Climate and Development
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tcld20
Use and economic benefits of indigenous seasonal
climate forecasts: evidence from Benin, West
Africa
Cocou Jaurès Amegnaglo, Akwasi Mensah-Bonsu & Kwabena Asomanin
Anaman
To cite this article: Cocou Jaurès Amegnaglo, Akwasi Mensah-Bonsu & Kwabena Asomanin
Anaman (2022): Use and economic benefits of indigenous seasonal climate forecasts: evidence
from Benin, West Africa, Climate and Development, DOI: 10.1080/17565529.2022.2027740
To link to this article:  https://doi.org/10.1080/17565529.2022.2027740
Published online: 23 Jan 2022.
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CLIMATE AND DEVELOPMENT
https://doi.org/10.1080/17565529.2022.2027740
Use and economic benefits of indigenous seasonal climate forecasts: evidence from
Benin, West Africa
Cocou Jaurès Amegnagloa,b, Akwasi Mensah-Bonsub and Kwabena Asomanin Anamanb
aEcole d’Agrobusiness et de Politiques Agricoles, Université Nationale d’Agriculture, Porto-Novo, Bénin; bDepartment of Agricultural Economics and
Agribusiness, University of Ghana, Legon, Ghana
ABSTRACT ARTICLE HISTORY
Since immemorial times, farmers especially in Africa have built and transmitted orally from one Received 10 February 2021
generation to another a complex network of seasonal climate knowledge enabling them to lower Accepted 6 January 2022
climate variabilities and vagaries. Despite the prominent role of this knowledge system in smallholder
farming, empirical studies relative to the production process, use and economic valuation of this KEYWORDSIndigenous seasonal climate
knowledge to inform decision-making are scanty. Travel cost method, descriptive statistics and a two- forecasts; maize; total
step Heckman method are used to analyse the use and economic value of indigenous seasonal expenses approach;
climate forecasts (ISCF) in Benin. ISCF were produced based on the observation of abiotic and biotic usefulness; valuation of
indicators in Kandi, Glazoué and Zè with the observations largely undertaken by local elders and forecasts; weak
professional traditional forecasters. Most farmers got ISCF either by travelling to visit a source of complementary assumption
knowledge or by sacrificing their time. The use of ISCF increased a maize producer’s net income by at
least 3%, implying that ISCF are valuables goods. The main factors driving the use and value of ISCF
were the use of fertilizer, large farm size, traditional African religions and access to market. Therefore,
policy to promote the integration of indigenous forecasting knowledge with modern forecasting
system should be taken.
1. Introduction
of crops to grow, type of agronomic practices to use and labour
Many African farmers use indigenous knowledge (IK) to fore- redistribution and allocation (Camacho-Villa et al., 2021;
cast climate patterns (Adanu et al., 2021; Nkuba et al., 2020; Radeny et al., 2019). However, the use of indigenous seasonal
Orlove et al., 2010; Radeny et al., 2019; Roncoli et al., 2002; climate forecasts (ISCF) depends on factors such as sex, reli-
Tume et al., 2019). According to UNESCO (2017), IK refers gion and education level (Ayal et al., 2015; Joshua et al.,
to the understandings, skills and philosophies developed by 2012). Roncoli et al. (2002) add that economic progresses
societies with long histories of interaction with their natural coupled with societal transformation tend to erode customary
surroundings. In a constrained environment characterized by systems and use of indigenous climate knowledge.
rain-fed farming system, lack of financial resources and access For rural and indigenous peoples, local knowledge informs
to technologies, the success of an agricultural venture depends decision-making about fundamental aspects of day-to-day life.
on the farmer managerial talent or ability to anticipate cor- Hiwasaki et al. (2014) and Ellen and Harris (1996) contended
rectly future climate patterns and use it advantageously to that IK is local, orally transmitted and is the consequence of
reduce losses and achieve food security and target income practical engagement in everyday life, and is constantly
(Adanu et al., 2021; Camacho-Villa et al., 2021). From times reinforced by experience and trial and error. Two schools of
immemorial, farmers have learned to forecast the climate to thought are arguing on the benefits generated by the use of
gain information in order to improve their decision-making IK. The first school of thought postulates that IK is unscien-
processes related to the production and marketing of agricul- tific, backward and its use limits the adoption of technological
tural goods. Farmers have used the appearance and/or the innovations (Gilchrist et al., 2005; Gupta, 2007; Huntington,
behavioural patterns of birds, insects, the movement and pos- 2000). The second school of thought argues that IK is widely
ition of moon, sun, cloud, the direction and strength of the used by the population since several centuries and their survi-
wind and plant phenology as data and signals that they process val through ages is a sign of their usefulness (Chang’a et al.,
to obtain climate information for their livelihood activities 2010; Rivero-Romero et al., 2016). These controversies sur-
(Adanu et al., 2021; Ingram et al., 2002; Nkuba et al., 2020; rounding the IK have partly led to a lack of studies on the
Orlove et al., 2010; Radeny et al., 2019; Rivero-Romero et al., economic value of ISCF.
2016; Roncoli et al., 2002; Roudier et al., 2014; Speranza Improving our understanding on the accuracy, reliability
et al., 2010; Tume et al., 2019). and value of ISCF could increase awareness of their usefulness.
Farmers use indigenous seasonal/weather climate knowl- Many investigations have previously explored indigenous sea-
edge to plan for land to cultivate, the timing of land prep- sonal climate knowledge (Adanu et al., 2021; Ayal et al., 2015;
aration, the purchase of seeds and fertilizer for planting, type Camacho-Villa et al., 2021; Kalanda-Joshua et al., 2011; Nkuba
CONTACT Cocou Jaurès Amegnaglo cjamegnaglo@st.ug.edu.gh; cocoujaures1@yahoo.fr P.O. Box 2667, Calavi, Benin
© 2022 Informa UK Limited, trading as Taylor & Francis Group
2 C. J. AMEGNAGLO ET AL.
et al., 2020; Radeny et al., 2019; Rivero-Romero et al., 2016; of this study is to analyse the use and economic value of ISCF
Roncoli et al., 2002; Speranza et al., 2010). However, these used by maize producers in Benin.
studies emphasize mainly on the documentation, description
and use of this IK by farmers. To the best of our knowledge,
none of these studies tried to establish the net benefit (value) 2. Materials and methods
of ISCF.
Most developing countries are investing in the meteorologi- 2.1. Conceptual framework
cal systems as a structural response to the emergency of cli- The success of small-scale agricultural venture depends on cli-
mate change and variability and to increase food production. mate realisations and on the farmer managerial talent in gen-
The significant and widespread use of the climate/weather eral and on the farmer managerial talent to anticipate correctly
information from the meteorological systems can justify the future climate patterns in particular (Figure 1). The talent to
rational of these investments. The estimation of the net forecast or get forecasts about future climate patterns helps
benefit of ISCF can also facilitate the dialogue between formal farmers to improve decision-making, reduce losses and
national meteorological system and local producers with the achieve food security and target income. Therefore, infor-
possibility of creation of participatory processes where climate mation about climate/weather is a fundamental factor in the
services are coproduced and better disseminated between pro- success of small-scale farming. Climate/weather information
ducers and end users. The estimation of the value of ISCF then can be obtained through two sources. Meteorologists can pro-
can improve the use of enhanced or modern seasonal forecast. vide enhanced data and information on past, present and poss-
This estimation of the net benefit of ISCF can help to better ible future states of the atmosphere (temperature, rainfall,
preserve and document IK related to climate and this IK can wind, cloudiness and air quality) derived from direct obser-
be fundamental in the adaptation to climate change (Radeny vations using instruments and/or using the human senses.
et al., 2019; Tume et al., 2019). Moreover, Radeny et al. Using IK and without instruments, indigenous communities
(2019) call for more studies on indigenous climate forecasting have mastered and performed a rich and complex system of
in order to better preservese this IK. The preservation and forecasting based on the observation of appearance and move-
documentation of ISCF and the integration of ISCF in a ment of biotic and abiotic indicators.
national/local meteorological programme are costly and this The limited access to enhanced (scientific) information
study can allow cost-benefit analysis of climate services pro- push farmers to rely on indigenous climate/weather fore-
grammes or projects. In this study, we attempted to answer casts. Having the information about the near and distant
the following research questions: How is the ISCF produced future (precipitations, wind speed and direction, cloudiness,
and disseminated? How do farmers value the ISCF? What relative humidity, minimum and maximum air and soil
are the factors influencing the use of ISCF? The main objective temperatures and pan evaporation), farmers can better
Figure 1. Conceptual framework.
CLIMATE AND DEVELOPMENT 3
choose which crop(s) to produce, manage efficiently inputs of informational services and a vector of other commod-
such as land, labour, fertilizer, financial assets devoted to ities, each of which may be produced by combining pur-
each crop, adopt improved technologies and crops varieties, chased inputs with time. Let U(v, x) be the quasi-concave,
intensify production, invest more in production and twice-differentiable utility function of a representative con-
storages facilities, adjust their daily decision (input timing sumer, where v is the number of visits to a source of infor-
and use, sowing period, marketing decisions), undertake mation and x is a vector of all other goods consumed by
activities that can minimize losses and risks due to adverse the individual at a given price vector p. Each visit to the
weather and so increase yield, quantity and quality of agri- information site has a cost c.
cultural production and take advantage of favourable con-
ditions (Hansen, 2002). By improving decision-making, The consumer has an income: Y = Y0 + wtw (1)
ISCF contribute to the outcome of the agricultural venture where Y0 is non-labour income, w is the wage rate per
and the difference between the outcome without ISCF and hour, tw is the work hours,
the one with ISCF is the net benefit of ISCF.
The consumer has a certain amount of time: T0
2.2. Theoretical framework = tw + vtv (2)
The true measure of the climate services benefits relates to where tv is the time spent on visit.
improved decisions made by farmers. These benefits relate to The user of the good must maximize its utility
the difference in net benefits or profits from using the selected subject to time and income restrictions. The basic model
forecast vis-à-vis other options. Net benefit is the difference implies that individuals are free to choose between work
between revenue and all costs of production including the and recreation, in which case the opportunity cost of
cost to access forecasts. Two broad methods are used to time is equal to the wage rate. In this manner, the utility
value climate forecasts. Some studies conducted on assessment function U(v, x) is maximized subject to the following
of the value of climate forecasts are based on ex-ante restrictions:
approaches (simulation models) whereas others are based on Y = cv+ px (4)
ex post approaches (observed, stated or revealed preference
approaches) (Amegnaglo et al., 2017; Meza et al., 2008; Sultan T0 = tw + vtv (5)
et al., 2010). Revealed preference approach or surrogate market
approach, which consists of inferring the value of non-market Bearing in mind the following relationships:
goods from the observed purchases of market goods necessary Y = cv+ px = Y0 + wtw (6)
for the enjoyment of non-market goods by individuals, can
also be used to value climate forecasts (World Meteorological Y0 + w(T0 − vtv) = cv+ px (7)
Organization [WMO], 2015). To have ISCF, farmers generally
either spend time observing the behaviour of different natural Y0 + wT0 − v(wtv + c)− px = 0 (8)
indicators like insects, birds, mammals, amphibians, threes The problem may be rewritten as:
and extra-terrestrials’ objects or travel to consult professional
traditional rainmakers, religious leaders, elders or friends. max {U(v, x)+ l(Y0 + wT0 − v(wtv + c)− px)} (9)v,x
The time that farmers spend in observing different natural
indicators can be used to determine the value of ISCF The first-order condition is:
(WMO, 2015). The total expenses method can be used to esti- dU
mate the benefits of ISCF for farmers who seek ISCF from − l(wtv + c) = 0 (10)dv
external and internal sources.1 The expenditures include travel
fees towards the site where the forecast is received, the pay- And making:
ments and/or donations made on the site in order to receive c∗ = wtv + c (total cost of visit) (11)
the ISCF.
The total expenses approach is often used in the context of Y∗ = Y0 + wT0 (maximum income) (12)
the estimation of the cost of travel by an economic agent from
a source to a site of interest as initially proposed by Hotelling The demand function may be expressed as: v
(1947). The Hotelling approach, Travel Cost Method (TCM), = f (Y∗, p, c∗) (13)
can be envisioned as part of the general analytic group that
can be called the total expenses approach (Douglas and Taylor, The demand for ISCF is function of the farmer’s income,
1999). Total expenses approach allows for the use of this gen- cost of visit to the information site and the price of other
eral method to value non-market goods beyond the traditional goods consumed. High income can allow individuals to
purview of recreational areas and environmental amenities devote a high share of their income to acquire ISCF.
and specifically allows for the valuation of non-market goods Also, the high cost of visits to information site reduces
that can also be used as inputs to produce market goods (Dou- the profit of the farmer; therefore, there is little incentive
glas and Taylor, 1999). for acquiring ISCF. High price of other goods consumed
Following Bockstael et al. (1987) and Bedate et al. reduces farmer ability to spend more on the acquisition
(2004), an individual maximizes utility by choosing a flow of ISCF.
4 C. J. AMEGNAGLO ET AL.
2.3. Empirical model elicited information from producers about the source and
use and all the expenditures attributable solely to the use of
A parametric approach was used to analyse factors that deter-
the ISCF. Benin is characterized by three main climatic
mine the benefits that farmers derived from the use of ISCF.
zones (Figure 2). Maize is produced in all of the three main cli-
The dependent variable is the logarithm of the expenditures
matic zones and is the major staple food in the country. Maize
incurred by farmers in order to enjoy the use of ISCF. The
is also the most produced crop in the country. Each climatic
two-step Heckman selection model is based on the main
zone has its own use of inputs such as water, fertilizer, herbi-
hypothesis that the process that determines the producer
cides for the production of maize.
decision to use or not ISCF is different from the process that
determines the amount spent by the producer to get the
ISCF (Heckman, 1979). This model also controls for the selec- 2.4.2 Sampling techniques
tion bias. A multi-stage cluster-based random sampling approach was
The model consists of two steps. Firstly, each farmer was used as the design to select the respondents of the study
asked whether they used ISCF or not. The respondent was (Table 2). The first stage of the design consisted of the ran-
expected to answer ‘Yes’ or ‘No’. The probit model was esti- dom selection of one municipality per climatic zone. The
mated in order to obtain the Inverse Mill’s Ratio (IMR). The second stage of the selection process consisted of the ran-
probability that the farmer uses indigenous climate knowledge dom selection of three districts per selected municipality.
can be expressed as a function of independent variables in The third stage of the selection process consisted of the
Equation (14) as: selection of two villages in each district. The fourth and
final stage was the selection of farmers to be interviewed
∑k with a structured questionnaire in the randomly selected
Pi = A0 + AjXij + Ui (14) villages. The optimal sample size for the number of farmers
j=1 selected for the whole study was 323.2 Oversampling was
where A is the coefficient of the jth socio-economic character- used and hence 396 farmers were chosen for the study indi-j
istic; X is the jth socio-economic characteristic of the ith pro- cating an oversampling of about 22.6%. This oversamplingij
ducer; U is the error term. The error term U is was done due to the possibility of some farmers refusingi i
independently and identically distributed with a normal prob- to participate in the study. In total, 396 farmers were inter-
ability distribution function. The independent variables used viewed, but data on 354 farmers were used for the analysis
are listed in Table 1. due to some missing data for 43 farmers.
If a given farmer uses ISCF, the second stage Ordinary Least
Squares (OLSs) equation would then be as follows: 2.4.3. Estimation of the economic value of ISCF
Farmers spend time in observing behaviour and phenology
lnTVE = a+ b′xi + gIMR+ ui (15) of natural indicators and the sacrifice of time made by
where lnTVE is the logarithm of the amount a farmer spends farmers in order to produce ISCF is valued by multiplying
in order to have access to ISCF; b is a k× 1 vector of unknown the time used by the average wage rate per hour in Benin
parameters; x is a k× 1 vector of known constants and u are as presented in Equation (16). For this study, the mediani i
independently and normally (0, s2). The independent vari- wage per hour in the agricultural sector estimated by Besa-
ables used are presented in Table 1. musca et al. (2013) is used to value the time farmers
sacrificed to generate their own forecasts. Besamusca et al.
(2013) estimated that the median wage per hour in agricul-
tural sector in Benin was USD 0.524 in 2012 based on a
2.4. Data collection sample of 2002 individuals.
2.4.1 Study area TEV = Time spent∗Median wage per hour rate (16)
A survey of maize producers was undertaken in Benin to 1
obtain relevant data from individual maize producers to deter- where TEV1 is the total economic value of ISCF based on
mine the aggregate benefits from the use of ISCF. The survey own production.
Table 1. Variable description for the two-step Heckman models.
Variables Description Measurement Expected signs
SEX Sex Dummy (male = 1, female = 0) +
LAND Current Farm Size Hectares +
EXPERIENCE Maize Farming Experience Years +
EDUCATION Education Dummy (1 = post-primary and 0 otherwise) –
OFFARM Off-Farm Participation Dummy (Yes = 1, No = 0) –
RELIGION Traditional Religion Adherents Dummy (Yes = 1, No = 0) +
MARKET Access to Market Dummy (Yes = 1, No = 0) –
EXTENSION Access to Extension Services Dummy (Yes = 1, No = 0) –
CREDIT Access to Credit Dummy (Yes = 1, No = 0) –
FBO FBO Membership Dummy (Yes = 1, No = 0) –
FERTILIZER Quantity of Fertilizer Used Kilogram +
REGION1 Kandi Dummy (Kandi = 1, Zè and Glazoué = 0)
REGION2 Glazoué Dummy (Glazoué = 1, Zè and Kandi = 0)
CLIMATE AND DEVELOPMENT 5
Figure 2. Location of the study areas and climatic zones in Benin.
Table 2. Summary of sampling procedure.
Sampling
steps Population Techniques Results
First Communes: Selection of Purposive sampling: Identification of commune per Kandi from the 14 communes in the Sudanian climate zone,
03 communes out of 77 climatic zone; Random sampling: Names of the Glazoué from the 15 communes in the Sudan-Guinean
communes are written on pieces of paper and three climate zone, Zè from the 34 communes in the Sub-
districts are randomly picked per municipality; humid Guinean Climate zone.
Second Districts: Selection of 03 Random selection: Names of the districts are written on Sonsoro, Kassakou and Donwari from the 10 districts of
districts per selected pieces of paper and three districts are randomly picked Kandi; Aklampa, Asanté and Zaffé from the 10 districts of
communes per commune selected; Glazoué; Tangbo-Djevie, Sedje-Denou and Djigbé from
the 11 districts of Zè.
Third Villages: Selection of two Random selection: Names of the villages are written on Two villages are randomly selected from each district
villages per districts pieces of paper and two villages are randomly picked per making up 18 villages for the nine districts and the three
districts selected municipalities.
Fourth Farmers: Selection of Random selection: Random selection of farmers based on Twenty-two (22) farmers are randomly selected from each
farmers per village the list based on the availability village based on identifiable clusters of houses and huts
in the different geographical areas of the village. A
consultation of the Chief of the Villages selected helps to
get an approximate number of farmers known to be
available in the village at the time of the study.
Farmers also get ISCF from external sources and TEV2 is significant time and they travel to the forecast sites during their
the total economic value of ISCF from these sources. Farmers leisure (free) time.
generally get the ISCF from external sources by using two The total economic value of ISCF from external sources
different strategies. Farmers get the forecasts individually or (TEV2) can be aggregated as follows:
by using community-based mechanisms. In some villages,
farmers contribute to organize public consultation days before TEV2 = cost of getting ISCF through group mechanisms
the starting of the rainy season. Other farmers seek the fore- + cost of getting ISCF individually
casts privately and have to travel to the source of ISCF and (17)
he has to pay for the service. The payment can be in cash or
in-kind. All the in-kind payments were valued at market The cost of getting ISCF individually is the number of visits
prices. In this part, travelling time and time spent on the fore- times the sum of travel cost and on-site payment for the fore-
casts site were not valued because most farmers do not spend casts. Since these travels to gather seasonal information are
6 C. J. AMEGNAGLO ET AL.
part of habitual traditional (and religious and/or festive) social of the farmers (57.3%) declared agriculture as their sole source
activities; in that case, the trip has more purposes and only a of income.
part (50%) of the cost should be used as the indicative value Only one-third of farmers interviewed interacted with
of the expected benefits of using the seasonal information. extension services during the last three farming seasons
The on-site payment can be in cash or in-kind. The in-kind (2012–2014), with a higher proportion of interaction in the
payments were valued at market prices. Therefore, the total north (66%) due to the production of cotton in the municipal-
economic value of ISCF from external sources (TEV2) can be ity of Kandi (Table 3). About 57% of farmers applied fertilizer
rewritten as follows: during the last cropping season.
TEV2 = cost of getting ISCF through group mechanisms
+ (0.5∗Travel cost to site+On site payments by farmers) (18) 3.2. Use of indigenous seasonal climate knowledge by
∗ number of visits farmers
The total economic value (TEV) of ISCF is the sum of the Farmers based some of their farming decisions on ISCF. A
value of the time used by farmers to produce their own ISCF large majority (83%) of farmers declared that they used indi-
(TEV1) and the cost that farmers incur to obtain ISCF from genous seasonal climate knowledge to plan ahead their farm-
external sources involving the costs of getting forecasts using ing operations during the 2014 major cropping seasons.
group mechanisms, the travel cost of an individual to a par- Similar results were obtained by Chang’a et al. (2010) and
ticular site to obtain forecasts and on-site payments to access Elia et al. (2014) in Tanzania, Roncoli et al. (2002) in Burkina
the forecasts once the farmer has reached the site (TEV2). Faso, Speranza et al. (2010) in Kenya, Kalanda-Joshua et al.
= + (2011) in Malawi, Nkuba et al. (2020) in Uganda, TumeTEV TEV1 TEV2 (19) et al. (2019) in Cameroun, Radeny et al. (2019) in Ethiopia,
Once the individual economic value of ISCF has been esti- Tanzania and Uganda. The proportion of farmers acting on
mated, it was multiplied by the number of maize farmers in ISCF decreased as one comes closer to the coastal regions.
Benin in order to estimate the total economic value of ISCF Indeed, during the 2014 farming season, 96.3, 88.7 and
at the national level. 61.9% of farmers in Kandi, Glazoué and Zè used ISCF to
Descriptive statistics including frequencies and means were guide their decision-making, respectively (Table 4). A regional
used to analyse the different sources of production of ISCF, analysis was performed because the three municipalities are
proportion of farmers acting on ISCF and dissemination chan- significantly different in terms of religious and cultural prac-
nels of these forecasts. Descriptive statistics (frequencies and tices, climatic and economic conditions and agricultural pro-
percentages analysis) were also used to determine the type of duction system (Amegnaglo et al., 2017; INSAE, 2015).
information provided by indigenous meteorological schemes Few farmers (7.4%) produced their own ISCF, 33.8% of
and the production step at which each type of information farmers got their forecasts from other members of their com-
produced is used. A proportion test was performed to analyse munity and 58.8% of farmers used both sources (Table 4).
the regional differences across responses. Farmers who did not have indigenous seasonal climate knowl-
edge allowing them to forecast climate by themselves con-
sulted various sources of information such as local elders,
professional traditional forecasters, religious leaders and
3. Results and discussion friends in decreasing order of importance, respectively
(Table 4). Most of the farmers who got forecasts from external
3.1. Socio-economic characteristics of survey
sources consulted more than one source and the consultation
respondents
of multiple external sources explained the fact that the sum of
About 38% of farmers interviewed came from Kandi Munici- the proportion of use of the various sources exceeded 100%.
pality, 32% from Zè and 30% from Glazoué (Table 3). Almost The difference in external sources used across regions was cer-
half of the respondents were Christians, one-third was adher- tainly due to religious differences across the three regions as
ents of the traditional African religions and the rest (about suggested by Roncoli et al. (2002) and Ingram et al. (2002).
20%) were Muslims. About 73% of the respondents were
male and one-third of the farmers were young (18–35 years).
3.3. Indigenous indicators used by farmers in seasonal
The mean age of sampled farmers was about 42 years and
climate forecasting
users of ISCF were slightly older than non-users. Respondents
with no schooling (formal education) constituted the largest Two-thirds of maize farmers who used seasonal climate fore-
group (61.6%), while primary school leavers were the second casts in Benin were aware of the indicators or elements used
most prominent class of respondents (28.5%). The average to generate indigenous meteorological knowledge. This pro-
farm size for the whole group was about 3.9 hectares and portion ranged from 58.6% in the southern zone (Zè) to
users of ISCF had slightly over three times the farmland of 73.8% in the northern zone (Kandi). They indicated different
non-users. The mean farming experience on maize production indigenous indicators of seasonal climate forecasting. The
is 22 years. Users of ISCF are more experienced (about 7 years) most used elements to forecast climate were trees (72.7%),
than non-users and this is not surprising knowing the role of birds (72.2%), extra-terrestrial objects (61.1%), winds
age in the production process of ISCF. For the majority of (58.9%), insects (44.9%) and sky (41.9%) (Table 5). The vast
farmers (73%), maize was their main crop and the majority majority of farmers (91%) who based their climate forecasts
CLIMATE AND DEVELOPMENT 7
Table 3. Summary of socio-economic characteristics of survey respondents per municipality based on averages and frequencies.
All farmers (n = 354) Users (n = 299) Non-Users (n = 60)
Items Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. ttest/P-Value
Sex (percentage male) 73.1 44.4 78.9 40.9 45.0 50.2 .0000***
Mean Age (Years) 41.7 12.6 43.3 12.8 33.9 8.2 .0000***
No education at all 61.6 48.7 61.9 48.6 60.0 49.4 .7830
Primary School 28.5 45.2 28.9 45.4 26.7 44.6 .7265
Post Primary School 9.9 29.9 9.2 28.9 13.3 34.3 .3278
Mean number of years of schooling 2.4 3.8 2.4 3.8 2.7 3.9 .6188
Christian 48.0 50.0 40.8 49.2 83.3 37.6 .0000***
Muslim 21.2 40.9 24.1 42.9 6.7 25.1 .0025***
Traditional African Religions 30.8 46.2 35.0 47.8 10.0 30.2 .0001***
Mean Farm size (ha) 3.9 4.4 4.4 4.6 1.5 1.5 .0000***
Use of fertilizer (%) 56.8 49.6 65.0 47.8 16.7 37.6 .0000***
Quantity of fertilizer used (Kg/Ha) 94.7 104.5 109.2 105.9 23.6 58.8 .0000***
Off-farm activity’ Participation (%) 42.7 49.5 36.1 48.1 75.0 43.7 .0000***
Maize farming Experience (years) 22.2 11.7 23.3 11.9 16.4 8.4 .0000***
Access to extension services (%) 33.9 47.4 37.4 48.5 16.7 37.6 .0019***
Access to credit (%) 52.2 50.0 52.4 50.0 51.7 50.4 .9199
Access to market (%) 89.8 25.2 93.2 25.2 73.3 57.6 .0000***
FBO’s membership (%) 12.7 33.3 13.6 34.4 8.2 27.6 .2458
* denotes statistical significance at the 10% level; ** denotes statistical significance at the 5% level; *** denotes statistical significance at the 1% level.
on their own knowledge used more than one element. Some- about 62.8% of farmers interviewed responded to ISCF
times when the indigenous indicators used to provide forecasts obtained by changing crop variety. The change of crop variety
are contradictory, religious rituals are performed by pro- was higher (Table 6) in Kandi and Glazoué, where farmers
fessional traditional forecasters to get a clearer picture of the were better off than in Zè. The change of crop acreage was
situation (Ingram et al., 2002). the third most employed response strategy by farmers in
Benin (Table 6). Almost all the farmers interviewed grew at
least two crops and each year they have to decide the quantity
3.4. Farmers’ response strategies to ISCF of inputs that must be devoted to each crop. Farmers can
Based on ISCF, farmers generally used non-intensi ed options reduce the quantity of land devoted or allocate zero lands tofi
such as change of sowing date, change of area devoted to crops, some crops with the aim to increase the quantity for other
the substitution of crops, change of crop density and change of crops more suited for the coming season based on the climate
crop variety in their production decision. These results are predictions.
similar to those found by Camacho-Villa et al. (2021) in Mex- About 41% of farmers intensified their production after get-
ico and Radeny et al. (2019) in Ethiopia, Tanzania and Uganda. ting ISCF (Table 6). This option was mostly used in areas
Intensi ed options (i.e. use and increase of organic and inor- (Kandi and Glazoué) where farmers had better access to agrofi
ganic fertilizer) were also used. Farmers also mixed the inten- fertilizer (NPK + Urea). The low use of this option in Zè was
sified and non-intensified strategies with the aim to decrease certainly due to the low use of agro fertilizer in this area and
production risks. Table 6 presents responses (strategies) used a policy should be undertaken to increase the use of fertilizer
by maize farmers in Benin. The majority of the farmers used (organic and chemical) in this area. Farmers who were pre-
more than two strategies (intensified and non-intensi ed). In viously using fertilizer with the access to meteorological infor-fi
all cases, farmers responded by either adopting non-intensi ed mation would maintain the current quantity of fertilizer usedfi
strategies or by mixing intensified and non-intensified strat- or increase it (Table 6).
egies. The most commonly used non-intensified strategies
were the change of planting date (76.3%), change of crop var-
iety/type (62.8%) and change of area planted by crop (49.2%) Table 5. Indigenous indicators of climate forecasting employed by maize farmers
(Table 6). When farmers have access to ISCF, they choose the expressed in percentage.
cultivar that can maximize their return. In the present study, All
Indigenous Indicators farmers Kandi Glazoué Zè
Behaviour of Birds 72.2 76.0 A, 55.7 C 87.8
B
Table 4. Sources of ISCF in Benin Expressed in percentage.
Behaviour of Insects 44.9 45.8 42.6 46.3
All farmers Kandi Glazoué Zè Behaviour of Mammals 14.6 18.8 A 6.6 C 17.1
Proportion of farmers using ISCF 83.0 96.3 A, B 88.7 C 61.9 Behaviour of Amphibians 21.7 37.5 A, 3.3 C 12.2
Sources of ISCF B
Own Production 7.4 6.9 B 3.2 C 13.5 Phenology of Trees 72.7 84.4 A, 55.7 C 70.7
External Production 33.8 26.2 B 35.8 44.6 B
Both 58.8 66.9 B 61.7 C 41.9 Behaviour of Extra-terrestrial Objects 61.1 65.6 B 63.9 C 46.3
External sources of ISCF (Moon, Sun & stars)
Local Elders 86.2 94.2 B 90.9 C 62.7 Behaviour of Sky 41.9 62.5 A, 29.5 C 12.2
Religious leaders or priests 48.5 19.0 A, B 65.9 C 83.0 B
Friends 7.3 8.2 5.6 7.8 Behaviour of Wind 58.9 63.5 B 63.9 C 39.0
A, B and C denote statistical significance at 5% based on proportion test between A, B and C denote statistical significance at 5% % based on proportion test
Kandi and Glazoué, Kandi and Zè and Glazoué and Zè, respectively. between Kandi and Glazoué, Kandi and Zè and Glazoué and Zè, respectively.
8 C. J. AMEGNAGLO ET AL.
Table 6. Response strategies used by maize farmers when planning for maize Table 8. Average cost incurred in getting ISCF through community-based
production after receiving ISCF in Benin expressed in percentage. mechanisms.
Preparedness Techniques All farmers Kandi Glazoué Zè Type All farmers Kandi Glazoué Zè
Non Intensified Strategies Individual 12.9 19.0 6.6 10.0
Change of planting dates 76.3 86.2 A, B 67.0 71.6 mechanism
Change of crop varieties 62.8 73.1 A, B 83.5 C 17.6 Community-based 67.6 55.4 92.3 55.0
Change crop acreage 49.2 39.2 A 71.1 C 37.8 mechanism
Change of crop types 30.9 52.3 A, B 14.4 14.9 Both 19.5 25.6 1.1 35.0
Change of land preparation 25.6 20.0 B 15.5 C 48.6 Individual share in 12.04 (14.56) 17.97 (18.66) 5.97 (7.38) 10.83 (9.72)
Change of crop spacing 24.9 40.8 A, B 9.9 17.6 USD in
Change of fields to plant maize 10.6 17.7 A 0.0 C 12.2 community-
Change of fertilizer application date 14.3 26.9 A, B 8.2 C 0.0 based
Change of chemicals application date 7.0 11.5 B 6.2 C 0.0 mechanisms
Intensified Strategies
A, B *The figures in brackets are the standard deviations of the means.Increase the use of fertilizer 41.2 63.1 41.2 C 2.7
Increase of the quantity of chemicals 10.3 15.4 B 9.3 C 2.7
A, B and C denote statistical significance at 5% % based on proportion test forecasters (72.5%), religious leaders (16.5%) and local elders
between Kandi and Glazoué, Kandi and Zè and Glazoué and Zè, respectively.
(11%).
On average, a farmer travelled about 4 km to reach the
3.5. Cost of using indigenous seasonal climate information site and spent 2.605 USD in terms of actual trans-
forecasts (ISCF) portation costs (Table 8). In this paper, travelling time and
time spent on the information site were not valued because
3.5.1. Value of time used to produce ISCF by farmers most farmers did not spend significant time on the site. More-
About 56% of the 198 farmers interviewed spent some time over, they travelled to the site during their leisure time.
observing behaviour and phenology of natural indicators as Majority (94.5%) of farmers paid consultation fees (cash or
a means of generating their own seasonal climate forecasts, in-kind) before getting the ISCF. The most common payment
suggesting that approximately two-thirds of farmers used form used to enjoy ISCF from external sources was cash pay-
ISCF. On average, a farmer spent 4 h and 20 minutes per sea- ment. About two-thirds of farmers interviewed used cash pay-
son (Table 7) in observing behaviour and phenology of natural ment only, while the other one-third of farmers used a mix of
indicators such as birds, insects, plants, amphibians, extra-ter- cash and in-kind payments. On average, a farmer spent 16
restrial objects. The computation of the total economic value (±17.46) USD. Farmers in Glazoué and Zè spent a higher
of ISCF based on own forecasts (TEV1) gives an average amount in consultation fee (cash or in-kind) compared to
benefit of 451.2 USD for the 198 farmers producing their farmers in Kandi. This was due to the difference in develop-
own forecasts. The benefit per farmer is around 2.28 USD ment between the three municipalities. Kandi was better off
for the 2014 major cropping season. in terms of wealth than Glazoué and Zè (INSAE, 2015). Poor
farmers spent more because they had low adaptive capacity
3.5.2. Value of ISCF acquired by farmers through external to production risks due to lack of resources and absence of
sources other climate change coping strategies (Roncoli et al., 2009;
About three-quarters of farmers interviewed got their ISCF Vogel, 2000; Ziervogel and Calder, 2003).
through community-based mechanisms (Table 8). This strat- The cost of getting ISCF individually was around 3706.05
egy certainly allowed farmers to discuss forecasts among them- USD for the whole community (Table 9). On average, a farmer
selves and share knowledge and experience. On average, a who sought ISCF individually spent about 41.64 USD. This
farmer paid 12 USD as a contribution for the organization of amount covered transportation costs, consultation fees and
the public consultation days. The total amount of money other stationary fees. This amount was much greater than
spent for the whole sample to get ISCF through community- the average cost of getting indigenous forecasts through com-
based mechanisms was about 2853.5 USD. One-third of farm- munity-based mechanism because community-based mechan-
ers who got seasonal climate forecasts from external sources ism helped to avoid transportation fees to the source of
sought the forecasts privately. Farmers (89 farmers) who forecasts and also reduced significantly payment on the site
sought ISCF privately consult professional traditional of forecasts. The computation of the total economic value of
Table 7. Distribution of time spent in observation of natural indicators.
All farmers Kandi Glazoué Zè
Number of farmers spending some time to get forecasts 198 96 61 41
Less than one hour 32.8% 32.3% 32.8% 36.6%
[1–2] hours 13.6% 9.4% 18.0% 17.1%
[2–3] hours 14.7% 11.4% 18.0% 17.1%
[3–4] hours 10.1% 7.3% 13.1% 12.2%
[4–5] hours 9.6% 12.5% 1.6% 14.6%
[5–10] hours 10.1% 15.6% 6.6% 2.4%
More than 10 hours 9.1% 12.5% 9.8% 0%
Average time spent 4.35 (5.00) 5.26 A (5.85) 4.07 C (4.77) 2.63 (1.70)
Opportunity cost of time spent (USD) 2.28 (2.62) 2.76 A (3.06) 2.13 C (2.50) 1.38 (0.89)
* A, B and C denote statistical significance at 5%% based on proportion test between Kandi and Glazoué, Kandi and Zè and Glazoué and Zè, respectively and 1US dollar
= 500 FCFA. The figures in brackets are the standard deviations of the means.
CLIMATE AND DEVELOPMENT 9
Table 9. Costs incurred to seek ISCF individually. religions were more likely to use ISCF than non-adherents.
Source All farmers Kandi Glazoué Zè Similarly, Joshua et al. (2012) found that Christianity reduces
Traditional 34.82 (53.49) 31.29 (52.63) 20.00 (20.89) 45.35(59.96) the farmers’ willingness to use ISCF forecasts in Zimbabwe.
forecaster Similarly, farmers with post-primary school educational qua-
Elders 1.79 (7.80) 0.80 (3.17) 10.48 (20.39) 1.52 (8.03)
Religion leaders 5.02 (22.30) 8.28 (28.25) n.a. n.a. lifications were less likely to use ISCF given their lower attach-
All sources 41.64 (54.35) 40.37 (55.13) 30.48 (19.05) 46.87 (59.30) ment to the indigenous cultural values as they would have been
less separated from their cultural background due to less Wes-
tern educational influences (Ayal et al., 2015).
ISCF from external sources (TEV2) amounted to a total of6 Participation in off-farm work reduced the probability of
559.55 USD and to an average of 24.84 USD per farmer as a using ISCF. Experience in maize farming increases the likeli-
cost of getting ISCF from external sources. hood of use of ISCF suggesting that farmers with increased
levels of experience searched for various forms of information
3.5.3. Total economic value of ISCF to reduce the risk of farm operations. Further, the experience
The total economic value of ISCF for the entire group of 354 would allow a farmer to have more insight into the use of
farmers interviewed over the 2014 maize cropping season ISCF and might have recognized/realized usefulness of these
was 7010.75 USD and this amount represented the minimum forecasts compared to less experienced farmers.
gain derived from the use of ISCF. This translated to 23.84 Farm size is positively and significantly related to the like-
USD per farmer for only farmers who used ISCF. The use of lihood of the use of ISCF. ISCF reduced losses due to the
ISCF increased a maize producer’s net income by at least risks induced by large farm size. Considering the geographical
3%. These results suggest that, given the revealed preference location, the results indicated that farmers living in Kandi were
approach based on expenditures actually incurred to consume more likely to use ISCF than farmers living in Zè. Similarly,
a good, ISCF are valuables goods. farmers living in Glazoué were more likely to use ISCF than
farmers living in Zè. The base municipality, Zè, is an area
3.5.4. Factors affecting the economic value of ISCF that is much closer to the economic capital city of Benin, Coto-
A two-step Heckman econometric analysis indicated that the nou. Hence farmers in Zè were less likely to have access to
coefficient of the inverse Mill’s ratio (IMR) was not statistically these forecasts because Roncoli et al. (2002) postulate that
signi cant (Table 10). This means that there was no selection economic progresses coupled with societal transformationfi
bias resulting from the use of non-zero amount values. There- tend to erode customary systems and use of IK such as indi-
fore, the second stage OLS is useful in determining the mean genous seasonal climate knowledge.
expenditure. The results of the OLS regression showed that adherents of
The results of the probit regression showed that at the 10% traditional African religions valued more ISCF than non-
level of statistical signi cance, all the variables were signi cant. adherents. Also, access to market increased the benefits thatfi fi
Men male farmers were more likely to use ISCF than their farmers derived from ISCF and therefore policymakers should
female counterparts. Ayal et al. (2015) suggested that the facilitate access to market by reducing transportation costs and
ISCF were the prerogative of men even though women were ensuring market availability in order to improve farmers’ live-
not excluded from getting and using the forecasts based on lihoods. Farm size and intensity of use of fertilizer positively
this knowledge. As expected, adherents of traditional African and significantly affected the benefits farmers derived from
the use of ISCF. Use of ISCF reduced losses due to the pro-
duction risks related to the use of fertilizer and large farm size.
Table 10. Results of the Heckman two-step selection regression analysis of the Access to credit significantly reduced benefits from ISCF.
determinants of benefits of ISCF in Benin. The negative relationship between access to credit and adop-
Probit OLS tion of improved technologies can either be due to the
P- t- P- inadequate access to credit (Diagne and Zeller, 2001) or the
Variable Coeff. t-value values Coeff. value values
allocation of credit obtained for production to smooth the con-
SEX 0.6721 2.73 .006*** 0.0738 0.41 .682
EXPERIENCE 0.0273 2.119 .035** −0.0008 −0.11 .914 sumption (Feder et al., 1990). Considering the geographical
EDUCATION −0.8589 −2.52 .012** 0.0093 0.04 .971 location, the results indicated that farmers in Glazoué derived
LOG (LAND) 0.5520 3.00 .003*** 0.4088 3.50 .000*** less benefits from the use of ISCF compared to farmers living
LOG 0.1053 2.55 .011**
(FERTILIZER) in Kandi and Zè. This may be due to the high climate instabil-
MARKET 0.5080 1.45 .148 0.5037 1.79 .073* ity experienced in Glazoué, this area is located in the transi-
EXTENSION −0.1829 −1.01 .313
− − − − tional zone.CREDIT 0.0737 0.30 .761 0.3621 2.26 .024**
FBO 0.5307 1.16 .106 0.1953 0.87 .382
OFFARM −0.6782 −2.75 .006*** −0.3314 −2.02 .043**
RELIGION 0.9729 3.26 .001*** 0.5354 2.36 .018** 4. Conclusions and policy recommendations
REGION1 1.5814 4.35 .000*** 0.1642 0.45 .652
REGION2 0.9047 2.89 .004*** −0.9169 −3.84 .000*** This paper analyses the economic value of indigenous seasonal
CONSTANT −1.3432 −2.72 .006*** 1.3937 2.41 .016** climate forecasts (ISCF) based on travel cost method, descrip-
Lambda 0.3191 0.58 .561 tive statistics and a two-step Heckman method and a random
Number of obs. = 354; Censored obs. = 60; Wald chi2 = 88.77; Prob > chi2 =
0.0000; survey of 354 maize farmers. Most farmers act on seasonal cli-
* denotes statistical significance at the 10% level; ** denotes statistical signifi- mate forecasts in Benin and receive their forecasts from tra-
cance at the 5% level; *** denotes statistical significance at the 1% level. ditional schemes. The indigenous seasonal climate
10 C. J. AMEGNAGLO ET AL.
knowledge is based on behaviour and phenology of trees, Notes
birds, extra-terrestrial objects, winds, insects and sky. The 1. In this study, external sources mean forecasts that are not pro-
most non-intensified strategies use are, in descending order, duced by the end-users of the forecasts. External sources include
change of planting date, change of crop variety, change of local elders, professional traditional forecasters, religious leaders
area planted by crop, change of crops planted, change of and friends.
land preparation, change of crop spacing and change of crop 2. The optimal sample size for the number of farmers selected for the
whole study was 323. The determination of the optimal sample size
fields. Farmers generally get the indigenous forecasts either is based on works of Babbie (2016) dealing with the sampling from
by producing their own forecasts based on observation of very large population sizes.
natural elements or by travelling to consult a source of infor-
mation. The valuation of the time spent and of the cost of over-
coming the distance between a farmer’s house and information
site gives a minimum bene t of 7010.75 USD for the whole Acknowledgementsfi
sample of farmers interviewed. These results suggest that indi- We thank all the participating farmers, chiefs and opinion leaders in the
genous seasonal climate knowledge in Benin is of substantial maize-producing regions of Benin for their extensive support and assist-
economic importance, especially in a context of smallholder ance towards the completion of this study. The contents of this document
are solely the liability of its authors and under no circumstances may be
farming system and of climate change/variability. The indigen- considered as a reflection of the position of the Cuomo Foundation and/
ous seasonal climate knowledge may be the starting point of an or the IPCC.
ex-ante adoption of climate technologies.
The main factors driving the use and value of ISCF are the
use of fertilizer, large farm size, traditional African religions Disclosure statement
and access to market. Regular interactions with formal insti- No potential conflict of interest was reported by the author(s).
tutions (formal education system and extension and credit ser-
vices) tend to reduce the use and value of ISCF. The results of
the regressions hold potential policy interventions. Policy- Funding
makers should find ways of integrating formal institutions in This document was produced with the financial support of the Cuomo
the design of climate services programmes and these pro- Foundation/IPCC and Alliance for Green Revolution in Africa
grammes should particularly target smallholders and women. (AGRA).
Investing in fertilizer development programme and improving
access to markets in Benin may increase the use and value of Notes on contributors
ISCF.
The results of this work show the societal, economic and Cocou Jaurès Amegnaglo A national of Benin, Cocou is an Assistant Pro-
cultural importance of indigenous seasonal climate knowledge fessor at the School of Agribusiness and Policy Analysis of the National
University of Agriculture in Benin. Cocou holds a PhD from the Univer-
for farmers. Therefore, the resource should be committed to sity of Ghana, Legon, in Applied Agricultural Economics and Policy with
studying and preserving indigenous knowledge in general a specialty in Environmental Economics and Production. He also
and indigenous seasonal climate forecasts especially. Policy- obtained a masters degree in Rural, Industrial and Environmental Econ-
makers should also integrate these indigenous forecasts in omics. He has worked and conducted research on climate change impacts
national meteorological and agricultural development strat- and financing solutions, agro-meteorology, production and institutional
economics. His research has been published in different journals and pre-
egies. This work also demonstrated the farmers’ need for sea- sented at various international conferences. He was a recipient of the
sonal climate information and so resources can be committed IPCC scholarship for his PhD research.
to studying and producing high-quality modern seasonal cli- Akwasi Mensah-Bonsu is Associate Professor and Head, Department of
mate forecasts for farmers. Also, policies that can improve Agricultural Economics and Agribusiness, University of Ghana, Legon.
farmers’ access to inputs and resources must be adopted by He holds a PhD in Development Economics from Vrije Universiteit,
policymakers. Amsterdam. His areas of research include development economics and
The valuation of ISCF in Benin focuses only on one crop policy analysis for agriculture, modelling of the agricultural sector
resources use and production efficiency analysis, benefit cost analysis,
(maize); however, a large majority of farmers in Benin produce project managing (including monitoring and evaluation) and environ-
at least two crops. The exclusion of other crops in the analysis mental issues. He has undertaken a number of research as well as other
may reduce the confidence that can be attached to the general extension works for both local and international agencies, including Gha-
conclusions derived from this analysis. Further in-depth analy- na’s Ministries of Food and Agriculture, and Finance, FAO, USAID/feed
sis is needed in order to understand and re ne the role and the future, DANIDA, UNDP, NEPAD and icipe.fi
value of ISCF in multi-cropping system. Furthermore, the tra- Kwabena Asomanin Anaman is a graduate of the University of Ghana
vel cost method used for the valuation of ISCF does not cap- (Bachelor in Agricultural Economics in 1977), and the University of Flor-
ida, Gainesville, Florida, United States (Master and PhD degrees in Food
ture non-use value of these forecasts and may lead to an and Resource Economics in 1981 and 1985 respectively). He is currently a
under-estimation of the value of these forecasts. Direct elicita- Professor in the Department of Agricultural Economics and Agribusiness,
tion methods or impact evaluation methods can be used on College of Basic and Applied Sciences, University of Ghana, Legon, Accra,
repeated data in order to reduce bias. Finally, the use of Heck- Ghana. He has a 36-year post-doctoral professional work experience in
man’s approach employed in this study does not capture the university/academia, government sector and private sector in civil society
organizations. This work experience includes 19 years of full-time
average treatment effect (ATE) of the use of indigenous climate research, teaching and government advisory work in three countries in
knowledge on farmers’ expenditures, future studies could the Asian-Pacific Region (Australia, Brunei and Papua New Guinea)
explore that possibility. and 15 years work in Ghana.
CLIMATE AND DEVELOPMENT 11
Professor Anaman was a member of the Joint Scientific Committee of the Feder, G., Lau, L.J., Lin, J.Y., & Luo, X. (1990). The relationship between
World Climate Research Programme, World Meteorological Organiz- credit and productivity in Chinese agriculture: A microeconomic
ation (WMO) from 2007 to 2010 and served as a member of three model of disequilibrium. American Journal of Agricultural
other expert panels of WMO from 1999 to 2016. He was also a member Economics, 72(5), 1151–1157. https://doi.org/10.2307/1242524
of the International Editorial Advisory Board of the Preventive Veterinary Gilchrist, G., Mallory, M., & Merkel, F. (2005). Can local ecological
Medicine journal (Elsevier Science Publishers) from December 1994 to knowledge contribute to wildlife management? Case studies of
December 2014. He was the Editor of the Ghana Policy Journal produced migratory birds. Ecology and Society, 10(1), 1–12. https://doi.org/10.
by the Institute of Economic Affairs, Accra over the period, March 2006 to 1098/rstb.2014.0271
July 2009 and the Editor of the Ghana Social Science Journal, produced by Gupta, A. (2007). Going to school in South Asia. (G. P. Group, Ed.).
the School of Social Sciences, University of Ghana from February 2012 to Greenwood Publishing Group.
July 2015. Professor Anaman served as a member of the Board of Direc- Hansen, J.W. (2002). Realizing the potential benefits of climate prediction
tors of the Bank of Ghana from July 2013 to January 2017. to agriculture : Issues, approaches, challenges. Agricultural Systems, 74
Professor Anaman has teaching, research, community service and exten- (3), 309–330. https://doi.org/10.1016/S0308-521X(02)00043-4
sion interests in resource and environmental economics, political econ- Heckman, J. (1979). Sample specification bias as a selection error.
omy and development, economic growth (firm, industry and Econometrica, 47(1), 153–162. https://doi.org/10.2307/1912352
macroeconomy), economics of agriculture and rural development, Hiwasaki, L., Luna, E., & Shaw, R. (2014). Process for integrating local
business economics/managerial economics, economics of family and and indigenous knowledge with science for hydro-meteorological
marriage, applied econometrics and statistics, and operations research. disaster risk reduction and climate change adaptation in coastal
He has published three books, co-authored 19 book chapters, and over and small island communities. The International Journal of
100 papers and articles, including 64 refereed articles in 33 academic Disaster Risk Reduction, 10, 15–27. https://doi.org/10.1016/j.ijdrr.
journals. 2014.07.007
Hotelling, H. (1947). The Economics of Public Recreation. The Prewitt
Report, National Parks Service. Washington, DC, USA.
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