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Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK's 4.5 million hectares of arable land whilst the country imports around half its food—nearly 40 per cent—from just four EU countries (The Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate-resilient and nutritious underutilised crops can help diversify the UK agrifood system, but research and investment in them is sporadic, piecemeal, and unfocused. In this paper, we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on UK Department for Environment, Food and Rural Affairs (Defra) Project CH0224, was delivered through literature and database searches and the expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second used the CropBASE digital knowledge base for underutilised crops. The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future. [ABSTRACT FROM AUTHOR]

In order to eliminate the limitations of traditional winter wheat yield prediction methods, the prediction models based on machine learning are used to improve the accuracy of winter wheat yield prediction. In this study, by collecting a large amount of domestic literature about wheat growth characteristics, the irrigation amount, fertilization amount, soil nutrient status, planting density, maximum leaf area index (LAImax), maximum aboveground dry matter accumulation (Dmax) and yield (Y) were chosen to develop the learning models. Using the data of the irrigation amount, fertilization amount, soil nutrient status and planting density as the training set, the regression prediction models (Gaussian process regression mode, linear regression model, regression tree mode and support vector machine model) were used to train and learn the data of the LAImax, Dmax and Y, respectively. The results show that the Gaussian regression model has the best precision compared to the other models. The coefficients of determination (R2) of the learning results of the Gaussian regression model for the LAImax, Dmax and Y are 0.9, 0.93 and 0.86, and the root mean square error (RMSE) is 0.57, 1125.1 and 640.41. Based on the data of the irrigation amount, nitrogen application amount, potassium application amount, phosphorus application amount, organic matter content, total nitrogen content, alkali-hydrolyzable nitrogen content, available phosphorus content, available potassium content and planting density, the method proposed in this paper can reliably predict the LAImax, the Dmax and Y of winter wheat. The results also have certain reference significance for the yield prediction of other crops. [ABSTRACT FROM AUTHOR]

Chromosome elimination resulting in haploids is achieved by rapid loss of chromosomes from one parent during the zygote stage and is an important procedure to produce doubled haploid (DH) lines in plants. During crosses between an emasculated wheat (Triticum aestivum L.) and maize (Zea mays L.) as pollen donors, the complete loss of maize chromosomes results in wheat haploid embryos. Through embryo rescue and chromosome doubling processes, pure lines with stable traits can be quickly obtained. The technique is called the "Wheat × Maize System". Although this technology is not new, it remains a practical approach to date. In order to optimize and improve this technology and to achieve its maximum potential in the winter wheat area of China, this paper reviews the previous and ongoing research and technical procedures for the production of wheat DH lines via the maize pollen induction and presents outlooks on DH research and its application in wheat breeding. [ABSTRACT FROM AUTHOR]

The main purpose of the paper is to highlight the impact of foliar fertilization during the various growth stages of winter wheat and its role in achieving high-quality and superior production. Foliar fertilizers play a crucial role in calibrating and forming active growth intervals correlated with productivity factors. The research was conducted over a two-year period using the Andrada winter wheat variety. It involved the application of four different foliar fertilizers: basic mineral fertilization, FoliMAX Orange (two treatments and three treatments), FoliMAX CerealsMIX (two treatments and three treatments), and Microfert U (three treatments). Depending on the treatment, two or three applications of foliar with foliar fertilizers were carried out at various stages of winter wheat development, including pre-flowering and grain formation. The research method used to record physiological parameters was non-destructive (the leaves were not detached from the plant) and was based on the use of the CIRAS-3 foliar gas analyzer, which simultaneously determines several physiological and environmental indicators. As an indicator read by the device, photosynthesis was chosen for leaf temperature and leaf transpiration, correlated with production and quality indices. By applying the foliar fertilizer treatments to the wheat culture, we managed to increase production and improve the quality. After using different foliar fertilizers, the assimilation and physiological parameters had higher values of over 30 μmolm−2·s−1. Foliar fertilization applied to the winter wheat variety resulted in an average production increase between 450 and 765 kg·ha−1 and quality indices with a protein percentage between 11.5 and 12.6%, gluten content between 21.5 to 24.0% and the Zeleny index between 29.0 and 39.0%, and the mass of one thousand grains was between 48.0 and 50.5 g. [ABSTRACT FROM AUTHOR]