Application of the cultural techniques of cotton leaf-age adjusting-controlling for promotng early maturity

According to the production index of lint cotton yield over 100kg per mu(1/15ha)and the relationship between the climate factors and the distribution of time and space for cottonboll setting in 1985—1990,we established a model of cultural techniques of cotton leaf-age-ad-justing-controlling for enhancing early maturity.By utilizing the principle of leaf-age model andthe principle of regulating nutrient distribution with DPC(Mepiquate chloride),the plants weretreated with DPC in proper time.A new type of population plant patterns was set up.It character-ized by high population density,dwarf,high boll weight,bolls near nodes,high energy and lowconsumption,short distance transport of nutrients,high yield and good quality.We put forward aset of management measures,i.e.adjusting boll setting stage,adjusting and controlling boll set-ting structure,stably applying N,increasing P,supplementing plants with K,increasing irrigationefficiency,techniques were applied and demonstrated in large areas in high yield

Physiological Response and Transcriptome Analysis of Cotton Leaves under Low Temperature Stress at the Two-leaf Stage

[Objectives]To investigate the effect of low temperature treatment on cotton leaves at the two-leaf stage.[Methods]Two cotton varieties(CN01 and SJB016(low temperature-tolerant))were used as the trial materials.They were treated at 25(CK)and 12℃(low temperature)for 0,3,6,12,24,48 and 72 h,respectively.Then,the changes in the contents of MDA,SS and Pro in the cotton leaves were analyzed.Based on the analysis results,RNA-seq verification was performed.[Results]Two cotton varieties(CN01 and SJB016(low temperature-tolerant))were used as the trial materials.They were treated at 25(CK)and 12℃(low temperature)for 0,3,6,12,24,48 and 72 h,respectively.Then,the changes in the contents of MDA,SS and Pro in the cotton leaves were analyzed.Based on the analysis results,RNA-seq verification was performed.[Conclusions]These genes may play an important role in improving the cold resistance of cotton.

Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.

Spectral Discrimination of Two Pigweeds from Cotton with Different Leaf Colors

To implement strategies to control Palmer amaranth (Amaranthus palmeri S. Wats.) and redroot pigweed (Amaranthus retroflexus L.) infestations in cotton (Gossypium hirsutum L.) production systems, managers need effective techniques to identify the weeds. Leaf light reflectance measurements have shown promise as a tool to distinguish crops from weeds. Studies have targeted plants with green leaves. This study focused on using leaf hyperspectral reflectance data to develop spectral profiles of Palmer amaranth, redroot pigweed, and cotton and to determine regions of the light spectrum most sensitive for pigweed and cotton discrimination. The study focused on cotton near-isogenic lines created to have bronze, green, or yellow colored leaves. Reflectance measurements within the 400 to 2500 nm spectral range were obtained from cotton and weed plants grown in a greenhouse in 2015 and 2016. Two scenarios were evaluated for the comparison: (1) Palmer amaranth versus cotton lines and (2) redroot pigweed versus cotton lines. Statistical significance (p ≤ 0.05) was determined with analysis of variance (ANOVA) and Dunnett’s test. Sensitivity measurements were tabulated to determine the optimal region of the light spectrum for weed and cotton line discrimination. Optimal bands for weed and cotton separation were 600 to 700 nm (both weeds versus cotton bronze and cotton yellow), 710 nm (Palmer amaranth versus cotton green), and 1460 nm (redroot pigweed versus cotton green). Spectral bands were identified for separating Palmer amaranth and redroot pigweed from cotton lines with bronze, green, and yellow leaves. Ground-based and airborne sensors can be tuned into the regions of spectrum identified, facilitating using remote sensing technology for Palmer amaranth and redroot pigweed identification in cotton production systems.

Yield, Leaf Senescence, and Cry1Ac Expression in Response to Removal of Early Fruiting Branches in Transgenic Bt Cotton

Two-year field experiments were conducted at Linqing, Yellow River valley of China, to study the plant response to the removal of early fruiting branches in transgenic Bt （Bacillus thuringiensis） cotton （Gossypium hirsutum L.） from 2003 to 2004. Plants were undamaged and treated by removing two basal fruiting branches （FB） at squaring to form the control and the removal treatment, respectively. The plant height, leaf area （LA）, dry weight of fruiting forms （DWFF）, the number of fruiting nodes （NFN）, photosynthetic （Pn） rate, and levels of leaf chlorophyll （Chl）, N, P, K, and Cry lAc protein in main- stem leaves were measured at a 10- or 20-d interval after FB removal, and the sink/source ratio as indicated by NFN/LA and DWFF/LA was determined. FB removal significantly increased the plant height, LA, and plant biomass in both years. Lint yields were increased 7.5 and 5.2% by removal compared with their controls in 2003 and 2004, respectively. Significant increases in boll size （5.7 and 5.1%） were also observed in removal than in control for both years. Either NFN/LA or DWFF/LA was significantly reduced by removal before 40 d after removal; however, both NFN/LA and DWFF/LA were significantly enhanced by FB removal at 80 d after removal compared to the untreated control. There was no significant difference in fiber quality in the first two harvests between removal and control, but fiber strength and micronarie in the third harvest were significantly improved by FB removal. In terms of leaf Chl, Pn rate, levels of total N, P, and K in late season, leaf senescence was considerably delayed by FB removal. Levels of CrylAc protein in the fully expanded young leaves were considerably higher in FB-excised plants than in control, indicating FB removal enhanced CrylAc expression. It is suggested that the yield and quality improvement with FB removal may be attributed to the increased NFN/LA or DWFF/LA in late season and delayed leaf senescence, respectively. FB removal can be a potential practice incorporated into the intensive cultivation system for enhancing transgenic Bt cotton production.

Relationship Between the N Concentration of the Leaf Subtending Boll and the Cotton Fiber Quality

This experiments were conducted in Nanjing （118~50＂E, 32~02＂N） and Xuzhou （117~11 ＂E, 34~15＂N）, Jiangsu Province, China, to study the response of fiber quality to the N concentration of the leaf subtending boll in cotton （Gossypium hirsutum L.）. Results suggested that the N dilution curve of the leaf subtending boll can accurately indicate the stage- specific plant N status for fiber development. Fiber strength is likely to be the most variable fiber quality index responding to the leaf N variation which is different in cultivars. Fiber length was the most stable index among strength, length, micronaire, and elongation. There existed an optimum leaf N concentration for fiber strength development in each stage. The optimum leaf N regression curve was very close to the N dilution curve in the middle positional fruiting branches under the 240 kg N ha-1 soil N application rate.

Genetic variation in LBL1 contributes to depth of leaf blades lobes between cotton subspecies, Gossypium barbadense and Gossypium hirsutum

Leaf is a essential part of the plants for photosynthetic activities which mainly economize the resources for boll heath. Significant variations of leaf shapes across the Gossypium sp. considerably influence the infiltration of sunlight for photosynthesis. To understand the genetic variants and molecular processes underlying for cotton leaf shape, we used F2 population derived from upland cotton genotype P30A （shallow-lobed leaf） and sea-island cotton genotype ISR （deep-lobed leaf） to map leaf deep lobed phenotype controlling genes LBL1 and LBL2. Genetic analysis and localization results have unmasked the position and interaction between both loci of LBL1 and LBL2, and revealed the co-dominance impact of the genes in regulating depth of leaf blades lobes in cotton. LBL1 had been described as a main gene and member of transcription factor family leucine zipper （HD-ZIPI） from a class I homologous domain factor Gorai.OO2G244000. The qRT-PCR results elaborated the continuous change in expression level of LBL1 at different growth stages and leaf parts of cotton. Higher expression level was observed in mature large leaves followed by medium and young leaves respectively. For further confirmation, plants were tested from hormonal induction treatments, which explained that LBL 1 expression was influenced by hormonal signaling. Moreover, the highest expression level was detected in brassinolides （BR） treatment as compared to other hormones, and this hormone plays an important role in the process of leaf blade lobed formation.

Small Scale Spatio-Temporal Variabilities in Soil Nitrogen, Leaf Nitrogen, and Canopy Normalized Difference Vegetation Index of Cotton

Strip plots have been increasingly used in agricultural field experiments to better reflect the true situation of crop production on farmers’ fields, but failure to account for spatially and temporally related errors when present in the data analysis of strip plot field experiments may cause inefficient assessment of treatment effect significance. The objective of this study was to investigate patterns and degrees of the spatial and temporal variabilities in soil inorganic N level, leaf N concentration, and Normalized Difference Vegetation Index (NDVI) of cotton under no-tillage and the influences of N treatments on these variabilities. A strip plot experiment was conducted on a private farm near Brazil, Gibson County, Tennessee from 2009 through 2011. Five N treatments of 0, 45, 90, 134, and 179 kg N ha-1 were implemented as side dress N in strip plots under a randomized complete block design with three replicates after 45 kg N ha-1 was applied in the form of chicken litter before cotton planting. Spatial variability was present in soil inorganic N before cotton planting and after harvest, and in leaf N and canopy NDVI at the early square and early, mid-, and late bloom stages although the patterns and degrees of the spatial variabilities sometimes varied with growth stages and years. Application of the in-season side-dress N treatments often reduced the spatial variations of leaf N and NDVI, but increased those of post-harvest soil inorganic N. Out results suggest that the spatial and temporal variabilities of soil inorganic N, leaf N, and NDVI are high, and should be taken into account if possible in the data analyses of N treatment effects on related soil properties and plant characteristics of cotton in strip plot field experiment research.

Effect of Some Environmental Factors on Incidence and Severity of Angular Leaf Spot of Cotton in Yola and Mubi, Adamawa State, Nigeria

Environmental factors such as relative humidity and rainfall generally have been found to increase the incidence, rate of spread and severity of diseases thereby reducing yield of crops. Study was conducted on five cotton varieties, which were artificially inoculated with bacterial blight pathogen to determine the effects of rainfall and relative humidity on incidence and severity of angular leaf spot (ALS) and yield of seed cotton in Yola and Mubi. Results showed that the severity of ALS was higher in Yola (58.65%) at 13 WAS assumed to be due to higher relative humidity range of 76% - 87% and low rainfall of 2 - 40.6 mm. This is assumed to have favoured disease development as against that of Mubi location which recorded lower severity (51.11%) due to lower relative humidity (42% - 55%) and rainfall (37 - 73 mm). Results further revealed that at 13 WAS, SAMCOT-8 had low incidence (66%) and severity (39%) in Yola. This was against the much higher corresponding incidence and severity of 82% and 42% respectively that was observed in Mubi during the same period. SAMCOT-10 and SAMCOT-9 varieties were found to be highly susceptible to the disease at the same period. SAMCOT-8 recorded the highest yield of 390.00 kg?ha?1 in Yola and 868.09 kg?ha?1 in Mubi while the lowest yields of 227.17 kg?ha?1 was observed on SAMCOT-10 in Yola while 461.61 kg?ha?1 was obtained on SAMCOT-9 in Mubi. The variation in yield among these varieties might be due to the differences in their reactions to the disease. There is a need to conduct further trials in these locations to confirm the level of resistance or other aspects of these varieties to the disease.

Effect of N fertilization rate on soil alkalihydrolyzable N, subtending leaf N concentration,fiber yield, and quality of cotton

Soil alkali-hydrolyzable nitrogen, which is sensitive to N fertilization rate, is one of the indicators of soil nitrogen supplying capacity. Two field experiments were conducted in Dongtai(120°19″ E, 32°52″ N), Jiangsu, China in 2009 and Dafeng(120°28″ E, 33°12″ N), Jiangsu province, China in 2010. Six nitrogen rates(0, 150, 300, 375, 450, and 600 kg ha^(-1)) were used to study the effect of N fertilization rate on soil alkali-hydrolyzable nitrogen content(SAHNC), subtending leaf nitrogen concentration(SLNC), yield, and fiber quality. In both Dongtai and Dafeng experiment station, the highest yield(1709 kg ha^(-1)), best quality(fiber length 30.6 mm, fiber strength 31.6 c N tex^(-1), micronaire 4.82), and highest N agronomic efficiency(2.03 kg kg^(-1)) were achieved at the nitrogen fertilization rate of 375 kg ha^(-1). The dynamics of SAHNC and SLNC could be simulated with a cubic and an exponential function,respectively. The changes in SAHNC were consistent with the changes in SLNC. Optimal average rate(0.276 mg day^(-1)) and duration(51.8 days) of SAHNC rapid decline were similar to the values obtained at the nitrogen rate of 375 kg ha^(-1)at which cotton showed highest fiber yield, quality, and N agronomic efficiency. Thus, the levels and strategies of nitrogen fertilization can affect SAHNC dynamics. The N fertilization rate that optimizes soil alkali-hydrolyzable nitrogen content would optimize the subtending leaf nitrogen concentration and thereby increase the yield and quality of the cotton fiber.

Spinning System for Pineapple Leaf Fiber via Cotton Spinning System by Solo and Binary Blending and Identifying Yarn Properties

By nature, pineapple fiber (PALF) obtained from pineapple leaf is a smooth, shiny and white natural cellulosic fiber. In current investigation of spinning technique for pineapple leaf fiber based on cotton spinning method comparison of produced yarn properties has been reported. For one of the investigations of this study, the fibers were cut into staple length and various properties of fibers were analyzed. Data and results acquired from this illustrated that there is excellent spinnability on the fiber. Two separate researches were conducted with consideration to produce resultant yarn by spinning of PALF. Yarn derived by solo spinning technique of 100% PALF and PALF blending with polyester and cotton fibers through binary blending technique in equal proportion has been spun, then yarn count, tensile properties, yarn evenness, hairiness have been evaluated and analyzed. The results from numerical simulations analysis indicated that yarn obtained from the Pineapple leaf fiber (PALF) had great potential to be used in apparels.

陆地棉叶片干质量的发育遗传研究

采用基因型与环境互作的加性-显性-母体遗传的非条件与条件分析即ADM遗传模型,研究7个陆地棉亲本及21个正交F1代在3种密度(2.4×10^(5)株/hm^(2)、1.8×10^(5)株/hm^(2)、1.2×10^(5)株/hm^(2))种植下,叶片干质量(LDW)发育遗传规律,探讨陆地棉叶片不同发育时期干质量、发育特征值与发育时间的关系,为优质亲本选育提供依据。结果表明:显性及其与环境互作效应存在于整个叶片干质量累积过程,在叶片不同发育时期或时段也存在较大的加性或母体效应影响,加性及母体与环境互作总体来说影响较小,叶片发育过程中以遗传主效应为主,而遗传主效应表达受环境调控较大;叶片干质量表现出明显杂种优势且易受环境条件影响的特点。不同发育时期或时段叶片干质量对最终叶片干物质的累积均有较大正向作用,但其遗传机制却各不相同。第20天、第30天对叶干质量具有较好选择效果。叶片干物质累积与叶片生长发育特点及亲本的选择有关,可根据各亲本的遗传效应预测值、环境稳定性进行陆地棉叶型育种。7个亲本中,大叶型育种时选择P5为亲本,小叶型育种时选择P2为亲本,以保证营养物质的有效储存与运输。

基于无人机多光谱的棉花多生育期叶面积指数反演

【目的】叶面积指数(leaf area index,LAI)是表征作物长势、光合、蒸腾的重要指标。论文旨在研究不同生育期、多生育期无人机多光谱数据棉花LAI估测模型,明确不同生育期间棉花LAI估测模型变化规律,为实时掌握棉花长势并因地制宜进行田间科学管理提供依据。【方法】利用大疆精灵4多光谱无人机获取棉花现蕾期、初花期、结铃期、吐絮期多光谱图像和RGB图像。选用归一化差植被指数(NDVI)、绿度归一化差植被指数(GNDVI)、归一化差红边指数(NDRE)、叶片叶绿素指数(LCI)、优化的土壤调节植被指数(OSAVI)5种多光谱指数和修正红绿植被指数(MGRVI)、红绿植被指数(GRVI)、绿叶指数(GLA)、超红指数(EXR)、大气阻抗植被指数(VARI)5种颜色指数分别建立棉花各生育期及棉花生长多生育期数据集合,结合打孔法获取地面LAI实测数据,使用机器学习算法中偏最小二乘(PLSR)、岭回归(RR)、随机森林(RF)、支持向量机(SVM)、神经网络(BP)构建棉花LAI预测模型。【结果】覆膜棉花LAI随着生育期的变化呈现先增长后下降的趋势,现蕾期、初花期、结铃期内侧棉花叶面积指数均值均显著大于外侧(P<0.05);选择的指数在各时期彼此间均呈显著相关(P<0.05),总体而言,多光谱指数与颜色指数间的相关性随着生育期的进行而呈现下降趋势,选择的指数在各时期均与棉花LAI相关性显著(P<0.05),多光谱指数相关系数介于0.35—0.85,颜色指数相关系数介于0.49—0.71,相关系数绝对值较大的指数多为多光谱指数,颜色指数与棉花LAI的相关系数绝对值较小;估测模型性能结果显示棉花各生育期模型中多光谱指数优于颜色指数,且各指数模型预测性能随着生育期的变化呈现一定规律性,NDVI是预测棉花LAI的最优指数。从模型结果上看,RF模型和BP模型在各生育期下获得了较高的估计精度。初花期LAI反演模型精度最高,最优模型验证集R2为0.809,MAE为0.288,NRMSE为0.120。多生育期最优模型验证集R2为0.386,MAE为0.700,NRMSE为0.198。【结论】棉花内外侧LAI在现蕾期、初花期、结铃期存在显著差异。在各生育期中,RF和BP模型是预测棉花LAI较优模型。NDVI在各指数中表现最好,是预测棉花LAI的最优指数。多生育期模型效果较单生育期明显下降,最优指数为GNDVI,最优模型为BP。本研究中预测棉花LAI的最优窗口期是初花期。研究结果可为无人机遥感监测棉花LAI提供理论依据和技术支持。

棉花叶片三维点云几何形状补全

棉花是新疆主要的经济作物之一,采用三维重建法自动获取棉花植株的表型数据,对实时监测棉花生长状态具有重要意义。由于遮挡、光线、分辨率等限制,获取的棉花植株点云不完整,因此本研究使用基于深度学习的点云补全PF-Net模型,对不完整的棉花叶片点云进行几何形态补全研究。首先采集多视角的棉花图像重建三维点云,然后利用聚类、最远点采样、归一化和数据增强等方法构建棉花叶片点云数据集,将数据带入PF-Net模型完成棉花叶片点云几何形态补全。结果表明,3种不同程度的缺失数据(25%,50%和75%)补全后的点云模型倒角距离值分别为11.8×10^(-3),9.9×10^(-3)和10.8×10^(-3),且补全后的棉花叶片点云可以保持原有的几何形态。因此,通过点云补全算法提供棉花植株叶片的完整三维模型,对棉花植株表型自动测量提供完整几何信息具有重要意义。

Nitrogen Concentration in Subtending Cotton Leaves in Relation to Fiber Strength in Different Fruiting Branches

Nitrogen（N） fertilizer experiments were conducted to investigate the optimal subtending leaf N concentration for fiber strength,and its relationship with activities of key enzymes（sucrose synthase and β-1,3-glucanase） and contents of key constituents（sucrose and β-1,3-glucan） involved in fiber strength development in the lower,middle and upper fruiting branches of two cotton cultivars（Kemian 1 and NuCOTN 33B）.For each sampling day,we simulated changes in fiber strength,activity of sucrose synthase and β-1,3-glucanase and levels of sucrose and β-1,3-glucan in response to leaf N concentration using quadratic eqs.;the optimal subtending leaf N concentrations were deduced from the eqs.For the same fruiting branch,changes in the optimal leaf N concentration based on fiber development（DPA） could be simulated by power functions.From these functions,the average optimal subtending leaf N concentrations during fiber development for the cultivar,Kemian 1,were 2.84% in the lower fruiting branches,3.15% in the middle fruiting branches and 3.04% in the upper fruiting branches.For the cultivar,NuCOTN 33B,the optimum concentrations were 3.04,3.28 and 3.18% in the lower,middle and upper fruiting branches,respectively.This quantification may be used as a monitoring index for evaluating fiber strength and its related key enzymes and constituents during fiber formation at the lower,middle and upper fruiting branches.

机采棉种植模式下不同棉花品种适应性研究

明确各品种在机采棉种植模式下形态、生理及产量因子变化,为长江流域棉花机械化生产中品种选择提供理论基础。通过2018—2019年大田试验,以‘中棉所63F2’(杂交棉品种)低密度(4.5万株/hm^(2))为对照,研究‘中棉所63F2’和‘中棉所425’(常规早熟棉品种)、‘中棉所50’(常规早熟棉品种)等3个棉花品种在高密度(9.0万株/hm^(2))机采棉种植模式下的品种生育进程、农艺性状、干物质积累及产量变化。结果表明,各品种在机采棉种植模式下,其叶面积指数、干物质积累量、成铃数及产量均高于对照;‘中棉所63F2’单铃重、籽棉产量高于2个常规早熟棉花品种,但未达显著水平。常规早熟棉品种‘中棉所425’、‘中棉所50’成熟较早、集中成铃,适宜长江流域一年两熟模式下连茬种植。

基于小型卷积神经网络的南疆棉花图像分类

针对南疆棉田图像分类特定应用场景,该文提出一种小型卷积神经网络模型,分别对幼苗期棉苗、缺苗穴和地膜及吐絮期叶片、成铃和吐絮铃图像进行自动化分类。该卷积神经网络由12层组成,包括交替堆叠的4个卷积层和4个最大池化层,以及1个展平层、1个Dropout层和2个密集连接层。采用智能手机拍照方法,获取幼苗期棉苗、缺苗穴和地膜图像13 920张,吐絮期叶片、成铃和吐絮铃图像21 427张。在普通笔记本电脑上部署TensorFlow、Keras深度学习框架和卷积神经网络模型,使用数据增强和添加Dropout层来消除过拟合。研究结果表明,小型卷积神经网络在幼苗期和吐絮期图像测试集的分类精度分别达到了0.999 3和0.975 7,模型具有很好的泛化能力,模型的训练时间约2 h。研究结果将为利用数字图像智能提取棉花缺苗信息及棉铃吐絮信息提供一定的参考。

基于CBAM-YOLO v7的自然环境下棉叶病虫害识别方法

针对自然环境下棉花叶片病害检测难度大和人工设计特征提取器难以获取与棉叶病虫害相近特征表达的问题,提出一种改进的注意力机制YOLO v7算法(CBAM-YOLO v7)。该模型在YOLO v7模型基础上,在Backbone与Head中间增加注意力机制CBAM,并在Head部进行4倍下采样,然后将CBAM-YOLO v7模型用于棉叶病虫害识别,并与YOLO v5和YOLO v7进行对比试验。试验结果表明:蚜虫和正常叶片检测方面,YOLO v7可取得好的检测结果;CBAM-YOLO v7对黄萎病、棉盲蝽、红蜘蛛棉叶病虫害图像检测的准确率高于其他模型。CBAM-YOLO v7的mAP为85.5%,相较于YOLO v5提高21个百分点,相较于YOLO v7提高4.9个百分点;单幅图检测耗时为29.26 ms,可为棉叶病害在线监测提供理论基础。

基于叶片生物量的南疆盐碱地棉花临界氮稀释曲线构建

为验证基于叶片生物量的西北内陆区(新疆南疆中熟棉区)盐碱地滴灌棉花临界氮稀释曲线年际间的稳定性和适应性,明确叶氮营养指数(LNNI)模型、叶片氮浓度(LNC)、氮水平(Nlevel)和籽棉产量(Y)间的关系模型对西北内陆区(新疆南疆中熟棉区)盐碱地滴灌棉花氮状况和适宜施氮量诊断、评价结果的一致性,通过2年定位5个氮水平(0、75、150、300、450 kg/hm^(2))的田间试验,建立了基于叶片生物量(LBM)不同棉花品种的临界氮(LNCc)稀释曲线、LNNI及LNC与Nlevel和Y间的关系模型。结果表明:(1)LNC与LBM符合幂指数关系,模型可分为两部分描述:新陆中55号(当LBM≥1.02 t/hm^(2)时,LNCc=3.41×LBM-0.38;LBM<1.02 t/hm^(2)时,LNCc=2.18%),新陆中78号(当LBM≥1.07 t/hm^(2)时,LNCc=4.08×LBM-0.43;当LBM<1.07 t/hm^(2)时,LNCc=2.61%);(2)新陆中55号和78号LNCc基于1∶1直线的R2、RMSE、ME分别为0.941、0.116 g/100 g、0.944和0.975、0.088 g/100 g、0.975,年际间具有较好的稳定性和适应性;(3)不同年际间,氮水平下新陆中55号和78号LNNI分别在0.76~1.12、0.70~1.09间波动,LNNI模型对新陆中55号和78号适宜氮水平诊断结果均以300 kg/hm^(2)处理最优;(4)LNC与Nlevel和Y关系模型得到新陆中55号和78号结铃期(BS)、吐絮期(OS)的LNC和Y分别为2.32%、1.95%和5850、5860 kg/hm^(2)及2.30%、1.99%和6070、5970 kg/hm^(2),适宜施氮量范围在313.3~380.0、335.0~385.0 kg/hm^(2)之间,这与氮水平和籽棉产量关系模型对新陆中55号和78号的适宜施氮量评价结果基本一致。研究结果可为西北内陆区(新疆南疆中熟棉区)盐碱地滴灌棉花氮状况精确诊断和适宜施氮量评价提供理论依据和方法参考。