Total Land Research Articles

Study on socio-economic status and farming practices of fish farmers in Pachrauta Municipality, Bara, Nepal

An interview-based questionnaire survey was done in 2021 to assess the socio-economic conditions, farming practices adopted, and problems faced by fish farmers and to analyze the cost and benefits of fish farming in the Fish super zone area of Pachrauta Municipality, Bara district. A simple random sampling technique was used to select 60 respondents for the study and the information collected was analyzed and processed by MS-Excel and SPSS. The majority of respondents were male, belonged to the age group of 25–36 years, and had a secondary level of school education. The mean total land holding size was found to be 3.65 ± 2.57 bigha with a total pond area of 1.38 ± 1.08 bigha and a small minority (18 %) practiced contract farming. Most of the farmers were found to have experience of 6–11 years (40 %). The farming system was polyculture and mostly Intensive (60 %). While the use of modern equipment was low (25 %), the majority of farmers (>90 %) practiced regular drying and liming of ponds. Mid-April to Mid-July was the most preferred time of stocking and the big fraction of feed used was locally prepared (70 %). Fertilizer use and Multiple harvesting were also common. Fish farming in the study area was found to fetch an average annual income of 0.95 million NRs. The major problems faced by farmers were Expensive feed (0.67), Lack of quality seeds (0.60), and Lack of proper marketing channels (0.55). Despite the constraints, Economic analysis revealed fish production in the study area as a profitable business with a BC ratio of 2.20 and a high gross margin value (Rs. 1330341.6 per hectare).

Production efficiency of wheat farmers in the Arsi Zone, Oromia Region of Ethiopia

Wheat is a cereal crop that contributes to food security; thus, Ethiopia must boost the production efficiency of wheat to meet the sustainable development goal of eradicating hunger and poverty. Consequently, a significant revolution is occurring in the Ethiopian wheat industry to improve production and productivity. Therefore, it is critical to understand the current level of wheat farmers’ efficiency, as its production is highly influenced by existing agricultural technologies and climate change, which makes it dynamic. Accordingly, this study employed the parametric Cobb–Douglas stochastic frontier and two-limit Tobit models to evaluate wheat farmers’ efficiency and determine their drivers in Ethiopia’s largest wheat-producing area, the Arsi Zone. A multistage sampling strategy was applied to obtain a representative sample of 422 wheat farmers. The model’s output suggested that the average technical, allocative, and economic efficiency scores were 80.8%, 88.1%, and 71.3%, respectively. It is confirmed that wheat farmers’ efficiencies can increase with household head age, education level, livestock ownership, contact with extension agents, wheat mechanization, and involvement in non/off-farm activities but decrease with household distance from the main market and total land holdings. To realize the potential gains from wheat cultivation in Ethiopia, the government needs to develop policies and strategies that enhance farmers’ education, livestock production, and extension contact, facilitate infrastructure, market development, and wheat mechanization, and promote non/off-farm activities.

The potential of agroforestry to buffer climate change impacts on suitability of coffee and banana in Uganda

AbstractCoffee, an important global commodity, is threatened by climate change. Agroforestry has been considered as one option to maintain or enhance coffee production. In this study, we use a machine learning ensemble consisting of MaxEnt, Random Forest and Boosted Regression Trees to assess climate change impacts on the suitability to grow Arabica coffee, Robusta coffee and bananas in Uganda by 2050. Based on this, the buffering potential of Cordia africana and Ficus natalensis, the two commonly used shading trees in agroforestry systems is assessed. Our robust models (AUC of 0.7–0.9) indicate temperature-related variables as relevant for Arabica coffee suitability, while precipitation-related variables determine Robusta coffee and banana suitability. Under current climatic conditions, only a quarter of the total land area is suitable for growing Arabica coffee, while over three-quarters are suitable for Robusta coffee and bananas. Our results suggest that climate change will reduce the area suitable to grow Arabica coffee, Robusta coffee and bananas by 20%, 9% and 3.5%, respectively, under SSP3-RCP7.0 by 2050. A shift in areas suitable for Arabica coffee to highlands might occur, leading to potential encroachment on protected areas. In our model, implementing agroforestry with up to 50% shading could partially offset suitable area losses for Robusta coffee—but not for Arabica coffee. The potential to produce valuable Arabica coffee thus decreases under climate change and cannot be averted by agroforestry. We conclude that the implementation and design of agroforestry must be based on species, elevation, and regional climate projections to avoid maladaptation.

Capturing the drivers of crop water footprints in Africa and its spatial patterns

Improving water efficiency in the agricultural sector is essential to ensure sustainable withdrawals and supply of freshwater in a context of increasing water scarcity and human water demand. The water footprint (WF) is an established metric of resource intensity while the drivers steering WF over time remain under-researched. To advance this line of research, this paper assesses the sign and magnitude of macroeconomic, climatic, and agronomic drivers on the agricultural crop WF in 43 countries of the African continent for the period 2002-2016, using econometric panel data techniques and considering potential spatial patterns. The results reveal a significant spatial dependence in the WF across neighbouring countries. Socioeconomic factors are the most important determinant of water productivity, indicating that economic development facilitates a falling water requirement per unit of production. A negative impact of the temperature variation on the WF is also found, while the share of total land dedicated to agriculture tends to increase the crop WF in the continent. These results support designing adequate agricultural and water management policies to achieve sustainable and resilient food systems capable of adapting to anticipated population growth, climate change and other future threats to human health, prosperity and environmental sustainability in Africa.

Socio-economic contributions and determinants of locals engagement with Boswellia papyrifera in lowland woodlands in Burie Zuria district, Ethiopia

Ethiopia's lowland woodlands are comprised of the major gum and resin-producing genera Acacia, Boswellia, and Commiphora. Boswellia papyrifera is primarily found in the degraded drylands, Burie Zuria district is the existence of the species; however, limited information is available on the woodland socio-economic contribution and determinants of locals' use of woodlands for their livelihoods, particularly Boswellia papyrifera. So to fill this gap, the study was conducted to examine the locals' socio-economic benefits from the woodland and to identify the determinants of locals' engagement in the collection of the benefits for better wise use and conservation policy implementation. The data was collected from household interviews, focus group discussions, and key informants. Species' socio-economic benefits were analyzed through descriptive statistics, whereas determinants of local's willingness to use woodlands were analyzed through an econometric model. The dependent variable used was annual income gained from the woodland and explanatory variables taken were gender, marital status, livestock number, family size, land holding size, age of the respondent, education level, wealth status, perception of local communities towards forest cover change, and distance from woodlands to the household's residence. Based on the results, the total annual income derived from the woodland by the sampled households' was a mean of 1759.45 USD. The result shows the woodland species shares 14.37 % of the total annual income, but because of legal investors Boswellia papyrifera had no contribution to this share. However, the local communities use woodlands for their livelihoods due to explanatory variables, and the usage percentage shows significant differences. The multiple linear regression results showed that the model was significant at a 5 % probability level, and 95.9 % of the variation was due to the explanatory variables. Age of respondent, family size, total land holding size, and education level showed positive and statistically significant relations to annual income derived from the woodland while other remaining variables showed non-significant. The result concludes that the species contributes significantly to local communities' livelihoods and engagement in gathering timber and non-timber products. The study recommended that awareness about the use of non-timber products in addition to forest products be created among the locals, and policies related to woodland sustainability and conservation should also be implemented.

Why should imminent international funds for solar photovoltaics go to families and the private sector and not to the government to stop the electricity crisis in Lebanon?

In Lebanon, the failure to supply oil-based electricity was mostly due to flawed management, increasing costs of bills of private generators, the rise of fuel prices, and the absence of subsidies. In response, individuals have started funding small-scale rooftop PV (RPV) to increase the solar share beyond the national target while the government has struggled for years to pass bidding phases for 12 utility-scale PV (USPV). In addition to publicly unavailable details, specifications, assessments, and termination plans about the 12 solar farms, their geographical sitting was not technically evaluated. Therefore, this study mapped USPV suitability in Lebanon using a multi-criteria decision-making algorithm. Although the highly suitable lands can supply 40% of the total land needed to cover the peak demand, the areas were not equally distributed throughout the governorates. Even if USPV can help reach national solar targets, investing in public-related projects can be perilous, especially with historical patterns of inappropriate allocation of funding, political instability, and absence of accountability. Thus, investing in RPV can be more attractive due to its efficiency, grid independence, and flexibility and should be oriented primarily towards low-income families and small entrepreneurs and later to wealthier people to ensure social equity and welfare.

Optimizing fenugreek (Trigonella foenum-graecum L.) oil yield and compositions in intercropping through growth-promoting bacteria and mycorrhiza

IntroductionBiofertilizers and intercropping are two main components in sustainable production systems.Materials and methodsA two-year (2020–2021) study was conducted in East Azarbaijan, Iran, to evaluate the effects of arbuscular mycorrhizal fungi (AMF), growth-promoting bacteria (GPB) and chemical fertilizer (CF) on fenugreek (Trigonella foenum-graecum L.) (F) oil yield and compositions in intercropping with Moldavian balm (Dracocephalam mobdavica L.) (MB). The cropping patterns included MB sole cropping, fenugreek sole cropping (F) and replacement intercropping ratios consisted of Moldavian balm : fenugreek (MB:F (1:1)), MB:F (2:2) and MB:F (4:2) and additive intercropping of MB:F (100:50).ResultsFor both years, among the intercropping patters, the highest seed and oil yields were obtained in MB:F (100:50) intercropping pattern treated with CF and AMF+GPB. In all cropping patterns except MB:F (4:2), the highest anthocyanin, total flavonoid, and mucilage contents were observed in plants received AMF+GPB. At all treatments, the linoleic, oleic, and linolenic acid were the main components of fenugreek oil. In MB:F (1:1), (2:2), (4:2), and (100:50) intercropping patterns, the linoleic acid content in AMF+GPB treatment, increased by 9.45%, 6.63%, 15.20%, and 7.82%, respectively, compared with sole fenugreek. The highest total land equivalent ratio (LERT) values were obtained in 2021 and MB:F (100:50) intercropping pattern treated with CF (1.70) and AMF+GPB (1.63).ConclusionsIn general, it could be concluded that MB:F (100:50) intercropping pattern treated with AMF+GPB improved the oil yield and unsaturated fatty acid contents of fenugreek compared with sole cropping and could be recommended in sustainable production systems.

Promoting low-carbon land use: from theory to practical application through exploring new methods

Cities are main carbon emissions generators. Land use changes can not only affect terrestrial ecosystems carbon, but also anthropogenic carbon emissions. However, carbon monitoring at a spatial level is still coarse, and low-carbon land use encounters the challenge of being unable to adjust at the patch scale. This study addresses these limitations by using land-use data and various auxiliary data to explore new methods. The approach involves developing a high-resolution carbon monitoring model and investigating a patch-scale low-carbon land use model by integrating high carbon sink/source images with the Future Land Use Simulation model. Between 2000 and 2020, the results reveal an increasing trend in both carbon emissions and carbon sinks in the Shangyu district. Carbon sinks can only offset approximately 3% of the total carbon emissions. Spatially, the north exhibits net carbon emissions, while the southern region functions more as a carbon sink. A total of 14.5% of the total land area witnessed a change in land-use type, with the transfer-out of cropland constituting the largest area at 96.44 km2, accounting for 50% of the total transferred area. Land-use transfer resulted in an annual increase of 77.72 × 104 t in carbon emissions between 2000 and 2020. Through land-use structure optimisation, carbon emissions are projected to increase by only 7154 t C/year from 2000 to 2030, significantly lower than the amount between 2000 and 2020. Further low-carbon land optimisation at the patch scale can enhance the carbon sink by 129.59 t C/year. The conclusion drawn is that there is considerable potential to reduce carbon emissions through land use control. The new methods developed in our study can effectively contribute to high-resolution carbon monitoring in spatial contexts and support low-carbon land use, promoting the application of low-carbon land use from theory to practice. This will provide technological guidance for land use planning, city planning, and so forth.

A Spatial Analysis of Urban Tree Canopy Using High-Resolution Land Cover Data for Chattanooga, Tennessee

Urban tree canopy (UTC) provides urban residents with numerous benefits, including positive mental and physical health, the mitigation and prevention of urban heat islands, and a sense of place. Numerous studies have shown that as the wealth of a community decreases, so does the amount of UTC found in the community; thus, wealthier communities are more likely to enjoy the benefits that urban forests provide. Emerging technologies in remote sensing and GIS are allowing for new opportunities to study and understand the relationships between urban neighborhoods and UTC. In this study, land cover data for Chattanooga, Tennessee were derived from high-resolution (50 cm) multispectral imagery to assess the previously unknown extent and distribution of UTC and to measure the extent of UTC by neighborhood and census block group level. Using exploratory regression analysis, variables representing income, population density, race, educational attainment, and urban heat islands were analyzed to investigate the influence of UTC on neighborhood characteristics. This study found that UTC represented half of the total land cover composition, the tree equity was not as profound as shown in other cities, and the lack of UTC likely influences the prevalence of urban heat islands. This study also shows the importance and utility of using high-resolution imagery and land cover to assess and understand the impact and distribution of UTC in urban environments.

Analysis of the Direction and Prediction of Built-Up Land Development in Slawi Sub-district, Tegal Regency, Indonesia in 2045

Abstract Slawi Sub-district is characterized by a high population density as it serves as both the capital of Tegal Regency and the central hub of the Tegal Regency government. The large growth of built-up land threatens the balance of the environment and uncontrolled spatial planning. This study aims to predict the development of built-up land in Slawi Subdistrict in 2045 and determine the direction of built-up land development in Slawi Subdistrict in 2007, 2015, 2023, and 2045. The research approach used Geographic Information System with the prediction of Cellular Automata (CA) Markov Chain prediction model. The inputs of the CA Markov model are the driving factors of land cover change consisting of road network, river network, government, settlement, and slope. The results showed that in 2045 the predicted built-up land area was 1,111.72 ha or 71.41% of the total land cover. The development of built-up land in Slawi Sub-district is closely related to the road distribution pattern, where the north and south are influenced by arterial road and rail road, while the east and west are influenced by collector roads.

Spatiotemporal evolution and driving mechanisms of desertification on the Mongolian Plateau

Desertification poses a severe ecological and environmental challenge in the Mongolian Plateau (MP). It is difficult to quantify desertification distribution using unified indicators in the entire MP, because of its complex physical geographic conditions and various climatic zones covered. To accurately address this challenge, the spatial distribution of desertification at a 30-m resolution from 1990 to 2020 were mapped in this study. The desertification potential occurrence zone was identified by using a moisture index on the MP firstly. The feature space model and five machine learning models were constructed to make the map based on Google Earth Engine and Landsat data. The spatiotemporal distribution of desertification were further analyzed, and the dominant drivers of desertification distribution and evolution were identified using Geodetector model. The results indicate that the potential occurrence area of desertification accounted for 83.88 % of the total land area. The gradient boosted tree model for desertification assessment has the best performance with the highest overall accuracy of 88.18 %. The year 2010 marked a pivotal transition from land degradation to land restoration in the MP. Between 2010 and 2020, desertified land continued to deteriorate extensively in the southern Mongolia, while Inner Mongolia, China, essentially entered a full recovery phase. Precipitation and land use emerged as the primary drivers of the spatial distribution of desertification on the Mongolian Plateau and Mongolia, with potential evapotranspiration and precipitation influencing the distribution of desertification in Inner Mongolia, China. Land use change was the primary driver of desertification evolution on the MP and Mongolia. This study constructs an indicator system and methodology suitable for desertification monitoring on the MP, addresses the lack of refined desertification data over a long time series, and provides scientific reference for decision-making support in combating desertification in this region, and other large arid and semi-arid areas in the world.

Spatiotemporal analysis of land cover change, projection, and fragmentation: an application of Google earth engine and machine learning approach on Baraitali forest, Bangladesh

ABSTRACT Understanding the spatiotemporal dynamics of land cover/use change and forest fragmentation from 1981 to 2021 across the Baraitali Forest was very crucial for modeling future land cover, formulation of sustainable and robust forest/land-management strategies, and policy. The study adopted a mixed modern tool of Google Earth Engine platform, Machine Learning Algorithms, ArcGIS software, Land Change Modeller of TerrSet software, and FRAGSTATS programs. The study revealed that total land cover from 1981 to 2021 found a negative change in vegetative coverage, and a positive change in settlement which was more visible within the 6 land cover classes. Projections of future land cover for the next 10 years anticipate that vegetation coverage will be reduced by 65.96 ha, while settlement will be enhanced by 41.91 ha in 2031. The study also found conversions/transition of land cover throughout the research area. Overall change of spatiotemporal patterns was leading to substantial forest fragmentation. The overall accuracy and Kappa statistics for all the supervised land cover classifications were satisfactory. The study will fill an information gap by providing the revealed information at national and regional scales and may contribute to understanding of global change.

Optimizing oil palm farming: Soil quality, fertilization and agro-environmental performance

The essential role of fertilization in the management of oil palm plantations cannot be overstated, with the understanding that effective strategies must be fine-tuned to accommodate the diversity of soil types present. The nuanced response of oil palm to fertilization under different soil conditions underscores the critical nature of this research. This study investigates the relationship between oil palm farming practices and agro-environmental performance of plantations. This research aims to understand the complex mechanisms influencing plantation productivity. Five agronomic field experiments were carried out on various soil types (Podzol, Inceptisol, Peat) during 2022. These experiments involved various types of soil and used different plant materials in a total land area of 33 hectares. The results of the peat soil trials revealed a positive effect of RP fertilizer on leaf phosphorus (P) content and a visible gradient in potassium (K) content with different doses of KCl. Average plantation yields peaked in 2022, showing the best yields ever recorded. On the contrary, experiments in podzolic soils showed that oil palm responded better to organic fertilizers than to mineral fertilizers in podzolic soils. The inceptisol soil revealed a significant increase in leaf potassium (K) content with KCl dose, confirming the positive effect of fresh oil palm empty fruit bunches (EFB) on potassium levels. In particular, EFB acts as a good K fertilizer, positively influencing the K content but negatively affecting the calcium content (Ca) due to K/Ca antagonism. These findings emphasize the need for diversified agricultural strategies to optimize oil palm cultivation, increase productivity, and reduce dependence on external input.

Impacts of Rural–Urban Labour Transfer and Land Transfer on Land Efficiency in China: A Analysis of Mediating Effects

In the midst of China’s ongoing rural–urban integration and development, a pivotal transformation involving the realignment of labour dynamics and land utilisation is underway. This paradigm shift has substantial implications for rural land use and agricultural productivity. Drawing from the field survey conducted in Zhejiang Province in 2019, this study puts non-agricultural employment, land transfer, and land efficiency into one econometric model and establishes a comprehensive framework to explain the mechanisms. Unlike existing research, this paper delves into the impact of different land-transfer behaviours, namely inflow and outflow, on land efficiency. The findings indicate that non-agricultural employment has no significant impact on land efficiency. Rural households acquiring land significantly enhance land efficiency, whereas relinquishing land shows no significance, thus addressing the gap in existing literature regarding the study of different transfer behaviours. Furthermore, to explore the underlying mechanisms, we investigate the mediating effect of land inflows on land efficiency, finding that it operates through plot size. In light of this, we propose that, in guiding land inflows, more emphasis should be placed on the integration and reorganisation of fragmented land rather than simply expanding the total land area, aiming to create large, well-managed areas of arable land by achieving concentrated and contiguous transferable land parcels.

Modeling the impact of sea level rise on endangered deer habitat

Numerous unique flora and fauna inhabit the Lower Florida Keys, including the endangered Florida Key deer, found nowhere else. In this vulnerable habitat of flat islands with low elevation, accelerated sea level rise poses a threat. Predicting the impact of sea level rise on vegetation and wildlife is crucial. This study used 5 Intergovernmental Panel on Climate Change (IPCC) sea level rise scenarios to assess their effects on No Name Key, Florida. The goal was to estimate changes in the Florida Key deer population relative to sea level rise using a lidar-derived elevation data and a vegetation map. The method used 2 cases to model the sea level rise impact. In Case 1, total non-submerged area at current sea level was determined. Using 5 IPCC scenarios, a new total non-submerged land area was estimated, and deer numbers were predicted for each scenario. In Case 2, upward migration of coastal vegetation combined with the coastal squeeze process was modeled. A distinct elevation range for each vegetation type at the current sea level was determined. Vegetation ranges were redistributed based on respective elevation ranges in the sea level rise scenarios. Areas for each vegetation type were recalculated, and Key deer numbers were estimated for each sea level rise scenario. Results under the worst emission scenario showed the following: (1) for case 1, the land area was reduced to 30 % of the current land area, corresponding to having about 27 deer, and (2) for case 2, the land area was reduced to 70 % of the current land area, having about 54 deer on No Name Key. The results indicated reduced non-submerged land area and less upland vegetation, particularly hardwoods/hammocks, by the year 2100. As less land area is available, a decline in Key deer population is expected as sea levels rise. Since Key deer favor upland vegetation, habitat affected by sea level rise will likely support a smaller deer population. The findings emphasize the need for precise, timely predictions of sea level rise impacts and long-term conservation strategies. Specifically designed measures are required to protect and maintain endangered wildlife, such as the Florida Key deer, residing on these vulnerable islands.

An assessment of anticipated future changes in water erosion dynamics under climate and land use change scenarios in South Asia

Water erosion has emerged as a significant global environmental challenge, particularly in South Asia, recognized as one of the regions hardest to hit globally by climate change. This study aims to identify the potential erosion hotspots in South Asia and to assess areas under risk in the future due to climate and land use change scenarios. We utilized the Revised Universal Soil Loss Equation (RUSLE), an empirical erosion model, to estimate erosion for both historical and future periods, considering two distinct Shared Socio-economic Pathways (SSP126 and SSP585). The baseline model predicts a mean water erosion rate of 10.6 t. ha−1. year−1 in South Asia, with more than 30 t. ha−1. year−1 recorded for 6.3 % of the total land area. Notably, Bangladesh and Nepal exhibit significantly higher erosion rates than other South Asian countries. Further, our projection indicates an + 10 % increase in erosion rates during the near future (2021–2040) under SSP585 compared to the historical estimates. A substantial rise of approximately + 31 % and + 39 % in water erosion rates in South Asia is anticipated during the far future periods of 2061–2080 and 2081–2100 under SSP585. At the country level, Bangladesh (+42 %), Bhutan (+108 %), and Nepal (+70 %) are expected to experience substantial increases in erosion rates during the 2081–2100 period, with India projecting a + 38 % increase. Additionally, a significant + 35 % expansion in the spatial extent of high erosion areas (>30 t. ha−1. year−1) is foreseen in 2081–2100 under SSP585. Furthermore, under the low climate change scenario (SSP126), the impact on the erosion rates is minimal, while under SSP585, a substantial increase in erosion rates is anticipated. Regions such as Southern India, the arid and semi-arid areas near the India-Pakistan border, and the Himalayan foothills are identified as hotspots for significant changes in erosion rates. Acknowledging a certain level of uncertainty, our study provides valuable insights into erosion risk areas in South Asia and their plausible future impacts due to climate and land use changes. This information can assist policymakers in South Asian countries in developing national-level strategies for soil conservation and climate resilience.

Mitigation of land-related impacts of solar deployment in the European Union through land planning policies

Solar power is space-intensive and will contribute to intensify land competition, a factor typically not captured by models. This study uses the Integrated Assessment Model WILIAM which explicitly represents the land use changes driven by solar energy expansion through a hard link of its energy and land modules including net energy restrictions. A Green Growth type transition is simulated for the European Union with a high renewable energy share target in electricity mix by 2050, testing different land use planning policies.The results show that a rapid deployment of solar power in land without land policies can intensify land use conflicts and increase associated land use change emissions. Land-use requirements for solar would be 1–1.4 % of total land (corresponding to 55–75 % of urban land), which could be problematic locally. The implementation of land-use protection and land siting policies could reduce 23 % of total land occupied bv solar photovoltaics panels (with respect to forest and cropland the area occupied could be reduced up to 88 %), and 23–47 % of the land use change associated emissions with respect to a scenario where not policies are applied. These results show the importance of integrating land use and energy planning policies to alleviate the undesired impacts.

Exploring shoreline changes and their implications in coastal communities using GIS and remote sensing techniques: The case of eastern beaches of Unguja island, Tanzania

Understanding shoreline dynamics and their impacts is crucial for the successful planning and development of coastal regions. This study focused on the research scarce area of the eastern coast of Unguja island, Tanzania to explore shoreline changes and their implications from 1990 to 2040 using GIS and remote sensing techniques. Four Landsat imagery taken in 1990, 2000, 2010 and 2020 were obtained and processed to determine past shoreline scenarios using Net-Shoreline-Movement (NSM), End-Point-Rate (EPR) and Linear-Regression-Rate (LRR) metrics in the DSAS software. Additionally, the LRR and the Kalman filter model were employed in the same software to determine future shoreline scenarios up to 2040. Overall results by LRR demonstrate that the shoreline of the eastern coast of Unguja was eroding at an average rate 15.560 m/year and its erosion rate is projected to reach 25.648 m/year by 2040. The total land eroded and accreted by EPR was 37,540,702.71 m² and 734,721.8037 m2, respectively. Periodically, high shoreline erosion rates occurred between 2000 and 2010 by the NSM (529.926 m, −798.133 m and −65.230 m) and EPR (53.299 m/year, −78.884 m/year and −6.617 m/year) results. Geographically, our study has identified that a significant portion of shoreline erosion occurs in the northern part of the island and some of its main identified impacts include the destruction of facilities along the coastal zone area. These findings call for the immediate and long-term interventions to mitigate adverse impacts of shoreline erosion and can inform other research in similar areas.

Spatio-Temporal Changes of Arable Land and Their Impacts on Grain Output in the Yangtze River Economic Belt from 1980 to 2020

The “Yangtze River Economic Belt Development Strategy” is one of China’s three major national development strategies. Enhancing the protection and quality of arable land in the Yangtze River Economic Belt (YEB) is pivotal for fostering regional growth. In this study, land use data spanning the years 1980 to 2020 in the YEB were extracted from the national land use database maintained by the Resource and Environment Data Center of the Chinese Academy of Sciences. Employing Geographic Information System (GIS) spatial analysis techniques and arable land change metrics, the study delineated the spatiotemporal characteristics of arable land alterations across the YEB for the period. Additionally, using grain output data at the prefecture level from 2011 to 2020, the paper calculated provincial grain output to analyze the impact of arable land changes over the last four decades on grain output. The findings revealed that: (1) From 1980 to 2020, the total arable land area in the YEB decreased by approximately 41,775 square kilometers, with the most significant decrease occurring in the downstream region. (2) From 1980 to 1990, the primary factor contributing to the decrease in arable land area was the expansion of water bodies, while from 1990 to 2020, the principal reason for the reduction in arable land area was the expansion of construction land. (3) From 1980 to 2020, the decrease in arable land area resulted in a net reduction of approximately 25.12 million tons in total grain output, with the largest decline observed in the downstream regions and the smallest decline in the upstream regions. (4) Consistent with the trends in arable land area reduction, the main reason for the decline in grain output from 1980 to 1990 was the expansion of water bodies encroaching upon arable land, whereas from 2000 to 2010, the primary cause of arable land reduction was the expansion of construction land areas. In conclusion, the research suggested that over the past four decades, the primary driver behind the reduction in arable land within the YEB has been the expansion of construction land areas. Particularly noteworthy was the period from 2000 to 2010, during which the impact of arable land reduction on grain output was most pronounced. This period coincided with the rapid economic development and accelerated urbanization process within the YEB.

Toward dual carbon targets: Spatial correlation on comprehensive carbon emission index in urban agglomerations based on a new evaluation model

China is confronted with substantial pressure to mitigate its carbon emissions. Urban agglomerations assume a pivotal role in the attainment of carbon peaking and carbon neutrality. This study constructed a carbon emission evaluation model from the perspectives of total quantity, intensity, efficiency, population, and land. A modified gravity model and social network analysis method was used to analyze the spatial correlation characteristics of six urban agglomerations, clarified their carbon emission spillover effects, and explored their influencing factors using the quadratic assignment procedure method. The results are as follows. (1) The carbon emission correlations in the Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing urban agglomerations increased, while Beijing-Tianjin-Hebei, Central Plains, and Middle Reaches of Yangtze River urban agglomerations decreased. (2) From a macro perspective, the Yangtze River Delta and Pearl River Delta urban agglomerations were at the core of the network, and the “bridging” role of the Middle Reaches of Yangtze River urban agglomeration was evident. From a micro perspective, the cities in the middle of urban agglomerations were more likely to become network cores. (3) Spillover effects between plates were present in the correlation network, with a gradual balance between the reception and spillover relationships. Most urban agglomerations exhibited an uneven distribution of community correlation capacity. (4) Geographic adjacency and urbanization level significantly promoted carbon emission correlation, although the direction and magnitude of other factors’ influence varied. The findings contribute to the implementation of low-carbon planning, carbon reduction strategies and regional sustainable development.