Hyperspectral Models for Estimating SPAD Value of Cotton Leaves under Waterlogging Stress

[Object] To setup the hyperspectral sensing models for estimating SPAD value of cotton leaves under waterlogging stress. [Method] Irrigation and drainage controllable plots were introduced to simulate the waterlogging stress treatment in the flowering and boll forming stage, during which the change characteristics of the cotton leaf spectral reflectance and SPAD value were observed after 1 d, 3 d, 6 d, 9 d waterlogging, respectively. To find out the hyperspectral sensing models for estimating SPAD value of cotton leaves under waterlogging stress, the correlation and regression relationships between SPAD value and spectrum parameters were analyzed. [Result] (1) The SPAD value of the fourth cotton leaf from the top was significantly lower than control when suffers from waterlogging for 3 d, when waterlogged 9 d the SPAD value decreased by around 15% compared with the control. (2) The cotton suffering from waterlogged damage in the flowering and boll forming stage caused the reflection peak in green light wave band became steep, while the near infrared spectral reflectance increased, and caused the reduction of red absorption and red edge position "blue shifts", the red edge position drifts towards short wave with 4～5 nm when suffers from waterlogging for 9 d. With increase of the waterlogged days, the red edge slope and red edge area increased with a maximum value at 6 d of waterlogging, meanwhile, the skewness and kurtosis of red edge increased. (3) After waterlogging, the SPAD value of the fourth cotton leaf from the top (chlorophyll content) had a remarkable correlation with red edge slope(Dr), red edge position(λr), green peak reflection(Rg), green peak position(λg), red well position(λo), blue edge area(SDb), yellow edge skewness(Sy), yellow edge kurtosis(Ky), red edge skewness(Sr), red edge kurtosis(Kr), etc. An experience linear, polynomial and exponential models for estimating SPAD value had been built through using the Sy, Sr, Kr as independent variables, respectively, their determination coefficient (R²) were greater than 0.9, and the root mean square error (RMSE) were less than 1; and an experience binary linear regression equation for estimating SPAD value had been built through multivariate regression using the λg, SDr/SDb(VI3), Sb, Sy, Ky as independent variables, the R² was as high as 0.973, and the RMSE was 0.393. [Conclusion] The model can be remote sensing model used as estimating leaf SPAD of cotton value under waterlogging stress.     

谷子杂交种与常规种光合和叶绿素荧光特性的比较

为了明确杂交谷的光合优势是否是其产量高于常规谷的一大主因,本研究于2013年对常规谷晋谷21号和杂交谷张杂谷5号的光合和叶绿素荧光特性进行比较。采用完全随机设计,每个品种种植5个小区,在拔节期和灌浆期分别测定各项光合指标。结果表明,在拔节期,张杂谷5号的净光合速率、PSⅡ实际光化学量子产量显著高于晋谷21号;但在灌浆期,张杂谷5号和晋谷21号之间的总叶绿素含量、净光合速率、蒸腾速率、气孔导度、胞间CO2浓度、PSⅡ实际光化学量子产量、光化学淬灭参数和非光化学淬灭参数都存在显著差异。总体杂交谷在拔节期和灌浆期的光合性能高于常规谷。     

基于随机森林回归算法的小麦叶片SPAD值遥感估算

使用机器学习中的随机森林(RF)回归算法构建小麦叶片SPAD值遥感反演模型。以2010—2013年江苏地区试验点稻茬小麦3个生育期(拔节、孕穗、开花)的叶片为材料,结合我国自主研发的环境减灾卫星HJ-1对研究区域进行同步监测,分析了各生育期叶片SPAD值与8种植被指数间的相关性;以0.01水平下显著相关的植被指数作为输入参数,使用RF回归算法构建了每个生育期的小麦SPAD反演算法模型,即RF-SPAD模型,以支持向量回归(SVR)和反向传播(BP)神经网络算法构建的SVR-SPAD模型和BP-SPAD模型作为比较模型,以R2和均方根误差(RMSE)为指标,分析了每个生育期3个模型的学习能力和回归预测能力,结果表明:RF-SPAD模型在3个生育期都表现出最强的学习能力,R2和RMSE在拔节期分别为0.89和1.54,孕穗期分别为0.85和1.49,开花期分别为0.80和1.71;RF-SPAD模型在3个生育期的回归预测能力都高于BP-SPAD模型,高于或接近于SVR-SPAD模型,R2和RMSE在拔节期分别为0.55和2.11,孕穗期分别为0.72和2.20,开花期分别为0.60和3.16。     

抽穗后遮荫对龙粳31产量及籽粒灌浆的影响

对水稻龙粳31抽穗后进行遮光处理,研究遮荫处理与未遮荫处理(CK)在产量及其构成、功能叶片的SPAD值和灌浆特性等方面的差异。结果表明:龙粳31抽穗后进行遮荫,主要影响千粒重和结实率两个产量因素,产量降低达到极显著水平;胁迫功能叶片SPAD值升高;遮光处理的强、弱势粒籽粒最大灌浆速率GR_(max)和平均灌浆速率GR_(mean)均低于对照。     

Continuous Monitoring of Visible and Near-Infrared Band Reflectance from a Rice Paddy for Determining Nitrogen Uptake Using Digital Cameras

Abstract

A two-band digital imaging system —one band for the visible red band (RED, 630?670 nm) and the other for the near infrared band (NIR, 820?900 nm)— was devised and positioned at a height of 12 m above a rice field of 300 m² in area during the 2007 growing season. The imaging system automatically logged bird’seye view images at 10-min intervals from 0800?1600 every day. Radiometric corrections for the pairs of two-band images were done using solar irradiance sensors and preceding calibrations to calculate daily band-reflectance and the normalized difference vegetation index (NDVI) values for 9 plots of rice plants, with 3 levels of planting density and basal fertilization. The daily- averaged reflectance values in the RED and the NIR bands showed different but smooth seasonal changing patterns according to the growth of plants. At the maximum tiller number and the panicle formation stages, the RED and NIR reflectance values had correlation coefficients (r) of 0.79 and 0.81 with above-ground nitrogen absorption per unit land area (NA, g m-²), respectively, whereas the NDVI using the two band reflectance values showed r-value of -0.13. An empirically derived equation for the NA using two band reflectance values showed r-value of 0.96 and a root mean square of error (RMSE) 0.5 g m^–2 (10% of the mean observed NA) in the estimation for the original (not validated) data set acquired at the maximum tiller number and the panicle formation stages. The results indicated that reflectance observation in the RED and NIR bands using the digital imaging system was potentially effective for assessing rice growth.     

Evaluating foliar nitrogen compounds as indicators of nitrogen status in Prunus persica trees

Mobile nitrogen (N) forms may be better N indicators of the N status of trees than total nitrogen (TN) due to their higher sensitivity to increasing N supply. A field experiment was carried out over a 3-year period to compare foliar concentrations of total N (TN), soluble N (SN), chlorophyll (Minolta SPAD readings), NH₄–N and NO₃–N as indicators of soil N availability in nectarine, Prunus persica L. Batsch, cv. ‘Fantasia’ (grafted on ‘Nemaguard’ peach, P. persica × P. davidiana) trees. Young trees were exposed to a range of fertilizer-N application rates. Based on correlation analysis, the best association between leaf N compounds with soil N supply and trunk diameter and/or fruit yield was obtained with TN and chlorophyll SPAD readings. Leaf concentrations of mobile N compounds (NH₄–N and NO₃–N) increased more than any other N compound under high N supply; however, their inconsistency among years and low leaf concentration difficult their use as N indicators. The optimum foliar TN for growth decreased with tree age, 4.4%, 3.6% and 3.3% in non-bearing 1-year-old trees, non-bearing 2-year-old trees and 3.3 fruit-bearing 3-year-old trees. The optimum SPAD readings were 40 in 2-year-old trees and 42 in 3-year-old trees. Stable N compounds (TN and chlorophyll SPAD) could be used to N diagnosis in the zone of N deficiency, and soluble N compounds (NH₄–N and NO₃–N) to diagnoses N excess.     

棉花SPAD值推荐施氮模型应用与修正

【目的】研究棉花SPAD值推荐施氮模型应用与修正,为棉花SPAD法推荐施肥模型在田间应用的准确性、普适性及合理性提供科学依据。【方法】采用田间小区试验,设3个处理:按照棉花生育时期测定SPAD值推荐施氮(N₁);按照棉花叶龄测定SPAD值推荐施氮(N₂);按当地高产模式推荐施氮(N₃);对比SPAD值、产量构成指标等数据变化特征,修正SPAD值推荐施氮模型。【结果】N₁处理与SPAD值关系式为y=0.090 3x+46.618, R²=0.855;N₂处理与SPAD值关系式为y=0.108 6x+48.666,R²=0.765,N₁处理的追氮量与SPAD值的相关性要高于N₂处理;M₁推荐的N₁处理追氮量与M₂推荐的N₂处理追氮量对产量的影响差异不显著;N₁的追氮量417.45 kg/hm²高于当地高产的追氮量,N₂的285.40 kg/hm²低于当地高产的追氮量,但是N₁和N₂的产量均明显低于当地高产产量;在棉花各个生育时期,N₁与N₂的追肥量与棉花需肥量趋势基本一致,但是各时期N₁的追肥量高于N₂的追肥量。【结论】棉花SPAD值推荐施氮模型具有科学性、可应用性,但在土壤、气候、管理模式变化时需要对模型参数进行修正,才能达到SPAD值与施氮量的最大相关性。修正SPAD值推荐施氮模型时可以利用原有模型的推荐施肥量、当地多年高产施肥量,通过反推快速得到当年就能使用的新推荐施肥公式。     

生物活性水对玉米种子萌发及幼苗生长的影响

以北农青贮308玉米种子为试验材料,分别用生物活性水稀释10、25、50、100、200、400、800、4000倍对玉米种子进行浸种处理,以蒸馏水为对照,比较种子的发芽率、发芽势、吸水值、贮藏物质转运率、生物量、根长、芽长、叶绿素含量。试验结果表明,除稀释10倍处理外,不同浓度生物活性水均能显著提高玉米种子的发芽率、发芽势及苗期的长势和叶绿素的含量,其中稀释100倍的生物活性水最有利于种子的萌发,稀释50倍的生物活性水在苗期对壮苗和促进株高效果最明显。     

Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions. Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field. The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor, soil and plant analyzer development (SPAD) chlorophyll meter, and two different types of leaf color charts (LCCs) for five basmati rice genotypes across different growth stages. Regression analysis was performed using normalized difference vegetation index (NDVI) recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University, Ludhiana (India) (PAU-LCC) and the International Rice Research Institute, Philippines (IRRI-LCC). The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination (R²) and minimum normalized root mean square error (NRMSE) at panicle initiation stage and explained 38.3%-76.4% variation in yield using genotype-specific models. Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained 53.4%-57.2% variation in grain yield. Normalizing different spectral indices with cumulative growing degree days (CGDD) decreased the accuracy of yield prediction. Normalization with days after transplanting (DAT), however, did not reduce or improve the predictability of yield. The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses. The NDVI-based genotype-specific models exhibited a robust linear correlation (R²=0.77, NRMSE=7.37%, n=180) between observed and predicted grain yields only at 35 DAT (i.e., panicle initiation stage), while the SPAD, PAU-LCC, and IRRI-LCC consistently provided reliable predictions (with respective R² of 0.63, 0.60, and 0.53 and NRMSE of 10%, 10%, and 13.6%) even with genotype invariant models with 900 data points obtained at different growth stages. The study revealed that unnormalized values of spectral indices, namely NDVI, SPAD, PAU-LCC, and IRRI-LCC, can be satisfactorily used for in-season estimation of grain yield for basmati rice. As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors, they can be preferably used by small farmers in developing countries.     

覆盖经营对高节竹叶片主要生理特征的影响

以连续2年林地覆盖和不覆盖高节竹（Phyllostachys prominens）林为对象,测定了叶片MDA、可溶性蛋白、可溶性糖含量和抗氧化酶活性、叶绿素值,研究短期林地覆盖经营对高节竹叶片生理特征的影响,结果表明：林地覆盖经营对高节竹2、3年生立竹叶片MDA、可溶性蛋白和可溶性糖含量影响不明显,但对1年生立竹有显著影响;对1~3年生立竹叶片SOD、POD和CAT活性总体上有较明显的影响,并显著提高了叶片叶绿素值。