Weather Simulation for Crop Management Models

Richardson, C. W.

doi:10.13031/2013.32484

Cited by 131 publications

(61 citation statements)

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“…In this study, the daily precipitation generator is a first-order two-state Markov chain (Larsen and Pense 1982, Roldom, J. and Woolhiser, DA 1982, Richardson 1985 and exponential and gamma distribution are used to generate the precipitation amount.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Single-site Daily precipitation Generator in northwestern of Benin

Etienne¹,

Hilaire²,

Ulrich³

et al. 2017

Int.J.Curr.Res.Aca.Rev.

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Article InfoStochastic weather generators used in meteorological and hydrological studies are mostly statistical models that produce random numbers resembling the observed data on which they have been fitted. They can generate weather sequences that statistically resemble the real observed data. In this study we developed a single-site daily rainfall generator for the simulations of rainfall occurrences and amounts in northwestern of Benin. A first-order two-state Markov chain was used to determine the occurrence of daily precipitation. The rainfall amounts on wet days were generated by using the oneparameter Exponential distribution and the two-parameter Gamma distribution. The Markov model was successful in simulating the rainfall occurrences and the Exponential distribution as the more reliability in preserving most of the important daily characteristics of the historical rainfall amounts. The test of the two distributions on data that are not taken into account in the elaboration of the model has shown that the exponential distribution reproduces well the statistics of the daily precipitation than the gamma distribution

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Section: Methodsmentioning

confidence: 99%

Evaluation of a Single-site Daily precipitation Generator in northwestern of Benin

Etienne¹,

Hilaire²,

Ulrich³

et al. 2017

Int.J.Curr.Res.Aca.Rev.

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“…The approach has been used successfully and studied extensively to generate rainfall (Larsen and Pense1982;Roldan and Woolhiser, 1982;Richardson 1985). Let X 0, X 1 , X 2 …Xn, be random variables distributed identically and taking only two values, namely 0 and 1, with probability one, i.e, 0 if the n th day is dry Xn= 1 if the n th day is wet Firstly, it may be assumed that, P(Xn+1=Xn+1|Xn=Xn, Xn-1=Xn-1,…, X0=X0)=P(Xn+1=Xn+1|Xn=Xn) Where X0, X1…Xn+1{ 0, 1 }.…”

Section: Rain Occurrencementioning

confidence: 99%

Markov chain modeling of daily rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia

Getachew

Teshome

2018

J.Degrade.Min.Land Manage.

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Abstract:An understanding and knowledge of seasonal rainfall distribution, e.g. length of the growing season and rainfall extremes, is very important in agro-based economies like Ethiopia, where 95% of the farmers depend on rainfed agricultural production. The distribution pattern of rainfall rather than the total amount of rainfall within the entire period of time is more important for studying the pattern of rainfall occurrence. Zero, first and second order Markov chain was used to describe the characteristics of rainfall occurrences in this woreda. The states considered were; dry (d) and rainy (r).The overall chance of rain and the fitted curve tells us that the chance of getting rain in the main rainy season is quadruple as compared to the small rainy season. The first order Markov chain model indicates that the probability of getting rain in the small rainy season is significantly dependent on whether the earlier date was dry or wet while the second order Marko chain indicates that during the main rainy season the dependence of the probability of rain on the previous two dates' conditions is less as compared with the small rainy season. Rainfall amounts are very variable and are usually modeled by a gamma distribution.Therefore, the pattern of rainfall is somewhat unimodial having only one extreme value in August. Onset, cessation and length of growing season of rainfall for the main rainy season show medium variation compared to the small rainy season.

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“…Solar radiation is derived from sunshine hours data Angstrom method 20 . This module generates daily weather data using the WGEN 21,22 or SIMMETEO 23,24 weather generators.…”

Section: Weather Modulementioning

confidence: 99%

Dry Biomass Partitioning of Growth and Development in Wheat (<i>Triticum aestivum</i> L.) Crop Using CERES-Wheat in Different Agro Climatic Zones of India

Singh¹,

Singh²,

Gill³

et al. 2017

Current Science

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The CERES-wheat crop growth simulation model has been calibrated and evaluated for two wheat cultivars (PBW 343 and PBW 542) for three sowing dates (30 October, 15 November and 30 November) during 2008-09 and 2009-10 to study partitioning of leaf, stem and grains at Ludhiana, Punjab, India. The experimental data and simulated model data were analysed on partitioning of leaf, stem and grains, and validated. It was found that the model closely simulated the field data from phenological events and biomass. Simulated biological and grain yield was in accordance with-field experiment crop yield within the acceptable range. The correlation coefficient between field-experiment and simulated yield data and biomass data varied significantly from 0.81 and 0.96. The model showed overestimation from field-experimental yield for both cultivars. The cultivar PBW 343 gave higher yield than cultivar PBW 542 on 15 November during both years. The model performance was evaluated and it was found that CERES-wheat model could predict growth parameters like days to anthesis and maturity, biomass and yield with reasonably good accuracy (error less than 8%) and also correlation coefficient between field-experimental and simulated yield data and biomass data varied from 0.94 and 0.95. The results showed that the correlation coefficient between simulated and districts yield varied from 0.41 to 0.78 and also significantly at all six selected districts. The results may be used to improve and evaluate the current practices of crop management at different growth stages of the crop to achieve better production potential.

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Weather Simulation for Crop Management Models

Cited by 131 publications

References 0 publications

Evaluation of a Single-site Daily precipitation Generator in northwestern of Benin

Evaluation of a Single-site Daily precipitation Generator in northwestern of Benin

Markov chain modeling of daily rainfall in Lay Gaint Woreda, South Gonder Zone, Ethiopia

Dry Biomass Partitioning of Growth and Development in Wheat (<i>Triticum aestivum</i> L.) Crop Using CERES-Wheat in Different Agro Climatic Zones of India

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