Structural variations in oil crops:Types,and roles on domestication and breeding

Structural variations(SVs),a newly discovered genetic variation,have gained increasing recognition for their importance,yet much about them remains unknown.With the completion of whole-genome sequencing projects in oil crops,more SVs have been identified,revealing their types,genomic distribution,and characteristics.These findings have demonstrated the crucial roles of SVs in regulating gene expression,driving trait innovation,facilitating domestication,making this an opportune time for a systematic review.We summarized the progress of SV-related studies in oil crops,focusing on the types of SVs and their mechanisms of occurrence,the strategies and methods for SV detection,and the SVs identified in oil crops such as rapeseed,soybean,peanut,and sesame.The various types of SVs,such as presence-absence variations(PAVs),copy number variations(CNVs),and homeologous exchanges(HEs),have been shown.Along with their genomic characterization,their roles in crop domestication and breeding,and regulatory impact on gene expression and agronomic traits have also been demonstrated.This review will provide an overview of the SV research process in oil crops,enabling researchers to quickly understand key information and apply this knowledge in future studies and crop breeding.

砂姜黑土地区降水分布与主要作物缺水量研究

本文讨论了砂姜黑土地区年内降水分布,分析了小麦、大豆、棉花、玉米的缺水情况.在此基础上,提出不同区域不同作物的灌溉问题.

气象要素缺失条件下不同机器学习模型计算参考作物蒸散量比较

为了实现气象要素缺失条件下对参考作物蒸散量(ET_(0))的预测,以山西果树研究所Adcon-Ws无线自动气象站2020-2021年每日最高气温(T_(max))、最低气温(T_(min))、2 m高风速(u_(2))、相对湿度(RH)和日照时数(n)数据为例,构建了9种气象要素缺失组合下的决策树(CART)、随机森林(RF)、梯度提升决策树(GBDT)、极端梯度提升(XGBoost)、支持向量机(SVR)、BP神经网络(BPNN)和深度学习(DL)7种ET_(0)机器学习模型,以PM公式计算值作为标准值,并与经验法Hargreaves-Samani、Irmak-Allen、Makkink和Priestley-Taylor进行对比。结果表明,在所有气象要素组合中,深度学习和BP神经网络均能取得较高的模拟精度并且有较好的泛化能力,其他模型在不同气象要素缺失组合中模拟精度和泛化能力有不同的排名,但整体效果较好的是支持向量机。不同气象要素对模型模拟ET_(0)的影响程度不同,影响由大到小排序依次为n、T_(max)、T_(min)、RH、u_(2)。与4种经验法相比,机器学习模型模拟精度均大于输入相同组合的经验法。