ABSTRACT
The aim of the study is to assess climate-smart water management practices for sustainable agriculture in the Lake Mutanda catchment area, Kisoro District, Uganda. The study was led by specific objectives, specifically: assessing the effects of flooding on agricultural productivity, and smallholder farmer's responses to rainfall variability for the past 20–30 years, investigating climate-smart water management practices for sustainable agriculture, and analyzing the seasonal variations of the physicochemical water quality parameters. The study used a mixed research design, which used mixed methodologies to collect and analyze data using both quantitative and qualitative approaches. A straightforward random sampling approach was used to determine the sample size. Three hundred and ninety-seven respondents participated in the study. Quantitative data was analyzed using the R programming language, and qualitative data was analyzed using ATLAS.ti. The study identified climate-smart water management practices like mulching, terracing, contour farming, conservation tillage, agroforestry, and fertilizer management. Most of the respondents (29.7%) cited a reduction in soil fertility, 57.9% of the total sampled respondents had noticed changes in rainfall variability, and 67.3% reported a decrease in rainfall. The majority (38% of the participants) use mulching to reduce running water into the catchment areas. Turbidity, temperature, and DO were beyond the WHO-permitted levels.
HIGHLIGHTS
Farmers need water for irrigation.
In Uganda, conservation farming techniques are becoming more and more well-liked.
For agriculture to be sustainable, crop varieties must be both drought- and climate-tolerant.
Agroforestry is another method of water management that is climate-smart.
Physicochemical water parameters of Lake Mutanda like turbidity and temperature were all found to be beyond the WHO-permitted levels.
INTRODUCTION
Agriculture consumes about 70% of freshwater worldwide (Chen et al. 2018; Lambrechts & Sheldon 2019). The three major objectives of climate-smart agriculture (CSA) are to increase climate change resilience, increase sustainable agricultural output and profitability, and decrease greenhouse gas (GHG) emissions from farming systems into the environment (CSA) (Israel et al. 2020). Around the world, less than half of the more than 13 billion hectares of land can be used for agriculture, including grazing. The potential arable land area of the world is around 3,031 million hectares, or 22% of the total land area (Lal 1990; Neupane et al. 2022). There are 2,154 and 877 million hectares of arable land in emerging and developed nations, or 28 and 15% of the total land area, respectively. A total of 1,461 million hectares, or 40% of the world's theoretically arable land, are under cultivation, with 784 million hectares or 36% – and 677 million hectares or 77% – of that total in countries that are developing and developed, respectively. The estimated 2,000 million hectares of formerly ecologically productive land that have been degraded or destroyed are not included in the 1,461 million hectares of land that is being cultivated (Rai 2022).
‘Climate-Smart Water Management Practices (CSWMS)’ are methods and procedures that strive to use, conserve, and manage water resources in the best possible way given the effects of climate change. These approaches increase resiliency, sustainability, and effectiveness by incorporating climate change concerns into water management techniques. The idea of being ‘climate-smart’ highlights the importance of addressing issues brought on by climate change, which include greater variability in rainfall patterns, higher temperatures, and unusual weather occurrences. Climate-smart water management techniques try to mitigate the effects of water-related behaviors on climate change while simultaneously adapting to these changes (Sikka et al. 2018). However, little attention has been made to improve the efficient water use conservation and economic viability of farming operations by promoting diverse income streams, fair pricing, and access to markets for small-scale farmers, and this justifies this study to fill the gaps.
Producing food, fiber, and additional crop-related goods in a way that protects the environment promotes the welfare of farmers and rural populations, and ensures economic longevity is the core premise behind the notion of sustainable agriculture (Bhatia et al. 2023). This helps to solve the drawbacks of conventional agriculture, including the depletion of renewable resources, loss of biodiversity, water pollution, and soil degradation. The goal of sustainable agriculture is to achieve a balance between providing for the present's food needs and preserving the capacity of the next generations to provide for themselves (Turyasingura et al. 2023b).
Globally, CSA has improved agricultural production (Hussain et al. 2022). Stable weather patterns, especially those related to temperature, precipitation patterns, and other seasons, are crucial for agriculture (Corwin 2021). These circumstances may be disturbed by climate change, which could result in shorter growing seasons, decreased crop yields, and altered pest and disease populations. Studies on climate change have indicated that the frequency and intensity of catastrophic weather conditions, which include droughts, floods, heat waves, and storms, are increasing, causing serious problems for the productivity of agriculture and food production networks thus, the need for CSA (Ahmed et al. 2022).
Mahdi & Singh (2022) found that the world's cultivated land area expanded by almost 13% between 1960 and 2000. This growth coincided with the tremendous expansion of the world's population, which exceeded double the number throughout that time. The increase in agricultural operations and the rising requirement for food production in order to meet the demands of an expanding population are highlighted by these trends.
Empirical evidence shows that changes in precipitation patterns affect the availability and distribution of water (Rocha et al. 2020), leading to water scarcity in some regions and increased risks of flooding in others (Dinar et al. 2019). Rising temperatures contribute to the melting of glaciers and ice caps, reducing water supplies in certain areas that depend on them. These water-related challenges can affect agriculture and food security as irrigation systems may be compromised, leading to crop failures and reduced food production. To support the aforementioned conclusions, it should be noted that climate change poses a growing danger to food security, which is defined as the availability, accessibility, and usage of food. Food scarcity, volatility in prices, and increased susceptibility may result from perturbations in agriculture and water supplies, particularly in developing nations where diets are more susceptible to climatic changes (Kargar Dehbidi et al. 2022). Additionally, because of altered growing circumstances that may have an influence on crop nutrient content and the accessibility of a variety of food options, climate change may have an impact on food quality and nutrition (Leisner 2020).
Historically, access to better water sources and the coverage of the water supply have been issues in many African communities. This condition is a result of a number of issues, including poor infrastructure, insufficient funding for sanitation and water supplies, population increase, and climate change (Armah et al. 2018), due to low adoption of CSWMS. Only 57% of the population, according to the WHO (2018), has access to services that provide the safe management of water for drinking, while 42% lack access to basic water supplies and 72% lack access to basic sanitation (Thomas et al. 2020). Africa currently lacks the data necessary to estimate the population utilizing a water source known to be ‘available when necessary’, indicating that more data is required to close this knowledge gap (Chirgwin et al. 2021).
In Sub-Saharan Africa (SSA), most people lack access to clean, safe drinking water due to poor farming practices in the area's water catchment zones, water quality has received little attention. Nearly 695 million people continue to use unimproved facilities, which serves as a support for the aforementioned results that about 109 million people use unsafe surface water (Walekhwa et al. 2022) and improved food security. The novelty of this study is emphasizing agriculture in recognition of its vital role in ensuring food security, in order to secure the production of inexpensive and sufficient food for the people (Rawat et al. 2024). The agricultural sector is very vulnerable to the effects of rainfall variability, notwithstanding its promise. Unfortunately, many developing countries make flimsy and insufficient efforts to protect this industry. This is because the negative effects on agricultural output and food insecurity could result from rainfall variability if they are not successfully addressed, posing a serious challenge to the SSA countries (Damte Darota et al. 2024).
Uganda's Vision 2040 is a long-term development plan that outlines the country's aspirations and goals for socioeconomic transformation by the year 2040 (Turyasingura et al. 2023a, 2023c; Banerjee et al. 2024). Providing universal access to clean drinking water for all Ugandans is one of Uganda Vision 2040′s main goals, which may be done through implementing CSA water management techniques. However, Uganda has made progress in expanding access to water supply, and challenges still remain. Factors such as population growth, limited financial resources, and inadequate infrastructure in certain areas can impede the goal of universal access to safe drinking water (Saturday et al. 2021). However, the commitment to achieving this goal is reflected in Uganda's Vision 2040, indicating the government's recognition of the importance of water and its commitment to ensuring access to safe drinking water for all Ugandans.
Although accurate data sets on the present CSWMS on water resources have become crucial, little is known about the state of the water quality and the CSA techniques used by the smallholder cultivators of crops in the Lake Mutanda watershed of southwest Uganda. One of the most crucial coping mechanisms for water quality management has been recognized as farmers modifying their agricultural techniques and farming systems to adapt to seasonal rainfall unpredictability at the farm level. To help farmers improve their individual effectiveness as a result of fishing and food crop production efficiency and to boost their returns on farming, however, their execution of these methods is still insufficient (Saturday et al. 2023).
Floods have frequently occurred in Kisoro District throughout history, influenced by both natural and man-made causes. The area has historically seen a lot of rainfall, which has been made worse by the hills and Lake Mutanda's close proximity. These elements work together frequently to cause flash floods and river overflows, which have an effect on the populations that are near watercourses. The Kisoro District floods are caused by a variety of factors such as increased deforestation, unsuitable farming methods, and inadequate infrastructure, which all lead to soil erosion and poor water drainage. Furthermore, as a result of climate change, rainfall patterns become more intense, increasing the likelihood of flooding (Saturday et al. 2023). In 2022, the Uganda Red Cross (URC) reported that 15 dead bodies had been recovered from the landside in Kisoro District. The most affected areas were Nyarusiza and Muramba sub-counties and Bunagana Town Council, where people lost their lives. The disaster led to extensive loss of property estimated to be over one billion Uganda shillings affecting 1,060 households (4,600) individuals who lost their property.
To guarantee the long-term supply and quality of the water from Lake Mutanda, sustainable water management techniques should be put into place. To balance human demands with environmental sustainability, this involves actions like watershed protection, water resource strategy, and monitoring of water consumption (Turyasingura et al. 2023a, 2023c). However, there is a higher risk of pollution from several sources due to expanding agricultural activity and a growing population. Algal blooms, nutrient enrichment, and decreased water quality can result from agricultural runoff that contains silt, pesticides, and fertilizers. If not properly handled, domestic and commercial wastewater can potentially contribute to pollution and harm the lake environment (Saturday et al. 2021). The above authors never reported water proper water management and protection which are crucial for ensuring the continued availability of water for various purposes, as well as preserving the ecological balance of the lake and its surrounding ecosystem thus, a gap to be filled.
In addition, vegetation that had previously shielded Lake Mutanda against erosion, as well as its effects, were removed to make way for farming and the establishment of tourist campsites. Additionally, due to soil erosion from farming activities near the lake, seasonal rainfall variability, such as fluctuations in precipitation, has increased turbidity in its waters (Tibihika et al. 2016). The authors also failed to raise awareness of the value of vegetation and how environmentally friendly management of land can encourage local neighborhoods and other interested parties to actively participate in preserving the ecosystem of the lake while preserving water quality for the well-being of current and future generations.
As a result, Lake Mutanda's water clarity has decreased, becoming unattractive and muddy as a result of contaminants brought on by bad agricultural practices in the lake's vicinity (Magumba 2000), and no research has been conducted to show how CSWMS improve water quality and management, leaving a void that this study will fill. Due to the public's continued use of subpar farming techniques and the low sensitivity and limited awareness of the CSWMS in the study area, there is a great need for this study. The aim of the study is to assess climate-smart water management practices for sustainable agriculture in the Lake Mutanda catchment area, Kisoro District, Uganda.
The study was guided by specific objectives, namely: assessing the effects of flooding on agricultural productivity; smallholder farmer's responses to rainfall variability for the past 20–30 years; and investigating climate-smart water management practices for sustainable agriculture and seasonal variations of the physicochemical water quality parameters in the Lake Mutanda catchment area.
METHODS
Description of the study area
Study research design
The study used a mixed research design, which used mixed methodologies to collect and analyze data using both quantitative and qualitative approaches.
Sampling design for social-economic data
Sample size

Sampling procedure
Households selected in Kisoro District by stratum (sub-counties)
Selected strata . | Total no. of households per selected stratum . | Sample size per selected stratum . | Percentage of selected household samples . |
---|---|---|---|
Busanza | 3,976 | 101 | 25 |
Nyakinama | 4,552 | 116 | 29 |
Kirundo | 4,419 | 113 | 29 |
Nyundo | 2,615 | 67 | 17 |
Total | 15,562 | 397 | 100 |
Selected strata . | Total no. of households per selected stratum . | Sample size per selected stratum . | Percentage of selected household samples . |
---|---|---|---|
Busanza | 3,976 | 101 | 25 |
Nyakinama | 4,552 | 116 | 29 |
Kirundo | 4,419 | 113 | 29 |
Nyundo | 2,615 | 67 | 17 |
Total | 15,562 | 397 | 100 |
From the above, HHA is the number of households per selected sampling area, THHA is total households in selected sampling areas, SS is sample size required, and PSS is proportionate sample size.
In order to learn more about crop farmers' experiences with the wet and dry seasons, as well as the climate-smart water management techniques they employ in the wet and dry seasons around water catchment areas for sustained flooding management and sustainable agriculture, the socioeconomic and demographic characteristics of households are being taken into consideration. To gather socioeconomic and biographical information, 397 smallholder crop producers participated in semi-structured and structured interviews using a mix of closed- and open-ended questions.
Interviews
In-depth information regarding the study was conducted in 2022, and it was 23–70 min in length and semi-structured in nature, allowing points to be explored alongside a common question list. Interviews were conducted to gather wide-ranging information on the smallholder farmers' responses on rainfall variability, the effect of rainfall variability on the quality of drinking water, sources of water contaminants, how communities manage flooding, and climate-smart water management practices for water quality management around Lake Mutanda. During data collection, all interviews were audio recorded with participants' consent and transcribed ethically.
Water sampling
For a total of 168 samples from Lake Mutanda, Kisoro District, water was sampled monthly for four months during both the dry and wet seasons (March–April wet season as well as June–July dry season of 2021) out of nine sampling locations of upper stream, middle stream, and lower stream. Water samples in the wet season were collected 1 week after heavy rains when the runoff reaches the lake to avoid errors in data collection, and for the dry season, water samples were collected at the beginning of the month to ease sampling. This study analysed temperature, DO, turbidity, electrical conductivity (EC), as well as pH, all of which have an impact on drinking water standards according to APHA (1976). The water sample collection was conducted from 6:00 morning to 12:00 noon before the water was destabilized by dipping 1 L polyethene plastic bottles about 0.3 m beneath the water's surface.
The three sampling stations used in the study were Busanza, Mukozi, as well as Mutanda Island with various sampling sites that were sampled 10 km apart from each other each station as shown below. These stations varied in the extent and kind of human activity in the lake and other lake-bordering areas. For proper sample collection, the upper Mutanda site contains the Busanza (U1), Iremera (U2), and Bucece (U3) stations; the middle Mutanda site contains the Mukozi (M1), Mukondero (M2), and Mutaba (M3) stations; and the lower Mutanda site contains the Mutanda Island (L1), Kyangushu (L2), and Buhungyiro (L3) stations.
We chose the sampling locations based on the region's topography, inlet and exit directions, and geographical features that made it easy to distinguish between the lake's upper and bottom portions depending on the flow of the outlets and the inlets. The bottles were often carefully cleaned with deionized water before any samples were taken in order to get rid of any potential pollutants, dust, or debris. After carefully rinsing, the bottles were dried to remove any potential contaminants before usage. The sampling vials used in this investigation were labeled with their station identifiers (Table 2) and washed four times in lake water to get rid of any potential contamination. Before being transferred to the National Water and Sewerage Corporation (NWSC) Central Laboratories in Kabale in addition to the Makerere University Laboratory for analysis, water samples obtained from various sampling sites were stored in an icebox for 24 h. Samples were kept in the lab in a refrigeration unit at 4 °C right until analysis.
The water quality at the sampling places
Sampling points . | Place code . | Place stations . | Latitude . | Longitude . |
---|---|---|---|---|
Upper Mutanda | U1 | Busanza | 1°10.534′S | 29°40.388′E |
U2 | Iremera | 1°11.223′S | 29°39.983′E | |
U3 | Bucece | 1°11.805′S | 29°40.327′E | |
Middle Mutanda | M1 | Mukozi | 1°11.742′S | 29°41.154′E |
M2 | Mukondero | 1°12.758′S | 29°40.472′E | |
M3 | Mutaba | 1°12.670′S | 29°40.913′E | |
Lower Mutanda | L1 | Mutanda Island | 1°13.313′S | 29°40.422′E |
L2 | Kyangushu | 1°13.625′S | 29°39.347′E | |
L3 | Buhungyiro | 1°15.062′S | 29°40.550′E |
Sampling points . | Place code . | Place stations . | Latitude . | Longitude . |
---|---|---|---|---|
Upper Mutanda | U1 | Busanza | 1°10.534′S | 29°40.388′E |
U2 | Iremera | 1°11.223′S | 29°39.983′E | |
U3 | Bucece | 1°11.805′S | 29°40.327′E | |
Middle Mutanda | M1 | Mukozi | 1°11.742′S | 29°41.154′E |
M2 | Mukondero | 1°12.758′S | 29°40.472′E | |
M3 | Mutaba | 1°12.670′S | 29°40.913′E | |
Lower Mutanda | L1 | Mutanda Island | 1°13.313′S | 29°40.422′E |
L2 | Kyangushu | 1°13.625′S | 29°39.347′E | |
L3 | Buhungyiro | 1°15.062′S | 29°40.550′E |
Physical water quality analysis
Physical water quality measures such as temperature, DO, turbidity, EC, as well as pH were measured onsite. The water temperature was determined using a temperature sensor within the DO meter (DO 5510 M.R.C type), which was also used to measure DO. Before recording DO and temperature readings, the probe was submerged in the water to a level of 0.3 m (Beutler et al. 2014).
A turbidity meter (Hach, 2100P, Colombia, USA, Arachem (m) Sdn. Bhd.) was used to measure the turbidity, and Nephelometric Turbidity Units (NTUs) were used to record the data.
A portable pH meter (HI98130 HANNA instruments, Mauritius, Iramac Sdn. Bhd.) was used to measure the pH. In the beginning, the pH meter was calibrated using buffer solutions with pH values of 4.01, 7.0, and 9.2, as per the directions in the manufacturer's manual.
A conductivity meter (HI 98130 HANNA instruments, Mauritius, Iramac Sdn. Bhd.) was used to test EC in-situ. Before recording the conductivity readings in microsiemens per centimeter (S cm−1), the conductivity meter was lowered into the water to a depth of 0.3 m.
A turbidity meter (Hach, 2100P, Colombia, USA, Arachem (m) Sdn. Bhd.) was used to measure the turbidity, and NTUs were used to record the data. The water turbidity meter was powered on and then given a 10- to 15-min warm-up period. A water sample was placed in a cuvette once the meter's reading steadied, and the reading was noted. After reading each sample, the cuvette was thoroughly cleaned with pure water in order to avoid mistakes or contamination.
A portable pH meter (HI98130 HANNA instruments, Mauritius, Iramac Sdn. Bhd.) was used to measure the pH. In the beginning, the pH meter was calibrated using buffer solutions with pH values of 4.01, 7.0, and 9.2, as per the directions in the manufacturer's manual. The pH of the lake water was subsequently measured by dipping the pH probe to a measurement depth of 0.3 m, letting it stabilize, and then recording the pH readings (Beutler et al. 2014). To achieve homogeneity, the calibrated meter probe was dipped in water and agitated for a few seconds. The pH measurement was read and recorded onsite after stabilization.
Statistical data analysis
The statistical information was examined using R (version 4.1.4). Quantitative data analysis was utilized in this work to offer information on the drinking water quality, causes of drinking water contamination, and how they control flooding and drought due to rainfall variability. Descriptive and inferential statistics were also used. After that, the key informant interviews' qualitative data were coded and processed, and major concepts were developed to help comprehend the phenomenon under research using ATLAS.ti. Last but not least, line graphs were used to depict data from Uganda's Meteorological Department in Kampala on seasonal rainfall variability, which were then connected with data on farmers' perceptions. The Mann–Whitney U test was used to analyze concentration differences for each water quality characteristic between the wet and dry seasons and the differences between sites. To determine whether there is a relationship between the measured physicochemical and bacteriological water quality parameters, the Spearman correlation coefficient (rho) was used, and the p-value < 0.05 was considered significant.
RESULTS AND DISCUSSION
Demographic characteristics
Out of 397 respondents that were selected to participate in the study, 61.5% were male respondents and 38.5% were female respondents. Based on the study findings, the number of males was bigger than that of their female counterparts due to the fact that males possess more energy to participate more in the rainfall variability water management practices to reduce stream and lake water contamination in Kisoro District than their female counterparts.
The findings indicated that 51.6% of the respondents were aged between 31 and 40 years, and 18.1% were in the age bracket between 21 and 30 years. The age of the respondents was considered for this study in order to acquire their knowledge based on their lifetime experience with the rainfall variability water management practices in Lake Mutanda and its catchments, in southwestern Uganda. Also, the maximum age during data collection was up to 90 as long as a farmer could go to the garden because local farmers in this area were still energetic to practice agriculture. This was because agricultural cultures have a special synergy between physical activity and mental resiliency, aging frequently defies conventional constraints. The 90-year-old upper age restriction for data collecting is indicative of the long-term health of the area's farmers, who continue to be actively involved in their trade. Even though they are becoming older, their relationship with the land gives them a sense of direction and lifts their spirits. Farming's physical demands – such as consistent exercise and outdoor work – promote lifespan, while crop management's mental stimulation fosters cognitive agility. In farming, then, one's age becomes a testimonial to the strength and determination of those who work the land.
On the marital status of the respondents, 35.5% were married, and 15.1% were widows. The study considered the marital status of respondents due to the fact that the majority of respondents were involved in clearing ridges and tree planting to avoid soil erosion, mulching, and monitoring of the lake during rainy seasons. In addition, Table 3 shows that in Kisoro District, Uganda, 27% of respondents had completed their secondary levels of education and only 9.1% had not completed any form of education. The majority of the respondents had completed the secondary level. As a result, they could adopt better methods of farming and new technology techniques involved in water quality management.
Demographics
Variable . | Frequency (n = 397) . | Percentage . |
---|---|---|
Gender | ||
Male | 244 | 61.5 |
Female | 153 | 38.5 |
Age | ||
21–30 | 72 | 18.1 |
31–40 | 205 | 51.6 |
41–50 | 74 | 18.6 |
Above 50 | 46 | 11.6 |
Marital status | ||
Single | 92 | 23.2 |
Married | 141 | 35.5 |
Separated | 104 | 26.2 |
Widows | 60 | 15.1 |
Education levels | ||
Non-formal education | 58 | 14.6 |
Primary | 103 | 25.9 |
Secondary | 107 | 27.0 |
Institutions | 93 | 23.4 |
Others | 36 | 9.1 |
Monthly income (USHS) | ||
Less than 100,000 | 232 | 58.4 |
200,000–400,000 | 108 | 27.2 |
500,000–900,000 | 28 | 7.1 |
More than 1,000,000 | 29 | 7.3 |
Variable . | Frequency (n = 397) . | Percentage . |
---|---|---|
Gender | ||
Male | 244 | 61.5 |
Female | 153 | 38.5 |
Age | ||
21–30 | 72 | 18.1 |
31–40 | 205 | 51.6 |
41–50 | 74 | 18.6 |
Above 50 | 46 | 11.6 |
Marital status | ||
Single | 92 | 23.2 |
Married | 141 | 35.5 |
Separated | 104 | 26.2 |
Widows | 60 | 15.1 |
Education levels | ||
Non-formal education | 58 | 14.6 |
Primary | 103 | 25.9 |
Secondary | 107 | 27.0 |
Institutions | 93 | 23.4 |
Others | 36 | 9.1 |
Monthly income (USHS) | ||
Less than 100,000 | 232 | 58.4 |
200,000–400,000 | 108 | 27.2 |
500,000–900,000 | 28 | 7.1 |
More than 1,000,000 | 29 | 7.3 |
Lastly, a household's monthly income was classified into four groups. It was found out that monthly income for 57.8% ranged from less than 100.00 UGX and 7.1% ranged from 500.000 to 900.000 UGX. Some water techniques could be fairly expensive and only accessible to wealthy family heads, which helps to support the conclusions. In poorer communities, for example, a substantial proportion of households were not aware of the rainfall variability water management practices thus polluting stream and Lake Mutanda water.
Effects of flooding on agricultural productivity
Concerning the effects of flooding on agricultural productivity, most of the respondents (29.7%) cited a reduction in soil fertility. Floodwaters' force can erode soil, removing important nutrients and topsoil (Rashmi et al. 2022). Reduced soil fertility and decreased agricultural output may result from this erosion (Chalise et al. 2019). Loaded soils become flooded, which may cause an oxygen shortage. Numerous soil functions, such as the activity of helpful soil bacteria, depend on oxygen. These microbes cannot function correctly without oxygen, which reduces the cycling of nutrients and speeds up the breakdown of organic materials (Mohamed et al. 2021).
In addition, 27.2% of the respondents mentioned the destruction of crops, 22.7% mentioned low produce, and 20.4% revealed that flooding leads to soil erosion in Kisoro District, thus affecting the water quality of Lake Mutanda and its catchments, and poor sustainable agriculture (Table 4). Crop losses and destruction are some of the most evident and direct effects of flooding on agriculture (Kassegn & Endris 2021). Crops submerged by floodwaters may suffer from oxygen deprivation, nutritional leaching, and increased vulnerability to pests and diseases (Brempong et al. 2023). Long-term standing water can drown plants, impede root development, and degrade plant tissues and seeds (Roberts & Marston 2011).
Effects of flooding on agricultural productivity
Effects . | Frequency (n = 397) . | Percentage . |
---|---|---|
Destruction of crops | 108 | 27.2 |
Reduces on soil fertility | 118 | 29.7 |
Leads to soil erosion | 81 | 20.4 |
Low produce | 90 | 22.7 |
Effects . | Frequency (n = 397) . | Percentage . |
---|---|---|
Destruction of crops | 108 | 27.2 |
Reduces on soil fertility | 118 | 29.7 |
Leads to soil erosion | 81 | 20.4 |
Low produce | 90 | 22.7 |
From the interview, flooding is a disaster for us, says Farmer 1. We lost the majority of our crops due to last year's torrential rains flooding our farms. The plants became suffocated by the standing water, and the soil became so compacted that it was difficult to replant. After such a blow, financial recovery is challenging. Farmer 2: ‘Flooding not only destroys our crops, but it also washes the rich topsoil away. As a result of falling yields over time, it is getting more difficult to produce adequate food for our family. We require assistance with better drainage systems and soil conservation’.
Another respondent revealed that ‘flooding occasionally brings in aggregates that temporarily improve the soil, but it's a two-edged sword. The nutrients we add with fertilizers can also be washed away by the same floods. Maintaining soil fertility is an ongoing battle’. It was also noted that ‘Our entire town is impacted by flooding. We have to purchase food from outside when we lose our harvests due to rising food prices. We must stop the poverty cycle's vicious loop’.
More than half, i.e., 225 (56.7%), of the respondents reported to have experienced flooding around water catchment areas and only 172 (43.3%) had not experienced flooding. This helped to maintain the water stream sources from contamination and also from contaminating lake water (Table 5).
Flooding experiences by the smallholder farmers
Response . | Frequency (n = 397) . | Percentage . |
---|---|---|
Yes | 225 | 56.7 |
No | 172 | 43.3 |
Response . | Frequency (n = 397) . | Percentage . |
---|---|---|
Yes | 225 | 56.7 |
No | 172 | 43.3 |
The main informants revealed that ‘smallholder farmers adopt several water conservation measures to maximize water consumption during times of insufficient rainfall. These strategies consist of setting up and operating small-scale reservoir structures like ponds or tanks, applying mulch to stop soil moisture from evaporating, and adopting effective watering techniques like drip irrigation’.
Despite the facts supplied by the sciences, farmers' decisions about water management methods to improve resilience against rainfall variability require a comprehensive grasp of many rainfall parameters such as intensity, distribution, onset and cessation times, temperatures, and drought episodes. However, the experience of local farmers with rainfall variability was first evaluated using a questionnaire in order to determine the various water management techniques they used in response to rainfall variability around Lake Mutanda and its catchments in southwest Uganda. The sampled farmers were asked to compare the current weather conditions to those of 20–30 years ago, and 57.9% of the total sampled respondents had noticed changes in rainfall variability (Table 6). Vinçon-Leite & Casenave (2019) cite a number of factors that may impact the water's cleanliness in the Kisoro District, fertilizer that is loading, and eutrophication of lakes like Mutanda, including local climate, rainfall fluctuations, and hydrodynamic conditions. Variability in rainfall influences the stability of the lake status and encourages the growth of cyanobacteria and eutrophication.
Smallholder farmers responses on rainfall variability in Kisoro District for the past 20–30 years
Variable . | Frequency (n = 397) . | Percentage . |
---|---|---|
Changes in rainfall variability in the past 20–30 years | ||
Yes | 230 | 57.9 |
No | 167 | 42.1 |
Rainfall for the last 20–30 years | ||
Greatly decreased | 161 | 40.6 |
Decreased | 106 | 26.7 |
No change | 27 | 6.8 |
Increased | 13 | 3.3 |
Rainfall distribution | ||
Very poor | 108 | 27.2 |
Poor | 119 | 30.0 |
No change | 86 | 21.7 |
Well | 45 | 11.3 |
Rainfall intensity | ||
Greatly decreased | 92 | 23.2 |
Decreased | 113 | 28.5 |
No change | 72 | 18.1 |
Increased | 45 | 11.3 |
Greatly increased | 75 | 18.9 |
Drought events | ||
Extremely low | 91 | 22.9 |
Low | 137 | 34.5 |
Moderate | 74 | 18.6 |
High | 55 | 13.9 |
Variable . | Frequency (n = 397) . | Percentage . |
---|---|---|
Changes in rainfall variability in the past 20–30 years | ||
Yes | 230 | 57.9 |
No | 167 | 42.1 |
Rainfall for the last 20–30 years | ||
Greatly decreased | 161 | 40.6 |
Decreased | 106 | 26.7 |
No change | 27 | 6.8 |
Increased | 13 | 3.3 |
Rainfall distribution | ||
Very poor | 108 | 27.2 |
Poor | 119 | 30.0 |
No change | 86 | 21.7 |
Well | 45 | 11.3 |
Rainfall intensity | ||
Greatly decreased | 92 | 23.2 |
Decreased | 113 | 28.5 |
No change | 72 | 18.1 |
Increased | 45 | 11.3 |
Greatly increased | 75 | 18.9 |
Drought events | ||
Extremely low | 91 | 22.9 |
Low | 137 | 34.5 |
Moderate | 74 | 18.6 |
High | 55 | 13.9 |
Regarding the rainfall for the last 20–30 years, 67.3% of the respondents reported a decrease in rainfall; 6.8% reported no change and 26% reported rainfall having increased. The distribution of rainfall in both long and short seasons was reported to be poor by 57.2% of the respondents; 21.7% reported no change, and only 21.1% reported well distribution of the rainfall. The study findings were in agreement with Singh et al. (2021) who reported that it is essential to comprehend the variables affecting rainfall variability for efficient management of water resources, agriculture, and climate change adaptation.
The findings were in support of Abegunde et al. (2019) and showed that there is a decline in rainfall in the semi-arid region of the sub-Sahara and increased rainfall in East and Central Africa. When asked about drought events, 34.5% reported low occurrence of droughts; 18.6% reported moderate events, 22.9% reported extremely low, and 13.9% reported high occurrence of drought events.
It was also found that only 39% of the respondents had ever heard about rainfall variability's effect on water quality, of which only 38.5% knew the causes (Table 7). This was in line with Khalid et al. (2020) who reported that water shortage becomes a serious problem during droughts as water sources including groundwater, water bodies, and rivers endure a decline. Water supply networks may become stressed due to a lack of water, which would increase competition for scarce water supplies. Inadequate dilution of harmful substances and higher concentrations of toxins in water bodies as a result, making investments in water facilities, such as storage facilities and reservoirs, can help reduce the impacts of drought and guarantee access to water during dry spells. Because of the drought, residents are forced to use contaminated water for their own members, their crops' irrigation, which lowers production, and their animals' drinking water, which causes crop wilting and low yield rates. In order to reduce runoff and manage floods, farmers must be enlightened by means of the climate village approach. Tree planting is one of the three pillars of CSA, which aims to reduce GHG emissions. Trees also act as carbon sinks.
Effects of rainfall variability on water quality
Response . | Frequency (n = 397) . | Percentage . |
---|---|---|
Heard about rainfall variability effect on water quality | ||
Yes | 155 | 39.0 |
No | 242 | 61.0 |
Know causes of these changes | ||
Yes | 153 | 38.5 |
No | 244 | 61.5 |
Response . | Frequency (n = 397) . | Percentage . |
---|---|---|
Heard about rainfall variability effect on water quality | ||
Yes | 155 | 39.0 |
No | 242 | 61.0 |
Know causes of these changes | ||
Yes | 153 | 38.5 |
No | 244 | 61.5 |
The seasonal rainfall trends for the three study stations were investigated from 1981 to 2020. Figure 2 depicts the results of Lake Mutanda's seasonal rainfall during the study period. It was found that the rainfall was increasing in both dry and wet seasons. It is clear that the seasonal rainfall increased in annual and dry seasons during the study period, and decreased in the wet season. The maximum increase in the magnitude of rain was observed for annual rainfall with 2.703 mm per year at Lake Mutanda, and the least increased dry seasonal rainfall, with 0.191 mm per year.
Climate-smart water management practices
In addition, 20% of the participants mentioned that terracing improves water quality because it reduces the flow of water during rainy seasons into the lake system, thus improving its quality as in line with Louw (2021). Many people need to be educated about water management methods, according to several of the key informants, in order to sustain the quality of the lake water in a region. People near Lake Mutanda have been implementing terracing, although only a small number of individuals do so. He continued by saying that farmers near Lake Mutanda use better water management techniques than those close to Lake Murehe, but more work needs to be done to enhance these techniques using CSA villages.
Additionally, it was discovered that 15% of the participants named contour farming as a water management strategy in the Kisoro District because it lowers soil erosion into lakes, preserves soil fertility and moisture, and increases overall crop yield. Mengstie (2009) reported that 45% of the farmers practised contour farming as a water conservation practice. It was also found that 12% of the participants mentioned conservation tillage, 9% mentioned agroforestry, and the least, 6%, said that they use less fertilizer to reduce the formation of algal blooms when fertilizers are washed into the catchments, thus reducing water contamination, as shown in Figure 4.
Some of the respondents revealed that ‘CSA has been recognized as a key tool that can be used to conquer the difficulties that climate change has posed to food systems and more effectively include agriculture in global climate negotiations. In fact, CSA helps farmers in Uganda, as well as important institutions and suppliers of services to farmers, develop the capacity to adapt to long-term climate change, to effectively respond to it, and to manage the risks that emerge from increased climate variability’.
From the interview held, some respondents said that ‘the agriculture industry has a chance to use CSA to help with climate change mitigation and resilience building through adaptation. CSA as agriculture boosts achievement of the nation's food security as well as growth goals while also enhancing resilience, increasing production in a sustainable manner, reducing or eliminating greenhouse gases when practicable. Additionally, these initiatives seek to reduce and adapt to the consequences of climate change as well as give disadvantaged people and smallholder farmers a fair and steady income and comfortable working conditions. A wide range of integrated choices that capitalize on the diversity of agricultural and fishing activities in Africa are included in CSA practices and technologies like the sustainable natural resource management’.
Local community leaders also said that ‘the local economy and social fairness may benefit greatly from CSA practices. A number of actors, including authorities, agriculturalists, civil society groups, international organizations, the commercial sector, and the scientific community, have started various CSA interventions in recent years due to the growing interest in the practice. Smallholder farmers in Uganda have recognized the impact of the climate-smart techniques adopted in this paper. The priority given to warming temperatures in the government's plan, however, varies from one nation to the next. The topic of climate change is not given any priority in southwestern Uganda. Climate change is still a particularly delicate topic because the agriculture sector is so important to the nation's economy’.
Climate-smart water management practices used by small holding farmers
In order to lessen the effects of rainfall unpredictability, small holding farmers must use CSWMP. The variety of variables, including ecological, socioeconomic, and institutional components, have an impact on how they choose to battle rainfall changes. The main goal of implementing climate-smart water management methods is to increase household income and improve food security.
According to the study, about 88% of farmers utilize it, and harvesting water is a common water management technique. In order to use rainwater or runoff for irrigation and livestock watering, water harvesting entails collecting and storing the water. In areas with variable patterns of precipitation or scarce access to alternative water sources, this approach especially helps farmers deal with water scarcity. Nevertheless, despite its advantages, only 16% of farmers currently use water collection. The perceived cost of installing water harvesting equipment is given as the justification for not implementing this technique. Some farmers, particularly those with little financial resources, may be discouraged from implementing water harvesting because it can involve an initial investment and continuous maintenance expenditures. It is important to keep in mind that the economic sustainability of water harvesting depends on a number of variables, including the climate of the area, the accessibility of other water sources, the size of the farm, and the precise water harvesting methods used. Recognizing the long-term advantages water harvesting practices can have for the environment and agricultural productivity, governments and agricultural organizations frequently provide incentives and support programs to encourage farmers to adopt them.
It was found that 80% of the farmers used agroforestry while only 33% did not use it. Agroforestry methods strategically grow trees next to crops to create microclimates that aid in water conservation and improve water usage efficiency (Sileshi et al. 2023). Tree canopies minimize water loss and maximize the availability of water for plant growth by reducing evaporation through the soil and crops. Agroforestry adoption is more likely among farmers who emphasize the preservation of water in the face of rising climate variability. As carbon sinks, trees in agroforestry systems take in and store carbon dioxide from the atmosphere as well. This aids in reducing GHG emissions and aids in the fight against climate change. Agroforestry is a climate-smart approach that farmers who care about the environment and want to lessen their carbon footprint may use. Agroforestry systems may need specialized technical knowledge and management skills that not all farmers have. Adoption may be hampered by a lack of access to training and services for extension. Implementing targeted actions and programs to encourage a wider acceptance of climate-smart water management methods requires an understanding of the obstacles and worries faced by farmers who choose not to use agroforestry. As shown in Table 8, most of the farmers practice water management practices to enhance food security and productivity.
Mutanda households' use of climate-smart water management practices
Climate-smart water management practice . | Users (N = 397) (%) . | Non-users (N = 397) (%) . |
---|---|---|
Water harvesting techniques | 88 | 16 |
Agroforestry | 80 | 33 |
Improved crop varieties | 78 | 24 |
Crop rotation | 66 | 15 |
On-farm water conservation | 50 | 35 |
Crop diversification | 42 | 58 |
Intercropping | 40 | 53 |
Change in crops cultivated | 39 | 69 |
Use of manure | 35 | 77 |
Integrated nutrient management | 24 | 78 |
Irrigation | 23 | 79 |
Integrated pest management use | 21 | 74 |
Changing in planting dates | 20 | 76 |
Livelihood diversification | 18 | 84 |
Climate-smart water management practice . | Users (N = 397) (%) . | Non-users (N = 397) (%) . |
---|---|---|
Water harvesting techniques | 88 | 16 |
Agroforestry | 80 | 33 |
Improved crop varieties | 78 | 24 |
Crop rotation | 66 | 15 |
On-farm water conservation | 50 | 35 |
Crop diversification | 42 | 58 |
Intercropping | 40 | 53 |
Change in crops cultivated | 39 | 69 |
Use of manure | 35 | 77 |
Integrated nutrient management | 24 | 78 |
Irrigation | 23 | 79 |
Integrated pest management use | 21 | 74 |
Changing in planting dates | 20 | 76 |
Livelihood diversification | 18 | 84 |
Correlation on CSWMP
The findings on the correlation coefficients of the error terms indicate that there is a positive and negative correlation between different climate-smart water management practices used by farmers. This means that the decision to use one practice affects the decision to use other practices for sustainable agriculture. Different correlation coefficients between the four practices mostly used around Lake Mutanda are found to be significant at the 1, 5, and 10% significance levels, as shown in Table 9. For instance, there is a favorable association between the use of various additional climate-smart water management techniques by farmers and their decision to use one practice. This shows that the implementation of complementary measures is stimulated by the application of one practice, resulting in a more thorough and integrated approach to water management on their farms. On the other hand, some other practices may exhibit a negative association with a few other practices. This suggests that farmers who select to practice them may be less likely to use particular associated activities. The restrictions of resources, competing demands between the practices used are as a result of this negative connection. These findings demonstrate how climate-smart water management techniques are interrelated, which is helpful information for Lake Mutanda officials and agricultural practitioners. Stakeholders can develop more efficient and coordinated strategies that promote the adoption of numerous complementary practices, promoting sustainable agriculture and management of water resources in the area, by recognizing these linkages.
Correlation on CSWMP
. | Mulching . | Terracing . | Contour farming . | Conservation tillage . | Agroforestry . |
---|---|---|---|---|---|
Mulching | 1 | ||||
Terracing | 0.01* | 1 | |||
Contour farming | 0.03* | 0.005 | 1 | ||
Conservation tillage | 0.012 | 0.002 | 0.001*** | 1 | |
Agroforestry | 0.001** | 0.001** | 0.02* | −0.014 | 1 |
. | Mulching . | Terracing . | Contour farming . | Conservation tillage . | Agroforestry . |
---|---|---|---|---|---|
Mulching | 1 | ||||
Terracing | 0.01* | 1 | |||
Contour farming | 0.03* | 0.005 | 1 | ||
Conservation tillage | 0.012 | 0.002 | 0.001*** | 1 | |
Agroforestry | 0.001** | 0.001** | 0.02* | −0.014 | 1 |
Note. *p < 0.05; **p < 0.01; and ***p < 0.001.
Novelty from the study
The study offered cutting-edge and flexible water management strategies, such as mulching, agroforestry, and terracing, that are designed to address the difficulties of climate change. The study focused on how small holding farmers in the Kisoro District can use climate-smart water management strategies to increase crop yield, decrease water waste, and have a smaller negative impact on the environment. The study critically highlighted the financial rewards for farmers that can result from adopting climate-smart water management strategies, including decreased input costs and greater earnings as a result of higher yields. Additionally, the current research showed how these water management techniques strengthen agricultural systems' resistance to extreme weather, water scarcity, and unpredictability of climate patterns. Finally, the researchers recommended governmental interventions and incentives based on their findings to promote the use of climate-smart water-related methods on a larger scale.
Seasonal variations of the physicochemical water quality parameters
In order to answer research question one on ‘what are the physicochemical qualities of water in response to rainfall variability in Lake Mutanda, southwestern Uganda’, physiochemical water quality parameters are reported in Table 10 and discussed below.
Seasonal variations of the physicochemical water quality parameters disaggregated by site and seasons (n = 132)
Sampling sites . | Season . | Turbidity (NTU) . | Temp. (°C) . | pH . | EC (μS/cm) . | DO . | |
---|---|---|---|---|---|---|---|
Upper Mutanda | Busanza | Wet | 7.95 ± 0.18 | 17.15 ± 0.01 | 7.09 ± 0.01 | 188.00 ± 5.66 | 4.60 ± 0.28 |
Dry | 2.39 ± 0.01 | 19.15 ± 0.07 | 7.12 ± 0.01 | 168.00 ± 2.83 | 3.05 ± 0.07 | ||
Iremera | Wet | 9.61 ± 0.84 | 17.15 ± 0.08 | 7.13 ± 0.04 | 168.00 ± 2.83 | 4.70 ± 0.42 | |
Dry | 2.66 ± 0.09 | 19.15 ± 0.07 | 7.12 ± 0.02 | 168.50 ± 0.71 | 3.05 ± 0.07 | ||
Bucece | Wet | 9.23 ± 1.02 | 17.10 ± 0.13 | 7.17 ± 0.04 | 158.0 ± 11.31 | 5.10 ± 0.08 | |
Dry | 2.58 ± 0.02 | 19.15 ± 0.07 | 7.16 ± 0.01 | 170.50 ± 2.12 | 3.10 ± 0.04 | ||
Middle Mutanda | Mukozi | Wet | 20.55 ± 1.06 | 17.10 ± 0.01 | 7.23 ± 0.11 | 181.50 ± 4.95 | 4.90 ± 0.20 |
Dry | 2.90 ± 0.20 | 19.15 ± 0.07 | 7.14 ± 0.06 | 166.50 ± 2.12 | 3.70 ± 0.02 | ||
Mukondero | Wet | 19.93 ± 0.25 | 17.15 ± 0.07 | 7.29 ± 0.06 | 145.50 ± 6.36 | 5.85 ± 0.64 | |
Dry | 3.17 ± 0.04 | 19.20 ± 0.14 | 7.16 ± 0.01 | 154.00 ± 8.49 | 4.05 ± 0.07 | ||
Mutaba | Wet | 25.73 ± 3.38 | 17.10 ± 0.02 | 7.29 ± 0.01 | 153.5 ± 10.61 | 5.80 ± 0.28 | |
Dry | 4.95 ± 0. 03 | 19.15 ± 0.07 | 7.18 ± 0.04 | 157.00 ± 9.90 | 3.50 ± 0.71 | ||
Lower Mutanda | Mutanda | Wet | 25.0 ± 10.4 | 17.15 ± 0.07 | 7.32 ± 0.03 | 165.5 ± 19.09 | 5.15 ± 0.07 |
Dry | 5.18 ± 0.06 | 19.15 ± 0.21 | 7.21 ± 0.01 | 157.00 ± 8.49 | 2.00 ± 0.04 | ||
Kyangushu | Wet | 38.0 ± 2.94 | 17.20 ± 0.11 | 7.30 ± 0.02 | 141.50 ± 6.36 | 5.65 ± 0.21 | |
Dry | 4.03 ± 0.01 | 19.15 ± 0.21 | 7.21 ± 0.12 | 144.50 ± 0.71 | 2.50 ± 0.02 | ||
Buhungyiro | Wet | 37.24 ± 1.00 | 17.15 ± 0.07 | 7.33 ± 0.04 | 139.5 ± 13.44 | 5.75 ± 0.49 | |
Dry | 4.96 ± 0.07 | 19.20 ± 0.14 | 7.27 ± 0.01 | 140.5 ± 10.61 | 1.50 ± 0.03 | ||
WHO | Standards | 2017 | < 5 | 25–30 | 6.5–8.5 | 250 | 6 |
Sampling sites . | Season . | Turbidity (NTU) . | Temp. (°C) . | pH . | EC (μS/cm) . | DO . | |
---|---|---|---|---|---|---|---|
Upper Mutanda | Busanza | Wet | 7.95 ± 0.18 | 17.15 ± 0.01 | 7.09 ± 0.01 | 188.00 ± 5.66 | 4.60 ± 0.28 |
Dry | 2.39 ± 0.01 | 19.15 ± 0.07 | 7.12 ± 0.01 | 168.00 ± 2.83 | 3.05 ± 0.07 | ||
Iremera | Wet | 9.61 ± 0.84 | 17.15 ± 0.08 | 7.13 ± 0.04 | 168.00 ± 2.83 | 4.70 ± 0.42 | |
Dry | 2.66 ± 0.09 | 19.15 ± 0.07 | 7.12 ± 0.02 | 168.50 ± 0.71 | 3.05 ± 0.07 | ||
Bucece | Wet | 9.23 ± 1.02 | 17.10 ± 0.13 | 7.17 ± 0.04 | 158.0 ± 11.31 | 5.10 ± 0.08 | |
Dry | 2.58 ± 0.02 | 19.15 ± 0.07 | 7.16 ± 0.01 | 170.50 ± 2.12 | 3.10 ± 0.04 | ||
Middle Mutanda | Mukozi | Wet | 20.55 ± 1.06 | 17.10 ± 0.01 | 7.23 ± 0.11 | 181.50 ± 4.95 | 4.90 ± 0.20 |
Dry | 2.90 ± 0.20 | 19.15 ± 0.07 | 7.14 ± 0.06 | 166.50 ± 2.12 | 3.70 ± 0.02 | ||
Mukondero | Wet | 19.93 ± 0.25 | 17.15 ± 0.07 | 7.29 ± 0.06 | 145.50 ± 6.36 | 5.85 ± 0.64 | |
Dry | 3.17 ± 0.04 | 19.20 ± 0.14 | 7.16 ± 0.01 | 154.00 ± 8.49 | 4.05 ± 0.07 | ||
Mutaba | Wet | 25.73 ± 3.38 | 17.10 ± 0.02 | 7.29 ± 0.01 | 153.5 ± 10.61 | 5.80 ± 0.28 | |
Dry | 4.95 ± 0. 03 | 19.15 ± 0.07 | 7.18 ± 0.04 | 157.00 ± 9.90 | 3.50 ± 0.71 | ||
Lower Mutanda | Mutanda | Wet | 25.0 ± 10.4 | 17.15 ± 0.07 | 7.32 ± 0.03 | 165.5 ± 19.09 | 5.15 ± 0.07 |
Dry | 5.18 ± 0.06 | 19.15 ± 0.21 | 7.21 ± 0.01 | 157.00 ± 8.49 | 2.00 ± 0.04 | ||
Kyangushu | Wet | 38.0 ± 2.94 | 17.20 ± 0.11 | 7.30 ± 0.02 | 141.50 ± 6.36 | 5.65 ± 0.21 | |
Dry | 4.03 ± 0.01 | 19.15 ± 0.21 | 7.21 ± 0.12 | 144.50 ± 0.71 | 2.50 ± 0.02 | ||
Buhungyiro | Wet | 37.24 ± 1.00 | 17.15 ± 0.07 | 7.33 ± 0.04 | 139.5 ± 13.44 | 5.75 ± 0.49 | |
Dry | 4.96 ± 0.07 | 19.20 ± 0.14 | 7.27 ± 0.01 | 140.5 ± 10.61 | 1.50 ± 0.03 | ||
WHO | Standards | 2017 | < 5 | 25–30 | 6.5–8.5 | 250 | 6 |
As can be seen from Table 10, the turbidity values during the wet seasons were the highest in lower mutanda (Mutanda: 25.0 ± 10.4 NTU, Kyangushu: 38.0 ± 2.94 NTU, and Buhungyiro: 37.24 ± 1.00 NTU) as compared with those recorded in middle Mutanda (Mukozi: 20.55 ± 1.06 NTU, Mukondero: 19.93 ± 0.25 NTU, and Mutaba: 25.73 ± 3.38 NTU) while the values recorded for upper Mutanda were the lowest (Busanza: 7.95 ± 0.18 NTU, Iremera: 9.61 ± 0.84 NTU, and Bucece: 9.23 ± 1.02 NTU). On the other hand, during the dry seasons, turbidity valves were highest in lower Mutanda (Mutanda: 5.18 ± 0.06 NTU, Buhungyiro: 4.96 ± 101 NTU, and Kyangushu: 4.03 ± 1.01 NTU) as compared with those recorded in middle Mutanda (Mutaba: 4.95 ± 0.03 NTU, Mukondero: 3.17 ± 0.04 NTU, and Mutanda: 2.90 ± 0.20 NTU) while the values recorded for upper Mutanda were the lowest (Iremera: 2.66 ± 0.09 NTU, Bucece: 2.58 ± 0.02 NTU, and Busanza: 2.39 ± 0.01 NTU). This agrees with Chen & Chang (2019) who found that the turbidity levels in the water were higher in wet seasons than in dry seasons. This is attributed to heavy rainfall that eroded soil particles into the water system leading to higher turbidity. The turbidity range across all study stations in the wet seasons exceeds the maximum value (5 NTU) set by the WHO for drinking water (WHO 2018). Waithaka et al. (2020) reported that the water quality parameters during the wet season recorded a higher turbidity of 79.00 ± 50.43 NTU and 11.60 ± 6.43 NTU. Newport et al. (2021) reported that increasing turbidity affects the development of visual systems and the behavioral ecology of fish from a wide range of habitats causing low productivity in fish farming. Hence, the water from Lake Mutanda cannot be consumed according to the WHO (2017) recommendations which estimate the turbidity value below 5 NTU. The high turbidity of the water during the wet season was attributed to the presence of suspended matter like clay, silt, organic matter (Hashmi et al. 2020), and microorganisms during the rainy season due to poor farming methods used by the local crop farmers around Lake Mutanda, which calls for immediate action, and water management policies in Kisoro District.
The temperature values during the dry season was slightly higher in the upper Mutanda (Busanza: 19.15 ± 0.07 °C, Iremera: 19.15 ± 0.07 °C, and Bucece: 19.15 ± 0.07 °C) as compared with those recorded in the wet seasons in upper Mutanda (Busanza: 17.15 ± 0.01 °C, Iremera: 17.15 ± 0.09 °C, and Bucece: 17.10 ± 0.13 °C). The current study is in line with Shukla et al. (2023) who note that agricultural runoff can also contribute to warmer lake temperatures. Fertilizers and pesticides used in agriculture can cause algae blooms, which can raise water temperatures during wet and dry seasons. The temperature range across all study stations from March to July did not exceed the maximum value (25 °C) for the global national regulations and standards for drinking water (WHO 2018).
The pH values during the wet seasons in lower Mutanda was found to be higher (Buhungyiro: 7.33 ± 0.04, Mutanda: 7.32 ± 0.03, and Kyangushu: 7.30 ± 0.02) as compared with those recorded in middle Mutanda (Mukondero: 7.29 ± 0.06 Mutaba: 7.29 ± 0.01, and Mukozi: 7.23 ± 0.11), and the lowest values recorded for upper Mutanda (Iremera: 7.13 ± 0.71 Bucece: 7.17 ± 0.04, and Busanza: 7.09 ± 0.01). During the dry seasons of June–July, higher pH values were recorded in lower Mutanda (Buhungyiro: 7.27 ± 0.01, Kyangushu: 7.21 ± 0.02, and Mutanda: 7.21 ± 0.01) as compared with those recorded in middle Mutanda (Mutaba: 7.18 ± 0.04, Mukondero 7.16 ± 0.01, and Mukozi: 7.14 ± 0.01), and the values recorded for upper Mutanda (Bucece: 7.16 ± 0.01 Busanza: 7.12 ± 0.03, and Iremera: 7.12 ± 0.02). In Mbarara, Uganda, Ojok et al. (2017) also noted that during the dry season, the mean pH of river water measured during the rainy season was higher (M = 8.400.548) than the sample taken in the dry season (M = 6.800.447). This study's findings show that Lake Mutanda water pH is within the acceptable range for drinking by the WHO (2017) standard threshold (6.5–8.5).
The EC values during the wet seasons were higher in upper Mutanda (Busanza: 188.00 ± 5.66 uS/cm, Iremera: 168.00 ± 2.83 uS/cm, and Bucece: 158.0 ± 11.31 uS/cm), middle region (Mukozi: 181.50 ± 4.95 uS/cm, Mutaba: 153.5 ± 10.61 uS/cm, and Mukondero: 145.50 ± 6.36 uS/cm), and lower region (Mutanda: 165 ± 19.09 uS/cm, and Kyangushu: 141.50 ± 6.36 uS/cm, and Buhungyiro: 139.5 ± 13.44 uS/cm), as compared with those recorded in the lower region. During the dry seasons of June–July, EC values in the upper region were found higher (Bucece: 170.50 ± 2.12 uS/cm, Busanza: 168.00 ± 2.83 uS/cm, and Iremera: 168.50 ± 0.71 uS/cm), middle region (Mukozi: 166.50 ± 2.12 uS/cm, Mutaba: 157.00 ± 9.90 uS/cm, and Mukondero: 145.50 ± 6.36 uS/cm), and lower region (Mutanda: 157 ± 8.49 uS/cm, Kyangushu: 144.50 ± 0.71 uS/cm, and Buhungyiro: 140.5 ± 10.61 uS/cm). Regarding EC, the results of the current investigation were within the WHO maximum allowed levels of 2,500 S/cm, which are outlined in international national drinking water rules and standards. As a result, the results accurately show that Lake Mutanda's water was not significantly ionized and contained a low degree of ionic concentration.
DO (mg/L) values during the wet seasons were higher in middle Mutanda (Mukondero: 5.85 ± 0.64 mg/L, Mutaba: 5.80 ± 0.28 mg/L, and Mukozi: 4.90 ± 0.20 mg/L) as compared with those recorded in lower Mutanda (Kyangushu: 5.65 ± 0.21 mg/L, Mutanda: 5.15 ± 0.07 mg/L, and Buhungyiro: 5.75 ± 0.49 mg/L). The values recorded in the upper Mutanda (Bucece: 5.10 ± 0.08 mg/L, Iremera: 4.70 ± 0.42 mg/L, and Busanza: 4.60.00 ± 0.28 mg/L). During the dry seasons of June–July, DO (mg/L) values in middle Mutanda (Mukondero 4.05 ± 0.07 mg/L, Mukozi: 3.70 ± 0.02 mg/L, and Mutaba: 3.50 ± 0.71 mg/L) were higher as compared with those recorded in upper Mutanda (Bucece: 3.10 ± 0.04 mg/L, Busanza 3.05 ± 0.07 mg/L, and Iremera: 3.05 ± 0.07 mg/L) and the values recorded for lower Mutanda (Kyangushu: 2.50 ± 0.02 mg/L, Buhungyiro: 1.50 ± 0.03 mg/L, and Mutanda: 2.00 ± 0.02 mg/L). The observed DO concentrations in the current study in the wet season were greater than the acceptable limit (6 mg/L) set by WHO/UNBS standards. Hence, exposure to extremely high levels of dissolved oxygen (hyperoxia) can be harmful to some aquatic organisms, particularly those adapted to low-oxygen environments, such as certain species of fish and invertebrates. In some cases, high levels of dissolved oxygen can cause increased competition between aquatic organisms for resources such as food and habitat, leading to ecological imbalances.
The findings are drawn from Lake Mutanda community members with the least basic computer literacy and good access to the internet. These findings should not be overly generalized because their relevance can change in various settings.
The classification of climate-smart water management techniques described here is meant to be a preliminary overview of techniques involved in sustainable agriculture rather than a full list. Further study should be conducted to assess the perception of the farmers on rainfall variability and seek to generalize findings more widely.
CONCLUSIONS AND FINAL REMARKS
In southwestern Uganda, the implementation of CSWMS, particularly for water quality management around water resources, remains limited. A significant proportion (38%) of local farmers around Lake Mutanda utilize mulching as a means to reduce soil erosion and enhance agricultural productivity, contributing to CSA initiatives. However, these practices are not widely adopted in communities neighboring rivers such as Ruhezamyenda and Mukya, which flow into Lake Mutanda. Despite its potential benefits, little effort has been made to scale up mulching in these areas. However, mulching practices are not widely adopted in areas near rivers and small streams like Ruhezamyenda and Mukya, leaving a gap for scaling up CSA. Analysis of various physicochemical parameters, including turbidity, temperature, and dissolved oxygen, revealed levels surpassing WHO-permitted standards. This underscores the critical need for climate-smart interventions in the study area to mitigate adverse impacts on water quality, thus safeguarding the livelihoods of local farmers who rely on these resources for agricultural activities and sustenance. Failure to address these issues could have significant long-term repercussions on agricultural productivity and community well-being. Continuous monitoring of water quality and data analyses are required to understand the water quality status of water bodies in different seasons. This study is helpful to water managers and administrators to formulate a plan to achieve healthy water through sustainable management. Also, there is a need to establish informative, educative, communicative and climate-smart agricultural programs for the populations that use poor farming methods that contaminate water sources through climate-smart village approach to ease the water quality status of the lake. Therefore, the study on farming methods that reduce the extent of lake water contamination throughout periods of excessive rainfall should be prioritized in policies that seek to stimulate the application of climate-smart water management practices at the local level. Thus, there is a need to help farmers improve their indigenous knowledge, which facilitates the use of improved water management practices. There is also a need to study the relationship between climate variability and water quality using Google Earth Software and Remote sensing techniques in Lake Mutanda.
FUNDING
This project was fully funded by the World Bank through ACE for CSA and Biodiversity Conservation, Haramaya University, Ethiopia, under the identification number IDA 57940.
RESEARCH ETHICS APPROVAL
This research was approved by the Mbarara University of Science and Technology Research Ethics Committee under protocol reference number (MUST-2022-666).
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.