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Evaluation of Rainfall and Temperature Conditions for a Perennial Crop in Tropical Wetland: A Case Study of Cocoa in Côte d’Ivoire

Evaluation of Rainfall and Temperature Conditions for a Perennial Crop in Tropical Wetland: A... Hindawi Advances in Meteorology Volume 2019, Article ID 9405939, 10 pages https://doi.org/10.1155/2019/9405939 Research Article EvaluationofRainfallandTemperatureConditionsforaPerennial Crop in Tropical Wetland: A Case Study of Cocoa in Coˆte d’Ivoire 1,2 1,2 1,2 1 Fide`le Yoroba , Benjamin K. Kouassi , Adama Diawara, Louis A. M. Yapo, 1 1 1 1 Kouakou Kouadio, Dro T. Tiemoko, Yves K. Kouadio , Ibrahim D. Kone´, and Paul Assamoi University F´elix Houphou¨et-Boigny, UFR SSMT, Laboratory of Atmosphere Physic and Mechanic Fluids (LAPA-MF), 22 BP 582 Abidjan 22, Coˆte d’Ivoire Geophysical Station of Lamto (GSL), BP 31 N’Douci, Cote d’Ivoire Correspondence should be addressed to Fide`le Yoroba; yorofidele@gmail.com Received 3 October 2018; Accepted 25 December 2018; Published 13 January 2019 Academic Editor: Helena A. Flocas Copyright © 2019 Fide`le Yoroba et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. +e rainfall and temperature conditions are evaluated for the first time during the 1989–2006 period, in six main cocoa production areas (Abengourou, Agboville, Daloa, Dimbokro, Guiglo, and Soubre) of Cote ˆ d’Ivoire using data from SODEXAM (ground- based observation) and the ex-CAISTAB. Statistical analysis shows an important sensitivity of cocoa production to rainfall conditions in all regions. It is worth noting that only the major rainy season from April to July and the rainfall amount of the little dry season from August to September affect the cocoa production for an 80% confidence level. +is influence varies from one cacao production area to another. Moreover, the effects related to temperature on the cocoa yield seem to represent a smaller contribution of climate impact than those related to precipitation during the studied period. +e temperature change remains in ° ° the acceptable range of values, between 25 C and 29 C, which is a favorable condition for cocoa growing. +ese findings are obtained despite the significant contributions from nonclimatic factors, to year-to-year variability in cocoa production. strong interannual variability mainly related to climate 1. Introduction variations [3] and the ignorance of many physical, biological, Across Cote ˆ d’Ivoire, cocoa farms have expanded consid- and socioeconomic processes. Cocoa growing requires rel- erably since 1980s thanks to a strong agricultural policy and atively more rigorous pedological and climatic conditions favorable agronomic and climatic conditions in many re- than other tropical crops like a palm tree, coffee, and rubber gions. Another favorable condition, in addition to propitious tree [4, 5]. For example, cocoa needs wetter conditions of agroclimatic conditions, to cocoa growing is the cheap labor about a minimum of 1200 mm to 1500 mm annual rainfall ˆ amount per year with a limited number of dry days (<90 availability [1]. +us, the economy of C ote d’Ivoire is mainly based on the pair coffee-cocoa. For example, in 1997, the days) [6] for its development. coffee-cocoa pair provided about 17% of the Gross Domestic On the other hand, West African countries, especially Product (GDP) and 38.6% of export earnings [2]. In ad- Cote ˆ d’Ivoire have experienced strong climate variability dition, according to the International CoCoa Organization characterized by a deficit on rainfall amount [7, 8]. Fur- (ICCO) statistics, Cote ˆ d’Ivoire produced 1,395,000 Tons of thermore, Freud et al. [9] showed that the rainfall trend in cocoa for the 2009-2010 seasons that represented 46.5% of the last few decades has led to a decrease in ecologically world production. favorable conditions for cocoa in many parts of the country. Cocoa is a perennial tropical plant, which contributes to In the same vein, several works as Nicholson [10], Lamb [11], the economy and development of many countries like Ghana Folland et al. [12], Lamb and Peppler [13], Fontaine and and Cote ˆ d’Ivoire. However, its production undergoes a Janicot [14], and Nicholson and Grist [15] evaluate possible 2 Advances in Meteorology mechanisms associated with these deficits in rain amount. 2 is dedicated to the description of the study area, the For example, these drought episodes are sequences of material, and the method used. Section 3 shows the results continuous rainfall deficit periods from 1960 to 1990, which and, the discussion is given in Section 4. Finally, conclusion were not only limited to the Sahel area but spread to the and perspectives are given at the end. coastal regions of West Africa [14, 16], especially in Cote ˆ d’Ivoire [17, 18]. +e dry conditions led to a serious dis- 2. Materials and Methods turbance in water resources and then a disaster for the 2.1. Study Area. +e area of interest is an extensive zone populations and cause a loss in livestock and a gradual composed of dense evergreen and semideciduous wet forests disappearance of certain export crops. covering an area of 170,320 km [29]. It represents about In agriculture, the consequences are significant and 53% of the Ivorian territory and is located between 2.5 W result in changes in the growing seasons: an occurrence of ° ° ° and 8.5 W and, between 4 N and 9 N, in the southern half of late or early rains and, particularly, long dry spells. +e the country (Figure 1). +e region is bounded at its northern effects of the changes are really important on the economy of part by the preforest Savannah zone commonly known as “V West African countries especially for a country like Cote Baoule” ´ and by the Atlantic Ocean in the south [24]. +ree d’Ivoire, 40% of whose resources are based on cocoa [19]. types of soils characterize this region: hydromorphic, fer- Amani [20] pointed out that farmers who have been most ralitic, and ferruginous soils [30]. +ese soils are mostly affected by this climate variability have used biotechnology favorable to agriculture and cocoa cultivation particularly. methods that involve selecting cloned or hybrid cocoa +is region is characterized by a bimodal rainy season: the species. +ese biotechnology methods were expected to major and first rainy season is between April and early-to- improve with the agrobiochemical supports (e.g., fertilizers, mid July with a peak in June and, the little rainy season herbicides, and insecticides) the cocoa production. How- extends from the mid-September to November with a peak ever, the methods did not show subsequent improvement in in October. Kouadio et al. [31] showed that the rainfall the production per hectare [21]. regime in the north of Gulf of Guinea is influenced by the Moreover, in the centre of Cote ˆ d’Ivoire, especially Intertropical Convergence Zone (ITCZ) and Sea Surface around Daoukro, considered as the old “main production Temperature (SST) anomalies at the equator and West zone”; Koukou-Tchamba et al. [22] and Kanohin et al. [23] Africa coast. underlined a decrease in cocoa production, and they at- tributed it to a decline of the rainfall in the region. +us, a new cocoa production “hot spot” has emerged towards the 2.2. Materials. +is study used ground-based observations southeast and southwest regions, which have not been (rainfall and temperature) from the National Meteorology of exploited before. Furthermore, a strong link between the Cote ˆ d’Ivoire (SODEXAM) and cocoa production data from main cocoa production areas and the recorded seasonal the ex-Caisse de Stabilisation du Cafe´ et du Cacao (ex- precipitation amount has been underlined [24]. +e cocoa CAISTAB) over the 1989–2010 period. +ese parameters growing has been also linked to other parameters like the are recorded across six stations (five rain gauge stations and temperature, during the flowering period, the type of the one synoptic station). +e selected and available stations soil, humidity, and insolation [25, 26]. +us, precipitation (Abengourou, Agboville, Dimbokro, Guiglo, Soubre, and and temperature are suggested to play an important role in Daloa) cover the study area and provide acceptable and the significant interannual variability of cocoa production uniform meteorological information about the considered experienced in Cote ˆ d’Ivoire during the past recent years. regions (Figure 2). However, Agoh and Augustin [27] show that these two climatic factors drive differently the interannual variability of cocoa production in a given area. For example, in the 2.3. Method. +e work is based on statistical analysis of cocoa Daloa region, cocoa yield is affected by the duration of yield in regard to the weather conditions. +e analyses assess sunshine and rainfall, while in the Gagnoa region, it depends the relationships between the climate and the cocoa yield. +e on the duration of sunshine and temperature. significance of any trend or signal is also analysed. +e water +erewith, some studies like [28] projected a decrease in requirements for the cocoa production are calculated based rainfall associated with a reduction in the number and length on values provided by Dian [30] and Mian [29]. +e main ° ° of consecutive dry days under 1.5 C and 2 C global warming rainy season from April to July must record an amount of rain based on Representative Concentration Pathways (RCP 8.5). above 700 mm [29] and the little dry season (August to +is study assesses the influence of precipitation and tem- September) must record at least 70 mm [30] to ensure a good perature on the development and cocoa yield, in six de- cocoa growing and production. +e considered temperature, partments (Abengourou, Agboville, Daloa, Dimbokro, and thereafter, is obtained by averaging the daily tempera- Guiglo, and Soubre) across Cote ˆ d’Ivoire. It tries to also tures over March-April (first flowering period) and improve our understanding of the relative and individual September-October (second flowering period) for each year. contributions of the two factors in estimating of climate change impacts on cocoa yields. +ese departments are 3. Results considered as the main cocoa production areas of the country. +e study is conducted over the period spreading 3.1. Interannual Variability of Cocoa Production. +e in- from 1989 to 2006. +e work is structured as follows; Section terannual variability of cocoa production is studied through Advances in Meteorology 3 8°0′0′′W 7°0′0′′W 4°0′0′′W 3°0′0′′W 8°0′0′′N 8°0′0′′N 7°0′0′′N 7°0′0′′N 6°0′0′′N 6°0′0′′N 5°0′0′′N 5°0′0′′N 8°0′0′′W 7°0′0′′W 6°0′0′′W 5°0′0′′W 4°0′0′′W 3°0′0′′W 0 45 90 180 (km) Dense evergreen wet forest Semi-deciduous wet forest Figure 1: Vegetation types of the study area (adapted to Kone et al. [26]). conditions in these two areas could similarly influence the the standardized anomalies calculated over the 1989–2006 period (Figure 3) in the six departments. +ese standardized cocoa yield and thus impact the variability of the cocoa production. anomalies vary from−3 to 3 and then suggest periods of deficit and abundance in cocoa production across all regions. For Abengourou, Guiglo, and Daloa, there is an alternate of 3.2. Cocoa Production Band Meteorological Parameters. deficit (before 1995 and from 2003) and abundance (from 1995 to 2002). A similar pattern is observed in Dimbokro Figures 4 and 5 show the variations of cocoa production in regard to the amount of rainfall during the main rainy with a deficit before 1993 followed by a scatter period where there are successive multiple deficits and abundances. +e season (April to July) and little dry season (August to September), and the temperature in the six regions, re- other departments (i.e., Agboville and Soubre) show a long period of deficit in the cocoa production before 2001 fol- spectively. A relative important correlation coefficient (0.43) is noticed between cocoa production and precipitation lowed by a period of abundance. +e displayed tendency amount during the main rainy season around Guiglo than (Figure 3), as it is expected from the standardized anomalies, shows a succession of decrease and increase in cocoa pro- that over the rest of departments (Figure 4). +e values of 0.31 and 0.32 are, respectively, obtained in Abengourou and duction. For example, there is a decrease in cocoa pro- duction from 1989 to 1995 followed by an increase, from Daloa. +e lowest correlations are found in Agboville (− 0.16), Dimbokro (0.02), and Soubre (0.14). In these latest regions, 1995 to 2003 in Guiglo and from 1995 to 1999 around Daloa and Abengourou. +e similarity in the variability of the it seems that there is no noticeable influence of rainfall on cocoa production, whereas, in Abengourou, Daloa, and cocoa production of Daloa and Abengourou may be due to their climate type and latitudinal location (Figure 2). +ese Guiglo, the rainfall amount of the main rainy season has no negligible impact on the variability of the cocoa production departments belong to the same climate type (climate of center) proposed by Kouadio et al. [32]. +e meteorological even if the correlation coefficients are below 0.5. +ese values 4 Advances in Meteorology Months Months Months Months Months Months Figure 2: Study area with the six reference departments and their mean ombrothermal diagram over the 1989–2006 period. Red contours delimit the important areas of cocoa production. of the correlation coefficients suggest an increase in the and b represents the potential nonlinear effects of pre- cocoa production with the rainfall. Figure 5 shows corre- cipitation or temperature, such as those that arise from lation values, between cocoa production and little dry interactions with other variables. +is equation ignores other season, below 0.23 across all the departments, except in aspects of meteorological conditions, such as relative hu- Agboville (0.32). +us, the assumed link may be marginal midity, wind speed, sunshine, and nonclimatic factors, such between the cocoa production variability and the rainfall as soils types, soil humidity, planted area, and number of the amounts during the little dry season from August to Sep- vegetative cycle. It represents a useful first-order estimate of tember. +e mean temperature values are between 25 C and year-to-year variability in cocoa yield for a considered de- partment over the studied period. All calculated coefficient 29 C (figure not shown) and offer favorable temperature conditions to the cocoa culture in the departments. +ere is a values of equation (1) are given in Table 1. +is table shows decrease in cocoa production with the mean temperature for that the increase in cocoa production is mainly due to the some department like Daloa (−0.06), Abengourou (−0.32), rainfall amounts of the major rainy season and the little dry and Dimbokro (−0.42) while for the remaining, the pro- season in Abengourou and Daloa. In Guiglo and Soubre, this duction increases with the mean temperature. increase is related to all the considered three parameters, while in Agboville, only the rainfall amount of the little dry season has remarkable impact on the cocoa production. In 3.3. Cocoa Production Response to Rainfall and Temperature Dimbokro, the negative correlation coefficients suggest a as the Predictors in a Multiple Linear Regression. In this decrease in cocoa production with both temperature and section, multiple regressions are used to evaluate sources of rainfall. +e significance of the correlation coefficients be- variations in the cocoa production (ΔY) in regard to both tween the cocoa production and the three defined param- rainfall amounts of the main rainy season (ΔP ), the little eters (i.e.,ΔP ,ΔP , andΔT) is assessed using indexes from rs rs ds dry season (ΔP ), and the growing season average tem- the Fisher–Snedecor and Student t-tests (see Tables 2 and 3). ds perature (ΔT). +e change in cocoa production (ΔY) for a Table 2 shows a high p value in Dimbokro (0.843) and given department is obtained by the following equation: Soubre (0.815), while it displays a low value in Abengourou (0.426), Agboville (0.442), Daloa (0.409), and Guiglo (0.219). ΔY � mΔP + mΔP + m ΔT+ b, (1) 1 rs 2 ds 3 +ese p values are associated with high values of coefficient where m , m , and m represent the sensitivity of cocoa of determination (R ) in Abengourou (0.175), Agboville 1 2 3 (0.169), Daloa (0.181), and Guiglo (0.263), while the values production to both rainfall amounts of the main rainy season, the little dry season and the temperature, respectively, are marginal in Dimbokro (0.055) and Soubre (0.063). In Precipitations (mm) Precipitations (mm) Precipitations (mm) 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 Jan Jan Jan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 Temperature (°C) Temperature (°C) Temperature (°C) Precipitations (mm) Precipitations (mm) Precipitations (mm) 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 Jan Jan Jan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 Temperature (°C) Temperature (°C) Temperature (°C) Advances in Meteorology 5 3 3 2 2 R = –0.24 R = 0.67 1 1 0 0 –1 –1 –2 –2 –3 –3 88 90 92 94 96 98 00 02 04 06 88 90 92 94 96 98 00 02 04 06 Years Years (a) (b) Years 3 3 R = 0.36 R = 0.11 1 1 –1 –1 –2 –2 –3 –3 88 90 92 94 96 98 00 02 04 06 88 90 92 94 96 98 00 02 04 06 Years Years (c) (d) 3 3 2 2 R = 0.18 R = 0.89 1 1 0 0 –1 –1 –2 –2 –3 –3 88 90 92 94 96 98 00 02 04 06 88 90 92 94 96 98 00 02 04 06 Years Years (e) (f) Figure 3: Standardized anomalies of cocoa production in the six reference departments of the study area during the 1989–2006 period. Red line indicates the trend curve. (a) Abengourou. (b) Agboville. (c) Daloa. (d) Dimbokro. (e) Guiglo. (f) Soubre. ´ these four departments where coefficient of determination is one of the three parameters could be inhibited by the in- crease of the two others. On the over hand, in Table 3, the t- noticeable, the equation of multiple regressions is relevant to explain the cocoa production. +en, the temperature and the values evaluate the importance of each variableΔP ,ΔP , rs ds rainfall during the main rainy season and the little dry season andΔT in the estimating cocoa production (ΔY) with an are crucial to explain the variation in cocoa production. 80% confidence level (t � 1.34). +e rainfall amounts (either +us, in Abengourou and Daloa, an increasing temperature main rainy season or little dry season) are suggested to be the induces a decrease in cocoa production while in the de- key parameters that influence the annual cocoa production partment of Agboville, the increase in rainfall amount in three departments: Agboville with ΔP (t� 1.5) and ds during the main rainy season tends to reduce the cocoa yield. Daloa and Guiglo with ΔP (t� 1.48 and t� 1.75, re- rs +e opposite evolution of cocoa production in regard to the spectively). But the mean temperature (ΔT) (t-values< 1.34 increased precipitation may be related to the influence of in all departments) seems to have negligible impacts on some other factors like the type of the soil and soil fertility, cocoa production in all departments. which are not considered in this study. +e increase of runoff, erosion, and relative humidity can damage the cocoa 4. Discussion trees if the soil conditions do not manage to inhibit their negative effects. In addition, it is possible that the increase in +e impact of the rainfall and the temperature conditions in the average growing season rainfall has a lower impact on cocoa yield across six departments (Agboville, Daloa, Gui- cocoa yield than the increase in rainfall during the critical glo, Abengourou, Dimbokro, and Soubre) of Cote ˆ d’Ivoire stages of cocoa trees growth. In this case, the intraseasonal has been highlighted. Cocoa production shows a strong temporal distribution of rainfall is a significant component annual variation from one department to another. Its var- for crops. In Guiglo, the decrease in cocoa production due to iation is characterized by a succession of periods of Standardized anomalies Standardized anomalies Standardized anomalies Standardized anomalies Standardized anomalies Standardized anomalies 6 Advances in Meteorology 4 4 cor_pr = 0.31 cor_pr = –0.16 2 cor_tp = –0.32 2 cor_tp = 0.16 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (a) (b) 4 4 cor_pr = 0.32 cor_pr = 0.02 2 cor_tp = –0.06 2 cor_tp = –0.42 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (c) (d) 4 4 cor_pr = 0.43 cor_pr = 0.14 2 cor_tp = 0.19 2 cor_tp = 0.2 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (e) (f) Figure 4: Standardized anomalies of rainfall of the main rainy season, temperature, and cocoa production in the six departments from 1989 to 2006. +e correlation coefficients between cocoa production and rainfall (blue) and temperature (red). (a) Abengourou. (b) Agboville. (c) Daloa. (d) Dimbokro. (e) Guiglo. (f) Soubre. ´ abundance and deficit during the 1989–2006 period. +e Soubre is more sensitive to rainfall amounts of the major deficit phase, before 1994 (Figure 3), has affected all the rainy season. Meanwhile, in the department of Agboville and departments and extended until the year 2000 in Agboville Dimbokro, the cocoa yield is mostly related to the rainfall and Soubre (Figure 1). +is phase in cocoa production amount of the little dry season (August to September). coincided with the period of rainfall deficit across West Moreover, Brou et al. [6] showed that the rainfall deficit Africa spreading from the Sahel [7, 17, 33–35] to the recorded during this period inhibited cocoa trees develop- Guinean coast areas, especially in Cote ˆ d’Ivoire [17]. +us, ment, and considerably reduced the cocoa production and the low cocoa production during this period could be related the quality of the beans. Many authors [36, 37] have shown to the drought that occurred from 1960 to 1990. +is dry that precipitation amount of the main rainy season (P ), the rs condition affected the whole West Africa from the Sahel to little dry season (P ) and the temperature (T) are essential ds the Guinean coast [14, 16–18]. It may imply a hydric stress for cocoa growing even if these parameters have different on the cocoa plants and then reduce the cocoa yield. In impacts depending on the region. Indeed, the cocoa pro- addition, these regional effects have been amplified by duction is conditioned by many factors, which some of them seasonal and local signals of precipitation variations. For are related to climate (i.e., precipitation amounts of the example, correlation coefficients (Figure 4) show that the major rainy season and the little dry season, and the tem- cocoa production in Abengourou, Daloa, Guiglo, and perature, etc.) and other exogenous factors such as land area, Standardized Standardized Standardized anomalies anomalies anomalies Standardized Standardized Standardized anomalies anomalies anomalies Advances in Meteorology 7 4 4 cor_pr = 0.2 cor_pr = 0.32 2 cor_tp = –0.32 2 cor_tp = 0.16 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (a) (b) 4 4 cor_pr = 0.22 cor_pr = –0.12 cor_tp = –0.42 2 cor_tp = –0.06 2 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (c) (d) 4 4 cor_pr = 0.21 cor_pr = 0.04 2 cor_tp = 0.19 2 cor_tp = 0.2 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (e) (f) Figure 5: Standardized anomalies of rainfall of the little dry season, temperature, and cocoa production in the six departments from 1989 to 2006. +e correlation coefficients between cocoa production and rainfall (blue) and temperature (red). (a) Abengourou. (b) Agboville. (c) Daloa. (d) Dimbokro. (e) Guiglo. (f) Soubr´e. soil moisture, soil types, etc., which are beyond the scope of +e Student t-values of the temperature are less than 1.34 this work. +e statistical analysis by Fisher–Snedecor and in all departments, so this parameter seems to have negli- Student’s t-test (Tables 2 and 3) show that onlyΔP and gible impacts on cocoa production during the 1989–2006 rs ΔP variables have significant impacts in the estimated period. In addition to the different impacts of the rainfall, ds cocoa productions in Agboville, Daloa, and Guiglo for an temperature has comparative influence on cocoa pro- 80% confidence level. +e influence of precipitation on duction, which changes from one location to another. For cocoa production was shown in the East Central region of instance, in Guiglo, Agboville, and Soubre, the cocoa pro- Cote ˆ d’Ivoire in several studies [23, 36, 38]. +ese works duction increases with the temperature while in the other departments, a rise of the temperature tends to inhibit the noticed an important interannual variability of cocoa pro- duction due to a decreasing and an irregular spatial dis- cocoa yield. +e relative lower values of the correlation tribution of the rain. +us, they concluded that the seasonal coefficients between the temperature and the cocoa pro- rainfall distributions remain one of the key factors influ- duction suggest negligible impact of the temperature. +is is encing the cocoa production. On the other hand, the low mainly attributed to the range of values of the temperature, values of the correlation coefficient between the cocoa yield which fluctuates between 25 and 29 C. +is range of values is and the rainfall of the little dry season imply that this a favorable condition for a good yield of cocoa and its growth seasonal rainfall cannot accurately describe the year-to-year [29]. Moreover, temperature is identified to have a more variability of the cocoa production. significant impact on crops [39]. Standardized Standardized Standardized anomalies anomalies anomalies Standardized Standardized Standardized anomalies anomalies anomalies 8 Advances in Meteorology Table 1: Multiple regression coefficients applied to the explanatory production departments. +e statistical analysis helped to variables (i.e., precipitation and temperature) and the explained determine the key meteorological parameters impacting the variable (i.e., cocoa production) for each studied department over cocoa production in each of the department of interest. +e the 1989–2006 period. results show that rainfall amount and its seasonal distribution are the most influencing factors for cocoa production. Tem- Correlation perature impacts on cocoa production are not negligible al- coefficients and Coefficients of the explanatory variables degree of of the constant b though they were not significant during the 1989–2006 period. Areas freedom +is is mainly due to the narrow range of variation in the ° ° temperature (25 C–29 C) in between favorable conditions for r F df b m m m 1 2 3 cocoa production. For example, in Abengourou, Daloa, and Aben 0.175 0.989 14 2.5E + 5 29.79 32.53 −7.9E + 3 Dimbokro, an increase of the temperature results on a decrease Agbo 0.169 0.951 14 −0.15E + 5 −7.09 16.13 1.1E + 3 Daloa 0.181 1.030 14 7.7E + 5 106.8 64.05 −28.5E + 3 in the cocoa production. In addition, temperature plays a vital Dimb 0.055 0.273 14 30.8E + 5 −86.31 −321.9 −10.5E + 4 and well-known role in evapotranspiration and water demand. Guigl 0.263 1.665 14 −5.3E + 5 86.28 56.92 19.2E + 3 It thus significantly affects growing seasons, water re- Soub 0.063 0.314 14 −25.8E + 5 116.3 26.01 100.7E + 3 quirements, and strategies to assure the availability of water to fulfill the demand. Lobell and Burke [40] and Ochieng et al. [37] underlined that uncertainties related to temperature Table 2: Statistic of the Fisher–Snedecor test. represented a greater contribution to climate change impact Areas R F V V P value 1 2 uncertainty than those related to rainfall system for most crops and regions. +e cocoa environment needs ecological and Abengourou 0.175 0.989 3 14 0.426 Agboville 0.169 0.951 3 14 0.442 pedological conditions, which are beyond the scope of the Daloa 0.181 1.030 3 14 0.409 current study, and none of them could be considered in- Dimbokro 0.055 0.273 3 14 0.843 dependently. +e linear model represented by equation (1) Guiglo 0.263 1.665 3 14 0.219 describes the relationship between meteorological conditions Soubre 0.063 0.314 3 14 0.815 and cocoa production. +us, the calculated determination 2 2 F � r V /V (1− r ); V � n− df− 1; V � df. n is the observation number 1 2 1 2 coefficient values indicate that this linear model using growing (n � 18), df is the number of degree of freedom (df� 14), P is the probability season temperature, seasonal rainfall, and the nonlinear terms for which the R values will be an artifact with a 95% confidence interval, related to precipitation and/or temperature explains only a part and F is the Fisher coefficient. of the cocoa yield variation (less than a third of variations) in a given region. However, this present result also show that the cocoa culture could be subject to combinations of stress factors Table 3: Statistic results of the Student’s t-test. that affect its yield or could respond nonlinearly to changes in Areas ΔP ΔP ΔT its development conditions and/or exhibit threshold responses. rs ds Taking into account other climate variables, such as the relative Abengourou 0.81 0.62 0.94 humidity, wind speed, and sunshine, and nonclimatic factors, Agboville 0.72 1.50 0.40 such as planted area, number of the vegetative cycle, soil Daloa 1.48 0.76 0.82 moisture and soil types, is useful to predict majority change in Dimbokro 0.19 0.49 0.64 Guiglo 1.75 1.15 0.56 cocoa production in the context of the climate variability and Soubre 0.56 0.12 0.80 changes. ΔP is the rainfall amount of the main rainy season,ΔP is the rainfall rs ds amount of the dry season, and ΔT is the growing season average tem- Data Availability perature. +e bold numbers are the t> t � 1.34 values with an 80% con- fidence level. +e cocoa, rainfall, and temperature data used to support the findings of this study were supplied by SODEXAM and ex- An exposure to important heat especially during the CAISTAB under license and so cannot be made freely available. growing season may considerably damage crop production. Requests for access to these data should be made to Laboratory +e rise of the temperature leads to an increasing evapo- of Atmosphere Physic and Fluids Mechanic (LAPA-MF) of the transpiration, which produces a considerable reduction in University Felix Houphouet-Boigny of Abidjan, 22 BP 582 water available for crop even with an associated augmen- Abidjan 22. tation in rainfall amount. Besides, Lobell and Burke [40] showed that the most critical need for agriculture in terms of Conflicts of Interest climate change impact assessments and adaptation efforts is the strengthening of knowledge about crop respond tem- +e authors declare that there are no conflicts of interest perature and the magnitude of regional temperature change. regarding the publication of this paper. 5. Conclusions Acknowledgments +is study assessed the meteorological constraints for cocoa +ey are grateful to the Geophysical Station of Lamto for its production in Coˆte d’Ivoire and, more specifically, in six cocoa financial support. Advances in Meteorology 9 [16] T. Lebel, F. Delclaux, L. Le Barbe, ´ and J. Polcher, “From GCM References scales to hydrological scales: rainfall variability in West [1] J. M. Kouadjo, Y. Keho, R. A. Mosso, and K. G. Toutou, Africa,” Stochastic Environmental Research and Risk Assess- “Production et offre du cacao et du caf´e en Cote ˆ d’Ivoire,” ment, vol. 14, no. 4, pp. 0275–0295, 2000. Rapport d’Enquete, ˆ Ministere de l’enseignement Superieur ´ et [17] E. Servat, J. E. Paturel, H. Lubes-Niel ` et al., “De differents ´ de la Recherche Scientifique, Ecole Nationale Sup´erieure de aspects de la variabilite´ de la pluviometrie ´ en Afrique de Statistique et d’Economie Applique´ (ENSEA) and the In- l’Ouest et Centrale non sahelienne,” Revue des sciences de ternational Institute of Tropical Agriculture (IITA), Abidjan, l’eau, vol. 12, no. 2, pp. 363–387, 1999. Cote d’Ivoire, 2002. [18] A. Diawara, F. Yoroba, K. Y. Kouadio et al., “Climate vari- [2] L. J. 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Freud, P. Petithuguenin, and J. Richard, Les Champs du Publication, Vol. 252, IAHS Publication, Wallingford, UK, Cacao: Un D´efi de Comp´etitivit´e Afrique-Asie, Karthala Edi- tions, Paris, France, 2000. [25] B. Dabin, N. Leneuf, and G. Riou, “Carte pedologique ´ de la [10] S. E. Nicholson, “+e nature of rainfall fluctuations in sub- Cote-d’Ivoire ˆ au 1/2.000.000,” in Notice Explicative, ORS- tropical West Africa,” Monthly Weather Review, vol. 108, TOM-IDERT, Abidjan, Cote ˆ d’Ivoire, 1960. no. 4, pp. 473–487, 1980. [26] M. Kone, ´ Y. L. Kouadio, F. R. N. Danho, D. F. Malan, and [11] P. J. Lamb, “Sub-saharan rainfall update for 1982; continued L. Coulibaly, “Evolution de la couverture foresti`ere de la Cote ˆ drought,” Journal of Climatology, vol. 3, no. 4, pp. 419–422, d’Ivoire des annees ´ 1960 au debut ´ du 21e siecle ` (evolution of the Cote ˆ d’Ivoire forest cover from the 1960s to the beginning [12] C. K. Folland, T. N. Palmer, and D. E. Parker, “Sahel rainfall of the 21st century),” International Journal of Innovation and and worldwide sea temperatures, 1901-85,” Nature, vol. 320, Applied Studies, vol. 2, no. 7, pp. 782–794, 2014. no. 6063, pp. 602–607, 1986. [27] D. K. P. Agoh and M. K. Augustin, “Analyse agroclimatique [13] P. J. Lamb and R. A. Peppler, “Further case studies of tropical ` ˆ ´ de la zone cacaoyere en Cote d’Ivoire,” Revue de Geographie de Atlantic surface atmospheric and oceanic patterns associated l’Universit´e Ouaga I Pr Joseph KI-ZERBO, vol. 2, pp. 45–68, with sub-Saharan drought,” Journal of Climate, vol. 5, no. 5, pp. 476–488, 1992. [28] N. A. B. Klutse, V. Ajayi, E. O. Gbobaniyi et al., “Potential [14] B. Fontaine and S. Janicot, “Sea surface temperature fields ° ° impact of 1.5 associated with West African rainfall anomaly types,” Journal C and 2 C global warming on consecutive dry and wet days over West Africa,” Environmental Research of Climate, vol. 9, no. 11, pp. 2935–2940, 1996. [15] S. 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Evaluation of Rainfall and Temperature Conditions for a Perennial Crop in Tropical Wetland: A Case Study of Cocoa in Côte d’Ivoire

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Hindawi Publishing Corporation
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Copyright © 2019 Fidèle Yoroba et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2019/9405939
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Abstract

Hindawi Advances in Meteorology Volume 2019, Article ID 9405939, 10 pages https://doi.org/10.1155/2019/9405939 Research Article EvaluationofRainfallandTemperatureConditionsforaPerennial Crop in Tropical Wetland: A Case Study of Cocoa in Coˆte d’Ivoire 1,2 1,2 1,2 1 Fide`le Yoroba , Benjamin K. Kouassi , Adama Diawara, Louis A. M. Yapo, 1 1 1 1 Kouakou Kouadio, Dro T. Tiemoko, Yves K. Kouadio , Ibrahim D. Kone´, and Paul Assamoi University F´elix Houphou¨et-Boigny, UFR SSMT, Laboratory of Atmosphere Physic and Mechanic Fluids (LAPA-MF), 22 BP 582 Abidjan 22, Coˆte d’Ivoire Geophysical Station of Lamto (GSL), BP 31 N’Douci, Cote d’Ivoire Correspondence should be addressed to Fide`le Yoroba; yorofidele@gmail.com Received 3 October 2018; Accepted 25 December 2018; Published 13 January 2019 Academic Editor: Helena A. Flocas Copyright © 2019 Fide`le Yoroba et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. +e rainfall and temperature conditions are evaluated for the first time during the 1989–2006 period, in six main cocoa production areas (Abengourou, Agboville, Daloa, Dimbokro, Guiglo, and Soubre) of Cote ˆ d’Ivoire using data from SODEXAM (ground- based observation) and the ex-CAISTAB. Statistical analysis shows an important sensitivity of cocoa production to rainfall conditions in all regions. It is worth noting that only the major rainy season from April to July and the rainfall amount of the little dry season from August to September affect the cocoa production for an 80% confidence level. +is influence varies from one cacao production area to another. Moreover, the effects related to temperature on the cocoa yield seem to represent a smaller contribution of climate impact than those related to precipitation during the studied period. +e temperature change remains in ° ° the acceptable range of values, between 25 C and 29 C, which is a favorable condition for cocoa growing. +ese findings are obtained despite the significant contributions from nonclimatic factors, to year-to-year variability in cocoa production. strong interannual variability mainly related to climate 1. Introduction variations [3] and the ignorance of many physical, biological, Across Cote ˆ d’Ivoire, cocoa farms have expanded consid- and socioeconomic processes. Cocoa growing requires rel- erably since 1980s thanks to a strong agricultural policy and atively more rigorous pedological and climatic conditions favorable agronomic and climatic conditions in many re- than other tropical crops like a palm tree, coffee, and rubber gions. Another favorable condition, in addition to propitious tree [4, 5]. For example, cocoa needs wetter conditions of agroclimatic conditions, to cocoa growing is the cheap labor about a minimum of 1200 mm to 1500 mm annual rainfall ˆ amount per year with a limited number of dry days (<90 availability [1]. +us, the economy of C ote d’Ivoire is mainly based on the pair coffee-cocoa. For example, in 1997, the days) [6] for its development. coffee-cocoa pair provided about 17% of the Gross Domestic On the other hand, West African countries, especially Product (GDP) and 38.6% of export earnings [2]. In ad- Cote ˆ d’Ivoire have experienced strong climate variability dition, according to the International CoCoa Organization characterized by a deficit on rainfall amount [7, 8]. Fur- (ICCO) statistics, Cote ˆ d’Ivoire produced 1,395,000 Tons of thermore, Freud et al. [9] showed that the rainfall trend in cocoa for the 2009-2010 seasons that represented 46.5% of the last few decades has led to a decrease in ecologically world production. favorable conditions for cocoa in many parts of the country. Cocoa is a perennial tropical plant, which contributes to In the same vein, several works as Nicholson [10], Lamb [11], the economy and development of many countries like Ghana Folland et al. [12], Lamb and Peppler [13], Fontaine and and Cote ˆ d’Ivoire. However, its production undergoes a Janicot [14], and Nicholson and Grist [15] evaluate possible 2 Advances in Meteorology mechanisms associated with these deficits in rain amount. 2 is dedicated to the description of the study area, the For example, these drought episodes are sequences of material, and the method used. Section 3 shows the results continuous rainfall deficit periods from 1960 to 1990, which and, the discussion is given in Section 4. Finally, conclusion were not only limited to the Sahel area but spread to the and perspectives are given at the end. coastal regions of West Africa [14, 16], especially in Cote ˆ d’Ivoire [17, 18]. +e dry conditions led to a serious dis- 2. Materials and Methods turbance in water resources and then a disaster for the 2.1. Study Area. +e area of interest is an extensive zone populations and cause a loss in livestock and a gradual composed of dense evergreen and semideciduous wet forests disappearance of certain export crops. covering an area of 170,320 km [29]. It represents about In agriculture, the consequences are significant and 53% of the Ivorian territory and is located between 2.5 W result in changes in the growing seasons: an occurrence of ° ° ° and 8.5 W and, between 4 N and 9 N, in the southern half of late or early rains and, particularly, long dry spells. +e the country (Figure 1). +e region is bounded at its northern effects of the changes are really important on the economy of part by the preforest Savannah zone commonly known as “V West African countries especially for a country like Cote Baoule” ´ and by the Atlantic Ocean in the south [24]. +ree d’Ivoire, 40% of whose resources are based on cocoa [19]. types of soils characterize this region: hydromorphic, fer- Amani [20] pointed out that farmers who have been most ralitic, and ferruginous soils [30]. +ese soils are mostly affected by this climate variability have used biotechnology favorable to agriculture and cocoa cultivation particularly. methods that involve selecting cloned or hybrid cocoa +is region is characterized by a bimodal rainy season: the species. +ese biotechnology methods were expected to major and first rainy season is between April and early-to- improve with the agrobiochemical supports (e.g., fertilizers, mid July with a peak in June and, the little rainy season herbicides, and insecticides) the cocoa production. How- extends from the mid-September to November with a peak ever, the methods did not show subsequent improvement in in October. Kouadio et al. [31] showed that the rainfall the production per hectare [21]. regime in the north of Gulf of Guinea is influenced by the Moreover, in the centre of Cote ˆ d’Ivoire, especially Intertropical Convergence Zone (ITCZ) and Sea Surface around Daoukro, considered as the old “main production Temperature (SST) anomalies at the equator and West zone”; Koukou-Tchamba et al. [22] and Kanohin et al. [23] Africa coast. underlined a decrease in cocoa production, and they at- tributed it to a decline of the rainfall in the region. +us, a new cocoa production “hot spot” has emerged towards the 2.2. Materials. +is study used ground-based observations southeast and southwest regions, which have not been (rainfall and temperature) from the National Meteorology of exploited before. Furthermore, a strong link between the Cote ˆ d’Ivoire (SODEXAM) and cocoa production data from main cocoa production areas and the recorded seasonal the ex-Caisse de Stabilisation du Cafe´ et du Cacao (ex- precipitation amount has been underlined [24]. +e cocoa CAISTAB) over the 1989–2010 period. +ese parameters growing has been also linked to other parameters like the are recorded across six stations (five rain gauge stations and temperature, during the flowering period, the type of the one synoptic station). +e selected and available stations soil, humidity, and insolation [25, 26]. +us, precipitation (Abengourou, Agboville, Dimbokro, Guiglo, Soubre, and and temperature are suggested to play an important role in Daloa) cover the study area and provide acceptable and the significant interannual variability of cocoa production uniform meteorological information about the considered experienced in Cote ˆ d’Ivoire during the past recent years. regions (Figure 2). However, Agoh and Augustin [27] show that these two climatic factors drive differently the interannual variability of cocoa production in a given area. For example, in the 2.3. Method. +e work is based on statistical analysis of cocoa Daloa region, cocoa yield is affected by the duration of yield in regard to the weather conditions. +e analyses assess sunshine and rainfall, while in the Gagnoa region, it depends the relationships between the climate and the cocoa yield. +e on the duration of sunshine and temperature. significance of any trend or signal is also analysed. +e water +erewith, some studies like [28] projected a decrease in requirements for the cocoa production are calculated based rainfall associated with a reduction in the number and length on values provided by Dian [30] and Mian [29]. +e main ° ° of consecutive dry days under 1.5 C and 2 C global warming rainy season from April to July must record an amount of rain based on Representative Concentration Pathways (RCP 8.5). above 700 mm [29] and the little dry season (August to +is study assesses the influence of precipitation and tem- September) must record at least 70 mm [30] to ensure a good perature on the development and cocoa yield, in six de- cocoa growing and production. +e considered temperature, partments (Abengourou, Agboville, Daloa, Dimbokro, and thereafter, is obtained by averaging the daily tempera- Guiglo, and Soubre) across Cote ˆ d’Ivoire. It tries to also tures over March-April (first flowering period) and improve our understanding of the relative and individual September-October (second flowering period) for each year. contributions of the two factors in estimating of climate change impacts on cocoa yields. +ese departments are 3. Results considered as the main cocoa production areas of the country. +e study is conducted over the period spreading 3.1. Interannual Variability of Cocoa Production. +e in- from 1989 to 2006. +e work is structured as follows; Section terannual variability of cocoa production is studied through Advances in Meteorology 3 8°0′0′′W 7°0′0′′W 4°0′0′′W 3°0′0′′W 8°0′0′′N 8°0′0′′N 7°0′0′′N 7°0′0′′N 6°0′0′′N 6°0′0′′N 5°0′0′′N 5°0′0′′N 8°0′0′′W 7°0′0′′W 6°0′0′′W 5°0′0′′W 4°0′0′′W 3°0′0′′W 0 45 90 180 (km) Dense evergreen wet forest Semi-deciduous wet forest Figure 1: Vegetation types of the study area (adapted to Kone et al. [26]). conditions in these two areas could similarly influence the the standardized anomalies calculated over the 1989–2006 period (Figure 3) in the six departments. +ese standardized cocoa yield and thus impact the variability of the cocoa production. anomalies vary from−3 to 3 and then suggest periods of deficit and abundance in cocoa production across all regions. For Abengourou, Guiglo, and Daloa, there is an alternate of 3.2. Cocoa Production Band Meteorological Parameters. deficit (before 1995 and from 2003) and abundance (from 1995 to 2002). A similar pattern is observed in Dimbokro Figures 4 and 5 show the variations of cocoa production in regard to the amount of rainfall during the main rainy with a deficit before 1993 followed by a scatter period where there are successive multiple deficits and abundances. +e season (April to July) and little dry season (August to September), and the temperature in the six regions, re- other departments (i.e., Agboville and Soubre) show a long period of deficit in the cocoa production before 2001 fol- spectively. A relative important correlation coefficient (0.43) is noticed between cocoa production and precipitation lowed by a period of abundance. +e displayed tendency amount during the main rainy season around Guiglo than (Figure 3), as it is expected from the standardized anomalies, shows a succession of decrease and increase in cocoa pro- that over the rest of departments (Figure 4). +e values of 0.31 and 0.32 are, respectively, obtained in Abengourou and duction. For example, there is a decrease in cocoa pro- duction from 1989 to 1995 followed by an increase, from Daloa. +e lowest correlations are found in Agboville (− 0.16), Dimbokro (0.02), and Soubre (0.14). In these latest regions, 1995 to 2003 in Guiglo and from 1995 to 1999 around Daloa and Abengourou. +e similarity in the variability of the it seems that there is no noticeable influence of rainfall on cocoa production, whereas, in Abengourou, Daloa, and cocoa production of Daloa and Abengourou may be due to their climate type and latitudinal location (Figure 2). +ese Guiglo, the rainfall amount of the main rainy season has no negligible impact on the variability of the cocoa production departments belong to the same climate type (climate of center) proposed by Kouadio et al. [32]. +e meteorological even if the correlation coefficients are below 0.5. +ese values 4 Advances in Meteorology Months Months Months Months Months Months Figure 2: Study area with the six reference departments and their mean ombrothermal diagram over the 1989–2006 period. Red contours delimit the important areas of cocoa production. of the correlation coefficients suggest an increase in the and b represents the potential nonlinear effects of pre- cocoa production with the rainfall. Figure 5 shows corre- cipitation or temperature, such as those that arise from lation values, between cocoa production and little dry interactions with other variables. +is equation ignores other season, below 0.23 across all the departments, except in aspects of meteorological conditions, such as relative hu- Agboville (0.32). +us, the assumed link may be marginal midity, wind speed, sunshine, and nonclimatic factors, such between the cocoa production variability and the rainfall as soils types, soil humidity, planted area, and number of the amounts during the little dry season from August to Sep- vegetative cycle. It represents a useful first-order estimate of tember. +e mean temperature values are between 25 C and year-to-year variability in cocoa yield for a considered de- partment over the studied period. All calculated coefficient 29 C (figure not shown) and offer favorable temperature conditions to the cocoa culture in the departments. +ere is a values of equation (1) are given in Table 1. +is table shows decrease in cocoa production with the mean temperature for that the increase in cocoa production is mainly due to the some department like Daloa (−0.06), Abengourou (−0.32), rainfall amounts of the major rainy season and the little dry and Dimbokro (−0.42) while for the remaining, the pro- season in Abengourou and Daloa. In Guiglo and Soubre, this duction increases with the mean temperature. increase is related to all the considered three parameters, while in Agboville, only the rainfall amount of the little dry season has remarkable impact on the cocoa production. In 3.3. Cocoa Production Response to Rainfall and Temperature Dimbokro, the negative correlation coefficients suggest a as the Predictors in a Multiple Linear Regression. In this decrease in cocoa production with both temperature and section, multiple regressions are used to evaluate sources of rainfall. +e significance of the correlation coefficients be- variations in the cocoa production (ΔY) in regard to both tween the cocoa production and the three defined param- rainfall amounts of the main rainy season (ΔP ), the little eters (i.e.,ΔP ,ΔP , andΔT) is assessed using indexes from rs rs ds dry season (ΔP ), and the growing season average tem- the Fisher–Snedecor and Student t-tests (see Tables 2 and 3). ds perature (ΔT). +e change in cocoa production (ΔY) for a Table 2 shows a high p value in Dimbokro (0.843) and given department is obtained by the following equation: Soubre (0.815), while it displays a low value in Abengourou (0.426), Agboville (0.442), Daloa (0.409), and Guiglo (0.219). ΔY � mΔP + mΔP + m ΔT+ b, (1) 1 rs 2 ds 3 +ese p values are associated with high values of coefficient where m , m , and m represent the sensitivity of cocoa of determination (R ) in Abengourou (0.175), Agboville 1 2 3 (0.169), Daloa (0.181), and Guiglo (0.263), while the values production to both rainfall amounts of the main rainy season, the little dry season and the temperature, respectively, are marginal in Dimbokro (0.055) and Soubre (0.063). In Precipitations (mm) Precipitations (mm) Precipitations (mm) 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 Jan Jan Jan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 Temperature (°C) Temperature (°C) Temperature (°C) Precipitations (mm) Precipitations (mm) Precipitations (mm) 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 0 20 60 100 140 180 220 260 300 Jan Jan Jan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 24 25 26 27 28 29 30 31 Temperature (°C) Temperature (°C) Temperature (°C) Advances in Meteorology 5 3 3 2 2 R = –0.24 R = 0.67 1 1 0 0 –1 –1 –2 –2 –3 –3 88 90 92 94 96 98 00 02 04 06 88 90 92 94 96 98 00 02 04 06 Years Years (a) (b) Years 3 3 R = 0.36 R = 0.11 1 1 –1 –1 –2 –2 –3 –3 88 90 92 94 96 98 00 02 04 06 88 90 92 94 96 98 00 02 04 06 Years Years (c) (d) 3 3 2 2 R = 0.18 R = 0.89 1 1 0 0 –1 –1 –2 –2 –3 –3 88 90 92 94 96 98 00 02 04 06 88 90 92 94 96 98 00 02 04 06 Years Years (e) (f) Figure 3: Standardized anomalies of cocoa production in the six reference departments of the study area during the 1989–2006 period. Red line indicates the trend curve. (a) Abengourou. (b) Agboville. (c) Daloa. (d) Dimbokro. (e) Guiglo. (f) Soubre. ´ these four departments where coefficient of determination is one of the three parameters could be inhibited by the in- crease of the two others. On the over hand, in Table 3, the t- noticeable, the equation of multiple regressions is relevant to explain the cocoa production. +en, the temperature and the values evaluate the importance of each variableΔP ,ΔP , rs ds rainfall during the main rainy season and the little dry season andΔT in the estimating cocoa production (ΔY) with an are crucial to explain the variation in cocoa production. 80% confidence level (t � 1.34). +e rainfall amounts (either +us, in Abengourou and Daloa, an increasing temperature main rainy season or little dry season) are suggested to be the induces a decrease in cocoa production while in the de- key parameters that influence the annual cocoa production partment of Agboville, the increase in rainfall amount in three departments: Agboville with ΔP (t� 1.5) and ds during the main rainy season tends to reduce the cocoa yield. Daloa and Guiglo with ΔP (t� 1.48 and t� 1.75, re- rs +e opposite evolution of cocoa production in regard to the spectively). But the mean temperature (ΔT) (t-values< 1.34 increased precipitation may be related to the influence of in all departments) seems to have negligible impacts on some other factors like the type of the soil and soil fertility, cocoa production in all departments. which are not considered in this study. +e increase of runoff, erosion, and relative humidity can damage the cocoa 4. Discussion trees if the soil conditions do not manage to inhibit their negative effects. In addition, it is possible that the increase in +e impact of the rainfall and the temperature conditions in the average growing season rainfall has a lower impact on cocoa yield across six departments (Agboville, Daloa, Gui- cocoa yield than the increase in rainfall during the critical glo, Abengourou, Dimbokro, and Soubre) of Cote ˆ d’Ivoire stages of cocoa trees growth. In this case, the intraseasonal has been highlighted. Cocoa production shows a strong temporal distribution of rainfall is a significant component annual variation from one department to another. Its var- for crops. In Guiglo, the decrease in cocoa production due to iation is characterized by a succession of periods of Standardized anomalies Standardized anomalies Standardized anomalies Standardized anomalies Standardized anomalies Standardized anomalies 6 Advances in Meteorology 4 4 cor_pr = 0.31 cor_pr = –0.16 2 cor_tp = –0.32 2 cor_tp = 0.16 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (a) (b) 4 4 cor_pr = 0.32 cor_pr = 0.02 2 cor_tp = –0.06 2 cor_tp = –0.42 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (c) (d) 4 4 cor_pr = 0.43 cor_pr = 0.14 2 cor_tp = 0.19 2 cor_tp = 0.2 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (e) (f) Figure 4: Standardized anomalies of rainfall of the main rainy season, temperature, and cocoa production in the six departments from 1989 to 2006. +e correlation coefficients between cocoa production and rainfall (blue) and temperature (red). (a) Abengourou. (b) Agboville. (c) Daloa. (d) Dimbokro. (e) Guiglo. (f) Soubre. ´ abundance and deficit during the 1989–2006 period. +e Soubre is more sensitive to rainfall amounts of the major deficit phase, before 1994 (Figure 3), has affected all the rainy season. Meanwhile, in the department of Agboville and departments and extended until the year 2000 in Agboville Dimbokro, the cocoa yield is mostly related to the rainfall and Soubre (Figure 1). +is phase in cocoa production amount of the little dry season (August to September). coincided with the period of rainfall deficit across West Moreover, Brou et al. [6] showed that the rainfall deficit Africa spreading from the Sahel [7, 17, 33–35] to the recorded during this period inhibited cocoa trees develop- Guinean coast areas, especially in Cote ˆ d’Ivoire [17]. +us, ment, and considerably reduced the cocoa production and the low cocoa production during this period could be related the quality of the beans. Many authors [36, 37] have shown to the drought that occurred from 1960 to 1990. +is dry that precipitation amount of the main rainy season (P ), the rs condition affected the whole West Africa from the Sahel to little dry season (P ) and the temperature (T) are essential ds the Guinean coast [14, 16–18]. It may imply a hydric stress for cocoa growing even if these parameters have different on the cocoa plants and then reduce the cocoa yield. In impacts depending on the region. Indeed, the cocoa pro- addition, these regional effects have been amplified by duction is conditioned by many factors, which some of them seasonal and local signals of precipitation variations. For are related to climate (i.e., precipitation amounts of the example, correlation coefficients (Figure 4) show that the major rainy season and the little dry season, and the tem- cocoa production in Abengourou, Daloa, Guiglo, and perature, etc.) and other exogenous factors such as land area, Standardized Standardized Standardized anomalies anomalies anomalies Standardized Standardized Standardized anomalies anomalies anomalies Advances in Meteorology 7 4 4 cor_pr = 0.2 cor_pr = 0.32 2 cor_tp = –0.32 2 cor_tp = 0.16 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (a) (b) 4 4 cor_pr = 0.22 cor_pr = –0.12 cor_tp = –0.42 2 cor_tp = –0.06 2 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (c) (d) 4 4 cor_pr = 0.21 cor_pr = 0.04 2 cor_tp = 0.19 2 cor_tp = 0.2 0 0 –2 –2 –4 –4 89 91 93 95 97 99 01 03 05 89 91 93 95 97 99 01 03 05 Years Years Precipitation Precipitation Temperature Temperature Cocoa production Cocoa production (e) (f) Figure 5: Standardized anomalies of rainfall of the little dry season, temperature, and cocoa production in the six departments from 1989 to 2006. +e correlation coefficients between cocoa production and rainfall (blue) and temperature (red). (a) Abengourou. (b) Agboville. (c) Daloa. (d) Dimbokro. (e) Guiglo. (f) Soubr´e. soil moisture, soil types, etc., which are beyond the scope of +e Student t-values of the temperature are less than 1.34 this work. +e statistical analysis by Fisher–Snedecor and in all departments, so this parameter seems to have negli- Student’s t-test (Tables 2 and 3) show that onlyΔP and gible impacts on cocoa production during the 1989–2006 rs ΔP variables have significant impacts in the estimated period. In addition to the different impacts of the rainfall, ds cocoa productions in Agboville, Daloa, and Guiglo for an temperature has comparative influence on cocoa pro- 80% confidence level. +e influence of precipitation on duction, which changes from one location to another. For cocoa production was shown in the East Central region of instance, in Guiglo, Agboville, and Soubre, the cocoa pro- Cote ˆ d’Ivoire in several studies [23, 36, 38]. +ese works duction increases with the temperature while in the other departments, a rise of the temperature tends to inhibit the noticed an important interannual variability of cocoa pro- duction due to a decreasing and an irregular spatial dis- cocoa yield. +e relative lower values of the correlation tribution of the rain. +us, they concluded that the seasonal coefficients between the temperature and the cocoa pro- rainfall distributions remain one of the key factors influ- duction suggest negligible impact of the temperature. +is is encing the cocoa production. On the other hand, the low mainly attributed to the range of values of the temperature, values of the correlation coefficient between the cocoa yield which fluctuates between 25 and 29 C. +is range of values is and the rainfall of the little dry season imply that this a favorable condition for a good yield of cocoa and its growth seasonal rainfall cannot accurately describe the year-to-year [29]. Moreover, temperature is identified to have a more variability of the cocoa production. significant impact on crops [39]. Standardized Standardized Standardized anomalies anomalies anomalies Standardized Standardized Standardized anomalies anomalies anomalies 8 Advances in Meteorology Table 1: Multiple regression coefficients applied to the explanatory production departments. +e statistical analysis helped to variables (i.e., precipitation and temperature) and the explained determine the key meteorological parameters impacting the variable (i.e., cocoa production) for each studied department over cocoa production in each of the department of interest. +e the 1989–2006 period. results show that rainfall amount and its seasonal distribution are the most influencing factors for cocoa production. Tem- Correlation perature impacts on cocoa production are not negligible al- coefficients and Coefficients of the explanatory variables degree of of the constant b though they were not significant during the 1989–2006 period. Areas freedom +is is mainly due to the narrow range of variation in the ° ° temperature (25 C–29 C) in between favorable conditions for r F df b m m m 1 2 3 cocoa production. For example, in Abengourou, Daloa, and Aben 0.175 0.989 14 2.5E + 5 29.79 32.53 −7.9E + 3 Dimbokro, an increase of the temperature results on a decrease Agbo 0.169 0.951 14 −0.15E + 5 −7.09 16.13 1.1E + 3 Daloa 0.181 1.030 14 7.7E + 5 106.8 64.05 −28.5E + 3 in the cocoa production. In addition, temperature plays a vital Dimb 0.055 0.273 14 30.8E + 5 −86.31 −321.9 −10.5E + 4 and well-known role in evapotranspiration and water demand. Guigl 0.263 1.665 14 −5.3E + 5 86.28 56.92 19.2E + 3 It thus significantly affects growing seasons, water re- Soub 0.063 0.314 14 −25.8E + 5 116.3 26.01 100.7E + 3 quirements, and strategies to assure the availability of water to fulfill the demand. Lobell and Burke [40] and Ochieng et al. [37] underlined that uncertainties related to temperature Table 2: Statistic of the Fisher–Snedecor test. represented a greater contribution to climate change impact Areas R F V V P value 1 2 uncertainty than those related to rainfall system for most crops and regions. +e cocoa environment needs ecological and Abengourou 0.175 0.989 3 14 0.426 Agboville 0.169 0.951 3 14 0.442 pedological conditions, which are beyond the scope of the Daloa 0.181 1.030 3 14 0.409 current study, and none of them could be considered in- Dimbokro 0.055 0.273 3 14 0.843 dependently. +e linear model represented by equation (1) Guiglo 0.263 1.665 3 14 0.219 describes the relationship between meteorological conditions Soubre 0.063 0.314 3 14 0.815 and cocoa production. +us, the calculated determination 2 2 F � r V /V (1− r ); V � n− df− 1; V � df. n is the observation number 1 2 1 2 coefficient values indicate that this linear model using growing (n � 18), df is the number of degree of freedom (df� 14), P is the probability season temperature, seasonal rainfall, and the nonlinear terms for which the R values will be an artifact with a 95% confidence interval, related to precipitation and/or temperature explains only a part and F is the Fisher coefficient. of the cocoa yield variation (less than a third of variations) in a given region. However, this present result also show that the cocoa culture could be subject to combinations of stress factors Table 3: Statistic results of the Student’s t-test. that affect its yield or could respond nonlinearly to changes in Areas ΔP ΔP ΔT its development conditions and/or exhibit threshold responses. rs ds Taking into account other climate variables, such as the relative Abengourou 0.81 0.62 0.94 humidity, wind speed, and sunshine, and nonclimatic factors, Agboville 0.72 1.50 0.40 such as planted area, number of the vegetative cycle, soil Daloa 1.48 0.76 0.82 moisture and soil types, is useful to predict majority change in Dimbokro 0.19 0.49 0.64 Guiglo 1.75 1.15 0.56 cocoa production in the context of the climate variability and Soubre 0.56 0.12 0.80 changes. ΔP is the rainfall amount of the main rainy season,ΔP is the rainfall rs ds amount of the dry season, and ΔT is the growing season average tem- Data Availability perature. +e bold numbers are the t> t � 1.34 values with an 80% con- fidence level. +e cocoa, rainfall, and temperature data used to support the findings of this study were supplied by SODEXAM and ex- An exposure to important heat especially during the CAISTAB under license and so cannot be made freely available. growing season may considerably damage crop production. Requests for access to these data should be made to Laboratory +e rise of the temperature leads to an increasing evapo- of Atmosphere Physic and Fluids Mechanic (LAPA-MF) of the transpiration, which produces a considerable reduction in University Felix Houphouet-Boigny of Abidjan, 22 BP 582 water available for crop even with an associated augmen- Abidjan 22. tation in rainfall amount. Besides, Lobell and Burke [40] showed that the most critical need for agriculture in terms of Conflicts of Interest climate change impact assessments and adaptation efforts is the strengthening of knowledge about crop respond tem- +e authors declare that there are no conflicts of interest perature and the magnitude of regional temperature change. regarding the publication of this paper. 5. Conclusions Acknowledgments +is study assessed the meteorological constraints for cocoa +ey are grateful to the Geophysical Station of Lamto for its production in Coˆte d’Ivoire and, more specifically, in six cocoa financial support. Advances in Meteorology 9 [16] T. Lebel, F. Delclaux, L. Le Barbe, ´ and J. Polcher, “From GCM References scales to hydrological scales: rainfall variability in West [1] J. M. Kouadjo, Y. Keho, R. A. Mosso, and K. G. 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Advances in MeteorologyHindawi Publishing Corporation

Published: Jan 13, 2019

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