DNDC模型在华北平原冬小麦区的率定和验证

为了研究DNDC模型在华北平原典型作物-冬小麦的适用性,利用DNDC模型对冬小麦不同灌溉制度下小麦生长进行模拟和验证,并对模型精度进行评价。基于2015和2016年冬小麦生育期实测数据,建立了不同灌溉制度下冬小麦DNDC模型,分析了不同作物参数对冬小麦生育期的生物量和产量的敏感性排序,率定和验证了DNDC模型参数,并对模型精度进行了评价。结果表明,作物需水量对产量和地上部生物量的敏感性较大;不同灌溉制度下冬小麦生育期的土壤水分、地上部生物量以及产量的实测值与模拟值的各项评价指标均在可接受范围内,DNDC模型可以较好的模拟华北地区冬小麦生育期间的土壤含水量、地上部生物量和产量,对冬小麦的生产有一定的指导意义。     

WOFOST作物模型在拉林河流域的适用性研究

为了解WOFOST作物模型对拉林河流域春玉米的适用性,以东北地区哈尔滨、双城、尚志、扶余等地春玉米为研究对象,建立模型运行所需的气候、土壤、作物数据库,模拟2013年春玉米发育期日序变化、2009年土壤含水率的变化、2009年哈尔滨站春玉米生物量累积过程以及2009―2013年春玉米产量,并与实测值进行对比分析。结果表明,生育期平均绝对误差MAE=1.57d,标准差σ=1.54d,均方根误差RMSE=1.85d;土壤含水率MAE在1.38%~1.75%之间,RMSE在1.87%~2.33%之间,模型效率指数MEI在0.48~0.73之间;叶干质量、茎干质量、穗干质量的MEI在0.82~0.94之间,产量RMSE和MEI分别为0.72 t/hm~2和0.15。各适用性指标均在可信范围内,表明经过参数校准后的WOFOST模型在拉林河流域具有一定的适用性。     

基于历史气象资料和WOFOST模型的区域产量集合预报

针对基于作物生长模型进行产量预报时气象要素变化对作物生长的实时影响不能得到充分反映,产量预报缺乏量化不确定性信息的突出问题,选择河北省保定市和衡水市冬小麦主产区为研究对象,提出构建历史气象集合作为预报期气象数据输入驱动WOFOST模型的冬小麦生长模拟,并通过实时更新不断向前滚动预报,从传统单一数值的预报转向基于集合的概率预报。结果表明:基于历史气象资料可以进行作物模型的区域产量集合预报,抽穗期至灌浆期是预报精度最高的时期,预报集合中位数与实测产量的皮尔逊相关系数(PCC)最高为0.563,平均绝对误差(MAE)最低为458 kg/hm~2。研究结果表明区域化产量集合预报具有较强的可行性,并为量化作物模拟系统不确定性、数值天气预报与作物模型的结合应用提供了参考。     

基于历史气象资料和WOFOST模型的冬小麦区域产量集合预报

针对基于作物生长模型进行产量预报时气象要素变化对作物生长的实时影响不能得到充分地反映,产量预报缺乏量化不确定性信息的突出问题,选择河北省保定市和衡水市冬小麦主产区为研究对象,提出构建历史气象集合作为预报期气象数据输入驱动WOFOST模型的冬小麦生长模拟,并通过实时更新不断向前滚动预报,从传统单一数值的预报转向基于集合的概率预报。结果表明:基于历史气象资料可以进行作物模型的区域产量集合预报,抽穗期至灌浆期是预报精度最高的时期,预报集合中位数与实测产量的皮尔逊相关系数(PCC)最高为0.56,平均绝对误差(MAE)最高为578 kg·hm~(-2)。研究结果表明区域化产量集合预报具有较强的可行性,并为量化作物模拟系统不确定性、数值天气预报与作物模型的结合应用提供了参考。     

从群体到个体尺度——基于数据的DSSAT和GreenLab作物模型连接探索

作物模型的研究涉及作物生长发育的复杂过程,空间上从分子到细胞、组织、器官、个体、群体等不同尺度,时间尺度上可以从秒到年。基于不同的研究需求,切换作物模型尺度,可使得作物模型的适用性更广泛灵活。其中,如何从群体尺度的作物模型转入个体尺度的作物模型是本研究的内容。本研究基于四个玉米品种的两个处理（灌溉和雨养）的已有的实验数据和基于这些数据的DSSAT系统的模拟数据,校准功能结构模型GreenLab的参数,以计算结果一致为指标,探索不同空间尺度模型建立接口的方法,比较不同模型的特点。结果表明,GreenLab模型可以复现DSSAT系统的模拟数据和实际测量数据,进一步可以反演出各种器官之间生物量的分配并进行三维可视化展示。最后讨论了不同空间尺度模型结合的优势及应用领域。     

冬小麦生物量和产量的AquaCrop模型预测

以华北地区冬小麦为研究对象,将AquaCrop作物生长模型应用到滴灌、喷灌、漫灌中,对模型主要参数如气象、土壤、作物特性等进行调整,并对作物产量和生物量模拟的有效方法进行了研究。模拟结果表明,产量和收获时地上部分生物量的模拟值与实测值较为接近且略高于实测值,模型性能指数均高于0.95。产量模拟效果优于生物量,滴灌模拟效果最好。     

基于MODIS与WOFOST模型同化的区域冬小麦成熟期预测

针对遥感技术只能获取作物的表征信息、对作物内在机理过程变化描述较为困难的问题,引入作物生长模型与遥感数据同化进行作物成熟期预测研究。以叶面积指数(LAI)作为耦合变量,以MODIS LAI(MCD15A3H产品)作为遥感数据源,结合2017—2018年实时气象数据以及气象预报数据,以2018年5月1日为预报时间节点,构建LAI归一化代价函数,采用复合形混合演化算法(Shuffled complex evolution-University of Arizona,SCE-UA)最小化代价函数,优化WOFOST作物模型的输入参数,用优化后的参数重新驱动WOFOST模型逐像元模拟冬小麦生长过程,得到研究区冬小麦成熟期的预测结果,并使用研究区内农业气象站点的观测数据进行验证。结果表明,冬小麦预测开花期、成熟期的均方根误差(RMSE)分别为2. 10、2. 48 d,预测精度较高。该方法能够为农作物的大区域成熟期预测提供重要理论基础。     

小麦模型算法集成平台构建与算法比较

为方便小麦模型算法比较与多算法集成模拟,本研究参考国内外主流作物模型CERES-Wheat、APSIM-Wheat、WheatSM、WOFOST、SWAT等的主要算法,集成了发育期、生物量、产量形成等模块的多种算法,构建了小麦模型算法集成平台（Wheat model algorithm integration platform, WMAIP）。发育期模块集成了小麦钟模型和热时两种算法;生物量模块集成了群体光合作用、光能利用效率和二氧化碳同化率3种算法;产量形成模块集成了籽粒灌浆、生物量转移和收获指数3种算法。基于模型平台组成了6个具有代表性的模拟模型。利用河北省吴桥县2017—2019年两年播期试验的田间观测数据结合2011—2014年3年播期耦合水分文献资料对模型进行参数校准与验证,并对特定模块的不同算法进行比较。结果表明,各模型的模拟结果与实测值均吻合良好,模拟误差在合理范围之内,其中发育期、地上部生物量、产量和土壤贮水量模拟值和实测值的归一化均方根误差（NRMSE）分别在0.56%~4.00%、16.13%~18.72%、12.48%~18.95%和10.78%~11.63%之间,模型集合的模拟效果优于单一模型。通过算法比较发现,发育期模块中热时法模拟播种至拔节阶段较优,小麦钟模型模拟播种至开花阶段和播种至成熟阶段较优;生物量模块中3种算法均为模拟小麦生物量的较佳模型,但在高辐射条件下,群体光合作用法模拟的生物量较高;产量模块中3种算法模拟的产量变化趋势较为一致,但生物量转移法效果略好。该平台集成了特定模块的多种算法,能较好地模拟土壤贮水量和冬小麦的生物学指标,在小麦模型算法比较与改进、集成模拟及气候变化影响评估方面具有较大的应用潜力。     

基于粒子滤波和多变量权重的冬小麦估产研究

为了构建能够反映作物长势的综合性指标以及准确估测作物产量,采用粒子滤波算法同化CERES-Wheat模型模拟和基于Landsat数据反演的叶面积指数(Leaf area index,LAI)、地上生物量和0~20 cm土壤含水率,获取冬小麦主要生育期以天为尺度的变量同化值,分析不同生育时期的LAI、地上生物量和土壤含水率同化值与实测单产的相关性,并应用熵值的组合预测方法确定不同状态变量影响籽粒产量的权重,进而生成综合性指数,并分析其与实测单产的相关性。结果表明,LAI、地上生物量和土壤含水率同化值和田间实测值间的均方根误差(Root mean square error,RMSE)以及平均相对误差(Mean relative error,MRE)均低于这些变量模拟值和实测值间的RMSE和MRE,说明数据同化方法提高了时间序列LAI、地上生物量和土壤含水率的模拟精度。基于不同状态变量的权重生成的综合性指数与实测单产间的相关性大于单个变量与实测单产间的相关性;基于综合性指数构建小麦单产估测模型,其估产精度(R2=0.78,RMSE为330 kg/hm2)分别比基于LAI、地上生物量和土壤含水率建立模型的估产精度显著提高,表明构建的综合性指数充分结合了不同变量在作物估产方面的优势,可用于高精度的冬小麦单产估测。     

气候变化条件下旱作玉米用水效率与单产变化趋势分析

以渭北旱塬合阳和长武2个试验站点为研究区域,通过多年的玉米田间试验数据评估CERES-Maize模型的适用性,再利用区域气候模式Reg CM4.0输出的气象数据对2050年前玉米单产及生产水足迹进行预测。结果表明:CERES-Maize模型可以很好地模拟雨养玉米产量和物候期,多数年份二者的绝对相对误差(Absolute relative error,ARE)在10%以内,CERES-Maize模型在渭北旱塬旱作农业区有很好的适用性。应用CERES-Maize模型模拟玉米生产水足迹,较传统水足迹计算方法得到的结果更为精确可靠。在RCP2.6气候情景下,随着温度升高和生育期有效降水量的增加,玉米产量呈上升趋势;在RCP8.5气候情景下,随着温度升高和生育期有效降水的减少,玉米产量呈下降趋势。气温上升幅度过大对玉米单产有明显的负面影响,降水与玉米用水效率呈正相关。为有效应对气候变化对旱作作物产量造成的负面影响,应采取减少温室气体排放量、增强土壤蓄水保墒能力、发展集雨补灌、筛选和培育节水抗旱新品种等措施。     

Regional simulation of maize production in tropical savanna fallow systems as affected by fallow availability

Upscaling of crop models from the field scale to the national or global scale is being used as a widespread method to make large-scale assessments of global change impacts on crop yields and agricultural production. In spite of the fact that soil fertility restoration and crop performance in many developing countries with low-input agriculture rely strongly on fallow duration and management, there are only few approaches which take into account the effect of fallowing on crop yields at the regional scale. The objectives of this study were to evaluate the sensitivity of maize yield simulations with the Environmental Policy Integrated Climate (EPIC) model to fallow availability at the field and regional scale and (2) to present a novel approach to derive a model-based estimate of the average fallow availability within a typical catchment of the sub-humid savanna zone of West Africa. Therefore, the EPIC model has been validated at the field scale and then incorporated into a spatial database covering a typical catchment within the sub-humid savanna zone of West Africa with 121 sub-basins. Maize-fallow rotations have been simulated within 2556 quasi-homogenous spatial units and then aggregated to the 10 districts within the catchment assuming three different scenarios of fallow availability: 100% of the bush-grass savanna area is available and used in fallow-crop rotations (FU100), 50% of the bush-grass savanna area is available and used in fallow-crop rotations (FU50) and 25% of the bush-grass savanna area is available and used in fallow-crop rotations (FU25). A new aggregation procedure has been developed which is based on changes in the frequency of fallow-cropland classes within the sub-basins to render the simulation results in the spatial database sensitive to changes in fallow availability. Comparison of the average simulated grain yield with the mean yield over the catchment shows that the simulations overestimate maize yields by 62%, 44% and 15% for scenario FU100, FU50 and FU25, respectively. The best agreement between simulated and observed crop yields at the district scale was found when using the assumption that 25% of the savanna is available as fallow land under the present cropping patterns, which corresponds to a fallow-cropland ratio of 0.9. Comparison with farm surveys shows that the combination of remote sensing and dynamic crop modelling with yield observations provides realistic estimates of effective fallow use at the regional scale.     

Regional crop modelling in Europe: The impact of climatic conditions and farm characteristics on maize yields

Impacts of climate variability and climate change on regional crop yields are commonly assessed using process-based crop models. These models, however, simulate potential and water limited yields, which do not always relate to observed yields. The latter are largely influenced by crop management, which varies by farm and region. Data on specific management strategies may be obtained at the field level, but at the regional level information about the diversity in management strategies is rarely available and difficult to be considered adequately in process-based crop models. Alternatively, understanding the factors influencing management may provide helpful information to improve simulations at the regional level.In this study, we aim to identify factors at the regional level that explain differences between observed and simulated yields. Observed yield data were provided by the Farm Accountancy Data Network (FADN) and Eurostat. The Crop Growth Monitoring System (CGMS), based on the WOFOST model, was used to simulate potential and water limited maize yields in the EU15 (i.e., the old member states of the European Union). Differences between observed and simulated maize yields were analysed using regression models including: (i) climatic factors (temperature and precipitation), (ii) farm size, (iii) farm intensity, (iv) land use, (v) income and (vi) subsidies. We assumed that the highest yields observed in a region were close to the yield potential as determined by climate and considered the average regional yields as also influenced by management. Model performance was analysed with respect to spatial and temporal yield variability.Results indicate that for potential yield, the model performed unsatisfactory in southern regions, where high temperatures increased observed yields which was in contrast to model simulations. When considering management effects, we find that especially irrigation and the maize area explain much of the differences between observed and simulated yields across regions. Simulations of temporal yield variability also diverted from observed data of which about 80% could be explained by the climatic factors (35%) and farm characteristics (50%) considered in the analysis. However, effects of specific factors differed depending on the regions. Accordingly, we propose different groups of regions with factors related to management which should be considered to improve regional yield simulations with CGMS.     

基于温度植被干旱指数的土壤水分空间变异性分析

【目的】深入探讨区域土壤水分空间变异及其尺度效应,优化灌区尺度土壤水分采样精度,并提供合理采样方案。【方法】以人民胜利渠灌区为研究区,利用Landsat 8遥感影像,构建了温度植被干旱指数,根据其与土壤水分的相关关系获得研究区土壤水分分布。利用经典统计学和地统计学分析方法对2种尺度下土壤水分分布进行了空间变异性分析。【结果】不同尺度土壤水分服从正态分布,随着研究尺度和分辨率的增大,土壤水分的空间变异系数逐步增大;地统计学分析表明小尺度的块金基台比(C0/(C0+C))小于0.25,具有较强的空间相关性,而灌区尺度的块金基台比大于0.25小于0.75,具有中等强度的空间相关性。灌区尺度所选不同分辨率下土壤水分的变异系数、变程以及块金基台比变化很小。【结论】人民胜利渠灌区尺度土壤水分的获取不适宜用插值法,比较适宜用遥感法。     

Analysis of two variants of a spatially distributed crop model,using wavelet transforms and geostatistics

Models of crop yield are important for the assessment and optimization of agricultural systems. It is therefore necessary that crop models are suitably validated. In many circumstances, a model is required for prediction at a particular spatial scale (e.g. at a within-field scale for precision agriculture), and validation of the model should account for this. We compared spatially explicit methods to validate a grain yield model applied to a transect of 267 contiguous 0.72 × 0.72 m plots on an arable field at Silsoe, eastern England. Grain yield of wheat was determined in each plot during two growing seasons, and a crop model was used to predict the yield retrospectively. We used two variants of the model, each of which used different spatial variables as input. Observed and predicted yield were then compared with non-spatial statistics, but also with wavelet transforms (i.e. the adapted maximal overlap discrete wavelet transform) and geostatistics (i.e. a linear mixed model estimated by residual maximum likelihood). The latter two are spatially explicit statistical methods. The most successful of the variants required as input the daily evolution of leaf-area index in each plot. Validation of this variant with spatial statistics revealed that (i) the variance of the predictions tended to underestimate that of the observations, particularly at relatively coarse spatial scales, however, in relative terms, the distribution of observed variance across scales was described adequately by the model; (ii) the correlation of the predictions with the observations was weak at relatively fine scales but strong at relatively coarse scales; (iii) there was evidence that the correlation of the predictions with the observations was not uniform across the transect at relatively fine scales, which was possibly due to the underlying soil variation; and, (iv) the spatial pattern of model error suggested that some of the fine-scale yield variation, especially in the first growing season, could be attributed to soil compaction, a process not included in the model. These details were not apparent with non-spatial statistics; wavelets and geostatistics are therefore more appropriate tools for validating a spatially distributed crop model. We conclude that this variant of the model is therefore potentially useful for precision agriculture where we need to predict crop behaviour within small management zones, at the scale of tens of metres, but not to predict yield at finer scales. We outline how the most appropriate statistical technique for a particular study depends on whether the observations can be sampled regularly in space, whether we can assume the statistics are uniform across the landscape, the number of spatial scales of interest, and whether interpolation of the predictions, observations, and errors is required.     

利用Aster高分辨率卫星图像估测灌溉小麦叶绿素（摘选）

无损识别作物属性为了解作物对各种环境影响的反应提供一种快速、准确的方法。作物叶绿素含量是判断作物健康情况和估测作物产量的重要指标。近年来,在局地和大尺度下通过遥感技术测定作物参数已作为地域特性管理的有力工具。在区域尺度上,评估了通过高分辨率卫星图像预测小麦属性的潜力。植被指数NDVI、RVI、GNDVIbr和其他波段比值都来自于Aster影响和相关作物叶绿素含量。通过导出植被指数,证明了利用Aster高分辨率卫星影像映射作物叶绿素含量空间变化的有效性。NDVI和红绿波段比被作为评价小麦叶绿素含量的敏感性指标（R=0.73和R=0.72）。结果表明,在区域范围内利用高空间分辨率卫星影像取得作物叶绿素水平对评估作物状况是一种行之有效的方法。      

顺序同化不同时空分辨率LAI的冬小麦估产对比研究

选择PyWOFOST模型为动态模型,以叶面积指数(LAI)为状态变量,遥感LAI为观测值,采用集合卡尔曼滤波(En KF)同化算法,研发了一种遥感LAI与作物模型同化的区域冬小麦产量估测系统。为消除云的污染,采用Savitzky-Golay(S-G)滤波算法重构时间序列MODIS LAI;通过构建地面观测LAI与3个关键物候期Landsat TM植被指数回归统计模型,获得区域TM LAI;通过融合3个关键物候期的TM LAI与时间序列S-G MODIS LAI,生成尺度转换LAI。对比分析3种不同时空分辨率的遥感LAI的同化精度,研究结果表明,同化尺度转换LAI获得了最高的同化精度,与官方县域统计产量相比,在潜在模式下,决定系数由同化前的0.24提高到0.47,均方根误差由602kg/hm2下降到478 kg/hm2。结果表明,遥感观测与作物模型的尺度调整对提高冬小麦同化模型精度具有重要作用,遥感LAI与作物模型的En KF同化方法是一种有效的区域作物产量估测方法。     

基于时间序列MODIS NDVI的冬小麦产量预测方法

选择我国河北、河南、山东3省作为研究区,在250 m空间分辨率的冬小麦种植区和1 km的冬小麦像元纯度图的基础上,分析了2000—2009年MODIS NDVI抽穗期峰值与单产的时间序列变化关系。采用Becker-Reshef等提出的去噪声修正后的冬小麦抽穗期NDVI峰值与单产进行回归分析建立冬小麦产量预测模型,并分析冬小麦预测精度的影响因素。最后,利用2010年地级市尺度的统计单产对所建立的预测模型进行精度验证,模型的平均估产误差约为7.49%。结果表明,基于冬小麦抽穗期NDVI峰值的产量预测方法在中国冬小麦主产区具有一定的应用潜力。     

The use of remote sensing for estimating ET of irrigated wheat and cotton in Northwest Mexico

Components of a satellite-based system for estimating the crop water requirements of irrigated vegetation have been combined, applied, and tested against field data in the Yaqui Valley, northwest Mexico. Frequent satellite observations have the potential to provide snap shots of cloud variability at the high spatial and temporal resolutions that are needed for making simple, near real-time estimates of incoming solar radiation and, thus, daytime evaporation required for irrigation scheduling. Less frequent polar orbiting satellites offer the capacity of following the vegetation development at higher spatial resolution. The operational framework for obtaining cloud cover has been developed and applied using hourly sampled, 1 km resolution, GOES-10 data received in real-time. The high-resolution, cloud-screening algorithm has proved to be efficient and reliable and has been used to provide high-resolution (4 km) estimates of solar radiation. Relationships between vegetation indices (NDVI and SAVI) and crop coefficients (the ratio of measured to reference evapotranspiration) have been derived with four different models (Shuttleworth, Penman, Priestley–Taylor and Makkink), using ground-based surface reflectance measured over the crop. Continuous measurements of surface fluxes and other meteorological variables were made following almost the entire vegetative cycle of the plant using a station equipped with standard meteorological instruments and an eddy-correlation system. Actual evapotranspiration was computed as the product of the estimated crop coefficients, derived from field radiometer measurements, and reference evapotranspiration. In comparison with ground data, RMSE values are on the order of 1 mm per day. Finally the opportunity to use high-resolution satellite data to make near real-time estimates of crop evaporation is discussed.     

Yield uncertainty at the field scale evaluated with multi-year satellite data

The level of yield risk faced by a farmer is an important factor in the design of appropriate management and insurance strategies. The difference between field scale and regional scale yield risk, which can be significant, also represents an important measure of the factors that cause the yield gap – the difference between average and maximum yields. While field scale yield risk is difficult to assess with traditional data sources, yield maps derived from remote sensing offer promise for obtaining the necessary data in any region. We analyzed remotely sensed yield datasets for two regions in Northwest Mexico, the Yaqui and San Luis Rio Colorado Valleys, in conjunction with time series of aggregated regional yields for 1976–2002. Regional scale yield risk was roughly 8% of average yields in both regions. Field scale yield risk was determined to be 58% higher than regional scale risk in both regions. The difference between field and regional scale risk accounted for 50% of the spatial variance in yields in the Yaqui Valley, and 70% in the San Luis Rio Colorado Valley, indicating that climatic uncertainty represents an important source of the spatial yield variability. This implies that accurate seasonal climate forecasts could substantially reduce yield losses in farmers’ fields. The results were shown to be fairly sensitive to assumptions about the magnitude and nature of errors in yield estimation, suggesting that improved understanding of estimation errors are needed to realize the full potential of remote sensing for yield risk analysis.