Agricultural Land Management Research Articles

Dynamics of living income for coffee farming households in agricultural area development policy: Socio-spatial integration

A decent income is an important part of overcoming economic disparities in agricultural development, especially in developing countries where most of the population are small farmers. As a developing country, Indonesia has also established a decent standard of living by setting a minimum wage as a reference for a decent income at the national and regional levels. However, this benchmark is not relevant to be applied uniformly at all levels of workers. This research determines the national coffee development area as the study center. We developed the Anker living wage methodology as a simple concept for determining living income for certain worker communities, especially for small farmers in rural areas who dominate the type of work in Indonesia. a socio-spatial approach is used to visualize the distribution of the dynamics of a decent life in various conditions of farming households. We found that 96.6% of coffee farming households in the national coffee development area had an inadequate living income, and only 3.4% were at an adequate level. We conclude that the current state of agricultural land management does not guarantee a decent income, even though efforts have been made to maximize agricultural crop productivity. The spatial description also shows that this condition is evenly distributed throughout residential areas. It is hoped that this approach can become an essential reference in implementing agricultural development programs that focus on welfare and equitable development as benchmarks for sustainable development goals in the future.

Integrated Assessment and Agricultural Planning in Selenium-Rich Hilly Soils: A Case Study on Land Use, Heavy Metal Contamination, and Nutrient Element Distribution

The strategic development of selenium-enriched soil cultivation is essential for effective agricultural land management. This research explores the sustainable utilization of selenium-rich soils in Qiongzhong County, Hainan Island. An extensive evaluation was conducted on 7266 surface soil samples, assessing the selenium concentrations, nutrient levels, heavy metals, pH values, and soil organic matter (SOM). In addition, analyses of 70 samples for organochlorine compounds and PAHs were performed. The results indicated average selenium content of 0.46 mg/kg, with 55.2% of the samples surpassing the selenium enrichment threshold of 0.4 mg/kg. According to the GB15618-2018 standards, 127 samples (1.75%) showed medium or high chromium-associated risks. No contamination from organochlorine compounds or PAHs was found, including the 16 priority-controlled PAHs with an average concentration of 30.3 µg/kg, confirming the soil’s high quality. The correlation and factor analysis identified surface enrichment as the main factor influencing selenium accumulation, presenting minimal environmental risks. Consequently, three categories of selenium-rich soil were defined: selenium-rich, pollution-free, and high-nutrient selenium-rich soil. Recommendations based on the soil characteristics and existing agricultural practices were made for the cultivation of selenium-rich vegetables, rubber, and nuts. This study lays a foundation for the sustainable management of selenium-rich soils, providing insights for further research and decision-making to optimize these resources, thus promoting environmental protection and agricultural sustainability.

Using convolutional neural network combined with multi-scale channel attention module to predict soil properties from visible and near-infrared spectral data

With the increasing demand for precision agriculture and sustainable land management, it is crucial to quickly and accurately predict soil properties. However, there are still challenges in predicting large-scale soil properties. This study proposes a new convolutional neural network architecture (MCA-CNN) combined with visible and near-infrared spectroscopy for predicting soil properties. The architecture introduces a multi-scale channel attention mechanism, aiming to more effectively capture complex features in the spectrum. The performance of the MCA-CNN model was tested on a large-scale dataset and compared with partial least squares (PLS), extreme gradient boosting tree (XGBoost), and conventional convolutional neural network (CNN) models. The predictive performance of different models in organic carbon (OC), nitrogen (N), pH, Clay, Salt, and Sand was analyzed. The results showed that the MCA-CNN model had the best predictive performance on all soil properties, achieving the lowest root mean square error (RMSE) and the highest coefficient of determination (R2), the R2 of OC, N, pH, Clay, Silt, and Sand are 0.95, 0.93, 0.95, 0.80, 0.58, 0.64, respectively. The RMSE of OC, N, pH, Clay, Silt, and Sand are 16.60 g kg−1, 0.96 g kg−1, 0.31, 4.89 %, 8.27 %, and 11.61 %, respectively. Further research has found that soil types (mineral soil and organic soil) affect the predictive performance of OC. It is necessary to distinguish between mineral soil and organic soil when predicting soil OC content. In addition, the effectiveness of the model transfer strategy was explored for situations with small soil sample sizes. By fine-tuning the pre-trained model, the performance of the model can be significantly improved, which provides a feasible solution for predicting soil properties in situations of data scarcity. In summary, this study not only confirms the potential application of deep learning in the field of soil science, but also provides new technological avenues for future soil management and agricultural practices.

Probabilistic crop type mapping for ex-ante modelling and spatial disaggregation

Agricultural land use and management fundamentally impacts the condition of natural resources like waterbodies, soils, and biodiversity. Modelling the anthropogenic effects on those resources over time requires detailed knowledge of the temporal and spatial distribution of crops. However, currently available crop type maps for Europe either lack the required spatial resolution or the temporal and spatial coverage. We develop and apply a probabilistic, spatially explicit crop type mapping approach that is suitable for ex-post and ex-ante modelling. The approach allows to quantify epistemic and aleatoric uncertainty related to estimated crop shares by providing an ensemble of maps. We implement the method for the EU-28 for the years 2010 – 2020, distinguishing between 28 different crop types at 1 km resolution. Based on a model of the data generating process that conceptually links field-, grid cell- and region-level crop acreages, our approach considers soil, climate, and topography information, as well as administrative data. The validation with ground-truthing data for France indicates that the generated crop type maps are plausible. The provided uncertainty intervals capture differences in uncertainty across space and time and correctly identify grid cells and crops where estimations are less precise. The generated maps constitute a unique data product of high practical value, e.g., for agri-environmental modelling applications. We see additional potential in using the approach to disaggregate the regional or national predictions of socio-economic ex-ante prediction models.

Cadastral-to-Agricultural: A Study on the Feasibility of Using Cadastral Parcels for Agricultural Land Parcel Delineation

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F1-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land.

Evaluating models of expert judgment to inform assessment of ecosystem viability and collapse.

Expert judgment underpins assessment of threatened ecosystems. However, experts are often narrowly defined, and variability in their judgments may be substantial. Models built from structured elicitation with large diverse expert panels can contribute to more consistent and transparent decision-making. We conducted a structured elicitation under a broad definition of expertise to examine variation in judgments of ecosystem viability and collapse in a critically endangered ecosystem. We explored whether variation in judgments among 83 experts was related to affiliation and management expertise and assessed performance of an average model based on common ecosystem indicators. There were systematic differences among individuals, much of which were not explained by affiliation or expertise. However, of the individuals affiliated with government, those in conservation and environmental departments were more likely to determine a patch was viable than those in agriculture and rural land management. Classification errors from an average model, in which all individuals were weighted equally, were highest among government agriculture experts (27%) and lowest among government conservation experts (12%). Differences were mostly cases in which the average model predicted a patch was viable but the individual thought it was not. These differences arose primarily for areas that were grazed or cleared of mature trees. These areas are often the target of restoration, but they are also valuable for agriculture. These results highlight the potential for conflicting advice and disagreement about policies and actions for conserving and restoring threatened ecosystems. Although adoption of an average model can improve consistency of ecosystem assessment, it can fail to capture and convey diverse opinions held by experts. Structured elicitation and models of ecosystem viability play an important role in providing data-driven evidence of where differences arise among experts to support engagement and discussion among stakeholders and decision makers and to improve the management of threatened ecosystems.

Exploring the position of farmers within the European green transition: transformation for whom?

Food systems have been framed as a “wicked problem” due to the complex socio-ecological impacts they foster, ranging from contributing nearly a quarter of anthropogenic greenhouse gas emissions to a myriad of social impacts (e.g., health, food safety, and food security). In the European green transition for food systems, multiple actors are involved. However, farmers play a unique and critical role as agricultural land managers and navigators of social, political, and environmental factors. Using cover cropping and intercropping as examples, we illustrate the complexities arising when decision-making and governance at multiple levels lead to tradeoffs and unexpected consequences at the farm scale. Amid complexity, we propose a conceptual model to address the question: how is an agricultural green transition best fostered? We find that changes are incremental, transformative or both depending on the level of analysis. Additionally, incoherence in agronomic recommendations across academic disciplines and policy agendas creates challenges at the farm scale that trickle up and can thwart sustainable agricultural land use. Although transdisciplinarity and knowledge production with farmers through co-creation are essential for food system transformation and can be part of the solution, it is crucial to examine the nature of change processes and to consider how knowledge and innovation are adopted. By balancing top-down and bottom-up approaches and distributing burden from the farm scale to governance and food systems, a more transformative green transition for European food systems with coherence across multiple agroecological objectives could be achieved.

Trade-Offs between Economic Benefits, GHG Emissions, and Nitrate, Phosphate, and Salinity Balances in Agricultural Land and Water Management

Trade-Offs between Economic Benefits, GHG Emissions, and Nitrate, Phosphate, and Salinity Balances in Agricultural Land and Water Management

Potensi precision farming dalam penerapan prinsip reduce untuk mengurangi limbah sumber daya pertanian

Background: The increase in agricultural production is often accompanied by excessive use of chemical inputs, contributing to environmental pollution. Precision farming is a modern approach that can address this challenge by optimizing input use through advanced technology. This paper explores the application of the reduce principle in precision farming to support efficient use of fertilizers and other resources. Method: The method used is a literature review of various published studies related to precision farming and agricultural land management. Findings: The application of the reduce principle in fertilization activities with precision farming can reduce fertilizer waste by up to 50%, increase land productivity by 10%, and minimize harmful environmental effects, such as groundwater pollution and greenhouse gas emissions. Precision farming also improves irrigation efficiency through precision irrigation technology. Conclusion: This study confirms that applying precision farming with a focus on the reduce principle not only supports sustainable agriculture but also provides economic benefits for farmers by reducing production costs. Innovations in precision farming technology, such as the integration of AI-based sensors and IoT, further enhance potential efficiencies in the future. Novelty/Originality of this article: Emphasizing the use of the reduce principle in precision farming as a primary strategy to achieve more efficient and environmentally friendly agriculture aligns with the goals outlined in the Sustainable Development Goals (SDGs) for responsible production.

Soil Erosion Risk Analysis in the Ría de Arosa (Pontevedra, Spain) Using the RUSLE and GIS Techniques

Soil erosion in coastal areas, driven by global change and human activity, poses a significant threat to ecological and economic stability. This research investigates water erosion in the southeast of the Ría de Arosa (Pontevedra, Spain), utilizing the Revised Universal Soil Loss Equation model and Geographic Information System technologies. Key factors analyzed include rainfall erosivity, soil erodibility, topography, land cover, and conservation practices. High-resolution maps (1 × 1 m pixels) identified areas at high risk of erosion. Vulnerable zones, such as coastal cliffs and vineyards, show severe erosion rates exceeding 50 t/ha/year (>5 mm/year), with the most extreme zones reaching up to 200 t/ha/year (>200 mm/year). These results emphasize that intervention could be required or recommended. Suggested measures include reforestation, effective agricultural land management, or the implementation of vegetative barriers to reduce erosion. These areas, characterized by steep slopes and sparse vegetation, are particularly susceptible to soil loss, necessitating specific conservation efforts. The results underscore the need for sustainable coastal management practices and preventive strategies to protect this vulnerable coastal zone. Implementing these measures is crucial to mitigating the impacts of soil erosion, preserving natural resources, and ensuring long-term ecological and economic resilience in the region.

An Innovative Heating Solution for Sustainable Agriculture: A Feasibility Study on the Integration of Phase Change Materials as Passive Heating Elements

In this study, we investigate an innovative option for the ecological management of agricultural land. The focus is on the use of phase change materials (PCMs) for passive temperature regulation in greenhouses and fruit crop fields in order to reduce yield losses due to unforeseen late frost events. The use of PCMs represents a novel approach to enhancing crop growth and extending growing seasons without relying on conventional energy-intensive methods, providing a stable microclimate that can protect plants from cold stress. This passive regulation of temperature helps to reduce the need for fossil fuel-based heating systems, thereby lowering greenhouse gas emissions and operational costs. The application of PCMs in agricultural settings is particularly innovative as it leverages naturally occurring temperature variations to create a self-sustaining, low-maintenance solution that aligns with the principles of sustainable farming. This approach not only improves energy efficiency but also contributes to the resilience of agricultural practices in the face of climate variability. This study focuses on the possible use of PCMs in passive heating modules for the protection of potted plants in greenhouses. Various PCMs such as paraffin, beeswax, and shea butter were tested. Experiments were then conducted, using one kind of paraffin-based PCM, in a specially designed module. In addition, an FEM simulation model (CFD) was built and tested. The model was used to perform detailed analyses of the heat transfer efficiency, fluid dynamics, and overall performance of the modules. The model can also be used for optimization purposes (e.g., efficiency improvements).

Effects of several long-term soil health treatments on populations of Pratylenchus penetrans and the soil microbial community

Soil suppressiveness can reduce the damage by plant parasitic nematodes (PPN) in agricultural soils and is conveyed by the activity of soil microorganisms. While natural suppressiveness has been reported, it is still poorly understood if soil suppressiveness can be elicited by manipulating the soil microbial community. In the present study we assessed the number of the Pratylenchus penetrans (Pp) and the bacterial and fungal community composition over 7 years in a long-term soil health experiment. The field experiment consisted of an organic and conventional agricultural land management system and three soil health treatments (SHT): an untreated control (CT), anaerobic disinfestation (AD) and a combination of marigold cover cropping, compost and chitin amendment (CB). The land management systems were kept continuously, while the soil health treatments were applied only twice in seven years. The microbial community significantly differed between the organic and conventional system, but there was no significant difference in Pp numbers between the two systems. However, both the CB treatment and to a lesser extent the AD treatment reduced Pp numbers and increased yield with the effect being the strongest in the years immediately after the treatment. Accordingly, both the bacterial and fungal community differed significantly between the treatments, the differences being largest in the years after the treatments. Notably, the CB treatment elicited both long-term changes in the microbial community and a reduction of Pp numbers lasting for at least three years. These results indicated that a combination of treatments can lead to an altered soil microbial community in combination with persisting suppressiveness of Pp.

МЕТОДЫ ОПРЕДЕЛЕНИЯ РАДИАЦИОННОГО БАЛАНСА ПОЧВЕННОГО И РАСТИТЕЛЬНОГО ПОКРОВА СЕЛЬСКОХОЗЯЙСТВЕННЫХ УГОДИЙ

In this article, an analysis of calculation methods for determining the radiation balance and photosynthetically active radiation of soil and vegetation cover of agricultural land is conducted. The lack of continuous actinometric observations of solar radiation and radiation balance at meteorological stations in the Eurasian region leads to the widespread use of empirical and semi-empirical models based on climatic and geographical indicators. Mathematical models based on climatic indicators, which vary in spatial and temporal aspects and are formed on the basis of the principle of natural analogies, provide reliable and accurate data. However, calculation methods that use geographical indicators do not have physical and mathematical meaning, resulting in significant errors in the data. The aim of the research is to analyze indirect methods to identify their physical and mathematical justification. The need for the development of quantitative methods for assessing the effective use of photosynthetically active radiation to improve forecasting and management of agricultural land is emphasized.

Complementarity of Sentinel-1 and Sentinel-2 Data for Soil Salinity Monitoring to Support Sustainable Agriculture Practices in the Central Bolivian Altiplano

Soil salinization will affect 50% of global cropland areas by 2050 and represents a major threat to agricultural production and food sovereignty. As soil salinity monitoring is costly and time consuming, many regions of the world undertake very limited soil salinity observation (in space and time), preventing the accurate assessment of soil salinity hazards. In this context, this study assesses the relative performance of Sentinel-1 radar and Sentinel-2 optical images, and the combination of the two, for monitoring changes in soil salinity at high spatial and temporal resolution, which is essential to evaluate the mitigation measures required for the sustainable adaptation of agriculture practices. For this purpose, an improved learning database made of 863 soil electrical conductivity (i.e., soil salinity) observations is considered for the training/validation step of a Random Forest (RF) model. The RF model is successively trained with (1) only Sentinel-1, (2) only Sentinel-2 and (3) both Sentinel-1 and -2 features using the Genetic Algorithm (GA) to reduce multi-collinearity in the independent variables. Using k-fold cross validation (3-fold), overall accuracy (OA) values of 0.83, 0.88 and 0.95 are obtained when considering only Sentinel-2, only Sentinel-1 and both Sentinel-1 and -2 features as independent variables. Therefore, these results highlight the clear complementarity of radar (i.e., Sentinel-1) and optical (i.e., Sentinel-2) images to improve soil salinity mapping, with OA increases of approximately 10% and 7% when compared to Sentinel-2 and Sentinel-1 alone. Finally, pre-sowing soil salinity maps over a five-year period (2019–2023) are presented to highlight the benefit of the proposed procedure to support the sustainable management of agricultural lands in the context of soil salinization on a regional scale.

Restructuring agricultural land use in Ukraine. Directions for development

The intensity of agricultural land use in Ukraine was analyzed based on soil quality and the values of the land-use intensity (LUI) index and the land-use capacity (LUC) index. In the administrative districts of Cherkasy region, the LUI index ranged from 0.07 to 1.0, and the LUC index ranged from 0.01 ha per 1000 UAH of gross output to 0.19 ha per 1000 UAH, which points to considerable difference in land-use intensity and agricultural land-use capacity. New approaches should be sought to improve the management and use of agricultural land. Functional land use areas should be restructured, in particular, by implementing non-traditional methods of agricultural land use. The results of the study indicate that land-use functions should be modified over an area of 5,343,900 ha, where 2,049,900 ha should be converted to crop rotations for soil protection, 208,000 ha – to protective forest margins; and 2,878,000 ha – to non-traditional forms of agricultural land use. Motives: The purpose of the article was to propose a methodological approach for assessing the intensity of agricultural land use and to suggest directions for land-use restructuring to improve its ecological status. Aim: In the proposed methodical approach, the intensity of agricultural land use was assessed with the land-use intensity (LUI) index and the land-use capacity (LUC) index. To reduce plowing and increase land-use intensity, the functions of degraded arable land should be modified over an area of 5,343,900 ha, where 2,049,900 ha should be converted to crop rotations for soil protection, 208,000 ha – to protective forest margins; and 2,878,000 ha – to non-traditional forms of agricultural land use. Results: The article substantiates the need to modify the functions of arable land over an area of 5,343,900 ha, where 2,049,900 ha should be converted to crop rotations for soil protection, 208,000 ha – to protective forest margins; and 2,878,000 ha – to non-traditional forms of agricultural land use.

Willingness to adopt green house gas mitigation measures: Agricultural land managers in the United Kingdom.

Rapid uptake of greenhouse gas (GHG) mitigation measures is central to reducing agricultural and land use emissions and meeting the UK Net Zero policy. The socioeconomic challenges and barriers to uptake are poorly understood, with yet unclear structural pathways to the uptake of GHG mitigation measures. Using an online survey of 201 agricultural land managers across the UK, and applying multiple linear regression and stepwise regression analysis, this research established farm and farmers' factors influencing perceptions and willingness to adopt GHG mitigation measures. The results consistently show that farm sector, farmers' business perception, and labour availability influence willingness to adopt GHG mitigation measures. Based on the farmers' qualitative feedback, other barriers to adoption include costs and concerns for profitability, lack of flexibility in land tenancy contracts, poor awareness and knowledge of the application of some GHG mitigation measures, perception about market demand e.g bioenergy crops, and scepticism about the future impacts of adopting varying GHG mitigation measures. In the midst of the ongoing net zero transition, this study identifies existing barriers to the uptake of GHG mitigation measures, and specifically, a substantial gap between farmers and the science of GHG mitigation measures and the need to incentivise a farm and farming community-led policy interventions to promote adoption of GHG mitigation measures.

Air Pollution and Blood Pressure: Evidence From Indonesia.

Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM2.5 concentrations at 0.5°×0.625° resolution across the IFLS domain. We assessed the association between PM2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM2.5 and blood pressure in Indonesia.

Quantitative prediction of soil chromium content using laboratory-based visible and near-infrared spectroscopy with different ensemble learning models

Soil heavy metal chromium pollution poses significant threats to human health and ecosystems, necessitating accurate quantitative prediction methods for effective monitoring and management. This study aims to develop robust predictive models for soil chromium content in farmland soils of Mojiang Hani Autonomous County, Pu’er City, Yunnan Province, China. These models utilize ensemble learning techniques based on visible and near-infrared spectroscopy. Operations before model building involved partitioning datasets with the Kennard-Stone algorithm to ensure representative training and testing sets. Visible and near-infrared spectroscopy-data preprocessing was performed using Savitzky-Golay smoothing and first-order derivative transformations to enhance signal quality. Bands selection was achieved through the Successive Projections Algorithm (SPA), effectively reducing data dimensionality and collinearity. Six ensemble learning models were constructed and assessed for their predictive performance: Bagging-DTR, Random Forest (RF), Adaboost-DTR, XGBoost-DTR, Stacking-1, and Stacking-2. These models utilized Decision Trees (DTR) and Linear Regression (LR) as base learners. Results demonstrated that ensemble models significantly outperformed individual base learners. Notably, the Stacking-2 model achieved the highest accuracy with an R^2 of 0.954, RMSE of 125.967 mg/kg, and RPD of 4.667. To validate the model’s practical applicability, spatial interpolation of soil Cr content was conducted using the Kriging method based on Stacking-2 model predictions. The spatial distribution maps of measured and predicted values exhibited high congruence, underscoring the model’s effectiveness in accurately mapping Cr distribution across the study area. This study underscores the efficacy of integrating ensemble learning with visible and near-infrared spectroscopy-data preprocessing and SPA for precise soil heavy metal prediction. The findings offer valuable insights and a scientific basis for enhanced soil quality monitoring, environmental risk assessment, and informed agricultural land management and pollution control.

Projecting the response of carbon sink potential to land use/land cover change in ecologically fragile regions

Introduction: The carbon storage service of ecosystems in ecologically fragile areas is highly sensitive to regional land use/land cover (LULC) changes. Predicting changes in regional carbon storage under different LULC scenarios is crucial for land use management decisions and exploring carbon sink potential. This study focuses on the Luan River Basin, a typical ecologically fragile area, to analyze the impact of LULC changes on carbon storage.Methods: The PLUS-InVEST model was employed to simulate LULC patterns for the year 2030 under three scenarios: natural development, cropland protection and urban development, and ecological protection. The model projected the future carbon sink potential of the basin under these scenarios.Results: From 2000 to 2020, carbon storage showed a trend of decrease followed by an increase. By 2030, compared to 2020, carbon storage is projected to increase by 16.97% under the ecological protection scenario and decrease by 22.14% under the cropland protection and urban development scenario. The increase in carbon storage was primarily due to the conversion of cropland and grassland to forestland, while the decrease was mainly associated with the conversion of forestland to grassland and cropland, and the transformation of grassland to cropland and construction land. In the potential LULC scenarios of 2030, certain regions within the basin exhibited unstable carbon sink potential, strongly influenced by LULC changes. These areas were predominantly characterized by artificially cultivated forests, shrubs, and agricultural land. Implementing appropriate forest management measures and optimizing agricultural land management practices are essential to enhance carbon sink potential in these regions. Population density, annual average temperature, and DEM (Digital Elevation Model) were the dominant factors driving the spatial variation of carbon sink potential in the Luan River Basin.Discussion: The research results provide a theoretical basis for rational planning of land use and the enhancement of carbon sink potential in ecologically fragile regions.

Impact of land fragmentation and fragmentation of property rights on the profit efficiency of rice cultivation and multiple farming in Taiwan

ABSTRACT This study assesses the impact of land fragmentation (LF) and property rights fragmentation on the profit efficiency of rice and multiple crop farming in Taiwan using a stochastic profit frontier model. Key findings indicate that LF negatively affects profit efficiency, particularly in rice farming; however, diversifying into multiple crops can mitigate this effect, suggesting the complexity of LF’s impact on agricultural efficiency. This research underscores the need for targeted policies to address LF and property rights issues, suggesting land consolidation and the promotion of leasing without ownership transfer as viable strategies. The findings advocate for a comprehensive approach to agricultural policy and land management practices, focusing on improving profit efficiency through strategic land use, diversification, and sustainable farming practices.