Dependence of rapeseed quality and yield on density, variety and year of production

Vujakovic, M.; Marjanović-Jeromela, Ana; Jovičić, Dušica; Marinković, Radovan

doi:10.5937/ratpov52-7188

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The strength of the correlation between two analysed traits may differ in different agroecological growing conditions. A strong negative correlation between oil and protein content was previously reported [48,52,53]. An increase in oil content in the seed can arise whether at the expense of decreasing protein content, or by reduction of other seed components [49].…”

Section: Discussionmentioning

confidence: 64%

Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

et al. 2021

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As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r2 values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively).

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Section: Discussionmentioning

confidence: 64%

Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

et al. 2021

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“…Moreover, the differences in leaf area among the three genotypes may be attributed to the differences in leaves per plant. In this concern, Al-Doori and Al-Dulaimy, (2011) and Vujaković et al, (2015) showed that taller genotypes had more leaves and leaf primordial than the others rapeseed genotypes. This might explain the consistent differences among the tested genotypes in all growth characters that were measured in this study.…”

Section: -Effect Of Rapeseed Genotypesmentioning

confidence: 91%

Field Performance of Three Rapeseed Genotypes (Brassica napus L.) Under Different Levels of Plant Density in Nineveh Governorate

Al-Doori¹

2021

CBERJ

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Two field experiments were conducted for rapeseed crop (Brassica napus L.) during two winter successive seasons 2018-2019, 2019-2020 at AL-Hamdanea location which is far Mosul city about (30km). Each experiment was conducted according to factorial experiment in randomized completely block design with three replications. Its included three plant population (111.110, 166.666 and 333.332 plants. hectar -1 ) with three genotypes of rapeseed crop (Talayeh, Okapi and Rainbow).The results could be summarized as:The plant population of 111.110 plants. hectar -1 gave the highest mean for characters stem diameter, number of primary branches, number of silique / plant, leaf area, number of seed per silique, weight of thousand seed, and oil, protein percentage in 2018-2019, 2019-2020 seasons, While the plant population of 333.332 plants. hectar -1 gave a high mean for plant height in both growing seasons.The genotypes differed significantly in all the related characteristics, the Okapi genotype come over the other genotypes in plant height, stem diameter, number of branches/plant, leaf area, number of silique/plant, number of seed/silique, 1000 seeds weight (g.) seed yield (ton.ha -1 ), oil, protein percentage of seeds and oil, protein yield (ton.ha -1 ) in both growing seasons. The interaction between plant population and genotypes was significant in plant height in both growing seasons.‫اجمللد‬ ، ‫األساسية‬ ‫لرتبية‬ ‫ا‬ ‫كلية‬ ‫أحباث‬ ‫جملة‬ ( ‫لعدد‬ ‫ا‬ ، ‫لسنة‬ ،)

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“…The yield components affected by different oilseed rape plant density were analyzed by McGregor (1987), Ozer (2003), Vujaković et al (2014) and Vujaković et al (2015). Marjanović-Jeromela et al (2007) tested seed yield and protein content of ten winter oilseed rape cultivars.…”

Section: Introductionmentioning

confidence: 99%

Winter Oilseed Rape (Brassica napus L.) Yield Components in Agro–ecological Conditions of Banja Luka Region

Kondić

Hajder

Marinković

et al. 2018

AGR

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Due to its numerous agricultural and industrial purposes, oilseed rape is a very important species. The total production of oilseed rape in the Republic of Srpska is faced with noticeable variation throughout years, with an increase in last three years. The oilseed rape yield is strongly influenced by environmental factors. The objective of this study was to analyze yield components of oilseed rape in agro–ecological conditions of Banja Luka. Four oilseed rape hybrids were used: PR46W21, PR46W20, PR46W14 and PR45DO3 in two growing seasons: 2012/13 and 2013/14. Yield components tested were: pod mass (g), seed mass per pod (g), pod index (%), number of seeds per pod and 1000–seed weight (g). Factorial 2×4 ANOVA and Fisher's LSD test were used to analyze data, with growing seasons (years) and hybrids as factors. Growing season significantly affected the pod mass (g), total seed mass per pod (g) and 1000–seed weight. Generally, higher yield component values were obtained in the second examined year (2013/14).

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Dependence of rapeseed quality and yield on density, variety and year of production

Cited by 3 publications

References 10 publications

Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

Field Performance of Three Rapeseed Genotypes (Brassica napus L.) Under Different Levels of Plant Density in Nineveh Governorate

Winter Oilseed Rape (Brassica napus L.) Yield Components in Agro–ecological Conditions of Banja Luka Region

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