scholarly journals Making sense of the link between tiller density and pasture production

1995 ◽  
pp. 83-87
Author(s):  
C. Matthew ◽  
A. Hernandez-Garay ◽  
J. Hodgson
Keyword(s):  
Leaf Area ◽  
Shoot Density ◽  
Logarithmic Scale ◽  
Data Set ◽  
Pasture Production ◽  
Density Data ◽  
Density Compensation ◽  
Area Effect ◽  
The Right ◽  
Tiller Density

Interpretation of tiller or shoot density data requires resolution of two independent, confounding effects, namely size/density compensation and what is here called the "leaf area effect". Size/density compensation implies that at higher herbage mass, individual tillers or shoots are larger, but the population density is correspondingly decreased. The leaf area effect represents difference in sward leaf area for two tiller populations. Such leaf area differences may be environmentally or genetically determined, but must of necessity be expressed through change in tiller size and/or tiller density as "yield components" of leaf area. The theoretical basis for distinguishing between size/density compensation and the leaf area effect is to consider tiller or shoot density and herbage yield, respectively, as X,Y co-ordinates in a size/density plot. When such a plot is drawn on a logarithmic scale, points along a line of -l/2 slope show size/ density compensation with respect to each other. Movement of points to the right or left of the size/ density compensation line is evidence of a leaf area effect. It is shown that when the size/density effects are removed from a data set in this way, rankings of experimental treatments for the leaf area effect can often be reversed compared with the ranking of uncorrected tiller density. Tiller density data corrected for size/density compen-. sation in this way appear to be a useful indicator of sward productivity. Keywords: sizeldensity compensation, sward productivity, tiller density

scholarly journals How much leaf area do insects eat? A data set of insect herbivory sampled globally with a standardized protocol

Ecology ◽  
2021 ◽  
Author(s):  
Gisele M. Mendes ◽  
Fernando A.O. Silveira ◽  
Carolina Oliveira ◽  
Wesley Dáttilo ◽  
Roger Guevara ◽  
...  
Keyword(s):  
Leaf Area ◽  
Insect Herbivory ◽  
Data Set ◽  
Standardized Protocol

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

Morphometry of leaf and shoot variables to assess aboveground biomass structure and carbon sequestration by different varieties of white mulberry (Morus alba L.)

2021 ◽  
Author(s):  
Ghulam Ali Bajwa ◽  
Muhammad Umair ◽  
Yasir Nawab ◽  
Zahid Rizwan
Keyword(s):  
Carbon Sequestration ◽  
Leaf Area ◽  
Aboveground Biomass ◽  
Shoot Density ◽  
Morus Alba ◽  
Coefficient Of Determination ◽  
Equivalent Diameter ◽  
Organic Carbon Content ◽  
Reasonable Accuracy ◽  
Leaf Density

AbstractMulberry is economically important and can also play a pivotal role in mitigating greenhouse gases. Leaf and shoot traits were measured for Morus alba var. Kanmasi, M. alba var. Karyansuban, M. alba var. Latifolia, and M. alba var. PFI-1 to assess aboveground biomass (AGB) and carbon sequestration. Variety-specific and multivariety allometric AGB models were developed using the equivalent diameter at breast height (EDBH) and plant height (H). The complete-harvest method was used to measure leaf and shoot traits and biomass, and the ash method was used to measure organic carbon content. The results showed significant (p < 0.01) varietal differences in leaf and shoot traits, AGB and carbon sequestration. PFI-1 variety had the greatest leaf density (mean ± SE: 1828.3 ± 0.3 leaves tree−1), Karyansuban had the largest mean leaf area (185.94 ± 8.95 cm2). A diminishing return was found between leaf area and leaf density. Latifolia had the highest shoot density per tree (46.6 ± 1.83 shoots tree−1), total shoot length (264.1 ± 2.32 m), dry biomass (16.69 ± 0.58 kg tree−1), carbon sequestration (9.99 ± 0.32 kg tree−1) and CO2 mitigation (36.67 ± 1.16 kg). The variety-specific AGB models b(EDBH) and b(EDBH)2 showed good fit and reasonable accuracy with a coefficient of determination (R2) = 0.98–0.99, standard error of estimates (SEE) = 0.1125–0.3130 and root mean square error (RMSE) = 0.1084–0.3017. The multivariety models bln(EDBH) and (EDBH)0.756 showed good-fitness and accuracy with R2 = 0.85–0.86, SEE = 1.6231–1.6445 and RMSE = 1.609–1.630. On the basis of these findings, variety Latifolia has good potential for biomass production, and allometric equations based on EDBH can be used to estimate AGB with a reasonable accuracy.

Broadband Combustion Noise Prediction With the Fast Random Particle Method

2014 ◽  
Author(s):  
Felix Grimm ◽  
Roland Ewert ◽  
Jürgen Dierke ◽  
Berthold Noll ◽  
Manfred Aigner
Keyword(s):  
Scaling Law ◽  
Particle Method ◽  
Reconstruction Algorithm ◽  
Combustion Noise ◽  
Frequency Noise ◽  
Noise Prediction ◽  
Data Set ◽  
Jet Flame ◽  
The Right ◽  
Random Particle

A new highly efficient, hybrid CFD/CAA approach for broadband combustion noise modeling is introduced. The inherent sound source generation mechanism is based on turbulent flow field statistics, which are determined from reacting RANS calculations. The generated sources form the right-hand side of the linearized Euler equations for the calculation of sound fields. The stochastic time-domain source reconstruction algorithm is briefly described with emphasis on two different ways of spatial discretization, RPM (Random Particle Method) and the newly developed FRPM (Fast RPM). The application of mainly the latter technique to combustion noise (CN) prediction and several methodical progressions are presented in the paper. (F)RPM-CN is verified in terms of its ability to accurately reproduce prescribed turbulence-induced one- and two-point statistics for a generic test and the DLR-A jet flame validation case. Former works on RPM-CN have been revised and as a consequence methodical improvements are introduced along with the progression to FRPM-CN: A canonical CAA setup for the applications DLR-A, -B and H3 flame is used. Furthermore, a second order Langevin decorrelation model is introduced for FRPM-CN, to avoid spurious high frequency noise. A new calibration parameter set for reacting jet noise prediction with (F)RPM-CN is proposed. The analysis shows the universality of the data set for 2D jet flame applications and furthermore the method’s accountance for Reynolds scalability. In this context, a Mach number scaling law is used to conserve Strouhal similarity of the jet flame spectra. Finally, the numerical results are compared to suitable similarity spectra.

scholarly journals Wind Speed Retrieval from Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data for Marine Application

2019 ◽  
Vol 11 (14) ◽  
pp. 1682 ◽  
Author(s):  
Torsten Geldsetzer ◽  
Shahid K. Khurshid ◽  
Kerri Warner ◽  
Filipe Botelho ◽  
Dean Flett
Keyword(s):  
Wind Speed ◽  
Low Noise ◽  
Quality Data ◽  
Band Model ◽  
Data Set ◽  
Wind Retrieval ◽  
Wind Speeds ◽  
Geophysical Model ◽  
The Right ◽  
Quality Threshold

RADARSAT Constellation Mission (RCM) compact polarimetry (CP) data were simulated using 504 RADARSAT-2 quad-pol SAR images. These images were used to samples CP data in three RCM modes to build a data set with co-located ocean wind vector observations from in situ buoys on the West and East coasts of Canada. Wind speeds up to 18 m/s were included. CP and linear polarization parameters were related to the C-band model (CMOD) geophysical model functions CMOD-IFR2 and CMOD5n. These were evaluated for their wind retrieval potential in each RCM mode. The CP parameter Conformity was investigated to establish a data-quality threshold (>0.2), to ensure high-quality data for model validation. An accuracy analysis shows that the first Stokes vector (SV0) and the right-transmit vertical-receive backscatter (RV) parameters were as good as the VV backscatter with CMOD inversion. SV0 produced wind speed retrieval accuracies between 2.13 m/s and 2.22 m/s, depending on the RCM mode. The RCM Medium Resolution 50 m mode produced the best results. The Low Resolution 100 m and Low Noise modes provided similar results. The efficacy of SV0 and RV imparts confidence in the continuity of robust wind speed retrieval with RCM CP data. Three image-based case studies illustrate the potential for the application of CP parameters and RCM modes in operational wind retrieval systems. The results of this study provide guidance to direct research objectives once RCM is launched. The results also provide guidance for operational RCM data implementation in Canada’s National SAR winds system, which provides near-real-time wind speed estimates to operational marine forecasters and meteorologists within Environment and Climate Change Canada.

scholarly journals There Is No Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation with Distributional Semantics

2016 ◽  
Vol 42 (4) ◽  
pp. 637-660 ◽  
Author(s):  
Germán Kruszewski ◽  
Denis Paperno ◽  
Raffaella Bernardi ◽  
Marco Baroni
Keyword(s):  
Large Data ◽  
Semantic Space ◽  
The Other ◽  
Similarity Function ◽  
Distributional Semantics ◽  
Application Domain ◽  
Data Set ◽  
Logical Negation ◽  
The One ◽  
The Right

Logical negation is a challenge for distributional semantics, because predicates and their negations tend to occur in very similar contexts, and consequently their distributional vectors are very similar. Indeed, it is not even clear what properties a “negated” distributional vector should possess. However, when linguistic negation is considered in its actual discourse usage, it often performs a role that is quite different from straightforward logical negation. If someone states, in the middle of a conversation, that “This is not a dog,” the negation strongly suggests a restricted set of alternative predicates that might hold true of the object being talked about. In particular, other canids and middle-sized mammals are plausible alternatives, birds are less likely, skyscrapers and other large buildings virtually impossible. Conversational negation acts like a graded similarity function, of the sort that distributional semantics might be good at capturing. In this article, we introduce a large data set of alternative plausibility ratings for conversationally negated nominal predicates, and we show that simple similarity in distributional semantic space provides an excellent fit to subject data. On the one hand, this fills a gap in the literature on conversational negation, proposing distributional semantics as the right tool to make explicit predictions about potential alternatives of negated predicates. On the other hand, the results suggest that negation, when addressed from a broader pragmatic perspective, far from being a nuisance, is an ideal application domain for distributional semantic methods.

Making more research count: a blockchain enabled one-stop shop for immutable behavioral research

foresight ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Christian Hugo Hoffmann
Keyword(s):  
Institutional Economics ◽  
New Technologies ◽  
Behavioral Science ◽  
Data Integrity ◽  
Original Data ◽  
Scientific Data ◽  
Data Set ◽  
Content Type ◽  
Blockchain Technology ◽  
The Right

Purpose The purpose of this paper is to offer a panoramic view at the credibility issues that exist within social sciences research. Design/methodology/approach The central argument of this paper is that a joint effort between blockchain and other technologies such as artificial intelligence (AI) and deep learning and how they can prevent scientific data manipulation or data forgery as a way to make science more decentralized and anti-fragile, without losing data integrity or reputation as a trade-off. The authors address it by proposing an online research platform for use in social and behavioral science that guarantees data integrity through a combination of modern institutional economics and blockchain technology. Findings The benefits are mainly twofold: On the one hand, social science scholars get paired with the right target audience for their studies. On the other hand, a snapshot of the gathered data at the time of creation is taken so that researchers can prove that they used the original data set to peers in the future while maintaining full control of their data. Originality/value The proposed combination of behavioral economics with new technologies such as blockchain and AI is novel and translated into a cutting-edge tool to be implemented.

A martingale approach to estimating confidence band with censored data

2014 ◽  
Vol 20 (4) ◽  
Author(s):  
Seung-Hwan Lee ◽  
Eun-Joo Lee
Keyword(s):  
Gaussian Process ◽  
Survival Function ◽  
Confidence Bands ◽  
Computer Assisted ◽  
Finite Sample ◽  
Real World Data ◽  
Data Set ◽  
Simulation Techniques ◽  
Simultaneous Confidence Bands ◽  
The Right

AbstractThis paper develops some non-parametric simultaneous confidence bands for survival function when data are randomly censored on the right. To construct the confidence bands, a computer-assisted method is utilized and this approach requires no distributional assumptions, so the confidence bands can be easily estimated. The procedures are based on the integrated martingale whose distribution is approximated by a Gaussian process. The supremum distribution of the Gaussian process generated by simulation techniques leads to the construction of the confidence bands. To improve the estimation procedures for the finite sample sizes, the log-minus-log transformation is employed. The proposed confidence bands are assessed using numerical simulations and applied to a real-world data set regarding leukemia.

A Dynamic Clustering Querying Algorithm Based on Grid in Manufacturing System

2011 ◽  
Vol 323 ◽  
pp. 89-93
Author(s):  
Jun Zeng
Keyword(s):  
Processing Speed ◽  
Manufacturing System ◽  
Data Sets ◽  
Dynamic Clustering ◽  
Data Set ◽  
Density Data ◽  
Clustering Center ◽  
And Performance

This article presents a querying algorithm of dynamic clustering based on grid in manufacturing system. The algorithm divides grids based on the location of nodes, and computes clustering center of grids, then queries based on clustering in the station, processing speed of this method are independent of size of data set, processing speed is quick, it can handle massive and multi-density data sets and performance is better in terms of accuracy and efficiency of querying.

Effects of a novel ryegrass endophyte on pasture production, dairy cow milk production and calf liveweight gain

2005 ◽  
Vol 45 (1) ◽  
pp. 11 ◽  
Author(s):  
S. J. Bluett ◽  
E. R. Thom ◽  
D. A. Clark ◽  
C. D. Waugh
Keyword(s):  
Milk Production ◽  
Milk Composition ◽  
Late Spring ◽  
Dairy Calves ◽  
Cow Milk ◽  
Plasma Prolactin ◽  
Pasture Production ◽  
Sward Height ◽  
Lolitrem B ◽  
Tiller Density

A 2-year evaluation of perennial ryegrass (Lolium perenne) infected with wild endophyte (Neotyphodium lolii), AR1 endophyte or no endophyte was carried out in Hamilton, New Zealand. In contrast to wild endophyte-infected ryegrass, AR1-infected ryegrass does not produce the alkaloids lolitrem B or ergovaline. Annual pasture production was similar across endophyte treatments, averaging 18.3 t DM/ha in year 1 and 13.8 t DM/ha in year 2, and ryegrass tiller density and botanical composition were unaffected by endophyte status. A combined analysis of 3 short-term milk production tests in late spring (Nov. 1999), summer (Jan. 2000) and autumn (Mar. 2000) in the first experiment, showed a 6.7% advantage in milk production to cows grazing AR1-infected ryegrass compared with those grazing wild endophyte-infected ryegrass pastures (P<0.05). Milk composition was similar in all test periods and ryegrass staggers was not observed on any treatment. In a second experiment, weaned dairy calves were continuously stocked on the pastures described above from late spring 2000 to autumn 2001 and grazed to a mean sward height of 5 cm. Average calf liveweight gain and total liveweight gain per hectare were similar across treatments over the 5-month period, averaging 0.8 kg/calf.day and 822 kg/ha, respectively. Calves grazing wild endophyte-infected ryegrass developed mild ryegrass staggers in January and February, coinciding with a peak lolitrem B concentration in this experiment of 2.3 mg/kg DM, while those grazing AR1-infected or endophyte-free ryegrass pastures did not develop staggers. Information is also presented on herbage and alkaloid intake, blood plasma prolactin concentration, and cow temperature and respiration rate. Results from this initial evaluation under dairying indicate that AR1-infected ryegrass can produce similar pasture yields as wild endophyte-infected ryegrass, while offering small improvements in milk yield with no incidence of ryegrass staggers in grazing animals.

Sign in / Sign up

Export Citation Format

Share Document