Modelling of the effect of dry periods on yielding of spring barley

Szulczewski, Wiesław; Żyromski, Andrzej; Biniak-Pieróg, Małgorzata; Machowczyk, Anna

doi:10.1016/j.agwat.2009.12.004

Cited by 9 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coefficient of residual mass (CRM) is the measurement of relations between the forecast and the measured values. When the value of CRM is 0, it indicates the ideal fit, its positive or negative values indicate, respectively, over or underestimation (Szulczewski et al 2010). When the level of trust a ¼ 0:05 it was verified that there is no reason why the hypothesis of fit of errors of model with the normal distribution should be rejected.…”

Section: Resultsmentioning

confidence: 98%

The influence of carbon content in the mixture of substrates on methane production

Mulka

Szulczewski

Szlachta

et al. 2015

Clean Techn Environ Policy

Self Cite

View full text Add to dashboard Cite

A lot of different chemical reactions take place in the biochemical process of biogas formation but the most important of them include the reaction of bonding carbon dioxide with hydrogen and the decomposition of acetic acid. Other factors, such as temperature, pH, etc., only limit the amount of methane or, in extreme cases, they even stop the process of methane formation. The paper presents an analysis of the influence of the amount of available carbon in the substrate and inoculum on biogas production, as well as of the validity of the relation between methane production and carbon/hydrogen ratio which is often mentioned in the literature. The analyses were made on the basis of the results of several dozen laboratory experiments on methane production for five groups of substrates: cultivated plants, animal faeces, plant waste, animal waste and municipal waste. This provided the basis for the formulation of the conclusion that there is no significant relation between the carbon/hydrogen ratio and methane production, and an alternative biogas calculator was suggested to estimate methane production with the known content of carbon in the substrate and inoculum. This calculator was also adapted to the conditions of agricultural biogas plants, and then it was tested in those conditions. It should also be mentioned that the innovative aspect of the study presented herein is the model developed for the estimation of methane production on the basis of carbon content only, providing estimates with a smaller error than in the case of the calculators!

show abstract

Section: Resultsmentioning

confidence: 98%

The influence of carbon content in the mixture of substrates on methane production

Mulka

Szulczewski

Szlachta

et al. 2015

Clean Techn Environ Policy

Self Cite

View full text Add to dashboard Cite

show abstract

“…The close contingence of traits allows to evaluate indirectly the parameters of one according to the indicators of the other. However, the contingence of traits of plant productivity varies significantly depending on the soil and climatic conditions of cultivation [22].…”

Section: Research Resultsmentioning

confidence: 99%

The Use of Multivariate Factor Analysis in the Selection of Spring Barley for Adaptability to Various Environmental Conditions

Akzhunis¹

2021

KAZATU

View full text Add to dashboard Cite

Multivariate analysis of variance of quantitative and morpho-biological traits of 155 samples of barley (Hordeum vulgare L.) of different ecological and geographical origin made it possible to identify the most adaptively valuable traits, which include the germination rate under saline conditions, the total weight of 14-day-old seedlings, the number and length of seminal roots. Of the production traits, the number of grains per ear, grain weight per ear and protein content in combination with early maturity are significant. Statistical analysis of the data has shown that the studied variety samples according to the “number of grains per ear” and “grain weight per ear” traits demonstrate a consistently high positive correlation relationship, regardless of the years of study and their cultivation areas. In this regard, the “number of grains per ear” trait can be used as the main factor in increasing crop yields in adverse ecosystems of the Aral Sea region of Kazakhstan.

show abstract

“…Then the relative root mean square error (RRMSE) 36 was the next measure method, which checked the relation between the data calculated from the model, the empirical values (RRMSE and CRM test). The closer the result is to 0, the better the fit of the model to the observed values:…”

Section: Verification Of the Modelmentioning

confidence: 99%

Brief analysis of the influence of elements on anaerobic digestion and mathematical model

Mulka¹,

Zheng²,

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

The main problem during the fermentation process is competition between microorganisms and provision of favorable conditions for archaea. The elements like magnesium, sodium and calcium play an important role during the anaerobic digestion, due to its capability to maintain normal life activities. Besides magnesium can work like effective catalyst during the fermentation process. Therefore, this work reports the influence of the mentioned elements on methane production. Moreover, mathematical model for biogas production for anaerobic digester was presented, which based on a continuous technology. This high-activity and cost-effective mathematical model could improve biogas production efficiency as a tool support for understanding fermentation process.

show abstract

Modelling of the effect of dry periods on yielding of spring barley

Cited by 9 publications

References 11 publications

The influence of carbon content in the mixture of substrates on methane production

The influence of carbon content in the mixture of substrates on methane production

The Use of Multivariate Factor Analysis in the Selection of Spring Barley for Adaptability to Various Environmental Conditions

Brief analysis of the influence of elements on anaerobic digestion and mathematical model

Contact Info

Product

Resources

About