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Environmental & Socio-economic Studies DOI: 10.2478/environ-2022-0016 Environ. Socio.-econ. Stud., 2022, 10, 3: 43-58 ________________________________________________________________________________________________ Original article Observed climate trends, perceived impacts and community adaptation practices in Côte d’Ivoire 1 1 2 Jean-Luc Kouassi *, Narcisse Wandan , Cheikh Mbow Laboratoire Science, Société et Environnement, UMRI Sciences Agronomiques et Génie Rural, Institut National Polytechnique Félix Houphouët-Boigny, BP 1093 Yamoussoukro, Côte d’Ivoire Centre de Suivi Ecologique, BP 15532 Dakar-Fann, Dakar, Sénégal E–mail address (*corresponding author): firstname.lastname@example.org ORCID iD: Jean-Luc Kouassi: https://orcid.org/0000-0002-0222-2438; Narcisse Wandan: https://orcid.org/0000-0002-2833- 5369; Cheikh Mbow: https://orcid.org/0000-0003-4620-4490 ______________________________________________________________________________________________________________________________________________ A B S T R A C T Climate change is a serious threat to local communities in West Africa. This study evaluated climatic trends and the perceptions of farmers to climate change in central Côte d'Ivoire. We surveyed 259 households across three agro-ecological zones. The knowledge of farmers about climate change was compared to observed trends of various climatic parameters from meteorological records (1973-2016). Results from trend analysis and descriptive analysis showed that the minimum, maximum and mean temperatures and rainfall showed a significant upward trend in all ecoregions. The average temperature and amount of rainfall increased by 3.2% (0.89°C) and 166.58% (645.5 mm) respectively over the 44 years. Local farmers perceived an increasing trend in temperature (all respondents) and a decreasing trend in rainfall (91.51%). Most of the respondents identified deforestation (76.83%), natural climate variation (50.97%) and wildfires (31.27%) as the main causes of these climatic disturbances, which induced plant dieback (92.66%), poor crop growth (59.46%) and crop loss (20.46%). The impacts on people and their assets encompassed a decrease in household income (63.71%), demolition of roofs (44..4%) and walls (43.91%) of houses, the scarcity of water points (39.38%) and the emergence of new diseases (30.89%). These climatic disturbances resulted in specific endogenous on-farm and off-farm strategies to adapt to the impacts of observed changes on their livelihoods. KEY WORDS: climate change, local perception, coping strategies, rural livelihoods, smallholder agriculture, N’Zi River Watershed ARTICLE HISTORY: received 30 May 2022; received in revised form 23 August 2022; accepted 24 August 2022 ______________________________________________________________________________________________________________________________________________ 1. Introduction cultivation area (IPCC, 2022; LÄDERACH ET AL., 2013). Climate change is the major and perhaps the most Across tropical West Africa, climate change has critical of all environmental challenges facing societies already led to warmer temperatures, extended in the current century. The global average surface periods of drought, loss of soil fertility and shorter temperature increased by +1.09°C over the last growing seasons (FAO ET AL., 2018). This threat century, with the hottest temperatures ever recorded has also undermined crop production with extreme in the last three decades (IPCC, 2022). Besides, climate events and amplified the occurrence of risky the Sixth Assessment Report of the Intergovernmental natural phenomena such as floods and wildfires Panel on Climate Change (IPCC) indicated that (IPCC, 2022). Consequently, climate change, by under a business as usual scenario, West African compromising household food and income security, countries will experience a 1.7°C increase in is expected to cause major economic losses and temperature by 2050 with no significant increase hunger for more than 70% of households (BUNN ET in rainfall and a marked reduction in suitable AL., 2019; FAO ET AL., 2018; LÄDERACH ET AL., 2013). In Côte d’Ivoire, the increase in temperature and changes they have experienced in their environment reduction in rainfall have had severe impacts on coupled with historical meteorological comparisons, major cash crops such as cocoa, coffee, rubber, palm its effects on their livelihoods and strategies to be oil and cashews (FAO ET AL., 2018; LÄDERACH ET AL., adopted. 2013). These tree crops also face several challenges This study aimed to analyze the understanding including soil erosion (KASSIN ET AL., 2012), land of farmers' perceptions of climate change and the tenure insecurity (CHAUVEAU, 2000), weather strategies developed by local farmers to adapt to variability and climate change (FAO ET AL., 2018; this threat in three ecoregions of Côte d’Ivoire. LÄDERACH ET AL., 2013). Tree crops mostly prosper in Especially, the study (i) assessed local farmers’ rainforests with fairly uniform temperatures, high perception towards variability in temperature and humidity, abundant rain, nitrogen-rich soil, and rainfall patterns from 1973-2016, (ii) verified protection from wind (WOOD & LASS, 2001). The the farmers’ perceptions by analyzing 44-year- economy of this country is largely dependent on meteorological data, (iii) assessed the impacts of these crops for export and local livelihoods, climate variability-induced shocks on farmers’ accounting for 21.2% of Gross Domestic Product and livelihoods and the environment and (iv) identified 47% of total exports (DSDI, 2016; FAO ET AL., 2018). ongoing adaptation strategies employed in response In recent decades, changes in climate and to the perceived variability in temperature and ecological conditions that have occurred at the rainfall. national scale have led to low crop productivity and triggered massive deforestation. The forest 2. Materials and methods cover has declined from about 7.9 million hectares in 1990 to 3.4 million hectares in 2015 (BNETD, 2.1. Study area 2016; FAO & SEP-REDD+, 2017). This deforestation affected rural people by exacerbating poverty and The study was conducted in the N’Zi River food insecurity (GERBER ET AL., 2014; KOUASSI ET Watershed (NRW) in central Côte d’Ivoire. This AL., 2021a). watershed is a tributary of the River Bandama and is As smallholder farmers rely heavily on agricultural located between 3°46′ W and 5°24′ W longitude and production, they are likely to suffer unstable income between 5°58′ N and 9°26′ N latitude. The altitude and food supplies due to increased input prices ranges between less than 100 meters above sea caused by the high demand and tensions on fossil level (masl) in the south and more than 600 masl fuel energy and phosphorus markets (BRUNELLE ET in the north (Fig. 1). The NRW covers an area of AL., 2015). Local communities have a lower capacity 35,309 km , nearly 11% of the national territory to adapt mainly because of economic reasons and straddles 80 subdistricts (KOUASSI ET AL., 2018). including the means of implementation and better Mainly composed of rural communities (70.64%), scaling up of resources. Nevertheless, they try to the population of the NRW was estimated at 2.9 respond to the changing climate via different million inhabitants in 2014 with a population indigenous coping and adaptation mechanisms density of 81.2 inhabitants per km² (INS, 2014). (YÉO ET AL., 2016). The watershed spans the three major climatic regions While several studies have been conducted on of Côte d’Ivoire and is covered by forest ecosystems the impacts of climate change on the livelihood of in the south, mosaic forest‒savannah ecosystems smallholder farmers, they are generally based on so-called “V Baoulé” in the centre and savannah the characterization of long-term trends in key ecosystems in the north (GOULA ET AL., 2006; climate indicators and their variability in relation GUILLAUMET, 1971). to a reference period (BIGOT ET AL., 2005; KANOHIN According to the Köppen-Geiger climate ET AL., 2012; YÉO ET AL., 2016). Some scientists have classification, the climate of the study area is of disapproved of these climate observations often Aw type (warm and humid). In the forest zone, an made by non-scientists (DE LONGUEVILLE ET AL., equatorial transition climate (Guinean climate) with 2020). They argued that farmers' perceptions of an annual rainfall between 1300 mm and 2400 mm climate regimes and variability can add valuable can be found (GOULA ET AL., 2006). In this area, the information to these conventional weather statistics vegetation is a dense moist forest with some (DE LONGUEVILLE ET AL., 2020). remaining forests on the tops of hills and in the With the climate projections showing continuous classified forests (GUILLAUMET, 1971). In the pre- deterioration of the climate and regarding forest zone, there is an attenuated equatorial requirements of developing sustainable landscape transition climate (Baoulean climate). The annual management strategies, it is important to rainfall ranges between 1500 mm and 2200 mm. understand the perceptions of local people of the The vegetation is characterized by a Guinean forest‒savannah mosaic, with forest patches along is mainly composed of grasslands and wooded the river (GOULA ET AL., 2006). These two ecoregions grasslands. In this phytogeographical region, the have two dry seasons and two rainy seasons. In the herbaceous cover, ranging in height from 20 cm Sudanian zone, the climate is characterized by a dry to 1.5 m, is composed of species of Panicum and tropical transition climate (Sudano-Guinean climate) Pennisetum, which are regularly affected by with two seasons (GOULA ET AL., 2006). The landscape wildfires (GUILLAUMET, 1971; KOUASSI ET Al., 2022). Fig. 1. Map of the N’Zi River Watershed The region is drained by numerous permanent collection, the localities of Pacobo, Abigui and and highly eroded water streams and tributaries Arikokaha were purposively selected in the forest, to the River N’Zi (PELTRE, 1977). The soils are pre-forest and Sudanian zones, respectively. In these moderately desaturated ferrallitic soils in the three localities, about 790 households were identified North and highly desaturated ferrallitic soils in the (INS, 2014). Centre and South (CPCS, 1967). However, hydro- A total of 259 households were randomly selected morphic soils can be found at the bottom of the and surveyed through the use of structured face- slopes and in the lowlands and tropical ferruginous to-face interviews. This sample size was calculated soils in savannah ecosystems. Agriculture dominates using a simple random sample formula at the 95% the economy of the watershed and employs two- confidence level with a ±5% margin of error (ROSS ET thirds of the population (DSDI, 2016). AL., 2002). Among the 259 surveyed farmers, 33.2% of them were in the forest zone, 32.82% in the 2.2. Data collection pre-forest zone and 33.98% in the Sudanian zone. Interviews were held with the heads of 2.2.1. Household survey agricultural households in January 2017 and focused on climatic manifestations for the period In this study, a stratified random sampling 1973-2016 or since they were over 18 years. The technique was used to select agricultural households. questionnaire was designed after a review of the The stratification was made according to the literature about farmers’ perceptions of climate phytogeographical location (ecoregion) and the change and its impacts. The questionnaire included administrative districts (sub-prefectures and information on farmers’ socio-economic profiles as localities). The stratification technique consisted well as their opinion of climate change manifestations. of considering the three ecoregions (forest, pre- The questionnaire also provided insights on how forest and Sudanian zones). For household data climate change impacted crops, assets and livelihood systems of farmers. Furthermore, the farmers were using the package trend (POHLERT, 2015) of R questioned about their perception of the causes statistical software (R CORE TEAM, 2019). of frequent climate change issues and their coping For household data, basic descriptive statistics, mechanisms for climate change manifestations such as mean, frequency and percentage were and their adaptation measures. A pilot survey was used to provide insights into farmers’ perception conducted before the data collection to identify of climate change. The significance of differences and revise unclear questions, ensure the suitability between the perceptions of climate pattern changes, of the data collection tools and allow enumerators to causes, impacts and coping strategies against familiarize themselves with the data collection climate change was assessed using the Kruskal- process. Data were collected using the Open Data Kit Wallis test for quantitative variables, differentiated (ODK) mobile data collection application (HARTUNG with Bonferroni adjustment used as a post hoc ET AL., 2010). The farmers' prior informed consent test and Pearson chi-squared test for qualitative was obtained and the anonymity of the study was variables at a 95% confidence interval. explained to the participants before they undertook These analyses were carried out using the the household survey. package questionr (BARNIER ET AL., 2017), and stats of the R statistical software (R CORE TEAM, 2019). 2.2.2. Climate data 3. Results In order to assess the impact of climate change on agriculture and local assets, the 44-year daily 3.1. Recorded climate trends climate data (precipitation, temperature, wind speed and relative humidity) ranging from 1973– The results of the Mann-Kendall test applied 2016 from all the five meteorological stations to the essential climatic variables for the period within the study area were obtained from the online 1973-2016 are presented in Table 1. The Mann- NOAA National Climatic Data Center (NATIONAL Kendall test showed a significant increasing trend CENTERS FOR ENVIRONMENTAL INFORMATION, 2017). at the 5% level (test Z> 0) within the minimum Data were then pooled and mean values computed (MIN), maximum (MAX) and mean (TM) to evaluate long-term trends in climatic variables. temperatures in all the ecoregions, except for the In addition to meteorological variables, the minimum and maximum temperatures of the forest Standardized Precipitation Index (SPI) was used and Sudanian zones. For rainfall, the results showed to quantify the precipitation deficit, the intensity, a significant upward trend in all the ecoregions of duration and frequency of drought episodes in the the NRW. The relative humidity (RH) showed a different ecoregions of the study area. The negative significant decreasing trend in the forest and (positive) values of the SPI indicate drought (wet) Sudanian zones while a non-significant uptrend conditions. SPI values less than –0.99, comprised was detected in the pre-forest zone. Across the between –1.99 and –1.50, and less than –2 show NRW, the relative humidity exhibited a significant moderate, severe and extreme droughts, respectively uptrend. The average wind speed (WDSP) and (SEILER ET AL., 2002). The rainfall and temperature maximum wind speed (MXSPD) showed a significant anomalies were computed by differencing observed increasing trend in all ecoregions, except the rainfall and temperature to their average over the forest zone which showed a statistically insignificant baseline period (1980–1990). uptrend. The reference evapotranspiration (ETo) showed a significant upward trend over the NRW 2.3. Data analysis except for the Sudanian zone, which presented non-significant values at the 5% level. The Sen’s In this study, both descriptive statistics and slope suggested that the rainfall, reference evapo- Mann-Kendall trend analysis were applied for the transpiration and relative humidity increased at an data analysis. The non-parametric Mann–Kendall annual rate of 5.68%, 5.28% and 0.43%, respectively test was used to identify the trend for essential in the whole NRW. The maximum, minimum and climate variables (temperature, rainfall, wind speed, average temperature increased respectively at a etc.) between 1973 and 2016 (KENDALL, 1970; MANN, rate of about 5.75%, 2.06% and 3.29% over the 1945). The Sen’s slope estimator was applied to 44 years. Conversely, the wind speed decreased estimate the magnitude of the trend (SEN, 1968). by 0.02% over the study period. Annually, the An upward trend is indicated by a positive Z maximum, minimum, and average temperatures value, while a downward trend is shown by a increased at rates of roughly 0.41%, 0.14%, and negative Z value. The trend analysis was computed 0.28% per year, respectively, while wind speed dropped at a rate of 0.02% per year. Table 1. Trends in climate series in the different ecoregions of the N’Zi River Watershed Ecoregions N’Zi River Watershed Climate Forest zone Pre-forest zone Sudanian zone variables Sen’s slope Test Z Sen’s slope Test Z Sen’s slope Test Z Sen’s slope Test Z -2 -2 -2 PRCP 8.40×10 5.129*** 4.22×10 3.931*** 0 2.475* 5.68×10 4.289*** -3 -3 -3 MAX 4.36×10 7.631*** 3.85×10 6.353*** 0 0.943ns 4.14×10 6.778*** -4 -4 -4 -3 MIN 8.11×10 3.417*** 3.89×10 1.088ns 5.49×10 4.283*** 1.39×10 4.499*** -3 -3 -4 -3 TM 2.56×10 7.115*** 2.26×10 5.738*** 1.86×10 3.185** 2.80×10 7.095*** -3 -3 -3 RH –6.69×10 –4.55*** 1.23×10 0.728ns 0 –2.43* 4.32×10 2.112* -2 -2 -2 ETo 8.35×10 8.092*** 5.45×10 4.921*** 0 0.96ns 5.28×10 5.647*** -5 -4 -4 -4 WDSP 5.51×10 1.172ns 9.74×10 14.9*** 1.12×10 4.607*** –1.98×10 –2.33* -3 -5 -5 MXSPD 0 –0.34ns 3.34×10 17.14*** 9.32×10 4.171*** 2.64×10 0.14ns ns: not significant; * p <0.05; ** p <0.01; *** p <0.001; PRCP: Precipitation; MAX: Maximum temperature ; MIN: Minimum temperature; TM: Average temperature; RH: Average relative humidity; ETo: Potential evapotranspiration; WDSP: Average wind speed; MXSPD: Maximum wind speed The number of rainy days in the dataset showed experienced in 1973, 2000 and 2001, severe drought an increase in all the ecoregions, with a rate of in 1976, and extreme drought in 1975 and 1999. roughly 121.26% at the NRW level (Fig. 2). On The magnitude of the extreme drought of 1975 average, the analysis of daily rainfall highlighted and 1999 was almost similar in all the ecoregions. 69, 47 and 34 rainy days in the forest, pre-forest Spatially, drought was mostly observed in the forest and Sudanian zones, respectively. The temperature and preforest zones. The average drought conditions and rainfall anomalies significantly increased by based on the SPI showed that the forest zone (β = +0.36 °C and +5.2 mm from 1973 to 2016 (Fig. 3). 0.04, R = 0.48, p < 0.05), pre-forest zone (β = 0.03, Overall, based on the SPI values, the study area R = 0.38, p = 0.01) and the whole NRW (β = 0.03, experienced two dry periods (1973-1976 and 1992- R = 0.40, p < 0.05) were significantly trending 2008) and two wet periods (1977-1991 and 2009- upwards, while the Sudanian zone showed a non- 2016) (Fig. 4). In the NRW, moderate drought was significant upward trend. Fig. 2. Number of rainy days over the three ecoregions of the N’Zi River Watershed Fig. 3. Monthly temperature (A) and rainfall (B) anomalies over the three ecoregions of the N’Zi River Watershed. Anomalies are the departures from the 1980–1990 standard averaging period. Fig. 4. Annual variation of the Standardized Precipitation Index (SPI) in the three different ecoregions of the NRW in central Côte d’Ivoire 3.2. Perceived climate change (82.63%). These farmers were mainly natives (88.8%), literate (56.37%) and Christian (52.9%) 3.2.1. Sociodemographic and socioeconomic growing mostly cash and food crops for economical characteristics of households purposes and their household subsistence, respectively. The mean farm size was 6.22 hectares Most respondents (81.85%) were men of about with about 4.25 hectares for cash crops and 1.96 48 years old (Table 2). As a result, the surveyed hectares for food crops. farmers were mostly adults (71.88%) and married Table 2. Socio-demographic characteristics of the sampled household in the N’Zi River Watershed (n = 259) Ecoregions N’Zi River Household attributes p-value Watershed Forest zone Pre-forest zone Sudanian zone Household proportion (%) 33.2 32.82 33.98 100 – Mean respondent age (years old) 44.73b 52.14a 46.94b 47.89 p < 0.05 Gender (male, %) 91.86 68.24 85.23 81.85 p < 0.05 Marital status (married, %) 82.56 97.65 68.18 82.63 p < 0.05 Education status (literate, %) 40 56.83 72.09 56.37 p < 0.05 Religion (Christians, %) 70.93 28.24 59.09 52.9 p < 0.05 Migration status (migrant, %) 27.91 3.53 2.27 11.20 p < 0.05 Duration of residence (years) 24.15b 39.27a 35.06a 32.72 p < 0.05 Occupation (farmer, %) 100 100 100 100 – Mean household size (no.) 4.91b 7.29a 5.64b 5.94 p < 0.05 Cultivated crops (no.) 2.41c 3.55a 2.99b 2.98 p < 0.05 Farm size (hectares) 4.07c 7.52a 7.06b 6.22 p < 0.05 Means within a column with the same letter are not significantly different from each other at the 5% level 3.2.2. Perception of climate change Most of the farmers (91.51%) experienced a decrease in the amount of rainfall in the landscape. The perception of farmers of the evolution of The Chi-squared test showed a significant climate patterns in the study area over the last difference between ecoregions and farmers' four decades is presented in Table 3. Most farmers perception of the start and duration of the rainy (98.07%) perceived that the start of the rainy season season and the change in the amount of rainfall. in these ecoregions was very late. Besides, more The perceptions of the causes of climate change than 98% of the farmers argued that the duration are presented in Table 4. The farmers stated that of the rainy season is reduced compared to previous the main causes of climate change observed in past years and the number of rainy days had decreased. decades were excessive deforestation (76.83%), In addition, 99.61% of the respondents highlighted natural variations in climate (50.93%) and wildfires that the early start of the dry season reduced the (31.27%). Only 11.3% of farmers attributed the duration of the rainy season. With a longer duration causes of climate deterioration to non-compliance of the dry season (98.84% of responses), most of with local norms and beliefs. In addition, about the respondents were unanimous on the increase 10% of farmers indicated that the observed in the intensity of pockets of heat and drought. climate change was a divine will. Table 3. Perceptions of farmers on changes in climate patterns Proportion (%) Chi-squared Perceptions attributes Pre-forest N’Zi River test Forest zone Sudanian zone zone Watershed+ Early 5.81 0 0 1.93 Beginning of the rainy p < 0.05 season Late 94.19 100 100 98.07 Shorter 96.51 100 100 98.84 Duration of the rainy p < 0.05 season Longer 3.49 0 0 1.16 Increase 1.16 0 0 0.39 Decrease 98.84 100 96.59 98.46 Number of rainy days p > 0.05 No change 0 0 2.27 0.77 Do not know 0 0 1.14 0.39 Increase 3.49 0 0 1.16 Decrease 96.51 98.82 79.55 91.51 Rainfall amount p < 0.05 No change 0 1.18 13.64 5.02 Do not know 0 0 6.82 2.32 Early 100 98.82 100 99.61 Start of the dry p > 0.05 season Late 0 1.18 0 0.39 Shorter 1.16 1.18 0 0.77 Duration of the dry p > 0.05 season Longer 98.84 98.82 100 99.23 Increase 100 100 100 100 Heat intensity p > 0.05 (temperature) Decrease 0 0 0 0 Increase 97.67 100 100 99.23 Existence of drought p > 0.05 period Decrease 2.33 0 0 0.77 Increase 1.16 0 0 0.39 Decrease 95.35 2.35 2.27 33.2 Flooding during the p < 0.05 rainy season No change 2.33 64.71 18.18 28.19 Do not know 1.16 32.94 79.55 38.22 Table 4. Perceptions of farmers about the causes of climate change in the study area Proportion (%) Perceived causes Forest zone Pre-forest zone Sudanian zone N’Zi River Watershed+ Abusive deforestation 74.42 92.94 63.64 76.83 Natural variations in climate 17.44 98.82 37.5 50.97 Wildfires 43.02 34.12 17.05 31.27 Non-compliance with social standards 32.56 0 1.14 11.2 God's will 27.91 0 2.27 10.04 Increase in cultivated areas 12.79 7.06 3.41 7.72 Non-respect of divinities 6.98 0 2.27 3.09 Transhumance 0 0 9.09 3.09 Do not know 1.16 0 15.91 5.79 + represents the overall statistics of the three ecoregions of the watershed 3.2.3. Perceived impacts of climate change The analysis of these results showed that the major perceived impacts of climate change on people The perceived impacts of climate change on crops and human settlements were mainly a decrease are presented in Table 5. The results showed that in household income (63.71%), the removal of the main impacts of climate change on crops were house roofs (47.1%), the scarcity of water supply plant dieback (92.66%), poor crop growth (59.46%), points (39.38%), the appearance of new diseases harvest losses (20.46%), the death of some crop (30.89%), the remoteness of water supply sources plants (13.13%), the scorching of crops (10.81%), (25.1%), poor water quality (22.01%) and the and non-maturation of crops (10.04%). These resurgence of certain diseases (16.22%). However, observations negatively affected crop yields and a small number of respondents (3.09%) noted an food security. increase in household income. The perceived impacts of climate change on humans and human settlements are shown in Table 6. Table 5. Perceived impacts of climate change on crops Proportion (%) Perceived impacts Forest zone Pre-forest zone Sudanian zone N’Zi River Watershed+ Plants dieback 88.37 95.29 94.32 92.66 Poor crop growth 52.33 89.41 37.5 59.46 Harvest losses 55.81 2.35 3.41 20.46 Death of crop plants 33.72 3.53 2.27 13.13 Scorching of crops 32.56 0 0 10.81 Immaturation of fruits and crops 9.3 8.24 12.5 10.04 Rotting of ears and fruits 10.47 0 1.14 3.86 Loss / dessication of seedlings and crops 9.3 1.18 0 3.47 Disruption of the agricultural calendar 9.3 1.18 0 3.47 Poor quality of harvested products 3.49 1.18 3.41 2.7 Breakage / destruction of stems and leaves 4.65 0 0 1.54 Attack by insects (caterpillars, termites, etc.) 4.65 0 0 1.54 Appearance of certain diseases 2.33 0 1.14 1.16 Lodging of crops 1.16 0 0 0.39 Storage losses 1.16 0 0 0.39 + represents the overall statistics of the three ecoregions of the watershed Table 6. Perceived impacts of climate change on people and their settlements Proportion (%) Perceived impacts on human Forest zone Pre-forest zone Sudanian zone N’Zi River Watershed Decrease in household income 98.84 94.12 0 63.71 Removal of house roofs 95.35 21.18 25 47.1 Scarcity of water points 67.44 1.18 48.86 39.38 Emergence of new diseases 4.65 32.94 54.55 30.89 Remoteness of water supply sources 60.47 2.35 12.5 25.1 Poor water quality 59.3 0 6.82 22.01 Resurgence of certain diseases 26.74 14.12 7.95 16.22 Do not know 1.16 1.18 7.95 3.47 Increase in household income 0 2.35 6.82 3.09 Demolishing the walls of houses 5.81 1.18 1.14 2.7 3.2.4. Adaptation strategies forest zone, 52.9% in the pre-forest zone and 64.8% in the Sudanian zone) stated that they did In a changing climate, farmers adopted not take any action. To fight against losses recorded adaptation strategies based on the perceived climate during the storage of agricultural products, the impacts on crops (Table 7). The adaptation strategies majority of the respondents (64.9%) claimed adopted to cope with the death of young plants modifying their conservation practices by using included the resumption of sowing and planting granaries, racks, etc. or taking no action in coping of crops (96.94%), the uprooting of impacted plants with this constraint (32.8%). (6.56%) and crop diversification (4.25%). Indeed, In the NRW, local communities developed by growing several plant species on the same farm strategies to adapt to the effects of climate change on (diversification), some crops could benefit others them and their habitat (Table 8). The respondents by providing various services (shade, fertilization, reinforced the construction materials and the pest control, etc.). Also, this practice minimized supports of their buildings to fight against the the risk of a bad harvest, a situation that could be strong winds and the torrential rains that caused disastrous in the case of monocropping. the removal of their house roofs (44.4%) and the The surveyed farmers who observed scorching of demolition of the walls of their houses (43.91%). crops, such as cereals (rice, corn, etc.) and Moreover, 24.77% of the farmers created drains to vegetables (tomatoes, etc.), testified that they did prevent the demolition of their walls or damage nothing to respond to these negative impacts (90.7% to their house foundations. More than 19% of in the forest zone, 95.3% in the pre-forest zone respondents claimed to go to the hospital regularly and 88.6% in the Sudanian zone). On the other or to use traditional medicine to cope with the onset hand, they stated that they used phytosanitary and recurrence of diseases (typhoid fever, malaria, products (57.9%) or confided in God (42.5%) to diarrhoea, etc.). Against water scarcity, the help them fight against crop loss. respondents in the Sudanian zone claimed to Besides, poor crop growth inevitably led to the collect water early in the morning at the pump or resumption of sowing, or planting, according to backwater (23.9%) or to collect water elsewhere 48.65% of the interviewees. For the non-maturity by motorbike or bicycle in containers (19.3%). In of crops, 44% of the respondents indicated that addition, a small share of the respondents (8.1%) they were not taking any action. Others, on the stated that they built water retention facilities. other hand, harvested very early at the start of the Also, farmers admitted to not taking any action about crop ripening process (19.7%) or they practice the decrease in household income (27.03%) and crop diversification and association (19.3%). poor water quality (30.79%) observed in the Among the respondents who noted the rotting study area. of ears and fruits, 56.8% (including 52.3% in the Table 7. Adaptation strategies developed against perceived climate change impacts on crops Proportion (%) Perceived impacts Adopted strategies Pre-forest Sudanian N’Zi River Forest zone zone zone Watershed+ Resume sowing and planting 97.67 97.65 95.45 96.94 Death of young Dig up dead plants 18.6 1.18 0 6.56 plants Practice crop diversification and association 0 7.06 5.68 4.25 Resume sowing and planting 6.98 91.76 47.73 48.65 No action 75.58 0 14.77 30.12 Poor crop growth Practice crop association and agroforestry 0 9.41 26.14 11.97 Apply chemical fertilizer 15.12 0 3.41 6.18 Use phytosanitary products 84.88 63.53 26.14 57.92 Harvest losses Surrender to God through prayers 0 52.94 73.86 42.47 No action 89.53 14.12 20.45 41.31 Scorching of crops No action 90.7 95.29 88.64 91.51 No action 94.19 5.88 31.82 44.02 Non-maturation Harvest as soon as ripening begins 3.49 56.47 0 19.69 of crops Practice crop diversification and association 0 17.65 39.77 19.31 Change conservation practices for 5.81 98.82 89.77 64.86 agricultural products Storage loss No action 94.19 0 4.55 32.82 No action 52.33 52.94 64.77 56.76 Rotting of ears Harvest impacted ears and fruits 27.91 4.71 11.36 14.67 and fruits Destroy diseased crops 0 21.18 12.5 11.2 + represents the overall statistics of the three ecoregions of the watershed Table 8. Adaptation strategies developed against the perceived impacts of climate change on humans and their habitat Proportion (%) Perceived impacts Adopted strategies Forest Pre-forest Sudanian N’Zi River zone zone zone Watershed+ Removal of house Reinforce construction materials and 98.84 16.47 18.18 44.4 roofs building supports Reinforce construction materials and 90.31 19.63 21.8 43.91 Demolishing house building supports walls Make drains 45.63 12.37 16.32 24.77 Getting to the hospital 0 22.35 19.32 13.9 Emergence of new diseases Cure oneself using traditional medicine 0 11.76 7.95 6.56 Getting to the hospital 23.12 29.21 15.69 22.67 Resurgence of certain diseases Cure oneself using traditional medicine 12.47 13.68 9.32 11.82 Fetch water elsewhere using containers 0 0 19.32 6.56 by motorbike or bicycle Scarcity and Collect water early in the morning at the distance from water 0 0 23.86 8.11 pump or backwater points Build water retention facilities 0 0 2.27 0.77 No action 0 52.94 28.41 27.03 Decrease in income Other 1.16 15.29 7.95 8.11 Poor water quality No action 16.42 56.3 19.67 30.79 3.3. Comparison between perceived and recorded farmers’ perceptions about drought spell trends changes in the landscape matched with the drought index trends from climate observations. Conversely, Our results showed a strong agreement inconsistencies were noted between the between NRW farmers’ perceptions of changes in perceptions of respondents about the rainfall temperature and recorded trends in the average change and calculated rainfall trends in all the temperature gathered from meteorological data ecoregions. The same inconsistencies were during the period 1973-2016 (Table 9). Also, observed with the number of rainy days. Table 9. Comparison between recorded and perceived change in climate variables in the N’Zi River Watershed from 1973–2016 Proportion (%) Chi-squared Climate variables Comparison N’Zi River test Forest zone Pre-forest zone Sudanian zone Watershed+ Good fit 3.49 0 0 1.16 Rainfall amount p < 0.05 Bad fit 96.51 100 100 98.84 Good fit 1.16 0 0 0.39 Number of rainy days p > 0.05 Bad fit 98.84 100 100 99.61 Good fit 100 100 100 100 Temperature p > 0.05 Bad fit 0 0 0 0 Good fit 97.67 100 100 99.23 Drought spells p > 0.05 Bad fit 2.33 0 0 0.77 + represents the overall statistics of the three ecoregions of the watershed 4. Discussion These temperature increases are particularly accentuated during years known as the El Niño The findings of this research revealed that the years such as 1973, 1983, 1998 and 2016, globally study area was subjected to climate variability recognized as the strongest (NULL, 2018; SARACHIK during the period between 1973 and 2016. & CANE, 2010). These temperature increases However, the intensity of the variation was not negatively impact agriculture and lead to the uniform over the whole area of the NRW given occurrence of environmental disasters such as the influence of local climates (equatorial climate wildfires (KOUASSI ET AL., 2018; 2022). in the South, subtropical climate in the North and The relative humidity increased in the forest an equatorial transition climate in the centre of zone, Sudanian zone and the whole NRW, but it the NRW). decreased in the pre-forest zone. These results Temperatures significantly increased during the could be explained by the increase in rainfall study period. Across the NRW, average temperatures patterns identified in these ecoregions. YAO ET AL. increased by around 3.29% (0.89°C) over the (2012) noted an increase in relative humidity period 1990-2016. This increase could be explained from 1960 to 2010 in the forest and Sudanian by the global changes underway since the end of zones and a decrease in relative humidity in the the 19th century (IPCC, 2022). The results of the pre-forest zone. KOUASSI ET AL. (2010) showed a identified average temperature trends remained steady decline in the relative humidity since 1973 lower than those observed throughout Côte d'Ivoire at the NRW level. by YAO ET AL. (2012) who noted an increase of Regarding rainfall, the results of this study 1.6°C in the average temperature at the national showed an increasing trend in the forest and level over the period 1961-2010 (i.e. 0.03°C per Sudanian zones and the whole NRW. Similar results year). However, this increasing temperature value were found in West Africa in recent studies of 0.89°C (i.e. 0.02°C per year) is consistent with (NICHOLSON ET AL., 2018a; 2018b). However, several the average increase of 1.09°C over the period studies about rainfall in Côte d’Ivoire and the NRW 1900-2021 (i.e. 0.01°C per year) observed at the revealed a negative trend at the beginning of the global level (IPCC, 2022) or of 0.18°C per decade 21st century (ASSEMIAN ET AL., 2013; GOULA ET AL., over the period 1979-2010 (i.e. 0.02°C per year) 2006, 2012). Occurring at a fine scale, rainfall recorded at the West African level (COLLINS, 2011). variations in any location at a given time can be determined by the climate features, biophysical as a season that would be shorter than usual conditions of the location as well as by the type rather than one that generated less rainfall and the number of rain gauges used for data (RONCOLI, 2006). Several authors demonstrated collection, explaining differences between the that farmers do not perceive climate trends in findings of this study and past studies. meteorological records although the climate affects The year 1998 has been identified as the agricultural activities (DE LONGUEVILLE ET AL., 2020; hottest year in the NRW over the period 1973- KOSMOWSKI ET AL., 2016). According to FOGUESATTO 2016. Earlier observations showed that 1998 was ET AL. (2020), perceptions of changing climate – the hottest year of the 20th century and with the regardless of their accuracy – are focused on the highest ENSO resulting in an increase in average expected change in farmers’ well-being and mental air temperature from 0.5°C to 1°C during the dry shortcuts experienced regarding the occurrence season of 1997-1998 in Africa (LEAN & RIND, 2009; of events. LU, 2005). The extreme weather conditions could be The perceived impacts of climate change on a major factor in the occurrence of increasingly crops were multiple, including the death of young frequent bushfires nationwide. plants, poor crop growth, non-maturation of The analysis of climate records showed that crops, death of standing crops, etc. These perceived the NRW experienced erratic climatic conditions impacts of climate change associated with observed in recent decades, in particular the environmental degradation led farmers to practice increase in temperature and the occurrence of crop diversification (YÉO ET AL., 2016) and adopt significant droughts. Local perceptions of climate new major climate-resilient crops such as cashew variability and change appear to corroborate in the savannah regions (KOFFI & OURA, 2019; RUF meteorological observations and scientific literature ET AL., 2019). In the forest zone, banana-cocoa in the study area. association and intercropping are used as a climate- The local farmers perceived climate change smart practice (FAO ET AL., 2018). Also, agroforestry manifestations including the increase in temperature, practices led to achieving mitigation and adaptation the decrease in rainfall and the number of rainy to climate change (KOUASSI ET AL., 2021b; MBOW ET days, the late start of the rainy season, etc., that AL., 2014) and enhanced yield (KOKO ET AL., 2013). occurred in their landscape. The main causes of Furthermore, they led to a lower income at the these climatic events were mainly deforestation, household level, the removal of house roofs and logging, bush or natural fires. These observations the upsurge of diseases such as malaria, dengue, are consistent with the analysis of climatic variables tropical fever, etc. As a result, crop destruction which and confirm several studies that have been carried led to low yields, food shortage and communities’ out on climate change and deforestation at the incomes declining, increased the cost of food and national (e.g. KOUASSI ET AL., 2021a; 2022; YAO ET exacerbated food insecurity (YÉO ET AL., 2016). AL., 2005) and regional (e.g. CODJOE ET AL., 2013; These results are consistent with the conclusions YAMBA ET AL., 2019) levels. However, perceptions of several studies conducted in West Africa (e.g. of rainfall patterns seemed to contradict the BOISSIÈRE ET AL., 2013; MERTZ ET AL., 2011). Other rainfall data and vary depending on the ecoregion. authors have highlighted the recurrence of infectious This inconsistency could be due to a great variability (cholera) and vector-borne diseases (malaria, marked by “false starts of rainy seasons”, the dengue, yellow fever, Burili ulcer, etc.) due to rising decrease in the number of rainy days and the temperatures associated with heavy rainfall recurrence of extreme rains (TA ET AL., 2016). These (CHRETIEN ET AL., 2015; MOORE ET AL., 2017; OMS climatic events cause a change in the growing ET AL., 2004). season and are harmful to sowing and plant growth. Local communities employ various adaptation Moreover, this contradiction could provide more measures to respond to climate change. The main localized contexts of climate variability, insufficiently adaptation strategies adopted by the local captured by the meteorological data used (BOMUHANGI communities included resuming sowing and ET AL., 2016). Perceptions of changes in the amount replanting seedlings and plants. Also, they used of rainfall do not match those of the populations crop diversification which has the advantage of of the surrounding surveyed localities (DOUMBIA compensating for the poor harvest of one crop with & DEPIEU, 2013; YÉO ET AL., 2016). DE LONGUEVILLE the harvest of another crop and reducing climate ET AL. (2020) showed that other personal and risks to household incomes (BUNN ET AL., 2019). environmental factors are important for determining Many crops are mixed and the common association climate change perceptions. Also, farmers consider for cash crops included cocoa-plantain, cocoa- rainfall as a process rather than as a quantity as cashew and cocoa-cashew-teak associations while they perceive a drier-than-normal rainy season food crop associations are composed of yams, des variabilités climatiques sur les ressources hydriques peanuts, taros, tomatoes, peppers and eggplants d’un milieu tropical humide. 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Environmental & Socio-economic Studies – de Gruyter
Published: Sep 1, 2022
Keywords: climate change; local perception; coping strategies; rural livelihoods; smallholder agriculture; N’Zi River Watershed
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