The Stability of Weed Seedling Population Models and Parameters in Eastern Nebraska Corn (Zea mays) and Soybean (Glycine max) Fields

Weed Science ◽  
1995 ◽  
Vol 43 (4) ◽  
pp. 604-611 ◽  
Author(s):  
Gregg A. Johnson ◽  
David A. Mortensen ◽  
Linda J. Young ◽  
Alex R. Martin
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Mean Value ◽  
Distribution Model ◽  
Ratio Test ◽  
Weed Species ◽  
Sampling Procedures ◽  
Grass Weed

Intensive field surveys were conducted in eastern Nebraska to determine the frequency distribution model and associated parameters of broadleaf and grass weed seedling populations. The negative binomial distribution consistently fit the data over time (1992 to 1993) and space (fields) for both the inter and intrarow broadleaf and grass weed seedling populations. The other distributions tested (Poisson with zeros, Neyman type A, logarithmic with zeros, and Poisson-binomial) did not fit the data as consistently as the negative binomial distribution. Associated with the negative binomial distribution is akparameter.kis a nonspatial aggregation parameter related to the variance at a given mean value. Thekparameter of the negative binomial distribution was consistent across weed density for individual weed species in a given field except for foxtail spp. populations. Stability of thekparameter across field sites was assessed using the likelihood ratio test There was no stable or commonkvalue across field sites and years for all weed species populations. The lack of stability inkacross field sites is of concern, because this parameter is used extensively in the development of parametric sequential sampling procedures. Becausekis not stable across field sites,kmust be estimated at the time of sampling. Understanding the variability in it is critical to the development of parametric sequential sampling strategies and understanding the dynamics of weed species in the field.

scholarly journals SPATIAL DISTRIBUTION AND SEQUENTIAL SAMPLING OF Brevipalpus phoenicis IN CITRUS

2016 ◽  
Vol 38 (4) ◽  
Author(s):  
WALTER MALDONADO JR ◽  
JOSÉ CARLOS BARBOSA ◽  
MARÍLIA GREGOLIN COSTA ◽  
PAULO CÉSAR TIBURCIO GONÇALVES ◽  
TIAGO ROBERTO DOS SANTOS
Keyword(s):  
Spatial Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Sampling Plan ◽  
Goodness Of Fit Test ◽  
Control Plan ◽  
Sequential Sampling Plan ◽  
Brevipalpus Phoenicis

ABSTRACT Among the pests of citrus, one of the most important is the red and black flat mite Brevipalpus phoenicis (Geijskes), which transmits the Citrus leprosis virus C (CiLV-C).When a rational pest control plan is adopted, it is important to determine the correct timing for carrying out the control plan. Making this decision demands constant follow-up of the culture through periodic sampling where knowledge about the spatial distribution of the pest is a fundamental part to improve sampling and control decisions. The objective of this work was to study the spatial distribution pattern and build a sequential sampling plan for the pest. The data used were gathered from two blocks of Valencia sweet orange on a farm in São Paulo State, Brazil, by 40 inspectors trained for the data collection. The following aggregation indices were calculated: variance/ mean ratio, Morisita index, Green’s coefficient, and k parameter of the negative binomial distribution. The data were tested for fit with Poisson and negative binomial distributions using the chi-square goodness of fit test. The sequential sampling was developed using Wald’s Sequential Probability Ratio Test and validated through simulations. We concluded that the spatial distribution of B. phoenicis is aggregated, its behavior best fitted to the negative binomial distribution and we built and validated a sequential sampling plan for control decision-making.

scholarly journals Spatial Distribution of Eggs of Alabama argillacea Hübner and Heliothis virescens Fabricius (Lepidoptera: Noctuidae) on Bt and non-BtCotton

2015 ◽  
Vol 87 (4) ◽  
pp. 2243-2253
Author(s):  
TATIANA R. RODRIGUES ◽  
MARCOS G. FERNANDES ◽  
PAULO E. DEGRANDE ◽  
THIAGO A. MOTA
Keyword(s):  
Spatial Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Heliothis Virescens ◽  
Negative Binomial ◽  
Model Fitting ◽  
Spatial Arrangement ◽  
Distribution Model ◽  
Aggregation Index ◽  
Alabama Argillacea

ABSTRACT Among the options to control Alabama argillacea (Hübner, 1818) and Heliothis virescens (Fabricius, 1781) on cotton, insecticide spraying and biological control have been extensively used. The GM'Bt' cotton has been introduced as an extremely viable alternative, but it is yet not known how transgenic plants affect populations of organisms that are interrelated in an agroecosystem. For this reason, it is important to know how the spatial arrangement of pests and beneficial insect are affected, which may call for changes in the methods used for sampling these species. This study was conducted with the goal to investigate the pattern of spatial distribution of eggs of A. argillacea and H. virescens in DeltaOpalTM (non-Bt) and DP90BTMBt cotton cultivars. Data were collected during the agricultural year 2006/2007 in two areas of 5,000 m2, located in in the district of Nova América, Caarapó municipality. In each sampling area, comprising 100 plots of 50 m2, 15 evaluations were performed on two plants per plot. The sampling consisted in counting the eggs. The aggregation index (variance/mean ratio, Morisita index and exponent k of the negative binomial distribution) and chi-square fit of the observed and expected values to the theoretical frequency distribution (Poisson, Binomial and Negative Binomial Positive), showed that in both cultivars, the eggs of these species are distributed according to the aggregate distribution model, fitting the pattern of negative binomial distribution.

Sequential Sampling for the Imported Cabbageworm, Pieris rapae (L.)

1966 ◽  
Vol 98 (7) ◽  
pp. 741-746 ◽  
Author(s):  
D. G. Harcourt
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Pieris Rapae ◽  
Sequential Sampling ◽  
Sampling Time ◽  
Field Conditions ◽  
Population Estimates ◽  
Population Means ◽  
Considerable Saving

AbstractSequential sampling, in which decisions depend upon the accumulated results of a series of observations, leads to considerable saving of time and money. A sequential plan, based on the negative binomial distribution and providing for population estimates in three infestation categories, was drawn up for use in control of the imported cabbageworm, Pieris rapae (L.), on cabbage. An appraisal of the plan under field conditions showed that it reduced the sampling time by 75% while rating the infestation correctly in 94 of 100 cases. With the six discrepancies, population means lay between the limits set for the infestation categories.

SPATIAL ARRANGEMENT OF THE EGGS OF HYLEMYA BRASSICAE (BOUCHÉ), AND A SEQUENTIAL SAMPLING PLAN FOR USE IN CONTROL OF THE SPECIES

1967 ◽  
Vol 47 (5) ◽  
pp. 461-467 ◽  
Author(s):  
D. G. Harcourt
Keyword(s):  
Spatial Pattern ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Sequential Sampling ◽  
Spatial Arrangement ◽  
Sampling Plan ◽  
Chi Square ◽  
Sequential Sampling Plan ◽  
Hylemya Brassicae

Counts of eggs of Hylemya brassicae (Bouché) in cabbage did not conform to the Poisson distribution owing to a preponderance of uninfested and highly infested plants. But when the negative binomial series was fitted to the observed distribution, the discrepancies were not significant when tested by chi-square. The spatial pattern may be described by expansion of (q—px)−k with a common k of 0.95.Three methods of transformation stabilized the variance of field counts. A sequential sampling plan based on the negative binomial distribution and providing for two infestation classes was drawn up for use in control of the insect in the stem brassicas.

scholarly journals Use of a negative binomial distribution to describe the presence of Sphyrion laevigatum in Genypterus blacodes

2013 ◽  
Vol 22 (4) ◽  
pp. 602-604 ◽  
Author(s):  
Patricio Peña-Rehbein ◽  
Patricio De los Ríos-Escalante ◽  
Raúl Castro ◽  
Carolina Navarrete
Keyword(s):  
Spatial Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Distribution Model ◽  
Economic Resource ◽  
Spatial Distribution Model

This paper describes the frequency and number of Sphyrion laevigatum in the skin of Genypterus blacodes, an important economic resource in Chile. The analysis of a spatial distribution model indicated that the parasites tended to cluster. Variations in the number of parasites per host could be described by a negative binomial distribution. The maximum number of parasites observed per host was two.

scholarly journals Testing the odds of inherent vs. observed overdispersion in neural spike counts

2016 ◽  
Vol 115 (1) ◽  
pp. 434-444 ◽  
Author(s):  
Wahiba Taouali ◽  
Giacomo Benvenuti ◽  
Pascal Wallisch ◽  
Frédéric Chavane ◽  
Laurent U. Perrinet
Keyword(s):  
Poisson Process ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Poisson Model ◽  
Probabilistic Methods ◽  
Distribution Model ◽  
Data Sets ◽  
Electrophysiological Recordings ◽  
Neurophysiological Data

The repeated presentation of an identical visual stimulus in the receptive field of a neuron may evoke different spiking patterns at each trial. Probabilistic methods are essential to understand the functional role of this variance within the neural activity. In that case, a Poisson process is the most common model of trial-to-trial variability. For a Poisson process, the variance of the spike count is constrained to be equal to the mean, irrespective of the duration of measurements. Numerous studies have shown that this relationship does not generally hold. Specifically, a majority of electrophysiological recordings show an “overdispersion” effect: responses that exhibit more intertrial variability than expected from a Poisson process alone. A model that is particularly well suited to quantify overdispersion is the Negative-Binomial distribution model. This model is well-studied and widely used but has only recently been applied to neuroscience. In this article, we address three main issues. First, we describe how the Negative-Binomial distribution provides a model apt to account for overdispersed spike counts. Second, we quantify the significance of this model for any neurophysiological data by proposing a statistical test, which quantifies the odds that overdispersion could be due to the limited number of repetitions (trials). We apply this test to three neurophysiological data sets along the visual pathway. Finally, we compare the performance of this model to the Poisson model on a population decoding task. We show that the decoding accuracy is improved when accounting for overdispersion, especially under the hypothesis of tuned overdispersion.

scholarly journals On the mechanistic roots of an ecological law: parasite aggregation

2019 ◽  
Author(s):  
Jomar F. Rabajante ◽  
Elizabeth L. Anzia ◽  
Chaitanya S. Gokhale
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Mechanistic Model ◽  
A Priori ◽  
Parasite Ecology ◽  
Mean And Variance ◽  
Parasite Aggregation ◽  
Sampling Procedures ◽  
Host Parasite

AbstractParasite aggregation, a recurring pattern in macroparasite infections, is considered one of the “laws” of parasite ecology. Few hosts have a large number of parasites while most hosts have a low number of parasites. Phenomenological models of host-parasite systems thus use the negative-binomial distribution. However, to infer the mechanisms of aggregation, a mechanistic model that does not make any a priori assumptions is essential. Here we formulate a mechanistic model of parasite aggregation in hosts without assuming a negative-binomial distribution. Our results show that a simple model of parasite accumulation still results in an aggregated pattern, as shown by the derived mean and variance of the parasite distribution. By incorporating the derived statistics in host-parasite interactions, we can predict how aggregation affects the population dynamics of the hosts and parasites through time. Thus, our results can directly be applied to observed data as well as can inform the designing of statistical sampling procedures. Overall, we have shown how a plausible mechanistic process can result in the often observed phenomenon of parasite aggregation occurring in numerous ecological scenarios, thus providing a basis for a “law” of ecology.

Sign in / Sign up

Export Citation Format

Share Document