Search results for: forest farming

Authors: Akingeneye Liliane

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The value chain concept in recent times is being used by businesses and organizations to develop and implement their businesses. Chili farming has been identified as a significant contributor to the economic growth of Bugesera district. However, numerous challenges have led to a decrease in production. The primary objective of this research was to assess the current Bugesera chili value chain, identify the bottlenecks in the value chain, and come up with interventions that can help increase the output of the Bugesera chili value chain, in a climate-smart way and enhance Long-term sustainability of the value chain. The research used a case study approach to fulfill its objectives, utilizing primary and secondary data sources. Qualitative and quantitative data were gathered through semi-structured interviews with 22 individual farmers, five exporters, and five supporters within the Bugesera district. A focus group discussion (FGD) with seven stakeholders was also conducted to validate the research findings. The study's results underscore the challenges faced by chili farmers and other actors in the chain, the perceptions of different stakeholders to contribute to chili production, and the importance of promoting strong collaboration among stakeholders in the chili value chain to establish a sustainable framework. Based on these findings, the study puts forward recommendations to address the identified challenges and improve the chili farming sector in Bugesera. The business canvas model, as a proposed recommendation, once implemented, is believed to represent the most effective approach to enhancing chili productivity in Bugesera and securing the long-term sustainability of an export-oriented chili value chain in the district.

Keywords: build, sustainability, chili value chain, export-oriented

Procedia PDF Downloads 43

Authors: G. Edwards, N. Dybro, L. J. Munkholm, C. G. Sørensen

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Planning agricultural operations requires an understanding of when fields are ready for operations. However determining a field’s readiness is a difficult process that can involve large amounts of data and an experienced farm manager. A consequence of this is that operations are often executed when fields are unready, or partially unready, which can compromise results incurring environmental impacts, decreased yield and increased operational costs. In order to assess timeliness of operations’ execution, a new scheme is introduced to quantify the aptitude of farm managers to plan operations. Two criteria are presented by which the execution of operations can be evaluated as to their exploitation of a field’s readiness window. A dataset containing the execution dates of spring and autumn operations on 93 fields in Iowa, USA, over two years, was considered as an example and used to demonstrate how operations’ executions can be evaluated. The execution dates were compared with simulated data to gain a measure of how disparate the actual execution was from the ideal execution. The presented tool is able to evaluate the spring operations better than the autumn operations as required data was lacking to correctly parameterise the crop model. Further work is needed on the underlying models of the decision support tool in order for its situational knowledge to emulate reality more consistently. However the assessment methods and evaluation criteria presented offer a standard by which operations' execution proficiency can be quantified and could be used to identify farm managers who require decisional support when planning operations, or as a means of incentivising and promoting the use of sustainable farming practices.

Keywords: operation management, field readiness, sustainable farming, workability

Procedia PDF Downloads 387

Authors: Shinoy Vengaramkode Bhaskaran, Kaushik Sathupadi, Sandesh Achar

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Human locomotion recognition is important in a variety of sectors, such as robotics, security, healthcare, fitness tracking and cloud computing. With the increasing pervasiveness of peripheral devices, particularly Inertial Measurement Units (IMUs) sensors, researchers have attempted to exploit these advancements in order to precisely and efficiently identify and categorize human activities. This research paper introduces a state-of-the-art methodology for the recognition of human locomotion patterns in a cloud environment. The methodology is based on a publicly available benchmark dataset. The investigation implements a denoising and windowing strategy to deal with the unprocessed data. Next, feature extraction is adopted to abstract the main cues from the data. The SelectKBest strategy is used to abstract optimal features from the data. Furthermore, state-of-the-art ML classifiers are used to evaluate the performance of the system, including logistic regression, random forest, gradient boosting and SVM have been investigated to accomplish precise locomotion classification. Finally, a detailed comparative analysis of results is presented to reveal the performance of recognition models.

Keywords: artificial intelligence, cloud computing, IoT, human locomotion, gradient boosting, random forest, neural networks, body-worn sensors

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Authors: Shailja Thakur

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In this paper it is shown that how farmers are suffering from all sides including vagaries of weather then price fluctuations, demand supply constraints, poor soil health etc. Also the ICT can prove to be of great help if incorporated rightly into Indian agriculture. Some innovative ways to reward farmers and distribution of subsidies to them can improve the current scenario.

Keywords: cost of farming, information and communication technology, innovative steps, roof gardening, vermicomposting

Procedia PDF Downloads 308

Authors: Alice Brites, Gerd Sparovek, Jean Paul Metzger, Ricardo Rodrigues

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The segregation between science and policy in decision making process hinders nature conservation efforts worldwide. Scientists have been criticized for not producing information that leads to effective solutions for environmental problems. In an attempt to bridge this gap between science and practice, we conducted a project aimed at supporting the implementation of the Brazilian Native Vegetation Protection Law (NVPL) implementation in São Paulo State (SP), Brazil. To do so, we conducted multiple open meetings with the stakeholders involved in this discussion. Throughout this process, we raised stakeholders' demands for scientific information and brought feedbacks about our findings. However, our main scientific advice was not taken into account during the NVPL implementation in SP. The NVPL has a mechanism that exempts landholders who converted native vegetation without offending the legislation in place at the time of the conversion from restoration requirements. We found out that there were no accurate spatialized data for native vegetation cover before the 1960s. Thus, the initial benchmark for the mechanism application should be the 1965 Brazilian Forest Act. Even so, SP kept the 1934 Brazilian Forest Act as the initial legal benchmark for the law application. This decision implies the use of a probabilistic native vegetation map that has uncertainty and subjectivity as its intrinsic characteristics, thus its use can lead to legal queries, corruption, and an unfair benefit application. But why this decision was made even after the scientific advice was vastly divulgated? We raised some possible reasons to explain it. First, the decision was made during a government transition, showing that circumstantial political events can overshadow scientific arguments. Second, the debate about the NVPL in SP was not pacified and powerful stakeholders could benefit from the confusion created by this decision. Finally, the native vegetation protection mechanism is a complex issue, with many technical aspects that can be hard to understand for a non-specialized courtroom, such as the one that made the final decision at SP. This example shows that science and decision-makers still have a long way ahead to improve their way to interact and that science needs to find its way to be heard above the political buzz.

Keywords: Brazil, forest act, science-based dialogue, science-policy interface

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Authors: N. Hedayat, E. Karamifar

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Sustainable food and fiber production is emerging as an irresistible option in agrarian planning. Although one should not underestimate the successes of the Green Revolution in enhancing crop production, its adverse environmental and ecosystem consequences have also been remarkable. The aim of this paper is to identify ways of improving crop production to ensure agricultural sustainability and environmental integrity. Systematic observations are used for data collection on intensive farming, deforestation and the environmental implications of industrial pollutants on agricultural sustainability at national and international levels. These were achieved within a comparative analytical model of data interpretation. Results show that while multiple factors enhance yield, they have a simultaneous effect in undermining the ecosystem and environmental integrity. Results show that application of excessive agrichemical have been one of the major cause of polluting the surface and underground water bodies as well as soil layers in affected croplands. Results consider rapid deforestation in the tropical regions has been the underlying cause of impairing the integrity of biodiversity and oxygen-generation regime. These, coupled with production of greenhouse gasses, have contributed to global warming and hydrological irregularities. Continuous production of pollutants and effluents has affected marine and land biodiversity arising from acid rains generated by modern farming and deforestation. Continuous production of greenhouse gases has also been instrumental in affecting climatic behavior manifested in recurring draughts and contraction of lakes and ponds as well as emergence of potential flooding of waterways and floodplains in the future.

Keywords: agricultural sustainability, environmental integrity, pollution, eco-system

Procedia PDF Downloads 401

Authors: Amit Barde, Dipali Khatave, Vaishali Savale, Atharva Chavan, Sapna Wagaj, Aditya Jilla

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AgriInnoConnect Pro is an advanced agricultural automation system designed to enhance irrigation efficiency and overall farm management through IoT technology. Using MIT App Inventor, Telegram, Arduino IDE, and Firebase Console, it provides a user-friendly interface for farmers. Key hardware includes soil moisture sensors, DHT11 sensors, a 12V motor, a solenoid valve, a stepdown transformer, Smart Fencing, and AC switches. The system operates in automatic and manual modes. In automatic mode, the ESP32 microcontroller monitors soil moisture and autonomously controls irrigation to optimize water usage. In manual mode, users can control the irrigation motor via a mobile app. Telegram bots enable remote operation of the solenoid valve and electric fencing, enhancing farm security. Additionally, the system upgrades conventional devices to smart ones using AC switches, broadening automation capabilities. AgriInnoConnect Pro aims to improve farm productivity and resource management, addressing the critical need for sustainable water conservation and providing a comprehensive solution for modern farm management. The integration of smart technologies in AgriInnoConnect Pro ensures precision farming practices, promoting efficient resource allocation and sustainable agricultural development.

Keywords: agricultural automation, IoT, soil moisture sensor, ESP32, MIT app inventor, telegram bot, smart farming, remote control, firebase console

Procedia PDF Downloads 43

Authors: L. P. K. Liyanage, N. R. P. Withana, A. L. Sandika

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The purpose of this study was to determine tourist and community perception-based sustainable tourism indicators as well as Human Pressure Index (HPI) and Tourist Activity Index (TAI). Study was carried out in Sinharaja forest which is considered as one of the major eco-tourism destination in Sri Lanka. Data were gathered using a pre-tested semi-structured questionnaire as well as records from Forest department. Convenient sampling technique was applied. For the majority of issues, the responses were obtained on multi-point Likert-type scales. Visual portrayal was used for display analyzed data. The study revealed that the host community of the Kudawa gets many benefits from tourism. Also, tourism has caused negative impacts upon the environment and community. The study further revealed the need of proper waste management and involvement of local cultural events for the tourism business in the Kudawa conservation center. The TAI, which accounted to be 1.27 and monthly evolution of HPI revealed that congestion can be occurred in the Sinharaja rainforest during peak season. The results provide useful information to any party involved with tourism planning anywhere, since such attempts would be more effective once the people’s perceptions on these aspects are taken into account.

Keywords: Kudawa Conservation Center, Sinharaja World Heritage Rainforest, sustainability indicators, community perception

Procedia PDF Downloads 151

Authors: Unity Chipfupa, Aluwani Tagwi, Edilegnaw Wale

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Climate change adaptation studies are challenged by a limited understanding of how non-cognitive factors such as psychological capital affect adaptation decisions of smallholder farmers. The concept of psychological capital has not been fully applied in the empirical literature on climate change adaptation strategies. Hence, the study was meant to assess how psychological capital endowment affects climate change adaptation among smallholder farmers. A multivariate probit regression model was estimated using data collected from 328 smallholder farmers in KwaZulu-Natal, South Africa. The findings indicate that, among other factors, self-confidence and hope or aspirations in farming influence climate change adaptation decisions of smallholders. The psychological capital theory proved to be comprehensive in identifying specific psychological dimensions associated with adaptation decisions. However, the non-alignment of approaches for measuring non-cognitive factors made it difficult to compare results among different studies. In conclusion, the study recommends the need for practical ways for enhancing smallholders’ endowment with key non-cognitive abilities. Researchers should develop and agree on a comprehensive framework for assessing non-cognitive factors critical for climate change adaptation. This will improve the use of positive psychology theories to advance the literature on climate change adaptation. Other key recommendations include targeted support for communities facing higher risks of climate change, improving smallholders’ ability to adapt, promotion of social networks and the inclusion of farming objectives as an important indicator in climate change adaptation research.

Keywords: adaptive capacity, climate change adaptation, psychological capital, multivariate probit, non-cognitive factors.

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Authors: Renata Silva-Mann, Sheila Valéria Álvares Carvalho, Robério Anastácio Ferreira, Laura Jane Gomes

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There is growing interest in defining indicators of sustainability, which are important for monitoring the conservation of native forests, particularly in areas of permanent protection. These indicators are references for assessing the state of the forest and the status of the depredated area and its ability to maintain species populations. The aim of the present study was to select genetic parameters as indicators of sustainability for Schinus terebinthifolius Raddi. Fragments located in riparian areas between the Sergipe and Alagoas States in Brazil. This species has been exploited for traditional communities, which represent 20% of the incoming. This study was carried out using the indicators suggested by the Organization for Economic Cooperation and Development, which were identified as Driving-Pressure-State-Impact-Response (DPSIR) factors. The genetic parameters were obtained in five populations located on the shores and islands of the São Francisco River, one of the most important rivers in Brazil. The framework for Schinus conservation suggests seventeen indicators of sustainability. In accordance with genetic parameters, the populations are isolated, and these genetic parameters can be used to monitor the sustainability of those populations in riparian area with the aim of defining strategies for forest restoration.

Keywords: alleles, molecular markers, genetic diversity, biodiversity

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Authors: Bissonnette Jean-François, Dossa Fabrice

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The project focuses on analyzing the spatial and temporal evolution of mangrove forest ecosystems near the city of Douala, Cameroon, in response to increasing human and environmental pressures. The selected study area, located in the Wouri River estuary, has a unique combination of economic importance, and ecological prominence. The study included valuable insights by conducting semi-structured interviews with resource operators and local officials. The thorough analysis of socio-economic data, farmer surveys, and satellite-derived information was carried out utilizing quantitative approaches in Excel and SPSS. Simultaneously, qualitative data was subjected to rigorous classification and correlation with other sources. The use of ArcGIS and CorelDraw facilitated the visual representation of the gradual changes seen in various land cover classifications. The research reveals complex processes that characterize mangrove ecosystems on Manoka and Cape Cameroon Islands. The lack of regulations in urbanization and the continuous growth of infrastructure have led to a significant increase in land conversion, causing negative impacts on natural landscapes and forests. The repeated instances of flooding and coastal erosion have further shaped landscape alterations, fostering the proliferation of water and mudflat areas. The unregulated use of mangrove resources is a significant factor in the degradation of these ecosystems. Activities including the use of wood for smoking and fishing, together with the coastal pollution resulting from the absence of waste collection, have had a significant influence. In addition, forest operators contribute to the degradation of vegetation, hence exacerbating the harmful impact of invasive species on the ecosystem. Strategic interventions are necessary to guarantee the sustainable management of these ecosystems. The proposals include advocating for sustainable wood exploitation techniques, using appropriate techniques, along with regeneration, and enforcing rules to prevent wood overexploitation. By implementing these measures, the ecological balance can be preserved, safeguarding the long-term viability of these precious ecosystems. On a conceptual level, this paper uses the framework developed by Elinor Ostrom and her colleagues to investigate the consequences of open access resources, where local actors have not been able to enforce measures to prevent overexploitation of mangrove wood resources. Governmental authorities have demonstrated limited capacity to enforce sustainable management of wood resources and have not been able to establish effective relationships with local fishing communities and with communities involved in the purchase of wood. As a result, wood resources in the mangrove areas remain largely accessible, while authorities do not monitor wood volumes extracted nor methods of exploitation. There have only been limited and punctual attempts at forest restoration with no significant consequence on mangrove forests dynamics.

Keywords: Mangroves, forest management, governance, open access resources, Cameroon

Procedia PDF Downloads 63

Authors: Bisemiire Anthony,

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PESH Poultry is a business project proposed specifically to solve unemployment and income-related problems affecting the youths in the Busia district. The project is intended to transform the life of the youth in terms of economic, social and behavioral, as well as the domestic well-being of the community at large. PESH Poultry is a start-up poultry farm that will be engaged in the keeping of poultry birds, broilers and layers for the production of quality and affordable poultry meat and eggs respectively and other poultry derivatives targeting consumers in eastern Uganda, for example, hotels, restaurants, households and bakeries. We intend to use a semi-intensive system of farming, where water and some food are provided in a separate nighttime shelter for the birds; our location will be in Lumino, Busia district. The poultry project will be established and owned by Bisemiire Anthony, Nandera Patience, Naula Justine, Bwire Benjamin and other investors. The farm will be managed and directed by Nandera Patience, who has five years of work experience and business administration knowledge. We will sell poultry products, including poultry eggs, chicken meat, feathers and poultry manure. We also offer consultancy services for poultry farming. Our eggs and chicken meat are hygienic, rich in protein and high quality. We produce processes and packages to meet the standard organization of Uganda and international standards. The business project shall comprise five (5) workers on the key management team who will share various roles and responsibilities in the identified business functions such as marketing, finance and other related poultry farming activities. PESH Poultry seeks 30 million Ugandan shillings in long-term financing to cover start-up costs, equipment, building expenses and working capital. Funding for the launch of the business will be provided primarily by equity from the investors. The business will reach positive cash flow in its first year of operation, allowing for the expected repayment of its loan obligations. Revenue will top UGX 11,750,000, and net income will reach about UGX115 950,000 in the 1st year of operation. The payback period for our project is 2 years and 3 months. The farm plans on starting with 1000 layer birds and 1000 broiler birds, 20 workers in the first year of operation.

Keywords: chicken, pullets, turkey, ducks

Procedia PDF Downloads 95

Authors: Maria da Conceição Proença

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This case study implements the evaluation of burned areas that suffered successive wildfires in Portugal mainland during the summer of 2017, killing more than 60 people. It’s intended to show that this evaluation can be done with remote sensing data free of charges in a simple laptop, with open-source software, describing the not-so-simple methodology step by step, to make it available for county workers in city halls of the areas attained, where the availability of information is essential for the immediate planning of mitigation measures, such as restoring road access, allocate funds for the recovery of human dwellings and assess further restoration of the ecological system. Wildfires also devastate forest ecosystems having a direct impact on vegetation cover and killing or driving away from the animal population. The economic interest is also attained, as the pinewood burned becomes useless for the noblest applications, so its value decreases, and resin extraction ends for several years. The tools described in this paper enable the location of the areas where took place the annihilation of natural habitats and establish a baseline for major changes in forest ecosystems recovery. Moreover, the result allows the follow up of the surface fuel loading, enabling the targeting and evaluation of restoration measures in a time basis planning.

Keywords: image processing, remote sensing, wildfires, burned areas evaluation, sentinel-2

Procedia PDF Downloads 212

Authors: Kollie B. Dogba, Willis Oluoch-Kosura, Chepchumba Chumo

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In Liberia, many of the agricultural households cultivate cassava for either sustenance purposes, or to generate farm income. Many of the concentrated cassava farmers reside in Nimba, a north-eastern County that borders two other economies: the Republics of Cote D’Ivoire and Guinea. With a high demand for cassava output and products in emerging Asian markets coupled with an objective of the Liberia agriculture policies to increase the competitiveness of valued agriculture crops; there is a need to examine the level of resource-use efficiency for many agriculture crops. However, there is a scarcity of information on the efficiency of many agriculture crops, including cassava. Hence the study applying an output-oriented method seeks to assess the economic efficiency of cassava farmers in Nimba County, Liberia. A multi-stage sampling technique was employed to generate a sample for the study. From 216 cassava farmers, data related to on-farm attributes, socio-economic and institutional factors were collected. The stochastic frontier models, using the Translog functional forms, of production and revenue, were used to determine the level of revenue efficiency and its determinants. The result showed that most of the cassava farmers are male (60%). Many of the farmers are either married, engaged or living together with a spouse (83%), with a mean household size of nine persons. Farmland is prevalently obtained by inheritance (95%), average farm size is 1.34 hectares, and most cassava farmers did not access agriculture credits (76%) and extension services (91%). The mean cassava output per hectare is 1,506.02 kg, which estimates average revenue of L$23,551.16 (Liberian dollars). Empirical results showed that the revenue efficiency of cassava farmers varies from 0.1% to 73.5%; with the mean revenue efficiency of 12.9%. This indicates that on average, there is a vast potential of 87.1% to increase the economic efficiency of cassava farmers in Nimba by improving technical and allocative efficiencies. For the significant determinants of revenue efficiency, age and group membership had negative effects on revenue efficiency of cassava production; while farming experience, access to extension, formal education, and average wage rate have positive effects. The study recommends the setting-up and incentivizing of farmer field schools for cassava farmers to primarily share their farming experiences with others and to learn robust cultivation techniques of sustainable agriculture. Also, farm managers and farmers should consider a fix wage rate in labor contracts for all stages of cassava farming.

Keywords: economic efficiency, frontier production and revenue functions, Nimba County, Liberia, output-oriented approach, revenue efficiency, sustainable agriculture

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Authors: Kaleem Ahmed, Jamal A. Khan

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Grouping patterns, habitat use, and overlap studies were conducted on five sympatric ungulate species sambar (Cervus unicolor), chital (Axis axis), muntjac (Muntiacus muntjac), goral (Nemorhaedus goral), and serow (Capricornis sumatraensis) in the Dabka watershed area within Indian West Himalayan range. Data on age, sex composition, group size, and various ecological and topographical factors governing the presence/absence of species within the study area were collected using a 250 km of a trail walk, 95 permanent circular plots of 10 m radius, and 3 vantage points with 58 scannings. The highest mean group size was recorded for chital (6.35 ± 0.50), followed by sambar (1.35 ± 0.10), goral (1.25 ±0.63), muntjac (1.12 ± 0.05), and serow (1.00 ± 0.00). Grouping pattern significantly varied among sympatric species (F = 85.10, df. = 6, P = 0.000). The highest mean pellet group density (/ha ± SE) was recorded for sambar (41.56 ± 3.51), followed by goral (23.31 ± 3.45), chital (19.21 ± 3.51), muntjac (7.43 ± 1.21), and serow (1.02 ± 0.10). Two-way variance analysis showed a significant difference in the density of the pellet group of all ungulate species across different study area habitats (F = 6.38, df = 4, P = 0.027). The availability-utilization (AU) analysis reveals that goral was mostly sighted in steep slopes, preferred > 2100 m altitudinal range with low shrub understory, avoided dense forest, and relatively more southern aspects were used. Chital had used a wide range of tree and shrub coverings with a preference towards moderate cover range (26-50%), preferred areas with low slope category ( < 25), avoided areas of high altitude > 900 m. Sambar avoided less tree cover (0-25), preferred slope category (26-500), altitudes between 1600-2100 m, and preferred dense forest with northern aspects. Muntjac used all elevation ranges in the study area with a preference towards the dense forest and northern aspects. Serow preferred high tree cover > 75%, avoided low shrub cover (0-25%), preferred high shrub cover 51-75%, utilized higher elevation > 2100 m, avoided low elevation range and northern aspects. All species occupied similar habitat types, forest or scrub, except for the goral, which preferred open spaces. Between muntjac and sambar, the highest overlap was found (65%), and there was no overlap between chital and serow, chital and goral. Aspect, slope, altitude, and vegetation characteristics were found to be important factors for the overlap of ungulate sympatric species. One major reason for their ecological separation at the fine-scale level is the differential use of altitude by ungulates in the present study. This is confirmed by the avoidance by chital of altitudes > 900 m and serow of < 2100 m.

Keywords: altitude, grouping pattern, Himalayas, overlap, ungulates

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Authors: Shah Rucksana Akhter Urme

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Agriculture is the main source of food for human consumption and feeding the world huge population, the pressure of food supply is increasing day by day. Undoubtedly, quality strain, improved plantation, farming technology, synthetic fertilizer, readily available irrigation, insecticides and harvesting technology are the main factors those to meet up the huge demand of food consumption all over the world. However, depended on this limited resources and excess amount of consuming lands, water, fertilizers leads to the end of the resources and severe climate effects has been left for our future generation. Agriculture is the most responsible to global warming, emitting more greenhouse gases than all other vehicles largely from nitrous oxide released by from fertilized fields, and carbon dioxide from the cutting of rain forests to grow crops . Farming is the thirstiest user of our precious water supplies and a major polluter, as runoff from fertilizers disrupts fragile lakes, rivers, and coastal ecosystems across the globe which accelerates the loss of biodiversity, crucial habitat and a major driver of wildlife extinction. It is needless to say that we have to more concern on how we can save the nutrients of the soil, storage of the water and avoid excessive depends on synthetic fertilizer and insecticides. In this case, eco- friendly cultivation could be a potential alternative solution to minimize effects of agriculture in our environment. The objective of this review paper is about organic cultivation following in particular biotechnological process focused on bio-fertilizer and bio-pesticides. Intense practice of chemical pesticides, insecticides has severe effect on both in human life and biodiversity. This cultivation process introduces farmer an alternative way which is nonhazardous, cost effective and ecofriendly. Organic fertilizer such as tea residue, ashes might be the best alternative to synthetic fertilizer those play important role in increasing soil nutrient and fertility. Ashes contain different essential and non-essential mineral contents that are required for plant growth. Organic pesticide such as neem spray is beneficial for crop as it is toxic for pest and insects. Recycled and composted crop wastes and animal manures, crop rotation, green manures and legumes etc. are suitable for soil fertility which is free from hazardous chemicals practice. Finally water hyacinth and algae are potential source of nutrients even alternative to soil for cultivation along with storage of water for continuous supply. Inorganic practice of agriculture, consuming fruits and vegetables becomes a threat for both human life and eco-system and synthetic fertilizer and pesticides are responsible for it. Farmers that practice eco-friendly farming have to implement steps to protect the environment, particularly by severely limiting the use of pesticides and avoiding the use of synthetic chemical fertilizers, which are necessary for organic systems to experience reduced environmental harm and health risk.

Keywords: organic farming, biopesticides, organic nutrients, water storage, global warming

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Authors: T. Garba, Y. Y. Babanyara, M. Isah, A. K. Muktari, R. Y. Abdullahi

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The increased in demand of fuel wood for heating, cooking and sometimes bakery has continued to exert appreciable impact on natural vegetation. This study focus on the use of multi-temporal data from land sat TM of 1986, land sat EMT of 1999 and lands sat ETM of 2006 to investigate the changes of Natural Vegetation resulting from charcoal production activities. The three images were classified based on bare soil, built up areas, cultivated land, and natural vegetation, Rock out crop and water bodies. From the classified images Land sat TM of 1986 it shows natural vegetation of the study area to be 308,941.48 hectares equivalent to 50% of the area it then reduces to 278,061.21 which is 42.92% in 1999 it again depreciated to 199,647.81 in 2006 equivalent to 30.83% of the area. Consequently cultivated continue increasing from 259,346.80 hectares (42%) in 1986 to 312,966.27 hectares (48.3%) in 1999 and then to 341.719.92 hectares (52.78%). These show that within the span of 20 years (1986 to 2006) the natural vegetation is depreciated by 119,293.81 hectares. This implies that if the menace is not control the natural might likely be lost in another twenty years. This is because forest cleared for charcoal production is normally converted to farmland. The study therefore concluded that there is the need for alternatives source of domestic energy such as the use of biomass which can easily be accessible and affordable to people. In addition, the study recommended that there should be strong policies enforcement for the protection forest reserved.

Keywords: charcoal, classification, data, images, land use, natural vegetation

Procedia PDF Downloads 365

Authors: Rachel Danielson, Brendan Bohannan, S.M. Tsai, Kyle Meyer, Jorge L.M. Rodrigues

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The Amazon Rainforest is a global diversity hotspot and crucial carbon sink, but approximately 20% of its total extent has been deforested- primarily for the establishment of cattle pasture. Understanding the impact of this large-scale disturbance on soil microbial community composition and activity is crucial in understanding potentially consequential shifts in nutrient or greenhouse gas cycling, as well as adding to the body of knowledge concerning how these complex communities respond to human disturbance. In this study, surface soils (0-10cm) were collected from three forests and three 45-year-old pastures in Rondonia, Brazil (the Amazon state with the greatest rate of forest destruction) in order to determine the impact of forest conversion on microbial communities involved in nitrogen fixation. Soil chemical and physical parameters were paired with measurements of microbial activity and genetic profiles to determine how community composition and process rates relate to environmental conditions. Measuring both the natural abundance of 15N in total soil N, as well as incorporation of enriched 15N2 under incubation has revealed that conversion of primary forest to cattle pasture results in a significant increase in the rate of nitrogen fixation by free-living diazotrophs. Quantification of nifH gene copy numbers (an essential subunit encoding the nitrogenase enzyme) correspondingly reveals a significant increase of genes in pasture compared to forest soils. Additionally, genetic sequencing of both nifH genes and transcripts shows a significant increase in the diversity of the present and metabolically active diazotrophs within the soil community. Levels of both organic and inorganic nitrogen tend to be lower in pastures compared to forests, with ammonium rather than nitrate as the dominant inorganic form. However, no significant or consistent differences in total, extractable, permanganate-oxidizable, or loss-on-ignition carbon are present between the two land-use types. Forest conversion is associated with a 0.5- 1.0 unit pH increase, but concentrations of many biologically relevant nutrients such as phosphorus do not increase consistently. Increases in free-living diazotrophic community abundance and activity appear to be related to shifts in carbon to nitrogen pool ratios. Furthermore, there may be an important impact of transient, low molecular weight plant-root-derived organic carbon on free-living diazotroph communities not captured in this study. Preliminary analysis of nitrogenase gene variant composition using NovoSeq metagenomic sequencing indicates that conversion of forest to pasture may significantly enrich vanadium-based nitrogenases. This indication is complemented by a significant decrease in available soil molybdenum. Very little is known about the ecology of diazotrophs utilizing vanadium-based nitrogenases, so further analysis may reveal important environmental conditions favoring their abundance and diversity in soil systems. Taken together, the results of this study indicate a significant change in nitrogen cycling and diazotroph community composition with the conversion of the Amazon Rainforest. This may have important implications for the sustainability of cattle pastures once established since nitrogen is a crucial nutrient for forage grass productivity.

Keywords: free-living diazotrophs, land use change, metagenomic sequencing, nitrogen fixation

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

Procedia PDF Downloads 106

Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: John Wangui, Charles Gachene, Stephen Mureithi, Boniface Kiteme

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Land use changes caused by demographic, natural variability, economic, technological and policy factors affect the goods and services derived from an ecosystem. In the past few decades, Lake Ol Bolossat catchment in Nyandarua County Kenya has been facing challenges of land cover changes threatening its capacity to perform ecosystems functions and adversely affecting communities and ecosystems downstream. This study assessed land cover changes in the catchment for a period of twenty eight years (from 1986 to 2014). Analysis of three Landsat images i.e. L5 TM 1986, L5 TM 1995 and L8 OLI/TIRS 2014 was done using ERDAS 9.2 software. The results show that dense forest, cropland and area under water increased by 27%, 29% and 3% respectively. On the other hand, open forest, dense grassland, open grassland, bushland and shrubland decreased by 3%, 3%, 11%, 26% and 1% respectively during the period under assessment. The lake was noted to have increased due to siltation caused by soil erosion causing a reduction in Lake’s depth and consequently causing temporary flooding of the wetland. The study concludes that the catchment is under high demographic pressure which would lead to resource use conflicts and therefore formulation of mitigation measures is highly recommended.

Keywords: land cover, land use change, land degradation, Nyandarua, Remote sensing

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Authors: Sakshi Yadav, Sushila Shekhawat

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This study addresses different types of points of view as seen in nature documentary films with the help of three eco documentaries, and it would be significant in understanding the role of the narrative point of view as a tool for showing and telling in documentaries. Narrative analysis of a film forms an essential aspect of the discourse of scholarship in film studies. Narration is the chain of events occurring in time and space. The notion of narrative provides the idea of coherence and wholeness to the story. There are various components that the narration carries, one of which is the perspective or point of view. The narrator plays the role of a mediator between the film and the audience; thus, his perspective influences the way the audience interprets the film. Feature films have been analyzed through narrative points of view; however, this research intends to conduct it from the angle of a nature documentary film. The study will examine narrative viewpoints unique to nature documentary films using three ecological documentary films-The Cove (2009), Tale of a forest (2012), and Before the flood (2016). This research will apply the framework of narrative theory and will investigate the impact of the different types of narrative points of view, as each portrays the human-nature relationship from a different standpoint, and it will also study the effect that the narrative point of view has on the mode of these eco documentaries.

Keywords: ecodocumentary, narrative, human-nature relationship, point of view

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Authors: Signe Voltelen, Kristin Astrup Aas

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In the Scandinavian cities, there is a raised focus on the potential of using urban agriculture in city development, both as a tool for handling challenges provoked by climate change and to develop new, and stronger social communities. During the last couple of years, Copenhagen has experienced an increase in extreme weather resulting in dramatical floods with huge humanitarian and economic consequences. As an approach for climate adaption and mitigation the government has made a strategy for changing a significant amount of the cities hard surfaces into green and absorbing surfaces. Including urban farms and gardens. In close collaboration with the municipality, it has been possible to implement citizen-run gardens under the different concepts climate adaption and food literacy. Like other European cities, Copenhagen has a historical tradition of small-scale farming for food security inside the city, and in the outskirts of the urban area. Lately, this tradition has gotten new relevance, and new initiatives are popping up. In addition to providing local food, the urban farm becomes a semi-public, semi-private room that invites to community and integration across ethnicity, social background, and age. The direct interaction in the process of farming creates a connection between the urban and the rural and are educational for people growing up and living their whole life in the dense city. In the paper, three local example models of urban agriculture are presented, and the experiences of their potential as tools for developing social and environmental sustainable cities is examined.

Keywords: city development, climate mitigation, community building, urban agriculture, urban- rural transition, food security

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Authors: Ina Dajci

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Biologic Materials presents groundbreaking transdisciplinary research aimed at fostering new collaborative models across the Built Environment, Forestry, and Agriculture sectors. This initiative seeks to establish innovative paradigms for local and global material flows by developing a biocompatible, regenerative material economy. The project focuses on creating materials derived from biowaste and silvicultural practices, ensuring the preservation of endangered indigenous and vernacular techniques through the integration of emerging biosciences. By utilizing biomaterials sourced from agricultural waste and forest byproducts, the initiative incorporates fabrication methods recognized by UNESCO as ‘intangible cultural heritage of humanity,’ which are currently at risk. The structural, mechanical, and environmental properties of these materials are enhanced through advanced CAD-CAM fabrication, along with energy-efficient biochemical and bacterial processes that promote healthy indigo coloration. Furthermore, the integration of AI technologies in species selection facilitates a novel partnership model, enabling designers to collaborate effectively with forest managers and silviculture practitioners. This collaborative approach not only optimizes the use of plant-based materials but also enhances biodiversity and climate resilience in regional ecosystems. Overall, this project embodies a holistic strategy for addressing environmental challenges while revitalizing traditional practices and fostering sustainable innovation.

Keywords: material, architecture, culture, heritage, ecology, environment

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Lucretia Ramnath

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Fishmeal is extensively used for fish farming but is an expensive fish feed ingredient. A cheaper alternate to fishmeal is single cell protein (SCP) which can be cultivated on fermentable sugars recovered from organic waste streams such as pulp and paper mill sludge (PPMS). PPMS has a high cellulose content, thus is suitable for glucose recovery through enzymatic hydrolysis but is hampered by lignin and ash. To render PPMS amenable for enzymatic hydrolysis, the PPMS waspre-treated to produce a glucose-rich hydrolysate which served as a feed stock for the production of fungal SCP. The PPMS used in this study had the following composition: 72.77% carbohydrates, 8.6% lignin, and 18.63% ash. The pre-treatments had no significant effect on lignin composition but had a substantial effect on carbohydrate and ash content. Enzymatic hydrolysis of screened PPMS was previously optimized through response surface methodology (RSM) and 2-factorial design. The optimized protocol resulted in a hydrolysate containing 46.1 g/L of glucose, of which 86% was recovered after downstream processing by passing through a 100-mesh sieve (38 µm pore size). Vogel’s medium supplemented with 10 g/L hydrolysate successfully supported the growth of Fusarium venenatum, conducted using standard growth conditions; pH 6, 200 rpm, 2.88 g/L ammonium phosphate, 25°C. A maximum F. venenatum biomass of 45 g/L was produced with a yield coefficient of 4.67. Pulp and paper mill sludge hydrolysate contained approximately five times more glucose than what was needed for SCP production and served as a suitable carbon source. We have shown that PPMS can be successfully beneficiated for SCP production.

Keywords: pulp and paper waste, fungi, single cell protein, hydrolysate

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Authors: Muhammad Idnan, Arshad Javid, Muhammad Nadeem

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The present study was conducted from April 2013 to March 2014 to observe the behavioral parameters of Chinkara (Gazella bennettii) under captive conditions by comparing the captive-born and wild-caught animals for conservation strategies. Understanding the behavioral conformations plays a significant role in captive management. Due to human population explosion and mechanized hunting, the captive breeding seems to be the best way for sports hunting, bush meat, for leather industry and horns for traditional medicinal usage. Primarily, captive management has been used on trial and error basis due to deficiency of ethology of this least concerned species. Behavior of [(20 wild-caught (WC) and 10 captive-bred (CB)] adult Chinkara was observed at captive breeding facilities for ungulates at Ravi Campus, University of Veterinary and Animal Sciences at Kasur district which is situated on southeast side of Lahore. The average annual rainfall is about 650 mm, with frequent raining during monsoon. A focal sample was used to observe the various behavioral patterns for CB and WC chinkara. A similarity was observed in behavioral parameters in WC and CB animals, however, when the differences were considered, WC male deer showed a significantly higher degree of agonistic interaction as compared to the CB male chinkara. These findings suggest that there is no immediate impact of captivity on behavior of chinkara nevertheless 10 generations of captivity. It is suggested that the Chinkara is not suitable for domestication and for successful deer farming, a further study is recommended for ethology of chinkara.

Keywords: Chinkara (Gazella bennettii), domestication, deer farming, ex-situ conservation

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Shinta Holdsworth, Jan Axmacher, Darren J. Mann

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Beetles represent the most diverse insect taxon, and they contribute significantly to a wide range of vital ecological functions. Examples include decomposition by bark beetles, nitrogen recycling and dung processing by dung beetles or pest control by predatory ground beetles. Nonetheless, research into the distribution patterns, species richness and functional diversity of beetles particularly from tropical regions remains extremely limited. In our research, we aim to investigate the distribution and diversity patterns of beetles and the roles they play in small tropical island ecosystems in the Kepulauan Seribu Marine National Park and on Java. Our research furthermore provides insights into the effects anthropogenic activities have on the assemblage composition and diversity of beetles on the small islands. We recorded a substantial number of highly abundant small island species, including a substantial number of unique small island species across the study area, highlighting these islands’ potential importance for the regional conservation of genetic resources. The highly varied patterns observed in relation to the use of different trapping types - pitfall traps and flight interception traps (FITs) - underscores the need for complementary trapping strategies that combine multiple methods for beetle community surveys in tropical islands. The significant impacts of human activities have on the small island beetle faunas were also highlighted in our research. More island beetle species encountered in settlement than forest areas shows clear trend of positive links between anthropogenic activities and the overall beetle species richness. However, undisturbed forests harboured a high number of unique species, also in comparison to disturbed forests. Finally, our study suggests that, with regards to different feeding guilds, the diversity of herbivorous beetles on islands is strongly affected by the different levels of forest cover encountered.

Keywords: beetle diversity, forest disturbance, island biogeography, island settlement

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

Procedia PDF Downloads 128