A multi-method analysis of forest fragmentation and loss: The case of ward 11, Chiredzi District of Zimbabwe

Forest fragmentation and loss seriously affect biodiversity. There is need to monitor and assess forest fragmentation and loss in communal areas for effective biodiversity management. In this study, we analysed the extent of forest fragmentation and loss in ward 11, Chiredzi district of Zimbabwe over a 14 year period (1989 to 2003). A multi-method design was adopted for triangulation and verification purposes. This involved the use of GIS and remote sensing techniques for analysis of satellite images of 1989 and 2003. Fragstats was used to compute the density, size and variation of patches between the two years. A patch area method for determining optimum quadrat size was proposed from for observations and measurements were done. Questionnaire surveys were used to complement data produced through GIS analysis. The non aligned block sampling design in which sample locations were randomly nested was used. Questionnaire surveys were used to collect qualitative data. Results show that there is ecologically significant fragmentation and loss of forest. Forest patches increased by 58.04% between 1989 and 2003. A loss of 32.47% of forest area was estimated. People’s perceptions confirm the conclusion that the forest has been significantly fragmented and lost due to collaborative effects of climatic changes and human activities.Key words: Forest fragmentation and loss, multi-method design, remote sensing, geographic informationsystem, Fragstats, patch area method

Quantifying changes in plant species diversity in a savanna ecosystem through observed and remotely sensed data

This study examined the impact of climate change on plant species diversity of a savanna ecosystem, through an assessment of climatic trends over a period of forty years (1974-2014) using Masvingo Province, Zimbabwe, as a case study. The normalised difference vegetation index (NDVI) was used as a proxy for plant species diversity to cover for the absence of long-term historical plant diversity data. Observed precipitation and temperature data collected over the review period were compared with the trends in NDVI to understand the impact of climate change on plant species diversity over time. The nonaligned block sampling design was used as the sampling framework, from which 198 sampling plots were identified. Data sources included satellite images, field measurements, and direct observations. Temperature and precipitation had significant (p < 0.05) trends over the period under study. However, the trend for seasonal total precipitation was not significant but declining. Significant correlations (p < 0.001) were identified between various climate variables and the Shannon index of diversity. NDVI was also significantly correlated to the Shannon index of diversity. The declining trend of plant species in savanna ecosystems is directly linked to the decreasing precipitation and increasing temperatures

Perceptions of climate change and adaptation to microclimate change and variability among smallholder farmers in Mhakwe Communal Area, Manicaland province, Zimbabwe

Climate change and the related increasing variability are real phenomena in sub-Saharan Africa. They are exacerbating climatic risks associated with small-scale agriculture in tropical regions. This study seeks to assess smallholder farmers' perceptions of climate change and also their adaptive strategies at the microscale in Mhakwe Communal Area in Zimbabwe. A mixed method research design was employed to carry out the study. The design was a triangulation of quantitative and qualitative data collection methods. A sample of 43 smallholder farmers was purposively selected because the population of smallholder farmers was unknown. The study noted that government agencies and non-governmental organisations were providing information about climate change and variability to smallholder farmers. Farmers practiced a number of adaptation strategies such as timing in planting, zero tillage, mulching, agroforestry and gardening. The study recommended that external agencies should focus on strengthening existing adaptive strategies. There is also need to scale-up programmes on capacity building with regards to dissemination of analysed weather andclimate data.Key Words: Climate change; Adaptation; Agriculture; Smallholder farmers, Vulnerabilit

Challenges and Opportunities for ‘little brothers’ in the Tourism Sector Matrix: The Case of Local Communities around Great Zimbabwe National Monument.

Pro-poor tourism seeks to improve opportunities and earnings of the socio-economically disadvantaged communities. This research explored ways in which poor and often marginalized local communities around the Great Zimbabwe National Monument can benefit through pro-poor tourism. The study adopted a mixed methods research framework. Research methods included key informant interviews, a questionnaire survey, participant observations and desktop research. The research findings indicate that local communities are deriving economic, social, cultural and environmental benefits from the tourism economic sub-sector. The benefits accruing to these local communities are derived from selling artefacts, handicrafts, agricultural produce and fruits to tourists as well as often lowly paid wage employment in hotels and lodges. There are also spill-over benefits as improved transport and communication services as the area is linked by an all-weather tarred road from Masvingo City and partial cellular network provision respectively. Moreover, some of the villagers are also involved in fishing in Lake Mutirikwi and its feeder rivers. However, accrual of the stated benefits remains unsatisfactory due to a plethora of factors including poor participation by the local villagers in community-based tourism projects as a result of lack of entrepreneurial skills, technical knowhow, capital and linkages to the mainstream tourism market. The study recommends a multistakeholder approach in building the capacity of local communities in terms of tourism product development and linkages with the mainstream tourism market. This maximization of benefits will enable the local villagers to play a stewardship role towards cultural and biophysical resources found in their area thereby contributing to employment and sustainable tourism development

A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018

Global greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene

Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale

Estimating biomass of savanna grasslands as a proxy of carbon stock using multispectral remote sensing

Limited research has been done to estimate the root biomass (belowground biomass) of savanna grasslands. The advent of remote sensing and related products have facilitated the estimation of biomass in terrestrial ecosystems, providing a synoptic overview on ecosystems biomass. Multispectral remote sensing was used in this study to estimate total biomass (belowground and aboveground) of selected tropical savanna grassland species. Total biomass was estimated by assessing the relationship between aboveground and belowground biomass, the Normalised Difference Vegetation Index (NDVI) and belowground biomass, and NDVI and total biomass. Results showed a positive significant relationship (p ¼ 0.005) between belowground and aboveground biomass. NDVI was significantly correlated (p ¼ 0.0386) to aboveground biomass and the Root Mean Square Error (RMSE) was 18.97 whilst the model BIAS was 0.019, values within acceptable ranges. A significant relationship (p ¼ 0) was found between belowground biomass and NDVI and the RMSE was 5.53 and the model BIAS was 0.0041. More so, a significant relationship (p ¼ 0.054) was observed between NDVI and total biomass. The positive relationships between NDVI and total grass biomass and the lack of bias in the model provides an opportunity to routinely monitor carbon stock and assess seasonal carbon storage fluctuations in grasslands. There is great potential in the ability of remote sensing to become an indispensable tool for assessing, monitoring and inventorying carbon stocks in grassland ecosystems under tropical savanna conditions

Comparison of oxidative stress biomarkers in Oreochromis mossambicus in minimally and highly disturbed aquatic environments in the Matabeleland region, Zimbabwe

Owing to their ability to provide a functional measure of organismal response to chemical stressors, oxidative biomarkers are useful in ecotoxicological studies to assess disturbance in aquatic environments. This study assessed the use of oxidative stress biomarkers in Oreochromis mossambicus (Peters, 1852) (Perciformes: Cichlidae) to distinguish between minimally and highly disturbed aquatic environments. A water quality index (WQI) and overall index of pollution (OIP) were used to characterize the target sites, namely the Mananda Dam (control, reference site) and Lower Mguza Dam (disturbed site). Forty male O. mossambicus samples were collected from each dam between April and August 2013. Values for the WQI and OIP indices were significantly higher for the Lower Mguza Dam than for the Mananda Dam (p < 0.05). Oxidative stress biomarker evaluation results showed that the activities of glutathione peroxidase (GPx), superoxide dismutase (SOD) and glutathione S-transferase (GST) as well as the malondialdehyde (MDA) concentration in the liver of O. mossambicus were significantly higher in fish collected from Lower Mguza Dam than those collected from Mananda Dam (p < 0.05). The activities of DT-diaphorase (DTD) and catalase (CAT) were significantly inhibited in fish from the Lower Mguza Dam, when compared to those collected from the Mananda Dam (p < 0.05). From these findings, it is evident that oxidative stress biomarkers, such as antioxidant enzyme activity and MDA accumulation, can be used to differentiate minimally and highly disturbed aquatic environments

Integration of mid-infrared spectroscopy and geostatistics in the assessment of soil spatial variability at landscape level

Knowledge of soil spatial variability is important in natural resource management, interpolation and soil sampling design, but requires a considerable amount of geo-referenced data. In this study, mid-infrared spectroscopy in combination with spatial analyses tools is being proposed to facilitate landscape evaluation and monitoring. Mid-infrared spectroscopy (MIRS) and geostatistics were integrated for evaluating soil spatial structures of three land settlement schemes in Zimbabwe (i.e. communal area, old resettlement and new resettlement; on loamy-sand, sandy-loam and clay soils, respectively). A nested non-aligned design with hierarchical grids of 750, 150 and 30 m resulted in 432 sampling points across all three villages (730–1360 ha). At each point, a composite topsoil sample was taken and analyzed by MIRS. Conventional laboratory analyses on 25–38% of the samples were used for the prediction of concentration values on the remaining samples through the application of MIRS–partial least squares regression models. These models were successful (R2 ? 0.89) for sand, clay, pH, total C and N, exchangeable Ca, Mg and effective CEC; but not for silt, available P and exchangeable K and Al (R2 ? 0.82). Minimum sample sizes required to accurately estimate the mean of each soil property in each village were calculated. With regard to locations, fewer samples were needed in the new resettlement area than in the other two areas (e.g. 66 versus 133–473 samples for estimating soil C at 10% error, respectively); regarding parameters, less samples were needed for estimating pH and sand (i.e. 3–52 versus 27–504 samples for the remaining properties, at same error margin). Spatial analyses of soil properties in each village were assessed by constructing standardized isotropic semivariograms, which were usually well described by spherical models. Spatial autocorrelation of most variables was displayed over ranges of 250–695 m. Nugget-to-sill ratios showed that, in general, spatial dependence of soil properties was: new resettlement > old resettlement > communal area; which was potentially attributed to both intrinsic (e.g. texture) and extrinsic (e.g. management) factors. As a new approach, geostatistical analysis was performed using MIRS data directly, after principal component analyses, where the first three components explained 70% of the overall variability. Semivariograms based on these components showed that spatial dependence per village was similar to overall dependence identified from individual soil properties in each area. In fact, the first component (explaining 49% of variation) related well with all soil properties of reference samples (absolute correlation values of 0.55–0.96). This showed that MIRS data could be directly linked to geostatistics for a broad and quick evaluation of soil spatial variability. It is concluded that integrating MIRS with geostatistical analyses is a cost-effective promising approach, i.e. for soil fertility and carbon sequestration assessments, mapping and monitoring at landscape level