Embracing the Emerging Precision Agriculture Technologies for Site-Specific Management of Yield-Limiting Factors

Precision agriculture (PA) is providing an information revolution using Global Positioning (GPS) and Geographic Information (GIS) systems and Remote Sensing (RS). These technologies allow better decision making in the management of crop yield-limiting biotic and abiotic factors and their interactions on a site-specific (SSM) basis in a wide range of production systems. Characterizing the nature of the problem(s) and public education are among the challenges that scientists, producers, and industry face when adapting PA technologies. To apply SSM, spatio-temporal characteristics of the problem(s) need to be determined and variations within a field demonstrated. Spatio-temporal characteristics of a given pathogen or pest problem may be known but may not be the only or primary cause of the problem. Hence, exact cause-and-effect relationships need to be established by incorporating GIS, GPS, and RS-generated data as well as possible interactions. Exploiting the potential of PA technologies in sustainable ways depends on whether or not we first ask ''''Are we doing the right thing?'''' (strategic) as opposed to ''''Are we doing it right?'''' (tactical).     

3D Participatory Sensing with Low-Cost Mobile Devices for Crop Height Assessment – A Comparison with Terrestrial Laser Scanning Data

The integration of local agricultural knowledge deepens the understanding of complex phenomena such as the association between climate variability, crop yields and undernutrition. Participatory Sensing (PS) is a concept which enables laymen to easily gather geodata with standard low-cost mobile devices, offering new and efficient opportunities for agricultural monitoring. This study presents a methodological approach for crop height assessment based on PS. In-field crop height variations of a maize field in Heidelberg, Germany, are gathered with smartphones and handheld GPS devices by 19 participants. The comparison of crop height values measured by the participants to reference data based on terrestrial laser scanning (TLS) results in R² = 0.63 for the handheld GPS devices and R² = 0.24 for the smartphone-based approach. RMSE for the comparison between crop height models (CHM) derived from PS and TLS data is 10.45 cm (GPS devices) and 14.69 cm (smartphones). Furthermore, the results indicate that incorporating participants’ cognitive abilities in the data collection process potentially improves the quality data captured with the PS approach. The proposed PS methods serve as a fundament to collect agricultural parameters on field-level by incorporating local people. Combined with other methods such as remote sensing, PS opens new perspectives to support agricultural development.     

GIS and Remote Sensing for Detecting Yield Loss in Cranberry Culture

The primary goal of our research is to develop key elements of a precision agriculture program applicable to high-value woody perennial crops, such as cranberries. These crop systems exhibit tremendous variability in crop yields and quality as imposed by variations in soil properties (water availability and nutrient deficiency) that lead to crop stress (disease development and weed competition). Some of the variability present in the growing environment results in persistent yield losses as well as crop-quality reductions. We are using state-of-the-art methodologies (GIS, GPS, remote sensing) to identify and map spatial variations of the crop. Through image-processing methods (NDVI and unsupervised classification), approximately 65% of the variation in yield was described using 4-m multispectral satellite data as a base image.     

上海九段沙互花米草种群动态遥感研究

该研究应用遥感技术(RS),结合地理信息系统(GIS)、全球定位系统(GPS)和野外实地调查,对长江口新生沙洲九段沙互花米草(Spartina alterniflora)引种7年来的种群扩散过程进行了调查分析。通过不同时相遥感卫星影像的解译,结合现场调查,分析了外来种互花米草种群自1997年引种后7年期间的种群扩散动态过程。至2004年,互花米草群落已从种植的100 hm²扩展到1 014 hm²。互花米草种群扩散过程可分为3个阶段:1997年的成活定居期;1998至2000年的滞缓期;2000年以后的快速扩散期。互花米草种群的增长速度远高于土著种芦苇(Phragmites australis),显示了互花米草极强的扩散能力、竞争优势和更广的生态幅。研究结果表明,3S技术可应用于植被分类、群落动态和生物入侵动态监测。3S技术具有宏观、快速、动态和综合的优势,可为外来植物的定量调查、分布格局和扩散动态研究提供有效的手段,为滩涂资源生物多样性保护和可持续开发利用提供科学依据。     

3S技术与生物多样性研究

生物多样性是地球生物圈和人类发展的基础,是国际社会所共同关注的焦点问题。3S技术是集遥感、全球定位与地理信息系统于一体的高新技术手段,为生物多样性研究提供强有力的技术支持。近年来该领域的研究越来越受到人们的重视,并取得了大量的研究成果。     

森林植被的自然火干扰

森林植被的自然火干扰邱扬（山西大学黄土高原研究所,太原０３０００６）ＮａｔｕｒａｌＦｉｒｅＤｉｓｔｕｒｂａｎｃｅｏｆＦｏｒｅｓｔＶｅｇｅｔａｔｉｏｎ．ＱｉｕＹａｎｇ（ＩｎｓｔｉｔｕｔｅｏｆＬｏｅｓＰｌａｔｅａｕ,ＳｈａｎｘｉＵｎｉｖｅｒｓｉｔｙ,...     

利用遥感光谱法进行农田土壤水分遥感动态监测

自 1 997年 4月至 1 998年 1 0月 ,在甘肃省定西县进行了大面积 0～ 5 0 cm土层农田土壤水分按每 1 5 d本底资料实际观测 ,对此间收到的 5幅 TM与 7幅 NOAA卫片数据资料进行了加工处理 ,并对地面光谱资料也进行了观测。在光谱反演与光谱和土壤水分相关性分析基础上 ,利用遥感技术和地理信息系统 ,初步建立了典型试验区 ( 3× 3km2 )遥感信息与土壤含水量之间的遥感光谱相关监测模型 ,做出了观测区土壤水分含量分布图和得到了大面积农田土壤水分宏观动态监测结果 ,并同地面实测土壤水分进行了精度校正。研究结果表明 ,文中提出的“光学植被盖度”概念 ,对土壤水分遥感监测研究是有益的 ,利用遥感光谱法和数学统计方法求出了有关物理参数 ,初步建立了 TM与 NOAA光谱水分监测模型 ,其模型监测 0～2 0 cm土层含水量的精度达到 90 %以上 ,实际监测土壤水分精度达到 72 .3% ;在遥感监测 2 0～ 5 0 cm土层土壤含水量中 ,利用遥感模型监测土壤水分精度达到 80 %以上 ,实际遥感监测精度达到 60 %左右 ,其结果可有效指导干旱半干旱雨养农业区春耕时间和动态监测大面积土壤墒情 ,可为农业生产提供科学依据。另外 ,经地面大量观测表明 ,一般来说 ,当土壤含水量为田间最大持水量的 5 5 %～ 85 %时 ,从生长状况和经济     

野生大熊猫生态学研究进展与前瞻

在现代食肉目动物中,大熊猫无疑是最为引人注目的物种之一,既在科学上具有重要的研究价值,同时亦是世界自然保护的象征。自20 世纪80 年代初期我国政府与世界自然基金会合作开展野生大熊猫生态学研究
以来,迄今已积累了大量有关该物种及其栖息地的生态学知识,近年来3S 技术及分子生物学技术的推广应用将野生大熊猫生态学研究提升到了一个崭新的高度。本文在综合大量历史文献的基础上,从栖息地生态学、觅食
生态学、繁殖生态学、行为生态学、分子生态学、种群生态学和群落生态学等不同方面就野生大熊猫生态学的研究现状进行了梳理,力图归纳已有研究发现,阐明研究结果的科学价值及在保护生物学上的意义。同时,结
合研究与保护管理工作的需要,对未来研究方向进行了前瞻。     

西北半干旱区深旋松耕作对马铃薯水分利用和产量的影响

探明深旋松耕作技术(VRT)对西北黄土高原半干旱区马铃薯阶段性耗水、个体和群体生长状况、产量、水分利用效率和经济收益的影响,可为寻求抗旱增产、资源高效利用的耕作方法提供依据.本研究采用随机区组设计,于2016和2017年设置旋耕15 cm (TT)、深松40 cm (DLT)、深旋松耕40 cm (VRT) 3种耕作方式,测定马铃薯不同生育时期0～200 cm土层土壤贮水量、叶片SPAD值、叶面积指数、植株干物质量和产量等指标,计算阶段耗水量、水分利用效率(WUE)、商品率、商品产量、纯收益和新增收益等指标,探究深旋松耕作对马铃薯生产效率和经济效益的影响.结果表明: 与TT和DLT相比,VRT能显著促进马铃薯在盛花期和块茎膨大期的耗水,2016和2017年分别较DLT、TT增加了46.7、35.7和27.2、47.3 mm.由于VRT促进马铃薯耗水,叶片SPAD值、干物质量和叶面积指数均显著提高,证明它能促进马铃薯个体和群体发育.基于较高的个体和群体生长量,VRT的马铃薯块茎产量显著提高,分别在2016和2017年较DLT和TT增加了156.8%、47.8%和24.8%、41.0%,WUE相应地提高了92.3%、19.2%和18.9%、26.6%.深旋松耕作使马铃薯商品薯产量显著增加,纯收益和新增纯收益显著提高,在2016和2017年分别达到 12631.9、11019.1和29498.3、18245.5元·hm^-2.深旋松耕作促进马铃薯花期和块茎膨大期耗水,使马铃薯叶片SPAD值、干物质量和叶面积指数显著提高,导致块茎产量和水分利用效率明显升高,并提高了商品薯产量和纯收益,是适宜于西北黄土高原半干旱区马铃薯种植的耕作技术.     

作物生产力模型及其应用研究

从农业生态环境的角度论述了作物生产力模型的产生背景,讨论了作物生产力模型发展的幼年期、少年期、青年期和成熟期4个阶段,从科学研究,农业作物管理和农业决策分析等方面论述了作物生产力模型在保护农业生态环境中的作用,讨论了作物生产力模型的不足之处主要为简单的模型的地区适应性不强,而复杂的模型则由于参数的难以获取,且不同研究区域基础数据格式的一致性问题,也导致模型的地区适应性比较弱,因而提出要建立通用,统一的数据格式,以使作物生产力模型在不同地区易于推广应用;最后针对作物生产力模型普遍适应性能比较弱的问题,对作物生产力模型与地理信息系统的结合进行了研究,并综述了目前在作物生产力模型的界面友好化方面的一些工作,提出建立通用的作物生产力模型界面是今后发展的重点所在。     

基于GIS和遥感技术的生态系统服务价值评估研究进展

生态系统服务价值评估是当前生态系统管理领域研究的热点,密切联系着人类的生活福祉.本文系统综述了基于地理信息系统(GIS)和遥感(RS)技术的生态系统服务价值评估的研究进展,归纳为以下3个特征：生态经济学理论作为价值定量计算的核心方法被普遍采纳;GIS和RS在数据获取、时空分析和集成平台方面发挥着关键作用;运用生态系统服务功能机理模型模拟自然现象与人类活动之间的关系.根据目前该领域的研究现状及不足,提出了可扩展、分布式的集成研究框架--“生产线”框架,探讨了GIS和RS技术与生态系统服务价值评估集成研究的未来发展趋势.     

Precision agriculture: a challenge for crop nutrition management

Precision agriculture was initiated in the mid 1980s, using newly available technologies, to improve the application of fertilizers by varying rates and blends as needed within fields. Presently, the concept has been adapted to a variety of practices, crops, and countries. Its adoption varies significantly by cropping system, regions, and countries but it is progressively introduced or evaluated around the world. Several types of challenges limit a broader adoption: socio-economical, agronomical, and technological. Socio-economical barriers are principally costs and lack of skills. Agronomical challenges are lack of basic information, inadequate sampling and scouting procedures, absence of site-specific fertilizer recommendations, misuse of information, and lack of qualified agronomic services. There are multiple technological barriers that relate to machinery, sensor, GPS, software, and remote sensing. However, these barriers will be progressively lifted and precision agriculture will be a significant component of the agricultural system of the future. It offers a variety of potential benefits in profitability, productivity, sustainability, crop quality, food safety, environmental protection, on-farm quality of life, and rural economic development.     

Simulating spatially-explicit crop dynamics of agricultural landscapes: The ATLAS simulator

The spatially-explicit AgriculTural LandscApe Simulator (ATLAS) simulates realistic spatial-temporal crop availability at the landscape scale through crop rotations and crop phenology. Intended to be linked to organism population dynamics, the simulator is developed in a multi-agent platform. The model relies on initial GIS inputs for landscape composition and configuration. Users define typical rotations and crop phenology stages to be included, according to their objectives. In the study, we present two applications to contrasting landscapes, where ATLAS is capable of simulating accurate composition (crop area) and configuration (crop clustering) dynamics. ATLAS has potential applicability to a range of contrasting agricultural landscapes. The benefits of such a simulator are the possibility to study the effects of various simulated management scenarios of crop spatial-temporal availability in relation to target organisms and/or specific ecological processes (e.g. pest, biological control), within a single model framework.     

群落结构和叶面积指数在具茨山植被次生演替中的变化

采取3S(GIS,RS和GPS)技术和野外作业相结合的方法,研究了河南省具茨山人为干扰后植被次生演替过程。在此基础上,从宏观和微观尺度对不同演替阶段典型群落的结构变化和植被叶面积指数(LAI)动态进行分析。结果表明:乔木阶段的植被LAI均值为4.1～5.5;灌木阶段为3.0～3.7;草本阶段为1.0～1.5。部分灌木林在生长季的LAI和盖度高于乔木林。随着演替进行,群落结构也发生很大变化,乔木种类和数量逐步上升。植被系统的复杂度和稳定性不断增强,其发挥的生态功能和生态服务价值随之提高。     

Definition and feasibility of isolation distances for transgenic maize cultivation

A major concern related to the adoption of genetically modified (GM) crops in agricultural systems is the possibility of unwanted GM inputs into non-GM crop production systems. Given the increasing commercial cultivation of GM crops in the European Union (EU), there is an urgent need to define measures to prevent mixing of GM with non-GM products during crop production. Cross-fertilization is one of the various mechanisms that could lead to GM-inputs into non-GM crop systems. Isolation distances between GM and non-GM fields are widely accepted to be an effective measure to reduce these inputs. However, the question of adequate isolation distances between GM and non-GM maize is still subject of controversy both amongst scientists and regulators. As several European countries have proposed largely differing isolation distances for maize ranging from 25 to 800 m, there is a need for scientific criteria when using cross-fertilization data of maize to define isolation distances between GM and non-GM maize. We have reviewed existing cross-fertilization studies in maize, established relevant criteria for the evaluation of these studies and applied these criteria to define science-based isolation distances. To keep GM-inputs in the final product well below the 0.9% threshold defined by the EU, isolation distances of 20 m for silage and 50 m for grain maize, respectively, are proposed. An evaluation using statistical data on maize acreage and an aerial photographs assessment of a typical agricultural landscape by means of Geographic Information Systems (GIS) showed that spatial resources would allow applying the defined isolation distances for the cultivation of GM maize in the majority of the cases under actual Swiss agricultural conditions. The here developed approach, using defined criteria to consider the agricultural context of maize cultivation, may be of assistance for the analysis of cross-fertilization data in other countries.     

Coregionalized single- and multiresolution spatially varying growth curve modeling with application to weed growth

Modeling of longitudinal data from agricultural experiments using growth curves helps understand conditions conducive or unconducive to crop growth. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data that help understand spatial patterns. A particularly common problem is capturing spatial variation in growth patterns over the entire experimental domain. Statistical modeling in these settings can be challenging because agricultural designs are often spatially replicated, with arrays of subplots, and interest lies in capturing spatial variation at possibly different resolutions. In this article, we develop a framework for modeling spatially varying growth curves as Gaussian processes that capture associations at single and multiple resolutions. We provide Bayesian hierarchical models for this setting, where flexible parameterization enables spatial estimation and prediction of growth curves. We illustrate using data from weed growth experiments conducted in Waseca, Minnesota, that recorded growth of the weed Setaria spp. in a spatially replicated design.     

Precision Genome Engineering and Agriculture: Opportunities and Regulatory Challenges

Plant agriculture is poised at a technological inflection point. Recent advances in genome engineering make it possible to precisely alter DNA sequences in living cells, providing unprecedented control over a plant''s genetic material. Potential future crops derived through genome engineering include those that better withstand pests, that have enhanced nutritional value, and that are able to grow on marginal lands. In many instances, crops with such traits will be created by altering only a few nucleotides among the billions that comprise plant genomes. As such, and with the appropriate regulatory structures in place, crops created through genome engineering might prove to be more acceptable to the public than plants that carry foreign DNA in their genomes. Public perception and the performance of the engineered crop varieties will determine the extent to which this powerful technology contributes towards securing the world''s food supply.

This article is part of the PLOS Biology Collection “The Promise of Plant Translational Research.”

Over the past 100 years, technological advances have resulted in remarkable increases in agricultural productivity. Such advances include the production of hybrid plants and the use of the genes of the Green Revolution—genes that alter plant stature and thereby increase productivity [1],[2]. More recently, transgenesis, or the introduction of foreign DNA into plant genomes, has been a focus of crop improvement efforts. In the US, more than 90% of cultivated soybeans and corn contain one or more transgenes that provide traits such as resistance to insects or herbicides [3]. Transgenesis, however, has limitations: it is fundamentally a process of gene addition and does not harness a plant''s native genetic repertoire to create traits of agricultural value. Furthermore, public concerns over the cultivation of crops with foreign DNA, particularly those generated by the introduction of genes from distantly related organisms, have impeded their widespread use. The regulatory frameworks created to protect the environment and to address public safety concerns have added considerably to the cost of transgenic crop production [4]. These costs have limited the use of transgenesis for creating crops with agriculturally valuable traits to a few high-profit crops, such as cotton, soybean, and corn.The tools of genome engineering allow DNA in living cells to be precisely manipulated (reviewed in [5]). Although genome engineering can be used to add transgenes to specific locations in genomes, thereby offering an improvement over existing methods of transgenesis, a more powerful application is to modify genetic information to create new traits. Traditionally, new traits are introduced into cultivated varieties through breeding regimes that take advantage of existing natural genetic variation. Alternatively, new genetic variation is created through mutagenesis. With genome engineering, it is possible to first determine the DNA sequence modifications that are desired in the cultivated variety and then introduce this genetic variation precisely and rapidly. The ability to control the type of genetic variation introduced into crop plants promises to change the way new varieties are generated. Already genome engineering is being used in crop production pipelines in the developed world, and this technology can also be used to improve the crops that feed the burgeoning populations of developing countries.