scholarly journals Evaluation of crop growing periods at some stations in northest Brazil

MAUSAM ◽  
2021 ◽  
Vol 47 (2) ◽  
Author(s):  
K. KARUNA KUMAR ◽  
J. A. TOMAS DA SILVA
Keyword(s):  
Soil Moisture ◽  
Markov Chain Model ◽  
Model Parameters ◽  
Chain Model ◽  
Conditional Probabilities ◽  
Minimum Period ◽  
First Order ◽  
Temperature And Precipitation ◽  
Order Markov Chain ◽  
Dry Soil

Results of a study of crop .growing periods at some stations in northeast Brazil are presented in this paper. Daily soil moisture values for a minimum period of 25 years are evaluated by means of a simple soil moisture model using temperature and precipitation data. A first order Markov chain model is applied to the soil moisture data and initial and conditional probabilities of wet and dry soil days are obtained. Soil moisture averages and probabilities are used to evaluate crop growing periods at the stations. The effect of uncertainties in the model parameters on the estimated growing periods is investigated.

MARKOV CHAIN MODEL FOR SEQUENCES OF FIELD WORKDAYS

1974 ◽  
Vol 54 (2) ◽  
pp. 137-148 ◽  
Author(s):  
HENRY HAYHOE ◽  
WOLFGANG BAIER
Keyword(s):  
Markov Chain ◽  
Computer Program ◽  
Markov Chain Model ◽  
Probability Model ◽  
Chain Model ◽  
Sequential Data ◽  
Conditional Probabilities ◽  
Farm Machinery ◽  
First Order ◽  
Order Markov Chain

A computer program was written to estimate from sequential data the parameters for a Markov chain probability model. The program was applied to the analysis of field workday probabilities at 10 selected locations across Canada. The suitability of the first-order Markov chain model was tested and the locations across Canada were compared as to the probability and conditional probabilities of workdays. These probabilities estimated by the program also provide data which should be of interest in farm machinery selection.

On the Internal Organization of Consumers' Cognitive Schemata

1989 ◽  
Vol 65 (1) ◽  
pp. 115-126 ◽  
Author(s):  
Roberto Friedmann ◽  
Richard Fox
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Operational Definition ◽  
Verbal Stimulus ◽  
Chain Model ◽  
Cognitive Schemata ◽  
First Order ◽  
Internal Organization ◽  
Order Markov Chain ◽  
Made In

An operational definition for internal-based versus external-based schema variables via the tangible versus intangible dichotomy is provided. Using strings of one-word associations made in response to a verbal stimulus, the stochastic structure associated with the use of these variables is investigated. Analysis shows that a first-order Markov chain model which allows for dependence between two consecutive schema variables is more appropriate than a Bernoulli model in the description of the internal organization of cognitive schemata. The phenomena of “chunking” and tangible versus intangible dominance are expressed in the context of the parameters associated with a first-order Markov chain.

A climatological study of soil moisture under corn crop at Campina Grande (NE Brazil)

MAUSAM ◽  
2021 ◽  
Vol 48 (3) ◽  
pp. 437-442
Author(s):  
K.KARUNA KUMAR ◽  
JOSE ANTONIO TOMAS DA SILVA ◽  
VIRGINIA DE FATIMA BEZERRA
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Markov Chain Model ◽  
Moisture Loss ◽  
Growth Stages ◽  
Chain Model ◽  
Growing Period ◽  
Corn Crop ◽  
Order Markov Chain ◽  
Different Growth Stages

ABSTRACT. Results of a climatological study of soil moisture under corn crop at Campina Grande (NE Brazil) are presented in this paper. Daily values of available moisture content during the crop growing period are evaluated for a period of 25 years. A six zone versatile soil moisture budget model is used for this purpose and approximately 5. 7.5, 12.5, 25, 25 and 25% of the available water capacity (AWC) are attributed to zones one to six respectively. Different root activity coefficients are assumed for the six zones in different growth stages and the dependence of these coefficients on moisture content is taken into consideration. The same moisture releasing characteristics are assumed for all soil zones. On rainy days moisture loss due to evapotranspiration is assumed to take place before precipitation. Four AWC values and three corn growing periods between March and September are considered in this study. A first order Markov chain model is applied to the daily soil moisture data. Soil moisture averages and probabilities are used to identify the optimum growing period for corn at this station. The irrigation requirements of the crop are briefly discussed.  

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