PLANTOLOGY
Archaeologists report earliest evidence for plant farming in east Africa
IMAGE:
LOCATED IN THE FOOTHILLS OF MOUNT ELGON NEAR THE KENYA-UGANDA BORDER, KAKAPEL ROCKSHELTER IS THE SITE WHERE WASHU ARCHAEOLOGIST NATALIE MUELLER AND HER COLLABORATORS HAVE UNCOVERED THE EARLIEST EVIDENCE FOR PLANT FARMING IN EAST AFRICA.
view moreCREDIT: STEVEN GOLDSTEIN
A trove of ancient plant remains excavated in Kenya helps explain the history of plant farming in equatorial eastern Africa, a region long thought to be important for early farming but where scant evidence from actual physical crops has been previously uncovered.
In a new study published July 10 in the Proceedings of the Royal Society B, archaeologists from Washington University in St. Louis, the University of Pittsburgh and their colleagues report the largest and most extensively dated archaeobotanical record from interior east Africa.
Up until now, scientists have had virtually no success in gathering ancient plant remains from east Africa and, as a result, have had little idea where and how early plant farming got its start in the large and diverse area comprising Kenya, Tanzania and Uganda.
“There are many narratives about how agriculture began in east Africa, but there’s not a lot of direct evidence of the plants themselves,” said WashU’s Natalie Mueller, an assistant professor of archaeology in Arts & Sciences and co-first author of the new study. The work was conducted at the Kakapel Rockshelter in the Lake Victoria region of Kenya.
“We found a huge assemblage of plants, including a lot of crop remains,” Mueller said. “The past shows a rich history of diverse and flexible farming systems in the region, in opposition to modern stereotypes about Africa.”
The new research reveals a pattern of gradual introductions of different crops that originated from different parts of Africa.
In particular, the remnants of cowpea discovered at Kakapel rock shelter and directly dated to 2,300 years ago constitute the earliest documented arrival of a domesticated crop — and presumably of farming lifeways — to eastern Africa. Cowpea is assumed to have originated in west Africa and to have arrived in the Lake Victoria basin concurrent with the spread of Bantu-speaking peoples migrating from central Africa, the study authors said.
“Our findings at Kakapel reveal the earliest evidence of domesticated crops in east Africa, reflecting the dynamic interactions between local herders and incoming Bantu-speaking farmers,” said Emmanuel Ndiema from the National Museums of Kenya, a project partner. “This study exemplifies National Museums of Kenya’s commitment to uncovering the deep historical roots of Kenya’s agricultural heritage and fostering an appreciation of how past human adaptations can inform future food security and environmental sustainability.”
Constantly changing landscape
Situated north of Lake Victoria, in the foothills of Mount Elgon near the Kenya-Uganda border, Kakapel is a recognized rock art site that contains archaeological artifacts that reflect more than 9,000 years of human occupation in the region. The site has been recognized as a Kenyan national monument since 2004.
“Kakapel Rockshelter is one of the only sites in the region where we can see such a long sequence of occupation by so many diverse communities,” said Steven T. Goldstein, an anthropological archaeologist at the University of Pittsburgh (WashU PhD ’17), the other first author of this study. “Using our innovative approaches to excavation, we have been uniquely able to detect the arrival of domesticated plants and animals into Kenya and study the impacts of these introductions on local environments, human technology and sociocultural systems.”
Mueller first joined Goldstein and National Museums of Kenya to conduct excavations at the Kakapel Rockshelter site in 2018. Their work is ongoing. Mueller is the lead scientist for plant investigations at Kakapel; the Max Planck Institute of Geoanthropology (in Jena, Germany) is another partner on the project.
Mueller used a flotation technique to separate remnants of wild and domesticated plant species from ashes and other debris in a hearth excavated at Kakapel. Although she has used this technique in her research in many other parts of the world, it is sometimes difficult to use this approach in water-scarce locations — so it has not been widely used in east Africa.
The scientists used direct radiocarbon dating on carbonized seeds to document the arrival of cowpea (also known as the black-eyed pea, today an important legume around the world) about 2,300 years ago, at about the same time that people in this area began to use domesticated cattle. Researchers also found evidence that sorghum arrived from the northeast at least 1,000 years ago. They also recovered hundreds of finger millet seeds, dating back to at least 1,000 years ago. This crop is indigenous to eastern Africa and is an important heritage crop for the communities that live near Kakapel today.
One unusual crop that Mueller uncovered was field pea (Pisum), burnt but perfectly intact. Peas were not previously considered to be part of early agriculture in this region. “To our knowledge, this is the only evidence of peas in Iron Age eastern Africa,” Mueller said.
The exceptional pea is pictured in the paper, and it represents its own little mystery. “The standard peas that we eat in North America were domesticated in the near east,” Mueller said. “They were grown in Egypt and probably ended up in east Africa by traveling down the Nile through Sudan, which is also likely how sorghum ended up in east Africa. But there is another kind of pea that was domesticated independently in Ethiopia called the Abyssinian pea, and our sample could be either one!”
Many of the plant remnants that Mueller and her team found at Kakapel could not be positively identified, Mueller said, because even modern scientists working in Kenya, Tanzania and Uganda today don’t have access to a good reference collection of samples of plants from east Africa. (As a separate project, Mueller is currently working on building such a comparative collection of Tanzania’s plants.)
“Our work shows that African farming was constantly changing as people migrated, adopted new crops and abandoned others at a local level,” Mueller said. “Prior to European colonialism, community-scale flexibility and decision-making was critical for food security — and it still is in many places.”
Findings from this study may have implications for many other fields, Mueller said, including historical linguistics, plant science and genetics, African history and domestication studies.
Mueller is continuing to work on identifying the wild plants in the assemblage, especially those from the oldest parts of the site, before the beginning of agriculture. “This is where human evolution occurred,” Mueller said. “This is where hunting and gathering was invented by people at the dawn of time. But there has been no archaeological evidence about which plants hunter-gatherers were eating from this region. If we can get that kind of information from this assemblage, then that is a great contribution.”
One unusual crop that Mueller uncovered was field pea, burnt but perfectly intact. Peas were not previously considered to be part of early agriculture in this region.
CREDIT
Courtesy of Proc. Royal Soc. B
JOURNAL
Proceedings of the Royal Society B Biological Sciences
ARTICLE TITLE
Early agriculture and crop transitions at Kakapel Rockshelter in the Lake Victoria region of eastern Africa
ARTICLE PUBLICATION DATE
10-Jul-2024
How a plant app helps identify the consequences of climate change
By leveraging millions of time-stamped observations, researchers can identify plant rhythms and ecological patterns year-round
IMAGE:
THE FLORA INCOGNITA APP MAKES IT EASY TO IDENTIFY PLANTS WITH A SMARTPHONE.
view moreCREDIT: FLORA INCOGNITA
Plants are known to respond to seasonal changes by budding, leafing, and flowering. As climate change stands to shift these so-called phenological stages in the life cycle of plants, access to data about phenological changes – from many different locations and in different plants – can be used to draw conclusions about the actual effects of climate change. However, conducting such analyses require a large amount of data and data collection of this scale would be unthinkable without the help of citizen scientists. “The problem is that the quality of the data suffers when fewer people engage as citizen scientists and stop collecting data,” says first author Karin Mora, research fellow at Leipzig University and iDiv.
Mobile apps like Flora Incognita could help solve this issue. The app allows users to identify unknown wild plants within a matter of seconds. “When I take a picture of a plant with the app, the observation is recorded with the (exact) location as well as a time stamp,” explains co-author Jana Wäldchen from the Max Planck Institute for Biogeochemistry (MPI-BGC), who developed the app with colleagues from TU Ilmenau. “Millions of time-stamped plant observations from different regions have been collected by now.” Although satellite data also records the phenology of entire ecosystems from above, they do not provide information about the processes taking place on the ground.
Plants show synchronised response
The researchers developed an algorithm that draws on almost 10 million observations of nearly 3,000 plants species identified between 2018 and 2021 in Germany by users of Flora Incognita. The data show that each individual plant has its own cycle as to when it begins a flowering or growth phase. Furthermore, the scientists were able to show that group behaviour arises from the behaviour of individuals. From this, they were able to derive ecological patterns and investigate how these change with the seasons. For example, ecosystems by rivers differ from those in the mountains, where phenological events start later.
The algorithm also accounts for the observational tendencies of Flora Incognita users, whose data collection is far from systematic. For example, users record more observations on the weekend and in densely populated areas. “Our method can automatically isolate these effects from the ecological patterns,” Karin Mora explains. “Fewer observations don’t necessarily mean that we can’t record the synchronisation. Of course, there are very few observations in the middle of winter, but there are also very few plants that can be observed during that time.”
It is known that climate change is causing seasonal shifts – for example, spring is arriving earlier and earlier. How this affects the relationship between plants and pollinating insects and therefore potentially also food security is still being subject to further research. The new algorithm can now be used to better analyse the effects of these changes on the plant world.
This study was funded by the Deutsche Forschungsgemeinschaft (DFG; FZT-118) and the iDiv Flexpool.
JOURNAL
Methods in Ecology and Evolution
METHOD OF RESEARCH
Data/statistical analysis
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Macrophenological dynamics from citizen science plant occurrence data
ARTICLE PUBLICATION DATE
9-Jul-2024
From genomes to gardens: introducing the HortGenome Search Engine for horticultural crops
IMAGE:
SEARCH INTERFACE AND RESULT PAGES IN HORTGENOME SEARCH ENGINE. A–E SCREENSHOTS OF THE SEARCH INTERFACES. F GENE LIST OF SEARCH RESULTS, INCLUDING PLANT NAME, GENE/TRANSCRIPT ID, GENOMIC LOCATION AND FUNCTIONAL DESCRIPTION.
view moreCREDIT: HORTICULTURE RESEARCH
The HortGenome Search Engine (HSE) introduces a groundbreaking tool that transforms the exploration of horticultural crops' genetics. Enabling swift access and analysis of data from over 500 plant species, HSE enhances our ability to decode complex genetic networks. This launch marks a pivotal advancement in horticultural studies, offering detailed insights into crop genetics critical for human nutrition and health.
As genomics profoundly reshapes our understanding of horticultural crops, researchers often grapple with dispersed and complex genomic data. This fragmentation significantly hinders effective analysis and application, presenting a clear demand for more cohesive research tools. Addressing this need is crucial for unlocking the full potential of genomic insights to enhance crop quality, diversity, and resilience in the face of growing agricultural demands.
Researchers from Beijing University of Agriculture, in collaboration with international scientists, have announced the development of HortGenome Search Engine (HSE), published in the prestigious journal Horticulture Research on April 8, 2024. The study (DOI: 10.1093/hr/uhae100) introduces a novel search engine designed to query and analyze genomic data of over 500 horticultural crops, enhancing our understanding of gene functions and crop improvement.
HSE consolidates genomic data from a diverse array of over 500 horticultural crops into a unified platform, providing easy access and efficient comparison of genetic information. Equipped with advanced tools like Basic Local Alignment Search Tool (BLAST) and Synteny Viewer, HSE streamlines gene querying and functional annotation processes. Features such as batch query interfaces and enrichment analysis simplify data navigation, boosting research efficiency. The engine's capability to identify crucial gene families, like the TCP transcription factors in tomatoes, highlights its essential role in driving forward genomic research and agricultural innovation.
Dr. Zhangjun Fei, co-developer of HSE, states, "The HortGenome Search Engine is a vital breakthrough in horticultural genomics, providing unparalleled access to extensive genomic data. This tool revolutionizes plant genomic research, significantly speeding up crop improvement discoveries and applications."
The HSE not only streamlines genomic research but also profoundly impacts crop breeding and genetic conservation. By simplifying genomic data access and analysis, HSE enables researchers to quickly pinpoint genes associated with desirable traits, thus accelerating the breeding of crop varieties that are more nutritious, resilient, and compatible with sustainable agricultural practices.
###
References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae100
Funding information
This work was supported by grants from the Beijing University of Agriculture (Start-up fund) to Y.Z., Young Teachers’ Research and Innovation Capacity Enhancement Program QJKC2022044 and Beijing Municipal Education Commission Scientific Research Plan Project KM202310020010 to S.Wang. The computing power was supported by the Alibaba Cloud.
About Horticulture Research
Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2022. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.
JOURNAL
Horticulture Research
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
HortGenome Search Engine, a universal genomic search engine for horticultural crops
LGNet revolutionizes plant disease detection for enhanced crop protection
NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE
IMAGE:
THE ACCURACY OF EACH EPOCH.
view moreCREDIT: THE AUTHORS
A research team has developed LGNet, a dual-branch network that combines convolutional neural networks (CNNs) and visual transformers (VTs) for plant disease identification. LGNet effectively fuses local and global features, achieving state-of-the-art recognition accuracies of 88.74% on the AI Challenger 2018 dataset and 99.08% on the self-collected corn disease dataset. This innovative approach enhances disease sensing capabilities and offers the potential for the development of efficient and robust plant disease recognition models, which are crucial for improving agricultural production and ensuring crop safety in diverse environments.
Safeguarding agricultural production is vital for economic growth, as plant diseases significantly threaten crop yields. The traditional methods of identifying plant diseases, which rely on the farmers' experience, are time-consuming and inadequate for large-scale cultivation. Recent advancements in image processing and deep learning have improved plant disease recognition, yet existing methods using only CNNs or VTs fall short due to their limited feature perception.
A study (DOI: 10.34133/plantphenomics.0208) published in Plant Phenomics on 21 Jun 2024, proposes LGNet, a dual-branch network combining CNNs and VTs that enhances both local and global feature extraction, achieving state-of-the-art performance on major datasets.
The research divided LGNet's parameters into two parts for training, utilizing pretrained weights on ImageNet 1k for the dual-branch backbone network and fine-tuning with different learning rates. The model was optimized with SGD, momentum, and weight decay, and trained on a Windows 11 system with an NVIDIA GeForce RTX 3090 GPU and PyTorch. For evaluation purpose, cross-entropy loss was used, while online data augmentation enhanced generalization. LGNet's performance was compared to single models ConvNeXt-Tiny and Swin Transformer-Tiny. The initial training accuracies were high for all models, but LGNet's accuracy improved significantly, surpassing the others by 1-2%. On the AI Challenger 2018 and SCD datasets, LGNet achieved 88.74% and 99.08% accuracy, respectively, outperforming the single models. Ablation experiments showed that both the AFF and HMUFF modules enhanced performance, with the full LGNet model achieving the best results, demonstrating the effectiveness of the dual-branch network and feature fusion techniques.
According to the study's lead researcher, Xin Zhang, “The development of robust plant disease recognition models, and improving the generalization ability of these models in real-world environments, is highly important for agricultural production.”
In summary, this study presents LGNet, a dual-branch network combining CNNs and VTs for enhanced plant disease identification. Future research will focus on knowledge distillation to create lightweight, high-performance models for mobile deployment and on obtaining more real-world data to enhance model robustness, thereby improving precision agriculture and ensuring crop safety.
###
References
DOI
Original Source URL
https://doi.org/10.34133/plantphenomics.0208
Funding information
This study was supported by the National Key Research and Development Program of China (2021YFE0113700), the National Natural Science Foundation of China (32360705;31960555), the Guizhou Provincial Science and Technology Program (2019-1410;HZJD[2022]001), the Outstanding Young Scientist Program of Guizhou Province (KY2021-026), and the Program for Introducing Talents to Chinese Universities (111 Program; D20023).
About Plant Phenomics
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
JOURNAL
Plant Phenomics
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Local and global feature-aware dual-branch networks for plant disease recognition
Exemplar-based data generation and leaf-level analysis for phenotyping drought-stressed poplar saplings
NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE
IMAGE:
LEAF POSTURE CALCULATION RESULTS.
view moreCREDIT: THE AUTHORS
A research team has developed a novel method combining computer vision and deep learning to phenotype drought-stressed poplar saplings, achieving 99% accuracy in variety identification and 76% accuracy in stress-level classification. This high-accuracy phenotyping approach leverages instance segmentation and multitask learning, offering precise drought-stress detection. The methods proposed hold significant potential for drought-resistant poplar screening and precise irrigation decision-making, fostering advancements in agricultural technology and plant stress management.
Poplar (Populus L.) is a fast-growing forest tree valued for its wood and role in protective forests. Current research focuses on enhancing woody biomass production despite abiotic and biotic stresses, with drought stress being a significant factor that impedes growth by affecting material transport and photosynthesis. However, traditional methods for identifying water-deficient plants or selecting drought-resistant varieties are inefficient and inaccurate.
A study (DOI: 10.34133/plantphenomics.0205) published in Plant Phenomics on 21 Jun 2024, explores innovative computer vision and deep learning technologies to improve drought-stress detection and phenotyping in poplar saplings.
The research utilized instance segmentation and leaf posture digitalization to analyze poplar saplings. The FasterRCNN model was used to segment leaves, midveins, and petioles, outperforming YOLO models in some aspects. The segmentation accuracy was evaluated using AP0.5 values, with FasterRCNN showing higher performance for leaf segmentation and YOLO excelling in midvein and petiole detection. The angles α and β, indicating leaf growth posture, were calculated, revealing errors only in incomplete segmentations. The mean absolute errors for petiole and midvein angle calculations were 10.7° and 8.2°, respectively. These results were validated with a new dataset, showing most errors within a range of [-5°, +5°]. The study confirmed the effectiveness of using segmentation models trained on a simulated dataset for accurate leaf posture analysis, despite some deviations under severe drought conditions. Further analysis indicated that the midvein's horizontal inclination angle was more affected by drought stress than the petiole's angle, proving the leaf posture calculation method's value in plant status analysis.
According to the study's lead researcher, Huichun Zhang, “The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.”
In summary, this study used computer vision and deep learning to phenotype drought-stressed poplar saplings, focusing on leaf posture calculation and stress-level identification. These methods significantly reduced manual annotation costs and demonstrated the potential for precise drought stress detection. Future research will focus on improving segmentation accuracy and expanding these techniques to other plant species for enhanced agricultural management.
###
References
DOI
Original Source URL
https://doi.org/10.34133/plantphenomics.0205
Funding information
This work is supported by the National Key Research and Development Program of China (2023YFE0123600), the National Natural Science Foundation of China (NSFC32171790, 32171818, and 62305166) and the Jiangsu Province Agricultural Science and Technology Independent Innovation Fund Project (CX(23)3126).
About Plant Phenomics
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
JOURNAL
Plant Phenomics
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Phenotyping for drought-stressed poplar sapling using exemplar-based data generation and leaf-level structural analysis
Synchrotron-based imaging techniques enhance understanding of soybean nodule structures for improved nitrogen fixation efficiency
NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE
IMAGE:
VISUAL ASSESSMENT OF THE ACCURACY OF THE (SEMI) AUTOMATIC SEGMENTATION OF SOYBEAN NODULE VASCULATURE USING BIOMEDISA’S SMART INTERPOLATION ALGORITHM.
view moreCREDIT: THE AUTHORS
A research team used synchrotron-based X-ray microcomputed tomography (SR-μCT) to non-invasively obtain high-quality 3D images of fresh soybean root nodules, quantifying the volumes of the central infected zone (CIZ) and vascular bundles (VBs). The study further employed synchrotron X-ray fluorescence imaging to visualize the distribution of iron and zinc within these tissues. This pioneering method enhances our understanding of nodule function in N2-fixation, with potential applications in breeding soybean cultivars for improved nitrogen-fixation efficiency and enhanced root nodule activity.
Nitrogen (N) is crucial for plant growth as it forms essential biomolecules. Modern agriculture relies on synthetic nitrogen fertilizers, which are energy-intensive and environmentally harmful. The legume-rhizobia symbiosis offers a sustainable alternative, efficiently fixing N2 in root nodules. However, the functional significance of nodule tissues in nitrogen fixation is not well understood.
A study (DOI: 10.34133/plantphenomics.0203) published in Plant Phenomics on 29 May 2024, aims to employ advanced imaging techniques to visualize and assess the functional structures in soybean root nodules, enhancing our understanding of nitrogen fixation efficiency.
This study utilized synchrotron radiation micro-computed tomography (SR-μCT) and X-ray fluorescence (SR-XRF) imaging to non-invasively visualize internal structures of fresh soybean root nodules, focusing on central infected zones (CIZ) and vascular bundles (VBs). SR-μCT provided high-quality, high-contrast images without extensive sample preparation, and Biomedisa's algorithm rapidly segmented nodular tissues. SR-XRF imaging revealed the distinct localization of iron within the CIZ and zinc within the VBs across three soybean genotypes, correlating with nitrogen fixation efficiencies. Despite limitations such as analyzing a single nodule per genotype, this innovative method demonstrated the potential of SR-μCT and SR-XRF for rapid, high-resolution phenotyping, offering valuable insights into nodule structure-function relationships. The study highlighted the utility of these techniques in advancing understanding of plant internal microstructures, suggesting that synchrotron imaging is a powerful tool for future research in this field.
According to the study's lead researcher, Leon Kochian, “The proposed methods enable the exploitation of root nodule’s anatomical features as novel traits in breeding, aiming to enhance N2-fixation through improved root nodule activity.”
In summary, this study highlights the functional importance of CIZ and VBs in soybean root nodules for nitrogen fixation. Using synchrotron-based X-ray microcomputed tomography (SR-μCT), high-quality, non-invasive 3D visualizations and volume quantifications of these tissues were achieved. Synchrotron X-ray fluorescence imaging further revealed the specific localization of iron and zinc within nodules, showcasing their roles. Future research could leverage deep neural networks for automatic segmentation and synchrotron X-ray fluorescence tomography for detailed 3D mapping, potentially enhancing nitrogen fixation efficiency through advanced soybean breeding strategies.
###
References
DOI
Original Source URL
https://doi.org/10.34133/plantphenomics.0203
Funding information
This research was supported by funding from a Canada Excellence Research Chairs (CERC) Grant to LVK, and from funding from the Global institute for Food Security, and the University of Saskatchewan, to LVK.
About Plant Phenomics
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
JOURNAL
Plant Phenomics
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Visualization and quantitative evaluation of functional structures of soybean root nodules via synchrotron X-ray imaging
Identification of a von Willebrand factor type A protein affecting both grain and flag leaf morphologies in wheat
SCIENCE CHINA PRESS
IMAGE:
FIGURE 1 MAP-BASED CLONING AND FUNCTIONAL CHARACTERIZATION OF RG1
view moreCREDIT: ©SCIENCE CHINA PRESS
This study is reported by Luxiang Liu’s group from the Institute of Crop Sciences, Chinese Academy of Agricultural science, Beijing. Wheat production is essential for global food security, and continual improvement to grain yield is necessary to meet the nutritional demands of the expanding human population. Grain size is positively correlated with grain weight, while the flag leaf serves as the major source of photosynthetic assimilates for grain development, therefore grain and flag leaf morphologies are determining factors in the final yield potential of wheat. Identification of genes involved in both grain and flag leaf morphologies remains is an essential step in precision breeding and cultivation of wheat with high yield potential. Chunyun Zhou, Hongchun Xiong, and their colleagues in Liu’s group identified a von Willebrand factor type A (vWA)-/Vwaint domain-containing protein, which affects grain size and flag leaf morphology, by map-based cloning from a round grain 1 (rg1) wheat mutant, and they revealed the molecular mechanism of phenotypic variations in the mutants.
The team identified a round grain (rg1) mutant with shorter flag leaf and plant height from the background of winter wheat variety Zhongmai 175, and fine mapped the rg1 gene to a 163 kb physical interval on chromosome 7D. Gene sequencing analysis indicated that the TraesCS7D02G010200 encoding a vWA and Vwaint domain-containing protein was the candidate gene of RG1. Missense mutations in seven round grain/short flag leaf mutants from backgrounds of Jing411 and Nongda5181 supported that TraesCS7D02G010200 was the causal gene for altered grain size and flag leaf morphology. Further protein structural modeling indicated that amino acid substitutions located at the critical sites for protein folding, especially in the highly conserved Vwaint domain, resulted in the round grain/short flag leaf phenotype. Further analyses on protein interaction and hormone content suggest that RG1 may regulate GA and ABA levels via interaction with the isoprenoid synthesis protein, MCT, which could contribute to reduced grain size and flag leaf length. Additionally, natural variation populations were also used to identify haplotypes that are associated with varying grain size-related traits.
This study reveals the functions of vWA domain in the regulation of grain size and flag leaf morphology. It provides a theoretical basis and genetic resources including RG1 haplotypes that can facilitate improvement of grain size and weight in wheat and potentially other cereal crops.
JOURNAL
Science China Life Sciences
DOI
Genomic data integration improves prediction accuracy of apple fruit traits!
Researchers reveal genomic data from different genotyping systems can be combined to obtain better genomic predictions of fruit traits at the seedling stage
IMAGE:
GENOMIC PREDICTION IS A POWERFUL STRATEGY IN FRUIT TREE BREEDING, AS IT ENABLES BREEDERS TO SELECT FRUIT TREES WITH DESIRED TRAITS AT THE SEEDLING STAGE. IN THIS STUDY, RESEARCHERS REVEALED THAT COMBINING DATASETS OBTAINED USING DIFFERENT GENOTYPING SYSTEMS IS A VIABLE APPROACH TO INCREASE THE ACCURACY OF GENOMIC PREDICTIONS. MOREOVER, THEY FOUND THAT CONSIDERING THE EFFECTS OF INBREEDING IN THESE MODELS CAN FURTHER INCREASE ACCURACY. IN THIS FIGURE, BRIX AND DEGREE OF MEALINESS ARE IMPORTANT FRUIT TRAITS IN APPLES.
view moreCREDIT: DR. MAI F. MINAMIKAWA FROM CHIBA UNIVERSITY, JAPAN
Over the past few decades, the world has witnessed tremendous progress in the tools used for genomic analysis. While it’s usually more common to associate these tools with the fields of biology and medicine, they have proven to be very valuable in agriculture as well. Using numerous DNA markers obtained from next-generation sequencing technologies, breeders can make genomic predictions and select promising individuals based on based on their predicted trait values.
Various systems and methodologies aimed at improving the quality of fruits use genetic analysis. One of them consists of genetic selection (GS) and genetic prediction (GP). This modern breeding approach uses statistical models to assess the entire genetic profile of a given individual based on previously collected genomes and their associated traits. This enables breeders to make predictions about the fruit traits that will be produced in the future at the seedling stage. In contrast, genome-wide association studies (GWAS) are instead focused on finding the exact genetic variants that are responsible for a particular fruit trait.
Until now, GP and GWAS have predominantly used DNA markers from a single system, and when the system in use became obsolete, it had to be re-analyzed using a more up-to-date system. However, it has been difficult to re-analyze populations for selection in fruit tree breeding that have been analyzed in previous systems, as it is not possible to re-obtain DNA from individuals discarded during selection. Thus, in a recent study published in Horticulture Research on 8 July 2024, a research team led by Associate Professor Mai F. Minamikawa from the Institute for Advanced Academic Research, Chiba University, Japan, set out to clarify whether combining apple data from different systems could lead to more accurate results when performing GP and GWAS. Other members of the team included Dr. Miyuki Kunihisa from the Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization, Japan, and Professor Hiroyoshi Iwata is from the Graduate School of Agricultural and Life Sciences at the University of Tokyo, Japan.
First, the researchers combined apple datasets acquired from two different genotyping systems, namely Infinium and genotyping by random amplicon sequencing direct (GRAS-Di). Then, they used these combined genotype markers to perform GP and GWAS for a total of 24 different fruit traits, including acidity, sweetness, harvest time, and solid soluble content. The team compared the performance of predictions made using models trained on either dataset alone or both combined.
The results were very encouraging; the accuracy of genomic predictions and the detection power of the GWAS system increased significantly when using the Infinium and GRAS-Di combined datasets for multiple fruit traits. This suggests there are benefits to combining data from different systems and leveraging historical data.
To push the envelope further, the researchers also trained the GP model in such a way that inbreeding effects were considered. Interestingly, these results also hinted at the combined approach performing better for certain traits, including Brix and Degree of mealiness. Still, these findings were less conclusive, as Dr. Minamikawa remarks, “Although the accuracy of GS for fruit traits in apples can be improved by data on inbreeding, further studies are needed to understand the relationship between fruit traits and inbreeding.”
Overall, the findings of this study hint at a convenient way of improving the accuracy of GS and GWAS by leveraging existing datasets. This could have many positive implications in agriculture, as Dr. Minamikawa highlights, “The challenges such as large plant size and long juvenile periods in fruit trees can be addressed by identifying superior genotypes from numerous individuals using high accuracy GS as seedling stage and detecting genetic variants for a target trait using precise GWAS.”
Let us hope further progress in this field makes the breeding of fruits more efficient and reliable so we can keep enjoying them in our diets!
About Associate Professor Mai F. Minamikawa
Dr. Minamikawa received a PhD degree from the Graduate School of Horticulture at Chiba University in 2013, where she currently holds the position of Tenure-track Associate Professor. She specializes in genomic selection, data science, and statistical and molecular genetics, with a particular focus on their application to fruit breeding. Worth noting, she is also a member of The Japanese Society of Breeding and The Japanese Society for Horticultural Science.
JOURNAL
Horticulture Research
METHOD OF RESEARCH
Data/statistical analysis
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: Application to apple fruit quality traits
ARTICLE PUBLICATION DATE
8-Jul-2024
From kale to carotenoid powerhouse: a breakthrough in plant nutrition
NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE
IMAGE:
BOABZR1.1 PROMOTED THE BIOSYNTHESIS OF CAROTENOIDS AND CHLOROPHYLLS IN CHINESE KALE. A PHENOTYPES OF T1 GENERATION OF BOABZR1.1-OE CHINESE KALE. B CONTENTS OF CAROTENOIDS AND CHLOROPHYLLS IN T1 GENERATION OF BOABZR1.1-OE CHINESE KALE. BARS ARE MEANS ± SD OF THREE BIOLOGICAL REPLICATES. THE SAME LETTER IN THE SAME HISTOGRAM INDICATES THAT THERE IS NO SIGNIFICANT DIFFERENCE BETWEEN THE VALUES TESTED BY LEAST SIGNIFICANT DIFFERENCE (LSD) (P < 0.05). C EXPRESSION OF CAROTENOID BIOSYNTHETIC GENES IN T1 GENERATION OF BOABZR1.1-OE CHINESE KALE. D EXPRESSION OF CHLOROPHYLL BIOSYNTHETIC AND DEGRADING GENES IN T1 GENERATION OF BOABZR1.1-OE CHINESE KALE.
view moreCREDIT: HORTICULTURE RESEARCH
A recent study has identified a crucial regulatory mechanism in Chinese kale, potentially revolutionizing its nutritional profile. By manipulating the BoaBZR1.1 transcription factor, researchers significantly enhanced carotenoid levels, crucial antioxidants for human health. This advancement opens pathways for improving vegetable nutrition through genetic engineering.
Carotenoids, vital antioxidants in plants, are integral for human health, enhancing immunity and preventing diseases. However, many vegetables, including Chinese kale, naturally exhibit low carotenoid levels. To address this nutritional gap, scientists are exploring genetic pathways to increase these beneficial compounds in crops, aiming to improve their health benefits significantly. This approach leverages advanced genetic techniques to potentially enrich the dietary value of commonly consumed vegetables.
Published in Horticulture Research in April 2024, the study (DOI: 10.1093/hr/uhae104) by Sichuan Agricultural University highlights the pivotal role of the BoaBZR1.1 gene in enhancing carotenoid biosynthesis in Chinese kale. This research pinpoints a key genetic target for augmenting vegetable nutritional qualities, providing valuable insights into the potential of genetic engineering to increase essential nutrients.
The study centered on BoaBZR1.1, a transcription factor in the brassinosteroid signaling pathway essential for plant growth and stress responses. Activation of BoaBZR1.1 led to a substantial increase in carotenoid biosynthesis gene expression, boosting both carotenoid and chlorophyll levels. This genetic enhancement not only raised the nutritional value but also enhanced the visual appeal of Chinese kale. The findings demonstrate an effective strategy to enhance the nutritional profiles of vegetables via targeted genetic engineering, potentially applicable to a wide range of crops. This approach marks a significant progression in agricultural biotechnology for improving dietary health.
Genetic engineering holds remarkable potential for overcoming dietary deficiencies," remarked Dr. Yi Tang, a study co-author and noted horticulturist. "This study illustrates how leveraging plant natural mechanisms can produce crops that are not only more nutritious but also more adaptable to environmental challenges."
The implications of this study extend beyond improving Chinese kale, offering prospects for enhancing other crops to address global nutritional deficits and support food security. This research paves the way for a future where crops are optimized for health benefits, potentially transforming global dietary patterns and contributing to sustainable agriculture.
###
References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae104
Funding information
This work was supported by National Natural Science Foundation of China (32372732, 32072586, 32372683, 31500247), Natural Science Foundation of Sichuan Province (2022NSFSC1689), Project of New Varieties Breeding of Sichuan Vegetable Innovation Team (sccxtd-2023-05), Agro-Industry Technology Research System of China (CARS-23-A07), ‘131’ Innovative Team Construction Project of Tianjin (201923), and the Guizhou Provincial Key Technology R&D Program ((2021) No.207).
About Horticulture Research
Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2022. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.
JOURNAL
Horticulture Research
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
BoaBZR1.1 mediates brassinosteroid-induced carotenoid biosynthesis in Chinese kale
From winter's rest to spring's bloom: PmDAM6 gene steers plant bud dormancy
NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE
IMAGE:
PROPOSED MODEL ILLUSTRATING HOW PMDAM6 REGULATES ABA, CKT, AND GA METABOLISM, LIPID BODY ACCUMULATION, AND CELL DIVISION IN THE DORMANT VEGETATIVE MERISTEM OF PRUNUS MUME.
view moreCREDIT: HORTICULTURE RESEARCH
This pivotal study explores the genetic orchestration of bud dormancy in woody perennials, a survival strategy crucial for enduring harsh climates. It focuses on the PmDAM6 gene, revealing its regulatory effects on lipid metabolism and phytohormone dynamics within dormant meristems, which dictate the plant's seasonal transition from rest to growth.
Plant dormancy's genetic mechanisms are vital for enhancing agricultural resilience and productivity. The interaction between lipid metabolism and hormone regulation significantly influences dormancy phases, essential for plant survival under varying climatic conditions. Exploring these biological challenges through genetic research is crucial for devising innovative strategies to ensure crop adaptability and sustainability.
Researchers at Kyoto University have made significant strides in understanding plant dormancy, a critical adaptation for woody perennials. In a study (DOI: 10.1093/hr/uhae102) published on April 9, 2024, in Horticulture Research, they reveal the intricate regulatory role of the PmDAM6 gene on lipid body accumulation and phytohormone metabolism in the dormant vegetative meristem, offering new insights into the genetic control of this vital process.
The study underscores the role of the Prunus mume DAM6 gene in managing lipid accumulation and phytohormone balance within dormant vegetative meristems. Enhanced DAM6 expression resulted in increased lipid bodies and reduced cell division by downregulating genes involved in lipid catabolism and the cell cycle. This genetic modulation also altered phytohormone levels, notably increasing abscisic acid while decreasing cytokinin and gibberellin. These findings indicate a complex regulatory network where lipid metabolism and hormone adjustments converge to manage dormancy. Transmission electron microscopy provided detailed visual evidence of DAM6's cellular impact, highlighting its potential as a target for enhancing plant adaptability and dormancy management.
Dr. Hisayo Yamane, the study's lead author, states, "Modulating DAM6 not only deepens our understanding of dormancy mechanisms but also catalyzes the development of strategies to enhance crop adaptation to changing climates, potentially revolutionizing agricultural practices."
Manipulating the DAM6 gene could transform agricultural practices by allowing precise control over dormancy periods, optimizing growth cycles, and enhancing crop resilience against environmental stresses. This breakthrough opens new avenues for breeding programs focused on improving food security amid global climate change challenges.
###
References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae102
Funding information
This research was supported by the Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science, Japan (Grant-in-Aid KAKENHI Nos. 18H02198 and 21H02186) to HY.
About Horticulture Research
Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2022. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.
JOURNAL
Horticulture Research
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Regulatory role of Prunus mume DAM6 on lipid body accumulation and phytohormone metabolism in the dormant vegetative
Flavonoid fortune: citrus genes illuminate path to nutrient richness
NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE
IMAGE:
CHANGES IN PHENOTYPE, CONTENT OF MAIN FGS, AND EXPRESSION LEVELS OF CIT7GLCTS IN CITRUS UNDER HLB INFECTION. (A), (B), AND (C) SHOW THE CHANGES IN PHENOTYPE, CONTENT OF MAIN FGS, AND EXPRESSION LEVELS OF CIT7GLCTS AFTER HLB INFECTION IN ‘SHATIAN’ PUMMELO, ‘SHATANG’ MANDARIN, AND ‘NEWHALL’ NAVEL ORANGE, RESPECTIVELY. HLB, HUANGLONGBING. NS, NOT SIGNIFICANT, *P < 0.05, **P < 0.01; ***P < 0.001; ****P < 0.0001.
view moreCREDIT: HORTICULTURE RESEARCH
Scientists have unlocked the genetic secrets behind the biosynthesis of flavonoid glycosides in citrus, a breakthrough that could fortify the nutritional potency and disease resistance of these fruits. This discovery refines our grasp of the genetic orchestration of these beneficial compounds, setting the stage for an upgrade in citrus nutrition and health.
Citrus fruits, celebrated for their zesty taste and dense flavonoid content, are integral to diets for their health-boosting properties. These natural compounds are linked to a spectrum of wellness benefits, yet the genetic blueprint directing their synthesis has been shrouded in mystery. Given their significance, illuminating the genetic pathways that govern flavonoid production is essential for advancing citrus cultivation and health science.
A team of researchers from Huazhong Agricultural University has charted new territory in the genetic landscape of citrus, with their revelations featured (DOI: 10.1093/hr/uhae098) in the esteemed Horticulture Research journal on April 25, 2024. The study zeroes in on the role of specific flavonoid 7-O-glucosyltransferase genes in the synthesis of flavonoid glycosides, uncovering a new layer of complexity in citrus biology.
Through rigorous transcriptomic and metabolomic scrutiny, the study spotlights six flavonoid 7-O-glucosyltransferase genes, four of which—CgUGT90A31, CgUGT89AK1, CgUGT73AC12, and CgUGT89D30—emerge as linchpins in flavonoid glycosylation. Their broad catalytic profiles across flavonoid substrates are revealed, highlighting their pivotal function in the flavonoid biosynthetic cascade. This genetic insight could prove instrumental in the precision breeding of citrus varieties with enhanced nutritional profiles and disease resistance.
Dr. Juan Xu, a preeminent figure in agricultural genomics and co-author of the study, underscores its impact: "Our research demystifies a critical aspect of citrus flavonoid production, offering a genetic roadmap for enriching the health attributes of citrus and potentially fortifying their defense mechanisms against afflictions like Huanglongbing."
The study's ramifications are profound, signaling a new era in agricultural biotechnology. With the genetic insights at hand, the cultivation of citrus breeds rich in flavonoids becomes a tangible goal, promising a bountiful harvest of fruits that are not only more nutritious but also more robust against pests and diseases, thereby enriching both public health and agricultural economies.
###
References
DOI
Original Source URL
https://doi.org/10.1093/hr/uhae098
Funding information
This work was supported by the National Natural Science Foundation of China (NSFC, No. 32002010) and the National Key Research and Development Program of China (Grant No. 2023YFD2300600).
About Horticulture Research
Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2022. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.
JOURNAL
Horticulture Research
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Four novel Cit7GlcTs functional in flavonoid 7-O-glucoside biosynthesis are vital to flavonoid biosynthesis shunting in citrus'
High throughput prediction of sugar beet root weight and sugar content in a breeding field using UAV derived growth dynamics
IMAGE:
THE MULTIPLE REGRESSION IN THE DIALLEL CROSS (2018, 2020, AND 2021). COEFFICIENTS OF DETERMINATION (R2) OF RW (TOP) AND SC (BOTTOM) FOR EACH DAT COMBINATION OF CC AND CH.
view moreCREDIT: THE AUTHORS
A research team employed an RGB camera on an unmanned aerial vehicle (UAV) to collect time series data on sugar beet canopy coverage and height. This data was used to predict root weight and sugar content with high accuracy. This innovative technique enhances breeder decision-making by providing pre-harvest selection criteria, reducing manual measurement needs. The UAV-based approach can also guide precision fertilization in production fields, demonstrating its value in improving agricultural efficiency and crop yield predictions.
Sugar beet (Beta vulgaris L.) is a vital crop for sugar production, yet its cultivation area has declined despite increased yields. Current research leverages heterosis to enhance sugar beet productivity, but traditional breeding methods are labor-intensive and inefficient. Although high-throughput UAV phenotyping demonstrated potential promise in other crops, it hasn't been fully explored for sugar beet yield and sugar content prediction.
A study (DOI: 10.34133/plantphenomics.0209) published in Plant Phenomics on 11 Jun 2024, aims to develop a high-throughput UAV method to accurately predict sugar beet root weight and sugar content, improving breeding efficiency and cultivar development.
The research employed UAV-based high-throughput phenotyping to assess yield and foliar growth in sugar beet breeding fields. Over three seasons, canopy coverage (CC) and canopy height (CH) were monitored and analyzed. In 2018, favorable conditions led to rapid early-season growth, while drought in 2020 reduced plant growth. In 2021, conditions were ideal, leading to good growth. Significant variation in root weight (RW) and sugar content (SC) was observed across the years, with analysis of variance (ANOVA) indicating significant differences among accessions. UAV flights every 30 days provided detailed growth patterns, with logistic models fitting CC data and Gompertz models fitting CH data. Integrals of these models were used for genetic analysis, revealing significant general and specific combining abilities (GCA and SCA) for RW, SC, CCint120, and CHint120, suggesting both additive and non-additive gene actions. Multiple regression analysis predicted RW and SC using CC and CH data, achieving high correlation coefficients (R2 = 0.89 for RW and 0.83 for SC). These findings highlight the potential of UAV-based phenotyping for efficient yield prediction and genetic analysis in the context of sugar beet breeding.
According to the study's lead researcher, Kazunori Taguchi, “Our simpleyet robust solution demonstrates how state-of-the-art remote sensing tools and basic analysismethods can be applied to small-plot breeder fields for selection purpose.”
In summary, this study utilized a UAV-based data-driven methodology to enhance breeder and farmer decision-making in sugar beet cultivation. This approach demonstrated that UAV-based phenotyping could efficiently predict sugar beet yield and assist in genetic analysis by providing significant data on growth patterns. Future applications may extend this method to other crops, guiding precision agriculture and improving breeding programs by integrating advanced remote sensing and machine learning techniques.
###
References
DOI
Original Source URL
https://doi.org/10.34133/plantphenomics.0209
Funding information
The multiple regression in the diallel cross (2018, 2020, and 2021). Coefficients of determination (R2) of RW (top) and SC (bottom) for each DAT combination of CC and CH.Image credit: The authors
About Plant Phenomics
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
JOURNAL
Plant Phenomics
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
High throughput prediction of sugar beet root weight and sugar content in a breeding field using UAV derived growth dynamics
