Search results for: forest farming

Authors: Dhananjoy Dutta

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Field studies in lower Gangetic plains of India reveal that over-abstraction of groundwater for irrigation to paddy leads to a substantial depletion of groundwater over the decades, resulting in negative effects on lowering of the water table, drying up of surface water sources and aquifer pollution with leached-out arsenic. The aggravating arsenic toxicity in drinking water is manifested in health problems and ‘arsenicosis’ of people. A social conflict arises between farmers, who intend to grow paddy for livelihoods, and the groundwater authority, which enacts the ‘Regulation Laws’ by putting a check on the excessive installation of private tube-wells for irrigation. Hence, considering the challenges of resource sustainability, health issues, and food security, the study calls for a paradigm shift in policy from further groundwater development to sustainable water resources management and evaluates some strategies integrating supply and demand side management for mitigating the problems.

Keywords: groundwater, irrigation, paddy farming, water table depletion, arsenic pollution, gangetic plains

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Authors: M. Feliciano, F. Maia, A. Gonçalves

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Olive oil production sector plays an important role in Portuguese economy. It had a major growth over the last decade, increasing its weight in the overall national exports. International market penetration for Mediterranean traditional products is increasingly more demanding, especially in the Northern European markets, where consumers are looking for more sustainable products. Trying to support this growing demand this study addresses olive oil production under the environmental and eco-efficiency perspectives. The analysis considers two consecutive product life cycle stages: olive trees farming; and olive oil extraction in mills. Addressing olive farming, data collection covered two different organizations: a middle-size farm (~12ha) (F1) and a large-size farm (~100ha) (F2). Results from both farms show that olive collection activities are responsible for the largest amounts of Green House Gases (GHG) emissions. In this activities, estimate for the Carbon Footprint per olive was higher in F2 (188g CO2e/kgolive) than in F1 (148g CO2e/kgolive). Considering olive oil extraction, two different mills were considered: one using a two-phase system (2P) and other with a three-phase system (3P). Results from the study of two mills show that there is a much higher use of water in 3P. Energy intensity (EI) is similar in both mills. When evaluating the GHG generated, two conditions are evaluated: a biomass neutral condition resulting on a carbon footprint higher in 3P (184g CO2e/Lolive oil) than in 2P (92g CO2e/Lolive oil); and a non-neutral biomass condition in which 2P increase its carbon footprint to 273g CO2e/Lolive oil. When addressing the carbon footprint of possible combinations among studied subsystems, results suggest that olive harvesting is the major source for GHG.

Keywords: carbon footprint, environmental indicators, farming subsystem, industrial subsystem, olive oil

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Authors: Sipumle Qapeshu, Bongiwe Mcata, Ajuruchukwu Obi

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Subsistence farmers practice farming for survival while commercial farmers produce to feed themselves and larger society with the motive to achieve highest profit. These types of farmers are characterised by growing what they eat, live without making regular purchases in the markets. The main objective of subsistence/peasant farmers is to ensure food security at household level. Cabbage is a crop that has been identified to have vital food nutrient sources like Vitamin A, B and C, protein, calcium, iron and antioxidative compounds beneficial for preventing cancer. This paper, therefore, looks at the potential that cabbage production has in enhancing household food security and also the challenges encountered by these cabbage producers. Primary data was obtained from 50 respondents, and linear regression model was used to analyse the data used. Income was used as food security measure. The results showed that three variables were statistically significant and they are gender (10%), education (5%) and household size (5%). Meaning that these are variables that influenced cabbage production by these households, and it also affects their food security status since income is affected.

Keywords: subsistence farmers, food security, cabbage, farming

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Akinola Helen Olufunke, Soetan Olatunbosun Jonathan, Adeleye Oludamola

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Introduction: Antibiotic resistance has grown significantly, which is a major cause for concern. There have not been many significant developments in antibiotics over the past few decades, and practically all of the ones that are currently in use are losing effectiveness against pathogenic germs. Researchers are starting to focus more on the physiologically active compounds found in plants, particularly phytobiotics in poultry production. Consumption of chicken products is among the greatest in the country, but numerous nations, including Nigeria, use excessive amounts of necessary antibiotics in poultry farming, endangering the safety of such goods (through antimicrobial residues). Drug resistance has become a widespread issue as a result of the risky use of antibiotics in the chicken production industry. In order to replace antibiotics, biotic or natural products like phytobiotics (also known as botanicals or phytogenics) have drawn a lot of interest. Phytobiotics or their components are thought to be a relatively recent category of natural herbs that have acquired acceptance and favor among chicken farmers. The addition of several phytobiotic additions to poultry feed has demonstrated its capacity to improve both the broiler and layer populations' productivity. Design: Experimental research design and cross-sectional study was carried out at every 300 purposively selected farming household in the six-geopolitical zone in Nigeria. Data Analysis: A semi-structured questionnaire was administered to each farmer, and quantitative data were analyzed using Statistical Package for Social Science (SPSS) while the Chi-square test was used to analyze factors mitigating the use of Phytobiotics. Result: The result shows that the benefits associated with the use of phytobiotics are contributed to growth promotion in chickens and enhancement of productive performance of broiler and layer, which could be attributed to their antioxidant activity. The result further revealed that factors mitigating the use of phytobiotics were lack of knowledge in the use of phytobiotics, overdose or underdose usage, and seasonal availability of the phytobiotics. Others are the educational level of the farmers, intrinsic motivation, income poultry farming experience, price of phytobiotics based additives feeds, and intensity of extension agents in visiting them. Conclusion: The difficulties associated with using phytobiotics in chicken farms limit their willingness to boost productivity. The study found that most farmers were ignorant, which prevented them from handling this notion and turning their poultry into a viable enterprise while also allowing them to be creative. They believed that packing phytobiotics-based additive feed was expensive, and lastly, the seasonal availability of some phytobiotics. Recommendation: Further research in phytobiotics use in Nigeria should be carried out in order to establish its efficiency, safety, and awareness.

Keywords: mitigating, antibiotics, phytobiotics, poultry farming

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Authors: P. V. Pramila , V. Mahesh

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Pulmonary Function Tests are important non-invasive diagnostic tests to assess respiratory impairments and provides quantifiable measures of lung function. Spirometry is the most frequently used measure of lung function and plays an essential role in the diagnosis and management of pulmonary diseases. However, the test requires considerable patient effort and cooperation, markedly related to the age of patients esulting in incomplete data sets. This paper presents, a nonlinear model built using Multivariate adaptive regression splines and Random forest regression model to predict the missing spirometric features. Random forest based feature selection is used to enhance both the generalization capability and the model interpretability. In the present study, flow-volume data are recorded for N= 198 subjects. The ranked order of feature importance index calculated by the random forests model shows that the spirometric features FVC, FEF 25, PEF,FEF 25-75, FEF50, and the demographic parameter height are the important descriptors. A comparison of performance assessment of both models prove that, the prediction ability of MARS with the `top two ranked features namely the FVC and FEF 25 is higher, yielding a model fit of R2= 0.96 and R2= 0.99 for normal and abnormal subjects. The Root Mean Square Error analysis of the RF model and the MARS model also shows that the latter is capable of predicting the missing values of FEV1 with a notably lower error value of 0.0191 (normal subjects) and 0.0106 (abnormal subjects). It is concluded that combining feature selection with a prediction model provides a minimum subset of predominant features to train the model, yielding better prediction performance. This analysis can assist clinicians with a intelligence support system in the medical diagnosis and improvement of clinical care.

Keywords: FEV, multivariate adaptive regression splines pulmonary function test, random forest

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Authors: Yung-Chung Chuang

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The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

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Authors: Rohana Pandukabhya Mahaliyanaarachchi, Maheshwari Sangeetha Elapatha, Mohamed Esham, Banagala Chathurika Maduwanthi

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The global climate change has become one of the imperative issues for the smallholder dominated agricultural sector and nature based tourism sector in Sri Lanka. Thus addressing this issue is notably important. The main objective of this study was to investigate the potential of agri-tourism as a sustainable adaptation option to mitigate some of the negative impacts of climate change in small scale agricultural sector in Sri Lanka. The study was carried out in two different climatic zones in Sri Lanka namely Low Country Dry Zone and Up Country Wet Zone. A case study strategy followed by structured and unstructured interviewers through cross-sectional surveys were adapted to collect data. The study revealed that there had been a significant change in the climate in regard to the rainfall patterns in both climatic zones resulting unexpected rains during months and longer drought periods. This results the damages of agricultural production, low yields and subsequently low income. However, to mitigate these adverse effects, farmers have mainly focused on using strategies related to the crops and farming patterns rather than diversifying their business by adopting other entrepreneurial activities like agri-tourism. One of the major precursor for this was due to lesser awareness on the concept of agri-tourism within the farming community. The study revealed that the respondents of both climatic zones do have willingness and potential to adopt agri-tourism. One key important factor identified was that farming or agriculture was the main livelihood of the respondents, which is one of the vital precursor needed to start up an agri-tourism enterprise. Most of the farmers in the Up Country Wet Zone had an inclination to start a farm guest house or a farm home stay whereas the farmers in the Low Country Dry Zone wish to operate farm guest house, farm home stay or farm restaurant. They also have an interest to open up a road side farm product stall to facilitate the direct sales of the farm. Majority of the farmers in both climatic zones showed an interest to initiate an agri-tourism business as a complementary enterprise where they wished to give an equal share to both farming and agri-tourism. Thus this revealed that the farmers have identified agri-tourism as a vital concept and have given the equal importance as given to farming. This shows that most of the farmers have understood agri-tourism as an alternative income source that can mitigate the adverse effects of climatic change. This study emphasizes that agri-tourism as an alternative income source that can mitigate the adverse effects of climatic change on small scale agriculture sector.

Keywords: adaptation, agri-tourism, climate change, small scale agriculture

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Authors: Ruijia Hu, Susanna T.Y. Tong

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Light detection and ranging (LiDAR) and four-channel red, green, blue, and near-infrared (RGBI) remote sensed imageries allow an accurate quantification and contiguous measurement of vegetation characteristics and forest structures. This information facilitates the generation of habitat structure variables for forest species distribution modelling. However, applications of remote sensing data, especially the combination of structural and spectral information, to support evidence-based decisions in forest managements and conservation practices at local scale are not widely adopted. In this study, we examined the habitat requirements of pileated woodpecker (Dryocopus pileatus) (PW) in Hamilton County, Ohio, using ecologically relevant forest structural and vegetation characteristics derived from LiDAR and RGBI data. We hypothesized that the habitat of PW is shaped by vegetation characteristics that are directly associated with the availability of food, hiding and nesting resources, the spatial arrangement of habitat patches within home range, as well as proximity to water sources. We used 186 PW presence or absence locations to model their presence and absence in generalized additive model (GAM) at two scales, representing foraging and home range size, respectively. The results confirm PW’s preference for tall and large mature stands with structural complexity, typical of late-successional or old-growth forests. Besides, the crown size of dead trees shows a positive relationship with PW occurrence, therefore indicating the importance of declining living trees or early-stage dead trees within PW home range. These locations are preferred by PW for nest cavity excavation as it attempts to balance the ease of excavation and tree security. In addition, we found that PW can adjust its travel distance to the nearest water resource, suggesting that habitat fragmentation can have certain impacts on PW. Based on our findings, we recommend that forest managers should use different priorities to manage nesting, roosting, and feeding habitats. Particularly, when devising forest management and hazard tree removal plans, one needs to consider retaining enough cavity trees within high-quality PW habitat. By mapping PW habitat suitability for the study area, we highlight the importance of riparian corridor in facilitating PW to adjust to the fragmented urban landscape. Indeed, habitat improvement for PW in the study area could be achieved by conserving riparian corridors and promoting riparian forest succession along major rivers in Hamilton County.

Keywords: deadwood detection, generalized additive model, individual tree crown delineation, LiDAR, pileated woodpecker, RGBI aerial imagery, species distribution models

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Authors: Erma Desmaliana, Saptahari Sugiri

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Limited availability of solid timber in large dimensions becomes a problem. The demands of timbers in Indonesia is more increasing compared to its supply from natural forest. It is associated with the issues of global warming and environmental preservation. The uses of timbers from HTI (Industrial Planting Forest) and HTR (Society Planting Forest), such as Jabon, is an alternative source that required to solve these problems. Having shorter lifespan is the benefit of HTI/HTR timbers, although they are relatively smaller in dimension and lower in strength. Engineering Wood Product (EWP) such as glulam (glue-laminated) timber, is required to overcome their losses. Glulam is fabricated by gluing the wooden planks that having a thickness of 20 to 45 mm with an adhesive material and a certain pressure. Glulam can be made a curved beam, is one of the advantages, thus making it strength is greater than a straight beam. This paper is aimed to know the material behavior of curved glue-laminated beam of Jabon timber. Preliminary methods was to gain physical and mechanical properties, and glue spread strength of Jabon timber, which following the ASTM D-143 standard test method. Dimension of beams were 50 mm wide, 760 mm span, 50 mm thick, and 50 mm rise. Each layer of Jabon has a thickness of 5 mm and is glued with polyurethane. Cold press will be applied to beam laminated specimens for more than 5 hours. The curved glue-laminated beams specimens will be tested about the bending behavior. This experiments aims to obtain the increasing of load carrying capacity and stiffness of curved glulam beam.

Keywords: curved glulam beam, HTR&HTI, load carrying, strength

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Authors: Anusha Jain, Santosh Vishwanathan, Praveen K. Gupta, Shwetha S., Kavitha S. N.

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Agriculture has a major impact on the Indian economy, with the highest employment ratio than any sector of the country. Currently, most of the traditional agricultural practices and farming methods are manual, which results in farmers not realizing their maximum productivity often due to increasing in labour cost, inefficient use of water sources leading to wastage of water, inadequate soil moisture content, subsequently leading to food insecurity of the country. This research paper aims to solve this problem by developing a full-fledged web application-based platform that has the capacity to associate itself with a Microcontroller-based Automated Irrigation System which schedules the irrigation of crops based on real-time soil moisture content employing soil moisture sensors centric to the crop’s requirements using WSN (Wireless Sensor Networks) and M2M (Machine To Machine Communication) concepts, thus optimizing the use of the available limited water resource, thereby maximizing the crop yield. This robust automated irrigation system provides end-to-end automation of Irrigation of crops at any circumstances such as droughts, irregular rainfall patterns, extreme weather conditions, etc. This platform will also be capable of achieving a nationwide united farming community and ensuring the welfare of farmers. This platform is designed to equip farmers with prerequisite knowledge on tech and the latest farming practices in general. In order to achieve this, the MailChimp mailing service is used through which interested farmers/individuals' email id will be recorded and curated articles on innovations in the world of agriculture will be provided to the farmers via e-mail. In this proposed system, service is enabled on the platform where nearby crop vendors will be able to enter their pickup locations, accepted prices and other relevant information. This will enable farmers to choose their vendors wisely. Along with this, we have created a blogging service that will enable farmers and agricultural enthusiasts to share experiences, helpful knowledge, hardships, etc., with the entire farming community. These are some of the many features that the platform has to offer.

Keywords: WSN (wireless sensor networks), M2M (M/C to M/C communication), automation, irrigation system, sustainability, SAAS (software as a service), soil moisture sensor

Procedia PDF Downloads 128

Authors: Danute Jablonskyte-Rasce, Laura Masilionyte

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The field experiments of different farming systems were conducted at Joniškėlis Experimental Station of the Lithuanian Research Centre for Agriculture and Forestry in 2006–2016. The soil of the experimental site was Endocalcari-Endohypogleyic Cambisol (CMg-n-w-can). The research was designed to identify the effects of dry matter and nitrogen accumulated in the above-ground biomass of various catch crops grown after winter wheat on soil mineral nitrogen variation during the autumn and spring period in the presence of intensive leaching complex. Research was done in the soil differing in humus status in the organic and sustainable cropping systems by growing various plant mixtures as catch crops: narrow-leafed lupine (Lupinus angustifolius L.) and oil radish (Raphanus sativus var. Oleifera L.), white mustard (Sinapis alba L.) and buckwheat (Fagopyrum exculentum Moench.) and white mustard as a sole crop. All crop and soil management practices have shown optimal efficiency in late autumn – stubble breaking, catch crops and straw used during the post-harvest period of the main crops, reduced Nmin migration into deeper (40–80 cm) soil layer. The greatest Nmin reduction in the 0–40 cm soil layer during the period from late autumn to early spring was identified in the sustainable cropping system having applied N30 for the promotion of straw mineralization and with no catch crops cultivation. The sustainable cropping system, having applied N30 for straw mineralization and growing white mustard in combination with buckwheat as catch crops, Nmin difference in the spring compared with its status in the autumn in the soil low and moderate in humus was lower by 70.1% and 34.2%, respectively.

Keywords: soil nitrogen, catch crops, ecological and sustainable farming systems, Cambisol

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Authors: Kgabo Pofu, Hintsa Araya, Dean Oelofse, Sonja Venter, Christian Du Plooy, Phatu Mashela

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Nematode resistance to the tropical root-knot (Meloidogyne species) nematodes is one of the most preferred nematode management strategies in development of smallholder resource-poor farming systems. Due to its pharmacological and ethnomedicinal applications, Artemisia annua is one of the underutilised crops that have attracted attention of policy-makers in rural agrarian development in South Africa. However, the successful introduction of this crop in smallholder resource-poor farming systems could be upset by the widespread aggressive Meloidogyne species, which have limited management options. The objective of this study therefore was to determine the degree of nematode resistance to the South African M. incognita and M. javanica population densities on A. annua seedlings. Uniform three-week-old seedlings in pots containing pasteurised growing medium under greenhouse conditions were inoculated using a series of eggs and second-stage juveniles of two Meloidogyne species in separate trials. At 56 days after inoculation, treatments were highly significant on reproductive factor (RF) for M. incognita and M. javanica on A. annua, contributing 87 and 89% in total treatment variation of the variables, respectively. At all levels of inoculation, RF values for M. incognita (0.17-0.79) and M. javanica (0.02-0.29) were below unity, without any noticeable root galls. Infection of A. annua by both Meloidogyne species had no significant effects on growth variables. In conclusion, A. annua seedlings are resistant to the South African M. incognita and M. javanica population densities and could therefore be explored further for use in smallholder resource-poor farming systems.

Keywords: ethnomedicial plants, medicinal plants, underutilised crops, plant parasitic nematodes

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Authors: Rahila Yilangai, Sonali Saha, Amartya Saha, Augustine Ezealor

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Gallery forests in sub-Saharan Africa are drastically disappearing due to intensive anthropogenic activities thus reducing ecosystem services, one of which is water provisioning. The role played by forest cover in regulating streamflow and water yield is not well understood, especially in West Africa. This pioneering 2-year study investigated the interrelationships between plant cover and hydrology in protected and unprotected gallery forests. Rainfall, streamflow, and evapotranspiration (ET) measurements/estimates over 2015-2016 were obtained to form a water balance for both catchments. In addition, transpiration in the protected gallery forest with high vegetation cover was calculated from stomatal conductance readings of selected species chosen from plot level data of plant diversity and abundance. Results showed that annual streamflow was significantly higher in the unprotected site than the protected site, even when normalized by catchment area. However, streamflow commenced earlier and lasted longer in the protected site than the degraded unprotected site, suggesting regulation by the greater tree density in the protected site. Streamflow correlated strongly with rainfall with the highest peak in August. As expected, transpiration measurements were less than potential evapotranspiration estimates, while rainfall exceeded ET in the water cycle. The water balance partitioning suggests that the lower vegetation cover in the unprotected catchment leads to a larger runoff in the rainy season and less infiltration, thereby leading to streams drying up earlier, than in the protected catchment. This baseline information is important in understanding the contribution of plants in water cycle regulation, for modeling integrative water management in applied research and natural resource management in sustaining water resources with changing the land cover and climate uncertainties in this data-poor region.

Keywords: evapotranspiration, gallery forest, rainfall, streamflow, transpiration

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Authors: Fabricia G. F. S. Rossato, Ieda G. Hidalgo, Andres Susseta, Felipe Casale, Leticia H. Nakamiti

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This research was conducted to show the useful source of biomass energy provided from forest waste and the black liquor from the pulping process. This energy source could be able to assist and improve its area environment in a sustainable way. The research will demonstrate the challenges from producing the biomass energy and the implantation of the pulp industry in the city of Três Lagoas, MS. – Brazil. Planted forest’s potential, energy production in the pulp industries and its consequence of impacts on the local region environmental was also studied and examined. The present study is classified as descriptive purposes as it exposes the characteristics of a given population and the means such as bibliographical and documentary. All the data and information collected and demonstrate in this study was carefully analyzed and provided from reliable sources such as official government agencies.

Keywords: Brazil, pulp industry, renewable energy, Três Lagoas

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Authors: Molin A., De Ramos J. M., Cadion L. G., Pico R. L.

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Malnutrition and food insecurity represent significant global challenges affecting millions of individuals, particularly in low-income and developing regions. The researchers created a solar-powered enzymatic composting bioreactor with an Arduino-based monitoring system for pH, humidity, and temperature. It manages mixed municipal solid wastes incorporating industrial enzymes and whey additives for accelerated composting and minimized carbon footprint. Within 15 days, the bioreactor yielded 54.54% compost compared to 44.85% from traditional methods, increasing yield by nearly 10%. Tests showed that the bioreactor compost had 4.84% NPK, passing metal analysis standards, while the traditional pit compost had 3.86% NPK; both are suitable for agriculture. Statistical analyses, including ANOVA and Tukey's HSD test, revealed significant differences in agricultural yield across different compost types based on leaf length, width, and number of leaves. The study compared the effects of different composts on Brassica rapa subsp. Chinesis (Petchay) and Brassica juncea (Mustasa) plant growth. For Pechay, significant effects of compost type on plant leaf length (F(5,84) = 62.33, η² = 0.79) and leaf width (F(5,84) = 12.35, η² = 0.42) were found. For Mustasa, significant effects of compost type on leaf length (F(4,70) = 20.61, η² = 0.54), leaf width (F(4,70) = 19.24, η² = 0.52), and number of leaves (F(4,70) = 13.17, η² = 0.43) were observed. This study explores the effectiveness of the enzymatic composting bioreactor and its viability in promoting carbon farming as a solution to food insecurity and malnutrition.

Keywords: malnutrition, food insecurity, enzymatic composting bioreactor, arduino-based monitoring system, enzymes, carbon farming, whey additive, NPK level

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Neha Devi, P. K. Joshi

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Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. One of the most significant forms of global disturbance, impacting community dynamics, biogeochemical cycles and local and regional climate across a wide range of ecosystems ranging from boreal forests to tropical rainforest is wildfire Assessment of fire danger is a function of forest type, fuelwood stock volume, moisture content, degree of senescence and fire management strategy adopted in the ground. Remote sensing has potential of reduction the uncertainty in mapping fuels. Hyperspectral remote sensing is emerging to be a very promising technology for wildfire fuels characterization. Fine spectral information also facilitates mapping of biophysical and chemical information that is directly related to the quality of forest fire fuels including above ground live biomass, canopy moisture, etc. We used Hyperion imagery acquired in February, 2016 and analysed four fuel characteristics using Hyperion sensor data on-board EO-1 satellite, acquired over the Shiwalik Himalayas covering the area of Champawat, Uttarakhand state. The main objective of this study was to present an overview of methodologies for mapping fuel properties using hyperspectral remote sensing data. Fuel characteristics analysed include fuel biomass, fuel moisture, and fuel condition and fuel type. Fuel moisture and fuel biomass were assessed through the expression of the liquid water bands. Fuel condition and type was assessed using green vegetation, non-photosynthetic vegetation and soil as Endmember for spectral mixture analysis. Linear Spectral Unmixing, a partial spectral unmixing algorithm, was used to identify the spectral abundance of green vegetation, non-photosynthetic vegetation and soil.

Keywords: forest fire fuel, Hyperion, hyperspectral, linear spectral unmixing, spectral mixture analysis

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Authors: Simone Cinquemani, Lorenzo Mantovani, Aleksander Dabek

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New technologies in agriculture are opening new challenges and new opportunities. Among these, certainly, robotics, vision, and artificial intelligence are the ones that will make a significant leap, compared to traditional agricultural techniques, possible. In particular, the indoor farming sector will be the one that will benefit the most from these solutions. Vertical farming is a new field of research where mechanical engineering can bring knowledge and know-how to transform a highly labor-based business into a fully autonomous system. The aim of the research is to develop a multi-purpose, modular, and perfectly integrated platform for crop production in indoor vertical farming. Activities will be based both on hardware development such as automatic tools to perform different activities on soil and plants, as well as research to introduce an extensive use of monitoring techniques based on machine learning algorithms. This paper presents the preliminary results of a research project of a vertical farm living lab designed to (i) develop and test vertical farming cultivation practices, (ii) introduce a very high degree of mechanization and automation that makes all processes replicable, fully measurable, standardized and automated, (iii) develop a coordinated control and management environment for autonomous multiplatform or tele-operated robots in environments with the aim of carrying out complex tasks in the presence of environmental and cultivation constraints, (iv) integrate AI-based algorithms as decision support system to improve quality production. The coordinated management of multiplatform systems still presents innumerable challenges that require a strongly multidisciplinary approach right from the design, development, and implementation phases. The methodology is based on (i) the development of models capable of describing the dynamics of the various platforms and their interactions, (ii) the integrated design of mechatronic systems able to respond to the needs of the context and to exploit the strength characteristics highlighted by the models, (iii) implementation and experimental tests performed to test the real effectiveness of the systems created, evaluate any weaknesses so as to proceed with a targeted development. To these aims, a fully automated laboratory for growing plants in vertical farming has been developed and tested. The living lab makes extensive use of sensors to determine the overall state of the structure, crops, and systems used. The possibility of having specific measurements for each element involved in the cultivation process makes it possible to evaluate the effects of each variable of interest and allows for the creation of a robust model of the system as a whole. The automation of the laboratory is completed with the use of robots to carry out all the necessary operations, from sowing to handling to harvesting. These systems work synergistically thanks to the knowledge of detailed models developed based on the information collected, which allows for deepening the knowledge of these types of crops and guarantees the possibility of tracing every action performed on each single plant. To this end, artificial intelligence algorithms have been developed to allow synergistic operation of all systems.

Keywords: automation, vertical farming, robot, artificial intelligence, vision, control

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

Abstract:

Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

Abstract:

Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Abdallah Bouguettoucha, Derradji Chebli, Tariq Yahyaoui, Hichem Attout

Abstract:

The effect of acid -basic treatment of lingocellulosic material (forest wastes wild carob) on Ethyl violet adsorption was investigated. It was found that surface chemistry plays an important role in Ethyl violet (EV) adsorption. HCl treatment produces more active acidic surface groups such as carboxylic and lactone, resulting in an increase in the adsorption of EV dye. The adsorption efficiency was higher for treated of lingocellulosic material with HCl than for treated with KOH. Maximum biosorption capacity was 170 and 130 mg/g, for treated of lingocellulosic material with HCl than for treated with KOH at pH 6 respectively. It was also found that the time to reach equilibrium takes less than 25 min for both treated materials. The adsorption of basic dye (i.e., ethyl violet or basic violet 4) was carried out by varying some process parameters, such as initial concentration, pH and temperature. The adsorption process can be well described by means of a pseudo-second-order reaction model showing that boundary layer resistance was not the rate-limiting step, as confirmed by intraparticle diffusion since the linear plot of Qt versus t^0.5 did not pass through the origin. In addition, experimental data were accurately expressed by the Sips equation if compared with the Langmuir and Freundlich isotherms. The values of ΔG° and ΔH° confirmed that the adsorption of EV on acid-basic treated forest wast wild carob was spontaneous and endothermic in nature. The positive values of ΔS° suggested an irregular increase of the randomness at the treated lingocellulosic material -solution interface during the adsorption process.

Keywords: adsorption, isotherm models, thermodynamic parameters, wild carob

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Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

Abstract:

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

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Authors: David Ahiamadia, Ramilan Thiagarajah, Peter Tozer

Abstract:

In Sub Saharan Africa, farm typologies have been used as a practical way to address heterogeneity among farming systems which is mostly done by grouping farms into subsets with similar characteristics. Due to the complexity in farming systems among farm households, it is not possible to formulate policy recommendations for individual farmers. As a result, this study employs a multivariate statistical approach using Principal Component Analysis (PCA) coupled with cluster analysis to reduce heterogeneity in a 615-household data set from the Africa Rising Baseline Evaluation Survey for 25 farming communities in Northern Ghana. Variables selected for the study were mostly socio-economic, production potential, production intensity, production orientation, crop diversity, food security, resource endowments, and climate risk variables. To avoid making some individuals in the subpopulation worse off when aclimate risk intervention is broadly implemented, the findings of the study also account for diversity in climate risk perception among the different farm types identified and their response strategies towards climate risk. The climate risk variables used in this study involve the most severeclimate shock types perceived by the household, household response to climate shock type, and reason for crop failure (i.e., maize, rice, and groundnut). Eventually, four farm types, each with an adequate level of homogeneity in climate risk perception and response strategies, were identified. Farm type 1 and 3 were wealthy with a lower degree of climate risk perception compared to farm type 2 and 4. Also, relatively wealthy farmers used asset liquidation as a climate risk management strategy, whereas poor farmers resorted to engaging in spiritual activities such as prayers, sacrifices, and divine consultations.

Keywords: smallholder, households, climate risk, variables, typologies

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Authors: Keisuke Takahata, Hiroshi Suetsugu

Abstract:

Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation.

Keywords: digital terrain model, satellite LiDAR, gaussian processes, uncertainty quantification

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Authors: Majid Samiee Zenoozian

Abstract:

This paper studies the susceptibility of local settlements in the gulf of Oman mangrove forest zone to flooding and progressesconsiderate of acuities and reactions to historical and present coastal flooding.it is indirect thaterosionsproduced in coastal zones by the change of mangrove undergrowthsubsequent from the enduring influence of persons since the late 19th century. Confronted with the increasing impact of climate change on climate ambitiousalarms such as flooding and biodiversity damage, handling the relationship between mangroves and their atmosphere has become authoritative for their defense. Coastal flood dangers are increasing quickly. We offer high resolution approximations of the financial value of mangroves forests for flood risk discount. We progress a probabilistic, process-based estimate of the properties of mangroves on avoidanceharms to people and property. More significantly, it also establishes how the incessantsqualor of this significant ecosystem has the potential to unfavorably influence the future cyclone persuadeddangers in the area.

Keywords: mangrove forest, coastal, flood, erosion

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Authors: S. Y. Cicekli

Abstract:

In this study, tree-dimensional model of tree was created by using terrestrial close range photogrammetry. For this close range photos were taken. Photomodeler Pro 5 software was used for camera calibration and create three-dimensional model of trees. In first test, three-dimensional model of a tree was created, in the second test three-dimensional model of three trees were created. This study aim is creating three-dimensional model of trees and indicate the use of close-range photogrammetry in forestry. At the end of the study, three-dimensional model of tree and three trees were created. This study showed that usability of close-range photogrammetry for monitoring tree and forests three-dimensional model.

Keywords: close- range photogrammetry, forest, tree, three-dimensional model

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Authors: Wencong Lu, Abdul Latif, Raza Ullah, Subhan Ullah

Abstract:

Agricultural production is subject to risk and the attitudes of producers toward risk, in turn, may be affected by certain socioeconomic characteristics of producers. Although, it is important to assess the risk attitude of farmers and their perception towards different calamitous risk sources for better understanding of their risk management adoption decisions, to the best of our knowledge no studies have been carried out to analyze the risk attitude and risk perceptions in the context of tobacco production in Pakistan. Therefore the study in hand is conducted with an attempt to overcome the gap in existing literature by analyzing different catastrophic risk sources faced by tobacco growers, their attitude towards risk and the effect of socioeconomic and demographic characteristics, farmers’ participation in contract farming and off-farm diversification on their risk attitude and risk perception. Around 78% of Pakistan’s entire tobacco crop and nearly all of the country’s Flue-Cured Virginia (FCV) tobacco is produced in Khyber Pakhtunkhwa (KPK) province alone. The yield/hectare of tobacco produced in KPK province is 14% higher than the global average and 22 % higher than national average. Khyber Pakhtunkhwa province was selected as main study area as nearly all of the country’s Flue-Cured Virginia (FCV) tobacco is produced in Khyber Pakhtunkhwa (KPK) province alone. Six districts were purposely selected based on their contribution in overall production for the last five years which accounts for more than 94.84% of the tobacco production in KPK province. Specific objectives taken into considerations for this study are the risk attitude of the farmers for growing FCV tobacco crop, farmers’ risk perception for different risk sources related to tobacco production (as far as the incidence and severity of each risk source is concerned) and the effect of socioeconomic characteristics, contract farming participation and off-farm diversification (income) on the risk attitude and risk perception of FCV tobacco growers.

Keywords: risk attitude, risk perception, contract farming, off-farm diversification, probit model

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Authors: Shih-Jen Feng, Nai-Chia Chao, Meng-Chi Shih, Chien-Chi Chang

Abstract:

Today, Taiwan agriculture operates under small business scale with economic insufficiency, due to aging population, unproductiveness, inadequate systematic management, insufficient agro-economic scale, and cultivation on agro-education. Moreover, because of farming special working method (physical tiring, shackled weather condition), environment (asymmetric distribution information), hours devoted (unbalance wealth), the willingness for younger generation to delicate into agriculture farming is rare. Although government had provided policies to harmonize the existing problem, significant result is unseen. Living lab (LL) is a methodology approach to sense, prototype and validate complex solutions in real life context. This paper contributes an innovative interaction methodology by probing under implementation of diverse LL sector merging big data analysis utilizing rural redevelopment and revitalization plan of Wufeng.

Keywords: living lab approach, historic rural redevelopment, innovation model, innovation approach

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Authors: Abel Kahuni

Abstract:

Cooperative game theory (CGT) postulates that groups of players are crucial units of the decision-making and impose cooperative behaviour. Accordingly, cooperative games are regarded as competition between coalitions of players, rather than between individual players. However, the basic supposition in CGT is that the cooperative is formed by all players. One of the emerging questions in CGT is how to develop cooperatives and fairly allocate the payoff. Cooperative Game Theory (CGT) may provide a framework and insights into the ways small holder farmers in rural resettlements may develop competitive advantage through marketing cooperatives. This conceptual paper proposes a non-competition model for small holder farmers of homogenous agri-commodity under CGT conditions. This paper will also provide brief insights into to the theory of cooperative games in-order to generate an understanding of CGT, cooperative marketing gains and its application in small holder farming arrangements. Accordingly, the objective is to provide a basic introduction to this theory in connection with economic competitive theories in the context of small holder farmers. The key value proposition of CGT is the equitable and fair sharing of cooperative gains.

Keywords: game theory, cooperative game theory, cooperatives, competition

Procedia PDF Downloads 76