invest the total EC during the first year. This

consideration was not taken by the model.

Transaction costs for adopting the proposed technologies are difficult to observe

and difficult to estimate. To find the information about the technologies, farmers must

travel to cities where extension services are located. Atriplex seedlings are not easy to

find because private nurseries are not interested by this shrub since the demand is still

very low.

The poor land tenure security in this region limited the farmers’ access to credit

facilities. The absence of credit system adapted to uncertain environment like this

region make it difficult to farmer to find adequate funding to invest in new

technologies. Several studies of adoption ([13] and [26]) have proven that secure

property rights to land, farm size, and wealth are reliable predictors of adoption

because they allow households to deal with uncertainty, overcome credit constraints,

and get better information about the technology. A secure right of property to land

makes it profitable for households to invest in new technologies.

Farmers in this region are relatively old (57 year in average) this can be a limiting

factor for technology adoption. Baron, Ervin, and Norris and Batie found that older

farmers are less likely to use soil conservation practices because of their shorter

planning horizons and less than perfect capitalization of yield changes in land prices.

Moreover, younger farmers may be more educated and more involved with more

innovative farming. Furthermore, the majority of interviewed farmers are illiterate

(75%) which can be also a serious handicap for technology adoption. Strauss, Forster,

Baron, Erwin and Ervin, and Norris and Batie found that education has a positive

impact on soil conservation technology adoption. It allows households to obtain

information about the technology.



Figure 4 Changes in adoption rate for Alley cropping

of Atriplex with barley by farm size.

Figure 5 Changes in adoption rate for Alley cropping

of Atriplex with barley (Aggregated results for all

farmers).

Figure 6 Changes in adoption rate for improved olive

trees by farm size.

Figure 7 Changes in adoption rate for improved olive

trees (Aggregated results for all farmers).

Figure 8 Changes in adoption rate for deep de-stoning

by farm size.

Figure 9 Changes in adoption rate for deep de-

stoning (Aggregated results for all farmers).

Figure 10 Changes in adoption rate for technologies

as a package by farm size.


as a package (Aggregated results for all farmers).


Morocco



as a package by zone.


as a package (Aggregated results for all zones).

5. CONCLUSION

The use of relatively simple, reliable and no data-intensive models for ex ante

technology changes assessment at farm and system levels is needed to provide timely

evaluations of potential impacts of alternative technologies and policies. In this study

minimum data methodology was proposed to assess the impact of financial incentives

on adopting technologies that reduce soil erosion.

Survey results show that farmers in this region are very aware of soil erosion and

that soil erosion was seriously damaging their land. It was estimated that the cost of

soil erosion represents up to 21% of total net revenue for small and medium-scale

farmers and around 35% for large-scale farmers.

Compared to field data the model results show very high adoption rates of the

proposed technologies (more than 90%) and without any financial incentives. We

could infer that this difference is due to un-observed costs that shift the distribution of

opportunity cost leftward and cause the model without these costs to over-predict

adoption. The no consideration of institutional and political constraints to adoption by

the model, such as access to credit, land tenure, farmers level of education and

availability of technical information represents a serious limitation for the

methodology.

Any improvement of the adoption rates of soil conservation technologies in the

study region should consider the problem of credit access, land tenure and extension

as a first step. Further research will be needed to assess the adequacy of this type of

simpler modeling approach in different ecological and economic settings.

Furthermore, in complex environment such us our region, we believe that this type of

approach must be coupled with qualitative analysis in order to better analyze and

understand the problem of technology adoption.

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CAN PAYMENTS FOR ECOSYSTEM SERVICES REDUCE SOIL EROSION? THE CASE OF EASTERN REGION OF MOROCCO

Engineering

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