Sensivity of Crop Yields to Temperature and Rainfall Daily Metrics in Senegal

Abstract

Senegal is a sub-Saharan country marked by rainfed agriculture, which is under the recurrent threat of climatic upheaval, mostly due to irregular rainfall and temperature. This study shows evidence of the influence of daily rainfall metrics on crop (groundnut and millet) yields. Statistical analysis has been carried out using observational datasets and over the period 1961-2018. The results show an increase in temperatures in our zone, which is in line with the decrease in groundnut yields. Also, significant correlations of 0.81 and 0.69 between the total rainfall indices and groundnut and millet have been found respectively. Rainfall intensity, length, and distribution would contribute up to 66% and 49% to the variability in groundnut and millet yields respectively. A decrease in crop yields is considerable during dry periods (18% for groundnut and 10% for millet) due to the occurrence of long dry spells and low rainfall distribution. The groundnut yield appears most affected by these indicators, while millet is more resistant in dry conditions. To face the major future challenges, it is essential to ensure that changes in these metrics are effectively taken into account in agro-climatic model simulations.

Full text

American Journal of Rural Development, 2020, Vol. 8, No. 1, 1-11
Available online at http://pubs.sciepub.com/ajrd/8/1/1
Published by Science and Education Publishing
DOI:10.12691/ajrd-8-1-1
Sensivity of Crop Yields to Temperature and
Rainfall Daily Metrics in Senegal
Abdou Kader Toure1,*, Moussa Diakhaté1, Amadou Thierno Gaye1, Mbaye Diop2, Ousmane Ndiaye3
1Laboratoire Physique de l’Atmosphère et de l’Océan - Siméon Fongang (LPAO-SF), UCAD, Dakar, Senegal
2Institut Sénégalais de Recherche Agricole (ISRA), Dakar, Senegal
3Agence Nationale de l’Aviation Civile et de la Météorologie (ANACIM), Dakar, Senegal
*Corresponding author: abdoukader.toure@ucad.edu.sn
Received January 04, 2020; Revised February 13, 2020; Accepted March 04, 2020
Abstract Senegal is a sub-Saharan country marked by rainfed agriculture, which is under the recurrent threat of
climatic upheaval, mostly due to irregular rainfall and temperature. This study shows evidence of the influence of
daily rainfall metrics on crop (groundnut and millet) yields. Statistical analysis has been carried out using
observational datasets and over the period 1961-2018. The results show an increase in temperatures in our zone,
which is in line with the decrease in groundnut yields. Also, significant correlations of 0.81 and 0.69 between the
total rainfall indices and groundnut and millet have been found respectively. Rainfall intensity, length, and
distribution would contribute up to 66% and 49% to the variability in groundnut and millet yields respectively. A
decrease in crop yields is considerable during dry periods (18% for groundnut and 10% for millet) due to the
occurrence of long dry spells and low rainfall distribution. The groundnut yield appears most affected by these
indicators, while millet is more resistant in dry conditions. To face the major future challenges, it is essential to
ensure that changes in these metrics are effectively taken into account in agro-climatic model simulations.
Keywords: temperature, rainfall, metrics, yield, climate impacts, sensivity
Cite This Article: Abdou Kader Toure, Moussa Diakhaté, Amadou Thierno Gaye, Mbaye Diop, and
Ousmane Ndiaye, “Sensivity of Crop Yields to Temperature and Rainfall Daily Metrics in Senegal.” American
Journal of Rural Development, vol. 8, no. 1 (2020): 1-11. doi: 10.12691/ajrd-8-1-1.
1. Introduction
Climate variation and change have a strong influence
on agriculture over West Africa (e.g. [1,2]). The impacts
of climate on agriculture vary from one region to another.
Developing countries in tropical latitudes tend to be
more exposed to socio-economic conditions. According to
the reference [3] report in many cases, their endemic
poverty increases the risk and severity of natural disasters
(e.g. [4,5,6]).
In Senegal, Reference [7] show that climatic factors
contribute to a major role in the distribution of plant
landscapes and soil types. In addition, the inter and intra
annual variability of precipitation has an impact on the
state of cultural intensity which is very low on average.
The climatic upheaval (decrease in rains, considerable
occurrence of extremes, increase in temperatures) will
certainly have repercussions on water resources and on
agricultural production (e.g. [8,9]) as well as on the
varietal map (e.g. [10]).
However, most of this work has focused only on the
impact of seasonal accumulations and not on seasonal
mean of rainfall metrics such as the intensity of rainy days,
the number of rainy days, the length of the season, the
dry and wet sequences, among others. Even though the
cumulative rainfall remains a good indicator of the
season's deficit or surplus, it is nevertheless insufficient to
explain the variability of agricultural yields (e.g [11]).
The length and the distribution of rain throughout the
season are key factors (e.g. [12]). Long or frequent dry
spell during the growing season (in the vegetative and
reproductive phases) can for instance lead a decrease of
agricultural yields (e.g. [13,14]). We also know that, for
example, false starts and early cessation of the rainy
season impact crop growth (e.g. [15]).
The objective of this study is to provide a
detailed analysis of the influence of rainfall metrics on
the speculations yields such as groundnut and millet in
Senegal. Similar analyzes were carried out by Reference
[16,17,18] but in other geographic areas and / or with
different methodologies. Reference [16] focused on Africa
and Asia and did not take daily rainfall metrics into
account. Reference [17] worked on the socio-economic
model to be adopted in Senegal by family farms for better
resilience against climate change. In the same perspective,
reference [18] looked only at the impact of climate
change on dry cereal crop yields by considering 35
possible climate scenarios combined with precipitation
anomalies and temperature anomalies using the SARRAH
agronomic model. Thus, the specificity of this study
comes down to the in-depth work of rainfall metrics and
temperature, a study based on the descriptive analysis of

2 American Journal of Rural Development
their influence on agricultural yields in Senegal. The study
is organize as follow. Section 1 present the study area,
section 2, the data and the methodology, section 3,
the results and finally, section 4, the conclusion and
discussions.
1.1. Study Area
Senegal is a country located in the far west of the
African continent with an area of 197,161 km2. Then 3.8
million ha of the land are arable (20% of the country's
surface) within which 3.352 million ha sown in 2018 (e.g.
[19]). Its rainfall is distributed in the North by about 300
mm / year, in the Center about 600-800 mm / year and
in the Southeast about 1200 mm / year (see figure
on inter-annual and decennial variability). Precipitation
occurs in a single rainy season which extends from
June - July to October - November and is caused by the
northward shift of the Intertropical Convergence Zone
(ITCZ) e.g.[20]). This precipitation is characterized by
high spatial and temporal variability and by periodic
droughts, particularly during the 1970s and 1980s (e.g.
[21]). However, since 2002, the amounts of annual rain
collected in Senegal, as in West Africa (e.g. [15,22,23]),
show a positive trend, without however reaching
the values of the 1950s. Its population is estimated at
16,209,125 inhabitants. 60% of this population live in
rural areas and 2/3 are less than 25 years old, according
to reference [24]. The agriculture is mainly rainfed.
According to the Reference [25], agriculture occupies
about 60% of the active population. It is largely dominated
by family farms and constitutes around 95% of the
country's agricultural land. However, the high variability
of intra- and inter-seasonal precipitation, coupled with
population growth and declining soil fertility, means that
local households continue to face considerable food
insecurity (reduced yields, drought, deficit food stocks,
among others).
The study focused mainly on the areas of the groundnut
basin which concern millet and groundnut speculations.
This zone is characterized by a generally flat relief with
three types of soils: tropical ferruginous soils weakly
leached on sand (Dior soils) on sandy-clayey sandstone
or battleships on shale, hydromorphic soils on clay
and halomorphic soils on clay alluvia. Leached tropical
ferruginous soils are suitable for a wider range of
crops because of their greater mineral richness but also
because they are located in areas that are relatively better
supplied with water. They are suitable for groundnut,
millet, corn, rain-fed rice, sorghum, cotton, arboriculture
and market gardening. Halomorphic soils on clay alluvia
are chemically rich, but their excessive salinity and the
difficulties of working the soil are the main constraints.
The soils with the widest range of suitability are mostly
alluvial hydromorphic soils which have the advantage of
having a great depth, a balanced texture, a large capacity
for water, a relatively good natural fertility (e.g. [26]).
The departments chosen are Bambey, Mbour, Thiès,
Kaolack, Fatick and Diourbel. Their choice is defined by
the actual availability of their climate data for the entire
study period. Local farming systems include a mixture of
compound fields and bush cultivated by rural households.
2. Data and Methods
2.1. Agronomic Data
The agronomic data for millet and groundnut cover all
42 agricultural departments of Senegal over the period
from 1961 to 2018. These data are provided by the
Direction de l'Analyse, de la Prévision et des Statistiques
Agricoles (DAPSA) and structured in terms of area (Ha),
production (Tons) and yield (Kg/Ha).
In Senegal, the small seeded varieties are intended for
the oil mill and the larger seeded varieties are theoretically
intended to be sold and consumed as such (in seed), they
are called groundnuts . Due to the North-South climatic
rainfall gradient, the northern part of the groundnut basin
(GB) requires varieties with short (90 days) or very short
(80 days) cycles. TRPs are produced in the South of
the GB and in Upper Casamance. In varietal terms for
Senegal, two new varieties appeared in the mid-1990s, the
variety with a very short cycle, GC8-35 (80 days), an
original creation, and the variety Fleur 11 (90 days),
an introduction-adaptation from China, which has the
same cycle but a higher yield than the 55-437. Varieties
55-33, 73-9-11 and SRV1-19 have been registered
(e.g. Table 1).
This table shows all the varieties listed and their cycle
in days.
Table 1. Presentation of the most commonly used groundnut
varieties and according to their cycle
Varieties cited (in bold, those used in the study area)
Early: 75 to 90 days, use in
oil mills or "green":
55-437 : 90j
Flower11 : 90-95 d
SRV1-19: 90d
55-33: 80d
73-9-11 :80d
GC8-35: 80d
78-936: 75d
Semi-late and late: 105 to 120 days
mixed use or TREE
73-33: 105-110j (mixed use *)
GH119-20: 115d
PC7 79-79 :120j
H75-0: 120d
28-206: 120d ( mixed, Casamance)
In order to distinguish the contribution of the metrics,
an evaluation is made in the study area essentially on the
groundnut basin where varieties 55-437 and flower 11 are
the most used according to the varietal map updated by
research in 1996 (e.g. [10]), due to the new climatic
conditions and new varieties, which take into account the
shift in isohyets (e.g. Figure 1). This is an "ideal" varietal
distribution based on recent regional agro-climatic data,
the results of multi-local experiments and available
varieties. Depending on the growing areas, the varieties
present also differ according to their destination, mouth or
oil mill. The varietal map was the main tool for guiding
seed production when the latter was centralized at the
State level (e.g. [27,28]).
In addition, millet is often grown in permutation with
groundnuts in this area for the most part. They are often
characterized by their vegetative cycle and productivity.
The Souna 3, Thialack 2 and IBV varieties are the most
used by farmers in this area. However, with the increase in
rainfall since the 2000s, some crops that were previously
abandoned are now being reintroduced (e.g. [17]). All the
varieties are listed in the Table 2.

American Journal of Rural Development 3
Figure 1. The 1996 variety map. This map takes into account the "southward shift of isohyets", a rainfall restriction of the order of 20% that affected the
north and center of the Senegalese groundnut basin in the early 1980s (e.g. [10]).
Table 2. Presentation of the most commonly used millet varieties and
according to their cycle
Varieties Areas Cycle (days) Yields (T/ha)
Souna 3 Fatick - Kaolack 85 - 95 2.4 - 3.5
IBV 8001 Kaolack - Fatick 90 2.4 - 3.4
IBV 8004 Thiès - Diourbel -
Louga 75 - 85 1 - 2.5
IBMV
8402
Thiès - Diourbel 75 - 95 2
ISMD
9507 Thiès - Diourbel 85 2.5
Gawane Thiès - Diourbel 85 2.5
Thialack 2 Fatick - Kaolack 95 2 - 3
2.2. Climatic Data
In this study, we used daily rainfall data from
meteorological stations in the departments of Bambey,
Diourbel, Fatick, Kaolack, Mbour and Thiès over the
period from 1961 to 2010. It should be noted that these
six departments are all included in an area of very
high agricultural production in Senegal called the
Groundnut Basin. However, it is important to point out
that the Groundnut Basin extends further south into other
administrative departments of the country.
In addition, the temperature data collected are at
monthly level and cover most of the study period from
1978 to 2008. They cover all regions of the country. In
addition, the data is divided into average temperature,
minimum temperature and maximum temperature. For this
purpose, we will divide them more into two groups: a
cold period (November, December, January, February,
March) and a hot period (April, May, June, July, August,
September, October). All these data come from the
National Agency for Civil Aviation and Meteorology
(ANACIM), which collects them.
2.3. Methods
Based on the data presented in the previous section,
rainfall metrics over the period 1961 to 2010 are
calculated following the definition of Expert Team on
Climate Change Detection and Indices (ETCCDI e.g.
[29]). Correlation tests are carried out to quantify the
relationships that may exist between the defined metrics
and millet and groundnut yields. Note that all data are
calculated using the moving average method (with a
5-year window) before correlation calculations. Finally,
dry and wet year composites are set up using Standard
Precipitation Index (SPI) to analyze the crop yield profile
during these periods. Indeed, according to reference [30],
the evolution of rainfall in Senegal from 1961 to 2010
makes it possible to divide the series into two parts: a dry
period from 1968 to 1998 including 31 years and two
wet periods from 1961 to 1967 and from 1999 to 2011
including 20 years. This decomposition makes it possible
to understand how rainfall metrics varie during these
periods and also to detect the role that they play in
agricultural productivity.
The analysis of rainfall is done in two phases
respectively with the highlighting of the whole period
from 1961 to 2010 then by a comparison between dry and
wet periods during which each is evaluated at the yields of
speculations such as groundnuts and millet.
During the study, temperature datasets (hot and cold
periods), and rainfall metrics (number of wet days NRR,
rain intensity SDII and the number of dry and wet days
during the winter period, length of season, start and end
dates etc.) and yields will be represented. Then, significant
links between mean rainfall and precipitation indices and
agricultural yields of in situ data are investigated through
a test of the significance of correlations between in situ
data and agricultural yields.

4 American Journal of Rural Development
3. Results of the Study
3.1. Evolution of Climate Metrics in Senegal
from 1961 to 2011
3.1.1. Temperature Evolution
According to reference [30], two seasons have been
distinguished in Senegal, the cool season from November
to March (5 months) and the warm one from April
to October (7 months). Figure 2 shows the temperature
evolution in Senegal for these two seasons. It is noticeable
that during all seasons, the temperature rises, but trend is
more important during the cold season in agreement with
the results of the Intergovernmental Panel on Climate
Change (IPCC e.g. [31]). Meaning that the temperature
rises more quickly during cold periods. In this respect, it is
important to take into account this factor which is a
major contributor to climate variation. Reference [18]
have shown that when warming exceeds +2°C, the
negative impacts caused by temperature rise cannot be
compensated by any change in precipitation. In addition,
temperature increases tend to shorten the crop growth
cycle (e.g. [32]). The continuous rise in temperature has a
greater impact on these interannual variations in yield, as
pointed out by reference [33].
Figure 2. Comparison of average hot and cold period temperatures from 1978 to 2008
Figure 3. Graphic representation of the annual total rainfall

American Journal of Rural Development 5
3.1.2. Evolution of rainfall patterns
The warm season consists mainly of the rainy season
expected mostly in the period from May to October. The
analysis of rainfall accumulation and metrics focused only
on the rainy period for this study.
Rainfall in Senegal and particularly in the groundnut
basin is marked overall by two trends: a decrease
from 1960 to 1980 and an increase from 1981 to 2011
(Figure 3). However, this situation includes wet and dry
years which, according to reference [30], have made it
possible to distinguish between two wet periods (1961 to
1967 and 1999 to 2011) and one dry period (1968 to 1998).
An exhaustive and comparative analysis is made between
1961 and 2011.
The graphical representation of the cumulative total
gives a global overview with a high interannual variation
in the period from 1960 to 2011. However, rainfall metrics
would give more information. A total of 14 metrics that
may impact on productivity are identified in the Table 3.
Table 3. Summary of the calculated rainfall indices and their
respective extremes
Rain metrics
Rating
Average value (min - max)
Number of rain NRR 20 - 52
Number of rain for 95
percentile
NR95p 1 - 4
Intensity of rain SDII 12 - 19
Length of season
LS
48 - 128
Date of start on season ONSET 170 - 230
Date of end of season OFSET 275 - 300
Length of wet spell LWS 0 - 3
Number of wet spell NWS 13 - 27
Consecutive wet spell
CWS
0 - 9
Consecutive wet day CWD 1-11
Length of dry spell LDS 4 - 12
Number of dry spell NDS 17 - 27
Consecutive dry spell CDS 1 - 10
Consecutive dry day
CDD
11 - 31
Daily rainfall data have been used to calculate several
metrics in order to attribute the most significant ones to
our agricultural yields, particularly for groundnut and
millet speculation (e.g. Figure 3).
3.2. Impacts of Rainfall Metrics on
Agricultural Yield Variability
In order to avoid subsequent bias using the linear
interpolation method (Replace missing values with the
average of the surrounding valid values; the interval of
neighboring points is the number of valid values above
and below the missing value used to calculate the mean),
the extreme values of agricultural yields were replaced by
the mean between the 3 preceding and 3 following years
that surround them (e.g. [34,35]). Also, in order to fill in
the missing data for the 1982 to 1985 period, the five-year
moving average (Rmean) is used.
From 1961 to 2011, the declining trend in groundnut
yield is consistent with the trend of rising temperatures
over time (e.g. Figure 2). Millet, on the other hand, is
reported to be more resilient to climate variability (e.g.
Figure 4). This results support the Reference [33]. Also
the trend in rainfall is quite similar to that of groundnut
yield. However, the variation in millet yield is not as
consistent. This may be due to the resilience of millet to
water stress. Nevertheless, the climate projections noted
predict a decline in overall agricultural yields. According
to the scenarios defined by the IPCC, these projections
show productivity declines, particularly for short- and
long-cycle millet, of 18% and 28% respectively by 2050
and about 50% by 2100.
In order to know the contribution of each rainfall index
to the agricultural productivity of millet and groundnut,
correlations are calculated between these metrics and the
yields of these speculations.
Figure 4. Graphical representation of groundnut and millet yields (top and bottom, respectively) in the raw state on the left and below the 5-year
moving average between 1960 and 2011 on the right.

6 American Journal of Rural Development
Figure 5. Correlation of millet (*) and groundnut (+) yields with rainfall metrics. Positive correlations in blue and negative correlations at the bottom
For groundnut, nine rainfall metrics are significantly
correlated (NRR, NR95p, NWS, LWS, CWD, CWS, DRY,
CDS, LDS). Thus, metrics of rain length and intensity
would contribute to increasing yields except that the
length of dry sequences and the consequent distribution of
these sequences increase with groundnut yield.
Table 4. Summary of the results obtained by the Stepwisefit method,
which defines the metrics likely to contribute to the regression. In
red are metrics that were not correlated on their own, in bold are
metrics that alone already contribute to agricultural productivity of
millet or groundnut yields. In yellow, the metrics selected by the
indicator selection method for regression
Stepwisefit results with the five-year moving average method
Rainfall metrics Groundnut Millet
LDS IN IN
LWS IN IN
NWS IN OUT
NDS OUT OUT
SDII IN OUT
NR95p OUT IN
NRR IN OUT
LS IN OUT
ONSET OUT OUT
OFFSET IN OUT
CWS IN OUT
CDS OUT IN
CWD OUT IN
CDD OUT OUT
Metrics of rain length and intensity would be
determinant to millet productivity and would appear to be
more sensitive to wet sequences over long periods of
time. Thus, seven rainfall metrics such as intensity
(SDII), number of rainy days (NRR), 95% extreme rainfall
(NR95p), number of wet sequences (NWS), length and
consecutive wet sequences (LWS, CWD, CWS) would
contribute to decreasing millet productivity (significantly
negative correlation).
The length and distribution of rainfall throughout the
season are key factors for some dry cereals (Salack et al.
2011). However, the combination of these indices would
give more credibility to the results obtained. To do this, a
multi-regression model is used to assess the combined
effects of these metrics on yields.
For groundnuts, eight metrics (LDS, LWS, NWS, SDII,
NRR, LS, Offset, CWS) are combined, while for millet
five rainfall indices (LDS, LWS, NR95p, CDS, CWD)
contribute to agricultural productivity (e.g. Table 4).
The results obtained with the regression of the selected
metrics are satisfactory for millet and groundnuts. The
correlations obtained for groundnut and millet are 0.81
and 0.69, respectively. In other words, metrics of rainfall
intensity, length and distribution (LDS, LWS, NWS, SDII,
NRR, LS, Offset, CWS) would account for 66% of the
change in groundnut yield. While those of millet (LDS,
LWS, NR95p, CDS, CWD) would contribute 48% of the
millet yield.
3.2.1. Composite Analysis: Wet and Dry Year
Comparisons
The average length of growing seasons is 93 days in
wet years and 83 days in dry years, a difference of about
10 days (e.g. Figure 7). Wet years are marked by very
early starts and late ends. The number of rainy days is
greater during wet periods (e.g. Figure 8). However, these
two periods are more differentiated by the occurrence of
extreme events such as rain breaks and dry and wet
sequence lengths, among others.

American Journal of Rural Development 7
Figure 6. Model reconstruction curves of in situ groundnut (a) and millet (b) yield data based on rainfall metrics. Continuous line the in situ data,
discontinuous line the model obtained with the metrics selected by the stepwise method for each speculation.
Figure 7. Comparison of Onset and Offset metrics of dry and wet years for six departments for the period 1960 to 2011

8 American Journal of Rural Development
Figure 8. Comparison of the number of wet and dry year NRR rainy days for six departments for the period 1960 to 2011
Table 5. Comparison of Onset and Offset (left) and NRR (right) metrics of dry and wet years for six departments for the period 1960 to 2011
Area
LS Wet Period
(day)
LS Dry Period
(day)
Diff
(Wet-Dry) (day)
Onset
For Wet
Onset
For Dry
Offset
For Wet
Offset
For Dry
Bambey 92 83 9 June 10th June 18th 10 Sept. 9 Sept
Diourbel 88 83 5 June 15th June 16th 11 Sept. 7 Sept.
Fatick 99 83 16 June 6th June 17th 13 Sept. 8 Sept
Kaolack 105 92 13 May 31st June 8th 13 Sept. 8 Sept
Mbour 88 82 6 June 17th June 18th 13 Sept. 8 Sept
Thiès 86 78 8 June 17th June 20th 11 Sept. 6 Sept
Mean LS 93 83 10
Kaolack and Fatick had maximum day lengths of 99
and 105 days respectively, exceeding the wet year average
of 93 days (Table 4). On the other hand, only the Kaolack
station is in excess of the 83-day dry year average. The
number of rainy days is much higher in wet periods and
at all stations. The length of (dry/wet) growing periods
affects groundnut and millet yields.
Average yields in wet years are significantly higher
than in dry years for all millet and groundnut speculations
(e.g. Figure 9). Average yields for wet and dry years are
734 kg/ha to 600 kg/ha for groundnuts and 551 kg/ha to
503 kg/ha for millet. Thus, they decrease by 18% for
groundnuts and 10% for millet, which is more resistant
to rainfall deficits. Figure 9 shows the situation obtained
according to the dry or wet period. In both cases,
groundnut have a higher yield despite the considerable
drop noted during the dry period. However, millet, which
is a dry cereal, is more likely to withstand water stress
In wet years, for groundnuts, the wet sequence
distribution and length metrics (NR95p, LWS, CWD,
CWS, NRR, NWS, SDII) are important in increasing yield
(e.g. Figure 10). On the other hand, the length and
occurrence of dry sequences (LDS, CDS) tend to reduce
groundnut productivity. In addition, millet is more
sensitive to the length of the season (LS) and the end date
of the season (Offset). However, during wet periods,
extreme rainfall (NR95p) and other wet sequences are
determinant to yield development. For this purpose, it
is important to know that millet can undergo the
photoperiodism effect if the insolation is not sufficient for
its chlorophyll. Reference [18] have shown that traditional
millet crops are more sensitive to photoperiods. However,
millet appears to be more resistant to future climatic
conditions.
In dry years, only consecutive wet sequences can
save groundnut yields. However, consecutive lengths and
sequences slow down productivity. For millet, the same
metrics mentioned above in wet years contribute with a
lower degree.
In both cases, the metrics are more sensitive in wet
periods than in dry periods. It is good to take into account
the contribution of certain indices which remain much
more determining and specific according to the given
speculation. In this regard, groundnut are most affected by
the length and intensity of wet sequences. Its performance
increases considerably with the increased input of these
metrics despite the inverse phenomenon caused by the
length and frequency of the dry sequence. In addition,
frequently used varieties such as North (55 - 33, 80 days),
Central (55 - 437, 90 days and Flower 11, 90 - 95 days, 73
- 33, 105 - 110 days), South 69 - 101 (105 - 110 days) are
more exposed to this yield reduction. The same
observation is noted for both short-cycle and long-cycle
millet. On this basis, varieties with a short growing cycle
and resistant to water stress must be multiplied for better
resilience to climate change.

American Journal of Rural Development 9
Figure 9. Comparison of millet (orange) and groundnut (blue) yields in dry and wet years.
Figure 10. Correlation of metrics in dry and wet years for millet and groundnut yields
4. Conclusion and Discussion
This study proposes a descriptive analysis of the
relationships between climatic parameters (temperature
and rainfall metrics) on groundnut and millet yields in the
groundnut basin of Senegal over the period 1961 to 2011.
Climatic data from the National Agency for Civil Aviation
and Meteorology (ANACIM) and agronomic data from
the Directorate of Analysis and Forecasting of Agricultural
Statistics (DAPSA) were used. With regard to rainfall, in
addition to the total accumulation index, 14 other rainfall
indicators indices were calculated and compared with
speculative returns. The direct correlations of each index
were first examined and then with a multilinear regression
model, the combined influences of several metrics on the
speculation were documented. A dry/wet year composite
analysis was also carried out to analyse the sensitivity of
speculation to the metrics.
The results show an increase in temperatures in our area.
A trend that is consistent with the decline in groundnut
yields according to Reference [18]. The latter claim that
the probability of yield reduction appears to be greater
in the Sudanian region (southern Senegal), due to an
exacerbated sensitivity to temperature changes compared
to the Sahelian region (northern Senegal). However, if we
consider millet, which is a dry cereal, the downward trend
is not very marked.
In addition, rainfall metrics play an important role and
are closely linked to agricultural yields. Also, the change
in productivity depends to a large extent on the sensitivity
of these indices to such speculation. Moreover, the trend
shows that groundnuts are the speculation that is most
affected by these metrics, while millet is more resistant.
The rainfall intensity, length and distribution metrics
(LDS, LWS, NWS, SDII, NRR, LS, Offset, CWS) and
(LDS, LWS, NR95p, CDS, CWD) would contribute 66%

10 American Journal of Rural Development
and 49% to the change in groundnut (r=0.81) and millet
(r=0.69) yields respectively. However, some varieties
with a short growing cycle, due to their ability to adapt,
remain less sensitive to deficits in rainfall intensity and
distribution.
Also, the decrease in crop yields is considerable during
dry periods (18% for groundnuts and 10% for millet) due
to the occurrence of long dry spells and low rainfall
distribution. In order to face the major future challenges, it
is essential to ensure that they are effectively taken into
account in agro-climatic model simulations in order to
reduce the uncertainties in projections. Thus, the potential
of the green economy must be expressed in the fight
against climate change and for the resilience of ecosystems,
populations and economies in terms of governance and
institutional and political capacities (e.g. [36]).
The impact of the metrics on these agricultural yields
creates doubts about the projections that metrics impacts
agricultural productivity (e.g. intra-seasonal dry spells that
have a differential impact depending on the phenological
stage of the crop according to reference [5])) in the future.
On this basis, these determinants need to be incorporated
in order to refine future projections. Also, oceanic
teleconnections with these indices remain a major challenge
to gauge their relationship and create synergy with
agricultural yields. Already, according to reference [37],
there is an interrelationship between groundnut yields,
interannual variability in rainfall and sea surface temperature.
However, it is important to look at the rainfall metrics to
see how they relate to these three parameters. For example,
reference [38] showed that oceanic forcing associated with
Sahelian rainfall vary with rainfall intensity. Reference
[14] have also shown that long dry spells (dependent on
false starts) are also sensitive to the combined warming of
the entire tropical Atlantic and equatorial Pacific and
Indian regions.
Acknowledgements
This document was produced with the financial support
of Mister Oumar SANE, Director of Agriculture in
Senegal. The contents of this document are solely
the liability of Abdou Kader TOURE and under no
circumstances may be considered as a reflection of
Agricultural direction in Senegal. Abdou Kader TOURE
is grateful to the University Cheikh Anta Diop of Dakar
where the LPAO-SF (the host lab) is located and
Bounama DIEYE for supporting this thesis. We thank
the reviewers for their constructive comments and
suggestions.
References
[1] HANSEN, J. W. (2002). Realizing the potential benefits of climate
prediction to agriculture: issues, approaches, challenges.
Agricultural Systems 74 309-30.
[2] ORAM, P. A. (1989). Sensitivity of agricultural production to
climatic change, an update. In International Symposium on
Climate Variability and Food Security in Developing Countries,
New Delhi (India), 5-9 Feb 1987. IRRI.
[3] PELLING, M., MASKREY, A., RUIZ, P., HALL, L., PEDUZZI, P.,
DAO, Q.-H., MOUTON, F., HEROLD, C. and KLUSER, S. (2004).
Reducing disaster risk: a challenge for development.
[4] SULTAN, B., DEFRANCE, D. and IIZUMI, T. (2019). Evidence of
crop production losses in West Africa due to historical global
warming in two crop models. Scientific Reports 9 1-15.
[5] SULTAN, B., ROUDIER, P. and TRAORE, S. (2015). Les impacts du
changement climatique sur les rendements agricoles en Afrique de
l’Ouest. In Les sociétés rurales face aux changements climatiques
et environnementaux en Afrique de l’Ouest Synthèses (B. Sultan,
R. Lalou, M. Amadou Sanni, A. Oumarou and M. A. Soumaré, ed)
pp 209-25. IRD, Marseille.
[6] NDONKY, A., LALOU, R. and NDIAYE, I. (2019). Analyse de la
diffusion spatiale du sanio dans le sud-ouest du bassin arachidier
du Sine (Sénégal). Cybergeo : European Journal of Geography.
[7] SYLLA C N ET AL. (2010). Deuxième communication nationale à
la Convention Cadre des Nations Unies sur les Changements
Climatiques. 112-3.
[8] A MOORHEAD. (2009). Climate change, agriculture and food
security: a strategy for change. Alliance of the CGIAR Centers.
[9] NDIAYE, O., WARD, M. N. and THIAW, W. M. (2011).
Predictability of Seasonal Sahel Rainfall Using GCMs and
Lead-Time Improvements Through the Use of a Coupled Model. J.
Climate 24 1931-49.
[10] CLAVEL, D. and N’DOYE, O. (1997). La carte variétale de
l’arachide au Sénégal. Agriculture et Développement.
[11] SARR, B., KAFANDO, L. and ATTA, S. (2011). Identification des
risques climatiques de la culture du maïs au Burkina Faso.
International Journal of Biological and Chemical Sciences 5
1659-75.
[12] BALME, M., GALLE, S. and LEBEL, T. (2005). Démarrage de la
saison des pluies au Sahel : variabilité aux échelles hydrologique
et agronomique, analysée à partir des données EPSAT-Niger. 16 8.
[13] WINKEL, T. and DO, F. (1992). Caractères morphologiques et
physiologiques de résistance du mil (Pennisetum glaucum (L.) R.
Br.) à la sécheresse. Agron. trop. (1975) 46 339-51.
[14] SALACK, S., GIANNINI, A., DIAKHATÉ, M., GAYE, Amadou. T.
and MULLER, B. (2014). Oceanic influence on the sub-seasonal to
interannual timing and frequency of extreme dry spells over the
West African Sahel. Clim Dyn 42 189-201.
[15] SALACK, S., MULLER, B. and GAYE, A. T. (2011). Rain-based
factors of high agricultural impacts over Senegal. Part I:
integration of local to sub-regional trends and variability. Theor
Appl Climatol 106 1-22.
[16] KNOX, J., HESS, T., DACCACHE, A. and WHEELER, T. (2012).
Climate change impacts on crop productivity in Africa and South
Asia. Environ. Res. Lett. 7 034032.
[17] MÜLLER, C., CRAMER, W., HARE, W. L. and LOTZE-CAMPEN, H.
(2011). Climate change risks for African agriculture. PNAS 108
4313-5.
[18] SULTAN, B., ROUDIER, P., QUIRION, P., ALHASSANE, A., MULLER,
B., DINGKUHN, M., CIAIS, P., GUIMBERTEAU, M., TRAORE, S. and
BARON, C. (2013). Assessing climate change impacts on sorghum
and millet yields in the Sudanian and Sahelian savannas of West
Africa. Environ. Res. Lett. 8 014040.
[19] PA. (2019). Rapport du Programme Agricole 2019, Ministère de
l’Agriculture et de l’Equipement rural du Sénégal.
[20] SULTAN BENJAMIN. (2002). Etude de la mise en place de la
mousson en Afrique de l’Ouest et de la variabilité intra-
saisonnière de la convection : Applications à la sensibilité des
rendements agricoles. thesis, Paris 7.
[21] ROUDIER, P., MULLER, B., D’AQUINO, P., RONCOLI, C., SOUMARE,
M. A., BATTE, L. and SULTAN, B. (2014). The role of climate
forecasts in smallholder agriculture: Lessons from participatory
research in two communities in Senegal. Climate Risk
Management 2 42-55.
[22] BODIAN, A. (2014). Caractérisation de la variabilité temporelle
récente des précipitations annuelles au Sénégal (Afrique de
l’Ouest). Physio-Géo. Géographie physique et environnement
297-312.
[23] FAYE, C., SOW, A. A. and NDONG, J. B. (2015). Étude des
sècheresses pluviométriques et hydrologiques en Afrique tropicale :
caractérisation et cartographie de la sècheresse par indices dans le
haut bassin du fleuve Sénégal. Physio-Géo. Géographie physique
et environnement 17-35.
[24] ANSD. (2019). Projections démographiques.Available at
http://www.ansd.sn/index.php?option=com_ansd&view=titrepubli
cation&id=30.
[25] FAO. (2011). The State of Food Insecurity in the World
2011.Available at http://www.fao.org/3/i2330e/i2330e00.htm.

American Journal of Rural Development 11
[26] KHOUMA M. (2002). Quatorzième réunion du Sous-Comité Ouest
et Centre Africain de Corrélation des Sols pour la Mise en Valeur
des Terres: Abomey, Bénin, 9 - 13 octobre 2000. Organisation des
Nations Unies pour l’Alimentation et l’Agriculture, Rome.
[27] MAYEUX A. (2007). Rapport de synthèse de l’opération de
production d’arachides de qualité 2006. 34.
[28] JACOB, N. and SCHILLING, R. L. (2001). Lévy-Type Processes and
Pseudodifferential Operators. In Lévy Processes: Theory and
Applications (O. E. Barndorff-Nielsen, S. I. Resnick and T.
Mikosch, ed) pp 139-68. Birkhäuser, Boston, MA.
[29] ETCCDI. (2013). Expert Team on Climate Change Detection and
Indices.Available at http://etccdi.pacificclimate.org/.
[30] SAGNA, P., NDIAYE, O., DIOP, C., NIANG, A. D. and SAMBOU, P.
C. (2016). Les variations récentes du climat constatées au Sénégal
sont-elles en phase avec les descriptions données par les scénarios
du GIEC ?
http://irevues.inist.fr/pollution-atmospherique. Available at
http://lodel.irevues.inist.fr/pollutionatmospherique/index.php?id=5
320&format=print.
[31] BARROS, V. R., DOKKEN, D. J., MACH, K. J., MASTRANDREA, M.
D., BILIR, T. E., CHATTERJEE, M., EBI, K. L., ESTRADA, Y. O.,
GENOVA, R. C. and GIRMA, B. (2017). Climate Change 2014:
Impacts, Adaptation, and Vulnerability. Part A: Global and
Sectoral Aspects. Contribution of Working Group II to the Fifth
Assessment Report of the Intergovernmental Panel on Climate
Change, CB Field and others (eds.), Cambridge, Cambridge
University Press.
[32] ROUDIER, P., KANE, C., LEBLOIS, A., SULTAN, B., WEBER, S.
(2019). Deux exemples de stratégies de gestion du risque agricole
en Afrique de l’Ouest : services climatiques et assurances
indicielles. researchgate.net 18.
[33] SCHLENKER, W. and LOBELL, D. B. (2010). Robust negative
impacts of climate change on African agriculture. Environ. Res.
Lett. 5 014010.
[34] HATHOUT, A. (1983). La régression d’après un nombre variable de
voisins. 9.
[35] BENZECRI, J.-P. (1982). Construction d’une classification
ascendante hiérarchique par la recherche en chaîne des voisins
réciproques. Cahiers de l’analyse des données 7 209-18.
[36] GAYE, A. T., LO, H. M., SAKHO-DJIMBIRA, S., FALL, M. S. and
NDIAYE, I. (2015). Sénégal: Revue du contexte socioéconomique,
politique et environnemental. 88.
[37] CAMBERLIN, P. and DIOP, M. (1999). Inter-Relationships Between
Groundnut Yield in Senegal, Interannual Rainfall Variability and
Sea-Surface Temperatures. Theor Appl Climatol 63 163-81.
[38] DIAKHATÉ, M., RODRÍGUEZ-FONSECA, B., GÓMARA, I., MOHINO,
E., DIENG, A. L. and GAYE, A. T. (2018). Oceanic Forcing on
Interannual Variability of Sahel Heavy and Moderate Daily
Rainfall. J. Hydrometeor. 20 397-410.
© The Author(s) 2020. This article is an open access article distributed under the terms and conditions of the Creative Commons
Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).