scholarly journals Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

2018 ◽  
Author(s):  
A.A Adnan ◽  
J. Diels ◽  
J.M. Jibrin ◽  
A.Y. Kamara ◽  
P. Craufurd ◽  
...  

AbstractMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha−1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok


2019 ◽  
Vol 1 (4) ◽  
pp. 37
Author(s):  
Yulizar Fikri ◽  
Ali Anis

This study aims to determine the analysis of the determinants of the composite stock price index in Indonesia. The independent variables in this study are inflation as X1, foreign exchange reserves as X2, exchange rates as X3, and economic growth as X4, and the dependent variable of the composite stock price index as Y. The data used are secondary data in the formof time series data from 2010Q1 until 2019Q2, with data collection techniques, namely documentation from Bank Indonesia publications, the Central Statistics Agency, investing. comsite and library research. The research methods used are: (1) Multiple Linear Regression, (2) Classical Assumption Test (3) coefficient of determination. The results of this study indicate that:(1) inflation does not significantly influence the composite stock price index. (2) foreign exchange reserves have a significant positive effect on the composite stock price index. (3) the rupiah exchange rate has an influence on the composite stock price index and (4) economic growth hasno significant effect on the composite stock price index.


2021 ◽  
Vol 12 (1) ◽  
pp. 52-65
Author(s):  
Armalinda Armalinda

This study aims to determine how much influence the Debt to Assets Ratio (DAR) and Debt to Equity Ratio (DER) have on the Return on Equity (ROE) of PT Bank Mandiri Tbk which are listed on the Indonesia Stock Exchange. The research design used in this research is associative/quantitative research. The population in this study is the annual financial statements of PT. Bank Mandiri Tbk for the period 2012-2019, while the sample was taken using time series data, namely the annual financial statements of PT. Bank Mandiri Tbk for the period 2012-2019 which consists of balance statements, income statements, and cash flow from funding activities from 2012 to 2019. The result of the coefficient of determination (R Square) is 0.813. This figure means that 0.813 or 81.3% of the diversity of data from financial performance data can be explained by the two independent variables, namely the Debt to Asset Ratio and the Debt to Equity Ratio. While the rest (1-0.813 = 0.817) or 18.7% is explained by other factors outside the study. The results of statistical tests show that the Asset Ratio and Debt to Equity Ratio together (simultaneously) have an effect on financial performance (Return on Equity).


2019 ◽  
Vol 3 (2) ◽  
pp. 124-139
Author(s):  
Juliana Putri ◽  
Salman Alfarisi

This study aims to determine the effect of the equivalent rate of profit sharing, interest rates on BPR deposits and the number of BPRS Offices on the number of mudharabah iB deposit customers at BPRS in Indonesia. The research method used is quantitative descriptive research using secondary data in the form of financial reports published by OJK in Sharia Banking Statistics (SPS) and Indonesian Banking Statistics (SPI) with time series data in the period of 2016-2018. The sample in this study all BPRS in Indonesia is 168 BPRS. Analysis of research using multiple linear regression analysis using application or supporting software namely PASW (Predictive Analytics SoftWare) Statistics 18, the results of research, it can be concluded that: 1) Equivalent rate of profit sharing (X1) has a significant negative effect of iB mudharabah deposit customers, 2) Variable interest rates on BPR deposits (X2) do not affect the number of mudharabah iB deposit customers. 3) The variable number of BPRS offices (X3) has a significant positive effect on the number of mudharabah iB deposit customers. 4) The coefficient of determination obtained is 0.586 or 58.6%. which means that 58.6% causes variable variable number of iB mudharabah (Y) deposit customers can be influenced by the equivalent rate of profit sharing, the level of BPR deposit rates and the number of BPRS offices, while the remaining 41.4% is influenced by other factors not included in the study.


Author(s):  
José Ramón Cancelo ◽  
Antoni Espasa

The authors elaborate on three basic ideas that should guide the implementation of business intelligence tools. First, the authors advocate for closing the gap between structured information and contextual information. Second, they emphasize the need for adopting the point of view of the organization to assess the relevance of any proposal. In the third place, they remark that any new tool is expected to become a relevant instrument to enhance the learning of the organization and to generate explicit knowledge. To illustrate their point, they discuss how to set up a forecasting support system to predict electricity consumption that converts raw time series data into market intelligence, to meet the needs of a major organization operating at the Spanish electricity markets.


2003 ◽  
Vol 51 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Z. Berzsenyi

The research agenda for crop science in the 21st century will depend largely on whether the present conditions regarding the global food surplus continue, or whether a food scarcity recurs. Crop production research is based chiefly on small-plot field experiments, the majority of which are either long-term experiments or experiments set up to investigate the specific agronomic responses of Martonvásár maize hybrids and wheat varieties. The sustainability of crop production is examined in long-term experiments. The agronomic responses of maize hybrids and wheat varieties are studied at various levels of biological organisation. Growth analysis facilitates the exact characterisation of agronomic responses and the grouping of response effects and types using multivariable methods. Continued experimentation coupled with crop simulation models and decision support systems are an ever more useful framework for analysing the complexity of agricultural systems.


2010 ◽  
Vol 10 (9) ◽  
pp. 1965-1975 ◽  
Author(s):  
T. Bleier ◽  
C. Dunson ◽  
C. Alvarez ◽  
F. Freund ◽  
R. Dahlgren

Abstract. Analysis of the 2007 M5.4 Alum Rock earthquake near San José California showed that magnetic pulsations were present in large numbers and with significant amplitudes during the 2 week period leading up the event. These pulsations were 1–30 s in duration, had unusual polarities (many with only positive or only negative polarities versus both polarities), and were different than other pulsations observed over 2 years of data in that the pulse sequence was sustained over a 2 week period prior to the quake, and then disappeared shortly after the quake. A search for the underlying physics process that might explain these pulses was was undertaken, and one theory (Freund, 2002) demonstrated that charge carriers were released when various types of rocks were stressed in a laboratory environment. It was also significant that the observed charge carrier generation was transient, and resulted in pulsating current patterns. In an attempt to determine if this phenomenon occurred outside of the laboratory environment, the authors scaled up the physics experiment from a relatively small rock sample in a dry laboratory setting, to a large 7 metric tonne boulder comprised of Yosemite granite. This boulder was located in a natural, humid (above ground) setting at Bass Lake, Ca. The boulder was instrumented with two Zonge Engineering, Model ANT4 induction type magnetometers, two Trifield Air Ion Counters, a surface charge detector, a geophone, a Bruker Model EM27 Fourier Transform Infra Red (FTIR) spectrometer with Sterling cycle cooler, and various temperature sensors. The boulder was stressed over about 8 h using expanding concrete (Bustartm), until it fractured into three major pieces. The recorded data showed surface charge build up, magnetic pulsations, impulsive air conductivity changes, and acoustical cues starting about 5 h before the boulder actually broke. These magnetic and air conductivity pulse signatures resembled both the laboratory rock stressing results and the 30 October 2007 M5.4 Alum Rock earthquake field data. The second part of this paper examined other California earthquakes, prior to the Alum Rock earthquake, to see if magnetic pulsations were also present prior to those events. A search for field examples of medium earthquakes was performed to identify earthquakes where functioning magnetometers were present within 20 km, the expected detection range of the magnetometers. Two earthquakes identified in the search included the 12 August 1998 M5.1 San Juan Bautista (Hollister Ca.) earthquake and the 28 September 2004 M6.0 Parkfield Ca. earthquake. Both of these data sets were recorded using EMI Corp. Model BF4 induction magnetometers, installed in equipment owned and operated by UC Berkeley. Unfortunately, no air conductivity or IR data were available for these earthquake examples. This new analysis of old data used the raw time series data (40 samples per s), and examined the data for short duration pulsations that exceeded the normal background noise levels at each site, similar to the technique used at Alum Rock. Analysis of Hollister magnetometer, positioned 2 km from the epicenter, showed a significant increase in magnetic pulsations above quiescient threshold levels several weeks prior, and especially 2 days prior to the quake. The pattern of positive and negative pulsations observed at Hollister, were similar, but not identical to Alum Rock in that the pattern of pulsations were interspersed with Pc 1 pulsation trains, and did not start 2 weeks prior to the quake, but rather 2 days prior. The Parkfield data (magnetometer positioned 19 km from the epicenter) showed much smaller pre-earthquake pulsations, but the area had significantly higher conductivity (which attenuates the signals). More interesting was the fact that significant pulsations occurred between the aftershock sequences of quakes as the crustal stress patterns were migrating. Comparing laboratory, field experiments with a boulder, and earthquake events, striking similarities were noted in magnetic pulsations and air conductivity changes, as well as IR signals (where instrumented). More earthquake samples, taken with the appropriate detectors and within 10–15 km proximity to large (>M5) earthquakes, are still needed to provide more evidence to understand the variability between earthquakes and various electromagnetic signals detected prior to large earthquakes.


2017 ◽  
Vol 33 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Housseyn Bouzeria ◽  
Abderrahmane N. Ghenim ◽  
Kamel Khanchoul

AbstractIn this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.


2021 ◽  
Vol 35 (2) ◽  
pp. 115-122
Author(s):  
Mohan Mahanty ◽  
K. Swathi ◽  
K. Sasi Teja ◽  
P. Hemanth Kumar ◽  
A. Sravani

COVID-19 pandemic shook the whole world with its brutality, and the spread has been still rising on a daily basis, causing many nations to suffer seriously. This paper presents a medical stance on research studies of COVID-19, wherein we estimated a time-series data-based statistical model using prophet to comprehend the trend of the current pandemic in the coming future after July 29, 2020 by using data at a global level. Prophet is an open-source framework discovered by the Data Science team at Facebook for carrying out forecasting based operations. It aids to automate the procedure of developing accurate forecasts and can be customized according to the use case we are solving. The Prophet model is easy to work because the official repository of prophet is live on GitHub and is open for contributions and can be fitted effortlessly. The statistical data presented on the paper refers to the number of daily confirmed cases officially for the period January 22, 2020, to July 29, 2020. The estimated data produced by the forecast models can then be used by Governments and medical care departments of various countries to manage the existing situation, thus trying to flatten the curve in various nations as we believe that there is minimal time to do this. The inferences made using the model can be clearly comprehended without much effort. Furthermore, it tries to give an understanding of the past, present, and future trends by showing graphical forecasts and statistics. Compared to other models, prophet specifically holds its own importance and innovativeness as the model is fully automated and generates quick and precise forecasts that can be tunable additionally.


Author(s):  
Pega Saputra

<p><em>This study describes the influence of SBI interest rate on the rupiah at Bank Indonesia studies. The method in this research is descriptive method with quantitative approach. Determination of the sample is based on time series data 2009-2015 period by using saturation sampling methods as many as 84 samples. This research was conducted at Bank Indonesia has the sole purpose of achieving and maintaining stability in the rupiah. This study uses simple linear regression analysis which includes the classical assumption and hypothesis testing in the form of the coefficient of determination (r</em><em>2</em><em>) and the partial test (t test). The results showed that the interest rate significantly influence the exchange rate. that the null hypothesis is rejected and the alternative hypothesis is accepted.</em></p>


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