Search results for: agriculture yield prediction

Authors: Brian H. Marsh, John H. Grove

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Gypsum is being applied to ameliorate subsoil acidity and to overcome the problem of very slow lime movement from surface lime applications. Reduced Crude Conversion Spent Lime (RCCSL) containing anhydrite was evaluated for use as a liming material with specific consideration given to the movement of sulfate into the acid subsoil. Agricultural lime and RCCSL were applied at 0, 0.5, 1.0, and 1.5 times the lime requirement of 6.72 Mg ha-1 to an acid Trappist silt loam (Typic Hapuldult). Corn [Zea mays (L.)]was grown following lime material application and soybean [Glycine max (L.) Merr.]was grown in the second year. Soil pH increased rapidly with the addition of the RCCSL material. Over time there was no difference in soil pH between the materials but there was with increasing rate. None of the observed changes in plant nutrient concentration had an impact on yield. Grain yield was higher for the RCCSL amended treatments in the first year but not in the second. There was a significant increase in soybean grain yield from the full lime requirement treatments over no lime.

Keywords: soil acidity, corn, soybean, liming materials

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Authors: M. Chavleishvili

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The paper presents the analysis of the current situation of agricultural development in Georgia. The investment environment that supports development of the agricultural sector is evaluated and the key priorities are identified. The analysis of the projects already implemented with state and EU support, as well as those that are being currently implemented is presented. The policy and the programs supporting development of agricultural sector are analyzed. Based on an analysis of the evaluations of experts and the primary accounting documents, the outcomes of investment programs, their advantages and disadvantages, are studied. Through identifying investment programs in the agricultural sector of Georgia, corresponding conclusions are made, based on which some recommendations are developed.

Keywords: agriculture, investments, investment programs, projects

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Authors: Qiming Zhang, Youda Ye, Qinxue Jiang

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Accurate prediction of external flowfield and aero-heating at the wall of hypersonic vehicle is very crucial for the design of aircrafts. Unstructured/hybrid meshes have more powerful advantages than structured meshes in terms of pre-processing, parallel computing and mesh adaptation, so it is imperative to develop high-resolution numerical methods for the calculation of aerothermal environment on unstructured/hybrid meshes. The inviscid flux scheme is one of the most important factors affecting the accuracy of unstructured/ hybrid mesh heat flux calculation. Here, a new hybrid flux scheme is developed and the approach of interface type selection is proposed: i.e. 1) using the exact Riemann scheme solution to calculate the flux on the faces parallel to the wall; 2) employing Sterger-Warming (S-W) scheme to improve the stability of the numerical scheme in other interfaces. The results of the heat flux fit the one observed experimentally and have little dependence on grids, which show great application prospect in unstructured/ hybrid mesh.

Keywords: aero-heating prediction, computational fluid dynamics, hybrid meshes, hybrid schemes

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Authors: Kashan Bashir, Salah Naji Ahmed Sufyan, Mirza Umar Baig

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In this study, n-dodecane, tetralin, decalin, and tetramethybenzene (TMBE) were used as model compounds of alkanes, naphthenic-aromatic, cycloalkanes and alkyl-benzenes presented in hydro-diesel. The catalytic cracking properties of four model compounds over Y zeolite catalyst (Y-Cat.) and ZSM-5 zeolite catalysts (ZSM-5-Cat.) were probed. The experiment results revealed that high conversion of macromolecular paraffin and naphthenic aromatics were achieved over Y-Cat, whereas its low cracking activity of intermediate products micromolecules paraffin and olefin and high activity of hydride transfer reaction goes against the production of value-added products (light olefin and gasoline). In contrast, despite the fact that the hydride transfer reaction was greatly inhabited over ZSM-5-Cat, the low conversion of macromolecules was observed attributed to diffusion limitations. Interestingly, the mixed catalyst compensates for the shortcomings of the two catalysts, and a “relay reaction” between Y-Cat and ZSM-5-Cat was proposed. Specifically, the added Y-Cat acts as a “pre-cracking booster site” and promotes macromolecules conversion. The addition of ZSM-5-Cat not only significantly suppresses the hydride transfer reaction but also contributes to the cracking of immediate products paraffin and olefin into ethylene and propylene, resulting in a high yield of alkyl-benzene (gasoline), ethylene, and propylene with a low yield of naphthalene (LCO) and coke. The catalytic cracking evaluation experiments of mixed hydro-LCO were also performed to further clarify the “relay reaction” above, showing the highest yield of LPG and gasoline over mixed catalyst. The results indicate that the Y-cat and ZSM-5-cat have a synergistic effect on the conversion of hydro-diesel and corresponding value-added product yield and selective coke yield.

Keywords: synergistic effect, hydro-diesel cracking, FCC, zeolite catalyst, ethylene and propylene

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Authors: Prakriti Diwan, S. Saraf

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The present study involves preparation and evaluation of microparticles of diclofenac sodium. The microparticles were prepared by the emulsion solvent evaporation techniques using ethylcellulose polymer. Four different batches of microspheres were prepared by varying the concentration of polymer from 50% to 80% w/w. The microspheres were characterized for drug content, percentage yield and encapsulation efficiency, particle size analysis and surface morphology. Microsphere prepared with high drug content produces higher percentage yield and encapsulation efficiency values. It was observed the increase in concentration of the polymer, increases the mean particle size of the microspheres. The effect of polymer concentration on the in vitro release of diclofenac from the microspheres was also studied. The production microparticles yield showed 98.74%, mean particle size 956.32µm and loading efficiency 97.15%. The results were found that microparticles prepared had slower release than microparticles (p>0.05). Therefore, it may be concluded that drug loaded microparticles are suitable delivery systems for diclofenac sodium.

Keywords: diclofenac sodium, emulsion solvent evaporation, ethylcellulose, microparticles

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Authors: Mohammadreza Delfieh, Seyed Ali Mohammad Modarres Sanavy, Rouzbeh Farhoudi

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Fennel is of most important medicinal plants which is widely used in food and pharmaceutical industries. In order to investigate the effect of different nitrogen nutritional systems including chemical, organic and biologic ones at different plant densities on yield, yield components and seed essential oil content and yield of this valuable medicinal plant, a field experiment was carried out in 2013-2014 agricultural season at Islamic Azad University of Shoushtar agricultural college in split plot design with 18 treatments and based on completely randomized blocks design. Different nitrogen system treatments consisting of: 1. N1 or control (Uniformly spreading urea fertilizer in the plot, 50% at planting time and 50% at stem elongation), 2. N2 (Uniformly spreading 50% of urea fertilizer in the plot at planting time and spraying the other 50% of urea fertilizer at stem elongation on fennel foliage), 3. N3 or cow manure, 4. N4 or biofertilizer (Inoculation of fennel seeds with Azotobacter and Azospirillum), 5. N5 or Integrated-1 (Cow manure + uniformly spreading urea fertilizer in the plot at stem elongation), 6. N6 or Integrated-2 (Cow manure + Inoculation of fennel seeds with Azotobacter and Azospirillum) were applied to the main plots. Three fennel densities consisting of: 1. FD1 (60 plant/m2), 2. FD2 (80 plant/m2) and 3. FD3 (100 plant/m2) were applied to subplots. Results showed that all of the traits were significantly affected by applied treatments (P 0.01). The interaction between treatments also were significant at 5 percent level for shoot dry weight and at 1 percent level for other traits. Based on the results, using the Integrated-1 treatment at 100 plant per m2 produced 94.575 g/m2 seed yield containing 3.375 percent of essential oil. Utilization of such combination not only could lead to a desirable fennel quantity and quality, but also is more consistent with environment.

Keywords: fennel (foeniculum vulgare mill.), nutritional system, nitrogen, biofertilizer, organic fertilizer, chemical fertilizer, density

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Authors: Manatchanok Tantiphiphatthana, Lin Peng, Rujira Jitrwung, Kunio Yoshikawa

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Hydrothermal liquefaction (HTL) is a technique for obtaining clean biofuel from biomass in the presence of heat and pressure in an aqueous medium which leads to a decomposition of this biomass to the formation of various products. A role of operating conditions is essential for the bio-oil and other products’ yield and also quality of the products. The effects of these parameters were investigated in regards to the composition and yield of the products. Chlorellaceae microalgae were tested under different HTL conditions to clarify suitable conditions for extracting bio-oil together with value-added co-products. Firstly, different microalgae loading rates (5-30%) were tested and found that this parameter has not much significant to product yield. Therefore, 10% microalgae loading rate was selected as a proper economical solution for conditioned schedule at 250oC and 30 min-reaction time. Next, a range of temperature (210-290oC) was applied to verify the effects of each parameter by keeping the reaction time constant at 30 min. The results showed no linkage with the increase of the reaction temperature and some reactions occurred that lead to different product yields. Moreover, some nutrients found in the aqueous product are possible to be utilized for nutrient recovery.

Keywords: bio-oil, hydrothermal liquefaction, microalgae, aqueous co-product

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Authors: Mohammad Mirwais Yusufi

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Skillful management is one of the most important success factors for today’s farms. When a farm is well managed, it can generate funds for its sustainability. Grape is one of the most diffused fruits in the world and one of the most important cash crops with high potential of production in Afghanistan as well. While there are several organizations intervening for improvement of this cash crop, the quality and quantity are still not satisfactory for producers and external markets. The situation has not changed over the years. Therefore, a survey was conducted in 2017 with 60 grape growers, supported by questionnaires in Mirbachakot, Kalakan and Shakardara districts of Kabul province. The purpose was to get an understanding of the current socio-demographic characteristics of farmers, management methods, constraints, farm size, yield and contribution of grape farming to household income. Findings indicate that grape farming was predominant 83.3% male, 16.6% female and small-scale farmers were the main grape producers, 60% < 1 ha of land under grape production. Likewise, 50% had more than > 10 years and 33.3% between 1-5 years’ experience in grape farming. The high level of illiteracy and diseases had significant digit effect on growth, yield and quality of grapes. The results showed that vineyard management operations to protect grapes from mechanical damage are very poor or completely absent. Comparing developed countries, table grape is one of the fruits with the highest input of technology, while in developing countries the cost of labor is low but the purchase of the equipment is very high due to financial situation. Hence the low quality and quantity of grape are influenced by poor management methods, such as non-availability of experts and lack of technical guidance in the study site. Thereby, the study suggested that improved agricultural extension services and managerial skills could contribute to addressing the problems.

Keywords: constraints, effect, management, Kabul

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Authors: Rehan Waheed, Abdul Shakoor

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Distortion produced during welding of thin metal plates is a problem in many industries. The purpose of this research was to study distortion produced during welding in 2mm Mild Steel plate by simulating the welding process using Finite Element Analysis. Simulation of welding process requires a couple field transient analyses. At first a transient thermal analysis is performed and the temperature obtained from thermal analysis is used as input in structural analysis to find distortion. An actual weld sample is prepared and the weld distortion produced is measured. The simulated and actual results were in quite agreement with each other and it has been found that there is profound deflection at center of plate. Temperature dependent material properties play significant role in prediction of weld distortion. The results of this research can be used for prediction and control of weld distortion in large steel structures by changing different weld parameters.

Keywords: welding simulation, FEA, welding distortion, temperature dependent mechanical properties

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Regina A. Tayong, Reza Barati

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A stable, fast and robust three-phase, 2D IMPES simulator has been developed for assessing the influence of; breaker concentration on yield stress of filter cake and broken gel viscosity, varying polymer concentration/yield stress along the fracture face, fracture conductivity, fracture length, capillary pressure changes and formation damage on fracturing fluid cleanup in tight gas reservoirs. This model has been validated as against field data reported in the literature for the same reservoir. A 2-D, two-phase (gas/water) fracture propagation model is used to model our invasion zone and create the initial conditions for our clean-up model by distributing 200 bbls of water around the fracture. A 2-D, three-phase IMPES simulator, incorporating a yield-power-law-rheology has been developed in MATLAB to characterize fluid flow through a hydraulically fractured grid. The variation in polymer concentration along the fracture is computed from a material balance equation relating the initial polymer concentration to total volume of injected fluid and fracture volume. All governing equations and the methods employed have been adequately reported to permit easy replication of results. The effect of increasing capillary pressure in the formation simulated in this study resulted in a 10.4% decrease in cumulative production after 100 days of fluid recovery. Increasing the breaker concentration from 5-15 gal/Mgal on the yield stress and fluid viscosity of a 200 lb/Mgal guar fluid resulted in a 10.83% increase in cumulative gas production. For tight gas formations (k=0.05 md), fluid recovery increases with increasing shut-in time, increasing fracture conductivity and fracture length, irrespective of the yield stress of the fracturing fluid. Mechanical induced formation damage combined with hydraulic damage tends to be the most significant. Several correlations have been developed relating pressure distribution and polymer concentration to distance along the fracture face and average polymer concentration variation with injection time. The gradient in yield stress distribution along the fracture face becomes steeper with increasing polymer concentration. The rate at which the yield stress (τ_o) is increasing is found to be proportional to the square of the volume of fluid lost to the formation. Finally, an improvement on previous results was achieved through simulating yield stress variation along the fracture face rather than assuming constant values because fluid loss to the formation and the polymer concentration distribution along the fracture face decreases as we move away from the injection well. The novelty of this three-phase flow model lies in its ability to (i) Simulate yield stress variation with fluid loss volume along the fracture face for different initial guar concentrations. (ii) Simulate increasing breaker activity on yield stress and broken gel viscosity and the effect of (i) and (ii) on cumulative gas production within reasonable computational time.

Keywords: formation damage, hydraulic fracturing, polymer cleanup, multiphase flow numerical simulation

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Authors: Abdelkader Hocine, Abdelhakim Maizia

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The exponential growth of reinforced fibers composite materials use has prompted researchers to step up their work on the prediction of their reliability. Owing to differences between the properties of the materials used for the composite, the manufacturing processes, the load combinations and types of environment, the prediction of the reliability of composite materials has become a primary task. Through failure criteria, TSAI-WU and the maximum stress, the reliability of multilayer tubular structures under pressure is the subject of this paper, where the failure probability of is estimated by the method of Monte Carlo.

Keywords: composite, design, monte carlo, tubular structure, reliability

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Authors: Awais Ahmad, Mostafa Muhammad Selim, Ali Abdullah Alderfasi

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Water limitation is undoubtedly a critical environmental constraint limiting the crop production under arid and semiarid areas. Mung bean is susceptible to both drought and water logging stresses. Therefore, present study was conducted to assess the water deficit stress consequences of yield components and water use efficiency in Mung bean. A field experiment was conducted at Educational Farm, Crop Production Department, College of Food and Agricultural Sciences, Kind Saud University, Saudi Arabia. Trail comprised of four irrigation levels — total amount of irrigation divided into irrigation intervals — (3, 5, 7 and 9 days interval) and three Mung bean genotypes; Kawmay-1, VC-2010 and King from Egypt, Thailand and China respectively. Experiment was arranged under split plot design having irrigation as main while genotype as subplot treatment, and replicated thrice. Plant height, 100 seed weight, biological yield, seed yield, harvest index and water use efficiency were recorded at harvesting. Results revealed that decrease in irrigation have significantly hampered all the studied parameters. Mung bean genotypes have also shown significant differences for all parameters, whereas irrigation genotype interaction was highly significant for seed yield, harvest index and water use efficiency (WUE) while it was significant for biological yield. Plant height and 100 seed weight were recorded non-significant for irrigation genotype interaction. A statistically highly significant correlation among recorded parameters was observed. Minimum irrigation interval (3 days) significantly produced maximum values while VC-2010 comparatively performed better under low irrigation levels. It was concluded that Mung bean may be successfully adopted under Saudi Arabian climate but it needs high water or frequent irrigation, however, genotypic differences are a hope to develop some improved varieties with high water use efficiency.

Keywords: mung bean, irrigation intervals, water use efficiency, genotypes, yield

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Authors: Auwal Garba, Rabiu Maijama’a, Abdullahi Muhammad Jalam

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Variability in climate has affected the agricultural production all over the globe. This concern has motivated important changes in the field of research during the last decade. Climate variability is believed to have declining effects towards rice production in Nigeria. This study examined climate variability and its impact on rice productivity in Dass Local Government Area, Bauchi State, by employing Linear Trend Model (LTM), analysis of variance (ANOVA) and regression analysis. Annual seasonal data of the climatic variables for temperature (min. and max), rainfall, and solar radiation from 1990 to 2015 were used. Results confirmed that 74.4% of the total variation in rice yield in the study area was explained by the changes in the independent variables. That is to say, temperature (minimum and maximum), rainfall, and solar radiation explained rice yield with 74.4% in the study area. Rising mean maximum temperature would lead to reduction in rice production while moderate increase in mean minimum temperature would be advantageous towards rice production, and the persistent rise in the mean maximum temperature, in the long run, will have more negatively affect rice production in the future. It is, therefore, important to promote agro-meteorological advisory services, which will be useful in farm planning and yield sustainability. Closer collaboration among the meteorologist and agricultural scientist is needed to increase the awareness about the existing database, crop weather models among others, with a view to reaping the full benefits of research on specific problems and sustainable yield management and also there should be a special initiative by the ADPs (State Agricultural Development Programme) towards promoting best agricultural practices that are resilient to climate variability in rice production and yield sustainability.

Keywords: climate variability, impact, productivity, rice

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Authors: Farag Ahwide, Souhel Bousheha

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A technical assessment has been made of electricity generation, considering wind turbines ranging between Vestas (V80-2.0 MW and V112-3.0 MW) and the air density is equal to 1.225 Kg/m3, at different towns in Libya. Wind speed might have been measured each 3 hours during 10 m stature at a time for 10 quite sometime between 2000 Furthermore 2009, these towns which are spotted on the bank from claiming Mediterranean ocean also how in the desert, which need aid Derna 1, Derna 2, Shahat, Benghazi, Ajdabya, Sirte, Misurata, Tripoli-Airport, Al-Zawya, Al-Kofra, Sabha, Nalut. The work presented long term "wind data analysis in terms of annual, seasonal, monthly and diurnal variations at these sites. Wind power density with different heights has been studied. Excel sheet program was used to calculate the values of wind power density and the values of wind speed frequency for the stations; their seasonally values have been estimated. Limit variable with rated wind pace to 10 different wind turbines need to be been estimated, which is used to focus those required yearly vitality yield of a wind vitality change framework (WECS), acknowledging wind turbines extending between 600 kW and 3000 kW).

Keywords: energy yield, wind turbines, wind speed, wind power density

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Authors: Tamrat Tesfaye, Bruce Sithole

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Box-Behnken Response surface methodology was used to determine the optimum processing conditions that give maximum extraction yield and whiteness index from mango seed. The steeping time ranges from 2 to 12 hours and slurring of the steeped seed in sodium metabisulphite solution (0.1 to 0.5 w/v) was carried out. Experiments were designed according to Box-Behnken Design with these three factors and a total of 15 runs experimental variables of were analyzed. At linear level, the concentration of sodium metabisulphite had significant positive influence on percentage yield and whiteness index at p<0.05. At quadratic level, sodium metabisulphite concentration and sodium metabisulphite concentration2 had a significant negative influence on starch yield; sodium metabisulphite concentration and steeping time*temperature had significant (p<0.05) positive influence on whiteness index. The adjusted R2 above 0.8 for starch yield (0.906465) and whiteness index (0.909268) showed a good fit of the model with the experimental data. The optimum sodium metabisulphite concentration, steeping hours, and temperature for starch isolation with maximum starch yield (66.428%) and whiteness index (85%) as set goals for optimization with the desirability of 0.91939 was 0.255w/v concentration, 2hrs and 50 °C respectively. The determined experimental value of each response based on optimal condition was statistically in accordance with predicted levels at p<0.05. The Mango seeds are the by-products obtained during mango processing and possess disposal problem if not handled properly. The substitution of food based sizing agents with mango seed starch can contribute as pertinent resource deployment for value-added product manufacturing and waste utilization which might play significance role of food security in Ethiopia.

Keywords: mango, synthetic sizing agent, starch, extraction, textile, sizing

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Authors: Fang-Bo Yu, Li-Bo Guan, Sheng-Dao Shan

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Biogas slurry was mixed with six commercial fungicides and screening against 11 phytopathogens was carried out. Results showed that inhibition of biogas slurry was different for the test strains and no significant difference between treatments of Didymella bryoniae, Fusarium oxysporum f. sp. vasinfectum, Aspergillus niger, Rhizoctonia cerealis, F. graminearum and Septoria tritici was observed. However, significant differences were found among Penicillium sp., Botrytis cinerea, Alternaria sonali, F. oxysporum F. sp. melonis and Sclerotinia sclerotiorum. The approach described here presents a promising alternative to current manipulation although some issues still need further examination. This study could contribute to the development of sustainable agriculture and better utilization of biogas slurry.

Keywords: anaerobic digestion, biogas slurry, phytopathogen, sustainable agriculture

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Authors: Getalem E. Haylia

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The Ribb dam is one of the irrigation projects in the Upper Blue Nile basin, Ethiopia, to irrigate the Fogera plain. Reservoir sedimentation is a major problem because it reduces the useful reservoir capacity by the accumulation of sediments coming from the watersheds. Estimates of sediment yield are needed for studies of reservoir sedimentation and planning of soil and water conservation measures. The objective of this study was to simulate the Ribb dam catchment sediment yield using SWAT model and to estimate Ribb reservoir effective life according to trap efficiency methods. The Ribb dam catchment is found in North Western part of Ethiopia highlands, and it belongs to the upper Blue Nile and Lake Tana basins. Soil and Water Assessment Tool (SWAT) was selected to simulate flow and sediment yield in the Ribb dam catchment. The model sensitivity, calibration, and validation analysis at Ambo Bahir site were performed with Sequential Uncertainty Fitting (SUFI-2). The flow data at this site was obtained by transforming the Lower Ribb gauge station (2002-2013) flow data using Area Ratio Method. The sediment load was derived based on the sediment concentration yield curve of Ambo site. Stream flow results showed that the Nash-Sutcliffe efficiency coefficient (NSE) was 0.81 and the coefficient of determination (R²) was 0.86 in calibration period (2004-2010) and, 0.74 and 0.77 in validation period (2011-2013), respectively. Using the same periods, the NS and R² for the sediment load calibration were 0.85 and 0.79 and, for the validation, it became 0.83 and 0.78, respectively. The simulated average daily flow rate and sediment yield generated from Ribb dam watershed were 3.38 m³/s and 1772.96 tons/km²/yr, respectively. The effective life of Ribb reservoir was estimated using the developed empirical methods of the Brune (1953), Churchill (1948) and Brown (1958) methods and found to be 30, 38 and 29 years respectively. To conclude, massive sediment comes from the steep slope agricultural areas, and approximately 98-100% of this incoming annual sediment loads have been trapped by the Ribb reservoir. In Ribb catchment, as well as reservoir systematic and thorough consideration of technical, social, environmental, and catchment managements and practices should be made to lengthen the useful life of Ribb reservoir.

Keywords: catchment, reservoir effective life, reservoir sedimentation, Ribb, sediment yield, SWAT model

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Authors: Mirza Waseem Abbas, Syed Danish Raza

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For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).

Keywords: change detection, area estimation, machine learning, urbanization, remote sensing

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Authors: Abubakar O. Sulaiman

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Agriculture was the main stay of Nigeria’s economy before the oil boom of the 1970s caused a gradual but steady shift from agriculture to crude oil as the major source of revenue and foreign exchange. The economy later experienced many symptoms of the 'Dutch disease', with exchange rate appreciation and erosion of competitiveness of the non-oil tradable goods. In order to reverse the worsening economic situations -high unemployment, galloping inflation, deteriorating balance of payment, declining economic growth, and fiscal deficits among others- the government, embarked on austerity measures in 1982 and Structure Adjustment Programme (SAP) in 1986. One of the cornerstones of SAP is the diversification of the economy from oil to non-oil. In the form of stocktaking, this paper investigates the impact of non-oil revenue on economic growth in Nigeria using quarterly time-series data from 1980 to 2019. The findings revealed that a long-run relationship exists between the variables (non-oil variables) and economic growth in Nigeria. Among the variables, (agriculture revenue, manufacturing revenue, revenue from services, and company income tax) contributed substantially to economic growth. The paper recommends that the government should continue to intensify efforts and policies in the diversification of the economy as it will bring about sustainable non-oil revenue and economic growth.

Keywords: non-oil revenue, economic growth, export, long run relationship

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Authors: Dijan Supramono, Eny Kusrini, Haisya Yuana

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Pyrolysis, a thermal cracking process in inert environment, may be used to produce bio-oil from biomass and plastic waste thus accommodating the use of renewable energy. Abundant amount of biomass waste in Indonesia are not utilised and plastic wastes are not well processed for clean environment. The aim of present work was to evaluate effect of mass ratio of plastic material to biomass in the feed blend of corn cobs and high density polyethylene (HDPE) of co-pyrolysis on bio-oil yield and chemical composition of bio-oil products. The heating rate of the co-pyrolysis was kept low and residence time was in the order of seconds to accommodate high yield of oil originating from plastic pyrolysis. Corn cobs have high cellulose and hemicellulose content (84%) which is potential to produce bio-oil. The pyrolysis was conducted in a laboratory-scale using a fixed bed reactor with final temperature of 500°C, heating rate 5 °C/min, flow rate N2 750 mL/min, total weight of biomass and plastic material of 20 g, and hold time after peak temperature of 30 min. Set up of conditions of co-pyrolysis should lead to accommodating the production of oil originating from HDPE due to constraint of HDPE pyrolysis residence time. Mass ratio of plastics to biomass in the feed blend was varied 0:100, 25:75, 50:50, 75:25 and 100:0. It was found that by increasing HDPE content up to 100% in the feed blend, the yield of bio-oil at different mass ratios prescribed above were 28.05, 21.55, 14.55, 9.5, and 6.3wt%, respectively. Therefore, in the fixed bed reactor, producing bio-oil is constrained by low contribution of plastic feedstock to the pyrolysis liquid yield. Furthermore, for the same variation of the mass ratio, yields of the mixture of paraffins, olefins and cycloalkanes contained in bio-oil were of 0, 28.35, 40.75, 47.17, and 67.05wt%, respectively. Olefins and cycloalkanes are easily hydrogenised to produce paraffins, suitable to be used as bio-fuel. By increasing composition of HDPE in the feed blend, viscosity and pH of bio-oil change approaching to those of commercial diesel oil.

Keywords: co-pyrolysis, corn cobs, fixed bed reactor, HDPE

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Kalpana Jamdhade, Anita Chorey, Bharti Tijare, V. M. Bhale

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A field experiment was conducted during summer season of 2011 at Agronomy research farm, Dr. PDKV, Akola, to study the effect of irrigation regime and nitrogen levels on growth and productivity of summer sesame. The experiment was laid out in split plot Design in which three irrigation scheduling on the basis of IW/CPE ratio viz., irrigation at 0.6, 0.8 and 1.0 IW/CPE ratios (I1, I2 and I3, respectively) and one irrigation scheduling based on critical growth stages of sesame (I4), in main plot and three nitrogen levels 0, 30 and 60 kg N ha-1 (N0, N1 and N2, respectively) in subplot. The result showed that plant height, number of leaves plant-1, leaf area and dry matter accumulation were maximum in irrigation scheduling at 1.0 IW/CPE ratio, which significantly superior over 0.6 IW/CPE ratio and irrigation at critical growth stages but were statistically at par with irrigation at 0.8 IW/CPE ratio. Nitrogen levels, application of 60 kg N ha-1 was recorded significantly superior all growth parameters over treatment 30 kg N ha-1 and 0 kg N ha-1. In case of yield attributes viz., No. of capsules plant-1, Test wt., grain yield and Stalk yield (qha-1) were maximum in irrigation scheduling at 1.0 IW/CPE ratio and were significantly superior over 0.8 IW/CPE ratio, 0.6 IW/CPE ratio and irrigation at critical growth stages. Application of 60 kg N ha-1 increased all yield attributing characters over application of 30 and 0 kg N ha-1. In case of economics of crop same trend was found and the highest B:C ration was obtained in irrigation scheduling at 1.0 IW/CPE ratio. Whereas, application of 30 kg N ha-1 was recorded highest B:C ration over application of 60 and 0 kg N ha-1. Interaction effect of irrigation and nitrogen levels were found to be non significant in summer season.

Keywords: irrigation regimes, nitrogen levels, summer sesame, agricultural technology

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Ahmed Elgharably, Nivien Nafady

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Arbuscular mycorrhizal fungi (AMF) and plant growth promoting microbes (PGPM) can promote plant growth under saline conditions. This study investigated how AMF and PGPM affected the growth and grain yield of wheat at different soil salinity levels (0, 75 and 150 mM NaCl). AMF colonization percentage, grain yield and dry weights and lengths of shoot and root, N, P K, Na, malondialdehyde, chlorophyll and proline contents and shoot relative permeability were determined. Salinity reduced NPK uptake and malondialdehyde and chlorophyll contents, and increased shoot Na concentration, relative permeability, and proline content, and thus declined plant growth. PGPM inoculation enhanced AMF colonization, P uptake, and K/Na ratio, but alone had no significant effect on plant growth and grain yield. AMF inoculation significantly enhanced NPK uptake, increased chlorophyll content and decreased shoot relative permeability, proline and Na contents, and thus promoted the plant growth. The inoculation of PGPM significantly enhanced the positive effects of AMF in controlling Na uptake and in increasing chlorophyll and NPK contents. Compared to AMF inoculation alone, dual inoculation with AMF and PGPM resulted in approximately 10, 25 and 25% higher grain yield at 0, 75 and 150 mM NaCl, respectively. The results provide that PGPM inoculation can maximize the effects of AMF inoculation in alleviating the deleterious effects of NaCl salts on wheat growth.

Keywords: mycorrhizal fungi, salinity, sodium, wheat

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Authors: Queen Suraajini Rajendran, Sai Hung Cheung

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Statistical down scaling models are required to connect the global climate model outputs and the local weather variables for climate change impact prediction. For reliable climate change impact studies, the uncertainty associated with the model including natural variability, uncertainty in the climate model(s), down scaling model, model inadequacy and in the predicted results should be quantified appropriately. In this work, a new approach is developed by the authors for statistical classification, statistical down scaling and uncertainty assessment and is applied to Singapore rainfall. It is a robust Bayesian uncertainty analysis methodology and tools based on coupling dependent modeling error with classification and statistical down scaling models in a way that the dependency among modeling errors will impact the results of both classification and statistical down scaling model calibration and uncertainty analysis for future prediction. Singapore data are considered here and the uncertainty and prediction results are obtained. From the results obtained, directions of research for improvement are briefly presented.

Keywords: statistical downscaling, global climate model, climate change, uncertainty

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Authors: Azizurahman Sakhizadah, Tsugiyuki Masunaga

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The use of sulfur fertilizer by Afghanistan farmers for wheat production has never been practiced, although sulfur deficiency has been expected for wheat production. A field experiment was conducted at Poza e Ishan Research Station Farm, Baghlan province, Afghanistan to examine the effect of sulfur fertilizer on growth and yield components of wheat. The experiment was laid out in randomize complete block design (RCBD), having three replications and eight treatments. The initial soil of experiment was alkaline (pH8.4), with textural class of sandy clay loam, available sulfur (40.8) mg kg-1, and Olsen-P (28.8) mg kg-1. Wheat variety, Kabul 013 was cultivated from November 2015 to June 2016. The recommended doses of nitrogen and Phosphors (Urea and DAP at 250 and 125 kg ha-1) were applied by broadcasting except control plot. Sulfur was applied by foliar spray (K2 SO4) at the rate of 10, 20, and 30 kg ha-1, split at tillering and flowering stages. The results demonstrated that sulfur application positively influenced on growth and yield of wheat crop with combination of nitrogen. Plant did not respond to sole sulfur application. Plant height, spike length, spikelet's number spike-1, were increased and yield g m-2 was also increased by 1.2, 19.1 and 25.1 % for 10, 20 and 30 kg sulfur ha-1 application.

Keywords: sulfur, nitrogen, wheat, foliar

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Authors: Tesfaye Walle Mekonnen

Abstract:

Maize is one of the most important staple food crops for most low-income households in the Sub-Saharan (SSA). Combined heat and drought stress is the major production threats that reduce the yield potential of biofortified maize and restrain various macro and micronutrient deficiencies highly prevalent in low-income people who rely solely on maize-based diets, SSA. This problem can be alleviated by crossing the biofortified inbred lines with different nutritional attributes, Fe, Zn, Protein, and Provitamin A, and developing agronomically superior and stable multi-nutrient maize of various genetic backgrounds. This aimed to understand the correlation between biofortified inbred lines per se and hybrid performance under combined heat and drought stress conditions (CSC). The experiment was conducted at CIMMYT, Zimbabwe, using α-lattice design with three replications. The hybrid effect was highly significant for zein fractions (α-, β-, γ- and δ-zein) zinc, (Zn), and iron (Fe) provitamin A, phytic acid, and grain yield. Under CSC, Fe, Zn concentration, provitamin A in grain and grain yield of hybrids were significantly decreased, however, the zein fraction content and phytic acid content increases in grain were increased under CSC. The phenotypic correlation between grain yield with Zn, Fe concentration, and Provitamin A in grain was strongly positive and higher under CSC than in well-watered conditions. The present investigation confirmed that under CSC, Fe, and Zn-enhanced hybrids could be forecasted to a certain scope based on the performance of and scientifically selected for desirable grain yield and related traits with CSC tolerance during hybrid development programs. In conclusion, the development of high-yielding and micronutrient-dense maize variety is possible under CSC, which could reduce the highly prevalent micronutrient in SSA.

Keywords: drought, Fe, heat, maize, protein, zein fractions, Zn

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Authors: Mursleen Yasin, Sunil Panchal, Michelle Mak, Zhonghua Chen

Abstract:

“The use of agricultural and horticultural waste to obtain beneficial products. Thus reduce its environmental impact and help the general population.” Every year 20 billion dollars of food is wasted in the world. All the energy, resources, nutrients and metabolites are lost to the landfills as well. On farm production losses are a main issue in agriculture. Almost 25% vegetables never leave the farm because they are not considered perfect for supermarkets and treated as waste material along with the rest of the plant parts. For capsicums, this waste is 56% of the total crop. Capsicum genus is enriched with a group of compounds called capsaicinoids which are a source of spiciness of these fruits. Capsaicin and dihydrocapsaicin are the major members comprising almost 90% of this group. The major production and accumulation site is the non-edible part of fruit i.e., placenta. Other parts of the plant, like stem, leaves, pericarp and seeds, also contain these pungent compounds. Capsaicinoids are enriched with properties like analgesic, antioxidants, anti-inflammatory, antibacterial, anti-virulence anti-carcinogenic, chemo preventive, chemotherapeutic, antidiabetic etc. They are also effective in treating problems related to gastrointestinal tract, lowering cholesterol and triglycerides in obesity. The aim of the study is to develop a standardised technique for capsaicinoids extraction and to identify better nutrient treatment for fruit and capsaicinoids yield. For research 3 capsicum and 2 chilli varieties were grown in a high-tech glass house facility in Sydney, Australia. Plants were treated with three levels of nutrient treatments i.e., EC 1.8, EC 2.8 and EC 3.8 in order to check its effect on fruit yield and capsaicinoids concentration. Solvent extraction procedure is used with 75% ethanol to extract these secondary metabolites. Physiological, post-harvest and waste biomass measurement and metabolomic analysis are also performed. The results showed that EC 2.8 gave the better fruit yield of capsicums, and those fruits have the higher capsaicinoids concentration. For chillies, higher EC levels had better results than lower treatment. The UHPLC analysis is done to quantify the compounds, and a decrease in capsaicin concentration is observed with the crop maturation. The outcome of this project is a sustainable technique for extraction of capsaicinoids which can easily be adopted by farmers. In this way, farmers can help in value adding of waste by extracting and selling capsaicinoids to nutraceutical and pharmaceutical industries and also earn some secondary income from the 56% waste of capsicum crop.

Keywords: capsaicinoids, plant waste, capsicum, solvent extraction, waste biomass

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