Harnessing genetic diversity in major staple crops through the development of new breeding capabilities is essential to ensure food security¹. Here we examined the genetic and phenotypic diversity of the A. E. Watkins landrace collection² of bread wheat (Triticum aestivum), a major global cereal, by whole-genome re-sequencing of 827 Watkins landraces and 208 modern cultivars and in-depth field evaluation spanning a decade. We found that modern cultivars are derived from two of the seven ancestral groups of wheat and maintain very long-range haplotype integrity. The remaining five groups represent untapped genetic sources, providing access to landrace-specific alleles and haplotypes for breeding. Linkage disequilibrium-based haplotypes and association genetics analyses link Watkins genomes to the thousands of identified high-resolution quantitative trait loci and significant marker–trait associations. Using these structured germplasm, genotyping and informatics resources, we revealed many Watkins-unique beneficial haplotypes that can confer superior traits in modern wheat. Furthermore, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritized quantitative trait loci in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilizing genetic diversity in crop improvement to achieve sustainable food security.

Key message: This review illustrates how far we have come since the emergence of GE technologies and how they could be applied to obtain superior and sustainable crop production. The main challenges of today’s agriculture are maintaining and raising productivity, reducing its negative impact on the environment, and adapting to climate change. Efficient plant breeding can generate elite varieties that will rapidly replace obsolete ones and address ongoing challenges in an efficient and sustainable manner. Site-specific genome editing in plants is a rapidly evolving field with tangible results. The technology is equipped with a powerful toolbox of molecular scissors to cut DNA at a pre-determined site with different efficiencies for designing an approach that best suits the objectives of each plant breeding strategy. Genome editing (GE) not only revolutionizes plant biology, but provides the means to solve challenges related to plant architecture, food security, nutrient content, adaptation to the environment, resistance to diseases and production of plant-based materials. This review illustrates how far we have come since the emergence of these technologies and how these technologies could be applied to obtain superior, safe and sustainable crop production. Synergies of genome editing with other technological platforms that are gaining significance in plants lead to an exciting new, post-genomic era for plant research and production. In previous months, we have seen what global changes might arise from one new virus, reminding us of what drastic effects such events could have on food production. This demonstrates how important science, technology, and tools are to meet the current time and the future. Plant GE can make a real difference to future sustainable food production to the benefit of both mankind and our environment.

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).

The unique rheological properties of bread wheat dough and the breadmaking quality of its flour are the main factors responsible for the global distribution and utilization of wheat. Recently, interest in the production and expansion of spelt wheat has been boosted due to its significance in the production of healthy food, mostly originated from organic production. The aim of this study was to examine and compare quality parameters (gluten content, Zeleny sedimentation volume, farinograph dough properties), protein content and composition (by the Dumas method, Size Exclusion (SE) and Reversed Phase (RP) High Performance Liquid Chromatography (HPLC) analyses) of five bread and five spelt wheat varieties grown under conventional and organic production in Hungary and under conventional production in Serbia. Most of the analyzed traits showed significant differences between varieties, wheat species and growing sites. Total protein content was significantly higher in spelt than in bread wheat and under conventional than under organic production. In comparison to spelt, bread wheat showed better breadmaking quality, characterized by a higher amount of glutenins (in particular high molecular weight glutenin subunits) and unextractable polymeric proteins. The proportion of the gliadins was also found to be different under conventional and organic systems. Spelt Ostro and Oberkulmer-Rotkorn and bread wheat varieties Balkan, Estevan and Pobeda proved suitable for low input and organic systems.

The objective of this study was to assess genotype by environment interaction for seed yield per plant in rapeseed cultivars grown in Northern Serbia by the AMMI (additive main effects and multiplicative interaction) model. The study comprised 19 rapeseed genotypes, analyzed in seven years through field trials arranged in a randomized complete block design, with three replicates. Seed yield per plant of the tested cultivars varied from 1.82 to 19.47 g throughout the seven seasons, with an average of 7.41 g. In the variance analysis, 72.49% of the total yield variation was explained by environment, 7.71% by differences between genotypes, and 19.09% by genotype by environment interaction. On the biplot, cultivars with high yield genetic potential had positive correlation with the seasons with optimal growing conditions, while the cultivars with lower yield potential were correlated to the years with unfavorable conditions. Seed yield per plant is highly influenced by environmental factors, which indicates the adaptability of specific genotypes to specific seasons.

The objectives of this study were to investigate: (1) the effects of genotype, environment, and their interactions on the oil content (OC), protein content (PC) and grain yield (GY) of 25 varieties of winter wheat, (2) the correlations among these traits in different environments, and (3) the effects of different climatic variables and their interactions with wheat genotypes for the examined traits. The field experiments were performed on three experimental sites in Serbia in 2009/10 and 2010/11. The most variable traits were GY and PC, while the variations of OC were lower. A significant positive correlation between wheat bran OC and GY was found in one, while highly significant negative correlations between PC and GY were found in three out of six environments. The partial least square regression (PLSR) triplots for protein content, oil content and grain yield enabled us to identify favorable and limiting climatic conditions for each trait and explained 31.9%, 32.6%, and 30.4% of the total variance, respectively. Cvs. Renesansa and Zvezdana were identified as genotypes with high average values for all traits, while cvs. Bankuty 1205 and Banatka were identified as potential sources of high protein content.