Agricultural Activities Research Articles

Climate smart agriculture in sub-Saharan Africa: Role of arbuscular mycorrhiza fungi

Climate change affects many areas of human lives, including agriculture. Interestingly, agriculture contributes to climate change, necessitating climate smart approaches to ensure a win-win situation; wherein agricultural production is sustainable and meets current and future needs. Climate smart agriculture is a collection of agricultural practices that increase productivity, adaptation, and mitigation of climate change contribution to agriculture. While many global regions depend on agriculture directly and indirectly, the sub-Saharan Africa region is directly dependent on agriculture. Existing “climate smart” activities in this region must be better defined and strengthened to achieve its objectives – increasing agricultural productivity, adapting agricultural systems, and mitigating emissions from agricultural activities. Among these climate smart activities are the significance of soil microorganisms, particularly arbuscular mycorrhizal fungi in sustainable agricultural systems, where they provide limited plant nutrients, control pests and diseases, aid drought adaptation, improve soil structure, reduce nutrient loss during leaching, leading to sustainable soil management.

Exploring Indigenous Knowledge Systems Integrated in Natural Sciences Practical Work in Selected South African Schools

This study used the nature of indigenous knowledge (NOIK) framework to explore indigenous knowledge systems (IKS) practices that were integrated when practical work in natural sciences was facilitated. In this interpretive explorative case study, data were collected by semistructured interviews, lesson observations and a focus group interview conducted with three purposively selected natural sciences teachers and two natural sciences district officials in the province of KwaZulu-Natal in South Africa. Thematic analysis techniques were used to analyze the data that were collected. Three findings on ways to integrate IKS in science practical work are the following. First, IKS was integrated in practical work through teaching of applied science and including the learners’ lived experiences of local technologies to solve problems. Second, practical work that integrated IKS was conducted as empirical observations of natural phenomena, which included genetic variation of plants and animals, including human beings, animal behavior, using moon cycles and the movement of the sun to guide day-to-day, cultural and agricultural activities. Third, some of the IKS included myths and superstitions that distorted the public understanding of science but were nevertheless used to persuade people to uphold certain cultural practices. The study recommends comparing NOIK and the nature of science when integrating IKS in the classroom to enable learners to distinguish myths from scientific facts.

Balancing Environmental Sustainability and Economic Viability in Luxembourgish Farms: An Agent-Based Model with Multi-Objective Optimization

To curb the impacts arising from the agricultural sector, the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi-objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint 3.0 life cycle impact assessment method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is built utilizing neighborhood interactions, through which a parameter known as farmer’s green consciousness is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e., when farmers’ green consciousness is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as the Environmental Footprint single score) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried out using the single score impact in the objective function, though this results in an 8% reduction in profitability over 10 years.

The Impact of Farmers' Socio-Economic Conditions Due to the Conversion of Agricultural Land in Setia Mulya Village, Bekasi Regency, Indonesia

Land conversion is the change of the original land function into another function. The conversion of laham affects the socio-economic conditions of farmers, especially in Setia Mulya Village. Socioeconomic conditions are a person's position in a community group that is determined by the type of economic activity, education, and income. The conversion of agricultural land can have an impact on the social and economic conditions of farmers. What is observed in this study is social conditions, economic conditions and their impact on farmers. The purpose of this study is to analyze changes in the socio-economic conditions of farmers due to land conversion and their impact on farmers' lives. The method used was a qualitative method with data sources obtained from observations, interviews, and documentation with farmers as respondents totaling 15 farmers, with sampling techniques using snowball sampling techniques. The results of the study showed changes in the socio-economic conditions of farmers due to land conversion that occurred in Setia Mulya Village. Changes in socio-economic conditions related to the age of farmers, changes in social status, changes in farmers' income, and farmers' household consumption. Farmers in Setia Mulya Village are cultivators so they have to pay rent to the landowner so that farming activities can continue. Although the area of agricultural land in Setia Mulya Village is very large, the high rental prices and the difficulty of irrigation flows make farmers have to look for decent agricultural land that can be used properly.

METHODS OF FORECASTING GRAIN CROP YIELD INDICATORS TAKING INTO ACCOUNT THE INFLUENCE OF METEOROLOGICAL CONDITIONS IN THE INFORMATION-ANALYTICAL SUBSYSTEM

Forecasting crop yields is one of the key challenges for the agricultural sector, especially in the context of a changing climate and unstable weather conditions. Kazakhstan, possessing significant territories suitable for growing grain crops, faces many challenges related to the effective management of agricultural activities. In this regard, yield forecasting becomes an integral part of planning and decision-making processes in agriculture. Information and analytical subsystems that integrate yield forecasting methods allow agribusinesses to estimate future production more accurately, minimise risks associated with climate change and optimise resource use. An important component of such systems is the consideration of weather conditions, as weather factors have a direct impact on crop growth and development. The purpose of this article is to develop and evaluate modern methods of forecasting grain yields taking into account the influence of weather conditions, as well as their integration into information-analytical subsystems to improve the accuracy of agricultural forecasting. To achieve this goal, the article addresses the following tasks: to analyse existing methods of yield forecasting and identify their advantages and disadvantages, to develop forecasting models, including machine learning methods such as gradient bousting and recurrent neural networks, to validate the developed models on the basis of historical data using cross-validation methods, to evaluate the effectiveness of the proposed methods and compare them with basic models such as linear regression and simple average, to evaluate the effectiveness of the proposed methods and to compare them with the basic models such as linear regression and simple average. This article reviews modern methods of forecasting grain crop yields in Kazakhstan, as well as technologies used in information-analytical subsystems. Particular attention is paid to the analysis of the influence of meteorological conditions on yields and the development of models that take this factor into account. The presented review and research results are aimed at improving the existing approaches to the management of agricultural processes under conditions of growing uncertainty caused by climate change. The article explores an important scientific task related to the development of methods for step-by-step forecasting of agrometeorological factors and grain yields, relying on the principle of analogy.

Determinan Pajanan Pestisida Terhadap Kadar Haemoglobin Wanita Usia Subur (WUS) di Kecamatan Dempo Selatan Pagar Alam

Women of childbearing age are a group that plays an important role in the agricultural sector not only in Indonesia but also in other agricultural countries around the world. The types of agricultural activities undertaken by WUS are sometimes the same as those of men, even those that allow contact with pesticides. Women of childbearing age in Pagaralam are at risk of experiencing health effects such as anemia, due to their exposure to Organophosphates. It is necessary to conduct research aimed at analyzing the determinants of pesticide exposure to the haemoglobin level in WUS as a basis for recommendations for efforts to prevent and overcome the effects of pesticide exposure. The design of this study was observational with a cross sectional study approach. The study population was women of childbearing age who were exposed to pesticides in the South Dempo District of Pagaralam, with a sample size of 120 WUS. Sampling using cluster sampling technique. Data collection was carried out through interviews using a questionnaire, observation using a checklist, measurement of body weight using a weight scale, height using a microtoise, and Hb levels using the Easy Touch GCHB tool. Data analysis of the results of the study was carried out univariately, and bivariate using the Chi- Square.The results of bivariate analysis of variables show that were significant with the haemoglobin level in women of childbearing age in South Dempo District were tenure, long time laundry spraying and length of time preparing pesticide. While variables that are not associated with haemoglobin levels are length of spraying, length of time mixed, frequency of spraying, and use of PPE. It is recommended that women of childbearing age reduce the length of time washing clothes spraying and the length of time preparing pesticides through good time management. Keywords : Haemoglobin level, pesticide exposure, woman of childbearing age

Impact of construction activities on agricultural land use for sustainable food production in Ile-Ife

The construction industry is a key player in global economic development and assumes a critical position in multifaceted demand for land resources through urbanisation process such as housing and infrastructure development. This pressure plays significant role in decimating available land resources for agricultural activities and has great implication for sustainable food production. This study examined land use change process in Ile-Ife using Landsat Image at 32m resolution over a period of 2002 to 2023. The features were classified into Baresurface, Built-up, Cultivation, Vegetation, and Waterbody with respective area of coverage of 0.9%, 21.0%, 30.3%, 47.4% and 0.3% at the baseline Year 2002. Findings showed that there was significant percentage increase from 2002 to 2023 of 166.7% for Baresurface (2.4%) and 106.2% for Built-up (43.3%). On the contrary, Cultivation area reduced to 22.2%, with a percentage decimation of 26.7%, Vegetation was reduced to 31.9% with a percentage reduction of 32.7% and Waterbody to 0.2% with a percentage reduction of 33.3%. Notably, majority of the percentage differences occurred between 2002 and 2014, which is largely attributable to increase in minimum wage. By implication, substantial arable land areas for cultivation and vegetation towards food production have been overtaken by built-up area and construction activities without any visible effort at exploring alternatives for agricultural practices towards food production. A concerted effort is required to stem the tide in ensuring sustainable food production.

Spatiotemporal Rainfall Analysis of El Nino, Neutral and La Nina Years Across Belg and Kiremt Seasons Over Oromia Region Ethiopia

ENSO is the primary natural hazard that contributes to food insecurity and poverty. It has been influencing countries’ climatic regimes for decades, bringing frequent floods and droughts. There is limited knowledge on El Niño and La Niña occurrences that affect agricultural activities in the Oromia Region and occur during the Belg and Kiremt seasons when rainfall is distributed. Evaluating the spatiotemporal rainfall performance of ENSO years spanning Belg and Kiremt seasons rainfall distribution over the research area is the primary goal. For the study’s spatial and temporal analyses, satellite rainfall data from 1981 to 2021 was obtained from CHG-UCSB and https://earlywarning.usgs.gov/fews/ewx_lite/index.html, respectively. The study’s findings demonstrated a substantial association between the Belg and Kiremt seasons spanning El Niño and La Niña periods, with a range of correlation values indicating a weak, moderate, and strong relationship. Because of the spatial and temporal dynamics of this study area, ENSO phases indicate that exceptionally dry years are probably linked to El Niño occurrences in the region, whereas extraordinarily rainy years are likely linked to La Niña events. Seasonal rainfall performance and rainfall variability are impacted by ENSO events and other regional systems, with both positive and negative effects observed throughout the research area. In addition to examining the ENSO (Neutral, El Niño, and La Niña) that have a major impact on the seasonal rainfall performances in the Oromia region, this study also looked at the water availability, yield production, pastoralist and agro-pastoralist activities, and water availability. In summary, the results of this research can help us better understand the mechanisms underlying regional ENSO events and their causes, which will be especially useful when future climate conditions change.

Regional Heterogeneity in Livelihood Strategies and Its Implications for Household Welfare: A Panel Data Analysis of Rural Vietnam

This study undertakes a meticulous examination of the Livelihood Strategy Diversity Index (LSDI) and its nuanced implications on household welfare in heterogeneous regions of rural Vietnam. Employing a unique panel dataset derived from the Vietnam Access to Resources Household Survey (VARHS) spanning from 2010 to 2018, the study employs both Ordinary Least Squares (OLS) and instrumental variable (IV) methods to investigate the intricate relationship between LSDI and household welfare, with a specific focus on income and dietary diversity. The empirical findings reveal compelling evidence of pronounced regional heterogeneity, highlighting the distinctive impacts of the LSDI on household welfare across diverse geographical areas. This study underscores the importance of region-specific strategies, advocating for a tailored and diversified approach to agricultural activities aligned with the unique context of each region. Furthermore, the findings emphasize the pivotal role of consolidating small plots as a strategic measure to alleviate agricultural land fragmentation, offering valuable insights into region-specific interventions for the enhancement of household well-being, encompassing both income and nutritional diversity.

THE ABUNDANCE OF Chlorella Sp. AND Chlamydomonas Sp.: WATER QUALITY, GROWTH, AND FEED CONVERSION RATIO OF WHITELEG SHRIMP (Litopenaeus Vannamei)

Chlorophyceae groups, such as Chlorella sp. and Chlamydomonas sp., are a stable group of phytoplankton with the highest abundance in Litopenaneus vannamei ponds. These two types can be used as determinant factors for water quality, growth, and Feed Conversion Ratio (FCR) in L. vannamei farming. This study aims to determine the correlation of Chlorella sp. and Chlamydomonas sp. on the water quality, growth, and FCR in L. vannamei farming. This study was conducted by survey and identification of plankton, water quality, growth, and FCR. The identified plankton was conducted using Principal Component Analysis (PCA) on water quality, growth, and FCR. The results of the study obtained a total abundance of Chlorella sp. and Chlamydomonas sp. of 91.93%. The water quality was found that most parameters comply with standards for L. vannamei farming activities, except for the parameters phosphate, nitrate, nitrite, Total Organic Matter (TOM), and ammonia. The abundance of Chlorella sp. and Chlamydomonas sp. has a strong correlation with a parameter of water quality in all ponds. The growth rate of Mean Body Weight in ponds is 0.4 – 24.43 grams/shrimp and has a strong correlation with the abundance of Chlorella sp. and Chlamydomonas sp. according to the significance value (p-value) of approximately 0.004 – 0.636. FCR value is 0.3-2.23 and has a strong correlation with the abundance of Chlorella sp. and Chlamydomonas sp. according to the eigenvalue of 1.032-1.670. The eigenvalue of the pond represents approximately 54.364% (pond 1), 55.658% (pond 2), 1.236, and 1.032 (pond 3) of the variable diversity. The results of the study showed that the abundance of Chlorella sp. and Chlamydomonas sp. is an indicator that influences the environment quality, growth, and Feed Conversion Ratio.

Unlocking the potential of electric and hybrid tractors via sensitivity and techno-economic analysis

The majority of agricultural vehicles in use today still rely on diesel-based propulsion, a major source of air pollution. Electrification is seen as a potential solution for decarbonizing these off-road vehicles but is hampered by higher upfront costs in energy storage and charging infrastructure. To better quantify these barriers, this paper proposes a techno-economic tool that can assist farmers in evaluating the total costs of ownership of electric and hybrid tractors. A pragmatic simulation model of the tractor is developed to predict the annual energy/fuel consumption and NOx emissions, while an economic model estimates the total acquisition, operation, and maintenance costs. For managing the energy in hybrid tractors, a new power split strategy based on model predictive control is developed, allowing the designer to balance energy efficiency and NOx emissions, while taking into account operational constraints of the electric powertrain. Additionally, we propose novel decision maps that allow farmers to quickly identify operating regions (in term of average load and yearly working time) where the deployment of electric/hybrid tractors is economically viable. To account for variability in the tractor’s operational conditions, we conduct both a deterministic sensitivity analysis with multi-parameter variation and a stochastic analysis of the total cost of ownership. The tool is validated with different mission profiles based on data from a California farm. The results show that, compared to diesel powertrains, electric tractors are more cost-effective for light-duty farming activities (engine loads less than 20%). On the other hand, hybrid powertrains are more economical for medium-duty tasks, where engine loads range from 20% to 60%.

Soil Nutrients Inversion in Open‐Pit Coal Mine Reclamation Area of Loess Plateau, China: A Study Based on ZhuHai‐1 Hyperspectral Remote Sensing

ABSTRACTSoil nutrients are crucial to assess land reclamation quality, and the use of various types of remote sensing data for soil nutrient inversion has been a key focus for soil monitoring. However, fewer studies have been conducted using satellite‐based hyperspectral remote sensing. To explore the potential of satellite‐based hyperspectral remote sensing in soil nutrient monitoring, this study selected soil organic matter, total nitrogen, available phosphorus, and available potassium content data from 83 sample sites using ZhuHai‐1 hyperspectral data. After spectral transformation and feature extraction, various inversion models were constructed, including partial least squares regression, support vector machine, recurrent neural network, and random forest. After verification by accuracy, the best spectral‐model combination was used for inversion. The results showed that the R‐squared range of the inversion models was 0.67748–0.78115. High content areas of soil organic matter and available potassium exhibited concentrated and contiguous features, while high content areas of total nitrogen and available phosphorus were more fragmented and fine‐grained. Alfalfa grassland plays a vital role in improving reconstructed soil in the early reclamation stage, and agricultural activities have differential impacts on soil nutrient accumulation. This study provides a theoretical basis for verifying the application capability of ZhuHai‐1 hyperspectral satellite data in soil monitoring.

THROUGH THE LENSES OF ACTOR-NETWORK APPROACH - THE DIGITAL TRANSFORMATION OF NIGERIAN AGRICULTURAL RESEARCH OUTREACH CENTRE

Agriculture’s goal in Nigeria is to provide supportive resources to sustain mankind, environmental resilience against climate change, nation’s economy, irrespective of geographical location or social status with a total land of 911,000 sq. km, and about (80%) is available for various agricultural purposes including arable land, permanent crops, pastures, and irrigated land. However, to achieve the set goal in the past, a series of agricultural information dissemination strategies such as radio, television, and print media were employed to raise adequate revenue for the agricultural sector and increase the food supply in Nigeria via Agricultural Research Outreach Centre in the National Agricultural Extension and Research Liaison Services adopted villages/ schools. The study was premised on a mixed method for counterfactuals to facilitate novelties. Despite all the efforts, these strategies did not yield the desired goal, thus, the digital transformation of the agricultural sector particularly to strengthen the extension system in Nigeria. This manner, demands the integration of the agricultural sector into the digital hub to create synergy among the stakeholders, source requisite agricultural information, and hybridize the means of disseminating agricultural information via participatory learning and action platforms. Also, create a Digital Participatory Learning and Action for Agricultural Extension (Di-PLA4AE). Thus, the paper trail on the concept of AROC, ICTs - Digitization, x-rayed the Actor-Network Approach (ANA) by Callon, its application, and the implications of digitization for agricultural activities to stakeholders and criticism in the context. More so, mirror the implications of the Di-PLA4AE and conclusion.

Economic Efficiency Of Pineapple Farming Using The Stochastic Frontier Approach

Central Aceh Regency is the province's largest pineapple producer. However, pineapple farming is facing the problem of low pineapple productivity and input use efficiency. The research aims to analyze the factors that influence the level of technical efficiency and the allocative and economic efficiency of pineapple farming in Pegasing District, Central Aceh Regency. The sampling process employs the stratified random sampling method. The total sample consisted of 62 respondents. The stochastic frontier is used. The research revealed that the production factor of land area, with a variable coefficient value of 0.0312, seeds with a variable coefficient value of seeds of 1.0152, and pesticides with a coefficient value of 5.6239, significantly influenced pineapple farming production in Pegasing District. Conversely, the labor factor, with a coefficient value of 0.0408, and fertilizer, with a coefficient value of 0.1198, had an insignificant effect. The use of inputs in pineapple farming has reached technical efficiency values, but there are ​still opportunities to increase optimally. However, neither allocatively nor economically have we achieved efficiency. The technical efficiency value for pineapple farmers was 0.80, the allocative efficiency value reached 0.427, and the economic efficiency was 0.351. Farmers, as actors in farming activities, need to pay attention to the use of the right combination of inputs; economic efficiency will be obtained from the use of production factors so that pineapple farmers' income will increase.

COVID-19 pandemic, household welfare and diversification strategies of smallholder farmers in Uganda

Agricultural activities in many sub-Saharan African (SSA) countries are subject to various risk factors that the COVID-19 compounds. Earlier studies on the effect of COVID-19 on smallholders neglect the issue of comparison with non-farm households. The study uses micro-level household datasets to explore the impact of the COVID-19 pandemic on household welfare, with a focus on farm households relative to their non-farm counterparts. We employed a binary probit model and Propensity Score Matching (PSM) approach and demonstrated that farm households witnessed important income reductions during the COVID-19 pandemic in Uganda. The study contributes to the design of evidence-based approaches to reducing farmers’ vulnerabilities to agricultural risks and pandemic-related shocks.

Projected changes in wet and dry extremes in the CMIP6 multi-model ensemble over the IGAD region of Eastern Africa

Wet and dry days, as well as wet and dry spells, are crucial pieces of information for rain-fed agriculture, food security, and numerous socioeconomic activities in East Africa. This study examines the projected changes in wet/dry days and wet/dry spells using data from the Multi-Model Ensemble (MME) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Statistical criteria and thresholds are used to project the changes in wet and dry days, as well as wet and dry spells, under the shared socioeconomic pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the annual, March–May (MAM), June–September (JJAS), and October-December (OND) seasons. The analysis indicates that the CMIP6 models generally overestimate the number of wet days (wet spells) across all seasons and scenarios at the 1 mm threshold. A projected 10–20% increase in total rainfall over the IGAD region is driven by an increase in rainfall intensity and the number of wet days under all scenarios during the near (2021–2050) and far (2071–2100) futures. A decrease in wet days and spells (increase in dry days and spells) is projected over South Sudan in JJAS, while an increase in wet spells is projected over Kenya, Somalia, and Sudan in OND during the near and far future compared to the baseline period. By the end of the century, wet days are projected to increase by approximately 20–30%, and dry spells are projected to decrease by 10–20% under the SSP1-2.6 and SSP5–8.5 scenarios. The EnsMean 10 CMIP6 tends to smooth out the extremes (wet or dry biases), creating a result that may appear more accurate but does not reflect the extremes or variations projected by individual models over South Sudan, Uganda, Kenya and Ethiopia. The projected patterns of wet and dry spells under SSP1-2.6 highlight the importance of mitigation efforts to limit temperature rise to below 1.5°. It is necessary to model how shifts in extreme dry and wet conditions will likely affect climate-sensitive sectors, such as agriculture and food security, in order to make well-informed decisions.

MEDIA IN SHAPING THE IMAGE OF POLISH AGRICULTURE FROM THE FARMERS’ PERSPPECTIVE

The research aimed to verify farmers’ views on the impact of content presented in various types of media on the image of Polish agriculture. The 147 responses obtained show that the surveyed farmers negatively assess the impact of traditional media on the image of Polish agriculture. Content presented via social media was positively received. How the media present aspects of agricultural activity and the farmer’s profession in contact with retail customers turned out to be more critical for farmers running smaller farms (36% of respondents rated this issue as necessary). Respondents recognized the importance of presence in social media, but the most frequently mentioned was gaining customers’ trust (73%). Social media presence was more important for farmers operating on smaller farms – those where retail sales were more common. The growing awareness of the needs of modern consumers is indicated by the high percentage of respondents noting that building relationships with customers requires reliable information about product features (53%), concern for the state of the natural environment (46%) and animal welfare (37%).

Seasonal variation and size distribution of aromatic acids in urban aerosols in Beijing, China

Aromatic acids are an integral component of organic acids in the atmosphere, contributing to the formation of climate-altering brown carbon (BrC). To better understand the sources and formation processes of aromatic acids, we collected size-segregated particulate matter samples in urban Beijing from April 2017 to January 2018, which were analyzed using solvent-extraction followed by gas chromatography/mass spectrometry. Phthalic acid (o-PhA) had the greatest average annual concentration, followed by terephthalic acid (p-PhA), 4-hydroxybenzoic acid (4-OHBA), dehydroabietic acid (DA), syringic acid (SA), 3-hydroxybenzoic acid (3-OHBA), isophthalic acid (m-PhA), and vanillic acid (VA). We identified distinct seasonal variations in aromatic acids, with o-PhA peaking in summer due to photochemical activity, while p-PhA and 4-OHBA elevated in autumn and summer, respectively, influenced by open waste and biomass burning. Wintertime variations in all aromatic acids were driven by complex meteorology and increased anthropogenic emissions, including rural biomass burning for cooking and heating. Particle size distribution of aromatic acids was affected by seasonal agricultural activities and dust storms, multiple emission sources, and formation mechanisms. The o-PhA has predominantly bimodal distribution, with diverse sources and complex formation mechanisms including gas- and aqueous-phase chemistry. The applicability of o-PhA as a tracer for specific secondary organic aerosols has been questioned due to its potential primary sources. The 3-OHBA, 4-OHBA, VA, SA, and DA exhibited bimodal or trimodal patterns during haze and non-haze periods across different particle size ranges. The seasonal variation in VA/SA and VA/4-OHBA ratios demonstrated the complexity of biomass burning types, influenced by season, particle size, meteorological conditions, and combustion sources.

Livestock farming and women empowerment in rural Bangladesh: a mixed method approach

BackgroundIn Bangladesh, livestock farming is not only a major source of animal protein, but it also provides opportunities for women to contribute to household income. Therefore, this study was undertaken to identify the empowerment status of women livestock farmers, factors influencing women's participation in livestock farming and its impact on women's empowerment.MethodsThe study was conducted in the Mymensingh district (administrative unit) of Bangladesh, due to the active participation of women in livestock farming activities. A total of 200 women were surveyed between May to November 2018. Additionally, prior to the final survey, 02 Focus Group Discussions (FGDs) were performed to gain insight into the context of the study areas. Furthermore, a total of 12 semi-structured in-depth interviews were performed with 12 experienced women livestock farmers to collect data regarding the challenges they encounter in livestock farming. Women empowerment was measured using four domains: decision-making process, ownership of assets, social and political awareness, and freedom of mobility. Descriptive statistics, and Heckman's endogenous treatment effect model were applied to analyze the data.ResultsThe results revealed that majority (75%) of women who are involved in livestock farming had a medium level of empowerment. Women's decision to participate in livestock farming was positively influenced by farm size, knowledge, extension contact, and training. According to the average treatment effect on the treated (ATT) value, the empowerment status of women is 16 points higher among participants in livestock farming compared to non-participants. Furthermore, women in rural areas face several obstacles such as economic issues and inadequate marketing facilities, which may prevent them from engaging in livestock farming.ConclusionsEducating women through raising awareness and providing training is warranted because there is a notable disparity in the empowerment status of women who are and are not involved in livestock farming. This will help women improve their social awareness, decision-making capacity, and mobility. Livestock initiatives that encourage women's involvement could be generated by various rural-focused organizations.

A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.