Vegetation Health Research Articles

Quantification of interactions among agricultural drought indices within Köppen–Geiger climate zones in Bangladesh

Agricultural productivity is highly correlated with climatic variations, including drought events. This study is aimed at performing a comprehensive assessment of agricultural drought conditions in the Köppen–Geiger climate zones in Bangladesh, based on the temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI). The zones are classified as follows: temperate dry winter with a hot summer zone, tropical savannah zone, and tropical monsoon zone. The range of VHI is optimized based on its correlation with TCI and VCI. The correlation among the stratified drought indices is also examined to quantify and measure the strength and direction of the linear relationships between them. Moreover, Mann–Kendall test is conducted to assess the statistical trends in the spatiotemporal propagation of droughts across different climate zones. The balanced correlation approach for VHI reveals that vegetation health is governed by the temperature conditions. Certain variations in the drought intensity, frequency, and duration are observed across climatic zones in earlier years while the recent years are noted with less droughts. Normal or no drought conditions are noticed mostly in the tropical monsoon zone through VCI and VHI. The correlations among the drought classes indicate that VHI is more strongly correlated with TCI than with VCI, while NDVI exhibits stronger correlations with VCI than with either VHI or TCI. The Mann-Kendall test revealed that VCI has significant downward trends in drought categories and an upward trend in normal conditions, whereas VHI and TCI displayed inconsistency in statistical trends. By extensively exploring the agricultural drought conditions within specific climate zones, this study offers valuable insights for agronomists and stakeholders involved in climate resilience planning and agricultural development in Bangladesh.

Unveiling Climate–Land Use and Land Cover Interactions on the Kerch Peninsula Using Structural Equation Modeling

This paper examines the effects of climatic factors, specifically temperature and precipitation, on land use and land cover (LULC) on the Kerch Peninsula using structural equation modeling (SEM). The Normalized Difference Vegetation Index (NDVI) was used as a mediator in the model to accurately assess the impact of climate change on vegetation and subsequent LULC dynamics. The results indicate that temperature exerts a significant negative influence on LULC in the early periods, inducing stress on vegetation and leading to land degradation. However, this influence diminishes over time, possibly due to ecosystem adaptation and the implementation of resilient land management practices. In contrast, the impact of precipitation on LULC, which is initially minimal, increases significantly, highlighting the need for improved water resource management and adaptation measures to mitigate the negative effects of excessive moisture. The NDVI plays a crucial mediating role, reflecting the health and density of vegetation in response to climatic variables. An analysis of lagged effects shows that both precipitation and temperature exert delayed effects on LULC, underscoring the complexity of water dynamics and ecosystem responses to climatic conditions. These results have important practical implications for land resource management and climate adaptation strategies. Understanding the nuanced interactions between climatic factors and LULC can inform the development of resilient agricultural systems, optimized water management practices, and effective land use planning. Future research should focus on refining models to incorporate nonlinear interactions, improving data accuracy, and expanding the geographic scope to generalize findings. This study highlights the importance of continuous monitoring and adaptive management to develop sustainable land management practices that can withstand the challenges of climate change.

Utilizing the Google Earth Engine for Agricultural Drought Conditions and Hazard Assessment Using Drought Indices in the Najd Region, Sultanate of Oman

Accurately evaluating drought and its effects on the natural environment is difficult in regions with limited climate monitoring stations, particularly in the hyper-arid region of the Sultanate of Oman. Rising global temperatures and increasing incidences of insufficient precipitation have turned drought into a major natural disaster worldwide. In Oman, drought constitutes a major threat to food security. In this study, drought indices (DIs), such as temperature condition index (TCI), vegetation condition index (VCI), and vegetation health index (VHI), which integrate data on drought streamflow, were applied using moderate resolution imaging spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform to monitor agricultural drought and assess the drought risks using the drought hazard index (DHI) during the period of 2001–2023. This approach allowed us to explore the spatial and temporal complexities of drought patterns in the Najd region. As a result, the detailed analysis of the TCI values exhibited temporal variations over the study period, with notable minimum values observed in specific years (2001, 2005, 2009, 2010, 2014, 2015, 2016, 2017, 2019, 2020, and 2021), and there was a discernible trend of increasing temperatures from 2014 to 2023 compared to earlier years. According to the VCI index, several years, including 2001, 2003, 2006, 2008, 2009, 2013, 2015, 2016, 2017, 2018, 2020, 2021, 2022, and 2023, were characterized by mild drought conditions. Except for 2005 and 2007, all studied years were classified as moderate drought years based on the VHI index. The Pearson correlation coefficient analysis (PCA) was utilized to observe the correlation between DIs, and a high positive correlation between VHI and VCI (0.829, p < 0.01) was found. Based on DHI index spatial analysis, the northern regions of the study area faced the most severe drought hazards, with severity gradually diminishing towards the south and east, and approximately 44% of the total area fell under moderate drought risk, while the remaining 56% was classified as facing very severe drought risk. This study emphasizes the importance of continued monitoring, proactive measures, and effective adaptation strategies to address the heightened risk of drought and its impacts on local ecosystems and communities.

Assessment of Fatty Acid and Oxylipin Profile of Resprouting Olive Trees Positive to Xylella fastidiosa subsp. pauca in Salento (Apulia, Italy).

Xylella fastidiosa subsp. pauca ST53 (XFP), the causal agent of olive quick decline syndrome (OQDS), was thoroughly investigated after a 2013 outbreak in the Salento region of Southern Italy. Some trees from Ogliarola Salentina and Cellina di Nardò, susceptible cultivars in the Gallipoli area, the first XFP infection hotspot in Italy, have resprouted crowns and are starting to flower and yield fruits. Satellite imagery and Normalized Difference Vegetation Index analyses revealed a significant improvement in vegetation health and productivity from 2018 to 2022 of these trees. Lipid molecules have long been recognized as plant defense modulators, and recently, we investigated their role in XFP-positive hosts and in XFP-resistant as well as in XFP-susceptible cultivars of olive trees. Here, we present a case study regarding 36 olive trees (12 XFP-positive resprouting, 12 XFP-positive OQDS-symptomatic, and 12 XFP-negative trees) harvested in 2022 within the area where XFP struck first, killing millions of trees in a decade. These trees were analyzed for some free fatty acid, oxylipin, and plant hormones, in particular jasmonic and salicylic acid, by targeted LC-MS/MS. Multivariate analysis revealed that lipid markers of resistance (e.g., 13-HpOTrE), along with jasmonic and salicylic acid, were accumulated differently in the XFP-positive resprouting trees from both cultivars with respect to XFP-positive OQDS symptomatic and XFP-negative trees, suggesting a correlation of lipid metabolism with the resprouting, which can be an indication of the resiliency of these trees to OQDS. This is the first report concerning the resprouting of OQDS-infected olive trees in the Salento area.

Identification of high-performing soil groups in grazing lands using a multivariate analysis method

Understanding and quantifying the complex relationships between soil properties and vegetation health is important for sustainable land management and optimising agricultural productivity. This study tested a spatial data-driven framework to identify the soil groups associated with pasture health using publicly available gridded soil attribute layers from Soil and Landscape Grid of Australia (SLGA) over two adjacent southeast Australian river catchments. Principal component analysis (PCA) followed by isocluster unsupervised classification was applied to seventeen SLGA soil attribute layers to identify dominant soil patterns, which showed good spatial agreement with the Enhanced Vegetation Index (EVI) data derived from Landsat-8 imagery. The soil class demonstrating the highest EVI values (HVR class) and the lowest EVI values (LVR class) were determined. A comparison of these classes with soil types defined in the New South Wales Soil Landscape maps confirmed that the HVR class is predominated by agriculturally productive, basalt-derived 'Ant Hill' soils. The cation exchange capacity (CEC) ranging from ∼10 to 20 meq/100 g, clay content ranging from 20 % to 30 %, pHc (pH in calcium chloride solution) between 5 and 6, pHw (pH in water) between 5.5 and 6.5 and SOC between 2.5 % to 4 % were associated with higher EVI values in the HVR soil class within the study area. This study demonstrated an effective framework for identifying key soil attributes that affect agricultural productivity and pinpointing critical locations for grazing land productivity.

50 years of mining-induced environmental changes: topography, hydrology, and vegetation health in Kazreti, Georgia

Globally, prioritizing short-term economic gains from mineral extraction has led to a critical dilemma: a planet rich in resources struggles with environmental degradation and a diminishing ability to sustain future generations. Open-pit mining exemplifies this paradox, causing significant environmental damage. In Georgia, this extractive industry presents environmental problems. Despite these known consequences, the long-term impacts of mining activities remain understudied. This study addressed this gap by analyzing the effects of open-pit mining on terrain morphology, and water dynamics in the Kazreti region over a 50-year period (1970–2020) and vegetation health over 35-year period (1987–2022). By integrating water quality assessment, spatial analysis and remote sensing, we revealed the significant human-induced changes to the region’s ecosystem. Spatial analysis results suggested that over 156.7 million cubic meters of bedrock have been fragmented by mining in southern East Georgia, with 125.5 million cubic meters deposited in valleys. Consequently, discernible shifts in the trajectories of water flow were observed based on the hydrological model. Additionally, a comparative analysis of NDVI and EVI values revealed a decline in vegetation health near mining zones, while remote forest areas remained stable. June typically showed healthier vegetation due to cooler temperatures and optimal growing conditions, while August presented lower vegetation health due to increased heat stress. Water quality revealed significant loadings of Cu (58–1855 μg l−1), Zn (54–2582 μg l−1), Mn (1–2167 μg l−1), and Cd (0.1–4.5 μg l−1), in local river systems, which are higher than the Georgian official guideline values (Cu - 1000, Zn - 1000, Mn—100, Cd—1 μg l−1). This study highlighted the need for a broader long-term monitoring strategy to assess the migration of these contaminants within the food web and the consequent socio-economic impact.

Claims highlighting health benefits may promote vegetable consumption: A cross-national comparison of consumer perspectives on the importance of vegetable taste, health, and nutrition

Claims highlighting health benefits may promote vegetable consumption: A cross-national comparison of consumer perspectives on the importance of vegetable taste, health, and nutrition

Hyperspectral Reflectance Assessment for Preliminary Identification of Degraded Soil Zones in Industrial Sites, India

The study explores the potential of next-generation satellite hyperspectral imaging systems for screening and predicting surface‐soil contamination and degradation by exploiting various spectral indices and signature‐matching techniques at a heavily industrialized area in India. The soil moisture content, desertification and salinity status, clay or fine material content, heavy metal content, vegetation health status, and stress levels were assessed from continuum-removed spectral reflectance values. Results indicated the presence of water in two tailings ponds, high salinity, and desertification values in most of the tailings ponds and dump sites, clay boundary liner along four ponds, high heavy metal indices along three ponds and all dump sites, highly stressed vegetation near all tailings ponds and coal dump sites, and pollutants in nearby water channels. The results suggest a strategy for the initial identification of priority areas for ground-based investigations and an alternative rapid methodology to monitor large industrial hubs in India.

Climatic teleconnection of the future trend of meteorological, GRACE-DSI, and vegetation-conditioned-based drought analysis in the Ganga Basin

ABSTRACT This study assesses drought in the Ganga River Basin using standard precipitation index (SPI), Gravity Recovery and Climate Experiment Drought Severity Index (GRACE-DSI), and vegetation condition index (VCI) via geographic information system (GIS) and non-parametric tests. Future SPI trends show increasing drought severity from 1986 to 2020. GRACE-DSI analysis (2002–2020) indicates a potential rise in drought conditions. VCI computations reveal vegetation health dynamics. Findings suggest an impending severe drought in the Ganga Basin, necessitating urgent water resource management. Drought causes are linked to precipitation changes and resource over-exploitation in changing climate conditions. The study emphasizes implementing measures like water conservation, sustainable land use, and groundwater recharge strategies to mitigate drought impacts.

Influence of Spotted Lanternfly (Lycorma delicatula) on Multiple Maple (Acer spp.) Species Canopy Foliar Spectral and Chemical Profiles

Invasive species have historically disrupted environments by outcompeting, displacing, and extirpating native species, resulting in significant environmental and economic damage. Developing approaches to detect the presence of invasive species, favorable habitats for their establishment, and predicting their potential spread are underutilized management strategies to effectively protect the environment and the economy. Spotted lanternfly (SLF, Lycorma delicatula) is a phloem-feeding planthopper native to China that poses a severe threat to horticultural and forest products in the United States. Tools are being developed to contain the spread and damage caused by SLF; however, methods to rapidly detect novel infestations or low-density populations are lacking. Vegetation spectroscopy is an approach that can represent vegetation health through changes in the reflectance and absorption of radiation based on plant physiochemical status. Here, we hypothesize that SLF infestations change the spectral and chemical characteristics of tree canopies. To test this hypothesis, we used a full range spectroradiometer to sample canopy foliage of silver maple (Acer saccharinum) and red maple (Acer rubrum) trees in a common garden in Berks County, Pennsylvania that were exposed to varying levels of SLF infestation. Foliar spectral profiles separated between SLF infestation levels, and the magnitude of separation was greater for the zero-SLF control compared with higher infestation levels. We found the red-edge and portions of the NIR and SWIR regions were most strongly related to SLF infestation densities and that corresponding changes in vegetation indexes related to levels of chlorophyll were influenced by SLF infestations, although we found no change in foliar levels of chlorophyll. We found no influence of SLF densities on levels of primary metabolites (i.e., pigments, nonstructural carbohydrates, carbon, and nitrogen), but did find an increase in the phenolic compound ferulic acid in response to increasing SLF infestations; this response was only in red maple, suggesting a possible species-specific response related to SLF feeding. By identifying changes in spectral and chemical properties of canopy leaves in response to SLF infestation, we can link them together to potentially better understand how trees respond to SLF feeding pressure and more rapidly identify SLF infestations.

Phenology- and light-based parameterization of stomatal conductance model for urban woody species in northern China

Surface ozone (O3) poses a significant threat to urban vegetation health, and assessing the O3 risk across woody species is of vital importance for maintaining the health of urban infrastructure. In the present study, Jarvis-type stomatal conductance model was parameterized for ten urban species in northern China. Incorporating the effects of time of day and diurnal O3 concentration significantly enhanced the model performance. For different plant functional types (greening trees, greening shrubs, and orchard-grown trees), three parameterizations were established to estimate stomatal O3 uptake (POD1, phytotoxic O3 dose over an hourly threshold of 1 nmol m−2 s−1). The differences in POD1 between greening trees and shrubs were primarily due to the difference in their stomatal sensitivity to light. Orchard-grown trees displayed the lowest O3 removal capacity (lowest value of POD1) because of their shorter growing season despite of high stomatal conductance. These results indicated that plant phenology and light responsiveness determined stomatal O3 uptake, and the three parameterizations developed here could be applicable to various urban species in northern regions. Among climatic factors for O3 risk assessment, O3 concentration was the most important factor determining annual variation of POD1, which was primarily driven by air temperature. However, when O3 pollution decreased, O3 concentration exhibited less dependence on temperature and more dependence on light. These findings provide crucial insights for urban policy-makers and environmental scientists aiming to mitigate O3 pollution effects and enhance urban vegetation health.

Optimizing land use for climate mitigation using nature based solution (NBS) strategy: a study on afforestation potential and carbon sequestration in Rajasthan, India

Rajasthan faces significant environmental challenges, including the pressing need for effective climate change mitigation strategies. Recognizing afforestation as a vital tool in this endeavor, this study leverages the latest in remote sensing and geospatial analysis to map out the state's afforestation potential. The goal is to assess land suitability across Rajasthan for afforestation projects and evaluate the potential carbon sequestration capabilities of different tree species. This aims to inform sustainable land management strategies that can contribute to climate change mitigation. By integrating satellite imagery, land cover data, terrain analysis, and vegetation indices, the study evaluates environmental factors such as slope, soil moisture, and vegetation health to identify areas optimal for tree planting. A carbon sequestration model was also developed to estimate sequestration rates based on species-specific growth patterns. Findings indicate that approximately 40% of Rajasthan is suitable for afforestation, with potential carbon sequestration rates ranging from 2 to 8 tons per hectare per year. Species such as Azadirachta indica (Neem) and Prosopis cineraria (Khejri) are identified as particularly effective for short-term sequestration, while Phyllanthus emblica (Amla) and Ziziphus mauritiana (Ber) are better suited for long-term carbon capture. This research highlights the importance of targeted afforestation using suitable tree species as a nature-based solution (NBS) for climate change mitigation in Rajasthan. The study offers a data-driven approach to enhancing ecosystem resilience and supports decision-making for climate adaptation in arid regions, highlighting the significant potential of Rajasthan's land for carbon sequestration through afforestation.

Monitoring the effects of climate change and topography on vegetation health in Tharparkar, Pakistan

ABSTRACT Pakistan's geographic position and socioeconomic profile make it one of the nations that are particularly susceptible to the negative effects of climate change. The Tharparkar district in Pakistan is of particular importance in this regard as it is an arid region with serious environmental issues like drought, desertification, and soil degradation. Therefore, the purpose of this study is to examine how topographic and climatic factors affect vegetation indicators in the Tharparkar. The study utilizes spatiotemporal data spanning over 20 years (2001–2020) collected from the satellites MOD11A2 and MOD13A3. The collected data are processed using a range of tools in ArcGIS 10.4.1, and the impact of topographic and climatic conditions is analyzed based on different vegetation indices, including EVI, NDVI, STVI, OSAVI, and SAVI. The findings reveal that temperature and precipitation, both of which are controlled by topographic features, such as elevation and slope, are the key elements affecting vegetation in Tharparkar. At high elevations, rainfall (>440 mm) and LST (>39 °C) are also high and where the slope is low the density of vegetation indices is high.

Assessing the significance of leaf chlorophyll content for measuring the vegetation health in mangrove species along the Kerala Coast, India – A multi-proxy approach

This study aimed to estimate the chlorophyll content of mangrove species distributed along the Kerala coast of India, and also to address the critical issue of assessing the health of these ecosystems. We identified 16 mangrove species and estimated their chlorophyll concentration levels through a combination of field reconnaissance, in situ radiometric measurements at 218 locations, and UV spectrophotometric (ultraviolet–visible) analysis in the laboratory, and estimated the vegetation health from spectral indices using IRS (Indian Remote Sensing) Resourcesat LISS-IV (Linear Imaging and Self-Scanning Sensor) satellite imagery. This study revealed that the dominant species in Kerala are Avicennia officinalis, A. marina, Rhizophora mucronata and Excoecaria agallocha, whereas, Bruguiera sexangula is sparsely spread. Approximately 14 % of the mangrove cover along the Kerala coast is under stress due to various factors, including climate change and anthropogenic influences. Significantly, two of the 16 species, Avicennia marina and Rhizophora appiculata appeared relatively healthy, with average chlorophyll concentration index (CCI) values of 311.89±40.13 µmol/m2 and 553.42±23.24 µmol/m2, respectively. However, other dominant species Avicennia officinalis, Rhizophora mucronata, and Excoecaria agallocha exhibited average CCI values of 582.35±147.61, 611.7±104.58, and 559.04±141.13 respectively, and seemed to be under stress, calling for serious conservation measures. Our study also revealed the potential of cost-effective IRS LISS-IV imagery for identifying mangrove species and for estimating the chlorophyll concentration and plant vigour together with field and laboratory observations and measurements.

Mechanisms for Data Acquisition to Train Artificial Intelligence Models for Detecting Increased Susceptibility to Fire Situations by Using Internet of Things Devices and Satellite Systems

Aim: Exploration and developing mechanisms of advanced data acquisition necessary for training an artificial intelligence model capable of effectively detecting areas with increased susceptibility to fire situations. The study focuses on utilizing data from satellite missions and ground-based sensors, which provide both high-resolution imagery and precise data on temperature, humidity, and other environmental factors. By analysing these diverse data sources, the research aims to create a comprehensive and efficient model capable of early detection of potential fire hazards, which is crucial for prevention for fire-prone situations. Project and methods: It centres on a project that aims to enhance fire detection and management through the integration of artificial intelligence with data acquired from satellite systems and internet of things devices. The methodologies employed in this project involve a combination of advanced data acquisition, machine learning techniques, and the synthesis of diverse environmental data to train artificial intelligence models that can predict and detect fire incidents more effectively. Results: Significant advancements in fire detection and management have been demonstrated through the integration of artificial intelligence (AI) with satellite data and IoT: 1. Enhanced monitoring capabilities the use of satellite data systems enabled real-time monitoring of thermal anomalies and vegetation health, crucial for early detection and effective monitoring of wildfires. This real-time capability allowed for quicker responses and more informed decision-making in firefighting efforts. 2. Effective integration of data sources: the integration of satellite and surface data proved to be effective in enhancing the predictive capabilities of the fire management systems. This comprehensive approach allowed for a better understanding of fire dynamics and contributed to more accurate and timely predictions. Conclusions: It could be emphasize the significant benefits and future potential of integrating artificial intelligence with satellite and internet of things data for improving fire detection and management. The integration of satellite imagery and internet of things sensor data is essential for enhancing the predictive accuracy of artificial intelligence systems. This integration allows for a comprehensive assessment of fire risks, providing actionable intelligence that is critical for prevention for fire-prone situations. These conclusions underscore the transformative potential of artificial intelligence in enhancing fire management systems. Keywords: data acquisition, artificial intelligence, IoT, satellite data systems, fire management systems

Assessing changes in land cover, NDVI, and LST in the Sundarbans mangrove forest in Bangladesh and India: A GIS and remote sensing approach

Mangrove ecosystems, although limited in diversity and area compared to tropical forests, provide essential ecological and economic services, such as carbon sequestration and coastal protection. The Sundarbans mangrove forest, shared by Bangladesh and India, is one of the largest mangrove ecosystems in the world and is crucial for biodiversity, economy, and climate regulation. Unfortunately, this ecosystem has been under severe stress over the years, with alarming rates of deforestation leading to habitat loss and a decline in ecosystem services. This study analyzes the spatiotemporal changes in the Sundarbans mangrove forest coverage from 1973 to 2023 using supervised image classification on Landsat images. It also assesses the relationship between the Normalized Difference Vegetation Index and Land Surface Temperature in the Sundarbans using MODIS data which were extracted in Google Earth Engine. It finds that, despite the loss of denser mangrove areas, an improvement in overall vegetation health is visible, which suggests a natural resilience within the Sundarbans mangrove forest. The Land Surface Temperature result shows a weak but statistically significant negative correlation with the Normalized Difference Vegetation Index, indicating that the depletion of the Sundarbans mangrove forest could have an impact on the area's surface temperature. As such, the study regressed the Normalized Difference Vegetation Index on Land Surface Temperature. The results confirm that although the Normalized Difference Vegetation Index has a statistically significant negative impact on Land Surface Temperature, the Coefficient of Determination is low. This suggests that other factors such as water bodies that intersect with the mangrove forest in the area may play an important role in influencing Land Surface Temperature. The paper reveals a nuanced picture of the Sundarbans' ecological state, with both declining mangrove densities and signs of vegetation recovery. It highlights the need for comprehensive conservation strategies to mitigate further ecosystem degradation.

Spatiotemporal analysis of land use/land cover changes and vegetation health in south Andaman district using remote sensing and GIS

Spatiotemporal analysis of land use/land cover changes and vegetation health in south Andaman district using remote sensing and GIS

A dataset of annual TCI, VCI, VHI and TVDI with the resolution of 1 km in the Yellow River Basin (2003–2022)

Primarily situated in arid and semi-arid climatic zones, characterized by naturally limited water resources, the Yellow River Basin is the most drought-affected among China's major basins. As global environmental and climate change continue to evolve, droughts in the Yellow River Basin have become increasingly frequent, making drought monitoring research a current focal point. Based on MODIS vegetation data and surface temperature products, this dataset undergoes quality control procedures such as cloud removal and data reconstruction to collect the data of annual Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI) in the Yellow River Basin from 2003 to 2022. The spatial scope of this dataset ranges from 32°10′N–41°50′N，95°53′E–119°05′E. The dataset is in GeoTiff format with a spatial resolution of 1km. It can provide foundational data and scientific support for drought disaster monitoring in the Yellow River Basin.

The invasion threat of the emerging alien cactus Cylindropuntia pallida (Rosa), F.M. Knuth in South Africa and the potential for control using herbicides

The emerging alien cactus Cylindropuntia pallida (Rose) F.M. Knuth originates from northern Mexico and introduced into South Africa in 1940s as an ornamental plant. Multiple populations of C. pallida have been detected in various areas of South Africa. C. pallida has effective propagule dispersal and rapid recruitment making it a likely key future invader, and thus, is a target for eradication in South Africa. To eradicate C. pallida populations, a foliar spray (i.e. using a 2% concentration of herbicide with fluroxypyr and triclopyr) has been applied to plants in nine populations, with population sizes ranging between 535 and 2701 plants and populations covering areas of 100 –1000 ha. The aims of the study were to investigate the efficacy of the foliar spray method used to eradicate C. pallida; to investigate the impacts of C. pallida invasions on native vegetation integrity; to apply species distribution models (SDMs) to identify suitable climates for C. pallida in South Africa; and to document the biomes vulnerable to the negative impact of C. pallida in South Africa. Results show that foliar spray killed many C. pallida plants (mean percentage of dead plants ± SE, 83.3 ± 6.4; n = 9; range, 70–96%), with adult plants taking about 2 months to die completely. The efficacy of the herbicide was not affected by plant size or the concentration of the herbicide used. The invaded site had significantly greater vegetation cover which persisted across winter compared to the uninvaded site, but the latter site’s vegetation cover significantly dropped in winter. Also, the invaded site had lower plant species diversity than the uninvaded site and was dominated by species in the Poaceae and Asteraceae plant families. Additionally, a normalised difference vegetation index (NDVI) analysis shows that the uninvaded site has higher vegetation cover and health than the invaded site wherein a notable decline in vegetation health was observed between 2019 and 2022. A large area (> 15 million hectares) was predicted to be suitable for invasion by C. pallida in provinces with arid and warm temperate climates - the fynbos and grassland biomes are the most vulnerable. Because of the observed negative impacts, high environmental compatibility, and high cost of clearing large infestations, we advocate for considering the biocontrol method for effectively managing C. pallida invasion in South Africa.

Rate of desertification, climate change and coping strategies: Insights from smallholder farmers in Ghana's Upper East Region

Climate change and desertification are global issues that are affecting agricultural activities in Sub-Saharan Africa. These phenomena have made it challenging for smallholder farmers to cope with the emerging global food crises. This article examines the evidence of desertification and climate change and how smallholder farmers in the Upper East Region of Ghana are responding to these threats. Primary data was analysed through interviews and focus group discussions. We used Landsat satellite-based annual mean Normalized Difference Vegetation Index, climate, crop production data, and reports from the Ministry of Food and Agriculture and other NGOs as secondary sources. We interviewed eighty respondents using case history and snowball sampling. The mean annual NDVI declined from 0.5 in 1998 to −0.10 in 2022. NDVI values suggest a decline in green vegetation health to a negative value, suggesting a non-vegetative surface. This means that the process of desertification has progressed to severe levels, resulting in little to no vegetation cover on the land surface. Similarly, rainfall data suggests a sharp decline in annual rainfall while farmers change the types of crops and the timing of planting. Farmers discussed the impact of climate change and desertification on their livelihoods. They have implemented approaches like using synthetic fertilisers, planting drought-resistant crops, practising dry-season farming, irrigating their crops, and building social support networks. Food insecurity and land degradation are major issues due to continuous cropping and population growth. Smallholder farmers suggest providing them with affordable, drought-resistant seedlings with shorter gestation periods to increase food production and avoid insecurity.