Correlation between temperature and phenology prediction error in rice (Oryza sativa L.)

For rice (Oryza sativa L.), simulation models like ORYZA2000 and CERES-Rice have been used to explore adaptation options to climate change and weather-related stresses (drought, heat). Output of these models is very sensitive to accurate modelling of crop development, i.e. phenology. What has to date received little attention in phenology calibration is the temperature range within which phenological models are accurate. Particularly the possible correlation between temperature and phenology prediction error has received little attention, although there are indications that such correlation exists, in particular in the study by Zhang et al. (2008). The implication of such correlation is that a phenology model that is accurate within the calibration temperature range can be less accurate at higher temperatures where it can systematically overestimate or underestimate the duration of the phase from emergence to flowering. We have developed a new rice phenology calibration program that is consistent with ORYZA2000 concepts and coding. The existing calibration program DRATES of ORYZA2000 requires an assumption of default cardinal temperatures (8, 30 and 42 °C) and then calculates cultivar specific temperature sums and development rates. Our new program estimates all phenological parameters simultaneously, including the cardinal temperatures. Applied to nine large datasets from around the world we show that the use of default cardinal temperatures can lead to correlation between temperature and phenology prediction error and temperature and RMSE values in the order of 4-18 days for the period from emergence to flowering. Our new program avoids such correlation and reduces phenology prediction errors to 3-7 days (RMSE). Our results show that the often made assumption of a rapid decrease in development rate above the optimal temperature can lead to poorer predictions and systematic errors. We therefore caution against using default phenological parameters for studies where temperatures may fall outside the range for which the phenological models have been calibrated. In particular, this applies to climate change studies, were this could lead to highly erroneous conclusions. More phenological research with average growing season temperatures above the optimum, in the range of 32-40 °C, is needed to establish which phenological model best describes phenology in this temperature range.     

TEOM1405F型PM2.5颗粒物分析仪校准方法的建立

对美国赛默飞世尔公司TEOM1405F颗粒物分析仪制定合理可行的校准方法,并且根据标准方法对仪器进行检定.校准结果为:环境温度传感器误差小于±2℃,环境气压传感器误差小于1 kPa,采样流量相对误差小于±3％,弹性常数K0＜±2％,绝缘阻抗＞20MΩ,满足校准方法技术要求.根据检定结果,该仪器监测数据准确可靠.     

啤酒大麦专用肥施用量对甘啤3号产量和品质的影响

通过两年对啤酒大麦专用肥施用量试验的研究,结果表明:不同的啤酒大麦专用肥施用量对啤酒大麦产量、蛋白质、筛选率、千粒重均有显著影响,应适当控制肥料的施用量,以啤酒大麦专用肥600kg/hm2为宜,不超过750kg/hm2。     

Durum wheat quality prediction in Mediterranean environments: From local to regional scale

Durum wheat is one of the most important agricultural crops in the Mediterranean area. In addition to yield, grain quality is very important in wheat markets because of the demand for high-quality end products such as pasta, couscous and bulgur wheat. Grain quality is directly affected by several agronomic and environmental factors. Our objective is to determine the general principles underlying how, in Mediterranean environments, grain protein content (GPC) is affected by these factors and provide a system model with high predictive ability. We initially evaluated the capability of the Delphi system to simulate GPC in the major Italian supply basins (Basilicata, Capitanata, Marche, Tuscany) for 9 years (1999–2007) a month ahead of harvesting and we then analyzed relations between Delphi system errors and selected environmental variables during flowering and grain filling stages. The results were evaluated on the basis of regression with observed GPC, while errors were calculated performing a linear correlation analysis with environmental variables. The model showed a high capability to reproduce the inter-annual variability, with important year to year differences, with better performance in the southern study areas (Basilicata and Capitanata). In this study the highest overestimation occurred in conjunction with the year (2004) characterized by the lowest quality in terms of GPC, lowest average temperature in May and highest yield production for the whole study period.     

Cone and seed production from stone pine (<Emphasis Type="Italic">Pinus pinea</Emphasis> L.) stands in Central Range (Spain)

Natural regeneration of stone pine (Pinus pinea L.) stands in the Central Range of Spain can be difficult to attain. The success of this regeneration is affected by factors such as the total amount of seed available, the short dispersal ability of the pinyon, the conditions required for germination, specific problems associated with the establishment, early survival of the seedlings due to severe summer heat and drought, competition for water and mineral resources and grazing damage. This study focuses on seed availability. The amount of available seeds depends on the number and size of the cones and the number of viable pinyons within the cones. In stone pine stands, both variables show great year to year variability. Both cone and seed production, for a given year, are also conditioned by the vigour and health of the tree, its size, the condition and attributes of the stand and the loss of seed through pests or predation. In this study, the main factors which influence cone and pinyon production are identified and a multivariate model to predict annual cone and viable seed production is developed. To consider the correlation among observations coming from the same tree, stand or year, random components are included in the model. The multivariate random structure allows for future calibration of the model for a given year from a small additional sample of observations. It is important to know the total amount of viable seed produced in a stand for a given year since regeneration cuttings for Stone pine should be concentrated in high crop years.     

应用近红外光谱技术分析稻米蛋白质含量

以稻谷、米粒、米粉3种形态的样品，应用近红外光谱技术(NIRS)和偏最小二阶乘法(PLS)，建立了6个稻米蛋白质含量近红外光谱数学模型，并对模型预测结果的准确性进行了评价。结果表明，糙米蛋白质含量的稻谷、糙米粒和糙米粉近红外光谱预测模型校正决定系数(R_C²)分别为0.893、0.971和0.987，校正标准差(RMSEC)分别为0.507、0.259和0.183；精米蛋白质含量的稻谷、精米粒和精米粉近红外光谱预测模型R_C²分别为0.897、0.984和0.986，RMSEC分别为0.497、0.186和0.190。模型内部交叉验证分析表明，预测糙米蛋白含量的稻谷、糙米粒和糙米粉模型内部交叉验证决定系数(R_CV²)分别为0.865、0.962和0.984，内部验证标准差(RMSECV)分别为0.557、0.290和0.205；预测精米蛋白含量的稻谷、精米粒和精米粉的模型R_CV²分别为0.845、0.951和0.979，RMSECV分别为0.594、0.316和0.233。模型外部验证分析表明，预测糙米蛋白含量的稻谷、糙米粒和糙米粉近红外光谱模型外部验证决定系数(R_V²)分别为0.683、0.801和0.939，外部验证标准差(RMSEV)为0.962、0.799和0.434；预测精米蛋白含量的稻谷、精米粒和精米粉近红外光谱的模型R_V²分别为0.673、0.921和0.959，RMSEV为0.976、0.513和0.344。用米粉建立的近红外光谱预模型准确性最高，米粒次之，基于稻谷的预测模型准确性相对较低；内部交叉验证和外部验证表明，近红外光谱分析技术与化学分析方法一致性较好，且能保证样品的完整性，在水稻优质育种和稻米品质分析中具有广泛的应用价值。     

用近红外光谱法测定大麦品质的研究

采用国标法和近红外光谱法（NIRS）对大麦籽粒水分、蛋白质、淀粉、赖氨酸进行分析,同时用IA-450型近红外分析仪对4个主要品质指标建立了定标方程。其相关系数Rc分别为0．9847、0．9947、0．9559、0．9742,标准误差为0．1649～9．0620,表明用近红外光谱法建立的大麦籽粒水分、蛋白质、淀粉、赖氨酸定标方程可直接用于大麦品质的快速测定和大批育种材料的筛选。     

近红外透射光谱法(NITS)分析大豆品质的研究

为了研究大豆品质性状的快速测定方法,以我国东北部四省区572份大豆样品为材料,采用近红外透射(NITS)技术非破坏性测定大豆的粗蛋白质、粗脂肪含量.调选出的校正集样品经实验室常规分析测定,建立其吸收光谱与化学成分间的关系模型,校正并优化原有测定方程.校正方程经预测获得了较高的预测集决定系数0.9757(蛋白质)、0.9549(脂肪)和较低的标准误差2.18(蛋白质)、0.88(脂肪).结果表明近红外透射光谱技术可广泛应用于大豆品种品质普查、大豆品质育种材料筛选和商品大豆质量分类分级.本研究试验用样品数量多、来源分布均匀、品种信息丰富,因此,所建立的近红外透射预测模型适用范围广.     

秦王川灌区田间土壤水分特征曲线与导水率的标定

运用相似性原理和标定理论，对秦王川灌区的土壤水分特征曲线和非饱和导水率进行了标定。通过分析认为，该标定结果对这一灌区的土壤有较好的代表性，可供水分运动研究之用     

Temperature compensation for near infrared reflectance measurement of apple fruit soluble solids contents

Near infrared reflectance (NIR) spectroscopy has been used successfully to measure soluble solids in apple fruit. However, for practical implementation, the technique needs to be able to compensate for fruit temperature fluctuations, as it was observed that the sample temperature affects the near infrared reflectance spectrum in a non-linear way. Temperature fluctuations may occur in practice because of varying weather conditions or improper conditioning of the fruit immediately after harvest. Two techniques were found well suited to control the accuracy of the calibration models for soluble solids with respect to temperature fluctuations. The first, and most practical one, consisted of developing a global robust calibration model to cover the temperature range expected in the future. The second method involved the development of a range of temperature dedicated calibration models. The drawback of the latter approach is that the required data collection is very large. When no precautions are taken, the error on the soluble solids content reading may be as large as 4%brix.