基于遥感和作物生长模型的作物产量差估测

传统的作物生长模型很难模拟大田的实际产量,因为大量的数据、复杂的数学运算以及误差传递限制了作物生长模拟模型的运用。目前为止实际产量仅能通过观测和实地调查获得。该文将NOAA-14 AVHRR遥感获取的冠层温度信息引入作物生长模型,利用冠气温差计算作物水分胁迫系数,可以近似地估计区域作物实际生长速率和产量,进而建立了遥感-作物模拟复合模型PS-X,提出了估算区域作物实际产量的方法。PS-X模型可在不同层次模拟作物的生长和产量,在PS-1、PS-2、PS-X水平计算的分别是作物的光温生产潜力、水分限制下的生产力和实际产量。利用该模型,论文分别模拟了邯郸地区1998年夏玉米的光温生产潜力、水分限制下的生产力和实际产量,并通过比较不同模拟水平下产量和农户调查产量进行区域产量差分析。结果表明：PS-1和PS-2水平之间的产量差主要由水分和土壤质地差异造成;PS-2与PS-X水平间的平均产量差异较大,占总产量差（PS-1与PS-X水平之差）的81.4%,主要由田间管理差异造成;对于平原地区,夏玉米产量估测精度可达90%以上;砂质土壤区估算冠层温度和水分胁迫系数比壤质、粘质土壤区要高,因此砂质土壤区模拟作物产量较低,这与PS-2计算结果、农户调查数据一致。研究证实,区域上应用遥感瞬时温度信息建立遥感-作物模拟复合模型进行估产是可行的。     

作物产量潜力极限研究

应用模型模拟方法研究近5 0年来主要作物产量演变趋势结果表明,作物长期进化过程中产量演变遵循S曲线增长规律,目前主要作物产量处于S曲线驻点(最快增长值点)左右,种植效益已开始下降,进一步提高产量日益困难,作物产量将不断接近其极限,多数作物未来产量潜力极限约为目前产量的2～3倍。提高作物产量潜力途径主要包括加强农田水利建设、提高作物育种手段、强化作物栽培措施、优化投入和改善作物生长的大环境等     

CO2倍增时江苏省主要粮食作物产量及生育期的变化

分析和评价全球气候变暖对粮食生产的影响,目前国内研究手段尚显不足.本文拟采用国外先进的大气环流模型和作物模型,并结合江苏的逐日历史气候资料和作物、土壤资料,研究其CO_2倍增时主要粮食作物产量和生育期的变化,从而为制定防范措施和今后的农业发展战略提供科学依据.     

作物对水分胁迫的反应

阐述了作物水分胁迫的生理生化机制及水分胁迫对作物营养、生育及产量的影响,讨论了水分胁迫反应机制在节水农业和可持续农业发展研究中应用的可能性。     

通过转基因手段改善作物产量性状

在世界范围内,目前的作物转基因研究主要针对于提高作物自身的抗逆性开展,而产量是一个较为复杂的农艺性状,受多种基因与环境的相互作用所控制.其中,光合作用、氮素同化、碳源分配、植株株型等生理过程是产量形成的基础.随着植物基因组测序工程的逐渐完善,控制上述生理过程的许多基因已被成功分离,并通过基因工程手段对其进行遗传修饰,最终获得了产量性状得以明显改善的转基因植物.虽然目前对于基因如何调控每个性状的机制尚未得到充分阐明,但是这些基因的获得及其增产功能的验证为作物的高产育种提供了一条全新的思路和一系列新方法.随着国家转基因生物新品种培育科技重大专项的实施,对功能基因的认识显得尤为重要,本文主要从影响作物产量形成的源、流、库、株型、氮素及水分利用率等几方面入手,对近10年来控制植物产量性状基因的相关研究进行了综述,期望为高产转基因作物新品种的培育提供一定的理论依据.     

作物水分生产函数研究进展

作物水分生产函数(cropwaterproductionfunctions,CWPF)一般指作物产量(cropyield,Y)与蒸散发(evapotranspiration, ET)之间的函数关系,是作物模型中联系水分和生产力的关键。本文系统地梳理了近半个世纪以来CWPF的相关研究,发现CWPF受多种因素影响,不同地区获得的田间试验结果往往差异较大;常用的CWPF模型多是基于统计信息,缺少坚实的物理基础和可靠的理论支撑,在跨地区、跨物种应用时存在一定缺点。同时基于碳同化过程的机制模型和更为复杂的作物模型也因为参数过多而不易在实际中应用。在以往研究的基础上,从公开发表的41篇文献中筛选出592组田间试验数据,发现小麦产量与ET基本呈线性关系,但数据分布相对离散,而玉米、棉花、水稻因数据量较少其产量与ET关系不明显。利用生长季降水量和累计蒸发皿蒸发数据对不同地区获得的小麦水分生产函数进行了修正,发现改进后的小麦水分生产函数表现出较好的跨地区应用潜力(r2从0.36提高到0.75),并提出了进一步的CWPF修正思路。指出通过改进函数关系虽然能提高统计模型的可移植性,但发展机制模型仍是未来CWPF研究的根本出路。     

作物产量农业气象统计预报基本模型研究

通过对作物产量农业气象统计预报基本模型的研究,综合考虑农业技术进步、气候变化对作物产量形成的直接效应和农业技术-气候相互作用对作物产量形成的间接效应,提出了一个包括农业技术趋势、气候变化和农业技术-气候相互作用三项的作物产量农业气象统计预报模型,并以江苏省苏南地区单季晚稻预报实例对模式作了检验.     

铁肥对黄淮海平原盐渍土主要农作物的增产效应

在缺铁的黄淮海平原浅层咸水型盐渍化土壤上施用铁肥对各种农作物都有显著的增产作用。自１９８７－１９９０年的该类土壤上进行的铁肥增产效应试验结果表明，农作物产量与根外喷施的铁肥浓度间存在有非线性回归。喷施硫酸亚铁的最适浓度为０．８％－１．０％。喷施适宜浓度硫酸亚铁，不仅提高了作物产量而且对作物产品品质也有良好的作用。     

作物对水分胁迫的反应

阐述了作物水分胁迫的生理生化机制及水分胁迫对作物营养、生育及产量的影响,讨论了水分胁迫反应机制在节水农业和可持续农业发展研究中应用的可能性。     

基于作物生长模型和遥感数据同化的区域玉米产量估算

为了将遥感观测到的玉米生长期间作物冠层方向反射波谱的时间序列变化信息用于区域玉米产量估算,该文将时间序列中分辨率成像光谱仪(moderate resolution imaging spectroradiometer,MODIS)数据和高空间分辨率LandsatTM遥感观测数据相结合,以叶面积指数(LAI)作为耦合作物生长模型(crop environment resource synthesis-Maize,CERES-Maize)和植被冠层反射率模型(scattering by arbitrarily inclined leaves,SAIL)的关键参数,提出了将耦合模型与时间序列遥感观测数据同化进行区域玉米产量估算的方案。该文选择吉林省榆树市为研究区,采用MODIS和LandsatTM2种尺度数据集,利用SCE-UA(shuffled complex evolution method developed at the University of Arizona)算法分别进行玉米产量同化估产研究,得到玉米单产空间分布的估计结果,结合遥感估算的种植面积求算榆树市玉米总产量。结果表明,与玉米统计总产量相比,2007、2008和2009年遥感数据同化估算的总产量误差分别为9.15%、14.99%和8.97%;与仅利用CERES-Maize模型模拟得到的产量误差相比,3a间遥感估算总产量的误差分别减小了7.49%、1.21%和5.23%,且采用MODIS和TM遥感数据估算的玉米产量表现了其空间差异性。利用榆树市3a间玉米产量的明显差异,分析了时序遥感数据对作物长势和产量变化信息的表达能力,同年份内时序归一化差值植被指数越大,对应的玉米产量越高;年际间遥感观测反射率的差异通过数据同化方法能够反映年际间玉米产量差的变化。该文提出的玉米估产方案为将来进一步结合多源遥感数据、植被冠层反射率模型与作物生长模型进行区域玉米估产研究提供了参考。     

气候波动对海伦市粮食产量影响的风险分析

粮食生产具有不确定性特征,气候波动是影响粮食产量年际变化的主要因素之一。本研究采用风险分析方法,基于海伦市22个乡镇1978～2007年大豆和玉米产量的统计资料,以相对气象产量作为农作物受气候波动影响程度的指标,采用非参数信息扩散方法中正态扩散模型,对各乡镇大豆和玉米产量不同减产程度出现的概率进行了计算,进而获得各乡镇大豆、玉米单产受气候波动影响的风险度。在此基础上,基于海伦市气象站逐日降雨量和气温资料,按照气候干燥度将所有年份分为偏干旱年份、正常年份和偏湿润年份,分别计算不同水热耦合年份下各乡镇粮食产量受气候波动影响的风险程度。结果表明,大豆和玉米极端减产年出现的概率高于极端高产年;北部大豆减产风险小于南部,玉米在总体上呈现出和大豆减产概率空间分布相反的趋势;在不同水热耦合年份中,海伦市旱田农作物生产风险具有空间差异性,其中大豆生产风险空间分布与河网相关性较玉米高。     

施氮和灌溉管理下作物产量和土壤生化性质

氮和水对作物生长非常重要,研究施氮和灌溉管理下作物产量、土壤性质以及它们的关系对我国农业科学有重要意义。本文以中国科学院封丘农业生态试验站为平台,研究了施氮(每季施氮150 kg·hm?2、190 kg·hm?2、230 kg·hm?2、270 kg·hm?2,以不施氮为对照)和灌溉(灌溉量达到0~20 cm、0~40 cm、0~60 cm土壤的田间持水量,以雨养为对照)管理下小麦-玉米轮作系统作物产量和土壤生化性质,以及它们之间的关系。研究结果表明,150~270 kg·hm?2施氮量对2008年、2009年玉米产量和2009年、2010年小麦产量无显著影响;灌溉对2010年玉米产量无显著影响,而2008年、2009年玉米产量随灌溉量增大而增加。尽管2008—2011年小麦产量随灌溉量变化趋势不一致,但与雨养相比灌溉提升了小麦产量。施氮在不同程度上提升了土壤全氮和速效氮含量、脱氢酶和脲酶活性、微生物生物量碳、基本呼吸和硝化势,稍微降低了土壤pH并大幅降低了速效磷含量(降幅48.7%~51.6%);灌溉提升了土壤全氮含量和脱氢酶活性,降低了全钾含量、脲酶活性、基本呼吸、硝化势。多元回归分析显示,某些土壤生化性质(全氮、溶解性有机碳、速效磷含量及微生物生物量碳氮、呼吸熵、硝化势)与2009年、2010年玉米产量很好地线性拟合。综上,土壤生化性质因施氮和灌溉发生不同程度的分异,因施氮和灌溉而分异的土壤生化性质能部分地鉴定作物产量。本研究方法可为产量主导因子的筛选及产量估算模型的建立提供一定参考依据。     

Stability analysis for grain yield of winter wheat in a long-term field experiment

The current study is based on a long-term field experiment that was conducted at the Rauischholzhausen field station of the University of Giessen (Germany). It includes six different crop rotation systems (CRSs), three mineral nitrogen (N) fertilization treatments and the varying annual weather conditions (AWCs) over 25 years (1993–2017). To ensure new insight into wheat cropping systems that have high yield stability, the dataset was assessed using different methods of stability analysis, including eco-valence, biplot and risk analysis. The results show that the factors which influence grain yield variation in winter wheat can be ranked in the following order: (1) N fertilization; (2) AWC; and (3) CRS. Compared to winter rye as the preceding cereal crop, field bean as the preceding legume crop had a clearly positive effect on the grain yield stability of winter wheat. Furthermore, the higher N fertilization level led to more stable grain yields of winter wheat for all investigated CRSs. Overall, in this study, crop rotation and N fertilization had a high impact on the yield stability of winter wheat. These are important factors to consider in agronomic management decisions under the increasingly difficult environmental conditions caused by climate change.     

Effects of tillage systems and rotations on crop production for a thin Black Chernozem in the Canadian Prairies

Soil degradation is the single most important threat to global food production and security. Wind and water erosion are the main forms of this degradation, and conservation tillage represents an effective method for controlling this problem. The objective of this study was to quantify the effects of three tillage methods [zero (ZT), minimum (MT) and conventional (CT)] and three four-year crop sequences [spring wheat (Triticum aestivum L.)–spring wheat–winter wheat–fallow; spring wheat–spring wheat–flax (Linum usitatissimum L.)–winter wheat; spring wheat–flax–winter wheat–field pea (Pisum sativum L.] on crop establishment, plant height, seed weight, soil water storage, crop water use, crop water use efficiency and grain yield over a 12-year period under Canadian growing conditions. Plant establishment was not adversely affected by tillage systems or crop sequences except for flax, where a small reduction was observed with ZT and MT. Conservation tillage showed a yield benefit over CT of 7%, 12.5% and 7.4% for field pea, flax and spring wheat grown on cereal stubble, respectively over the 12 years of the study. Much of the yield increase was due to an increase in soil water in the 0–30 cm soil layer with ZT and MT. However, tillage systems had no effect on grain yield for spring wheat grown on fallow and field pea stubble due to a lack of differences in spring soil water content. Flax grown in sequence with cereals only yielded higher than when it was grown in the sequence which included field pea, even though flax was seeded on spring wheat stubble in both cases. Winter wheat yielded higher when grown on flax stubble than on spring wheat stubble. The results indicate that a one-year non-cereal break crop was enough to alleviate the negative effects of consecutive cereal crops on winter wheat. Spring wheat grown on field pea stubble always yielded more than when grown on cereal stubble. A 10% increase in water use efficiency was observed with flax grown with ZT and MT management. Crop sequence improved water use efficiency in flax and spring wheat. Growing spring wheat on field pea stubble as opposed to growing it on cereal stubble resulted in a 10% increase in water use efficiency. Overall, rainfall accounted for 73%, 72%, 67% and 65% of total water used by field pea, flax, winter wheat and spring wheat, respectively. This explains the large year effect as a result of variation in growing (May–August) season precipitation. The non-significant tillage system by year interaction implies that the positive benefits of ZT and MT occur over a wide range of growing conditions, while the absence of a tillage system by crop sequence interaction suggests that knowledge developed under CT management also applies to ZT and MT. The results of this study support the large shifts towards in conservation tillage being observed in the Canadian prairies.     

气候变化对我国主要粮食作物产量的影响及适应措施

过去几十年气候变化对我国主要粮食作物产量产生了重要影响,为了研究作物产量对气候变化的响应和适应,保障粮食安全,基于国内相关研究文献,分析归纳了研究方法,综述了国内小麦、玉米和水稻等主要粮食作物产量对气候变化的响应和适应,得出如下结论:（1）作物产量对气候变化响应的研究方法主要包括田间试验观测、统计分析和作物模型模拟等方法,其中田间观测法最直观,统计分析法可操作性强、应用最为普遍,作物模型模拟机理性强,可以定量描述气候因子对作物产量的影响,外推效果好;（2）近几十年来,小麦生育期内气温升高和辐射变化使我国北方小麦增产0.9%~12.9%,南方小麦减产1.2%~10.2%;气候变暖对玉米产量贡献率为-41.4%~0.4%;水稻生育期内气温升高和辐射增强有利于东北地区水稻产量增加,增产贡献率为1.01%~3.29%,而辐射减弱对长江流域等南方主要水稻种植区的水稻产量（长江流域晚熟稻除外）产生不利影响;（3）未来气候变化情境下小麦应从延长生殖生长期、增加籽粒数量和提高收获指数等方面培育新品种应对气候变暖对作物产量的不利影响;耐高温和长生殖生长期的玉米品种可以用来应对气温、降水等气候因子的变化;水稻则应选育耐高温品种应对气温和辐射等因子的变化所带来的作物生产上的风险。     

作物单产估算模型研究进展与展望

作物单产估算是服务现代农业的一项重要内容,也是农业监测的难点之一,及时准确的产量模拟对国家农业决策、农田生产管理、粮食仓储安全等都有重要意义。利用模型对作物生长发育和产量形成过程进行动态模拟是当前产量估算的主流方式。本文通过对比当前主流模型构建的理论基础,将估产模型分为经验统计模型、作物生长模型、光能利用率模型和耦合模型4种类型,并对比分析4种模型的优缺点,得到了各个模型的优势和不足。同时分别分析了遥感技术在4种估产模型中的应用,对模型中遥感数据的使用方法、限制因素、解决办法等进行了总结,并分析了遥感技术在作物估产模型方面使用的优势、不足和应用前景。分析了模型发展过程中存在的问题和限制因素,最后对模型的研究热点和发展趋势进行了展望,总结了遥感数据的使用方法、不同模型的耦合、现有模型的优选3个作物估产模型研究需要重点关注的方向。     

Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model

To assess the scope for enhancing productivity of soybean (Glycine max L. Merr.), the CROPGRO-Soybean model was calibrated and validated for the diverse soybean-growing environments of central and peninsular India. The validated model was used to estimate potential yields (water non-limiting and water limiting) and yield gaps of soybean for 21 locations representing major soybean regions of India. The average water non-limiting potential yield of soybean for the locations was 3020 kg ha⁻¹, while the water limiting potential was 2170 kg ha⁻¹ indicating a 28% reduction in yield due to adverse soil moisture conditions. As against this, the actual yields of locations averaged 1000 kg ha⁻¹, which was 2020 and 1170 kg ha⁻¹ less than the water non-limiting potential and water limiting potential yields, respectively. Across locations the water non-limiting potential yields were less variable than water limited potential and actual yields, and strongly correlated with solar radiation during the season (R² = 0.83, p ≤ 0.01). Both simulated water limiting potential yield (R² = 0.59, p ≤ 0.01) and actual yield (R² = 0.33, p ≤ 0.05) had significant but positive and curvilinear relationships with crop season rainfall across locations. The gap between water non-limiting and water limiting potential yields was very large at locations with low crop season rainfall and narrowed down at locations with increasing quantity of crop season rainfall. On the other hand, the gap between water limiting potential yield and actual farmers yield was narrow at locations with low crop season rainfall and increased considerably at locations with increasing amounts of rainfall. This yield gap, which reflects the actual yield gap in rainfed environment, is essentially due to non-adoption of improved crop management practices and could be reduced if proper interventions are made. The simulation study suggested that conservation of rainfall and drought resistant varieties in low rainfall regimes; and alleviation of water-logging and use of water-logging tolerant varieties in high rainfall regimes will be the essential components of improved technologies aimed at reducing the yield gaps of soybean. Harvesting of excess rainfall during the season and its subsequent use as supplemental irrigation would further help in increasing crop yields at most locations.     

Crop yield forecasting on the Canadian Prairies using MODIS NDVI data

Although Normalised Difference Vegetation Index (NDVI) data derived from the advanced very high resolution radiometer (AVHRR) sensor have been extensively used to assess crop condition and yield on the Canadian Prairies and elsewhere, NDVI data derived from the new moderate resolution imaging spectroradiometer (MODIS) sensor have so far not been used for crop yield prediction on the Canadian Prairies. Therefore, the objective of this study was to evaluate the possibility of using MODIS-NDVI to forecast crop yield on the Canadian Prairies and also to identify the best time for making a reliable crop yield forecast. Growing season (May-August) MODIS 10-day composite NDVI data for the years 2000-2006 were obtained from the Canada Centre for Remote Sensing (CCRS). Crop yield data (i.e., barley, canola, field peas and spring wheat) for each Census Agricultural Region (CAR) were obtained from Statistics Canada. Correlation and regression analyses were performed using 10-day composite NDVI and running average NDVI for 2, 3 and 4 dekads with the highest correlation coefficients (r) as the independent variables and crop grain yield as the dependent variable. To test the robustness and the ability of the generated regression models to forecast crops grain yield, one year at a time was removed and new regression models were developed, which were then used to predict the grain yield for the missing year. Results showed that MODIS-NDVI data can be used effectively to predict crop yield on the Canadian Prairies. Depending on the agro-climatic zone, the power function models developed for each crop accounted for 48 to 90%, 32 to 82%, 53 to 89% and 47 to 80% of the grain yield variability for barley, canola, field peas and spring wheat, respectively, with the best prediction in the semi-arid zone. Overall (54 out of 84), the % difference of the predicted from the actual grain yield was within ±10%. On the whole, RMSE values ranged from 150 to 654, 108 to 475, 204 to 677 and 104 to 714 kg ha⁻¹ for barley, canola, field peas and spring wheat, respectively. When expressed as percentages of actual yield, the RMSE values ranged from 8 to 25% for barley, 10 to 58% for canola, 10 to 38% for field peas and 6 to 34% for spring wheat. The MAE values followed a similar trend but were slightly lower than the RMSE values. For all the crops, the best time for making grain yield predictions was found to be from the third dekad of June through the third dekad of July in the sub-humid zone and from the first dekad of July through the first dekad of August in both the semi-arid and arid zones. This means that accurate crop grain yield forecasts using the developed regression models can be made one to two months before harvest.     

不同施肥制度对作物产量及土壤磷素肥力的影响

在潮棕壤上连续进行18年的定位试验, 研究了不同施肥制度对作物产量和土壤磷素肥力的影响.结果表明: 与对照处理相比, 施用磷肥有明显的增产和稳产作用.不施磷肥处理, 土壤磷素收支赤字, 土壤全磷和速效磷含量均明显下降, 且土壤磷收支的盈亏值与土壤速效磷的增减量呈显著直线相关; 施磷肥处理, 土壤磷素收支盈余, 18年间耕层土壤全磷含量均明显提高, 提高幅度为0.02～0.04 g·kg-1, 土壤速效磷含量亦明显增加, 特别是在试验的后几年, 土壤速效磷含量似有加速上升的趋势.施氮肥对玉米有明显增产作用, 施磷钾肥对玉米增产作用不明显; 大豆则相反, 施氮肥增产作用不明显, 施磷钾肥有显著增产作用.