CAPSA
Centre for Alleviation of Poverty through Sustainable Agriculture
Vol. 27 No. 2
December 2010
Assessment of Sustainable Agricultural Technologies
fact sheet
14/2015
Background
A transformation of the agricultural sector requires the adoption of new and innovative
approaches that support sustainable outcomes. National and international agricultural research
organizations from the public and private sector are providing solutions for enhanced agricultural
sustainability, and many of these have value beyond the specific setting for which they were
developed. However, decision makers at all levels, including farmers, extension workers and
programme managers, require better tools to determine practices and innovations relevant to
specific situations. Decision-making tools based on farm profit-maximization, such as linear
programming, do not take into consideration aspects that enhance sustainability and are,
therefore, not suited to the sustainability agenda.
The development of the universal sustainable development agenda agreed on at the 2012
United Nations Conference on Sustainable Development (Rio+20) is based on the principles of
economic profitability, social justice and environment-friendliness. Decision-making tools to
optimize sustainability outcomes from the use of new technologies and innovations, should thus
take into account the three pillars of sustainable development. Yet, this is not an easy task as
sustainability lacks an intrinsic value and different stakeholders have different value
assumptions, for example: the relationship between economic development and human wellbeing, how the future should be different, the relationship between resource allocation and
consumption and views of what should be sustained. This brief introduces a tool to support
decision-making by providing information on different aspects of sustainability.
Technology - practice - idea - innovation
The terms technology (set) or best practice should be understood in the broadest
possible sense as agricultural innovation, as “an idea, practice, or object that is
perceived as new by an individual or other unit of adoption” (Rogers, 2003, 2013).
Although the word 'innovation' is the most appropriate from the point of view of social
science, the term 'technology' is commonly understood and frequently used by other
disciplines and extension practitioners. In view of the broad definition of innovation, this
brief uses the term technology as a synonym for innovation and best practice.
Evaluating technologies for sustainable agriculture
Analytical framework
Given the different dimensions to sustainability, we construct a framework taking these into
account. The three pillars of sustainable development can also be interpreted in the context of
sustainable agriculture, where:
Economic profitability means ensuring farm and household viability, providing employment
security and ensuring the economic security of the community.
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Environmental friendliness means using renewable resources within their regenerative capacity,
limiting the use of non-renewable resources, creating substitutes for loss of non-renewable
resources, protecting the sink function of nature from pollution and maintaining ecosystem
stability and resilience.
Social justice means not compromising the ability of future generations to meet their needs,
recognizing the right for development, ensuring equal treatment of women and men, and
ensuring decent labour conditions.
We add the dimension of ‘technical sustainability’ to our framework. The speed and rate of
adoption of an innovation depend on the potential adopter's personal characteristics, the nature
of the social system, the type of adoption decision, the extent of promotion by the change agent
as well as the specific attributes of the innovation itself that determine its usefulness for the
potential adopter (Rogers, 2003, 2013).
Criteria for sustainable agriculture
A literature search provided the basis for the framework criteria. A total of 104 sustainability
criteria relevant for agricultural technologies were identified and reduced by eliminating
indicators irrelevant for developing countries, and by merging similar indicators. Criteria, for
which data collection would have been too costly, were also eliminated. As a result, 27 criteria
were initially identified as highly relevant to describe various aspects of technologies in the
context of sustainability, but the research process, due to data limitations, further reduced the
criteria as shown in Figure 1.
Figure 1. Hierarchy scheme for analysis and composite sustainability indicator calculation
Overall
objective
(Indicator)
Sub-objective
(Dimension of
Sustainability)
Criteria
Impaction local biodiversity
Environment
Economy
Impaction natural biological processes
Quantity
Energy use characteristics
Type
Water use characteristics
Efficiency
Net present value
Quantity
Pollution
Suitable
technology
Land area required
Share of female adopter
Society
Risk of disturbance
Persons involved (workload)
Payment of person involved
(employment potential)
Technology
Complexity/Simplicity/Transferability
To decide on their relative importance, a criteria weight distribution was developed in
consultation with a team of interdisciplinary scientists and project partners. Table 1 shows
essential criteria and corresponding weights assigned to these by consensus.
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Table 1. Weights assigned to dimensions, criteria, and subcriteria of the framework
Note:
1
Represents workload
2
Represents employment potential
3
Represents vulnerable groups
4
Represents suitability for poor landless people
The data on different criteria had different units of measurement, making aggregation into one
indicator difficult. This problem can be overcome by normalizing the data using the equation (1):
(1)
Where IN is the normalized value of the criterion of a given technology, IA is the actual criteria
value of the technology in absolute terms, Imin and Imax are the smallest and largest value of this
criterion present in the data set of all collected technologies, respectively (compare Krajnc and
Glavič, 2005).
To calculate the composite sustainability indicator, the sum of all weighted and normalized
criteria values was built:
(2)
where CSI is the composite sustainability indicator, w is the weight of criterion i, and cv is the
criteria value of criterion i (compare Krajnc and Glavič, 2005).
Data was collected with the help of a questionnaire e-mailed to more than 300 technology
experts. Data from 32 technologies is included in the analysis here. An average normalized net
present value (NPV) was estimated for technologies for which no economic data was available1.
1
These technologies are tomato grafting, treadle pump & microirrigation technology, vermitechnology, windmill,
chili and sweet pepper grafting, school gardens, crotalaria, and rainbow trout aquaculture.
3
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The robustness of the model in view of the chosen criteria weights was assessed by establishing
strong and weak dominance rules (Sharpe and Andrews, 2012) and by ranking all technologies
according to these rules. The weighting scheme shown in Table 1 was compared to four other
possible weighting schemes: 1) the dimensions are weighted equally (25 per cent each); 2) the
dimension ‘technology’ is considered with a weight of 16 per cent, and the other three
dimensions with 28 per cent, each (intermediate scheme between the original scheme and
scheme 1); 3) criteria weights generated by experts using the Analytical Hierarchy Process
(AHP); and 4) equal weights of all criteria (9.09 per cent). One technology strongly dominates
another when it ranks higher in all weighting schemes compared. One technology weakly
dominates another when it ranks higher or equal in all weighting schemes compared.
Technologies were ranked accordingly and the rankings compared.
Application of the tool
Technologies were grouped into more suitable and less suitable categories based on a nonhierarchical cluster analysis (K means) of the Composite Sustainability Index (CSI) (see Table 2).
Groups A to C represent the 14 most suitable technologies based on data presently available
and the analytical assumptions made.
Table 2. Sustainability clusters of selected technologies
-
-
-
-
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The results of a robustness analysis (not displayed here) reveal that the model reliably identifies
all technologies in cluster A and B, which rank highest in respect to the composite sustainability
indicator, as well as those in cluster F with the lowest CSI.
Three technologies from three different groups, namely vermitechnology (cluster A - green line),
broomgrass farming (Cluster C - blue line), and mini hatchery (Cluster E - red line) are shown in
a radar chart (see Figure 2) that reveals more details on the performance of the three examples.
If the line is close to the outer edge of the diagram, the technology performs well in terms of the
particular criterion. All technologies perform well in terms of water consumption. The hatchery
hardly uses any water except for cleaning, broomgrass farming is a rain-fed culture and
vermitechnology requires little water to keep the substrate moist. Only the hatchery uses energy
from a non-renewable source, thus contributing to the low CSI of this technology. It has no
impact on biological processes while broomgrass prevents soil erosion and vermitechnology has
a positive impact on nutrient cycling. Vermicompost has a better impact on biodiversity than the
mini hatchery and broomgrass farming. The latter has a negative impact on biodiversity because
land areas formerly covered with a diversity of wild plants are cultivated only with broomgrass.
All technologies require little input in terms of work and broomgrass cultivation can create local
jobs in peak periods. None of the compared technologies has a risk of disturbing the
neighborhood or creating social conflict. All technologies are suitable for female adopters, but
broomgrass farming involves some hard work to prepare the soil during the planting period.
Hatcheries and vermitechnology can be operated on a few square metres of land while
broomgrass usually requires a more extended area. Vermitechnology has by far the highest NPV
per hectare for the set of technologies included in the analysis. From the amount of knowledge
and skills a person has to master for its successful operation, the mini hatchery appears to be
the most complex and broomgrass farming the easiest of the three technologies.
Figure 2. Suitability radar chart for vermitechnology (green),
broomgrass farming (blue), and mini hatchery (red)
Water
Complexity
Net Present Value
Energy
Biological Processes
Biodiversity
Land Area Required
Gender
Risk of Disturbance
Workload
Employment Opportunity
Strengths and limitations of the framework
The tool described here and its results can be considered a snapshot of sustainability as defined
through our framework at a given point in time. Detailed individual values of the composite
sustainability indicator are not displayed and should not be considered as the ultimate truth due
to the dynamic character of a technology's sustainability over time. The analysis becomes even
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more interesting if repeated after a few years with up-to-date data that takes into account
changes in a technology's performance. With knowledge of the strengths and weaknesses of a
given technology in respect to sustainability, experts might innovate and improve it. Moreover,
prices are likely to change over time and hence affect indicator results. The analysis can thus
yield interesting results if repeated after a few years with up-to-date data that takes into account
changes in a technology's performance.
Although the objective underlying the framework has a sound justification, several inherent
issues and limitations need to be kept in mind when interpreting results and formulating
extension recommendations on the suitability of technologies.
Firstly, the analytical framework described in this brief should not be considered as a tool for
comparing the sustainability of different types of technology. Rather, it provides information on
various aspects of sustainability for a given technology and can serve as a decision tool to
compare different but related technologies.
Secondly, combining biophysical information with social and economic information into a single
indicator involves the problem of incommensurability between the different dimensions of
sustainability (Rigby and others, 2001). Another issue regarding composite indicator calculation
relates to compensation between the values of its components. For example, a low risk of
neighbourhood disturbance cannot balance a higher risk of soil erosion. However, this issue can
be addressed by retaining information on the contribution of subobjectives and subcriteria, for
instance through sustainability polygons, webs or radars.
Also, our assessment is based on inputs, rather than actual sustainability outcomes. This is due
to the limited availability of impact data. “It is commonly the case that assessments of
sustainability operate by prediction rather than direct evaluation of impact. … One of the key
issues is the extent to which one can map with confidence from inputs to environmental impact”
(Rigby and others, 2001). However, like others, we believe that while our assumptions of the
impact are crude, these are, nevertheless, robust.
Policy implications and research outlook
Indicators such as the one detailed here can be powerful communication tools for policymakers
and other decision takers, and it is thus important that these are well-grounded in sound
methods and assumptions.
The global agreement on the sustainable development goals (UN, 2012), requires a better
understanding of what constitutes sustainability in the context of agricultural approaches. The
Open Working Group on Sustainable Development has proposed a target, namely the
implementation of resilient agricultural practices that increase productivity and production, that
help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme
weather, drought, flooding and other disasters, and that progressively improve land and soil
quality (target 2.4 in the Outcome document2). Policymakers will, therefore, need to agree on
monitoring tools to assess progress towards the proposed targets and indicators, and methods
such as the CSI described here, will play an important role in this process.
However, to enhance the usefulness of sustainability indicators, more research is required on
the relationship and interaction of the various dimensions of sustainability. The tools and
methodologies described here will also benefit from the availability of enhanced data.
2
http://sustainabledevelopment.un.org/content/documents/4518SDGs_FINAL_Proposal%20of%20OWG_
19%20July%20at%201320hrsver3.pdf
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Incorporated criteria based on expert judgments could be measured more accurately and
additional criteria, for which no data is available, can be considered in future. For instance, the
impact of technologies on climate change mitigation was not considered within the proposed
framework due to insufficient data to address this criterion. However, including this aspect would
add value to the proposed framework. Finally, more experience with the sustainability impact of
technologies will allow determination of thresholds for each criterion against which a technology
can be assessed. This would facilitate interpretation of results because every criterion would be
displayed as being either above, on, or below the threshold of acceptable sustainability. This
approach is also suggested in the Sustainability Assessment of Food and Agriculture systems
(SAFA) framework (FAO, 2013) but was beyond the scope of the present study.
Selected references
Dantsis, T., and others (2010). A methodological approach to assess and compare the sustainability
level of agricultural plant production systems. Ecological Indicators, vol. 10, Issue 2, pp. 256-263.
Food and Agriculture Organization of the United Nations (1995). FAO Trainer's Manual, Vol. 1:
Sustainability Issues in Agricultural and Rural Development Policies. Rome, FAO.
__________ (2013). Sustainability Assessment for Food and Agriculture Systems (SAFA) Guidelines,
version 3.0. Rome, FAO. Available from http://www.fao.org/nr/sustainability/sustainabilityassessments-safa/en/
Forman, E., and K. Peniwati (1998). Aggregating individual judgments and priorities with the analytic
hierarchy process. European Journal of Operational Research, vol. 108, Issue 1, pp. 165-169.
Godfray, H. C., and others (2010). Food security: The challenge of feeding 9 billion people. Science,
vol. 327, No. 5967, pp. 812-818.
Krajnc, D., and P. Glavič (2005). A model for integrated assessment of sustainable development.
Resources, Conservation and Recycling, vol. 43, Issue 2, pp. 189-208.
Kriesemer, K. and D. Virchow (2012). Analytical framework for the assessment of agricultural
technologies, Report published by SATNET Asia project and Food Security Center (FSC). Available
from: http://www.satnetasia.org/public/SATNET-Asia-Analytical-Framework.pdf.
Pannell, D. J., and S. Schilizzi (1999). Sustainable agriculture: A matter of ecology, equity, economic
efficiency or expedience? Journal of Sustainable Agriculture, vol. 13, Issue 4, pp. 57-66.
Rigby, D., and others (2001). Constructing a farm level indicator of sustainable agricultural practice.
Ecological Economics, vol. 39, Issue 3, pp. 463–478.
Rogers, E. M. (2003, 2013). Diffusion of Innovations. Free Press.
Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of
Operational Research, vol. 48, Issue 1, pp. 9–26.
Sharpe A., and B. Andrews (2012). An assessment of weighting methodologies for composite
indicators: The case of the index of economic well-being. CSLS Research Report No. 2012-10.
Ontario, Canada, 49 pp.
United Nations (2012). Resolution 66/288. The Future We Want. New York.
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About SATNET Asia
SATNET Asia is a network funded by the European Union. It is implemented by the Centre for
Alleviation of Poverty through Sustainable Agriculture (CAPSA) of the United Nations Economic and
Social Commission for Asia and the Pacific (UNESCAP) in collaboration with the Asian and Pacific
Centre for Transfer of Technology (APCTT), AVRDC - The World Vegetable Center, the Food Security
Center of the University of Hohenheim and the Trade and Investment Division of UNESCAP.
SATNET Asia was launched in 2012 to support innovation for sustainable agriculture by strengthening
South-South dialogue and intraregional learning. Operating in 10 countries of South and South-East
Asia, SATNET facilitates knowledge transfer through the development of a portfolio of best practices on
sustainable agriculture, trade facilitation and innovative knowledge sharing. Based on this documented
knowledge, it delivers a range of capacity-building programmes to network participants who play roles
as change agents and innovators, such as farmer organizations, traders, the private sector, the public
sector and policymakers. This will enable network participants to transfer this knowledge to those who
need it most – smallholder farmers and small-scale entrepreneurs.
Because the public sector no longer predominates agricultural development, SATNET explicitly aims to
include the following groups in the innovation process: universities, private companies that develop and
sell technology products or provide trade facilitation services, agricultural foundations, farmer
organizations and NGOs. For, and together with, these target groups, the project aims to create a
knowledge environment that is focused on poverty reduction and conducive to continuous and
sustainable innovation.
A detailed sustainability assessment of different technologies is available at
www.satnetasia.org/database. The database will be updated on a continuous basis. Currently, the
following technologies are included:
 Crotalaria is Effective Against Nematode Damage of Chili in South-East Asia
 Broomgrass Farming
 Small-scale Distillation Unit
 Leasehold Riverbed Vegetable Farming
 Vermitechnology
 Mobile Cricket Raising Units
 Sand-based Mini-hatcheries for Chicken and Ducks
 Eggplant Fruit and Shoot Borer Integrated Pest Management
 Floating Vegetable Garden
 Biointensive School Gardens: Enhancing Nutritional and Agro-biodiversity Outcomes
 System of Rice Intensification (SRI)
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This factsheet was produced jointly by the Food Security Centre,
University of Hohenheim, and CAPSA-ESCAP
Funded by the
European Union
This publication has been produced with the assistance of the European Union. The contents of this publication are
the sole responsibility of University of Hohenheim and ESCAP and can in no way be taken to reflect the views of the European Union.