以农机资本经营促进大型农机自我更新

1998年，安达市被黑龙江省财政厅确定为农机资本经营试点市。几年来，在黑龙江省财政厅的大力支持下，在省农机局的精心指导下，取得了较好的经济和社会效益。实施农机资本经营，无论对地方财政建设，还是对农机事业自我完善和更新发展，都起到了积极的推进作用。为此，以实施农机资本经营为出发点，论述了大型农机的自我完善、自我更新的发展思路。     

Changing technology for training irrigation managers

This article describes developments in microcomputer and video-disc technology which may be used in the training of managers for irrigation projects.     

风景园林研究的跨界融合与量化革新——以香港大学城市环境与人类健康实验室为例

以香港大学城市环境与人类健康实验室这一新兴学术共同体作为研究切入点,使用文献综述与案例研究的方法深入探讨学术共同体在推动风景园林学科的发展和进步中扮演的关键角色。研究结果发现：该实验室在以往的风景园林学科内容、框架和知识体系基础上,运用新兴技术并结合定量的统计学方法,将城市环境与居民健康紧密相连,为快速城市化地区的环境和人类健康问题提供高效、精准、人文关怀且切实可行的解决方案,并极大地拓展了风景园林学科的研究领域;实验室的核心关注点在于城市感知：探究城市环境特征如何以及在何种程度上通过心理、生理等途径影响人类健康;在健康城市领域,这支先锋团队担任着理论研究、技术探索以及教育者的角色。     

滇东地质高背景区土壤镉的伴矿景天修复及其对后茬水稻的影响

云南是我国典型的重金属元素地球化学高背景区,超积累植物能否有效修复镉（Cd）地质高背景土壤,从而实现农产品安全生产,尚鲜见报道。以云南石林、富源和罗平等三地的典型Cd地质高背景农田土壤为对象,开展温室盆栽试验,探究Cd、锌（Zn）超积累植物伴矿景天对土壤Cd的吸取修复效果,及对后茬水稻生长和Cd吸收的作用。结果表明,种植三季伴矿景天使三地土壤全量Cd分别下降24.8%、30.9%和58.8%;伴矿景天在富源玄武岩风化物母质发育的土壤上长势最好,与富源土壤肥力相对最高有关;伴矿景天在罗平石灰岩风化物发育的土壤上重金属吸取修复效果最佳,与罗平土壤呈中性、酸化作用更为明显有关,这些差异由土壤母质、养分与重金属有效性等综合影响所致。经伴矿景天吸取修复三季后,全生育期淹水种稻,其糙米和秸秆Cd浓度均大幅降低,可实现糙米Cd安全生产。未经植物吸取修复,旱作处理下水稻糙米Cd存在超标风险。植物吸取修复后种植水稻,再结合水分管理措施,可进一步降低稻米中Cd浓度,实现稻米安全生产,为Cd地质高背景区土壤的农作物安全生产提供理论依据和技术支撑。     

新疆亚洲玉米螟暴发成灾驱动机制分析及其治理对策

亚洲玉米螟Ostrinia furnacalis (Guenée)为新疆玉米生产中的主要害虫, 2010年以来呈整体暴发态势, 并造成了极为严重的产量损失, 已成为制约当前新疆玉米高产稳产和粮食产能提升最重要的害虫, 然而有关其暴发成灾的驱动机制尚不清楚。为此, 本文通过分别概述近年来新疆气候、作物种植结构、栽培耕作措施以及亚洲玉米螟田间发生世代的变化情况, 综合分析了上述变化对亚洲玉米螟越冬幼虫存活、个体生长发育、取食偏好和种群增殖扩张等方面造成的具体影响。在此基础上, 结合已有大量研究成果, 系统解析了新疆绿洲生态区亚洲玉米螟种群暴发成灾的驱动机制。此外, 本文还介绍了新疆植保科技工作者在亚洲玉米螟综合治理技术研究与应用方面取得的相关成果, 简述了田间治理采取的措施和取得的防治成效, 最终为亚洲玉米螟的持续有效治理提供一定参考。     

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates. In this study, we analyzed a database of thinning trees from a forest management plan in the Contendas de Sincorá National Forest, southwestern Bahia State, Brazil. The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm. Adjustments, validation and statistical selection of four volumetric models were performed. Due to the difference in height values for the same diameter and the low correlation between both variables, we do not suggest models which only use the diameter at breast height (DBH) variable as a predictor because they accommodate the largest estimation errors. In comparing the best single entry model (Hohenald-Krenn) with the Spurr model (best fit model), it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion (AIC) and adjusted determination coefficient (R2 adj), which are poorer than the second best model (Schumacher-Hall). Regarding the minimum sample size, errors in estimation (root mean square error (RMSE) and bias) of the best model decrease as the sample size increases, especially when a larger number of trees with DBH≥15.0 cm are randomly sampled. Stratified sampling by diameter class produces smaller volume prediction errors than random sampling, especially when considering all trees. In summary, the Spurr and Schumacher-Hall models perform better. These models suggest that the total variance explained in the estimates is not less than 95%, producing reliable forecasts of the total volume with shell. Our estimates indicate that the bias around the average is not greater than 7%. Our results support the decision to use regression methods to build models and estimate their parameters, seeking stratification strategies in diameter classes for the sample trees. Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest. Stratified sampling of the data set for model adjustment and selection is necessary, since we find significant results with mean error square root values and bias of up to 70% of the total database.     

Role of endoplasmic reticulum stress in trimethylamine N-oxide-induced oxidative stress in human umbilical vein endothelial cells

AIM: To investigate whether endoplasmic reticulum stress is involved in trimethylamine N-oxide (TMAO)-mediated oxidative stress in human umbilical vein endothelial cells (HUVECs). METHODS: The cell viability was examined by CCK-8 assay. The cells were stained by DCFH-DA, and the intracellular level of reactive oxygen species (ROS) was observed by phase-contrast microscopy and detected by flow cytometric analysis. The protein levels of phospho-IRE-1α, IRE-1α and GRP78/BiP were detected by Western blot. RESULTS: TMAO exerted no significant effect on the viability of HUVECs. For a long period (>24 h), even a low concentration (10 μmol/L) of TMAO increased the oxidative stress level in the HUVECs (P<0.05). TMAO increased the phosphorylation level of IRE-1α and significantly up-regulated the protein level of GRP78/BiP in HUVECs (P<0.01). Pretreatment with STF-083010, an inhibitor of IRE1α, for 1 h reduced TMAO-induced oxidative stress in HUVECs (P<0.05). CONCLUSION: Endoplasmic reticulum stress is involved in TMAO-induced oxidative stress in HUVECs.     

A novel approach to estimating the competitive ability of Cirsium arvense in cereals using unmanned aerial vehicle imagery

Eight experiments were carried out in Denmark to determine the yield loss of spring barley due to Cirsium arvense in farmers' fields and to suggest and evaluate a novel approach for quantifying C. arvense infestation in large plots. Literature about the competitive ability of C. arvense is old, scattered and inconclusive, and existing models for estimating crop yield loss are based on data from North America. This study showed that C. arvense coverage could be quantified from unmanned aerial vehicle imagery using a manual image analysis procedure. This gave similar results as scoring the coverage. Yield loss of spring barley due to C. arvense infestation assessed at harvest was given by Y = 100·(1−exp(−0.00170·X)) where Y is the percentage of crop yield loss and X is the percentage of C. arvense coverage. The yield loss was much lower than estimates from models that have been developed in North America. It is speculated that the main reason for this is the later emergence of C. arvense than the crop due to lower soil temperatures in spring. Grain moisture increased linearly with C. arvense coverage: M = 0.0310·X where M is the proportional (%) increase in grain moisture and X is the proportion (%) of C. arvense coverage. Automated image analysis procedures are needed to estimate C. arvense coverage on field scales, and further experiments are needed to reveal whether the low competitive ability of C. arvense found in this study is representative for Northern Europe.