Assessing the utility of topographic variables in predicting structural complexity of tree stands in a reforested urban landscape

The transformation of natural landscapes into impervious built-up surfaces through urbanization is known to significantly interfere with the ecological integrity of urban landscapes and accelerate climate change and associated impacts. Although urban reforestation is widely recognised as an ideal mitigation practice against these impacts, it often has to compete with other lucrative land uses within an urban area. The often limited urban space provided for reforestation therefore necessitates the optimization of the ecological benefits, which demands spatially explicit information. The recent proliferation of tree stands structural complexity (SSC) and topographic data offer great potential for determining the ecological performance of reforested areas across an urban landscape. This study explores the potential of using topographic datasets to predict SSC in a reforested urban landscape and ranks the value of these topographic variables in determining SSC. Tree structural data from a reforested urban area was collected and fed into a tree stand structural complexity index, which was used to indicate ecological performance. Topographic variables (Topographic Wetness Index, slope, Area Solar Radiation and elevation)- were derived from a Digital Elevation Model (DEM) and used to predict SSC using the Partial Least Squares (PLS) regression technique. Results show that SSC varied significantly between the topographic variables. Results also show that the topographic variables could be used to reliably predict SSC. As expected, the Topographic Wetness Index and slope were the most important topographic determinants of SSC while elevation was the least valuable. These results provide valuable spatially explicit information about the ecological performance of the reforested areas within an urban landscape. Specifically, the study demonstrates the value of topographic data as aids to urban reforestation planning.     

青海省海东市不同等级降水和旱涝关系研究

利用1961～2015年青海省海东市5气象站逐日降水资料,根据中国气象局降水等级划分标准和旱涝Z指数的计算方法,分别统计海东市夏、冬半年不同等级雨量和雨日及计算Z指数,利用线性回归法分析海东市55年来不同等级雨日和雨量和Z指数的变化趋势,并采用偏相关系数、相对贡献率和合成分析等方法研究不同等级雨日和雨量对旱涝的影响。得出以下结论：①海东市夏(冬)半年降水以小雨(雪)和中雨(雪)为主,雨日和雨量及占年比例均呈现自南向北递增的空间分布。②夏半年小雨和大到暴雨的雨量及雨日呈减小趋势,中雨雨量和雨日呈增加趋势,冬半年小雪、中雪和大到暴雪的雨量及雨日均呈减小趋势。③夏、冬半年Z指数均表现为不显著的减小趋势。④夏、冬半年不同等级雨量和雨日与Z指数偏相关系数和相对贡献率分析,夏半年中雨和大到暴雨的雨量及雨日对Z指数有显著的影响,冬半年小雪的雨量和雨日对Z指数变化影响最大。⑤采用合成分析法的标准化值,无论是夏半年还是冬半年,在偏涝年,不同等级降水雨日和雨量基本都为正值,在偏旱年,不同等级降水雨日和雨量基本都为负值。在偏涝年,不同等级降水雨日和雨量差异夏半年主要表现在大到暴雨和中雨雨日及雨量,小雨雨日及雨量差异较小,而冬半年主要表现在小雪雨日及雨量,中雪次之,大到暴雪最小；在偏旱年,不同等级降水雨日和雨量差异夏半年主要表现在中雨雨日及雨量,大到暴雨次之,小雨最小,冬半年主要表现在小雪和中雪雨日及雨量,大到暴雪差异较小。     

汾渭平原农户冬小麦氮磷养分投入调查与分析

为明确汾渭平原农户冬小麦施肥现状，并指导农户合理施肥，在汾渭平原冬小麦种植区选取22个区/县，连续2年进行农户冬小麦产量及氮、磷养分投入调查。结果表明，汾河平原和渭河平原农户冬小麦平均产量分别为6 949和6 442 kg·hm^-2，其中中等产量水平（5 500 kg·hm^-2）以上的农户超过80%。汾河平原农户冬小麦N和P₂O₅的平均投入量分别为272.6和139.1 kg·hm^-2，施氮不足、适中和过量的农户分别占11.84%、23.68%和64.47%，施磷不足、适中和过量的农户分别占14.03%、7.89%和78.07%。渭河平原农户N和P₂O₅的平均投入量分别为196.3和147.2 kg·hm^-2，施氮不足、适中和过量的农户分别占 28.63%、31.97%和39.40%，施磷不足、适中和过量的农户分别占5.95%、14.50%和79.56%。汾河平原农户冬小麦氮肥基肥和追肥比例分别为60.3%和39.7%，而渭河平原农户冬小麦氮肥基肥和追肥比例分别为 97.3%和2.7%。此外，汾河平原农户冬小麦N、P₂O₅的平均偏生产力分别为30.4和59.6 kg·kg^-1，渭河平原农户冬小麦N、P₂O₅的平均偏生产力分别为38.4和56.1 kg·kg^-1。总之，在汾渭平原农户冬小麦种植过程中，氮、磷肥过量施用依然严重，导致肥料利用率低，因此，未来需进一步加强对农民的宣传培训，使其科学合理施肥，促进农业绿色发展。     

基于PLSR-BP复合模型的红壤有机质含量反演研究

对红壤地区土壤有机质进行快速预测,以满足智慧农业与精准施肥的需要。以江西省奉新县北部为研究区域,采用1 km×1 km标准格网划分研究区进行采样,共得到红壤样本248个。对土壤光谱进行了包含分数阶导数在内的3种数学变换方法,将经过P=0.01显著性检验的波段用于模型的构建,选用偏最小二乘回归(PLSR)和BP神经网络建立土壤有机质含量预测模型。结果表明:当对红壤光谱数据进行1.5阶导数变换后再使用PLSR-BP复合模型对土壤有机质含量进行预测时的结果为最优,训练集R~2=0.89,RMSE=4.68g·kg~(-1),验证集R~2=0.87,RMSE=5.55g·kg~(-1),RPD=2.75。1.5阶导数对红壤光谱数据的变换能够更好地突出与有机质相关的特征信息,有助于其含量预测。PLSR-BP复合模型预测精度优于单一模型,能够较好地预测红壤有机质含量,为精准农业快速监测红壤有机质含量提供了新的途径。     

青海省海东市不同等级降水和旱涝关系研究

青海省海东市是青海省重要的粮食生产基地,干旱直接影响着农业的有序发展。利用1961—2015年青海省海东市5个气象站逐日降水资料,笔者分析各级雨量和雨日及Z指数的变化趋势,并研究各级雨量和雨日对旱涝的影响。结果表明：（1）夏（冬）半年降水以小雨（雪）和中雨（雪）为主;夏半年小雨和大到暴雨（中雨）的雨量及雨日呈减小（增加）趋势,冬半年各级雨量及雨日均呈减小趋势;（2）夏、冬半年Z指数均表现为不显著的减小趋势;（3）采用偏相关系数和相对贡献率分析,夏半年中雨和大到暴雨雨量及雨日对Z指数有显著的影响,冬半年小雪的雨量和雨日对Z指数变化影响最大;（4）采用合成分析法的标准化值,在偏涝年,各级雨量和雨日基本都为正值,夏半年差异主要表现在大到暴雨和中雨雨量和雨日,而冬半年差异主要表现在小雪雨量和雨日;在偏旱年,各级降水雨量和雨日基本都为负值,夏半年差异主要表现在中雨雨量和雨日,冬半年差异主要表现在小雪和中雪雨量和雨日。     

Investigating the effects of simulated transport vibration on tomato tissue damage based on vis/NIR spectroscopy

Visible and near infrared (vis/NIR) spectroscopy combined with chemometrics were investigated to evaluate the effects of simulated transport vibration levels on damage of tomato fruit. A total of 280 tomato samples were randomly divided into 5 groups; each group was subjected to vibration at different acceleration levels. A total of 230 samples (46 from each group) were selected as a calibration set; whereas 50 samples (10 from each group) were selected as a prediction set. Raw spectra, differentiation (the first derivative) spectra, extended multiplicative scatter correction (EMSC) processed spectra and standard normal variant combined with detrending (SNV–DT) processed spectra were used for calibration models. SNV–DT processed spectra had the best performance using for partial least squares (PLS) analysis. The PLS analysis was implemented to calibrate models with different wavelength bands including visible, short-wave near infrared (SWNIR) and long-wave near infrared (LWNIR) regions. The best PLS model was obtained in the vis/NIR (600–1600 nm) region. Using a grid search technique and radial basis function (RBF) kernel, four least squares support vector machine (LS–SVM) models with different latent variables (7, 8, 9, and 10 LVs) were compared. The optimal model was obtained with 9 LVs and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for the best prediction by LS–SVM were 0.984, 0.137 and 0.003, respectively. The results showed that vis/NIR spectroscopy could be applied as a reliable and rapid method for predicting the effect of vibration levels on tissue damage of tomato fruit.     

基于连续投影算法和光谱变换的冬小麦生物量高光谱遥感估算

为探索基于全波段冠层高光谱以及变换光谱的冬小麦地上部生物量的遥感估算方法，以2016、2017年冬小麦田间试验为基础，通过对冠层光谱和地上部生物量的相关性分析，筛选拔节期、抽穗期的冬小麦冠层光谱、一阶导数光谱、对数变换光谱和连续统去除光谱对地上部生物量的敏感波段，并结合偏最小二乘法(PLS)分别建立拔节期和抽穗期基于SPA算法的冬小麦地上部生物量估测模型，再与基于任意两波段组合的最佳归一化光谱指数、比值光谱指数、差值光谱指数和已报道光谱指数的冬小麦地上部生物量估测模型进行比较。结果表明：(1)SPA算法较好地利用了全波段冠层光谱信息，并显著降低了光谱维度，不同变换光谱的地上部生物量敏感波段个数在4~14之间；(2)拔节期和抽穗期冠层光谱与地上部生物量的相关性高于开花期和灌浆期，各生育时期一阶导数光谱与地上部生物量之间的相关性优于连续统去除光谱、对数变换光谱和光谱指数；(3) 利用抽穗期一阶导数光谱敏感波段建立的预测模型和验证模型达到了较高的精度，其预测模型的决定系数和均方根误差分别为0.78和0.87 t·hm^-2，验证模型的决定系数和均方根误差分别为 0.84和0.69 t·hm^-2，预测相对偏差为2.74。这说明，抽穗期是估算地上部生物量的最佳生育时期，且基于冠层一阶导数变换光谱，结合连续投影算法和偏最小二乘回归方法所构建抽穗期地上部生物量估算模型具有最优的精度和预测能力，可用于地上部生物量的定量估算。     

Agronomic response and water productivity of almond trees under contrasted deficit irrigation regimes

We investigated the long-term effects of different deficit irrigation (DI) options on tree growth, shoot and leaf attributes, yield determinants and water productivity of almond trees (Prunus dulcis, cv. Marta) grown in a semiarid climate in SE Spain. Three partial root-zone drying (PRD) irrigation treatments encompassing a wide range of water restriction (30%, 50% and 70% of full crop requirements, ET_c) and a regulated deficit irrigation treatment (RDI, at 50% ET_c during kernel-filling) were compared over three consecutive growth seasons (2004–2006) to full irrigation (FI). The results showed that all deficit irrigation treatments have a negative impact on trunk growth parameters. The magnitude of the reduction in trunk growth rate was strongly correlated through a linear relationship with the annual volume of water applied (WA) per tree. Similarly, a significant relationship was found between WA and the increase in crown volume. In contrast, leaf-related attributes and some yield-related parameters (e.g., kernel fraction) were not significantly affected by the irrigation treatments. Except in PRD₇₀, individual kernel weight was significantly reduced in the deficit irrigated treatments. Kernel yield, expressed in percent of the maximum yield observed in the FI treatment, showed a linear decrease with decreasing WA and a slope of 0.43, which implies that a 1% decrease in water application would lead to a reduction of 0.43% in yield. Water productivity increased drastically with the reduction of water application, reaching 123% in the case of PRD₃₀. Overall, our results demonstrate the prevalence of direct and strong links between the intensity of the water restriction under PRD – i.e., the total water supply during the growing season – and the main parameters related to tree growth, yield and water productivity. Noteworthy, the treatments that received similar annual water volumes under contrasted deficit irrigation strategies (i.e., PRD₇₀ and RDI) presented a similar tree performance.     

Genotypic differences of maize in grain yield response to deficit irrigation

This study was undertaken to investigate genotypic differences of five maize cultivars in grain yield response to two different modes of deficit irrigation, conventional deficit irrigation and partial root zone irrigation. Three irrigation treatments were implemented: (1) FULL irrigation, the control treatment where plant water requirement, 100% Class-A pan evaporation, was fully met and the furrows on both sides of the plant rows were irrigated; (2) partial root zone irrigation (PRI), 35% deficit irrigation, compared to FULL treatment, was applied in every other furrow thus irrigating only one side of the plant rows. The furrows irrigated were alternated every irrigation; (3) conventional deficit irrigation (CDI), the same amount of water as PRI was applied in furrows on both sides of the plant rows, similar to FULL irrigation treatment. Five maize cultivars (P.31.G.98, P.3394, Rx:9292, Tector and Tietar) showing extreme growth response to water stress were selected out of ten cultivars tested with earlier completed greenhouse-pot experiment. A split-plot experimental design, comprising three irrigation treatments and five maize cultivars with four replicates, was used during two years of work, in 2005 and 2006. Total of nine irrigations, with one-week irrigation interval, were annually applied using a drip-irrigation system. Soil water status was monitored using a neutron moisture gauge, in addition to measuring leaf water potential and above-ground biomass production throughout the growing season. Grain yield and other yield attributes were measured at harvest as well as assessing differences in plant root distributions. Decrease in grain yield and harvest index of the tested cultivars, compared to FULL treatment, was proportionally less under PRI than CDI. Whether or not a significant yield advantage can be obtained under PRI compared to CDI showed significant (P < 0.05) genotypic variability. Tector and Tietar among the tested cultivars of maize showed significantly higher grain yield (P < 0.05) under PRI than CDI. The yield advantage of the genotypes (P.3394 and Tector) under PRI compared to CDI seems related to their enhanced root biomass developed under PRI.