woody vegetation characterization in yilou, burkina faso
TRANSCRIPT
2015
Wageningen UR, Farming Systems Ecology Chairgroup
Droevendaalsesteeg 1 – 6708 PB Wageningen - The Netherlands
Woody vegetation characterization
in Yilou, Burkina Faso
Woody species inventories in farmer’s
fields, Piliostigma reticulatum fertility
island description.
Author: Timothée Cheriere, Organic Agriculture master student
Coordination : Georges Félix, PhD student at Farming System Ecology Chairgroup Johannes Scholberg, assistant professor and researcher at FSE
Bilibilfou, liuula maanda toko.
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Acknowledgement
Being part of my study program of organic agriculture, this MSc thesis played an important role in my
understanding of the principles of agro-ecology, and was an important experience to know myself
better. All this would not have been possible without the following people.
I want to thank:
- For his time and advices, Johannes Scholberg, assistant professor at Farming System
Ecology chairgroup, who was my supervisor and a precious help during my research work
- For his advices and expertise, Laurent Cournac, researcher at IRD (Institut de Recherche
et Développement)
- For his kindness, time and knowledge, Jean-Marie Douzet from CIRAD (Centre de
coopération international en recherche agronomique pour le développement)
- Yilou’s inhabitant for their kindness and welcome in the village
Special thanks to
- Jérôme, Jean, Joseph, Parfait and Fidel for their time, happiness, involvement in the
project and all the knowledge they share with me during our discussions.
- Georges Félix, Phd student at Farming System Ecology group, without whom this
incredible life experience would not have been possible, for his time, happiness, energy,
advices, support and his respect.
Barka woussogo. Wênd na kô-d panga!
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Executive summary
Agriculture in Burkina Faso faces several challenges, land is a scarce resource, fallow time
has been reduced if not suppressed, animal pressure increases and climate is changing.
Furthermore, the growing urban population requires an increasing amount of food. In Yilou,
a village in sudano-sahelian part of Burkina Faso, the project WASSA (Woody Amendment
for Sudano-Sahelian Agriculture) aims to describe the use of woody resources in the area
and more particularly the use of Piliostigma reticulatum, an evergreen shrub developing
during the dry season. The present work focuses of woody resources in farmer’s fields and
the description of Piliostigma reticulatum’s fertility island in cropped fields.
Detailed inventory of woody biomass available at field scale were carried out in 25 couple of
“Best” and “Worst” fields, classified as such by the farmers in interviews. Several parameters
were measured (height, crown diameters, basal circumference, diameter at breast height)
and specie name and sanitary observation were recorded. Piliostigma reticulatum and
Guiera senegalensis are the dominant species in terms of occurrence in fields and by their
number in the given fields. This lead to think that mulching soils with Piliostigma reticulatum
leaves was largely adopted by farmers of the village. However, no significant difference in
management practices of shrubs and trees could be highlighted between “Best” and “Worst”
fields. There are numerous other factors to explain the differences in use of the woody
resources, most of the time not controllable by the farmer, i.e. animal grazing, tree products
collection by villagers…
Fertility islands occurring at the bottom of shrubs like Guiera senegalensis and Piliostigma
reticulatum, by accumulation of organic matter and sediments, are known to be richer in
fertile elements than the open field. Nonetheless, descriptive studies were carried out in
fallows. A microprofilometer inspired by Bochet et al. (2000) was built to physically measure
the height difference existing across four axis (North-South, North east-South west, East-
West and South east-North west). Fertility island around Piliostigma reticulatum in cultivated
fields was shown to exist, with an average soil elevation between the open field and the
fertility island of 3.5 cm. Fertility island profiles were extremely diverse from almost not
existing to Gauss like curve. Also, no clear relation between wind direction or slope and the
shape of the fertility island could be found. This is probably due to Zaï formation or
ploughing practiced by farmers. Further investigation are needed to describe fertility change
due to the islands in cropped fields.
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Table of Contents Introduction ............................................................................................................................................. 4
Research scope ........................................................................................................................................ 6
Material and method .............................................................................................................................. 7
1. Experimental site ......................................................................................................................... 7
2. Woody species diversity and biomass inventory ........................................................................ 7
3. Fertility island characterization ................................................................................................... 8
A. Determination of the fertility island shape ............................................................................. 8
B. Integral value calculation ...................................................................................................... 10
C. Approximation of the height of fertility island ...................................................................... 11
D. Soil volume approximation.................................................................................................... 11
Inventory results analysis ...................................................................................................................... 12
1. Species diversity ........................................................................................................................ 12
2. Representation of species within fields .................................................................................... 14
3. Comparison between “Best” and “Worst” fields woody vegetation ........................................ 16
4. Farmer management ................................................................................................................. 17
5. Discussion of inventory results .................................................................................................. 19
Determination of fertility island shape ................................................................................................. 21
1. Visual analysis ............................................................................................................................ 21
2. Integral analysis ......................................................................................................................... 22
3. Discussion for microtopography ............................................................................................... 26
General Discussion ................................................................................................................................ 28
Conclusion ............................................................................................................................................. 29
Bibliography ........................................................................................................................................... 30
Table of figures ...................................................................................................................................... 32
Appendix ................................................................................................................................................ 33
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Introduction Burkina Faso is experiencing a remarkable population growth and during the last fifty years,
population tripled (FAOSTAT, 2014). Moreover, according to FAO predictions, population will grow
from 17.5 million in 2014 to approximately 40 million by 2050. One of the key issues for sub-Saharan
farmers is to sustain population growth and to produce sufficient food surpluses to feed the growing
urban population. Even if theoretically worldwide food production is enough to feed the global
human population, studies have shown that food production ideally should occur where it is being
consumed (Tscharntke et al. 2012). Therefore, Burkina Faso needs to improve its agricultural
productions by increasing its food supply in order to feed its population and increment its food
sovereignty.
With scarce rainfalls and limited resources, farmers have mainly increased food production through
increase of cropped area (Barbier et al. 2009; FAOSTAT, 2014). Because of population growth, land is
becoming a scarce resource. As a consequence farmers seek methods to enhance yields in their fields
to increase overall food production but face several challenges. When possible, they used more of
external inputs (i.e. NPK, pesticides) and adapted their farming system and introduced enhanced
agronomic practices (i.e. contour bunds, tillage, different varieties and species…). Marginal lands,
considered less fertile, were then cropped and fallow time traditionally used to restore soil
properties were reduced (Vierich & Stoop 1990; Barbier et al. 2009; Bationo et al. 2007; Roose et al.
1999).
A more intensive use of lands through management practices such as crop residue grazing by
livestock, use of less fertile lands and reduction of fallow time may lead to soil properties
degradation (Tittonell & Giller 2013; Bationo et al. 2007). Tittonell and Giller (2012) suggested that
with proper agronomic management of crop yield in Africa may be increased even with the limited
resources that are locally available. Numerous papers show that yields can be increased via
application of organic amendments and inorganic fertilizers (Yelemou et al. 2014; Bationo et al.
2007). Moreover, there is a clear consensus that one of the most important points to boost yields is
to restore inherent soil fertility (Bationo et al. 2007; Roose et al. 1999; Tittonell et al. 2012; Lahmar et
al. 2012). Organic matter in the soil is a prevailing element for aggregate stability. Aggregate
improves soil resistance to erosion and degradation by rain impact and organic matter contained in
this aggregate is an important source of nutrients. However, most farmers are relatively poor and
capital is not available for them so they cannot invest in external inputs while land and labour
resources at times may also be limited. In most cases inorganic fertilizers are considered as too
expensive or it may not be readily available (Barbier et al. 2009). As farmers cannot invest in
production inputs, they produce only few surpluses which could be sold in the market and therefore
are locked into so called ‘poverty trap’ (Tittonell & Giller 2013). Although they cannot invest in
external inputs to improve their soils’ quality and therefore their production, Scherr (2000) proposed
that with adapted research, poor farmers could improve their income through increased production
while also sustaining and/or enhancing land productivity.
In sub-Saharan West Africa, soil aggradation, as opposed to soil degradation, has been proposed as a
viable strategy to restore soil properties and increase yields. In Burkina Faso, the practice of the Zaï
has been reintroduced, from the Yatenga region in the 1980’s, after the drought of the 1970’s to
improve productivity of highly degraded lands which are locally called Zipellé. This technique consists
of digging micro-basins in the soil, surrounded by a small mound which may be amended with
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organic material. This micro-relief in the landscape will trap dusts and Aeolian-based erosion of crop
residues and fertile top soil while during the rainy season, the holes effectively capture and retain
run offs thereby concentrate water and nutrients in the root zone. Organic matter (manure, leaves) is
routinely applied at the bottom of the pits, which attracts termites that in turn contribute to
enhancing soil porosity, which improve soil water infiltration, retention and over crop water supply
(Roose et al. 1999).
Often crop seeds are planted in the vicinity of the applied manure in the Zaï hole which enhances
nutrient use efficiency as well. Woody shrubs growing after practicing Zaï such as Piliostigma
reticulatum (Roose et al. 1999), are kept by farmers for forage production at the end of the dry
season, medicinal use and other traditional practices or soil application to improve soil quality
(Yelemou et al. 2007). It is also a common practice for farmers to use Piliostigma reticulatum leaves
to cover highly degraded parts of the fields to improve inherent soil fertility and biological activity
and in general this does not conflict with other uses such as thicker branches as firewood or use of
pods for livestock (Yelemou et al. 2014; Yelemou et al. 2007). P. reticulatum thus can be an asset and
readily available on-farm renewable resource for resource-challenged farmers in Burkina Faso.
Indeed, field observations and research studies have shown that presence of shrubs have a positive
impact on soil fertility within a certain radius and this leads to the formation of the so called ‘fertility
islands’. However, the component contributing to this increased fertility such as litter, roots or
capture of dusts and organic debris carried by the wind are not well known (Lahmar and Yacouba,
2012). If farmers want to effectively manage this resource, it is necessary to have a better
understanding how much leafy biomass would be available at the field scale. This in order to better
manage and allocate this resource across different potential uses including its use as a soil for soil
amendment.
The use of Zaï combined with Piliostigma reticulatum as a soil mulch can play a central role in soil
aggradation in Burkina Faso as they complement and reinforce each other. Therefore, it is necessary
to investigate the influence of the presence of shrubs in the fields on soil quality and to elucidate the
rational farmers have for retaining and managing the shrubs and how they may contribute to
enhancing farming systems efficiency. Also, it is important to investigate shrub management
techniques within a specific farm context and see if techniques are implemented in a standard
manner or evolve over time based on specific settings and demands. Such understanding may
facilitate the adoption of this soil conservation technique to other social, economic and climatic
environments (Lahmar et al. 2012; Roose et al. 1999; Scherr 2000; Tittonell et al. 2012). An important
element therefore, is to better understand the composition of species and the available Piliostigma
reticulatum’s biomass in agricultural fields and to characterize its effects on soil properties. Linking
this data to farmers’ practises will contribute to the understanding of the underlying motivations and
reasons for preservation of woody vegetation in fields to enhance yields.
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Research scope
The present master thesis focuses on two distinct subjects exploring relation between Piliostigma
reticulatum and soil fertility. Data collection took place in Yilou, Bam province, approximately 80
kilometers north from Ouagadougou.
First there will be a work on the relation at the farm scale, exploring the possible link between field
quality perception by the farmer and the presence of Piliostigma reticulatum through the following
research question:
Is there a link between the presence woody vegetation (trees and shrubs) and farmers’
perception of soil quality and crop productivity?
Hypothesis: the fields perceived as poor by farmers have more woody vegetation due to the
necessity to restore soil properties to increase soil productivity.
The second research question will focus the influence of the shrub on soil fertility, particularly its
aerosols capture abilities.
What is the influence of the dust trapping constituted by Piliostigma reticulatum on soil
fertility?
Hypothesis: Piliostigma reticulatum retain and concentrate soil fertility at the base of the
shrub which constitute a fertility island and enhances crop productivity around the shrub.
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Material and method
1. Experimental site Yilou (13°1′N 1°33′W) is a village of approximately 3500 inhabitants, located in Guibaré departement
(Bam province), 80km North of Ouagadougou. It is characterized by Sudano-Sahelian climate, with
annual rain-fall of 400 to 600 mm concentrated during the rainy season, usually between June and
October. Lixisols are dominant in Yilou (Tittonell et al. 2012). This type of soils is characterized by a
lower clay content in the topsoil than in subsoil and, when degraded, regenerate very slowly (Fao
2006). Millet and sorghum are the main food crops, intercropped with cowpea and peanuts (pers.
obs.). Free range livestock is common, managed in herd or in small groups of few animals. Market
access is ensured through national road 22, which crosses the village (Tittonell et al. 2012). Also,
Yilou is, from the words of Jerome Ouedraogo, the main agricultural market of the department. In
addition to cereals, during the dry season and partly during the rainy season, some inhabitants of
Yilou produce commercial tomatoes, eggplant, cucumber and a number of horticultural crops on the
border of the Nakambé river, located South of the village.
2. Woody species diversity and biomass inventory Woody vegetation in farmers’ fields have an influence on the field productivity [reference], and
perceived impact of the woody vegetation on the field properties have consequences on farmer’s
management of this vegetation (Leenders et al. 2005). In order to explore existing relations between
presence of woody vegetation on fields and productivity perception, vegetation inventories were
conducted on 60 plots. Sample selection was based on plot-level surveys carried out by Georges Félix
in 2014. Farmers were asked to show the two most extreme fields of their farming system, the one
were production was perceived “best” and the one where production is usually perceived as “worst”.
Geographical location, rotations, soil types and management operations were recorded in 2014.
Vegetation inventory was therefore done in this study on coupled “best” and “worse” fields as
described during the interviews. This to verify the hypothesis that higher woody vegetation presence
enhances crop productivity. In each field, trees and shrubs parameters were recorded according to
forestry methodologies (e.g. Vade-mecum du forestier (2006), Kabré (2010) and Cabral (2011)). The
following parameters were recorded:
- Species
- Sanitary state (alive, alive and prunned, alive but parasited, or dead)
- Phenological state (leaves, leaves and flowers, leaves and fruits, all three, only flowers,
only fruits, or bare)
- Basal circumference (Circumf base, in cm) for shrub types or Diameter at breast height
(DBH1.30 m, in cm) for tree types
- Height (H, in m)
- Largest crown diameter (D, in m) and its perpendicular diameter (d, in m). Crown area
projection was estimated using the following equation (Cabral 2011):𝐶𝑟𝑜𝑤𝑛 𝑎𝑟𝑒𝑎 =
(𝐷+𝑑
2) ∗ 𝜋
- Number of stems (Ns)
- Average stem size (in cm) of multiple stem shrub types
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Results were reported in a data record sheet (appendix 1), similar to the one used by Gaëlle Feur
(2014) to assess landscape-scale woody biomass and species diversity in Yilou.
Detailed inventory was carried out for 11 “best” fields and their corresponding “worst” fields,
meaning every single tree and shrub were measured.
As time was limiting and workload to conduct full inventory, for 19 other couples of “best and worst”
fields, inventory was simplified by counting the number of individual tree or shrub in a pre-
determined size classes and describing only one representative individual per size, one
corresponding to the average height and crown area (see Fig. 1).
Figure 1: Principle of size class (Cheriere, 2015)
3. Fertility island characterization Soil properties around shrubs, such as Piliostigma reticulatum, enhance yields compared to the open
field. Despite the production and deposition of organic matter by the shrub itself through leaf falls,
this improved soil conditions under the canopy can be explain by several factors. First, during the dry
season, there is a retention of mineral material transported during wind erosion events (Lozano et al.
2013; Leenders et al. 2005; Okin et al. 2006; Wezel et al. 2000), then during the rainy season, mineral
material is retained from water erosion flows (Lahmar et Yacouba 2012) and finally, during both
seasons, organic matter is stopped and concentrated at the shrubs base (Lahmar et Yacouba 2012;
Wezel et al. 2000). This accumulation of organic matter and soil particles under shrub’s canopy is
called a fertility island. Studies have explored the difference of fertility between the soil under shrub
canopy and the open land (Wezel et al. 2000; Lozano et al. 2013) and showed significant differences
in soil properties. However, the different component contributing this soil improvement have not
been investigated yet (Lahmar et Yacouba 2012). Also, fertility island description was made in fields
during their fallow period (Wezel et al. 2000) or in abandoned fields (Okin et al. 2006). This
description has not been done in cultivated fields yet.
A. Determination of the fertility island shape
Sediment trapped by shrubs are spread around the shrub according to the main wind direction and
slope direction for the water erosion aspect. Indeed, behind the shrub, wind speed is decreased and
particles deposition is more important (Leenders et al. 2011). On slopes, because of water erosion, a
spatial pattern occurs around vegetation elements (Bochet et al. 2000).
1rst size class 2nd size class 3rd size class
Shrub described for the size class
Shrub described for the size class
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Figure 3: Microprofilometer used for measurements (Cheriere, 2015) Red arrow shows the stick used to keep point n°33 at the same spot.
To determine the shape of the fertility island around P. reticulatum and test the existence of general
shape of the island, micro-topographies were done along four transects:
- A North to South transect to get the slope direction (G. Félix observations in Yilou).
- A North-East to South-West transect to get the influence of the two dominant winds in the
region (Sivakumar & Gnoumou 1987).
- An East to West transect to have the perpendicular transect to the wind influence.
- A North-West to South-East transect for the perpendicular to the slope influence.
On each transect, soil height at the shrub centre was taken as a reference. Then, soil height was
measured with a microprofilometer, built for this purpose (Fig. 3) and inspired by the design
reported in Bochet et al. (2000)(Fig. 3).
In each transect (195 cm long), 65 points were taken, 1 every 3 centimetre. The middle point, n°33,
was kept as a reference for all transects, corresponding to the centre of the shrub. It was determined
by a stick placed in the centre of the shrub (figure 3). The edges of the micro-profilometer are
outside the shrub and the fertility island. Points near n°1 and n°65 are closest to the open field
Figure 2: micro-profilometer used by Bochet et al. (from Bochet et al., 2000)
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values. Similarly, points within the basis of the shrub (i.e. in a radius calculated with basal
circumference of each shrub) and with n°33 as central point are considered being fertility island
measurements.
To facilitate later analysis of the data, point n°1 has always been taken in the north for north to south
transect (N->S), in the north east for north east to south west transect (NE->SW), in the east for east
to west transect (E->W) and in the south for the south west to north east transect (SW->NE).
Shrubs for the measurement were selected to match several criteria:
(1) No physical obstacle e.g. other shrub or trees, rocks, within 5 meter radius around the shrub.
(2) Also, if there were signs of frequent animal or human passage, such as a path or a dirt road,
within at ten meter radius, the shrub was not chosen.
(3) The last rejection criteria was presence of rocks, massive stump or roots at soil surface or termite
colonies within the shrubs, because such element would introduce a bias for the analysis.
Dendrometric data were also collected to obtain as many information as possible about every chosen
Piliostigma. For every shrub measured, basal circumference (Cbase), height (h), large crown diameter
(D), small crown diameter (d), number of stem in the shrub (#stems) and an estimated average
diameter of the stems (diamstem) has been recorded. To complete those information, the
orientation of the large diameter was noted.
B. Integral value calculation
For analysis, data measured on the field were transformed to match the following criteria:
- For the same shrub, n°33 was set to the same value for the 4 axes.
- The minimal value, H0, of the 4 axes was set to 0.
Figure 4: 3D representation of the microtopographies.
As the values obtained across the transect constitute a curve, an approximation of the integral was
calculated with the following formula:
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∑ 3𝑥𝑖
65
𝑖=1
In a theoretical shrub without soil micro relief, the value would be close to 0. The lower the value,
the smaller the difference between the lowest points and the highest and therefore the size of the
microrelief. Integral value represents the area under the curve. This value can help characterisation
of the relief occurring in the transect.
C. Approximation of the height of fertility island
Another data treatment proposed is the following: an open field level is calculated (average of the
first 10 points) for both sides of the transect on the hypothesis that the first ten points are at field
average height. In the centre the average value of height were calculated for points located within
the shrub basal radius (Figure 4). Those calculations provided 4 values. For example on N->S, there
was Lateral N, Centre N, Centre S and Lateral S.
Figure 5: Illustration of the lateral value/central values analysis The black line corresponds to the shrub diameter at soil level (calculated from the basal circumference), red lines are the lateral average and the green lines are the central average.
D. Soil volume approximation
As there are 4 axes and 8 radius for each shrub, the area under point x concerned to approximate the
volume of soil is 1/8 of the ring positioned under point x.
dx = (distance from point 33 to point x) – 1.5 cm
Dx = (distance from point 33 to point x) + 1.5 cm
Ring area (Rax) = πDx² - πdx²
Figure 6: Visual support for calculation method
D
d
Point x Point 1 Point 33 Point 65
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Then, to obtain an approximation of the volume of the shrub fertility island, the following formula is
used:
𝑉𝑜𝑙 = ∑ ∑ 𝑅𝑎𝑥 ∗ ℎ𝑥65𝑗=1
4𝑖=1
With i the number of axis (N->S, NE->SW, E->W, SE->NW), j the number of point per axis and hx the
observed height at point x.
Inventory results analysis
1. Species diversity 25 couple of fields were scouted to inventory woody vegetation. Each couple had a “Best” field and a
“Worst” field. This rating was made during interviews with the farmer. The following observations
explores the differences or similarities which may exist between Best and Worst field regarding the
woody vegetation.
Regardless from the number of individuals observed per specie, 44 species were observed during
vegetation inventories. Among those species, 43 were observed in Best fields whereas only 33 were
observe in Worst fields. However, in both Best and Worst fields, only 19 and 20 species respectively
were observed in 5 or more fields. Those groups of species have a strong common basis: Acacia
nilotica, Acacia seyal, Adansonia digitata, Azaridachta indica, Balanites aegyptica, Calotropis procera,
Cassia sieberiana, Combretum aculeatum, Combretum micranthum, Combretum glutinosum,
Diospyros mespiliformis, Faidherbia albida, Guiera senegalensis, Lannea microcarpa, Piliostigma
reticulatum, Sclerocarya birrea, Vitellaria paradoxa and Ziziphus Mauritania. Anogeissus leiocarpus
appears in “Best” fields’ species group. Whereas Tamarindus indica and Gardenia sokotensis appear
only in the “Worst” group.
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ha
goss
ypiif
olia
Par
kia
big
lob
osa
Bo
mb
ax c
ost
atu
m
Eup
ho
rbia
sp
.
Lon
cho
carp
us
laxi
flo
rus
Aca
cia
sp.
Xim
enia
am
eric
ana
Pee
per
ga
Co
nb
risa
ca
Ecal
yptu
s ca
mal
du
len
sis
Gar
den
ia t
ern
ifo
lia
Go
np
ayan
de
ga
Kh
aya
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ega
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sis
Man
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dic
a
Myt
ragi
na
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Sab
a se
neg
alen
sis
Ster
culia
set
iger
a
Waa
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Only 6 species have been observed in 15 or more of the Best fields and 4 species in the Worst fields.
Piliostigma reticulatum, Lannea microcarpa, Guiera senegalensis and Combretum micranthum where
in 15 fields or more in both Best and Worst fields. In Best fields, Balanites aegyptica and Ziziphus
mauritania were present in more than 15 fields but were not in Worst fields.
Figure 8: Occurence of species in "Worst" fields
06/03/2015 P a g e | 14
The fields that underwent the inventory had unequal surfaces. Therefore the number of species can
observed can be linked to the surface. On Figure 9, the link between number of species and surface
seems stronger for worst fields than for best fields as the R² are respectively 0.6924 and 0.3038.
2. Representation of species within fields Piliostigma reticulatum and Guiera senegalensis are the most represented species in the fields. Their
number is often very large compared to the second or third specie when ranked by number of
individuals observed in the fields. Also, out of 50 fields, Piliostigma reticulatum rank first 32 times. In
each of the 50 fields inventoried, when Piliostigma reticulatum has been observed, it is present in the
top three species per number of individuals, mainly in first and second.
Jatropha gossypiifolia, Combretum glutinosum and Combretum micranthum can be quite numerous
when present in the field but not in the proportions of Guiera senegalensis or Piliostigma
reticulatum. In one case, Ousseni Guira’s worst field, Acacia seyal was counted 119 times. As it was a
recently cleared fallow, it can be considered as an exception.
The 5 previous species are shrub species or in general managed as shrubs (Piliostigma reticulatum).
Tree species are present in a lower extent. Balanites aegytica, Diospyros mespiliformis, Azaridachta
indica, Lannea microcarpa and Ziziphus mauritania may happen in an important number in fields (up
to 30). However the can be managed as low vegetation, and coppiced regularly. Only Lannea
microcarpa is almost exclusively kept as a tree.
First specie number 2nd specie number 3rd specie number
Guiera senegalensis 328 Piliostigma reticulatum 107 Diospyros mespiliformis 28
Jatropha gossypiifolia 61 Piliostigma reticulatum 24 Euphorbia sp. 9
Piliostigma reticulatum 359 Combretum glutinosum 77 Guiera senegalensis 75
Piliostigma reticulatum 381 Combretum glutinosum 86 Guiera senegalensis 24
y = 2.4098x + 8.0768 R² = 0.3038
y = 6.0534x + 4.3606 R² = 0.6924
0
5
10
15
20
25
0 1 2 3 4 5 6 7
Nu
mb
er o
f sp
ecie
s o
bse
rved
Field's surface
Best
Worst
Linear (Best)
Linear (Worst)
Figure 9: Number of species observed per field's surface
06/03/2015 P a g e | 15
Piliostigma reticulatum 54 Guiera senegalensis 30 Faidherbia albida 7
Piliostigma reticulatum 416 Lannea microcarpa 16 Combretum glutinosum 10
Piliostigma reticulatum 61 Jatropha gossypiifolia 42 Azaridachta indica 21
Combretum micranthum 60 Guiera senegalensis 32 Piliostigma reticulatum 4
Piliostigma reticulatum 13 Guiera senegalensis 2 Balanites aegyptica 2
Piliostigma reticulatum 10 Combretum glutinosum 9 Combretum micranthum 8
Piliostigma reticulatum 72 Lannea microcarpa 9 Balanites aegyptica 7
Piliostigma reticulatum 354 Balanites aegyptica 13 Faidherbia albida 9
Guiera senegalensis 380 Piliostigma reticulatum 265 Combretum micranthum 69
Piliostigma reticulatum 52 Combretum glutinosum 9 Eucalyptus camaldulensis 4
Guiera senegalensis 331 Piliostigma reticulatum 193 Balanites aegyptica 11
Piliostigma reticulatum 275 Adansonia digitata 11 Lannea microcarpa 8
Piliostigma reticulatum 954 Guiera senegalensis 417 Ziziphus mauritania 20
Guiera senegalensis 45 Combretum micranthum 20 Piliostigma reticulatum 5
Piliostigma reticulatum 111 Guiera senegalensis 11 Lannea microcarpa 9
Lannea microcarpa 1 Tamarindus indica 1 Azaridachta indica 1
Piliostigma reticulatum 44 Balanites aegyptica 2 several 1
Azaridachta indica 3 Faidherbia albida 3 Piliostigma reticulatum 2
Piliostigma reticulatum 324 Lannea microcarpa 30 Combretum glutinosum 15
Piliostigma reticulatum 47 Combretum micranthum 41 Azaridachta indica 29
Balanites aegyptica 3 Azaridachta indica 2 several 1 Table 1: 3 most represented species per Best field and their number.
First specie number 2nd specie number 3rd specie number
Vitellaria paradoxa 2 Tamarindus indica 1 - 0
Piliostigma reticulatum 305 Guiera senegalensis 161 Diospyros mespiliformis 28
Piliostigma reticulatum 100 Combretum glutinosum 18 Guiera senegalensis 8
Piliostigma reticulatum 33 Combretum glutinosum 22 Guiera senegalensis 10
Guiera senegalensis 35 Piliostigma reticulatum 10 several 1
Piliostigma reticulatum 147 Guiera senegalensis 54 Piliostigma thonningii 35
Guiera senegalensis 525 Piliostigma reticulatum 203 Combretum micranthum 76
Guiera senegalensis 44 Combretum micranthum 36 Piliostigma reticulatum 7
Piliostigma reticulatum 245 Guiera senegalensis 64 Combretum micranthum 8
Piliostigma reticulatum 187 Guiera senegalensis 27 Combretum micranthum 4
Piliostigma reticulatum 189 Guiera senegalensis 17 Combretum micranthum 12
Piliostigma reticulatum 46 Combretum micranthum 9 several 2
Guiera senegalensis 602 Piliostigma reticulatum 309 Acacia seyal 119
Piliostigma reticulatum 31 Guiera senegalensis 21 several 1
Piliostigma reticulatum 64 Guiera senegalensis 44 Balanites aegyptica 32
Piliostigma reticulatum 224 Diospyros mespiliformis 57 Guiera senegalensis 30
Piliostigma reticulatum 331 Lannea microcarpa 12 several 5
Guiera senegalensis 47 Piliostigma reticulatum 11 Cassia sieberiana 8
Guiera senegalensis 240 Piliostigma reticulatum 70 Combretum micranthum 51
Piliostigma reticulatum 11 Calotropis procera 5 Lannea microcarpa 4
Piliostigma reticulatum 57 Guiera senegalensis 4 Gardenia sokotensis 3
06/03/2015 P a g e | 16
Azaridachta indica 24 Piliostigma reticulatum 1 Sclerocarya birrea 1
Piliostigma reticulatum 76 Faidherbia albida 13 Azaridachta indica 7
Piliostigma reticulatum 2 Azaridachta indica 2 - 0
Lannea microcarpa 21 Piliostigma reticulatum 10 Azaridachta indica 8 Table 2: 3 most represented specie per Worst field and their number.
3. Comparison between “Best” and “Worst” fields woody vegetation Vegetation was grouped in three categories, potential source of mulching material (i.e.: Piliostigma
reticulatum, Piliostigma thoningii, Guiera senegalensis and Combretum micranthum), other low
vegetation (<2m high), and high vegetation (>2m).
The main vegetation type available in fields, is the “mulchable” woody vegetation. With the
exception of 4 fields without any of this species, where the total number of individuals is really low
(circa 10 individuals in total). The distribution of this proportion between the best and worst fields
groups is similar. No element suggest the presence of more “mulchable” elements in best fields than
in worst.
Figure 10: Percentage of "mulchable" individuals in the fields
The same happens for the distribution of the low vegetation in both fields’ groups except for the
extreme values (n°23, 24 and 25).
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Best Worst
06/03/2015 P a g e | 17
Figure 11: Proportion of low vegetation (<2m) in the fields
Finally, it seems that the proportion of high vegetation is slightly larger in Best fields than in Worst
fields.
Figure 12: Proportion of high vegetation (>2m) in the fields
4. Farmer management
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Best Worst
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Best Worst
06/03/2015 P a g e | 18
Proportions of different vegetation in both Best and Worst fields of a same farmer are often different
(see figures below). Also, no general trend appears in favour of one category of field.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Best Worst
Figure 13: Percentage of vegetation potentialy available for mulching in fields
06/03/2015 P a g e | 19
Figure 15: Percentage of high vegetation in fields (>2m)
5. Discussion of inventory results Piliostigma reticulatum is known by villagers to have positive impact on soil structure when applied
as a mulch. Therefore, its presence on almost every field, seems to confirm a spreading of this use
amongst farmers.
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Isso
uf
Sin
aré
Idri
ssa
San
fo
Sou
mai
la G
anso
re 1
Fati
mat
a Ye
ta
Bo
uka
ry S
oré
Pau
l Saw
ado
go
Zaka
ria
Gu
ira
Pam
ou
ssa
Zan
go
Ge
org
ette
Ab
ibo
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ued
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go
Mar
iam
Rab
o
Har
ou
na
Saw
ado
go
Sayo
ub
a Sa
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ogo
Ou
ssen
i Gu
ira
Idri
ssa
Ou
ed
rao
go
Lass
ane
So
ré
Pat
enem
a Sa
wad
ogo
Ham
ido
u S
awad
ogo
Ou
sman
e Za
ngo
Mas
sou
rou
Ou
edra
ogo
Sala
m S
awad
ogo
2
Ab
ibo
u S
awad
ogo
Dan
iel Z
ou
ngr
ana
Sala
m S
awad
ogo
1
Sou
mai
la G
anso
re 2
Ch
rist
ine
Ab
ibo
u O
ued
rao
go
Best Worst
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Isso
uf
Sin
aré
Idri
ssa
San
fo
Sou
mai
la G
anso
re 1
Fati
mat
a Ye
ta
Bo
uka
ry S
oré
Pau
l Saw
ado
go
Zaka
ria
Gu
ira
Pam
ou
ssa
Zan
go
Ge
org
ette
Ab
ibo
u O
ued
rao
go
Mar
iam
Rab
o
Har
ou
na
Saw
ado
go
Sayo
ub
a Sa
wad
ogo
Ou
ssen
i Gu
ira
Idri
ssa
Ou
ed
rao
go
Lass
ane
So
ré
Pat
enem
a Sa
wad
ogo
Ham
ido
u S
awad
ogo
Ou
sman
e Za
ngo
Mas
sou
rou
Ou
edra
ogo
Sala
m S
awad
ogo
2
Ab
ibo
u S
awad
ogo
Dan
iel Z
ou
ngr
ana
Sala
m S
awad
ogo
1
Sou
mai
la G
anso
re 2
Ch
rist
ine
Ab
ibo
u O
ued
rao
go
Best Worst
Figure 14: Percentage of low vegetation (<2m)
06/03/2015 P a g e | 20
The slightly higher presence of high vegetation in best fields might have several explanations. First,
high vegetation provides straw storage, shelter and shadow for the animals that often gather under
and leave there their excrements in the fields, which fertilises the soil nearby. Also, trees are
important wind breaks and reduce soil erosion. Finally, some species produces fruits, wood, leaves
and other by products used in daily life. Those elements may increase the perception of the quality of
the field.
The higher link between specie number and surface might come from a necessity to restore soil in
worst fields. Having more species might come from a less intensive management by farmers and a
lower interest in clearing the field. In Best fields, more intensive management might lead some
farmer to clear more space and keep only the species he believes are the most interesting for him.
The analysis of the results per ha was hazardous because most of the fields are unevenly covered by
vegetation. When calculated, individuals/ha and species/ha lead to exaggerated results. Small fields
had a high diversity and a large number of trees per ha and large fields had few species and
individuals per ha, which is not representative of the reality. This flaw in the methodology can be
tackled by defining an area of the same size to facilitate the data analysis. Defining areas of 2500 m²
(50x50m) and proceeding to inventory the vegetation in those squares would help the analysis. Also,
it could be possible to have one or two sampling areas in small fields and 4 to 5 in large fields in order
to calculate average values.
06/03/2015 P a g e | 21
Determination of fertility island shape
1. Visual analysis When plotting microtopographies data from point 1 to 65, different profiles can be observed. In
Figure 16, three profiles can be distinguished, corresponding to three different transects of the
fertility island. First, a rather flat profile such as Topo34T with the edges almost at the same level
than the centre values. Secondly, the profiles such as Topo4T that show a distinct difference between
the edges and the centre of the profile. Finally, Topo38T reveal a profile which resemble to a Gauss
curve. Those profiles can be found in the different axis and the most common are similar to Topo4T
and Topo34T (see appendix 2 for the 4 axis with the 21 curves).
Figure 16: 3 Examples of fertility island profiles from the north to south transect
Except for diversity in profiles, especially in E->W, there is, visually, no obvious difference between
the different transects when looking for a general trend in the curves (Appendix 2). However, the
existence of the fertility island seems to be confirmed in each transect, at least physically.
Intra fertility island visual analysis revealed several information. Some fertility islands studied tend to
have their 4 profiles quite similar in shape, such as Topo56T. This can lead to think of a regular shape
of the soil relief under the shrub. Nonetheless, some differences can be seen when looking at the
symmetry of the curves, for example, the SE->NW axis of Topo56T has a gentle slope in its north west
section (n°33 to n°65) and a steeper slope in its south east part (Figure 17).
0
5
10
15
20
25
1 3 5 7 9 11131517192123252729313335373941434547495153555759616365
Topo34T Topo38T Topo4T
06/03/2015 P a g e | 22
Figure 17: Topo56T profiles
Also, some other graphs reveal large differences in profiles. In those graphs, curves show large
differences in profiles but some similarities can be found between curves. E->W and SE->NW seem to
share a close pattern for Topo29T and the first half of N->S and NE->SW seem also quite close in
slope.
Figure 18: Topo29T profiles
2. Integral analysis Integrals values for each curve were approximated. Each integral calculation provided 4 values: the
first half of the transect, the second half, the total value and the difference value from the two half.
As the reference value of the lowest point has been set to 0, a rather flat curve, and therefore no
distinct fertility island or with little relief differences, has an integral value close to 0. A distinct
mount shape or high differences in measured values will lead to a high integral value. In the data
gathered, a dissymmetry between the two half of the curves are frequent as we could see on the
previous part. As a consequence, focus will be on difference between the first half of the transect
0
1
2
3
4
5
6
7
8
9
10
1 3 5 7 9 11131517192123252729313335373941434547495153555759616365
N->S NE->SO E->O SE->NO
0
2
4
6
8
10
12
14
16
1 3 5 7 9 11131517192123252729313335373941434547495153555759616365
N->S NE->SO E->O SE->NO
06/03/2015 P a g e | 23
and the second half (e.g.: (n°1 to n°33 integral)-(n°33 to n°65 integral) which corresponds to North
part – South part (N-S) on N->S). Table 3 presents a summary of the differences of the two half of
each axis.
N - S NE - SW E - W SE - NW
Topo27T 96 -130.5 60.9 -51.6 Topo28T 294.3 -70.2 51.3 15.3 Topo29T -281.7 27.9 326.1 141.9 Topo30T 21.3 6.9 -6.3 -59.1 Topo33T -52.8 231 19.5 -201.6 Topo34T -14.7 133.8 93.9 -119.7 Topo38T 70.2 -102.3 -14.1 -297.3 Topo39T -85.8 -97.8 -41.7 57.6 Topo3T 171 18.6 19.8 121.2
Topo16T 11.7 110.7 34.2 56.1 Topo53T 90 248.7 114.6 -8.4 Topo54T 15 96.6 283.5 -28.5 Topo55T 18 242.4 114.3 6 Topo56T 6.9 -115.8 -44.4 -128.1 Topo43T -113.7 -60.6 -370.5 -16.2 Topo45T 163.2 274.5 -248.7 -31.2 Topo46T -48.6 -319.2 26.1 -116.7 Topo4T 54.9 -51.3 -33.3 -199.2 Topo7T -21.9 -60.3 -215.28 -104.4 Topo9T 233.1 -38.1 -158.7 72.6
Topo52T 152.1 -14.7 -72 -246.6 Number of values <0 7 11 10 14 Number of values >0 14 10 11 11
Table 3: Summary of the difference between half integral for each axis
From the summary of this table it is possible to see two trends: on NE->SW and E->W transects, there
is no side showing larger values than the other. Indeed about half of the values are positive and the
other half negative. On N->S 14 values are positive leading to think that the north side tends to have
a higher values than the south part. Same occurs on SE->SO were there are 14 negative values
possibly signifying that NW half integral value is larger.
06/03/2015 P a g e | 24
Figure 19: Diagram with Quartiles and Medians of the difference between integrals of each axis studied
To further the description of the previous data, quartiles were calculated. In figure 19, first quartile,
median and third quartile are presented per axis. On N->S and NW->SE axis, there are more large
values on, respectively, north and north west part than in south or south east. Indeed in the north
part 25% of the data have a value superior to 30 whereas, in the south part, 25% percent of the
values are lower than -5. This reinforce the idea of a trend for larger integral value in the north part
of the N->S transect. Same idea applies for NW->SE transect, it seems that there are larger integral
values in the north west part of the transect. On E->W transect, the distribution looks even, with
little difference between the values of the quartiles (25% of the values < -45 and 25% of the values
>60). Finally, for NE->SW the first quartile has a relatively high value with 25% of the values above
110 but the shift does not seem to happen in favour of north east part because the third quartile also
has a high value of -70.
When statistically analysing the distribution of the integrals differences, through a paired t-test
procedure, there is no significant difference in the integral value on the E->W and NE->SW axis as it
was possible to foresee from the previous graph. Even though the difference on N->S is not
statistically significant (p-value = 0.09816), it is not as clear as on the previous axis. Finally, the value
of the integrals is higher in the NW part than in the SE part of SE->NW (p-value = 0.02405).
Comparing average values of the lateral to the central points of a semi-axis, with a t-test, the results
are always significant (p-value<0.001). In table 4, SE half transect is taken as an example, the other
results are present in the appendix 3. The average height of the relief under the shrub, in this part is
3.52cm. Overall average height difference between lateral soil level and fertility island centre is
3.74cm.
06/03/2015 P a g e | 25
n° pilio axis Lat SE Centre SE Centre-Lat
Topo27T SE->NW 10.34 15.52 5.18 Topo28T SE->NW 12.22 15.42 3.20 Topo29T SE->NW 14.10 15.95 1.85 Topo30T SE->NW 15.83 17.61 1.77 Topo33T SE->NW 11.53 18.40 6.87 Topo34T SE->NW 15.83 21.52 5.69 Topo38T SE->NW 16.77 21.65 4.88 Topo39T SE->NW 15.42 19.85 4.42 Topo3T SE->NW 13.90 19.75 5.85
Topo16T SE->NW 16.06 18.94 2.88 Topo53T SE->NW 16.08 17.95 1.87 Topo54T SE->NW 14.96 17.65 2.70 Topo55T SE->NW 16.11 17.74 1.63 Topo56T SE->NW 14.12 19.79 5.67 Topo43T SE->NW 17.68 19.86 2.18 Topo45T SE->NW 15.54 18.77 3.22 Topo46T SE->NW 15.66 18.94 3.28 Topo4T SE->NW 15.30 17.39 2.09 Topo7T SE->NW 12.64 16.21 3.56 Topo9T SE->NW 13.48 16.31 2.84
Topo52T SE->NW 15.08 17.31 2.24
Average = 3.52
Table 4: South east table of results for lateral and central average comparison
In order to explore the existence of a relationship of the fertility island shape and shrub, integral
values were plotted against dendrometric parameters of the shrub. When checking for a relation
between the differences between the half integrals and the various shrubs parameters no specific
pattern such as a linear relation was highlighted. Shrubs parameters checks were: D, d, h, Cbase,
#stems, crown surface, D*h, d*h and number of stems per cm² at the base of the shrub (calculated
from Cbase and #stems).
In table 5, results of volume calculation are presented. The volumes range from 0.08 to 0.34 m3 for
the extreme values but the average value is 0.13 m3 with a standard deviation of 0.05. This represent
a soil accumulation relatively important.
Table 5: Soil volume under the Piliostigma reticulatum
n° pilio Volume (cm3) Vol m3
Topo27T 160090.71 0.16
Topo28T 139050.67 0.14
Topo29T 168289.94 0.17
Topo30T 123732.66 0.12
Topo33T 341440.24 0.34
Topo34T 111805.81 0.11
Topo38T 122960.06 0.12
Topo39T 103122.04 0.10
Topo3T 112521.16 0.11
06/03/2015 P a g e | 26
Topo16T 92830.16 0.09
Topo53T 141038.36 0.14
Topo54T 139884.06 0.14
Topo55T 130705.48 0.13
Topo56T 101375.74 0.10
Topo43T 116602.21 0.12
Topo45T 156240.45 0.16
Topo46T 147250.95 0.15
Topo4T 105062.02 0.11
Topo7T 121769.64 0.12
Topo9T 82322.68 0.08
Topo52T 93687.58 0.09
3. Discussion for microtopography The fertility island appears to exist at the base of the shrubs, this suggest that shrubs in the fields,
even coppiced before cropping season, have the ability to maintain a fertility island as it occurs in
fallows. Therefore, they have a role of element trapping and aeolian erosion control.
Data collection is the major source of bias in this experiment. Indeed, the measurement were done
with a precision of more or less 2 mm and sometimes in un-optimal conditions for example with
wind.
In the methodology, using the integrals values to try to show a greater soil accumulation at the
bottom of the shrub is not evident. Indeed, various element can change the calculation results. First
the assumption of the natural slope and shape of the fertility island being preserved is unlikely to be
verified, because of the use of the fields for crops. In addition, tillage can create holes in parts of the
transect outside of the fertility island, which would alter the integral’s value. As a consequence,
conclusion can be drawn about absolute measurement values but the link between a higher integral
value in one side and a higher soil accumulation is not obvious.
Although, from literature study, main wind direction in dry season was expected to come from north
east. When checking the meteorological station data, it appeared that main wind direction were
coming from north west and north (Figure 20).
06/03/2015 P a g e | 27
Figure 20: Wind occurences between december 2014 and march 2015 in Yilou. (from data collected by CIRAD, 2015)
Under the hypothesis that integral value is a sign of soil amount under the shrubs, there would be a
higher soil accumulation in the north west side of the shrubs and, even though not statistically
significant (0.05<p-value<0.1), in the north. Regarding the axis of accumulation, it is coherent with
the wind profile obtain through CIRAD meteorological station in Yilou. Furthermore, the
accumulation taking place before the shrub when looking from the wind provenance, can be
explained by trapping saltation particles. Saltation particles are coarse element not light enough to
be in suspension in the wind and moving near soil surface (Okin et al. 2006). Also, fine elements can
be extracted from the wind by reducing its speed and deposited behind the shrub. This should result
in a greater integral value in this part but it is probably destroyed by tillage.
The shape of the island at the base of the shrub seems relatively regular and do not share the
patterns described in Okin et al (2006). This shape can come from the changing high speed wind
directions during storms (Leenders et al. 2011). More likely, this shape comes from land
management and more particularly tillage. Indeed, when preparing the field for crops, farm either
plow or prepare zaï holes. As it is know that the shrubs have a positive impact on the soil farmer
preserve it. During an interview a farmer, Noël, said: “We plough the field in parallel lines, when we
encounter a shrub, we lift the plough to avoid destruction of the roots”. This way, they keep the
shrub but as the fertility island is wider than the shrub base, some soil is spread around during
ploughing. This soil tillage are probably the cause of the disappearance of the shape of the island.
0%2%4%6%8%
10%12%14%16%
N
NNE
NE
ENE
E
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
Wind occurrence
06/03/2015 P a g e | 28
General Discussion Assessing the woody diversity in farmer fields is not enough to show trends in farmer’s practices. The
results presented above did not reveal anything particular between “Best” and “Worst” fields.
Indeed, Woody vegetation management in farmer’s field is driven by numerous and complex factors.
As a matter of fact, animals roaming in the fields, feeding on crop residues, shrubs and trees, villagers
collecting raw material for construction, cooking or medicine preparation are putting pressure on
woody resources. Also, soil quality and cropping practices history determine the percentage of trees
on the fields. However, this inventories revealed the large adoption of Piliostigma reticulatum
preservation for mulch amongst farmers. It also highlighted some innovative practices used by some
farmers, especially in Piliostigma reticulatum management, which was, in Salam’s fields, managed as
tree and shrub at the same time (see figure
21).
From the interview of Salam Sawadogo, the
presence of trees in the field, well
managed, is beneficial for the production.
Indeed, Salam has a micromanagement of
the parts of his fields:
- In areas under certain tree species
such as Faidherbia albida, he doesn’t use
any mulch. Because he observed that it
wasn’t necessary thank to the shadow and
the leave falling on the ground.
- In areas really dry, he applies twice
as much mulch as in semi dry areas to
promote termite activities and favour the
improvement of soil structure.
- In semi dry areas, he applies mulch
only once after the first rainfalls.
The understanding of the micro-practices
of the farmers on their fields, is really
important to understand the link between
the presence of woody biomass, its
quantity, its use and their influence on the
production.
Even though in immediate proximity of the trees crop yields may be reduced, trees have a positive
influence on crop productivity as it creates a microclimate and can play a role as hydraulic lift (Vetaas
1992; Kessler 1992; Jonsson et al. 1999). Also trees have their own utility producing fruits, wood and
leafs which can compensate for yield losses.
Fertility island characterization is interesting to assess its potential to retain sediments from erosive
phoenomenons (Okin et al. 2006). Also, to further the analysis and description of its beneficial effect,
it would be necessary to complete this physical description by a chemical description, to establish the
Figure 21: Piliostigma reticulatum managed as a tree (Cheriere, 2015)
06/03/2015 P a g e | 29
existence of a gradient of fertility as suggested in Wezel (2000). And to complement it, propose an
experiment on Sorghum yields, in the radius of Piliostigma reticulatum.
Conclusion This master thesis, part of a larger project, aimed at describing woody biomass in farmer’s fields and
characterise fertility islands at the base of Piliostigma reticulatum. Even though some species were
only found in “Best” fields and some in “Worst” fields, they were observed only once. A group of
species was present in a large number of fields in various proportions but no clear difference could
be established between “Best” and “Worst” fields. Indeed, several other factor have to be taken into
account, amongst them, the pressure on woody resources, the farmer practices and objectives, the
management of land at the community level and of course, soils characteristics. Woody vegetation
has an important role to play in soil protection and accumulation of biomass, especially shrubs. A
more detailed description of Piliostigma reticulatum role in soil fertility and erosion reduction in
farmer’s fields would help improve management of this vegetation to enhance crops production.
However, preservation of Piliostigma reticulatum in fields, tend to show a wide adoption of mulching
technic amongst the farmers of the village, compared to other village were this shrub is slashed and
burned.
It was established in this work that even in fields under cultivation fertility island exist at the base of
Piliostigma reticulatum. It has an average height of 3.5 cm and represents a soil volume of 0.13 m3.
No spatial pattern could be highlighted as fields are ploughed or tilled for Zaï preparation. A more
detailed research about the composition of fertility island and the processes of its formation might
improve management methods and soil fertility and protection.
06/03/2015 P a g e | 30
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Table of figures
Figure 1: Principle of size class (Cheriere, 2015) ..................................................................................... 8
Figure 2: micro-profilometer used by Bochet et al. (from Bochet et al., 2000) ...................................... 9
Figure 3: Microprofilometer used for measurements (Cheriere, 2015) ................................................. 9
Figure 4: 3D representation of the microtopographies. ....................................................................... 10
Figure 5: Illustration of the lateral value/central values analysis The black line corresponds to the
shrub diameter at soil level (calculated from the basal circumference), red lines are the lateral
average and the green lines are the central average. ........................................................................... 11
Figure 6: Visual support for calculation method ................................................................................... 11
Figure 7: Occurence of species in "Best" fields ..................................................................................... 12
Figure 8: Occurence of species in "Worst" fields .................................................................................. 13
Figure 9: Number of species observed per field's surface .................................................................... 14
Figure 10: Percentage of "mulchable" individuals in the fields ............................................................ 16
Figure 11: Proportion of low vegetation (<2m) in the fields ................................................................. 17
Figure 12: Proportion of high vegetation (>2m) in the fields ................................................................ 17
Figure 13: Percentage of vegetation potentialy available for mulching in fields .................................. 18
Figure 14: Percentage of low vegetation (<2m) .................................................................................... 19
Figure 15: Percentage of high vegetation in fields (>2m) ..................................................................... 19
Figure 16: 3 Examples of fertility island profiles from the north to south transect .............................. 21
Figure 17: Topo56T profiles ................................................................................................................... 22
Figure 18: Topo29T profiles ................................................................................................................... 22
Figure 19: Diagram with Quartiles and Medians of the difference between integrals of each axis
studied ................................................................................................................................................... 24
Figure 20: Wind occurences between december 2014 and march 2015 in Yilou. (from data collected
by CIRAD, 2015) ..................................................................................................................................... 27
Figure 21: Piliostigma reticulatum managed as a tree (Cheriere, 2015) .............................................. 28
Table 1: 3 most represented species per Best field and their number. ................................................ 15
Table 2: 3 most represented specie per Worst field and their number................................................ 16
Table 3: Summary of the difference between half integral for each axis ............................................. 23
Table 4: South east table of results for lateral and central average comparison ................................. 25
Table 5: Soil volume under the Piliostigma reticulatum ....................................................................... 25
06/03/2015 P a g e | 33
Appendix Appendix 1. Vegetation inventory sheet
Land type: Vegetation class:
Field’s owner: Longitude :
Soil type: Latitude:
Note:
species N° Sanitary state Phenol Cb C 1.30 HT D crown
1 D d
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Cb = Basal Circumference C 1.30 = Circumference at 1.30 meter height Ht= Total Height D crown = Diameter of the crown D= D1 d=D2 Code sanitary state : alive :1; alive and pruned :2; alive and parasite :3; dead :4 Phenology code: with leaves = 1; with fruits = 2; with flowers =3; with leaves and fruits=4; with leaves
and flowers=5; with flowers and fruits =6; bare=7
06/03/2015 P a g e | 34
Appendix 2: Every curve on the 4 axis
0
5
10
15
20
25
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
N->S
Topo27T Topo28T Topo29T Topo30T Topo33T Topo34T Topo38T
Topo39T Topo3T Topo16T Topo53T Topo54T Topo55T Topo56T
Topo43T Topo45T Topo46T Topo4T Topo7T Topo9T Topo52T
0
5
10
15
20
25
30
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
NE->SW
Topo27T Topo28T Topo29T Topo30T Topo33T Topo34T Topo38T
Topo39T Topo3T Topo16T Topo53T Topo54T Topo55T Topo56T
Topo43T Topo45T Topo46T Topo4T Topo7T Topo9T Topo52T
06/03/2015 P a g e | 35
0
5
10
15
20
25
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
E->W
Topo27T Topo28T Topo29T Topo30T Topo33T Topo34T Topo38T
Topo39T Topo3T Topo16T Topo53T Topo54T Topo55T Topo56T
Topo43T Topo45T Topo46T Topo4T Topo7T Topo9T Topo52T
0
5
10
15
20
25
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
SE->NW
Topo27T Topo28T Topo29T Topo30T Topo33T Topo34T Topo38T
Topo39T Topo3T Topo16T Topo53T Topo54T Topo55T Topo56T
Topo43T Topo45T Topo46T Topo4T Topo7T Topo9T Topo52T
06/03/2015 P a g e | 36
Appendix 3: Summary tables of lateral height against central height.
n° pilio axe Lat N Centre N N Lat-Centre Centre S Lat S S Centre-Lat
Topo27T N->S 15.89 17.89 2.00 17.57 16.83 0.74 Topo28T N->S 18.12 18.43 0.31 18.56 15.71 2.85 Topo29T N->S 19.42 20.24 0.82 20.03 18.73 1.30 Topo30T N->S 17.12 19.39 2.27 18.34 17.80 0.54 Topo33T N->S 15.04 18.03 2.99 18.52 12.59 5.93 Topo34T N->S 14.30 20.97 6.67 20.95 16.28 4.67 Topo38T N->S 14.67 19.35 4.68 18.83 15.53 3.30 Topo39T N->S 12.17 17.36 5.19 16.73 12.26 4.47 Topo3T N->S 12.20 18.57 6.37 17.57 8.64 8.93
Topo16T N->S 10.18 16.13 5.96 17.34 11.30 6.04 Topo53T N->S 10.11 15.08 4.97 14.69 8.91 5.78 Topo54T N->S 11.20 17.47 6.27 18.55 8.22 10.33 Topo55T N->S 11.37 15.35 3.99 18.25 13.82 4.43 Topo56T N->S 13.30 15.50 2.20 15.73 15.21 0.52 Topo43T N->S 17.11 21.10 3.99 18.37 13.34 5.03 Topo45T N->S 17.10 20.97 3.87 17.49 17.08 0.41 Topo46T N->S 15.24 17.99 2.75 18.15 14.88 3.27 Topo4T N->S 13.47 18.36 4.89 17.07 15.74 1.33 Topo7T N->S 13.76 21.09 7.33 19.43 16.58 2.85 Topo9T N->S 12.86 20.78 7.92 19.21 15.70 3.51
Topo52T N->S 13.39 19.89 6.50 21.91 13.88 8.03 Average
4.38
4.01
06/03/2015 P a g e | 37
n° pilio axe Lat NE Centre NE Centre - Lat Centre SO Lat SO Centre-Lat
Topo27T NE -> SO 17.28 26.25 8.98 22.42 18.50 3.92 Topo28T NE -> SO 7.56 18.33 10.77 17.17 10.03 7.14 Topo29T NE -> SO 8.44 19.54 11.10 18.29 14.18 4.11 Topo30T NE -> SO 19.51 18.42 -1.09 18.42 19.61 -1.19 Topo33T NE -> SO 18.22 20.17 1.95 18.77 16.98 1.79 Topo34T NE -> SO 17.74 21.19 3.45 18.56 17.39 1.17 Topo38T NE -> SO 18.10 18.53 0.43 19.60 18.89 0.71 Topo39T NE -> SO 2.17 13.43 11.26 11.55 2.83 8.72 Topo3T NE -> SO 4.76 13.00 8.24 14.31 5.15 9.16
Topo16T NE -> SO 3.20 15.77 12.57 15.29 4.45 10.84 Topo53T NE -> SO 4.66 10.45 5.80 14.69 4.94 9.75 Topo54T NE -> SO 15.28 17.41 2.14 17.29 16.71 0.58 Topo55T NE -> SO 14.02 16.83 2.81 17.60 15.88 1.72 Topo56T NE -> SO 17.96 20.05 2.09 20.35 18.58 1.77 Topo43T NE -> SO 14.86 18.17 3.32 16.88 15.56 1.32 Topo45T NE -> SO 16.47 22.93 6.46 19.81 16.27 3.54 Topo46T NE -> SO 15.16 20.20 5.04 18.50 16.70 1.80 Topo4T NE -> SO 14.96 18.37 3.41 19.34 15.42 3.92 Topo7T NE -> SO 19.10 20.75 1.65 21.07 17.01 4.06 Topo9T NE -> SO 17.48 18.21 0.74 20.76 17.53 3.23
Topo52T NE -> SO 18.66 19.75 1.10 20.04 18.35 1.69 Average
4.87
3.80
n° pilio axe Lat E Centre E Cent-Lat Centre O Lat O Centre- Lat
Topo27T E->O 14.93 19.93 4.99 22.09 20.30 1.79 Topo28T E->O 18.32 19.48 1.16 19.20 18.64 0.56 Topo29T E->O 12.33 16.03 3.69 13.97 10.94 3.03 Topo30T E->O 13.48 17.34 3.86 13.75 11.05 2.70 Topo33T E->O 16.61 17.43 0.82 18.79 18.84 -0.05 Topo34T E->O 18.08 18.91 0.83 18.05 18.68 -0.63 Topo38T E->O 15.99 19.34 3.35 20.37 14.76 5.61 Topo39T E->O 15.36 21.09 5.73 22.29 19.87 2.42 Topo3T E->O 17.26 19.60 2.34 20.27 15.81 4.46
Topo16T E->O 13.30 18.12 4.82 19.58 13.75 5.83 Topo53T E->O 12.87 18.57 5.71 17.25 13.91 3.34 Topo54T E->O 14.37 17.79 3.43 18.28 15.52 2.76 Topo55T E->O 15.40 17.77 2.37 18.50 14.68 3.82 Topo56T E->O 12.56 18.45 5.89 20.05 14.80 5.25 Topo43T E->O 17.54 19.33 1.79 17.69 17.65 0.04 Topo45T E->O 17.10 18.07 0.97 18.71 16.64 2.07 Topo46T E->O 19.32 18.45 -0.87 18.95 19.89 -0.94 Topo4T E->O 16.53 17.66 1.13 20.25 19.37 0.88 Topo7T E->O 14.90 22.37 7.47 21.05 14.84 6.21 Topo9T E->O 14.81 19.45 4.64 15.73 13.33 2.40
Topo52T E->O 12.50 18.29 5.79 16.20 12.58 3.62 Average
3.33
2.63
06/03/2015 P a g e | 38
n° pilio axis Lat SE Centre SE Centre-Lat Centre NW Lat NW Centre-Lat
Topo27T SE->NW 10.34 15.52 5.18 16.14 9.76 6.38
Topo28T SE->NW 12.22 15.42 3.20 15.81 11.27 4.54
Topo29T SE->NW 14.10 15.95 1.85 15.02 13.45 1.57
Topo30T SE->NW 15.83 17.61 1.77 16.48 10.91 5.57
Topo33T SE->NW 11.53 18.40 6.87 16.08 14.28 1.80
Topo34T SE->NW 15.83 21.52 5.69 20.41 17.42 2.99
Topo38T SE->NW 16.77 21.65 4.88 18.38 15.89 2.49
Topo39T SE->NW 15.42 19.85 4.42 18.32 15.61 2.71
Topo3T SE->NW 13.90 19.75 5.85 20.15 13.25 6.90
Topo16T SE->NW 16.06 18.94 2.88 19.03 16.47 2.56
Topo53T SE->NW 16.08 17.95 1.87 19.79 15.53 4.26
Topo54T SE->NW 14.96 17.65 2.70 17.42 15.35 2.07
Topo55T SE->NW 16.11 17.74 1.63 18.67 16.57 2.10
Topo56T SE->NW 14.12 19.79 5.67 18.76 16.25 2.51
Topo43T SE->NW 17.68 19.86 2.18 20.43 17.93 2.50
Topo45T SE->NW 15.54 18.77 3.22 20.08 18.23 1.85
Topo46T SE->NW 15.66 18.94 3.28 19.59 17.56 2.03
Topo4T SE->NW 15.30 17.39 2.09 16.19 12.13 4.06
Topo7T SE->NW 12.64 16.21 3.56 17.21 12.51 4.70
Topo9T SE->NW 13.48 16.31 2.84 18.95 13.89 5.06
Topo52T SE->NW 15.08 17.31 2.24 16.81 14.75 2.06
Average 3.52 3.37