scholarly journals Evaluating irrigated rice yields in Japan within the Climate Zonation Scheme of the Global Yield Gap Atlas

2021 ◽  
pp. 1-12
Author(s):  
S. Ishikawa ◽  
T. Nakashima ◽  
T. Iizumi ◽  
M. C. Hare
Keyword(s):  
Production Capacity ◽  
Aridity Index ◽  
Data Sets ◽  
Yield Gap ◽  
Yield Data ◽  
Irrigated Rice ◽  
Rice Yields ◽  
The Stability ◽  
Temperature Seasonality ◽  
Government Statistics

Abstract The Global Yield Gap Atlas (GYGA) is an international project that addresses global food production capacity in the form of yield gaps (Yg). The GYGA project is unique in employing its original Climate Zonation Scheme (CZS) composed of three indexed factors, i.e. Growing Degree Days (GDD) related to temperature, Aridity Index (AI) related to available water and Temperature Seasonality (TS) related to annual temperature range, creating 300 Climate Zones (CZs) theoretically across the globe. In the present study, the GYGA CZs were identified for Japan on a municipality basis and analysis of variance (ANOVA) was performed on irrigated rice yield data sets, equating to actual yields (Ya) in the GYGA context, from long-term government statistics. The ANOVA was conducted for the data sets over two decades between 1994 and 2016 by assigning the GDD score of 6 levels and the TS score of 2 levels as fixed factors. Significant interactions with respect to Ya were observed between GDD score and TS score for 13 years out of 21 years implying the existence of favourable combinations of the GDD score and the TS score for rice cultivation. The implication was also supported by the observation with Yg. The lower values of coefficient of variance obtained from the CZs characterized by medium GDD scores indicated the stability over time of rice yields in these areas. These findings suggest a possibility that the GYGA-CZS can be recognized as a tool suitable to identify favourable CZs for growing crops.

Hybrid Neural Models For Rice Yields Times Forecasting

2012 ◽  
Author(s):  
Ruhaidah Samsudin ◽  
Puteh Saad ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Data Sets ◽  
Ann Model ◽  
Yield Data ◽  
Rice Yields ◽  
Artificial Neural Network Ann

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

Importance of basic cultivation techniques to increase irrigated rice yields in Tanzania

2017 ◽  
Vol 15 (4) ◽  
pp. 847-859 ◽  
Author(s):  
Nobuhito Sekiya ◽  
Motonori Tomitaka ◽  
Nobuaki Oizumi ◽  
Adam Gabriel Pyuza ◽  
Richard James Shayo ◽  
...  
Keyword(s):  
Irrigated Rice ◽  
Rice Yields ◽  
Cultivation Techniques

A Statistical Test for the Stability of Covariance Structure

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Nur Syahidah Yusoff ◽  
Maman Abdurachman Djauhari
Keyword(s):  
Covariance Matrix ◽  
Complex Structure ◽  
Covariance Structure ◽  
Statistical Test ◽  
Dispersion Measure ◽  
Data Sets ◽  
Scalar Representation ◽  
Multivariate Dispersion ◽  
Sample Covariance ◽  
The Stability

The stability of covariance matrix is a major issue in multivariate analysis. As can be seen in the literature, the most popular and widely used tests are Box M-test and Jennrich J-test introduced by Box in 1949 and Jennrich in 1970, respectively. These tests involve determinant of sample covariance matrix as multivariate dispersion measure. Since it is only a scalar representation of a complex structure, it cannot represent the whole structure. On the other hand, they are quite cumbersome to compute when the data sets are of high dimension since they do not only involve the computation of determinant of covariance matrix but also the inversion of a matrix. This motivates us to propose a new statistical test which is computationally more efficient and, if it is used simultaneously with M-test or J-test, we will have a better understanding about the stability of covariance structure. An example will be presented to illustrate its advantage

Controlled Freezing of Nonideal Solutions With Application to Cryosurgical Processes

1991 ◽  
Vol 113 (4) ◽  
pp. 430-437 ◽  
Author(s):  
H. M. Budman ◽  
J. Dayan ◽  
A. Shitzer
Keyword(s):  
Cooling Rate ◽  
Freezing Process ◽  
Data Sets ◽  
Tracking Accuracy ◽  
Loop Control ◽  
Set Point ◽  
New Device ◽  
Loop Control System ◽  
The Stability ◽  
Controlled Freezing

Success of a cryosurgical procedure, i.e., maximal cell destruction, requires that the cooling rate be controlled during the freezing process. Standard cryosurgical devices are not usually designed to perform the required controlled process. In this study, a new cryosurgical device was developed which facilitates the achievement of a specified cooling rate during freezing by accurately controlling the probe temperature variation with time. The new device has been experimentally tested by applying it to an aqueous solution of mashed potatoes. The temperature field in the freezing medium, whose thermal properties are similar to those of biological tissue, was measured. The cryoprobe temperature was controlled according to a desired time varying profile which was assumed to maximize necrosis. The tracking accuracy and the stability of the closed loop control system were investigated. It was found that for most of the time the tracking accuracy was excellent and the error between the measured probe temperature and the desired set point is within ±0.4°C. However, noticeable deviations from the set point occurred due to the supercooling phenomenon or due to the instability of the liquid nitrogen boiling regime in the cryoprobe. The experimental results were compared to those obtained by a finite elements program and very good agreement was obtained. The deviation between the two data sets seems to be mainly due to errors in positioning of the thermocouple junctions in the medium.

Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R199-R217 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Genyang Tang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Anelastic Attenuation ◽  
Seismic Resolution ◽  
Text Filtering ◽  
The Stability ◽  
Reflectivity Inversion

Seismic data are nonstationary due to subsurface anelastic attenuation and dispersion effects. These effects, also referred to as the earth’s [Formula: see text]-filtering effects, can diminish seismic resolution. We previously developed a method of nonstationary sparse reflectivity inversion (NSRI) for resolution enhancement, which avoids the intrinsic instability associated with inverse [Formula: see text] filtering and generates superior [Formula: see text] compensation results. Applying NSRI to data sets that contain multiples (addressing surface-related multiples only) requires a demultiple preprocessing step because NSRI cannot distinguish primaries from multiples and will treat them as interference convolved with incorrect [Formula: see text] values. However, multiples contain information about subsurface properties. To use information carried by multiples, with the feedback model and NSRI theory, we adapt NSRI to the context of nonstationary seismic data with surface-related multiples. Consequently, not only are the benefits of NSRI (e.g., circumventing the intrinsic instability associated with inverse [Formula: see text] filtering) extended, but also multiples are considered. Our method is limited to be a 1D implementation. Theoretical and numerical analyses verify that given a wavelet, the input [Formula: see text] values primarily affect the inverted reflectivities and exert little effect on the estimated multiples; i.e., multiple estimation need not consider [Formula: see text] filtering effects explicitly. However, there are benefits for NSRI considering multiples. The periodicity and amplitude of the multiples imply the position of the reflectivities and amplitude of the wavelet. Multiples assist in overcoming scaling and shifting ambiguities of conventional problems in which multiples are not considered. Experiments using a 1D algorithm on a synthetic data set, the publicly available Pluto 1.5 data set, and a marine data set support the aforementioned findings and reveal the stability, capabilities, and limitations of the proposed method.

scholarly journals Data-collection strategy for challenging native SAD phasing

2016 ◽  
Vol 72 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Vincent Olieric ◽  
Tobias Weinert ◽  
Aaron D. Finke ◽  
Carolin Anders ◽  
Dianfan Li ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sets ◽  
Yield Data ◽  
Guide Rna ◽  
Multiple Data ◽  
Data Collection Strategy ◽  
Sad Phasing ◽  
Anomalous Signal ◽  
Multiple Data Sets ◽  
Low X

Recent improvements in data-collection strategies have pushed the limits of native SAD (single-wavelength anomalous diffraction) phasing, a method that uses the weak anomalous signal of light elements naturally present in macromolecules. These involve the merging of multiple data sets from either multiple crystals or from a single crystal collected in multiple orientations at a low X-ray dose. Both approaches yield data of high multiplicity while minimizing radiation damage and systematic error, thus ensuring accurate measurements of the anomalous differences. Here, the combined use of these two strategies is described to solve cases of native SAD phasing that were particular challenges: the integral membrane diacylglycerol kinase (DgkA) with a low Bijvoet ratio of 1% and the large 200 kDa complex of the CRISPR-associated endonuclease (Cas9) bound to guide RNA and target DNA crystallized in the low-symmetry space groupC2. The optimal native SAD data-collection strategy based on systematic measurements performed on the 266 kDa multiprotein/multiligand tubulin complex is discussed.

Methodology for substantiating the indicators of economic entities for solving the problems of determining their reliability and stability in the organizational network

Innotrans ◽  
2021 ◽  
pp. 14-20
Author(s):  
Vasiliy M. Say ◽  
◽  
Dariya Yu. Gorelova ◽  
Keyword(s):  
Regression Analysis ◽  
Economic Activity ◽  
Production Capacity ◽  
Control Points ◽  
Microsoft Excel ◽  
High Performing ◽  
Organizational Network ◽  
Correlation And Regression Analysis ◽  
Analysis Package ◽  
The Stability

The article proposes a method for selecting parameters based on correlation and regression analysis in order to further include significant variables in the equation of stability of an enterprise of an organizational network. The sample consists of ten businesses and ten hypothetical control points. Three subsystems are accepted for consideration: technical and economic (nine indicators), organizational and legal (seven indicators) and human resources (seven indicators of economic activity of a legal entity). The proposed methodology allows us to justify significant indicators for each of the three subsystems for determining the stability of network elements (enterprises), depending on the overall trend of production capacity: a successfully developing enterprise; a high-performing enterprise; a low-performing one. This method allows us to predict the possible volume of deliveries from enterprises - elements of the network. To achieve this goal, it is proposed to use the apparatus of multivariate analysis, in particular, correlation and regression analysis, which allows us to determine the presence of links and their closeness between factors, as well as the degree of influence on the studied value. The calculations were performed using the “Correlation” and “Regression” tools included in the “Data Analysis” package of the Microsoft Excel program.

scholarly journals Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

2020 ◽  
Vol 21 (6) ◽  
pp. 1263-1290
Author(s):  
Gerald Blasch ◽  
Zhenhai Li ◽  
James A. Taylor
Keyword(s):  
Crop Production ◽  
Pattern Analysis ◽  
Production Systems ◽  
Principal Component ◽  
Temporal Variations ◽  
Data Sets ◽  
Yield Data ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Cereal Fields

Abstract Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

The control and ‘fitness for purpose’ of UK official statistics

2019 ◽  
pp. 119-132
Author(s):  
David Rhind
Keyword(s):  
New Technologies ◽  
Data Sets ◽  
Official Statistics ◽  
Politics And Government ◽  
The Public ◽  
Economic Statistics ◽  
Institutional Structures ◽  
The Uk ◽  
Government Statistics ◽  
The Impact

This chapter describes the evolution of UK Official Statistics over an 80 year period under the influence of personalities, politics and government policies, new user needs and changing technology. These have led to changing institutional structures – such as the Statistics Commission - and periodic oscillations in what statistics are created and the ease of their accessibility by the public. The chapter concludes with the impact of the first major statistical legislation for 60 years, particularly as a consequence of its creation of the UK Statistics Authority. This has included major investment in quality assurance of National and Official Statistics and in professional resourcing. These changes are very welcome, as is the statutory specification of government statistics as a public good by the 2007 Statistics and Registration Service Act. But problems of access to some data sets and the pre-release of key economic statistics to selected groups of users remain. Given the widespread societal consequences of the advent of new technologies, what we collect and how we do it will inevitably continue to change rapidly.

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