鲁中肉羊初生体尺体重的遗传参数估计

为估计鲁中肉羊初生重、体高、体长、胸宽、胸深、胸围和管围的遗传参数,利用2018年上半年出生的611只鲁中肉羊的初生体尺体重数据,采用AI-REML (Average information restricted maximum likelihood)算法,借助DMU软件分析以产羔数为固定效应、个体加性遗传效应为随机效应的多性状动物模型。结果表明:鲁中肉羊初生重、胸宽、胸深、体高、体长、胸围和管围的遗传力分别为0.16、0.10、0.22、0.44、0.43、0.46和0.52,各性状之间遗传相关为-0.517～0.773,表型相关为-0.197～0.503。说明鲁中肉羊初生重、胸宽和胸深为低遗传力性状,体高、体长、胸围和管围为中等遗传力性状。     

设施蔬菜土壤连作障碍及治理措施

连作障碍是设施蔬菜栽培中的一个关键问题。设施土壤障碍因子已严重影响蔬菜产量和品质,解决这一问题需要从土壤管理、施肥、灌溉、耕作等多方面采取措施。本文从土壤酸化板结、土壤次生盐渍化、根系分泌物、土壤病虫和土壤微量元素变化等方面综述了设施蔬菜连作障碍及治理措施。     

中国草地资源调查历程及规范化遥感解译框架探索

面对生态退化风险，我国草地资源迫切需要现代遥感技术建立规范化遥感解译框架来支撑草地新时期的调查与管理。本研究通过对文献分析和历史资料的整理，梳理我国草地资源调查工作历程，分析草地资源调查技术的进展与不足。在此基础上，结合遥感标准光谱端元空间的优势，勾画草地资源类型学-草地资源调查-管理之间的关联与尺度效应，提出多尺度融合嵌套的草地资源遥感解译框架，并建议推动草地系统知识共享平台的搭建，为草地资源适应性管理研究与实践提供重要支撑。     

A spatial frequency/spectral indicator-driven model for estimating cultivated land quality using the gradient boosting decision tree and genetic algorithm-back propagation neural network

Cultivated land quality (CLQ) is related to national food security. Rapid and high-precision monitoring of CLQ is crucial for the sustainable development of agriculture. However, current satellite image-based evaluation methods that only consider the crop's spatial spectrum characteristics in the key growth stages cannot accurately estimate CLQ. This study proposes a new method based on time-series spectral data of crop growth to improve the accuracy of CLQ estimation. This study was conducted in the Conghua District of Guangzhou, Guangdong Province, China. The results showed that seven spectral indicators were determined as the optimal indicators based on the gradient boosting decision tree (GBDT) and variance inflation factor (VIF). And the genetic algorithm-back propagation neural network (GA-BPNN) model provided more accurate CLQ estimates than the partial least squares regression (PLSR) model, indicating a nonlinear relationship between CLQ and the indicators. In addition, the GA-BPNN model with a normalized root mean square error (NRMSE) of 9.91% demonstrates the excellent potential for mapping CLQ over large areas. The model based on the seven optimal indicators of crop phenology provided higher accuracy than the GA-BPNN model based on the normalized difference vegetation index (NDVI) indicators in the spatial domain, significantly decreasing the NRMSE of the CLQ estimates by 3.17%. This further implied that the spectral indicators in the spatial frequency domain can improve the accuracy of estimating CLQ.     

Object-based classification of urban plant species from very high-resolution satellite imagery

Cities are facing too many challenges. Urban vegetation, in particular trees, are essential as they provide services in terms of air pollution mitigation, freshness, biodiversity, and citizens’ well-being. Accurate data on location, species, and structural characteristics are essential for quantifying their benefits. However, the cost of measuring thousands of individual trees through field campaigns can be prohibitive and reliable information on domestic gardens is lacking due to difficulties in acquiring systematic data. The main objective of this study was to investigate the suitability of very-high resolution satellite imagery, e.g., WorldView-2, for detecting, delineating, and classifying the urban plant species in both public and private areas. The characterization of urban vegetation is difficult due to the complexity of the urban environment (buildings, shadows, open courtyards), the diversity of species and the spatial proximity between trees. To overcome these constraints, an object-based classification was developed with the selection of new relevant spectral and texture-based features for each plant species. Four spectral bands (blue, green, yellow, red) and four texture features (i.e., energy, entropy, inverse difference moment, Haralick correlation) were found to be the most efficient attributes for object-based classification from WV-2 images. Then, a classification of plant species, by using a Random Forest classifier, and ground validation were performed. In the two study areas, Aix-en-Provence (France) and Florence (Italy), 22 and 20 dominant plant species, and grassland, were identified and classified with an overall accuracy of 84% and 83%, respectively. The highest classification accuracy was obtained for Pinus spp. and Platanus acerifolia in Aix-en-Provence, and for Celtis australis and Cupressus sempervirens in Florence. The lowest classification accuracy was obtained for Quercus spp. in Aix-en-Provence, and Magnolia grandiflora in Florence.     

水稻吸收转运铁的生物地球化学机制研究进展

铁的生物吸收转移是环境中铁生物地球化学循环的重要过程之一，不仅控制着铁在稻米的累积，且影响水稻累积锌等养分元素及镉等重金属。阐明土壤-水稻体系中铁的吸收转移机制对深入理解稻田铁的环境行为与归趋具有重要的科学意义。主要介绍了水稻中铁吸收转运的功能基因、土壤-水稻系统铁同位素分馏及水稻植株中铁分布的光谱分析方法与手段，重点总结铁在水稻根部吸收及植物体内的转运过程，探讨铁对水稻根部锌和镉吸收的影响机制，可为提高水稻产量及改善稻米品质提供科学依据。     

基于光谱相似性匹配的土壤有机碳估算

全球土壤光谱库的建立,为利用可见-近红外光谱预测土壤属性提供了参考集,如何从光谱库中挑选合适的建模集以实现对局部地区土壤有机碳的高精度预测,是一个值得研究的问题.本研究基于欧氏距离、马氏距离和光谱角三种光谱相似性指数,探索利用全球光谱库预测局部地区土壤有机碳的有效策略,并比较了不同光谱相似性指数、不同建模集数量及不同建...     

基于光谱变换的滨海湿地土壤全氮含量建模预测

基于133个滨海湿地土样的全氮（TN）含量和光谱反射率（R）及其对数（lgR）、对数的一阶微分（（lgR）''）、倒数（1/R）、倒数的一阶微分（（1/R）''）、一阶微分（R''）、平方根（√R）、一阶微分的倒数（1/（R）''）变换，采用偏最小二乘回归（PLSR）、随机森林回归（RFR）和支持向量机回归（SVR）3种算法分别建立土壤TN含量估测模型。结果表明：①土壤TN含量与光谱变换形式相关性由高到低为：（1/R）''> R''> （lgR）''> 1/R > lgR > 1/（R）''> √R > > R，经光谱变换，土壤TN含量与变换光谱的相关性均高于R，其中与（1/R）''的Pearson相关系数最大为0.746。②PLSR和SVR基于R''、（1/R）''、（lgR）''和1/（R）''变换构建的模型、RFR方法构建的所有模型R²均大于0.732，均可用于滨海湿地土壤TN含量的估算。③基于1/（R）''建立的SVR模型预测精度最高，其R²为0.987，RMSE为0.057 g/kg，MAE为0.050 g/kg，是预测滨海湿地土壤TN含量的最优模型，可为准确获取滨海湿地土壤TN含量提供稳定方法。     

基于1D-CNN的土壤全氮近红外光谱预测模型

摘要：【目的】传统的基于近红外光谱数据预测土壤全氮的方法需要对原始光谱数据做复杂的预处理，筛选出与土壤全氮含量相关性高的敏感波长之后进行模型的回归拟合。本文提出一种一维卷积神经网络（1D-CNN）模型，可以在对数据进行简单预处理甚至无处理的情况下达到非常理想的结果，实现用近红外光谱技术对土壤全氮含量的预测。【方法】于江苏无锡采集410个土壤样品，利用半微量开氏法(NY/T 53-1987)测定土壤的全氮含量，并利用NIR Quest 512光谱仪，在室内环境下对每份土壤样品做光谱检测，并用均值中心化(CT)、标准正态变换(SNV)、趋势校正(DT)对光谱进行预处理，运用偏最小二乘回归（PLS）、BP神经网络、1D-CNN方法建立土壤全氮含量的回归预测模型。每种模型在采用不同预处理方法的数据集上做十折交叉验证，记录预测模型的决定系数(R2)和均方根误差(RMSE)的平均值，并对比三种预处理方法对模型精度的影响。【结果】证明了本文提出的1D-CNN模型基于土壤近红外光谱数据预测土壤全氮含量的可靠性。使用原始数据与经均值中心化、标准正态变换、趋势校正预处理的数据训练得到的1D-CNN模型的决定系数分别为0.907、0.931、0.922、0.964，构建的PLS回归模型决定系数为0.856、0.863、0.861、0.880，训练的BP神经网络的决定系数为0.874、0.907、0.901、0.911。【结论】本文提出的1D-CNN模型在原始数据和经预处理的光谱数据上的表现都优于PLS和BP神经网络，且可以证明，对光谱数据进行预处理能够有效提高1D-CNN模型的性能，尤其是趋势校正对模型的提升效果最明显。研究表明，1D-CNN能更好地提取光谱特征并建立其与含氮量的映射关系，有效地避免过拟合，在未经过预处理的光谱数据上依然能够达到一定的精度。     

Calibration of caesium-137 measurements to provide quantitative erosion rate data

The caesium-137 technique for investigating rates and patterns of soil loss has now been successfully applied in a wide range of environments. However, some uncertainty still surrounds the ability of the technique to provide quantitative estimates of rates of erosion. The wide range of calibration relationships that have been employed by individual workers to derive erosion rates from estimates of the amount of ¹³⁷Cs (caesium-137) lost from the soil profile, emphasize the uncertainty involved. Existing calibration procedures, involving both empirical relationships and theoretical models and accounting procedures are reviewed and their limitations and inconsistencies are identified. Further research is required in this important area, if the clear potential of the caesium-137 technique is to be fully realized.