Content uploaded by Cory Whitney
Author content
All content in this area was uploaded by Cory Whitney on Feb 09, 2021
Content may be subject to copyright.
Forest Policy and Economics 125 (2021) 102403
Available online 6 February 2021
1389-9341/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Stochastic simulation of restoration outcomes for a dry afromontane forest
landscape in northern Ethiopia
Yvonne Tamba
a
,
*
, Joshua Wafula
a
, Cory Whitney
b
,
c
, Eike Luedeling
b
,
c
, Negusse Yigzaw
d
,
Aklilu Negussie
d
, Caroline Muchiri
a
, Yemane Gebru
d
, Keith Shepherd
a
, Ermias Aynekulu
a
a
World Agroforestry (ICRAF), UN Avenue, P.O. BOX 30677, Nairobi, Kenya
b
Center for Development Research (ZEF), University of Bonn, Genscherallee 3, D-53113 Bonn, Germany
c
University of Bonn, INRES – Horticultural Sciences, Auf dem Hügel 6, D-53121 Bonn, Germany
d
WeForest, P.O.BOX 25450/1000, Addis Ababa, Ethiopia
ARTICLE INFO
Keywords:
Forest and landscape restoration
Decision analysis
Monte Carlo risk analysis
Value of information
Uncertainty
ABSTRACT
Forest and Landscape Restoration (FLR) is carried out with the objective of regaining ecological functions and
enhancing human well-being through intervention in degrading ecosystems. However, uncertainties and risks
related to FLR make it difcult to predict long-term outcomes and inform investment plans. We applied a Sto-
chastic Impact Evaluation framework (SIE) to simulate returns on investment in the case of FLR interventions in a
degraded dry Afromontane forest while accounting for uncertainties. We ran 10,000 iterations of a Monte Carlo
simulation that projected FLR outcomes over a period of 25 years. Our simulations show that investments in
assisted natural regeneration, enrichment planting, exclosure establishment and soil-water conservation struc-
tures all have a greater than 77% chance of positive returns. Sensitivity analysis of these outcomes indicated that
the greatest threat to positive cashows is the time required to achieve the targeted ecological outcomes. Value
of Information (VOI) analysis indicated that the biggest priority for further measurement in this case is the
maturity age of exclosures at which maximum biomass accumulation is achieved. The SIE framework was
effective in providing forecasts of the distribution of outcomes and highlighting critical uncertainties where
further measurements can help support decision-making. This approach can be useful for informing the man-
agement and planning of similar FLR interventions.
1. Introduction
According to the United Nations Environmental Programme, degra-
dation of terrestrial and marine ecosystems undermines the well-being
of 3.2 billion people and costs about 10% of the annual global gross
product in loss of species and ecosystem services (UNEP, 2019). In
Ethiopia, land and forest resource degradation across the different
production systems of the country is considered a major impediment for
sustainable development, causing considerable negative impacts on the
national economy (Gashaw et al., 2014). A rapidly growing population,
combined with increasingly frequent droughts, prevalent poverty and
lack of alternative employment opportunities, is leading to over-
exploitation of the country’s natural resources (Tesfaye et al., 2014).
The traditional customary resource management systems that commu-
nities have relied on for generations are therefore being challenged
(Scull et al., 2017). Novel approaches to restoration, such as forest and
landscape restoration (FLR), may offer effective and integrated strate-
gies for sustainable and integrated landscape management.
FLR is a planned process where forest landscapes are restored with
the goal of ecological integrity and improved human well-being. In
practice, FLR projects follow guiding principles that dictate a focus on
landscapes and natural ecosystems, participatory governance, context-
specic approaches, adaptive management and restoration of multiple
functions for multiple benets (Gitz et al., 2020). The denition of FLR is
broad, allowing for exibility in how the process is implemented in local
landscapes, while the underlying set of guiding principles were devel-
oped to ensure restoration quality. Despite being adopted as a vehicle for
transformation in multiple initiatives that target degraded landscapes
(such as the Convention on Biological Diversity (CBD, 2010), the United
Nations Framework Convention on Climate Change’s REDD+goals
(COP 16, 2011), the United Nations Conventions to Combat Deserti-
cation (Chotte et al., 2019), and the United Nation’s Decade of
* Corresponding author.
E-mail address: Y.Tamba@cgiar.org (Y. Tamba).
Contents lists available at ScienceDirect
Forest Policy and Economics
journal homepage: www.elsevier.com/locate/forpol
https://doi.org/10.1016/j.forpol.2021.102403
Received 15 September 2020; Received in revised form 15 January 2021; Accepted 18 January 2021
Forest Policy and Economics 125 (2021) 102403
2
Ecosystem Restoration (FAO, 2019), there is still a need for empirical
evidence to support scaling-up efforts. Case studies that meet all the
criteria of FLR are few due to the recency of the concept, and the lack of a
standard method for assessing FLR outcomes (Stanturf et al., 2019). A
variety of methods have been developed to address the need for scien-
tic tools to support decision-making on specic components of resto-
ration outcomes, such as soil health (land degradation surveillance
framework; (Vågen et al., 2013), soil nutrient deciencies (Munialo
et al., 2019), soil organic matter content (Zomer et al., 2017), biomass
accumulation (Romijn et al., 2019), rangeland/grazing management
and governance (Sircely et al., 2019), as well as the economics of land
degradation (Nkonya et al., 2015). However, assessment metrics that
integrate both socioeconomic and biophysical outcomes are still lacking
(Chomba et al., 2020).
In Ethiopia, there have been several interventions that meet the FLR
criteria of sustainable land management, including the Integrated Food
Security Project and, more recently, the landscapes for people, food, and
nature initiatives (Nigussie et al., 2017; Weldesemaet, 2015). Substan-
tial investment is required but often cannot be secured due to evidence
gaps that threaten the success of management strategies. Another reason
for limited investments is the long-term planning horizon of FLR, which
dampens enthusiasm for funding (Kusters et al., 2018; McGonigle et al.,
2020; Pistorius et al., 2017). To evaluate and justify investments in
sustainable land management, development practitioners commonly
employ deterministic cost-benet analysis approaches that are hinged
upon precise models of system functions, such as the Restoration Op-
portunities Assessment Methodology (ROAM) manual and the Restora-
tion Diagnostic (IUCN and WRI, 2014; World Resources institute, 2015).
However, deterministic models often fall short of adequately supporting
decisions when data are scarce or of low quality (Wendt, 1975), or
where complex system functions introduce risk and uncertainty (Lued-
eling and Shepherd, 2016). Effective planning and prioritization of in-
terventions may be compromised by uncertainty in the denition of
restoration objectives, failure to identify the most efcient practices and
failure to identify the socio-economic and cultural drivers of deforesta-
tion (Cortina et al., 2011; McGonigle et al., 2020; Yet et al., 2020). At-
tempts by managers to value restoration outcomes also face difculties
when assigning monetary values to ecosystem services with low market
values, such as carbon sequestration, regulation of hydrological cycles
and improved micro-climates (de Groot et al., 2010).
Decision support approaches that holistically evaluate decision op-
tions based on plausible ranges of costs and benets while accounting
for uncertainties and risks could overcome these knowledge barriers.
Furthermore, they could strengthen the capacity of managers to use
continuous learning and monitoring systems to track their progress to-
wards their goals (Rumpff et al., 2011). It is also possible to take stock of
the successes and failures of restoration policies and efforts undertaken
and to learn lessons for improved natural resource management and
protection (Cronkleton et al., 2018). Through these approaches, we can
prioritize critical uncertainties where targeted research could enhance
clarity on expected outcomes. In this study, we demonstrated the
application of a stochastic impact evaluation (SIE) framework to (i)
predict bio-physical and socio-economic outcomes of FLR practices, (ii)
identify knowledge gaps that constrain effective decision making and,
(iii) provide insights that aid in adaptive management of FLR efforts.
2. Materials and methods
2.1. Study area
Desa’a forest is one of the oldest remaining dry Afromontane forests
along the western escarpment of the Great Rift Valley in northern
Ethiopia (Lat. 13◦53′– 13◦56′N and Long. 39◦48′- 39◦51′E) (Fig. 1).
It lies between 900 and 3100 m above sea level. Based on rainfall data
from the Ethiopian Meteorological Agency for 2006 to 2015, the mean
annual rainfall was about 602 mm (Mokria et al., 2015). Desa’a is an
even-aged secondary forest, hosting about 90 tree and shrub species, and
dominated by Wild African wild Olive (Olea europaea subsp. cuspidata)
and African Juniper (Juniperus procera) (Aynekulu et al., 2009). The
forest is of high ecological and socio-economic importance as it has the
potential to conserve biodiversity and soils, supply biomass for fuelwood
and construction, regulate water and carbon cycles and offer a host of
other ecosystem services (Teklay et al., 2013). Despite its protected
status as a state forest, about 70% of dense forest, with a canopy cover of
more than 40%, has been deforested and degraded since the 1970s
(WeForest, 2018). This is mainly due to forest land conversion to agri-
culture land and settlements, over-extraction of woody biomass for fuel
and timber, re, and free grazing (Aynekulu et al., 2011).
2.2. Methods
2.2.1. FLR interventions
To restore the degraded Desa’a forest, WeForest, a non-prot orga-
nisation with support from the Ethiopian government, launched a long-
term FLR programme that proposed investments in a portfolio of scal-
able restoration and livelihood interventions. The interventions are ex-
pected to achieve socioeconomic benets by promoting economic
resilience of vulnerable communities and incentivizing improved natu-
ral resource governance. The targeted beneciaries of the interventions
were subsistence farmers. The following FLR interventions aimed to
restore degraded forest functions:
•Assisted natural regeneration (ANR) of degraded forest. The ANR
intervention involved restricting access to the forest products
through social fencing facilitated by local by-laws and governance
structures in a process termed “exclosure”. Social fencing was
enforced by community members trained as forest guards, and
community participation was encouraged through livelihood devel-
opment interventions.
•Enrichment planting and assisted natural regenertation of up to 1000
native trees per hectare in the open forest areas with canopy cover of
less than 40% but more than 10%, where assisted natural regener-
ation has low potential to restore vegetation.
•Grazing land exclosure, where communally owned grasslands were
protected from free grazing to encourage natural regeneration of
woody vegetation. The community was allowed access to harvest
grass for livestock feed (cut and carry method).
•Soil and water conservation, where gully restoration and in-situ
water harvesting structures were established to reduce soil erosion
and improve water inltration.
The project also implemented a set of livelihood improvement
interventions:
•Beekeeping, where two to three modern beehives were distributed
among 3280 beekeepers to establish apiaries around their home-
steads with the aim of promoting non-timber forest products.
•Sheep rearing, where three to ve sheep were distributed among
7650 female-headed households to provide alternative sources of
income to forest products. Small ruminants were chosen due to their
resilience to harsh climatic conditions, ease of liquidity to meet
household nancial needs and the proximity of animal feed in the
form of fodder from exclosures established on communal grazing
lands.
•High-value fruit trees, where eight to thirteen apple tree seedlings
were distributed among 5465 targeted farmers (whose farms were
located within the FLR restoration project area) with the aim of
diversifying incomes and reducing demand for forest commodities.
•Poultry farming, where ten poultry birds were distributed among
7650 impoverished female-headed households to provide livelihood
benets through sale and consumption of poultry products.
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
3
Fig. 1. Map of Desa’a state forest in Ethiopia (adapted from WeForest, 2018). Forest zones are demarcated based on vegetation density and human inuence. The
core zone is an area of dense forest with canopy cover ≥40%. Buffer zone 1 includes areas categorized as open forest where vegetation cover is greater than 10% but
less than 40%. Buffer zone 2 denotes areas that are communally owned and made up of fragmented open forests and grazing lands where vegetation cover is ≤10%.
Development zone denotes areas covered by community settlements.
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
4
•Energy efcient stoves that reduce demand for fuelwood from the
forest were distributed among 10,390 households to promote alter-
native energy sources.
2.2.2. Stochastic simulation of FLR outcomes
We used the SIE framework (Fig. 2), based on Luedeling and Shep-
herd (2016) to simulate FLR outcomes. SIE is a mixed methods approach
that has been widely used to simulate outcomes under uncertainty and
risk for investments in honey value chains (Wafula et al., 2018), water
supply (Luedeling et al., 2015), irrigation development (Yigzaw et al.,
2019), management of reservoir sedimentation (Lanzanova et al., 2019),
and to determine the value of ecosystem services in rangelands (Favretto
et al., 2017). In this study, we applied SIE as an iterative ve-step pro-
cess that supports decisions by integrating evidence and expert opinion
in quantitative simulations of decision impact pathways (Fig. 2).
Step 1: Decision framing
Decision framing is a crucial step where decision-makers need to
explicitly dene their problem and target outcomes. This rst step ad-
dresses questions regarding the short-term and long-term outcomes, the
targeted beneciaries and the type of decision under consideration
(prioritizing vs planning) (Luedeling and Shepherd, 2016). To clearly
dene the intervention’s social, economic and biophysical impacts, we
carried out semi-structured interviews with representatives from the
implementing agency. The interviews provided insights on the objec-
tives of the FLR project, implementation strategies and the targeted
outcomes. A 25 year horizon was chosen to support long-term planning
that incorporates key uncertainties. Through these interactions, the
decision problem that emerged was whether the selected FLR in-
terventions will be able to restore the degraded forest to provide sus-
tainable socioeconomic and biophysical benets.
To further clarify the decision context, we conducted semi-structured
interviews with ve government ofcers, seven development
practitioners and one academic staff of Mekelle University. We applied
purposive sampling to identify interviewees who had expertise in
environmental management, forest resource management and agricul-
tural value chains. We also held a focus group discussion with 12 male
and eight female members of the local community, two development
agents and two community leaders in the project area. The objective of
the discussion was to elicit perspectives from members of the commu-
nity on the historical trends in land use and land cover in the forest
landscape, how the community expected the FLR interventions to
change the trends in use of forest resources, and the potential barriers to
implementation that they could foresee.
Step 2: Conceptual modelling
We followed a participatory model development process with the
aim of conceptualizing the decision’s impact pathways and identifying
the cost, benet and risk variables that would be parameterized in a
simulation model. We held a workshop with 17 stakeholders from six
development agencies, ve representatives of state agencies and two
researchers from Mekelle University, Ethiopia and elicited relevant
variables. We then consolidated the resulting impact pathways and
causal relationships between costs, benets, and risks to generate the
overall conceptual structure of the decision model (Do et al., 2020)
(Fig. 3).
The variable estimates (Tamba et al., 2020) were used to feed the
mathematical model, which was then run as a Monte Carlo (MC)
simulation with 10,000 iterations using the decisionSupport package
(Luedeling and Whitney, 2018; Luedeling et al., 2020) in the R pro-
gramming language (R Core Team, 2017). For each run, the model
produced a projection of the NPV, computed by adding up discounted
net benets over a 25-year simulation period.
Step 3: Developing a mathematical model
In the third step, the conceptual model was translated into a math-
ematical model to quantify the impact of nine FLR interventions as the
Fig. 2. Sequence of activities in the Stochastic Impact Evaluation approach (adapted from Yigzaw et al., 2019). Step 1 denes the decision context by identifying
stakeholders and engaging them in a participatory research process. Step 2 creates a conceptual model of the decision’s impact pathways and describes the re-
lationships between the cost, benet and risk variables identied by stakeholders. Step 3 translates the conceptual model into a mathematical model with causal
relationships between variables rewritten as equations. In step 4, the values of model parameters are estimated by calibrated subject matter experts, and a Monte
Carlo (MC) simulation of the cost-benet analysis is run to project the distribution of returns. To analyse the sensitivity of the model, the Variable Importance in the
Projection (VIP) is computed based on the results of a Partial Least Squares regression analysis. Expected Value of Perfect Information (EVPI) analysis serves to
identify variables with high information value for the specic decision. Step 5 is where the model is rened when necessary. The process is iterative and allows for
multiple cycles until the decision maker has sufcient information to make a decision.
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
5
change in outcomes with intervention relative to existing land use
systems.
Risks: Risk factors were considered as the probability of occurrence
of risk (used to generate a binomial distribution describing whether the
respective events occur or not in the simulations) and the magnitude of
impacts on expected benets, if these events occur. We identied two
classes of risks. The rst was a class of risks that had a random chance of
occurring in any given year (random risks). These were simulated by
computing the annual likelihood of occurrence and the impact on out-
comes when the risks do occur using the chance_event function of the
decisionSupport package. For example, for anthropogenic risk, we
computed the chance that community members would encroach into the
forest (for various reasons, such as illegal logging, charcoal burning,
fuelwood harvesting). We then estimated the magnitude of loss of pre-
viously computed benet streams. The second class of risk factors was
conditional risks. For these, the risk events were associated with other
events whose occurrence was uncertain. For instance, the risk of the
community encroaching on the forest to graze their livestock was
determined by the probability that exclosures were ineffective given the
community’s demand for fodder and the probability of poor imple-
mentation of social fencing.
Costs: We categorized costs into ‘individual costs’ incurred by ben-
eciaries and ‘program costs’ incurred by the implementing agency. For
the livelihood development activities, the cost of acquiring assets was
borne by the implementing agency and calculated only for the rst year,
while operating costs were borne by the individuals and considered
annually over the 25-year period. Opportunity costs were added to the
cost per individual. The cost of restoration interventions included the
cost of acquiring materials (which was a one-off investment) and
recurrent expenditure on technical labour and maintenance. As some
labour was provided as in-kind payment by the community, we
considered that only a part of the labour costs was paid in cash.
Benets: For livelihood interventions, we quantied the expected
increase in income per household as the main benet. The beekeeping
intervention would provide revenues from the sale of honey. For energy-
efcient cookstoves, we accounted for the benets indirectly as house-
hold health cost savings and reduced dry wood harvesting costs. Sheep
rearing would mostly provide revenues from the sale of sheep. Poultry
farming would provide revenues from the sale of poultry products.
Apple trees would generate revenues from the sale of fruits.
For restoration interventions, the benets were expected to accrue to
the entire community and therefore quantied as communal benets.
This assessment targeted provisioning and regulating ecosystem ser-
vices, since these have direct use values. We then applied a mix of
market and non-market pricing strategies. The main benet expected
from ANR and enrichment planting would be the increase in carbon
stocks as vegetation regenerates. In addition to regeneration, there
would be agricultural benets from a favourable microclimate, resulting
in improved yields for surrounding farmers. To simulate the carbon
sequestration benet of enrichment planting, ANR and exclosure
establishment, we used the gain-loss method that sums up changes in
biomass stock for the specic land-use category (IPCC, 2006). We
determined biomass stocks using two approaches:
-An exponential function adapted from guidelines from the Inter-
governmental Panel on Climate Change was used to simulate the annual
increment in biomass per replanted tree.
biomass per tree =a
1+exp − (BCEF*(b−c) ) (1)
where a =maximum marketable volume, BCEF =biomass conversion
and expansion factor, b =simulation period, c =stem maturity age.
-We computed biomass growth as a function of the mean annual
increment per hectare of regenerated forest area and exclosure:
Fig. 3. Conceptual model developed by stakeholders showing the expected costs, benets and risks of programme activities. We collected estimates of model
variables (yellow bubbles) from calibrated subject matter experts and passed the inputs through the model to arrive at the value of outcome variables (blue bubbles)
under risk and uncertainty (red rectangles). (For interpretation of the references to colour in this gure legend, the reader is referred to the web version of
this article.)
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
6
biomass per ha =
mean annual increment*mean biomass per ha (2)
We then quantied the change in carbon stocks that would result
from restoration activities (ANR, enrichment planting, and exclosure)
using the gain-loss method. Emphasis was placed on gains to estimate
the impact of successful restoration. With this approach, we determined
the change by the product of the area of land and the incremental
biomass stock per unit of land area. The impact of social fencing was
used as a proxy for forest areas gained from avoided deforestation and
degradation (Eqs. 3& 4).
∆Carbon gain from avoided deforestation and degradation =
biomassstock per ha*∆areasocialfencing+enrichment*carbonfraction (3)
Where carbon fraction of dry matter =0.47 (IPCC, 2006)
We also quantied carbon losses from random events of re and
disease outbreaks and calculated carbon accumulation per hectare of
restored and conserved forest based on the mean annual increase in
carbon stocks (Eqs. 1, 2). We then used the benet transfer method to
determine the market price for carbon.
The impact of exclosure establishment was assessed by valuing the
change in the quantity of grass produced when land use shifted from
communal grazing to exclosures with cut-and-carry harvesting. The use
value of establishing exclosures was determined by the amount of fodder
produced in exclosures relative to the amount produced by grazing
lands. The non-use value of carbon sequestration was found by calcu-
lating the mean annual increment of above- ground biomass (Eq. 2). For
investments in soil and water conservation, the avoided-cost method
was used to quantify the primary benet of reducing costs to the com-
munity related to removal of sediments from a community dam (Pan-
agos et al., 2015; Cheboiwo et al., 2018).
For each intervention, we quantied the expected net benets by
subtracting the aggregate costs from risk-adjusted benets (Eq. 5) and
then discounted the net benet to nd the net present value (Eq. 6).
riskscaler =probability of risk occuring ×impact of risk (5)
Net Benefiti=∑
n
1
∑
t
1
[Total Benefiti× (1−risk scaler) ] − Total costsi
(6)
where n =number of targeted beneciary households,
t =number of simulation years.
NPVi=NetBenefiti
(1+r)t.(7)
where NPV =Net present value, r =discount rate, and t =year
Step 4: Model parameterization and simulation
We used expert knowledge elicitation and literature review to assign
probability distributions for all model variables and operationalize the
model. However, expert opinion can be subjective and susceptible to
biases such as overcondence or under-condence (Hubbard, 2014; Yet
et al., 2016). To reduce these biases, we conducted a calibration training
of subject matter experts during a model validation workshop. The
training aimed to improve the capacity of subject matter experts to make
estimates for which they are 90% condent that the actual values lie
within the provided ranges. We used Klein’s Pre-mortem (Klein, 2007)
and the equivalent-bet technique, which have been proven to
measurably improve an expert’s ability to provide accurate estimates
(Hubbard, 2014).
Step 5: Model Renment
We used Value of Information (VOI) analysis to identify important
knowledge gaps where further measurement efforts could provide
clarity on the best decision (Wilson, 2015). We did this by computing the
expected value of perfect information (EVPI). EVPI represents the op-
portunity loss that could be incurred by a decision-maker due to lack of
information on a specic variable (Felli and Hazen, 1998; Hubbard,
2014). Applied in this way, the EVPI computation can help to determine
where further measurements may help reduce uncertainty on decision
outcomes. We also applied Partial Least Squares (PLS) regression anal-
ysis to the MC simulation results and used Variable-Importance-in-the-
Projection (VIP) sensitivity analysis to assess the input parameters
(Luedeling and Gassner, 2012). The VIP statistic represents the direction
and strength of each input variable’s relationship with the output vari-
able (Wold et al., 2001).
3. Results
3.1. Returns from livelihood interventions
Model results for livelihood interventions showed that most in-
terventions would have positive NPVs for the 25-year simulation period.
Returns on investments in fruit trees and beekeeping had a 0.4% chance
of loss but beekeeping had a wider range of returns than fruit trees
(Table 1). Poultry farming and efcient cooking stoves both had no
chance of loss but were less protable than fruit trees and beekeeping.
The net present value of returns to sheep rearing had the highest pos-
sibility of loss (60%) and the lowest prots of all livelihood
interventions.
3.2. Return from restoration interventions
The simulated NPV of ANR cashows had a possibility of negative
returns (Table 1). The distribution showed minor variation over time,
with the median return for each year progressively increasing but never
exceeding 4000 USD ha
−1
. VIP analysis of outcomes revealed 9 variables
that the projected returns were sensitive to. The impact of ANR on yields
in the surrounding agricultural area, market price of carbon, annual rate
of deforestation, and viability of carbon marketing were the 4 most
highly ranked variables correlated with ANR outcomes (Fig. 4d). VOI
analysis revealed that there were no critical knowledge gaps to be lled
(Fig. 4b).
The model simulated positive returns on the enrichment planting
intervention in 89.8% of model runs. Annual outcomes varied signi-
cantly with a high likelihood of losses in the rst 5 years after planting. If
further clarity on this outcome is needed, priority should be given to
reducing uncertainty related to carbon markets (Fig. 5b). VIP analysis
highlighted 13 variables with a coefcient value above the threshold of
0.8 (Fig. 5d). The most sensitive variables in this case were strongly
related to carbon markets (cost of carbon and risk of lack of carbon
markets) and the tree population (annual rate of deforestation, number
of replanted trees per ha and risk of wildres) (Fig. 5d). Grazing land
exclosure was the riskiest restoration intervention with a 77.2% likeli-
hood of positive returns. Annual cashows (Fig. 6c) revealed possibil-
ities of net losses in the initial years and and expectation of breaking
even in the 10
th
year.
∆areasocial fencing+enrichment =avoided loss in forest area due to agricultural and settlement expansion+
avoided loss in forest area due illegal commercial logging (4)
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
7
Table 1
Summary of returns on Forest Landscape Restoration (FLR) interventions. The range represents the 90% condence interval of the total Net Present Value (NPV),
considering a 25-year simulation period. Also shown are the chance of loss for each intervention and the value of information (VOI) for further measurement for each
intervention, expressed as the Value of Perfect Information (EVPI).
NPV in USD (n =10,000, 90% C.I.) VOI
Intervention Lower bound Median Upper bound Chance of loss EVPI (USD) Critical knowledge gap
Beekeeping 1594 4517 10,961 0.1% 0.4 Honey yield per hive
Cookstoves 1165 2008 3140 0% 0 –
Sheep rearing −1258 −165 1013 60.0% 209 Cost of Labour
Poultry farming 624 1053 1569 0.0% 0 –
High Value trees 1482 4292 8023 0.2% 0.4 Max. fruit yield potential
Grazing land exclosure −13,119 9800 50,785 22.9% 2000 Biomass maturity age
Assisted natural Regeneration 13,231 20,215 30,286 0% 0 –
Soil water conservation 1104 4141 7401 1.2% 7.9 Rate of soil loss
Enrichment planting −492 3212 13,852 10.2% 56 Market price per ton of carbon
Fig. 4. Projected outcome of the decision to implement ANR in Desa’a (a), high decision-value variables (b), the respective cashows (c) and important variables
(determined by VIP analysis of PLS regression models) (d). The results were produced through MC simulation (10,000 model runs) of ANR performance over 25
years. In the PLS plot, green bars indicate positive correlations of uncertain variables with the outcome variable, while red bars indicate negative correlations. Blue
bars indicate variables that did not meet the threshold of the model sensitivity analysis. (For interpretation of the references to colour in this gure legend, the reader
is referred to the web version of this article.)
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
8
Further measurements to pinpoint the maturity age of woody
biomass in exclosures (EVPI =1330 USD) and, to a lesser extent, the
maximum carbon stock that can be accumulated in the exclosures and
the rate of deforestation would reduce ambiguity for the decision-
makers (Fig. 6b). The NPV for soil water conservation efforts had a
98.84% chance of positive outcomes (Fig. 7a). If further clarity is
necessary, the analysis identied the rate of soil loss in the forest as a
source of uncertainty (Fig. 7b). Despite this uncertainty, outcomes are
most likely to be positive. Sensitivity analysis revealed ve variables
with a signicant correlation with the projected outcomes (Fig. 7d).
4. Discussion
4.1. Livelihood interventions
Beekeeping is the most protable among the livelihood interventions
that were investigated. Energy-efcient cookstoves are also promising,
although this intervention would not provide direct income, but save on
the cost of extracting fuelwood from the forest, improve health and
reduce carbon emissions from forest degradation (Grieshop et al., 2011).
Indirect income from reduced fuelwood needs and savings on health
costs might not be enough nancial incentive to encourage community
members to adopt energy-saving stoves (Okuthe and Akotsi, 2014). For
households targeted for sheep rearing, the enterprise is risky with a 60%
chance of incurring losses. This outcome indicates uncertainty, as it does
not offer sufcient evidence to support the decision to roll out the
intervention. Measurements to gain a better understanding of labour
requirements of sheep rearing can help eliminate uncertainty about this
outcome. Identifying the ideal number of sheep to distribute to com-
munity members for the intervention to make economic sense and the
effect of a drought event on sheep rearing ventures can also help to gain
clarity on outcomes. Poultry farming is protable and could effectively
provide an alternative source of income for the most resource poor
households (Pica-Ciamarra and Otte, 2010). Investment in planting of
fruit trees would also generate positive returns, but these returns will not
be realised in the rst few years, since apple trees take several years to
Fig. 5. Projected outcome of the decision to undertake enrichment planting in Desa’a (a), high decision-value variables (b), the respective cashows (c) and
important variables (determined by VIP analysis of PLS regression models) (d). The results were produced through MC simulation (10,000 model runs) of enrichment
planting outcomes over 25 years. In the PLS plot, green bars indicate positive correlations of uncertain variables with the outcome variable, while red bars indicate
negative correlations. Blue bars indicate variables that did not meet the threshold of the model sensitivity analysis. (For interpretation of the references to colour in
this gure legend, the reader is referred to the web version of this article.)
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
9
reach their maximum production potential.
4.2. Restoration interventions
Model results showed that the restoration strategy with ANR accel-
erates recovery of natural forest by reducing disturbance. Enrichment
planting achieves the same outcome but through active replanting in the
fragmented forest zones. While our simulations showed that both in-
terventions were likely to have promising outcomes, the likelihood of
positive monetary returns was higher with ANR than enrichment
planting. This agrees with the results of a meta-analysis of forest resto-
ration interventions in tropical forests. Crouzeilles et al. (2017) report
that restoration outcomes measured by vegetation structure and biodi-
versity were higher for natural regeneration than for tree planting. Our
ndings on the differences in quantities of sequestered carbon for
replanting compared with regeneration are explained by slower rates of
accumulation in replanted trees and high quantities of sequestered
carbon in old trees (K¨
ohl et al., 2017). Furthermore, the difference in
projected returns on enrichment planting relative to ANR is also
explained by the differences in costs, as lower implementation costs are
incurred with ANR as compared to enrichment planting (Chazdon et al.,
2016; Shono et al., 2007). Nevertheless, projected NPVs per ha for both
interventions were higher than those of most alternative land uses. This
makes both interventions effective and protable to achieve biophysical
outcomes (Pistorius et al., 2017).
VOI analysis revealed that there were no high value variables for the
ANR intervention. However, there were variables of importance that
would determine the magnitude of positive cashows. When the effect
of restoration on yields in adjacent agricultural lands was considered,
we found that the projected returns increased.A study on the effect of
increased tree cover on agriculture in southern Ethiopia simulated a 5%
increase in wheat production on lands adjacent to reforested forests and
hedgerows (Yang et al., 2020). This result is attributed to improved soil
moisture, temperature regulation and increased soil nutrient availability
for agricultural lands bordering forest.
Exclosure establishment would also generate substantial benets
Fig. 6. Projected outcome of improved exclosure management on the economic value of ecosystem goods and services in exclosure (a), high decision-value variables
(b), the respective cashows (c) and important variables (determined by VIP analysis of PLS regression models) (d). The results were produced through MC
simulation (10,000 model runs) of 25 years of exclosure performance. In the PLS plot, green bars indicate positive correlations of uncertain variables with the
outcome variable, while red bars indicate negative correlations. Blue bars indicate variables that did not meet the threshold of the model sensitivity analysis. (For
interpretation of the references to colour in this gure legend, the reader is referred to the web version of this article.)
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
10
when compared to the alternative, a communal free grazing land use
system. However, while regulating services are expected to improve
with the establishment of exclosure, the impact on feed resources will be
negative. Exclosures could create competition for livestock feed re-
sources among community members by restricting access to grazing
land (Birhane et al., 2018). Results of VOI analysis showed that
achieving greater precision in estimating the time required to achieve
maximum biomass accumulation should be prioritized. This knowledge
could potentially improve management of exclosures by making the
valuation of carbon stock more precise. Nonetheless, the range of out-
comes projected by the model brackets the deterministic result projected
by Mekuria (2013) (about 3000 USD ha
−1
) when assessing the changes
in regulating ecosystem services following establishment of exclosure on
communal grazing lands in Ethiopia. The trend in returns showed that
over time, the cash ows increase with exclosure age and level off after
the exclosure reaches its production potential. This was also found by
Mekuria (2013), who compared biomass productivity in ve, ten, fteen
and twenty-year old exclosures with communal grazing land, nding the
greatest difference in older exclosures.
4.3. Implications for FLR actors and policy-makers
While FLR is widely expected to have positive socioeconomic out-
comes, measurements of ecosystem benets are sorely lagging behind
the recognition that they exist (Matzek, 2018). This is the result of a
shortage of technical experts who can address the methodological con-
cerns and philosophical objections that come up when attempting to
monetize nature’s services. Uncertainty in measurement results from
practical challenges in monitoring ecosystem services (de Groot et al.,
2010), and the acknowledgement that restoration efforts cannot fully
recover the natural ‘pre-disturbance’ ecosystem functions (Crouzeilles
et al., 2016).
For long-term planning of FLR, managers and policy-makers should
pay more attention to biological factors. The time lag to production will
affect the distribution of returns, hence low returns should be expected
in the rst few years and greater returns towards the end of the
Fig. 7. Projected outcome of introducing soil and water conservation measures in Desa’a. (a), high decision-value variables (b), the respective cashows (c) and
important variables (determined by VIP analysis of PLS regression models) (d). The results were produced through MC simulation (10,000 model runs) of the
performance of conservation structures over 25 years. In the PLS plot, green bars indicate positive correlations of uncertain variables with the outcome variable,
while red bars indicate negative correlations. Blue bars indicate variables that did not meet the threshold of the model sensitivity analysis. (For interpretation of the
references to colour in this gure legend, the reader is referred to the web version of this article.)
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
11
simulation period. Managers therefore need to be prepared to evaluate
the outcomes of their interventions during the early phase of their
project where implementation costs are incurred, and net losses are
likely. A portfolio analysis to identify the best combinations of in-
terventions could help buffer against the risk of losses in the rst few
years. Also, varietal selection to prioritize tree species that accelerate
ecosystem recovery can help minimize losses. It is important for man-
agers and policy-makers to note that socioeconomic factors, i.e. the
drivers of deforestation and the viability of carbon trading were more
likely to determine whether actual returns matched desired outcomes
than the biophysical determinants of returns. Without strategic man-
agement, exclosures are expected to lead to a resource constraint for
livestock farmers, as they reduce availability of feed resources, but may
not be able to offset this effect through positive revenues from carbon
credits. Even when paired with cut-and-carry harvesting, this interven-
tion may lead to a net reduction in fodder supply, which may discourage
community participation. Incentivizing pastoral communities by
providing a livestock insurance policy against drought could help ach-
ieve community buy-in and improve revenues from carbon credits.
There is also no doubt that successful implementation of FLR pro-
grammes requires key socioeconomic mechanisms be put in place to
ensure there are clear rights, roles and benet sharing arrangements
between different stakeholders and community members (Yami et al.,
2013).
Holistic and stochastic valuation of forest restoration costs and
benets can provide realistic estimates of the plausible ranges of returns
of interventions, considering all outcome dimensions that are relevant in
a particular context. Since the objectives of FLR programmes can thus be
better captured than in traditional evaluations that rely on precise
measurements, this method is suitable for accounting for costs and
benets of such programmes. To realistically value ecosystem benets,
FLR actors should base their predictions on expert knowledge of the
local context rather than on benchmark estimates carried over from
different contexts (Stålhammar and Pedersen, 2017). The use of distri-
butions when estimating the value of variables rather than best-bet es-
timates avoids overly hopeful predictions that could misguide planning
(Luedeling et al., 2019).
The outcomes of this study indicate positive returns for most in-
vestments. This is a clear indication that investments in FLR pro-
grammes can succeed in reversing degradation in the long term.
However, initial costs incurred to establish livelihood interventions,
mobilize communities, strengthen social governance structures, and
provide capacity building and training can result in net losses in the rst
few years. Therefore, FLR actors may need signicant nancial support
to see their interventions through to the medium and long term (Pis-
torius et al., 2017).
4.4. Limitations of the study
The study did not explicitly consider the overall socio-economic
environment and the challenges that social factors present to imple-
menters of participatory forest management. Insecure land tenure, low
levels of technical and technological capacity and a lack of benet-
sharing agreements pose an additional risk to restoration outcomes
(Chazdon et al., 2016; Lemenih et al., 2014). For instance, in the case of
land tenure, landless individuals may not have access to economic in-
centives that would discourage over-harvesting of forest resources.
Incomplete consideration of land rights may mean that the returns
presented here are only applicable to households that own land and have
direct access to the benets. Further consideration of biotic factors may
also be warranted. The effectiveness of restoration and choice of resto-
ration approach are linked with the regeneration potential of the species
considered. While our analysis did not differentiate between regenera-
tion potentials of the two climax tree species, evidence from previous
studies indicated that J. procera has low potential for ecosystem recovery
and might not be effectively restored by the ANR approach (Aynekulu
et al., 2009).
5. Conclusions
Predicting FLR outcomes is a difcult endeavour, since outcomes are
often achieved through complex mechanisms with many uncertainties
and risks preventing robust decision making. Development practi-
tioners, landscape restoration programme managers and researchers can
overcome these challenges by applying stochastic methods and partici-
patory research approaches. Decision analysis tools that apply stochastic
impact evaluation are suitable for decision makers who are not only
constrained by imperfect knowledge of complex systems, but also need
to consider a range of social, economic, and biophysical factors to pre-
dict project impacts. The SIE framework enabled us to clearly dene the
objectives of FLR activities and quantify the expected impacts on land
use and land cover trends.
Engagement of subject matter experts, decision-makers and com-
munity members enabled us to develop a decision model that incorpo-
rated priorities and beliefs of stakeholders and decision-makers. The
process provided an avenue for stakeholders to express their uncertainty
about the relevant variables, including those considered difcult to
measure. Thus, we conducted a robust cost-benet analysis and pre-
sented distributions of plausible decision outcomes to decision-makers.
In this way, research outcomes were translated into economic impacts
for easy integration into decision-making processes. Future studies may
benet from considering the impact of social governance structures on
FLR interventions.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Acknowledgements
This research was carried out under the CGIAR Research Program on
Water, Land and Ecosystems (WLE) with support from CGIAR Fund
Donors (http://www.cgiar.org/about-us/our-funders/). We also appre-
ciate the support accorded to us by the implementing agency (WeForest
Ethiopia) and its partners. We thank community members from Kalaa-
min village in Tigray, Ethiopia, Mekelle University and state agencies for
their input to the decision model development process. Their insights
were critical in understanding complex interactions between commu-
nity members and Desa’a Forest. In addition, the paper beneted greatly
from the comments of reviewers.
References
Aynekulu, E., Denich, M., Tsegaye, D., 2009. Regeneration response of Juniperus procera
and Olea europaea subsp cuspidata to Exclosure in a dry Afromontane Forest in
northern Ethiopia. Mt. Res. Dev. 29, 143–152. https://doi.org/10.1659/mrd.1076.
Aynekulu, E., Denich, M., Tsegaye, D., Aerts, R., Neuwirth, B., Boehmer, H.J., 2011.
Dieback affects forest structure in a dry Afromontane forest in northern Ethiopia.
J. Arid Environ. 75, 499–503. https://doi.org/10.1016/j.jaridenv.2010.12.013.
Birhane, E., Mengistu, T., Seyoum, Y., Hagazi, N., Putzel, L., Rannestad, M.M., Kassa, H.,
2018. Exclosures as forest and landscape restoration tools: lessons from Tigray
region, Ethiopia. Int. For. Rev. https://doi.org/10.1505/146554817822330498.
CBD, 2010. Aichi Biodiversity Targets (Aichi Bidiversity Targets).
Chazdon, R.L., Broadbent, E.N., Rozendaal, D.M.A., Bongers, F., Zambrano, A.M.A.,
Aide, T.M., Balvanera, P., Becknell, J.M., Boukili, V., Brancalion, P.H.S., Craven, D.,
Almeida-Cortez, J.S., Cabral, G.A.L., De Jong, B., Denslow, J.S., Dent, D.H.,
DeWalt, S.J., Dupuy, J.M., Dur´
an, S.M., Espírito-Santo, M.M., Fandino, M.C.,
C´
esar, R.G., Hall, J.S., Hern´
andez-Stefanoni, J.L., Jakovac, C.C., Junqueira, A.B.,
Kennard, D., Letcher, S.G., Lohbeck, M., Martínez-Ramos, M., Massoca, P., Meave, J.
A., Mesquita, R., Mora, F., Mu˜
noz, R., Muscarella, R., Nunes, Y.R.F., Ochoa-
Gaona, S., Orihuela-Belmonte, E., Pe˜
na-Claros, M., P´
erez-García, E.A., Piotto, D.,
Powers, J.S., Rodríguez-Velazquez, J., Romero-P´
erez, I.E., Ruíz, J., Saldarriaga, J.G.,
Sanchez-Azofeifa, A., Schwartz, N.B., Steininger, M.K., Swenson, N.G., Uriarte, M.,
Van Breugel, M., Van Der Wal, H., Veloso, M.D.M., Vester, H., Vieira, I.C.G.,
Bentos, T.V., Williamson, G.B., Poorter, L., 2016. Carbon sequestration potential of
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
12
second-growth forest regeneration in the Latin American tropics. Sci. Adv. https://
doi.org/10.1126/sciadv.1501639.
Cheboiwo, J., Langat, D., Muga, M., Kiprop, J., 2018. Economic Analysis of Forest
Landscape Restoration Options in Kenya.
Chomba, S., Sinclair, F., Savadogo, P., Bourne, M., Lohbeck, M., 2020. Opportunities and
constraints for using farmer managed natural regeneration for land restoration in
sub-Saharan Africa. Front. For. Glob. Chang. 3 https://doi.org/10.3389/
ffgc.2020.571679.
Chotte, J.L., Aynekulu, E., Cowie, A., Campbell, E., Vlek, P., Lal, R., Kapovi´
c-
Solomun, M., von Maltitz, G., Kust, G., Barger, N., Vargas, R., Gastrow, S., 2019.
Realising the Carbon Benets of Sustainable Land Management Practices: Guidelines
for Estimation of Soil Organic Carbon in the Context of Land Degradation Neutrality
Planning and Monitoring.
COP 16, 2011. Framework Convention on Climate Change / Conference of the Parties /
2010 / 7 / Add.1. In: Report of the Conference of the Parties on Its Sixteenth Session,
Held in Cancun from 29 November to 10 December 2010. Addendum. Part Two:
Action Taken by the COP at Its Sixteenth Session.
Cortina, J., Amat, B., Castillo, V., Fuentes, D., Maestre, F.T., Padilla, F.M., Rojo, L., 2011.
The restoration of vegetation cover in the semi-arid Iberian southeast. J. Arid
Environ. https://doi.org/10.1016/j.jaridenv.2011.08.003.
Cronkleton, P., Artati, Y., Baral, H., Paudyal, K., Banjane, M.R., Liu, J.L., Tu, T.Y.,
Putzel, L., Birhane, E., Kassa, H., 2018. How do property rights reforms provide
incentives for forest landscape restoration? Comparing evidence from Nepal, China
and Ethiopia. Int. For. Rev. 19 https://doi.org/10.1505/146554817822330506.
Crouzeilles, R., Curran, M., Ferreira, M.S., Lindenmayer, D.B., Grelle, C.E.V., Rey
Benayas, J.M., 2016. A global meta-analysis on the ecological drivers of forest
restoration success. Nat. Commun. 7, 1–8. https://doi.org/10.1038/ncomms11666.
Crouzeilles, R., Ferreira, M.S., Chazdon, R.L., Lindenmayer, D.B., Sansevero, J.B.B.,
Monteiro, L., Iribarrem, A., Latawiec, A.E., Strassburg, B.B.N., 2017. Ecological
restoration success is higher for natural regeneration than for active restoration in
tropical forests. Sci. Adv. 3 https://doi.org/10.1126/sciadv.1701345.
de Groot, R.S., Alkemade, R., Braat, L., Hein, L., Willemen, L., 2010. Challenges in
integrating the concept of ecosystem services and values in landscape planning,
management and decision making. Ecol. Complex. https://doi.org/10.1016/j.
ecocom.2009.10.006.
Do, H., Luedeling, E., Whitney, C., 2020. Decision analysis of agroforestry options reveals
adoption risks for resource-poor farmers. Agron. Sustain. Dev. 40 https://doi.org/
10.1007/s13593-020-00624-5.
FAO, 2019. New UN Decade on Ecosystem Restoration Offers Unparalleled Opportunity
for Job Creation, Food Security and Addressing Climate Change (News Artic).
Favretto, N., Luedeling, E., Stringer, L.C., Dougill, A.J., 2017. Valuing Ecosystem Services
in Semi-arid Rangelands through Stochastic Simulation. L. Degrad. Dev. 28, 65–73.
https://doi.org/10.1002/ldr.2590.
Felli, J.C., Hazen, G.B., 1998. Sensitivity analysis and the expected value of perfect
information. Med. Decis. Mak. https://doi.org/10.1177/0272989X9801800117.
Gashaw, T., Bantider, A., G/Silassie, H, 2014. Land degradation in Ethiopia : causes,
impacts and rehabilitation techniques land degradation in Ethiopia : causes, impacts
and rehabilitation techniques. J. Environ. Earth Sci. 4, 98–104.
Gitz, V., Place, F., Koziell, I., Pingault, N., van Noordwijk, M., Alexander, M., Minang, P.,
2020. A joint stocktaking of CGIAR work on forest and landscape restoration. In:
A Joint Stocktaking of CGIAR Work on Forest and Landscape Restoration. https://
doi.org/10.17528/cifor/007669.
Grieshop, A.P., Marshall, J.D., Kandlikar, M., 2011. Health and climate benets of
cookstove replacement options. Energy Policy 39, 7530–7542. https://doi.org/
10.1016/j.enpol.2011.03.024.
Hubbard, D.W., 2014. How to Measure Anything: Finding the Value of Intangibles in
Business. https://doi.org/10.1002/9781118983836.
IPCC, 2006. 2006 IPCC guidelines for National Greenhouse gas Inventories Volume 4
agriculture, forestry and other land use chapter 4 forest land 2006. Forestry.
IUCN, WRI, 2014. A guide to the Restoration Opportunities Assessment Methodology
(ROAM): Assessing forest landscape restoration opportunities at the national or sub-
national level. (Road-test Ed).
Klein, G., 2007. Performing a project premortem. Harv. Bus. Rev. 85, 18. Available
online at: https://hbr.org/2007/09/performing-a-project-premortem.
K¨
ohl, M., Neupane, P.R., Lotomran, N., 2017. The impact of tree age on biomass growth
and carbon accumulation capacity: a retrospective analysis using tree ring data of
three tropical tree species grown in natural forests of Suriname. PLoS One 12.
https://doi.org/10.1371/journal.pone.0181187.
Kusters, K., Buck, L., de Graaf, M., Minang, P., van Oosten, C., Zagt, R., 2018.
Participatory planning, monitoring and evaluation of multi-stakeholder platforms in
integrated landscape initiatives. Environ. Manag. https://doi.org/10.1007/s00267-
017-0847-y.
Lanzanova, D., Whitney, C., Shepherd, K., Luedeling, E., 2019. Improving development
efciency through decision analysis: Reservoir protection in Burkina Faso.
Environmental Modelling & Software 115, 164–175. https://doi.org/10.1016/j.
envsoft.2019.01.016.
Lemenih, M., Kassa, H., Kassie, G.T., Abebaw, D., Teka, W., 2014. Resettlement and
woodland management problems and options: A case study from North-Western
Ethiopia. L. Degrad. Dev. https://doi.org/10.1002/ldr.2136.
Luedeling, E., Gassner, A., 2012. Partial least squares regression for analyzing walnut
phenology in California. Agric. For. Meteorol. 158–159, 43–52. https://doi.org/
10.1016/j.agrformet.2011.10.020.
Luedeling, E., Oord, A.L., Kiteme, B., Ogalleh, S., Malesu, M., Shepherd, K.D., de
Leeuw, J., 2015. Fresh groundwater for Wajir-ex-ante assessment of uncertain
benets for multiple stakeholders in a water supply project in Northern Kenya.
Front. Environ. Sci. 3, 1–18. https://doi.org/10.3389/fenvs.2015.00016.
Luedeling, E., Shepherd, K., 2016. Decision-focused agricultural research. Solutions
September 46–54.
Luedeling, E., Whitney, C.W., 2018. R Package decisionSupport: Controlled Burns in
Conifer Forests.
Luedeling, E., Borner, J., Amelung, W., Schiffer, K., Shepherd, K., Rosenstock, T.S., 2019.
Forest restoration: overlooked constraints Forest restoration: expanding agriculture
Forest restoration: transformative trees. Science (80-.) 366, 315. https://doi.org/
10.1126/science.aay7988.
Luedeling, E., G¨
ohring, L., Schiffer, K., 2020. decision support: quantitative support of
decision making under uncertainty. CRAN Archive. 366 (6463), 315.
Matzek, V., 2018. Turning delivery of ecosystem services into a deliverable of ecosystem
restoration. Restor. Ecol. 26, 1013–1016. https://doi.org/10.1111/rec.12872.
McGonigle, D.F., Rota Nodari, G., Phillips, R.L., Aynekulu, E., Estrada-Carmona, N.,
Jones, S.K., Koziell, I., Luedeling, E., Remans, R., Shepherd, K., Wiberg, D.,
Whitney, C., Zhang, W., 2020. A knowledge brokering framework for integrated
landscape management. Front. Sustain. Food Syst. https://doi.org/10.3389/
fsufs.2020.00013.
Mekuria, W., 2013. Changes in regulating ecosystem services following establishing
exclosures on communal grazing lands in Ethiopia: a synthesis. J. Ecosyst. https://
doi.org/10.1155/2013/860736.
Mokria, M., Gebrekirstos, A., Aynekulu, E., Br¨
auning, A., 2015. Tree dieback affects
climate change mitigation potential of a dry afromontane forest in northern
Ethiopia. For. Ecol. Manag. 344, 73–83. https://doi.org/10.1016/j.
foreco.2015.02.008.
Munialo, S., Hall, O., Francisca Archila Bustos, M., Boke Ol´
en, N., Onyango, M.C., Oluoch
Kosura, W., Marstorp, H., G¨
oran, D., 2019. Micro-spatial analysis of maize yield gap
variability and production factors on smallholder farms. Agric. https://doi.org/
10.3390/agriculture9100219.
Nigussie, Z., Tsunekawa, A., Haregeweyn, N., Adgo, E., Nohmi, M., Tsubo, M., Aklog, D.,
Meshesha, D.T., Abele, S., 2017. Factors inuencing small-scale farmers’ adoption of
sustainable land management technologies in North-Western Ethiopia. Land Use
Policy 67. https://doi.org/10.1016/j.landusepol.2017.05.024.
Nkonya, E., Mirzabaev, A., von Braun, J., 2015. Economics of land degradation and
improvement - a global assessment for sustainable development. Economics of Land
Degradation and Improvement - A Global Assessment for Sustainable Development.
https://doi.org/10.1007/978-3-319-19168-3.
Okuthe, I.K., Akotsi, E.N., 2014. Adoptions of improved biomass cook stoves by
households : an example from homabay county. Int. J. Humanit. Soc. Sci. 4 (9(1)),
191–206.
Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K.,
Montanarella, L., Alewell, C., 2015. The new assessment of soil loss by water erosion
in Europe. Environ. Sci. Pol. https://doi.org/10.1016/j.envsci.2015.08.012.
Pica-Ciamarra, U., Otte, J., 2010. Poultry, food security and poverty in India: looking
beyond the farm-gate. Worlds. Poult. Sci. J. https://doi.org/10.1017/
S0043933910000358.
Pistorius, T., Carodenuto, S., Wathum, G., 2017. Implementing Forest landscape
restoration in Ethiopia. Forests 61, 1–19. https://doi.org/10.3390/f8030061.
R Core Team, 2017. R: A language and environment for statistical computing. R Found.
Stat. Comput.. ISBN 3-900051-07-0.
Romijn, E., Coppus, R., De Sy, V., Herold, M., Roman-Cuesta, R.M., Verchot, L., 2019.
Land restoration in Latin America and the Caribbean: An overview of recent, ongoing
and planned restoration initiatives and their potential for climate change mitigation.
Forests. https://doi.org/10.3390/f10060510.
Rumpff, L., Duncan, D.H., Vesk, P.A., Keith, D.A., Wintle, B.A., 2011. State-and-transition
modelling for adaptive management of native woodlands. Biol. Conserv. 144,
1224–1236. https://doi.org/10.1016/j.biocon.2010.10.026.
Scull, P., Cardelús, C.L., Klepeis, P., Woods, C.L., Frankl, A., Nyssen, J., 2017. The
resilience of Ethiopian church forests: interpreting aerial photographs, 1938–2015.
L. Degrad. Dev. https://doi.org/10.1002/ldr.2633.
Shono, K., Cadaweng, E.A., Durst, P.B., 2007. Application of assisted natural
regeneration to restore degraded tropical forestlands. Restor. Ecol. https://doi.org/
10.1111/j.1526-100X.2007.00274.x.
Sircely, J., Conant, R.T., Boone, R.B., 2019. Simulating rangeland ecosystems with G-
range: model description and evaluation at global and site scales. Rangel. Ecol.
Manag. https://doi.org/10.1016/j.rama.2019.03.002.
Stålhammar, S., Pedersen, E., 2017. Recreational cultural ecosystem services: how do
people describe the value? Ecosyst. Serv. https://doi.org/10.1016/j.
ecoser.2017.05.010.
Stanturf, J.A., Kleine, M., Mansourian, S., Parrotta, J., Madsen, P., Kant, P., Burns, J. ,
Bolte, A., 2019. Implementing forest landscape restoration under the Bonn
Challenge: a systematic approach. Ann. For. Sci. https://doi.org/10.1007/s13595-
019-0833-z.
Tamba, Y., Wafula, J., Yigzaw, N., Whitney, C., Lüdeling, E., 2020. Data for the
evaluation of forest and landscape restoration in Northern Ethiopia. Mendeley Data
v1.
Teklay, A., Abera, B., Giday, M., 2013. An ethnobotanical study of medicinal plants used
in kilte awulaelo district, Tigray region of Ethiopia. J. Ethnobiol. Ethnomed. https://
doi.org/10.1186/1746-4269-9-65.
Tesfaye, S., Guyassa, E., Joseph Raj, A., Birhane, E., Wondim, G.T., 2014. Land use and
land cover change, and Woody vegetation diversity in human driven landscape of
Gilgel Tekeze catchment, Northern Ethiopia. Int. J. For. Res. https://doi.org/
10.1155/2014/614249.
UNEP, 2019. New UN Decade on Ecosystem Restoration Offers Unparalleled Opportunity
for Job Creation, Food Security and Addressing Climate Change [WWW Document].
Y. Tamba et al.
Forest Policy and Economics 125 (2021) 102403
13
Vågen, T-G, Winowiecki, LA, Tamene Desta, L, Tondoh, JE, 2013. The Land Degradation
Surveillance Framework (LDSF) - Field Guide v3. World Agroforestry Centre,
Nairobi, Kenya.
Wafula, J., Karimjee, Y., Tamba, Y., Malava, G., Muchiri, C., Koech, G., De Leeuw, J.,
Nyongesa, J., Shepherd, K., Luedeling, E., 2018. Probabilistic assessment of
investment options in honey value chains in Lamu County. Kenya. Front. Appl. Math.
Stat. 4, 1–11. https://doi.org/10.3389/fams.2018.00006.
WeForest, 2018. THE GREAT RIFTVALLEY DRY AFROMONTANE Desa ’ a State Forest
Management Plan.
Weldesemaet, Y.T., 2015. Enhancing food security through forest landscape restoration:
lessons from Burkina Faso, Brazil, Guatemala, Viet Nam, Ghana, Ethiopia and
Philippines, Enhancing food security through forest landscape restoration: Lessons
from Burkina Faso, Brazil, Guatemala, Viet Nam, Ghana, Ethiopia and Philippines.
https://doi.org/10.2305/iucn.ch.2015.fr.2.en.
Wendt, D., 1975. Some criticism of stochastic models generally used in decision making
experiments. Theory Decis. https://doi.org/10.1007/BF00169107.
Wilson, E.C.F., 2015. A practical guide to value of information analysis.
Pharmacoeconomics. https://doi.org/10.1007/s40273-014-0219-x.
Wold, S., Sj¨
ostr¨
om, M., Eriksson, L., 2001. PLS-regression: A basic tool of chemometrics.
In: Chemometrics and Intelligent Laboratory Systems, pp. 109–130. https://doi.org/
10.1016/S0169-7439(01)00155-1.
World Resources institute, 2015. The Restoration Diagnostic: A Method for Developing
Forest Landscape Restoration Strategies by Rapidly Assessing the Status of Key
Success Factors.
Yami, M., Mekuria, W., Hauser, M., 2013. The effectiveness of village bylaws in
sustainable management of community-managed exclosures in northern Ethiopia.
Sustain. Sci. 8, 73–86. https://doi.org/10.1007/s11625-012-0176-2.
Yang, K.F., Gergel, S.E., Baudron, F., 2020. Forest restoration scenarios produce
synergies for agricultural production in southern Ethiopia. Agric. Ecosyst. Environ.
295 https://doi.org/10.1016/j.agee.2020.106888.
Yet, B., Constantinou, A., Fenton, N., Neil, M., Luedeling, E., Shepherd, K., 2016.
A Bayesian network framework for project cost, benet and risk analysis with an
agricultural development case study. Expert Syst. Appl. https://doi.org/10.1016/j.
eswa.2016.05.005.
Yet, B., Lamanna, C., Shepherd, K., Rosenstock, T., 2020. Project evaluation using
Bayesian networks (PEBN): prioritizing agricultural development investments under
climatic and socio-political risk. PLoS One 15 (6). https://doi.org/10.1371/journal.
pone.0234213.
Yigzaw, N., Mburu, J., Ackello-Ogutu, C., Whitney, C., Luedeling, E., 2019. Stochastic
impact evaluation of an irrigation development intervention in northern Ethiopia.
Sci. Total Environ. 685, 1209–1220. https://doi.org/10.1016/j.
scitotenv.2019.06.133.
Zomer, R.J., Bossio, D.A., Sommer, R., Verchot, L.V., 2017. Global sequestration
potential of increased organic carbon in cropland soils. Sci. Rep. https://doi.org/
10.1038/s41598-017-15794-8.
Y. Tamba et al.
