Data and monitoring needs for a more ecological agriculture
Identifieur interne : 000628 ( Istex/Corpus ); précédent : 000627; suivant : 000629Data and monitoring needs for a more ecological agriculture
Auteurs : David P M. Zaks ; Christopher J. KucharikSource :
- Environmental Research Letters [ 1748-9326 ] ; 2011.
Abstract
Information on the life-cycle environmental impacts of agricultural production is oftenlimited. As demands grow for increasing agricultural output while reducing its negativeenvironmental impacts, both existing and novel data sources can be leveraged to providemore information to producers, consumers, scientists and policy makers. We review thecomponents and organization of an agroecological sensor web that integrates remotesensing technologies and in situ sensors with models in order to provide decisionmakers with effective management options at useful spatial and temporal scales formaking more informed decisions about agricultural productivity while reducingenvironmental burdens. Several components of the system are already in place, but byincreasing the extent and accessibility of information, decision makers will have theopportunity to enhance food security and environmental quality. Potential roadblocks toimplementation include farmer acceptance, data transparency and technology deployment.
Url:
DOI: 10.1088/1748-9326/6/1/014017
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As demands grow for increasing agricultural output while reducing its negativeenvironmental impacts, both existing and novel data sources can be leveraged to providemore information to producers, consumers, scientists and policy makers. We review thecomponents and organization of an agroecological sensor web that integrates remotesensing technologies and in situ sensors with models in order to provide decisionmakers with effective management options at useful spatial and temporal scales formaking more informed decisions about agricultural productivity while reducingenvironmental burdens. Several components of the system are already in place, but byincreasing the extent and accessibility of information, decision makers will have theopportunity to enhance food security and environmental quality. Potential roadblocks toimplementation include farmer acceptance, data transparency and technology deployment.</div></front></TEI><istex><corpusName>iop</corpusName><author><json:item><name>David P M Zaks</name><affiliations><json:string>Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of WisconsinMadison, 1710 University Avenue, Madison, WI 53726, USA</json:string><json:string>Author to whom any correspondence should be addressed</json:string><json:string>E-mail: zaks@wisc.edu</json:string><json:string>E-mail: davidzaks@gmail.com</json:string></affiliations></json:item><json:item><name>Christopher J Kucharik</name><affiliations><json:string>Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of WisconsinMadison, 1710 University Avenue, Madison, WI 53726, USA</json:string><json:string>Department of Agronomy, University of WisconsinMadison, 1575 Linden Drive, Madison, WI53706, USA</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>agriculture</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>environmental monitoring</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>science policy</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>agroecology</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>food security</value></json:item></subject><language><json:string>eng</json:string></language><originalGenre><json:string>letter</json:string></originalGenre><abstract>Information on the life-cycle environmental impacts of agricultural production is oftenlimited. As demands grow for increasing agricultural output while reducing its negativeenvironmental impacts, both existing and novel data sources can be leveraged to providemore information to producers, consumers, scientists and policy makers. We review thecomponents and organization of an agroecological sensor web that integrates remotesensing technologies and in situ sensors with models in order to provide decisionmakers with effective management options at useful spatial and temporal scales formaking more informed decisions about agricultural productivity while reducingenvironmental burdens. Several components of the system are already in place, but byincreasing the extent and accessibility of information, decision makers will have theopportunity to enhance food security and environmental quality. 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Res. Lett.</title><idno type="pISSN">1748-9326</idno><idno type="eISSN">1748-9326</idno><imprint><publisher>IOP Publishing</publisher><date type="published" when="2011"></date><biblScope unit="volume">6</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="1">1</biblScope><biblScope unit="page" to="10">10</biblScope><biblScope unit="production">Printed in the UK</biblScope></imprint></monogr><idno type="istex">092840B4C18A423F07516A4364E2462EA5F2942D</idno><idno type="DOI">10.1088/1748-9326/6/1/014017</idno><idno type="PII">S1748-9326(11)74549-9</idno><idno type="articleID">374549</idno><idno type="articleNumber">014017</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2011</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Information on the life-cycle environmental impacts of agricultural production is oftenlimited. As demands grow for increasing agricultural output while reducing its negativeenvironmental impacts, both existing and novel data sources can be leveraged to providemore information to producers, consumers, scientists and policy makers. We review thecomponents and organization of an agroecological sensor web that integrates remotesensing technologies and in situ sensors with models in order to provide decisionmakers with effective management options at useful spatial and temporal scales formaking more informed decisions about agricultural productivity while reducingenvironmental burdens. Several components of the system are already in place, but byincreasing the extent and accessibility of information, decision makers will have theopportunity to enhance food security and environmental quality. Potential roadblocks toimplementation include farmer acceptance, data transparency and technology deployment.</p></abstract><textClass><keywords scheme="keyword"><list><head>keywords</head><item><term>agriculture</term></item><item><term>environmental monitoring</term></item><item><term>science policy</term></item><item><term>agroecology</term></item><item><term>food security</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2011">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/092840B4C18A423F07516A4364E2462EA5F2942D/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="corpus iop not found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="ISO-8859-1"</istex:xmlDeclaration><istex:docType SYSTEM="http://ej.iop.org/dtd/iopv1_5_2.dtd" name="istex:docType"></istex:docType><istex:document><article artid="erl374549"><article-metadata><jnl-data jnlid="erl"><jnl-fullname>Environmental Research Letters</jnl-fullname><jnl-abbreviation>Environ. 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Lett.</jnl-abbreviation><jnl-shortname>ERL</jnl-shortname><jnl-issn>1748-9326</jnl-issn><jnl-coden>ERLNAL</jnl-coden><jnl-imprint>IOP Publishing</jnl-imprint><jnl-web-address>stacks.iop.org/ERL</jnl-web-address></jnl-data><volume-data><year-publication>2011</year-publication><volume-number>6</volume-number></volume-data><issue-data><issue-number>1</issue-number><coverdate>January-March 2011</coverdate></issue-data><article-data><article-type type="letter" sort="regular"></article-type><type-number type="letter" numbering="article" artnum="014017"></type-number><article-number>374549</article-number><first-page>1</first-page><last-page>10</last-page><length>10</length><pii>S1748-9326(11)74549-9</pii><doi>10.1088/1748-9326/6/1/014017</doi><copyright>IOP Publishing Ltd</copyright><ccc>1748-9326/11/014017 + 10$33.00</ccc><printed>Printed in the UK</printed></article-data></article-metadata><header><title-group><title>Data and monitoring needs for a more ecological agriculture</title><short-title>Data and monitoring needs for a more ecological agriculture
</short-title><ej-title>Data and monitoring needs for a more ecological agriculture
</ej-title></title-group><author-group><author address="erl374549ad1" alt-address="erl374549ad3" email="erl374549ea1 erl374549ea2"><first-names>David P
M</first-names><second-name>Zaks</second-name></author><author address="erl374549ad1 erl374549ad2"><first-names>Christopher J</first-names><second-name>Kucharik</second-name></author><short-author-list>D P M Zaks and C J Kucharik</short-author-list></author-group><address-group><address id="erl374549ad1"><orgname>Center for Sustainability and the Global Environment, Nelson Institute for Environmental
Studies, University of Wisconsin–Madison</orgname>, 1710 University Avenue, Madison, WI
53726,
<country>USA</country></address><address id="erl374549ad2"><orgname>Department of Agronomy, University of Wisconsin–Madison</orgname>, 1575 Linden Drive,
Madison, WI 53706,
<country>USA</country></address><address id="erl374549ad3" alt="yes">Author to whom any correspondence should be addressed</address><e-address id="erl374549ea1"><email mailto="zaks@wisc.edu">zaks@wisc.edu</email></e-address><e-address id="erl374549ea2"><email mailto="davidzaks@gmail.com">davidzaks@gmail.com</email></e-address></address-group><history received="10 November 2010" accepted="7 March 2011" online="24 March 2011"></history><abstract-group><abstract><heading>Abstract</heading><p indent="no">Information on the life-cycle environmental impacts of agricultural production is often
limited. As demands grow for increasing agricultural output while reducing its negative
environmental impacts, both existing and novel data sources can be leveraged to provide
more information to producers, consumers, scientists and policy makers. We review the
components and organization of an agroecological sensor web that integrates remote
sensing technologies and <italic>in situ</italic> sensors with models in order to provide decision
makers with effective management options at useful spatial and temporal scales for
making more informed decisions about agricultural productivity while reducing
environmental burdens. Several components of the system are already in place, but by
increasing the extent and accessibility of information, decision makers will have the
opportunity to enhance food security and environmental quality. Potential roadblocks to
implementation include farmer acceptance, data transparency and technology deployment.</p></abstract></abstract-group><classifications><keywords><keyword>agriculture</keyword><keyword>environmental monitoring</keyword><keyword>science policy</keyword><keyword>agroecology</keyword><keyword>food security</keyword></keywords></classifications></header><body refstyle="alphabetic"><sec-level1 id="erl374549s1" label="1"><heading>Introduction</heading><p indent="no">The global agricultural system has provided food, feed, fiber and fuel to a population that
has quadrupled over the past century. While output of agricultural products has increased
over time, so too have negative environmental impacts (<cite linkend="erl374549bib53">MEA 2005</cite>, <cite linkend="erl374549bib28">Foley <italic>et al</italic> 2005</cite>). The market prices of most agricultural goods produced
today do not reflect the life-cycle environmental impacts of production, transportation and
consumption. Such information must be available if we are to have a more informed
market, one that internalizes the environmental costs of agricultural production currently
borne by society.</p><p>The challenge to provide for a larger, more affluent population in the coming decades while
decreasing the environmental impacts of agriculture is increasingly clear to both scientists
and policy makers (<cite linkend="erl374549bib91">World Bank 2008</cite>, <cite linkend="erl374549bib27">Federoff <italic>et al</italic> 2010</cite>, <cite linkend="erl374549bib33">Godfray <italic>et al</italic> 2010</cite>). Improved monitoring, cataloging, interpreting and
dissemination of data about the status and trends of agroecosystems is needed if
agricultural products are to be delivered with smaller environmental footprints and if their
prices are to reflect the life-cycle costs of production.</p><p>Farmers and land managers have become the <italic>de facto</italic> managers of the largest anthrome,
on earth—agroecosystems (<cite linkend="erl374549bib24">Ellis and Ramankutty 2008</cite>). Often
they do not have the proper resources for managing agroecosystems to maximize
productivity and deliver ecosystem services simultaneously. In most cases, farmers
make management decisions based on assessment of local conditions, previous
experience and desired outcomes. Their knowledge can be supplemented with
management recommendations derived from satellites, on-the-ground sensors and
computer models that monitor and help forecast environmental conditions and
crop needs. These new observations are like an added ‘pair of eyes’ that can help
improve management decisions (<cite linkend="erl374549bib65">Porter <italic>et al</italic> 2009</cite>). Limited examples
of this adaptive management cycle exist where precision agriculture (PA) tools
have been adopted, but there is a need for improved monitoring and information
dissemination infrastructure to aid in decision-making (<cite linkend="erl374549bib12">Bramley 2009</cite>, <cite linkend="erl374549bib49">Lindenmayer and Likens 2010</cite>).</p><p>While the whole structure of an improved agroecological monitoring system has
yet to be designed, researchers in both public and private sectors are currently
developing many elements. These elements bridge remote sensing and ground-based
monitoring systems with real-time, smart, wireless, internet-connected sensor webs
(<cite linkend="erl374549bib72">Rundel <italic>et al</italic> 2009</cite>, <cite linkend="erl374549bib1">Adamchuk <italic>et al</italic> 2004</cite>). New technologies
can assist in analysis and reporting of spatial and temporal variability across the
agroecological landscape, while models can be used to transform raw data into useful
information assets in the decision-making process (<cite linkend="erl374549bib51">McLaren <italic>et al</italic> 2009</cite>, <cite linkend="erl374549bib35">Hale and Hollister 2009</cite>). With these new data streams, systems must be
designed to aggregate, coordinate, organize and synchronize within and between monitoring
networks.</p><p>Expanding the current agroecological monitoring and analysis systems will not only require
new technologies, but cooperation between governments, academia, private industries and
farmers as well. Policy and economic incentives that explicitly value public goods will be
vital to the success of any system. To overcome these challenges, an innovative
multidisciplinary approach that leverages the available tools to deliver a more ecologically
sound agriculture will be required. The momentum needed to implement this type of system
can be initiated by policies to reduce the life-cycle impacts of agricultural production. This
will require a more robust system to collect, analyze and disseminate data on
the functioning of the agricultural system. Putting these data in the hands of
decision makers has the potential to decrease environmental impact while increasing
efficiency of production. In this context, we include a variety of actors in the
term ‘decision maker’ whose choices can have an impact on the agroecological
landscape.</p><p>There are many challenges related to sustainability to be addressed at the intersection of
science, technology, agriculture, and policy. Availability of data on the dynamics of the
agroecological system will be a necessary input toward information used in decision-making
processes at the forefront of ensuring the sustainability of these systems. Here we review
the state of on-going monitoring activities and propose pathways to implement an
enhanced agroecological monitoring system that can assist producers, consumers, policy
makers and scientists to make more informed decisions at the interface of the food system
and environment.</p></sec-level1><sec-level1 id="erl374549s2" label="2"><heading>Gaps in tools currently used to facilitate decisions in the agricultural sector</heading><p indent="no">Predicting the impact of the global food system on the environment requires data
assets on system functioning, responses to change, and the potential impacts of
management decisions. Development of extensive datasets and numerous models has been
progressing, although enhancements in data collection methodology, aggregation and
dissemination to decision makers at a range of scales are necessary to meet both
production and environmental goals. Gaps in the patchwork of currently available data
prohibit a broad evaluation of the current state and trends of environmental
impacts from agriculture, and more effective responses could be implemented if
adequately informed models and indicators were available to decision makers
(<cite linkend="erl374549bib58">O’Malley <italic>et al</italic> 2009</cite>).
</p><sec-level2 id="erl374549s2.1" label="2.1"><heading>Ground-based and remote data collection</heading><p indent="no">Observations of agroecosystems monitor changes in agricultural or ecosystem processes, but
seldom both (<cite linkend="erl374549bib50">Lovett <italic>et al</italic> 2007</cite>). Commonly collected production data
include the crop type (<cite linkend="erl374549bib52">McNairn <italic>et al</italic> 2009</cite>), phenology/crop progress
(<cite linkend="erl374549bib75">Sakamoto <italic>et al</italic> 2005</cite>), area covered (<cite linkend="erl374549bib67">Ramankutty <italic>et al</italic> 2008</cite>),
and yield (<cite linkend="erl374549bib54">Monfreda <italic>et al</italic> 2008</cite>, <cite linkend="erl374549bib90">Wang <italic>et al</italic> 2010</cite>, <cite linkend="erl374549bib71">Ross <italic>et al</italic> 2008</cite>). The US Department of Agriculture’s Foreign Agriculture
Service provides agrometeorological data through their Crop Explorer tool that integrates
stations, models and satellites on a regional scale (USDA 2010). Other satellite systems,
such as SPOT (Satellite Pour l’Observation de la Terre), can be contracted to provide
imagery that assists in monitoring a range of agricultural parameters. The spatial and
temporal scales at which remote sensing and ground-based monitoring are conducted are
seldom coordinated, which can hamper efforts to synthesize crop data at regional and
global scales.</p><p>Environmental field data most often collected include quantification of soil water
(<cite linkend="erl374549bib69">Robock <italic>et al</italic> 2000</cite>, <cite linkend="erl374549bib92">Zhang <italic>et al</italic> 2010</cite>), greenhouse gases
(<cite linkend="erl374549bib5">Baldocchi 2008</cite>, <cite linkend="erl374549bib13">Bréon and Ciais 2010</cite>) and nutrient cycling
(<cite linkend="erl374549bib7">Batjes 2009</cite>). However, environmental data collection methods in
agroecological landscapes vary based on the scale of interest and intended purpose.
Irrigated areas can be detected from satellites, but on-farm water use data are best
collected at the field scale. Fertilizer use (or run-off) can be calculated through production,
sales data and application records, but these data are not often spatially referenced.
Mapping of the global distribution of fertilized lands has only recently been accomplished
(<cite linkend="erl374549bib66">Potter <italic>et al</italic> 2010</cite>). Integrated monitoring of crop input needs and
environmental variables is rarely undertaken in unison. A unified infrastructure of data
assets has yet to emerge to organize the ongoing collection of ground-based and remotely
sensed data.
</p></sec-level2><sec-level2 id="erl374549s2.2" label="2.2"><heading>Models</heading><p indent="no">Models are useful tools for synthesizing data, simulating the relationship between
environmental conditions and agroecosystem variables, and exploring such scenarios
as potential management decisions, climate change, increased atmospheric
CO<sub>2</sub>, and their impacts. Model output can sometimes replace field data when data
collection is too costly, impractical or time consuming. Models can also be used to
project the end of season field conditions based on initialization with field data and
scenarios of seasonal weather. Some models are designed on first principles and
validated by field data, while others are designed to reproduce the variability seen in
observations. Agronomic models that use input data (soils, climate, etc) in order
to perform simulations rely both on the underlying structure of the model and
on the quality of the input data. Therefore, if input data are limited or of poor
quality, the model results may be inaccurate. Many models report a static set of
results, but internet-based models can produce user-generated simulations on-the-fly
(<cite linkend="erl374549bib23">Eckman <italic>et al</italic> 2009</cite>).</p><p>There are three general modeling frameworks to simulate the components of the
agricultural system that range in scale and complexity. Detailed crop models, like DSSAT
(the Decision Support System for Agrotechnology Transfer), are used in both research and
management activities (<cite linkend="erl374549bib39">Jones <italic>et al</italic> 2003</cite>). Agroecosystem models, like
Agro-IBIS (Integrated BIosphere Simulator) and LPJmL (Lund–Potsdam–Jena
managed Land Dynamic Global Vegetation and Water Balance Model), incorporate
crop specific modules into existing models designed to study the interactions of
ecosystems with environmental drivers of change (<cite linkend="erl374549bib47">Kucharik 2003</cite>, <cite linkend="erl374549bib11">Bondeau <italic>et al</italic> 2007</cite>). Integrated models, such as IMPACT (International
Model for Policy Analysis of Agricultural Commodities and Trade) and BLS (Basic
Linked System) World Food Model, incorporate socioeconomic parameters into
environmental and agronomic relationships to simulate the broader food system
(<cite linkend="erl374549bib70">Rosegrant <italic>et al</italic> 2008</cite>, <cite linkend="erl374549bib62">Parry <italic>et al</italic> 2004</cite>). Given this variety of
tools, there are many opportunities to improve the data flow between agroecological
sensors, models, and end-users. Tighter integration between models and decision support
systems can provide added value to decision-making processes by providing timely, relevant
information to the hands of decision makers.
</p></sec-level2><sec-level2 id="erl374549s2.3" label="2.3"><heading>Indicators</heading><p indent="no">In lieu of real-time data on the functioning of agroecosystems, a variety of indicators have
been developed to relay information about the state of a system and how it might
be changing. The <cite linkend="erl374549bib36">Heinz Center (2008)</cite> defines an indicator as ‘a
specific, well-defined, and measurable variable that reflects some key characteristic
that can be tracked through time to signal what is happening within and across
ecosystems.’ These indicators include information on the extent, chemical, biological
and physical characteristics, amongst other goods and services provided by
agroecosystems. Data collected at various spatial and temporal scales are used as inputs
for indicators, but the use of an indicator can mask the complexity of a system
(<cite linkend="erl374549bib63">Payraudeau and Van Der Werf 2005</cite>). Current sets of indicators are useful,
as they can assist in targeting regions and variables that are poorly monitored. The Heinz
Center developed a thorough set of indicators for United States ecosystems, but adequate
data existed to calculate only 30% of indicators, while another 30% had partial data, and
40% of indicators had insufficient data for calculation (<cite linkend="erl374549bib36">Heinz Center 2008</cite>).
The Organisation for Economic Co-operation and Development (OECD) created a similar
set of indicators for the <cite linkend="erl374549bib36">Heinz Center (2008)</cite> to provide information
about the environmental performance of agriculture for their 30 member states
(<cite linkend="erl374549bib57">OECD 2001</cite>). Of the 37 indicators they developed, only 20 (54 per cent)
were deemed to be scientifically sound; on average, 18 of the 30 countries (60 per
cent) had adequate data. The paucity of data available to activate indicators
further highlights the need for improved data collection, standardization and
distribution.
</p></sec-level2></sec-level1><sec-level1 id="erl374549s3" label="3"><heading>Improvements in agroecological monitoring systems</heading><p indent="no">Improved monitoring and integrated decision-making can help overcome many of the
challenges that face the agroecological system. Many of these technologies are already
available (<cite linkend="erl374549bib32">Gebbers and Adamchuk 2010</cite>), but have seldom been
incorporated in a systematic manner. Benefits from enhanced observations are likely to
emerge when monitoring networks, reporting across different spatial and temporal scales,
are integrated so as to reveal novel system behaviors. We propose that relaying these data,
trends and management recommendations to decision makers in the field, or those crafting
policy can bring about a positive feedback loop which can help achieve desired production
and environmental outcomes.</p><p>Agroecosystems not only have their own ecological behavior, they are embedded within
ecologies at larger scales. We suggest that monitoring should encompass the nested scales at
which decisions are made across agroecological systems. These systems respond to drivers
across many dimensions of space and time; from daily variation in soil micronutrients
within a field to changes in weather over days and seasons, and climatic changes over
decades. Monitoring current conditions gives decision makers the ability to manage with
greater precision, while documenting trends over a longer length of time can help reveal
potential thresholds and discontinuities, and assist in both short-term forecasts (e.g. end of
season yields) and long-term adaptations. On-farm monitoring systems and portable
imaging systems should be able to capture differences within and between fields, while
earth observing systems can obtain a broader perspective of changes. The integration of
ground-based monitoring networks with remotely sensed data into Earth system
models has the potential to offer added value to scientists and decision makers
alike.</p><p>At the farm scale, data from enhanced monitoring will likely be used if it is presented
in a form that is easily integrated into existing decision-making structures
(<cite linkend="erl374549bib44">Kitchen 2008</cite>). If data from on-the-ground and remote systems is going to
be utilized by several parties (e.g. farmers, scientists, policy makers, consumers), additional
infrastructure will be needed to aggregate, process, model, store and disseminate data
products. Wireless systems are already in place, at a small scale, to aggregate data from
several monitors within a field (<cite linkend="erl374549bib89">Wang <italic>et al</italic> 2006</cite>), and for satellite
systems (<cite linkend="erl374549bib22">Duveiller and Defourny 2010</cite>). However, an integrated
information infrastructure foundation will be needed to transform raw data into
useful products, and thereby ensuring effectiveness. Standards-based data transfer
protocols from Open Geospatial Consortium (<cite linkend="erl374549bib55">Nash <italic>et al</italic> 2009</cite>, <cite linkend="erl374549bib45">Kooistra <italic>et al</italic> 2009</cite>) have been developed to seamlessly integrate multiple
data sources.</p><p>To be effective, an agroecological monitoring system must capture changes over a range of
processes. While crops in different biomes have individual biotic and abiotic needs, a
general framework for monitoring needs can still be assembled. Agricultural inputs
including nutrients and water support productivity, which is usually measured as yield.
Records of crop varieties, planting extent and timing are also useful to treat food security
issues. Ecological indicators such as soil type and fertility and meteorological indicators
such as solar radiation and humidity help us understand longer-term production and
environmental changes.</p><p>These data are the basic building blocks needed to ensure that the timing, magnitude and
location of management decisions have the desired agroecological outcomes. The spatial
scale and frequency of data collection will vary depending on the needs of the manager,
with more frequent temporal data and greater spatial resolution preferred. The regional to
continental extent of monitoring activities must include well-managed, highly productive
systems as well as those in sub-optimal locations or with limited management in
order to capture the full range and responses of agronomic and environmental
variables.</p><p>Environmental sensor technology has continued to improve because of increased availability
and sensor capacity and decreased cost. Given the extensive nature of agriculture, sensors
capable of providing data at spatial and temporal scales useful to on-the-ground managers
have been increasingly adopted (<cite linkend="erl374549bib48">Lamb <italic>et al</italic> 2008</cite>). These improved sensors
take advantage of innovations in data collection technology, data transfer capabilities
between sensors and to the internet, on-the-fly data processing, and renewable
and energy efficiency technologies (e.g. <cite linkend="erl374549bib64">Pierce and Elliott 2008</cite>, <cite linkend="erl374549bib83">Sun <italic>et al</italic> 2009</cite>, <cite linkend="erl374549bib17">Conover <italic>et al</italic> 2009</cite>). This portends reduced
monitoring costs, increased data availability, better spatial and temporal measurement
scales and the ability to monitor more components.
</p><sec-level2 id="erl374549s3.1" label="3.1"><heading>Soil physical and chemical properties</heading><p indent="no">Soil sensors to monitor nutrients, physical properties and sub-surface dynamics are already
available. A range of sensors exist depending on the variable in question and
includes electrical, optical, mechanical, acoustic, pneumatic and electrochemical
types (<cite linkend="erl374549bib1">Adamchuk <italic>et al</italic> 2004</cite>). Optical methods have shown the most
promise for nutrient sensing (<cite linkend="erl374549bib78">Sinfield <italic>et al</italic> 2010</cite>). Observation of the
correlations between primary properties of optical sensing and quantities such
as pH, cation exchange capacity and microbial activity have been documented
(<cite linkend="erl374549bib56">Nduwamungu <italic>et al</italic> 2009</cite>, <cite linkend="erl374549bib2">Allen <italic>et al</italic> 2007</cite>). While the
majority of soil sensors are ground-based, airborne hyperspectral imaging has
been used to measure soil organic carbon (<cite linkend="erl374549bib81">Stevens <italic>et al</italic> 2010</cite>). In
comparison to traditional in-lab soil testing, these new approaches have been
shown to reduce costs as much as 80% (<cite linkend="erl374549bib56">Nduwamungu <italic>et al</italic> 2009</cite>, <cite linkend="erl374549bib89">Wang <italic>et al</italic> 2006</cite>, <cite linkend="erl374549bib43">Kim <italic>et al</italic> 2009</cite>), and do not require
disturbing the soil structure (<cite linkend="erl374549bib77">Serrano <italic>et al</italic> 2010</cite>). The accuracy of
some measurements is not as great as their laboratory counterparts. For example, <cite linkend="erl374549bib15">Christy (2008)</cite> used an on-the-go near infrared reflectance spectroscopy
sensor to map within-field soil organic matter with the laboratory measurements
and sensor values that were in agreement with a RMSE of 0.52% and an
<italic>R</italic><sup>2</sup>
of 0.67. While not as accurate, the increase in sampling resolution, decrease in
cost and synergy with other management activities has led to their increased
acceptance.
</p></sec-level2><sec-level2 id="erl374549s3.2" label="3.2"><heading>Water</heading><p indent="no">Monitoring soil moisture status, coupled with vegetation vigor, is necessary in order
to understand how cropping systems respond to highly variable soil moisture
conditions (<cite linkend="erl374549bib61">Ozdogan <italic>et al</italic> 2010</cite>). In irrigated systems, crop water
needs require higher resolution data than those commonly used so that water
is provided in a more efficient manner given heterogeneous soil conditions
(<cite linkend="erl374549bib34">Greenwood <italic>et al</italic> 2010</cite>, <cite linkend="erl374549bib74">Sadler <italic>et al</italic> 2005</cite>). Soil moisture
sensors have been developed for below-ground, above-ground and remote monitoring. <cite linkend="erl374549bib14">Champagne <italic>et al</italic> (2010)</cite> used a ground-based network to test the ability of a
satellite-based passive microwave sensor with promising results. Data from in-ground soil
moisture and temperature sensors in a cotton field were transmitted via radio frequency
identification (RFID) chips to a central processor to assist in site-specific irrigation
scheduling (<cite linkend="erl374549bib88">Vellidis <italic>et al</italic> 2008</cite>). A ground-based optical remote sensing
system was fixed to a center-pivot irrigation site to provide <italic>in situ</italic> measurements. These
were used to compute a water deficit index, thus improving irrigation decisions
(<cite linkend="erl374549bib16">Colaizzi <italic>et al</italic> 2003</cite>).
</p></sec-level2><sec-level2 id="erl374549s3.3" label="3.3"><heading>Crop identification</heading><p indent="no">Crop identification and yield monitoring data can be used to advise markets on crop
production and progress, and for food security related questions, input to models, and
on-farm management (<cite linkend="erl374549bib9">Blaes <italic>et al</italic> 2005</cite>, <cite linkend="erl374549bib60">Ozdogan 2010</cite>).
Current approaches for crop identification across large areas use optical sensors, radar
sensors, or a combination of the two (<cite linkend="erl374549bib52">McNairn <italic>et al</italic> 2009</cite>). Jang <italic>et al</italic> (2009) used a series of Landsat images supplemented with the MODIS normalized
difference vegetation index (NDVI) to discriminate between crop types with success. <cite linkend="erl374549bib52">McNairn <italic>et al</italic> (2009)</cite> used a combination of optical sensors and radar for
individual crop classifications across Canada with accuracies of 80–90%. Yield can be
estimated from monitoring sensors located on the tractor (<cite linkend="erl374549bib71">Ross <italic>et al</italic> 2008</cite>),
or combined with satellite images (which have been calibrated with <italic>in situ</italic> data) for a
broader spatial coverage (<cite linkend="erl374549bib21">Dobermann and Ping 2004</cite>). Satellite remote
sensing images can be used in conjunction with yield-forecasting models, but often need
on-the-ground validation (<cite linkend="erl374549bib90">Wang <italic>et al</italic> 2010</cite>).
</p></sec-level2><sec-level2 id="erl374549s3.4" label="3.4"><heading>Processing and visualization</heading><p indent="no">Data collected by <italic>in situ</italic> or remote sensors are rarely useful by themselves in decision-making
either on or off the farm. Integrating this information with other observations and
numerical models, and then effectively communicating it to the decision maker can help to
fully leverage these new data sources. Emerging systems include features such as on-the-fly
error correction and modeling and the distribution of results to the internet or cellular
phones. The Intelligent Sensorweb for Integrated Earth Sensing combines <italic>in situ</italic>
measurements, crop growth models and online maps of predicted crop and range yields and
transmits the product to managers (<cite linkend="erl374549bib85">Teillet <italic>et al</italic> 2007</cite>). In South Africa,
where the internet is less accessible, <cite linkend="erl374549bib79">Singels and Smith (2006)</cite> report
on a system to provide advice on irrigation scheduling to small-scale sugarcane
farmers via cell phone, a technology that is much more readily available. Similarly, <cite linkend="erl374549bib4">Antonopoulou <italic>et al</italic> (2009)</cite> created a personalized spatial model that
incorporates policy, market, environmental, and agronomic information to the user via cell
phone in Greece. In areas where agroecological monitoring may not be available, web
crawlers ‘mine’ data from websites to provide information on the changing state
of the system (<cite linkend="erl374549bib30">Galaz <italic>et al</italic> 2009</cite>). While these examples do not
necessarily provide the backbone to a novel monitoring infrastructure, they highlight
what is possible with existing data assets and push the boundaries for future
innovations.
</p></sec-level2><sec-level2 id="erl374549s3.5" label="3.5"><heading>Agroecological sensor webs</heading><p indent="no">The monitoring systems described here have been deployed for decision-making at the farm
and regional scales. The advent of internet-connected real-time wireless sensors presents a
new opportunity to integrate data from a wide variety of sources and process
them in a manner that the output is useful to decision makers. These sensor webs
can reveal emergent biogeochemical properties of the agricultural system that
may not have been otherwise observable with current monitoring infrastructure
(<cite linkend="erl374549bib87">Van Zyl <italic>et al</italic> 2009</cite>). New tools are required to build an agricultural
information infrastructure for data organization, synthesis and integration as data
become available from individual networks across the agroecological landscape
(<cite linkend="erl374549bib35">Hale and Hollister 2009</cite>) (figure <figref linkend="erl374549fig1">1</figref>).
<figure id="erl374549fig1" parts="single" width="page" position="float" printstyle="normal" orientation="port"><graphic><graphic-file version="print" format="EPS" scale="100" filename="images/7454901.eps"></graphic-file><graphic-file version="ej" format="JPEG" filename="images/7454901.jpg"></graphic-file></graphic><caption id="erl374549fc1" type="figure" label="Figure 1"><p indent="no">Major components of the agroecological sensor web and information infrastructure.
Individual sensors including remote (e.g. satellite, aircraft, unmanned drone), automated <italic>in
situ</italic> and direct human observations collect data at multiple spatial scales. These data are
processed for quality control and data from multiple networks across spatial scales
are aggregated. These data are used as inputs to agronomic, agroecological and
integrated models. The output of the models can be used by producers for real-time
management and long-term planning, consumers to discriminate products based on their
environmental footprints, scientists to study the dynamics of agroecosystems and policy
makers to guide policies to further reduce environmental impacts of agricultural
production. Image credits (L to R): DPMZ and Flickr users stawarz, ostrosky and ciat.</p></caption></figure></p><p>As the amount of data produced by environmental and ecological sensor systems has
grown, techniques in database management, informatics, statistics, spatial processing and
visualization have emerged to meet the challenge of data handling, processing and
storage (e.g. <cite linkend="erl374549bib51">McLaren <italic>et al</italic> 2009</cite>, <cite linkend="erl374549bib86">Uslaender <italic>et al</italic> 2010</cite>, <cite linkend="erl374549bib6">Ball <italic>et al</italic> 2008</cite>, <cite linkend="erl374549bib40">Jurdak <italic>et al</italic> 2008</cite>). For the most part, these
new techniques have emerged at the fringes of traditional disciplines, for example,
by bringing together biologists and computer scientists to contribute new tools
(<cite linkend="erl374549bib8">Benson <italic>et al</italic> 2010</cite>). Many of these tools can be applied to building an
agroecological sensor web if these data are used as model input, and the model output is
rapidly disseminated directly to the decision maker in a form that is deemed useful for the
specific context (e.g. policy or land management).
</p></sec-level2></sec-level1><sec-level1 id="erl374549s4" label="4"><heading>Discussion</heading><p indent="no">The limitation to implementing an enhanced agroecological monitoring infrastructure is not
the sensor technology; rather it is leveraging the output data for decision-making on several
levels that would reduce the negative impacts of agriculture on the environment. Unlike the
current approach of precision agriculture, agroecological data must be paired with
innovative policies to incentivize simultaneous production and environmental goals.
There are many potential users of enhanced agroecological data, and there is
no one-size-fits-all approach that incorporates data collection, processing and
dissemination. The challenge ahead is not purely technical, as there are potential
social barriers, such as privacy concerns, to data collection and dissemination.
Designing useful products from such a system will require input from end-users to
ensure their applicability and relevance to the challenges at hand. Information and
decision support tools from agroecological monitoring can be used throughout the
supply chain of products, from producers and consumers to policy makers and
scientists.
</p><sec-level2 id="erl374549s4.1" label="4.1"><heading>Producers</heading><p indent="no">Technology vendors, scientists and policy makers can extol the virtues of sensor web
technology ad nauseam, but results will be limited to scientific results until a significant
proportion of the agricultural community adopts it. For producers in developing countries,
here is an opportunity to leapfrog the traditional development pathways and adopt the
latest methods and technologies. But the acceptance of precision agriculture technology
has been relatively slow thus far (<cite linkend="erl374549bib18">Daberkow and McBride 2003</cite>, <cite linkend="erl374549bib82">Sumberg 2005</cite>). One strategy to avoid falling from the ‘peak of inflated
expectations’ to the ‘trough of disillusionment’ of technology adoption, as described by <cite linkend="erl374549bib48">Lamb <italic>et al</italic> (2008)</cite> is to ensure the delivery of decision-relevant information
to the producer, compared to raw data which has limited usefulness. Before too much hype
is made about the many potential benefits of agroecological sensor webs, the systems sensor
data need to be incorporated into decision support systems that allow the producer to
explicitly understand potential trade-offs between management decisions and ecological and
production outcomes (<cite linkend="erl374549bib29">Fountas <italic>et al</italic> 2006</cite>). Some elements of this can be
seen in precision agriculture systems currently deployed, although many systems lack the
ability to provide real-time information on trade-offs related to economic and
environmental outcomes of management decisions. Access to this type of management
information can decrease costs for producers as agricultural inputs could be targeted.
While some systems are in development, additional work is needed to ensure
the transparency, reliability and ease-of-use of the software and its integration
into current agricultural management tools. Once these objectives are met, there
is a higher likelihood that a rapid adoption of agroecological sensor webs will
ensue.
</p></sec-level2><sec-level2 id="erl374549s4.2" label="4.2"><heading>Consumers</heading><p indent="no">Informed consumers have the ability to shift markets through changes in their purchasing
habits. Eco-labels and certifications are emerging as an approach to inform consumers
about the products they purchase. Labels for products such as organic foods, sustainable
wood products and energy-saving appliances have been growing in recent years
(<cite linkend="erl374549bib38">Ibanez and Grolleau 2008</cite>, <cite linkend="erl374549bib46">Kotchen 2006</cite>). Additional food
labels provide information on how the items were produced, such as fair-trade, shade-grown
or dry-farmed (<cite linkend="erl374549bib37">Howard and Allen 2010</cite>). While comparisons can currently
be made between products with eco-labels and those without, little specific information is
communicated to the consumer about the life-cycle impacts. As supply chains shift
to increase the transparency of their products, data from agroecological sensor
webs can be used to communicate the back-story of the product to the consumer
(<cite linkend="erl374549bib59">Opara and Mazaud 2001</cite>). Building on the success of other eco-labels, <cite linkend="erl374549bib26">Faludi (2007)</cite> proposed ‘eco-nutrition’ labels that mimic the current labels
on food products (figure <figref linkend="erl374549fig2">2</figref>). These labels would communicate energy, resource, water, toxins and social
scores of the product’s life-cycle to the user and would allow for more in-depth comparisons
among products. Similar graded eco-labels can provide consumers with information on
multiple environmental performance indicators (<cite linkend="erl374549bib10">Bleda and Valente 2009</cite>).
Labels like these could be improved with enhanced agroecological sensor web
technology.
<figure id="erl374549fig2" parts="single" width="column" position="float" printstyle="normal" orientation="port"><graphic><graphic-file version="print" format="EPS" scale="100" filename="images/7454902.eps"></graphic-file><graphic-file version="ej" format="JPEG" filename="images/7454902.jpg"></graphic-file></graphic><caption id="erl374549fc2" type="figure" label="Figure 2"><p indent="no">A representation of an eco-label that integrates data from several monitoring
sources and clearly communicates the life-cycle environmental impacts of the
product to the consumer. Figure adapted with permission from Faludi J 2007 <italic>The
Eco-Nutrition Label</italic> (available at <webref url="http://www.worldchanging.com/archives/007256.html">http://www.worldchanging.com/archives/007256.html</webref>).</p></caption></figure></p></sec-level2><sec-level2 id="erl374549s4.3" label="4.3"><heading>Science</heading><p indent="no">Many of the models that simulate crops, ecosystems and economies are hampered by a
scarcity of data about the system of interest. Limitations in computing power to run the
simulations are being lifted as computers have become cheaper and more powerful.
Integration of new data at higher spatial and temporal resolution, supplemented by
historical data, can improve the precision and accuracy of model output. In addition, new
streams of multivariate and multidisciplinary data will require the expertise of many
disciplines to unravel the agroecological complexities veiled under the many new layers of
information.</p><p>While new data streams, such as those from an agroecological sensor web, may assist in
further refining these models, they can also help to elucidate previously unknown or poorly
understood relationships within the modeled system. Some newly collected data may not
even fit into the structure of current models, and in this case new models will need to be
built that can harness an input dataset with increased dimensionality over time and space.
These new models can also help to create links between disciplines, especially in the
physical and social sciences that are needed to solve problems and produce solutions for
policy makers. These systems can also help to strengthen partnerships between developed
and developing countries and foster the co-development of new models and knowledge
sharing.
</p></sec-level2><sec-level2 id="erl374549s4.4" label="4.4"><heading>Policy</heading><p indent="no">At the most basic level, the interactions between policy makers and the agricultural system
occur both at the marketplace and through the regulatory structure. Broad polices such as
the US Department of Agriculture Conservation Reserve Program and markets for
ecosystem goods and services have similar goals of striking a balance between production of
agricultural goods and protection of vital ecosystem services. However, they must often rely
on generalized information that lacks a connection between a parcel of land and its delivery
of ecosystem goods and services. By incorporating data from agroecological sensor webs
into a policy framework, a structure can be developed to provide incentives for
lands that produce a suite of ecosystem goods and services, as well as the ability
to value these services separately. These incentives can be modified according
to updated data, an improvement on a program that values all areas equally.
Performance-based incentives can reward producers for meeting environmental targets
while decreasing the environmental burdens of production. Monitoring compliance
for this type of incentive would be streamlined through the use of sensor web
data.</p><p>In the near term, the fundamental data gap in need of attention is the monitoring of
greenhouse gases (GHG) from agricultural lands. As policies are negotiated to reduce
GHGs across the US economy, emissions from agriculture may be excluded from
a cap-and-trade system because they are hard to measure, monitor and verify
(<cite linkend="erl374549bib80">Smith <italic>et al</italic> 2007</cite>, <cite linkend="erl374549bib19">Dale and Polasky 2007</cite>). The added
transaction costs, and uncertainty in emissions reductions, have marginalized the 7 per cent
of US GHGs emitted by agricultural activities (<cite linkend="erl374549bib25">EPA 2010</cite>). Improved
monitoring of carbon dioxide, nitrous oxide and methane from agriculture could provide
the information and incentives necessary for carbon markets, policy makers and farmers to
reduce emissions from the production life-cycle.
</p></sec-level2><sec-level2 id="erl374549s4.5" label="4.5"><heading>Getting from here to there: innovation, investment and transparency</heading><p indent="no">Many of the technologies highlighted here have yet to be deployed at the scale necessary to
display the emergent properties of an agroecological sensor web (table <tabref linkend="erl374549tab1">1</tabref>). As the focus on agricultural innovation shifts to incorporate both
production and environmental objectives, information and communications technologies
(ICT) are likely to play a larger role. As investments and technological breakthroughs
in ICT have generally been focused on sectors other than agriculture, there is
tremendous potential to apply the technology already available to agroecological uses
(<cite linkend="erl374549bib76">Sassenrath <italic>et al</italic> 2008</cite>). Important advances are likely in approaches to
transmit, store, process and aggregate data from multiple sources to aid in site-specific
decision-making.</p><table id="erl374549tab1" width="42pc"><caption id="tc1" label="Table 1"><p indent="no">Summary of the current and potential future components of an agroecological sensor web.</p></caption><tgroup cols="3"><colspec colnum="1" colname="col1" align="left"></colspec><colspec colnum="2" colname="col2" align="left"></colspec><colspec colnum="3" colname="col3" align="left"></colspec><thead><row><entry></entry><entry>Current</entry><entry>Future</entry></row></thead><tbody><row><entry>Markets for food, carbon and other EGS</entry><entry>Carbon markets limited by lack of available data: nutrient markets in their infancy</entry><entry>Markets informed by data from agroecological sensor web</entry></row><row><entry><italic>In situ</italic> and remote monitoring</entry><entry>Yield monitoring becoming common in high-input system, remote sensing algorithm output
not explicitly designed for agricultural users</entry><entry>Access to data available to on-farm decision makers via internet and cellular phones in
real-time</entry></row><row><entry>Data transparency</entry><entry>Limited public availability of data</entry><entry>Agricultural sousveillance joins other energing monitoring systems</entry></row><row><entry>Product labeling</entry><entry>Product certification labeling lacks differentiation between impact categories</entry><entry>Products assigned grades based on water, carbon, nutrient, biodiversity impacts</entry></row><row><entry>Scientific models</entry><entry>Models driven and validated by limited observations</entry><entry>Social economic and environmental data streams used to constantly update model
validation and modify projections</entry></row><row><entry>Impacts of production</entry><entry>60% of ecosystem services negatively impacted by agriculture</entry><entry>Reduced environmental impacts, increased food security</entry></row><row><entry>Social and environmental costs of production</entry><entry>External to product cost</entry><entry>Internalized in product cost</entry></row></tbody></tgroup></table><p>Sensor webs have already emerged at several spatial scales. Examples include the Global
Earth Observing System of Systems (<cite linkend="erl374549bib41">Justice and Becker-Reshef 2007</cite>),
National Ecological Observatory Network (<cite linkend="erl374549bib42">Keller <italic>et al</italic> 2008</cite>) and
Chesapeake Bay Environmental Observatory (<cite linkend="erl374549bib6">Ball <italic>et al</italic> 2008</cite>). These systems
are driven by their own science questions and can serve as useful building blocks to address
new agroecological questions. Like these examples, an agroecological sensor web
will require the buy-in and support from many entities, expanding beyond the
public sector. While the metrics for success will be variable, explicit goal setting
amongst sensor web partners will be necessary to avoid unrealistic or unattainable
goals.</p><p>The cost of installing an agroecological monitoring system is likely to vary as a function of
the area covered, the variables tracked and the degree of integration with other systems.
The diversity of potential stakeholders and end-users of the data introduces a variety of
actors to help burden the cost of such a system and help bring it to fruition. Even with
high initial capital costs, the benefits are likely to be high. Private benefits can include
increased yields and decreased costs from inputs; public benefits include increased
availability of ecosystem services from biodiversity, carbon sequestration, and water
infiltration. Public and private investments will be essential to realize these benefits
(<cite linkend="erl374549bib3">Alston <italic>et al</italic> 2009</cite>).</p><p>The availability of data on how management decisions affect the provision of ecosystem
goods and services can help to inform markets and offset the costs of monitoring
(<cite linkend="erl374549bib84">Swinton <italic>et al</italic> 2007</cite>, <cite linkend="erl374549bib19">Dale and Polasky 2007</cite>). Tracking
changes in production and ecosystem service delivery facilitates the internalization of costs
from agriculture that were previously external. While developing these markets will require
outside capital, as markets grow they should provide returns that help to build additional
monitoring capacity.</p><p>The increased availability in wireless monitoring devices is not limited to the agricultural
sector, but examples can be found in cell phone photography and closed caption television
systems (<cite linkend="erl374549bib20">Dennis 2008</cite>). The public availability of these new data streams has
had a positive social benefit since they act as a deterrent for ‘anti-social behaviors’
(<cite linkend="erl374549bib31">Ganascia 2010</cite>). The rise of sousveillance, where societal monitoring
activities are becoming widespread and data are publicly available, has yet to be explored
for agricultural applications. If the maximum benefit of sensor web technology is
to be realized, then the data collected will need to have both on- and off-farm
uses.</p><p>To date, most data collected on-farm for management purposes is not used in other ways
and scientific and census data are usually collected independently. For example, The US
National Agricultural Statistics Survey (NASS) promotes ‘confidentiality and data security’
in regard to data about agricultural production. With additional production and
environmental data being potentially readily available, making all collected data publicly
available and transparent has its merits. Consumers will be able to trace the origins of their
products and monitor the conditions under which they were produced. This will compel
producers to improve the environmental performance of their products. Increased
transparency in the agricultural system can close the gap between producers
and consumers through the monitoring and open distribution of agroecological
data.
</p></sec-level2></sec-level1><sec-level1 id="erl374549s5" label="5"><heading>Conclusions</heading><p indent="no">The grand challenge for agriculture over the next generation is to reduce its environmental
impact while producing enough food, feed and fiber for a larger and wealthier
population (<cite linkend="erl374549bib68">Robertson and Swinton 2005</cite>). While the first green
revolution brought increases in productivity, these carried environmental costs and the
next generation of farmers will be in the vanguard to reduce those impacts. This
cohort will be an increasingly digitally connected group and will have access to
unprecedented numbers of novel tools. As new farmers will be recruited into an occupation
that has steadily decreased in size over the past generation, the image of the
farmer needs to be recast as a 21st century steward of the land, equipped with
digital tools, knowledge and skills to meet increasingly stringent multi-functional
demands.</p><p>Moving enhanced agroecological monitoring infrastructure from research lab to farm
field will take commitments and investments from a diverse array of stakeholders
(<cite linkend="erl374549bib73">Sachs <italic>et al</italic> 2010</cite>). Not only are there many technical elements in need of
further development in the proposed system, but also the agricultural sector has been slow
to adopt other potentially important innovations. Therefore the social, economic and
environmental stakeholders in the system will need to be on board before a successful
introduction is possible.</p><p>The opportunity to establish a global agroecological monitoring infrastructure along with
information dissemination tools comes at a time when increasing agricultural demands and
reducing environmental impacts from production have gained attention at the highest
political levels. While this is a formidable challenge, we have recently entered an era where
monitoring, data processing, numerical modeling and communications technologies have
the ability to give agroecological decision-making new dimensions. Policies that provide
incentives to create these new data streams and leverage their data to put an objective
value on the ecosystem goods and services connected to agriculture are paramount. While
the investment needed for such a system is large, the return on investment, as measured by
agricultural productivity and reduced environmental impacts, can be great as
well.</p></sec-level1><acknowledgment><heading>Acknowledgments</heading><p indent="no">The authors would like to thank George Allez, Sam Batzli, Carol Barford, Molly Jahn and
the two anonymous reviewers for their useful comments and suggestions, and Jeremy Faludi
for allowing us to adapt a version of figure <figref linkend="erl374549fig2">2</figref> for use in the manuscript. Images from figure <figref linkend="erl374549fig1">1</figref> are credited (from L to R): DPMZ, and Flickr users stawarz,
ostrosky and ciat under the Creative Commons license. DPMZ was supported by the
National Science Foundation grant 144-144PT71 and CJK was supported by the
National Aeronautics and Space Administration’s Interdisciplinary Earth Science
Program.
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<crossref><cr_doi>http://dx.doi.org/10.1029/2009WR008800</cr_doi><cr_issn type="print">0043-1397</cr_issn><cr_issn type="electronic"></cr_issn></crossref></journal-ref></reference-list></references></back></article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="eng"><title>Data and monitoring needs for a more ecological agriculture</title></titleInfo><titleInfo type="abbreviated"><title>Data and monitoring needs for a more ecological agriculture</title></titleInfo><titleInfo type="alternative" lang="eng"><title>Data and monitoring needs for a more ecological agriculture</title></titleInfo><name type="personal"><namePart type="given">David P M</namePart><namePart type="family">Zaks</namePart><affiliation>Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of WisconsinMadison, 1710 University Avenue, Madison, WI 53726, USA</affiliation><affiliation>Author to whom any correspondence should be addressed</affiliation><affiliation>E-mail: zaks@wisc.edu</affiliation><affiliation>E-mail: davidzaks@gmail.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Christopher J</namePart><namePart type="family">Kucharik</namePart><affiliation>Center for Sustainability and the Global Environment, Nelson Institute for Environmental Studies, University of WisconsinMadison, 1710 University Avenue, Madison, WI 53726, USA</affiliation><affiliation>Department of Agronomy, University of WisconsinMadison, 1575 Linden Drive, Madison, WI53706, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="letter">letter</genre><originInfo><publisher>IOP Publishing</publisher><dateIssued encoding="w3cdtf">2011</dateIssued><copyrightDate encoding="w3cdtf">2011</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><note type="production">Printed in the UK</note></physicalDescription><abstract>Information on the life-cycle environmental impacts of agricultural production is oftenlimited. As demands grow for increasing agricultural output while reducing its negativeenvironmental impacts, both existing and novel data sources can be leveraged to providemore information to producers, consumers, scientists and policy makers. We review thecomponents and organization of an agroecological sensor web that integrates remotesensing technologies and in situ sensors with models in order to provide decisionmakers with effective management options at useful spatial and temporal scales formaking more informed decisions about agricultural productivity while reducingenvironmental burdens. Several components of the system are already in place, but byincreasing the extent and accessibility of information, decision makers will have theopportunity to enhance food security and environmental quality. Potential roadblocks toimplementation include farmer acceptance, data transparency and technology deployment.</abstract><subject><genre>keywords</genre><topic>agriculture</topic><topic>environmental monitoring</topic><topic>science policy</topic><topic>agroecology</topic><topic>food security</topic></subject><relatedItem type="host"><titleInfo><title>Environmental Research Letters</title></titleInfo><titleInfo type="abbreviated"><title>Environ. Res. Lett.</title></titleInfo><genre type="journal">journal</genre><identifier type="ISSN">1748-9326</identifier><identifier type="eISSN">1748-9326</identifier><identifier type="PublisherID">erl</identifier><identifier type="CODEN">ERLNAL</identifier><identifier type="URL">stacks.iop.org/ERL</identifier><part><date>2011</date><detail type="volume"><caption>vol.</caption><number>6</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>1</start><end>10</end><total>10</total></extent></part></relatedItem><identifier type="istex">092840B4C18A423F07516A4364E2462EA5F2942D</identifier><identifier type="DOI">10.1088/1748-9326/6/1/014017</identifier><identifier type="PII">S1748-9326(11)74549-9</identifier><identifier type="articleID">374549</identifier><identifier type="articleNumber">014017</identifier><accessCondition type="use and reproduction" contentType="copyright">IOP Publishing Ltd</accessCondition><recordInfo><recordContentSource>IOP</recordContentSource><recordOrigin>IOP Publishing Ltd</recordOrigin></recordInfo></mods></metadata><enrichments><json:item><type>multicat</type><uri>https://api.istex.fr/document/092840B4C18A423F07516A4364E2462EA5F2942D/enrichments/multicat</uri></json:item></enrichments><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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