Viewed, Doi:10.20944/Preprints201702.0014.V1

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

VARIABILITY OF TEMPERATURE, PRECIPITATION AND POTENTIAL EVAPOTRANSPIRATION TIME SERIES ANALYSIS IN REPUBLIC OF BENIN

1*Fèmi E. Hounnou, 1Houinsou Dedehouanou

1Laboratory of Rural Economics and Farm Management; School of Economics, Socio-Anthropology and Communication for rural development; Faculty of Agronomic Sciences, University of Abomey-Calavi, 01 BP 526 Republic of Benin Cotonou, Benin.

ABSTRACT

Long-term temperature and precipitation trends are two of the major determinants of climate variability at geographic scale. This study aims to determine past trends in climatic parameters in different regions of Benin. Six stations throughout Benin provided daily records of precipitation, potential evapotranspiration, minimum and maximum temperatures. Those records were used for the detection of probable trends. Time series data (1960-2016) of mean daily maximum and minimum temperature, mean daily precipitation and potential evapotranspiration were analyzed. Descriptive statistics, graphs and test of comparison were used in order to demonstrate possible trends or differences. The results indicate that the minimum and maximum temperatures have increased at all region of Benin. Likewise, precipitation during the same period revealed a decreasing trend. In addition, the annual precipitation analysis showed disparate coefficients of variation according to the stations explaining the abnormal distribution with large discrepancies among years. The comparison of the mean state of the minimum and maximum temperatures between the periods 1960-1988 and 1989-2016 exhibited a significant difference at five stations. About potential evapotranspiration, the mean difference indicated the shortage through five stations, but significant only at one station. However, there was no difference between the two periods with respect to average annual precipitation.

Keywords: Climate change, Temperature, Potential evapotranspiration, Mean comparison test, Benin.

1. INTRODUCTION Developing countries, particularly African countries in the south of the Sahara, are experiencing a high population growth with proven impacts on food security [1]. Food security is considered by [2] as a situation that requires the availability, accessibility and proper use of food. In order to achieve such results, agriculture has been recognized as the essential pillar of food security [3]. www.ijaer.in Copyright © IJAER 2018, All right reserved Page 991

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ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

In addition to its role in job and income generation, agriculture is a source of food availability in response to the food need for population growth. Thus, food security, hunger and poverty reduction and human development are largely dependent on the agricultural sector and are a main priorities for the various public policies [4]. In addition, in agricultural-oriented countries such as Benin, securing food availability is a major challenge. Indeed, the production of staple food is largely rainfed and is subject to significant fluctuations due to climate variability with negative effects on the agricultural productivity [5]. Global warming caused by Green House Gas (GHG) emissions in the atmosphere is nowadays indisputable [6]. During the past century, concentration of atmospheric CO2 had increased significantly, that induced the rising of global average temperature of 0.74 °C compared to the pre-industrialization era [7]. The adverse impacts of climate change on natural resources, the biodiversity through the many services they provide to humanity have generated renewed interest for precipitation trends analysis in recent decades [8]. According to [9], extreme phenomena represent the important aspect of climate analyses and it is generally accepted that changes in the precipitation set may be associated with changes in the frequency or quantity of precipitation by events, or the combination of frequency and amount of precipitation. For [8,10], daily precipitation series should be analyzed in order to improve the understanding of precipitation behavior in a country or a region. Precipitation and air temperature are two key variables in the field of climate science and Hydrology [11]. According to those authors, precipitation is an essential component in the relationship between rainy and dry periods and influences the assessment of floods and droughts as well as mitigation strategies. Also, temperature plays an important and well-known role in evaporation, perspiration and water demand (both for animal and human), and thus significantly affects water requirements and strategies to ensure its availability. The effects of precipitation and temperature variations raised up an explicit assessment of their trend and their impact on the sectors of activity directly or indirectly related to natural environment for the water resources management. Temperature and precipitation variations influence Potential Evapotranspiration (PET). Considered as one of the most significant components of the hydrological cycle, PET is influenced directly or indirectly by the atmospheric parameters, soil and plant characteristics, and water availability [12].

Ref. [13] defines climatic variability as changes in the average state and other climatic statistics on all spatial and temporal scales, beyond individual weather events. According to this report, the term "climatic variability" is often used to refer to deviations from climate statistics over a period of time (for example, one month, one season or one year) compared to long-term statistics for the same period calendar. Climatic variability is measured by these deviations, which are www.ijaer.in Copyright © IJAER 2018, All right reserved Page 992

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

usually characterized as anomalies. Whereas climate change refers to a statistically significant change in the average climate state or its variability, persisting for an extended period of time (often decades or more). Africa is one of the continent most vulnerable to climate change and variability due to its low capacity for adaptation [14]. According to [15], the great climatic parameter variations and the occurrence of extreme climatic events are identified as a threat to maintain human livelihoods and their well-being. Temperature fluctuations and the disproportionate distribution of precipitation over time and space create a challenge for practicing economic activities, food insecurity and household poverty reduction. According to [16], Benin has been confronted in the last decade with increasingly unpredictable rains and sometimes disturbances of a whole rainy season. Benin brings together several regions with diversified physical (soil, hydrological, plant) and climatic characteristics providing an important asset for the country. Agricultural activities are vulnerable to temperature rising and persistence droughts; intense and unusual precipitation can lead to erosion and flooding. The reduction of agricultural production combined with population growth can lead to food unavailability and the blow to food insecurity. The fifth IPCC report [17] states that understanding the evolution of precipitation is crucial for the implementation of adaptation strategies in order to manage and reduce the risks of precipitation variability on systems such as agriculture and forestry and to strengthen the level of resilience. For this reason, this study aims to determine the seasonal variability of climatic parameters by analyzing trends in temperature and precipitation variations over the period 1960-2016 in Benin. Benin was periodically affected by climatic irregularities parameters resulting in droughts, floods, thunderstorms with strong winds and rains. According to data from the Second National Communication, Benin has experienced since 1984 more than 15 major disasters with significant consequences (including drought in 1984; floods in 1985, 1994, 1996, 1997, 2006 and 2010; thunderstorms in 2005; etc.) [18]. Drought sequences are recorded throughout the country, affecting the livelihoods of households, especially activities directly linked to natural conditions. In 2009, Minister of agriculture of Benin demonstrated during Climate Change Vulnerability Assessment that four agro-ecological zones are particularly vulnerable. According to [19], current climate challenges, with the resulting uncertainties and consequences for environment, well-being and production activities of rural communities, accentuate difficulties and poverty level. Thus the goal of Beninese households’ food security is threatened.

Atmospheric temperature trend In recent years, many scientific studies have been involved in climate variability trends analysis [6, 8, 11, 15, 20-22]. Overall, temperature trend seems to be clear globally (11). According to www.ijaer.in Copyright © IJAER 2018, All right reserved Page 993

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

[17], global mean annual temperature for land surface and ocean air in combination had increased by 0.65 – 1.06 °C during the period 1880 – 2012. Differences or at least uniformity of the findings occur as soon as the spatial scale is reduced. The average land surface temperature has increased in all parts of the world by referencing (23) in Asia; (24,25) in Europe; [26,27] in America; [15,16,28,29] in Africa. However, some countries, regions or localities have non- significant trends in temperature evolution [16,27]. Ref [11] determined that the variation in the long-term events of temperature appears to be present, especially at relatively small spatial and temporal scales.

Precipitation trend Previous studies on long-term precipitation event show a variation on the distribution and amount of precipitation on different spatial and temporal scales. Depending on geographical position, ref [11,30] have reported that during the twentieth century, annual precipitation average at the land surface is increased from 7 to 12 % in high and mid-latitudes and towards the upper end of the scale between 30 and 85 °C and only 2 % in the latitude regions from 0 to 55 °C South. At smaller scale, ref [31] showed during their research on long-term precipitation (1901 – 2009) in the Mediterranean region that trends were generally negative. A slightly positive trend has been detected in the sub-regions of North Africa, southern Italy and the west of the Iberian Peninsula [11]. Contrasts in the research results were found in the Middle East by [32]; in Europe by [33,34]; in America by [27,35] with trends increasing or decreasing trends of climatic parameters at the different meteorological stations in the same country. Studies in recent decades have indicated that extreme precipitation events are common in some regions and rare in others [36]. In such a situation of fluctuating precipitation, ref [37] believe that understanding and anticipating climate events is a major challenge for economic development and food security in sub-Saharan Africa. Previous authors cited above suggest that the long-term trend of temperature and precipitation is characterized by greater spatial variability, indicating a higher proportional dependence on regional and local conditions. In this case, small scale analysis (tens or hundreds of thousands of km²) may be required in practical implementations. Potential evapotranspiration (PET) The total losses of water from vegetation – both as evaporation from the soil and transpiration from the plants is qualified as evapotranspiration [38]. Changing in climatic conditions will affect evapotranspiration, which affects the crop water requirement, and then water management for agriculture sector and food production [39]. As mentioned by [14], the recent global temperature increases have been the highest in the last century. From this result, questions related to spatial and temporal changes in climatic variable water loss is become the core of www.ijaer.in Copyright © IJAER 2018, All right reserved Page 994

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

many debates, and many scholar works are increasingly going on universal [40-44]. Accordingly to [45], evapotranspiration is evaluated through potential evapotranspiration (PET), which represents the maximum evaporative demand on a reference grass crop under climatic conditions where water availability is not a limiting factor. Turoglu [46] mentioned that PET is directly related to temperature and represents an important parameter in the water balance. Consequently, temperature and precipitation factors play a decisive role on PET. Several authors have investigated on the PET fluctuation through spatial and temporal scale in different regions and most of them lead to changes in evapotranspiration, certainly due to the warming of the earth [30,47-52]. But, [54] has indicated that climate warming is almost proved universally, the PET trend is not obvious and may increase or decrease in function of climatic events and locations. The global warming will lead to a possible intensification of the hydrological cycle resulting from increase of precipitation and PET [55,56]. Many studies in this domain reveal contradictory results. In instance, [57] in England; [41] in France; [58] in Africa (Nile River); [59] in Burkina- Faso; [60] in Benin; [61] in India; [62-64] in Iran; [65] in China; etc. have pointed out the increasing trends of PET in the long-term whereas downward trends were revealed by [40,66] in India; [67] in Central and East Asia (Tibetan Plateau); [38,65] in China; [68] in America; [53] in Benin; etc. This study aims at comparing PET state by two periods of time in order to find out which period has PET deficit. 2. METHODOLOGY 2.1. Study Area Benin is a small country located in West Africa with 114763 km² area [69]. It is surrounded by four neighboring countries: Burkina-Faso to the north-west, Niger to the north, Nigeria to the east, Togo to the West, and the Atlantic Ocean to the south. Benin is highly dependent on natural resources and agriculture growth is critical for many households related to climate sensitive sectors. Benin is covered by several types of climate. To the south, the climate is subequatorial with two rainy seasons and two dry seasons, but disturbances are recorded in recent years making it difficult to separate the seasons. In the north, the climate is of Sudanian region, with one rainy season and one dry season. Precipitation varies from 900 mm to 1450 mm per year and temperatures fluctuate between 22 °C and 37 °C. Taking into account country diversity, Program of Governmental Actions (PAG) has made clusters in seven of agricultural development poles [70].

2.2. Data used Data used in this study concern daily maximum temperature, daily minimum temperature, daily precipitation and potential evapotranspiration. Provided by Agency for Aerial Navigation Safety in Africa and Madagascar (ASECNA) and Benin National Institute of Agriculture Researches www.ijaer.in Copyright © IJAER 2018, All right reserved Page 995

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(INRAB), the data were recorded at different weather stations of Benin (Cotonou, Bohicon, Savè, Parakou, Natitingou and Kandi) during the period of 1960-2016 (Table 1). These weather stations serve as the center for weather condition reading and prediction for the whole country. As agriculture is an important sector for the Beninese economy and contributes up to 25 % of GDP and provides up to 55 % of the country's jobs [71], the analysis of precipitation and temperature seems to be based on the fact that almost 95% of the Agricultural sector in Sub- Saharan Africa is based on natural condition [5,72]. The variability of climatic conditions in the study area is evident [16,73]. In this study, the decade, monthly and periodic precipitation and PET mean have been calculated just as monthly and periodic temperatures (minimum and maximum) average.

Table 1: Geographic coordinates of weather stations in Benin

Station Longitude (°C) Latitude (°C) Altitude (m) Bohicon 2.07 7.17 166 Cotonou 2.38 6.35 4 Kandi 2.93 11.13 290 Natitingou 1.38 10.32 460 Parakou 2.6 9.35 392 Savè 2.47 8.03 198 Source: Obada et al. [53]

2.3. Methods of data analysis Data analyses were carried out on precipitation and temperature trends recorded by the weather stations in Benin over 57 years. Descriptive statistics in particular parameters such as mean, median, standard deviation, variance, Kurtosis and Skewness statistics, and coefficient of variation were used. Linear regression was also used to determine the rate of precipitation and temperature evolution. For this purpose, statistical data processing has been done in Microsoft Excel spreadsheet software and Stata 14 software. In addition, a recourse was made to Student's test to verify the existence of a significant difference between the averages of the climatic parameters (temperature, precipitation and PET) studied over two periods: ranging from 1960 to 1988 and 1989 to 2016.

3. RESULTS AND DISCUSSION 3.1. Analysis of precipitation trend Precipitation trends in Benin throughout the weather stations of the study area were analyzed over 57 years. The distribution of mean precipitation is quite diversified and varies from one month to another and from one region to another (Table 5). In fact, the southern stations www.ijaer.in Copyright © IJAER 2018, All right reserved Page 996

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ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

(Bohicon, Cotonou and Savè) have an average precipitation (April to October) greater than 40 mm (decadally) compared to the months from May to September in the northern stations (Kandi, Natitingou and Parakou). Maximum precipitation is recorded in southern Benin between June and July and those in the north, is observed during August and September (Table 5). Based on the monthly standard deviations (Table 5), the precipitation distribution is normal from March to October for the southern region and April to October for the north one during the study period. In addition, the coefficients of variation of the monthly mean precipitation differ from one station to another. Indeed, these coefficients are ranging between 40 and 270 % at Bohicon; 40 and 210 % at Cotonou; 30 and 750 % at Kandi; 30 and 440 % at Natitingou; 30 and 350 % at Parakou; and 40 and 280 % at Savè. Previous researches have considered the coefficient of variation to be an ideal indicator for the analysis of climatic trend parameters. Thus, ref [74] by following the coefficient of variation (CV) values had established a degree of precipitation variability as low (CV ˂ 20 %), moderate (20 % ˂ CV ˂. 30 %), high (CV ˃ 30 %), very high (CV ˃ 40 %) and extremely high (CV ˃ 70 %) inter-annual variability of precipitation. Based on this classification and the precipitation data used, it follows that the variability of precipitation is moderate only in the northern stations (Kandi, Natitingou, and Parakou). In contrast, Bohicon, Cotonou and Savè stations highlighted a very high variability in inter-annual precipitation since their coefficients of variation are greater than or equal to 40 %. However, the coefficient of variation values for all stations are greater than 20 % (Table 5). This result implies that the variability of the country's annual precipitation is moderate. The coefficient of variation is also different across the stations and indicates a certain homogeneity in the variations of precipitation. Those results corroborate with what [15] detected in the southern region of Ethiopia. Moreover, the evolution of annual precipitation is highly variable among stations. Trends across the stations show a continual decrease in precipitation at the spatial scale according to the stations and trends in the various stations of Benin show a continual decrease in precipitation by temporal scale (Graph 1). Indeed, during the period 1960 to 2016, Cotonou station has recorded the highest annual precipitation and those of Bohicon and Savè the lowest. Rates of decreasing were higher at Natitingou (-3,598) and Savè (-3,018) compared to Kandi, which had the lowest rate (-0.196). These results reveal that during the period 1960 - 2016, the annual precipitation has decreased considerably throughout Benin territory.

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

2500 yNatitingou = -3.5979x + 8379.5 yKandi = -0.1961x + 1405.3 2250 yBohicon = -0.6336x + 2388.4 yCotonou = -1.1634x + 3637.7 2000 yParakou = -1.4195x + 3979.9 y = -3.0181x + 7094.7 Bohicon_P 1750 Savè Cotonou_P 1500 Kandi_P 1250 Natitingou_P 1000 Précipitation e en mm en e Précipitation Parakou_P 750 Savè_P

500

1992 2000 2008 2016 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1994 1996 1998 2002 2004 2006 2010 2012 2014 Année

Graph 1: Annual precipitation average through weather stations in Benin Source: ASECNA, 2017 The result indicates that the amount of precipitation across study areas is extremely variable. This result is similar to the climatic variability found in different regions of sub-Saharan Africa [6,15,75-78]. According to those authors, precipitation in sub-Saharan Africa is highly variable and these variations are more observable in areas where populations are over dependence on climate sensitive sectors (agriculture). This variability in precipitation could have a negative impact on the social life of the rural population where the majority of them practice rain-fed agriculture as main source of income. In the same sense as those of [15] in Ethiopia, the results reveal that during the last two decades, Benin is characterized by an uneven and unstable distribution of precipitation amount during the rainy seasons. The inter-seasonal analysis is thus at the center of decision-making and adaptive forecasting. Referring to [79], this analysis is described as crucial because agricultural yields are more related to the inter-seasonal instability of precipitation than to the simple annual accumulation. In south-Benin, [28] reported that the beginning of the rainy season may be early accompanied by late end or vice versa. This could be due to poor agricultural yields resulting from the low distribution and unpredictable amounts of precipitation during each crop year. This result can provide the same effect of what reported by [80] that bad harvests, total harvest losses, forage shortages are consequences of low precipitation and uneven and unpredictable distribution during agricultural campaigns. In addition, precipitation instability, distribution patterns, frequencies and probabilities of drought period during the step of crop growing are crucial factors affecting planning, performance and management of agricultural operations [15]. www.ijaer.in Copyright © IJAER 2018, All right reserved Page 998

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ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

3.1.1. Difference in precipitation variation during the periods 1960-1988 and 1989-2016 The analyses revealed a difference in precipitation variation across stations during the 1960-1988 and 1989-2016 periods. Indeed, the average decades (ten days accumulate mean) differences calculated over these two periods highlight an excess (between 0.17 and 0.86 mm) of precipitation during the period 1989-2016 at Bohicon, Cotonou, Kandi and Parakou stations while Natitingou and Savè stations have a deficit of precipitation (1.44 and 1.57 mm respectively) over 10 days during the same period (Table 1). However, for the two periods considered, the average precipitation is not statistically different at all the weather stations in Benin. This result show that the different weather events cannot be associated to climate change, but to climate variability. Ref [81] found similarly results in Rwanda when they had worked on precipitation, air temperature and PET under changing climate conditions. It follows that with the difference in precipitation observed during the periods 1960-1988 and 1989-2016 and the definitions of [13] on climate variability and change, that the different regions of Benin face the phenomena of climatic variability. Indeed, the observation made in the stations of Natitingou and Savè is consistent with the results of [29] over the period 1950-2010, which revealed a high pronounced decrease in precipitation in the northern of Benin particularly in Natitingou. The precipitation surplus observed at Bohicon, Cotonou, Kandi and Parakou stations attest the slight resumption of precipitation reported by [82] in the Gulf of Guinea during the last two decades. These authors have linked the recent floods recorded in last years in West Africa and even in Africa in general to the resumption of precipitation and their poor distribution at temporal and spatial scales. Ref [29] also highlighted this positive trend in southern Benin. The statistical similarity of precipitation between the period 1960-1988 and the period 1989-2010 could be explained by the results of [16], which showed that the inter-annual variability of precipitation during the period 1951-2010 in Benin is linked to short periods of deficit, alternating with a few years of short surpluses.

Table 1: Difference of decade precipitation mean between 1960-1988 and 1989-2016 periods

Stations Mean (mm) Standard deviation (mm) Mean difference Probability 1960-1988 1989-2016 1960-1988 1989-2016 (mm) (Test t of Student) Bohicon 30.26 31.12 20.34 20.83 0.86 0.858 Cotonou 36.5 37.0 30.74 29.95 0.49 0.944 Kandi 27.20 27.7 31.52 31.29 0.50 0.946 Natitingou 33.87 32.42 33.13 32.19 -1.44 0.850 Parakou 31.21 31.38 27.16 28.45 0.17 0.979 Savè 30.28 28.71 22.73 20.92 -1.57 0.758 Source : ASECNA, 2017

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

3.2. Analysis of temperature evolution The cycle and management of water are influenced by the temperature of each region of the world. Graphs 2 and 3 indicate, respectively, the trends in the maximum and minimum temperature of the different regions in Benin. Using a linear regression model, the rate of change in the maximum temperature is defined by the regression curve, which in this case is about 0.015; 0.017; 0.018; 0.027; 0.027 and 0.032 °C respectively for the stations of Cotonou, Bohicon, Kandi, Natitingou, Parakou and Savè during the period 1960-2016. For the minimum temperature, the rate of change or increase is higher at the Kandi (0,036 °C) station and low at Natitingou (0.014 °C) during the same period. Those values found across Benin stations are much lower than what obtained at the world level which extends to the order of 0.6 °C during the last century. However, these values are higher than those obtained by [6,15] in Ethiopia respectively in the south (0.0013 °C) and in the North (0.0026 °C for the maximum and 0.0067 °C for the minimum temperature). This result also indicates that the minimum temperature has increased more than the maximum temperature. This could be easily observable and justifies the fact that the Benin population feels and declares the rise in temperature over the last three decades [28]. 3.2.1. Difference in maximum and minimum temperature variation during 1960-1988 and 1989-2016 The average minimum temperatures have increased in all weather stations in Benin (Table 5). The difference of average minimum temperature varies from 0.44 °C (Natitingou) to 1.07 °C (Kandi) (Table 2). In addition, the probabilities of t-test (mean-comparison test) applied to the average differences of the two periods are less than 5 % in all stations except for Kandi (Table 2), which certifies a positive and statistically significant difference of the average minimum temperature of the periods 1960-1988 and 1989-2016.

Table 2: Difference of minimal temperature mean between 1960-1988 and 1989-2016 periods

Stations Mean (°C) Standard deviation (°C) Mean difference Probability (Test 1960-1988 1989-2016 1960-1988 1989-2016 (°C) t of Student) Bohicon 22.63 23.29 0.81 0.86 0.66 0.000 Cotonou 24.25 24.98 0.86 0.94 0.73 0.000 Kandi 20.93 22.00 3.15 2.95 1.07 0.000 Natitingou 20.75 21.18 1.57 1.83 0.44 0.001 Parakou 20.87 21.83 1.45 1.28 0.96 0.000 Savè 21.87 22.67 0.77 0.89 0.80 0.000 Source : ASECNA, 2017 www.ijaer.in Copyright © IJAER 2018, All right reserved Page 1000

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ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

For the maximum temperature, the differences in mean between the two periods are positive and statistically significant in all weather stations in the study area except for Kandi. However, this difference is higher at Savè station (0.77) compared to other stations (Table 3). Table 3: Difference of maximum temperature mean between 1960-1988 and 1989-2016 periods

Stations Mean (°C) Standard deviation (°C) Mean Probability (Test 1960-1988 1989-2016 1960-1988 1989-2016 Difference (°C) t of Student) Bohicon 32.47 32.94 2.24 2.34 0.47 0.005 Cotonou 30.13 30.63 1.44 1.48 0.50 0.000 Kandi 34.32 34.65 2.77 2.90 0.34 0.108 Natitingou 33.11 33.73 2.68 2.76 0.62 0.002 Parakou 32.58 33.17 2.63 2.81 0.59 0.003 Savè 32.77 33.54 2.56 2.62 0.77 0.000 Source : ASECNA, 2017 From the observation of the variation in the minimum and maximum temperatures through all stations in Benin, there is a general increase in temperature over the last three decades with reference to the last three previous decades. This confirms the results of [29], which showed a bullish trend in the average temperature during the period 1950 to 2010 of different localities of the country. During the same period, [16] mentioned an increase in the order of 1 °C of the average temperature in the different regions of Benin, except the coastline zone. Kandi station has the highest temperature trend unlike Cotonou with the lowest trend of the maximum temperature during the period considered. The four other weather stations in the country have a maximum temperature trend close to Kandi trend. The observation at Cotonou station could be explained by its close proximity to the ocean and the humidity of the air around the coastal zone [83].

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ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

38 37 ykandi = 0.0184x - 1.9842 36 yNatitingou = 0.0274x - 20.96 35 Bohicon_Tma 34 33 Cotonou_Tma 32 Kandi_Tma 31 30 Natitingou_Tma 29 y = 0.0145x + 1.5514 Parakou_Tma 28 Cotonou y = 0.0266x - 19.916 27 Parakou Savè_Tma 26 ySavè = 0.0316x - 29.724 y = 0.017x - 1.0057

25 Bohicon

1980 1984 2010 1962 1964 1966 1968 1970 1972 1974 1976 1978 1982 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2012 2014 2016 1960 Graph 2: Maximum temperature mean at weather stations in Benin Source: ASECNA, 2017

For the minimum temperature, the station of Cotonou recorded the highest values followed by the station of Bohicon (Graph 3). In contrast, the lowest minimum temperature values were observed at Natitingou station.

26 yCotonou = 0.0271x - 29.32 25 24

23 Bohicon_Tmi 22 Cotonou_Tmi 21 Kandi_Tmi 20 y Bohicon= 0.0289x - 34.362 Natitingou_Tmi 19 ySavè = 0.0292x - 35.713 18 Parakou_Tmi yKandi = 0.0355x - 49.115 Savè_Tmi 17 yParakou = 0.0344x - 47.126

16 yNatitingou = 0.0143x - 7.5582

1960 1964 1968 2006 2010 2014 1966 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2008 2012 2016 1962 Graph 3: Minimal temperature trend at weather stations in Benin Source: ASECNA, 2017

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ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

Similar increasing trends in mean temperature have been reported in south of Spain (with ratio of 0.16 °C to 0.4 °C per decade), in France (with ratio of 0.2°C to 0.41°C per decade) respectively by [41,42]. Accordingly to [40], the warming climate is likely to lead to increased potential evapotranspiration and increased irrigation needs in area with low precipitation. The availability of water for the practice of different activities, especially agriculture, in Benin is found to confront many adverse factors. The trends observed through a general decrease in precipitation added to an increase in the minimum and maximum temperatures will influence the evolution of water resources and constitute a threat for water availability and especially soil moisture for crop growth.

3.3. Potential evapotranspiration The potential evapotranspiration (PET) is essential for hydrological cycle in reflecting the maximum water demand of environment to maintain water balance. Ref. [38] mentioned that some studies have reported significant decreasing trends in PET during last decades throughout worldwide areas.

This study showed that temperature (maximum and minimum) averages are greater at 1989-2016 period than 1960-1988 period. This result confirms the global warming issue certified by [14]. Others studies such as [38,44,46,53,78,84-88] have noticed the upward in temperature trends. Ref. [56] has estimated that global warming will lead to a possible intensification of the hydrological cycle resulting from increase of precipitations and PET. But, many studies showed controversial or different results about PET trends analysis. Some of them had pointed out upward trends in PET time series analysis [81,89,90] while other studies had showed downward trends [12,86,91]. This study showed that PET differences between 1989-2016 and 1960-1988 periods are negative for all stations except for Cotonou station (Table 4). The difference of PET is significant only at Parakou station; and the question is how PET trends could be possible with regard to the temperature trends at those stations. Ref. [92] explained that one of expected consequences of temperature rising is that the air near the surface should be drier, which should result in an increase in the rate of evaporation from terrestrial open water bodies. Additionally, temperature upward trend is able to enhance PET as generally advocated [38]. Ref. [44] in Tunisia found that there was any annual trend of PET during 34 years (1973-2007). They used each part of PET to show a significant trend, respectively, increasing and decreasing of radiation and aerodynamic terms of PET. Then, this opposite trend of the two components explained the cancelled trend of their sum. The most factors which had been found sensitive to PET variation was mainly the net shortwave radiation, the actual vapor pressure and the long wave radiation. According to their explanation, the sum in study should be negative.

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Study [87] had shown that despite temperature rising and relative humidity decreasing over the catchment, the combined effect of shortened sun duration and wind speed negated the rising effect of those factors and caused significant decrease of PET. According to those authors, changes of mean annual PET in 1970-2008 were firstly affected by sun duration followed by wind speed, relative humidity and daily temperature respectively. Also, they had found that the significant declines of wind speed and sun duration are responsible for the decrease of annual PET, but the increase of daily temperature and decrease of relative humidity play a complementary role of enhancing PET. Although daily temperature is often seen as the primary driver of evapotranspiration changes [14], it is not a decisive determinant for the PET variation in some part of the world, even though daily temperature has increased significantly over the past 50 years. In china, it was showed that the decreasing trend of PET is primarily attributed to wind speed due to its significant decreasing trend and high sensitivity [38]. This study mentioned that the positive contribution of temperature rising to PET is offset, to large extent, by the effect of wind speed and sunshine duration. Then, PET has declined while climate warming has increased. The decreasing PET has been attributed to the decrease in wind speed and net radiation [84]. Ref. [93] had suggested that the reducing effect of wind on PET could be explained by the lower amount of water vapor carried by the wind. With high correlation between PET rates and temperature, other factors in addition to rising temperatures also affect PET [85]. Thus, they explained the phenomena by increasing humidity and higher CO2 that both tend to reduce transpiration and counteract the higher temperature effects on PET. Like oceans, rivers, lakes, lagoon, and other water flat warm and evaporate more water into the atmosphere, regional humidity is likely to increase. Also, by regarding CO2 concentrations increase, leaf stomata partially close in response to maintain the CO2 concentration inside the stomata. Thus, with respect to those authors, the effect of higher CO2 concentration and relative humidity could partially offset the temperature impact on PET while the daily temperature increases. There was also found that temperature rising induced glacier melting, besides, different water body areas increases could cause too solar radiation and PET decreases [94].

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Table 4: Difference of potential evapotranspiration mean between 1960-1988 and 1989-2016 periods

Stations Mean (mm) Standard deviation (mm) Mean difference Probability t 1960-1988 1989-2016 1960-1988 1989-2016 (mm) Différence Bohicon 42.93 40.82 4.90 4.33 -2.11 0.0570 Cotonou 45.24 45.84 4.98 4.45 0.59 0.5969 Kandi 48.78 45.72 8.29 6.29 -3.05 0.0828 Natitingou 41.45 41.36 6.52 5.41 -0.09 0.9516 Parakou 46.15 40.97 9.00 5.40 -5.18 0.0042 Savè 41.34 41.19 4.82 5.68 -0.29 0.8149 Source : ASECNA, 2017 CONCLUSION In sub-Saharan Africa, variations in climatic parameters are complex and are not fully understood. This study provided a description of the inter-annual and periodic variability of precipitation, temperatures and potential evapotranspiration in the different regions of Benin. Results indicate that precipitation and temperatures (minimum and maximum) from 1960 to 2016 varied from one region to another and from one year to another. Between the periods 1960- 1989 and 1989-2016, precipitation was found to be statistically homogeneous throughout all weather Benin stations. On the other hand, the minimum and maximum temperatures are more elevated in all regions except for Kandi for the maximum temperature. Concerning PET, there is a negative gap compared to first period considered and significant at one station. This study suggests another analysis on wind and sun duration in Benin to justify the PET trend. Results address to maintain soil moisture on farms to change or improve practices or strategies. Under these conditions, the implementation of responses to changing climate stimuli must be at the center of the country's growth policies and the development of Benin's rural communities. The most efficient and urgent adaptation strategies are to make available the information of future projections of climatic events to reduce risk of production. Sustainable adaptation and mitigation strategies are needed to address the effects of temperature and precipitation variability on agricultural productivity and the use of natural resources. REFERENCES [1] ADB 2011. La croissance démographique impacte fortement la disponibilité alimentaire en Afrique. [2] FAO 2008. Rapport sur le développement du monde. Thème C : Quels sont les liens qui unissent la production agricole et la sécurité alimentaire ?

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[3] UNDP. 2015. Agriculture, sécurité alimentaire et développement humain au Benin. Rapport national sur le développement humain ; 144p. [4] Djamen P. 2016. Développer le secteur semencier pour augmenter la productivité agricole en Afrique de l’Ouest et du Centre : Leviers et principes d’actions. FARM and CORAF, 11p.

[5] Doukpolo B. 2014. Changements climatiques et productions agricoles dans l'Ouest de la République Centrafricaine. Sciences de la Terre. Thèse Doctorale de l’Université d’Abomey- Calavi 338p. [6] Asfwa A., Simane B., Hassen A., Bantider A. 2018. Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin. Weather and Climate Extremes 19, 29-41. https://doi.org/10.1016/j.wace.2017.12.002. [7] UNFCCC 2007. Climate Change: Impacts, Vulnerabilities and Adaptation in Developing Countries. United Nations Framework Convention on Climate Change (UNFCCC), Bonn, Germany.

[8] Caloiero T., Coscarelli R., Ferrari E., Sirangelo B. 2016. Trends in the daily precipitation categories of Calabria (southern Italy), ScienceDirect, Procedia Engineering 162, 32 – 38. [9] IPCC 2013. Summary for Policymakers. Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge. [10] Todeschini S. 2012. Trends in long daily rainfall series of Lombardia (northern Italy) affecting urban stormwater control, Int. J. Climatol. 32, 900–919. [11] Chattopodhay S., Edwards D. R. 2016. Long-Term Trend Analysis of Precipitation and Air Temperature for Kentucky, United States. Climate, 4, 10; doi:10.3390/cli4010010. [12] Sonali P., Kumar D. N. 2016. Spatio-temporal variability of temperature and potential evapotranspiration over India. Journal of Water and Climate Change 07.4, 810-822. doi: 10.2166/wcc.2016.230. [13] IPCC 2001. The Scientific Basis; Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change; Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A., Eds.; Cambridge University Press: Cambridge, UK. [14] IPCC 2007. Bilan 2007 des changements climatiques. Contribution des Groupes de travail I, II et III au quatrième Rapport d’évaluation du Groupe d’experts intergouvernemental sur l’évolution du climat [Équipe de rédaction principale, Pachauri, R.K. et Reisinger, A.]. GIEC.Genève : 103 p. ISBN 92-9169-222-0. www.ijaer.in Copyright © IJAER 2018, All right reserved Page 1006

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ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

[15] Hayelom B., Chen Y., Marsie Z., Negash M. 2017. Temperature and Precipitation Trend Analysis over the Last 30 Years in Southern Tigray Regional State, Ethiopia, Preprints, Not peer-reviewed, doi:10.20944/preprints201702.0014.v1. [16] Boko M., Kosmowski, Vissin E. W. 2012. Les Enjeux du Changement Climatique au Bénin, Konrad-Adenauer-Stiftung, Programme pour le Dialogue Politique en Afrique de l’Ouest ; 71p.

[17] IPCC 2014. Summary for policymakers. In: 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 [Field, C.B. Cambridge Univ. Press. Cambridge, United Kingdom New York, NY, USA, p. 1–32.

[18] MEHU 2011. Deuxième Communication Nationale du Bénin sur les changements climatiques. [19] Hounkponou S. K., 2015. Rapport d’étude sur les impacts des changements climatiques en matière d’adaptation : cas du bassin de Tèwi dans la commune de Dassa-Zoumè au Bénin, Réseau climat développement, 32 p. [20] Boyles P. R., Raman S. 2003. Analysis of climate trends in North Carolina (1949–1998). Environ. Int., 29, 263–275. [21] Sayemuzzaman M., Jha M. K., Mekonnen A., Schimmel K. A. 2014. Subseasonal climate variability for North Carolina, United States. Atmos. Res. 145–146, 69–79. [22] Kumar M., Denis D. M., Suryavanshi S. 2016. Long-term climatic trend analysis of Giridih district, Jharkhand (India) using statistical approach, Model. Earth Syst. Environ. 2:116 DOI 10.1007/s40808-016-0162-2. [23] Zhao P., Jones P., Cao L., Yan Z., Zha S., Zhu Y., Yu Y., Tang G. 2014. Trend of surface air temperature in Eastern China and associated large-scale climate variability over the last 100 years. Am. Meteorol. Soc., 27, 4693–4703. [24] Rio D. S., Herrero L., Pinto-Gomes C., Penas A. 2011. Spatial analysis of mean temperature trends in Spain over the period 1961–2006. Glob. Platin. Chang. 78, 65–75. [25] Ceppi P., Scherrer S. C., Fischer A. M., Appenzeller C. 2012. Revisiting Swiss temperature trends 1959–2008. Int. J. Climatol. 32, 203–213. [26] Martinez J. C., Maleski J. J., Miller F. M. 2012. Trends in precipitation and temperature in Florida, USA. J. Hydrol. 452–453, 259–281.

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

[27] Karmeshu N. 2012. Trend Detection in Annual Temperature & Precipitation Using the Mann Kendall Test—A Case Study to Assess Climate Change on Select States in the Northeastern United States. Master’s Thesis, University of Pennsylvania, Philadelphia, PA, USA. [28] Alle C. S. U. Y., Afouda A. A., Agbossou K. E., Guibert H. 2013. Evolution des descripteurs intrasaisonniers des saisons pluvieuses au sud-Bénin entre 1951 et 2010. American Journal of Scientific Research 94 : 55-68. [29] Amoussou E. Totin V. S. H., Cledjo F. P., Allagbe Y. B. S., Akognongbe J. S. A., Houedenou C. Mahe G. Camberlin P., Boko M., Perard J. 2016. Evolution climatique du Benin de 1950 à 2010 et son influence sur les eaux de surface. XXIXe Colloque de l’Association Internationale de Climatologie, Lausanne - Besançon 2016, 229-236. [30] Xu C., Gong L., Jiang T., Chen D. 2006. Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment. J. Hydrol. 327, 81–93.

[31] Philandras C. M., Nastos P. T., Kapsomenakis J., Douvis K. C., Tselioudis C. S., Zerefos C. S. 2011. Long term precipitation trends and variability within the Mediterranean region. Nat. Hazards Earth Syst. Sci. 11, 3235–3250. [32] Abbaspour K. C., Faramarzi M., Ghasemi, S. S., Hong, Y. 2009. Assessing the impact of climate change on water resources in Iran. Water Resour. Res., 45, W10434, doi:10.1029/2008WR007615. [33] Bocheva L., Marinova T., Simeonov P., Gospodinov I. 2008. Variability and trends of extreme precipitation events over Bulgaria (1961–2005). Atmos. Res., 93, 490-497.

[34] Longobardi A., Villani P. 2009 Trend Analysis of annual and seasonal rainfall time series in the Mediterranean area. Int. J. Climatol. [35] Jones R. J., Schwartz S. J., Ellis N. K., Hathaway M. J., Jawdy M. C. 2015. Temporal variability of precipitation in the Upper Tennesse Valley. J. Hydrol. 3, 125–138. [36] Easterling D. R., Kunkel K. E., Wehner M. F., Sun L. 2016. Detection and attribution of climate extremes in the observed record. Weather Clim. Extrem 11, 17–27. [37] Sultan B., Alhassane A., Barbier B., Baron C., Bella-Medjo T. M., Berg A., Dingkuhn M., Fortilus J., Kouressy M., Leblois A., Marteau R., Muller B., Oettli P., Quirion P., Roudier P., Traore S. B., Vaksmann M. 2012. La question de la vulnérabilité et de l’adaptation de

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

l’agriculture sahélienne au climat au sein du programme AMMA. La Météorologie Spécial AMMA : 64-72. (37) [38] Yin Y., Wu S., Chen G., Dai E. 2010. Attribution analyses of potential evapotranspiration changes in China since the 1960s. Theor. Appl. Climatol. 101, 19–28. DOI 10.1007/s00704-009- 0197-7.

[39] Zhang Y. F., Deng J. L., Guan D. X., Jin C. J., Wang A. Z., Wu J. B., Yuan F. H. 2011. Spatiotemporal changes of potential evapotranspiration in Songnen plain of north east China. Ying Yong Sheng Tai Xue Bao 22(7): 1 702–10.

[40] Chattopdhyay N., Hulme M. 1997. Evaporation and potential evapotranspiration in India under conditions of recent and future climate change. Agricultural and forest meteorology, 87:55-73. [41] Chaouche K., Neppel L., Dieulin C., Pujol N., Ladouche B., Martin E., Salas D., Caballero, Y. 2010. Analyses of precipitation, temperature and evapotranspiration in a French Mediterranean region in the context of climate change. Comptes Rendus Geoscience 342(3):234- 243. [42] Espadafor M., Lorite I. J., Gavilán P., Berengena J. 2011. An analysis of the tendency of reference evapotranspiration estimates and other climate variables during the last 45 years in Southern Spain. Agricultural Water Management, vol. 98, pp.1045-1061, 4.

[43] Tao X.-E., Chen H., Xu C.-Y., Hou Y.-K., Jie M.-X. 2015. Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River Basin, China. Water Science and Engineering, vol. 8, pp. 273-281. [44] Mansour M., Hachicha M., Mougou A. 2017. Trend Analysis of Potential Evapotranspiration Case of Chott-Meriem Region (The Sahel of Tunisia). International Journal of Agriculture Innovations and Research. Volume 5, Issue 5, ISSN (Online) 2319-1473. [45] Allen R., Periera L., Raes D., Smith M. 1996. Irrigation and Drainage: Crop Evapotranspiration (Guidelines for Computing Crop Water Requirements). Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, p. 56.

[46] Turoglu H. 2016. Hydrology and water resources sensitivity of streamflow to climate change in Melen river basin (NW Turkey). Hydrology and Water Resources, 16th International Multidisciplinary Scientific GeoConference SGEM 2016. Book3 Vol. 1, 645-652 pp. DOI: 10.5593/SGEM2016/B31/S12.084.

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

[47] McVicar T. R., Van Niel T. G., Li L. T., Hutchinson M. F., Mu X. M., Liu Z. H. 2007. Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. J. Hydrol., 338, 196–220. [48] Li Z., Zheng F., Liu W. 2012. Spatiotemporal characteristics of reference evapotranspiration during 1961–2009 and its projected changes during 2011–2099 on the Loess Plateau of China. Agric. For. Meteorol., 154–155, 147–155. [49] Fan Z. X., Thomas A. 2013. Spatiotemporal variability of reference evapotranspiration and its contributing climatic factors in Yunan Province, SW China, 1961–2004. Clim. Chang. 116, 309–325.

[50] Alemu H., Senay G. B., Kaptue A. T., Kovalskyy V. 2014. Evapotranspiration variability and its association with vegetation dynamics in the Nile Basin, 2002–2011. Remote Sens. 6, 5885–5908. https://doi.org/10.3390/rs6075885. [51] Valipour M. 2014. Study of different climatic conditions to assess the role of solar radiation in reference crop evapotranspiration equations. Arch. Agron. Soil Sci., 61, 679–694. [52] Xu Y. P., Pan S., Fu G., Tian Y., Zhang X. 2014. Future potential evapotranspiration changes and contribution analysis in Zhejiang Province, East China. J. Geophys. Res. Atmos., 118, 2174–2192. [53] Obada E., Alamou E. A., Zandagba J., Afouda A. 2017. Trends and Changes in Recent and Future Penman-Monteith Potential Evapotranspiration in Benin (West Africa). Hydrology, 4, 38; doi:10.3390/hydrology4030038. [54] Rim C.-S. 2009. The effects of urbanization, geographical and topographical conditions on reference evapotranspiration. Climatic Change, vol. 97, pp. 483-514.

[55] Labat D., Goddéris Y., Probst J. L., Guyot J. L. 2004. Evidence for Global Runoff Increase Related to Climate Warming. Adv. Water Res. 27, 631–642. [56] Huntington T. G. 2006. Evidence for intensification of the global water cycle: Review and synthesis. Journal of hydrology, 319:83-95. [57] Kay A. L., Bell V. A., Blyth E. M., Crooks S. M., Davies H. N., Reynard N. S. A. 2013. Hydrological perspective on evaporation: Historical trends and future projections in Britain. J. Water Clim. Chang. 4, 193–208. [58] Onyutha C. 2016. Statistical analyses of potential evapotranspiration changes over the period 1930–2012 in the Nile River riparian countries. Agricultural and Forest Meteorology 226–227 80–95. http://dx.doi.org/10.1016/j.agrformet.2016.05.015. www.ijaer.in Copyright © IJAER 2018, All right reserved Page 1010

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

[59] Ibrahim B. 2012. Caractérisation des Saisons de Pluies au Burkina Faso Dans un Contexte de Changement Climatiques sur le Bassin de Nakambé. Thèse de Doctorat, Institut International d’Ingénierie de l’Eau et de l’Environnement (2iE), l’Université Pierre et Marie Curie. [60] Nounangnonhou T. C., Fifatin F.-X. N, Sanya E. A. 2016. Modélisation et simulation des tendances climatiques à l’horizon 2040 sur le bassin du fleuve Ouémé en République du Bénin. Afrique SCIENCE 12(6) 48 – 56. [61] Jhajharia D., Pandey P. K., Dabral P. P., Kumar R., Choudhary R. 2015. Variability in Temperature and Potential Evapotranspiration over West Siang in Arunachal Pradesh. Jour. Ind. Geil. Cong., 7, 37–43.

[62] Kousari M. R., Asadi Zarch M. A., Ahani H., Hakimelahi H. 2013. A survey of temporal and spatial reference crop evapotranspiration trends in Iran from 1960 to 2005. Climatic Change, vol. 120, pp. 277-298. [63] Tabari H., Grismer M. E., Trajkovic S. 2013. Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Science. 31:107-117. DOI 10.1007/s00271-011-0295-z. (63) [64] Talaee H. P., Somee’e S. B., Ardakani S. S. 2013. Time trend and change point of reference evapotranspiration over Iran. Theor Appl Climatol. DOI 10.1007/s00704-013-0978-x. [65] Gao G., Chen D., Xu C. Y., Simelton E. 2007. Trend of estimated actual evapotranspiration over China during 1960– 2002. Journal of Geophysical Research, vol. 112. [66] Darshana, Pandey A., Pandey. R. P. 2013. Analysing trends in reference evapotranspiration and weather variables in the Tons River Basin in Central India. Stochastic Environmental Research and Risk Assessment, vol. 27, 1407-1421. DOI 10.1007/s00477-012-0677-7.

[67] Chen, S.B.; Lui, Y.F.; Axel, T. 2006. Climatic change on the Tibetan Plateau: Potential evapotranspiration trends from 1961–2000. Clim. Chang. 76, 291–319. [68] Irmak S., Kabenge I., Skaggs K. E., Mutiibwa D. 2012. Trend and magnitude of changes in climate variables and reference evapotranspiration over 116-yr period in the Platte River Basin, central Nebraska–USA. Journal of Hydrology, vol. 420–421, pp. 228-244. [69] INSAE, PAM and MAEP. 2015. Analyse Globale de la Vulnérabilité et de la Sécurité Alimentaire, Available at: http://www.insae-bj.org/autres_enquetes.html?file=files/enquetes- recensements/autres/Benin_AGVSA_Rapport.pdf.

[70] PAG 2016. Programme d’Actions du Gouvernement béninois 2016-2021, 98 p.

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

[71] World Bank. 2017. Economy Profile of Benin. Doing Business 2018; World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/28657 License: CC BY 3.0 IGO. [72] Bronkhorst S. 2012. Rareté de ressources et conflit entre pasteurs et agriculteurs au Sud- Kordofan, Soudan », Cultures & Conflits [En ligne], 88 | hiver 2012, mis en ligne le 15 mars 2014. URL : http://conflits.revues.org/18589 ; DOI : 10.4000/conflits.18589. [73] Boko M., Niang I., Nyong A., Vogel C., Githeko A., Medany M., Osman Elasha B., Tabo R., Yanda P. 2007. Climate change: Impacts, Adaptation and Vulnerability. pp. 433–67. In: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J. and Hanson, C.E. (Eds.). Climate change 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge: Cambridge University Press). [74] Hare W. 2003. Assessment of Knowledge on Impacts of Climate Change – Contribution to the Specification of Art. 2 of the UNFCCC,” W issenschaf tlicher b eira t der b und esregierung g lob ale u mwel tveränd erungen. [75] Badjana H. M., Hounkpe K., Wala K., Batawila K., Akpana K., Edjame, K. S. 2014. Analyse de la variabilité temporelle et spatiale des séries climatiques du nord du Togo entre 1960 to 2010. European Scientific Journal. 10(11) ISSN: 1857 – 7881. [76] Ezin V., Yabi I., Kochoni E. G. M., Ahanchédé A. 2014. Effect of flooding and salinity as a result of climate change on tomato production in the coastal zone of Benin. African Journal of Food Science, 8(7), 368-375. [77] Oguntunde P. O., Lischeid G., Abiodun B., Dietrich O. 2014. Analysis of spatial and temporal patterns in onset, cessation and length of growing season in Nigeria, Agricultural and Forest Meteorology 194:77–87. DOI: 10.1016/j.agrformet.2014.03.017. (77) [78] Balogoun I., Ahoton E. L., Saïdou A., Bello D. O., Amadji L. G., Ahohuendo C. B., Babatoundé S., Chougourou C. D., Ahanchede A. 2016. Effect of climatic factors on cashew (Anacardium occidentale L.) productivity in Benin. Journal of Earth Science & Climate Change (JESCC). 7: 329. doi:10.4172/2157-7617.1000329.

[79] Ingram K. T., Roncoli C., Kirshen P. 2002. Opportunities and constraints for farmers of west Africa to use seasonal precipitation forecasts with Burkina Faso as a case study, Agricultural Systems 74(3):331-349, DOI: 10.1016/S0308-521X(02)00044-6.

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

[80] Addisu S., Selassie Y. G., Fissha G., Gedif B. 2015. Time series trend analysis of temperature and rainfall in lake Tana Sub-basin, Ethiopia. Environ. Syst. Res., 4: 25. https://doi.org/10.1186/s40068-015-0051-0. [81] Haggag M., Kalisa J. C., Abdeldayem A. W. 2016. Projections of precipitation, air temperature and potential evapotranspiration in Rwanda under changing climate conditions. African Journal of Environmental Science and Technology, Vol. 10(1), pp. 18-33. DOI: 10.5897/AJEST2015.1997. [82] Crétat J., Vizy K. E., Cook H. K. 2013. How well are daily intense rainfall events captured by current climate models over Africa? Climate Dynamics, 42, 2691-2711.

[83] Fink A. H., Paeth H., Ermert V., Pohle S., Diederich M. 2010. Meteorological processes influencing the weather and climate of Benin, 135-149. [84] ElNesr M., Alazba A., Abu-Zreig M. 2010. Analysis of Evapotranspiration Variability and Trends in the Arabian Peninsula. American Journal of Environmental Sciences 6 (6): 535-547.

[85] Snyder R. L., Moratiel R., Song Z., Swelam A., Jomaa I., Shapland T. 2011. Evapotranspiration Response to Climate Change. Proc. XXVIIIth IHC – IS on Water Use in a Changing World Eds.: J.E. Fernández and M.I. Ferreira Acta Hort. 922, ISHS 2011: 91-98. [86] Patle G. T., Singh D. K., Sarangi A., Rai A., Khanna M., Sahoo R. N. 2013. Temporal variability of climatic parameters and potential evapotranspiration. Indian Journal of Agricultural Sciences 83 (5): 518–24. [87] Ye X., Li X., Liu J., Xu C.-Y., Zhang Q. 2014. Variation of reference evapotranspiration and its contributing climatic factors in the Poyang Lake catchment, China. Hydrol. Process. 28, 6151–6162. DOI: 10.1002/hyp.10117.

[88] Park C. E., Jeang S. J., Ho S. H., Park H., Plao S., Kim J., Feng S. 2017. Dominance of climate warming effects on recent drying trends over wet monsoon regions. Atmos. Chem. Phys., 17, 10467–10476, 2017 https://doi.org/10.5194/acp-17-10467-2017. [89] Sun S., Chen H., Wang G., Li J., Mu M., Yan G., Xu B., Huang J., Wang J., Zhang F., Zhu S. 2016. Shift in potential evapotranspiration and its implications for dryness/wetness over Southwest China. J. Geophys. Res. Atmos., 121, 9342–9355, doi:10.1002/2016JD025276. [90] Xing W., Wang W., Shao Q., Yu Z., Yang T., Fu J. 2016. Periodic fluctuation of reference evapotranspiration during the past five decades: Does Evaporation Paradox really exist in China? Scientific Reports | 6:39503 | DOI: 10.1038/srep39503.

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[91] Shenbin C., Yunfeng L., Thomas A. 2006. Climatic change on the Tibetan Plateau: Potential Evapotranspiration trends from 1961–2000. Climatic Change 76: 291–319. DOI: 10.1007/s10584-006-9080-z. [92] Roderick M. L., Farquhar G. D. 2002. The cause of decreased pan evaporation over the past 50 years. Science 298:1410-1411.

[93] Huo Z., Dai X., Feng S., Kang S., Huang G. 2013. Effect of climate change on reference evapotranspiration and aridity index in arid region of China. J. Hydrol. 492, 24–34. [94] Liao J., Shen G., Huang W. 2011. The response of lake variations to climate change in Tibetan Plateau in last 40 years. ASPRS 2011 Annual Conference Milwaukee, Wisconsin, 11p.

APPENDIX Table 5: Descriptive statistics of monthly precipitation over weather stations in Benin during 1960 à 2016

Statistic Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Bohicon Mean 1.8 10.4 25.0 42.9 45.8 55.3 46.4 37.5 54.5 34.0 6.2 3.1 Min 0.0 0.0 0.3 8.7 15.7 21.0 9.0 1.0 13.9 2.4 0.0 0.0 Max 16.6 64.0 63.0 96.3 93.0 131.1 130.0 97.2 165.8 73.6 34.6 41.1 SD 3.8 11.7 14.4 20.8 19.0 23.9 25.9 23.3 24.0 17.8 7.8 8.3 SV 14.2 136.2 207.9 434.2 362.1 569.1 672.3 545.2 574.0 318.6 61.1 69.4 CV 2.1 1.1 0.6 0.5 0.4 0.4 0.6 0.6 0.4 0.5 1.3 2.7 Std E 0.5 1.5 1.9 2.8 2.5 3.2 3.4 3.1 3.2 2.4 1.0 1.1 Med 0.0 7.6 23.9 39.8 45.9 52.1 37.4 31.5 53.0 32.3 3.7 0.0 Kurtosis 10.0 9.1 2.6 3.1 2.7 3.9 3.5 2.5 9.6 2.2 5.9 15.7 Skewness 2.7 2.1 0.4 0.8 0.5 1.0 0.9 0.6 1.8 0.2 1.8 3.6 Cotonou Mean 6.6 13.5 29.4 45.4 64.0 111.4 41.6 20.5 42.1 45.0 13.3 6.5 Min 0.0 0.0 4.7 4.1 22.1 16.6 1.9 0.0 7.7 0.3 0.1 0.0 Max 76.7 77.2 103.7 137.4 121.8 287.1 204.4 169.9 151.3 107.9 90.1 58.9 SD 13.5 16.7 22.7 24.8 24.3 53.9 37.7 27.5 25.4 24.3 17.4 12.7 SV 2907. 1422. 302. 183.2 278.5 514.9 613.4 590.9 754.6 643.8 592.7 160.6 8 8 8 CV 2.1 1.2 0.8 0.5 0.4 0.5 0.9 1.3 0.6 0.5 1.3 2.0 Std E 1.8 2.2 3.0 3.3 3.2 7.1 5.0 3.6 3.4 3.2 2.3 1.7 Med 0.6 9.5 23.7 42.2 59.1 99.5 33.4 12.5 37.7 41.1 8.6 0.5 Kurtosis 17.3 7.0 4.5 4.7 2.4 3.6 8.4 18.1 7.4 2.5 11.5 11.4 Skewness 3.6 1.9 1.4 1.0 0.4 0.8 2.0 3.5 1.5 0.2 2.8 2.9 Kandi www.ijaer.in Copyright © IJAER 2018, All right reserved Page 1014

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

Mean 0.0 0.8 2.4 12.6 35.5 48.9 67.4 88.4 53.3 6.7 0.0 0.1 Min 0.0 0.0 0.0 0.2 2.8 16.8 35.6 32.9 18.5 0.0 0.0 0.0 Max 1.7 12.7 19.5 52.3 74.5 86.6 111.9 183.6 102.8 38.6 0.7 2.5 SD 0.2 2.4 4.1 11.8 17.2 16.1 18.1 25.2 20.5 8.0 0.1 0.5 SV 0.1 5.7 16.5 140.1 295.0 258.8 327.5 635.1 420.7 64.2 0.0 0.2 CV 7.5 3.2 1.7 0.9 0.5 0.3 0.3 0.3 0.4 1.2 6.9 4.3 Std E 0.0 0.3 0.5 1.6 2.3 2.1 2.4 3.3 2.7 1.1 0.0 0.1 Med 0.0 0.0 0.2 8.9 33.0 49.9 66.4 84.3 54.2 3.3 0.0 0.0 Kurtosis 55.0 19.3 9.6 4.9 2.7 2.6 2.8 5.2 2.7 6.6 54.1 20.2 Skewness 7.3 4.1 2.5 1.5 0.5 0.1 0.4 0.9 0.5 1.7 7.3 4.3 Natitingou Mean 0.5 1.7 7.2 26.6 37.1 48.2 67.7 94.6 75.2 25.1 2.2 0.4 Min 0.0 0.0 0.0 4.2 0.0 24.1 24.2 30.3 29.1 0.4 0.0 0.0 Max 14.0 12.8 20.8 87.8 70.8 97.0 124.8 177.3 117.9 77.8 26.3 7.6 SD 2.2 3.1 5.8 15.4 14.7 18.1 25.2 26.8 23.2 17.3 5.2 1.3 SV 4.7 9.7 33.6 238.6 216.5 326.9 634.7 716.5 540.3 300.7 26.8 1.6 CV 4.4 1.9 0.8 0.6 0.4 0.4 0.4 0.3 0.3 0.7 2.4 3.5 Std E 0.3 0.4 0.8 2.0 1.9 2.4 3.3 3.5 3.1 2.3 0.7 0.2 Med 0.0 0.0 5.9 23.1 36.1 45.1 62.8 95.4 72.4 22.5 0.0 0.0 Kurtosis 31.1 6.1 2.0 6.1 2.9 3.5 2.4 3.5 1.9 4.8 13.8 21.5 Skewness 5.3 2.0 0.4 1.5 0.1 1.1 0.5 0.2 0.1 1.3 3.3 4.2 Parakou Mean 0.9 3.2 12.5 27.6 42.8 53.8 62.4 71.7 65.6 21.9 1.9 0.9 Min 0.0 0.0 0.0 4.4 13.4 12.4 24.7 19.8 22.7 0.3 0.0 0.0 Max 16.5 22.9 45.5 73.1 107.0 97.9 122.8 164.7 107.2 63.7 23.0 18.0 SD 2.7 5.8 11.0 15.2 19.2 21.4 24.7 27.3 20.3 15.6 4.7 3.1 SV 7.3 34.2 120.0 230.2 367.5 456.4 610.6 746.6 410.1 243.0 22.0 9.7 CV 3.0 1.8 0.9 0.5 0.4 0.4 0.4 0.4 0.3 0.7 2.5 3.5 Std E 0.4 0.8 1.5 2.0 2.5 2.8 3.3 3.6 2.7 2.1 0.6 0.4 Med 0.0 0.0 10.1 23.8 37.9 52.8 59.1 75.4 63.7 17.1 0.0 0.0 Kurtosis 23.4 5.8 3.8 3.3 4.4 2.0 2.5 4.1 2.3 3.3 12.1 23.3 Skewness 4.4 2.0 1.1 0.7 1.1 0.2 0.4 0.5 0.0 0.9 3.0 4.5 Savè Mean 2.0 6.4 21.3 37.2 39.4 51.9 51.6 52.2 53.4 27.5 2.6 1.2 Min 0.0 0.0 1.2 9.5 9.9 16.4 9.8 12.7 10.4 0.0 0.0 0.0 Max 109. 190. 133. 32. 22.5 26.0 60.3 70.7 77.2 118.5 71.3 17.7 9 6 9 8 SD 4.3 6.9 14.3 16.7 16.7 20.8 30.8 28.3 21.1 15.7 5.7 3.3 SV 203. 278. 279. 431. 947. 802. 32. 18.3 47.5 447.3 245.7 10.8 7 5 6 5 8 5 6 CV 2.1 1.1 0.7 0.4 0.4 0.4 0.6 0.5 0.4 0.6 2.2 2.8 Std E 0.6 0.9 1.9 2.2 2.2 2.8 4.1 3.8 2.8 2.1 0.8 0.4 Med 0.0 3.3 20.5 35.3 38.5 50.1 44.8 46.7 53.6 24.7 0.0 0.0 www.ijaer.in Copyright © IJAER 2018, All right reserved Page 1015

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

Kurtosis 16. 11.8 2.9 2.9 2.2 2.3 3.0 8.9 3.3 3.6 2.6 14.3 5 Skewness 2.8 0.9 0.7 0.4 0.4 0.7 1.9 0.8 0.6 0.4 3.5 3.3

Table 6: Descriptive statistics of monthly maximum temperature over weather stations in Benin during 1960 à 2016

Statistic Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Bohicon Mean 34.6 36.1 35.4 34.0 32.7 30.8 29.5 29.2 30.2 31.8 33.9 34.2 Min 33.4 34.2 33.9 32.7 31.3 26.4 27.8 27.5 29.0 30.7 31.9 32.7 Max 36.3 37.7 38.3 36.8 34.1 32.1 30.9 30.7 31.8 33.4 35.3 35.9 SD 0.67 0.82 1.04 0.87 0.59 0.83 0.7 0.69 0.65 0.54 0.67 0.71 SV 0.44 0.67 1.08 0.75 0.34 0.69 0.48 0.48 0.42 0.29 0.45 0.51 CV 0.02 0.02 0.03 0.03 0.02 0.03 0.02 .02 0.02 0.02 0.02 0.02 Std Er 0.09 0.11 0.14 0.11 0.08 0.11 0.09 0.09 0.09 0.07 0.09 0.09 Med 34.6 36.2 35.3 34.0 32.6 30.9 29.5 29.2 30.4 31.8 33.8 34.2 Kurtosis 2.69 2.38 3.09 3.31 2.89 15.67 2.66 2.73 2.30 3.27 3.56 2.41 Skewness 0.24 -0.00 0.81 0.55 0.28 -2.71 -0.10 -0.14 -0.09 0.24 -0.17 0.00 Cotonou Mean 31.11 31.86 32.09 31.77 31.12 29.4 28.22 27.97 28.68 29.84 31.20 31.29 Min 30.1 29.21 30.87 30.51 30.12 28.26 26.4 26.5 27.42 28.2 29.66 29.91 Max 32.55 33.55 33.47 33.02 32.31 30.81 29.37 28.9 29.6 30.82 32.02 32.74 SD 0.56 0.77 0.58 0.61 0.55 0.56 0.67 0.52 0.53 0.54 0.54 0.74 SV 0.31 0.6 0.33 0.374 0.30 0.32 0.45 0.27 0.28 0.29 0.29 0.54 CV 0.028 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 Std E 0.07 0.10 0.08 0.08 0.07 0.07 0.09 0.07 0.07 0.07 0.07 0.01 Med 31.06 31.9 32.11 31.77 31.05 29.45 28.3 28.06 28.74 29.95 31.28 31.34 Kurtosis 2.59 4.51 2.95 2.35 2.38 2.48 2.49 2.88 2.39 3.56 3.24 1.99 Skewness 0.35 -0.3 0.30 0.07 0.13 0.13 -0.33 -0.48 -0.39 -0.74 -0.71 0.05 Kandi Mean 33.66 36.57 38.82 38.83 36.34 33.47 31.05 30.2 31.23 34.23 35.59 34.05 Min 29.06 32.81 36.6 36.24 33.49 31.25 29.61 28.26 29.73 32.36 33.52 30.88 Max 36.48 39.5 40.94 40.99 41. 7 38.84 32.66 32.79 32.77 36.12 37.59 36.38 SD 1.51 1.47 1.06 1.12 1.38 1.14 0.80 0.78 0.75 0.9 0.97 1.27 SV 2.29 2.15 1.13 1.25 1.91 1.3 0.65 0.61 0.57 0.80 0.94 1.63 CV 0.04 0.04 0.03 0.03 0.04 0.03 0.03 0.03 0.02 0.03 0.03 0.04 Std E 0.20 0.19 0.14 0.15 0.18 0.15 0.11 0.10 0.1 0.12 0.13 0.17 Med 33.87 36.57 38.7 38.78 36.37 33.36 30.98 30.26 31.3 34.25 35.47 34.16

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

Kurtosis 3.15 2.74 2.42 2.39 6.04 9.85 2.14 3.97 2.34 2.54 2.07 2.36 Skewness -0.58 -0.34 -0.08 -0.24 1.16 1.72 0.25 0.25 -0.16 0.09 0.1 -0.39 Natitingou Mean 34.58 36.71 37.50 35.99 33.83 31.62 29.67 29.14 30.40 32.58 34.68 34.59 Min 31.42 33.93 34.95 33.1 32.01 29.44 27.78 27.07 28.71 30.07 32.28 32.16 Max 37.25 38.93 39.27 38.49 36.32 33.89 31.35 30.79 31.65 34.62 36.97 36.67 SD 1.07 1.05 0.87 1.24 0.91 0.90 0.77 0.81 0.69 0.91 1.13 1.08 SV 1.16 1.10 0.76 1.53 0.82 0.81 0.59 0.65 0.48 0.84 1.27 1.17 CV 0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.02 0.03 0.03 0.03 Std E 0.14 0.14 0.12 0.16 0.12 0.12 0.10 0.11 0.09 0.12 0.15 0.14 Med 34.71 36.74 37.57 36.04 33.80 31.64 29.6 29.22 30.47 32.55 34.78 34.71 Kurtosis 3.47 3.16 2.93 2.43 2.68 2.84 2.7 2.81 2.43 3.37 2.36 2.37 Skewness -0.10 -0.49 -0.39 0.15 0.26 -0.11 0.13 -0.34 -0.41 -0.06 -0.12 -0.00 Parakou Mean 34.57 36.46 36.77 35.28 33.06 31.01 29.37 28.65 29.73 31.62 34.11 34.25 Min 31.63 34.48 34.16 32.49 31.45 29.71 27.89 26.44 28.14 29.11 31.11 32.04 Max 37.25 38.82 39.75 38.83 35.92 32.93 31.68 30.58 31.57 33.49 36.88 37.16 SD 1.08 1.05 1.09 1.21 0.95 0.74 0.75 0.81 0.78 0.85 1.07 1.09 SV 1.18 1.1 1.18 1.46 0.91 0.54 0.57 0.66 0.60 0.71 1.14 1.2 CV 0.03 0.03 0.03 0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 Std E 0.15 0.14 0.15 0.16 0.13 0.10 0.10 0.11 0.11 0.12 0.15 0.15 Med 34.68 36.40 36.82 35.26 32.94 30.93 29.41 28.67 29.75 31.52 34.15 34.2 Kurtosis 3.35 2.65 4.12 3.53 3.05 2.89 4.09 2.88 2.77 3.48 3.63 3.00 Skewness -0.20 0.35 0.17 0.25 0.55 0.39 0.76 -0.05 0.27 0.05 0.12 0.38 Savè Mean 35.38 36.94 36.36 34.54 33.19 31.36 29.71 29.09 30.24 31.92 34.40 34.93 Min 34.01 34.76 34.41 32.14 31.77 29.50 27.79 26.80 28.28 30.23 32.07 32.80 Max 37.74 39.30 38.57 37.19 35.20 33.24 31.68 30.99 32.03 33.29 36.32 37.22 SD 0.87 0.93 1.06 1.05 0.80 0.74 0.85 0.76 0.76 0.75 0.94 1.03 SV 0.76 0.86 1.12 1.10 0.64 0.55 0.72 0.58 0.58 0.56 0.89 1.05 CV 0.02 0.03 0.03 0.03 0.02 0.02 0.03 0.03 0.03 0.02 0.03 0.03 Std E 0.12 0.12 0.14 0.14 0.11 0.10 0.11 0.10 0.10 0.10 0.12 0.14 Med 35.35 36.86 36.29 34.45 33.19 31.27 29.74 29.21 30.22 31.94 34.44 34.79 Kurtosis 3.34 3.22 2.24 2.92 2.45 3.05 2.62 3.52 3.10 2.26 2.46 2.51 Skewness 0.75 0.08 0.17 0.34 0.04 0.09 0.09 -0.41 0.00 -0.14 -0.04 0.44

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

Table 7: Descriptive statistics of monthly minimal temperature over weather stations in Benin during 1960 à 2016

Statistic Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Bohicon Mean 22.63 23.96 24.31 23.98 23.47 22.65 22.11 21.81 22.11 22.51 23.22 22.82 Min 20.62 22.44 22.92 22.41 22.37 21.51 20.81 20.24 20.44 21.34 21.5 20.1 Max 24.55 25.61 25.99 26.01 24.66 24.13 23.23 23.03 23.34 23.76 24.67 24.52 SD 1.03 0.79 0.78 0.78 0.58 0.59 0.56 0.65 0.64 0.62 0.70 0.99 SV 1.06 0.62 0.60 0.61 0.34 0.35 0.32 0.42 0.41 1.38 0.5 0.98 CV 0.05 0.03 0.03 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.04 Std Er 0.14 0.10 0.10 0.10 0.08 0.08 0.07 0.09 0.08 .08 0.09 0.13 Med 22.77 23.99 24.29 24.05 23.44 22.68 22.08 21.88 22.09 22.5 23.33 22.89 Kurtosis 2.16 2.19 2.21 2.66 2.34 2.33 2.81 2.87 2.46 2.15 2.32 2.95 Skewness -0.32 0.05 0.02 -0.01 0.29 -0.03 -0.19 -0.46 -0.29 0.04 -0.23 -0.47 Cotonou Mean 24.02 25.66 26.27 25.87 24.93 24.03 23.97 23.7 23.89 24.10 24.7 24.25 Min 20.6 23.74 24.53 23.84 23.72 22.89 22.95 22.32 22.61 22.89 23.36 22.02 Max 26.14 27.9 27.63 27.5 26.44 25.17 25.05 24.72 25 25.28 26.06 25.74 SD 1.19 0.9 0.72 0.87 0.65 0.59 0.57 0.58 0.56 0.59 0.72 0.9 SV 1.41 0.81 0.52 0.75 0.43 0.34 0.32 0.34 0.32 0.35 0.52 0.80 CV 0.05 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.03 0.04 Std E 0.16 0.12 0.1 0.12 0.09 0.08 0.08 0.08 0.07 0.08 0.1 0.12 Med 24.18 25.69 26.3 25.95 24.8 24.09 24.04 23.68 23.95 24.1 24.69 24.23 Kurtosis 3.2 2.60 2.43 2.65 2.44 2.28 1.99 2.52 2.59 2.28 1.80 2.46 Skewness -0.61 0.01 -0.05 -0.26 0.38 -0.28 -0.07 -0.32 -0.16 -0.19 -0.02 -0.31 Kandi Mean 16.60 19.84 24.08 26.03 24.88 23.21 22.3 21.99 21.78 21.98 18.48 16.43 Min 14.3 17.14 21.19 23.61 23.19 21.35 21.24 20.86 20.72 20.24 15.39 14.02 Max 19.65 23.86 27.73 27.92 26.8 24.56 23.65 22.92 22.76 23.46 21.55 19.92 SD 1.25 1.49 1.37 0.83 0.79 0.67 0.61 0.5 0.54 0.85 1.62 1.29 SV 1.56 2.21 1.87 0.69 0.63 0.45 0.37 0.25 0.29 0.73 2.63 1.66 CV 0.08 0.07 0.06 0.03 .03 0.03 0.03 0.02 0.02 0.04 0.09 0.08 Std E 0.17 0.2 0.18 0.11 0.10 0.09 0.08 0.07 0.07 0.11 0.21 0.17 Med 16.70 19.89 23.99 26.15 24.76 23.22 22.27 22.02 21.8 21.82 18.15 16.2 Kurtosis 2.31 2.70 3.05 3.59 2.66 3.22 2.09 2.13 1.93 2.1 2.29 2.68 Skewness 0.14 0.32 0.14 -0.44 0.14 -0.27 0.16 -0.17 -0.09 0.11 0.16 0.21 Natitingou Mean 18.92 20.98 23.20 23.68 22.76 21.68 21.13 20.96 20.68 20.54 18.65 18.41 Min 14.96 18.17 21.58 21.82 21.37 20.54 20.25 20.16 19.77 19.36 16.42 13.67 Max 21.36 23.74 25.08 25.57 24.04 22.98 22.25 21.79 21.34 21.84 20.31 20.09 SD 1.21 0.99 0.83 0.72 0.65 .55 0.5 0.41 0.46 0.60 0.78 1.17 SV 1.47 0.97 0.69 0.53 0.43 0.31 0.25 0.17 0.21 0.36 0.60 1.38 www.ijaer.in Copyright © IJAER 2018, All right reserved Page 1018

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939 Volume:04, Issue:04 "July-August 2018"

CV 0.06 0.05 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.03 0.04 0.06 Std E 0.16 0.13 .11 0.1 0.09 0.07 0.07 0.05 0.06 0.08 0.10 0.16 Med 18.85 20.98 23.19 23.7 22.77 21.74 21.12 20.97 20.84 20.51 18.54 18.58 Kurtosis 5.48 4.29 2.43 3.12 2.51 2.66 2.43 2.04 1.77 2.32 3.16 7.25 Skewness -1.23 -0.32 0.20 0.05 0.04 -0.11 0.44 0.02 -0.24 0.25 0.09 -1.81 Parakou Mean 19.38 21.77 23.41 23.40 22.56 21.73 21.35 21.13 20.93 21.22 20.45 19.2 Min 15.97 19.91 21.83 21.61 21.13 20.10 19.75 19.46 19.12 19.39 17.68 16.9 Max 22.79 24.34 24.9 25.27 23.67 23.03 22.28 22.05 22.06 22.35 22.45 21.18 SD 1.31 1.08 0.8 .74 0.63 0.59 0.59 0.57 .65 0.70 1.26 1.21 SV 1.72 1.16 0.64 0.56 0.39 0.35 0.35 0.32 0.42 0.5 1.58 1.47 CV 0.07 0.05 0.03 0.03 0.03 0.03 0.03 0.03 0.03 .03 0.06 0.06 Std E 0.18 0.15 0.11 0.10 0.09 0.08 0.08 0.08 0.09 0.1 0.17 0.17 Med 19.40 21.55 23.46 23.48 22.61 21.84 21.43 21.18 20.99 21.39 20.5 19.09 Kurtosis 3.05 2.16 2.10 3.33 2.41 3.01 2.78 3.67 2.87 2.94 2.22 2.01 Skewness -0.18 0.27 -0.21 -0.24 -0.25 -0.47 -0.46 -0.97 -0.62 -0.78 -0.31 -0.13 Savè Mean 21.53 23.15 23.69 23.38 22.89 22.20 21.78 21.42 21.63 21.91 22.26 21.47 Min 19.12 21.76 22.22 22.00 21.88 21.15 20.76 20.09 20.36 20.73 20.81 19.31 Max 23.79 24.80 25.43 24.87 24.13 23.80 22.88 22.39 22.74 23.20 23.63 23.54 SD 1.03 0.83 0.82 0.76 0.59 0.59 0.54 0.57 0.57 0.58 0.84 1.03 SV 1.07 0.69 0.67 0.57 0.35 0.34 0.29 0.33 0.33 0.34 0.71 1.05 CV 0.05 0.04 0.03 0.03 0.03 0.03 0.02 0.03 0.03 0.03 0.04 0.05 Std E 0.14 0.11 0.11 0.10 0.08 0.08 0.07 0.08 0.08 0.08 0.11 0.14 Med 21.61 23.11 23.67 23.45 22.89 22.30 21.78 21.50 21.63 21.97 22.14 21.56 Kurtosis 2.79 2.17 2.13 2.34 2.36 2.51 2.41 2.59 2.27 2.22 1.54 2.28 Skewness -0.21 0.21 0.12 0.08 0.21 0.11 0.05 -0.32 -0.15 -0.05 -0.04 -0.15