|
|
||||
|
||||
|
|
|
|
|
|
||||
|
AGROFORESTRY INNOVATIONS IN AFRICA: CAN THEY IMPROVE SOIL FERTILITY ON WOMEN FARMERS’ FIELDS? Christina
H. Gladwin, Jennifer S. Peterson, and Robert Uttaro Abstract: Most observers
agree that the verdict is still out for agroforestry innovations known
as improved fallows, which may take a decade for farmers to test properly.
First farmers plant several small plots of different tree species,
cut them after two years and plant a cash or food crop, and then wait
to see the results of that harvest. Because the improved fallow
cycle takes four or five years, farmers’ adoption or adaptation
of this technology takes a lot longer than adoption of an improved
seed or a new fertilizer. Until the experiment fails, African farmers
– like most researchers – are willing to experiment, probably
due to the lack of other options available as soil fertility amendments
in Africa today. This is especially true for women farmers,
even more so for female headed households whose lack of adult family
labor presents them with severe cash and credit constraints.
This paper describes their adoption decision processes when presented
with new agroforestry technologies such as improved fallows in western
Kenya, southern Malawi, and eastern Zambia. INTRODUCTION Agricultural experts claim that the food security situation
in Africa now is analogous to the situation of Asia 40 years ago and
Latin America 30 years ago, before the Green Revolution transformed
both continents and enabled them to structurally transform their economies
from being overly dependent and dominated by a stagnant agricultural
sector to becoming more diversified with fledgling but growing manufacturing,
services, and agricultural sectors. [1] Until Asian and Latin American policy
makers adopted development strategies that encouraged small farmers
to increase their agricultural productivity on small landholdings
by adopting yield-increasing inputs of production, this structural
transformation was not possible. [2] The bio-physical root cause of low per capita food production
is soil fertility depletion; the nutrient capital of African countries
is now being mined, just like mineral deposits of metals or fossil
fuels. [3] Smaling et al. estimate that soils
in sub-Saharan Africa are being depleted at annual rates of 22 kilograms
per hectare (kg/ha) for nitrogen (N), 2.5 kg/ha for phosphorus (P),
and 15 kg/ha of potassium (K). [4] Soil fertility depletion is all the
more alarming, given that recurring devaluations and removal of fertilizer
subsidies, mandated by structural adjustment reforms, have made inorganic
fertilizers unaffordable for most African smallholders. [5]
In contrast to the situation when Asian and Latin American farmers
were encouraged to intensify their crop production by adopting nitrogen-responsive
crop varieties and increasing plant populations, African entrepreneurs
face constraints that make it difficult for them to freely compete
in an open fertilizer market. Among these factors are the small
volume of fertilizer most African countries import, high transportation
costs within most African countries, and high storage costs. [6]
As a result, fertilizers are six to eight times more expensive at
the farm gate in Africa than in Asia or Latin America. Sanchez and colleagues of the International Center for
Research on Agroforestry (ICRAF) recommend a two-pronged strategy
to stop this mining, the first to replenish phosphorus nutrients and
the second to replenish nitrogen. The first strategy involves
the high phosphorous fixing soils of Africa, an estimated 530 million
hectares where phosphorus-fixation is now considered an asset, and
not a liability as previously thought. Here, inorganic phosphorus
fertilizers are necessary to overcome phosphorus depletion on these
soils. [7] Large applications of phosphorus fertilizer
can become “phosphorus capital” as sorbed or fixed phosphorus,
almost like a savings account, because most phosphorus sorbed is slowly
desorbed back into the soil solution during 5-10 years.
The larger the initial application rate, the longer the residual effect.
If phosphorus is applied as a one time application of phosphate rock,
it can be helped to desorb by the decomposition of organic inputs
that produce organic acids to help acidify the phosphate rock, e.g.,
the organic acids in tithonia (Tithonia diversifolia), a common
shrub in western Kenya. While phosporus replenishment still requires an externally sourced chemical
input that may be beyond the reach of the small farmer, this is not
necessarily the case for nitrogen. To reverse nutrient depletion of nitrogen in African
soils, a second strategy exists, namely an increased use of organic
sources of nitrogen nutrients. The organic sources of nitrogen
include: animal manures and compost, biomass transfers of organic
matter into the field, and also more efficient use of trees and shrubs
whose deep roots capture nutrients from subsoil depths beyond the
reach of crop roots and transfer them to the topsoil via decomposition
of tree litter. By strategic planting of trees, nitrogen lost
over the last 20 years can be replenished with nitrogen from agroforestry
innovations such as hedgerow intercropping with leucaena (Leucaena
leucocephala (Lam.) De Wit), biomass transfer with tithonia, manures
improved with calliandra (Calliandra calothyrsus Meissner),
and improved fallow systems using nitrogen-fixing shrubs like sesbania
(Sesbania sesban), tephrosia (Tephrosia vogelii), pigeon
pea (Cajanus cajan), and gliricidia (Gliricidia sepium). Questions persist about this innovative approach to Africa’s soil degradation crisis, however, and center on the issue of whether the nitrogen demands of food crops can be met in full with only organic sources of nutrients. ICRAF scientists claim that biophysically, organic sources can produce mid-range level yields of 4 tons/ha, but not the 6 tons/ha that could result from combinations of organic and inorganic fertilizers. Such combinations are needed because recovery of nitrogen by the crop from leaves of leguminous plants is lower (10-30%) than recovery from nitrogen inorganic fertilizers (20-40%). To reach these higher crop yields, more research is needed on the synergistic effects of combining the different kinds of organic and inorganic fertilizers. [8] AGROFORESTRY INNOVATIONS: A SOLUTION FOR AFRICAN WOMEN FARMERS? Whether or not this innovative approach to replenishing
Africa’s soil fertility is a success is likely to depend on its
adoption by African rural women, who by custom produce the food crops
in many African societies, while men produce the export crops. [9]
As food producers, women farmers are the key to reversing the crisis
and increasing domestic food production in Africa. Yet their
lack of power inside their own households is a unique problem facing
them in their roles as African food producers; food producers in Asia
and Latin America, although small farmers, were not similarly constrained
during the time of their Green Revolution. This is what we call
the invisibility factor in the African food security literature,
most of which is de-linked from the women in development (WID) literature.
Food security analysts correctly argue that development strategies
need to reach African smallholders to be effective, but they ignore
the fact that the constraints facing women smallholders may be an
important part of the problem. Eicher, for example, consistently
does not mention that 45% of the smallholders responsible for Zimbabwe’s
second Green Revolution (1980-1986) are women; nor does he indicate
the percentage of hybrid maize adopted by women nor the percentage
of fertilizer subsidies benefiting women. [10] Similarly, Smale’s report on
Malawi’s delayed Green Revolution does not indicate women’s
adoption of hybrid maize; [11] yet women’s maize varieties, as shown
here by Uttaro, are mostly local maize varieties, while hybrid varieties
are mostly cash crops sold by men. Because women farmers are such important players
in African agriculture, the success of any strategy to replenish African
soils needs to answer questions such as: will women farmers
adopt agroforestry innovations to provide their soils with needed
nitrogen, or will they face constraints to adoption more severe than
those facing men farmers? Will women have more limiting factors
to adoption? Do women have different motivations and reasons
to adopt than men? Finally, do women in female headed households
(FHHs) differ from women in male headed households (MHHs), and are
the former more constrained than the latter? Previous ethnographic and policy research suggest women
have more limiting factors to adoption than men. Rocheleau finds
an interaction between gendered property relations and gendered resource
uses, user groups, landscapes, and ecosystems in Western Kenya, a
region where agroforestry had been practiced since the 1600s. [12]
As population increased and fallow lands became smaller and trees
more scarce, people began planting trees, since they were no longer
able to gather as many products from the forest and communal lands.
Women did not own land but played an important role, as decisions
about where sons would cultivate was the mother’s; while wives
and daughters also had usufruct rights to the land and its products.
With the advent of land reformation laws in 1956, men aged 18 and
over were automatically entitled to land titleship by the colonial
government. [13] This policy lowered women’s
status in the lineage system since sons no longer had to go through
their mothers to acquire land. Women’s rights to land,
trees, animals and water became subject to male permission. Today,
however, with the implementation of more and more agroforestry projects
in the area and continued decreases in fallow lands, women have begun
to plant trees despite the traditional taboo that holds that bad luck
will ensue should they do so. Scherr finds that gender differences in agroforestry
practices are still quite significant. [14]
In one study, men had 50% more trees on their farms and almost 30%
higher tree density. Men tended to plant trees in cropland while
women’s farms had more trees used primarily for fuelwood.
These differences reflect men and women’s differential ability
to independently decide how trees will be used and allocated.
Women are not permitted to make decisions without consulting their
husbands, and are also less likely to question men or their policies
at the institutional and state levels. This power differential
between men and women lays the foundation for gender bias from household
level decisions to policy level decisions. AGROFORESTY
ADOPTION DECISION TREE MODELS In order to definitively answer questions about whether
or not factors like power differentials influence agroforestry adoption
decisions, in this paper we propose a testable model of the adoption
decision process, and test it on a gender-disaggregated sample of
both adopters and non-adopters. Here, as in the paper by Uttaro,
we use “ethnographic decision trees” or hierarchical
decision models [15] whose
usefulness comes from their relatively high prediction rate: at least
80% of the historical choices made by farmers interviewed in an area
are predicted by a decision tree model. Previously, decision
tree models have been used to predict farmers’ choices between
chemical fertilizer and manure in Guatemala and Malawi, to increase
fertilizer use in Mexico, to use credit for fertilizer in Mexico,
Malawi, and Cameroon, to adopt other agroforestry technologies such
as hedgerow intercropping in Kenya and Malawi, and to use grain legumes
as soil-fertility-amendments in Malawi and E. Zambia. [16]
Decision trees predict because they are cognitive-science
models, which aim to process information in the same way humans
do,
[17] as opposed to artificial-intelligence
methods which are not so concerned with modeling the exact process
that humans use but seek some alternative processing technique that
approximates the human solution, e.g., linear programming models or
multiple regression models of choice (probit, logit, and tobit analysis).
Because cognitive science models aim to represent psychological reality
and to mimic the mental processes people use, they should be better
descriptions of human information processing and better predictors
of human choice than are artificial-intelligence models. When a decision tree is correctly specified, it allows
the research team to identify the main factors limiting adoption at
a specified time, and if possible, to recommend policy intervention
to alleviate these constraints and speed up adoption. [18]
These limiting factors may change or disappear over time, however;
and the model is assumed valid only for the time period during which
it is tested and should be retested at later times. Given low
adoption rates, the research team may gradually conclude that the
chances of much future adoption of the technology are not good, if
there are a number of structural factors persistently blocking adoption
(e.g., lack of land) that are not amenable to policy intervention
(as opposed to limiting factors that are easily changed, e.g., lack
of knowledge or seeds or credit). In this case, the usefulness
of the adoption decision tree model is in sending the designers of
the technology, the biophysical scientists, back to the drawing board
to redesign. Such was the case of the first application of decision
tree modeling to agroforestry innovations, e.g., hedgerow intercropping
(HI), implemented in on-farm trials in Western Kenya since the
late 1980s. Much adoption work has been done by ICRAF social
scientists using ethnographic decision tree modeling on the adoption
and expansion of hedgerow intercropping (HI) or alley cropping. [19]
Their work showed that women farmers’ constraints of lack of
knowledge, labor, and land did not allow many of them to plant hedges
of leucaena or calliandra in between rows of maize, the subsistence
crop. Their conclusions were matched by those of Deirdre
Williams of the University of Florida Soils Management CRSP (collaborative
research support program supported by USAID) project, “Gender
and Soil Fertility in Africa.” [20]
Williams also interviewed 40 women farmers in Maseno, western Kenya,
and found less than 20 percent adoption (Gladwin et al. 1997: 225-227),
due to a number of structural factors blocking adoption (e.g., lack
of land (5 cases) and labor (4 cases)) as well as limiting factors
more amenable to policy change (e.g., lack of knowledge (15 cases)
and seeds (2 cases) and termite problems (2 cases)). [21] She concluded the future chances
of women’s adoption of HI in western Kenya were not good.
Williams also modeled women farmers’ decisions
to adopt or not adopt biomass transfer innovations with a subsample
of 23 women farmers in western Kenya. [22] Biomass transfer involves the use
of leaves and strems from shrubs (Tithonia diversifolia and
Lantana camara) for mulch. These shrubs are homestead
border markers found everywhere in rural western Kenya, are under
the control of women, but are traditionally used for goat fodder and
medicine for stomach ailments and not for mulch. Williams model
of women farmers’ decisions not to use tithonia leaves
and cuttings as mulch on their food crops shows why. In this
decision tree, for brevity not presented here, more than half the
women in the test sample did not pass the first two constraints, “Have
access to tithonia or lantana shrubs growing nearby?” and “Know
about this technology?” Other constraints included women's
lack of labor: many female heads of households and women with
small children felt they did not have the time themselves or access
to the labor required to cut and carry enough biomass from these shrubs
to adequately mulch their crops. The amount of biomass required
to produce significant soil fertility benefits is very large;
by some estimates, 7 tons per hectare of leafy dry matter (and triple
that for fresh biomass) is being used in ICRAF’s biomass transfer
experiments.
[23] Other women had problems with termites
coupled with no farming practice (like putting on ash) to help with
this problem; still others felt they needed the tree or shrub more
for fodder or medicine than for soil improvement. The cumulative
result of all these constraints was that the decision model predicted
only four of the 23 women in the test sample should use tithonia for
soil improvement. The trees are relatively simple to design and test,
as Uttaro’s model of the decision to adopt improved fallows (IF)
in figure 1 shows, read from top to bottom. [24]
They have alternatives in set notation ({ }) at the top
of the tree, decision outcomes in boxes ([ ])
at the end of the paths of the tree, and decision criteria in diamonds
(< >) at the nodes of the tree. There
are only two alternatives or decision outcomes in this set, [Plant
an improved fallow now] and [Don’t Plant now] and they are mutually
exclusive. The only trick to the trees is eliciting the decision
criteria from the decision makers themselves, who are the experts
in making their decisions. They alone know how they make their
choices, and so their decision criteria should be elicited from them
in ethnographic interviews and by participant observation and other
participatory methods (e.g., role playing).
Given a particular sample of data from decision makers
who have decided to both adopt and not adopt improved fallows, e.g.,
Uttaro’s sample of 60 farmers interviewed in Zomba, southern
Malawi, in 1998, one can test the tree easily by putting the
data from each individual choice (as a separate, independent case,
like a Bernoulli trial) down the tree and counting the
errors in prediction on each path. Results of testing this model
shows that lack of land was the most serious constraint to IF adoption
in the Zomba region: most of the households in his sample engaged
in continuous cropping. Nine informants (15%) had farms large
enough to leave part of it fallow; and eight (13%) usually
left part of the farm fallow (criteria 1 and 2). Only three
of the eight farmers left their land fallow for two or more years
(criterion 3). Of the three informants left, only two FHHs had
any trees or shrubs that improve soil fertility in fallow areas (criterion
5); and they both lacked the knowledge of how to plant an improved
fallow so that they would get higher yields after returning the land
to maize production (criterion 7). In short, no farmer of the
60 used improved fallows. The gender-disaggregated data, moreover,
show women face no additional constraints limiting their adoption
of improved fallows. The future prospects for improved fallow systems in two heavily-population
regions of Africa (southern Malawi, western Kenya) would thus appear
to be poor. Even if information was disseminated about the use
and management of trees and bushes in fallow systems, farmers would
still need to have land available to place into fallow. And
with population growth rates among the highest in Africa, that is
something unlikely to occur in both southern Malawi and western Kenya.
EASTERN ZAMBIA: THE EXCEPTION THAT PROVES THE RULE? Our initial ethnographic results in African locations
prior to 1998 were discouraging, as they showed women farmers tend
not to adopt agroforestry innovations such as biomass transfers,
hedgerow intercropping, and improved fallows. Why? Their
main limiting factors were lack of knowledge of the new technology,
lack of access to seeds or seedlings, and cash or credit to acquire
them. Yet structural factors -- lack of land and labor -- were
also limiting women’s adoption, and in our judgment, they posed
more serious problems to adoption prospects than the factors more
amenable to policy intervention such as lack of knowledge or seedlings.
Moreover, they were much more severe for women than men, and even
more severe for female-headed households. We were therefore
discouraged about the chances of agroforestry innovations replacing
inorganic fertilizers as women’s soil fertility amendments in
the near future. But could we extend these results to all of sub-Saharan
Africa? In a word, no. Conditions in Africa are so diverse,
so location-specific, so dependent on historical contingencies and
socio-economic specificities (note the postmodern influence here)
that results that hold in Western Kenya and Malawi cannot easily be
generalized to other locations in Africa. Recent research results
from ICRAF’s on-farm trials of improved fallow systems with Sesbania
sesban in Eastern Zambia seem to agree. [25] In 1988, ICRAF began to test improved
fallow technologies at Msekera Research Station, Eastern Zambia, and
in 1992/93 some on-farm trials of the improved fallows began in four
villages chosen by ICRAF scientists to be representative of the diverse
agro-climatic, socioeconomic conditions in the eastern Zambia region. Small plots of improved fallows, ranging in size from
10 meters by 10 meters to 30 meters by 20 meters, are planted for
two years with nitrogen-fixing tree species (Sesbania sesban
or Gliricidia seedlings or direct-seeded Tephrosia vogelii
or Cajanus cajan (pigeon pea)), and followed by two or three
years of maize. By far the most promising, although it
may look like a “dinky little tree,” is sesbania, grown
in a dimba nursery three to six weeks before the rainy season.
Results over the five year cycle showed improved fallows increased
total maize production 87% over unfertilized maize (even without any
yield in years one and two); although estimates varied about the advantage
of IFs over fully-fertilized maize (with 112 kg N/ha). Kwesiga
and Beneist found maize yields following two-year improved fallows
approach those of fully fertilized fields, [26] but Franzel et al. found fully-fertilized
maize yielded 2.5 times more than IFs over five years. [27]
The differing estimates did not matter for farmers, however, because
with the rising prices of fertilizer in the Eastern Province, fully
fertilized maize was no longer an option. In many cases even
partially fertilized maize was not an option because farmers had neither
the cash nor the access to credit to purchase fertilizer. By
1997, therefore, the multi-year trials of improved fallow technologies
(IF) were a major success story: over 3000 farmers had participated,
49 percent of whom were women farmers. In 1998/99, USAID sponsored
an extension project managed by World Vision (WV), an NGO operating
in Africa to improve food security. The aim of the five-year
WV project was to extend improved fallow technologies to the entire
region of eastern Zambia with the aim of reaching 50,000 farmers,
not just the farmers in the four villages in which ICRAF was concentrating
its on-farm trials. By fall 2001, the WV project had registered
10,000 farmers in the region as participants in on-farm tests of IF
technologies. These numbers suggest that the IF technologies are a
major success story at a time when Africa can boast of few success
stories. Yet the question still unanswered is: why are
improved fallows being adopted so readily in Eastern Zambia, especially
by women, and not in southern Malawi right across the border?
Is their success due to the fact that E. Zambia is a region of lower
population density than the other regions so that women farmers have
enough land to put some of it in fallow, or is it just a delayed reaction
to structural adjustment policies that have raised the price of inorganic
fertilizers to levels so high that women farmers have finally “adjusted”
by deciding to “grow their own fertilizer” and adopt a substitute
soil-fertility amendment? To answer this question, Jen Scheffee
Peterson of the UF project, “Gender and Soil Fertility in Africa,”
in collaboration with ICRAF and later World Vision, interviewed women
farmers who both were and were not testing and expanding their on-farm
trials of improved fallows. To elicit decision criteria from them, men and women
adopters and non-adopters were interviewed in 1998 using open-ended
eliciting techniques described by cognitive anthropologists, first
by Peterson with three women in each of the four villages targeted
by ICRAF with on-farm trials of improved fallows since 1992/93.
Profiles of each farmer and stories about each farmer’s adoption
process were then formulated, and an initial “composite model”
was built to represent the decision process of the group of 12 farmers
interviewed. Gladwin and Peterson then jointly refined Peterson’s
initial composite model during another 18 interviews in June, 1998,
and designed a questionnaire so that Peterson could test the revised
decision model (figures 2 and 3) during personal interviews with another
test sample of 81 women farmers and 40 men farmers who also resided
in the camps surrounding ICRAF’s four target villages. [28] Women in both FHHs and MHHs were
interviewed, as well as men so that there were three sub-samples.
The samples were chosen, after discussions with Steven Franzel and
Donald Phiri of ICRAF, such that half the sample of each gender would
be testers, who planted at least one improved fallow plot,
and half non-testers, who did not plant even one improved fallow
plot. Half of the sample of testers would be testers-expanders,
who planted at least two improved fallow plots, and half testers-non-expanders,
who planted only one improved fallow plot. [29] Different versions of the adoption decision model were
tested first by Peterson using an Excel spreadsheet, and then by Gladwin
using simple SPSS syntax programs, by including different criteria
elicited from different decision makers. Different orderings
of the decision criteria were also tested, although the order of the
criteria does not usually affect the prediction rate of a simple {Plant
an IF; don’t} model.
[30] The different orders and criteria
generated different decision trees, some of which are presented elsewhere.
[31] For brevity, only the model with
the best fit to these data is included here. The model in figures
2-3 is a close approximation to the first model elicited by Peterson
and Gladwin, so that it has “descriptive adequacy,” meaning
it matches informants’ statements about how they decided to plant
an improved fallow. It differs from the model first elicited
in minor ways, however, by the inclusion of other criteria, which
upon testing were shown to “cut” the sample of decision
makers into adopters and non-adopters. |
|
||||
|
|
|
|
|
|
||||
