Msimu wa Kupanda:
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Date
2007
Authors
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Journal ISSN
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Publisher
Wageningen University
Abstract
Soil fertility decline is the major single factor explaining the decrease in per capita food production in sub-Saharan Africa. Integrated soil fertility management (ISFM) is an approach to improving or restoring soil productivity, based on combinations of organic and mineral fertilisers, improved germplasm and hb-fixation. but its adoption by farmers has been limited. Smallholder farms in sub-Saharan Africa are highly diverse, heterogeneous and dynamic, and operate in complex socio-ecological environments. Much of the heterogeneity within the farming systems is caused by spatial soil variability. This affects the performance of ISFM technologies, which must be then targeted strategically within heterogeneous farming systems to ensure their propensity to enhance the efficiency of resource (e.g. land, labour, nutrients) use al inrm scale. An analytical framework in which systems analysis is aided by survey, experiments and simulation modelling was used to analyse farming futures in the highlands of East Africa. Case study farms from six moderate to high potential agricultural areas in central and western Kenya and eastern Uganda were characterized to identify the diversity of livelihood strategics and understand the main drivers of farm heterogeneity. Constraints to the performance of ISFM technologies and opportunities for efficient targeting of resources within heterogeneous smallholder farms were analysed considering short and long-term horizons, scaling up from field to farm scale, and contextualising livelihood opportunities at regional scale.
Across sites, population densities varied from 250 to 1000 inhabitants km'2, which translated in 11 to 4 months year'1 of food self-sufficiency. Based on resource endowment, dependence on off-farm income and production objectives, households were grouped into five Farm Types: I. Subsidised by off-farm employment; 2. Marketoriented, cash-crops farms; 3. expanding, medium resource endowment farms; 4. Subsisting, partly on non-farm activities; and 5. Dependent, wage labourers. Despite their differences in access to resources for soil management, these Fann Types differed more in the degree of soil heterogeneity than in the average fertility status at farm scale. Across sites, soil heterogeneity was smaller on farms owning more cattle.
The productivity of maize, the main crop in most of the region, was highly variable within individual farms, strongly influenced by variation in both current crop management (e.g. planting dates, fertilizer rates) and soil fertility (influenced by past soil and crop management). In a classification and regression tree analysis (CART), resource use intensity, planting density, and time of planting were the principal variables determining yield, but at low resource intensity, total soil N and soil Olsen P became important yield-determining factors. Soil heterogeneity also affected crop responses to fertilisers from a maximum of 4.4-fold to -0.5-fold relative to the control in soils varying in organic C and P availability. Across sites in western Kenya, P was the most limiting nutrient for crop production, and P availabilities > 10 mg kg'1 were only measured in soils with > 10 g kg'1 organic C. Such co-variation is induced by day-to-day management decisions fanners make when facing trade-offs in the allocation of their limited resources. A study using inverse modelling allowed
analysing tradeoffs of this nature, coupling the dynamic crop/soil simulation model
DYNBAL with a Metropolis-type of search algorithm (MOSCEM) and linking crop
husbandry practices to labour availability. In a heterogeneous farm, the allocation ol
fertiliser and labour favoured the fields around the homestead, where the efficiency ol
nutrient capture was the largest. Productivity could be increased up to a certain
threshold beyond which N losses by leaching and soil erosion losses increased
abruptly, when fertilisers were applied to the most degraded outllelds of the farm.
These fields must be rehabilitated through ISFM technologies ensuring organic mailer
additions, before crops growing on them can respond to nutrient applications.
However, the quality of manure common in smallholder farms (e.g.. 23 - 35% C. <*.5
1.2% N. 0.1 - 0.3% P) and their availability are restrictive. This prevents a quick
(hysteretic) soil restoration. Competing uses for crop residues on the farm lin~.ii I lie
capacity of fertilisers to restore soil fertility. In simulations using the crop/soil model
for long-term dynamics FIELD, which was developed, calibrated and tested agam>s 4
independent datasets, soils receiving combined manure and fertiliser applications over
12 years stored between 1.1 to 1.5 t C ha’1 year’1 when 70% of the crop residue was
retained in the field, and between 0.4 to 0.7 t C ha’1 year’1 when only 10% of residues
were retained. In mixed crop-livestock systems, crop residues are used to feed
livestock, which in turn provide manure to fertiliser crops. When farmers in western
Kenya designed ideal farms through participator}' prototyping, they emphasised on I he
importance of such interactions, but tended to overestimate the necessary nutrient
Hows. A study using the farm-scale model FARMSIM, which integrates FIELD with
livestock and manure-cycling models dynamically, showed that although tightlymanaged
crop-livestock interactions allowed a more efficient tfse of nutrients brought
in the system as fertilisers, the trajectory of change from the current to the ideal
fanning system is hardly feasible for a majority of farmers. Sustainable intensification should be an aim in the design of ISFM options, partly by
intensification of nutrient inputs (removing constraints) and partly by implementing
qualitative changes in the configuration of the farming systems (removing
inefficiencies). However, the context in which farming systems operate cannot be
overlooked. Based on their agroecological potential and market opportunities, and
conditioned by population pressure, different sites or regions have a certain propensity
to stimulate either: hanging-in (subsistence), stepping-up (market orientation) or
steppine-out (off/non-farm income) livelihood strategics.
Description
Keywords
Farn typology, Livelihood strategies, Near-infrared spectroscopy, Africa Trade-off analysis, Sub-saalan, Farm-scale modelling, Soil fertility gradients, Farming system design