干旱条件下APSIM模型修正及华北冬小麦产量模拟效果

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

2009/2010年度异常气候对北京冬小麦生产的影响

通过收集小麦主产区气象数据,结合田间"三情"监测点调查结果,采用比较分析方法,针对2009/2010年度北京地区出现的异常气候对冬小麦不同生育期产生的影响进行了分析。结果表明:2009/2010年冬小麦全生育期遭遇播种-出苗期干旱、初冬强降温、冬季持续极端低温、春季低温霜冻和灌浆后期连阴寡照等异常气候造成的农业气象灾害,对小麦造成的影响包括生育期推迟、群体不足、个体生长较弱、产量下降等。     

中国作物生长模拟监测系统构建及应用

该文系统阐述了中国作物生长模拟监测系统（Crop Growth Simulating and Monitoring System in China, CGMS-China）的构建方法及其在国家级农业气象业务中的应用。CGMS-China是基于WOFOST、Oryza2000、WheatSM、ChinaAgroys 4个作物模型构建的系统,在作物长势监测评估、农业气象灾害影响评估、作物产量预报等农业气象业务中均有应用。该系统可进行作物长势监测、产量预报、农业气象灾害影响评估。利用CGMS-China模拟输出的地上生物量、叶面积指数、穗质量,建立作物长势评估指标,可对小麦、玉米、水稻进行实时长势监测与评估。通过CGMS-China对2014年8月中旬华北黄淮夏玉米的干旱产量损失评估和2016年6月22日早稻高温热害的产量损失预估表明,CGMS-China对农业气象灾害影响评估的效果较好。利用CGMS-China对2014年冬小麦主产省进行产量预报,各省的平均预报相对误差为7%。与此同时,在CGMS-China中利用遥感数据同化方法,对山西洪洞县进行产量预报,预报相对误差小于11%。该系统在国家级农业气象业务中具有良好的应用前景。     

小麦生长发育模拟模型在华北冬麦区适用性验证

对小麦生长发育模拟模型(WheatSM)在华北冬麦区的适用性进行验证结果表明,小麦生长发育模拟模型(WheatSM)对冬小麦生育期及产量的模拟有较高模拟精度,模拟值与实测值拟合良好,且适用性较佳。     

甘肃省冬小麦水分盈亏指数的改进及其应用

冬小麦是甘肃省主要粮食作物,水分是限制该省旱作冬小麦生产最主要的因素,降水时空分布不均造成冬小麦干旱常年发生,因此准确监测甘肃省冬小麦干旱及定量评估灾害损失可为决策部门制定农业防灾减灾措施及保障区域粮食安全提供科学依据。利用甘肃省1981—2014年3个农业气象观测站土壤水分和冬小麦生育状况观测资料,38个冬小麦种植县气象资料、产量资料及干旱灾情资料,考虑冬小麦休闲期土壤储水及生育期水分盈亏量修订了作物水分盈亏指数,修订后作物水分盈亏指数与土壤储水、冬小麦产量要素和产量损失相关性较高,能客观、准确、定量地反映干旱对甘肃冬小麦产量的影响;利用水分盈亏指数分别建立冬小麦产量要素和产量损失评估模型。结果表明:产量要素评估模型均通过0.01水平显著性检验,产量损失评估模型大部分通过0.05水平显著性检验。产量要素评估模型回代结果与冬小麦产量要素实测值间均通过0.01水平的F检验;产量损失评估模型验证结果表明,68.4%种植县通过0.05水平F检验,基本能准确评估全省大部分地方干旱对冬小麦产量造成的损失。修订后作物水分盈亏指数能客观反映甘肃省冬小麦干旱,建立的评估模型能准确评估干旱对冬小麦造成的损失,具有一定的应用价值。     

不同发育期干旱对冬小麦灌浆和产量影响的模拟

为探明不同强度干旱对农作物生长过程所产生的影响,本文基于作物生长模型WOFOST,采用数值模拟方法,模拟分析了河南省郑州地区冬小麦在拔节期、抽穗期和灌浆期分别发生不同程度干旱、两个发育期以及3个发育期都发生不同程度干旱对冬小麦灌浆过程和产量的影响。结果表明,当单一发育期供水减少10~30 mm时,干旱导致小麦灌浆强度在正常灌浆后的第14~18 d下降,可使小麦减产1.34%~12.5%,且以抽穗期干旱影响最大,其次是灌浆期干旱,拔节期干旱影响最小;当两个发育期供水都减少10~20 mm时,干旱导致小麦灌浆强度在正常灌浆后的第10~17 d下降,可使小麦减产4.94%~21.88%,且以抽穗期和灌浆期干旱影响为最大,其次是拔节期和灌浆期干旱,拔节期和抽穗期干旱影响最小;当3个发育期供水都减少5~15 mm时,干旱导致小麦灌浆强度在正常灌浆后的第11~16 d下降,可使小麦减产3.93%~24.84%。可见,干旱致使土壤水分亏缺,影响了作物正常的灌浆强度,进而导致作物减产。干旱发生时段与程度不同,造成作物的减产率也不尽相同,多个发育期干旱导致小麦的减产率往往大于单一发育期干旱相叠加的效应。     

不同气候区和不同产量水平下APSIM-Wheat模型的参数全局敏感性分析

量化作物模型的参数敏感性和模拟结果的不确定性对模型的标定和应用具有重要意义。为了探讨小麦生长模型(APSIM-Wheat)在不同气候区和不同产量水平下参数的敏感性,以及由于参数造成模拟结果的不确定性,以华北栾城、黄土高原长武、四川盐亭和新疆乌兰乌苏4个不同气候区下的典型冬小麦生产地为分析对象,运用扩展傅里叶幅度检验法(extended Fourier amplitude sensitivity test,EFAST)的全局敏感性分析方法,量化了小麦生长模型(APSIM-Wheat模型)在3种产量水平下(潜在、雨养和实际产量)的开花期、成熟期、产量、生育期的蒸散(evapotranspiration,ET)对品种、土壤和生化等33个参数的敏感性和不确定性。发现:1影响开花期和成熟期较为敏感的参数依次是:始花期积温、出苗到拔节积温、春化指数、光周期因子、灌浆期积温;2影响产量较敏感的参数依次为:春化指数、出苗到拔节积温、每茎谷粒质量、潜在灌浆速率、光周期指数、最大谷粒质量和辐射利用效率(radiation use efficiency,RUE);影响生育期蒸散较为敏感的参数依次为:春化指数、出苗到拔节积温、光周期指数、始花期积温;3不同产量水平下,参数敏感性差异不大,4个不同气候类型下的冬小麦开花期、成熟期、产量和生育期的蒸散对参数的敏感性基本一致;4不同气候区下,开花期和成熟期对模型参数敏感性差异很小,但产量和生育期的蒸散对参数敏感性有差异。该研究为APSIM-Wheat模型的区域应用和模型调参提供了科学指导依据。     

增温对河南省冬小麦产量的影响分析

研究历史气候变化背景下增温对作物产量的影响是识别气候变化对农业影响的有效途径。本文利用河南省历史气候资料和冬小麦平均单产资料, 通过提取气候产量、建立不同时段回归方程的方法, 分析了河南省气候变暖对冬小麦产量的影响。结果表明: 河南省1961~1981 年冬小麦气候产量与气温距平显著相关, 1987 年以后产量波动随气温变化的相关性减弱。20 世纪80 年代中期以后河南省冬小麦全生育期气温显著升高, 增温幅度为0.81 ℃?10a^-1。相对于变暖前的1961~1981 年, 1991~2000 年和2001~2007 年增温带来的单产增加量占实际增产量的15.6%~20.7%; 但显著升温后的2001~2007 年相对于1991~2000 年增温带来的增产量仅占实际增产量的1.0%, 冬小麦单产对气温的敏感性降低。     

基于APSIM模型的灌溉降低冬小麦产量风险研究

华北平原是我国冬小麦主产区,干旱是影响该地区冬小麦产量稳定的最主要的灾害之一。进行产量风险评估以及如何通过灌溉降低干旱产量风险对于该地区冬小麦稳产高产具有重要的现实意义。该文利用澳大利亚的APSIM农业生产系统模拟模型,以华北平原北京和山东禹城为例,分析了不同降水年型条件下冬小麦的产量风险;通过不同灌溉方案的设计和模拟,分析了不同的灌溉方案在各种年型条件下对降低冬小麦产量风险的作用。结果表明：北京和禹城地区冬小麦生育期内绝大部分年份降水不能满足作物的需水,严重缺水年型出现的频率均在30%左右,两地该年型的平均产量仅为2 445和2 466 kg/hm2,产量风险较高。灌溉对于降低产量风险具有明显的作用,但需根据不同的缺水年型选择适宜的灌溉方案。在兼顾冬小麦稳产高产和提高水分利用效率的前提下,严重和中度缺水年型进行3次补充灌溉,分别为底墒水、拔节水和开花水,而在轻度缺水年型条件下,底墒水和拔节水两次灌溉即可大大降低干旱带来的产量风险,灌水定额为50～70 mm,且随缺水程度的降低和灌溉次数的增加,可以适当减小灌水定额。     

宁夏南部山区小麦干旱监测与灾损评估模型研究

研究计算了宁夏回族自治区南部山区 1961～ 2 0 0 0年参考作物蒸散量 ,并利用FAO提供的山区小麦Kc值和历年小麦生育期资料模拟历年逐旬小麦需水量。通过土壤水分平衡方程各分量的订正 ,求算出历年小麦生长期间实际耗水量和水分满足率 ,运用包络产量和作物水分生产函数建立了小麦单产干旱监测与灾损评估模型 ,应用效果良好 ,可满足监测与灾损评估需要     

Effect of crop type and crop growth on atmospheric nitrogen deposition

Varying atmospheric nitrogen (N) depositions for different crops were observed at Bad Lauchstädt (Saxony‐Anhalt, Germany) when using the ITNI system (ITNI = Integrated Total Nitrogen Input), which is based on the ¹⁵N isotope dilution method. These differences were only partly explained by climatic influences. The effects of crops on the atmospheric N deposition measured by the ITNI system are discussed. For this purpose, data of six different plant species recorded in 1998 were re‐analyzed. It was found that the airborne N input is closely correlated with the morphology and metabolism of crops. Daily atmospheric N depositions of 129.0–360.8 g per hectare were measured for the plant species used. The nutritional supply of plants, especially with N, is another factor of influence on the N input from the atmosphere which should be considered. To investigate this aspect, a pot experiment was conducted with the grass Lolium perenne at three different N levels. An increase in the airborne N uptake (corresponding to N fertilization) was observed as biomass production rose.     

作物模型研究进展

作物模型的研究和应用有利于科研成果的综合集成、作物种植管理决策的现代化和辅助国家决策,是农业研究中的重要工具。一个完整的作物模型一般包括作物生长模块、水分运动模块与氮素迁移转化模块。这三者相互联系,相互影响。本文对上述3个模块的发展历程以及应用比较广泛、综合性比较强的几个模型进行综述,对比各自的优缺点,指出了作物模型今后的发展方向。     

遥感信息与作物生长模型的区域作物单产模拟

利用外部数据同化作物生长模型提高区域作物单产模拟精度是近年来的研究热点.该文以遥感反演的叶面积指数(LAI)作为结合点,以黄淮海粮食主产区典型县市夏玉米为研究对象,在区域尺度利用全局优化的复合形混合演化( SCE-UA)算法进行了遥感反演LAI信息同化EPIC (environmental policy integra...     

基于遥感数据与作物生长模型同化的作物长势监测

遥感观测和作物生长模型模拟是进行农作物长势监测的2种有效手段,并具有较好的互补性,构建二者的同化系统是目前农业遥感研究领域的热点。同化涉及多学科的交叉集成,十分有必要将同化方法中的模型、算法、数据进行集成,构建基础作物模型同化系统平台,降低科学研究和应用的难度。采用模块化结构设计,将同化系统构建所需的主要模型、算法、数据等进行有机结合和无缝集成,实现基于极快速模拟退火算法的遥感数据与CERES-Wheat作物生长模型的同化原型系统构建。此外,通过所开发的系统利用地面高光谱作为遥感数据,通过同化小麦叶面积指数对同化系统进行了检验和初步应用。同化LAI与实测结果较好的拟合度,表明所开发的同化系统基本可行,能为遥感技术与作物模型的基础研究和应用提供一个平台。     

Least limiting water range and crop yields as affected by crop rotations and tillage

Crop rotation and the maintenance of plant residues over the soil can increase soil water storage capacity. Root access to water and nutrients depends on soil physical characteristics that may be expressed in the Least Limiting Water Range (LLWR) concept. In this work, the effects of crop rotation and chiselling on the soil LLWR to a depth of 0.1 m and crop yields under no‐till were studied on a tropical Alfisol in São Paulo state, Brazil, for 3 yr. Soybean and corn were grown in the summer in rotation with pearl millet (Pennisetum glaucum, Linneu, cv. ADR 300), grain sorghum (Sorghum bicolor, L., Moench), congo grass (Brachiaria ruziziensis, Germain et Evrard) and castor bean (Ricinus comunis, Linneu) during fall/winter and spring, under no‐till or chiselling. The LLWR was determined right after the desiccation of the cover crops and before soybean planting. Soil physico‐hydraulic conditions were improved in the uppermost soil layers by crop rotations under zero tillage, without initial chiselling, from the second year and on, resulting in soil quality similar to that obtained with chiselling. In seasons without severe water shortage, crop yields were not limited by soil compaction, however, in a drier season, the rotation with congo grass alone or intercropped with castor resulted in the greatest cover crop dry matter yield. Soybean yields did not respond to modifications in the LLWR.     

沙漠绿洲灌区膜下滴灌作物需水量及作物系数研究

根据 2001年甘肃民勤小坝口灌溉试验站自动气象站观测的气象资料，依据FAO Penman—Monteith公式计算出作物生育期内参考作物蒸发蒸腾量，结合实测的充分供水条件下作物耗水量，对膜下滴灌条件下大田作物的需水规律和作物系数进行了研究。确定了该地区膜下滴灌棉花和玉米各个生育阶段的作物系数，并建立了作物系数和有效积温及播种后天数的函数关系。该结果为该沙漠绿洲灌区膜下滴灌条件下棉花、玉米的水分管理提供了科学依据。     

Soil properties and crop yields in a dryland Vertisol sown with cotton-based crop rotations

Information on the effects of growing cotton (Gossypium hirsutum L.)-based crop rotations on soil quality of dryland Vertisols is sparse. The objective of this study was to quantify the effects of growing cereal and leguminous crops in rotation with dryland cotton on physical and chemical properties of a grey Vertisol near Warra, SE Queensland, Australia. The experimental treatments, selected after consultations with local cotton growers, were continuous cotton (T₁), cotton–sorghum (Sorghum bicolor (L.) Moench.) (T₂), cotton–wheat (Triticum aestivum L.) double cropped (T₃), cotton–chickpea (Cicer arietinum L.) double cropped followed by wheat (T₄) and cotton–wheat (T₅). From 1993 to 1996 land preparation was by chisel ploughing to about 0.2 m followed by two to four cultivations with a Gyral tyne cultivator. Thereafter all crops were sown with zero tillage except for cultivation with a chisel plough to about 0.07–0.1 m after cotton picking to control heliothis moth pupae. Soil was sampled from 1996 to 2004 and physical (air-filled porosity of oven-dried soil, an indicator of soil compaction; plastic limit; linear shrinkage; dispersion index) and chemical (pH in 0.01 M CaCl₂, organic carbon, exchangeable Ca, Mg, K and Na contents) properties measured. Crop rotation affected soil properties only with respect to exchangeable Na content and air-filled porosity. In the surface 0.15 m during 2000 and 2001 lowest air-filled porosity occurred with T₁ (average of 34.6 m³/100 m³) and the highest with T₃ (average of 38.9 m³/100 m³). Air-filled porosity decreased in the same depth between 1997 and 1998 from 45.0 to 36.1 m³/100 m³, presumably due to smearing and compaction caused by shallow cultivation in wet soil. In the subsoil, T₁ and T₂ frequently had lower air-filled porosity values in comparison with T₃, T₄ and T₅, particularly during the early stages of the experiment, although values under T₁ increased subsequently. In general, compaction was less under rotations which included a wheat crop (T₃, T₄, T₅). For example, average air-filled porosity (in m³/100 m³) in the 0.15–0.30 m depth from 1996 to 1999 was 19.8 with both T₁ and T₂, and 21.2 with T₃, 21.1 with T₄ and 21.5 with T₅. From 2000 to 2004, average air-filled porosity (in m³/100 m³) in the same depth was 21.3 with T₁, 19.0 with T₂, 19.8 with T₃, 20.0 with T₄ and 20.5 with T₅. The rotation which included chickpea (T₄) resulted in the lowest exchangeable Na content, although differences among rotations were small. Where only a cereal crop with a fibrous root system was sown in rotation with cotton (T₂, T₃, T₅) linear shrinkage in the 0.45–0.60 m depth was lower than in rotations, which included tap-rooted crops such as chickpea (T₄) or continuous cotton (T₁). Dispersion index and organic carbon decreased, and plastic limit increased with time. Soil organic carbon stocks decreased at a rate of 1.2 Mg/ha/year. Lowest average cotton lint yield occurred with T₂ (0.54 Mg/ha) and highest wheat yield with T₃ (2.8 Mg/ha). Rotations which include a wheat crop are more likely to result in better soil structure and cotton lint yield than cotton–sorghum or continuous cotton.     

Tillage and crop management impacts on soil loss and crop yields in northwestern Ethiopia

Lack of appropriate agronomic practices is one of the major causes for soil erosion and low yields in teff (Eragrostis tef [Zucc.]) production in Ethiopia. A 3-yr study was conducted at the Aba Gerima watershed in northwestern Ethiopia, to investigate the effects of two tillage practices (reduced tillage [RT] and conventional tillage [CT]), two planting methods (row planting [RP] and broadcast planting [BP]), and two compaction options (with [+T] and without [–T] trampling) on soil loss and teff yields in a split-split plot arrangement. Sediment concentration ranged from 0.01 to 5.37 g L^?1 (mean, 0.25 g L^?1) in our study. Accordingly, the estimated total (August–October) soil loss ranged from 0.2 to 0.5 t ha^–1 (mean, 0.3 t ha^–1). The sediment concentration and total soil loss were significantly influenced (P < 0.05) by tillage, planting methods, and trampling only in the third monitoring year. RT reduced soil loss by 19% relative to that of CT, whereas RP resulted in a 13% reduction in soil loss over BP. The ?T plots showed a 15% reduction in soil loss as compared to + T plots. Results revealed significant increase in soil total carbon and nitrogen in RT and –T. Less soil loss and greater teff grain yield were obtained in plots with improved agronomic practices (RT and RP) compared to conventional ones (CT and BP). Based on our findings we conclude that the use of RT, RP, and –T practices can effectively minimize soil loss without any crop yield penalty.     

农作物秸秆资源利用浅析

中国农作物秸秆综合利用主要集中在能源、饲料和肥料等领域。虽然已取得了一批成果,但就总体而言,与发达国家尚有较大差距。文章指出,中国发展秸秆资源的综合利用,要立足促进农业经济发展和农民增收。首先要解决“四个转变”,重点要研发实用性强,推广简便适用、易操作、农民容易接受的技术,推动农业农村经济的发展。     

Interpolation type and data computation of crop yield maps is important for precision crop production

Technological advances in precision agriculture in the last two decades have made yield monitoring and mapping an economically feasible option or practice for farmers. Differentially corrected Global Positioning System (GPS)-equipped yield monitoring system on a combine allows collection of georeferenced yield data which when coupled with a geographic information system (GIS) can generate yield maps via several interpolation techniques. Scientists and practitioners have reported to use multiple different types of interpolation techniques to process yield data. However, one of the aspects that still need to be elucidated is the influence of the different interpolation methods on the quality of the resulting thematic yield maps. The objective of this study was to investigate the influence of three interpolation methods (i.e., inverse of distance, inverse of square distance, and ordinary kriging) commonly used in developing yield maps. An index for the comparison of errors (ICE) was proposed to provide an objective criterion for selecting an experimental variogram model to use with the kriging. Results indicate that inverse distance squared performed slightly better in predicting yields than either inverse distance or ordinary kriging. With a mean absolute difference varying from 0.04 to 0.32 t ha^?1 corresponding to a relative deviation from 1.20 to 7.53%, the management decisions can differ in some cases based on the type of interpolation implemented.