Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects

Globally it has been estimated that only one third of applied N is recovered in the harvested component of grain crops (Raun and Johnson 1999). This represents an incredible waste of resource and the overuse has detrimental environmental and economic consequences. There is substantial variation in nutrient use efficiency (NUE) from region to region, between crops and in different cropping systems. As a consequence, both local and crop specific solutions will be required for NUE improvement at local as well as at national and international levels. Strategies to improve NUE will involve improvements to germplasm and optimized agronomy adapted to climate and location. Essential to effective solutions will be an understanding of genetics (G), environment (E) and management (M) and their interactions (G x E x M). To implement appropriate solutions will require agronomic management, attention to environmental factors and improved varieties, optimized for current and future climate scenarios. As NUE is a complex trait with many contributing processes, identifying the correct trait for improvement is not trivial. Key processes include nitrogen capture (uptake efficiency), utilization efficiency (closely related to yield), partitioning (harvest index: biochemical and organ-specific) and trade-offs between yield and quality aspects (grain nitrogen content), as well as interactions with capture and utilization of other nutrients. A long-term experiment, the Broadbalk experiment at Rothamsted, highlights many factors influencing yield and nitrogen utilization in wheat over the last 175 years, particularly management and yearly variation. A more recent series of trials conducted over the past 16 years has focused on separating the key physiological sub-traits of NUE, highlighting both genetic and seasonal variation. This perspective describes these two contrasting studies which indicate G x E x M interactions involved in nitrogen utilization and summarizes prospects for the future including the utilization of high throughput phenotyping technology

Feeding the world sustainably - efficient nitrogen use

Globally, overuse of nitrogen (N) fertilizers in croplands is causing severe environmental pollution. In this context, Gu et al. suggest environmentally friendly and cost-effective N management practices and Hamani et al. highlight the use of microbial inoculants to improve crop yields, while reducing N-associated environmental pollution and N-fertilizer use

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe

A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery

(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions

Exploiting genetic variation in nitrogen use efficiency

Cereals are the most important sources of calories and nutrition for the human population, and are an essential animal feed. Food security depends on adequate production and demands are predicted to rise as the global population rises. The need for increased yields will have to be coupled to the efficient use of resources including fertilisers such as nitrogen to underpin the sustainability of food production. Although optimally performing crops with high yields require a balanced mineral nutrition, nitrogen fundamentally drives growth and yield as well as requirements for other nutrients. It is estimated that globally only 33% of applied nitrogen fertiliser is recovered in the harvested grain, indicative of a huge waste of resource and potential major pollutant and is thus a major target for crop improvement. Both agronomy and breeding will contribute to improved nitrogen use efficiency (NUE) and an important component of the latter is harnessing germplasm variation. This review will consider the key traits involved in NUE, the potential to exploit genetic variation for these specific traits, and the approaches to be utilised

Fatal encephalitis due to the scuticociliate Uronema nigricans in sea-caged, southern bluefin tuna Thunnus maccoyii

A syndrome characterized by atypical swimming behaviour followed by rapid death was first reported in captive southern bluefin tuna Thunnus maccoyii (Castelnau) in the winter of 1993. The cause of this behaviour was found to be a parasitic encephalitis due to the scuticociliate Uronema nigricans (Mueller). Based on parasitological and histological findings, it is proposed that the parasites initially colonise the olfactory rosettes and then ascend the olfactory nerves to eventually invade the brain. Possible epidemiological factors involved in the pathogenesis of the disease include water temperature (>18 degrees C) and the immune status of the fish

Next Generation dsRNA-Based Insect Control: Success So Far and Challenges

RNA interference (RNAi) is a method of gene silencing where dsRNA is digested into small interfering RNA (siRNA) in the presence of enzymes. These siRNAs then target homologous mRNA sequences aided by the RNA-induced silencing complex (RISC). The mechanism of dsRNA uptake has been well studied and established across many living organisms including insects. In insects, RNAi is a novel and potential tool to develop future pest management means targeting various classes of insects including dipterans, coleopterans, hemipterans, lepidopterans, hymenopterans and isopterans. However, the extent of RNAi in individual class varies due to underlying mechanisms. The present review focuses on three major insect classes viz hemipterans, lepidopterans and coleopterans and the rationale behind this lies in the fact that studies pertaining to RNAi has been extensively performed in these groups. Additionally, these classes harbour major agriculturally important pest species which require due attention. Interestingly, all the three classes exhibit varying levels of RNAi efficiencies with the coleopterans exhibiting maximum response, while hemipterans are relatively inefficient. Lepidopterans on the other hand, show minimum response to RNAi. This has been attributed to many facts and few important being endosomal escape, high activity dsRNA-specific nucleases, and highly alkaline gut environment which renders the dsRNA unstable. Various methods have been established to ensure safe delivery of dsRNA into the biological system of the insect. The most common method for dsRNA administration is supplementing the diet of insects via spraying onto leaves and other commonly eaten parts of the plant. This method is environment-friendly and superior to the hazardous effects of pesticides. Another method involves submergence of root systems in dsRNA solutions and subsequent uptake by the phloem. Additionally, more recent techniques are nanoparticle- and Agrobacterium-mediated delivery systems. However, due to the novelty of these biotechnological methods and recalcitrant nature of certain crops, further optimization is required. This review emphasizes on RNAi developments in agriculturally important insect species and the major hurdles for efficient RNAi in these groups. The review also discusses in detail the development of new techniques to enhance RNAi efficiency using liposomes and nanoparticles, transplastomics, microbial-mediated delivery and chemical method

Substantial increase in yield predicted by wheat ideotypes for Europe under future climate

A substantial increase in food production is needed for global food security. Europe is the largest wheat producer, delivering 35% of wheat globally, but its future genetic yield potential is yet unknown. We estimated the genetic yield potential of wheat in Europe under 2050 climate by designing in silico wheat ideotypes based on genetic variation in wheat germplasm. To evaluate the importance of heat and drought stresses around flowering, a critical stage in wheat development, sensitive and tolerant ideotypes were designed. Ideotype yields ranged from 9 to 17 t ha−1 across major wheat growing regions in Europe under 2050 climate. Both ideotypes showed a substantial increase in yield of 66−89% compared to current local cultivars under future climate. Key traits for wheat improvements under future climate were identified. Ideotype design is a powerful tool for estimating crop genetic yield potential in a target environment, along with the potential to accelerate breeding by providing target traits for improvements

DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

Crop yield is an essential measure for breeders, researchers and farmers and is comprised of and may be calculated by the number of ears/m2, grains per ear and thousand grain weight. Manual wheat ear counting, required in breeding programmes to evaluate crop yield potential, is labour intensive and expensive; thus, the development of a real-time wheat head counting system would be a significant advancement. In this paper, we propose a computationally efficient system called DeepCount to automatically identify and count the number of wheat spikes in digital images taken under the natural fields conditions. The proposed method tackles wheat spike quantification by segmenting an image into superpixels using Simple Linear Iterative Clustering (SLIC), deriving canopy relevant features, and then constructing a rational feature model fed into the deep Convolutional Neural Network (CNN) classification for semantic segmentation of wheat spikes. As the method is based on a deep learning model, it replaces hand-engineered features required for traditional machine learning methods with more efficient algorithms. The method is tested on digital images taken directly in the field at different stages of ear emergence/maturity (using visually different wheat varieties), with different canopy complexities (achieved through varying nitrogen inputs), and different heights above the canopy under varying environmental conditions. In addition, the proposed technique is compared with a wheat ear counting method based on a previously developed edge detection technique and morphological analysis. The proposed approach is validated with image-based ear counting and ground-based measurements. The results demonstrate that the DeepCount technique has a high level of robustness regardless of variables such as growth stage and weather conditions, hence demonstrating the feasibility of the approach in real scenarios. The system is a leap towards a portable and smartphone assisted wheat ear counting systems, results in reducing the labour involved and is suitable for high-throughput analysis. It may also be adapted to work on RGB images acquired from UAVs