The priority of management factors for reducing the yield gap of summer maize in the north of Huang-Huai-Hai region,China

Understanding yield potential, yield gap and the priority of management factors for reducing the yield gap in current intensive maize production is essential for meeting future food demand with the limited resources. In this study, we conducted field experiments using different planting modes, which were basic productivity(CK), farmer practice(FP), high yield and high efficiency(HH), and super high yield(SH), to estimate the yield gap. Different factorial experiments(fertilizer, planting density, hybrids, and irrigation) were also conducted to evaluate the priority of individual management factors for reducing the yield gap between the different planting modes. We found significant differences between the maize yields of different planting modes. The treatments of CK, FP, HH, and SH achieved 54.26, 58.76, 65.77, and 71.99% of the yield potential, respectively. The yield gaps between three pairs: CK and FP, FP and HH, and HH and SH, were 0.76, 1.23 and 0.85 t ha~(–1), respectively. By further analyzing the priority of management factors for reducing the yield gap between FP and HH, as well as HH and SH, we found that the priorities of the management factors(contribution rates) were plant density(13.29%)fertilizer(11.95%)hybrids(8.19%)irrigation(4%) for FP to HH, and hybrids(8.94%)plant density(4.84%)fertilizer(1.91%) for HH to SH. Therefore, increasing the planting density of FP was the key factor for decreasing the yield gap between FP and HH, while choosing hybrids with density and lodging tolerance was the key factor for decreasing the yield gap between HH and SH.     

Regional distribution of wheat yield and chemical fertilizer requirements in China

Quantification of currently attainable yield and fertilizer requirements can provide detailed information for assessing the food supply capacity and offer data support for agricultural decision-making. Datasets from a total of 5 408 field experiments were collected from 2000 to 2015 across the major wheat production regions in China to analyze the spatial distribution of wheat yield, the soil nutrient supply capacity(represented by relative yield, defined as the ratio of the yield under the omission of one of nitrogen(N), phosphorus(P) and potassium(K) to the yield under the full NPK fertilizer application), and N, P and K fertilizer requirements by combining the kriging interpolation method with the Nutrient Expert Decision Support System for Wheat. The results indicated that the average attainable yield was 6.4 t ha~(-1), with a coefficient of variation(CV) of 24.9% across all sites. The yields in North-central China(NCC) and the northern part of the Middle and Lower reaches of the Yangtze River(MLYR) were generally higher than 7 t ha~(-1), whereas the yields in Southwest China(SWC), Northeast China(NEC), and the eastern part of Northwest China(NWC) were usually less than 6 t ha~(-1). The precentage of area having a relative yield above 0.70, 0.85, and 0.85 for N, P, and K fertilizers accounted for 52.3, 74.7, and 95.9%, respectively. Variation existed in N, P, and K fertilizer requirements, with a CV of 24.8, 23.9, and 29.9%, respectively, across all sites. More fertilizer was needed in NCC and the northern part of the MLYR than in other regions. The average fertilizer requirement was 162, 72, and 57 kg ha~(-1) for N, P_2O_5, and K_2O fertilizers, respectively, across all sites. The incorporation of the spatial variation of attainable yield and fertilizer requirements into wheat production practices would benefit sustainable wheat production and environmental safety.     

玉米茎秆抗倒伏遗传的研究进展

茎秆倒伏严重影响玉米产量、品质和机械化收获,是当前玉米生产和育种亟待解决的主要问题之一。加强对玉米茎秆抗倒伏性的研究,对提高品种抗倒伏能力具有重要意义。本文综述了玉米茎秆倒伏的主要影响因素及其遗传特征。茎秆倒伏与茎秆自身的强度密切相关。茎秆强度越高,抗倒伏性越强。茎秆强度受茎秆所处的发育阶段、茎秆内部结构和外部形态,及其细胞壁成分等影响。处于分生组织的茎秆细胞分裂旺盛,较易折断,而进入生殖生长后,茎秆表皮、厚壁组织增厚,维管束发育成熟,对茎秆的支撑作用增强。茎秆细胞壁的主要成分——纤维素、半纤维素、木质素、可溶性糖、无机物等均可提升茎秆强度。目前,研究者借助高通量表型平台,利用玉米连锁群体和自交系群体,采用各种定位方法,鉴定到一系列影响茎秆形态、强度、细胞壁成分的相关QTL和候选基因。研究表明,基于单倍型的QTL定位方法比基于单个SNP的定位效果好。一致性QTL分析将不同遗传群体的研究整合到一起,能够提高QTL结果的通用性。茎秆强度的遗传基础复杂,受微效多基因控制,位点间具有加性效应。茎秆成分QTL中的候选基因涉及细胞壁代谢、转录因子、蛋白激酶等。MAIZEWALL是玉米细胞壁相关基因的重要数据库。目前该数据库包含1 156个玉米细胞壁生物学相关的候选基因,为该领域的深入研究提供强大的资源。已鉴定到一系列影响玉米茎秆细胞壁成分、茎秆形态和强度的基因,其功能涉及纤维素合成路径,如纤维素合成酶类、Cobra类、糖基转移酶和核糖转运蛋白类;苯丙烷路径基因,如控制bm1—bm5的相关基因;植物激素类,如赤霉素、生长素、油菜素甾醇相关基因;转录因子如NAC、MYB;miRNA（ZmmiR528）以及F-box基因（stiff1）等。今后应积极探索不同发育时期玉米茎秆倒伏的力学机制;广泛发展自然群体或育种群体进行遗传分析;采取多种定位策略,提高抗倒伏相关基因鉴定的功效;针对优良等位基因,开发各类分子标记,加强抗倒伏分子标记辅助选择。本文将为玉米茎秆抗倒伏遗传机制解析及抗倒伏玉米品种的分子育种提供参考。     

采用双模态联合表征学习方法识别作物病害

基于深度卷积神经网络的视觉识别方法在病害诊断中表现出色,逐渐成为了研究热点。但是,基于深度卷积神经网络建立的视觉识别模型通常只利用了图像模态的数据,导致模型的识别准确率和鲁棒性,都依赖训练数据集的规模和标注的质量。构建开放环境下大规模的病害数据集并完成高质量的标注,通常需要付出巨大的经济和技术代价,限制了基于深度卷积神经网络的视觉识别方法在实际应用中的推广。该研究提出了一种基于图像与文本双模态联合表征学习的开放环境下作物病害识别模型(bimodalNet)。该模型在病害图像模态的基础上,进行了病害文本模态信息的嵌入,利用两种模态病害信息间的相关性和互补性,实现了病害特征的联合表征学习。最终bimodalNet在较小的数据集上取得了优于单纯的图像模态模型和文本模态模型的效果,最优模型组合在测试集的准确率、精确率、灵敏度、特异性和F1值分别为99.47%、98.51%、98.61%、99.68%和98.51%。该研究证明了利用病害图像和病害文本的双模态表征学习是解决开放环境下作物病害识别的有效方法。     

基于多源数据的华北平原夏玉米种植区划研究

精准识别农业生产环境信息和农业生产特征,对气象、土壤和作物等多源数据进行综合分类,是提高农业资源利用效率和优化农业种植结构的基础。本研究基于近20年（1998~2017年）气象数据和华北五省的玉米单产统计数据,首先构建了华北平原气候资源和玉米生产时空分布特征数据库,研究区内的降雨量、活动积温、日照时数、太阳辐射和玉米单产均存在显著的时空变化;利用作物精细种植区划方法,将华北平原夏玉米种植区分为极不适宜区、不适宜区、较适宜区、适宜区、极适宜区五大类,各类面积分别占总体的比例约为10%、11%、25%、30%、24%;进一步通过环境类别归属度分析方法,将每一大类分为5小类,概率大于75%的相对稳定区域约占总面积的63%,小于75%的波动区域约占37%;极不适宜区、不适宜区和较适宜区,三类时空分布比较稳定,隶属度为100%分别占各类面积的87.67%、70.41%和84.28%,波动区主要发生在极适宜区和适宜区,以及适宜区和较适宜区之间。本研究构建的华北平原夏玉米精细区划结果,对提高研究区资源利用效率和优化玉米产业布局具有重要的指导意义。     

改性尿素硝酸铵溶液调控氮素挥发和淋溶的研究

为了提高肥料的利用率,以尿素硝酸铵溶液为原料、聚氨酸为保护剂,复合抑制剂NBPT(N-丁基硫代磷酰三胺)和DMPP(3,4-二甲基吡唑磷酸盐)为材料,开发出改性尿素硝酸铵溶液(YUL1和YUL2),研究其对华北平原夏玉米追肥过程中的氨挥发和淋溶损失的调控效果。田间试验设置6个处理:不施氮肥(CK)、农民习惯追施尿素(CN)、优化追施尿素(CNU)、优化追施尿素硝酸铵溶液(UAN)、优化追施改性尿素硝酸铵溶液(YUL1)和优化追施改性尿素硝酸铵溶液(YUL2)。采用扫描电镜和能谱仪分析相关指标变化,在夏玉米喇叭口期追施氮肥后15d内进行田间原位连续动态观测氨挥发和土壤铵态氮和硝态氮变化,并在玉米成熟期测定产量,计算经济效益。结果表明,改性尿素硝酸铵溶液清澈无杂质,流延后成膜表面光滑、致密,抑制剂在膜表面分布均匀;能谱测试膜层表面磷硫含量增高,证明复合抑制剂与尿素硝酸铵溶液达到有效融合。在同等优化施氮量下:与CNU相比, YUL1氨挥发总量显著降低19.3%, YUL2增加9.6%;与UAN相比, YUL1、YUL2分别显著降低57.3%和42.0%。与其他施氮处理相比, YUL1和YUL2夏玉米季生长中后期0～20 cm土层依然保持相对较高的氮素含量水平,夏玉米收获后土壤硝态氮含量分别比CNU高46.0%和43.4%,比UAN高45.6%和44.7%;180～200cm土层硝态氮含量显著低于其他处理。在保证产量和净收益的同时,改性尿素硝酸铵肥料显著降低了氮素的氨挥发和淋溶损失浓度,尿酶抑制剂含量相对较高的YUL1抑制氨挥发的效果更好,硝化抑制剂含量相对高的YUL2硝态氮向下淋失的风险更小。     

沼液淹没土壤抑制根结线虫及对土壤线虫群落的影响

为探索沼液抑制根结线虫的效果,本研究通过盆栽试验,以番茄为试供作物,对比了种植前沼液淹没土壤(BSS)、种植期间浇灌沼液(BS)和加热(HE)3种方法对根结线虫的防控效果。结果表明,与不采取任何措施的对照(CK)处理相比, BSS处理抑制根结线虫效果最为明显,防效高达97.1%,根结指数分别比HE和BS处理降低96.9%和92.9%。HE处理尽管在处理土壤后显著降低了根结线虫数量,但在最后破坏性取样时(结束试验)出现反弹,根结线虫数量甚至高于CK处理。对于土壤线虫群落,CK处理中以植食性线虫为主(81.8%);两个沼液处理中食细菌线虫占优势(平均78.3%),且其中的杂食捕食性线虫在土壤前处理后消失,在试验结束时又重新出现,但所占比例依然非常低。沼液淹水方式的高效防控效果揭示了利用沼液防控根结线虫的关键期在于线虫入侵到植物根部之前的幼虫期。然而,在盆栽系统中,沼液淹水的方式也对作物生长表现出了一定的抑制趋势。高量沼液施用防控病害的同时引发的植物毒害作用以及环境污染风险,需要进一步开展田间研究。     

基于级联卷积神经网络的番茄花期识别检测方法

对作物花期状态的准确识别是温室作物执行授粉的前提。为提高花期状态识别的准确度，该研究以温室番茄为例提出了一种基于级联卷积神经网络的番茄花朵花期识别方法。首先采用改进的端到端的特征金字塔网络FS-FPN实现番茄花束的分割，然后采用Prim最小生成树对分割后的花束图片进行花期识别优先级排序，最后将已排序的分割花束图片输入改进的Yolov3网络，实现番茄花朵花期状态的精准识别。在由1600幅包含花蕾期、全开期、谢花期、初果期4类花期状态的番茄花束图像构成的数据集上，所提方法对番茄花朵花期平均检测时间为12.54 ms，平均检测精度分别比Mask R-CNN和SPP-Net提高了3.67%和2.39%，识别错误率比改进前的Yolov3网络降低了1.25%。最终将该方法部署到番茄授粉机器人上，并在大型玻璃温室内进行验证，结果表明，所提方法对番茄花朵各花期的检测精度分别为花蕾期85.71%、全开期95.46%、谢花期62.66%、初果期88.34%，该研究结果可为智能授粉机器人的精准作业提供重要依据。