<figure><img src="image_1.png" /></figure>

THE GEOGRAPHICAL BULLETIN

May 2023

Volume 64

Number 1
Gamma Theta Upsilon

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THE GEOGRAPHICAL BULLETIN

*The Geographical Bulletin* is a peer-reviewed journal published semi-annually (May and November) by Gamma Theta Upsilon, the international geography honor society. In keeping with its goals of promoting geography awareness, GTU offers *The Geographical Bulletin* as an open-access online journal that does not charge authors for either submission or subsequent publication. While *The Geographical Bulletin* began publication as an outlet for student research in 1970, it welcomes submissions from established geographers, researchers, and other scholars. Many students who have gone on to graduate school in geography and related fields have seen their first professional publication appear in *The Bulletin*. To encourage student publication, Gamma Theta Upsilon awards an annual "Best Student Paper Award" with a two-hundred-dollar (USD) prize. Published articles are available free of charge in PDF form on the GTU website, as well as being indexed through EBSCO's databases and selectively abstracted in Current Geographical Publications of the American Geographical Society, Geo-Abstracts, and Sociological Abstracts. For questions about *The Geographical Bulletin*, including potential article submission, please visit the current journal website (https://digitalcommons.kennesaw.edu/thegeographicalbulletin/), the GTU website, or contact:

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E-mail: npullen@kennesaw.edu, mpatters@kennesaw.edu
THE GEOGRAPHICAL BULLETIN

May
2023

Vol. 64
No. 1

EDITOR

**Nancy Hoalst-Pullen**
Kennesaw State University
Kennesaw, GA

**Mark W. Patterson**
Kennesaw State University
Kennesaw, GA

EDITORIAL BOARD

**Stacey Brown Amilian**
Southern Illinois University Edwardsville
(SIUE)
Edwardsville, IL

**Mark D. Bjelland**
Calvin University
Grand Rapids, MI

**Jessie Clark**
University of Nevada – Reno
Reno, NV

**H. Jason Combs**
University of Nebraska-Kearney
Kearney, NE

**Tim Hawkins**
Shippensburg University
Shippensburg, PA

**Falguni Mukherjee**
Sam Houston State University
Huntsville, TX

**Nathanial Trumbull**
University of Connecticut
Storrs, CT

A Note from the Editors .................................................... 1

RESEARCH ARTICLES

A Seasonal Wind Climatology of The Great Lakes Region of The United States: 1990-2019
*By Anna Allen and Michael A. Davis* .................................... 5

Impacts And Perceptions Of Climate Variability On Shea (Vitellaria Paradoxa) And African Locust Bean (Parkia Biglobosa) In Burkina Faso
*By Joseph Yaméogo and Isidore Pawendkisgou Yanogo* .......................... 17

Heating Up at the Plate: Relationships between Temperature, Climate Type in Place of Birth, and MLB Batting Performance
*By Nathan M. Hitchens and Reuben J. Allen* .................................. 31

Spatial Patterns of Micromobility Ridership: A Multi-City Analysis
*By Mohammad Anwar Alattar and Mark Beecroft* ............................ 41

*The Geographical Bulletin* 64(1): 1-57

ISSN 2163-5900 © The Author(s).

The Geographical Bulletin © 2023 Gamma Theta Upsilon
# Gamma Theta Upsilon

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School of Earth, Environment and Society
Bowling Green State University
Bowling Green, OH 43403
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Department of Geography and Anthropology
University of Wisconsin – Eau Claire
Eau Claire, WI 54701
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Department of Environmental and Geosciences
Sam Houston State University
Huntsville, TX 77341
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Ohio Northern University
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### Co-EDITOR, THE GEOGRAPHICAL BULLETIN

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Department of Geography and Anthropology
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Kennesaw, GA 30144
Email: mpatters@kennesaw.edu
# Impacts And Perceptions Of Climate Variability On Shea
(Vitellaria paradoxa) And African Locust Bean
(Parkia biglobosa) In Burkina Faso

Joseph Yaméogo¹* and Isidore Pawendkisgou Yanogo¹

¹Department of Geography, Norbert ZONGO University, Koudougou, Burkina Faso

*Corresponding author: josephyameogo10@gmail.com

## ABSTRACT

This article examines manifestations of climate variability and its impacts on tree species (shea, African locust bean) in agricultural fields through an analysis of the opinion of the field managers. The Likert-type survey form was prepared for this purpose for the collection of primary data in the field. A sample of 110 households out of a total of 480 was selected, and secondary data (rainfall, temperature and wind) were also collected through Burkina Faso's national meteorological agency. Descriptive statistics were used to analyse the primary data collected. The study revealed that 90% of agroforestry parkland managers perceive climate variability through rainfall, temperature and wind. The meteorological data also showed trends in the variability of rainfall, temperature and wind between 1990 and 2020. They also observed that climate variability has an impact on shea and African locust through the disruption of the phenological phases (foliage, flowering, fruiting and fructification) of the trees, the death of young shea seedlings and stunted growth of shea, as well as the reduction in fruit productivity of both species.

**Keywords:** agroforestry parkland, climate variability, phenology, shea, African locust bean

## INTRODUCTION

The main production systems promoted by rural communities in West Africa are traditional agroforestry systems (Cook & Grut, 1989). These are set up by farmers who deliberately decide to leave tree species here and there for their personal needs. The result is a landscape of agroforestry parkland in the various countries of West Africa. In Burkina Faso, the agrarian landscape is dominated by agroforestry parkland, where species such as *Viltellaria paradoxa*, *Parkia biglobosa*, *Lannea microcarpa*, *Tamarindus indica* and *Adansonia digitata* are mainly present in agricultural fields (Yaméogo, et al. 2010). Since the droughts of the 1970s, these agroforestry parklands have deteriorated in density and age (Gijsbers, Kessler, Knevel 1994). Today, with the increase in climatic extremes (prolonged droughts, heavy rainfall, floods, increased heat) and biotic stresses (Coulibaly, et al. 2014), the dominant species in agroforestry parkland are experiencing phenological disturbances and a reduction in fruit production capacity

(*Ximenia americana*, *Balanites aegyptiaca*, *Vitellaria paradoxa*, *Boscis senegalensis*, *Detarium microcarpum*) (Gaisberger, et al. 2017; Lompo, Lykke, Lankoande 2018; Bondé, et al. 2019a). While local perceptions of the effects of climate variability on the phenological phase and productivity of tree species in agroforestry parks remain almost non-existent in West Africa (Avaligbé, Yabi, Bello 2021). Yet the importance of knowing how human populations perceive, interpret, and manage the impacts of climate change at the local scale enables effective resource management policies to be put in place (Da Silva, Da Silva, Albuquerque 2016; Da Silva, De Almeida Campus, Da Silva Oliveira 2020).

This study fills this gap by assessing the impacts of climate variability on tree species in agricultural fields through the opinions of rural people who manage trees. The main hypothesis is that managers (farmers) in agroforestry parkland perceive the manifestations of climate variability and its effects on the phenology and productivity of tree species (shea and African locust bean) in the commune of Nébiélianayou in
Burkina Faso. The main objective of this study is to analyse the manifestations of climatic variability and its impacts on tree species (shea, African locust bean) in the agroforestry park-land in the rural commune of Nébiélianayou. In particular, it aims to: examine the perceptions of climate variability by the respondents, analyse climate variability in the light of climatic data, and determine the effects of this climate variability on the phenology and fruit production of shea and African locust bean trees through the severity index or the strength of opinion of the respondents.

# THEORETICAL FRAMEWORK

## Shea and African Locust Bean: Two Tree Species
Emblematic of Agroforestry Parkland in Burkina
Faso

The shea tree is indigenous to sub-Saharan Africa (Azong-
nide, Issa, Houetchegnon 2019). It belongs to the Sapotaceae
family (Guira 1997). Four essential phases characterise the
phenology of the shea tree: foliation, flowering, fruiting and
leafing (Guira 1997). Average annual production varies be-
tween 400 and 1000 kg of kernels/tree and fruiting begins
at the age of 10 (Eyog Matig, Gandé, Dossou 2002). Fruit
abundance varies greatly from year to year and from tree to tree
(Guira & Zongo 2002). The shea fruit is very fleshy, sweet and
edible (Eyog Matig, Gandé, Dossou 2002). Burkina Faso has
over 280 million shea trees for an estimated potential produc-
tion of 1,247,295 tonnes of shea kernels per year, of which
the actual exploitation rate is only around 32%, or 404,412
tonnes (MEEVCC 2017). Shea provides income for the local
population and supports the country's economy (Sawadogo
2018). However, it is in decline due to human activities and
changing climatic conditions.

The African locust bean, which belongs to the Mimo-
saceae family, is widespread in the Sudanian savannahs of
sub-Saharan Africa (Lompo, et al. 2017). It is generally not
felled when open forests are cleared. Over the years, the
species has become ubiquitous in Burkina Faso's agricultural
fields. The African locust bean is a deciduous tree with a very
wide crown and can reach a height of 20 m (Lamien, et al.
2011). It can grow in a wide range of climatic conditions, in
environments where annual rainfall is between 600 and 1,500
mm and the dry season lasts between five and seven months
(Lamien, et al. 2011). The same authors also note that the
phenological phases are composed of foliation, flowering,
and fruiting. However, the start of flowering depends on the
amount of water in the soil (Lamien, et al. 2011).

Perception of Climate Variability and Its Effects
on Natural Resources: A Debated Reality

Climate change studies have relied heavily on instrumental
data, and as a result, local people's perceptions of the environ-
mental effects of climate variability are poorly understood or
quantified (Savo, et al. 2016). Thus, many studies have focused
on local perceptions of the impacts of climate variability on
water resources, food security (Poudel, et al. 2017; Thapa &
Hussain 2021), livestock (Debela, et al. 2015; Megersa, et
al. 2014), agriculture (Mavhura, Manyangadze, Aryal 2022),

forests (Dey, et al. 2017; Ofoegbu, et al. 2016), local environ-
ment (Appiah & Guodaar 2022).

However, the ability of individuals to assess the impacts of
climate change is debated, and this is due to the complexity
of climate risks which does not allow the public to perceive
them adequately (Etkin & Ho 2007). In addition, the majority
of individuals assess climate risks at an abstract and cognitive
level, which may lead to an underestimation of the perception
of the dangers of climate change on the local environment
(Menny, et al. 2011). The abstract nature of climate change
induced by psychological distance may also lead to biases in
the perception of climate variability and its effects (Van Lange
& Huckelba 2021). Thus, the assessment of environmental
problems induced by climate variability must be related to
the degree of perceived control (Michel-Guillou 2014), which
can only be done on the basis of the individual's presence in
the environment over several years or even decades, and give
the individual several response options on issues related to the
impacts of climate variability. This makes it easy to transcribe
the effects of climate variability on the local environment.
For this reason, studies (Masud, et al. 2016; Pickson & He
2021) are deploying more methods, including the severity
index, to capture the strength of people's opinions in order to
understand the perception of climate variability and its effects
on the local environment.

Severity Index Method or Strength of Opinion of
Respondents

Introduced by Al-Hazmi 1987 cited by Al-Hammad and
Assaf 1996, the index method has been most often used in
the field of evaluating the performance problems of construc-
tion companies (Al-Hammad 2000; Assaf & Al-Hejji 2006;
Mahamid 2016). Subsequently, this method is deployed in
environmental studies (Longe, Longe, Ukpebor 2009; Ogun-
niyi, Oladejo, Adepoju 2014; Masud, et al. 2017) to assess the
strength of respondents' opinions on environmental issues. It
is for this reason that Ferdushi, Ismail, Kamil 2019 note that
the index method is the strength of respondents' opinions.
In other words, the index method expresses the strength of
opinion of respondents. The latter consists of offering several
indicator options to respondents, who will rate them on a
Likert-type scale from 0 to 4 as follows: strongly disagree (0),
disagree (1), neutral (2), agree (3) and strongly agree (4) (Pick-
son & He 2021). The Likert scale is an important element of
this method. It allows respondents to choose several response
options, ranging from binary responses (agree or disagree) to
neutral responses (Somekh & Cathy 2005).

RESEARCH METHODOLOGY

The Study Area

The study area is located in Burkina Faso, in the central-west
region, in the commune of Nébiélianayou (Fig.1).

The municipality's climate is predominantly North Suda-
nese, with rainfall ranging from 600 to 900 mm per year. The
local landscape is made up of agroforestry parklands, domi-
nated by species such as shea and African locust bean (Fig. 2).
<figure><img src="image_2.png" /></figure>

Source: BNDT, 2014

Date: 20/09/2022

Author: YAMEOGO Joseph

<caption>Figure 1. Location of the study areas.</caption>

## Sampling Procedure and Sample Size

The choice of study areas was based on the high climatic variability and the presence of tree species such as shea and African locust in agricultural fields. As part of the study, 110 households managing agroforestry parkland were interviewed. In the commune, three villages were selected: Logo, Aziga and Pala. The distribution of respondents in the study villages was based on the security context (Burkina Faso has been facing terrorism since 2016). Table 1 shows the distribution of respondents by study village.

## Data Sources and Collection Methods

Primary and secondary data were collected. For primary data, an exploratory study was first carried out in October-November 2021 to select the six study villages and find out the total number of households with trees (shea, African locust bean) in their agricultural fields. Thereafter, the surveys were conducted between April 2022 and June 2022, i.e., three months, in the villages of Logo, Aziga, and Pala. The selection of respondents in the study villages was based on four criteria: (1) being able to describe the phenological phases of shea and

<figure><img src="image_3.png" /></figure>

Image: March 2022 (dry season period)

This photo shows an African locust bean in full view. In the background is a shea agroforestry parkland and houses in the village of Aziga.

<figure><img src="image_4.png" /></figure>

Image: July 2022 (period of the rainy season)

This photo shows an agroforestry parkland (maize + shea) in the village of Aziga.

<caption>Figure 2. The landscape of the study area. Plate photo taken by J. Yaméogo.</caption>
<table><caption>Table 1. Target populations sampled in the study sites</caption><thead><tr><th>Nébiélianayou rural municipality</th><th>Total number of households</th><th>Target population</th></tr></thead><tbody><tr><td>Study villages</td><td></td><td></td></tr><tr><td>Logo</td><td>149</td><td>35</td></tr><tr><td>Aziga</td><td>76</td><td>20</td></tr><tr><td>Pala</td><td>255</td><td>55</td></tr><tr><td>Total</td><td>480</td><td>110</td></tr></tbody></table>

Source: General Census of Population and Housing, 2019

African locust bean production; (2) having knowledge of the manifestations of climatic variability; (3) having shea and African locust bean in the agricultural fields; and finally (4) being 40 years of age or older. Primary data included managers' perceptions of climate variability, and the influences of climate variability on phenological phases and fruit production on shea and African locust bean. The study also used secondary data from scientific journals, downloaded from the Internet, and books from the libraries of University Aube Nouvelle and Norbert ZONGO University. Meteorological data (rainfall, temperature, wind) from the Boromo synoptic station were collected from Burkina Faso's National Meteorological Agency (BFNMA).

## Data Analysis Method

### Primary Data

Descriptive statistics such as sum, mean, percentage were used to present the climatic perceptions of tree managers in agricultural fields, and the calculation of the severity index (SI) whose formula is defined as follows (Al-Hammad & Asaaf 1996) :

$$ \text{IS}: \left( \frac{\sum_{i=0}^{4} b_i x_i}{4 \sum_{i=0}^{4} x_i} \right) \times 100 $$

Where, $b_i$ represents the index of a class, which is constant and indicates the weight given to the class; $x_i$ shows the response frequency, i.e., for $i=0,1,2,3,4$ Furthermore, $b_0, b_1, b_2, b_3, b_4$ denote the response frequencies corresponding to $x_0=0, x_1=1, x_2=2, x_3=3, x_4=4$.

The severity index was used to assess the intensity of the impact of climatic variability on the phenology and hence productivity of and African locust bean as perceived by rural populations in the study areas.

### Secondary Data

These are meteorological data from the Boromo station in the Mouhoun region of Burkina Faso. Data analysis was based on the Buishand statistical test and linear regression. The Buishand test is more sensitive to breaks in the middle of the time series (Wijngaard, Klein Tank, Konnen 2003). The Buishand procedure refers to the same model and assumptions as the

<table><caption>Table 2. Evaluation criteria for the severity index</caption><thead><tr><th>Criteria</th><th>Severity index</th></tr></thead><tbody><tr><td>X<sub>0</sub> = strongly disagree</td><td>0.00≤SI<12.5</td></tr><tr><td>X<sub>1</sub> = disagree</td><td>12.5≤SI<37.5</td></tr><tr><td>X<sub>2</sub> = neutral</td><td>37.5≤SI<62.5</td></tr><tr><td>X<sub>3</sub> = agree</td><td>62.5≤SI<87.5</td></tr><tr><td>X<sub>4</sub> = strongly agree</td><td>87.5≤SI<100</td></tr></tbody></table>

Source: Pickson & He, 2021

Lee and Heghinian approach (Traoré, et al. 2017). According to the authors, the assumption is that there is a uniform distribution of the position of the breakpoint t, the Buishand U-statistic is defined by the series of equation (Rosine, et al. 2015 cited by Traoré, et al. 2017):

$$ U = \frac{\sum_{k=1}^{N=1} (S_k / \sigma_x)^2}{N(N+1)} $$

Where the terms $S_k$ and $\sigma_x$ are the partial sum (equation (3)) and the standard deviation equation (4), respectively (Traoré, et al. 2017):

$$ S_k = \sum_{i=1}^{k} (X_i - \bar{X}) $$ $$ \sigma_x^2 = \frac{1}{N} \sum_{i=1}^{N} (X_i - \bar{X})^2 $$

The KHRONOSTAT software developed by the IRD (Institute of Research for Development, France) was used to carry out the Buishand test. Finally, the climate data (rainfall, temperature, and wind) were applied to Linear regression, which is a non-parametric test that was used to study trends in rainfall and temperature change data (Chepkoech, et al. 2017).

## RESULTS

### Tree Managers' Perceptions of Climate Variability

Local perceptions of climate change and variability are generally based on observations of the climatic variables that affect their lives (Weber 2010). To the question, “Do you know about climate variability?” over 80% of respondents in the three villages said they had a very good knowledge of climate variability. Variables such as rainfall, temperature and wind are usually used by households managing agroforestry parkland (Avaligbé, Yabi, Bello 2021). These variables were therefore selected for this study.

### Local Perceptions of Rainfall Variability

In the study areas, 92% of the respondents observed a decrease in annual rainfall. 88% of the respondents observed an increase
in rainfall variability and 84% of the respondents observed an increase in the frequency of droughts between 1990 and 2020. In addition, 93.92% of those questioned felt that the intensity of rainfall had increased, while 82.24% noted a decrease in the length of the rainy season over the last 30 years (1990-2020).

## Local Perceptions of Temperature Variability

95.54% of the managers of the agroforestry parklands believe that the average, minimum and maximum temperatures have increased over the last 30 years, while 5.45% believe that the opposite is true.

## Local Perceptions of Wind Trends

Two types of wind are encountered in Burkina Faso. These are the dry season wind, known as the Harmattan, and the rainy season wind, known as the Monsoon. The frequencies of the violence of the monsoon wind and the harmattan (accompanied by dust mists) in the dry season were used by the respondents to assess changes in the wind over the last few decades. In the commune of Nébiélianayou, 83.64% of respondents noted an increase in the violence of monsoon winds in the rainy season, compared with 16.36% in the dry season. As for dry season winds, 80% of managers have noticed an increase in harmattan speeds accompanied by dust mists. However, only 20% found harmattan speeds to be stable.

## Trend of Climate variability with Meteorological Data

### Rainfall Variability between 1990 and 2020

The Buishand statistical test revealed a break in the rainfall time series in 2007 (Fig. 3).

Fig. 3 shows that the rainfall data show an annual fluctuation, but with an increase in rainfall after the break (2007). Furthermore, Fig. 4 also shows that pre-season rainfall, the rainy season and post-season rainfall periods are subject to inter-annual variability between 1990 and 2020.

## Temperature Variability between 1990 and 2020

Fig. 5 shows that average temperatures follow a sawtooth evolution, with an increase between 1990 and 2020.

The trends in maximum (max) and minimum (min) temperatures are also increasing (Fig. 6).

Fig. 6 shows that maximum temperatures underwent a break in 2007, while minimum temperatures underwent two breaks, first in 2005 (rising) and a second in 2008 (falling). Overall, temperatures have increasing trends (maximum temperature (y=+0.0099x+35.359; minimum temperature (y=0.027x+21.682).

### Trend of wind evolution between 1990 and 2020

Fig. 7 shows a fluctuation of the wind between 1990 and 2020; however, the overall wind trend is decreasing (y= -0.0091x+19.269).

However, in the pre-season, which corresponds to the months of April and May, and incidentally the rainy months of June and July, the wind speed increases and varies between 0.9 m/s and 1.3 m/s (data BFNMA 1990-2020). This affects farmers, as the wind speed destroys crops and blows off tree flowers during the period (May-July). The end of the post-rainy season corresponds to a reduction in wind speed (varying between 0.6 m/s and 0.8 m/s) (data BFNMA 1990-2020).

<figure><img src="image_5.png" /><figcaption>Figure 3. Buishand test applied to rainfall data.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4. High variability in pre-rainy season, rainy season and post-rainy season periods.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 5. Trends in average temperatures.</figcaption></figure>

## Perceived impacts of Climate Variability on shea and African locust bean

### Perceived impacts of rainfall variability on shea and African locust bean

In the commune of Nébiélianayou, in the northern Sudanian zone, households report that rainfall variability has impacts on shea and African locust bean. Table 3 below summarises

the impacts and indicators associated with rainfall variability. The frequency of drought, shortening of the rainy season and violence of the rains result in the death of young shoots, rapid wilting of the species' leaves, stunted growth of young offspring (shea, African locust bean), stunted fruiting of both species and rapid growth of invasive tree species.

The phenological phases affected by this rainfall variability are leafing, flowering, and fruiting. The severity indices vary between 55% and 75%. For the respondents, the recurrence
<figure><img src="image_8.png" /><figcaption>Figure 6. Trends in maximum and minimum temperatures.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 7. Declining trend in wind speed between 1990 and 2020.</figcaption></figure>

of pockets of drought affects shea and African locust bean. The latter ranked stunted growth of young shoots (IS=75%), death of young shoots (IS=74%), stunted fruiting of trees (IS=69%), rapid growth of certain tree pests (IS=63.86%) as first, second, third and fourth respectively. This means that the recurrence of pockets of drought in shea, African locust bean on stunted growth of young offspring, death of young shoots, stunted fruiting of trees, rapid growth of some pest species. The wilting of leaves is poorly perceived by the respondents, hence an IS=57%, which means a neutral opinion.

As for the reduction of the rainy season, it causes the death of young shoots (IS=74%), stunted growth of young offshoots (IS=72.95%), stunted fruiting of trees (IS=70%) and rapid growth of some pest species (IS=64%). The wilting of tree leaves is not attributable to the reduction of the rainy season,

as the respondents could not give a clear answer to this question. Therefore, there IS=55% reflects a neutral opinion of their response.

For the violence of the rains, it is responsible for the delay in the growth of young shoots (IS=74%), then for the death of young shoots (IS=73%), then for the delay in the fruiting of trees (IS=69%), and finally for the rapid growth of certain harmful species (IS=64%). The delay in the growth of young shoots and the delay in fruiting led to a decrease in the productivity of both species (shea and African locust bean). In addition, the decline in tree productivity was attributed to the invasive shea tree species, which has increased over the past decades in the area. According to the respondents, this is due to the increase in rainfall variability, which leads to a greater migration of birds to the area that leaves the seeds of these
species on the African locust bean and especially on the shea. Subsequently, these invasive species grow on the shea tree or the African locust bean, causing the growth of the attacked species to slow down.

## Perceived impacts of the rising temperature trend on shea, African locust bean

Average, minimum and maximum temperatures have been rising in recent decades. According to the respondents, the increase in all aspects of temperature has an effect on the phenology and productivity of species such as shea and African locust bean that dominate the agricultural fields in this area. Table 4 shows that the simultaneous increase in average, minimum and maximum temperatures affect the phenological phases of the trees such as leafing, flowering, and fruiting. The death of young shoots, the increase in the frequency of tree pests and the decrease in tree productivity were also mentioned by the respondents. Opinion strengths (severity index) ranged from 72% to 76%. The respondents ranked first the delay in fruiting

(IS=76%), the decrease in fruit productivity (IS=76%), then the decrease in fruit set (IS=73.4%), the decrease in flowering time (IS=73), and finally the frequency of tree pests and death of young shoots.

For the respondents, the disruption of the phenological phases is linked to the decline in productivity of shea and African locust bean. Fig.8 summarises the process of phenological phase disruption and the decline in shea and African locust bean productivity as observed by the respondents.

### Perceived impacts of wind variability on shea, African locust bean

In recent years, wind speeds have increased. In addition, dry-season winds are frequently accompanied by dust mists. These various changes in wind speed over the decades have had an impact on the phenology and productivity of species such as shea and African locust bean. Table 5 shows that the effects of this change in wind speed led to disturbances in the phenological phases (foliage, flowering, fruit set, and fructifica-

<table><caption>Table 3. Impacts of rainfall variability on the phenology and productivity of shea and African locust bean in the municipality of Nébiélianayou.</caption><thead><tr><th rowspan="2">Nébiélianayou</th><th rowspan="2">Productivity</th><th colspan="3">Phenological phase chosen by the respondents</th><th colspan="6">Frequency of drought pockets</th><th rowspan="2">Severity index</th><th rowspan="2">Rank</th></tr><tr><th>Tree foliage</th><th>Flowering</th><th>Fruiting</th><th colspan="5">Frequency analysis</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th>SDA (0)</th><th>DA (1)</th><th>N (2)</th><th>A (3)</th><th>SA (4)</th><th>Total</th><th></th><th></th></tr></thead><tbody><tr><td>Death of young shoots</td><td></td><td></td><td></td><td></td><td>0</td><td>2</td><td>14</td><td>279</td><td>32</td><td>327</td><td>74%</td><td>2<sup>nd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0.61%</td><td>4.28%</td><td>85.32%</td><td>9.79%</td><td>100%</td><td></td><td></td></tr><tr><td>Tree leaf wilt</td><td></td><td></td><td></td><td></td><td>0</td><td>18</td><td>44</td><td>183</td><td>4</td><td>249</td><td>57%</td><td>4<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>7.23%</td><td>17.67%</td><td>73.49%</td><td>1.61%</td><td>100%</td><td></td><td></td></tr><tr><td>Stunting of young shoots</td><td></td><td></td><td></td><td></td><td>0</td><td>4</td><td>8</td><td>276</td><td>40</td><td>328</td><td>75%</td><td>1<sup>st</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>1.22%</td><td>2.44%</td><td>84.15%</td><td>12.20%</td><td>100%</td><td></td><td></td></tr><tr><td>Delayed fruiting of trees</td><td></td><td></td><td></td><td></td><td>0</td><td>9</td><td>30</td><td>210</td><td>56</td><td>305</td><td>69%</td><td>3<sup>rd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>2.95%</td><td>9.84%</td><td>68.85%</td><td>18.36%</td><td>100%</td><td></td><td></td></tr><tr><td>Rapid growth of some pest species</td><td></td><td></td><td></td><td></td><td>0</td><td>18</td><td>18</td><td>201</td><td>44</td><td>281</td><td>63.86%</td><td>5<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td>0%</td><td>6.4%</td><td>6.4%</td><td>71.5%</td><td>15.66%</td><td>100%</td><td></td><td></td></tr><tr><th colspan="13">Nebielanayou</th></tr><tr><th colspan="13">Reduction in the length of the rainy season</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th colspan="5">Frequency analysis</th><th rowspan="2">Severity index</th><th rowspan="2">Rank</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th>FDA (0)</th><th>DA (1)</th><th>N (2)</th><th>A (3)</th><th>FA (4)</th><th>Total</th></tr><tr><td>Death of young shoots</td><td></td><td></td><td></td><td></td><td>0</td><td>5</td><td>8</td><td>279</td><td>32</td><td>324</td><td>74%</td><td>1<sup>st</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td>0%</td><td>1.54%</td><td>2.47%</td><td>86.11%</td><td>9.88%</td><td>100%</td><td></td><td></td></tr><tr><td>Tree leaf wilt</td><td></td><td></td><td></td><td></td><td>0</td><td>18</td><td>38</td><td>183</td><td>4</td><td>243</td><td>55%</td><td>5<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td>0%</td><td>7.41%</td><td>15.64%</td><td>75.31%</td><td>1.65%</td><td>100%</td><td></td><td></td></tr><tr><td>Stunting of young shoots</td><td></td><td></td><td></td><td></td><td>0</td><td>2</td><td>20</td><td>255</td><td>44</td><td>321</td><td>72.95%</td><td>1<sup>st</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td>0%</td><td>0.62%</td><td>6.23%</td><td>79.44%</td><td>13.71%</td><td>100%</td><td></td><td></td></tr><tr><td>Delayed fruiting of trees</td><td></td><td></td><td></td><td></td><td>0</td><td>9</td><td>28</td><td>213</td><td>56</td><td>306</td><td>70%</td><td>3<sup>rd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td>0</td><td>2.94%</td><td>9.15%</td><td>69.61%</td><td>18.30%</td><td>100%</td><td></td><td></td></tr><tr><td>Rapid growth of some pest species</td><td></td><td></td><td></td><td></td><td>0</td><td>18</td><td>18</td><td>201</td><td>44</td><td>281</td><td>64%</td><td>4<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td>0%</td><td>6.41%</td><td>6.41%</td><td>71.53%</td><td>15.66%</td><td>100%</td><td></td><td></td></tr><tr><th colspan="13">North Sudanese (Nébiélianayou)</th></tr><tr><th colspan="13">Violence of the rains</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th colspan="5">Frequency analysis</th><th rowspan="2">Severity index</th><th rowspan="2">Rank</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th>SDA (0)</th><th>DA (1)</th><th>N (2)</th><th>A (3)</th><th>FA (4)</th><th>Total</th></tr><tr><td>Death of young shoots</td><td></td><td></td><td></td><td></td><td>0</td><td>6</td><td>6</td><td>279</td><td>32</td><td>323</td><td>73%</td><td>2<sup>nd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>1.86%</td><td>1.86%</td><td>86.4%</td><td>9.91%</td><td>100%</td><td></td><td></td></tr><tr><td>Tree leaf wilt</td><td></td><td></td><td></td><td></td><td>0</td><td>18</td><td>38</td><td>183</td><td>4</td><td>243</td><td>55%</td><td>5<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>7.41%</td><td>15.64%</td><td>75.31%</td><td>1.65%</td><td>100%</td><td></td><td></td></tr><tr><td>Stunting of young shoots</td><td></td><td></td><td></td><td></td><td>0</td><td>4</td><td>14</td><td>261</td><td>48</td><td>327</td><td>74%</td><td>1<sup>st</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>1.22%</td><td>4.28%</td><td>79.82%</td><td>14.68%</td><td>100%</td><td></td><td></td></tr><tr><td>Delayed fruiting of trees</td><td></td><td></td><td></td><td></td><td>0</td><td>9</td><td>30</td><td>210</td><td>56</td><td>305</td><td>69%</td><td>3<sup>rd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>2.95%</td><td>9.84%</td><td>68.85%</td><td>18.36%</td><td>100%</td><td></td><td></td></tr><tr><td>Rapid growth of some pest species</td><td></td><td></td><td></td><td></td><td>5</td><td>48</td><td>19</td><td>27</td><td>11</td><td>110</td><td>64%</td><td>4<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>43.64%</td><td>17.27%</td><td>24.55%</td><td>10%</td><td>100%</td><td></td><td></td></tr></tbody></table>

Source: Field surveys, April 2022 and June 2022;

Phenological phase selected by respondents;

No observation of phenological phase by respondents

Decline in productivity or factors leading to decline in productivity of trees (shea, African locust bean); SDA: Strongly Disagree; DA: Disagree; N: Neutral; A: Agree; SA: Strongly Agree;

PR: Percent of respondents
tion) of species such as shea and black locust. Thus, abortion of flowering, decline in fruit set, and fruit set have higher severity indices. Abortion of flowering tops the list with an IS of 90.87%, followed by the decline of fruit set, with an IS of 87.98%. The decrease in fruit set came third, with an SI of 86.54%. Finally, the decline in productivity comes fifth, with an index of 85.10%. The drop in productivity of the African carob tree and especially the shea tree is the result of a combination of a drop in flowering, fruit set, and fructification.

According to 70% of respondents, the hot harmattan winds accompanied by dust mists during the flowering period dry out the flowers of both species. The dust mists, which carry sand, then stick to the flowers of both species, leading to a drop in fruit set. As a result, fructification and productivity are reduced.

# DISCUSSION

## Perceptions of Climate Variability and Weather Data: A Convergence of Trends with a Number of Limitations

The present study revealed that farmers managing agro-forestry parklands in the three villages studied are aware of climate variability between 1990 and 2020. This is reflected in their perception of rainfall variability, temperature increase (minimum, maximum, average) and wind increase (harmattan and/or monsoon). These different perceptions are similar to meteorological data from 1990 to 2020. However, there is a discrepancy between perceptions and meteorological data in the areas studied. This concerns the annual quantities of water and the steady increase in wind speed. Households managing agroforestry have difficulty remembering annual water quantities at any given time because of the passage of time (30 years). They therefore focus on the appearance of pockets of drought during this period and conclude that annual rainfall totals are decreasing. Similar findings such as Limantol, et al. (2016) that

89.5% of farmers surveyed wrongly perceive that rainfall has decreased over the years (1972-2012) in the Vea watershed in Ghana. Simelton, et al. (2013), in southern Africa (Botswana, Malawi) also note a trend mismatch between rainfall data and local perceptions of rainfall unpredictability at the start of the rainy season. The discrepancy between the two types of knowledge (scientific and local perceptions) could be explained by the fact that scientific knowledge follows certain rigorous procedures in its production process, whereas local knowledge is unregulated, random or unorganized, irrelevant definitions of meteorological indicators (Tume, Kimengsi, Fogwe 2019; Dickinson, et al. 2017).

## Impact of Climate Variability on Tree Species in Agricultural Fields

The study found that tree managers in agricultural fields believe that the impact of climate variability results from the combination of climatic variables such as rainfall, temperature, and wind. Each variable contributes in one way or another to influence the phenology and productivity of shea and African locust bean. For example, rainfall variability causes death or stunting of young shea shoots, wilting of tree leaves, and delays in tree fruiting. Temperature and wind cause a decrease in flowering, fruit set, and fruiting. This inevitably leads to a decline in shea and African locust bean productivity in the three study villages. Several other studies in Burkina Faso and West Africa corroborate the results of the study. Indeed, in Dano, in the southwest of Burkina Faso, the occurrence of winds in recent years has led to increased dust mists that cause fruit trees such as shea, African locust bean, and mango to die back (Sanfo, et al. 2014). In the Centre-West of Burkina Faso, notably in the province of Sissili, managers (15.50% of respondents) of shea parks also acknowledge that the strength and speed of the winds affect the ability of the trees to retain their flowers and eventually their fruit (Elias 2015). In northern Burkina Faso, in the village of Sillia, the causes of tree degradation in agricultural

<figure><img src="image_10.png" /><figcaption>Figure 8. Link between phenology disruption and reduced productivity of shea, African locust bean. Source: Field surveys, April-June 2022</figcaption></figure>
<table><caption>Table 4. Impact of rising temperatures on the phenology and productivity of species (shea, African locust bean) in the municipality of Nébiélianayou.</caption><thead><tr><th rowspan="2">North Sudanese (Nébiélianayou)</th><th rowspan="2">Productivity</th><th colspan="4">Phenological phase chosen by the respondents</th><th colspan="5">Temperature frequency (average, minimum and maximum)</th><th rowspan="2">Total</th><th rowspan="2">Severity index</th><th rowspan="2">Rank</th></tr><tr><th>Tree foliage</th><th>Flowering</th><th>Fruit set</th><th>Fruiting</th><th>SDA (0)</th><th>DA (1)</th><th>N (2)</th><th>A (3)</th><th>SA (4)</th></tr></thead><tbody><tr><td>Death of young shea shoots</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>2</td><td>40</td><td>210</td><td>68</td><td>320</td><td>72.73%</td><td>5<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>0.62%</td><td>12.5%</td><td>65.62%</td><td>21.25%</td><td>100%</td><td></td><td></td></tr><tr><td>Frequency of parasites on trees</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>6</td><td>16</td><td>240</td><td>56</td><td>318</td><td>72%</td><td>4<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1.89%</td><td>5.03%</td><td>75.47%</td><td>17.61%</td><td>100%</td><td></td><td></td></tr><tr><td>Decrease in flowering time</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>6</td><td>18</td><td>249</td><td>48</td><td>321</td><td>73%</td><td>3<sup>rd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>1.87%</td><td>5.61%</td><td>77.57%</td><td>14.95%</td><td>100%</td><td></td><td></td></tr><tr><td>Decreased fruit set</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>8</td><td>14</td><td>237</td><td>64</td><td>323</td><td>73.4%</td><td>2<sup>nd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2.48%</td><td>4.33%</td><td>73.37%</td><td>19.81%</td><td>100%</td><td></td><td></td></tr><tr><td>Delayed fruiting of trees</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>10</td><td>10</td><td>195</td><td>120</td><td>335</td><td>76%</td><td>1<sup>st</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2.99%</td><td>2.99%</td><td>58.21%</td><td>35.82%</td><td>100%</td><td></td><td></td></tr></tbody></table>

Source: Field surveys, April 2022 and June 2022;

Phenological phase selected by respondents ;

No observation of phenological phase by respondents

Decline in productivity or factors leading to decline in productivity of trees (shea, African locust bean); SDA: Strongly Disagree; DA: Disagree; N: Neutral; A: Agree; SA:

Strongly Agree; PR: Percent of respondents

<table><caption>Table 5. Impacts of wind trends (mists of dust, harmattan winds, monsoon winds) on phenology and productivity.</caption><thead><tr><th rowspan="2">Nébiélianayou</th><th rowspan="2">Productivity</th><th colspan="4">Phenological phase chosen by the respondents</th><th colspan="5">Wind frequency (Dust vortex, harmattan, monsoon)</th><th rowspan="2">Total</th><th rowspan="2">Severity index</th><th rowspan="2">Rank</th></tr><tr><th>Tree foliage</th><th>Flowering</th><th>Fruit set</th><th>Fruiting</th><th>SDA (0)</th><th>DA (1)</th><th>N (2)</th><th>A (3)</th><th>SA (4)</th></tr></thead><tbody><tr><td>Death of young shoots</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>16</td><td>50</td><td>120</td><td>0</td><td>186</td><td>44.71%</td><td>6<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>8.6%</td><td>26.88%</td><td>64.52%</td><td>0%</td><td>100%</td><td></td><td></td></tr><tr><td>Decline in tree productivity</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>2</td><td>6</td><td>150</td><td>196</td><td>354</td><td>85.1%</td><td>4<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.56%</td><td>1.69%</td><td>42.37%</td><td>55.37%</td><td>100</td><td></td><td></td></tr><tr><td>Frequency of tree pests</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>23</td><td>100</td><td>36</td><td>4</td><td>163</td><td>39.2%</td><td>5<sup>th</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td></td><td>14.11%</td><td>61.35%</td><td>22.09%</td><td>2.45%</td><td>100%</td><td></td><td></td></tr><tr><td>Aborted flowering</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>2</td><td>4</td><td>144</td><td>228</td><td>378</td><td>90.87%</td><td>1<sup>st</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.53%</td><td>1.06%</td><td>38.10%</td><td>60.32%</td><td>100%</td><td></td><td></td></tr><tr><td>Decrease in fruit set</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>1</td><td>2</td><td>135</td><td>228</td><td>366</td><td>87.98%</td><td>2<sup>nd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.27%</td><td>0.55%</td><td>36.89%</td><td>62.30%</td><td>100%</td><td></td><td></td></tr><tr><td>Aborted tree fruiting</td><td></td><td></td><td></td><td></td><td></td><td>0</td><td>2</td><td>6</td><td>132</td><td>220</td><td>360</td><td>86.54%</td><td>3<sup>rd</sup></td></tr><tr><td>PR</td><td></td><td></td><td></td><td></td><td></td><td>0.56%</td><td>1.67%</td><td>36.67%</td><td>61.11%</td><td>100%</td><td></td><td></td><td></td></tr></tbody></table>

Source: Field surveys, April 2022 and June 2022;

Phenological phase selected by respondents ;

No observation of phenological phase by respondents

Decline in productivity or factors leading to decline in productivity of trees (shea, African locust bean); SDA: Strongly Disagree; DA: Disagree; N: Neutral; A: Agree; SA: Strongly Agree; PR: Percent of respondents
fields are due to rainfall deficit (56.86% of respondents) and drought (29.41% of respondents) (Ouédraogo, et al. 2017). Other studies, such as that by Lamien, et al. 2006 in Burkina Faso, show that parasitic plants have no significant impact on shea flowering. The difference with this result lies in the fact that the study was carried out on a few shea branches (Lamien, et al. 2006), whereas our study is based on shea trees with all branches affected by parasitic plants. As a result, a drop in flowering was observed, and consequently a decline in fruit set and fruiting of the shea tree in the study villages.

In southern Mali, early cessation of rains and frequent droughts can affect water availability, which affects the flowering and fruiting phases of shea (Sanogo, Sanogo, Ba 2016). Okullo, Hall, Obua (2004) in Uganda, Glèlè Kakaï, et al. (2011) in Benin, Burkina Faso (Bondé, et al. 2019b; Ouédraogo, et al. 2020) concur that shea production is strongly correlated with the climatic conditions of the locality where the species is found. In Niger State, Nigeria, several species (Adansonia digitata, Vitellaria paradoxa, Parkia biglobosa, Tamarindus indica, and Vitex doniana) found in agricultural fields are affected by climate variability/change. The main impacts of climate change on these species are a reduction in species production (58.21% of respondents) and change in species spatial distribution (12.97% of respondents) (Allakonon, Guidigan, Belarmain 2021). Species extinction, premature or early fruiting, and tree mortality were also mentioned as impacts by 5.67%, 3.17%, and 1.73% of respondents respectively (Al-lakonon, Guidigan, Belarmain 2021). In northern Ghana, it is the species (shea, African locust bean) present in agricultural fields that are vulnerable to climate variability, leading to reduced fruit production and yield (Derbile, Kanlisi, Frederick 2022). Nyon, Adesina, Osman (2007) further point out that this situation is the result of ecological stresses induced by increasing temperatures and decreasing precipitation that alter the functioning of ecological systems, especially in terms of growth and development.

## CONCLUSIONS

The study revealed that the study area is subject to climatic variability, which is also perceived by agroforestry parkland managers. This perception is based on climatic variables such as rainfall, temperature, and wind. These climatic variables, because they vary over time and increase, have a negative impact on the phenological phases and productivity of shea and African locust bean. It is therefore important that the issue of climatic variability and its effects on the environment should no longer be the sole preserve of the so-called 'hard' sciences but should also be approached from the angle of perception, to capture people's view of the phenomenon and its impact. Following this study, it would be useful for managers to take an interest in other wild tree species, such as Lannea microcarpa, and Gardenia erubescens, to diversify their sources of income. As for the local, provincial, and regional authorities, they must carry out development initiatives in these villages to prevent the population from sinking further into a spiral of permanent vulnerability.

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Joseph Yaméogo is a PhD student at the Norbert ZONGO University in Koudougou, Burkina Faso. His research interests are climatology and environmental geography.

Isidore Pawendkisgou Yanogo is an associate professor of geography in the geography department of the Norbert ZONGO University in Koudougou, Burkina Faso. His research focuses on themes related to geography: Spaces, Societies and Regional Planning.

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