Abstract
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).
Highlights
High and stable yield and oil content are the most important traits in rapeseed (Brassica napus L.) breeding programs
That year was favourable for rapeseed growing and over half of the examined genotypes yielded more than 2950 kg/ha
The current study suggests that random forest regression (RFR) and Artificial neural networks (ANNs) modelling can be successfully exploited for the purpose of rapeseed oil and protein content, seed yield, oil and protein yield, and 1000 seed weight prediction, based on the year of production and genotype
Summary
High and stable yield and oil content are the most important traits in rapeseed (Brassica napus L.) breeding programs. According to [1], in the last five years the world average rapeseed yield was about 2.1 t/ha. Rapeseed seed yield and quality vary depending on location, cultivar and their mutual interaction [2,3]. Seed yield is mainly affected by environmental variation such as climatic factors (temperature, precipitation, length of photoperiod, abiotic stresses), soil type, and cultivation practice (density and time of sowing, fertilization). Due to the abovementioned factors, seed yield prediction is an exceedingly challenging task. Being able to forecast low yield leaves space to make on-time warning and develop a strategy to maintain a stable food supply chain. It is forecasted that in the near future precipitation levels will rise in northern Europe, which is among others expected to reflect on higher seed yield [4].
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