Search results for: tomato yield prediction

Authors: M. Bhattarai, B. B. Rana, M. Kamimukai, I. Takamure, T. Kawano, M. Murai

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The sd1-d allele at the sd1 locus on chromosome 1, originating from Taiwanese variety Dee-geo-woo-gen, has been playing important role for developing short-culm and lodging-resistant indica varieties such as IR36 in rice. The dominant allele SD1 for long culm at the locus is differentiated into SD1-in and SD1-ja which are harbored in indica and japonica subspecies’s, respectively. The sd1-d of an indica variety IR36 was substituted with SD1-in or SD1-ja by recurrent backcrosses of 17 times with IR36, and two isogenic tall lines regarding the respective dominant alleles were developed by using an indica variety IR5867 and a japonica one ‘Koshihikari’ as donors, which were denoted by '5867-36' and 'Koshi-36', respectively. The present study was conducted to examine the effect of sd1-d on yield and related traits as compared with SD1-in and SD1-ja, by using the two isogenic tall lines. Seedlings of IR36 and the two isogenic lines were transplanted on an experimental field of Kochi University, by the planting distance of 30 cm × 15 cm with two seedlings per hill, on May 3, 2017. Chemical fertilizers were supplied by basal application and top-dressing at a rate of 8.00, 6.57 and 7.52 g/m², respectively, for N, P₂O₅ and K₂O in total. Yield, yield components, and other traits were measured. Culm length (cm) was in the order of 5867-36 (101.9) > Koshi-36 (80.1) > IR36 (60.0), where '>' indicates statistically significant difference at the 5% level. Accordingly, sd1-d reduced culm by 41.9 and 20.1 cm, compared with SD1-in and SD1-ja, respectively, and the effect of elongating culm was higher in the former allele than in the latter one. Total brown rice yield (g/m²), including unripened grains, was in the order of IR36 (611) ≧ 5867-36 (586) ≧ Koshi-36 (572), indicating non-significant differences among them. Yield-1.5mm sieve (g/m²) was in the order of IR36 (596) ≧ 5867-36 (575) ≧ Koshi-36 (558). Spikelet number per panicle was in the order of 5867-36 (89.2) ≧ IR36 (84.7) ≧ Koshi-36 (79.8), and 5867-36 > Koshi-36. Panicle number per m² was in the order of IR36 (428) ≧ Koshi-36 (403) ≧ 5867-36 (353), and IR36 > 5867-36, suggesting that sd1-d increased number of panicles compared with SD1-in. Ripened-grain percentage-1.5mm sieve was in the order of Koshi-36 (86.0) ≧ 5867-36 (85.0) ≧ IR36 (82.7), and Koshi-36 > IR36. Thousand brown-rice-grain weight-1.5mm sieve (g) was in the order of 5867-36 (21.5) > Koshi-36 (20.2) ≧ IR36 (19.9). Total dry weight at maturity (g/m²) was in the order of 5867-36 (1404 ) ≧ IR36 (1310) ≧ Kosihi-36 (1290). Harvest index of total brown rice (%) was in the order of IR36 (39.6) > Koshi-36 (37.7) > 5867-36 (35.5). Hence, sd1-d did not exert significant effect on yield in indica genetic background. However, lodging was observed from the late stage of maturity in 5867-36 and Koshi-36, particularly in the former, which was principally due to their long culms. Consequently, sd1-d enables higher yield with higher fertilizer application, by enhancing lodging resistance, particularly in indica subspecies.

Keywords: rice, dwarfing gene, sd1-d, SD1-in, SD1-ja, yield

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju

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The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.

Keywords: comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events

Procedia PDF Downloads 261

Authors: R. Behrooz, D. Jahanfar, D. Reza

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Organic farming is a fundamental principle in sustainable agriculture. Preventing excessive contamination of water and soil with pesticides and chemical fertilizers is important in order to produce healthy food. For this purpose, two potato cultivars (Sante and Marfona) and seven levels of fertilizer (non-fertilizer, chemical fertilizer, granulated chicken manure, common manure, compost, vermicompost and tea compost) were evaluated by factorial experiment based on randomized complete block design (RCBD) with three replications. According to the results, the effect of different manure was significant on number of tubers per plant, tuber weight per plant and tuber yield. The highest value of these traits was obtained by using of chicken manure which was significantly superior to other treatments. However, there was no significant difference between the two varieties. According to the results, the use of chicken manure has produced the highest potato yield even in comparison with the use of chemical fertilizer.

Keywords: organic farming, organic manure, potato, tuber yield

Procedia PDF Downloads 154

Authors: David Boyajian, Tadeh Zirakian

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Stability and performance of steel plates are characterized by geometrical buckling and material yielding. In this paper, the geometrical buckling and material yielding behaviors of low yield point (LYP) steel plates are studied from the point of view of their application in steel plate shear wall (SPSW) systems. Use of LYP steel facilitates the design and application of web plates with improved buckling and energy absorption capacities in SPSW systems. LYP steel infill plates may yield first and then undergo inelastic buckling. Hence, accurate determination of the limiting plate thickness corresponding to simultaneous buckling and yielding can be effective in seismic design of such lateral force-resisting and energy dissipating systems. The limiting thicknesses of plates with different loading and support conditions are determined theoretically and verified through detailed numerical simulations. Effects of use of LYP steel and plate aspect ratio parameter on the limiting plate thickness are investigated as well. In addition, detailed studies are performed on determination of the limiting web-plate thickness in code-designed SPSWs. Some practical recommendations are accordingly provided for efficient seismic design of SPSW systems with LYP steel infill plates.

Keywords: buckling, low yield point steel, plates, steel plate shear walls, yielding

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Authors: N. Zandi, M. Sitarz, J. Jastrzebski, M. Vagheian, J. Choinski, A. Stolarz, A. Trzcinska

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¹³⁵La with the half-life of 19.5 h can be considered as a good candidate for Auger therapy. ¹³⁵La decays almost 100% by electron capture to the stable ¹³⁵Ba. In this study, all important possible reactions leading to ¹³⁵La production are investigated in details, and the corresponding theoretical yield for each reaction using the Monte-Carlo method (MCNPX code) are presented. Among them, the best reaction based on the cost-effectiveness and production yield regarding Poland facilities equipped with medical cyclotron has been selected. ¹³⁵La is produced using 16.5 MeV proton beam of general electric PET trace cyclotron through the ¹³⁵Ba(p,n)¹³⁵La reaction. Moreover, for a consistent facilitating comparison between the theoretical calculations and the experimental measurements, the beam current and also the proton beam energy is measured experimentally. Then, the obtained proton energy is considered as the entrance energy for the theoretical calculations. The production yield finally is measured and compared with the results obtained using the MCNPX code. The results show the experimental measurement and the theoretical calculations are in good agreement.

Keywords: efficient ¹³⁵La production, proton cyclotron energy measurement, MCNPX code, theoretical and experimental production yield

Procedia PDF Downloads 141

Authors: Abadi Berhane, Walelign Worku, Berhanu Abrha, Gebre Hadgu

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Sesame is an important oilseed crop in Ethiopia, which is the second most exported agricultural commodity next to coffee. However, there is poor soil fertility management and a research-led farming system for the crop. The AquaCrop model was applied as a decision-support tool, which performs a semi-quantitative approach to simulate the yield of crops under different soil fertility levels. The objective of this experiment was to calibrate and validate the AquaCrop model for simulating the growth and yield of sesame under different nitrogen fertilizer levels and to test the performance of the model as a decision-support tool for improved sesame cultivation in the study area. The experiment was laid out as a randomized complete block design (RCBD) in a factorial arrangement in the 2016, 2017, and 2018 main cropping seasons. In this experiment, four nitrogen fertilizer rates, 0, 23, 46, and 69 Kg/ha nitrogen, and three improved varieties (Setit-1, Setit-2, and Humera-1). In the meantime, growth, yield, and yield components of sesame were collected from each treatment. Coefficient of determination (R2), Root mean square error (RMSE), Normalized root mean square error (N-RMSE), Model efficiency (E), and Degree of agreement (D) were used to test the performance of the model. The results indicated that the AquaCrop model successfully simulated soil water content with R2 varying from 0.92 to 0.98, RMSE 6.5 to 13.9 mm, E 0.78 to 0.94, and D 0.95 to 0.99, and the corresponding values for AB also varied from 0.92 to 0.98, 0.33 to 0.54 tons/ha, 0.74 to 0.93, and 0.9 to 0.98, respectively. The results on the canopy cover of sesame also showed that the model acceptably simulated canopy cover with R2 varying from 0.95 to 0.99 and a RMSE of 5.3 to 8.6%. The AquaCrop model was appropriately calibrated to simulate soil water content, canopy cover, aboveground biomass, and sesame yield; the results indicated that the model adequately simulated the growth and yield of sesame under the different nitrogen fertilizer levels. The AquaCrop model might be an important tool for improved soil fertility management and yield enhancement strategies of sesame. Hence, the model might be applied as a decision-support tool in soil fertility management in sesame production.

Keywords: aquacrop model, normalized water productivity, nitrogen fertilizer, canopy cover, sesame

Procedia PDF Downloads 79

Authors: Theresa K., Shanmugasundaram R., Kennedy J. S.

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Nutrient supplements are indispensable for raising crops and to reap determining productivity. The nutrient imbalance between replenishment and crop uptake is attempted through the input of inorganic fertilizers. Excessive dumping of inorganic nutrients in soil cause stagnant and decline in yield. Imbalanced N-P-K ratio in the soil exacerbates and agitates the soil ecosystems. The study evaluated the fertilization practices of conventional (CFs), organic and Integrated Nutrient Management system (INM) on soil quality using key indicators and soil quality indices. Twelve rice farming fields of which, ten fields were having conventional cultivation practices, one field each was organic farming based and INM based cultivated under monocropping sequence in the Thondamuthur block of Coimbatore district were fixed and properties viz., physical, chemical and biological were studied for four cropping seasons to determine soil quality index (SQI). SQI was computed for conventional, organic and INM fields. Comparing conventional farming (CF) with organic and INM, CF was recorded with a lower soil quality index. While in organic and INM fields, the higher SQI value of 0.99 and 0.88 respectively were registered. CF₄ received with a super-optimal dose of N (250%) showed a lesser SQI value (0.573) as well as the yield (3.20 t ha⁻¹) and the CF6 which received 125 % N recorded the highest SQI (0.715) and yield (6.20 t ha⁻¹). Likewise, most of the CFs received higher N beyond the level of 125 % except CF₃ and CF₉, which recorded lower yields. CFs which received super-optimal P in the order of CF₆&CF₇>CF₁&CF₁₀ recorded lesser yields except for CF₆. Super-optimal K application also recorded lesser yield in CF₄, CF₇ and CF₉.

Keywords: rice cropping system, soil quality indicators, imbalanced fertilization, yield

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Authors: Jessica Morales Perdomo, Daniel Rodriguez Caicedo, Fernando Cantor Rincon

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Tuta absoluta known as the tomato leaf miner, is one of the main pests in tomato crops in South America and the main pest in many European countries. Apanteles gelechiidivoris is a parasitoid of third instar Tuta absoluta larvae. Our studies have demonstrated that this parasitoid can cause up to 80% mortality of T. absoluta larvae in the field. We investigated cold storage of A. gelechiidivoris pupae as a method of mass production of this parasitoid. This storage method does not interfere with biological characteristics of the parasitoid. In this study, we evaluated the effect of different temperatures (4, 8 and 12°C) and different time duration (7, 14, 21 or 28 days) of cold storage on biological parameters of A. gelechiidivoris pupae and adults. The biological parameters of the parasitoid evaluated were: adult emergence time, lifespan, parasitism percentage and sex ratio. We found that the adult emergence time was delayed when the parasitoid pupae were stored at 4°C and 8°C. The shortest adult emergence was recorded when pupae were stored for seven days. The lowest adult emergence was found for pupae stored at 4°C and decreased significantly as the days of storage increased. We found high percentages of adult emergence when pupae were stored at 8°C and 12°C for seven days. Adult lifespan decreased with increasing days of cold storage. Adults emerging from pupae stored at 8°C during seven and 14 days showed the longest lifespan (nine days). The lowest parasitism rate was recorded at 4°C at every time point. The highest percentage of parasitism (80%) was found at 8°C during seven days of storage. The treatments had no effect on adults the sex ratio. The results suggest that A. gelechiidivoris pupae can be stored for up to 14 days at 8°C without affecting the efficacy of the parasitoid in the field.

Keywords: biological control, cold storage, massive rearing, quality control

Procedia PDF Downloads 372

Authors: Alla V. Goncharova, Tatyana A. Karpenyuk, Yana S. Tsurkan, Rosa U. Beisembaeva, Togzhan D. Mukasheva, Ludmila V. Ignatova, Ramza Z. Berzhanova

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In this work the effect of culturing conditions of filamentous fungi Penicillium raistrickii, Penicillium anatolicum, Fusarium sp. on biomass yield, the content of total lipids and fatty acids was studied. It has been established that in time the process of lipids accumulation correlated with biomass growth of cultures, reaching maximum values in stationary growth phase. Biomass yield and accumulation of general lipids was increased by adding zinc to the culture medium. The more intensive accumulation of biomass and general lipids was observed at temperature 18°C. Lowering the temperature of culturing has changed the ratio of saturated: Unsaturated fatty acids in the direction of increasing the latter.

Keywords: biomass, culturing conditions, fungi, fatty acids (FA), growth dynamics, lipids

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Authors: Azizul Haque, Kamrun Nahar

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Field experiments were conducted to reduce the chemical fertilizers with the integrated use of straight and phospho- vermicompost with chemical fertilizers in T. aman-Boro rice cropping pattern at the BINA farm, Mymensingh during 2019-20. Six treatments were used in the experiment for both the crops. The treatments used for T. aman rice (Binadhan 17) with straight vermicompost were as follows: T1: Native soil fertility, T2: 100% N from Chemical Fertilizer (CF), T3:70%N from CF, T4: 30% N from vermicompost-3 + 70% N from CF and T5:30% N from vermicompost-4 + 70% N from CF and T6: 100% PKS only. The treatments of Boro rice (var. Binadhan -10) with phospho-vermicompost were: T1: Native soil fertility, T2: 100% NPKS from chemical fertilizer (CF), T3:75% NKS from CF (Non IPNS) with 1 t ha-1 Phospho-vermicompost (P-Vermicom), T4: 100% NKS (IPNS) with 2 t ha-1 P-Vermicom, T5: 100% NKS from CF (Non IPNS) with 2 t ha-1 P-Vermicom and T6: 100% NKS. The experiments were conducted in a Randomized Complete Block Design with three replications. The treatment T5 (5.5 t ha-1) gave maximum grain yield of T.aman rice followed by the treatment T4 (5.4 t ha-1). But the treatmentsT5, T4, and T2 gave identical grain yields of T. aman rice. Similar results were observed in case of straw yields of T. Aman rice. The result indicated that 70% N from CF with 30% N from either straight vermicompost-3 or straight vermicompost-4 gave comparable yield to the sole application of 100% N from CF alone. Therefore, 30% chemical fertilizers (N, P, K and S) could be saved with the integrated (IPNS) use of vermicompost-3 or vermicompost-4 in the cultivation of T. aman rice. Application of Phospho-vermicompost significantly influenced the yield and yield contributing characters of Boro rice (Binadhan-10). The treatment T4 (7.23.0 t ha-1) gave maximum grain yield of Boro rice followed by the treatments T2 and T5. But the treatments T2 and T5 produced statistically similar grain yields. The results from the treatment T4 (100% NKS (IPNS) with 2.0 t ha-1P-Vermicom) indicated that full demand of P could be met up from 2 t ha-1 Phospho-vermicompost with IPNS chemical fertilizers (NKS) which was sufficient for attaining the highest grain yield of Boro rice than that of the treatment T2 (100% NPKS from CF) and the treatmentT5 (100% NKS from CF (Non IPNS) + 2 t ha-1 Phospho-vermicompost). The results revealed that 100% P and substantial amount of N (21%), K (44.6%) and S (53.7%) fertilizers could be saved with the integrated use of Phospho-vermicompost in the cultivation of Boro rice. In case of Boro rice partial cost benefit analysis showed that the application of Phospho-vermicompost (@2 tha--1) with IPNS chemical fertilizes (NKS) gave higher return of Tk. 18,213 / - than that of only 100% chemical fertilizer. Therefore, use of Phospho-vermicompost was beneficial for the cultivation of Boro rice in combination with suitable dose of chemical fertilizers.

Keywords: phosphovermicompost, cropping pattern, rice yield, chemical fertilizer

Procedia PDF Downloads 103

Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: B. Medjahed, M. A. Didi, B. Guezzen

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This work concerns with the experimental study of the adsorption of the Ni(II) on bentonite. The effects of various parameters such as contact time, stirring rate, initial concentration of Ni(II), masse of clay, initial pH of aqueous solution and temperature on the adsorption yield, were carried out. The study of the effect of the ionic strength on the yield of adsorption was examined by the identification and the quantification of the present chemical species in the aqueous phase containing the metallic ion Ni(II). The adsorbed species were investigated by a calculation program using CHEAQS V. L20.1 in order to determine the relation between the percentages of the adsorbed species and the adsorption yield. The optimization process was carried out using 2³ factorial designs. The individual and combined effects of three process parameters, i.e. initial Ni(II) concentration in aqueous solution (2.10⁻³ and 5.10⁻³ mol/L), initial pH of the solution (2 and 6.5), and mass of bentonite (0.03 and 0.3 g) on Ni(II) adsorption, were studied.

Keywords: adsorption, bentonite, factorial design, Nickel(II)

Procedia PDF Downloads 159

Authors: Marilyn S. Painagan, Willie Jones B. Saliling

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This paper reports an optimized cropping calendar and land suitability for maize in North Cotabato derived from modeling crop productivity over time and space. Using Quantum GIS, eight representative soil types and 0.3o x 0.3o climate grids shapefiles were intersected to form thirty two pedoclimatic zones within the boundaries of the province. Surveys were done to ascertain crop performance and phenological properties on field. Based on these surveys, crop parameters were calibrated specific for a variety of maize. Soil properties and climatic data (daily precipitation, maximum and minimum temperatures) from pedoclimatic zones were loaded to the FAO Aquacrop Water Productivity Model along with the crop properties from field surveys to simulate yield from 1980 to 2010. The average yield per month was computed to come up with the month of planting having the highest and lowest probable yield in a year assuming that all lands were planted with maize. The yield attributes were visualized in the Quantum GIS environment. The study revealed that optimal cropping patterns varied across North Cotabato. Highest probable yield (8000 kg/ha) can be obtained when maize is planted on May and September (sandy clay-loam soils) in the northern part of the province while the lowest probable yield (1000 kg/ha) can be obtained when maize is planted on January, February and March (clay loam soils) at the northern part of the province. Yields are simulated on the basis of varieties currently planted by farmers of North Cotabato. The resulting maps suggest where and when maize is most suitable to achieve high yields. There is a need to ground truth and validate the cropping calendar on field.

Keywords: aquacrop, quantum GIS, maize, cropping calendar, water productivity

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

Procedia PDF Downloads 119

Authors: Taramani Yadav, Gajender Kumar, R. K. Yadav, H. S. Jat

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Soil Salinity and irrigation water salinity is critical threat to enhance agricultural food production to full fill the demand of billion plus people worldwide. Salt affected soils covers 6.73 Mha in India and ~1000 Mha area around the world. Irrigation scheduling of saline water is the way to ensure food security in salt affected areas. Research experiment was conducted at ICAR-Central Soil Salinity Research Institute, Experimental Farm, Nain, Haryana, India with 36 treatment combinations in double split plot design. Three sets of treatments consisted of (i) three regimes of irrigation viz., 60, 80 and 100% (I1, I2 and I3, respectively) of crop ETc (crop evapotranspiration at identified respective stages) in main plot; (ii) four levels of irrigation water salinity (sub plot treatments) viz., 2, 4, 8 and 12 dS m-1 (iii) applications of two PBRs along with control (without PBRs) i.e. salicylic acid (G1; 1 mM) and thiourea (G2; 500 ppm) as sub-sub plot treatments. Grain yield of wheat (Triticum aestivum) was increased with less amount of high salt loaded irrigation water at the same level of salinity (2 dS m-1), the trend was I3>I2>I1 at 2 dS m-1 with 8.10 and 17.07% increase at 80 and 100% ETc, respectively compared to 60% ETc. But contrary results were obtained by increasing amount of irrigation water at same level of highest salinity (12 dS m-1) showing following trend; I1>I2>I3 at 12 dS m-1 with 9.35 and 12.26% increase at 80 and 60% ETc compared to 100% ETc. Enhancement in grain yield of wheat (Triticum aestivum) is not need to increase amount of irrigation water under saline condition, with salty irrigation water less amount of irrigation water gave the maximum wheat (Triticum aestivum) grain yield.

Keywords: Irrigation Scheduling, Saline Environment, Triticum aestivum, Yield

Procedia PDF Downloads 144

Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

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Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

Procedia PDF Downloads 154

Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

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Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

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Authors: Takele Nemomsa, Girma Mamo, Tesfaye Balemi

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Climate change refers to a change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or variance of its properties and that persists for an extended period, typically decades or longer. In Ethiopia; Maize production in relation to climate change at regional and sub- regional scales have not been studied in detail. Thus, this study was aimed to analyse the impact of climate change on maize yield in Ambo Districts, Central Ethiopia. To this effect, weather data, soil data and maize experimental data for Arganne hybrid were used. APSIM software was used to investigate the response of maize (Zea mays) yield to different agronomic management practices using current and future (2020s–2080s) climate data. The climate change projections data which were downscaled using SDSM were used as input of climate data for the impact analysis. Compared to agronomic practices the impact of climate change on Arganne in Central Ethiopia is minute. However, within 2020s-2080s in Ambo area; the yield of Arganne hybrid is projected to reduce by 1.06% to 2.02%, and in 2050s it is projected to reduce by 1.56 While in 2080s; it is projected to increase by 1.03% to 2.07%. Thus, to adapt to the changing climate; farmers should consider increasing plant density and fertilizer rate per hectare.

Keywords: APSIM, downscaling, response, SDSM

Procedia PDF Downloads 383

Authors: Ahmed Boucherit, Hussein Khalaf, Eduardo Paredes, José Luis Todolí

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Solvent extraction studies of molybdenum (VI) with two organophosphorus reagents namely TBP and D2EHPA have been carried out from aqueous acidic solutions of HCl, H2SO4 and H3PO4 under microwave irradiations. The extraction efficiencies of the investigated extractants in the extraction of molybdenum (Vl) were compared. Extraction yield was found unchanged when microwave power varied in the range 20-100 Watts from H2SO4 or H3PO4 but it decreases in the range 20-60 Watts and increases in the range 60-100 Watts when TBP is used for extraction of molybdenum (VI) from 1 M HCl solutions. Extraction yield of molybdenum (VI) was found higher with TBP for HCl molarities greater than 1 M than with D2EHPA for H3PO4 molarities lower than 1 M. Extraction yield increases with HCl molarities in the range 0.50 - 1.80 M but it decreases with the increase in H2SO4 and H3PO4 molarities in the range of 0.05 - 1 M and 0.50 - 1 M, respectively.

Keywords: extraction, molybdenum, microwave, solvent

Procedia PDF Downloads 642

Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Harneet Walia, Morteza Zihayat

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Customer acquisition has become one of the critical issues of any business in the 21st century; having a healthy customer base is the essential asset of the bank business. Term deposits act as a major source of cheap funds for the banks to invest and benefit from interest rate arbitrage. To attract customers, the marketing campaigns at most financial institutions consist of multiple outbound telephonic calls with more than one contact to a customer which is a very time-consuming process. Therefore, customized direct marketing has become more critical than ever for attracting new clients. As customer acquisition is becoming more difficult to archive, having an intelligent and redefined list is necessary to sell a product smartly. Our aim of this research is to increase the effectiveness of campaigns by predicting customers who will most likely subscribe to the fixed deposit and suggest the most suitable month to reach out to customers. We design a Time Aware Upsell Prediction Framework (TAUPF) using two different approaches, with an aim to find the best approach and technique to build the prediction model. TAUPF is implemented using Upsell Prediction Approach (UPA) and Clustered Upsell Prediction Approach (CUPA). We also address the data imbalance problem by examining and comparing different methods of sampling (Up-sampling and down-sampling). Our results have shown building such a model is quite feasible and profitable for the financial institutions. The Time Aware Upsell Prediction Framework (TAUPF) can be easily used in any industry such as telecom, automobile, tourism, etc. where the TAUPF (Clustered Upsell Prediction Approach (CUPA) or Upsell Prediction Approach (UPA)) holds valid. In our case, CUPA books more reliable. As proven in our research, one of the most important challenges is to define measures which have enough predictive power as the subscription to a fixed deposit depends on highly ambiguous situations and cannot be easily isolated. While we have shown the practicality of time-aware upsell prediction model where financial institutions can benefit from contacting the customers at the specified month, further research needs to be done to understand the specific time of the day. In addition, a further empirical/pilot study on real live customer needs to be conducted to prove the effectiveness of the model in the real world.

Keywords: customer acquisition, predictive analysis, targeted marketing, time-aware analysis

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Authors: Abolfazl Hedayatipoor, Mohammad Younesi Alamooti

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In order to study the effect of conservative tillage on a number of physical properties of soil and the yield of rainfed wheat, an experiment in the form of a randomized complete block design (RCBD) with three replications was conducted in a field in Aliabad County, Iran. The study treatments included: T1) Conventional method, T2) Combined moldboard plow method, T3) Chisel-packer method, and T4) Direct planting method. During early October, the study soil was prepared based on these treatments in a field which was used for rainfed wheat farming in the previous year. The apparent specific gravity of soil, weighted mean diameter (WMD) of soil aggregates, soil mechanical resistance, and soil permeability were measured. Data were analyzed in MSTAT-C. Results showed that the tillage practice had no significant effect on grain yield (p < 0.05). Soil permeability was 10.9, 16.3, 15.7 and 17.9 mm/h for T1, T2, T3 and T4, respectively.

Keywords: rainfed agriculture, conservative tillage, energy consumption, wheat

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Authors: Bunjirtluk Jintaridth

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Utilization of silicon for sustainable rice cultivation in acid sulfate soils was studied for 2 years. The study was conducted on Rungsit soils in Amphoe Tanyaburi, Pathumtani Province. The objectives of this study were to assess the effect of high quality organic fertilizer in combination with silicon and chemical fertilizer on rice yield, chemical soil properties after using soil amendments, and also to assess the economic return. A Randomized Complete Block Design (RCBD) with 10 treatments and 3 replications were employed. The treatments were as follows: 1) control 2) chemical fertilizer (recommended by Land Development Department, LDD 3) silicon 312 kg/ha 4) high quality organic fertilizer at 1875 kg/ha (the recommendation rate by LDD) 5) silicon 156 kg/ha in combination with high quality organic fertilizer 1875 kg/ha 6) silicon at the 312 kg/ha in combination with high quality organic fertilizer 1875 kg/ha 7) silicon 156 kg/ha in combination with chemical fertilizer 8) silicon at the 312 kg/ha in combination with chemical fertilizer 9) silicon 156 kg/ha in combination with ½ chemical fertilizer rate, and 10) silicon 312 kg/ha in combination with ½ chemical fertilizer rate. The results of 2 years indicated the treatment tended to increase soil pH (from 5.1 to 4.7-5.5), percentage of organic matter (from 2.43 to 2.54 - 2.94%); avail. P (from 7.5 to 7-21 mg kg-1 P; ext. K (from 616 to 451-572 mg kg-1 K), ext Ca (from 1962 to 2042.3-4339.7 mg kg-1 Ca); ext Mg (from 1586 to 808.7-900 mg kg-1 Mg); but decrease the ext. Al (from 2.56 to 0.89-2.54 cmol kg-1 Al. Two years average of rice yield, the highest yield was obtained from silicon 156 kg/ha application in combination with high quality organic fertilizer 300 kg/rai (3770 kg/ha), or using silicon at the 312 kg/ha combination with high quality organic fertilizer 300 kg/rai. (3,750 kg/ha). It was noted that chemical fertilizer application with 156 and 312 kg/ha silicon gave only 3,260 และ 3,133 kg/ha, respectively. On the other hand, half rate of chemical fertilizer with 156 and 312 kg/ha with silicon gave the yield of 2,934 และ 3,218 kg/ha, respectively. While high quality organic fertilizer only can produce 3,318 kg/ha as compare to rice yield of 2,812 kg/ha from control. It was noted that the highest economic return was obtained from chemical fertilizer treated plots (886 dollars/ha). Silicon application at the rate of 156 kg/ha in combination with high quality organic fertilizer 1875 kg/ha gave the economic return of 846 dollars/ha, while 312 kg/ha of silicon with chemical fertilizer gave the lowest economic return (697 dollars/ha).

Keywords: rice, high quality organic fertilizer, acid sulfate soil, silicon

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Authors: Pragnyashree Mishra, Shradhanjali Mohapatra

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The present investigation is carried out to know the effect of foliar feeding of nitrogen and gibberellic acid on vegetative growth, flowering behaviour and yield of calendula variety ‘Golden Emporer’. The experiment was laid out in RBD in rabi season of 2013-14. There are 16 treatments are taken at different level such as nitrogen (at 0%,1%,2%,3%) and GA3 (at 50 ppm,100ppm,150 ppm). Among them maximum height at bud initiation stage was obtained at 3% nitrogen (27.00 cm) and at 150 ppm GA3 (26.5 cm), fist flowering was obtained at 3% nitrogen(60.00 days) and at 150 ppm GA3 (63.75 days), maximum flower stalk length was obtained at 3% nitrogen(3.50 cm) and at 150 ppm GA3 (5.42 cm),maximum duration of flowering was obtained at 3% nitrogen(46.00 days) and at 150 ppm GA3 (46.50days), maximum number of flower was obtained at 3% nitrogen (89.00per plant) and at 150 ppm GA3 (83.50 per plant), maximum flower weight was obtained at 3% nitrogen(1.25 gm per flower) and at 150 ppm GA3 (1.50 gm per flower), maximum yield was was obtained at 3% nitrogen (110.00 gm per plant) and at 150 ppm GA3 (105.00gm per plant) and minimum of all character was obtained when 0% nitrogen0 ppm GA3. All interaction between nitrogen and GA3 was found in significant except the yield .

Keywords: calendula, golden emporer, GA3, nitrogen and gibberellic acid

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Authors: Tanu Aneja, Harsha Malaviya

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Capturing pose information in cricket shots poses several challenges, such as low-resolution videos, noisy data, and joint occlusions caused by the nature of the shots. In response to these challenges, we propose a CondDGConv-based framework specifically for cricket shot prediction. By analyzing the spatial-temporal relationships in batsman shot sequences from an annotated 2D cricket dataset, our model achieves a 97% accuracy in predicting shot types. This performance is made possible by conditioning the graph network on batsman 2D poses, allowing for precise prediction of shot outcomes based on pose dynamics. Our approach highlights the potential for enhancing shot prediction in cricket analytics, offering a robust solution for overcoming pose-related challenges in sports analysis.

Keywords: action recognition, cricket. sports video analytics, computer vision, graph convolutional networks

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Authors: Yeshona Sewsynker

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This study presents three microwave-assisted alkalic salt pretreatments to enhance delignification and enzymatic saccharification of corn cobs. The effects of process parameters of salt concentration (0-15%), microwave power intensity (0-800 W) and pretreatment time (2-8 min) on reducing sugar yield from corn cobs were investigated. Pretreatment models were developed with the high coefficient of determination values (R2>0.85). Optimization gave a maximum reducing sugar yield of 0.76 g/g. Scanning electron microscopy (SEM) and Fourier Transform Infrared analysis (FTIR) showed major changes in the lignocellulosic structure after pretreatment. A 7-fold increase in the sugar yield was observed compared to previous reports on the same substrate. The developed pretreatment strategy was effective for enhancing enzymatic saccharification from lignocellulosic wastes for microbial biofuel production processes and value-added products.

Keywords: pretreatment, lignocellulosic biomass, enzymatic hydrolysis, delignification

Procedia PDF Downloads 499

Authors: Shivahari Revathi Venkateswaran

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Segmenting customers plays a significant role in churn prediction. It helps the marketing team with proactive and reactive customer retention. For the reactive retention, the retention team reaches out to customers who already showed intent to disconnect by giving some special offers. When coming to proactive retention, the marketing team uses churn prediction model, which ranks each customer from rank 1 to 100, where 1 being more risk to churn/disconnect (high ranks have high propensity to churn). The churn prediction model is built by using XGBoost model. However, with the churn rank, the marketing team can only reach out to the customers based on their individual ranks. To profile different groups of customers and to frame different marketing strategies for targeted groups of customers are not possible with the churn ranks. For this, the customers must be grouped in different segments based on their profiles, like demographics and other non-controllable attributes. This helps the marketing team to frame different offer groups for the targeted audience and prevent them from disconnecting (proactive retention). For segmentation, machine learning approaches like k-mean clustering will not form unique customer segments that have customers with same attributes. This paper finds an alternate approach to find all the combination of unique segments that can be formed from the user attributes and then finds the segments who have uplift (churn rate higher than the baseline churn rate). For this, search algorithms like fast search and recursive search are used. Further, for each segment, all customers can be targeted using individual churn ranks from the churn prediction model. Finally, a UI (User Interface) is developed for the marketing team to interactively search for the meaningful segments that are formed and target the right set of audience for future marketing campaigns and prevent them from disconnecting.

Keywords: churn prediction modeling, XGBoost model, uplift segments, proactive marketing, search algorithms, retention, k-mean clustering

Procedia PDF Downloads 71

Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

Procedia PDF Downloads 113

Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

Procedia PDF Downloads 86

Authors: E. O. Adeleye

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Field experiments were conducted to investigate the effect of soil tillage techniques and poultry manure application on the soil properties and yam (Dioscorea rotundata) performance in Ondo, southwestern Nigeria for two farming seasons. Five soil tillage techniques, namely ploughing (P), ploughing plus harrowing (PH), manual ridging (MR), manual heaping (MH) and zero-tillage (ZT) each combined with and without poultry manure at the rate of 10 tha-1 were investigated. Data were obtained on soil properties, nutrient uptake, growth and yield of yam. Soil moisture content, bulk density, total porosity and post harvest soil chemical characteristics were significantly (p>0.05) influenced by soil tillage-manure treatments. Addition of poultry manure to the tillage techniques in the study increased soil total porosity, soil moisture content and reduced soil bulk density. Poultry manure improved soil organic matter, total nitrogen, available phosphorous, exchangeable Ca, k, leaf nutrients content of yam, yam growth and tuber yield relative to tillage techniques plots without poultry manure application. It is concluded that the possible deleterious effect of tillage on soil properties, growth and yield of yam on an alfisol in southwestern Nigeria can be reduced by combining tillage with poultry manure.

Keywords: poultry manure, tillage, soil chemical properties, yield

Procedia PDF Downloads 446