[前沿资讯] CABI launches new agriRxiv, the dedicated agricultural preprint service for agricultural research 进入全文
agriRxiv – pronounced agri-archive, and previously known as AgriXiv – is to be relaunched with a new website offering researchers and students access to preprints across agriculture and allied sciences. Preprints are drafts of research articles that authors typically share with the wider community for feedback before submitting their final version to a journal and formal peer-review. They bring many advantages: preprints allow researchers to share their results rapidly, in just a few days; they reach a global audience and there’s no charge for authors or readers; they provide an early forum for discussion and allow authors to get informal feedback on their article prior to submitting an improved version to a journal for peer review. The new and enhanced platform, which is hosted and managed by CABI, aims to benefit from the organisation’s greater global reach and sustainability. It draws on CABI’s extensive expertise in agricultural data, information and knowledge dissemination. agriRxiv will benefit from the technical and content management infrastructure used to deliver CABI’s subscription products, like CAB Abstracts – the leading English-language bibliographic information service providing access to the world’s applied life sciences literature, which has almost 10 million records. As well as being able to submit preprints, users will, over time, be able to search and filter records with far more precision, and stay abreast of all the latest submissions, news and updates from the agriRxiv platform.
An analysis of new climate model projections by Australian researchers from the ARC Centre of Excellence for Climate Extremes shows southwestern Australia and parts of southern Australia will see longer and more intense droughts due to a lack of rainfall caused by climate change. But Australia is not alone. Across the globe, several important agricultural and forested regions in the Amazon, Mediterranean and southern Africa can expect more frequent and intense rainfall droughts. While some regions like central Europe and the boreal forest zone are projected to get wetter and suffer fewer droughts, those droughts they do get are projected to be more intense when they occur. The research published in Geophysical Research Letters examined rainfall-based drought using the latest generation of climate models (known as CMIP6), which will inform the next IPCC assessment report on climate change.
Background Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season. Results Algorithms and feature extraction techniques were combined to develop a regression model to predict final yield from imagery, achieving an accuracy of over 90.72% by RF and 91.36% by XGBoost. Conclusions Results provide practical information for the selection of phenotypes for breeding coming from UAS data as a decision support tool, affording constant operational improvement and proactive management for high spatial precision.
Bangladesh is on track to lose all of its forestland in the next 35-40 years, leading to a rise in CO2 emissions and subsequent climate change, researchers said. However, that is just one of the significant land-use changes that the country is experiencing. A new study uses satellite and census data to quantify and unravel how physical and economic factors drive land-use changes. Understanding this relationship can inform climate policy at the national scale in Bangladesh and beyond. The study, led by University of Illinois at Urbana-Champaign atmospheric sciences professor Atul Jain and postdoctoral researcher Xiaoming Xu, is published in the journal Regional Environmental Change.
来自俄科院西伯利亚分院网站的报道，该分院克拉斯诺亚尔斯克科学中心物理所研发出北极土壤状况卫星信息分析用途数学模型，可将所获得的包括表层状态、湿度和温度等永冻土卫星信息转换成程序可处理的数据并进行分析，所开发的软件系统可详细评估北极地区状况，跟踪永冻土对气候变化的反应。相关成果发布在《International Journal of Remote Sensing》学术期刊。 物理所开发的数学模型借助于卫星系统可确定北极土壤永冻土表层的状况，其算法是建立在复介电常数测量基础上的，适用于融化和冷冻的矿物质土壤，可监控永冻土表层的温度、湿度等状态参数。构建模型所采用的土壤样品来自于亚马尔半岛的北极苔原，科研人员考察了3种不同黏土含量的北极土样以精确土壤参数。在对土样分析过程中发现，土壤是由被水层包裹着的细小颗粒组成，具有固定的介电性能，且数值主要取决于土壤的湿度，变量参数的减少使得科研人员简化了所研发数学模型的复杂程度。