Article
Environmental Sciences
Fatimah Mahmood, Muhammad Fahim Khokhar, Zafar Mahmood
Summary: This study aims to quantify the impact of climate change on crop productivity in South Asia, particularly on wheat, rice, and cotton. The results show a significant increasing trend in temperature and high inter-annual variability in precipitation. When temperature exceeds specific threshold values, it significantly reduces crop productivity. Furthermore, the region is rapidly heading towards exceeding temperature and threshold values at an alarming rate.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Ecology
Katarzyna Sekiewicz, Irina Danelia, Vahid Farzaliyev, Hamid Gholizadeh, Grzegorz Iszkulo, Alireza Naqinezhad, Elias Ramezani, Peter A. Thomas, Dominik Tomaszewski, Lukasz Walas, Monika Dering
Summary: Predicting the effects of climatic changes on species requires understanding the factors shaping the spatial genetic composition. In this study, we found that the colonization history is the most important driver of the genetic pattern of Oriental beech, and local climate also influences the genetic composition. The loss of genetic resources due to projected habitat loss may increase the vulnerability of the Azerbaijan and Hyrcanian populations to environmental change, which could ultimately affect the species' adaptation and the stability of forest ecosystems in the Caucasus ecoregion.
ECOLOGY AND EVOLUTION
(2022)
Article
Ecology
Melania Vega, Christian Quintero-Corrales, Alicia Mastretta-Yanes, Alejandro Casas, Victorina Lopez-Hilario, Ana Wegier
Summary: By assembling chloroplast genomes of 23 wild, landraces, and breeding lines, we found that the evolutionary history of cotton in Mexico involves multiple events of introgression and genetic divergence. The Mexican landraces were found to arise from multiple wild populations, and their chloroplast organizations were preserved. However, genetic diversity decreases as a consequence of domestication, mainly in transgene-introgressed individuals, highlighting the importance of biosecurity and agrobiodiversity conservation.
ECOLOGY AND EVOLUTION
(2023)
Article
Plant Sciences
Nerea Larranaga, Maarten van Zonneveld, Jose I. Hormaza
Summary: The study shows that cherimoya was transported from Mesoamerica to Peru through long-distance sea-trade routes across the Pacific Ocean, providing new insights into pre-Columbian crop exchange between Mesoamerica and the Andes.
Article
Environmental Sciences
Konstantin Ash
Summary: A recent study shows that there is an independent association between fallow agriculture and out-migration from Syrian agricultural regions before the onset of the Syrian conflict. The use of weather as an instrument for agricultural outcomes is also called into question.
REGIONAL ENVIRONMENTAL CHANGE
(2023)
Article
Agronomy
S. Dzikiti, D. Lotter, S. Mpandeli, L. Nhamo
Summary: This study investigates the energy and water balance of rooibos fields and its relationship with crop yield. It reveals that implementing water conservation and weed management practices early in the growing season can save substantial amounts of soil moisture, sustaining rooibos production under low rainfall conditions.
AGRICULTURAL WATER MANAGEMENT
(2022)
Article
Plant Sciences
Lance F. Merrick, Adrienne B. Burke, Zhiwu Zhang, Arron H. Carter
Summary: This study aimed to dissect the genetic architecture of seedling emergence in wheat using one multi-trait genome-wide association study (MT-GWAS) and three single-trait GWAS (ST-GWAS) models. The ST-GWAS models identified 107 significant markers across 19 chromosomes, while the MT-GWAS identified 82 significant markers across 14 chromosomes. The FarmCPU and BLINK models were able to identify both small effect markers and large effect markers on chromosome 5A.
FRONTIERS IN PLANT SCIENCE
(2022)
Review
Genetics & Heredity
Karansher S. Sandhu, Lance F. Merrick, Sindhuja Sankaran, Zhiwu Zhang, Arron H. Carter
Summary: The adoption of genomic selection and phenomics tools in plant breeding programs has significantly increased in the past decade. Genomic selection has shown potential in selecting superior genotypes with high precision and accelerating the breeding cycle, while phenomics aims to alleviate phenotyping bottlenecks and explore new large-scale phenotyping and data acquisition methods. This review discusses the lessons learned from genomic selection and phenomics in six self-pollinated crops and their implementation schemes, with a focus on rice, wheat, soybean, common bean, chickpea, and groundnut.
FRONTIERS IN GENETICS
(2022)
Article
Genetics & Heredity
Ruicai Long, Fan Zhang, Zhiwu Zhang, Mingna Li, Lin Chen, Xue Wang, Wenwen Liu, Tiejun Zhang, Long-Xi Yu, Fei He, Xueqian Jiang, Xijiang Yang, Changfu Yang, Zhen Wang, Junmei Kang, Qingchuan Yang
Summary: This study reported the chromosome-level genome sequence and genomic variation database of Zhongmu-4, a widely cultivated alfalfa cultivar in China, as well as the polymorphic genes associated with agronomic traits. The expansion of gene families and increase of repetitive elements observed in Zhongmu-4 genome may contribute to its larger genome size compared to Medicago truncatula. The population structure analysis revealed the influence of geography on the genetic divergence of alfalfa. The findings of this study will facilitate genetic research and genomics-assisted breeding for alfalfa variety improvement.
GENOMICS PROTEOMICS & BIOINFORMATICS
(2022)
Review
Food Science & Technology
Yang Hu, Stephanie M. Sjoberg, Chunpen (James) Chen, Amber L. Hauvermale, Craig F. Morris, Stephen R. Delwiche, Ashley E. Cannon, Camille M. Steber, Zhiwu Zhang
Summary: This review examines the application, limitations, and potential alternatives to the Hagberg-Perten falling number (FN) method used in the global wheat industry. The FN test indirectly detects the presence of alpha-amylase, which is responsible for starch degradation. Low FN/high alpha-amylase in wheat grains results in poor end-product quality and significant losses for various stakeholders. The FN method has limitations such as sampling variability, high cost, labor intensiveness, destructive nature, and inability to differentiate between different causes of low FN. Seeking faster, cheaper, and more accurate alternatives can improve breeding and avoid inadvertent mixing of high- and low-FN grain, thus preserving the value of wheat grain.
COMPREHENSIVE REVIEWS IN FOOD SCIENCE AND FOOD SAFETY
(2022)
Article
Agriculture, Dairy & Animal Science
Chun-Peng J. Chen, Gota Morota, Kiho Lee, Zhiwu Zhang, Hao Cheng
Summary: Precision livestock farming in the swine industry focuses on using computer vision technology to automate monitoring pig activity, but the performance is highly dependent on the quality of imagery features and requires intensive effort in labeling ground truths for training datasets.
JOURNAL OF ANIMAL SCIENCE
(2022)
Article
Plant Sciences
Ryokei Tanaka, Di Wu, Xiaowei Li, Laura E. Tibbs-Cortes, Joshua C. Wood, Maria Magallanes-Lundback, Nolan Bornowski, John P. Hamilton, Brieanne Vaillancourt, Xianran Li, Nicholas T. Deason, Gregory R. Schoenbaum, C. Robin Buell, Dean DellaPenna, Jianming Yu, Michael A. Gore
Summary: With low vitamin E content in maize grain, the use of the multikernel genomic best linear unbiased prediction (MK-GBLUP) approach can improve the accuracy of predicting vitamin E content. By leveraging existing genomic and transcriptomic information, combined with biological knowledge of known QTL and candidate causal genes, the prediction models can be further enhanced.
Article
Plant Sciences
Fan Zhang, Junmei Kang, Ruicai Long, Mingna Li, Yan Sun, Fei He, Xueqian Jiang, Changfu Yang, Xijiang Yang, Jie Kong, Yiwen Wang, Zhen Wang, Zhiwu Zhang, Qingchuan Yang
Summary: Fall dormancy (FD) is a crucial trait for alfalfa cultivar selection. We conducted genomic prediction of FD using machine learning methods and achieved high prediction accuracy.
HORTICULTURE RESEARCH
(2023)
Review
Biochemical Research Methods
Tingting Guo, Xianran Li
Summary: Predicting phenotype with genomic and environmental information using machine learning methods is challenging but necessary. This review discusses the progress of phenotype prediction models enabled or improved by machine learning. The applications are categorized into scenarios based on genotypic information, environmental information, and both, highlighting the practicality, advantages, and challenges of modeling complex relationships. The potential of leveraging machine learning and genetics theories to develop models that predict phenotype and interpret biological consequences is also discussed.
CURRENT OPINION IN BIOTECHNOLOGY
(2023)
Article
Agriculture, Multidisciplinary
Zhou Tang, Meinan Wang, Michael Schirrmann, Xianran Li, Robert Brueggeman, Sindhuja Sankaran, Arron H. Carter, Michael O. Pumphrey, Yang Hu, Xianming Chen, Zhiwu Zhang
Summary: In this study, a neural network-based image classifier called RustNet was developed to efficiently monitor wheat fields for stripe rust. The model uses deep learning to process images and videos captured from affordable devices, enabling high-throughput pheno-typing for early detection of rust and improved control efficiency.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Letter
Biochemistry & Molecular Biology
Bosen Zhang, Haiyan Huang, Laura E. Tibbs-Cortes, Adam Vanous, Zhiwu Zhang, Karen Sanguinet, Kimberly A. Garland-Campbell, Jianming Yu, Xianran Li
Article
Plant Sciences
Ming Li Wang, Brandon Tonnis, Xianran Li, John Bradly Morris
Summary: Sesame is an important oilseed crop with natural genetic variation. The identification and utilization of genetic allele variation from the germplasm collection can improve seed quality.
Article
Plant Sciences
Tingting Guo, Jialu Wei, Xianran Li, Jianming Yu
Summary: This study examines the consistency of parameter estimation for reaction norms of genotypes across different subsets of environments for sorghum and rice genetic populations. The results show that both sample size and environmental mean range of the subset affect the consistency. Additionally, high accuracy of genomic prediction is obtained for reaction norm parameters of untested genotypes using models built from tested genotypes under subsets with a large range or a large sample size.
JOURNAL OF EXPERIMENTAL BOTANY
(2023)
Article
Mycology
Y. P. Fu, Y. T. Dai, K. W. T. Chethana, Z. H. Li, L. Sun, C. T. Li, H. L. Yu, R. H. Yang, Q. Tan, D. P. Bao, Y. J. Deng, S. X. Wang, Y. F. Wang, F. H. Tian, L. L. Qi, L. L. Shu, P. S. Jia, L. C. Chen, M. Y. Chen, Q. X. Hu, H. Tan, T. T. Song, Z. W. Zhang, G. Bonito, G. Zervakis, S. J. Xiao, K. D. Hyde, Y. Li, X. H. Yuan
Summary: This study presents high-quality genomes, transcriptomes, and re-sequencing data of edible and medicinal mushrooms, and constructs an integrated omics database (MushDB) for a better understanding of mushroom domestication. Using multi-omics data, the researchers identified potential functional genes contributing to mushroom domestication, including genes involved in starch and sucrose metabolism and mitogen-activated protein kinase signaling pathway. The function of one key gene in low temperature adaptation and cultivation environment was validated using CRISPR/Cas9 system.
