Climate Information Services and Farm-Level Decision-Making in the Pra River Basin of Ghana

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Attoh, Ishmeal Amoah, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5501950/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Agriculture is the backbone of many economies and provides livelihood to many nations in Africa. However, the sector is largely rainfed and faces considerable challenges due to the impacts of climate variability and unpredictable rainfall patterns. Despite advances in climate information services, farmers have limited access to reliable climate information to make adaptive decisions. Studies have shown that farmers with limited access hardly rely on the information they get access to. Using the Pra River basin as a case, this study assessed climate information’s influence on farmers' farm-level decisions by interviewing 382 farmers and evaluated the performance of seasonal forecasts from the European Centre for Medium-Range Weather Forecast (ECMWF) in meeting farmers’ needs. Rainfall onset, amount and cessation were the three most prioritised needs ranked in descending order. Most farmers preferred to receive climate information at a 2-month lead time. However, ECMWF-S5 only provided the second-ranked need (rainfall amount) at a 1-month lead time with the highest skill in forecasting rainfall in the study area at correlation coefficient, RMSE (ranging between 40.31–52.84, 68.85–75.82, 92.17-101.07), and KGE within the ranges of 0.7–0.9, 40–53, and 0.6–0.8, respectively. The skill of ECMWF-S5 decreases with increasing lead times. ECMWF-S5 rainfall forecast information at a 1-month lead time is advisable and likely to positively impact the decision-making process for farming in the Pra River Basin. It is recommended that other forecast models, including indigenous techniques be combined to improve the forecast's lead time and determine the onset and cessation for adaptive farming. Climate Change Climate information services hydroclimatic needs lead time rainfall forecast ECMWF-S5 model forecast accuracy Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Climate change is a significant barrier to efforts to enhance the livelihoods of people living in rural areas of Sub-Saharan Africa and a significant contributor to food insecurity. Climate variability and change have reportedly garnered much attention in recent years because of the detrimental effects of changing weather conditions on human life (Rehman and Ozturk, 2020). Increased greenhouse gas emissions are to blame for the rise in global temperatures, which has altered the world's water cycle and precipitation patterns (Pachauri et al. , 2014). The main long-term effects of climate change can be broadly categorized as follows: (i) a decrease in agricultural productivity and food security due to a change in rainfall patterns, (ii) a worsening of water security due to variations in rainfall patterns, and (iii) a decrease in fish resources due to a temperature rise (IPCC, 2018). Nyadzi et al. (2018) reported that due to fluctuating climatic circumstances, there have been difficulties in deciding the type of seed to plant, the best time to plant, and the best time for cultural practices on the field. Other studies have shown that farmers start planning as soon as they see signs of rainfall onset, and such delays could affect production (Nyadzi et al., 2019). The difficulty in predicting weather and seasonal climate significantly impacts the precision of farm-level decisions, particularly those requiring planning months before the coming season (Lawson et al. , 2019). Agricultural climate information services involve the development, production, and location specificity of information and knowledge obtained from various climate science and meteorology research to assist in decision-making at all levels of agricultural production in society (Naab et al., 2019; Sarku et al., 2024). Researchers over the years have focused on the kind of weather and climate information that tends to inform agricultural decision-making, the quality of the existing information, and how the information available can further enrich the lives of its users (Sarku et al., 2024; Naab et al., 2019). However, farmers' ability to accept and adopt any adaptation practice is also dependent on education, age, farming experience, income, size of farm (acreage under cultivation), and access to information (Bessah et al., 2021a). As much as climate services are pivotal in agriculture, little or less has been achieved in many developing nations. According to Shah et al. (2020), farmers may incur extra costs in re-sowing seeds due to the rains' delays, which slows crop germination and growth. The quest for seasonal weather forecast information focused on smallholder farmers surged after the El Nino 1997/98 event (Gerlak et al. , 2018). As reported by Gudoshava et al.(2024), almost all national meteorological agencies across Africa provide information on seasonal climate conditions through monthly climate outlooks, agrometeorological bullets, and extreme weather indexes. Breakthroughs in the science community have made it possible to provide short-, medium-, and long-term climate services to assist in farmers' decision-making (Nyadzi et al. , 2019; Johnson et al. , 2019; Gubler et al. , 2020). Furthermore, only farmers with larger farm sizes and a high level of climate change awareness are more likely to access climate information services (Bessah et al. , 2021a). Smallholder farmers in rural areas continue to be sceptical of the forecasts provided by the agencies. According to a multi-stakeholder cross-sectoral assessment, climate information failed to contribute meaningfully to development across Africa due to market atrophy caused by a long-term interaction between ineffective demand by development stakeholders and an insufficient supply of actionable climate information (Hansen et al., 2019; Huysen et al., 2018). This study looks at the issue from a different lens by focusing on providing location-specific information and as such the concerns of the end users play a pivotal role in the dissemination and adoption of the adaptation strategy. Therefore, this research aimed to assess the potential of climate information services in influencing farmers' decisions and the performance of ECMWF seasonal forecasts in meeting farmer's needs in the Pra River basin. The specific objectives were: To assess farmers' perception of the significance of climate information services in influencing their decisions. To assess the accuracy of the European Center for Medium-Range Weather Forecast- System 5 model to predict seasonal total rainfall at different lead times. Results of this research can support decision-makers in determining the optimal time to execute agricultural policy, in that, the result from this study will give policymakers the information to provide needed resources at the required times. Also, gaining insight into the local conditions of the end users (farmers) is a way of bridging the gap that exists between the producers (researchers) and local farmers (users) concerning their limited ability to use existing climate information services. 2. MATERIALS AND METHODS 2.1 Study Area The Pra River basin (PRB) covers a land area of 23,331 km 2 and is situated within latitudes 5.00° N and 7.21° N and longitudes 2.30° W and 0.43° W of Ghana (Figure 1). The basin cuts across four administrative regions, namely Ashanti, Eastern, Western, and Central regions, respectively, with an annual average discharge of 4174 Mm 3 /year. It has four main tributaries: the Anum, Birim, Offin, and Oda rivers, which have their main supply from Kwahu-Mampong and flow southwards for about 240 km before joining the Gulf of Guinea (Osei et al., 2021). The Pra River basin is known mainly for its rich agricultural lands and mineral resources. It has several mining companies (both large scale and small scale) located in the area, providing natives of the area an alternative means of livelihood. The Pra River Basin is the major producer of the most valued economic crop in Ghana (cocoa), the leading producer of cassava, which is the second food security crop in Ghana, and one of the top three areas in the country producing the first food security crop, that is, maize (Nutsukpo et al. , 2013; MoFA, 2016). A shift from a bi-modal to a mono-modal pattern of rainfall commencing in late March and ending in November, according to previous studies in the basin, might lower rainfall by 1.77 % between 2020 and 2049 (Bessah et al. , 2020a). In the same period, the average temperature has been predicted to rise by 1.51°C (Bessah et al. , 2018). Surface water yields could be reduced by 2-16 mm due to the expected changes in rainfall and temperature, with flood and drought effects across the basin (Bessah et al. , 2020b). Farmers perceive themselves to be vulnerable to higher temperatures and fluctuations in rainfall, according to Bessah et al. (2021b), especially during the growing season, with an evident influence on their yield. Although most farmers are responding to climate change by changing planting dates, only approximately 30% have access to meteorological information, primarily obtained through radio broadcasts that have shown to be unreliable over time (Bessah et al. , 2021b). Data Sources Primary data was obtained from 382 respondents using semi-structured questionnaires, three (3) stakeholder discussions in three districts, and an expert discussion at the Ghana Meteorological Agency (GMet). Rainfall hindcast data from 1993-2015 were obtained from the European Centre for Medium-Range Weather Forecast System 5 (ECMWF-S5) at one-, two- and three-month lead times. Also, Global Precipitation and Climatology Centre (GPCC) monthly rainfall data v2020 at 0.25° × 0.25° resolution were obtained for the same period and used as the observed data. GPCC data was used because it is a gauge-based gridded precipitation estimate that reproduce observed precipitation (Ahmed et al. , 2019; Nashwan et al. , 2019; Wange, 2017). GPCC uses measurements obtained from rain gauges, weather stations, and other ground-based precipitation observations. These measurements are collected in real-time and provide direct information about the amount of precipitation at specific locations. Atiah et al. (2020) have established that GPCC v7 and CHIRPS v2 are Ghana's best reference gauge measurements. Data from GPCC and ECMWF were extracted in R software from seven (7) meteorological stations in the basin (Figure 1). Sample size and sampling techniques The study adopted the approach used by Bessah et al. (2021a, b). The sample size of 399 individuals using the Yamane simplified formula was calculated. Also, the selection was based on earlier research by Bessah et al. (2021a, b) in the same study area. Assin Foso Municipality, Obuasi East and Atiwa West districts located in the Central, Ashanti, and Eastern Regions, respectively, were purposely selected to cover all regions in the basin and have a diverse representation of respondents. Five communities were purposely sampled, and 30 respondents were randomly selected from each community for the questionnaire administration. One community was selected from Assin North District and three from the Assin Foso to track activities since 2019 after the first study. Data collection A semi-structured questionnaire was built into the Kobo App (Lakshminarasimhappa, 2021) to administer to sampled respondents. The questions focused on local farmers' understanding of their environment (climate conditions) and opinions of climate information services. Understanding their unique climatic needs and the relevance of climate information in making decisions such as the type of crop and species to select when cultivating the land preparation type, irrigation schedule (if any), fertilizer and chemical application time when needed, and harvest time were investigated. A total of 382 respondents participated in the administered questionnaire. A total of 45 persons took part in the stakeholder discussion comprised of Eight (8), eleven (11), and thirteen (13) farmers from Assin Foso, Atiwa West, and Obuasi East districts, respectively, plus thirteen (13) Agric extension officers). Six (6) experts from the seasonal forecast, daily forecast, hydro-climatology, and instrumentation departments of GMet were engaged in scientific discussions on the findings from the field and information from the literature. Both stakeholder and expert discussions were recorded and transcribed for analysis. Data analysis Questionnaires were imported into the Statistical Package for Social Sciences (SPSS) version 29.0 for the descriptive and cross-tabulation analysis to establish the patterns and distribution between certain dependent and independent variables. Data gathered from the stakeholder and expert discussions were transcribed and analyzed based on themes. Rainfall forecast data was analyzed with R-statistic. The performance of ECMWF was assessed using various forecast accuracy indices like Pearson correlation coefficient (r) because the model uses it to assess the linear relationship between the ensemble means and the observation (Gubler et al., 2020), Root mean square error (RMSE), bias, and Klint-Gupta efficiency (KGE) (Amekudzi et al., 2016; Gubler et al., 2020). Pearson correlation coefficient has a value between -1 and 1, where r = 0 means the variables are not associated or related linearly, while r = 1 means all fall precisely on the best-fit line with a positive slope (Schober et al., 2018). RMSE computes the average magnitude of the differences between predicted and actual values. If the RMSE is low, the model effectively forecasts the observed data, and vice versa. A positive bias value indicates overestimation, whiles a negative bias denotes underestimation. KGE is a statistical metric used in hydrological modelling to compare the performance of simulated or predicted values to observed values. According to Kling et al. (2012), it is an improvement over traditional efficiency metrics such as Nash-Sutcliffe Efficiency (NSE) and correlation coefficient (r). 3. RESULTS AND DISCUSSION 3.1 Socio-demographic characteristics of respondents Out of the sample size of 382, the majority (66.5%) of the respondents were males and the dominant age group was 40–59 years (47.6%) (Table 1 ). Most farmers (51.6%) possessed either a middle school diploma or a JSS certificate (thus, about 12 schooling years). About 63.1% of the farmers engaged in rainfed agriculture, with 88.2% of this number having land sizes of more than a hectare and almost all cultivated maize and/or cassava (Table 1 ). About 67% of the farmers have more than ten (10) years of experience. Farmers cultivate maize more than any other crop because of its versatility as a staple crop. Rice was the least cultivated due to its water requirement and the nature of agriculture practiced within the locality. Most farmers engaged in rainfed agriculture due to the size of their lands and the fragmented nature of their lands due to land litigations. The farmers also reported practicing rainfed agriculture due to financial constraints and the vast initial capital needed for irrigation equipment. Amongst the other crops cultivated by the farmers were cocoyam, taro, oil palm, cocoa, para rubber, ginger, and coconut. Each crop, especially cocoa, para rubber, and coconut, was either cultivated commercially by a small proportion of the farmers or individuals with keen interest in producing that. As reported by farmers, crops like cocoyam, taro and ginger were only planted in their backyards for sustenance. Table 1 Socio-demographic characteristics of the study area (N = 382) Categories (n = 382) Frequency Percentages (%) Male 254 66.5 Female 128 33.5 Age groups(n = 382) 0–19 3 0.8 20–39 138 36.1 40–59 182 47.6 60–79 55 14.4 80–99 3 0.8 100+ 1 0.3 Education(n = 382) Elementary/Primary 64 16.8 Middle school lever/JHS 197 51.6 SSS/O-Level/WASSCE 50 13.1 Tertiary 14 3.7 No formal education 57 14.9 Farming type (n = 382) Rain-fed 241 63.1 Irrigated 6 1.6 Both irrigated and rainfed 135 35.3 Farming experience (years)(n = 382) 1–5 62 16.2 6–10 64 16.8 11–15 63 16.5 16–20 69 18.1 21–25 36 9.4 > 25 88 22.0 Farm sizes (ha) (n = 382) 4 98 25.7 Cultivated crops (n = 382 for each crop) Maize 317 83.0 Rice 78 20.4 Cassava 333 87.2 Yam 103 27.0 Plantain 318 83.3 Vegetables 264 69.1 Others 317 8230 3.2 Perception of Farmers on Climate Information Services The study sought to gather information on the perception of farmers on currently accessible climate forecast information. It examined their knowledge of climate information services and the relevance of accurate weather forecast on their livelihood activities. Table 2 shows that about 73% of the total respondents reported having some level of access to climate information. 36.9% believe that accurate weather forecast information increases their productivity and yield. Additionally, 46.4% express concern about considerable climate variability, viewing climate information as a potential solution to address climate change issues. They also recognise the value of reliable weather forecast information in guiding favourable farming decisions throughout the agricultural cycle. About 34.8% reported using forecast information to boost their yield, income, and profit margins (Table 2 ). During stakeholder discussions, farmers confirmed the relevance of incorporating climate information into their planning processes to help avoid waste of money, thereby increasing their seasonal yield. The recent changes in rainfall onset due to climate change have compelled many farmers to engage in multiple planting cycles within a season to secure sufficient rainfall for their production (Guido et al., 2021 ). Unfortunately, this practice result in reduced profit margins and, at times, substantial financial losses. Table 2 Farmers’ perception of climate and weather services Relevance of accurate weather forecast (%) Reason for using weather forecast (%) Good seed usage 6.1 Too much climate variability 46.4 High yield 36.9 My existing forecast methods are unreliable 2.1 Appropriate water management 8.1 Hope it improves crop yield 34.8 Save money 22.8 Better water management 4.3 Enough food for my family 17.0 Others (specify) 12.4 Others (specify) 9.1 The results obtained are in agreement with the findings of Lechthaler and Vinogradova ( 2017 ), who reported that climate information is a vital tool due to its ability to provide individuals with information and predictions that help reduce the risks associated with climate and weather-related issues and as such increases the efficiency of farmers’ decisions. According to Zongo et al. ( 2016 ), farmers are aware of the risks associated with climate variability and change, and that about 93% of studied farmers in Sahelian and Sudano-Sahelian areas of Burkina Faso see climate information as a means of battling the adverse effects of climate change. Farmers' perception of crop failures resulting from poor rainfall distribution and frequency could potentially be averted using accurate climate information. 3.3 Communicating weather and climate information at preferred Lead times The results show that 52.4% of the respondents prefer to receive climate information 2 months before the start of the season (Fig. 2 ). Alternatively, some farmers believe 1 month or 3 months ahead of the season is sufficient for adequate preparation and as such they prefer to receive the information within this range. For instance, during the stakeholder discussions, most farmers stated the importance of receiving information early, specifically within the range of 1 to 2 months before the beginning of the farming season, as an ideal timeframe for effective planning and execution of farm activities. However, climate and weather forecasting experts assert that providing information 1-, 2- or 3-month lead times is not practical. However, experts from the climate and weather forecast field perceive 1, 2, or 3 months are not feasible for providing the information. According to the GMet expert "The farther ahead you forecast, the greater the margin of error and the more inaccurate the information becomes. And the closer you are to the season, the more accurate and reliable your information". This underscores the dependency of the usability of climate information on the producers' capacity to deliver timely data to farmers. Experts suggest that the optimum time for providing weather or climate information is the last day of February, even though the official start of the season is perceived to be in March, Nevertheless, this can only be achieved after a recorded rainfall amount of 20mm or more has occurred for at least10 days within a month according to experts from the Ghana meteorological agency. About 30% of the respondents perceive the radio as the most accessible communication medium due to the minimum financial investment required to acquire a radio set. Agric Extension Officers (20.6%) are also preferred by 20.6% to disseminate climate information with the added benefit of interpreting the provided information. Other sources include mobile phones (17.5%), Television sets (10.2%), and leaders of farmer-based organizations (3.5%). These results align with the findings of Baffour-Ata et al. ( 2022 ), who reported that farmers prefer radios and television sets for weather and climate information due to their easy accessibility, extensive coverage, and low maintenance costs. Studies by Baffour-Ata et al. ( 2022 ) also support these claims, emphasizing the regular delivery of weather forecasts in news segments by many radio and television stations. However, Yegbemey and Egah ( 2021 ) foresee a promising future in disseminating information through mobile phones to smallholder farmers (Baffour-Ata et al., 2022 ; Partey et al., 2020 ). Peculiar hydroclimatic information needs and their potential influence on Farm Decisions The majority of farmers (31.6%) identified rainfall onset as the most needed hydroclimatic information, followed by seasonal rainfall amount (18.7%) and rainfall cessation (10.9%). Others include seasonal rainfall distribution (8.9%), temperature (7.2%) and wind speed (0.8%), while some farmers did not express specific preferences. Farmers believe that having information about the start of the rainfall is essential for effective planning, enabling them to organize their activities to coincide with major part of the rainy season. At each stage of the production cycle, farmers recognize the need for specific information to optimize the effective and efficient use of resources. For instance, during the initial stages of production (pre-planting, land preparation stage, nursery), rainfall onset was a crucial need for farmers because they help them prepare well to meet the rains (Table 3 ). Information on rainfall onset also aids in selecting variety to plant and the landscape orientation to farm on during a season. Rainfall onset, seasonal rainfall amount and seasonal rainfall distribution were the most important information needed by farmers at every stage of the production cycle except for harvest and post-harvest management, where rainfall cessation and temperature were perceived to be crucial needs (Table 3 ). The findings concur with that of Roncoli et al. (2009) that farmers in West African base critical farming decisions—such as when to plough, when to plant, when to irrigate etc., —on advanced information regarding rainfall onset and cumulative rainfall. Hansen et al. ( 2019 ) further support the significance of information on the cumulated seasonal rainfall during the rainy season for farm decisions, given the information. by helping assist farmers in assessing whether the season will experience a dry spell, a wet period or the normal seasonal conditions. According to Lechthaler and Vinogradova ( 2017 ), knowledge of weather and climate services at the harvesting stage of production is relevant for the preservation of harvested crops contributing to the economic well-being of farmers. It enables farmers to enhance the value of their produce and maximize their returns on investment. Barriers to the usage of climate information services Several political, socioeconomic, and cultural barriers hinder the effective adoption and utilisation of weather and climate information. About 42.0% of the respondents expressed distrust in the reliability of the provided information, deeming it unrealistic and unsuitable for decision-making. This scepticism stems from farmers' negative experiences, with 13.4% reporting failures in its practical application (see Table 4 ). Antwi-Agyei et al. (2021) noted that farmers find the information from weather and climate service institutions insufficient for seasonal planning. Studies by Kabobah et al. ( 2018 ) also reported that farmers to early warning signs exhibit minimal usage because of mistrust over the years due to previously failed projections. Forecasts over the years consider the regional and global conditions rather than the localised conditions experienced by farmers contributes to this scepticism. Any forecast that does not capture information about farmer's local environment is perceived as inaccurate, further fostering doubt in the credibility of such information. Table 3 Most important hydroclimatic need, as ranked by farmers at every stage of production Decisions Most important hydroclimatic information need percentages (%) 1 2 3 1 2 3 Seed type and variety RO SRA SRD 52.1 22.7 16.5 Land size and allocation SRA RO SRD 37.4 31.9 18.1 Land preparation RO SRA SRD 78.5 9.7 5.2 Nursery RO SRA SRD 45.3 27.4 10.5 Transplanting RO SRA SRD 51.6 20.0 14.7 Direct seeding RO SRA SRD 63.8 13.4 11.8 Supplementary irrigation N/A SRD SRA 91.0 6.0 2.5 Amount of water for irrigation N/A SRD SRA 91.0 6.0 2.5 Fertilizer application SRA RO SRD 37.1 23.2 22.0 Time of fertilizer app. RO SRA SRD 49.5 17.8 15.2 Weed control method SRA SRD RO 29.8 22.5 21.5 Time for weed control RO SRA SRD 34.0 19.6 17.8 Pesticide application method SRA SRD RO 38.2 18.8 15.7 Time of pesticide app. RO SRA N/A 40.8 19.9 12.3 Harvesting time RC N/A SRA 57.6 21.2 6.8 Harvesting method N/A RC TMP 39.0 37.2 8.6 Post-harvest TMP RC OTH 57.9 11.0 3.7 Selling N/A RC TMP 47.6 20.9 12.3 NB: SRA = seasonal rainfall amount; SRD = Seasonal rainfall distribution; RO = Rainfall onset; RC = Rainfall cessation; N/A = Not applicable; TMP = Temperature; OTH = Others Given the level of inaccuracies with climate information, farmers have several indigenous means of determining the rainfall onset, such as croaks of frogs, tree growth characteristics, and seasonal activities of insects and animals. During the stakeholder discussions mention that they use certain ecological indicators to forecast some of the important climate information such as rainfall onset. For instance, a farmer from Assin Dompem in the Central Region of Ghana said, "I use indicators such as the emergence of certain insect species to determine whether it is going to rain or not. And to also know if the season is going to produce more rain or not". Table 4 Possible barriers that prevent the usage of climate information services Barriers Respective (%) Complex for me to understand 2.1 Not realistic 42.0 I don't perceive it/ I don't care 10.4 Bad experiences in the past 13.4 I don't know about them 0.2 The way I do it works for me 2.1 I don't have access to this information 11.7 Others 18 Performance of ECMWF-S5 model in forecasting seasonal rainfall at different lead times in the Pra River Basin. Figure 3 and Table 5 depict the ECMWF-S5's skill in predicting rainfall events over the Pra River Basin spanning the period from 1993 to 2015. S51, S52, and S53 correspond to 1-, 2-, and 3-month lead times, respectively, of the ECMWF-S5, compared against observed rainfall events from GPCC data. The findings suggest that the ECMWF-S5 model exhibits the capability to predict seasonal rainfall anomalies within the study area. Nevertheless, the accuracy of the model declines with an increase in lead time. For instance, at 1-month lead time, the correlation coefficient was within the range of 0.7–0.9, signifying a strong positive correlation between the model and the observed GPCC records. However, the correlation coefficient for the 2-month and 3-month lead times ranged from 0.35–0.5 and − 0.01–0.07 respectively (Fig. 3 ), indicating a very weak negative correlation between the model outcome and the observed forecast for 3-month lead time. RMSE increased with increasing lead time. The RMSE at 1-month lead time was 40–55 mm, while that of 2-month and 3-month lead times were within the ranges of 68–76 mm and 90–105 mm, respectively (Table 5 ). The smaller the RMSE, the more accurate the forecast was compared to an observed event and vice versa. Only positive biases were recorded across all stations at the various lead times. This indicates an overestimation across the model for all lead times in the study area. Higher KGE values were recorded for the model at a 1-month lead time (0.66–0.75). KGE for 2-month and 3-month lead times were lower falling between 0.3–0.4 and − 0.05–0.06, respectively. Table 5 Performance evaluation of the ECMWF-S5 at the seven meteorological stations RMSE Bias KGE values Stations S51 S52 S53 S51 S52 S53 S51 S52 S53 Atieku 43.61 75.82 101.07 0.9 0.95 0.89 0.73 0.4 -0.03 Dunkwa 45.76 75.73 99.56 0.98 1.03 0.97 0.73 0.41 0 Kibi 40.31 68.85 92.17 1.16 1.22 1.13 0.77 0.43 0.06 Twifo-Praso 43.61 75.82 101.07 0.9 0.95 0.89 0.73 0.4 -0.03 Akim-Oda 46.97 75.53 96.76 1.05 1.1 1.03 0.75 0.36 -0.01 Konongo 46.13 69.68 93.17 1.07 1.12 1.05 0.73 0.44 0.04 Kumasi 52.84 73.29 95.79 1.06 1.12 1.05 0.66 0.41 0.03 Results showed that ECMWF-S5 prediction skills differed by station and at different lead times. This demonstrates that the accuracy of the ECMWF-S5 forecasts varies throughout the study area. According to Nyadzi et al. ( 2019 ), providing forecast information at lead times beyond 1-month has proven problematic and useless in the worst-case scenario. Studies by Atiah et al. ( 2023 ) report that discrepancies that happen due to increasing lead times could be attributed to different temporal resolutions and elevation changes. Studies by Nyadzi et al. ( 2019 ) again reported that receiving the information one month before the season begins is likely to influence farmers' decision-making in Northern Ghana. Farmers, according to Nyamekye et al. ( 2021 ), are unlikely to employ forecasts at a 3-month lead time owing to fluctuations that may develop, and they would also be unable to do anything due to the 3-month window of opportunity. It implies that farmers in the Pra River Basin could rely on the ECMWF-S5 forecast at a 1-month lead time to plan their activities. CONCLUSION This study sought to assess the potential of climate information services in influencing farmers' decisions in the Pra River basin. Farmers are aware of and well-informed about the changing climate and weather patterns over time. Farmers receive climate information to some extent, which serves as a tool for adapting to changes in climate and weather conditions. Farmers, on the other hand, refuse to use the information they receive because they believe it is flawed and difficult to apply in their daily activities. Farmers' information does not capture the conditions of their local environment; thus, using such information can harm their economic activities. The most important hydroclimatic needs of farmers were rainfall onset, rainfall amount, and cessation. They regard these requirements as critical to plan their seasonal and even day-to-day activities. These phenomena can affect/influence planting dates, seed type and variety, land type to choose when to prepare the land for planting, and when to harvest during the production period. ECMWF-S5 model has the potential to forecast rainfall over the study area, however, the forecast accuracy declines with increasing lead times. Therefore, a 1-month lead time was suitable to meet farmers' demands for climate information (rainfall amount and possible estimated onset) in the Pra River Basin and can be used during their decision-making process. The study recommends that future studies compare the potential of other forecast models at different spatial resolutions. Also, the accuracy of estimating rainfall onset forecast models should be studied. The study is also recommended to be replicated in other regions to assess the model's effectiveness across diverse regions. LIMITATIONS As a limitation, the study only focused on the stated study areas based on the researcher's prior knowledge of the readiness and accessibility of the farmers within the area. Therefore, it can not be used as a tool for generalization to other areas. Also, as a limitation the study only covered very key information areas and environments because of the limited resources available to the researcher. Note A major caveat of the paper is that seasonal forecasts are best presented probabilistically, and therefore should be verified as such. Here, only skill estimates of deterministic forecasts are presented. Declarations ACKNOWLEDGEMENT This research was extracted from an MPhil thesis submitted to Kwame Nkrumah University of Science and Technology, Ghana. We acknowledge the farmers, Directors of the Department of Agriculture and Extension Officers from the participating districts involved in this study. Moreover, we thank the Ghana Meteorological Agency for participation and appreciate ECMWF-S5 and GPCC data providers for making it accessible online. FUNDING DECLARATION The study was funded by Carnegie Corporation, New York of the Future Africa Research Leadership Fellowship at the University of Pretoria and the International Foundation for Science, Stockholm, Sweden, through a grant [W_6201-2] to Enoch Bessah. DATA AVAILABILITY STATEMENT The dataset generated during and/or analysed during the current study is available from the corresponding author upon reasonable request. ETHICAL STATEMENT The current protocol used for the study was approved by Kwame Nkrumah University of Science and Technology’s Committee on Humanities and Social Sciences Research and Ethics committee in accordance with the Ghana’s Data Protection Act (Act 843) and the Declaration of Helsinki. References Ahmed K, Shahid S, Wang X, Nawaz N, Khan N. 2019. Evaluation of gridded precipitation datasets over arid regions of Pakistan. Water, 11(2), p.210. Alie MEK, Yateh M, Bavumiragira JP, Liao Z. Identifying challenging barriers to farmers' adaptation to climate change in Bo district, Sierra Leone: A review. J Water Clim Change. 2024;15(7):2992–3014. Amekudzi LK, Osei MA, Atiah WA, Aryee JN, Ahiataku MA, Quansah E, Preko K, Danuor SK, Fink AH. 2016. Validation of TRMM and FEWS satellite rainfall estimates with rain gauge measurement over Ashanti Region, Ghana. Atmospheric and Climate Sciences, 6(04), p.500. Antwi-Agyei P, Stringer LC. Improving the effectiveness of agricultural extension services in supporting farmers to adapt to climate change: Insights from northeastern Ghana. Clim Risk Manage. 2021;32. https://doi.org/10.1016/j.crm.2021.100304 . Atiah WA, Tsidu GM, Amekudzi LK. Investigating the merits of gauge and satellite rainfall data at local scales in Ghana, West Africa. Weather Clim Extremes. 2020;30. https://doi.org/10.1016/j.wace.2020.100292 . Atiah WA, Bendito EG, Muthoni FK. 2023. Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa. Baffour-Ata F, Antwi-Agyei P, Nkiaka E, Dougill AJ, Anning AK, Kwakye SO. 2022. Climate information services available to farming households in Northern Region, Ghana. Weather, Climate, and Society, 14(2), pp.467–480. Bessah E, Donkor E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO, Strapasson A. 2021a. Determinants of maize farmers’ access to climate information services in Ghana, In: Handbook of Climate Change Management. Edited by Leal Filho W., Luetz J., Ayal D., Springer, Cham. Pages 4173–4192. Bessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO. The impact of varying spatial resolution of climate models on future rainfall simulations in the Pra River Basin (Ghana). J Water Clim Change. 2020a;11(4):1263–83. Bessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO. Variable resolution modeling of near future mean temperature changes in the dry sub-humid region of Ghana. Model Earth Syst Environ. 2018;4:919–33. Bessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO, Strapasson A. Hydrological responses to climate and land use changes: The paradox of regional and local climate effect in the Pra River Basin of Ghana. J Hydrology: Reg Stud. 2020b;27:100654. Bessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO, Strapasson A, Donkor E. Gender-based variations in the perception of climate change impact, vulnerability and adaptation strategies in the Pra River Basin. Int J Clim Change Strateg Manag. 2021b;13(4/5):435–62. Gerlak AK, Guido Z, Vaughan C, Rountree V, Greene C, Liverman D, Trotman AR, Mahon R, Cox SA, Mason SJ, Jacobs KL. Building a framework for process-oriented evaluation of regional climate outlook forums. Weather Clim Soc. 2018;10(2):225–39. Gubler S, Sedlmeier K, Bhend J, Avalos G, Coelho CAS, Escajadillo Y, Jacques-Coper M, Martinez R, Schwierz C, De Skansi M, Spirig C. Assessment of ECMWF SEAS5 seasonal forecast performance over South America. Weather Forecast. 2020;35(2):561–84. Gudoshava M, Otieno G, Koech E, Misiani H, Ongoma JG, Heinrich-Mertsching C, Wachana C, Endris HS, Mwanthi A, Kilavi M, Mwangi E. 2024. Advances, gaps and way forward in provision of climate services over the Greater Horn of Africa. Frontiers in Climate , 6 , p.1307535. Guido Z, Lopus S, Waldman K, Hannah C, Zimmer A, Krell N, Knudson C, Estes L, Caylor K, Evans T. Perceived links between climate change and weather forecast accuracy: new barriers to tools for agricultural decision-making. Clim Change. 2021;168:1–20. Guye M, Legesse A, Mohammed Y. 2022. Indigenous weather forecasting among Gujii pastoralists in southern Ethiopia: Towards monitoring drought. Pastoralism, 12(1), p.43. Hansen JW, Vaughan C, Kagabo DM, Dinku T, Carr ER, Körner J, Zougmoré RB. Climate services can support african farmers' context-specific adaptation needs at scale. Front Sustainable Food Syst. 2019;3:21. IPCC., 2018. Global warming of 1.5° C. Summary for Policymakers. Contribution of Working Groups I, II and III to the 48th Session of the IPCC. Johnson SJ, Stockdale TN, Ferranti L, Balmaseda MA, Molteni F, Magnusson L, Tietsche S, Decremer D, Weisheimer A, Balsamo G, Keeley SP. SEAS5: the new ECMWF seasonal forecast system. Geosci Model Dev. 2019;12(3):1087–117. Kabobah L, Nukpezah D, Ntiamoa-Baidu Y. Adaptive capacity of farmers to climate change in the Kassena Nankana Municipality of Ghana: Implications for climate adaptation strategies. West Afr J Appl Ecol. 2018;26:14–26. Lakshminarasimhappa MC. Web-Based and smart mobile App for data collection: Kobo Toolbox / Kobo Collect. J Indian Libr Association (JILA). 2021;57(2):72–9. Lawson ET, Alare RS, Salifu ARZ, Thompson-Hall M. Dealing with climate change in semi-arid Ghana: understanding intersectional perceptions and adaptation strategies of women farmers. Volume 85. GeoJournal; 2020. pp. 439–52. Lechthaler F, Vinogradova A. The climate challenge for agriculture and the value of climate services: Application to coffee-farming in Peru. Eur Econ Rev. 2017;94:45–70. Naab FZ, Abubakari Z, Ahmed A. The role of climate services in agricultural productivity in Ghana: The perspectives of farmers and institutions. Clim Serv. 2019;13:24–32. Nashwan MS, Shahid S, Wang X. 2019. Uncertainty in estimated trends using gridded rainfall data: a case study of Bangladesh. Water, 11(2), p.349. Nutsukpo DK, Jalloh A, Zougmoré R, Nelson GC, Thomas TS. 2013. Ghana. In: West African Agriculture and Climate Change: A Comprehensive Analysis. Chapter 6. International Food Policy Research Institute, Washington, D.C., pp.141–172. Available from: http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/127450 Nyadzi E, Nyamekye AB, Werners SE, Biesbroek RG, Dewulf A, Van Slobbe E, Long HP, Termeer CJ, Ludwig F. Diagnosing the potential of hydro-climatic information services to support rice farming in northern Ghana. NJAS-Wageningen J Life Sci. 2018;86:51–63. Nyadzi E, Werners ES, Biesbroek R, Long PH, Franssen W, Ludwig F. 2019. Verification of seasonal climate forecast toward hydroclimatic information needs of rice farmers in Northern Ghana. Weather, Climate, and Society, 11(1), pp.127–142. Nyamekye AB, Nyadzi E, Dewulf A, Werners S, Van Slobbe E, Biesbroek RG, Termeer CJ, Ludwig F. 2021. Forecast probability, lead time and farmer decision-making in rice farming systems in Northern Ghana. Climate Risk Management, 31, p.100258. OECD-SWAC. An Atlas of the Sahara‐Sahel. Geography, Economics and Security; 2014. Osei MA, Amekudzi LK, Quansah E. 2021. Characterisation of wet and dry spells and associated atmospheric dynamics at the Pra River catchment of Ghana, West Africa. Journal of Hydrology: Regional Studies , 34 , p.100801. Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Church JA, Clarke L, Dahe Q, Dasgupta P, Dubash NK. 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (p. 151). Partey ST, Dakorah AD, Zougmoré RB, Ouédraogo M, Nyasimi M, Nikoi GK, Huyer S. Gender and climate risk management: evidence of climate information use in Ghana. Clim Change. 2020;158(1):61–75. https://doi.org/10.1007/s10584-018-2239-6 . Rehman A, Ma H, Ozturk I. Decoupling the climatic and carbon dioxide emission influence to maize crop production in Pakistan. Air Qual Atmos Health. 2020;13:695–707. Sarku R, Kranjac-Berisavljevic G, Tröger S. 2024. Just transformations in climate information services provision: Perspectives of farmers in southern Ghana. Climate and Development , pp.1–15. Schober P, Boer C, Schwarte LA. Correlation coefficients: appropriate use and interpretation. Anesth analgesia. 2018;126(5):1763–8. Shah H, Siderius C, Hellegers P. 2020. Cost and effectiveness of in-season strategies for coping with weather variability in Pakistan's agriculture. Agricultural Systems , 178 , p.102746. Sheffield J, Wood EF, Chaney N, Guan K, Sadri S, Yuan X, Olang L, Amani A, Ali A, Demuth S, Ogallo L. A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bull Am Meteorol Soc. 2014;95(6):861–82. van Huysen T, Hansen J, Tall A. Scaling up climate services for smallholder farmers: Learning from practice. Clim Risk Manage. 2018;22:1–3. Wange XJ. Evaluation of the performance of gridded precipitation products over Balochistan Province, Pakistan. Desalination Water Treat. 2017;1:14. Yegbemey RN, Egah J. 2021. Reaching out to smallholder farmers in developing countries with climate services: A literature review of current information delivery channels. Climate Services, 23, p.100253. Zongo B, Diarra A, Barbier B, Zorom M, Yacouba H, Dogot T. 2016. Farmers' perception and willingness to pay for climate information in Burkina Faso. Additional Declarations No competing interests reported. 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Attoh","email":"","orcid":"","institution":"International Water Management Institute","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"M.N.A.N.","lastName":"Attoh","suffix":""},{"id":447053653,"identity":"5267c83d-206f-4d51-8a4b-daec9ebc5032","order_by":3,"name":"Ishmeal Amoah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ishmeal","middleName":"","lastName":"Amoah","suffix":""},{"id":447053657,"identity":"3895e79c-cebe-4ce8-819e-67a3e1ddc022","order_by":4,"name":"Andrews Afrifa","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Andrews","middleName":"","lastName":"Afrifa","suffix":""},{"id":447053660,"identity":"3fed44c0-568b-466e-9311-d0fadd42c1ea","order_by":5,"name":"Willem A. 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Agodzo","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sampson","middleName":"K.","lastName":"Agodzo","suffix":""}],"badges":[],"createdAt":"2024-11-22 06:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5501950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5501950/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81506979,"identity":"a3ab38bd-9ad4-4a91-87e2-29cc25fc4fbf","added_by":"auto","created_at":"2025-04-28 05:33:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228274,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the study area and the various meteorological stations\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5501950/v1/16bcdd90f06c4ef18d47a0ca.jpg"},{"id":81506981,"identity":"5a1c0ffa-6f67-4b50-be24-f9df111890c8","added_by":"auto","created_at":"2025-04-28 05:33:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":324706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePreferred lead times of farmers\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5501950/v1/84cf3d6f021842fee71df932.jpg"},{"id":81506990,"identity":"d7fda8f8-b567-4314-acd8-4dedea78122f","added_by":"auto","created_at":"2025-04-28 05:33:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":368183,"visible":true,"origin":"","legend":"\u003cp\u003eA Taylor diagram showing the various statistics for rainfall measurement at seven (7) meteorological stations in the Pra River Basin for 1-month (S51), 2-month (S52), and 3-month (S53) lead times\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5501950/v1/c123b221fcf6aff8e02c3baa.jpg"},{"id":82115221,"identity":"d3a5a245-788e-48d5-8a72-f8047e6710bc","added_by":"auto","created_at":"2025-05-07 02:23:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1887797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5501950/v1/590e4189-1a47-4298-8879-9b22fe6e8b53.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate Information Services and Farm-Level Decision-Making in the Pra River Basin of Ghana","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eClimate change is a significant barrier to efforts to enhance the livelihoods of people living in rural areas of Sub-Saharan Africa and a significant contributor to food insecurity. Climate variability and change have reportedly garnered much attention in recent years because of the detrimental effects of changing weather conditions on human life (Rehman and Ozturk, 2020). Increased greenhouse gas emissions are to blame for the rise in global temperatures, which has altered the world's water cycle and precipitation patterns (Pachauri \u003cem\u003eet al.\u003c/em\u003e, 2014). The main long-term effects of climate change can be broadly categorized as follows: (i) a decrease in agricultural productivity and food security due to a change in rainfall patterns, (ii) a worsening of water security due to variations in rainfall patterns, and (iii) a decrease in fish resources due to a temperature rise (IPCC, 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNyadzi \u003cem\u003eet al.\u003c/em\u003e (2018) reported that due to fluctuating climatic circumstances, there have been difficulties in deciding the type of seed to plant, the best time to plant, and the best time for cultural practices on the field. Other studies have shown that farmers start planning as soon as they see signs of rainfall onset, and such delays could affect production (Nyadzi et al., 2019). The difficulty in predicting weather and seasonal climate significantly impacts the precision of farm-level decisions, particularly those requiring planning months before the coming season (Lawson \u003cem\u003eet al.\u003c/em\u003e, 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAgricultural climate information services involve the development, production, and location specificity of information and knowledge obtained from various climate science and meteorology research to assist in decision-making at all levels of agricultural production in society (Naab et al., 2019; Sarku et al., 2024). Researchers over the years have focused on the kind of weather and climate information that tends to inform agricultural decision-making, the quality of the existing information, and how the information available can further enrich the lives of its users (Sarku et al., 2024; Naab et al., 2019). However, farmers' ability to accept and adopt any adaptation practice is also dependent on education, age, farming experience, income, size of farm (acreage under cultivation), and access to information (Bessah \u003cem\u003eet al.,\u003c/em\u003e 2021a). As much as climate services are pivotal in agriculture, little or less has been achieved in many developing nations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to Shah et al. (2020), farmers may incur extra costs in re-sowing seeds due to the rains' delays, which slows crop germination and growth. The quest for seasonal weather forecast information focused on smallholder farmers surged after the El Nino 1997/98 event (Gerlak \u003cem\u003eet al.\u003c/em\u003e, 2018). As reported by Gudoshava et al.(2024), almost all national meteorological agencies across Africa provide information on seasonal climate conditions through monthly climate outlooks, agrometeorological bullets, and extreme weather indexes. Breakthroughs in the science community have made it possible to provide short-, medium-, and long-term climate services to assist in farmers' decision-making (Nyadzi \u003cem\u003eet al.\u003c/em\u003e, 2019; Johnson \u003cem\u003eet al.\u003c/em\u003e, 2019; Gubler \u003cem\u003eet al.\u003c/em\u003e, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, only farmers with larger farm sizes and a high level of climate change awareness are more likely to access climate information services (Bessah \u003cem\u003eet al.\u003c/em\u003e, 2021a). Smallholder farmers in rural areas continue to be sceptical of the forecasts provided by the agencies. According to a multi-stakeholder cross-sectoral assessment, climate information failed to contribute meaningfully to development across Africa due to market atrophy caused by a long-term interaction between ineffective demand by development stakeholders and an insufficient supply of actionable climate information (Hansen et al., 2019; Huysen et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study looks at the issue from a different lens by focusing on providing location-specific information and as such the concerns of the end users play a pivotal role in the dissemination and adoption of the adaptation strategy. \u0026nbsp;Therefore, this research aimed to assess\u0026nbsp;the potential of climate information services in influencing farmers' decisions and the performance of ECMWF seasonal forecasts in meeting farmer's needs in the Pra River basin. The specific objectives were:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo assess farmers' perception of the significance of climate information services in influencing their decisions.\u003c/li\u003e\n \u003cli\u003eTo assess the accuracy of the European Center for Medium-Range Weather Forecast- System 5 model to predict seasonal total rainfall at different lead times.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eResults of this research can support decision-makers in determining the optimal time to execute agricultural policy, in that, the result from this study will give policymakers the information to provide needed resources at the required times.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, gaining insight into the local conditions of the end users (farmers) is a way of bridging the gap that exists between the producers (researchers) and local farmers (users) concerning their limited ability to use existing climate information services.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e2.1 Study Area\u003c/p\u003e\n\u003cp\u003eThe Pra River basin (PRB) covers a land area of 23,331 km\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eand is situated within latitudes 5.00\u0026deg; N and 7.21\u0026deg; N and longitudes 2.30\u0026deg; W and 0.43\u0026deg; W of Ghana (Figure 1). The basin cuts across four administrative regions, namely Ashanti, Eastern, Western, and Central regions, respectively, with an annual average discharge of 4174 Mm\u003csup\u003e3\u003c/sup\u003e/year. It has four main tributaries: the Anum, Birim, Offin, and Oda rivers, which have their main supply from Kwahu-Mampong and flow southwards for about 240 km before joining the Gulf of Guinea (Osei et al., 2021). The Pra River basin is known mainly for its rich agricultural lands and mineral resources. It has several mining companies (both large scale and small scale) located in the area, providing natives of the area an alternative means of livelihood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Pra River Basin is the major producer of the most valued economic crop in Ghana (cocoa), the leading producer of cassava, which is the second food security crop in Ghana, and one of the top three areas in the country producing the first food security crop, that is, maize (Nutsukpo \u003cem\u003eet al.\u003c/em\u003e, 2013; MoFA, 2016). A shift from a bi-modal to a mono-modal pattern of rainfall commencing in late March and ending in November, according to previous studies in the basin, might lower rainfall by 1.77 % between 2020 and 2049 (Bessah \u003cem\u003eet al.\u003c/em\u003e, 2020a). In the same period, the average temperature has been predicted to rise by 1.51\u0026deg;C (Bessah \u003cem\u003eet al.\u003c/em\u003e, 2018). Surface water yields could be reduced by 2-16 mm due to the expected changes in rainfall and temperature, with flood and drought effects across the basin (Bessah \u003cem\u003eet al.\u003c/em\u003e, 2020b). Farmers perceive themselves to be vulnerable to higher temperatures and fluctuations in rainfall, according to Bessah \u003cem\u003eet al.\u003c/em\u003e (2021b), especially during the growing season, with an evident influence on their yield. Although most farmers are responding to climate change by changing planting dates, only approximately 30% have access to meteorological information, primarily obtained through radio broadcasts that have shown to be unreliable over time (Bessah \u003cem\u003eet al.\u003c/em\u003e, 2021b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Sources\u003c/p\u003e\n\u003cp\u003ePrimary data was obtained from 382 respondents using semi-structured questionnaires, three (3) stakeholder discussions in three districts, and an expert discussion at the Ghana Meteorological Agency (GMet). Rainfall hindcast data from 1993-2015 were obtained from the European Centre for Medium-Range Weather Forecast System 5 (ECMWF-S5) at one-, two- and three-month lead times. Also, Global Precipitation and Climatology Centre (GPCC) monthly rainfall data v2020 at 0.25\u0026deg; \u0026times; 0.25\u0026deg; resolution were obtained for the same period and used as the observed data. GPCC data was used because it is a gauge-based gridded precipitation estimate that reproduce observed precipitation (Ahmed \u003cem\u003eet al.\u003c/em\u003e, 2019; Nashwan \u003cem\u003eet al.\u003c/em\u003e, 2019; Wange, 2017). GPCC uses measurements obtained from rain gauges, weather stations, and other ground-based precipitation observations. These measurements are collected in real-time and provide direct information about the amount of precipitation at specific locations. Atiah \u003cem\u003eet al.\u003c/em\u003e (2020) have established that GPCC v7 and CHIRPS v2 are Ghana\u0026apos;s best reference gauge measurements. Data from GPCC and ECMWF were extracted in R software from seven (7) meteorological stations in the basin (Figure 1).\u003c/p\u003e\n\u003cp\u003eSample size and sampling techniques\u003c/p\u003e\n\u003cp\u003eThe study adopted the approach used by Bessah et al. (2021a, b). The sample size of 399 individuals using the Yamane simplified formula was calculated. Also, the selection was based on earlier research by Bessah \u003cem\u003eet al.\u003c/em\u003e (2021a, b) in the same study area. Assin Foso Municipality, Obuasi East and Atiwa West districts located in the Central, Ashanti, and Eastern Regions, respectively, were purposely selected to cover all regions in the basin and have a diverse representation of respondents. Five communities were purposely sampled, and 30 respondents were randomly selected from each community for the questionnaire administration. One community was selected from Assin North District and three from the Assin Foso to track activities since 2019 after the first study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData collection\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A semi-structured questionnaire was built into the Kobo App (Lakshminarasimhappa, 2021) to administer to sampled respondents. The questions focused on local farmers\u0026apos; understanding of their environment (climate conditions) and opinions of climate information services. Understanding their unique climatic needs and the relevance of climate information in making decisions such as the type of crop and species to select when cultivating the land preparation type, irrigation schedule (if any), fertilizer and chemical application time when needed, and harvest time were investigated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA total of 382 respondents participated in the administered questionnaire.\u0026nbsp;A total of 45 persons took part in the stakeholder discussion comprised of Eight (8), eleven (11), and thirteen (13) farmers from Assin Foso, Atiwa West, and Obuasi East districts, respectively, plus thirteen (13) Agric extension officers). Six (6) experts from the seasonal forecast, daily forecast, hydro-climatology, and instrumentation departments of GMet were engaged in scientific discussions on the findings from the field and information from the literature. Both stakeholder and expert discussions were recorded and transcribed for analysis.\u003c/p\u003e\n\u003cp\u003eData analysis\u003c/p\u003e\n\u003cp\u003eQuestionnaires were imported into the Statistical Package for Social Sciences (SPSS) version 29.0 for the descriptive and cross-tabulation analysis to establish the patterns and distribution between certain dependent and independent variables. Data gathered from the stakeholder and expert discussions were transcribed and analyzed based on themes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRainfall forecast data was analyzed with R-statistic. The performance of ECMWF was assessed using various forecast accuracy indices like Pearson correlation coefficient (r) because the model uses it to assess the linear relationship between the ensemble means and the observation (Gubler et al., 2020), Root mean square error (RMSE), bias, and Klint-Gupta efficiency (KGE) (Amekudzi \u003cem\u003eet al.,\u003c/em\u003e 2016; Gubler et al., 2020). Pearson correlation coefficient has a value between -1 and 1, where r = 0 means the variables are not associated or related linearly, while r = 1 means all fall precisely on the best-fit line with a positive slope (Schober et al., 2018). RMSE computes the average magnitude of the differences between predicted and actual values. If the RMSE is low, the model effectively forecasts the observed data, and vice versa. A positive bias value indicates overestimation, whiles a negative bias denotes underestimation. KGE is a statistical metric used in hydrological modelling to compare the performance of simulated or predicted values to observed values. According to Kling \u003cem\u003eet al.\u003c/em\u003e (2012), it is an improvement over traditional efficiency metrics such as Nash-Sutcliffe Efficiency (NSE) and correlation coefficient (r).\u003c/p\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Socio-demographic characteristics of respondents\u003c/h2\u003e \u003cp\u003eOut of the sample size of 382, the majority (66.5%) of the respondents were males and the dominant age group was 40\u0026ndash;59 years (47.6%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most farmers (51.6%) possessed either a middle school diploma or a JSS certificate (thus, about 12 schooling years). About 63.1% of the farmers engaged in rainfed agriculture, with 88.2% of this number having land sizes of more than a hectare and almost all cultivated maize and/or cassava (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). About 67% of the farmers have more than ten (10) years of experience. Farmers cultivate maize more than any other crop because of its versatility as a staple crop. Rice was the least cultivated due to its water requirement and the nature of agriculture practiced within the locality. Most farmers engaged in rainfed agriculture due to the size of their lands and the fragmented nature of their lands due to land litigations. The farmers also reported practicing rainfed agriculture due to financial constraints and the vast initial capital needed for irrigation equipment. Amongst the other crops cultivated by the farmers were cocoyam, taro, oil palm, cocoa, para rubber, ginger, and coconut. Each crop, especially cocoa, para rubber, and coconut, was either cultivated commercially by a small proportion of the farmers or individuals with keen interest in producing that. As reported by farmers, crops like cocoyam, taro and ginger were only planted in their backyards for sustenance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of the study area (N\u0026thinsp;=\u0026thinsp;382)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;382)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentages (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge groups(n\u0026thinsp;=\u0026thinsp;382)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEducation(n\u0026thinsp;=\u0026thinsp;382)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary/Primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school lever/JHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSS/O-Level/WASSCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFarming type (n\u0026thinsp;=\u0026thinsp;382)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRain-fed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrrigated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth irrigated and rainfed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFarming experience (years)(n\u0026thinsp;=\u0026thinsp;382)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFarm sizes (ha) (n\u0026thinsp;=\u0026thinsp;382)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.1-4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCultivated crops (n\u0026thinsp;=\u0026thinsp;382 for each crop)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCassava\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlantain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Perception of Farmers on Climate Information Services\u003c/h2\u003e \u003cp\u003eThe study sought to gather information on the perception of farmers on currently accessible climate forecast information. It examined their knowledge of climate information services and the relevance of accurate weather forecast on their livelihood activities. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that about 73% of the total respondents reported having some level of access to climate information. 36.9% believe that accurate weather forecast information increases their productivity and yield. Additionally, 46.4% express concern about considerable climate variability, viewing climate information as a potential solution to address climate change issues. They also recognise the value of reliable weather forecast information in guiding favourable farming decisions throughout the agricultural cycle. About 34.8% reported using forecast information to boost their yield, income, and profit margins (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During stakeholder discussions, farmers confirmed the relevance of incorporating climate information into their planning processes to help avoid waste of money, thereby increasing their seasonal yield. The recent changes in rainfall onset due to climate change have compelled many farmers to engage in multiple planting cycles within a season to secure sufficient rainfall for their production (Guido et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Unfortunately, this practice result in reduced profit margins and, at times, substantial financial losses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFarmers\u0026rsquo; perception of climate and weather services\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRelevance of accurate weather forecast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReason for using weather forecast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood seed usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eToo much climate variability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMy existing forecast methods are unreliable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppropriate water management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHope it improves crop yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSave money\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBetter water management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnough food for my family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOthers (specify)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (specify)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results obtained are in agreement with the findings of Lechthaler and Vinogradova (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who reported that climate information is a vital tool due to its ability to provide individuals with information and predictions that help reduce the risks associated with climate and weather-related issues and as such increases the efficiency of farmers\u0026rsquo; decisions. According to Zongo et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), farmers are aware of the risks associated with climate variability and change, and that about 93% of studied farmers in Sahelian and Sudano-Sahelian areas of Burkina Faso see climate information as a means of battling the adverse effects of climate change. Farmers' perception of crop failures resulting from poor rainfall distribution and frequency could potentially be averted using accurate climate information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Communicating weather and climate information at preferred Lead times\u003c/h2\u003e \u003cp\u003eThe results show that 52.4% of the respondents prefer to receive climate information 2 months before the start of the season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Alternatively, some farmers believe 1 month or 3 months ahead of the season is sufficient for adequate preparation and as such they prefer to receive the information within this range. For instance, during the stakeholder discussions, most farmers stated the importance of receiving information early, specifically within the range of 1 to 2 months before the beginning of the farming season, as an ideal timeframe for effective planning and execution of farm activities.\u003c/p\u003e \u003cp\u003eHowever, climate and weather forecasting experts assert that providing information 1-, 2- or 3-month lead times is not practical. However, experts from the climate and weather forecast field perceive 1, 2, or 3 months are not feasible for providing the information. According to the GMet expert \u003cem\u003e\"The farther ahead you forecast, the greater the margin of error and the more inaccurate the information becomes. And the closer you are to the season, the more accurate and reliable your information\".\u003c/em\u003e This underscores the dependency of the usability of climate information on the producers' capacity to deliver timely data to farmers. Experts suggest that the optimum time for providing weather or climate information is the last day of February, even though the official start of the season is perceived to be in March, Nevertheless, this can only be achieved after a recorded rainfall amount of 20mm or more has occurred for at least10 days within a month according to experts from the Ghana meteorological agency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAbout 30% of the respondents perceive the radio as the most accessible communication medium due to the minimum financial investment required to acquire a radio set. Agric Extension Officers (20.6%) are also preferred by 20.6% to disseminate climate information with the added benefit of interpreting the provided information. Other sources include mobile phones (17.5%), Television sets (10.2%), and leaders of farmer-based organizations (3.5%). These results align with the findings of Baffour-Ata et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who reported that farmers prefer radios and television sets for weather and climate information due to their easy accessibility, extensive coverage, and low maintenance costs. Studies by Baffour-Ata et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also support these claims, emphasizing the regular delivery of weather forecasts in news segments by many radio and television stations. However, Yegbemey and Egah (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) foresee a promising future in disseminating information through mobile phones to smallholder farmers (Baffour-Ata et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Partey et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePeculiar hydroclimatic information needs and their potential influence on Farm Decisions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe majority of farmers (31.6%) identified rainfall onset as the most needed hydroclimatic information, followed by seasonal rainfall amount (18.7%) and rainfall cessation (10.9%). Others include seasonal rainfall distribution (8.9%), temperature (7.2%) and wind speed (0.8%), while some farmers did not express specific preferences. Farmers believe that having information about the start of the rainfall is essential for effective planning, enabling them to organize their activities to coincide with major part of the rainy season.\u003c/p\u003e \u003cp\u003eAt each stage of the production cycle, farmers recognize the need for specific information to optimize the effective and efficient use of resources. For instance, during the initial stages of production (pre-planting, land preparation stage, nursery), rainfall onset was a crucial need for farmers because they help them prepare well to meet the rains (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Information on rainfall onset also aids in selecting variety to plant and the landscape orientation to farm on during a season. Rainfall onset, seasonal rainfall amount and seasonal rainfall distribution were the most important information needed by farmers at every stage of the production cycle except for harvest and post-harvest management, where rainfall cessation and temperature were perceived to be crucial needs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The findings concur with that of Roncoli \u003cem\u003eet al.\u003c/em\u003e (2009) that farmers in West African base critical farming decisions\u0026mdash;such as when to plough, when to plant, when to irrigate etc., \u0026mdash;on advanced information regarding rainfall onset and cumulative rainfall. Hansen et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) further support the significance of information on the cumulated seasonal rainfall during the rainy season for farm decisions, given the information. by helping assist farmers in assessing whether the season will experience a dry spell, a wet period or the normal seasonal conditions. According to Lechthaler and Vinogradova (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), knowledge of weather and climate services at the harvesting stage of production is relevant for the preservation of harvested crops contributing to the economic well-being of farmers. It enables farmers to enhance the value of their produce and maximize their returns on investment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBarriers to the usage of climate information services\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral political, socioeconomic, and cultural barriers hinder the effective adoption and utilisation of weather and climate information. About 42.0% of the respondents expressed distrust in the reliability of the provided information, deeming it unrealistic and unsuitable for decision-making. This scepticism stems from farmers' negative experiences, with 13.4% reporting failures in its practical application (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Antwi-Agyei \u003cem\u003eet al.\u003c/em\u003e (2021) noted that farmers find the information from weather and climate service institutions insufficient for seasonal planning. Studies by Kabobah et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also reported that farmers to early warning signs exhibit minimal usage because of mistrust over the years due to previously failed projections. Forecasts over the years consider the regional and global conditions rather than the localised conditions experienced by farmers contributes to this scepticism. Any forecast that does not capture information about farmer's local environment is perceived as inaccurate, further fostering doubt in the credibility of such information.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMost important hydroclimatic need, as ranked by farmers at every stage of production\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDecisions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMost important hydroclimatic information need\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003epercentages (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed type and variety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand size and allocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand preparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNursery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransplanting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect seeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupplementary irrigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmount of water for irrigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizer application\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of fertilizer app.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeed control method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime for weed control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide application method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of pesticide app.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarvesting time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarvesting method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-harvest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNB: SRA\u0026thinsp;=\u0026thinsp;seasonal rainfall amount; SRD\u0026thinsp;=\u0026thinsp;Seasonal rainfall distribution; RO\u0026thinsp;=\u0026thinsp;Rainfall onset; RC\u0026thinsp;=\u0026thinsp;Rainfall cessation; N/A\u0026thinsp;=\u0026thinsp;Not applicable; TMP\u0026thinsp;=\u0026thinsp;Temperature; OTH\u0026thinsp;=\u0026thinsp;Others\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGiven the level of inaccuracies with climate information, farmers have several indigenous means of determining the rainfall onset, such as croaks of frogs, tree growth characteristics, and seasonal activities of insects and animals.\u003c/p\u003e \u003cp\u003eDuring the stakeholder discussions mention that they use certain ecological indicators to forecast some of the important climate information such as rainfall onset. For instance, a farmer from Assin Dompem in the Central Region of Ghana said, \u003cem\u003e\"I use indicators such as the emergence of certain insect species to determine whether it is going to rain or not. And to also know if the season is going to produce more rain or not\".\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePossible barriers that prevent the usage of climate information services\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarriers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespective (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex for me to understand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot realistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don't perceive it/ I don't care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad experiences in the past\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don't know about them\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe way I do it works for me\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI don't have access to this information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePerformance of ECMWF-S5 model in forecasting seasonal rainfall at different lead times in the Pra River Basin.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depict the ECMWF-S5's skill in predicting rainfall events over the Pra River Basin spanning the period from 1993 to 2015. S51, S52, and S53 correspond to 1-, 2-, and 3-month lead times, respectively, of the ECMWF-S5, compared against observed rainfall events from GPCC data. The findings suggest that the ECMWF-S5 model exhibits the capability to predict seasonal rainfall anomalies within the study area. Nevertheless, the accuracy of the model declines with an increase in lead time. For instance, at 1-month lead time, the correlation coefficient was within the range of 0.7\u0026ndash;0.9, signifying a strong positive correlation between the model and the observed GPCC records. However, the correlation coefficient for the 2-month and 3-month lead times ranged from 0.35\u0026ndash;0.5 and \u0026minus;\u0026thinsp;0.01\u0026ndash;0.07 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating a very weak negative correlation between the model outcome and the observed forecast for 3-month lead time. RMSE increased with increasing lead time. The RMSE at 1-month lead time was 40\u0026ndash;55 mm, while that of 2-month and 3-month lead times were within the ranges of 68\u0026ndash;76 mm and 90\u0026ndash;105 mm, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The smaller the RMSE, the more accurate the forecast was compared to an observed event and vice versa. Only positive biases were recorded across all stations at the various lead times. This indicates an overestimation across the model for all lead times in the study area. Higher KGE values were recorded for the model at a 1-month lead time (0.66\u0026ndash;0.75). KGE for 2-month and 3-month lead times were lower falling between 0.3\u0026ndash;0.4 and \u0026minus;\u0026thinsp;0.05\u0026ndash;0.06, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance evaluation of the ECMWF-S5 at the seven meteorological stations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBias\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eKGE values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eS51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eS52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eS53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eS51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eS52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eS53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eS51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eS52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eS53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtieku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDunkwa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKibi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwifo-Praso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkim-Oda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKonongo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKumasi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults showed that ECMWF-S5 prediction skills differed by station and at different lead times. This demonstrates that the accuracy of the ECMWF-S5 forecasts varies throughout the study area. According to Nyadzi et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), providing forecast information at lead times beyond 1-month has proven problematic and useless in the worst-case scenario. Studies by Atiah et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) report that discrepancies that happen due to increasing lead times could be attributed to different temporal resolutions and elevation changes. Studies by Nyadzi et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) again reported that receiving the information one month before the season begins is likely to influence farmers' decision-making in Northern Ghana. Farmers, according to Nyamekye et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), are unlikely to employ forecasts at a 3-month lead time owing to fluctuations that may develop, and they would also be unable to do anything due to the 3-month window of opportunity. It implies that farmers in the Pra River Basin could rely on the ECMWF-S5 forecast at a 1-month lead time to plan their activities.\u003c/p\u003e\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study sought to assess the potential of climate information services in influencing farmers' decisions in the Pra River basin. Farmers are aware of and well-informed about the changing climate and weather patterns over time. Farmers receive climate information to some extent, which serves as a tool for adapting to changes in climate and weather conditions. Farmers, on the other hand, refuse to use the information they receive because they believe it is flawed and difficult to apply in their daily activities. Farmers' information does not capture the conditions of their local environment; thus, using such information can harm their economic activities. The most important hydroclimatic needs of farmers were rainfall onset, rainfall amount, and cessation. They regard these requirements as critical to plan their seasonal and even day-to-day activities. These phenomena can affect/influence planting dates, seed type and variety, land type to choose when to prepare the land for planting, and when to harvest during the production period.\u003c/p\u003e \u003cp\u003eECMWF-S5 model has the potential to forecast rainfall over the study area, however, the forecast accuracy declines with increasing lead times. Therefore, a 1-month lead time was suitable to meet farmers' demands for climate information (rainfall amount and possible estimated onset) in the Pra River Basin and can be used during their decision-making process. The study recommends that future studies compare the potential of other forecast models at different spatial resolutions. Also, the accuracy of estimating rainfall onset forecast models should be studied. The study is also recommended to be replicated in other regions to assess the model's effectiveness across diverse regions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLIMITATIONS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs a limitation, the study only focused on the stated study areas based on the researcher's prior knowledge of the readiness and accessibility of the farmers within the area. Therefore, it can not be used as a tool for generalization to other areas. Also, as a limitation the study only covered very key information areas and environments because of the limited resources available to the researcher.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eA major caveat of the paper is that seasonal forecasts are best presented probabilistically, and therefore should be verified as such. Here, only skill estimates of deterministic forecasts are presented.\u003c/p\u003e \u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eACKNOWLEDGEMENT\u003c/p\u003e\n\u003cp\u003eThis research was extracted from an MPhil thesis submitted to Kwame Nkrumah University of Science and Technology, Ghana. We acknowledge the farmers, Directors of the Department of Agriculture and Extension Officers from the participating districts involved in this study. Moreover, we thank the Ghana Meteorological Agency for participation and appreciate ECMWF-S5 and GPCC data providers for making it accessible online.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by Carnegie Corporation, New York of the Future Africa Research Leadership Fellowship at the University of Pretoria and the International Foundation for Science, Stockholm, Sweden, through a grant [W_6201-2] to Enoch Bessah.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset generated during and/or analysed during the current study is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current protocol used for the study was approved by Kwame Nkrumah University of Science and Technology’s Committee on Humanities and Social Sciences Research and Ethics committee in accordance with the Ghana’s Data Protection Act (Act 843) and the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed K, Shahid S, Wang X, Nawaz N, Khan N. 2019. Evaluation of gridded precipitation datasets over arid regions of Pakistan. Water, 11(2), p.210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlie MEK, Yateh M, Bavumiragira JP, Liao Z. Identifying challenging barriers to farmers' adaptation to climate change in Bo district, Sierra Leone: A review. J Water Clim Change. 2024;15(7):2992\u0026ndash;3014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmekudzi LK, Osei MA, Atiah WA, Aryee JN, Ahiataku MA, Quansah E, Preko K, Danuor SK, Fink AH. 2016. Validation of TRMM and FEWS satellite rainfall estimates with rain gauge measurement over Ashanti Region, Ghana. Atmospheric and Climate Sciences, 6(04), p.500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntwi-Agyei P, Stringer LC. Improving the effectiveness of agricultural extension services in supporting farmers to adapt to climate change: Insights from northeastern Ghana. Clim Risk Manage. 2021;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crm.2021.100304\u003c/span\u003e\u003cspan address=\"10.1016/j.crm.2021.100304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtiah WA, Tsidu GM, Amekudzi LK. Investigating the merits of gauge and satellite rainfall data at local scales in Ghana, West Africa. Weather Clim Extremes. 2020;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.wace.2020.100292\u003c/span\u003e\u003cspan address=\"10.1016/j.wace.2020.100292\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtiah WA, Bendito EG, Muthoni FK. 2023. Evaluating Seasonal Rainfall Forecast Gridded Models over Sub-Saharan Africa.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaffour-Ata F, Antwi-Agyei P, Nkiaka E, Dougill AJ, Anning AK, Kwakye SO. 2022. Climate information services available to farming households in Northern Region, Ghana. Weather, Climate, and Society, 14(2), pp.467\u0026ndash;480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessah E, Donkor E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO, Strapasson A. 2021a. Determinants of maize farmers\u0026rsquo; access to climate information services in Ghana, In: Handbook of Climate Change Management. Edited by Leal Filho W., Luetz J., Ayal D., Springer, Cham. Pages 4173\u0026ndash;4192.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO. The impact of varying spatial resolution of climate models on future rainfall simulations in the Pra River Basin (Ghana). J Water Clim Change. 2020a;11(4):1263\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO. Variable resolution modeling of near future mean temperature changes in the dry sub-humid region of Ghana. Model Earth Syst Environ. 2018;4:919\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO, Strapasson A. Hydrological responses to climate and land use changes: The paradox of regional and local climate effect in the Pra River Basin of Ghana. J Hydrology: Reg Stud. 2020b;27:100654.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO, Strapasson A, Donkor E. Gender-based variations in the perception of climate change impact, vulnerability and adaptation strategies in the Pra River Basin. Int J Clim Change Strateg Manag. 2021b;13(4/5):435\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerlak AK, Guido Z, Vaughan C, Rountree V, Greene C, Liverman D, Trotman AR, Mahon R, Cox SA, Mason SJ, Jacobs KL. Building a framework for process-oriented evaluation of regional climate outlook forums. Weather Clim Soc. 2018;10(2):225\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGubler S, Sedlmeier K, Bhend J, Avalos G, Coelho CAS, Escajadillo Y, Jacques-Coper M, Martinez R, Schwierz C, De Skansi M, Spirig C. Assessment of ECMWF SEAS5 seasonal forecast performance over South America. Weather Forecast. 2020;35(2):561\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGudoshava M, Otieno G, Koech E, Misiani H, Ongoma JG, Heinrich-Mertsching C, Wachana C, Endris HS, Mwanthi A, Kilavi M, Mwangi E. 2024. Advances, gaps and way forward in provision of climate services over the Greater Horn of Africa. \u003cem\u003eFrontiers in Climate\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, p.1307535.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuido Z, Lopus S, Waldman K, Hannah C, Zimmer A, Krell N, Knudson C, Estes L, Caylor K, Evans T. Perceived links between climate change and weather forecast accuracy: new barriers to tools for agricultural decision-making. Clim Change. 2021;168:1\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuye M, Legesse A, Mohammed Y. 2022. Indigenous weather forecasting among Gujii pastoralists in southern Ethiopia: Towards monitoring drought. Pastoralism, 12(1), p.43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen JW, Vaughan C, Kagabo DM, Dinku T, Carr ER, K\u0026ouml;rner J, Zougmor\u0026eacute; RB. Climate services can support african farmers' context-specific adaptation needs at scale. Front Sustainable Food Syst. 2019;3:21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC., 2018. Global warming of 1.5\u0026deg; C. Summary for Policymakers. Contribution of Working Groups I, II and III to the 48th Session of the IPCC.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson SJ, Stockdale TN, Ferranti L, Balmaseda MA, Molteni F, Magnusson L, Tietsche S, Decremer D, Weisheimer A, Balsamo G, Keeley SP. SEAS5: the new ECMWF seasonal forecast system. Geosci Model Dev. 2019;12(3):1087\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKabobah L, Nukpezah D, Ntiamoa-Baidu Y. Adaptive capacity of farmers to climate change in the Kassena Nankana Municipality of Ghana: Implications for climate adaptation strategies. West Afr J Appl Ecol. 2018;26:14\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLakshminarasimhappa MC. Web-Based and smart mobile App for data collection: Kobo Toolbox / Kobo Collect. J Indian Libr Association (JILA). 2021;57(2):72\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawson ET, Alare RS, Salifu ARZ, Thompson-Hall M. Dealing with climate change in semi-arid Ghana: understanding intersectional perceptions and adaptation strategies of women farmers. Volume 85. GeoJournal; 2020. pp. 439\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLechthaler F, Vinogradova A. The climate challenge for agriculture and the value of climate services: Application to coffee-farming in Peru. Eur Econ Rev. 2017;94:45\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaab FZ, Abubakari Z, Ahmed A. The role of climate services in agricultural productivity in Ghana: The perspectives of farmers and institutions. Clim Serv. 2019;13:24\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNashwan MS, Shahid S, Wang X. 2019. Uncertainty in estimated trends using gridded rainfall data: a case study of Bangladesh. Water, 11(2), p.349.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNutsukpo DK, Jalloh A, Zougmor\u0026eacute; R, Nelson GC, Thomas TS. 2013. Ghana. In: West African Agriculture and Climate Change: A Comprehensive Analysis. Chapter 6. International Food Policy Research Institute, Washington, D.C., pp.141\u0026ndash;172. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/127450\u003c/span\u003e\u003cspan address=\"http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/127450\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyadzi E, Nyamekye AB, Werners SE, Biesbroek RG, Dewulf A, Van Slobbe E, Long HP, Termeer CJ, Ludwig F. Diagnosing the potential of hydro-climatic information services to support rice farming in northern Ghana. NJAS-Wageningen J Life Sci. 2018;86:51\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyadzi E, Werners ES, Biesbroek R, Long PH, Franssen W, Ludwig F. 2019. Verification of seasonal climate forecast toward hydroclimatic information needs of rice farmers in Northern Ghana. Weather, Climate, and Society, 11(1), pp.127\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyamekye AB, Nyadzi E, Dewulf A, Werners S, Van Slobbe E, Biesbroek RG, Termeer CJ, Ludwig F. 2021. Forecast probability, lead time and farmer decision-making in rice farming systems in Northern Ghana. Climate Risk Management, 31, p.100258.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD-SWAC. An Atlas of the Sahara‐Sahel. Geography, Economics and Security; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsei MA, Amekudzi LK, Quansah E. 2021. Characterisation of wet and dry spells and associated atmospheric dynamics at the Pra River catchment of Ghana, West Africa. \u003cem\u003eJournal of Hydrology: Regional Studies\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e, p.100801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, Church JA, Clarke L, Dahe Q, Dasgupta P, Dubash NK. 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (p. 151).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartey ST, Dakorah AD, Zougmor\u0026eacute; RB, Ou\u0026eacute;draogo M, Nyasimi M, Nikoi GK, Huyer S. Gender and climate risk management: evidence of climate information use in Ghana. Clim Change. 2020;158(1):61\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10584-018-2239-6\u003c/span\u003e\u003cspan address=\"10.1007/s10584-018-2239-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehman A, Ma H, Ozturk I. Decoupling the climatic and carbon dioxide emission influence to maize crop production in Pakistan. Air Qual Atmos Health. 2020;13:695\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarku R, Kranjac-Berisavljevic G, Tr\u0026ouml;ger S. 2024. Just transformations in climate information services provision: Perspectives of farmers in southern Ghana. \u003cem\u003eClimate and Development\u003c/em\u003e, pp.1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchober P, Boer C, Schwarte LA. Correlation coefficients: appropriate use and interpretation. Anesth analgesia. 2018;126(5):1763\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah H, Siderius C, Hellegers P. 2020. Cost and effectiveness of in-season strategies for coping with weather variability in Pakistan's agriculture. \u003cem\u003eAgricultural Systems\u003c/em\u003e, \u003cem\u003e178\u003c/em\u003e, p.102746.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheffield J, Wood EF, Chaney N, Guan K, Sadri S, Yuan X, Olang L, Amani A, Ali A, Demuth S, Ogallo L. A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bull Am Meteorol Soc. 2014;95(6):861\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Huysen T, Hansen J, Tall A. Scaling up climate services for smallholder farmers: Learning from practice. Clim Risk Manage. 2018;22:1\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWange XJ. Evaluation of the performance of gridded precipitation products over Balochistan Province, Pakistan. Desalination Water Treat. 2017;1:14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYegbemey RN, Egah J. 2021. Reaching out to smallholder farmers in developing countries with climate services: A literature review of current information delivery channels. Climate Services, 23, p.100253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZongo B, Diarra A, Barbier B, Zorom M, Yacouba H, Dogot T. 2016. Farmers' perception and willingness to pay for climate information in Burkina Faso.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate Change, Climate information services, hydroclimatic needs, lead time, rainfall forecast, ECMWF-S5 model, forecast accuracy","lastPublishedDoi":"10.21203/rs.3.rs-5501950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5501950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgriculture is the backbone of many economies and provides livelihood to many nations in Africa. However, the sector is largely rainfed and faces considerable challenges due to the impacts of climate variability and unpredictable rainfall patterns. Despite advances in climate information services, farmers have limited access to reliable climate information to make adaptive decisions. Studies have shown that farmers with limited access hardly rely on the information they get access to. Using the Pra River basin as a case, this study assessed climate information\u0026rsquo;s influence on farmers' farm-level decisions by interviewing 382 farmers and evaluated the performance of seasonal forecasts from the European Centre for Medium-Range Weather Forecast (ECMWF) in meeting farmers\u0026rsquo; needs. Rainfall onset, amount and cessation were the three most prioritised needs ranked in descending order. Most farmers preferred to receive climate information at a 2-month lead time. However, ECMWF-S5 only provided the second-ranked need (rainfall amount) at a 1-month lead time with the highest skill in forecasting rainfall in the study area at correlation coefficient, RMSE (ranging between 40.31\u0026ndash;52.84, 68.85\u0026ndash;75.82, 92.17-101.07), and KGE within the ranges of 0.7\u0026ndash;0.9, 40\u0026ndash;53, and 0.6\u0026ndash;0.8, respectively. The skill of ECMWF-S5 decreases with increasing lead times. ECMWF-S5 rainfall forecast information at a 1-month lead time is advisable and likely to positively impact the decision-making process for farming in the Pra River Basin. It is recommended that other forecast models, including indigenous techniques be combined to improve the forecast's lead time and determine the onset and cessation for adaptive farming.\u003c/p\u003e","manuscriptTitle":"Climate Information Services and Farm-Level Decision-Making in the Pra River Basin of Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 05:33:07","doi":"10.21203/rs.3.rs-5501950/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"768caba6-4f4b-448f-bd66-5021a14dded8","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T02:23:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 05:33:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5501950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5501950","identity":"rs-5501950","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}