基于CERES-Maize模型的吉林春玉米遗传参数调试

为了应用CERES-Maize模型模拟吉林春玉米生长发育和产量形成,需要确定主栽春玉米品种的遗传参数。采用晚熟春玉米品种‘吉单159’和‘先玉335’多年田间观测数据和同期气象、土壤等数据进行遗传参数调试,并对模型的模拟效果进行验证。结果表明:2个春玉米品种生育期的模拟值和实测值散点均在1:1线周围。‘吉单159’播种至开花天数和播种至成熟天数RMSE值分别为3.78和10.90。‘先玉335’播种至出苗天数、播种至开花天数和播种至成熟天数RMSE值分别为1.12、3.20和4.85。2个品种春玉米的地上部生物量和叶面积指数的模拟值与实测值均在抽雄期接近,三叶期和七叶期差异最大。‘吉单159’和‘先玉335’产量的RRMSE值分别为4.31%和4.81%。说明CERES-Maize模型对吉林春玉米生育期和产量模拟效果较好,地上部生物量次之,叶面积指数一般。总之,CERES-Maize模型能够应用于吉林省气候变化对春玉米影响的评价。     

ORYZA2000模型模拟北京地区旱稻的适应性初探

利用北京昌平2年旱稻田间试验结果，对ORYZA2000模型模拟旱稻生长发育的适应性做了初步研究。根据2003年旱稻田间试验结果，对模型进行调试，获得了旱稻的基本作物参数，包括旱稻不同生育阶段的发育速率、干物质分配系数、比叶面积、最大根深等。利用2002年的数据对模型模拟的生物量、叶面积和产量等结果进行了检验。结果表明，ORYZA200能够比较准确地模拟旱稻的生物量、叶面积动态变化过程及最终产量，尤其是在模拟穗生物量方面具有较高的准确性。地上部总生物量、绿叶生物量、茎生物量、穗生物量、叶面积指数和产量的相对均方根误差NRMSE值分别为45%、35%、57%、37%、35%和23%。     

基于ORYZA2000模型模拟贵阳地区一季中稻的适应性初探

为了验证ORYZA2000模型在贵阳地区一季中稻的适应性,以贵阳地区2008-2009年气象资料为基础,采用2008-2009年‘金优527’、‘黔南优2058’、‘Q优6号’的田间试验数据对ORYZA2000模型进行校正,同时获取相关作物参数和开展模拟评价。结果表明,ORYZA2000模型对3个品种的地上部总生物量、茎生物量、穗生物量、绿叶生物量、叶面积指数、产量的NRMSE分别为：16.4%、22.5%、23.5%、26.5%、26.1%、11.3%。经过参数调试后的ORYZA2000模型能够比较准确地模拟贵阳地区一季中稻的生物量、叶面积动态变化及产量,尤其是在模拟产量、总生物量方面具有较高的准确性。     

基于ALMANAC的山西省冬小麦模拟

为了准确的监测山西省冬小麦动态长势和预测产量,本研究使用ALMANAC作物生长模型对山西省洪洞县高、中、低产田的冬小麦产量进行了模拟。收集了模型需要的作物属性、土壤、气象及田间管理措施等众多参数并根据实际情况对参数进行了调整,结果表明：冬小麦模拟产量的相对误差(RE)为-7.8%~5.7%,叶面积指数的RE为-12.5%~13.6%,水地最大叶面积指数最大;与背景态相比生育期提前,叶面积指数水地变化不大,旱地低较多,温度主要是对生育期的影响,而水分则对叶面积指数产生较大影响。冬小麦的产量和叶面积指数的动态变化能够被ALMANAC模型较好地模拟;而且模型能够模拟不同水分条件下冬小麦的叶面积指数及气候变化对冬小麦影响。     

紫花苜蓿光合生产与干物质积累模拟模型研究

依据紫花苜蓿生物学特性，通过田间试验和广泛收集资料，构建了紫花苜蓿光合生产与干物质积累模拟模型。该模型包括光合作用、呼吸作用、叶面积消长、干物质积累、同化物分配和产量形成等过程，考虑了温度对光合作用的影响。计算得到紫花苜蓿不同生育时期的干物质转换系数( β )和同化物分配分配系数[C(d)_I]，确定了主要紫花苜蓿品种的光合参数(a 和P_max)。分别利用北京和太原不同年份和不同品种的试验资料对地上部生物量、产量和叶面积指数模型进行了检验。结果表明，模型对叶面积动态、地上部生物量和产量模拟效果较好，叶面积指数、地上部生物量、茎和叶生物量的决定系数分别为0.98、0.95、0.96和0.88（n=20），产量均方差(RMSE)为103 kg hm^-2，相对均方差(NRMSE)为2.1% (n=102)。模型不仅具有较强的机理性，而且有较好的拟合性。     

基于MODIS-NDVI的春玉米叶面积指数和地上生物量估算

为了构建春玉米叶面积指数和地上生物量的估算模型,基于辽宁省大田条件下9个站点春玉米不同生育期叶面积指数LAI和地上鲜生物量数据,利用MODIS提取的10种植被指数NDVI、RVI、DVI、PVI、EVI、GNDVI、RDVI、SAVI、OSAVI和NLI反演春玉米LAI和地上鲜生物量。结果表明：10种植被指数与春玉米LAI和地上鲜生物量相关性显著,采用植被指数反演春玉米LAI和地上鲜生物量是可行的;分别基于回归分析法和人工神经网络法,利用10种植被指数反演春玉米LAI和地上鲜生物量,利用回归分析法结合OSAVI和NDVI反演春玉米LAI和地上鲜生物量效果较好;利用人工神经网络法进行模拟反演采用RVI、DVI和EVI效果最佳,反演精度高于回归分析法。     

辽宁地区玉米生长发育及产量对温度和降水的响应

为了研究温度和降水对玉米生长发育及产量的影响,为合理利用不同气候条件保证玉米生长提供依据,采用大田试验数据和气候资料,分析了辽宁地区玉米生长发育及产量特征对温度、降水和热量条件的响应。结果表明：玉米各生育期生长天数,七叶—拔节期受平均温度影响,三叶—七叶期受降水量影响,播种—三叶期和乳熟—成熟期受前期积温影响;降水量与播种—三叶期的玉米地上干重相关显著,平均气温与三叶—七叶期的叶面积指数相关显著,前期积温对玉米生物量增长影响最大;在同等积温增加条件下,辽东地区地上生物量增加最多,辽西地区最少。不同生育期玉米受到播期和气候条件的影响程度不同,不同地区间的果实性状及产量差异较大。     

基于寒地春小麦AquaCrop与WOFOST模型适应性验证分析

使用AquaCrop模型与WOFOST模型对哈尔滨地区春小麦生长进行模拟,以春小麦的地上生物量和产量及生育期土壤体积含水量为指标,对比分析两个模型的模拟精度。结果表明:使用经过校正的两个模型均能够较为准确地模拟哈尔滨地区春小麦的生长发育情况及产量形成,观测值与模拟值一致性较好,误差均在合理范围内。在非正常年际AquaCrop模型模拟结果与实测结果偏差较大,说明该模型适合于正常年景的作物生长模拟,WOFOST模型适应性更强。在对2011年土壤水分含量的模拟中,两个模型观测值与模拟值总体趋势相同,误差均在合理范围内。总体来说,经过修正和校准后的AquaCrop模型与WOFOST模型均适合寒地春小麦的模拟研究。     

GECROS模型在黄淮海地区模拟夏玉米生长的适应性评价

GECROS是荷兰瓦赫宁根农业大学近些年开发的机理性更强、算法更简要的作物生长模型。本文利用黄淮海地区夏玉米试验数据进行GECROS模型的适应性评价,为模型进一步开展区域应用提供依据。结果表明,GECROS基本能够反映黄淮海地区夏玉米的发育进程。模型模拟夏玉米抽雄期的绝对偏差在6.0 d以内,平均为2.1 d;模拟成熟期的绝对偏差在8.0 d以内,平均为3.4 d。GECROS描述夏玉米干物质积累和叶面积扩展过程的准确度较高。模拟雌穗总重的归一化均方根误差在7.8%~33.8%之间,平均为18.6%;模拟植株地上总重的归一化均方根误差在11.2%~32.6%之间,平均为20.7%;模拟LAI的绝对偏差在0.28~0.55之间,平均为0.41,模拟籽粒产量的绝对偏差在20.3~229.0 g m–2之间,平均为80.9 g m–2。利用GECROS模型相对评价作物生长状况或环境影响基本可行。但GECROS模拟夏玉米发育进程仍存在低值偏高、高值偏低的现象;在土壤水分胁迫较重时,描述的生物量积累过程有偏低情况;描述LAI扩展的总体效果差于生物量累积的效果。GECROS仍需进一步完善。     

内蒙古黄土高原秸秆还田对玉米农田土壤水热状况及产量的影响

内蒙古清水河县地处黄土高原的边缘地带,沟壑纵横,是典型的北方干旱缺水地区。为明确秸秆翻耕还田对该区域玉米农田土壤蓄水保墒效果及产量形成特性的影响,通过连续2年的田间小区试验,探究0kg/hm ²（CK）、3 000kg/hm ²（SF1）、6 000kg/hm ²（SF2）和12 000kg/hm ²（SF3）4种秸秆还田量下玉米农田全生育期地温、土壤水分、植株生长发育、水分利用效率和产量的变化规律。结果表明：秸秆还田促进了玉米叶面积指数提高和地上部生物量的积累,2018年,SF2处理叶面积指数和地上部干物质积累量较CK分别提高13.17%和10.70%;玉米全生育期0~30cm土层土壤温度、0~80cm土层土壤含水率、生育期贮水量和农田耗水量、水分利用效率和产量均表现为SF2>SF3>SF1>CK;2018年,SF1、SF2和SF3产量分别较CK提高了8.5%、11.4%和9.3%。秸秆翻耕还田措施能够显著改善玉米农田土壤水热状况、促进植株生长、提升子粒产量和水分利用效率,其中6 000kg/hm ²还田处理效果最好,可作为节水保墒栽培模式在内蒙古黄土高原推广应用。     

基于分期播种的气候变暖对东北春玉米生长发育与产量的影响研究

为揭示东北春玉米对未来气候变化的响应程度,提出切实可行的适应对策,分析以东北地区北部哈尔滨地区主栽春玉米‘久龙8号’（中晚熟品种）和东北地区南部锦州地区主栽春玉米‘农华101’（晚熟品种）为试验品种,开展分期播种试验;同时以‘久龙8号’为例,进行春玉米适应未来气候的地理分期播种试验。分期播种试验以当地正常播种期为对照,研究播种期分别推迟10天和20天情景下,不同熟性春玉米品种的响应情况;地理分期播种试验以哈尔滨当地正常播种期为对照(CK),同时设计3个未来气候变暖情景(CW1—CW3),通过对3个未来气候变暖情景与对照情景的对比,分析气候变化对东北春玉米生长发育状况和产量构成要素的影响。结果表明,推迟播种日期10天以上,对哈尔滨地区春玉米生长不利,对锦州地区无明显影响;若保持当前春玉米品种不变,东北春玉米发育期在不同气候变暖情景下,均表现出缩短趋势,缩短时间为12~20天不等;百粒重和单产则表现为增产的趋势,增产83~188 g/m2,增产幅度为9.85%~22.36%。     

Increased utilization of lengthening growing season and warming temperatures by adjusting sowing dates and cultivar selection for spring maize in Northeast China

Global warming has lengthened the theoretical growing season of spring maize in Northeast China (NEC), and the temperatures during the growing season have increased. In practise, crop producers adjust sowing dates and alternate crop cultivars to take advantage of the lengthening growing season and increasing temperatures. In this study, we used crop data and daily weather data for 1981–2007 at five locations in NEC to quantify the utilization of the lengthening growing season and increasing temperatures by adjusting sowing dates and cultivar selection for spring maize production. If these two positive factors are not fully utilized, then it is important to know the potential impacts of these climatic trends on spring maize grain yields. The results show that in NEC, both the actual and theoretical growing seasons are lengthening, i.e., the sowing dates have been advanced and the maturity dates have been delayed. The actual sowing dates are 1–8 days later and the actual maturity dates are 6–22 days earlier than the theoretical perspective. Advancing sowing dates and changing cultivars led to 0–5 days and 6–26 days extension of the growing season. For the potential thermal time (TT), adjusting the sowing dates decreased the unutilized TT before sowing, while the cultivar selection increased the utilized TT and decreased the unutilized TT after maturity. On average, the unutilized heating resource before sowing is less than that after the maturity date (0.3–1.9% vs. 2.1–7.8%). During 1981–2007, for per day extension of the growing season, the spring maize grain yield increased by 75.2 kg ha⁻¹. The spring maize grain yields have increased by 7.1–57.2% when both early sowing and changing cultivars during 1981–2007. In particular, adjusting the sowing dates increased the grain yield by 1.1–7.3%, which was far less than the increase effect (6.5–43.7%) from switching to late maturing cultivars. Therefore, selecting late maturing cultivars is an important technique to improve maize grain yields in NEC under the global warming context. Nevertheless, if the currently unutilized TT were fully explored, the local spring maize grain yield would have increased by 12.0–38.4%.     

AquaCrop模型在东北黑土区作物产量预测中的应用研究

东北黑土区是我国玉米和大豆生产基地,为了实现利用AquaCrop模型优化管理和预测产量,本文基于作物小区田间试验和大田观测数据,采用OAT(one factor at a time)法分析了该模型参数的敏感性,率定了敏感性高的参数,并对率定后的模型进行了验证。结果表明:玉米和大豆产量均对影响经济产量的收获指数十分敏感,二者虽然对冠层和根系生长参数都敏感,但有所差异:玉米对冠层衰减系数(canopy decline coefficient,CDC)更为敏感,而大豆则对限制冠层伸展的水分胁迫系数曲线的形状因子(shape factor for water stress coefficient for canopy expansion,Pexshp)更为敏感;玉米因根系深对最大有效根深(maximum effective rooting depth,Zx)更敏感,大豆因根系浅对根区根系伸展曲线的形状因子(shape factor describing root zone expansion,Rexshp)更敏感。由于玉米需水量大,对冠层形成和枯萎前的作物系数(crop coefficient before canopy formation and senescence,KcTr,x)和归一化水分生产力(normalized water productivity,WP*)很敏感,大豆则是一般敏感。率定后模型模拟玉米产量与实测产量的回归系数由0.34提升至0.89,模拟大豆产量与实测产量的回归系数由0.80提升至0.88。进一步用大田实测产量的验证结果表明:预测的玉米与大豆产量与实测产量间回归方程的决定系数(coefficient of determination,R2)分别为0.775和0.779,均方根误差(root mean square error,RMSE)分别为1.076 t hm^–2和0.299 t hm^–2,标准均方根误差(normalized root mean square error,NRMSE)分别为0.097和0.178,模拟效率(model efficiency,ME)分别为0.747和0.730,率定后的AquaCrop模型能较精准地模拟东北黑土区玉米和大豆产量,可用于产量预测或优化管理。     

Attainable yield achieved for plastic film-mulched maize in response to nitrogen deficit

Nitrogen (N) stress limits the yields of maize (Zea mays L.) that have been plastic film-mulched in northwest China. Using the tested Hybrid-Maize simulation model, which was combined with field experiments using four levels of N fertilisers (0, 100, 250 and 400 kg N ha⁻¹), we aimed to understand the variability of the attainable yield in response to N stress under plastic film mulching. We show that the application of N250 or N400 results in 100% simulated potential LAI, which is, thus, close to 100% of the simulated potential of both biomass and grain yield. However, N stress treatments significantly decreased the biomass and grain yields, achieving only 40–50% of the simulated potential (N0 treatment) and 70–80% of the simulated potential (N100 treatment). Growth dynamic measurements showed that N stress significantly decreased the LAI, delaying the source capacity growth (canopies) around the silking stage and resulting in lower final kernel numbers. The lower LAI resulted in decreased dry matter accumulation and allocation during the reproductive stage; this decrease led to a decrease in the kernel growth rate and in the grain filling duration, which resulted in a significantly lower kernel weight. This knowledge could be helpful for the optimisation of N management to close the yield gaps of dryland maize in semi-arid monsoon climate regions.     

Using the CERES-Maize model in a semi-arid Mediterranean environment. Evaluation of model performance

The CERES-Maize model was tested in a semi-arid Mediterranean environment during a period of 2 years under three different soil moisture conditions (well-watered and two limited irrigation regimes). In well-watered plots, growth and yield were adequately simulated by the model (differences between simulated values and observations were less than 10%). Results suggest that the absence of air humidity among the model inputs does not limit the CERES-Maize performance, even under dry-air conditions. On the contrary, under mild soil water shortage, CERES-Maize underestimated the leaf area index (LAI) (up to 26% for maximum LAI), above-ground biomass (up to 23%) and grain yield (up to 15%). Mismatches between observations and predictions increased with water stress level (by up to 46, 29 and 23% for maximum LAI, biomass and grain yield, respectively). It is suggested that the functions describing leaf growth and senescence and those calculating the soil water deficit functions should be modified to adapt CERES-Maize to Mediterranean environments.     

冬小麦–夏玉米与双季玉米种植模式产量及光温资源利用特征比较

优化传统冬小麦-夏玉米模式并探索新型种植模式是挖掘黄淮海区周年高产潜力,提高资源利用效率的重要途径。本研究以冬小麦-夏玉米传统种植模式为对照(CK),建立了冬小麦–夏玉米优化种植模式(T1)和双季玉米模式(T2),于2009—2012年在河南新乡进行田间试验,对其周年资源分配、产量及资源利用效率进行了比较。结果表明:(1)T1模式通过播/收期调整,协调了两季的光、温资源分配比例(0.7∶1.0和1.4∶1.0);T2模式两季积温基本为均等分配,光照资源分配比例为1.5∶1.0。(2)资源分配的变化引起了产量的变化。与CK比,T1模式周年产量平均增幅为7.8%,其产量的增加主要来自于夏玉米季,T1模式夏玉米季平均叶面积指数(MLAI)、生物量和产量均显著高于CK,且冬小麦晚播并未造成减产。双季玉米(T2)是"双C4作物"的新型种植模式,其第1季的MLAI、生物量和产量均显著高于CK和T1,第2季(除MLAI外)显著低于CK和T1。T2与T1周年产量差异不显著,但显著高于CK,平均增幅为9.2%。另外,T2模式周年日产量显著高于CK和T1,平均增幅分别为53.9%和46.2%。(3)T2模式周年光、温生产效率及籽粒光能利用效率显著高于CK和T1,平均增幅分别为30.5%和23.3%,15.5%和9.7%,30.3%和23.0%。综上所述,T1和T2高产高效模式建立的核心均是充分利用C4作物玉米高物质生产能力的优势,二者的建立为黄淮海区周年产量潜力的挖掘及种植结构调整提供了思路。     

AquaCrop模型在东北黑土区作物产量预测中的应用研究

Northeast black soil area is the production area of maize and soybean in China. In order to optimize the agricultural management and forecast crop yield with AquaCrop model, we use OAT (one factor at a time) method to analyze the sensitivity of the model parameters based on the experiment and field observation data, and to validate the model after calibrated the high sensitivity parameters. The results of sensitivity analysis showed that the yields of maize and soybean were both extremely sensitive to the reference harvest index (HI₀) and the parameters of canopy growth and root growth. The difference was that maize was more sensitive to the canopy decline coefficient (CDC), while soybean was more sensitive to the shape factor for water stress coefficient for canopy expansion (Pexshp). Maize was more sensitive to the maximum effective rooting depth (Z_x) because of its deep root, while soybean was more sensitive to the shape factor describing root zone expansion (Rexshp) because of its short roots. Maize was extremely sensitive to the crop coefficient before canopy formation and senescence (Kc_Tr,x) and the normalized water productivity (WP^*) due to the large water demand, while soybean was only generally sensitive. After calibrated the high sensitivity parameters with experiment data, the regression coefficient of simulated yield and measured yield of maize increased from 0.34 to 0.89, and the regression coefficient of simulated yield and measured yield of soybean increased from 0.80 to 0.88. Furthermore, the validation results of field observation data indicated that the determination coefficients (R²), the root mean square error (RMSE), the normalized root mean square error (NRMSE) and the model efficiency (ME) of the AquaCrop model of maize and soybean were 0.775 and 0.779, 1.076 t hm^-2 and 0.299 t hm^-2, 0.097 and 0.178, 0.747 and 0.730, respectively. The calibrated AquaCrop model can accurately simulate the yield of corn and soybean in the black soil area of Northeast China, and is useful for yield prediction and optimal management.