Molecular Breeding in an Industrial Setting

To develop better varieties for farmers and to increase overall agricultural productivity, Monsanto Company has continued to lead innovations in plant biotech, breeding and molecular breeding. In Molecular breeding, nongel based SNP markers have replaced traditional gel-based markers. Our advanced SNP discovery pipeline enabled us to rapidly identify thousands of single nucleotide polymorphisms （SNP） for use as markers in any crops of choice. High-density SNP maps have been successfully constructed for major crops and these maps have provided a useful tool for QTL identification, Marker-Assisted Breeding, Association Study as well as biotech trait integration. Our high level of automation, in conjunction with accurate data tracking and analytical systems, has made it possible to achieve high throughput and unattended 24-hour operations for reliable and cost-effective large volume genotyping and data analysis. As genetic information continues to accumulate, confirmed associations between markers and traits grow. Such information will allow breeders to gain insight into the genetic determinants underlying complex agronomic traits. Furthermore,     

Spatial aggregation of soil property predictions in support of local land management

Over the past decades, numerous practical applications of Digital Soil Mapping have emerged to respond to the need of land managers. One important contribution to this effort is the release of regional‐scale soil maps from the GlobalSoilMap (GSM) project. While the GSM project aims at producing soil property predictions on a fine 90 × 90 m grid at the global scale, land managers often require aggregated information over larger areas of interest (e.g. farms, watersheds, municipalities). This study evaluated a geostatistical procedure aiming at aggregating GSM grids to a land management scale, thereby providing land suitability maps with associated uncertainty for the French region ‘Languedoc‐Roussillon’ (27 236 km²). Specifically, maps were derived from three GSM prediction grids (pH, organic carbon and clay content) by calculating the proportion of ‘suitable’ agricultural land within a municipality, where suitability was defined as having soil property values below or above a predefined threshold (pH < 5.5, OC < 10 g/kg, clay > 375 g/kg). Calculation of these nonlinear spatial aggregates and the associated uncertainty involved a three‐step approach: (i) sampling from the conditional probability distributions of the soil properties at all grid cells by means of sequential Gaussian simulation applied to a regression kriging model, (ii) transformation of soil properties to suitability indicators for all grid cell samples generated in the first step and (iii) spatial aggregation of the suitability indicators from grid cells to municipalities. The maps produced show large differences between municipality areas for all three land suitability indicators. The uncertainties associated with the aggregated suitability indicators were moderate. This approach demonstrated that fine‐scale GSM products may also fulfil user demands at coarser land management scales, without jeopardizing uncertainty quantification requirements.     

A Method to Efficiently Apply a Biogeochemical Model to a Landscape

Biogeochemical models offer an important means of understanding carbon dynamics, but the computational complexity of many models means that modeling all grid cells on a large landscape is computationally burdensome. Because most biogeochemical models ignore adjacency effects between cells, however, a more efficient approach is possible. Recognizing that spatial variation in model outputs is solely a function of spatial variation in input driver variables such as climate, we developed a method to sample the model outputs in input variable space rather than geographic space, and to then use simple interpolation in input variable space to estimate values for the remainder of the landscape. We tested the method in a 100 km×260 km area of western Oregon, U.S.A. , comparing interpolated maps of net primary production (NPP) and net ecosystem production (NEP) with maps from an exhaustive, wall-to-wall run of the model. The interpolation method can match spatial patterns of model behavior well (correlations>0.8) using samples of only 5 t o 15% of the landscape. Compression of temporal variation in input drivers is a key step in the process, with choice of input variables for compression largely determining the upper bounds on the degree of match between interpolated and original maps. The method is applicable to any model that does not consider adjacency effects, and could free up computational expense for a variety of other computational burdens, including spatial sensitivity analyses, alternative scenario testing, or finer grain-size mapping.     

Methods for mapping local food production capacity from agricultural statistics

Interest in local food security has increased in the last decade, stemming from concerns surrounding environmental sustainability, small scale agriculture, and community food security. Promotions for consumption of locally produced foods have come from activists, non-governmental organizations, as well as some academic and government research and policy makers. Methods to empirically assess the types and quantities of crops and animals produced locally (i.e., local food production capacity) are under-developed, hindering the ability of policy makers to effect innovative local food security policy. In this paper, we demonstrate methods to estimate local food production capacity using regularly gathered federal Agricultural Census and survey data for a Canadian province. The methods are generalizable to other provinces and nations. Operating at the sub-provincial scale of Local Health Area (LHA), our goal is to integrate census farmland and survey yield data to construct local food production estimates in each LHA. We also assess the stability of these surveyed agricultural yields over time to determine the temporal extent of data required for reasonable representation of product yields. We find that provincial yield data may be used to construct reasonable estimates of local scale food production, due to the high level of regionalization in productive farmland of each product in the province. However, many products exhibit significant yield variability over time, suggesting that, for some foods, local production capacity is a dynamic and variable concept. The methods developed will be useful for researchers and government officials alike, as well as a first step towards more advanced modeling of current local food capacity and future potential.     

中国作物新基因发掘：现状、挑战与展望

中国作物新基因发掘是实现中国作物种质资源优势向基因资源优势转变和作物分子育种的基础。本文对中国近10年来水稻、小麦、玉米、大豆、棉花和油菜等主要作物基因发掘研究的进展进行了分析和评述。中国作物基因发掘也取得了一系列突破性进展：(1)创制出一批具有特色的基因发掘材料,包括基于中国作物遗传多样性的核心种质、基于优异资源的遗传分离群体和基于人工诱变的突变体等;(2)基因发掘技术和方法有所突破,尤其是在针对基因特点整合各种基因发掘技术、改进基因/QTL的生物统计算法等,提高了基因发掘的效率;(3)作物农艺性状的标记与基因定位已成为常规遗传研究方法,初步定位了一批抗病、抗逆、优质、养分高效、高产相关基因/QTL,其中,有500多个基因已精细定位;(4)以水稻为代表的作物基因克隆及功能研究在国际上受到瞩目,在主要作物中已克隆了300多个基因,其中,在目标作物中验证的基因数超过70个。在国际作物基因发掘高效化、规模化及实用化发展过程中,中国作物基因发掘也取得了重要进展。然而,中国作物基因发掘的数量和质量还远远不能满足作物分子育种的需求,与国际作物基因发掘也存在差距,具体表现为不同作物基因发掘研究进展不平衡、发掘基因的数量还相对有限、已发掘的基因中具有重大利用价值的基因不多。针对中国基因发掘面临的问题和世界各国以及跨国生物技术公司争夺基因的巨大挑战,作者提出了中国作物基因发掘应重点提高基因发掘效率,加强重要基因克隆及基因的价值评估,以生物产业发展需求为导向的基因发掘策略。     

葫芦科作物果实品质性状的分子标记与定位研究进展

近年来,人们对果实品质的要求不断提高,而品质性状基因的表达易受环境因素的影响,在一定程度上制约了品质育种的发展。随着分子标记技术的应用,分子标记辅助选择育种成为提高果实品质的有效途径。根据有关文献结合作者课题组的研究成果,综述了葫芦科作物果实品质性状的分子标记与定位研究进展,并对未来葫芦科果实品质育种的研究方向进行了展望,以期为提高果实品质提供参考。     

Indigenous and spontaneous vegetation: their relationship to urban development in the city of Leeds, UK

The concepts of indigenous and spontaneous vegetation are considered as subsets of the broader category, semi-natural vegetation. Indigenous vegetation is conceptualised as that which has originated in a rural landscape and developed over a time period of several centuries or more, either naturally or as a result of traditional management methods, together with the environmental conditions supporting it. In contrast, spontaneous vegetation is that which has naturally colonised derelict urban sites, mainly within the last century. An investigation, using map and aerial photographic data, examined the relative proportions of potentially indigenous and spontaneous vegetation on proposed development sites in Leeds, a large industrial city in the UK. Results suggested that spontaneous vegetation occupied approximately 13% by area of the development sites while possible indigenous vegetation occupied as little as 5%. However, further analysis suggested that approximately 25% of the proposed development sites were within 0.5 km of a designated site containing indigenous vegetation. It is argued that both types of semi-natural vegetation can make positive contributions to the urban environment but that public perception of spontaneous vegetation and, in particular, the locations where it occurs, are often negative. Further research into public perceptions of semi-natural vegetation and ways of incorporating it into urban development are required.     

祁连冰沟流域浅雪层光谱特征 分析与遥感算法反演

祁连山冰沟流域地形复杂,积雪深度较浅且破碎化严重,针对MODIS标准积雪面积比例产品在该地区监测精度较差的问题,本研究基于冰沟流域浅雪光谱特征分析及结合野外实测经验,探索浅雪的光谱特征对MODIS浅雪面积比例提取精度的影响;然后通过线性回归法、线性混合像元分解法及BP神经网络模型3种方法分别构建了针对研究区的MODIS积雪制图算法,并利用同时相的Landsat 8OLI二值积雪数据作为真值对上述3种制图方法进行精度验证。结果表明,1)浅雪的光谱反射率对基于NDSI阈值法的MODIS浅雪提取精度几乎没影响,MODIS提取浅雪精度差的主要原因为该地区复杂的地形而导致的积雪分布破碎化,即混合像元的大量存在;2)利用BP神经网络模型反演积雪面积比例的最佳输入参数组合为(ρ_1~ρ_7)+NDSI+DEM;3)线性混合像元分解模型在该研究区的积雪面积比例提取精度较低,BP神经网络模型精度最好;4)在地形复杂区域,多因素模型(BP神经网络模型)相对于单因素模型(一元线性回归模型)具有较好的积雪面积比例提取精度和稳定性,可以为研究区MODIS积雪面积比例的反演提供一种理想的方法。     

一个玉米叶夹角突变体的表型鉴定及遗传分析

玉米叶夹角是高密度育种的重要影响因子,与株型育种及产量高度相关;叶夹角相关QTL/基因的鉴定不仅有助于剖析叶夹角的遗传基础,也为玉米叶夹角及株型的遗传改良提供重要的分子靶点。以自交系‘LY8405’自然突变获得的突变体‘FU1603’为主要研究材料,开展叶夹角性状的表型鉴定、遗传分析及定位等试验。结果表明,与‘LY8405’相比,‘FU1603’穗三叶的叶夹角显著降低(P0.05),而且伴随有叶片卷曲等表型;且在植株的株型、穗部性状及籽粒性状上均发生了显著的改变(P0.05)。遗传分析表明‘FU1603’为一个单隐性核基因控制的突变体(χ~2值0.5)。采用BSA (Bulked segregant analysis)方法筛选到与突变位点紧密连锁的3个SSR标记C1-2、C1-16和C1-18,利用‘FU1603’与‘B73’自交系杂交产生的160个F_2单株开展了上述3个连锁标记的共分离分析;进一步利用此3个标记筛选后代重组交换单株,最终将控制叶夹角的突变位点定位在玉米第一染色体1.02 bin标记C1-18和C1-2之间9 Mb范围之内,为后续该位点的精细定位及克隆奠定良好的基础。     

林木数量性状位点定位的统计方法

本世纪初数量遗传学作为一门独立的学科而产生,以研究实验群体或自然群体的数量性状的遗传变异,它主要研究两个基本问题:(1)总的表型变异中有多少比例属于遗传的影响?(2)有多少遗传因子参与这种遗传变异?如遗传力的概念可以反映遗传因子对表型变异的贡献,它可用亲属间的相关性加以估计.这些参数量已在微生物、动植物中有大量的研究,遗传参数估算对于制定适当的育种计划和理解生物性状的表型进化有重要的作用.