山西省冬小麦生产潜力时空分布与气象因子分析

估算一定区域内作物生产潜力,对于明确该区域作物生产力及其限制因子具有重要意义。采用农业生态区域法和生产潜力递减机制法估算山西省冬小麦生产潜力,并解析其与主要气象因子的关系。结果表明,2004-2013年山西省冬小麦平均光合、光温、气候和土地生产潜力分别介于63.63~74.81、10.86~12.97、3.68~5.65和2.71~4.73t/hm ²。山西冬小麦生产潜力分布特征为南部普遍低于中部地区,中部盆地地区普遍低于东西部高海拔地区。与2004-2008年相比,2009-2013年晋中西部地区和吕梁冬小麦光合和光温生产潜力有所增加,而晋中东部和长治东部的冬小麦气候和土地生产潜力有所降低。限制冬小麦气候和土地生产潜力的主要气象因子是生育期降水量和日平均温度。     

不同土壤条件下氮肥处理对小麦产量及品质的影响

为探讨不同土壤条件下氮肥处理对小麦产量及品质的影响,采用盆栽试验,以Egypt1和津强7号为供试材料,研究不同土壤条件下氮肥处理对小麦产量及品质的影响。试验设黑土、潮土2种土壤类型和不追肥、拔节期追氮肥、挑旗期追氮肥3种肥料处理,3次重复。结果表明: 在2种土壤条件下,小麦籽粒产量和各蛋白质组分产量表现为黑土>潮土,除球蛋白产量外,土壤处理间均差异显著。不同时期追施氮肥均显著提高了籽粒产量和蛋白质产量。黑土和潮土配合追施氮肥都可以有效提高小麦籽粒产量,改善品质。     

柯桥区茶叶气候风险区划

本文研究了茶叶种植区的主要气象灾害,为各种植区提供不同类型的精细化气象服务做准备。利用浙江省区域气象自动站2004—2016年的数据、地理信息数据,对柯桥区茶叶气象灾害风险进行评估,并在此基础上划分气候风险区划。结果发现：柯桥区南北地势不同带来了气候的明显差异,针对茶叶生长发育的需求,对柯桥区南北气候进行对比分析,发现南部虽然易发生气象灾害,但在气候适宜性综合评价上更适合茶叶生长。因此,本研究有助于为当地茶叶风险预报提供理论依据,有助于整体上优化茶叶生产布局。     

苗后除草剂喷施时期对杂草防治及冬油菜产量和品质的影响

为了探究苗后除草剂不同施用时期对北方冬油菜田间杂草的防控效果及其对冬油菜生长、产量和品质的影响,以当地主栽甘蓝型冬油菜天油2266和白菜型冬油菜天油H614为试验材料,选用对冬油菜安全的苗后除草剂高效氟吡甲禾灵,设置4个喷施时期处理,于2018-2019年开展大田试验。结果表明,越冬后不同时期喷施苗后除草剂,甘蓝型冬油菜田间杂草总数较对照减少57.54%~92.56%,杂草总鲜重较对照降低75.40%~97.48%,第2时期（3月25日）处理产量较对照增加354.50kg/hm²;白菜型冬油菜田间杂草株防效和鲜重防效分别为55.91%~92.02%和37.17%~97.46%,产量较对照增加2.29%~16.77%。因此,在冬油菜大量返青但未封行前施用苗后除草剂,可显著防控田间杂草并增加冬油菜籽粒产量。     

施氮处理对不同筋型小麦产量和品质的影响

为探究施氮处理对不同筋型小麦的植株性状、籽粒产量和品质的影响,采用盆栽试验,选用强筋小麦品种中麦578（A1）和中麦5051（A2）、弱筋小麦品种扬麦15（A3）和扬麦24（A4）为供试品种,在施氮量相同的条件下进行底施（B1）和追施（B2）处理。结果表明：在其他栽培措施相同条件下,强筋小麦穗长、总小穗数、千粒重和籽粒产量均优于弱筋小麦,其中A1籽粒产量分别比A2、A3和A4高0.44%、49.81%和15.27%;施氮处理中B2的株高、穗长、穗粒数、总小穗数和籽粒产量均高于B1;不同处理组合中,强筋小麦品种A1B2的植株和产量性状优于其他处理;强筋小麦品种的籽粒蛋白质含量和蛋白质产量均高于弱筋小麦品种,且具有极显著差异（P<0.01)。本试验中强筋小麦品种中麦5051在氮肥追施处理中可以兼顾籽粒产量、蛋白质含量和蛋白质产量。     

播期播量对小麦中麦895产量和品质的影响

为给小麦新品种中麦895的推广提供技术支撑，2013-2015在关中中部灌区，采用二次D饱和最优设计方法研究了播期与播量对中麦895产量及品质的效应。结果表明，播期和播量显著影响中麦895的产量，其高产适宜的播期为10月12日-19日，适宜的播量为170.2 kg·hm^-2～206.5 kg·hm^-2。不同处理之间成穗数、千粒重和产量差异极显著，穗粒数差异显著。播量为185.5 kg·hm^-2时，达到最高产量的适宜播期为10月14日-22日；播期为10月14日时，达到最高产量适宜的播量为130.5 kg·hm^-2～207.3 kg·hm^-2。播期和播量可显著影响中麦895的蛋白质含量和容重，对加工品质和拉伸品质没有显著影响；适期偏晚播种有利于提高中麦895的成穗数和千粒重以获得高产。     

除草剂减量施用对麦田杂草的防治效果及对叶绿素SPAD值的影响

探讨除草剂减施对麦田杂草的防治效果,以及对小麦生长过程中叶绿素SPAD值变化的影响。结果表明,药后45 d,10%苯磺隆WP 9.00 g/hm~2处理(减施60.00%)、10%苯磺隆WP 9.00 g/hm~2+5%(唑啉草酯+炔草酯)EC 30.00 g/hm~2处理(减施60.00%)、7%双氟·炔草酯OD 63.00 g/hm~2处理(减施25.00%)和28%唑草·苯磺隆WP 16.80 g/hm~2处理(减施33.33%)对麦田阔叶杂草的鲜质量防效分别为98.64%、98.99%、98.54%和98.68%,表明除草剂减量施用对麦田杂草仍具有较好的防效。施用除草剂后,小麦叶片叶绿素SPAD值呈现先下降再上升的趋势。药后3 d、5 d、7 d,28%唑草·苯磺隆WP 33.60 g/hm~2处理(增施33.33%)小麦叶片叶绿素SPAD值显著低于7%双氟·炔草酯OD 63.00 g/hm~2处理(减施25.00%)、10%苯磺隆WP 9.00 g/hm~2处理(减施60.00%)和空白对照(CK)的叶绿素SPAD值,低于其他处理的叶绿素SPAD值。     

Evaluation of wheat and emmer varieties for artisanal baking,pasta making,and sensory quality

Identifying varieties best suited to local food systems requires a comprehensive understanding of varietal performance from field to fork. After conducting four years of field trials to test which varieties of ancient, heritage, and modern wheat grow best on organically managed land, we screened a subset of varieties for bread, pastry, pasta, and cooked grain quality. The varieties evaluated were three lines of emmer (T. turgidum L. ssp. dicoccum Schrank ex Schübl) and eleven lines of common wheat (Triticum aestivum L.), including two modern soft wheat varieties, four soft heritage wheat varieties, four hard modern wheat varieties, and one hard heritage wheat variety. A diverse group of bakers, chefs, researchers, and consumers compared varieties for qualities of interest to regional markets. Participants assessed differences in sensory profiles, pasta making ability, and baking quality for sourdough, matzah crackers, yeast bread, and shortbread cookies. In addition to detecting significant differences among varieties for pasta, sourdough, and pastry quality, participants documented variation in texture and flavor for the evaluated products. By demonstrating which varieties perform best in the field, in the bakery, and on our taste buds, these results can support recommendations that strengthen the revival of local grain economies.     

Genome-wide association mapping of phenolic acids in tetraploid wheats

Phenolic acids are major components of cell walls in wheat and have important implications on human health as antioxidants with anti-tumor activity. Our objectives were to identify phenolic acid genes in wheat by single nucleotide polymorphisms (SNPs) detected within the coding sequences of candidate genes, and to identify chromosomal regions associated with single phenolic acids and total soluble phenolic compounds. A set of candidate genes involved in the biosynthesis of hydroxycinnamic acid derivatives were identified by comparative genomics. SNPs found in the coding sequences of six genes (PAL1, PAL2, C4H, C3H, COMT1 and COMT2) were used to determine their chromosomal location and accurate map position on two reference consensus linkage maps. The genome-wide association study (GWAS), based on genotyping a tetraploid wheat collection with 81,587 gene-associated SNPs, detected 22 quantitative trait loci (QTL) distributed on almost all durum wheat chromosomes. Two QTL for p-coumaric acid were coincident with the phenylalanine ammonia-lyase (PAL2) and p-coumarate 3-hydroxylase (C3H) genes on chromosome arms 2AL and 1AL, respectively. The availability of candidate gene-based markers can allow elucidating the mechanism of phenolic acids accumulation in wheat kernels and exploiting the genetic variability of phenolic acids content for the nutritional improvement of wheat end-products.     

Prediction of baking results from farinograph measurements by using stepwise linear regression and artificial neuronal networks

In an effort to extract additional data from farinograph experiments a model was developed to simulate the measurements and correlate the parameters of the model with results from baking tests. This additional information can be used in bakeries to predict the baking properties of the flours and adjust the recipes to maintain a constant product quality. For this eight different flours were characterized with a farinograph and 13 different results from baking experiments. An approach with five nonlinear differential equations was able to model the farinograph measurements very well (average R² = 0.995 ± 0.005). While a stepwise multilinear regression only showed weak correlations in cross validation between a single parameter of the model and the baking volume (R² = 0.745) and the volume yield (R² = 0.796) respectively, the artificial neuronal network was more successful. For the baking weight (R² = 0.926), the dough yield gross (R² = 0.909) and net (R² = 0.913) strong correlations were found. A good correlation for the baking volume (R² = 0.853) was also determined, while the volume yield showed comparable results to the linear regression (R² = 0.792).