Chapter 4
Estimation of feed supply (continued)

TABLE 4.18
Diagnostic horizons and properties of soil unite

( indicates mutually exclusive horizons

TABLE 4.19
Soil unit ratings for pasture at high, intermediate and low levels of inputs

¹    See Table 4.12 for soil unit names

4.2.7 Slope limitations and soil erosion

Limitations imposed by slope are taken into account in three steps. Step one defines the slopes which are permissible for pastures production, and as a model variable this is defined as slopes less than 45%.

Step two involves the computation of potential topsoil loss which is estimated, by inputs level, through a modified Universal Soil Loss Equation (Wischmeier and Smith 1978).

TABLE 4.20
Soil phase ratings for pasture at high, intermediate and low levels of inputs

¹    See Table 4.14 for soil phase names

Step three relates the estimated top-soil losses to yield losses through a set of equations given in Table 4.22, taking into account soil susceptibility, and level of inputs. The reduced impact at intermediate and high inputs is due to the compensating effect of fertilizer applications at their normal rates of use.

Steps two and three are described in detail in Technical Annex 2, and Mitchell (1986). Table 4.23 shows the soil units ranked in three classes according to their susceptibility to yield losses in relation to loss of topsoil, on the basis of organic matter content, soil depth and on the presence of other unfavourable subsoil conditions.

In estimating yield losses from equations in Table 4.22 regeneration capacity of soils is taken into account in calculating net loss of top soil. Regeneration capacities of topsoils by thermal and moisture regimes are given in Table 4.24.

TABLE 4.21
Stoniness ratings for pasture at high, intermediate and low levels of inputs

4.2.8 Land suitability

All three assessments: the climatic suitability, the edaphic suitability and the soil erosion hazard, are required to determine the ecological land suitability for grassland/pasture production of each climate-soil unit of the land resources inventory. In essence the land suitability assessment takes account of all the inventoried attributes of land and compares them with the requirements of pasture species, to give an easy to understand picture of the suitability of land for grassland/pasture production.

The results of the land suitability assessment are presented in five basic suitability classes, each linked to attainable yields for the three levels of inputs considered. For each level of inputs, the land suitability classes are: very suitable (VS) 80% or more of the maximum attainable yield; suitable (S) 60% to less than 80% of the maximum attainable yield; moderately suitable (MS) - 40% to less than 60% of the maximum attainable yields; marginally suitable (mS) - 20% to less than 40%; and not suitable (NS) - less than 20%.

Table 4.22
Relationships between topsoil loss and yield loss

Y = productivity loss in percent

X = soil loss in cm

Table 4.23
Ranking of soils (Kenya Soil Survey) according to their susceptibility to productivity loss per unit of topsoil loss

TABLE 4.24
Regeneration capacity of topsoil (mm/year) by length of growing period (LGP) and thermal zone (derived from Hammer (1981)

Land suitability assessment is achieved by applying the programme illustrated in Figure 4.3.

Firstly, the temperature requirements of the grass and legume species with regard to photosynthesis and phenology are compared with prevailing temperature conditions of each thermal zone. If they do not accord, all the growing period zones in that thermal zone are classified as not suitable. If the temperature conditions of a thermal zone do partially or fully accord with the crop thermal requirements, all growing period zones in that thermal zone are considered for further suitability assessment according to the thermal zone rating.

This further assessment comprises application of length of growing period suitability to the computed areas of the various growing period zones by LGP-Pattern zone. Thus if the thermal zone rating of a particular growing period zone is S1, then potential yield biomass value for the growing period zone is not modified. If the thermal zone rating of the growing period zone is S3, then the potential yield biomass value for the computed extents of the period zone is decreased by 50%. The thermal and moisture suitability assessments are described in Section 4.2.3.

The length of growing period suitability and yields are applied according to the LGP-Pattern make up of LGP zone. All pasture species and yields are matched to the individual component length of growing period,

i.e. L1, L2¹, L2² L3¹, L3², L3³, L4¹, L4², L4³ and L4⁴. The LGP-Pattern evaluation for each crop is achieved by taking into account the constituent component lengths of each LGP-Pattern, thus providing a profile of variability in potential yields over time (e.g. average yield, maximum yield, minimum yield).

The next step is an appraisal of the soil units present in each growing period zone. The rating of soil units, for pasture and level of inputs under consideration, is applied to the computed area of the growing period zone occupied by each soil unit. The appraisal, undertaken on the basis of the soil ratings as described in Section 4.2.6, leads to appropriate modifications of the climatic suitability assessment and the attainable total and consumable biomass. Subsequently, the ratings for the different soil textures, phases and stoniness are applied consecutively.

Finally, limitations imposed by slope are taken into account to arrive at the final land suitability appraisal for pasture, for the level of inputs under consideration.

The five classes of land suitabilities are related to attainable yield as a percentage of the maximum attainable yield under the optimum climatic, edaphic and landform conditions. Consequently the results provide an assessment of pasture production potentials of each land unit, which in turn can, be aggregated for any given area in Kenya.

Generalized results of land suitability assessment for pasture production at intermediate level of inputs are presented in Figure 4.4, and in Technical Annex 1.8. It should be noted that the generalized results presented include a subdivision of the not suitable class (zero to less than 20% of the maximum attainable yield) into two classes (1) very marginally suitable (more than zero to less than 20% of maximum attainable yield) and (2) not suitable (zero yield).

FIGURE 4.3
Schematic presentation of the land suitability assessment programme for pasture production

FIGURE 4.4
Generalized land suitability for rainfed pasture production at intermediate level of inputs

4.3 Fodder from Browse, Fodder Trees and Fuelwood Trees

In the low rainfall areas (LGP < 120 days), natural woody vegetation including leguminous shrubs and trees can be important in the nutrition of domestic stock. However, relatively little is known about the digestibility of biomass materials from browse. By comparison with the large amount of herbage from grasslands or natural pastures, the quantity of fodder biomass from natural woody vegetation is limited. Contribution of browse biomass is assumed to be included in the estimates of biomass from grasslands and pastures given in Table 4.9, and no separate account is taken at this stage of the model development and application.

Trees are sown for fodder in Kenya, and the main species are Acacia, Calliandra, Gliricidia, Grevillea, Leucaena and Sesbania. Again the potential contribution from sown fodder trees is assumed to be included in the estimates of biomass from pastures given in Table 4.9, and no seperate account is taken at this stage of model development and application. However, the land suitability procedure for seperately quantifying fodder biomass from fodder trees is identical to the procedure for quantifying wood biomass from fuelwood trees in Technical Annex 1.6. Consequently, it is now possible, if required, to provide for a separate assessment of fodder from fodder trees.

Where trees are considered for fuelwood production and carry palatable foliage, it is assumed that about 10% (i.e. 3.3% of mean annual wood biomass increments given in Technical Annex 1.6) of the foliage may be utilized by stock without affecting fuelwood yields. Fuelwood species that can contribute fodder are: Acacia gerrardia, A. nilotica, A. Senegal, Calliandra calothyrus, Casuarina equisetifolia, Conocarpus lancifolius, Eucalyptus camaldulensis, E. citridoria, E. tereticornis, Parkinsonia aculeata, Sesbania sesban.

4.4 Fodder from Fallow Land

In the crop productivity model (Technical Annex 1.4), fallow requirements for crop rotation options are formulated. At low level of inputs, fallow land is assumed to carry natural bush vegetation; at intermediate and high levels of inputs, fallow land is assumed to carry sown grass-legume pasture.

Biomass production from natural fallow under low inputs, and from sown pasture under intermediate and high inputs is taken as one-third of that from normal sown or permanent pastures given in Table 4.9. It is further assumed that only 50% of the biomass may be utilized by stock.

4.5 Fodder from Fodder Crops

Fodder grasses, legumes and cereals are grown for fodder production in Kenya. Main fodder grasses are Pennisetum purpureum(Napier or Bana grass), Setaria splendida(Giant setaria), Sorghum sudanense(Sudan grass) and Tripsacum laxacum(Guatemala grass). Main fodder legume species are Centrosema pubescens, Lablab purpureus or Lablab niger(Hyacinth bean), Macroptilium atropurpureum(Siratro), Vigna spp. and Stylosanthesspp. Main fodder cereals are maize, oat, pearl millet and sorghum.

A separate assessment of biomass potential from fodder grasses, legumes and cereals is possible according to the land suitability methodology described in Technical Annex 3. However, at this stage of model development and application, the range of biomass potentials from pastures given in Table 4.9, are found to adequately cover the biological potentials of fodder crops.

4.6 Crop Residues, By-products and Primary Products

In areas with more than 120 days growing period, crop residues are an important source of fodder particularly for the low and intermediate technology livestock systems. Important residues are the haulms of groundnut, cowpea and other grain legumes, and the staves (stalks) of sorghum, maize and millet, and straw from rice, wheat, barley and oat. Quantities of residues that may be available have been estimated by applying the residue factor (Cr) and the corresponding utilization coefficients (Cru) and (Cbu) to crop yields (Table 4.25).

By-products, defined as edible materials remaining after a crop has been processed, are bran and germ meal from cereal milling; molasses and bagasse from sugar milling; and cakes (cotton, soybean, groundnut) from oilseeds. Quantities of crop by-products that may be available have been estimated by applying the byproduct factor (Cb) and the corresponding utilization coefficients (Cbu) onto crop yields (Table 4.25).

The term primary product applies to grain used for the purpose of feeding to animals either directly in an unprocessed form or in a processed form. Main cereals used in Kenya are maize, sorghum, wheat and barley. Direct grain feeding is used mainly at the high level of technology in the dairy and meat production systems with cattle and goat. The intensive livestock industries of poultry and pig production tend to rely on processed feeds.

4.7 Feed Supply Potential (Primary Productivity)

When Part I of the livestock productivity model (Figure 2.1) is applied to the land resources inventory, feed supply potential of each agro-ecological cell are quantified by feed source (Figure 4.1), as described earlier.

Once feed supply potential or primary productivity has been quantified, it is possible to quantify livestock productivity potential of livestock systems at specified performance levels and feed requirements. These aspects are taken into account in Parts II, III, IV and V of the model.

TABLE 4.25
Crop residua (Cr) and by-product (Cb) factors