Lead (Pb) is a non-threshold toxin capable of inducing toxic effects at any blood level but availability of soil screening criteria for assessing potential health risks is limited. The oral bioaccessibility of Pb in 163 soil samples was attributed to sources through solubility estimation and domain identification. Samples were extracted following the Unified BARGE Method. Urban, mineralisation, peat and granite domains accounted for elevated Pb concentrations compared to rural samples. High Pb solubility explained moderate-high gastric (G) bioaccessible fractions throughout the study area. Higher maximum G concentrations were measured in urban (97.6 mg kg−1) and mineralisation (199.8 mg kg−1) domains. Higher average G concentrations occurred in mineralisation (36.4 mg kg−1) and granite (36.0 mg kg−1) domains. Findings suggest diffuse anthropogenic and widespread geogenic contamination could be capable of presenting health risks, having implications for land management decisions in jurisdictions where guidance advises these forms of pollution should not be regarded as contaminated land.

To protect and improve water quality in the Great Barrier Reef, the Queensland Government's Reef 2050 Water Quality Improvement Plan targets that 90% of sugarcane, horticulture, cropping and grazing lands in priority areas be managed using best management practices for sediment, nutrient and pesticides by 2025. Progress towards this target is insufficient and variable across catchments and industries. The motivation to adopt improvements in management practices is heavily influenced by social, economic, cultural and institutional dimensions. In this paper we synthesise the literature on how these human dimensions influence decision making for land management practice and highlight where future investment could be focussed. We highlight that focussing on —1) investigating systems to support landholder decision making under climate uncertainty (risk); 2) generating a better understanding of the extent and drivers of landholder transaction cost; 3) understanding if there are competing ‘right’ ways to farm; and 4) improving understanding of the social processes, trust and power dynamics within GBR industries and what these means for practice change— could improve practice change uptake in the future.

In recent decades, significant advances have been made in understanding the generation, fates and consequences of water quality pollutants in the Great Barrier Reef ecosystem. However, skepticism and lack of trust in water quality science by farming stakeholders has emerged as a significant challenge. The ongoing failures of both compulsory and particularly voluntary practices to improve land management and reduce diffuse agricultural pollution from the Great Barrier Reef catchment underlines the need for more effective communication of water quality issues at appropriate decision-making scales to landholders. Using recent Great Barrier Reef catchment experiences as examples, we highlight several emerging themes and opportunities in using technology to better communicate land use-water quality impacts and delivery of actionable knowledge to farmers, specifically supporting decision-making, behavior change, and the spatial identification of nutrient generation ‘hotspots’ in intensive agriculture catchments. We also make recommendations for co-designed monitoring-extension platforms involving farmers, governments, researchers, and related agencies, to cut across stakeholder skepticism, and achieve desired water quality and ecosystem outcomes.

The accumulation of potential toxic elements (PTEs) in soils is usually conditioned by parental material or anthropogenic sources. To achieve correct land management and land degradation neutrality, it is necessary to spatially detect them. However, there are several areas over the world with high concentrations of PTE but without efficient maps and tools to correctly find solutions and apply control measures. The current study attempts to identify the concentrations, sources, and spatial distributions of the main PTEs such as As, Cd, Cr, Cu, Ni, Pb, and Zn in a non-explored area combining fieldwork and geostatistical analysis. In order to accomplish this goal, a total of 90 soil samples were collected in agricultural and natural areas in the Piedemonte Llanero, Colombia. The chemical analysis was conducted by acid digestion and determined through ICP-OES. Then, ordinary kriging was applied to spatially analyze the most vulnerable areas. Our results demonstrated the effectiveness of these techniques and it is noted that the agricultural areas presented the highest concentrations and represented the potential source of PTEs. On the other hand, the natural areas presented the following concentrations of PTEs Cr (17.10 mg/kg), As (2.92 mg/kg), Cu (7.57 mg/kg), Ni (8.63 mg/kg), Cd (0.17 mg/kg), Pb (8.80 mg/kg), and Zn (27.57 mg/kg) lower than agricultural soils. This information was a key first step to be presented to the policymakers and stakeholders to organize soil sustainable management plans for the Piedemonte Llanero in Colombia.

Farmland abandonment, as a manifestation of the low efficiency of the rural economy, has a profound impact on the process of agricultural and rural modernization. This study uses the 2016 China Labor Force Dynamic Survey data based on 8116 samples from 104 cities, constructs the Tobit model and IV Tobit model to analyze the land abandonment quantitatively from the perspective of environmental pollution. The results show that (1) environmental pollution can significantly increase the probability and area of land abandonment, (2) there is significant regional heterogeneity in the impact of environmental pollution on land abandonment, (3) the impact of environmental pollution on land abandonment varies significantly with different family sizes and land management scales, but the land management scale is relatively more sensitive. This study provides a deeper understanding of the relationship between environmental pollution and land abandonment in China and provides a basis for formulating relevant policies to strengthen the treatment of environmental pollution to solve the dilemma of land abandonment, which is of great practical significance to sustainable development of rural economy and the guarantee of food security in China.

Over the past couple of decades, the world has witnessed a rise in greenhouse gas (GHG) emissions and rising income inequality that threatens human well-being. Addressing these challenges and ensuring sustainable development become a pressing issue for policymakers. This paper investigates the impact of GHG emissions on income inequality in Africa. The study uses a panel data set from 49 countries from 1981 to 2015 and shows that GHG emission widens income inequality. The result is robust for alternative emission indicators. A direct implication is that climate change policy should be designed to narrow income inequality. It is emphasized that mitigation actions should focus on the agriculture sector. Hence, intervention towards energy-smart agriculture, land conservation practices, exploiting the job creation potential, and strengthening value addition in the agricultural sector is decisive. Reforming agriculture reduces emission, narrows income inequality, and realizes sustainable development on the continent.

Changes in land management and climate alter vegetation dynamics; however, the factors driving vegetation changes remain elusive at multiple spatiotemporal levels. Here, we assess the drivers of changes in greenness from 2000 to 2015 in Northwest China (NW China). We used multiple stepwise linear regression (MSLR), redundancy analysis (RDA), and 12 other models to quantify the impacts of precipitation and temperature metrics, gross domestic product (GDP), population, and grazing intensity on the normalized difference vegetation index (NDVI) at three administrative levels (county, town, and village), four temporal levels (yearly, May, July, and September), two vegetation types (woodland and grassland), and at annual precipitation gradients of <200, 200–400, and >400 mm. The results suggest that NW China underwent vegetation greening from 2000 to 2015. Precipitation and temperature were the most influential factors contributing to the NDVI change. Population was the main determinant of NDVI under the precipitation gradient of <200 mm, and the effect of GDP on NDVI was moderate. On the temporal scale, annual precipitation, precipitation before the previous year, and precipitation in the current year determined the NDVI in May, July, and September, respectively, for both woodland and grassland. At multiple scales, climate change was the primary driver of vegetation change in NW China, rather than human disturbance. These findings expand our understanding on drivers of NDVI at multiple levels over a long period. Measures to manage decreasing vegetation coverage may be more effective and could be implemented sooner based on predicted climate change in drylands worldwide.

In this review, we explore the concept, approach, and future research of hydrological connectivity and its assessment at multiscales, because according to the literature, an integrated review upon hydrological connectivity is lack. Systematic studies illustrate the effects of (i) human activities (i.e., dam construction, groundwater extraction, water flow regulation and diversion, and land management) and (ii) natural factors (i.e., climate, soil characteristics, vegetation, and topography) on hydrological connectivity. Approaches (i.e., soil water content patterns, runoff patterns and processes, numerical models, and index of hydrological connectivity) applied to evaluate hydrological connectivity are examined in detail. Lastly, hydrological connectivity at multiscales is indicated. This review concludes with a discussion of potential research trends that can improve understanding of hydrological connectivity. Reported records showed that few studies were published on hydrological connectivity from 1980 to 2003, whereas the evolution of these studies is temporally promising since 2003. We cannot define a standard concept of hydrological connectivity that works in all environments. We desire to show different concepts of hydrological connectivity in different environments. The degree and nature of hydrological connectivity are not static due to the influences of human activities and changes of natural factors. The index of hydrological connectivity and numerical models are the most significant approaches to assess the changes in hydrological connectivity. This study showed that considering hydrological connectivity in social-economical-ecological-hydrological frameworks can prevent its negative effects on surface or subsurface water quantity and quality and is beneficial for sound water sources management.

The contents of total arsenic (tAs), inorganic arsenic (iAs), Cd, Cr, Cu, Mn, Ni, Pb, Sb, and Zn in 135 rice grain samples from Zijiang River basin were determined, and the probabilistic distribution of noncarcinogenic and carcinogenic risks associated with ingesting locally produced rice was determined by Monte Carlo simulation. Further, multivariate statistical analysis was used to analyze the potential sources of the heavy metals in rice grains. The average concentrations of the heavy metals in rice grains were ranked as follows: Mn (17.314 mg/kg) > Zn (16.043 mg/kg) > Cu (2.013 mg/kg) > Ni (1.332 mg/kg) > Cr (0.571 mg/kg) > Cd (0.283 mg/kg) > tAs (0.241 mg/kg) > Pb (0.145 mg/kg) > Sb (0.027 mg/kg). These heavy metals were significantly enriched in some rice grain samples. The analysis of potential sources indicated that As, Pb, Sb, and Zn were mainly derived from mining and smelting and agricultural activities; Cd, Cu, Mn, and Ni were mainly derived from the agricultural activities; Cr were mainly derived from the natural source. The results of Monte Carlo simulation indicated that ingestion of rice grown in the area may pose health risks for children, adult males, and adult females. The noncarcinogenic risks were mainly from As, Cd, Mn, Ni, and Sb, and the carcinogenic risk was mainly from As, Cd, and Ni. This study could provide basic information for land management and rice intake in the study area.

In this study, we simulated heterotrophic CO₂(Rh) fluxes at six European peatland sites using the ECOSSE model and compared them to estimates of Rh made from eddy covariance (EC) measurements. The sites are spread over four countries with different climates, vegetation and management. Annual Rh from the different sites ranged from 110 to 540 g C m⁻². The maximum annual Rh occurred when the water table (WT) level was between −10 and −25 cm and the air temperature was above 6.2 °C. The model successfully simulated seasonal trends for the majority of the sites. Regression relationships (r²) between the EC-derived and simulated Rh ranged from 0.28 to 0.76, and the root mean square error and relative error were small, revealing an acceptable fit. The overall relative deviation value between annual EC-derived and simulated Rh was small (−1 %) and model efficiency ranges across sites from −0.25 to +0.41. Sensitivity analysis highlighted that increasing temperature, decreasing precipitation and lowering WT depth could significantly increase Rh from soils. Thus, management which lowers the WT could significantly increase anthropogenic CO₂, so from a carbon emissions perspective, it should be avoided. The results presented here demonstrate a robust basis for further application of the ECOSSE model to assess the impacts of future land management interventions on peatland carbon emissions and to help guide best practice land management decisions.