大豆叶面积变化对田间微气象条件及产量的影响

作者通过1977～1981年和1987～1989年,分别在绥化、哈尔滨和合江地区的田间试验和生产调查,发现大豆叶面积指数的大小可造成不同的田间微气象环境条件,叶面积指数6左右时,植株似倒非倒,株间采光量和CO_2分布较为合理,光合效率高,大豆单株生育良好,群体产量高;在生产上可依据当地土地条件、施肥水平、不同品种和不同种植方式用叶面积动态变化方程式计算出适宜的密度,以使大豆群体最大叶面积指数保持在6左右,创造较理想的田间微气象效应。     

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.     

基于机载LiDAR数据的玉米叶面积指数反演

以机载LiDAR离散点云数据为数据源,基于植被冠层孔隙率与叶面积指数的关系,提出一种反演大田玉米叶面积指数的方法。对反演LAI和实测LAI进行对比分析,结果表明:基于Axelsson改进的不规则三角格网加密方法可以将地面点和非地面点分开,结合高分辨率影像能够提取出玉米冠层点云;基于孔隙率反演LAI,尼尔逊参数的选择对结果影响很大,利用扫描天顶角模拟尼尔逊参数,LAI反演结果接近于真实情况。利用机载LiDAR点云数据能精确地反演大田玉米LAI,该研究方法适用于中等高度的农作物,可以扩展到甜菜、甘蔗等其他中等高度农作物。     

西安市生态环境植被信息的遥感反演

西安市自然生态环境问题突出,植被信息作为地表生态环境评价与监测的重要参数之一,对区域生态环境的监测及生态环境建设具有重要意义。为此,通过LandsatTM影像提取的植被指数与叶面积指数关系的相关分析,确定适合反映研究区植被信息的VI。结果证明,用NDVI作参考来监测植被状况是合理的,相关系数为0.7205。基于此进一步分析了植被指数与生态环境参数的关系,为西安市的生态环境监测提供依据。     

Development of a ground truthing method for determination of rangeland biomass using canopy reflectance properties

Veld condition assessment and establishment of grazing capacity norms provide guidelines for the formulation of sustainable practices. However, conventional monitoring methods are becoming inadequate to meet future challenges, where quantification of spatial and temporal variation is required. This study proposes an on-site remote sensing method for monitoring above-ground biomass in rangelands. A preliminary model was formulated, based on simple regression relationships between canopy reflectance properties and aboveground biomass. This model was validated in semi-arid environments (Nama-karoo and Kalahari) within the framework of spatial and temporal experimental trials. Model accuracy was found to be primarily a function of canopy structure and vegetation composition where increases in dwarf shrub presence resulted in greater variations of both NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) measurements. Temporal variation in model accuracy could also be observed which seemed to be associated with precipitation events. It was concluded that the proposed remote sensing method has potential as a ground truthing technique for determination of rangeland biomass. Indications are that this method is well suited for use in grass dominated veld types. With further refinement, implementation of this technique should also be possible in dwarf shrublands.     

乾宁狼尾草和中序狼尾草的某些生物学特性初探与核型分析

从牧草种质资源鉴定利用角度,对中国产狼尾草属的两个新野生种农艺性状生长、栽植密度以及核型分析等方面,进行了比较研究。结果表明,乾宁狼尾草与中序狼尾草在自然状况下,结实率低,分别仅为41.2％和17.8％,但两者营无性繁殖的地下茎十分发达。生长分析的各项指标上,乾宁狼尾草最大叶面积指数13.1比中序狼尾草3.56大,平均净同化作用率前者小,后者大(36.2和54.1克平方米~(-2)周~(-1)),两者差异均极显著(P=0.01),平均相对生长率分别为0.56和0.52克~(-1)周~(-1),与其它各项均不显著。不同栽植密度,使得草种对光照条件、CO_2流通率以及单位植株营养面积表现出巨大差异,导致形成的生物量也不尽同。乾宁狼尾草与中序狼尾草分别以35cm和25cm行距栽植时,当年生物产量较高;从营养成分上,不单含高量的粗蛋白,营养期与开花期分别达21.45％、16.02％和21.05％、16.88％;赖氨酸含量也表现出同步优势。经染色体数目及核型分析,两种的体细胞染色体数均为36,属四倍体(X=9)。其核型彼此无差异。初步可认定两种,亲缘关系属同源染色体组。     

精准施药中叶面积指数探测研究

精准施药技术的研究以提高农药的利用率为目的,是精准施药的发展方向。果树叶面积指数能为精准施药提供重要参考依据,通过叶面积指数及生物量探测可以进行大面积果树的长势监测并计算其药量的需求,进而对作物进行指导性施药。为此,结合国内外研究现状阐述了各类叶面积指数探测技术和计算方法,分析了精准施药技术中靶标探测的方法及叶面积指数探测在精准施药中的应用,并对精准变量施药中叶面积指数探测方法进行了展望。     

Simulation of transpiration, drainage, N uptake, nitrate leaching, and N uptake concentration in tomato grown in open substrate

Free-drainage or “open” substrate system used for vegetable production in greenhouses is associated with appreciable NO₃⁻ leaching losses and drainage volumes. Simulation models of crop N uptake, N leaching, water use and drainage of crops in these systems will be useful for crop and water resource management, and environmental assessment. This work (i) modified the TOMGRO model to simulate N uptake for tomato grown in greenhouses in SE Spain, (ii) modified the PrHo model to simulate transpiration of tomato grown in substrate and (iii) developed an aggregated model combining TOMGRO and PrHo to calculate N uptake concentrations and drainage NO₃⁻ concentration. The component models simulate NO₃⁻-N leached by subtracting simulated N uptake from measured applied N, and drainage by subtracting simulated transpiration from measured irrigation. Three tomato crops grown sequentially in free-draining rock wool in a plastic greenhouse were used for calibration and validation. Measured daily transpiration was determined by the water balance method from daily measurements of irrigation and drainage. Measured N uptake was determined by N balance, using data of volumes and of concentrations of NO₃⁻ and NH₄⁺ in applied nutrient solution and drainage. Accuracy of the two modified component models and aggregated model was assessed by comparing simulated to measured values using linear regression analysis, comparison of slope and intercept values of regression equations, and root mean squared error (RMSE) values. For the three crops, the modified TOMGRO provided accurate simulations of cumulative crop N uptake, (RMSE = 6.4, 1.9 and 2.6% of total N uptake) and NO₃⁻-N leached (RMSE = 11.0, 10.3, and 6.1% of total NO₃⁻-N leached). The modified PrHo provided accurate simulation of cumulative transpiration (RMSE = 4.3, 1.7 and 2.4% of total transpiration) and cumulative drainage (RMSE = 13.8, 6.9, 7.4% of total drainage). For the four cumulative parameters, slopes and intercepts of the linear regressions were mostly not statistically significant (P < 0.05) from one and zero, respectively, and coefficient of determination (r²) values were 0.96-0.98. Simulated values of total drainage volumes for the three crops were +21, +1 and −13% of measured total drainage volumes. The aggregated TOMGRO-PrHo model generally provided accurate simulation of crop N uptake concentration after 30-40 days of transplanting, with an average RMSE of approximately 2 mmol L⁻¹. Simulated values of average NO₃⁻ concentration in drainage, obtained with the aggregated model, were −7, +18 and +31% of measured values.     

病害胁迫下玉米LAI遥感反演研究

为了在病害发生条件下进行玉米LAI的遥感估算,针对41个不同抗性的玉米自交系品种,通过人工接种方法,获得了不同病害严重程度(1～9级)的LAI数据,同时采集了地面高光谱和无人机多光谱数据,构建了K近邻算法、支持向量机、梯度提升分类树和决策分类树分类模型对病害进行分类,对玉米种质资源抗病性进行了划分。基于不同玉米病害胁迫程度分类结果,采用随机森林回归、梯度提升回归树、极端梯度增强算法、轻量梯度提升机4种机器学习模型对玉米LAI进行反演,讨论了不同模型在病害胁迫下的鲁棒性。研究结果表明,对不同生育期玉米病害程度进行划分,基于地面高光谱识别精度分别为84.72%(梯度提升分类树)、47.67%(支持向量机)、55.05%(K近邻算法)、83.02%(决策分类树)。基于病害分类结果,本文利用无人机多光谱数据估算了不同病情等级胁迫下的玉米LAI。构建了4种集成学习模型对不同病情等级的LAI进行估算,4个LAI反演模型的总体反演精度(rRMSE)分别为：19.11%(梯度提升回归树)、15.94%(轻量梯度提升机)、14.51%(随机森林回归)和15.45%(极端梯度增强算法)。其中极端梯度增强算...     

种植密度对玉米-大豆带状间作下大豆光合、产量及茎秆抗倒的影响

【目的】阐明玉米-大豆带状间作下大豆植株冠层在不同种植密度下的光环境变化规律,明确种植密度对间作大豆叶片光合特性、产量形成及茎秆抗倒的影响,为构建寡日照地区间作大豆合理群体密度提供理论参考。【方法】本研究以大豆（川豆-16）和玉米（正红-505）为试验材料。采用双因素随机区组设计,主因素为种植方式,设玉米-大豆带状间作和大豆带状单作2个水平,副因素为大豆的3个种植密度（PD1=17株/m²,PD2=20株/m²,PD3=25株/m²),研究种植密度对间作大豆冠层内部光环境变化、叶片光合特性、植株生长动态、田间倒伏率及产量构成等的影响。【结果】2年结果表明,在玉米-大豆带状间作系统中,大豆生长中后期受高位作物玉米遮荫和自荫性增加的影响,其植株群体冠层内部的光合有效辐射（PAR）、叶面积指数（LAI）、叶片光合能力、分枝数及产量显著降低,但受玉米影响的程度因大豆种植密度的不同而不同。在间作模式下,PD1和PD2处理的大豆植株群体冠层光合有效辐射比PD3处理分别增加了45.4%和24.8%,净光合速率分别增加了46.1%和12.3%,单株有效荚数分别增加了53.2%和27.2%,单株分枝数分别增加了270.4%和140.9%,田间倒伏率分别降低了50.3%和19.3%。相关性分析发现,间作大豆的田间倒伏率与冠层内部光合有效辐射、叶片净光合速率、茎秆抗折力、茎叶干物质比、单株分枝数及单株有效荚数呈显著负相关,与株高、叶面积指数和单株无效荚数呈显著正相关。【结论】在玉米-大豆带状间作模式下,20株/m²的大豆密度（PD2）有利于创造良好的群体冠层内部光环境,降低植株田间大豆倒伏率,增加光合产物积累,从而提高大豆产量。