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

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

Overexpression of a NAC transcription factor delays leaf senescence and increases grain nitrogen concentration in wheat

Increasing the duration of leaf photosynthesis during grain filling using slow-senescing functional stay-green phenotypes is a possible route for increasing grain yields in wheat (Triticum aestivum L.). However, delayed senescence may negatively affect nutrient remobilisation and hence reduce grain protein concentrations and grain quality. A novel NAC1-type transcription factor (hereafter TaNAC-S) was identified in wheat, with gene expression located primarily in leaf/sheath tissues, which decreased during post-anthesis leaf senescence. Expression of TaNAC-S in the second leaf correlated with delayed senescence in two doubled-haploid lines of an Avalon 3 Cadenza population (lines 112 and 181), which were distinct for leaf senescence. Transgenic wheat plants overexpressing TaNAC-S resulted in delayed leaf senescence (stay-green phenotype). Grain yield, aboveground biomass, harvest index and total grain N content were unaffected, but NAC over-expressing lines had higher grain N concentrations at similar grain yields compared to non-transgenic controls. These results indicate that TaNAC-S is a negative regulator of leaf senescence, and that delayed leaf senescence may lead not only to increased grain yields but also to increased grain protein concentrations.D. Zhao, A. P. Derkx, D.-C. Liu, P. Buchner, M. J. Hawkesfor

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

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

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

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

The Functional Diversity of the High-Affinity Nitrate Transporter Gene Family in Hexaploid Wheat: Insights from Distinct Expression Profiles

High-affinity nitrate transporters (NRT) are key components for nitrogen (N) acquisition and distribution within plants. However, insights on these transporters in wheat are scarce. This study presents a comprehensive analysis of the NRT2 and NRT3 gene families, where the aim is to shed light on their functionality and to evaluate their responses to N availability. A total of 53 NRT2s and 11 NRT3s were identified in the bread wheat genome, and these were grouped into different clades and homoeologous subgroups. The transcriptional dynamics of the identified NRT2 and NRT3 genes, in response to N starvation and nitrate resupply, were examined by RT-qPCR in the roots and shoots of hydroponically grown wheat plants through a time course experiment. Additionally, the spatial expression patterns of these genes were explored within the plant. The NRT2s of clade 1, TaNRT2.1-2.6, showed a root-specific expression and significant upregulation in response to N starvation, thus emphasizing a role in N acquisition. However, most of the clade 2 NRT2s displayed reduced expression under N-starved conditions. Nitrate resupply after N starvation revealed rapid responsiveness in TaNRT2.1-2.6, while clade 2 genes exhibited gradual induction, primarily in the roots. TaNRT2.18 was highly expressed in above-ground tissues and exhibited distinct nitrate-related response patterns for roots and shoots. The TaNRT3 gene expression closely paralleled the profiles of TaNRT2.1-2.6 in response to nitrate induction. These findings enhance the understanding of NRT2 and NRT3 involvement in nitrogen uptake and utilization, and they could have practical implications for improving nitrogen use efficiency. The study also recommends a standardized nomenclature for wheat NRT2 genes, thereby addressing prior naming inconsistencies

Root phenotyping and root water uptake calculation using soil water contents measured in a winter wheat field

Non-destructive phenotyping of roots and measurement of root water uptake from different soil layers in the field are vital for improving water management and facilitating the development of drought-resistant crop varieties, but difficult because of their opaqueness. As a result, indirect methods using easy-to-measure variables such as soil water content have been used as alternatives. However, the inherent measurement errors could undermine the robustness and reliability of these methods. This paper proposes a new method to bridge this knowledge gap by using soil water content profiles measured at two time points to calculate root uptake and root-length density. It is based on the Richards' equation by treating root uptake from different soil layers between the two time points as random unknown numbers; their distributions are calculated using the Bayesian framework, solved by the Markov Chain Monte Carlo method. We applied the method to 39 winter wheat lines grown in a silt-clay loam field. Soil water content profile measured at the first time point from each plot served as the initial condition, and water content measured at the second time point was the target to match the model for calculating average root water uptake and root-length density between the two time points. The results show that the measured soil water contents fall within the 95% confidence interval of the calculated soil water contents. The inherent soil water measurement errors lead to uncertainties in the calculated root water uptake for all lines, but such uncertainties decrease with soil depth. Although the soil types and agronomic management were the same for all 39 lines, their root water uptake from different soil layers varies considerably, with some lines more capable of using subsoil water than others. Generally, the calculated and measured root-length densities agree well, albeit the degree of the agreement varies with lines. While this paper focuses on methodology and applies the method to one growth stage spanning one month only, the consistent results for all 39 lines indicates the method is robust and can be applied to other crops cultivated in different conditions. Given the growing interest in improving root traits to enhance water use efficiency, the proposed method has important implications as phenotyping roots and understanding their water uptake from different soil layers in the field is a prerequisite to achieve this crucial target