Promoting crop diversification in European agriculture is a key pillar of the agroecological transition. Diversifying crops generally enhances crop productivity, quality, soil health and fertility, and resilience to pests and diseases and reduces environmental stresses. Moreover, crop diversification provides an alternative means of enhancing farmers’ income. Camelina (Camelina sativa (L.) Crantz) reemerged in the background of European agriculture approximately three decades ago, when the first studies on this ancient native oilseed species were published. Since then, a considerable number of studies on this species has been carried out in Europe. The main interest in camelina is related to its (1) broad environmental adaptability, (2) low-input requirements, (3) resistance to multiple pests and diseases, and (4) multiple uses in food, feed, and biobased applications. The present article is a comprehensive and critical review of research carried out in Europe (compared with the rest of the world) on camelina in the last three decades, including genetics and breeding, agronomy and cropping systems, and end-uses, with the aim of making camelina an attractive new candidate crop for European farming systems. Furthermore, a critical evaluation of what is still missing to scale camelina up from a promising oilseed to a commonly cultivated crop in Europe is also provided (1) to motivate scientists to promote their studies and (2) to show farmers and end-users the real potential of this interesting species.

Legumes and brassicas have much in common: importance in agricultural history, rich biodiversity, numerous forms of use, high adaptability to diverse farming designs, and various non-food applications. Rare available resources demonstrate intercropping legumes and brassicas as beneficial to both, especially for the latter, profiting from better nitrogen nutrition. Our team aimed at designing a scheme of the intercrops of autumn- and spring-sown annual legumes with brassicas for ruminant feeding and green manure, and has carried out a set of field trials in a temperate Southeast European environment and during the past decade, aimed at assessing their potential for yields of forage dry matter and aboveground biomass nitrogen and their economic reliability via land equivalent ratio. This review provides a cross-view of the most important deliverables of our applied research, including eight annual legume crops and six brassica species, demonstrating that nearly all the intercrops were economically reliable, as well as that those involving hairy vetch, Hungarian vetch, Narbonne vetch and pea on one side, and fodder kale and rapeseed on the other, were most productive in both manners. Feeling encouraged that this pioneering study may stimulate similar analyses in other environments and that intercropping annual legume and brassicas may play a large-scale role in diverse cropping systems, our team is heading a detailed examination of various extended research.

Jerusalem artichoke is an excellent source of inulin. Inulin has valuable nutritional and functional attributes, and therefore it is needed to know inulin content in different accessions of Jerusalem artichoke tubers. We used rapid high-performance liquid chromatography (HPLC) method following water extraction to determine inulin content in Jerusalem artichoke tubers. HPLC conditions included Rezex RCM-monosaccharide Ca 2 þ column, deionized water as mobile phase and light scattering detection. It was found that inulin content of Jerusalem artichoke tubers ranged from 8.16 to 13.46% of fresh weight. The maximum value of inulin content in 12 accessions of Jerusalem artichoke was detected in TUB CG 32.

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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