Validation of a commercially available cELISA test for canine neosporosis against an indirect fluorescent antibody test (IFAT)

A commercially available competitive enzyme-linked immunosorbent assay (cELISA, VMRD^®) was validated for the detection of Neospora caninum antibodies in the serum of dogs, using as a reference test an indirect fluorescent antibody test (IFAT, Fuller^®). A partial verification approach was used. A total of 618 dogs were screened with cELISA and a subset of positive and negative sera (n = 237) were then tested with IFAT. Naïve relative sensitivity (SE_nv) and naïve relative specificity (SP_nv) of cELISA were calculated and then corrected (SE_corr; SP_corr) for studies with partial validation. Results showed a SE_nv of 72% and a SP_nv of 89.3%; corrected estimates showed a SE_corr of 47% and a SP_corr of 96%. ROC analysis showed that the cutoff recommended by the manufacturer (30%) corresponded to the highest naïve sensitivity (72%) combined with a good naïve specificity (90%) of cELISA. Corrected estimates of SE and SP for partial verification method revealed that SE of the cELISA is lower and SP is higher than naïve estimates. The results suggest to use this test for confirmation of a clinical suspicion of neosporosis, and to use some techniques for adjustment of misclassification in prevalence and risk-factor studies.     

苏北沿海滩涂盐土上油葵盐肥效应研究

2008年在江苏沿海海涂进行田间试验研究了不同盐分含量土壤上油葵盐肥耦合效应.结果表明:①土壤盐分含量在2.9 ～ 3.6 g/kg时对油葵籽粒产量影响不大,但随着土壤盐分含量的增加,在5.9 ～ 6.8 g/kg时,油葵籽粒产量显著下降.随土壤盐分含量的增加,茎叶生物产量、根生物产量、株高与籽粒产量变化趋势相似.随着N肥和P肥使用量的增加,油葵籽粒产量显著增加,油葵茎叶和根生物产量与株高随着N肥和P肥使用量增加的变化趋势与籽粒产量变化相似.②随着土壤盐分含量的增加,N和P效应越明显,油葵茎粗、盘直径、盘重、茎叶重和根重在不同土壤盐分含量下,随着施N、P量的增加,其变化趋势与油葵主茎高度的变化趋势相似.③各处理措施均能影响油葵籽粒产量,经方差分析,土壤盐分含量、N肥施用量、P肥施用量、盐肥交互作用、NP肥交互作用均呈极显著关系,从处理间区别看,影响油葵产量的主要因素是土壤盐分含量,N肥和P肥次之.     

植物基因功能验证技术概述

随着生物学研究在分子水平上的不断深入和推进,越来越多的基因得以发现并成功克隆,对基因功能的研究显得日益重要,这也是后基因组时代功能基因组学的重要研究内容。植物中存在的众多基因发挥着不同的作用,控制着不同的合成途径,基因各自发挥的功能对植物起着至关重要的作用。总结了有关基因功能验证技术方面的研究,并将常用于验证基因功能的方法做一归纳,以便为后续的试验研究提供生物学基础。     

CENTURY模型在新疆天山山区的适用性分析

利用模型评价气候变化对草地生产力的影响,对草地的可持续利用及防灾减灾具有重要意义。本文选取新疆天山山区乌鲁木齐市牧业气象试验站、昭苏、巴里坤为代表站点,基于各站多年围封草地观测资料,对草地生态系统模型（CENTURY模型）进行适用性分析。结果表明：CENTURY模型模拟的乌鲁木齐市牧业气象试验站、昭苏和巴里坤地上生物量模拟值与实测值呈显著相关（P<0.05）,决定系数（R²）分别为0.39,0.51,0.38,均方根误差（RMSE）分别为34.09,37.15,14.29 g C·m^-2,CENTURY模型可以用于气候变化对新疆天山山区草地生产力的影响研究。基于验证后的模型对1961-2016年地上生物量模拟显示：巴里坤呈显著减少趋势、昭苏呈极显著增加趋势、乌鲁木齐市牧业气象试验站呈波动减少趋势,但差异不显著。     

AquaCrop模型在西北胡麻生物量及产量模拟中的应用和验证

为了预测水分和养分对胡麻籽粒产量、生物量与水分生产率的影响,使用FAO研发的水分驱动作物模型AquaCrop对胡麻在不同灌溉与氮磷水平下的生长情况进行模拟和验证。试验分别于2011年、2012年在甘肃省榆中县良种场进行,试验设置4个灌溉水平,3个氮水平,3个磷水平。模型性能的评价采用模型效率(E)、决定系数(R2)、均方根误差(RMSE)和平均绝对误差(MAE)等统计指标。分析结果表明:AquaCrop模型校正的籽粒产量和生物量在不同灌溉与氮磷水平处理下的预测误差统计值为:0.97E0.99,0.11RMSE0.33,0.11 t·hm 2MAE0.42 t·hm 2,与2012年的试验观察数据(0.96E0.99,0.11RMSE0.42,0.11 t·hm 2MAE0.39 t·hm 2)基本一致;同时,群体覆盖(CC)与生物量的模拟结果与测定值也非常拟合。AquaCrop模型在充分灌溉处理下预测胡麻产量,比非充分灌溉处理下具更高的准确性。因而,水分驱动模型AquaCrop在西北胡麻区不同的灌溉与田间管理措施下有较高的模拟精确性,具有广阔的应用前景和价值。     

Satellite remote sensing for water erosion assessment: A review

Water erosion creates negative impacts on agricultural production, infrastructure, and water quality across the world. Regional-scale water erosion assessment is important, but limited by data availability and quality. Satellite remote sensing can contribute through providing spatial data to such assessments. During the past 30 years many studies have been published that did this to a greater or lesser extent. The objective of this paper is to review methodologies applied for water erosion assessment using satellite remote sensing. First, studies on erosion detection are treated. This comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes. Second, the assessment of erosion controlling factors is evaluated. Four types of factors are discussed: topography, soil properties, vegetation cover, and management practices. Then, erosion mapping techniques are described that integrate products derived from satellite remote sensing with additional data sources. These techniques include erosion models and qualitative methods. Finally, validation methods used to assess the accuracy of maps produced with satellite data are discussed. It is concluded that a general lack of validation data is a main concern. Validation is of utmost importance to achieve regional operational monitoring systems, and close collaboration between the remote sensing community and field-based erosion scientists is therefore required.     

Evaluating an environmental indicator: Case study of MERLIN, a method for assessing the risk of nitrate leaching

The need to achieve acceptable levels of nitrate in drinking water has led to the development of simulation models and indicators for assessing the environmental performance of agricultural practices. These indicators are necessarily based on simplifications in order to meet the practical constraints of feasibility, but they should nevertheless meet scientific standards, especially as regards their validation. The overall objective of this paper is to evaluate the MERLIN indicator and sub-indicators, an assessment method developed by French agricultural advisors. This tool estimates and classes the risks of water pollution by nitrates, integrating farmer practices both during crop cultivation and in between two successive crops, as well as soil sensitivity to leaching. The evaluation was performed according to the methodological framework proposed by Bockstaller and Girardin [Bockstaller, C., Girardin, P., 2003. How to validate environmental indicators? Agric. Syst. 76, 639–653]: design, output and end-use validation. Design validation involved submission of the method to experts and checking the sub-indicators against literature. Output validation was carried out by comparing real values to indicator outputs. End-use validation was based on information gathered by users. In particular we compared output data from the original method with that of users that had adapted the method to their situation, in order to assess the consequences of these changes. The first step confirmed that the assumptions of MERLIN and its sub-indicators are scientifically sound. However, the weighting of the different sub-indicators raised questions. The second output validation step gave acceptable results for the EQUIF sub-indicator but the MERLIN test highlighted the need for additional experimental data before validation. This approach also showed that improvements in the precision of parameters do not necessarily increase the accuracy of the classification. The last step confirmed that the indicator is considered useful by decision-makers but also reveals that in some cases users adapt parameter values to their situation. This has lead to the production of a user guide which defines the method more clearly to avoid numerous adaptations by users.     

Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions

Various crop growth simulation models exist for rice but thorough validation and evaluation reports are scarce. We present the model ORYZA2000, which simulates the growth and development of rice under conditions of potential production and water and nitrogen limitations. The model was evaluated against a data set of five field experiments with irrigated rice performed at IRRI between 1991 and 1993, with nitrogen levels varying from 0 to 400 kg ha⁻¹ in different splits and timings of application. We compared simulated and measured leaf area index (LAI) and biomass of leaves, stems, panicles, and total aboveground biomass by graphics; by the slope, intercept, and adjusted coefficient of correlation; by Student’s t test of means; and by absolute and normalized root mean square errors (RMSE). On average, RMSE was 690–1280 kg ha⁻¹ for total biomass, 350–380 kg ha⁻¹ for leaf biomass, 460–790 kg ha⁻¹ for stem biomass, and 380–580 kg ha⁻¹ for panicle biomass. Yield was simulated with an RMSE of 840–850 kg ha⁻¹ and a normalized RMSE of 11–13%. For these crop variables, normalized RMSE values were 65–84% higher than the typical coefficients of variation associated with their measurements. Simulated LAI generally exceeded measured values, especially at low levels of nitrogen application. We can use ORYZA2000 to support N field experiments and investigate optimum N application regimes with quantified errors of simulation. The developed database and quantitative goodness-of-fit parameters serve as references for future model improvements.     

An overview of the crop model

is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil–crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.     

Non-destructive leaf area estimation model for faba bean (Vicia faba L.)

In this study, a leaf area estimation model was developed for faba bean (Vicia faba L.) using the linear measurements such as lamina length (L) and width (W) by stepwise regression analysis. Leaflets from fourteen faba bean genotypes, including three cultivars (Eresen-87, Filiz-99 and Lara), two lines (FLIP85-172FB and FLIP86-116FB) and nine local genotypes, were used to develop the model in 2003-2004. The proposed leaf area (LA) estimation model is LA = 0.919 + 0.682LW, R² = 0.977. In 2004–2005 growing season, this model was validated by measuring new leaf samples from the different level of the plant canopy (lower, middle and upper) of faba bean cvs. Eresen-87 and Filiz-99 sown in both autumn and late winter. Produced model in this study can be reliably used for estimating area of leaf samples from the different level of the plant canopy of faba bean cvs. Eresen-87 and Filiz-99 sown in both autumn and late winter.