美国玉米和小麦产量动态预测遥感模型

粮食安全问题一直倍受世界各国关注,及时、准确地了解其他国家或地区的粮食生产状况,对于中国粮食贸易和粮食宏观调控,具有十分重要的意义.本文以美国冬小麦和玉米为研究对象,在分析各作物空间分布及生长季节的基础上,利用土地利用数据剔除非耕地信息,使提取的归一化植被指数(NDVI)客观地反映各作物的生长状况.以1998-2007年的SPOT VEGETATION旬最大值合成NDVI资料为数据源,研究了美国玉米和小麦生长季的旬NDVI与产量的关系,确定了不同月份的建模因子,分别建立了美国玉米和冬小麦不同月份的产量动态预报模型.通过对各模型估算产量与实际产量进行比较,各模型预报结果的相对误差大部分在3%以内,精度较高,说明建立的作物产量动态预报模型实用可行,能够投入产量预报业务应用.     

黄淮海多熟种植农业区作物历遥感检测与时空特征

多熟种植是高强度农业土地利用的重要特征,但由于缺乏在空间和时间上清晰描述农业多熟种植和作物种植历时空分布的数据,使得区域尺度农田生态系统碳动态估计、农田生产力监测与模拟等有很大的不确定性。黄淮海农业区是以冬小麦-夏玉米二熟制为主的我国粮食主产区,冬小麦和夏玉米分别为光合作用途径为C3和C4的作物,已有研究证明如果在估算生态系统生产力时不考虑一年两季作物及其光能利用率的差异则会导致生产力估算结果过低。研究结合农业气象站点地面作物物候观测数据和空间分辨率500m、8d合成的MOD IS时间序列数据,分析研究区二熟制作物的生长过程、物候特征和作物历的空间差异,发展基于EVI和LSWI时间序列曲线检测多熟区各季作物种植历的方法,获取黄淮海农业区空间表述清晰的熟制和各季作物的生长开始与结束时间数据,并应用农业气象站点数据对方法和所获取的作物历数据进行了比较验证。论述的方法和提取的各季作物的作物历时空数据将能够应用于区域尺度农田生产力估算、生物地球化学循环模拟和农业生态系统监测。     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

遥感信息应用于区域尺度水分限制条件下作物生长模拟的研究进展

第2生产水平（即水分限制条件）下的大范围作物生长动态模拟研究具有十分重要的现实意义.目前,区域尺度上水分限制条件下作物生长模拟存在一定的难度,而遥感信息与作物生长模拟模型的结合,可以为区域尺度水分限制条件下作物生长发育模拟及产量估算提供了一条行之有效的途径.本文简要回顾了遥感与作物生长模拟模型结合研究的发展概况,指出了区域尺度水分限制条件下作物生长模拟需要解决的问题,并在已有遥感反演土壤水分状况研究的基础上,简述了遥感信息应用于区域尺度水分限制条件下作物生长模拟的研究方法,并探讨了当前该领域研究的其他可能途径及需要进一步研究和解决的科学问题.     

土壤干旱胁迫对作物影响的模拟研究进展

用于模拟土壤干旱胁迫对作物影响的模型分为两类,一是水分管理模型,此类模型并不模拟作物的生长发育,但可以用于灌溉管理;二是作物生长模拟模型,这类模型模拟作物生长的主要过程(如叶片生长、生物量的积累与分配等),通常以实际蒸腾与潜在蒸腾的比值估算土壤干旱胁迫对作物光合的影响,近年来发展的耦合模型将植物的碳同化、蒸腾、能量平衡以及气孔行为相耦合,使得土壤干旱胁迫对作物影响的模拟更具机理性。本文从不同模型模拟土壤干旱对作物影响的原理入手,阐述了水分管理模型(FAO水分生产函数模型)、作物生长模型(Aqua Crop模型、CERES-Maize模型、WOFOST模型、EPICphase模型、耦合模型)等具有代表性模型是如何模拟土壤干旱胁迫对作物生长发育和(或)产量影响的,提出了作物模型模拟土壤干旱胁迫影响时应着力解决的问题:完善干旱对作物物候的影响模拟;考虑花期不遇对作物产量影响的模拟;考虑后续持续影响的模拟机制;发展更加基于物理和生理过程的模型。提出:作物模型的发展还需要多领域如模型程序员、田间试验、植物生理学家的相互协同与发展,田间试验研究是作物模型发展不可或缺的数据来源与坚实基础。     

气候变化背景下东北三省主要粮食作物产量潜力及资源利用效率比较

以东北地区喜温作物和喜凉作物的潜在种植区为研究区域,基于研究区域内65个气象台站1961—2010年地面气象观测数据,结合作物生育期资料,应用作物产量潜力逐级订正法,分析不同作物各级产量潜力时空分布特征,明确作物各级产量潜力受气候资源限制程度,比较气候资源利用效率差异.结果表明： 1961—2010年,东北三省6种作物（玉米、水稻、春小麦、高粱、谷子和大豆）的光温产量潜力呈明显的西高东低的空间分布特征,作物气候产量潜力除春小麦外其他作物均呈现南高北低的空间分布规律.6种作物受温度限制的产量潜力损失率呈东高西低的空间分布特征,大豆受温度限制引起的产量潜力损失率最高,平均为51%,其他作物为33%～41%;因降水制约引起的潜力损失率分布有明显的区域性差异,在松嫩平原和长白山区各有一个高值区,春小麦因降水亏缺引起的产量潜力损失率最高,平均为50%,其他4种雨养作物集中在8%～10%.东北三省各作物生长季内光能利用效率在0.9%～2.7%,其中玉米>高粱>水稻>谷子>春小麦>大豆;雨养条件下,玉米、高粱、春小麦、谷子和大豆各作物的降水利用效率在8～35 kg·hm^-2·mm^-1,其中玉米>高粱>春小麦>谷子>大豆.在光能利用效率和降水利用效率均较低的长白山区和小兴安岭南部地区,可采取合理密植、选择适宜品种、适时施肥、蓄水保墒耕作以及优化作物布局等措施提高资源利用效率.     

基于更新和同化策略相结合的遥感信息与水稻生长模型耦合技术的研究

将遥感与作物模型耦合有利于提高作物模型在区域尺度应用时的精度。基于集合平方根滤波算法(Ensemble Square RootFilter,EnSRF)和粒子群优化算法(Particle Swarm Optimization,PSO),以叶面积指数(Leaf Area Index,LAI)和叶片氮积累量(Leaf Nitrogen Accumulation,LNA)共同作为同化耦合点和过程更新点,将同化与更新策略相结合,研究建立了基于遥感信息与水稻生长模型(RiceGrow)耦合的水稻生长与产量预测技术。结果表明,将更新和同化策略结合后,利用RiceGrow模型模拟的水稻生长指标和产量结果更接近于实测值。其中LAI、LNA和产量与实测值间的RMSE分别为0.94、0.47 g/m2和320.15 kg/hm2;RiceGrow模型直接模拟LAI、LNA和产量的RMSE为1.25、1.24 g/m2和516.83 kg/hm2;而单纯基于同化策略模拟LAI、LNA和产量的RMSE为1.01、0.59 g/m2和335.70 kg/hm2。此外,基于该技术的模型区域尺度预测结果能较好地描述水稻生长和产量的时空分布状况,生长指标及区域总产量的模拟相对误差均小于20%。显示基于更新和同化策略相结合的遥感与模型耦合技术具有较高的预测精度,从而为区域尺度作物生长和产量预测提供了技术支撑。     

黄淮海平原林网保护区夏玉米生长过程的数值模拟

利用改进了产量生态学模型ＳＵＣＲＯＳ,对黄淮海平原林网保护区夏玉米的生长过程进行了数值模拟,并与田间监测资料做了比较,分析了影响夏玉米生长的各种生理、生态学因子,结果表明,改进后的模型能成功模拟夏玉米的生长过程,考虑病虫害、杂草影响后,模型输出的叶面积指数、器官生物量与生长监测资料十分一致。与单作农田比较,由于林网地区小气候条件的改善,夏玉米单产提高６．８％左右。播种密度、播种日期与籽粒产量关系的     

基于遥感与模型耦合的冬小麦生长预测

遥感的空间性、实时性与作物生长模型的过程性、机理性优势互补,将两者有效耦合已成为提高作物生长监测预测能力的重要手段之一。提出了一种基于地空遥感信息与生长模型耦合的冬小麦预测方法,该方法基于初始化/参数化策略,以不同生育时期的小麦叶面积指数(LAI)和叶片氮积累量(LNA)为信息融合点将地面光谱数据(ASD)及HJ-1 A/B CCD、Landsat-5 TM数据与冬小麦生长模型(WheatGrow)耦合,反演得到区域尺度生长模型运行时难以准确获取的部分管理措施参数(播种期、播种量和施氮量),在此基础上实现了对冬小麦生长的有效预测。实例分析结果表明,LNA较LAI对模型更敏感,以之作为耦合点的反演效果较好。另外,抽穗期是遥感信息与生长模型耦合的最佳时机,对播种期、播种量和施氮量反演的RMSE值分别达到5.32 d、14.81 kg/hm²、14.11 kg/hm²。生长模型与遥感耦合后的模拟结果很好地描述了冬小麦长势和生产力指标的时空分布状况,长势指标的模拟相对误差小于0.25,籽粒产量模拟的相对误差小于0.1。因此研究结果可为区域尺度冬小麦生长的监测预测提供重要理论依据。     

WOFOST模型在内蒙古河套灌区模拟玉米生长全程的适应性

在河套灌区引入成熟的作物模型并进行适应性验证,可为进一步开展玉米生长监测及估产提供依据和基础。本文利用河套灌区巴彦淖尔农业气象试验站2012年玉米观测数据,结合当地气象、土壤资料对荷兰瓦赫宁根大学开发的WOFOST模型进行参数校准,并利用2013年玉米观测数据和2001—2011年农业气象观测资料对模型的区域适用性进行验证,获得了玉米的基本作物参数,包括各发育阶段比叶面积、最大CO2同化率、单叶光能利用率等。结果表明:通过校准作物参数,WOFOST模型可以较好地模拟LAI扩展、生物量的动态积累过程,LAI、各器官生物量及最终产量的模拟值与实测值吻合较好;独立样本检验中,模型模拟LAI的绝对偏差平均值为0.75,叶生物量、茎生物量、贮存器官生物量、地上部总生物量、产量的归一化均方根误差分别为33%、26%、17%、18%和13%;模拟2001—2011年玉米产量的归一化均方根误差为7.5%。参数校准后的模型对LAI、各器官生物量、产量的模拟结果较为符合实际,WOFOST模型能够适用于河套地区玉米生产过程生理、生态因子诊断、评估等。     

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10 degrees N-17 degrees N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel-Guillot-Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.     

Analysis of climate signals in the crop yield record of sub‐Saharan Africa

Food security and agriculture productivity assessments in sub‐Saharan Africa (SSA) require a better understanding of how climate and other drivers influence regional crop yields. In this paper, our objective was to identify the climate signal in the realized yields of maize, sorghum, and groundnut in SSA. We explored the relation between crop yields and scale‐compatible climate data for the 1962–2014 period using Random Forest, a diagnostic machine learning technique. We found that improved agricultural technology and country fixed effects are three times more important than climate variables for explaining changes in crop yields in SSA. We also found that increasing temperatures reduced yields for all three crops in the temperature range observed in SSA, while precipitation increased yields up to a level roughly matching crop evapotranspiration. Crop yields exhibited both linear and nonlinear responses to temperature and precipitation, respectively. For maize, technology steadily increased yields by about 1% (13 kg/ha) per year while increasing temperatures decreased yields by 0.8% (10 kg/ha) per °C. This study demonstrates that although we should expect increases in future crop yields due to improving technology, the potential yields could be progressively reduced due to warmer and drier climates.     

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO₂], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly ?0.5 Mg ha^?1 per °C. Doubling [CO₂] from 360 to 720 μmol mol^?1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO₂] among models. Model responses to temperature and [CO₂] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.     

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.     

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near‐term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.     

Actual and potential yield for rainfed and irrigated maize in China

A crop yield model (YIELD), that uses climatic and environmental data to calculate yield and water consumption for a variety of major crops was applied specifically to maize (grain corn) in the region of China and Korea, by examining the parameters of potential and actual yield. A network of 241 stations provided the seasonal climatic input, which consisted of data averaged over approximately a 20 year period. Among the simulated results, highest yields under full irrigation (first growing season) occurred in the Yangtze River area, northward to Korea, Kweichou and Szechwan Provinces, and northcentral China, whereas least yield was found for the western interior. High yields exceeded 12,000 kg/ha per harvest. Under rainfed conditions, only the Yangtze River region retained its predominance. In order to achieve optimum crop yields, about 800 mm of irrigation water was needed in northcentral China, contrasted with none required in the south and east of China. Making certain dietary assumptions, the calculated grain corn production could potentially support between 700 and 400 million people, depending on the irrigation strategies adopted. If corn were used as feed stock for beef, only between 100 and 60 million persons could be supported. A sensitivity analysis was applied to determine the degree of error introduced by faulty, uncertain, or missing environmental input data for the stations utilized in this study.     

Climate‐driven simulation of global crop sowing dates

Aim To simulate the sowing dates of 11 major annual crops at the global scale at high spatial resolution, based on climatic conditions and crop‐specific temperature requirements. Location Global. Methods Sowing dates under rainfed conditions are simulated deterministically based on a set of rules depending on crop‐ and climate‐specific characteristics. We assume that farmers base their timing of sowing on experiences with past precipitation and temperature conditions, with the intra‐annual variability being especially important. The start of the growing period is assumed to be dependent either on the onset of the wet season or on the exceeding of a crop‐specific temperature threshold for emergence. To validate our methodology, a global data set of observed monthly growing periods (MIRCA2000) is used. Results We show simulated sowing dates for 11 major field crops world‐wide and give rules for determining their sowing dates in a specific climatic region. For all simulated crops, except for rapeseed and cassava, in at least 50% of the grid cells and on at least 60% of the cultivated area, the difference between simulated and observed sowing dates is less than 1 month. Deviations of more than 5 months occur in regions characterized by multiple‐cropping systems, in tropical regions which, despite seasonality, have favourable conditions throughout the year, and in countries with large climatic gradients. Main conclusions Sowing dates under rainfed conditions for various annual crops can be satisfactorily estimated from climatic conditions for large parts of the earth. Our methodology is globally applicable, and therefore suitable for simulating sowing dates as input for crop growth models applied at the global scale and taking climate change into account.     

An economic analysis of sweet sorghum cultivation for ethanol production in North China

Sweet sorghum [Sorghum bicolor (L.) Moench] is a promising non‐food energy crop. The objective of this study was to determine the economic costs and input sensitivity of sweet sorghum compared to cotton, maize, and sunflower, at two saline‐alkali sites in Shandong (Wudi County) and Inner Mongolia (Wuyuan County) provinces of China. The data were collected quantitatively based on a face‐to‐face interview with 100 and 67 sweet sorghum growers at the two sites, respectively. The sweet sorghum grown at Wudi had lower external input (5469 CNY ha^?1), higher net return (7305 CNY ha^?1) and benefit‐cost ratio (2.36) than its reference crop, cotton (18 454 CNY ha^?1; 3615 CNY ha^?1; 1.24). At Wuyuan, the sweet sorghum showed contrasting economic performance (19 541 CNY ha^?1; ?3438 CNY ha^?1; 0.91), which was largely because of the higher labor costs compared to sunflower (10 896 CNY ha^?1; 15 133 CNY ha^?1; 2.49); and maize (9108 CNY ha^?1; 14 760 CNY ha^?1; 2.76). The productivity of sweet sorghum per unit of external input costs (kg CNY^?1) was 13.12 for Wudi and only 3.26 for Wuyuan. Based on the Cobb‐Douglas production function, the external inputs of diesel and seed had a significantly positive impact on the profitability of sweet sorghum at Wudi but not at Wuyuan. However, the costs of irrigation and film cover were significantly negative. The energy crop, sweet sorghum, showed a better return to scale on investment than cotton and sunflower.     

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi‐year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO₂ concentration [CO₂]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model‐based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well‐controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO₂] and temperature.