| Source of technology and funding |
Research conducted by national agricultural research
systems (NARS) in West Asia and North Africa, in collaboration with
the International Centre for Agricultural Research in the Dry Areas
(ICARDA) and the International Food Policy Research Institute (IFPRI),
and cofinanced by IFAD and the Arab Fund for Economic and Social Development
(AFESD) |
| Expected Benefit: |
Quantitative and qualitative evaluation of the impact
of research. Improved mechanisms for technology dissemination, based
on economic and environmental assessments |
| Targeted Groups: |
National and international institutions, development
planners, project implementers, extension agents, and end-users |
| Commodities: |
Barley and sheep |
| Agro-ecological zones: |
Arid and semi-arid zones (200 to 350 mm annual precipitation) |
| Target region and countries: |
West Asia and North Africa (WANA) – Algeria,
Iraq, Jordan, Lebanon, Libyan Arab Jamahiriya, Morocco, Syria and
Tunisia |
| Keywords: |
Keywords: adoption; impact; constraints to technology
adoption |
Introduction
Among the many factors that contribute to growth in agricultural productivity,
technology is the most important. The rate of adoption of a new technology
is subject to its profitability and the degree of risk and uncertainty
associated with it, and is highly influenced by the capital requirement,
agricultural policies, and the socio-economic characteristics of farmers.
The question of adoption or non-adoption is important; however, intensity
of adoption is actually the most critical criterion in the adoption process.
Producers benefit from the adoption of new technology through opportunities
to lower production costs, either by increasing outputs from the same
inputs or by maintaining the same output from reduced inputs. New technology,
such as new crop varieties, may change the optimal levels of inputs used.
Thus, an understanding of the effect of new varieties on input demand
and productivity is crucial for better understanding of potential diffusion
of the technology among farmers. Widespread adoption of new production
technology might also be expected to have important market effects. Market-level
impact can then be estimated by aggregating the farm responses, based
on an assumed national adoption level.
Objectives and activities
Measuring the effectiveness of a project in developing and transferring
improved technologies to end-users in an important step in assessing its
impact. This measurement was to be be achieved by:
- Identifying and analysing the socio-economic characteristics that
influence adoption, including the acceptance of technology, adoption
rates, performance, and constraints in adoption.
- Analysing the degree of adoption of a new technology.
- Analysing the intensity of adoption.
- Measuring farm-level impact of introduced technologies.
- Assessing market-level impact of technologies adopted.
These components were to be analysed using cross-sectional data from
farmers in targeted areas. An understanding of how individual characteristics
tend to influence adoption decisions could improve the effectiveness of
technology in enhancing growth in productivity.
Studies conducted within the framework of the Mashreq/Maghreb (M&M)
project included:
- Economic evaluation – at farm, community and national levels
– of technologies being developed and promoted by the Project.
Initially this focused on assessments of the impact of the project’s
activities at both the farm and community level in the selected communities
within the project.
- Monitoring the rate of adoption of technology among farmers and pastoralists
participating in the project and identifying the constraints limiting
adoption and benefits, thus providing feedback to technical and policy
researchers.
- Evaluation of cost effectiveness of technology transfer mechanisms
used by the project, in the interests of possible improvement.
- The objective of these studies were to provide the basis for assessing
the potential impact of extending the project’s activities to
other areas.
Asessing adoption
Sampling approach and methodology
The approach used in the studies involved interviews with three main
categories of farmers, depending on the type of participation in project
activities.
1. The first group consisted of farmers that hosted demonstrations of
the technology under consideration. This group was named “ participants
in demonstrations” or “ demonstration farmers”.
2. The second group involved neighbours and participants in field days
on the technology under consideration. This group was termed “field
day attendees”.
3. The third group comprised non-participant farmers. These farmers
did not host any demonstrations nor attend any field days, and therefore
served as the control group that provided background information concerning
the farmer knowledge and perceptions about the technologies. This category
was termed “ non-participants”. They were selected at random
from the population of farmers in the areas where the demonstrations and
field days took place.
Both probit and logit models were used to assess adoption rate (measured
by the percentage of farmers using the technology on a continuing basis)
and degree of adoption (measured, for example, by the proportion of land
under the new crop variety), where the probability of adoption depends
on the characteristics of the farmers. If the estimated coefficient of
a particular variable is positive, it means that higher values of that
variable result in a higher probability of adoption. A lower value implies
a lower probability of adoption. Intensity of adoption (measured by the
amount of modern inputs used per unit area) was analysed using a multiple
linear regression model.
The explanatory variables for the three models – probit, logit
and multiple linear regression –were farm size, weather risk, farming
enterprise and profitability.
1. Farm size is expected to have a positive relationship with the adoption
of improved crop cultivars and fertilizer.
2. The attitude of farmers to risk is an important factor in adoption.
It is generally understood that risk-averse farmers are reluctant to invest
in innovations of which they have little first-hand experience. Weather
variability is taken as a major risk factor in rainfed farming.
3. Farming enterprise is another factor that may affect adoption, and
can be positive or negative, depending on its relative contribution to
farm income.
4. Profitability is the most important determinant of the rate of adoption
and diffusion. Both the rate and extent of diffusion are positively related
to changes in the profitability of the technology.
Assessing impact
1. At the farm level
Three methods are commonly used for assessing the economic impacts at
farm level of a technology.
- The first method is to calculate the relative cost and revenue differences
between the proposed technology and the existing production systems,
within a set of gross-margin budgets.
- The second method is to build a set of representative-farm linear
programming models, which incorporate the output from the gross-margin
analyses, but in addition consider the overhead and other costs associated
with the adoption of new technology.
- The third method is multiple regression analysis of farm production
data using information obtained from technology adoption surveys. A
production function is also used to isolate the impact of varietal technology
on total factor productivity.
2. At market level
Widespread adoption of new production technology might also be expected
to have important market effects. A widely used method in estimating the
ex ante market impacts of a technology is to calculate the economic surplus
changes and distributions from the technology adoption. Economic surplus
comprises both consumer’s and producer’s surplus. This method
is based on the assumption that technology adoption leads to an outward
shift in the product’s supply curve. Certain assumptions are required
about the slopes of the supply and demand curves, the nature of the supply
shift, and the relationship between producer and consumer prices. In addition,
some base or initial equilibrium sets of prices and quantities are used
for making these calculations. The internal rate of return to investment
in the technology can be estimated using the economic surplus approach.
M&M illustrative examples for adoption
1. Adoption of Improved Barley Technologies
Results of a farm survey of 250 barley farmers in Iraq showed that all
farmers who participated in the project demonstrations adopted an improved
cultivar or fertilizer, or both, whereas only 37% of field-day attendees
and non-participant farmers adopted fertilizers.
Farm size and profitability are the most significant factors affecting
the three indicators of adoption –adoption rate, degree of adoption
and the intensity of adoption.
Similarly, a farm survey of 138 farmers in Syria showed that the use
of improved technologies greatly increased among participating farmers,
and the rate of increased adoption was much greater among participating
farmers than among non-participants. Factors constraining increased use
of improved cultivars include lack of farmer knowledge about them, availability,
and preferences regarding grain colour.
Likewise, 285 farmers in Jordan were interviewed to study the adoption
of improved barley technology. The adoption rates were considerably higher
among project participants than among farmers who did not participate.
There was a considerable difference in technology use according to location,
intended utilization of production, and source of household income.
Results of farm surveys of 88 barley producers in Morocco’s Khouribga
province show that the level of adoption of new barley varieties in the
province was quantitatively and qualitatively higher than had been indicated
in previous studies, with 76% of surveyed farmers cultivating new varieties,
with 36% of their total barley area sown to such varieties. The major
constraints identified by farmers were non-availability of seed and the
high price of certified seed.
In Algeria, Tunisia and the Libyan Arab Jamahiriya, diagnostic surveys
were conducted in targeted areas; first, to identify major constraints,
and, second, to assess farmers’ attitudes toward technological packages
developed by the project. In Tunisia, attention has focused on the adoption
potential of an improved barley variety, and sheep management and feeding
techniques. In Algeria, absence of fertilizer use, absence of mechanization,
high mortality in lambs and scarcity of feed have been identified as major
constraints in the project area. More than 90% of the farmers interviewed
expressed interest in new cultivars of barley. In the Libyan Arab Jamahiriya,
only 3% of farmers interviewed used improved barley varieties; of the
remainder, only 30% were aware of the availability of improved varieties.
Low adoption is attributed to lack of seed and information.
2. Adoption of improved animal production technologies
Sheep owners have been exposed to a number of new technologies in animal
production in the Mashreq countries, such as the introduction of bekia
(Vicia sativa) in rotation with barley; the use of sponges and PMSG hormone;
urea-treated straw; agricultural by-product feed blocks; early weaning
of lambs; and Vitamin A injection. Nearly 500 sheep owners in the region
were interviewed to investigate the level of adoption and identify constraints
limiting the adoption of such technologies. The perception of farmers
gave an insight into the factors likely to limit the adoption of the technologies
tested –constraints such as flock size, availability of inputs and
the labour needed to apply the technologies. Results of a sample survey
of 149 sheep owners in Iraq indicated that 94% of the participants in
field demonstrations had used feed blocks at least once. The adoption
rate was 36% among the attendees of field days. In contrast, only 4.2%
of the non-participants adopted the feed block technology. Production
system, availability of extension services and flock sizes are three important
factors affecting the adoption of feed blocks.
M&M illustrative examples for impact
Impact of Improved Barley Technologies
A cross-sectional survey of 495 barley farmers in Iraq was conducted
to evaluate farm-level impact of an improved barley variety. A Cobb-Douglas
production function was estimated to study the impact of varietal technology
on total factor productivity. The estimates show that total factor productivity
of the improved cultivar was about 19% higher than that of the local barley
cultivar. That is, given the same level of inputs, the yield advantage
of the improved variety over the conventional variety was about 19%. The
yield advantage of the improved cultivar increased to 43% in more favourable
environment. The yield advantage is the magnitude of the neutral upward
shift in the barley production function resulting from the introduction
of the improved variety. Since the shift in the production function is
of neutral type, it implies that the improved cultivar gives a higher
output per unit of input than the local variety.
To study the economic impact of the improved cultivar on supply of barley
and the demand for variable inputs, an indirect normalized profit function
within the framework of duality theory was specified and estimated. The
impact of the improved barley cultivar on the demand for seed, fertilizer,
machinery and labour was then calculated from the derived factor demand
functions. It is clear that the use of the improved cultivar would increase
the demand for seed by up to 23%, for fertilizers by up to 22%, and for
machinery and labour by up to 29%. This means that the use of the improved
cultivar requires higher input levels compared to that of the local variety.
These results have important policy implications in that the supplies
of seed, fertilizers, machinery and labour should be increased to the
levels of new demands in order to increase the efficiency of barley production
under rainfed conditions in Iraq. The estimated price elasticity showed
that output support price policy is more effective in increasing barley
production than is subsidizing input prices, such as seed and fertilizers.
Similarly, the impact of output support on input use was more effective
than the combined or separated effect of subsidizing seed and fertilizer
prices.
To assess market-level impact of improved barley varieties, gross research
benefits were calculated using an economic surplus model for 24 years
(1977–2000). The internal rate of return (IRR) was computed. With
the exception of Morocco, which is a large country and has an IRR of about
70%, all the countries had returns on barley research investment lower
than 50%. Iraq and Jordan had estimated IRRs of 41% and 43%, respectively.
The IRR was 37% for Syria, 39% for Tunisia and 32% for Algeria.
