Influences of agricultural phenology dynamics on land surface biophysical processes and climate feedback: A review
LIUFengshan收稿日期:2016-12-15
修回日期:2017-03-24
网络出版日期:2017-08-07
版权声明:2017《地理学报》编辑部本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
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1 引言
陆地地表过程和气候是紧密耦合在一起的。气候强烈影响生物分布和地表特征;地表特征变化通过改变陆—气之间能量和水分交换及大气化学组成（如CO2、CH4、O3等）影响气候。目前全球变化对地表动态过程的影响研究较多,而地表动态对地表生物物理过程及气候的反馈研究较少[1]。受全球气候变化（如温度升高、降水变率增加等）和人类活动（如土地利用变化、土地管理变化等）的影响,地表要素的变化范围和强度日益提升,通过改变地表水热收支等过程而成为局地、区域和全球尺度气候变化的重要驱动力[2-5]。地表动态过程对气候的长期扰动进一步影响生态系统结构和功能[6],并潜在威胁人类的粮食安全和生活质量。深入理解地表动态对地表生物物理过程及气候的反馈,可望提高全球变化与地表过程相互作用研究。农业生态系统是与人类生产和生活密切相关、并受气候变化和人类活动影响最频繁的生态系统。增加农田面积和提高单位面积产量是满足人类对粮食需求增加的主要方式,两种粮食增产方 式均极大地改变了农业生态系统的地表动态过程。有研究表明,通过地表反照率和粗糙度机制,土地管理变化与土地利用变化对温度的影响具有相似的强度[7];农田具体的管理措施在一定时期内对温度、降水和大气环境产生了影响[8-12]。考虑到未来人口增长和生活标准提高会继续推动粮食需求的持续增加,在有限的土地资源和稀缺的宜耕种农田条件下,提高单位面积产量将成为保障粮食安全的主要方式,研究农田地表动态通过地表生物物理过程对气候的调节功能和时空特征,将成为缓解气候变化和保障粮食安全的重要课题。
在植物物候对地表生物物理过程和气候的影响研究中,目前关注较多的是自然物候的贡献,尤其是全球变暖背景下森林物候与气候变化的相互作用[13-15]。最新的研究表明,欧洲森林展叶时间提前,显著降低了全球变暖[16]。通过定义作物的种植期、苗期、灌浆期、开花期、成熟期和收获期等关键时期,农业物候为人类主观控制下的农田生态系统提供了客观的研究依据。在生物物理过程研究中,农业物候与农业生态系统地表动态过程、地表水热平衡和作物—大气物质交换等过程具有密切联系,为农业与气候之间的交互作用提供了规律性的研究依据。例如农业物候的季节动态与归一化植被指数（NDVI）和叶面积指数（LAI）等密切相关[17];反照率在玉米的不同生育期（发芽、开花和成熟等）具有不同的差别,并受种植日期影响[18];陆面过程模型中对农业生态系统能量、水分和碳收支的模拟精度最终依赖于物候的模拟,模型中作物的提前发育会造成物质和能量模拟的过大误差[19]。
通过控制陆—气界面的物质、能量和动量交换过程,农业物候信息不仅对历史气候变化研究提供了依据,而且影响未来气候变化的程度和方向。利用档案资料,Ge等[20]重建了过去170年华东地区的春季物候指数,其变化为区域植物—物候关系和长期气候变化对生物物理系统的影响提供了重要信息[21]。利用CanESM2模型耦合静态物候和动态物候进行模拟,发现未来生长季延长造成更高的植物生产力和生物量;动态物候造成北美春季主要受反照率降低引起的增温效应影响;同时在美国东南部,在RCP 8.5（到2100年辐射能增加8.5 W m-2）条件下,将会造成温度上升和降水下降,抵消二氧化碳的施肥效应,从而强调了动态物候通过生物物理过程对气候的调节[22]。
本文以农业物候动态为例,从农业物候动态的事实、农业物候动态在陆面过程模型中的表达、对生物物理过程的影响及其气候效应等方面进行了综合分析,并指出了未来需要细化研究的4个问题。该综述对于提高地表物候动态在陆面过程模型和大气环流模型中的认识有一定的帮助。
2 农业物候动态的事实
小麦、玉米和大豆是种植面积最广泛的作物[23]。根据作物产量和收获面积数据,世界五大主要产粮基地和作物分别为美国中西部玉米、南美洲东南部大豆、西非玉米、中亚小麦带和东亚小麦[24]。但是这3种作物物候变化特征的研究集中在中国、美国和欧洲等农业比较发达的地区,对于西非和中亚等地区物候的研究进展较少（表1）。这些地区的站点观测资料证明,过去半个世纪,小麦、玉米和大豆等作物的物候发生了显著的变化。如在1981-2005年期间,美国玉米和大豆的种植期分别提前了10 d和12 d[25];欧洲小麦和玉米的种植时间提前近3周[26-28];中国农业气象站点的农业物候观测资料,其统计规律普遍达到显著水平[29-34]。农业物候较为一致的变化规律为种植时间的提前和灌浆期的延长[25-26, 33]。灌浆期延长增加了有机物积累的时间;提前种植有助于延长作物生长期和生育期,提高LAI和干物质固定过程。虽然不同地区物候的变化幅度存在差异,但是这种地区差异性并不能改变农业物候变化存在的事实,引起了人们探究其变化机理的强烈兴趣。Tab. 1
表 1
表 1世界主要作物在主要分布区的物候变化特征及其驱动因素
Tab. 1Characteristics of phenological change and controlling factors for the staple crops around the world
| 分布区 | 作物 | 物候期变化 | 驱动因素 | 文献 |
|---|---|---|---|---|
| 中国 | 冬小麦 | 种植期、出苗期、休眠期分别延迟1.5 d/decade、1.7 d/decade和1.5 d/decade;春季发芽、开花和成熟分别提前1.1 d/decade、2.7 d/decade和1.4 d/decade。 | 温度增加缩减生长期、品种积温增加延长生殖生长期、日照长度降低延长营养生长期。 | [29-31] |
| 中国 | 夏玉米 | 36.6%站点成熟期延长,41.1%站点生育期延长;生殖生长期延长2.4~3.7 d/decade。 | 平均温度增加降低生育期,品种更新延迟开花期和成熟期;适应温度增加的种植期提前策略。 | [32-34] |
| 中国 | 玉米 | 种植、拔节和开花期提前,成熟期推迟,营养生长期缩短,生殖生长期和生育期延长。 | 全球变暖加快玉米发育和缩短生长期;降水减少一定程度缩短生长期;品种更新延长生长期。 | [34] |
| 美国 | 玉米 | 种植期提前4.2 d/decade;种植—收获期增长5 d/decade;成熟—收割期变短3 d/decade。 | 生殖生长期有效积温需求增加14%;生长期更长的玉米品种;灌溉增加和施肥增加与品种更新的交互作用。 | [25] |
| 美国 | 大豆 | 种植期提前4.9 d/decade;收割期提前4.9 d/decade。 | 气温升高造成种植期提前,并有助于维持成熟期的稳定;品种更新造成更长的生殖生长期。 | [25] |
| 欧洲中部和北部 | 禾本科(小麦、燕麦和玉米) | 种植期提前1~3周;开花期和成熟期提前1~3周。 | 根据超过1500条站点记录设定模型,燕麦和小麦种植期—开花期发育依赖温度和昼长,玉米依赖温度;3种植物的开花期—成熟期发育仅依赖温度。 | [26] |
| 西班牙 | 禾谷类作物(燕麦、小麦、黑麦、大麦和玉米) | 冬小麦春季的物候期提前;小麦和燕麦旗叶鞘肿提前30 d/decade;开花期提前10 d/decade。 | 物候开始之前的温度变化是物候趋势的主要因素;人类干预降低物候变化对产量的影响。 | [27] |
| 德国 | 玉米 | 播种期、出苗期、开始收获期分别提前1.7 d/decade、3.3 d/decade和1.3 d/decade;播种—出苗间隔减小1.6 d/decade;出苗—收获增加2.1 d/decade。 | 春季温度增加使提前播种成为可能;5月份强烈增温加速了植物发育,对出苗期影响最严重。 | [28] |
| 哈萨克 斯坦 | 小麦 | NDVI峰值提前4~7 d | 积温增加;苏联解体的影响。 | [38] |
新窗口打开
农业物候变化的原因主要包括两方面：以温度升高为代表的全球气候变化和以品种更新为代表的人为管理措施[35-36]。温度的升高致使作物在春季的种植时间提前成为可能,也成为秋季收获时间延迟的必要条件。但是,温度的升高加快了作物的发育速度和有效积温的积累过程,在作物品种不变条件下,作物的成熟时间和中间的各个生育时期将会提前,成为产量降低的潜在威胁。通过选择生育期长和积温需求大的作物品种,搭配合理的施肥、灌溉、种植密度等管理措施,更加充分利用增加的农业气候资源[37],有效延长了作物生育期,尤其以提高产量为目的对生殖生长期的延长成为农业物候的重要特征。在人为干预较少的地区（如哈萨克斯坦[38]）,温度升高将主导农业物候的变化;重视粮食生产的地区,强烈的人为干预活动抵消温度升高的影响,直至反转物候期变化实现对特定生育阶段（如生殖生长期）的延长和产量的增加。中国冬小麦和夏玉米物候的变化同时受到轮作系统的影响,即冬小麦只能在秋末夏玉米收获后才能种植,夏玉米也只能在春末冬小麦收获后方能播种,作物的物候期受到上一茬作物的影响。
3 农业物候动态监测及其在陆面过程模型中的表达
农业物候动态的监测方法主要有地面观测法、遥感监测法和模型模拟法[39]。地面观测方法是对个体和小区域范围的作物生长节律利用人工观察进行记录的方法,具有时间精度高、易于操作等优点,是物候研究中最基本的方法;主要问题是提供的信息具有时空局限性[40]。遥感监测方法主要是根据任何目标物都具有发射、反射和吸收电磁波的性质,利用传感器对地物波谱信息进行记录,具有监测尺度广、反映作物群体特征等优点;该方法需要结合地面观测数据对其加以本地化,并存在一定的误差[41-42]。模型模拟法主要是指在个体和种群水平上通过研究植物生长节律的生理发生机制,建立物候模型来研究植物物候的时空变化;农业物候的参数化和数字化为研究物候动态与环境的作用和反作用提供了便利,有助于从机理上反映作物的生长过程;存在的问题是模型在模拟地表物候动态方面还有较大的误差[43]。受外在环境因素影响造成的数据和过程的不确定性,地面观测法和遥感监测法获取的记录很难验证物候在生物物理过程及其气候效应中的作用。模型模拟法是陆面过程和气候变化研究中常用的方法,对作物生长的数字化表达具有明显的优势[19, 44]：在空间尺度上,模型模拟法可以符合大气研究中从小气候、局地气候、区域气候到全球气候等各种尺度,提供研究范围内地表与大气的物质和能量通量以及研究范围外通过侧向交换提供的物质和能量通量;时间尺度上,不仅可以分析历史物候变化的贡献,更为未来作物种植的方向及其气候变化的走势和调控提供指导依据。
陆面过程模型中表达农业物候动态主要通过两种方法：环境控制方案和人为设定方案。环境控制方案是根据物候与环境变量的关系（尤其是气象环境）,当环境变量到达一定程度,则某一特定物候期就会发生。有效积温和日长是控制作物发育的关键因子,当这2个参数达到设定的临界值,则意味着某个物候期的发生。在CLM3.5-CornSoy模型中,有效积温控制了玉米和大豆从出苗—展叶—灌浆等时期的发展,收获则由日长控制[19];冬小麦的发育受制于温度、春化作用和光周期[45];SiBcrop模型中,作物发芽和后续生育期的进展根据有效积温和种植日期设定[46-47]。人为设定方案是指某些物候期的发生根据模型开发者和使用者的实际情景进行人为设定,不随外界环境条件变化而变化。如模型中进入特定物候期的有效积温阈值是不同的,通常根据实际情况或研究需要进行人为设定[19];对于最适温度和极端温度等有效积温的计算依据、农业物候期出现的判定依据等参数,都具有固定的属性和数值,但受到人为因素的控制并具有一定的地域性[48]。
现有的模拟农业物候和生长的模型通常具有详细的物候学特征和生理学过程算法。如CROPGRO和CERES作物模型,具有详细的生理学和物候学特征,在气象、土壤和管理数据驱动下,可估算谷类和豆科等作物的光合作用、干物质分配和水热通量等参数[49]。Gervois等[44]和de Noblet-Ducoudr′e等[50]在全球动态植被模型（ORCHIDEE）中耦合作物模型（STICS）,从而提高玉米、小麦和大豆等作物的生长过程及其对碳和水分交换的模拟精度。这些作物模型的开发为物候的数字化表达及其对环境的定量影响提供了便利。
4 农业物候动态对地表生物物理过程的影响
地表物候模型的引入对陆面过程的影响是多方面的。首先,物候参数是作物生长最基础的表达。作物模型的耦合表达了作物的生长和发育过程,主要包括物候学（阶段发育）和形态学（作物生长和器官）发育。物候学发展包括生育阶段的变化,同时改变生物量的分配格局;形态学发展涉及作物生命周期中各种器官发育的开始和结束,对形态学的模拟试图提供对叶片、分蘖和籽粒等的信息[51]。其次,地表物候动态引起地表过程的变化。模型根据季节发育阶段把光合固碳分配到作物不同部位[47],如分配到根部改变土壤水分供给过程;分配到叶片改变LAI和冠层结构;分配到茎部改变株高;生殖生长期的发生和果实的出现,极大地限制其他器官的生长和地表特征的变化。不同物候期对光合物质的分配决定了地表LAI及作物结构的动态,成为地表形态学过程的重要控制因素。再次,生理特征同样受到物候的影响。尤其以冠层导度和Rubiso活性,是模型中光合作用、呼吸作用、蒸散发过程等的主要控制机制[46, 48]。因此,农业物候动态对形态学和生理学等参数都具有显著的控制作用,为陆面过程模型中LAI、地表反照率、辐射收支、水分移动等过程的模拟提供动态且精确的计算依据。地表生物物理过程响应农业物候变化具有以下基本特征。在物候早期,地上作物叶片覆盖面积少,地表生物物理过程各分量如地表反照率、净辐射、潜热通量等受土壤因素的贡献较大,但是作物生长速度快,作物的贡献比重迅速增加;作物高度和冠层结构简单,地表粗糙度和零平面位移等变量较低,动量交换过程不活跃。物候盛期,作物高度和冠层发育完整,地表粗糙度、零平面位移、净辐射和潜热通量最高,地表反照率、感热通量和土壤热通量最低,并具有相对的稳定性。物候末期,作物生理过程下降明显,主要影响地表能量分配过程,造成净辐射主要用于感热分配。收获期对作物的收割和移除,对地表特征和生物物理过程的改变是急速的,造成地表粗糙度、零平面位移和地表反照率等变量显著降低,作物残茬对土壤水分和蒸发有一定的保护作用。对山东省位山站冬小麦地表水热通量的研究表明,净辐射与潜热通量在冬小麦不同生长期表现为：越冬期<拔节抽穗期<灌浆成熟期;感热通量表现为：拔节抽穗期<灌浆成熟期<越冬期[52]。冬小麦越冬期感热最大,潜热最小,体现了该时期冬小麦净辐射主要分配到感热,灌浆成熟期比拔节抽穗期更加复杂的冠层结构和LAI,有利于蒸腾作用和对太阳辐射的捕获。
在站点和区域尺度陆面过程研究中,众多的作物模型或农业物候算法被耦合进陆面过程模式中。根据收集的资料（表2）,共有7种作物生长模型、5种农业物候算法被耦合进9种陆面过程模型中,用于表达农业生态系统的地表动态。改进后的陆面过程模型,实现了对多种农业种植系统的模拟（单作、轮作、休耕等）;通过敏感性分析和统计分析方法,证明了农业物候动态对生物物理过程的重要性;提高对农业物候和生长过程的表达,降低对LAI、碳通量、地表水热通量及冠层截流等分量的模拟误差。农业物候算法的耦合,增强了模型在农业生态系统研究的通用性,对不同地区农田地表上的水热平衡具有更强的表达能力。如SiBcrop模型同时提高了美国小麦、大豆、玉米和中国的冬小麦—夏玉米轮作系统的模拟精度[46-47]。
Tab. 2
表2
表2陆面过程模型耦合作物模型对改进地表水热平衡理解的贡献
Tab. 2Contribution of land surface and crop model coupling to understanding surface energy and water balance
| 模型a | 研究对象 | 结果 | 原因 | 文献 |
|---|---|---|---|---|
| Agro-IBIS 作物动态生长模型 | 美国玉米和大豆 | 农业物候变化改变了地表水热平衡,种植期提前造成6月份潜热增加,感热降低;成熟期—收割期降低增加10月份净辐射。 | 利用有效积温实现物候期的变化。 | [25] |
| BATS CERES3.0 | 中国农田 | 冠层截流、作物蒸腾、土壤蒸发、潜热和感热通量都具有显著的影响;降低LAI和表层土壤水分系统误差,提高地表通量模拟精度。 | 增加了作物生长和发育过程。 | [51] |
| BATS CERES-Maize | 美国玉米 | LAI从5变为1,潜热变化30%~45%,感热变化20%~35%;蒸发和蒸腾对潜热贡献受LAI强烈影响。 | 基于生理学的物候期和有机质积累及分配过程。 | [53] |
| CLASS 碳氮模型 | 加拿大农田 | 提高了NEP模拟与实测数据的决定系数;有机质分配过程更加合理。 | 添加了农业物候方案和农田管理措施的查找表 | [54] |
| CLM CornSoy | 美国大豆和玉米 | 碳通量的模拟与物候模拟有紧密大量联系;对LAI、能量和碳通量的模拟与实测值的相关性更好。 | 利用有效积温精确表达出苗—灌浆期和灌浆期—收割期;解除对LAI最大值的限制。 | [19] |
| CLM农业物候模型 | 北美洲玉米、大豆和谷类 | 更加真实的作物LAI;更清晰的展示春季种植和秋季收割;在低LAI期更好的影响潜热通量;展示了物候的重要性。 | 利用温度驱动农业物候和碳分配的季节变化。 | [55] |
| ISAM 作物动态生长模型 | 美国玉米—大豆轮作系统 2001-2004年 | 与静态作物比较,LAI季节变化、冠层高度、根深、土壤水分吸收和蒸腾、碳通量、水热通量、对生长季潜热和碳通量提高较多,对感热影响较小。 | 作物动态包含了考虑了光、水和养分胁迫;LAI季节动态模拟的提升;根系分布过程更好的模拟土壤水分吸收和蒸腾。 | [56] |
| JULES InfoCrop | 印度农田 | 蒸散发模拟误差,湿润季节从7.5~24.4 mm month-1下降到5.4~11.6 mm month-1,干旱季节从10~17 mm month-1下降到2.2~3.4 mm month-1。 | 添加了作物生长的模型。 | [48] |
| JULES SUCROS | 欧洲农田 | 显著提高农田模拟与实测数据的相关性;更好的捕获欧洲作物生长状态的时空特征;表明作物结构和物候对陆—气交互作用的重要性。 | 作物动态生长;包含果实器官、从种植到收割的物候周期等特征的农业系统特征过程。 | [57] |
| LPJ DGVMs | 全球农田 | 温带禾本科种植日期、作物冠层季节发育更好;产量和碳积累过程更好;农业扩张造成蒸腾降低5%,蒸发增加40% | 物候的具体参数化,并与叶面积发育结合起来。 | [58] |
| ORCHIDEE STICS | 法国和美国冬小麦和玉米 | 对不同气候区中的蒸散发、生物量积累过程模拟更好。 | 增加了对叶面积、养分胁迫,植物高度的模拟;改善了有机质分配、水分胁迫、羧化作用等过程。 | [44] |
| SiB2农业物候模型 | 美国小麦、 大豆、玉米 | 提高LAI和碳通量;更好的模拟生长季的开始和结束、收割、轮作系统的季节动态。 | 针对特定作物开发出的物候方案和对应的生理学参数,取代旧的基于NDVI计算通量的算法。 | [46] |
| SiB2农业物候模型 | 华北平原冬小麦—夏玉米轮作 | 精确模拟LAI、碳通量、潜热通量、土壤水分含量和产量。 | 针对特定作物开发出的物候方案和对应的生理学参数,取代旧的基于NDVI计算通量的算法。 | [47] |
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地表物候动态的耦合为生物物理过程提供了更多的细节,使得利用陆面过程模型分析历史物候期变化对地表水热平衡的影响成为可能。美国玉米种植期提前,造成6月份的潜热增加,感热减少;成熟期至收割期间隔时间的缩短增强了10月份的净辐射[25]。在Agro-IBIS中延长物候期,发现物候期延长前后对总体生物物理过程的影响不大;但在NDVI提高0.1条件下,潜热、感热和土壤热通量的最大变化幅度分别达到45 W m-2、-20 W m-2和-25 W m-2 [10],这种现象是由于物候期的变化时间在总生长期的占比偏低,地表生物物理过程的波动集中在物候期发生变化的时间。物候期变化对地表反照率也有一定的影响,农业物候期延长增加LAI和冠层高度,通过叶冠内对短波辐射的多重反射降低反照率[59],作物收割造成土壤裸露出来对反照率的影响受土壤反照率的影响,土壤反照率比冠层反照率低（高）,则地表反照率下降（上升）[60]。
陆面过程模型耦合作物模型研究物候动态对地表能量平衡的影响成为地表动态过程对气候反馈的重要内容。但是,已开展的研究集中在地表形态学特征变化（如LAI、NDVI）对地表反照率和水热平衡的影响,忽略了生理学特征变化对冠层导度及其控制下的能量分配的影响,更缺少对气象数据和物候变化交互作用的研究。气候变化背景下,作物生理特征的变化是提高特定物候期光合效率和维持产量的重要保证[61-65],尤其在生殖生长期对气孔与大气间水分和CO2交换的影响,必将改变冠层导度和潜热分配比例,并受到气象条件的制约。系统研究特定气象条件下作物形态学和生理学变化对生物物理过程的影响,有助于正确评估农业物候动态在气候变化中的贡献。
5 农业物候动态通过调节地表生物物理过程对气候的反馈
在大气环流模型中研究气候对农业物候的影响,通常借助温度—物候响应函数[66],如GDD10, 30,提取物候期中位于10~30 ℃区间温度并减去10 ℃后得到的有效积温作为物候的预测指标[67];APSIM模型利用0~44 ℃区间温度—物候的多重线性函数表达玉米物候期动态[68];Parent等建立了酶促反应公式表达高、中和低纬度地区各种玉米基因型对温度的反应[69]等。虽然不同响应函数和模型结构对农业物候的数字化结果存在差异[66, 70],但是气候变化对农业物候的影响引起了学术界的重视,成为作物适应和产量预测的重要兴趣点。通过耦合农业物候模型,提高了陆面过程模型和大气环流模型中陆—气界面物质和能量交换过程的模拟精度,增强了对农业生态系统气候效应的认识和理解[71]。例如,通过植被蒸腾作用,春季作物提前生长对东亚地区的温度升高具有强烈的抑制作用[72]。利用站点和局地尺度数据[7]及模型模拟结果[10],温带地区农业物候期延长的生物物理过程通常表现出蒸腾—冷却效应超过反照率—升温效应,造成物候期延长总体以降温为主。通过对比华北平原单作和轮作系统,6月份是轮作收获期和单作生育盛期,两种农业生态系统的差异造成了潜热通量、气温、降水和区域环流的显著变化[8]。物候期提前改变了作物蒸腾作用和土壤水分循环等过程,通过向大气供给更多水分影响龙卷风年际变化[73];通过径流过程成为洪水的影响因子[74]。因此,更多的研究结果表明地表能量分配是农业物候动态的主要影响过程,是温度、水分、环流等过程发生变化的主要机理。
在大气环流模型中耦合农业物候模型,不仅为大气边界层中水热通量交换提供了更准确的数据,而且为研究气候与作物间的交互作用提供了可能。气候在季节、年际和年代际尺度的变化影响农业地表动态过程,改变的地表动态经由边界层特征对大气施加影响,这种交互式的气候—物候模型更加真实的反应气候与农田生态系统的相互关系[56, 71]。ECHAM5和JSBACH耦合模型模拟结果表明,很多地区物候超过土壤水分对降水的贡献,与降水具有很高的结合强度[75]。Osborne等[76]把GLAM中一年生作物模型加入到HadAM3气候模型中的MOSES陆面过程方案中,该耦合模型中大气条件和作物生长二者之间交互作用,作物通过影响低层大气条件影响气候,改变的气候同时影响作物的生长和发育;该模型真实的模拟了气候对一年生作物季节生长的影响,再现了降雨和作物产量之间的关系。
6 展望
本文的结构框架图如图1所示。在全球气候变化和人为管理措施影响下,农业物候期发生了显著的变化。种植期和灌浆期响应春季温度升高而提前,生殖生长期迎合产量增加而延长,其他物候期也发生了相应的变化。农业物候期的波动幅度可以达到1个月,从而对区域范围内的地表特征和生物物理过程及其对气候的反馈产生了不可忽略的影响。在陆面过程模型和大气环流模型中耦合作物模型是研究物候变化对地表水热平衡和陆—气边界层特征影响的重要手段。作物模型中详细的生长和发育机理算法提供了农业物候学和生理学过程的准确动态,改善了陆面过程模型和大气环流模型中对地表动态过程的表达,进而增强了对地表反照率、净辐射、潜热、感热等生物物理过程和气温、降水、环流等大气过程的模拟,并实现了地表物候动态对生物物理过程影响和物候与气候相互作用关系的定量研究。
显示原图|下载原图ZIP|生成PPT图1农业物候动态对地表生物物理过程的影响及气候反馈示意图
-->Fig. 1Flowchart of the influences of agricultural phenology dynamic on biophysical process and climate feedback
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温带地区开展的研究表明,农业物候动态在区域范围内对特定时期的地表水热平衡产生了显著影响,地表能量分配机制超过反照率机制,主导了物候变化对气候的反馈效应。未来农田地区气候变化需要重视农业生态系统动态及其通过地表生物物理途径的气候效应,并需要继续开展以下方面的工作：
（1）通过完善耦合作物模型的陆面过程模型,加强全球变化对地表农业物候动态的影响及其反馈综合研究。由于农业生态系统的复杂性,现有的陆面过程模型对农业生态系统的模拟还远远不足,需要结合地表物候和陆—气水热通量的观测资料,发展完善耦合作物模型的陆面过程模型。
（2）受近红外反射率与LAI成正比关系的影响,地表反照率与LAI在野外观测中经常表现出正比关系[59],但是模型模拟结果极少呈现或分析这种关系。更多地关注不同光谱波段地表反射率与作物动态的关系有助于提高模型参数的优化,更准确地刻画物候变化前后的辐射收支动态。
（3）物候变化不仅仅改变了作物形态学特征,更引起了其生理学特征的变化[61-65]。由于形态学数据的易获取性和生理学参数的难定量化,造成模型的研究偏向于形态学的影响规律。但是生理学特征是控制地表能量分配的主要机制,过去几十年新的作物品种引起的生理学特征变化及其对地表水热平衡和气候效应的影响是一个重要的机理探讨过程,并对日和月尺度的生物物理过程有直接的贡献。
（4）需要重视不同气候区农业物候变化对气候反馈效应的差异。如有冰雪覆盖的高纬度地区,种植期提前对地表反照率和能量分配的影响,地表反照率机制是否超过地表能量分配机制,从而造成该区域的温度升高?在干旱和湿润地区农业物候延长的气候效应有何差别?基于它们的表现,应该采取不同的物候管理策略应对或缓解区域气候变暖。
The authors have declared that no competing interests exist.
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| [1] | Abstract By altering fluxes of heat, momentum, and moisture exchanges between the land surface and atmosphere, forestry and other land-use activities affect climate. Although long recognized scientifically as being important, these so-called biogeophysical forcings are rarely included in climate policies for forestry and other land management projects due to the many challenges associated with their quantification. Here, we review the scientific literature in the fields of atmospheric science and terrestrial ecology in light of three main objectives: (i) to elucidate the challenges associated with quantifying biogeophysical climate forcings connected to land use and land management, with a focus on the forestry sector; (ii) to identify and describe scientific approaches and/or metrics facilitating the quantification and interpretation of direct biogeophysical climate forcings; and (iii) to identify and recommend research priorities that can help overcome the challenges of their attribution to specific land-use activities, bridging the knowledge gap between the climate modeling, forest ecology, and resource management communities. We find that ignoring surface biogeophysics may mislead climate mitigation policies, yet existing metrics are unlikely to be sufficient. Successful metrics ought to (i) include both radiative and nonradiative climate forcings; (ii) reconcile disparities between biogeophysical and biogeochemical forcings, and (iii) acknowledge trade-offs between global and local climate benefits. We call for more coordinated research among terrestrial ecologists, resource managers, and coupled climate modelers to harmonize datasets, refine analytical techniques, and corroborate and validate metrics that are more amenable to analyses at the scale of an individual site or region. |
| [2] | |
| [3] | <p>Land use and land cover change (LULCC) strongly influence regional and global climate by combining both biochemical and biophysical processes. However, the biophysical process was often ignored, which may offset the biogeochemical effects, so measures to address climate change could not reach the target. Thus, the biophysical influence of LULCC is critical for understanding observed climate changes in the past and potential scenarios in the future. Therefore, it is necessary to identify the mechanisms and effects of large-scale LULCC on climate change through changing the underlying surface, and thus the energy balance. The key scientific issues on understanding the impacts of human activities on global climate that must be addressed including: (1) what are the basic scientific facts of spatial and temporal variations of LULCC in China and comparative countries? (2) How to understand the coupling driving mechanisms of human activities and climate change on the LULCC and then to forecasting the future scenarios? (3) What are the scientific mechanisms of LULCC impacts on biophysical processes of land surface, and then the climate? (4) How to estimate the contributions of LULCC to climate change by affecting biophysical processes of land surface? By international comparison, the impacts of LULCC on climate change at the local, regional and global scales were revealed and evaluated. It can provide theoretical basis for the global change, and have great significance to mitigate and adapt to global climate changes.</p> |
| [4] | The Simple Biosphere Model (SiB2) and the 265×65202km resolution National Land use/Land Cover database were used to investigate the effects of Land Use/Cover Change (LUCC) on land surface energy balance and climate in Jilin Province, northeast China, from 1990 to 2005. The spatial patterns of the components of surface energy balance (i.e., net radiation (R 63), latent heat (LH), sensible heat (SH), and albedo (α)) and climate (i.e., canopy temperature (T c), diurnal temperature range (DTR)), as well as the roles of land cover type in variations of energy balance and climate, were investigated. The results showed that there were general similar trends in R 63, LH, SH, and α in the LUCC process. The spatial patterns of T c and DTR also showed consistent relationships with LUCC processes. Leaf area index (LAI) and canopy conductance (g c) were found to be the key factors in controlling the spatial patterns of the components of surface energy balance and T c. Using linear correlation method, the gaps of the components of surface energy balance were well-explained by the differences of LAI and g c, and R 63 had a better correlation with T c and DTR, in the process of LUCC. The surface energy partitioning of R 63 into LH and SH could not only dampen or strengthen the temperature difference, but also change the relative size of albedo-based R 63 when the albedo gap was small, between land cover types. |
| [5] | The Community Atmosphere Biosphere Land Exchange (CABLE) model has been coupled to the UK Met Office Unified Model (UM) within the existing framework of the Australian Community Climate and Earth System Simulator (ACCESS), replacing the Met Office Surface Exchange Scheme (MOSES). Here we investigate how features of the CABLE model impact on present-day surface climate using ACCESS atmosphere-only simulations. The main differences attributed to CABLE include a warmer winter and a cooler summer in the Northern Hemisphere (NH), earlier NH spring runoff from snowmelt, and smaller seasonal and diurnal temperature ranges. The cooler NH summer temperatures in canopy-covered regions are more consistent with observations and are attributed to two factors. Firstly, CABLE accounts for aerodynamic and radiative interactions between the canopy and the ground below; this placement of the canopy above the ground eliminates the need for a separate bare ground tile in canopy-covered areas. Secondly, CABLE simulates larger evapotranspiration fluxes and a slightly larger daytime cloud cover fraction. Warmer NH winter temperatures result from the parameterization of cold climate processes in CABLE in snow-covered areas. In particular, prognostic snow density increases through the winter and lowers the diurnally resolved snow albedo; variable snow thermal conductivity prevents early winter heat loss but allows more heat to enter the ground as the snow season progresses; liquid precipitation freezing within the snowpack delays the building of the snowpack in autumn and accelerates snow melting in spring. Overall we find that the ACCESS simulation of surface air temperature benefits from the specific representation of the turbulent transport within and just above the canopy in the roughness sublayer as well as the more complex snow scheme in CABLE relative to MOSES. |
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| [7] | Anthropogenic changes to land cover (LCC) remain common, but continuing land scarcity promotes the widespread intensification of land management changes (LMC) to better satisfy societal demand for food, fibre, fuel and shelter(1). The biophysical effects of LCC on surface climate are largely understood(2-5), particularly for the boreal(6) and tropical zones(7), but fewer studies have investigated the biophysical consequences of LMC; that is, anthropogenic modification without a change in land cover type. Harmonized analysis of ground measurements and remote sensing observations of both LCC and LMC revealed that, in the temperate zone, potential surface cooling from increased albedo is typically offset by warming from decreased sensible heat fluxes, with the net effect being a warming of the surface. Temperature changes from LMC and LCC were of the same magnitude, and averaged 2 K at the vegetation surface and were estimated at 1.7 K in the planetary boundary layer. Given the spatial extent of land management (42-58% of the land surface) this calls for increasing the efforts to integrate land management in Earth System Science to better take into account the human impact on the climate(8). |
| [8] | The North China Plain (NCP) is one of the most important agricultural regions in Asia and produces up to 50% of the cereal consumed in China each year(1,2). To meet increasing food demands without expanding croplands, annual agricultural practice in much of the NCP has changed from single to double cropping(3,4). The impact of double cropping on the regional climate, through biophysical feedbacks caused by changes in land surface conditions, remains largely unknown. Here we show that observed surface air temperatures during the inter-cropping season (June and July) are 0.40 degrees C higher over double cropping regions (DCRs) than over single cropping regions (SCRs), with increases in the daily maximum temperature as large as 1.02 degrees C. Using regional climate modelling, we attribute the higher temperatures in DCRs to reduced evapotranspiration during the inter-cropping period. The higher surface temperatures in June and July affect low-level circulation and, in turn, rainfall associated with the East Asian monsoon over the NCP and neighbouring countries. These findings suggest that double cropping in the NCP can amplify the magnitude of summertime climate changes over East Asia. |
| [9] | Increases in temperature extremes are of major concern for agricultural production. However, this study identifies a connection between agricultural intensification and less extreme summer temperatures over the agriculturally dominated US Midwest. |
| [10] | Abstract The potential impacts of climate change in the Midwest United States present unprecedented challenges to regional agriculture. In response to these challenges, a variety of climate-smart agricultural methodologies have been proposed to retain or improve crop yields, reduce agricultural greenhouse gas emissions, retain soil quality and increase climate resilience of agricultural systems. One component that is commonly neglected when assessing the environmental impacts of climate-smart agriculture is the biophysical impacts, where changes in ecosystem fluxes and storage of moisture and energy lead to perturbations in local climate and water availability. Using a combination of observational data and an agroecosystem model, a series of climate-smart agricultural scenarios were assessed to determine the biophysical impacts these techniques have in the Midwest United States. The first scenario extended the growing season for existing crops using future temperature and CO2 concentrations. The second scenario examined the biophysical impacts of no-till agriculture and the impacts of annually retaining crop debris. Finally, the third scenario evaluated the potential impacts that the adoption of perennial cultivars had on biophysical quantities. Each of these scenarios was found to have significant biophysical impacts. However, the timing and magnitude of the biophysical impacts differed between scenarios. |
| [11] | This study analysed the changes in cropland greenness during 1982-2006 in the North China Plain (NCP) and investigated the warming/cooling effects of the greenness changes. The results show that while spring cropland greenness increased, early summer cropland greenness substantially decreased from 1982 to 2006. In contrast to the cooling and wetting effects of the greenness increase in spring, the greenness reduction in early summer had warming and drying effects. The cooling/warming effects of cropland greenness changes accounted for 47% of the spatial variance of daily maximum temperature (Tmax) change in spring and 44% in early summer. The wetting/drying effects of cropland greenness changes accounted for 48% of the spatial variance of daily minimum specific humidity (SPHmin) change in spring and 19% in early summer. The cooling-wetting/warming-drying effects mainly resulted from the distinct partitioning of surface net radiation between surface latent heat flux and sensible heat flux over cropland with different greenness. Canopy transpiration plays a dominant role. The increased (decreased) cropland greenness corresponds to high (low) transpiration rate, less (more) sensible heat flux and high (low) humidity, and consequently cooling-wetting (warming-drying) effects. In comparison, there was little change in surface net radiation, although surface albedo and emissivity had changed with greenness change. 2013 IOP Publishing Ltd. |
| [12] | It is well known that expansion of agriculture into natural ecosystems can have important climatic consequences, but changes occurring within existing croplands also have the potential to effect local and global climate. To better understand the impacts of cropland management practices, we used the NCAR CAM3 general circulation model coupled to a slab-ocean model to simulate climate change under extreme scenarios of irrigation, tillage, and crop productivity. Compared to a control scenario, increases in irrigation and leaf area index and reductions in tillage all have a physical cooling effect by causing increases in planetary albedo. The cooling is most pronounced for irrigation, with simulated local cooling up to ~8掳C and global land surface cooling of 1.3掳C. Increases in soil albedo through reduced tillage are found to have a global cooling effect (~0.2掳C) comparable to the biogeochemical cooling from reported carbon sequestration potentials. By identifying the impacts of extreme scenarios at local and global scales, this study effectively shows the importance of considering different aspects of crop management in the development of climate models, analysis of observed climate trends, and design of policy intended to mitigate climate change. |
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| [14] | |
| [15] | 中国温带季风区是我国重要的农业区,春季霜冻常对该地区的植物造成严重的损害.本文利用“中国物候观测网”12个站点的物候观测数据和对应站点气象资料,应用物候模型方法,对1963-2009年各站点的霜冻频次和多种木本植物的始花期进行了分析,并对植物在花期的霜冻风险进行了评估.结果表明,1963-2009年,研究区内东北地区和华北地区的始花期分别以-1.52天/10a(P＜0.01)和-2.22天/10a(P＜0.01)的速度提前.在同一时段,研究区春季霜冻日数显著减少,终霜冻日显著提前.综合考虑花期和霜冻频次的变化,霜冻风险指数,即木本植物花期受到霜冻的物种数占调查总数的百分比,在东北地区以-0.37％/10a的速度降低(不显著)；而在华北地区,霜冻风险指数则以-1.80％/10a的速度显著下降(P＜ 0.01).这表明过去半个世纪研究区植物花期霜冻风险在降低,且存在显著的区域差异.该结论可为农业和森林管理者制订应对春季霜冻害的决策提供参考. . 中国温带季风区是我国重要的农业区,春季霜冻常对该地区的植物造成严重的损害.本文利用“中国物候观测网”12个站点的物候观测数据和对应站点气象资料,应用物候模型方法,对1963-2009年各站点的霜冻频次和多种木本植物的始花期进行了分析,并对植物在花期的霜冻风险进行了评估.结果表明,1963-2009年,研究区内东北地区和华北地区的始花期分别以-1.52天/10a(P＜0.01)和-2.22天/10a(P＜0.01)的速度提前.在同一时段,研究区春季霜冻日数显著减少,终霜冻日显著提前.综合考虑花期和霜冻频次的变化,霜冻风险指数,即木本植物花期受到霜冻的物种数占调查总数的百分比,在东北地区以-0.37％/10a的速度降低(不显著)；而在华北地区,霜冻风险指数则以-1.80％/10a的速度显著下降(P＜ 0.01).这表明过去半个世纪研究区植物花期霜冻风险在降低,且存在显著的区域差异.该结论可为农业和森林管理者制订应对春季霜冻害的决策提供参考. |
| [16] | Abstract Earlier spring leaf unfolding is a frequently observed response of plants to climate warming. Many deciduous tree species require chilling for dormancy release, and warming-related reductions in chilling may counteract the advance of leaf unfolding in response to warming. Empirical evidence for this, however, is limited to saplings or twigs in climate-controlled chambers. Using long-term in situ observations of leaf unfolding for seven dominant European tree species at 1,245 sites, here we show that the apparent response of leaf unfolding to climate warming (ST, expressed in days advance of leaf unfolding per °C warming) has significantly decreased from 1980 to 2013 in all monitored tree species. Averaged across all species and sites, ST decreased by 40% from 4.0 ± 1.8 days °C(-1) during 1980-1994 to 2.3 ± 1.6 days °C(-1) during 1999-2013. The declining ST was also simulated by chilling-based phenology models, albeit with a weaker decline (24-30%) than observed in situ. The reduction in ST is likely to be partly attributable to reduced chilling. Nonetheless, other mechanisms may also have a role, such as 'photoperiod limitation' mechanisms that may become ultimately limiting when leaf unfolding dates occur too early in the season. Our results provide empirical evidence for a declining ST, but also suggest that the predicted strong winter warming in the future may further reduce ST and therefore result in a slowdown in the advance of tree spring phenology. |
| [17] | Abstract The degree to which the interannual variability of vegetation phenology affects hydrological fluxes over land is investigated through a series of simulations with the Mosaic land surface model, run both offline and coupled to the NASA Seasonal-to-Interannual Prediction Project (NSIPP) atmospheric general circulation model (GCM). Over a 9-yr period, from 1982 to 1990, interannual variations of global biophysical land surface parameters (i.e., vegetation density and greenness fraction) are derived from Normalized Difference Vegetation Index (NDVI) data collected by the Advanced Very High Resolution Radiometers (AVHRRs). First the sensitivity of evapo-transpiration to interannual variations in vegetation properties is evaluated through offline simulations that ignore feedbacks between the land surface and the atmospheric models, and interannual precipitation variations. Evapo-transpiration is shown to be highly sensitive to variations in vegetation over wet continental surfaces that are not densely vegetated. The sensitivity is reduced by a saturation effect over dense vegetation covers and physiological control due to environmental stress over arid and semiarid regions. Correlations between evapotranspiration and vegetation anomalies are reduced markedly in offline runs that impose interannual variations in both vegetation and precipitation. They are also strongly reduced in the coupled simulations. Although interannual variations in vegetation properties still influence transpiration and interception loss at the global scale in these runs, their impact on large-scale regional climate is much weaker, apparently because the impact is drowned out by atmospheric variability. |
| [18] | <a name="Abs1"></a>The albedo (<i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i>) of vegetated land surfaces is a key regulatory factor in atmospheric circulation and plays an important role in mechanistic accounting of many ecological processes. This paper examines the influence of the phenological stages of maize (<i>Zea mays</i>) and cowpea (<i>Vigna unguiculata</i>) fields on observed albedo at a tropical site in Ghana. The crops were studied for the first and second planting dates in the year 2002. Crop management was similar for both seasons and measurements were taken from 10 m×10-m plots within crop fields. Four phenological stages were distinguished: (1) emergence, (2) vegetative, (3) flowering, and (4) maturity. <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i> measured from two reference surfaces, short grass and bare soil, were used to study the change over the growing seasons. Surface <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i> was measured and simulated at sun angles of 15, 30, 45, 60, and 75°. Leaf area index (LAI) and crop height (CH) were also monitored. Generally, <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i> increases from emergence to maturity for both planting dates in the maize field but slightly decreases after flowering in the cowpea field. For maize, the correlation coefficient (<i>R</i>) between <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i> and LAI equals 0.970, and the <i>R</i> between <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i> and CH equals 0.969. Similarly, for cowpea these <i>R</i>s are 0.988 and 0.943, respectively. A modified albedo model adequately predicted the observed <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i>s with an overall <i>R</i>>0.860. The relative difference in surface <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i> with respect to the <i><img src="/content/PXX79CK079GQ3GQW/xxlarge945.gif" alt="agr" align="BASELINE" border="0"></i> values measured from the two reference surfaces is discussed. Data presented are expected to be a valuable input in agricultural water management, crop production models, eco-hydrological models and in the study of climate effects of agricultural production, and for the parameterization of land-surface schemes in regional weather and climate models. |
| [19] | Abstract A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water, and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to Earth system models is relatively rare. Here we evaluated two such models (CLM4-Crop and CLM3.5-CornSoy), both implemented within the Community Land Model (CLM) framework, at two AmeriFlux corn-soybean sites to assess their ability to simulate phenology, energy, and carbon fluxes. Our results indicated that the accuracy of net ecosystem exchange and gross primary production simulations was intimately connected to the phenology simulations. The CLM4-Crop model consistently overestimated early growing season leaf area index, causing an overestimation of gross primary production, to such an extent that the model simulated a carbon sink instead of the measured carbon source for corn. The CLM3.5-CornSoy-simulated leaf area index (LAI), energy, and carbon fluxes showed stronger correlations with observations compared to CLM4-Crop. Net radiation was biased high in both models and was especially pronounced for soybeans. This was primarily caused by the positive LAI bias, which led to a positive net long-wave radiation bias. CLM4-Crop underestimated soil water content during midgrowing season in all soil layers at the two sites, which caused unrealistic water stress, especially for soybean. Future work regarding the mechanisms that drive early growing season phenology and soil water dynamics is needed to better represent crops including their net radiation balance, energy partitioning, and carbon cycle processes. |
| [20] | AbstractExtending phenological records into the past is essential for the understanding of past ecological change and evaluating the effects of climate change on ecosystems. A growing body of historical phenological information is now available for Europe, North America, and Asia. In East Asia, long-term phenological series are still relatively scarce. This study extracted plant phenological observations from old diaries in the period 1834–1962. A spring phenology index (SPI) for the modern period (1963–2009) was defined as the mean flowering time of three shrubs (first flowering of Amygdalus davidiana and Cercis chinensis, 50% of full flowering of Paeonia suffruticosa) according to the data availability. Applying calibrated transfer functions from the modern period to the historical data, we reconstructed a continuous SPI time series across eastern China from 1834 to 2009. In the recent 3065years, the SPI is 2.1–6.365days earlier than during any other consecutive 3065year period before 1970. A moving linear trend analysis shows that the advancing trend of SPI over the past three decades reaches upward of 4.165d/decade, which exceeds all previously observed trends in the past 3065year period. In addition, the SPI series correlates significantly with spring (February to April) temperatures in the study area, with an increase in spring temperature of 1°C inducing an earlier SPI by 3.165days. These shifts of SPI provide important information regarding regional vegetation-climate relationships, and they are helpful to assess long term of climate change impacts on biophysical systems and biodiversity. |
| [21] | . |
| [22] | This study focuses on projected changes to vegetation characteristics and their interactions with the atmosphere under future climatic conditions over North America, using four transient climate change simulations of the Canadian Regional Climate Model (CRCM5). Here CRCM5 performs dynamical downscaling of the Canadian Earth System Model (CanESM2) simulated data, for Representative Concentration Pathways (RCPs) 4.5 and 8.5. For each RCP, two CRCM5 simulations are performed鈥攐ne with static vegetation phenology and the other with dynamic vegetation phenology鈥攆or the 1950-2100 period over North America. The dynamic vegetation model used here is the Canadian Terrestrial Ecosystem Model. Results show that the extension of the growing season under future climatic conditions in the dynamic phenology simulations leads to higher annual vegetation productivity and biomass. In comparison with projected changes based on CRCM5 with static phenology, CRCM5 with phenology dynamics leads to an albedo-mediated warming enhancement across most of North America in spring. In summer, results suggest a warming enhancement in the northern latitudes and an attenuation of warming for more southern regions due to hydrological feedbacks. Furthermore, results suggest that vegetation enhances its water-use efficiency with rising atmospheric COconcentrations. Over southeastern United States, in the dynamic phenology simulation corresponding to the RCP8.5 scenario, the adverse effects of the projected increase in temperatures and decrease in precipitation on vegetation dominate the COfertilization effect, leading to decreasing trends in productivity during the 2071-2100 period. This study thus clearly demonstrates that phenology dynamics modulate greenhouse gas-mediated warming through various biophysical feedbacks. |
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| [24] | The majority of the world’s food production capability is inextricably tied to global precipitation patterns. Changes in moisture availability—whether from changes in climate from anthropogenic greenhouse gas emissions or those induced by land cover change (LCC)—can have profound impacts on food production. In this study, we examined the patterns of evaporative sources that contribute to moisture availability over five major global food producing regions (breadbaskets), and the potential for land cover change to influence these moisture sources by altering surface evapotranspiration. For a range of LCC scenarios we estimated the impact of altered surface fluxes on crop moisture availability and potential yield using a simplified linear hydrologic model and a state-of-the-art ecosystem and crop model. All the breadbasket regions were found to be susceptible to reductions in moisture owing to perturbations in evaporative source (ES) from LCC, with reductions in moisture availability ranging from 7 to 17% leading to potential crop yield reductions of 1–17%, which are magnitudes comparable to the changes anticipated with greenhouse warming. The sensitivity of these reductions in potential crop yield to varying magnitudes of LCC was not consistent among regions. Two variables explained most of these differences: the first was the magnitude of the potential moisture availability change, with regions exhibiting greater reductions in moisture availability also tending to exhibit greater changes in potential yield; the second was the soil moisture within crop root zones. Regions with mean growing season soil moisture fractions of saturation >0.5 typically had reduced impacts on potential crop yield. Our results indicate the existence of LCC thresholds that have the capability to create moisture shortages adversely affecting crop yields in major food producing regions, which could lead to future food supply disruptions in the absence of increased irrigation or other forms of water management. |
| [25] | Crop yields are affected by many factors, related to breeding, management and climate. Understanding these factors, and their relative contributions to historical yield increases, is important to help ensure that these yield increases can continue in the future. Two important factors that can affect yields are planting dates and the crop's growing degree day (GDD) requirements. We analyzed 25 years of data collected by the USDA in order to document trends in planting dates, lengths of the vegetative and reproductive growth periods, and the length of time between maturity and harvest for corn and soybeans across the United States. We then drove the Agro-IBIS agroecosystem model with these observations to investigate the effects of changing planting dates and crop GDD requirements on crop yields and fluxes of water and energy. Averaged across the U.S., corn planting dates advanced about 10 days from 1981 to 2005, and soybean planting dates about 12 days. For both crops, but especially for corn, this was accompanied by a lengthening of the growth period. The period from corn planting to maturity was about 12 days longer around 2005 than it was around 1981. A large driver of this change was a 14% increase in the number of GOD needed for corn to progress through the reproductive period, probably reflecting an adoption of longer season cultivars. If these changes in cultivars had not occurred, yields around 2005 would have been 12.6 bu ac(-1) lower across the U.S. Corn Belt, erasing 26% of the yield increase from 1981 to 2005. These changes in crop phenology, together with a shortening of the time from maturity to harvest, have also modified the surface water and energy balance. Earlier planting has led to an increase in the latent heat flux and a decrease in the sensible heat flux in June, while a shorter time from maturity to harvest has meant an increase in net radiation in October. (C) 2011 Elsevier B.V. All rights reserved. |
| [26] | The phenological development of cereal crops from emergence through flowering to maturity is largely controlled by temperature, but also affected by day length and potential physiological stresses. Responses may vary between species and varieties. Climate change will affect the timing of cereal crop development, but exact changes will also depend on changes in varieties as affected by plant breeding and variety choices. This study aimed to assess changes in timing of major phenological stages of cereal crops in Northern and Central Europe under climate change. Records on dates of sowing, flowering, and maturity of wheat, oats and maize were collected from field experiments conducted during the period 1985-2009. Data for spring wheat and spring oats covered latitudes from 46 to 64掳N, winter wheat from 46 to 61掳N, and maize from 47 to 58掳N. The number of observations (site-year-variety combinations) varied with phenological phase, but exceeded 2190, 227, 2076 and 1506 for winter wheat, spring wheat, spring oats and maize, respectively. The data were used to fit simple crop development models, assuming that the duration of the period until flowering depends on temperature and day length for wheat and oats, and on temperature for maize, and that the duration of the period from flowering to maturity in all species depends on temperature only. Species-specific base temperatures were used. Sowing date of spring cereals was estimated using a threshold temperature for the mean air temperature during 10 days prior to sowing. The mean estimated temperature thresholds for sowing were 6.1, 7.1 and 10.1掳C for oats, wheat and maize, respectively. For spring oats and wheat the temperature threshold increased with latitude. The effective temperature sums required for both flowering and maturity increased with increasing mean annual temperature of the location, indicating that varieties are well adapted to given conditions. The responses of wheat and oats were largest for the period from flowering to maturity. Changes in timing of cereal phenology by 2040 were assessed for two climate model projections according to the observed dependencies on temperature and day length. The results showed advancements of sowing date of spring cereals by 1-3 weeks depending on climate model and region within Europe. The changes were largest in Northern Europe. Timing of flowering and maturity were projected to advance by 1-3 weeks. The changes were largest for grain maize and smallest for winter wheat, and they were generally largest in the western and northern part of the domain. There were considerable differences in predicted timing of sowing, flowering and maturity between the two climate model projections applied. |
| [27] | Over recent years, the Iberian Peninsula has witnessed an increase both in temperature and in rainfall intensity, especially in the Mediterranean climate area. Plant phenology is modulated by climate, and closely governed by water availability and air temperature. Over the period 1986–2012, the effects of climate change on phenology were analyzed in five crops at 26 sites growing in Spain (southern Europe): oats, wheat, rye, barley and maize. The phenophases studied were: sowing date, emergence, flag leaf sheath swollen, flowering, seed ripening and harvest. Trends in phenological response over time were detected using linear regression. Trends in air temperature and rainfall over the period prior to each phenophase were also charted. Correlations between phenological features, biogeographical area and weather trends were examined using a Generalized Lineal Mixed Model approach. A generalized advance in most winter-cereal phenophases was observed, mainly during the spring. Trend patterns differed between species and phenophases. The most noticeable advance in spring phenology was recorded for wheat and oats, the “ Flag leaf sheath swollen” and “Flowering date” phenophases being brought forward by around 302days/year and 102day/year, respectively. Temperature changes during the period prior to phenophase onset were identified as the cause of these phenological trends. Climate changes are clearly prompting variations in cereal crop phenology; their consequences could be even more marked if climate change persists into the next century. Changes in phenology could in turn impact crop yield; fortunately, human intervention in crop systems is likely to minimize the negative impact. |
| [28] | Distinct changes in air temperature since the end of the 1980s have led to clear responses in plant phenology in many parts of the world. In Germany phenological phases of the natural vegetation as well as of fruit trees and field crops have advanced clearly in the last decade of the 20th century. The strongest shift in plant development occurred for the very early spring phases. The late spring phases and summer phases reacted also to the increased temperatures, but they usually show lower trends. Until now the changes in plant development are still moderate, so that no strong impacts on yield formation processes were observed. But further climate changes will probably increase the effect on plants, so that in the future stronger impacts on crop yields are likely. |
| [29] | Climate change in the last three decades could have major impacts on crop phenological development and subsequently on crop productivity. In this study, trends in winter wheat phenology are investigated in 36 agro-meteorological stations in the North China Plain (NCP) for the period 1981-2009. The study shows that the dates of sowing (BBCH 00), emergence (BBCH 10) and dormancy (start of dormancy) are delayed on the average by 1.5, 1.7 and 1.5 days/decade, respectively. On the contrary, the dates of greenup (end of dormancy), anthesis (BBCH 61) and maturity (BBCH 89) occur early on the average by 1.1, 2.7 and 1.4 days/decade, respectively. In most of the investigated stations, GP2 (dormancy to greenup), GP3 (greenup to anthesis) and GP0 (entire period from emergence to maturity) of winter wheat shortened during the period 1981-2009. Due, however, to early anthesis, grain-filling stage occurs at lower temperatures than before. This, along with shifts in cultivars, slightly prolongs GP4 (anthesis to maturity). Comparison of field-observed CERES (Crop Environment Resource Synthesis)-wheat model-simulated dates of anthesis and maturity suggests that climate warming is the main driver of the changes in winter wheat phenology in the NCP. The findings of this study further suggest that climate change impact studies should be strengthened to adequately account for the complex responses and adaptations of field crops to this global phenomenon. |
| [30] | Weather and climate variability are predicted to impact food security by altering crop growth, phenology, and yield processes. Adaptation measures are critical for reducing future vulnerability of crop production to warming weather and climate variability. It is therefore vital to investigate the shifts in crop phenological processes in response to weather/climate variability. This study analyzes the trends in the dates of winter wheat ( Triticum aestivum L.) phenology in relation to average temperature of different growth stage and the adaptation of the crop to weather/climate variability in China. The results suggest that the phenological phases of winter wheat have specific regional patterns in China. There are also significant shifts in the dates of winter wheat phenology and the duration of the growth stages in the investigated 30-year period of 1980鈥2009. While the date of sowing winter wheat delays, the dates of post-winter phenological phases (e.g., heading and maturity dates) advances in most areas of China. Detailed analysis shows that the changes in the phenological phases of winter wheat are strongly related to temperature trends. Temporal trends in phenological phases of winter wheat are similar in characteristics to corresponding trends in temperature. Although warming weather and climate variability is the main driver of the changes in winter wheat phenology, temperature is lower than before in most of the investigated stations during the period from heading to maturity鈥攎ainly the grain-filling stage. This is mainly due to the early heading and maturity dates, which in turn not only prolong growth stages but also enhance productivity of winter wheat. This could be a vital adaptation strategy of winter wheat to warming weather with beneficial effects in terms of productivity. |
| [31] | Investigating the spatiotemporal changes of crop phenology in field is important to understand the processes and mechanisms of crop response and adaption to ongoing climate change. Here, the wheat phenology at more than 100 national agro-meteorological experiment stations across China spanning the years 1981-2007 was examined. Spatiotemporal changes of wheat phenology and seasonal temperature, as well as the correlations between them were presented. During the investigation period, heading dates advanced significantly at 43 stations from the 108 investigated stations: maturity dates advanced significantly at 41 stations from the 109 investigated stations. Lengths of growing period (from sowing to maturity) and vegetative growing period (from sowing to heading) were significantly reduced at about 30% of the investigated stations, especially for spring wheat in northwestern China, despite thermal accumulation during the periods increased. In contrast, although significantly and negatively related to mean temperature, lengths of reproductive growing period (from heading to maturity) increased at 60% of the investigated stations, owing to increase in crop cultivars thermal requirements or/and decrease in mean temperature. The results showed that besides the complex influences of agronomic factors, climate change contributed substantially to the shift of wheat phenology. Mean day length during vegetative growing period had a decreasing trend at most of the investigated stations owing to delay of sowing date or/and advancement of heading date, which counterbalanced the roles of temperature in controlling the duration of vegetative growing period. In-depth analyses showed that thermal requirements from sowing to almost each development stage increased, however the thermal requirements to complete each single development stage changed differently, which tended to increase yield and adapt to ongoing climate change. Our findings have important implications for improving climate change impact studies, for breeding scientists to breed higher yielding cultivars, and for agricultural production to cope with ongoing climate change. (c) 2012 Elsevier B.V. All rights reserved. |
| [32] | As climate change could significantly influence crop phenology and subsequent crop yield, adaptation is a critical mitigation process of the vulnerability of crop growth and production to climate change. Thus, to ensure crop production and food security, there is the need for research on the natural (shifts in crop growth periods) and artificial (shifts in crop cultivars) modes of crop adaptation to climate change. In this study, field observations in 18 stations in North China Plain (NCP) are used in combination with Agricultural Production Systems Simulator (APSIM)-Maize model to analyze the trends in summer maize phenology in relation to climate change and cultivar shift in 1981–2008. Apparent warming in most of the investigated stations causes early flowering and maturity and consequently shortens reproductive growth stage. However, APSIM-Maize model run for four representative stations suggests that cultivar shift delays maturity and thereby prolongs reproductive growth (flowering to maturity) stage by 2.4613.702day per decade (d 10a 611 ). The study suggests a gradual adaptation of maize production process to ongoing climate change in NCP via shifts in high thermal cultivars and phenological processes. It is concluded that cultivation of maize cultivars with longer growth periods and higher thermal requirements could mitigate the negative effects of warming climate on crop production and food security in the NCP study area and beyond. |
| [33] | Maize phenology observations at 112 national agro-meteorological experiment stations across China spanning the years 1981-2009 were used to investigate the spatiotemporal changes of maize phenology, as well as the relations to temperature change and cultivar shift. The greater scope of the dataset allows us to estimate the effects of temperature change and cultivar shift on maize phenology more precisely. We found that maize sowing date advanced significantly at 26.0% of stations mainly for spring maize in northwestern, southwestern and northeastern China, although delayed significantly at 8.0% of stations mainly in northeastern China and the North China Plain (NCP). Maize maturity date delayed significantly at 36.6% of stations mainly in the northeastern China and the NCP. As a result, duration of maize whole growing period (GPw) was prolonged significantly at 41.1% of stations, although mean temperature (Tmean) during GPw increased at 72.3% of stations, significantly at 19.6% of stations, and Tmean was negatively correlated with the duration of GPw at 92.9% of stations and significantly at 42.9% of stations. Once disentangling the effects of temperature change and cultivar shift with an approach based on accumulated thermal development unit, we found that increase in temperature advanced heading date and maturity date and reduced the duration of GPw at 81.3%, 82.1% and 83.9% of stations on average by 3.2, 6.0 and 3.5days/decade, respectively. By contrast, cultivar shift delayed heading date and maturity date and prolonged the duration of GPw at 75.0%, 94.6% and 92.9% of stations on average by 1.5, 6.5 and 6.5days/decade, respectively. Our results suggest that maize production is adapting to ongoing climate change by shift of sowing date and adoption of cultivars with longer growing period. The spatiotemporal changes of maize phenology presented here can further guide the development of adaptation options for maize production in near future. |
| [34] | To identify countermeasures to the effects of climate warming on crop production, we mustunderstand the changes in crop phenology and the relationships between phenology and climatechange and cultivar. We used summer maize phenological and climate data in the North ChinaPlain, collected from 1981 to 2010. This study analyzed the spatiotemporal trends inphenological data and lengths of different growing phases, mean temperatures and rainfall.The analyses showed that sowing, jointing and anthesis occurred relatively early at 13 (48.1%),11 (40.7%) and 13 (48.1%) stations, respectively. Maturity dates were delayed significantly at10 (37.0%) stations. The lengths of the vegetative growing phases, vegetative and reproductivegrowing phase at most stations showed a negative trend. The lengths of the reproductivegrowing phase increased at 25 (92.6%) stations, respectively. Furthermore, at most stations, thecorrelations between Tmeans and lengths of the various growing phases were negative, whereasthe correlations between rainfall and lengths of various growing phases were positive.Furthermore, a field experiment, including four summer maize cultivars which were introducedduring the 1950s, 1970s, 1990s and 2000s, was carried out during 2012 to 2014. The analysesshowed that the durations of the various growing phases increased significantly. These resultsindicated that climate warming accelerates summer maize growth and shortens the growingperiods of maize growth, whereas cultivars shift might prolong the maize growing season.Therefore, the maize cultivars with more longer whole growing period should be adopt in theNorth China Plain under the trend of global warming and the adaptation strategy of maizeproduction under climate change should include crop phenology in response to climate change.The findings presented here could guide the development of options to adapt maize productionto climate change in the North China Plain and other areas with similar ecologies. |
| [35] | Growing evidence suggests that the warming trend observed in many parts of the world has considerably modified crop phenology during the last decades but little is known about the impact of changes in crop management on crop phenology and possible interactions with temperature increase, and whether responses can be generalized across crop types. Here we evaluate the effects of climate and management on crop phenology by using observations for winter rapeseed and winter rye obtained in Germany for the period 1960 to 2013 by using piecewise linear regressions of temperature and phenology data on year. We show that long-term trends in crop phenology are crop-specific. The length of the vegetative phase of winter rapeseed declined by 4.8days per decade in the period 1979-2013. However, the corresponding decline for winter rye was only 1.3days per decade in the period 1978-2013 with the difference caused by change in management practices such as the introduction of early flowering cultivars or changes in sowing and harvest dates of winter rapeseed and winter rye during the last decades in Germany. The length of the reproductive phase of winter rye declined by 0.9days per decade between 1976 to 2013 in response to the warming trend in that period. In contrast, the extended use of late maturing cultivars with a longer grain filling period and changed planting densities over-compensated for the effect of increasing temperature on the length of the reproductive phase of winter rapeseed and caused an increasing trend of 2.0days per decade between 1992 and 2013. The sowing date of winter rye advanced by 1.3days per decade in the period 1972-2013. The length of the phase between maturity and harvest increased considerably for both crops and compensated partly for the effect of increasing temperature to shorten the preceding phenological phases. We conclude that it is essential to account for interactions between climate and crop management in climate change impact analysis and assessment studies and that differences among crops need to be considered. |
| [36] | <h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">A detailed dynamic crop stand phenological model is presented for winter barley and winter rye. The modelled phenological stages are described in different scale units (FEEKES [1–20], BBCH [1–100] and DC [1–100]) and a differentiation in mathematical approaches and in parameterisation is made between the germination, the vegetative and the generative phases. The following driving forces are taken into account: temperature, day length, drought stress and nitrogen availability. Drought stress is described using the ratio of actual to potential evapotranspiration and nitrogen availability is described using the nitrogen content in the above-ground biomass. Vernalisation is considered as a process influencing phenology and is described normalised between 0 and 1. In the paper the model parameters are listed separately for winter barley and winter rye. Model parameterisation and validation results are presented for three different German locations (Müncheberg, Hohenfinow, Halle). The comparison of calculated and observed phenological phases (emergence, shooting, flowering, maturity) gives an <em>R</em><sup>2</sup> between 0.87 and 0.99 for winter rye and between 0.92 and 0.99 for winter barley. For these phenological stages the maximum mean deviation between calculations and observations is 5 days. For tillering an insufficient agreement was occurred only. Investigations regarding the geographical extrapolation of the model are carried out for two different German locations (Dedelow and Mariensee). The model-experiment-comparison results for all phenological phases show a sufficient accuracy (<em>R</em><sup>2</sup> = 0.98, <em>N</em> = 82), followed by the presentation and discussion of their results. Model simulation runs with drought stress and not enough nitrogen availability show the dominance of drought stress induced phenology acceleration.</p> |
| [37] | The change tendency and regular patterns of agroclimatic resources, agrometeorological disasters including drought, flood, heat wave and low temperature disasters, and agricultural pests and diseases in China under global climate change are reviewed in this paper. The facts of the climate change impacts on agricultural production in China are revealed from the changes of agricultural production potential, crop cultivation system and crop quality. The potential impacts of future climate change on agricultural production in China are discussed, and the adaptation measures of agricultural production to climate change are summarized. Based on the temporal and spatial patterns of agroclimatic resources in China and new situation and new problems of Chinese agricultural production under climate change, the shortcomings of the study on the impacts of climate change on agricultural production in China are pointed out. Moreover, the future tasks related to the study on the impacts of climate change on agricultural production in China are emphasized, in order to provide scientific decision support for agricultural production security and national food security. . The change tendency and regular patterns of agroclimatic resources, agrometeorological disasters including drought, flood, heat wave and low temperature disasters, and agricultural pests and diseases in China under global climate change are reviewed in this paper. The facts of the climate change impacts on agricultural production in China are revealed from the changes of agricultural production potential, crop cultivation system and crop quality. The potential impacts of future climate change on agricultural production in China are discussed, and the adaptation measures of agricultural production to climate change are summarized. Based on the temporal and spatial patterns of agroclimatic resources in China and new situation and new problems of Chinese agricultural production under climate change, the shortcomings of the study on the impacts of climate change on agricultural production in China are pointed out. Moreover, the future tasks related to the study on the impacts of climate change on agricultural production in China are emphasized, in order to provide scientific decision support for agricultural production security and national food security. |
| [38] | Kazakhstan is the second largest country to emerge from the collapse of the Soviet Union. Consequent to the abrupt institutional changes surrounding the disintegration of the Soviet Union in the early 1990s, Kazakhstan has reportedly undergone extensive land cover/land use change. Were the institutional changes sufficiently great to affect land surface phenology at spatial resolutions and extents relevant to mesoscale meteorological models? To explore this question, we used the NDVI time series (1985–1988 and 1995–1999) from the Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset, which consists of 10 days maximum NDVI composites at a spatial resolution of 8 km. Daily minimum and maximum temperatures were extracted from the NCEP Reanalysis Project and 10 days composites of accumulated growing degree-days (AGDD) were produced. We selected for intensive study seven agricultural areas ranging from regions with rain-fed spring wheat cultivation in the north to regions of irrigated cotton and rice in the south. We applied three distinct but complementary statistical analyses: (1) nonparametric testing of sample distributions; (2) simple time series analysis to evaluate trends and seasonality; and (3) simple regression models describing NDVI as a quadratic function of AGDD. The irrigated areas displayed different temporal developments of NDVI between 1985–1988 and 1995–1999. As the temperature regime between the two periods was not significantly different, we conclude that observed differences in the temporal development of NDVI resulted from changes in agricultural practices. In the north, the temperature regime was also comparable for both periods. Based on extant socioeconomic studies and our model analyses, we conclude that the changes in the observed land surface phenology in the northern regions are caused by large increases in fallow land dominated by weedy species and by grasslands under reduced grazing pressure. Using multiple lines of evidence allowed us to build a case of whether differences in land surface phenology were mostly the result of anthropogenic influences or interannual climatic fluctuations. |
| [39] | 基于植物物候的遥感监测对于研究植被对气候变化的响应具有重要的科学价值。本文在阐述植物物候遥感监测原理及其通用技术流程的基础上,分别从植被类型及其所处的地理条件、遥感数据源及其预处理、植物物候遥感识别方法和植物物候遥感监测结果评价4个方面分析了影响植物物候遥感监测精度的因素,并针对当前研究中存在的不足,探讨了提高植物物候遥感监测精度的可行性途径,即建立高分辨率的近地面遥感定点观测及数据共享网络,发展普适性更强的卫星遥感时序数据去噪及植被指数曲线重建方法,寻求稳定性更高的植物物候期遥感识别方法,探索综合运用地面观测、遥感监测与模型模拟实现物候观测空间尺度拓展的可能性。 . 基于植物物候的遥感监测对于研究植被对气候变化的响应具有重要的科学价值。本文在阐述植物物候遥感监测原理及其通用技术流程的基础上,分别从植被类型及其所处的地理条件、遥感数据源及其预处理、植物物候遥感识别方法和植物物候遥感监测结果评价4个方面分析了影响植物物候遥感监测精度的因素,并针对当前研究中存在的不足,探讨了提高植物物候遥感监测精度的可行性途径,即建立高分辨率的近地面遥感定点观测及数据共享网络,发展普适性更强的卫星遥感时序数据去噪及植被指数曲线重建方法,寻求稳定性更高的植物物候期遥感识别方法,探索综合运用地面观测、遥感监测与模型模拟实现物候观测空间尺度拓展的可能性。 |
| [40] | Abstract Recent warming of Northern Hemisphere (NH) land is well documented and typically greater in winter/spring than other seasons. Physical environment responses to warming have been reported, but not details of large-area temperate growing season impacts, or consequences for ecosystems and agriculture. To date, hemispheric-scale measurements of biospheric changes have been confined to remote sensing. However, these studies did not provide detailed data needed for many investigations. Here, we show that a suite of modeled and derived measures (produced from daily maximum–minimum temperatures) linking plant development (phenology) with its basic climatic drivers provide a reliable and spatially extensive method for monitoring general impacts of global warming on the start of the growing season. Results are consistent with prior smaller area studies, confirming a nearly universal quicker onset of early spring warmth (spring indices (SI) first leaf date, 611.2daysdecade 611 ), late spring warmth (SI first bloom date, 611.0daysdecade 611 ; last spring day below 5°C, 611.4daysdecade 611 ), and last spring freeze date (611.5daysdecade 611 ) across most temperate NH land regions over the 1955–2002 period. However, dynamics differ among major continental areas with North American first leaf and last freeze date changes displaying a complex spatial relationship. Europe presents a spatial pattern of change, with western continental areas showing last freeze dates getting earlier faster, some central areas having last freeze and first leaf dates progressing at about the same pace, while in portions of Northern and Eastern Europe first leaf dates are getting earlier faster than last freeze dates. Across East Asia last freeze dates are getting earlier faster than first leaf dates. |
| [41] | . |
| [42] | <p>植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器，其发生时间可以反映陆地生态系 统对气候变化的快速响应。近年来，遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点， 已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理 方法的基础上，从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆 盖、植物物候与农作物估产等方面系统阐述了近5 年来国内外遥感物候学研究的重要进展，并针对目前研究中存 在的问题，提出近期遥感物候研究的主要方向：(1)发展一种更具普适性的物候生长季节划分方法；(2)通过开展植物 群落的物候观测和选择合适的尺度转换方法，统一地面与遥感的空间信息；(3)定量分析植物物候变化对人类活动 的响应机制；(4)选择适宜的数学方法和模型，实现各种不同分辨率遥感数据的融合；(5)通过动态模拟，预测植物物 候对未来气候变化的响应。</p> . <p>植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器，其发生时间可以反映陆地生态系 统对气候变化的快速响应。近年来，遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点， 已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理 方法的基础上，从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆 盖、植物物候与农作物估产等方面系统阐述了近5 年来国内外遥感物候学研究的重要进展，并针对目前研究中存 在的问题，提出近期遥感物候研究的主要方向：(1)发展一种更具普适性的物候生长季节划分方法；(2)通过开展植物 群落的物候观测和选择合适的尺度转换方法，统一地面与遥感的空间信息；(3)定量分析植物物候变化对人类活动 的响应机制；(4)选择适宜的数学方法和模型，实现各种不同分辨率遥感数据的融合；(5)通过动态模拟，预测植物物 候对未来气候变化的响应。</p> |
| [43] | Recent shifts in phenology are the best documented biological response to current anthropogenic climate change, yet remain poorly understood from a functional point of view. Prevailing analyses are phenomenological and approximate, only correlating temperature records to imprecise records of phenological events. To advance our understanding of phenological responses to climate change, we developed, calibrated, and validated process-based models of leaf unfolding for 22 North American tree species. Using daily meteorological data predicted by two scenarios (A2: +3.2 pC and B2: +1 pC) from the HadCM3 GCM, we predicted and compared range-wide shifts of leaf unfolding in the 20th and 21st centuries for each species. Model predictions suggest that climate change will affect leaf phenology in almost all species studied, with an average advancement during the 21st century of 5.0 days in the A2 scenario and 9.2 days in the B2 scenario. Our model also suggests that lack of sufficient chilling temperatures to break bud dormancy will decrease the rate of advancement in leaf unfolding date during the 21st century for many species. Some temperate species may even have years with abnormal budburst due to insufficient chilling. Species fell into two groups based on their sensitivity to climate change: (1) species that consistently had a greater advance in their leaf unfolding date with increasing latitude and (2) species in which the advance in leaf unfolding differed from the center to the northern vs. southern margins of their range. At the interspecific level, we predicted that early-leafing species tended to show a greater advance in leaf unfolding date than late-leafing species; and that species with larger ranges tend to show stronger phenological changes. These predicted changes in phenology have significant implications for the frost susceptibility of species, their interspecific relationships, and their distributional shifts. |
| [44] | doi: |
| [45] | By Enli Wang and Thomas Engel; Simulation of phenological development of wheat crops |
| [46] | Croplands are man-made ecosystems that have high net primary productivity during the growing season of crops, thus impacting carbon and other exchanges with the atmosphere. These exchanges play a major role in nutrient cycling and climate change related issues. An accurate representation of crop phenology and physiology is important in land-atmosphere carbon models being used to predict these exchanges. To better estimate time-varying exchanges of carbon, water, and energy of croplands using the Simple Biosphere (SiB) model, we developed crop-specific phenology models and coupled them to SiB. The coupled SiB-phenology model (SiBcrop) replaces remotely-sensed NDVI information, on which SiB originally relied for deriving Leaf Area Index (LA!) and the fraction of Photosynthetically Active Radiation (fPAR) for estimating carbon dynamics. The use of the new phenology scheme within SiB substantially improved the prediction of LA! and carbon fluxes for maize, soybean, and wheat crops, as compared with the observed data at several AmeriFlux eddy covariance flux tower sites in the US mid continent region. SiBcrop better predicted the onset and end of the growing season, harvest, interannual variability associated with crop rotation, day time carbon uptake (especially for maize) and day to day variability in carbon exchange. Biomass predicted by SiBcrop had good agreement with the observed biomass at field sites. In the future, we will predict fine resolution regional scale carbon and other exchanges by coupling SiBcrop with RAMS (the Regional Atmospheric Modeling System). |
| [47] | The North China Plain is one of the most important crop production regions in China. However, water resources in the area are limited. Accurate modeling of water consumption and crop production in response to the changing environment is important. To better describe the two-way interactions among climate, irrigation, and crop growth, the crop phenology and physiology scheme of the SiBcrop model was coupled with the Simple Biosphere model version 2 (SiB2) for simulating crop phenology, as well as the crop production and evapotranspiration of winter wheat and summer maize, two of the main crops in the region. In the coupled model, the Leaf Area Index (LAI) produced by the crop phenology and physiology scheme was used in estimating the sub-hourly energy and carbon fluxes. Observations obtained from two typical eddy covariance sites located in this region were used to validate the model. The coupled model was able to simulate carbon and energy fluxes, soil water content, biomass carbon, and crop yield with high accuracy, especially for the latent heat flux and carbon flux. The LAI was also well-simulated by the model. Therefore, the coupled model is capable of assessing the responses of water resources and crop production to the changes of future climate and irrigation schedules. |
| [48] | Land-Surface Models (LSMs) are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land-surface–atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the LSM JULES with the crop growth model InfoCrop. JULES in its current version (v3.4) does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parametrized it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges River basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm month, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 5.4–11.6 mm month. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm month, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 2.2–3.4 mm month. The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops–atmosphere interactions. |
| [49] | Statistical models using historical data on crop yields and weather to calibrate relatively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale (e.g. global scale, national scale, provincial scale and county scale). Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally. . Statistical models using historical data on crop yields and weather to calibrate relatively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale (e.g. global scale, national scale, provincial scale and county scale). Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally. |
| [50] | Agriculture is still accounted for in a very simplistic way in the land-surface models which are coupled to climate models, while the area it occupies will significantly increase in the next century according to future scenarios. In order to improve the representation of croplands in a Dynamic Global Vegetation Model named ORCHIDEE (which can be coupled to the IPSL1 climate model), we have (1) developed a procedure which assimilates some of the variables simulated by a detailed crop model, STICS, and (2) modified some parameterisations to avoid inconsistencies between assimilated and computed variables in ORCHIDEE. Site simulations show that the seasonality of the cropland-atmosphere fluxes of water, energy and CO2 is strongly modified when more realistic crop parameterisations are introduced in ORCHIDEE. A more realistic representation of wheat and corn croplands over Western Europe leads to a drying out of the atmosphere at the end of summer and during autumn, while the soils remain wetter, specially at the time when winter crops are sowed. The seasonality of net CO2 uptake fluxes is also enhanced and shortened. |
| [51] | In this study, the Crop Estimation through Resource and Environment Synthesis model (CERES3.0) was coupled into the Biosphere-Atmosphere Transfer Scheme (BATS), which is called BATS CERES, to represent interactions between the land surface and crop growth processes. The effects of crop growth and development on land surface processes were then studied based on numerical simulations using the land surface models. Six sensitivity experiments by BATS show that the land surface fluxes underwent substantial changes when the leaf area index was changed from 0 to 6 m<sup>2</sup> m <sup>−2</sup>. Numerical experiments for Yucheng and Taoyuan stations reveal that the coupled model could capture not only the responses of crop growth and development to environmental conditions, but also the feedbacks to land surface processes. For quantitative evaluation of the effects of crop growth and development on surface fluxes in China, two numerical experiments were conducted over continental China: one by BATS CERES and one by the original BATS. Comparison of the two runs shows decreases of leaf area index and fractional vegetation cover when incorporating dynamic crops in land surface simulation, which lead to less canopy interception, vegetation transpiration, total evapotranspiration, top soil moisture, and more soil evaporation, surface runoff, and root zone soil moisture. These changes are accompanied by decreasing latent heat flux and increasing sensible heat flux in the cropland region. In addition, the comparison between the simulations and observations proved that incorporating the crop growth and development process into the land surface model could reduce the systematic biases of the simulated leaf area index and top soil moisture, hence improve the simulation of land surface fluxes. |
| [52] | 以位山试验站典型农田为对象,利用位山站2005-10-10～2006-06-10日的实验观测数据,探讨了冬小麦整个生长期农田的热、碳通量特征,并运用SiB2(simple biosphere model Version2)模型对热、碳通量进行了模拟分析,结果表明,农田的热、碳通量在冬小麦生长过程中表现出明显的日变化,这些通量的最大值基本出现在正午前后;热、碳通量的日际变化也较明显,其中净辐射与潜热通量在冬小麦不同生长期表现为:越冬期拔节抽穗期灌浆成熟期;感热通量表现为:拔节抽穗期灌浆成熟期越冬期;而CO2通量为:越冬期灌浆成熟期拔节抽穗期.对以上通量及地表温度的模拟表明,SiB2模型能较好地模拟冬小麦生长期中农田热、碳通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值的一致性较好,线性相关系数R2分别达0.985、0.637、0.481、0.725、0.499与0.877,其中感热通量与CO2通量模拟偏差较大.另外,按冬小麦生长期分阶段对农田以上分量模拟结果表明,SiB2模型在冬小麦拔节抽穗期模拟效果最好,并发现模型对叶面积指数敏感. . 以位山试验站典型农田为对象,利用位山站2005-10-10～2006-06-10日的实验观测数据,探讨了冬小麦整个生长期农田的热、碳通量特征,并运用SiB2(simple biosphere model Version2)模型对热、碳通量进行了模拟分析,结果表明,农田的热、碳通量在冬小麦生长过程中表现出明显的日变化,这些通量的最大值基本出现在正午前后;热、碳通量的日际变化也较明显,其中净辐射与潜热通量在冬小麦不同生长期表现为:越冬期拔节抽穗期灌浆成熟期;感热通量表现为:拔节抽穗期灌浆成熟期越冬期;而CO2通量为:越冬期灌浆成熟期拔节抽穗期.对以上通量及地表温度的模拟表明,SiB2模型能较好地模拟冬小麦生长期中农田热、碳通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值的一致性较好,线性相关系数R2分别达0.985、0.637、0.481、0.725、0.499与0.877,其中感热通量与CO2通量模拟偏差较大.另外,按冬小麦生长期分阶段对农田以上分量模拟结果表明,SiB2模型在冬小麦拔节抽穗期模拟效果最好,并发现模型对叶面积指数敏感. |
| [53] | |
| [54] | Land surface models are useful tools for estimating the contribution and response to climate change of C dynamics in various terrestrial ecosystems. In many land surface models, plant phenological algorithms are incorporated based on field studies in forests. However, to simulate adequately the C cycle over a large area, there is a need to include and validate algorithms for other ecosystems. The Carbon and Nitrogen-coupled Canadian Land Surface Scheme (CN-CLASS) is a land surface model that has been applied successfully to the study of C stocks in forest ecosystems. The objective of this study is to incorporate a simple crop phenology algorithm into CN-CLASS and validate its ability to simulate C cycles at an agricultural site in southern Ontario, Canada. The model was validated on a corn crop (Zea mays L.) in 2005 and 2008 based on measurements of aboveground biomass and net ecosystem productivity (NEP), as well as a well-tested agricultural model, DayCENT (the daily time-step version of the CENTURY model). The modified CN-CLASS showed similar dynamics of biomass allocation compared with field measurements and DayCENT simulations. Regression analysis indicated that the modifications improved the NEP simulation for a corn field, with the coefficient of determination (R-2) relating simulated and observed NEP increasing from 0.51 in the original CN-CLASS to 0.78 in the modified model. Other crop species could be further validated to expand the model application to crop rotation studies and large areas covered by forests and crop fields in consideration of land management practices. |
| [55] | The Community Earth System Model, version 1 (CESM1) is evaluated with two coupled atmosphere-land simulations. The CTRL (control) simulation represents crops as unmanaged grasses, while CROP represents a crop managed simulation that includes special algorithms for midlatitude corn, soybean, and cereal phenology and carbon allocation. CROP has a more realistic leaf area index (LAI) for crops than CTRL. CROP reduces winter LAI and represents the spring planting and fall harvest explicitly. At the peak of the growing season, CROP simulates higher crop LAI. These changes generally reduce the latent heat flux but not around peak LAI (late spring/early summer). In midwestern North America, where corn, soybean, and cereal abundance is high, simulated peak summer precipitation declines and agrees better with observations, particularly when crops emerge late as is found from a late planting sensitivity simulation (LateP). Differences between the CROP and LateP simulations underscore the importance of simulating crop planting and harvest dates correctly. On the biogeochemistry side, the annual cycle of net ecosystem exchange (NEE) also improves in CROP relative to Ameriflux site observations. For a global perspective, the authors diagnose annual cycles of CO2 from the simulated NEE (CO2 is not prognostic in these simulations) and compare against representative GLOBALVIEW monitoring stations. The authors find an increased (thus also improved) amplitude of the annual cycle in CROP. These regional and global-scale refinements from improvements in the simulated plant phenology have promising implications for the development of the CESM and particularly for simulations with prognostic atmospheric CO2. |
| [56] | No abstract available. |
| [57] | The increasing demand for ecosystem services, in conjunction with climate change, is expected to significantly alter terrestrial ecosystems. In order to evaluate the sustainability of land and water resources, there is a need for a better understanding of the relationships between crop production, land surface characteristics and the energy and water cycles. These relationships are analysed using the Joint UK Land Environment Simulator (JULES). JULES includes the full hydrological cycle and vegetation effects on the energy, water, and carbon fluxes. However, this model currently only simulates land surface processes in natural ecosystems. An adapted version of JULES for agricultural ecosystems, called JULES-SUCROS has therefore been developed. In addition to overall model improvements, JULES-SUCROS includes a dynamic crop growth structure that fully fits within and builds upon the biogeochemical modelling framework for natural vegetation. Specific agro-ecosystem features such as the development of yield-bearing organs and the phenological cycle from sowing till harvest have been included in the model. This paper describes the structure of JULES-SUCROS and evaluates the fluxes simulated with this model against FLUXNEf measurements at 6 European sites. We show that JULES-SUCROS significantly improves the correlation between simulated and observed fluxes over cropland and captures well the spatial and temporal variability of the growth conditions in Europe. Simulations with JULES-SUCROS highlight the importance of vegetation structure and phenology, and the impact they have on land-atmosphere interactions. (C) 2010 Elsevier B.V. All rights reserved. |
| [58] | In order to better assess the role of agriculture within the global climate-vegetation system, we present a model of the managed planetary land surface, Lund-Potsdam-Jena managed Land (LPJmL), which simulates biophysical and biogeochemical processes as well as productivity and yield of the most important crops worldwide, using a concept of crop functional types (CFTs). Based on the LPJ-Dynamic Global Vegetation Model, LPJmL simulates the transient changes in carbon and water cycles due to land use, the specific phenology and seasonal CO2 fluxes of agricultural-dominated areas, and the production of crops and grazing land. It uses 13 CFTs (11 arable crops and two managed grass types), with specific parameterizations of phenology connected to leaf area development. Carbon is allocated daily towards four carbon pools, one being the yield-bearing storage organs. Management (irrigation, treatment of residues, intercropping) can be considered in order to capture their effect on productivity, on soil organic carbon and on carbon extracted from the ecosystem. For transient simulations for the 20th century, a global historical land use data set was developed, providing the annual cover fraction of the 13 CFTs, rain-fed and/or irrigated, within 0.5 degrees grid cells for the period 1901-2000, using published data on land use, crop distributions and irrigated areas. Several key results are compared with observations. The simulated spatial distribution of sowing dates for temperate cereals is comparable with the reported crop calendars. The simulated seasonal canopy development agrees better with satellite observations when actual cropland distribution is taken into account. Simulated yields for temperate cereals and maize compare well with FAO statistics. Monthly carbon fluxes measured at three agricultural sites also compare well with simulations. Global simulations indicate a similar to 24% (respectively similar to 10%) reduction in global vegetation (respectively soil) carb |
| [59] | Using a six year data set of eddy covariance flux measurements of sensible and latent heat, soil heat flux, net radiation, above-ground phytomass and meteorological driving forces energy partitioning was investigated at a temperate mountain grassland managed as a hay meadow in the Stubai Valley (Austria). The main findings of the study were: (i) Energy partitioning was dominated by latent heat, followed by sensible heat and the soil heat flux; (ii) When compared to standard environmental forcings, the amount of green plant matter, which due to three cuts varied considerably during the vegetation period, explained similar, and partially larger, fractions of the variability in energy partitioning; (iii) There were little, if any, indications of water stress effects on energy partitioning, despite reductions in soil water availability in combination with high evaporative demand, e.g. during the summer drought of 2003. |
| [60] | In the light of daunting global sustainability challenges such as climate change, biodiversity loss and food security, improving our understanding of the complex dynamics of the Earth system is crucial. However, large knowledge gaps related to the effects of land management persist, in particular those human-induced changes in terrestrial ecosystems that do not result in land-cover conversions. Here, we review the current state of knowledge of ten common land management activities for their biogeochemical and biophysical impacts, the level of process understanding and data availability. Our review shows that ca. one-tenth of the ice-free land surface is under intense human management, half under medium and one-fifth under extensive management. Based on our review, we cluster these ten management activities into three groups: (i) management activities for which data sets are available, and for which a good knowledge base exists (cropland harvest and irrigation); (ii) management activities for which sufficient knowledge on biogeochemical and biophysical effects exists but robust global data sets are lacking (forest harvest, tree species selection, grazing and mowing harvest, N fertilization); and (iii) land management practices with severe data gaps concomitant with an unsatisfactory level of process understanding (crop species selection, artificial wetland drainage, tillage and fire management and crop residue management, an element of crop harvest). Although we identify multiple impediments to progress, we conclude that the current status of process understanding and data availability is sufficient to advance with incorporating management in, for example, Earth system or dynamic vegetation models in order to provide a systematic assessment of their role in the Earth system. This review contributes to a strategic prioritization of research efforts across multiple disciplines, including land system research, ecological research and Earth system modelling. |
| [61] | Knowledge on the changes in yield potential and associated physiological traits is essential for understanding the main yield-limiting factors and guiding future breeding strategies. Our objective was to identify physiological traits associated with genetic gains in grain yield of winter wheat (Triticum aestivum L.) in Shandong province, China. Thirteen milestone cultivars and two advanced lines released from 1969 to 2006 were examined over 3 yr at Tai'an during 2006 to 2009. The genetic gain in grain yield was 62 kg ha(-1) yr(-1), largely associated with increased kernels per square meter, biomass, and harvest index (HI) and reduced plant height. Significant genetic changes were also observed especially for apparent leaf area index (LAI) at heading and anthesis, chlorophyll content (Chl) at anthesis, photosynthesis rate during grain filling, and stem water-soluble carbohydrate (WSC) content at anthesis. Comparing genotypes having Rht-D1b and others with both Rht-D1b and Rht8c (Rht-D1b+Rht8c) showed increased grain yield, thousand kernel weight, kernels per spike, kernel weight per spike, HI, canopy temperature depression, and Chl at anthesis and LAI at heading with the latter but no difference in height. The results suggested that genetic gains in grain yield in Shandong province were mainly contributed by increases in kernels per square meter and biomass, which were achieved through improving crop photosynthesis at and after heading, and the source for grain filling may have benefited from increased WSC in stems at anthesis. |
| [62] | Abstract Crop biomass production is a function of the efficiencies with which sunlight can be intercepted by the canopy and then converted into biomass. Conversion efficiency has been identified as a target for improvement to enhance crop biomass and yield. Greater conversion efficiency in modern soybean [ Glycine max (L.) Merr.] cultivars was documented in recent field trials, and this study explored the physiological basis for this observation. In replicated field trials conducted over three successive years, diurnal leaf gas exchange and photosynthetic CO2 response curves were measured in 24 soybean cultivars with year of release dates (YOR) from 1923 to 2007. Maximum photosynthetic capacity, mesophyll conductance and nighttime respiration have not changed consistently with cultivar release date. However, daily carbon gain was periodically greater in more recently released cultivars compared with older cultivars. Our analysis suggests that this difference in daily carbon gain primarily occurred when stomatal conductance and soil water content were high. There was also evidence for greater chlorophyll content and greater sink capacity late in the growing season in more recently released soybean varieties. Better understanding of the mechanisms that have improved conversion efficiency in the past may help identify new, promising targets for the future. |
| [63] | Wheat (Triticum aestivum L.) cultivars with high canopy temperature depression (CTD) tend to have higher grain yield under dry, hot conditions. Therefore, CTD has been used as a selection criterion to improve adaptation to drought and heat. The CTD is a result of the leaf's energy balance, which includes terms determined by environment and physiological traits. We hypothesized that one or more of several physiological traits contributed to consistent CTD differences among three closely-related winter wheat lines grown under dryland conditions. For three years we measured several leaf traits, including CTD, leaf dimension, gas exchange rates, and carbon-13 isotope discrimination (). Soil water content was also monitored. Data showed that daytime CTD was related to the leaf size in these wheat lines. The most drought-tolerant line, TX86A8072, had consistently smaller and narrower leaves than TX86A5606, the least drought tolerant. For TX86A8072, dryland and irrigated average noon CTD was -0.8掳C, and average flag leaf area (LA) 11 cm2, for TX86A5606, values were -1.7掳C and 12.5 cm2, respectively. However, TX86A8072 also had higher CTD (i.e., lower temperatures) than TX86A5606 at night, despite a theoretically greater sensible heat transfer coefficient, suggesting greater nighttime transpiration (T). Implications of these traits on nighttime leaf energy balance and possible adaptive roles of nighttime T are discussed. |
| [64] | Abstract Our objective was to investigate the physiological basis of genetic progress in grain yield in CIMMYT spring wheat (Triticum aestivum L.) cultivars developed from 1966 to 2009 in irrigated, high-potential conditions. Field experiments were conducted during three growing seasons in northwest Mexico (2008–2009, 2009–2010, and 2010–2011) examining 12 historic CIMMYT semidwarf spring wheat cultivars released from 1966 to 2009. The linear rate of genetic gain in grain yield was 30 kg ha611 yr611 (0.59% yr611; R2 = 0.58, P = 0.01). Grain yield progress was associated with increased aboveground dry matter (AGDM) at harvest (R2 = 0.80, P < 0.001) and heavier grain weight (R2 = 0.46, P < 0.05). There was a positive linear association between AGDM and plant height (R2 = 0.43, P < 0.05) and between grain weight and the date of complete canopy senescence (CCS) among the 12 cultivars (R2 = 0.36, P < 0.05). There was no change in grains per square meter or harvest index (HI) with year of release (YoR). Grain weight was positively associated with potential grain weight (PGW), and PGW, in turn, was positively associated with rachis length per spikelet among the cultivars. Overall spike dry matter (DM) per square meter at anthesis (GS61) +7 d did not change with YoR. There was a trend for a linear increase in AGDM of fertile shoots (expressed as g m612) at GS61 +7 d with YoR, but this was counteracted by spike partitioning decreasing overall during the 43-yr period from 0.25 to 0.23. There was a linear increase in preanthesis flag-leaf stomatal conductance with YoR (P < 0.05). There was no change in grain growth response to a degraining treatment imposed at GS61 +14 d (mean grain weight response +5.5%) indicating that the degree of source limitation to grain growth appeared to be small and unchanged in the older and modern cultivars. Generally, these results indicated that the rate of genetic progress in CIMMYT spring wheat has slowed but has not plateaued in recent decades, while genetic gains were associated with increase in both potential and final grain weight. |
| [65] | Abstract A field study was conducted at CCS Haryana Agricultural University, Hisar, during two consecutive rabi seasons of 2002-03 and 2003-04 on wheat genotypes. The main plots treatment consisted of three irrigation schedules viz., normal irrigation (Control), two irrigations at 45 and 85 DAS (limited irrigation) and no post sowing irrigation (rainfed) and in sub-plots five genotypes were grown namely WH 896, WH 912, WHD 935, WHD 936, PDW 233, Raj 1555. The restricted irrigation decreased the leaf water potential (LWP), canopy temperature depression (CTD), transpiration rate, stomatal conductance and photosynthesis significantly over irrigated control, while, significant increase was observed in plant water retention. Reduction in grain yield under rainfed condition was 23.4 per cent. Reduced irrigation application decreased the yield attributes with maximum reduction in number of grains per spike. Genotype PDW 233 yielded significantly higher than all other tested genotypes. It maintained higher plant water status and higher rate of photosynthesis than other genotypes. |
| [66] | Accurate prediction of phenological development in maize (Zea mays L.) is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were (i) to evaluate the precision of eight thermal functions, (ii) to assess the effects of source data on the ability to differentiate among thermal functions, and (iii) to attribute the precision of thermal functions to their response across various temperature ranges. Data sets used in this study represent >1000 distinct maize hybrids, >50 geographic locations, and multiple planting dates and years. Thermal functions and calendar days were evaluated and grouped based on their temperature response and derivation as empirical linear, empirical nonlinear, and process-based functions. Precision in predicting phase durations from planting to anthesis or silking and from silking to physiological maturity was evaluated. Large data sets enabled increased differentiation of thermal functions, even when smaller data sets contained orthogonal, multi-location and -year data. At the highest level of differentiation, precision of thermal functions was in the order calendar days < empirical linear < process based < empirical nonlinear. Precision was associated with relatively low temperature sensitivity across the 10 to 26 degrees C range. In contrast to other thermal functions, process-based functions were derived using supra-optimal temperatures, and consequently, they may better represent the developmental response of maize to supra-optimal temperatures. Supra-optimal temperatures could be more prevalent under future climate-change scenarios, but data sets in this study contained few data in that range. |
| [67] | |
| [68] | Abstract In cool-temperate climates, potential maize grain yields are variable and often small. Low temperature prolongs growth duration, reduces crop growth rate, and increases the risk of frost terminating grain filling prematurely. The objectives of this study were (1) to assess the performance of a radiation- and temperature-driven maize simulation model in a cool-temperate climate and (2) to modify the model to allow the effects of temperature and solar radiation on growth and yield to be simulated in both warm and cool climates.Modifications to the model to improve simulation in the cool climate included a changed phenology response to low temperature, a reduction in radiation-use efficiency and rate of harvest index increase at low temperature, and an increased time lag between silking and the start of grain growth at low temperature. The modified model gave good agreement between observed independent datasets and simulated values of grain and total biomass yield in tropical, subtropical and cool-temperate locations; root mean square deviations of the comparisons averaged across all locations were about 12% of the mean values. Thus the utility of the model has been enhanced for a wider range of climates. The study also showed that the conclusion from previous analyses with the model in warm climates that the highest potential maize yields occur at locations with a combination of high incident radiation, low temperature and long growth duration may not be valid if mean temperature during growth is less than ca. 18掳C. However, this condition would only occur in cool-temperate climates. |
| [69] | 61 Rates of tissue expansion, cell division and progression in the plant cycle are driven by temperature, following common Arrhenius-type response curves. 61 We analysed the genetic variability of this response in the range 6-37°C in seven to nine lines of maize (Zea mays), rice (Oryza spp.) and wheat (Triticum aestivum) and in 18 species (17 crop species, different genotypes) via the meta-analysis of 72 literature references. 61 Lines with tropical or north-temperate origins had common response curves over the whole range of temperature. Conversely, appreciable differences in response curves, including optimum temperatures, were observed between species growing in temperate and tropical areas. 61 Therefore, centuries of crop breeding have not impacted on the response of development to short-term changes in temperature, whereas evolution over millions of years has. This slow evolution may be a result of the need for a synchronous shift in the temperature response of all developmental processes, otherwise plants will not be viable. Other possibilities are discussed. This result has important consequences for the breeding and modelling of temperature effects associated with global changes. |
| [70] | Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking. |
| [71] | |
| [72] | This study investigates the impact of increased vegetation greening on the springtime temperature over east Asia for 1982-2000. An analysis of station-based temperature records and satellite-measure normalized difference vegetation index (NDVI) indicates that slight warming (<0.4掳C 10-yr) occurred over regions that experienced large increase in NDVI (>=0.08 10-yr). On the contrary, strong warming (>=0.8掳C 10-yr) occurred over regions that exhibited minor changes in NDVI (<0.04 10-yr). For the most part, this inverse NDVI-temperature relationship observed with the daily maximum temperature. Thus, it is suggested that the decrease in warming was mostly attributable to the increase in evapotranspiration associated with increased vegetation greening. Earlier vegetation growth may have further strengthened the effect of this vegetation-evaporation on spring temperature. |
| [73] | This study, through the inclusion of a simpleparameterization of the phenologicaldevelopment of spring wheat in evapotranspirationsimulations for 1988鈥2000, at a representativearid grassland and a representative transitionalgrassland site, delineated the inter-annualvariability of the seasonal moisture flux from theCanadian Prairie agro-ecosystem. Theagro-ecosystem's contribution to atmospheric boundary-layermoisture, at these representative sites, wasrelated to the seasonal pattern of tornado days in thegrassland eco-climatic zone for the averageyear, for a warmer/drier year and for a cooler/wetteryear. The following conclusions were drawn:(1) The moisture flux from the Prairie agro-ecosystemdisplays considerable inter-annualvariability due, in the main, to the rate andtiming of crop phenological development andassociated biophysical parameters, and (2) themoisture flux from the Prairie agro-ecosystemtranslates directly into changes in atmosphericboundary-layer moisture, which subsequentlyaffects the magnitude of the potential energyavailable for deep convection and the seasonalpattern of tornado days. For expansive agriculturalareas, representing the inter-annual variabilityof crop phenological development in land surfacemodels is critical to the successful simulationof the surface moisture flux, and thus thethermodynamic properties of the atmospheric boundarylayer. Therefore, it is of particularimportance to Prairie climate and climate change modelling. |
| [74] | A programme of field experiments at the Pontbren catchment in Wales has, since autumn 2004, been examining the effects of land use change on flooding. The Pontbren catchment possesses a long history of artificial drainage of its clay soils and intensification of sheep farming. Increased flood runoff has been noted within the last decades, as has the mitigating effect of trees at field scale. To examine the local and catchment-scale effects of land management within the catchment, including the potential advantages of planting additional trees, a multidimensional physically based model has been developed and conditioned on data from an intensely instrumented hillslope. The model is used to examine the effects of planting a small strip of trees within a hillslope. Results demonstrate that careful placement of such interventions can reduce magnitudes of flood peaks by 40% at the field scale. The challenges associated with upscaling these results to the Pontbren and Upper Severn catchments are discussed. |
| [75] | Soil moisture and phenology are seasonally varying modes of the land system. Due to their seasonal persistence, they have the ability to predictably influence seasonal weather. Hence, their use in seasonal forecasts can potentially improve the skill of the forecasts. However a complete measure of their influence in geographical locations and in different seasons is not known. As a result, modern seasonal forecasting techniques have not been able to fully exploit their persistence in improving skill of seasonal forecasts. By measuring similarity between model ensemble members that are forced by soil moisture and phenology respectively, in this study, we identify global hot spots where soil moisture and phenology impact key atmospheric variables in spring and summer seasons. Results indicate that over South East Asia (SEA) and the Sahel the phenology and soil moisture impact precipitation to an equal extent. Results show that 5-7 % of the variance in Indian summer monsoon precipitation is caused by soil moisture and phenology anomalies. Prior to the monsoon they influence predictors of the SEA monsoon. Hence, their persistence can be used to improve skill of seasonal forecasts, particularly of mesoscale systems like the SEA monsoon. |
| [76] |
