【目的】机械粒收是玉米生产的发展方向，收获质量是影响其推广应用的主要因素。中国玉米机械粒收还处于起步阶段，目前在西北和东北等春播玉米区推广应用面积较大，黄淮海夏播玉米区正在积极开展试验示范。本研究通过分析黄淮海夏玉米机械粒收质量及其影响因素，为该技术的推广应用提供支持。【方法】2013—2015年累计选用了23个玉米品种，在黄淮海典型代表区河南新乡开展试验研究。2013年和2015年在收获期分别进行2次机械收获，2014年1次机械收获。收获当天测定各个品种的收获前籽粒含水率，并调查测产。机械收获后从机仓随机取一定量籽粒样品，立即测定收获后籽粒含水率，然后手工分拣样品，测定籽粒破碎率和杂质率；收获后，在田间选取3个代表性样区，调查落穗损失和落粒损失。【结果】2013—2015年，籽粒破碎率共调查131个样点，结果显示，收获时玉米籽粒含水率在20.80%—41.08%，籽粒破碎率变幅为4.98%—41.36%，籽粒破碎率随着籽粒含水率的提高明显升高；破碎率低于8%的有38个样点，占比29.01%，籽粒含水率低于26.92%时，收获的玉米籽粒能够满足破碎率8%以下的要求。机收杂质率共调查134个样点，杂质率0.37%—5.28%，杂质率低于3%的样点有107个，占比79.85%，杂质率也随着籽粒含水率的升高而增加；2013—2014年，籽粒含水率低于28.27%时，杂质率能够低于3%的国家标准；2015年收获时籽粒含水率虽然较高，但杂质率均在3%以下。田间损失率共调查108个样点，变幅为0.18%—2.85%（落穗率和落粒率），均能满足国家标准，损失率不是影响机械收获质量的限制因素。在本试验条件下，籽粒含水率低于26.92%时，破碎率和杂质率分别低于8%和3%，田间损失率也符合国家标准，能够满足机械粒收质量要求。研究还发现，籽粒含水率相近的不同品种之间，机械收获的破碎率和杂质率也存在显著差异，表明品种固有的理化特性对机械收获质量也有影响。【结论】收获时的籽粒含水率是影响机械粒收质量的关键因素，在相同籽粒含水率条件下，品种之间收获质量表现出显著差异。由于年际间热量等条件的不同，收获时的籽粒含水率存在一定幅度的变动，但通过选择适宜品种、科学安排播种和收获时间，以河南新乡为代表的黄淮海夏玉米区完全能够保证玉米机械粒收质量。

The pericarp weight comprises <17 % of wheat grain weight at harvest. The pericarp supports the hydration and nutrition of both the embryo and endosperm during early grain filling. However, studies of the pericarp and its association with final grain weight have been scarce. This research studied the growth dynamics of wheat pericarp from anthesis onwards and its relationship to final grain weight under contrasting plant densities and night warming.Two spring wheat cultivars contrasting in kernel weight (Bacanora and Kambara) were sown in field conditions during seasons 2012-13 and 2014-15. Both genotypes were grown under contrasting plant density (control, 370 plants m-2; and low plant density, 44 plants m-2) and night temperatures, i.e. at ambient and increased (>6 °C) temperature for short periods before and after anthesis. From anthesis onward, grains were harvested every 3 or 4 d. Grain samples were measured and the pericarp was removed with a scalpel. Whole grain and pericarp fresh and dry weight were weighed with a precision balance. At harvest, 20 grains from ten spikes were weighed and grain dimensions were measured.Fresh weight, dry matter and water content of pericarp dynamics showed a maximum between 110 and 235 °Cd. Maximum dry matter of the pericarp ranged between 4.3 and 5.7 mg, while water content achieved values of up to 12.5 mg. Maximum values and their timings were affected by the genotype, environmental condition and grain position. Final grain weight was closely associated with maximum dry matter and water content of the pericarp.Maximum pericarp weight is a determinant of grain weight and size in wheat, which is earlier than other traits considered as key determinants of grain weight during grain filling. Better growing conditions increased maximum pericarp weight, while higher temperature negatively affected this trait.© The Author(s) 2020. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Maize is a highly important crop to many countries around the world, through the sale of the maize crop to domestic processors and subsequent production of maize products and also provides a staple food to subsistance farms in undeveloped countries. In many countries, there have been long-term research efforts to develop a suitable hardness method that could assist the maize industry in improving efficiency in processing as well as possibly providing a quality specification for maize growers, which could attract a premium. This paper focuses specifically on hardness and reviews a number of methodologies as well as important biochemical aspects of maize that contribute to maize hardness used internationally. Numerous foods are produced from maize, and hardness has been described as having an impact on food quality. However, the basis of hardness and measurement of hardness are very general and would apply to any use of maize from any country. From the published literature, it would appear that one of the simpler methods used to measure hardness is a grinding step followed by a sieving step, using multiple sieve sizes. This would allow the range in hardness within a sample as well as average particle size and/or coarse/fine ratio to be calculated. Any of these parameters could easily be used as reference values for the development of near-infrared (NIR) spectroscopy calibrations. The development of precise NIR calibrations will provide an excellent tool for breeders, handlers, and processors to deliver specific cultivars in the case of growers and bulk loads in the case of handlers, thereby ensuring the most efficient use of maize by domestic and international processors. This paper also considers previous research describing the biochemical aspects of maize that have been related to maize hardness. Both starch and protein affect hardness, with most research focusing on the storage proteins (zeins). Both the content and composition of the zein fractions affect hardness. Genotypes and growing environment influence the final protein and starch content and, to a lesser extent, composition. However, hardness is a highly heritable trait and, hence, when a desirable level of hardness is finally agreed upon, the breeders will quickly be able to produce material with the hardness levels required by the industry.

Genetic relationships between the phenotypic means and plasticities of kernel size and weight revealed the common genetic control of these traits in maize. Kernel size and weight are crucial components of grain yield in maize, and phenotypic plasticity in these traits facilitates adaptations to changing environments. Elucidating the genetic architecture of the mean phenotypic values and plasticities of kernel size and weight may be essential for breeding climate-robust maize varieties. Here, a maize nested association mapping (CN-NAM) population and association panel were grown in different environments. A joint linkage analysis and genome-wide association mapping were performed for five kernel size and weight phenotypic traits and two phenotypic plasticity measures. The mean phenotypes and plasticities were significantly correlated. The overall results of quantitative trait locus (QTL) and candidate gene analyses indicated moderate and high levels of common genetic control for the two traits. Furthermore, the mean phenotypes or plasticities of the hundred-kernel weight and volume were commonly regulated to a high degree. One pleiotropic locus on chromosome 10 simultaneously controlled the mean phenotypic values and plasticities of kernel size and weight. Therefore, the plasticity of kernel size and weight might be indirectly selected during maize breeding; however, selecting for high or low plasticity in combination with high or low mean phenotypic values of kernel size and weight traits may be difficult.

玉米机械粒收过程中出现的籽粒破碎、果穗遗漏、籽粒散落等影响收获质量的现象是机械粒收推广过程中备受关注的问题。开展机械粒收质量及其影响因素研究, 是确定适宜粒收时期、指导品种改良等的基础, 对于机械粒收技术的推广普及具有重要意义。本研究于2015年和2017年在中国农业科院新乡综合试验站, 以黄淮海夏玉米区生产用品种为试材, 采用同一收获机和操作人员分期收获, 调查不同收获期籽粒含水率变化以及破碎率、杂质率、落粒率和落穗率等机械粒收质量指标, 分析籽粒含水率与粒收质量指标的关系。结果显示, 随着收获期推迟, 籽粒含水率逐渐降低, 籽粒破碎率和落粒率呈先降低后升高趋势, 杂质率逐渐降低, 落穗率逐渐增加。2年参试样本籽粒含水率分布在9.68%~41.36%之间, 破碎率与籽粒含水率的关系符合y = 0.068x ²-2.743x+31.09 (R ²= 0.79 ^**, n = 140)模型; 含水率在15.47%~24.78%之间时, 破碎率低于5%; 含水率为20.05%时, 破碎率最低。杂质率与籽粒含水率的关系符合y = 0.0158e ^0.1111 ^x(R ²= 0.66 ^**, n = 140)模型, 杂质率随着含水率降低逐渐降低并趋于稳定。落粒率与籽粒含水率符合y = 0.006x ²-0.236x+3.479 (R ²= 0.42 ^**, n = 127)模型, 含水率为20.37%时, 落粒率最低。落穗率与籽粒含水率符合y = 2578.7645/x ^2.2453(R ²= 0.35 ^**, n = 140)模型, 当含水率低于16.15%时, 落穗率将超过5%。研究还发现, 即使籽粒含水率相近, 不同品种的收获质量(特别是籽粒破碎率)也存在显著差异。本研究的结果表明, 破碎率是决定机械粒收质量的关键因素, 以破碎率5%和落穗率5%为标准, 黄淮海夏玉米适宜机械粒收的籽粒含水率范围为16.15%~24.78%, 籽粒含水率在20%左右时, 收获质量最佳。