Germplasm Screening of Green Manure Rapeseed through the Effects of Short-Term Decomposition on Soil Nutrients and Microorganisms

The rapidly emerging fertilizer rapeseed used as green manure has wide applications for use. However, there have been few studies on its decomposition and effects on soil nutrients and microorganisms after its decay. In this study, 12 rapeseed lines to be screened were decomposed through a randomized block field design with two green-manure-specific varieties as the controls. The contents of nitrogen, phosphorus, and potassium from the plants, soil nutrients, and microbial changes after degradation were measured. There were substantial differences in the rates of decomposition and cumulative release of nutrients among the different lines after 30 days of rolling. The contents of phosphorus and potassium in the soil were 1.23–2.03 and 3.93–6.32 times those before decomposition, respectively. In addition, there was a significant difference in the relative content of soil microorganisms at the phylum level after the decomposition of different species of rapeseeds. Most of the top 20 bacterial groups significantly correlated with the characteristics of plant decomposition and soil nutrient content, including Proteobacteria, Actinomycetes, Armatimonadetes, Rokubacteria, and Planctomycetes. A principal component analysis showed that the soil microorganisms and nutrients are the leading factors that enable the evaluation of the decomposing characteristics of green manure rapeseed. Numbers 5 (purple leaf mustard) and 8 (Xiafang self-seeding) were more effective than two controls, which can be used as excellent types of germplasm to promote the breeding of green manure rapeseed.

Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa

Background Cabbage white butterflies (Pieris spp.) can be severe pests of Brassica crops such as Chinese cabbage, Pak choi (Brassica rapa) or cabbages (B. oleracea). Eggs of Pieris spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (B. nigra). Unravelling the genetic basis of this egg-killing trait in Brassica crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by P. brassicae eggs in B. rapa. Results A germplasm screening of B. rapa 56 accessions, representing the genetic and geographical diversity of a B. rapa core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for Pieris brassicae-induced cell death on chromosomes A02 (Pbc1), A03 (Pbc2), and A06 (Pbc3). The three QTLs Pbc1-3 contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with A. thaliana suggested that Pbc1 and Pbc2 are novel QTLs associated with this trait, while Pbc3 contains also LecRK-I.1, a gene of A. thaliana previously associated with cell death induced by a P. brassicae egg extract. Conclusions This study provides the first genomic regions associated with the Pieris egg-induced HR-like cell death in a Brassica crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.

Growing Jatropha (Jatropha curcas L.) as a Potential Second-Generation Biodiesel Feedstock

Dwindling supplies of fossil fuels and their deleterious impacts on human health and the global environment have intensified the search for substitute energy sources. Biodiesel has been identified as a promising renewable energy substitute for diesel fuel due to several comparable and sustainable properties. However, approximately 95% of biodiesel is derived from edible oil crops, threatening the current food supplies. Therefore, the biodiesel production potential from inexpensive, non-edible, and non-conventional bioenergy crops, such as Jatropha (Jatropha curcas L.), has attracted the attention of many researchers, policymakers, and industries globally. Jatropha is considered to be the second-generation biofuel feedstocks for biodiesel production. However, sustainable biodiesel generation from J. curcas oil has not yet been attained, owing to different socio-economic, ecological, and technical factors. This study aimed to synthesize the information from the existing literature on the present status and to identify the knowledge gaps for future research on Jatropha by providing comprehensive information regarding its origin and distribution, morphology, phenology, and reproduction, genetic diversity, its productivity, oil content, and fatty acid composition, the methodology used for extracting biodiesel, and agronomic, economic, and environmental aspects of biodiesel production. The germplasm screening of J. curcas and the exploration of its adaptability and agronomic potential across diverse climates are highly desired to promote this crop as an alternative biofuel crop, particularly in arid and semi-arid regions. Moreover, future research should focus on developing, optimizing, and modernizing the technologies involving seed collection, the processing of seeds, oil extraction, and the production of biodiesel.

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

Understanding the Sorghum–Colletotrichum sublineola Interactions for Enhanced Host Resistance

Improving sorghum resistance is a sustainable method to reduce yield losses due to anthracnose, a devastating disease caused by Colletotrichum sublineola. Elucidating the molecular mechanisms of sorghum–C. sublineola interactions would help identify biomarkers for rapid and efficient identification of novel sources for host-plant resistance improvement, understanding the pathogen virulence, and facilitating resistance breeding. Despite concerted efforts to identify resistance sources, the knowledge about sorghum–anthracnose interactions remains scanty. Hence, in this review, we presented an overview of the current knowledge on the mechanisms of sorghum-C. sublineola molecular interactions, sources of resistance for sorghum breeding, quantitative trait loci (QTL), and major (R-) resistance gene sequences as well as defense-related genes associated with anthracnose resistance. We summarized current knowledge about C. sublineola populations and its virulence. Illustration of the sorghum-C. sublineola interaction model based on the current understanding is also provided. We highlighted the importance of genomic resources of both organisms for integrated omics research to unravel the key molecular components underpinning compatible and incompatible sorghum–anthracnose interactions. Furthermore, sorghum-breeding strategy employing rapid sorghum germplasm screening, systems biology, and molecular tools is presented.

NIRS Estimation of Drought Stress on Chemical Quality Constituents of Taro (Colocasia esculenta L.) and Sweet Potato (Ipomoea batatas L.) Flours

Taro (Colocasia esculenta (L.) Schott) and sweet potato (Ipomoea batatas (L.) Lam.) are important food crops worldwide, whose productivity is threatened by climatic constraints, namely drought. Data calibration, validation, and model development of high-precision near-infrared spectroscopy (NIRS) involving multivariate analyses are needed for the fast prediction of the quality of tubers and shoots impacted by drought stress. The main objective of this study was to generate accurate NIRS models for quality assessment of taro and sweet potato accessions (acc.) subjected to water scarcity conditions. Seven taro and eight sweet potato acc. from diverse geographical origins were evaluated for nitrogen (N), protein (Pt), starch (St), total mineral (M), calcium oxalate (CaOx), carbon isotope discrimination (Δ13C), and nitrogen isotopic composition (δ15N). Models were developed separately for both crops underground and aboveground organs. N, Pt, St, and M models could be used as quality control constituents, with a determination coefficient of prediction (r2pred) between 0.856 and 0.995. δ13C, δ15N, and CaOx, with r2pred between 0.178 and 0.788, could be used as an informative germplasm screening tool. The approach used in the present study demonstrates NIRS’s potential for further research on crop quality under drought.

Quantitative PCR Assays Developed for Diaporthe helianthi and Diaporthe gulyae for Phomopsis Stem Canker Diagnosis and Germplasm Screening in Sunflower (Helianthus annuus)

Phomopsis stem canker of sunflower is caused by two fungal pathogens, Diaporthe helianthi and Diaporthe gulyae, in the United States. In this study, two quantitative PCR (qPCR) assays were developed to detect and quantify D. helianthi and D. gulyae in sunflower. The two assays differentiated the two fungi from each other, other species of the genus Diaporthe, and pathogens, and they have high efficiency (>90%). The qPCR assays detected the two pathogens on plant samples exhibiting Phomopsis stem canker symptoms sampled from commercial sunflower fields in Minnesota, Nebraska, North Dakota, and South Dakota. Furthermore, the assays were used to screen cultivated sunflower accessions for resistance to D. helianthi and D. gulyae. The disease severity index (DSI) of the accessions significantly correlated (P < 0.0001) with the amount of pathogen DNA from the qPCR assays. The qPCR assays identified PI664232 and PI561918 to be significantly less susceptible (P ≤ 0.05) to D. helianthi and D. gulyae, respectively, when compared with the susceptible check cultivar HA 288, and this was in agreement with the DSI. These results suggest that the qPCR assays for D. helianthi and D. gulyae can be used as a reliable tool to diagnose Phomopsis stem canker and screen sunflower germplasm for disease resistance.