Water Resources Commission of Ghana

Analyzing changes in land use and land cover (LULC) is crucial for ensuring long-term ecological sustainability. This study investigated the spatio-temporal dynamics of LULC, with a particular focus on irrigation expansion and its effects on ecosystem services in the Lake Tana sub-basin. An ensemble machine learning algorithm, random forest, was employed in the LULC classification for 1985, 2003, and 2021. The Normalized Vegetation Index (NDVI) was used to differentiate between rain-fed and irrigated lands, and the impact of LULC dynamics on Ecosystem Service Values (ESVs) was evaluated using modified valuation coefficients tailored to Ethiopian biome conditions. Based on the classified maps, the agricultural land increased significantly from 43.95 to 55.22%, and the irrigated agriculture rose from 1.24 to 8.69% between 1985 and 2021, whereas grassland and shrub land decreased from 14.38 to 5.35% and 18.85 to 15.34%, respectively, within the same periods. The largest gain was observed for irrigated agriculture, while the greatest loss was observed for grasslands over 36years. The total ESVs of the sub-basin were estimated at US$2.98 billion in 1985, US$2.96 billion in 2003, and US$2.995 billion in 2021. Over the 36years, approximately US$0.015 billion (0.52%) in ESVs was gained, highlighting the effects of LULC changes on ecosystem services. To mitigate these impacts, strategic land use planning should incorporate ecosystem service valuation into decision-making, reinforce regulatory frameworks, and advance sustainability initiatives. Future research should focus on enhancing classification accuracy by integrating higher-resolution satellite imagery, incorporating socio-economic variables, and simulating future ESV scenarios under various development pathways.

Aquaculture has surpassed capture fisheries in terms of production and is among the fastest growing food sectors. It has great potential to contribute to food security and nutrition, poverty reduction, jobs, and environmental sustainability. Fish seed is increasingly considered to be a major driver and disabler of aquaculture development. However, little is known about how fish seed systems operate, their challenges and opportunities, or entry points for strengthening them. This study analyzes primary data on the challenges and opportunities faced by various actors along the fish seed chain, documents the lessons from a fish seed project (Ghana Tilapia Seed Project, 2019–2022), and provides an analysis of entry points for strengthening fish seed systems. Using an analytical framework that tracks germplasm base, seed production and quality, seed availability and distribution, and the information flow along the fish seed value chain, we analyze the case of Ghana, the top producer of farmed tilapia in sub-Saharan Africa. The study uses a mixed-methods approach, including value chain analysis, action-oriented research methods, and statistical analysis of survey data. Findings indicate that the initial rapid growth in tilapia production in Ghana was partly due to an improved local strain released in 2004; however, the recent stagnation is largely caused by seed-related issues (poor maintenance and improvement of germplasm base, seed quality and availability, lack of information and coordination, and lack of enforcement of regulations). This study highlights the successes and lessons learned from the Ghana Tilapia Seed Project on broodstock distribution, training on fingerling production, establishment of nurseries, and training of fish farmers. The lessons highlight the need for policy changes and capacity building related to strain development and broodstock management. These findings fill the large gap in evidence on the functioning of fish seed systems and how to strengthen them. They can directly inform ongoing country-level efforts and programs aiming to develop aquaculture. • Initial rapid growth in tilapia production in Ghana is partly due to improved strain. • Recent stagnation is largely due to challenges with low seed quality and availability. • New broodstock, hatchery training, and new nurseries increased seed supply and demand. • Training and digital tools led to more farmer's knowledge, productivity, and income. • Seed policy change and capacity to evaluate and manage multiple strains are needed.

ABSTRACTClimate change induces high variability in drought patterns and extreme precipitation indices in rainfed cocoa farming, impacting cocoa production. This study evaluated water stress, meteorological and agricultural drought conditions, and critical extreme precipitation indices in the world's two largest cocoa‐producing nations from 1981 to 2022. The results revealed a significant reduction in total annual precipitation (PRCPTOT), in the last three decades, with the greatest decline in the 1991–2000 and 2011–2022 periods. Ghana experienced the most significant reduction up to 15% (200 mm/year) in the last decade, attributed to a substantial decrease in wet days number (RR1) up to 25 days per year, a reduction in maximum consecutive wet days (CWD) up to 6 days per year, and an increase in maximum consecutive dry days (CDD) up to 15 days per year. Moreover, there was a notable decline in the Simple Daily Intensity Index (SDII), with reductions of up to 4 mm/day in certain areas, contributing to increased drought frequency, severity, and duration. In the most recent decade (2011–2022), particularly during the extremely dry years of 2013 and 2015, cocoa‐growing regions in Ghana (GHA) and eastern Côte d'Ivoire (CIV) experienced prolonged agricultural drought expressed by soil moisture deficit, typically extending from May to September. Additionally, large portions of central and eastern Ghana, as well as northeastern Côte d'Ivoire experienced sustained water stress, with over three consecutive months of total monthly precipitation falling below 100 mm, negatively impacting cocoa productivity. The decrease in the yield in the range of 2.5% to 37% was noted in the dry years and the following years, varying according to the country depending on the severity of the drought. Sensitivity analysis highlights cocoa yield's responsiveness to drought and water stress, particularly in specific years when water stress occurred, such as 1984,1985, 1989, 1995, 1999, 2000, and 2008. Considering the observed trends in precipitation patterns and their impact on cocoa production, it is crucial to acknowledge the inherent uncertainty of future precipitation patterns due to climate change. To address this challenge effectively, our study underscores the importance of identifying and closely monitoring regions currently facing water stress, as determined by precipitation and drought indicators. Over the analysed period (1981–2022), we have noted shifts in the distribution of water‐stressed areas, highlighting the dynamic nature of this issue. Consequently, we advocate for a targeted approach to implement cocoa supplementary irrigation in consistently water‐stressed regions.

International audience

Migration and social transformation are major drivers of socio-economic development. Yet, the linkages between social transformation and migration in Ghana are poorly understood. This article seeks to shed light on how social transformation affects or is affected by migration, using mixed methods with transformationalist and social change theoretical lenses. At the same time, there have been retrogressive transformations in the economic conditions, technology and demography have improved and increased, respectively, and political and cultural factors have remained relatively the same over the past decade. Although there is a perceived bi-directional relationship between social transformation and migration, social transformation exerts greater influence on migration than migration has on social transformation except for higher educational attainment and improved household income. Therefore, the relationship between social transformation and migration is not balanced in our study area as the former influences more than the latter.

Increased groundwater extraction for irrigation is leading to the intrusion of saline seawater into many wells in some communities in Anloga District, Volta Region, Ghana, with resultant adverse effects on crop productivity. This study investigated the water quality of selected wells in four communities of the district for their suitability for irrigation. Water samples were collected from 10 representative wells, purposively selected out of 60, to determine the pH, electrical conductivity, total dissolved salt by means of a pH/EC/TDS/Temperature portable meter, and the concentrations of sodium, potassium, calcium, magnesium, sulphates, chlorides, bicarbonate and nitrate, using standard chemical laboratory procedures. The geochemical characterization of the sampled water sources was carried out using the Piper trilinear diagram, and revealed the dominance of Na-Cl facies of the groundwater in Tegbi, Whuti and Anyanui, suggesting seawater encroachment connected with groundwater abstraction. The water samples from wells in Woe indicated the Ca-Mg-Cl-SO4 mixed type and are considered suitable for irrigation. About 50% of the sampled wells could be classified under the high salinity-low sodium hazard (C3-S1), 20% are under very high salinity-medium sodium hazard (C4-S2), another 20% under very high salinity-high sodium hazard (C4-S3) and 10% under medium salinity-low sodium (C2-S1) classes. These wells are prone to salinity, and farmers’ discretion is recommended through the adoption of conditional use of the irrigation water such as growing of salt-tolerant crops.

This study interrogates the state of social-ecological landscapes (SEL) in West Africa, focusing on two case studies: the Mankran SEL in Ghana (case study 1) and the Doma–Rutu SEL in Nigeria (case study 2). Using a mix of methods, the assessment was framed by the Drivers Pressure State Impact Response (DPSIR) model tailored for SEL evaluation (DPSIR-SEL). In the Mankran landscape, land use patterns shifted significantly from 2008 to 2018, with cash crop cultivation peaking at 30% in 2015 before declining to 14.5% by 2018. Water quality assessments in the Mankran micro-watershed indicated that several parameters, including Total Suspended Solids (TSS) at 914.41 ± 1974 mg/L, lead at 18.73 ± 17.26 µg/L, and arsenic at 53.41 ± 86.66 µg/L, exceeded World Health Organization (WHO) standards, raising concerns about potential contamination. In contrast, the Doma–Rutu landscape in Nigeria experienced land use and land cover (LULC) changes from 2000 to 2022, characterized by the expansion of residential and agricultural areas alongside modifications to natural water bodies and vegetation. Water quality issues have emerged, with elevated levels of electrical conductivity, total dissolved solids, and salinity. Furthermore, Focus Group Discussions (FGDs) revealed persistent herder-farmer conflicts in Nigeria, which have historically constrained crop production due to various environmental and social factors. The intertwined challenges faced by both the Mankran and Doma–Rutu landscapes underscore the urgent need for sustainable and inclusive resource management, adaptive land-use strategies, and proactive measures to safeguard water quality.

Water quality is deteriorating in the world's freshwater bodies, and Lake Tana in Ethiopia is becoming unpleasant to biodiversity. The objective of this study is to retrieve non-optical water quality data, specifically total nitrogen (TN) and total phosphorus (TP) concentrations, in Lake Tana using Machine Learning (ML) techniques applied to Landsat 8 OLI imagery. The ML methods employed include Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RF), XGBoost Regression (XGB), AdaBoost Regression (AB), and Gradient Boosting Regression (GB). The XGB algorithm provided the best result for TN retrieval, with determination coefficient (R2), mean absolute error (MARE), relative mean square error (RMSE) and Nash Sutcliff (NS) values of 0.80, 0.043, 0.52, and 0.81 mg/L, respectively. The RF algorithm was most effective for TP retrieval, with R2 of 0.73, MARE of 0.076, RMSE of 0.17 mg/L, and NS index of 0.74. These methods accurately predicted TN and TP spatial concentrations, identifying hotspots along river inlets and northeasters. The temporal patterns of TN, TP, and their ratios were also accurately represented by combining in-situ, RS and ML-based models. Our findings suggest that this approach can significantly improve the accuracy of water quality retrieval in large inland lakes and lead to the development of potential water quality digital services.