LARS-WG天气发生器在黄土高原的适应性研究

 利用黄土高原地区西安、兰州、太原、银川、呼和浩特和延安6个气象站1951—1990年的日序列的降雨量、最高温度、最低温度及日照时间,用LARS-WG天气发生器模拟1991—2000年的日气象资料,并用1991—2000年的实际观测值与之比较,对LARS-WG天气发生器适应性进行了检验。通过线性回归、平方根误差和平均偏差评价表明:通过长序列日气象资料,LARS-WG天气发生器能准确地模拟黄土高原6个站点的日最高温度、日最低温度的月分布和年辐射总量。年降水及其月分布值普遍高于实际降水值。     

DSSAT-CERES模型在黄土高原丘陵沟壑地区春玉米生产中的适用性评价

【目的】本研究在黄土高原丘陵沟壑区引入农业技术转化决策系统(DSSAT, Decision Support System for Agrotechnology Transfer)研究春玉米生长过程,旨在组建该区土壤、 气象、 作物品种的DSSAT模型数据库,验证和评估DSSAT模型在黄土丘陵沟壑地区的适用性,为模型在黄土丘陵沟壑地区的应用奠定基础。【方法】研究主要基于陕西安塞农田生态系统国家野外科学观测研究站2002、 2003、 2005年春玉米田间试验结果,采用DSSAT4.5内嵌的GLUE(Generalized Likelihood Uncertainty Estimation)软件,结合2002、 2003、 2005三年CK处理的大田试验数据运行GLUE软件3000次,对黄土高原丘陵沟壑区玉米品种沈单10 进行参数率定,然后运行DSSAT4.5中的CERES-Maize模型对其他处理条件下的春玉米生育期、 产量、 生物量进行模拟,并结合田间实测数据进行对比验证,对模型参数的适用性进行评价,从而评价CERES-Maize模型在该区春玉米种植中的适用性。【结果】 1)2002年和2005年对春玉米生育时期的模拟误差均在3天以内,产量模拟的归一化的均方根误差(n-RMSE)和平均相对误差(MRE)均小于10%,生物量模拟的误差稍大,但均在可接受范围内。2)2003年模拟的生育期时间和春玉米实测生育期时间基本吻合,唯独出苗日期相差较大,为9天;产量模拟的n-RMSE和MRE分别为6.47%和5.03%;地上生物量模拟误差稍大,模拟结果的n-RMSE和MRE分别为31.81%和30.21%。【结论】在正常降雨条件的情况下(2002年和2005年),CERES-Maize模型能够较好的模拟黄土高原丘陵沟壑区春玉米的生长过程、 地上部分生物量以及收获产量;在降雨较少的年份(2003年),模型对作物生产实际情况的再现能力相对变弱,因此CERES-Maize模型还不能很好的模拟干旱年份下作物的生长过程。     

多模式集合模拟气候变化对玉米产量的影响

气候模式驱动作物模型是气候变化影响评估的主要手段。但是,单一气候模式输出和作物模型的结构差异使得研究结果存在不确定性。多模式集合的概率预估可以有效减少研究结果的不确定性。为此,本文利用1981—2009年东北地区海伦、长岭、本溪3地区农业气象站的历史气象资料和玉米作物数据,分别建立了作物统计模型并验证了APSIM机理模型在研究区域的适用性。在此基础上,与CMIP5在RCP4.5情景下的8个全球模式结合,尝试基于多模式集合评估了未来2010—2039年时段和2040—2069年时段气候变化对玉米产量的可能影响(相对于1976—2005年基准时段)。研究结果表明,APSIM模型对玉米生长发育和产量形成有很好的模拟能力。玉米生育期的模拟误差(RMSE)为3~4 d,产量的RMSE为0.6~0.8 t?hm~(-2)。建立的产量统计模型表明,玉米出苗阶段(5月中旬)的温度增加对产量增加有积极作用,而开花到成熟阶段(7月中旬到9月上旬)的温度和降水的增加、光照的不足均不利于产量增加。与1976—2005年基准时段相比,气候因素影响下2010—2039年玉米产量减少3.8%(海伦)~7.4%(本溪),减产的概率为64%(长岭)~73%(本溪);2040—2069年时段减产6.4%(海伦)~10.5%(本溪),减产的概率为74%(海伦)~83%(本溪)。未来2010—2039年时段和2040—2069年时段基于机理模型模拟的产量降低分别为6.6%(海伦)~8.9%(本溪)和9.7%(海伦)~13.7%(本溪),均高于相应时段基于统计模型得到的0.9%(海伦)~6.0%(本溪)和2.0%(长岭)~7.3%(本溪)减产结果。     

Assessing potential production of maize using simulation models for land evaluation in Brazil

Abstract. The paper highlights the advantages of modelling to simulate dynamic processes and to couple environmental data more closely with effects on crops. The CERES-Maize crop model and the DSSAT software package were selected for the work and the region of the Triângulo Mineiro in the state of Minas Gerais was selected as the study area. The results showed that there are differences in the potential production of the Latosols of the Triângulo Mineiro and this should be taken into account when extrapolating or transferring technology for other regions within the same cerrado ecosystem of central-west Brazil. It is essential to improve the number of points with weather data collection since, as demonstrated for Frutal, the climate of the Triângulo can determine differences in potential agricultural production as well as risk for production. The study showed that the use of crop models can improve land suitability evaluations, for land use planning, or establishing strategies for rural funding.     

冰雹对美国玉米带作物产量影响模拟

为了尝试计算机模拟方法能否成为制定作物保险保费的辅助性工具,该研究运用计算机模拟模型预测冰雹对作物产量的影响。通过对现有EPIC(综合气候因素的环境政策)模型增加冰雹天气模块,即冰雹事件发生概率的数学模型,模拟冰雹对作物产量的影响。除此之外,该研究还模拟了干旱和霜冻等天气因素对美国Iowa,Illinoi和Indiana等3个玉米带州作物产量的影响。首先介绍数据来源及处理方法,讨论建立冰雹模拟模型过程以及对模型进行有效性检验;然后运用统计分析方法对模型模拟结果与实际观察结果进行比较,检验模型模拟结果的准确性。结果表明EPIC模型可以达到95%甚至更高的产量预测的准确性;同时,冰雹灾害模拟结果也可以达到一个较为合理的准确性(R2>0.7)。这些结果表明本研究所建立的增加冰雹模块的EPIC模型可以作为一个较为可靠的冰雹引起的作物产量损失的预测方法.该模型可以用来模拟冰雹事件发生的概率以及其对各种作物产量造成的损失。     

地形对漫川漫岗黑土区大豆产量的影响

为研究黑土区田块尺度地形对大豆产量造成的影响,在海伦东兴合作社具有明显地形起伏的地块,采集大豆田间试验数据,考虑温度、太阳辐射、坡度、土壤养分等因素,运用作物生长模型DSSAT(Decision Support Systemfor Agrotechnology Transfer)模型对各样点进行参数率定及验证,得出以下结论:1)DSSAT模型的模拟产量与实际产量的相对均方根误差为7.9%,模拟结果表现为优,表明运用作物模型模拟不同地形上的产量变异具有可行性;2)地形通过影响作物生长环境因子的时空差异决定产量差异,田块尺度温度、水分和坡度是影响产量差异的主要因素;3)坡顶和坡底的产量相对较高,且产量变异性较小,阳坡虽然接收到更多的光照,却由于水分胁迫造成减产,坡底和平缓坡顶水肥保持较好,易获得高产。研究成果为田间精细管理与田块尺度耕地高效利用提供科学依据。     

基于DSSAT模型的氮肥管理下华北地区冬小麦产量差的模拟

为了评价氮肥管理对华北地区冬小麦产量差的影响,利用大田试验数据和田间调查的方法,应用DSSAT模型分析了吴桥不同氮肥水平下冬小麦多年平均可获得产量及产量差,并研究了不同地块产量差和氮肥农学效率差的分布。结果表明,不同地块冬小麦产量差异显著,但产量变异较小,地块间施肥水平存在明显差异,且变异较大。模型分析确定222 kg/hm2为最佳施氮肥量,对应的最大可获得产量为7618 kg/hm2,地块产量与最大可获得产量有较大差距,当地冬小麦产量具有一定的提升空间。75%的农户地块的施氮量高于最佳施氮量,且氮肥农学效率普遍偏低。因此,生产中应优化氮肥管理方案,适当减氮并调整施肥时期和改进技术,提高氮肥农学效率,以实现冬小麦生产高产高效。在保障国家粮食安全和保护自然环境双重压力的背景下,通过合理的氮肥管理来缩减冬小麦产量差对提高中国粮食总产及保持农业可持续发展具有重要的意义。     

Predictions of soil water and crop growth dynamics using the agroecosystem models THESEUS and OPUS

The agroecosystem models THESEUS and OPUS were tested with data obtained from three agricultural experimental field plots on sandy soils without groundwater located at the moraine landscape in East Brandenburg, Germany. At each of these plots, a separate agricultural management practice was applied. Measurements of soil water contents, pressure heads, above‐ground crop biomass, and crop yield from these three plots were compared with the corresponding simulation results of both models. The comparisons of simulated with measured outputs were analyzed using the modeling‐efficiency index IA. According to these analyses, both models simulated adequately the time courses of volumetric soil water contents and above‐ground crop biomass, but the time courses of pressure heads were predicted with a lower quality by both models. As for the pressure heads, the yields simulated with both models showed greater discrepancies in comparison with the observed ones. This indicates the need of a site‐specific parameter calibration of the crop‐growth modules, especially for that included in OPUS .     

Nitrogen uptake kinetics of key staple cereal crops in different agro-ecological regions of the world

The study was undertaken to assess the effect of environmental, management, and stress factors on nitrogen uptake patterns through the crops’ growth cycle and to associate temporal patterns of N uptake with biomass and grain yields. Existing complete experimental data, provided by several institutional databases and through an extensive literature review, were utilized together with crop simulation models (CSMs) to synthesize yield and N uptake profiles of the key staple cereal crops in selected agro-ecologies. Approximately 465 observations were identified for combined maize grain yield and plant N uptake: 156 for rice and 254 for wheat. The Decision Support System for Agrotechnology Transfer (DSSAT), which comprises CSMs and data that integrate capabilities on soils, daily weather, crops, and management, was used in combination with field information to first validate the CERES-maize, -rice, and -wheat models. The most noteworthy results from synthesis of the data set for the three key cereals were as follows: (a) N uptake continued to increase with time until physiological maturity with adequate N supply; (b) significant effect of soil N status on N uptake kinetics was observed at zero N; (c) N uptake profile was also influenced by the planting date, with the summer planting showing higher uptake than other planting dates; (d) field methods of N application influenced N uptake kinetics: a one-time injected or subsurface-applied urea continued to provide an adequate amount of N throughout the crop growth phase that was comparable or even higher than with broadcasting multiple splits; (e) N uptake was also dependent on crop cultivars, including stages of vegetative and reproductive phases, with shorter vegetative and longer reproductive phases showing continuous N uptake and lesser dependence on N remobilization; and (f) predictions suggested that modest changes in ambient temperature and atmospheric carbon dioxide (CO₂) concentrations would not significantly alter the N uptake kinetics, with the uptake rate expected to increase under future climate change scenarios. The combined data suggest that no one N uptake kinetic pattern fits all crops under all environments and management practices.     

Impacts of climate change on irrigated potato production in a humid climate

The impacts of climate change on the irrigation water requirements and yield of potatoes (Solanum tuberosum L.) grown in England have been assessed, by combining the downscaled outputs from an ensemble of general circulation models (GCM) with a potato crop growth model. The SUBSTOR-Potato model (embedded within the DSSAT program) was used to simulate the baseline and future irrigation needs (mm) and yield (t ha⁻¹) for selected emissions scenario (SRES A1FI and B1) for the 2050s, including CO₂ fertilisation effects. The simulated baseline yields were validated against independent experimental and field data using four reference sites. Probabilistic distribution functions and histograms were derived to assess GCM modelling uncertainty on future irrigation needs. Assuming crop husbandry factors are unchanged, farm yields would show only marginal increases (3-6%) due to climate change owing to limitations in nitrogen availability. In contrast, future potential yields, without restrictions in water or fertiliser, are expected to increase by 13-16%. Future average irrigation needs, assuming unconstrained water availability, are predicted to increase by 14-30%, depending on emissions scenario. The present ‘design’ capacity for irrigation infrastructure would fail to meet future peak irrigation needs in nearly 50% of years. Adaptation options for growers to cope with these impacts are discussed.     

干旱条件下APSIM模型修正及华北冬小麦产量模拟效果

干旱是影响华北地区冬小麦产量的主要农业气象灾害之一,作物生长模型是评估干旱对作物产量影响主要方法之一,但作物生长模型对极端天气气候条件下（如干旱）作物产量模拟效果仍存在不确定性。为提高作物模型在干旱条件下对作物产量模拟的精准性,该研究利用调参验证后的农业生产系统模型（agricultural production systems simulator,APSIM）,通过查阅与华北地区冬小麦相关的186篇大田试验文献获得1 876对观测数据,以作物水分亏缺指数为干旱指标,评估APSIM模型在冬小麦拔节-开花和开花-成熟阶段干旱对产量影响的模拟效果,提出APSIM在拔节-开花和开花-成熟阶段干旱对小麦产量影响的修正系数。基于历史气候条件、SSP245和SSP585未来气候情景资料,分析了冬小麦拔节-开花和开花-成熟阶段干旱时空分布特征,并采用修正系数校正后的APSIM模型评估华北地区冬小麦拔节-开花和开花-成熟阶段不同等级干旱对其产量的影响。结果表明,APSIM模型低估了拔节-开花阶段干旱对冬小麦产量影响程度,轻旱、中旱和重旱校正系数分别为0.85、0.91和0.85;APSIM模型可准确模...     

Simulating management effects on crop production, tile drainage, and water quality using RZWQM-DSSAT

The objective of this study was to explore if more crop-specific plant growth modules can improve simulations of crop yields, and N in tile flow under different management practices compared with a generic plant growth module. We calibrated and evaluated the Root Zone Water Quality Model (RZWQM) with the Decision Support for Agrotechnology Transfer (DSSAT v3.5) plant growth modules (RZWQM-DSSAT) for simulating tillage (NT — no till, RT — ridge till, CP — chisel plow, and MP — moldboard plow), crop rotation {CC — continuous corn, and CS — corn (Zea mays L.)-soybean [Glycine max (L.) Merr.]}, and nitrogen (N) (SA — single application at preplant, and LSNT — late spring soil N test based application) and manure (SM — fall injected swine manure) management effects on crop production and water quality. Data from 1978 to 2003 from a water quality experiment near Nashua (Nashua experiments), Iowa, USA, were used. The model was calibrated using data from one treatment plot and validated for the rest of the plots. Simulated management effects on annual N loading in tile flow were agreeable with measured effects in 85%, 99%, 88%, and 78% of the cases for tillage, crop rotation (CS vs. CC), N application timing (SA vs. LSNT), and swine manure applications (SM vs. SA), respectively. On average, the LSNT plots were simulated to have 359 kg ha^− 1 higher corn yield compared to SA, when the observed increase was 812 kg ha^− 1. Grain yield simulations were not sensitive to differences between RT and NT, between SM and SA treatments, and between CS and CC. We conclude that considering the uncertainties of basic input data, processes in the field, and lack of site specific weather data, the results obtained with this RZWQM-DSSAT hybrid model were not much better than the results obtained earlier with the generic crop growth module.     

秒尺度温室番茄作物-环境互作模型构建与验证

为了解决现有温室模型时间尺度不统一的问题,该研究建立了一个时间尺度统一的温室番茄作物-环境互作模型,描述作物与环境之间的相互作用,提高模型的精准性。首先,将番茄作物生长模型拆分成SUPPLY、PARTITION、GROWTH3个子模块,针对3个模块在由天数量级时间尺度到秒数量级时间尺度变换时存在的问题,通过模型替换、结构改造、参数辨识等方法对时间尺度进行了转换,并利用EFAST敏感性分析算法将模型中的不确定参数分为敏感参数和不敏感参数两类。然后,在秒时间尺度番茄作物生长模型的基础上,考虑番茄作物对温室环境的实时反馈,结合小气候模型形成包含未知参数的"通用"的互作模型结构。最后,利用贝叶斯优化方法及番茄生产温室的实际数据,分别对互作模型中生长模型和小气候模型的未知参数进行参数辨识,确定互作模型全部结构与参数,得到可用的互作模型。利用该研究得到的秒时间尺度生长模型对2015—2018年上海崇明A8温室番茄产量进行模拟,其与真实产量值间的均方根误差在7.34～18.85 g/m~2之间,平均相对误差在5.8%～18%之间,均小于TOMGRO模型与Integrated模型,可以更好地预测产量变化。含作物反馈的小气候环境模型经参数辨识后,模拟番茄作物3个不同生长时期(幼苗期、开花坐果期、结果期)的环境因子(温室内温度、湿度、CO_2浓度)变化的平均相对误差均在3%～6%之间,且相较于未考虑作物反馈的一般小气候模型有更好的模拟效果。互作模型的建立将作物与温室小气候环境统一成一个模型,可以为温室环境控制提供模型基础。     

冬小麦物候期对土壤水分胁迫的响应机制与模拟

作物模型在农业生产管理和决策中发挥着重要作用,而物候期模拟是作物模型正确模拟作物生长发育和产量形成过程的基础。作物模型模拟物候发育的常用算法一般是基于积温的计算,同时也考虑光周期和春化作用的影响,但是水分胁迫对物候发育的次级影响却较少被考虑在内。该研究以连续2季(2013-2014和2014-2015)的遮雨棚下土柱试验和连续3季(2012-2013、2013-2014和2014-2015)的遮雨棚下大田试验数据和前人研究成果为基础提出了冬小麦物候期对水分胁迫的响应机制理论假设,并以土壤相对有效含水率为水分胁迫指标校正冬小麦物候期水分胁迫响应函数。该研究以2014-2015生长季土柱试验各处理试验数据来建立冬小麦物候期水分胁迫响应函数,确定发育加速点A、发育减速点D和发育停止点S所对应的相对有效含水率值分别为0.30、0.10和0。结果发现拔节期和开花期模拟值和观测值之间的均方根误差(root mean square error,RMSE)分别为0.8和1.7 d,绝对相对误差(absolute relative error,ARE)分别低于0.68%和2.09%。然后用2013-2014生长季土柱试验各处理数据进行验证,结果发现拔节期和开花期模拟值和观测值之间的RMSE分别约为0.9和1.1 d,ARE分别在1.37%和1.68%以下。最后再用3年独立大田试验数据对上述修正后的冬小麦物候期算法进行验证,结果发现开花期和成熟期的模拟值与观测值之间的RMSE分别约为2.4和2.0 d,ARE分别低于4.21%和2.67%;与DSSAT-CERES-Wheat模型的模拟结果进行比较,发现修正算法能反映出水分胁迫对冬小麦物候期造成的差异(有提前也有推迟),而DSSAT-CERES-Wheat模型无法体现这种差异,且开花期和成熟期的模拟值与观测值之间的RMSE分别约为4.0和5.5 d,误差最大分别为8和6 d。这表明校正后的冬小麦物候期算法模拟精度得到了较大提高,能在一定程度上描述和量化水分胁迫对冬小麦物候期的影响机制,可用来模拟不同水分胁迫条件下不同品种冬小麦的物候期。     

Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model

Global/Regional Circulation Models (GCM/RCM) predict the interannual climate variability better than the absolute values of meteorological variables. Statistical bias-correction methods increase the quality of daily model predictions of incoming solar radiation, maximum and minimum temperatures and rainfall frequency and amount. However, when bias-corrected forecasts/hindcasts are used by dynamic crop models, timing of dry-spell occurrences generate the largest uncertainty during the linking process. In this study, we used 20 ensemble members of an 18-year period provided by the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) regional spectral model coupled to the National Center for Atmospheric Research Community Land Model (CLM2). The daily seasonal-climate hindcast was bias-corrected and used as input to the CERES-Maize model, thus producing 20 crop yield ensemble members. Using observed weather data for the same period, a time series of simulated crop yields was produced. Finally, principal component (PC) regression analysis was used to predict this time series using the crop yield ensemble members as predictors. Between 13.7 and 28.8% of the simulated corn yield interannual variability was explained using only one principal component (p < 0.05), and estimated yields were in the correct tercile by margins of 16.7 to 38.2% beyond chance. Predictability of simulated corn yields using principal components was improved relative to the use of bias-corrected daily hindcasts. Bias-correcting all meteorological variables used by the crop model increased predictability skills compared with use of raw hindcasts, individual bias-correction of rainfall, and climatological values.     

作物生长模型的应用研究进展

作物生长模型不仅能够进行单点尺度上作物生长发育的动态模拟,而且能够从系统角度评价作物生长状态与环境要素的关系。本文通过梳理当前作物生长模型应用的诸多研究成果,剖析模型在气候变化对农业生产影响研究、作物生长模型区域应用中的关键问题,总结了当前以作物生长模型为核心的农业决策支持系统开发的研究情况,意在促进作物生长模型在生态、农业、区域气候资源和气候变化等研究中更广泛地应用。结果表明,作物生长模型在国内外的研究与应用广泛而深入,在气候变化背景下,应用作物生长模型进行历史时期气候条件和农业气象灾害对作物生产状况和产量的影响研究已相当广泛且相对成熟。利用全球气候模式（GCM）或区域气候模式（RCM）构建未来气候变化情景,再与作物生长模型耦合已发展成为评估未来气候变化对农业生产影响的重要手段。通过集成与整合多作物生长模型、多气候模式集合模拟、优化气候模拟数据订正方法可有效降低气候变化对农业生产影响评估的不确定性。遥感数据同化技术能够将站点模型运用到区域尺度上评价不同环境因子对农业生产的影响,拓宽了作物生长模型的应用尺度范围并有效提高作物产量估算的精度。以作物生长模型为核心的农业决策支持系统的研究与应用越来越多元化,是辅助农业生产管理和决策的重要工具。然而,由于作物生态系统的复杂性,作物生长模型模拟结果仍存在很大的不确定性,今后对作物生长机理及过程间耦合机制的探索还需加强,以便进一步完善和改进模型,促进作物生长模型更广泛地应用。     

土壤水分平衡与作物生长模拟模型的开发与验证

基于模块化和面向对象化程序设计思想,根据土壤水分平衡过程和作物生长发育的特点,采用VisualBasic程序设计语言,实现了界面友好的土壤水分平衡与作物生长模拟模型。在土壤、作物和气象参数数据文件的支持下,对红壤性水稻土上的作物生长过程进行了模拟和验证,田间验证结果表明,冬小麦田间0～5,5～15,30～35cm三个土壤深度土壤含水量模拟值与实测值相对误差分别为7．0％、8．1％、4．5％。小麦、早稻、晚稻、玉米产量模拟值与实测值之间的相对误差分别为6．7％、2．4％、5．3％、1．9％。     

基于聚类法筛选历史相似气象数据的玉米产量DSSAT-CERES-Maize预测

根据陕西杨凌、合阳、长武3个站点各2 a玉米试验,在对玉米生长模拟模型CERES-Maize进行调试、验证的基础上,探索在生育期内进行动态产量预测的方法并验证.研究将目标生育期内未知气象数据分别用试验地的多年历史同期数据代替,结合生育期实时数据对应生成多个完整的气象数据序列运行模型预测产量.随着生育期的推进,逐日在气象数据序列中融入目标年实测的气象数据,从播种至收获动态模拟玉米产量.此外该研究使用改进前后的K-NN算法从历史气象年份中筛选目标年的气象相似年份进而预测产量.通过对3种方法预测精度及预测效率对比,确定改进的K-NN算法最优.研究表明,玉米生育前期产量预测可靠性和准确率均较差,抽雄后预测精度迅速提高;利用改进的K-NN算法在3个站点全生育期预测产量的平均绝对相对误差的均值分别为9.9%、19.8%、17.9%,抽雄后预测产量的平均绝对相对误差在0.2%~12.6%之间,相比于使用全部历史年份数据进行全生育期产量预测,模拟所需时间从61 min缩短至25 min.对该方法中降雨因子的筛选进一步改进可提高预报精度,未来有望达到业务应用水平.     

WGDWS天气发生器在中国五大气候区的适用性

为了检验基于干湿期的天气发生器(Weather Generator based on Dry and Wet Spells,WGDWS)在中国不同气候区的应用效果,该研究利用中国五大主要气候区16个站点57 a的逐日天气数据,通过对比生成与实测气象要素统计值,及比较WGDWS与随机天气模拟器(Daily Weather...     

Estimation of yield and dry matter of rapeseed using logistic model under water salinity and deficit irrigation

The inadequacy of most models to simulate crop growth and yield is due to complexity, difficulty to understand, and lack of input data. Therefore, several simple crop growth models are presented to reduce these failures. In this investigation, yield and aboveground dry matter (DM_above) of rapeseed were simulated by two logistic growth models that were based on days after planting (DAP) and growing degree days (GDD) under water salinity and deficit irrigation, in a 2-year experiment. Data of first and second year were used for calibration and validation of the model, respectively. The coefficients of logistic function were determined as a function of irrigation water salinity and sum of applied water and rainfall in spring of the first year. Results indicated that logistic function based on GDD-predicted DM_above during growing season more accurately than logistic function based on DAP. Furthermore, seed yield of rapeseed was estimated based on harvest index with a good accuracy. Therefore, logistic function based on GDD that is based on the cumulative heat units can be used for different weather conditions and planting dates to determine rapeseed DM_above and yield under water salinity and deficit irrigation.