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The global Land-Potential Knowledge System (LandPKS): Supporting evidence-based, site-specific land use and management through cloud computing, mobile applications, and crowdsourcing



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JAN/FEB 2013—VOL. 68, NO. 1
Jeffrey E. Herrick , Kevin C. Urama, Jason W. Karl, John Boos, Mari-Vaughn V. Johnson, Keith D. Shepherd, Jon
Hempel, Brandon T. Bestelmeyer, Jonathan Davies, Jorge Larson Guerra, Chris Kosnik, David W. Kimiti, Abra-
ham Losinyen Ekai, Kit Muller, Lee Noreet, Nicholas Ozor, Thomas Reinsch, José Sarukhan, and Larry T. West
The global Land-Potential Knowledge System (LandPKS):
Supporting evidence-based, site-specic land use and management
through cloud computing, mobile applications, and crowdsourcing
Jeffrey E. Herrick is Research Soil Scientist and
Brandon T. Bestelmeyer and Jason W. Karl are
Research Ecologists at the Jornada Experimental
Range, USDA Agricultural Research Service,
Las Cruces, New Mexico. Kevin C. Urama is
Executive Director and Nicholas Ozor is Senior
Research Ofcer at the African Technology
Studies Network, Nairobi, Kenya. John Boos is
Geospatial Advisor and Chris Kosnik is Team
Leader at the US Agency for International
Development, Washington, DC. Mari-Vaughn
V. Johnson is Agronomist and Lee Noreet is
Modeling Team Leader/Soil Scientist at the
USDA Natural Resource Conservation Service,
Temple, Texas. Keith D. Shepherd is Principal
Soil Scientist at the World Agroforestry Center
(ICRAF), Nairobi, Kenya. Jon Hempel is Director,
and Larry T. West is Soil Survey Research and
Laboratory National Leader at the National Soil
Survey Center, Lincoln, Nebraska. Jonathan
Davies is Coordinator at the IUCN Global
Drylands Initiative, Nairobi, Kenya. Jorge Larson
Guerra is Use of Biodiversity Coordinator and
José Sarukhan is National Coordinator at
National Commission for Knowledge and Use
of Biodiversity (CONABIO), Mexico City, Mexico.
David W. Kimiti is Graduate Research Assistant
at the New Mexico State University, Las
Cruces, New Mexico. Abraham Losinyen Ekai
is Ford Foundation Scholar at Duke University,
Durham, North Carolina. Kit Muller is Strategic
Planner at the US Department of the Interior
Bureau of Land Management, Washington, DC.
Thomas Reinsch is National Leader for World
Soil Resources at the USDA Natural Resource
Conservation Service, Beltsville, Maryland.
gricultural production must
increase significantly to meet
the needs of a growing global
population with increasing per capita con-
sumption of food, fiber, building materials,
and fuel. Consumption already exceeds
net primary production in many parts of
the world (Imhoff et al. 2004).
In addition to reducing consumption,
there are two options to meet these needs:
production intensification and land con-
version. Both strategies present unique
opportunities, challenges, and risks. The
largest gains achievable through agricul-
tural intensification will likely occur on
lands with the largest unrealized produc-
tion potential, or yield gap. These lands
have high potential production and low
current production. Similarly, the highest
returns on investments to be gained by land
conversion should occur on lands with the
highest potential production, assuming
similar infrastructure, per acre conversion
costs, and other market conditions.
The biggest long-term risk for both
strategies is that application of nonsustain-
able land management practices will result
in soil degradation that is often costly, if
not impossible, to reverse. Exploiting these
opportunities and minimizing risks depend
on careful matching of production systems
with the sustainable production potential
of each type of land. Similar analyses can
be applied to biodiversity conservation to
prioritize land conservation and restora-
tion efforts.
The ability to match land use with land
potential is limited by four factors: (1)
current land potential evaluation systems,
while addressing potential productivity
and degradation resistance, do not con-
sider resilience, (2) it is virtually impossible
to identify, access, and interpret all rel-
evant scientific and local knowledge and
information, (3) this information is often
provided in the form of maps at a scale that
is far too coarse for field-scale management,
and (4) by the time land classification sys-
tems are established and maps developed,
the information is often obsolete. Farmers
and scientists are constantly innovating and
adapting, effectively changing land poten-
tial to support different types of production.
This paper describes how a new
cloud-based Land-Potential Knowledge
System (LandPKS; www.landpotential.
org; figure 1) will allow land potential
to be defined explicitly and dynamically
for unique and constantly changing soil
and climate conditions and to be updated
based on new evidence about the success
or failure of new management systems
on different soils. The knowledge engine
(figure 2), together with simple applica-
tions for mobile phones, will also facilitate
more rapid and complete integration and
dissemination of local and scientific knowl-
edge about sustainable land management.
Figure 1
Land-Potential Knowledge System. See for more information and
opportunities to participate.
Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
JAN/FEB 2013—VOL. 68, NO. 1
This system expands the concept of an
Ecological Knowledge System (Herrick
and Sarukhan 2007) through the use of
mobile technologies and cloud computing
and making more extensive use of crowd-
sourcing both knowledge and information
(Karl and Herrick forthcoming).
The system will include both simple,
mobile phone interfaces and more sophis-
ticated web tools that can be accessed via
personal computers and linked with other
decision tools. Individual producers will be
able to use the system to answer questions
about sustainable land management options
at the field scale, while policymakers will
be able to aggregate data across larger areas
without losing key pieces of information,
such as the presence of small, highly produc-
tive, biodiverse, or vulnerable sites within a
region. It will also provide extension work-
ers with the ability to instantaneously access
the best available information and interpret
it in the context of local socioeconomic
conditions and local values, including crop
preferences, while scientists will have access
to a global georeferenced database for cali-
brating remote sensing imagery and testing
hypotheses globally. Finally, as a social net-
working tool, it will allow individual
producers to easily connect with others
facing similar challenges on similar types
of land.
Overview. Land potential includes three
elements: potential production of one
or more ecosystem services, degradation
resistance, and resilience, or the capacity
to recover following degradation. While
some definitions of resilience integrate
both degradation resistance and capacity
to recover, we distinguish them because
different soil, vegetation, and landscape
properties and processes affect resistance
and resilience to different disturbance
types in different ways (figure 3) (Seybold
et al. 1999). For example, a flat, shallow,
loamy soil may be relatively resistant but
not resilient to water erosion. Conversely,
the same soil may have low resistance to
compaction, but recover relatively quickly
and completely (high resilience). Similarly,
sustainable cultivation of steep slopes tends
to be limited by low soil erosion resistance,
while recovery following soil erosion also
Figure 2
Knowledge engine for the Land-Potential Knowledge System.
Figure 3
Changes in productivity in response to an acute disturbance, such as an extreme storm
event on a freshly plowed field, demonstrate the difference in long-term productivity
potentials for resilient and nonresilient land. Resistant land loses little soil in response
to the disturbance. Potential productivity on land that is resilient but not resistant will
be impacted by the disturbance, but will quickly recover and regain the previous poten-
tial productivity levels. Land that is resilient due to its recovery capacity may have
relatively deep soils that change little with depth. Land that is nonresilient in response
to extreme storm events following tillage often has shallow soils, or soils in which the
lower horizons contain more clay than the surface, resulting in reduced infiltration
(adapted from Seybold et al. 1999).
Potential productivity, including variability due to weather
Resistant to degradation
Neither resistant nor resilient
Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
JAN/FEB 2013—VOL. 68, NO. 1
tends to be limited on otherwise produc-
tive shallow soils (figure 4).
A general definition of land potential
that combines these three elements can
be adapted from the definition of sus-
tainability provided in the report “Our
Common Future:” land potential is the
capacity of land to support ecosystem ser-
vices required to meet “the needs of the
present without compromising the ability
of future generations to meet their own
needs” (Brundtland Commission 1987).
Land potential can also be defined in terms
of the capacity of land to support more
specific land use objectives, including its
potential to provide the resources neces-
sary for one or more species to complete
their life cycles and reproduce. The value
of applying the land potential concept to
biodiversity conservation is that it allows
the potential future range of species to be
predicted based on habitat requirements,
rather than relying solely on historic or
existing plant and animal community pat-
terns. This is particularly important where
climate change and invasive species mod-
ify the conditions necessary for species of
interest to survive and reproduce. The fact
that there are so many different land use
objectives means that it can be difficult to
generate land potential evaluations that
address all needs (FAO 2007).
For most purposes, however, land poten-
tial can be evaluated based on knowledge of
basic soil profile characteristics, topography,
and climate. For example, a nonsaline, deep,
well-drained, medium-textured soil with a
slope of less than 2% in a 1,000 mm (39.4
in) summer-dominated precipitation zone
clearly has greater potential to sustainably
support a wide range of ecosystem services
than a steep, shallow saline soil receiving
200 mm (7.9 in) of rain per year. Soil tex-
ture and depth largely determine soil water
and nutrient supplying capacity. Erosion
risk for bare soil can be predicted with the
inclusion of topographic attributes eas-
ily derived from digital elevation models.
Additional information may be required
in regions where the soil parent mate-
rial, age, or hydrology result in unique soil
characteristics, including clay mineralogy,
unusually high or low pH, and high salinity
and sodicity. Feedbacks between vegetation,
soil, and climate, including both hydrology
and nutrient dynamics, are also impor-
tant, particularly over longer time periods
(Peters et al. 2004).
Spatial Variability. Even across areas
that share common rainfall and tempera-
ture patterns, production potentials may
differ considerably, with some areas having
shallow, highly eroded soils and other areas
characterized by deep, relatively fertile soils
that hold water long into droughts. Land
degradation risk and recovery potential also
vary widely: some soils recover quickly fol-
lowing tillage or overgrazing, while others
may require centuries or millennia.
It is more widely recognized that land
potential also varies at global and regional
scales. In Antarctica, climate limits produc-
tion to near zero and resilience is limited by
both low resistance to soil erosion (Tejedo
et al. 2009) and low recovery potential due
to both climate and shallow soil depths.
Conversely, all other continents have both
regions of low land potential and limited
resilience and regions with extremely high
levels of productivity, degradation resis-
tance, and resilience.
Change Over Time. Global circulation
models predict that the currently observed
spatial variability of land potential will be
compounded by increased climate vari-
ability through the 21st century, creating
even more heterogeneity.
A number of different evaluation sys-
tems have been developed and applied
around the world in attempts to improve
our understanding and management
of land potential. An extensive review
and analysis of these systems is forth-
coming (United Nations Environment
Programme International Resource Panel,
Two that have been widely applied glob-
ally are the USDA’s Land Capability
Classification (LCC) system (Klingebiel
and Montgomery 1961) and the Food and
Agriculture Organization’s Agroecological
Zoning (AEZ) system (FAO 2007). The
Land Capability Classification was devel-
oped in the 1950s and focused on general
biophysical limitations to sustainable crop
production. The AEZ, developed within
the Framework for Land Evaluation
(FAO 1976), is a more holistic approach
Figure 4
Simplified, generalized patterns of potential production (P) and resistance and resil-
ience (R) based on climate, resistance to erosion, soil depth, soil texture, and potential
for soil organic matter accumulation and soil structure development (under natural con-
ditions for water-limited regions). The Land-Potential Knowledge System will improve
and localize these predictions to individual fields.
Flat, shallow
sandy soils (low
water storage)
Flat, loamy
soils (high
water storage)
Steep loamy soils
(high water stor-
age and water
erosion risk)
Flat loamy soils
(high water
Steep loamy
soils (high
water storage
and water
erosion risk)
Low rainfall
Potential production and resilience
Moderate rainfall
Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
JAN/FEB 2013—VOL. 68, NO. 1
that addresses both the biophysical and
socioeconomic factors that may affect pro-
duction of a particular crop in a specific
area. Both the LCC and AEZ effectively
regard crop production as the highest and
best use of land. They reflect differences in
degradation resistance but not the capacity
of the land to recover following degrada-
tion (resilience).
The Food and Agriculture
Organization’s Framework for Land
Evaluation was recently updated in rec-
ognition of the need to address climate
change, biodiversity, and desertification,
incorporate new technologies for land
evaluation, and address the benefits of par-
ticipatory approaches (FAO 2007). This
revision is based on a set of eight principles
(FAO 2007):
1. Land suitability should be assessed and
classified with respect to specified kinds
of land use and services.
2. Land evaluation requires a compari-
son of benefits obtained and the inputs
needed on different types of land to
assess the productive potential, envi-
ronmental services, and sustainable
3. Land evaluation requires a multidisci-
plinary and cross-sectoral approach.
4. Land evaluation should take into
account the biophysical, economic,
social, and political contexts, as well as
the environmental concerns.
5. Suitability refers to use of services on
a sustained basis; sustainability should
incorporate productivity, social equity,
and environmental concerns.
6. Land evaluation involves a comparison
of more than one kind of use of service.
7. Land evaluation needs to consider all
8. The scale and the level of decision
making should be clearly defined prior
to the land evaluation process.
The LandPKS will ultimately support
the application of each of these prin-
ciples for land evaluation at farm field to
national scales. The LandPKS will facilitate
the integration and application of local
and scientific information and knowledge
(Herrick et al. 2010), including existing
land evaluation systems through the adop-
tion of crowdsourcing, mobile phone, and
innovative decision support system tech-
nologies (Karl and Herrick forthcoming).
Overview. Development of the LandPKS
takes advantage of recent advances in
cloud computing, digital soil mapping,
Global Positioning System–enabled cam-
era phones, and mobile applications. These
technologies allow knowledge and infor-
mation about land potential to be gathered,
integrated, and shared globally, while global
databases and generic models make exist-
ing knowledge more accessible and allow
similar sites to be more easily matched (fig-
ure 5) (Bestelmeyer et al. 2011; Herrick
and Sarukhan 2007). Global Positioning
System–enabled mobile phones can be used
to capture and transmit geolocated photo-
graphs of soil, land use, and erosion features.
Applications can be used to record
additional information about a site using
drop-down menus, text-input, and pic-
ture matching. Slope and color can
also be determined with many phones,
and the USDA (Agricultural Research
Service–Jornada and Natural Resources
Conservation Service–Lincoln), in col-
laboration with World Agroforestry Centre
(Shepherd and Walsh 2007), is currently
developing an application that will increase
the quality of color determinations using
an integrated calibration system. Cloud
computing allows completion of relatively
sophisticated analyses requiring access to
large databases, while innovative analysis
approaches allow different types of data
with currently unspecified error rates to
be integrated (e.g., Hubbard 2010). These
approaches facilitate integration of local
knowledge with, for example, the increas-
ing amounts of information being made
available by the Global Soil Map con-
sortium. This initiative was established in
response to the rapidly increasing demand
for soil information (Sanchez et al. 2009).
The consortium includes leading global
soil science institutions and is committed to
producing digital soil maps that will predict
important soil properties at a fine resolution
using state-of-the-art and emerging tech-
nologies for soil mapping (figure 6).
These and other tools and tech-
nologies allow local knowledge to be
crowdsourced and different sources of
knowledge and information to be cross-
referenced, enabling the LandPKS to
generate site-specific interpretations about
land potential which can be immediately
shared with others, including farmers and
scientists, who may have additional insights.
The LandPKS extends earlier efforts to
integrate local and scientific informa-
tion (Barrios et al. 2006; Herrick et al.
2010) by allowing site-specific conclu-
sions to be instantaneously updated based
on input from other locations with similar
soil and climate characteristics. As a result,
the accuracy, precision, and relevance of
the knowledge engine at the core of the
LandPKS will increase with each use.
Phased Development. The LandPKS
is being developed and implemented
through a phased, modular approach (table
1) designed to complement, rather than
replace, new and existing land evaluation,
database development, and soil mapping
Figure 5
Similar soil profiles occurring in areas with similar climate in the (a) southwest United
States and (b) northwest Namibia. Both are shallow loamy sands on low gradients
underlain by a partially rubblized petrocalcic horizon (inset).
Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
JAN/FEB 2013—VOL. 68, NO. 1
initiatives, as well as government extension
efforts and local and international devel-
opment projects. Characteristics unique
to LandPKS include the ability to provide
site-specific information based on simple
soil descriptions, to effectively integrate
local and scientific knowledge through
expert systems that assess multiple sources
of qualitative and quantitative knowl-
edge and information, and to provide
an interactive self-learning platform that
simultaneously collects and shares knowl-
edge and information among a broad
range of users.
Using a phased, modular approach
will (a) allow stakeholders to apply early
versions of the system to make basic
determinations about land potential; (b)
maximize opportunities for them to con-
tribute knowledge and information to
the system, including providing instant
feedback on initial determinations; and
(c) ensure that the system is sufficiently
flexible and dynamic to take advantage
of and contribute to future tools, tech-
nologies, and information and knowledge
sources. In particular, we are encouraged
by current progress and future plans of,
among others, the Food and Agriculture
Organization; Global Soil Map; African
Soil Information System; European
Environment Agency, including Eye on
Earth; and several Consultative Group
on International Agricultural Research
centers; as well as a number of sustain-
able land management knowledge systems
such as World Overview of Conservation
Approaches and Technologies (WOCAT).
Finally, we believe that the growth of the
semantic web (Villa 2007) and new tools
that essentially automate mobile appli-
cation development will significantly
increase the functionality of the system,
while reducing maintenance costs.
Leadership and Participation.
Development of the LandPKS is being
led by the USDA together with a large
and growing group of partners. Funding
from the US Agency for International
Development is supporting initial develop-
ment and pilot implementation in Africa,
and the Africa Technology Policy Studies
Network is providing leadership and coor-
dination throughout the continent. An
open source, open participation format is
being applied to both development and
implementation. In order to facilitate broad
participation, the amount of additional
information provided by the user will be
tiered based on user information needs,
knowledge, input device (mobile phone vs.
computer keyboard), time availability, and
technical capacity (figure 7).
Figure 6
An example of a first generation digital
soil map derived from existing soil map
data (Soil Survey Geographic Database
and US General Soil Map) using spatially
map unit component weighted means
calculation: soil organic carbon in the sur-
face 5cm (Bliss et al. 2009). Red indicates
low and blue indicates high soil organic
carbon values.
Table 1
Phased approach to the development and implementation of the Land-Potential
Knowledge System. This approach will continue to evolve based on input from a
rapidly growing group of partners.
Phase I (2012/13 to 2015) Phase II (2014/15 to 2016) Phase III (2015 to 2018)
Information sources Existing online Existing online Existing online
databases databases + pilot user- databases + user-
contributedeld contributedeld
observations observations
Knowledgesources Publishedscientic PhaseI+pilotuser- PhaseIIfull
sources contributed local implementation
knowledge andrenement
Knowledge system Simple decision Phase I + pilot expert Phase II full
(mechanism) support based on system iteratively implementation and
basicusersoil/site integratinguser renement
descriptionandexisting responsestosite-specic
land evaluation systems queries designed to
increase accuracy and
relevance of output
Knowledgesystem Basicbiophysicalland PhaseI+identication PhaseII+identication
(output) potentialforsustainable ofspecicSLMpractices ofsuccessfulSLM
agricultural production with emphasis on options from other
(suitability for grazing, practices already in regions being applied
crop production, and use locally on similar soils that are
identicationofgeneral potentiallyrelevantin
typesofSLMpractices)* localsocioeconomic
Connections with Develop system so that Initiate connections and Ensure that
other mobile phone/ it is open and has develop capability for independent
Web-based services† capacity to connect and other organizations to use connection with
identifypotential LandPKSasaplatform LandPKSissupported
connections. for their own products
Implementation Focused on USDA–US Available for pilot Full implementation
Agency for International application by other
Development pilot areas organizations
Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
JAN/FEB 2013—VOL. 68, NO. 1
Overview. The LandPKS is very much
a means to multiple ends, rather than an
end in itself. During an extended infor-
mal scoping process in 2012, we reviewed
the project objectives and strategy with
diverse stakeholders, including pastoralists
and farmers in Turkana, Kenya (Losinyen
2012), and northern Namibia; leadership
and technical staff of the USDA Natural
Resources Conservation Service and US
Department of the Interior Bureau of
Land Management; global scientists; and
participants in two African Technology
Policy Studies Network meetings, includ-
ing representatives of a broad range of
government ministries and the African
Union. Based on these conversations, we
identified the following functions: connect
producers with each other; directly support
land management decisions by farm-
ers, ranchers, and pastoralists, including
through extension; inform land planning
by governments and investments in land
management by governments, nongovern-
mental, and overseas development assistant
organizations; improve other decision sup-
port systems and geographic information
system products; and promote innovation
(figure 1).
We recognize that it is impossible to
optimize the LandPKS for all of these
functions. We are committed to ensuring
that other developers can easily connect
with and leverage the LandPKS to create
products that more effectively address one
or more of these functions locally, nation-
ally, or globally.
Connect Producers with Each Other.
The local knowledge database developed
as part of the LandPKS will include all of
the elements necessary to support social
networking. It will allow individual pro-
ducers to easily connect with others facing
similar challenges on similar types of land.
This is particularly important in countries
where extension services are limited. It can
also be used to support extension activi-
ties by allowing facilitators working on
farmer-to-farmer exchanges identify pro-
ducers who are most likely to be able to
benefit from each other.
Support Land Management Decisions.
Integrating existing knowledge and infor-
mation enables fine-scale interpretation
of available knowledge and information
at any location where a user provides
descriptive soils information (figure 7).
This is important because land potential
often varies more at finer scales than it is
possible to map. Accuracy of pixel-level
predictions based on digital soil maps,
such as those being developed by the
Africa Soil Information System, will be
further improved by user-provided pho-
tos, responses to simple questions about
soil texture and depth, land use and cover,
and new diagnostic soil color applications
and protocols for mobile phones. We are
working to leverage the extensive work
that Africa Soil Information System has
done to develop more sophisticated rela-
tionships based on near- and mid-infrared
spectroscopy (Shepherd and Walsh 2007).
Both Africa Soil Information System and
USDA Natural Resources Conservation
Service databases include visible bands that
can be acquired using cameras included
with many mobile devices.
Inform Land Use Planning, Land
Tenure, and Targeted Investments
Designed to Sustainably Increase
Production. In addition to the obvi-
ous benefits for land use planning, the
LandPKS can, as desired by individual
countries, be used to help ensure that
land tenure reform programs result in an
equitable distribution of land based on
its potential, rather than simply distribut-
ing areas of equal size. LandPKS can also
help governments, funders, and nongov-
ernmental organizations identify specific
locations within each region where invest-
ments in specific projects, such as technical
support and drought assistance, are likely
to have the greatest long-term impact and
return on investment, while helping to
select the interventions that are likely to
have the greatest impact.
Improve Other Decision Support
Systems and Geographic Information
System Products. One of the most fre-
quently cited future benefit of the
LandPKS by scientists is improving soil,
Figure 7
Illustration of potential use of the LandPKS to guide land use management decisions in
an area of northern Namibia where grasslands, savannas, and woodlands are being con-
verted to annual crop production. User uploads geolocated soil photos and local knowl-
edge (1); which is then integrated with global knowledge and information and sent back
to the user as coded land management options, together with a request for additional
information (2); which is again entered on the mobile phone (3); resulting in a refined
suite of coded management options (4). The number of iterations and complexity of user
input will be flexible, depending on user technical capacity and time availability.
Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
JAN/FEB 2013—VOL. 68, NO. 1
land use, and land cover maps by provid-
ing consistent data, including photographs,
at potentially millions of points globally.
User inputs including soil, vegetation,
land use, and land use history informa-
tion will be made available, subject to any
country-specific limitations, in an online
In addition to using the raw data pro-
vided by users, the LandPKS is being
designed to allow other land management–
related applications and other software to
make more soil-specific recommendations
about, for example, improved crop variet-
ies, fertilizer forms and rates, and specific
management interventions. The number
of mobile applications supporting one
or more aspects of land use management
and conservation planning is increas-
ing rapidly, but few explicitly address the
critical importance of soils and, more
generally, land potential, for localizing
One particularly intriguing initia-
tive that is already explicitly integrating
soil information is the development of
innovative models through the informa-
tion systems and strategic research of the
Consultative Group on International
Agricultural Research Program on Water,
Land and Ecosystems using Applied
Information Economics (figure 8)
(Hubbard 2010; Shepherd and Hubbard
2012). Land and water intervention deci-
sions are being modelled in a way that
captures the uncertainties in costs and
impacts on agro-ecosystem productivity,
environment and human well-being, and
trade-off preferences of stakeholders. This
probabilistic modelling approach identifies
and quantifies the value of information
and further research for improving inter-
vention decisions and provides a holistic
business case for intervention programs
that includes environmental sustainabil-
ity and equity concerns (Shepherd and
Hubbard 2012).
Promote Innovation. Perhaps the
most exciting function of the LandPKS
is promoting innovation. Agriculture
has continued to largely rely on the
research-demonstration-extension model
of innovation. The LandPKS can acceler-
ate innovation in at least three ways. The
first is by reducing the failure frequency in
the implementation of new technologies
by reducing the probability that the tech-
nologies will be applied on land that does
not have the potential to respond. The
second is by allowing for virtual instanta-
neous sharing of successes and failures to
all producers with similar land potential.
Scientists, too, can benefit by replacing
preliminary trials with a global search of
all producers who have already tried a
similar approach on similar soils and can
use the same process to validate conclu-
sions across diverse soils. While there are
clearly a number of risks associated with
this crowdsourcing approach, we believe
that they can be minimized by cross-ref-
erencing different types of knowledge and
information, including producer obser-
vations, measurements, photographs, and
physical models. This general approach
to reducing the uncertainty of individual
conclusions has been widely applied in the
business sector (Hubbard 2010).
We fully recognize that in many cases the
ability to sustainably increase agricultural
production and biodiversity conservation
and to address other objectives is not lim-
ited by knowledge and information about
biophysical land potential. Access to mar-
kets, prices for agricultural products and
inputs, social and political instability, and
local food preferences frequently con-
strain land use decisions. We acknowledge
this to be true in many and perhaps most
cases. However, we also believe that while
not sufficient, an understanding of land
potential is necessary to select the most
sustainably productive land use or uses
and management systems, and that this
understanding can also be used to select
from a range of options that is already
limited by nonbiophysical factors. By
starting with the biophysical potential of
the land, we provide a foundation for inte-
grating other factors, such as crop prices.
Socioeconomic factors will be integrated
as LandPKS evolves, either by linking to
other programs, or as an integral part of
the system itself.
Finally, by facilitating global con-
nections, the LandPKS increases the
probability that innovative solutions will
Figure 8
An example of how the LandPKS is being designed to both inform and be informed by
other related decision support systems. In this illustration, probabilistic modelling is
applied to the development and selection of land management interventions. The model
will determine the uncertainty of onsite and offsite impacts of interventions, as well
as behavioral factors like the adoption rate of a new practice or how incentives change
behavior. Ultimately, the effects of an intervention and the quantified preferences are
combined into a single monetized value so that interventions of different types and
sizes can be compared (Shepherd and Hubbard 2012).
Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
JAN/FEB 2013—VOL. 68, NO. 1
be developed and communicated more
rapidly and systematically. For example, an
innovative approach to rehabili tating land-
scapes dissected by gullies developed in
one country can be instantaneously shared
with producers in other countries, who
can then test it under local conditions.
The rapid expansion of internet acces-
sibility through mobile phone networks
together with simple mobile applications
and expert knowledge systems provide
new opportunities to connect farmers,
extension and development workers, and
policymakers with site-specific knowledge
and information. The amount of electroni-
cally available knowledge and information
about land potential, including resilience, is
also rapidly increasing through the efforts
of a number of organizations throughout
the world. The proposed Land-Potential
Knowledge System will leverage these
emerging trends to connect land managers
committed to sustainable land management
with the most relevant and up-to-date
knowledge and information available.
We thank all of the individuals who participated in the
LandPKS scoping discussions during the past year. In
addition to the coauthors listed here and a large body
of literature (only a small portion of which is cited),
a large number of individuals have also contributed
to conversations that led to the development of the
approach, including but by no means limited to Fee
Busby (Professor, Utah State University, Logan, Utah),
Joel Brown (Scientist, Natural Resource Conservation
Service, Las Cruces, New Mexico), Ericha Courtright
(Research Assistant, USDA-Agricultural Research
Service, Las Cruces, New Mexico), Kris Havstad
(Lead Scientist, USDA-Agricultural Research Service,
Las Cruces, New Mexico), Elisabeth Huber-Sannwald
(Professor, IPICyT, San Luis Potosi, Mexico), Mike
Pellant (Great Basin Restoration Initiative Coordinator,
Bureau of Land Management, Boise, Idaho), Debra
Peters (Research Scientist, USDA-Agricultural
Research Service, Las Cruces, New Mexico) , David
Pyke (Research Ecologist, US Geological Survey,
Corvallis, Oregon), James Reynolds (Professor, Duke
University, Durham, North Carolina), Pat Shaver
(Rangeland Management Specialist, Corvallis,
Oregon), Chloe Stull-Lane (Program Quality Manager
African Drylands, Mercy Corps, Nairobi, Kenya),
Dennis Thompson (National Rangeland and Pasture
Lead, USDA Natural Resource Conservation Service,
Washington, DC), Jason Taylor (Monitoring National
Implementation Lead, Bureau of Land Management,
Denver, Colorado), Gordon Toevs (National
Monitoring Lead, Bureau of Land Management,
Washington, DC), Justin van Zee (Research Assistant,
USDA-Agricultural REsesearch Service, Las Cruces,
New Mexico), Nick Webb (Post-doctoral Research
Scientist, USDA Agricultural Research Service, Las
Cruces, New Mexico) and Skye Wills (Soil Scientist,
Natural Resource Service, Lincoln, Nebraska). Pat
Shaver (Rangeland Management Specialist, USDA
Natural Resources Conservation Service, Corvallis,
Oregon), Corinna Riginos (Post-doctoral Research
Associate, University of Wyoming, Laramie, Wyoming),
and Carey Farley (Team Leader, CARE International
funded by US Agency for International Development,
Nairobi, Kenya) were instrumental in beta testing
some of the rapid protocols that will be implemented
as part of the approach, as were Uhangatenua Kapi,
Colin Nott, Matthias Metz, Cornelis van der Waal,
and the Agra field crew (all associated with and sup-
ported by the United States Millennium Challenge
Corporation and the Namibia Millennium Challenge
Account, Windhoek, Namibia).
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taxonomy of rangeland change. IX International,
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forecasting catastrophic events. Proceedings of the
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N.J. McKenzie, M.L. de Mendonca-Santos, B.
Minasny, L. Montanarella, P. Okoth, C.A. Palm,
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M.G. Walsh, L.A. Winowieck, and G-L. Zhang.
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resilience: A fundamental component of soil quality.
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Copyright © 2013 Soil and Water Conservation Society. All rights reserved. 68(1):5A-12A Journal of Soil and Water Conservation
... Degraded lands in Kenya and especially in the North, when continually put into production, without restoration or other conservation measures, can become irreversibly unproductive, jeopardizing the livelihoods of millions of people who depend on these systems (Herrick et al., 2013;Vågen et al., 2014;Winowiecki et al., 2018 increased conflict as livestock and wildlife migration routes become closed due to changes in land ownership. ...
... Degraded lands in Kenya and especially in the North, when continually put into production, without restoration or other conservation measures, can become irreversibly unproductive, jeopardizing the livelihoods of millions of people who depend on these systems (Herrick et al., 2013;Vågen et al., 2014;Winowiecki et al., 2018). Current adaptation strategies that include shifts from purely pastoral to agro-pastoral systems have resulted in increased conflict as livestock and wildlife migration routes become closed due to changes in land ownership. ...
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The Kenyan rangelands contribute significantly to the country's GDP through livestock production and tourism. With dependence on rain-fed pastures, climate variability coupled with human induced factors such as overgrazing have adversely affected the rangeland ecosystems. And while indigenous communities and conservation experts already use their knowledge of the landscape to make decisions, this information is usually localized. Earth observation imagery provides a bigger picture that can complement indigenous knowledge and improve decision making. This research leverages on data from the MODIS receiver located at the Regional Centre for Mapping of Resources for Development (RCMRD) to develop the indices for the web-based Rangelands Decision Support Tool (RDST). The tool (RDST) automates data processing and provides an easy to use interface for accessing indices for rangeland monitoring. MODIS Normalized Difference Vegetation Index (NDVI), anomalies and deviation indices are provided on the tool at decadal, monthly, and seasonal time steps. Users begin their assessments by selecting their monitoring units and an NDVI index that responds to their specific questions. These questions respond to assessing current conditions, monitoring trends and changes in vegetation, and evaluating proxies for drought conditions. The information can then be overlaid with other ancillary datasets (roads, water sources, invasive species, protected areas, place names, conflict areas, migration routes), for context. At the click of a button, the information can be downloaded as a map for further analysis or application in sub regional decision making. Information and maps generated by this tool are being used decision making tool by rangeland managers in the counties and in other management units (conservancies and ranches). Specifically, inform adjustments to existing grazing plans, managing movement of livestock from designated grazing areas in wet and dry season, monitoring the success of rehabilitation efforts and resilience of the rangeland ecosystems, monitoring drought, managing scarce water resources, and monitoring the spread of invasive species. Successful implementation and application for decision making has relied heavily on local indigenous knowledge and capacity building on use of the earth observation indices. The SERVIR project service planning engagement approach was used in engagements with stakeholders. This improved their participation in co-development of the tool and indices; and in adoption of the tools for decision making.
... LandPKS uses knowledge from field samples for regular updating soil data at 250m resolution. The soil pH, organic carbon (c/kg), bulk density (g/cm 3 ), soil water content, taxonomy groups (kPa) and soil texture (sand/silt/clay) were analysed as physical characteristics 29 . ...
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Crop survival and growth requires identification of correlations between appropriate suitable planting season and relevant climatic and environmental characteristics. Climatic and environmental conditions may cause water and heat stress at critical stages of crop development and thus affecting planting suitability. Consequently, this may affect crop yield and productivity. This study assesses the influence of climate and environmental variables on rain-fed sunflower planting season suitability in Tanzania. Data on rainfall, temperature, slope, elevation, soil and land use/or cover were accessed from publicly available sources using Google Earth Engine. This is a cloud-based geospatial computing platform for remote sensed datasets. Tanzanian sunflower production calendar of 2022 was adopted to mark the start and end of planting across country. The default climate and environmental parameters from FAO database were used. In addition, Pearson correlation was used to evaluate the relationship between rainfall, temperature over Normalized Difference Vegetation Index (NDVI) from 2000 to 2020 at five-years interval for January-April and June-September, for high and poor suitability season. The results showed that planting suitability of sunflower in Tanzania is driven more by rainfall than temperature. It was revealed that intra-annual planting suitability increases gradually from short to long- rain season and diminishes towards dry season of the year. January-April planting season window showing highest suitability (41.65%), whereas June-September indicating lowest suitability (0.05%). Though, not statistically significant, rainfall and NDVI were positively correlated with r = 0.65 and 0.75 whereas negative correlation existed between temperature and NDVI with r = -0.6 and − 0.77. We recommend sunflower subsector interventions that consider appropriate intra-regional and seasonal diversity as an important adaptive mechanism to ensure high sunflower yields.
... However, a major breakthrough to collect detailed and local-scale management activity data is possible by engaging land users themselves in providing local-scale management activity data via a crowd-sourcing model [102]. Initial efforts using the LandPKS system [103] show promise in not only collecting management activity data but also in mobilizing local knowledge about soil characteristics at the field scale, that could provide inputs to modelbased assessment. ...
Full-text available
The importance of building/maintaining soil carbon, for soil health and CO2 mitigation, is of increasing interest to a wide audience, including policymakers, NGOs and land managers. Integral to any approaches to promote carbon sequestering practices in managed soils are reliable, accurate and cost-effective means to quantify soil C stock changes and forecast soil C responses to different management, climate and edaphic conditions. While technology to accurately measure soil C concentrations and stocks has been in use for decades, many challenges to routine, cost-effective soil C quantification remain, including large spatial variability, low signal-to-noise and often high cost and standardization issues for direct measurement with destructive sampling. Models, empirical and process-based, may provide a cost-effective and practical means for soil C quantification to support C sequestration policies. Examples are described of how soil science and soil C quantification methods are being used to support domestic climate change policies to promote soil C sequestration on agricultural lands (cropland and grazing land) at national and provincial levels in Australia and Canada. Finally, a quantification system is outlined – consisting of well-integrated data-model frameworks, supported by expanded measurement and monitoring networks, remote sensing and crowd-sourcing of management activity data – that could comprise the core of a new global soil information system.
... Seasonal field data collectors determined soil texture-by-feel as part of a rangeland assessment study of communal areas in Namibia and recorded in the citizen science-Land Potential Knowledge System (LandPKS) Data Portal (Herrick et al., 2013;, 2018). Texture was estimated using the field textureby-feel method as part of a rangeland assessment study of communal areas in Namibia in August 2014 using the LandPKS mobile application and methodology (Herrick et al., 2017). ...
Full-text available
Estimating soil texture is a fundamental practice universally applied by soil scientists to classify and understand the behavior, health, and management of soil systems. While the accuracy of both the soil texture class and the estimates of the percentage of sand and clay is generally accepted when completed by trained soil scientists, similar estimates by “citizen scientists” or less experienced seasonal resource scientists are often questioned. We compared soil texture classes determined by texture-by-feel and laboratory analyses for two groups: professional soil scientists who contributed to the USDA-NRCS National Soil Characterization Database and seasonal field technicians working on rangeland inventory and assessment programs in the Western United States and Namibia. Texture accuracy was compared using a confusion matrix to evaluate classification accuracy based on the assumption that laboratory measurements were correct. Our results show that the professional soil scientists predicted the laboratory-determined texture class for 66% of the samples. Accuracy for seasonal field technicians was between 27 and 41%. When a “correct” prediction was defined to include texture classes adjacent to the laboratory-determined texture based on a standard USDA texture triangle, accuracy increased to 91% for professionals and 71 to 78% for seasonal field technicians. These findings highlight the need to improve options for increasing the accuracy of field-textured estimates for all soil texture observers, with relevance to career soil scientists, seasonal technicians, and citizen scientists. Opportunities for improving soil texture accuracy include training, calibration, and decision support tools that go beyond simple dichotomous keys.
... Seasonal field data collectors de- termined soil texture-by-feel as part of a rangeland assessment study of com- munal areas in Namibia and recorded in the citizen science-Land Potential Knowledge System (LandPKS) Data Portal ( Herrick et al., 2013;, 2018). Texture was estimated using the field texture- by-feel method as part of a rangeland assessment study of communal areas in Namibia in August 2014 using the LandPKS mobile application and methodology (Herrick et al., 2017). ...
Full-text available
Estimating soil texture is a fundamental practice universally applied by soil scientists to classify and understand the behavior, health, and management of soil systems. While the accuracy of both the soil texture class and the estimates of the percentage of sand and clay is generally accepted when completed by trained soil scientists, similar estimates by “citizen scientists” or less experienced seasonal resource scientists are often questioned. We compared soil texture classes determined by texture-by-feel and laboratory analyses for two groups: professional soil scientists who contributed to the USDA-NRCS National Soil Characterization Database and seasonal field technicians working on rangeland inventory and assessment programs in the Western United States and Namibia. Texture accuracy was compared using a confusion matrix to evaluate classification accuracy based on the assumption that laboratory measurements were correct. Our results show that the professional soil scientists predicted the laboratory-determined texture class for 66% of the samples. Accuracy for seasonal field technicians was between 27 and 41%. When a “correct” prediction was defined to include texture classes adjacent to the laboratory-determined texture based on a standard USDA texture triangle, accuracy increased to 91% for professionals and 71 to 78% for seasonal field technicians. These findings highlight the need to improve options for increasing the accuracy of field-textured estimates for all soil texture observers, with relevance to career soil scientists, seasonal technicians, and citizen scientists. Opportunities for improving soil texture accuracy include training, calibration, and decision support tools that go beyond simple dichotomous keys.
... General approaches to information transfer include (1) collaborative development of STMs that include the managers who will use them (see Sect. 9.6.1;Knapp et al. 2011a), (2) initiation of collaborative adaptive management projects at the scale of landscapes that include STM development and use as key components (Bestelmeyer andBriske 2012), (3) the use of web-based technologies and mobile devices to link users to STMs pertaining to specific localities (Herrick et al. 2013), and (4) the distillation of STM information into simple presentation materials (such as pictorial field guides, web-based materials) and the use of field-based workshops to enable understanding of these materials. The use of STMs for management will require concerted efforts by scientists, government agencies, educators, and technical experts and cannot be limited to the production of reports, publications, and associated databases by a handful of managers and ecologists. ...
Full-text available
State and transition models (STMs) are used to organize and communicate information regarding ecosystem change, especially the implications for management. The fundamental premise that rangelands can exhibit multiple states is now widely accepted and has deeply pervaded management thinking, even in the absence of formal STM development. The current application of STMs for management, however, has been limited by both the science and the ability of institutions to develop and use STMs. In this chapter, we provide a comprehensive and contemporary overview of STM concepts and applications at a global level. We first review the ecological concepts underlying STMs with the goal of bridging STMs to recent theoretical developments in ecology. We then provide a synthesis of the history of STM development and current applications in rangelands of Australia, Argentina, the United States, and Mongolia, exploring why STMs have been limited in their application for management. Challenges in expanding the use of STMs for management are addressed and recent advances that may improve STMs, including participatory approaches in model development, the use of STMs within a structured decision-making process, and mapping of ecological states are described. We conclude with a summary of actions that could increase the utility of STMs for collaborative adaptive management in the face of global change.
Full-text available
As eco–environmental effects have become important considerations in the construction and planning of production, living, and ecological spaces, we used a combination of quantitative and qualitative methods to analyze the eco–environmental effects’ spatiotemporal evolution and present appropriate ideas for ecological restoration based on the land use data. The results show that during the research period both an improvement and degradation of the regional eco–environment occurred simultaneously. In the earlier period, the ecological environment tended to be worse, while in the later period, the eco–environmental quality was dramatically enhanced. Pasture ecological land in the study area had the strongest positive impact on the eco–environmental quality, while the negative effect of agricultural production land was severe. The quality of the regional eco−environment was enhanced (degraded) due to the extension (contraction) of ecological land. The construction of an ecological environment is a complex engineering task. Although the eco−environment in most areas of the study area showed an improving trend, the overall eco–environment remains relatively fragile. In the course of supporting high–quality regional social and economic growth and pursuing high–level environmental preservation strategies, we should take corresponding measures to protect and repair the regional ecological environment.
The relationship between Indigenous, Local and Science knowledge systems has been the subject of much debate over the past few decades, especially in ecology and natural resource management. In this monograph, we review available scholarship to develop a pragmatic framework for representation of knowledge systems in general, with specific emphasis on productive engagement between individuals from different communities and cultures. We distill operational definitions/explanations of fundamental concepts associated with data, information, knowledge and wisdom. With these concepts clarified, we re‐consider previous applications of socio‐cultural knowledge system thinking, focusing on system structure and function. Our analysis leads to convergence on a set of fundamental knowledge system processes and actor roles that have emerged repeatedly across many of the scholarly disciplines. We embed these key concepts within a general framework for operational characterization of socio‐cultural knowledge systems. In order to demonstrate existing and potential applications of the knowledge system framework, we present and discuss major trends in recent ecology and natural resource management literature. Finally, we propose that continued and collaborative development of this general framework can serve as a pragmatic tool for individuals from Indigenous, Local and Science knowledge systems who wish to engage in reciprocal and meaningful dialogue with members of other knowledge systems, especially regarding the highly uncertain global future of ecology and natural resource management.
Full-text available
Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location non-impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing Machine Learning Algorithms (MLA) for operational mapping of Potential Natural Vegetation (PNV). The following MLA were considered: neural networks (nnet package), random forest (ranger), gradient boosting (gmb), K-nearest neighborhood (class) and cubist. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief and lithologic variables, were used as explanatory variables. The overall results show that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated at 68% (33% with spatial Cross Validation) with the most important predictors being total annual precipitation, monthly temperatures and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers and month of the year, respectively. Further developments of PNV mapping could include using GBIF records to map global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at 1 km spatial resolution are available for download via .
Full-text available
Research in extremely delicate environments must be sensitive to the need to minimize impacts caused simply through the presence of research personnel. This study investigates the effectiveness of current advice relating to travel on foot over Antarctic vegetation-free soils. These are based on the concentration of impacts through the creation of properly signed and identified paths. In order to address these impacts, we quantified three factors - resistance to compression, bulk density and free-living terrestrial arthropod abundance - in areas of human activity over five summer field seasons at the Byers Peninsula (Livingston Island, South Shetland Islands). Studies included instances of both experimentally controlled use and natural non-controlled situations. The data demonstrate that a minimum human presence is sufficient to alter both physical and biological characteristics of Byers Peninsula soils, although at the lowest levels of human activity this difference was not significant in comparison with adjacent undisturbed control areas. On the other hand, a limited resilience of physical properties was observed in Antarctic soils, thus it is crucial not to exceed the soil’s natural recovery capability.
On the Ground The same collaborative Internet technologies that fundamentally changed how businesses communicate, create products and services, and ultimately succeed have the potential to contribute greatly to meeting knowledge challenges of rangeland management. Web 2.0 tools, like wikis, crowd-sourcing, and content aggregation (i.e., mashups), are currently used in natural resource science and have the potential to increase our understanding of rangeland ecosystems and improve management decision making in the future. Taking advantage of this explosion of information will require a change in focus from discrete and isolated projects to comprehensive knowledge systems that can be tapped (and supplemented as necessary) to respond to new management issues as they arise.
The quantity of soil organic carbon (SOC) stocks forms a foundation for understanding potential sequestration or release of carbon in the future in response to changes in land management and climate. We have made new maps and databases of SOC stocks for the conterminous United States from the State Soil Geographic (SSURGO) database, developed by the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS). These data have more than 100 times more spatial detail than the previous maps formed from the State Soil Geographic (STATSGO) data developed by NRCS in 1994. The SSURGO data are now 90% complete for the conterminous United States. We show relationships between the SOC stocks and other spatial data such as land cover, elevation, slope, aspect, relief, landscape position, and Federal land ownership status. The new data are expected to improve spatially explicit modeling of regional carbon dynamics, and reduce the uncertainty of estimation for scenarios of future soil carbon change.
Science-based approaches to agricultural and environmental management are needed to accelerate development progress in the world's poorest countries. We present a diagnostic surveillance framework modelled on medical diagnostic approaches for evidence-based management of agriculture and environment in developing countries. Infrared (IR) spectroscopy can play a pivotal role in making the surveillance framework operational, by providing a rapid, low cost and highly reproducible diagnostic screening tool. We review the wide applicability of IR spectroscopy for setting up measurement systems for the management of soils, crops, agricultural inputs, livestock health, agricultural products and water quality. IR spectroscopy is already being used in the design of soil surveillance systems, but the principles are generally applicable. A new evidence-based interpretation approach to plant analysis, combining plant and soil IR spectroscopy measurements, is proposed. Finally, an idealised design is proposed for making IR spectroscopy-based diagnostic surveillance operational in developing countries over the next ten years. Large area surveillance frameworks for agricultural and environmental problems will deploy integrated spectral indicators of soil, crop and livestock health and water quality. Spectral indicators will help to quantify risk factors associated with problems and assess intervention impacts. Smallholder farmers will have access to IR spectroscopy-based analysis of soils, crops and inputs through a network of hand-held or mobile IR spectroscopy units. Agricultural processing industries will make extensive use of IR spectroscopy on the factory floor to add value to agricultural produce and improve food safety. Regional centres of scientific and technological excellence will be required to support (i) high quality laboratory reference analyses, (ii) development of IR spectroscopy calibration databases and interpretation systems and (iii) up-grading of scientific and technical skills through training and education. Key challenges for adoption of this design include (i) building human capacity in science- and technology-based approaches, (ii) development of rugged low cost IR spectroscopy instrumentation and (iii) development of decision support systems to interpret IR spectroscopy data into management recommendations.
Globalization of labor and capital can increase the rate and extent of global environmental degradation, while enhancing the ability of ecologists to respond rapidly and collaboratively to mitigate these impacts. Nevertheless, ecological research remains focused at local and regional levels, with collaboration limited by national borders and funding. New initiatives are required to increase the utility and availability of environmental research to nat- ural resource owners, managers, and policy makers in the public and private sectors, whose decisions affect land and other forms of natural capital. We propose a four-part strategy to increase the effectiveness of ecological sci- ence in addressing environmental issues in an era of globalization: (1) develop an Ecological Knowledge System, (2) increase our ability to anticipate, identify, and rapidly address new research needs, (3) increase the number and diversity of participants in all phases of research and decision-making processes, and (4) increase the flexibility of funding sources.
Soil resilience has recently been introduced into soil science to address sustainability of the soil resource and to combat soil degradation. The concept of soil resilience and its relationship to soil quality have not been well defined or well developed. The main objectives of this paper are to clarify the concept of soil resilience and its relationship to soil quality and to present a framework for its assessment. A review of the literature on the assessment and quantification of soil resilience is presented and discussed. The concept of soil resilience in combination with resistance is presented as an important component of soil quality, a key element of sustainability. Factors that affect soil resilience and resistance are soil type and vegetation, climate, land use, scale, and disturbance regime. Maintenance of recovery mechanisms after a disturbance is critical for system recovery. Three approaches for assessing soil resilience are presented: (i) directly measuring recovery after a disturbance, (ii) quantifying the integrity of recovery mechanisms after a disturbance, and (iii) measuring properties that serve as indicators of those recovery mechanisms. Research is needed in the development of indicators or quantitative measures of the ability of soils to recover from specific disturbances.
The increasing attention paid to local soil knowledge results from a greater recognition that farmer knowledge can offer many insights into the sustainable management of tropical soils and that the integration of local and technical knowledge systems helps extension workers and scientists work more closely with farmers. A participatory approach and a methodological guide were developed to identify and classify local indicators of soil quality and relate them to technical soil parameters, and thus develop a common language between farmers, extension workers and scientists. This methodological guide was initially developed and used in Latin America and the Caribbean-LAC (Honduras, Nicaragua, Colombia, Peru, Venezuela, Dominican Republic), and was later improved during adaptation and use in eastern African (Uganda, Tanzania, Kenya, Ethiopia) through a South–South exchange of expertise and experiences. The aim of the methodological guide is to constitute an initial step in the empowerment of local communities to develop a local soil quality monitoring and decision-making system for better management of soil resources. This approach uses consensus building to develop practical solutions to soil management constraints identified, as well as to monitor the impact of management strategies implemented to address these constraints. The particular focus on local and technical indicators of agroecosystem change is useful for providing farmers with early warnings about unobservable changes in soil properties before they lead to more serious and visible forms of soil degradation. The methodological approach presented here constitutes one tool to incorporate local demands and perceptions of soil management constraints as an essential input to relevant research for development activities. The participatory process followed was effective in facilitating farmer consensus; for example, about which soil related constraints were most important and what potential soil management options could be used. Development of local capacities for consensus building constitute a critical step prior to collective action by farming communities resulting in the adoption of integrated soil fertility management strategies at the farm and landscape scale.
Abstract I present a comprehensive conceptualization that unifies representation and integration of natural data while at the same time providing a workable computational framework for developing andr unning simulation models. The approach is based on a long-standing principle of scientific investigation: the separation of the ontological character of the object of study from the semantics of the observation context, the latter including location in space, time, classification schemata, and any other observation-related aspects. I will show how the object-oriented paradigm allows an efficient implementation of the concept through the abstract model of a domain class, which relates to the idea of aspect in software engineering. This conceptualization allows us to factor out two fundamental causes of complexity and awkwardness in the representation of knowledge about natural system: (a) the distinction between data and models, which are both seen here as generic modules or information sources; (b) the multiplicity of states in data sources, which is handled through the hierarchical composition of independently defined domain objects, each accounting for all states in one well-known observational dimension, such as space or time. This simplification leaves modelers free to work with the bare conceptual bones of the problem, encapsulating away complexities connected to data format, scale, and the fact that data may either come from known data sources or be obtained by simulation. I will show how a software system, based on established representational frameworks, can be efficiently designedt o allow an operationali mplementation oft he approach, referringt o - 2 - explicit ontologies to unambiguously categorize the semantics of the objects of study, and allowing the independent definition of a global observation context that users or