Cocoa farmers in Nigeria adopt crop diversification to safeguard the food security of their households. Although credit and land are thought to play a vital role in crop diversification, they continue to have limited access to credit and land. This study investigated the linkages between access to credit, land use, crop diversification, and food security with a focus on cocoa farming households. A multistage sampling procedure was used to obtain data for the study. Data were analyzed with the aid of descriptive statistics, the Heifindahl index, the Tobit regression model, the food consumption score, and the ordered Logit regression model. The results for the entire respondents showed mean values of 55 years for age, 31 years for farming experience, 6 people for household size, and 5 ha for farm size. Heifindahl index shows 38.67 % of the respondents had low crop diversification in the study area. Tobit regression model reveals that access to credit, farming experience, cooperative organization, access to extension service, farm size, distance to farms, and labour are the main albeit significant factors that determine crop diversification among cocoa farming households. Food consumption score revealed that 46.67 % were poor, 30.67 % were at the borderline and about 27.67 % were within the acceptable threshold. The ordered logit model revealed that crop diversification index, formal education, access to credit, farm size, land use, and farming experience have a significant influence on the food security of households. The study concluded that there is a positive relationship between access to credit, land use, crop diversification, and food security. Therefore, the government and financial institutions should make credit facilities accessible to cocoa farmers to improve their livelihood.

Humans have greatly altered earth\'s terrestrial water cycle with the majority of fresh water being used for agriculture. Irrigation changes spatial and temporal water availability and alters mosquito abundance and phenology. Previous studies evaluating the effect of irrigation on mosquito abundance and mosquito-borne disease have shown inconsistent results and little is known about the effect of irrigation on variability in mosquito abundance. We examined the effect of irrigation, climate and land cover on mosquito abundance and human West Nile virus (WNV) disease cases across California. Irrigation made up nearly a third of total water inputs, and exceeded precipitation in some regions. Abundance of two key vectors of several arboviruses, including WNV, Culex tarsalis and the Culex pipiens complex, increased 17-21-fold with irrigation. Irrigation reduced seasonal variability in C. tarsalis abundance by 36.1%. Human WNV incidence increased with irrigation, which explained more than a third (34.2%) of the variation in WNV incidence among California counties. These results suggest that irrigation can increase and decouple mosquito populations from natural precipitation variability, resulting in sustained and increased disease burdens. Shifts in precipitation due to climate change are likely to result in increased irrigation in many arid regions which could increase mosquito populations and disease.

Since China proposed building a national park system in 2017, the establishment of a planning system for nature reserves, with national parks as the main body, is being actively promoted around the country. Among them, scientific ecological space management and control zoning (ESMCZ) is an important link in maintaining the ecological stability of national parks. How to zone national parks and how to improve the precision of zoning has become a new task for national parks. Therefore, this study takes the Giant Panda National Park as the study area, takes ecosystem services and land use/cover change as the research perspective, integrates the InVEST model, PLUS model and bayes belief network (BBN) model, and builds a set of ecological space management and control zoning (ESMCZ) spatial zoning framework based on raster scale, dividing the study area into strictly protected zone, ecological buffer zone, ecological control zone and controlled development zone. The results showed that: (1) The study area showed an increasing trend in water conservation, soil conservation and carbon storage from 2005 to 2020, and the habitat quality index was generally high. The spatial heterogeneity of ecosystem services in the study area was significant, and the effect of a single factor on ecosystem services was most pronounced. (2) Large variation in area for different land uses under natural development scenarios and ecological protection scenarios. In both scenarios, the area of cultivated land, the area of grassland and the area of unused land decrease relative to 2020, and the area of forested land, the area of water and the area of constructed land increase relative to 2020. (3) The Giant Panda National Park is divided into strictly protected zone, ecological buffer zone, ecological control zone and control development zone, of which the strictly protected zone have the largest area and the best ecosystem background condition, and the control development zone have the smallest area and the worst ecosystem background condition. (4) The ecological space management and control zoning (ESMCZ) framework provides a more refined method for the secondary zoning of nature reserves such as the Giant Panda National Park, which is valuable for the implementation of zoning and categorization management for ecological conservation in the Giant Panda National Park.

To realize the energy and resource utilization from organic solid waste, a two-phase microbial desalination cell (TPMDC) was constructed using dewatered sludge and kitchen waste as the anode substrate. The performance of electricity generation and composting efficacy was investigated, along with a comprehensive assessment of the potential health risks associated with the land use of the resulting mixed compost products. Experimental outcomes revealed a maximum open-circuit voltage of 0.893 ± 0.005 V and a maximum volumetric power density of 0.797 ± 0.009 W/m³. After 90 days of composting enhanced by microbial electrochemistry, a significant organic matter removal rate of 31.13 ± 0.44 % was obtained, and the anode substrate electric conductivity was reduced by 30.02 ± 0.04 % based on the anode desalination. Simultaneously, there was an increase in the content of available nitrogen, phosphorus, and potassium, as well as an improvement in the seed germination index. The forms of heavy metals shifted from bioavailable to stable residual states. The non-carcinogenic hazard index (HI) values for heavy metals and polycyclic aromatic hydrocarbons (PAHs) during the land use of compost products were less than 1, and the total carcinogenic risk (TCR) values for heavy metals and PAHs were below the acceptable threshold of 10-4. The occupational population risk of infection from five pathogens was higher than that of the general public, with all risk values ranging from 8.67 × 10-8 to 1, where the highest risk was attributed to occupational exposure to Legionella. These outcomes demonstrated that the mixture of dewatered sludge and kitchen waste was an appropriate anode substrate to enhance TPMDC stability for electricity generation, and its compost products have promising land use suitability and acceptable land use risk, which will provide important guidance for the safe treatment and disposal of organic solid waste.

A key driver of the African savannah elephant population decline is the loss of habitat and associated human-elephant conflict. Elephant physiological responses to these pressures, however, are largely unknown. To address this knowledge gap, we evaluated faecal glucocorticoid metabolite (fGCM) concentrations as an indicator of adrenal activity and faecal thyroid metabolite (fT3) concentrations as an indicator of metabolic activity in relation to land use, livestock density, and human landscape modification, while controlling for the effects of seasonality and primary productivity (measured using the normalized difference vegetation index). Our best-fit model found that fGCM concentrations to be elevated during the dry season, in areas with higher human modification index values, and those with more agropastoral activities and livestock. There was also a negative relationship between primary productivity and fGCM concentrations. We found fT3 concentrations to be higher during the wet season, in agropastoral landscapes, in locations with higher human activity, and in areas with no livestock. This study highlights how elephants balance nutritional rewards and risks in foraging decisions when using human-dominated landscapes, results that can serve to better interpret elephant behaviour at the human-wildlife interface and contribute to more insightful conservation strategies.

This paper aims to evaluate the driving style effects, through the construction of driving cycles, on the polluting gases, in the context of urban freight transportation. For this, the method used was the construction of cycles through the Vehicle Specific Power (VSP) parameter, which considers instantaneous vehicle and road parameters better to represent driving patterns and freight transportation\'s environmental impacts. The study was conducted in Fortaleza city, Ceará, Brazil, with a professional driver\'s group. The road types, land use and traffic light location were considered to analyze and discuss the results. The results show collector roads presented higher speeds than arterial roads, and the use of the land around the road also directly impacted vehicle driving patterns. Regarding CO2 emissions, higher concentrations measured were observed on the arterial roads.

Protected areas in South Asia face significant challenges due to human disturbance and deforestation. The ongoing debate surrounds the recent surge in illegal encroachment of forest buffer zones in the Musali divisional secretariat division (DSD), which has led to a significant loss of forest cover over the past three decades. In this context, detecting changes in forest cover, assessing forest health, and evaluating environmental quality are crucial for sustainable forest management. As such, our efforts focused on assessing forest cover dynamics, forest health, and environmental conditions in the DSD from 1988 to 2022. We employed standardized image processing techniques, utilizing Landsat-5 (TM) and Landsat-8 (OLI) images. However, the forest area in the DSD has shown minimal changes, and environmental conditions and forest health have illustrated considerable spatial-temporal variations over the 34 years. The results indicated that 8.5 km2 (1.9%) of forest cover in the DSD has been converted to other land use classes. Overall, the Normalized Difference Vegetation Index (NDVI) has declined over time, while Land Surface Temperature (LST) exhibits an increasing trend. The regression results demonstrated a robust inverse relationship between LST and NDVI. The declining vegetation conditions and the increasing LST contribute to an increase in environmental criticality. The derived maps and indices will be beneficial for forest authorities in identifying highly sensitive locations. Additionally, they could enable land use planners to develop sustainable land management strategies.

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in three different urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and mixed land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. Statistics on accident in three zones show that among the mentioned land uses, commercial areas have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75 % of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 85 % accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 81 % accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 75 % accuracy.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods\' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

As a new product of rapid urbanization, the sprawl of urban construction land can objectively reflect urban land use efficiency, which is of great significance to China\'s new urban construction. This study aimed to summarize the expansion patterns and utilization efficiency of urban construction land in China from the perspectives of the status, speed and trends of expansion, and to uncover the key factors that lead to the differential distribution of the expansion of construction land. It can also provide land management experience for other countries with rapid expansion of construction land. The results show the following. (1) The expansion of China\'s construction land presents a \"point-line-plane\" pattern of evolution, forming changing stages of point-like aggregation, linear series and planar spread. (2) China\'s construction land shows the characteristics of disorderly spread, a low utilization rate and low output efficiency. The speed of expansion presents clear characteristics of being high in the east and low in the west, mostly concentrated in the Yangtze River Delta, Pearl River Delta and the Beijing-Tianjin-Hebei urban agglomeration. Shanghai, Beijing, Shenzhen and Guangzhou have the highest intensity of construction land use. In Shandong Peninsula and eastern coastal areas, the intensity of the construction land use is generally high. In Xinjiang and Xizang, the intensity of construction land use is relatively low. (3) The urban economic level, population size, industrial structure, foreign investment and land policies have significant effects on the spatial distribution of the expansion of construction land.