Processing quality of winter-wheat is affected by genotype, environmental conditions, and crop husbandry practices. In the present study, a data set of 17 quality-related traits for 211 main winter-wheat varieties in China during 2006 to 2018 was extracted from China Wheat Quality Report. Analysis was carried out to evaluate the quality status and variations, to reveal correlation between quality-related traits, as well as to identify key influencing factors. Results indicated that the quality indicators of medium-gluten or medium-strong-gluten wheat varieties were acceptable, whereas those of weak- and strong-gluten wheat varieties were far below national standard, especially hardness index (HI), crude protein content (CPC), wet gluten content (WG), and water absorption for weak-gluten wheat and sedimentation value (SV), stability time (ST), and stretch area (SA) for strong-gluten wheat, respectively. Correlation analysis showed that WA, WG, development time, HI, CPC, falling number, ST, and tractility directly affected the overall quality of winter-wheat. CPC, SV, and WG in medium-gluten wheat had no significant correlation with the processing quality of noodles score, whereas gluten index significantly correlated with noodle score (P < 0.001). This implied that protein quality might play a more important role than protein quantity in determining medium-gluten wheat quality. Furthermore, analysis of variance showed that genetic characteristics (cultivars) had significant influences on the restriction indexes (SV, ST, and SA) of strong-gluten wheat, whereas genetic characteristics, environment conditions, and crop growing practices (cultivars, locations, and years) significantly affected the restriction indexes (HI, CPC, WG, and WA) of weak-gluten wheat. The results suggest that improvement of Chinese strong-gluten wheat should mainly focus on cultivating new varieties. As to weak-gluten wheat, cultivation and husbandry practices should be paid more attention to limit undesired high grain protein content.

It is a challenge to maintain high yields while improving grain quality of wheat under limited water and nitrogen supply conditions. To achieve a simultaneous improvement of yield and quality, a field trial was conducted from 2021 to 2023. Treatments with different timings (5, 10, and 15 ​d after anthesis) and frequencies of spraying nitrogen (1, 2, and 3 times) were set up after anthesis under a water-saving cultivation system. Spraying nitrogen after anthesis significantly increased grain yield and protein content by 4.99% and 6.00%, respectively. The increase in grain yield was mainly due to the improvement in grain weight, which was attributed to increased grain filling and starch synthesis. Spraying nitrogen once at 15 ​d after anthesis (T15 D) was optimal. T15 D treatment increased the photosynthetic pigment content and the enzyme activities of carbon and nitrogen metabolism of the flag leaves. T15 D increased the pre-anthesis nitrogen remobilization to grains by 4.71%-9.06% compared to the other spraying treatments, and promoted the accumulation of nitrogen in the grains. Moreover, T15 D optimized the protein composition of the grains and improved processing quality at maturity. In conclusion, spraying nitrogen at 15 ​d after anthesis may be an effective measure to simultaneously improve the yield and quality of winter wheat under a water-saving cultivation system.

小麦是中国第三大粮食作物，在农业生产和食品工业中占有重要地位。随着中国经济和社会的发展，消费者的生活需求正在发生变化,工业化和城市化使食品工业，特别是传统食品工业化加速发展。与此同时，食品工业对食品原料的专用化和规模化，即品种的质量、规格和数量，提出了更高的要求。如何研究和解决这一矛盾，已成为小麦产业和食品工业可持续发展的新问题。本文以国内发表的论著为基础，整理、分析了小麦品质改良历史、研究进展、存在问题；讨论了小麦产业链和食品加工业的可持续发展对优质小麦的需求；结合农业生产和食品工业的发展趋势提出建议，供小麦育种、生产、收贮、食品加工等部门和研究人员参考。

【目的】研究基因型和环境对新疆冬小麦品种蛋白质品质性状的影响。【方法】选用762份新疆冬小麦品种种植于新疆3个地点,检测籽粒蛋白质含量、湿面筋含量、面筋指数、沉淀值以及面团揉合特性等8个蛋白质品质性状。【结果】新疆冬小麦品种的籽粒蛋白质含量为14.44%,湿面筋含量为32.37%,面筋指数为61.86%,Zeleny沉淀值为26.25 mL,峰值时间为2.67 min,峰值高度为53.09%,8 min宽度为15.30%,8 min面积为107.16%TQ×min,多数品种属中筋类型。不同地点间小麦蛋白质品质性状存在差异,蛋白质数量性状表现为阿克苏点>石河子点>伊宁点,蛋白质质量性状表现为伊宁点>石河子点>阿克苏点,3个地点间的蛋白质质量性状表现出显著差异。基因型、环境及G×E互作效应对蛋白质品质性状均有影响,但作用大小不同,环境为47.62%~68.94%,G×E为23.34%~41.38%,基因型为7.71%~11.00%。不同类型的蛋白质品质性状稳定性不同,其中籽粒蛋白质含量、湿面筋含量、Zeleny沉淀值、峰值时间、8 min宽度和8 min面积稳定性表现为石河子点>阿克苏点>伊宁点,峰值高度稳定性表现为伊宁点>石河子点>阿克苏点,面筋指数稳定性表现为阿克苏点>伊宁点>石河子点。【结论】基因型、环境及G×E对新疆冬小麦品种蛋白质品质性状均有显著影响作用,依次为环境>G×E>基因型;新疆小麦品种多属中筋类型,除籽粒蛋白质含量和湿面筋含量外,其他蛋白品质性状均未达到国标优质强筋的标准,且强筋小麦品种较少;不同地点的蛋白质品质特性不同,阿克苏点蛋白质数量性状总体优于石河子点与伊宁点,伊宁点蛋白质质量性状表现总体优于石河子点和阿克苏点;不同品质性状的稳定性不同,籽粒蛋白质含量、湿面筋含量、Zeleny沉淀值、峰值时间、8 min宽度和8 min面积在石河子点稳定性较好,峰值高度在伊宁点稳定性较好,面筋指数在阿克苏点稳定性较好;小偃54号、小偃22号、百农68、中优206、中麦175等,可供新疆冬小麦蛋白质品质改良利用。

【目的】氮素的精准监测和合理施用对小麦健康生长、产量及品质提升、减少农田环境污染与资源浪费尤为重要。为精准监测小麦生长关键生育期植株氮含量，探索机器学习方法构建的植株氮含量预测模型的迁移能力。【方法】小区试验于2020—2022年在河南省商水县开展，在冬小麦拔节期、孕穗期、开花期和灌浆期，采用M600大疆无人机搭载K6多光谱成像仪获取5波段（Red、Green、Blue、Rededge、Nir）多光谱影像。基于5个波段冠层反射率提取20种植被指数和40种纹理特征，采用相关分析从65个影像特征中筛选冬小麦植株氮含量敏感特征。基于筛选出的敏感特征，采用BP神经网络（BP）、随机森林（RF）、Adaboost、支持向量机（SVR）4种机器学习回归方法构建植株氮含量预测模型，并对模型预测效果和在不同水处理条件下模型的迁移预测能力进行分析。【结果】（1）植株氮含量与影像特征的相关系数通过0.01极显著水平检验的包括22个光谱特征和29个纹理特征。（2）4种机器学习回归方法构建的冬小麦植株氮含量预测模型存在差异，RF和Adaboost方法预测植株氮含量集中于95%的置信区间，多分布于1﹕1直线附近，而BP和SVR方法预测的植株氮含量分布相对较为分散；RF方法构建的预测模型R 2最大，RMSE最小，MAE中等，分别为0.81、0.42%和0.29%；SVR方法构建的预测模型R 2最小，RMSE和MAE较大，分别为0.66、0.54%和0.40%。（3）以W1处理（按需灌溉）实测植株氮含量为训练集，采用BP、RF、Adaboost和SVR方法构建的模型对W0处理冬小麦植株氮含量迁移预测R 2分别为0.75、0.72、0.72和0.66；以W0处理（自然状态）实测植株氮含量为训练集，BP、RF、Adaboost和SVR方法构建的模型对W1处理冬小麦植株氮含量迁移预测R 2分别为0.51、0.69、0.61和0.45。【结论】4种机器学习方法构建的冬小麦植株氮含量预测模型均表现出了较强的迁移预测能力，尤以RF和Adaboost方法构建的模型预测效果和迁移能力为好。

【目的】探索无人机遥感在氮效率分类识别中的潜力，构建小麦品种氮效率分类方法，为氮高效品种筛选提供理论依据和技术支持。【方法】通过6个成熟期与氮效率密切相关的农学指标（产量、植株氮积累、氮素生理利用效率、植株干生物量、籽粒总吸氮量、N收获指数）构建主成分综合值，并对其进行K-Means聚类分析，将121个小麦品种划分为氮高效型、氮中效型和氮低效型3种类型。利用无人机遥感平台搭载多光谱相机，在小麦拔节期、孕穗期和开花期获取无人机遥感影像，并提取34种植被指数，分析植被指数与氮效率综合值的相关性；对比支持向量机（SVM）、随机森林（RF）和K最近邻（KNN）分类方法的氮效率分类模型精度，使用总体分类精度（OA）和Kappa系数比较不同生育时期下小麦品种氮效率分类识别的能力；并使用3种不同的特征集筛选方法（ReliefF算法、Boruta算法和RF-RFE算法）对优化的特征子集进行综合评价，确立适宜的小麦品种氮效率分类识别方法。【结果】随着小麦生育时期的不断推进，植被指数与氮效率综合值的相关性逐渐提高，开花期最高（r=0.502）；利用植被指数全特征集对小麦品种氮效率进行分类，对于单生育时期数据而言，以开花期的SVM模型分类效果最好（OA=77.1%，Kappa=0.591），拔节期最差（OA=65.6%，Kappa=0.406）；总体而言，多生育时期数据融合的品种氮效率分类精度高于单生育时期，其中以拔节期+孕穗期+开花期3个生育时期数据融合的SVM模型的分类效果最优（OA=80.6%，Kappa=0.669）。为减少多生育时期数据融合的特征集变量数量，比较分析RF-RFE、Boruta和ReliefF 3种算法的特征优化效果，基于RF-RFE算法得到的优化特征子集分类精度最高，其OA和Kappa系数比全特征集分类模型分别提高了4.0%和10.1%，其中，以3个生育时期数据融合的分类效果最好（OA=85.4%，Kappa=0.749）。【结论】确立6个氮效率指标—主成分分析—K-Means氮效率评价方法；RF-RFE算法有效优化多生育时期组合的特征子集数量，且获得较高的分类精度，确立基于多生育时期组合—RF-RFE—SVM技术融合的小麦品种氮效率分类模型，为小麦氮高效品种的快速准确分类鉴定提供理论依据和技术支撑。

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

以不同基因型的小麦品种(13个强筋小麦品种和2个中筋小麦品种), 不同试验地点(馆陶、宁晋、藁城)和不同施氮水平(0、180、240和300 kg hm -2)的田间试验, 综合分析基因型、环境对小麦产量、品质、氮肥利用率的影响, 以期为优质强筋小麦品种选育、栽培调控及高产提质增效的协同目标提供科学依据。研究表明, 不同小麦品种产量在9289~10,088 kg hm -2之间, 强筋小麦品种平均产量9548 kg hm -2, 比中筋小麦品种减产3.1%。藁城、宁晋、馆陶三地的产量分别达9932、9433和9223 kg hm -2。不同小麦品种籽粒蛋白质均值14.5%, 湿面筋28.5%, 沉淀指数39.5 mL, 稳定时间15.4 min, 拉伸能量87.5 cm 2, 最大拉伸阻力428.8 BU。藁优5218、藁优5766、冀麦738、科农2009、师栾02-1、藁优2018、冀麦867综合品质表现较好。馆陶和宁晋的小麦品质性状相对较好, 藁城的品质较差。氮肥利用率随施氮量增加呈降低趋势, N180处理的氮肥农学效率、氮肥吸收利用效率、氮肥生理利用率最高, 依次为4.3 kg kg -1、26.2%、16.6 kg kg -1。兼顾产量、品质、效率三方面, 藁城适宜种植品种有冀麦738、冀麦867、师栾02-1; 宁晋有师栾02-1、科农2009、冀麦738; 馆陶有藁优5766、藁优2018、师栾02-1。综合考虑小麦产量、籽粒品质和氮素利用率, 180 kg hm -2为本研究条件下的最佳施氮量。

在大田条件下，研究了钾肥施用量对强筋小麦郑麦9023和弱筋小麦豫麦50号籽粒品质、粉质仪参数和淀粉糊化特性的影响。结果表明，适宜的钾肥施用量可明显改善郑麦9023和豫麦50号的品质和淀粉糊化特性，增施钾肥能够提高强筋小麦籽粒干、湿面筋含量、形成时间、稳定时间、评价值和稀懈值，降低强筋小麦峰值黏度、低谷黏度、最终黏度、降落值和弱化度。钾肥施用量对弱筋小麦主要品质性状的影响与强筋小麦呈相反趋势。在豫西地区，强筋小麦和弱筋小麦最适宜的钾肥施用量（K2O）均为112．5kg／hm^2。

Although crop-growth monitoring is important for agricultural managers, it has always been a difficult research topic. However, unmanned aerial vehicles (UAVs) equipped with RGB and hyperspectral cameras can now acquire high-resolution remote-sensing images, which facilitates and accelerates such monitoring. To explore the effect of monitoring a single crop-growth indicator and multiple indicators, this study combines six growth indicators (plant nitrogen content, above-ground biomass, plant water content, chlorophyll, leaf area index, and plant height) into the new comprehensive growth index (CGI). We investigate the performance of RGB imagery and hyperspectral data for monitoring crop growth based on multi-time estimation of the CGI. The CGI is estimated from the vegetation indices based on UAV hyperspectral data treated by linear, nonlinear, and multiple linear regression (MLR), partial least squares (PLSR), and random forest (RF). The results are as follows: (1) The RGB-imagery indices red reflectance (r), the excess-red index (EXR), the vegetation atmospherically resistant index (VARI), and the modified green-red vegetation index (MGRVI), as well as the spectral indices consisting of the linear combination index (LCI), the modified simple ratio index (MSR), the simple ratio vegetation index (SR), and the normalized difference vegetation index (NDVI), are more strongly correlated with the CGI than a single growth-monitoring indicator. (2) The CGI estimation model is constructed by comparing a single RGB-imagery index and a spectral index, and the optimal RGB-imagery index corresponding to each of the four growth stages in order is r, r, r, EXR; the optimal spectral index is LCI for all four growth stages. (3) The MLR, PLSR, and RF methods are used to estimate the CGI. The MLR method produces the best estimates. (4) Finally, the CGI is more accurately estimated using the UAV hyperspectral indices than using the RGB-image indices.