Chlorophyll Stability is an Indicator of Drought Tolerance in Peanut

Chlorophyll stability during drought might be a promising criterion for selection for drought resistance in peanut. The study describes two field trials conducted at Khon Kaen University, Thailand which investigate genotype × drought interactions in a wide range of peanut germplasm in general and assess the relationship between chlorophyll stability and genotypic performance in particular, under drought. Two field experiments (during 2003/2004 and 2004/2005 dry seasons) were conducted in a split plot design with three water regimes [field capacity, 2/3 available water (AW) and 1/3 AW] as main, and 12 peanut genotypes as subtreatments, replicated four times. Observations on total dry matter (TDM), chlorophyll density (ChlD) (chlorophyll content per unit leaf area), chlorophyll content (chlorophyll content per plant) and SPAD chlorophyll meter readings (SCMR) were recorded at 30, 60 and 90 days after emergence. Transpiration (T) and transpiration efficiency (TE) were computed using the data on amount of water input and TDM. Drought stress significantly reduced TDM, T and chlorophyll content across genotypes but significantly increased TE and ChlD in peanut. However, there were significant differences among genotypes for TE and chlorophyll parameters. The genotype × drought interaction effects for chlorophyll characters (content and density) were not significant suggesting a strong genetic effect. The correlation coefficients between TDM and chlorophyll content (r = 0.51, P = 0.01 to r = 0.91, P = 0.01) and between TE and ChlD (r = 0.46, P = 0.05 to r = 0.77, P = 0.01) were positive and significant. These findings suggest that chlorophyll parameters are strongly linked with drought tolerance in peanut. There were highly significant and positive relationships between ChlD and SCMR (r = 0.67, P = 0.01 to r = 0.93, P = 0.01), between SCMR and TE (r = 0.41, P = 0.05 to r = 0.80, P = 0.01) suggesting that SCMR could be used as a tool for rapid assessment of relative chlorophyll status in peanut genotypes as well as for the indirect selection of drought tolerance in peanut.     

基于叶片SPAD的棉花氮营养指数估算研究

为了探究不同施氮处理对棉花叶片SPAD值、生物量和氮营养指数(NNI)的影响,通过两年田间试验,设置5个施氮水平（即0、120、240、360、480 kg/hm²）,开展棉花叶片SPAD值、氮素吸收分配规律和生物量变化的研究。研究结果发现：棉叶SPAD值随施氮量增加呈增加趋势,在N4处理下获得最大值;建立棉花氮浓度稀释模型：N_c=3.02W_max^-0.24,分析表明棉叶SPAD与NNI之间呈正相关关系,相关系数R²为0.05~0.74;经验证,棉花不同生育时期的模型模拟性能较好,出苗后97天时相关系数达到最大。棉花叶片SPAD值与临界氮浓度存在显著的正相关关系,可以作为棉花氮营养诊断的指标。     

豫中上六片烤烟不同采收期对烤后烟叶品质的影响

为明确豫中烟区烤烟上六片适宜的成熟采收特征、成熟度与烟叶产质量关系，确定最有利于质量提升的成熟度标准，形成优质上六片烟叶成熟采收技术规程，以中烟100为试验材料，设置不同采收梯度，在田间进行烟叶成熟特征描述和SPAD值测定，并对烤后烟叶经济性状、外观质量、物理性状、化学成分和感官指标进行相关分析。结果表明：延迟10~11 d采收的上六片烟叶呈桔黄至深桔黄色，烟叶厚薄适中，结构疏松，成熟度好，油分足，光泽强，色度浓，弹性好，经济效益较高，感官质量评价最好。此时，上六片烟叶的田间成熟特征为叶脉基本全白，叶片颜色为淡黄色,成熟斑较多，上三片和下三片相对应的SPAD值分别为5.4~5.7和6.4~6.9。因此，豫中烟区上六片烟叶应适当延迟10~11 d采收，此时可获得产质量均较好的优质上部烟叶。     

叶绿素仪与植株硝酸盐浓度测试对冬小麦氮营养诊断准确性的比较研究

通过研究不同土壤氮素供应对冬小麦拔节期叶绿素仪SPAD读数和植株硝酸盐浓度的影响,以及两种方法在冬小麦拔节期与土壤Nmin、植株全氮和吸氮量的关系,比较了SPAD叶绿素仪和植株硝酸盐测试两种氮素营养诊断方法的准确性。认为两种方法都可以比较好的反映冬小麦的氮素营养状况,植株硝酸盐浓度与SPAD读数相比能更好的反映作物的氮素营养状况。     

基于SPAD值确定东北黑土区水稻最佳施氮量及追肥时间

为确定东北黑土区水稻最佳施氮量及最佳追肥时间,采用田间试验的方式,使用SPAD-502,以"天"为单位,连续45天测定不同处理(N1~N6;SPAD40、SPAD38、SPAD36、SPAD34和SPAD32)下水稻叶片SPAD值的动态变化。研究结果表明:追施氮肥7天时间内SPAD值明显上升,随施氮量的增加水稻叶片SPAD值明显升高且峰值较大,同时到达峰值时间越早;当施氮量为170 kg N/hm2时产量最高能达到7078 kg/hm2,SPAD值为40时进行追肥产量最高,达到9078 kg/hm2。因此,在东北黑土区水稻最佳施氮量为170 kg N/hm2,当SPAD值为40时可用于确定分蘖期到孕穗期的最佳追肥时间。     

半湿润气候区土壤水分对冬小麦叶绿素值的影响

利用天水地区2007～2010年的冬小麦叶绿素含量(SPAD值)和土壤含水量实际观测资料,分析了土壤水分含量和SPAD值在冬小麦生育期内的动态变化过程,研究了冬小麦叶片SPAD值与不同深度土层土壤水分含量的关系,以及SPAD值与冬小麦生长量、产量构成要素和地段实产的关系。结果表明:(1)在冬小麦生育期内土壤含水量呈现减小趋势,叶片SPAD值则经历了一个先升高后降低的过程,浅层土壤含水量更易受降水的影响而升高;(2)土壤水分含量对冬小麦叶片SPAD值的影响主要集中开花期至乳熟期,土壤水分含量高的年份叶片SPAD值较高,反之则较低;(3)中浅层土壤水分含量与10d后冬小麦叶片SPAD值有显著的正相关性;(4)冬小麦叶片SPAD值与部分生长量和产量构成要素存在相关关系,但是与地段实际产量关系较弱。     

基于高光谱数据的晚籼稻品种剑叶SPAD值估测

本研究测定了长江中下游不同晚籼稻品种的剑叶SPAD值及反射光谱数据,分析了原始光谱及其变换数据与SPAD值的相关关系,建立了不同晚籼稻品种剑叶SPAD值估测模型,并采用平均偏差率对模型精度进行品种间验证.结果表明,水稻剑叶SPAD值与原始光谱的敏感波段位于710～720 nm之间,与一阶微分光谱的敏感波段为690～70...     

Identification of quantitative trait loci associated with iron deficiency chlorosis resistance in groundnut (Arachis hypogaea)

Iron deficiency chlorosis is an important abiotic stress affecting groundnut production worldwide in calcareous and alkaline soils with a pH of 7.5–8.5. To identify genomic regions controlling iron deficiency chlorosis resistance in groundnut, the recombinant inbred line population from the cross TAG 24 × ICGV 86031 was evaluated for associated traits like visual chlorosis rating and SPAD chlorophyll meter reading across three crop growth stages for two consecutive years. Thirty-two QTLs were identified for visual chlorosis rating (3.9%–31.8% phenotypic variance explained [PVE]) and SPAD chlorophyll meter reading [3.8%–11% PVE] across three stages over 2 years. This is the first report of identification of QTLs for iron deficiency chlorosis resistance-associated traits in groundnut. Three major QTLs (>10% PVE) were identified at severe stage, while majority of other QTLs were having small effects. Interestingly, two major QTLs for visual chlorosis rating at 60 days (2013) and 90 days (2014) were located at same position on LG AhXIII. The identified QTLs/markers after validation across diverse genetic material could be used in genomics-assisted breeding.     

喷水时长对葡萄叶片SPAD值和叶绿素含量的影响分析

为研究极端干旱区葡萄微喷条件下不同喷水时长对葡萄叶片SPAD值和叶绿素含量的影响,通过对葡萄各生育期叶绿素含量和SPAD值的测定,分析不同喷水时长处理下的SPAD值日变化及连日变化特征、SPAD值与叶绿素含量相关性及统计检验。结果显示,在不同喷水时长条件下,各处理的SPAD值呈先减小后增大的变化趋势,日变化值在40.1~45.1之间,其中,每天喷水1 h处理（WP1）的SPAD值(43.5)日平均值最高,其次是每天喷水2h处理（WP2）和每天喷水3 h处理（WP3）,分别为42.9和42.6,对照处理（CK）的SPAD值最低(41.2)。葡萄叶片SPAD值与叶绿素a、叶绿素b、总叶绿素含量间均达到极显著正相关关系,其回归方程分别为Ca=0.0915VSPAD-1.787、Cb=0.0682VSPAD-1.9327、Ct=0.1597VSPAD-3.7197,表明利用SPAD值预测叶绿素a含量、b含量与总叶绿素含量是可行的,为快速测定大田葡萄叶片叶绿含量提供了新的方法。     

基于Sentinel-2数据的干旱区典型绿洲植被叶绿素含量估算

以渭干河-库车河绿洲(渭-库绿洲)为研究区,采用在机器学习方面具有明显优势的随机森林回归算法,对绿洲内的4种典型植被(棉花、芦苇、杨树、大枣)叶片的叶绿素相对含量(soil and plant analyzer development,SPAD)考。进行估算和验证。首先基于"红边"处光谱信息丰富的哨兵2号(Sentinel-2)影像和由其衍生的一阶微分、二阶微分影像各提取23种对叶绿素敏感的宽波段光谱指数,加入3种影响植物生长的土壤参量(土壤含水量,土壤有机质,土壤电导率)作为影响叶片SPAD的特征变量,再根据以上特征变量对每种植被叶片各建立3种方案的SPAD估算模型,从而实现对绿洲内植被叶绿素的监测。结果表明:①影像经一阶微分再提取的植被指数相比原位光谱植被指数,在SPAD估测模型中起到了更重要的作用,在随机森林算法的重要性排序中位居前列;②4种植被叶片的SPAD估测模型都取得了不错的效果,芦苇叶片尤为显著,确定系数(R^2)达到了0.926;③分析对比3种方案下模型预测能力,方案3(包含土壤参量)的预测能力卓越〔2.143<相对百分比偏差(RPD)<2.692〕,其预测能力排序为:方案3>方案1>方案2,土壤属性和模型预测结果有较强的非线性相关。Sentinel-2数据具有理想的估算绿洲植被叶绿素含量的潜力,提供了一种高效、低成本、潜在高精度的方案来估算叶绿素含量,可为干旱区绿洲农业、生态系统实现更有效的保护和管理提供参考。