Mesolevel

General procedure

The mesolevel nutrient-balance calculation utilized tabular data. The approach involved establishing relations between land use and soils in order to compensate for the lack of spatial data. For example, in Ghana, farmers grow cocoa mainly on top and slope positions, where orthic Acrisols are located. As it is easier to adapt tabular data rather than spatial data, it is possible to include exceptions. In particular, compared with the macrolevel, it enables an improved description of crop management data, which is very important at the mesolevel. It was not possible to create a reconnaissance or semi-detailed land-use map for the mesolevel study areas. Mesolevel data for a map-based procedure were not available and macrolevel data would have provided no added value and failed to be sufficiently representative. At the mesolevel, a 1-km grid is too coarse for representing physiographic differences with sufficient accuracy. If higher resolution data were available, it would be necessary to develop a new procedure for the creation of a land-use map. This is because a suitability approach is not appropriate at the mesolevel, where socio-economic factors are far more important. Aerial photographs and satellite images with fast field checks can also provide the basis for creating a land-use map. However, these were not available for the study areas. The data availability for the three countries was very different, which prohibited a generic approach at the mesolevel.

IN1 Mineral fertilizer

The sources of data on mineral fertilizer were farm surveys, recommended fertilizer rates and macrolevel data, depending on the data availability in each study area. Recommended fertilizer rates are generally much higher than the actual application rates because not all farmers wish or can afford to apply these quantities. Therefore, in order to prevent overestimation, the fertilizer rates were multiplied by a factor representing the ratio between the harvested area at the mesolevel and the harvested area at national level.

IN2 Organic fertilizer

The amount of available manure was derived from the number of livestock within the study area, using excretion, nutrient-content and nutrient-loss factors. The application per crop was derived from farm surveys or estimates. Local nutrient-content values were used where available.

IN3 Atmospheric deposition

The atmospheric deposition was derived from the macrolevel. Rainfall data from local weather stations were used or, where these were not available, they were derived from the macrolevel.

IN4 N fixation

Nitrogen fixation was treated in a way similar as that for the macrolevel. Where available, specific data related to N fixation should be included, e.g. agroforestry systems with N-fixing trees.

IN5 Sedimentation

Irrigation was not relevant for the three case-study areas. Sedimentation was estimated for river-valley crops, e.g. rice. The LAPSUS model was not used at the mesolevel because of the lack of spatial data.

OUT1 Crop products

Crop production data were multiplied by the nutrient contents of the crops. Where available, local nutrient-content factors were used because these can differ significantly from the average continental values used at the macrolevel.

OUT2 Crop residues

Crop-residue removal factors were derived from farm surveys or estimated by local experts. These factors were multiplied by the production and the nutrient-content factors of the crop residues.

OUT3 Leaching

The calculations of the leaching of N and K utilized the macrolevel regression models.

OUT4 Gaseous losses

The estimation of the gaseous N losses utilized the macrolevel regression model.

OUT5 Erosion

Estimates of erosion were made for each crop. These estimates took regional differences in topography and soils into account and were based on literature and expert knowledge. The LAPSUS model was not used because of the lack of spatial data.

Ghana

For Ghana, nutrient balances were calculated for two districts: Nkawie and Wassa Amenfi. The amount of data available in Ghana was small because there have been no studies on soil fertility and nutrient management in the cocoa area. Therefore, a significant amount of the data was derived from the macrolevel. A soil map (scale: 1:250 000) (Soil Research Institute, 1999) was available for both districts, but representative chemical characteristics of the soils were unknown. Therefore, the soil properties were derived from the WISE database (Batjes, 2002). Local weather stations were the source of the rainfall data, averaged for 1997 - 99. Nutrient balances were made for the following crops: cocoa, maize, cassava, plantain, cocoyam, yam, rice, vegetables and oil-palm. The area under annual crops and the macrolevel data provided the basis for estimating the area under fallow: 14 600 ha (13 percent) in Nkawie District, and 7 300 ha (2.7 percent) in Wassa Amenfi District. Annex 9 lists all the primary data.

TABLE 17
Harvested areas and manure for each crop in two districts, Ghana

TABLE 18
Estimated erosion per crop for cocoa-based system

TABLE 19
Soil properties for the upper AEZs in Embu District

Source: Stoorvogel et al. (2000).

Kenya

Many data were available for Kenya because it has been the subject of many soil fertility and nutrient management studies. However, not all of the data were useful because they were very scattered or based on only a few farms. The study area comprised the tea - coffee - dairy zone of Embu District, defined mainly by AEZ2 of the VARINUTS study (SC-DLO et al., 2000). The area consists mainly of Nitisols. A soil map of Embu District (Ministry of Agriculture, 1987) was available, but only one soil type was differentiated within the study area. This study utilized the soil properties as determined in the VARINUTS study (Table 19). Annex 9 lists all the primary data.

The nutrient balance was calculated for the following crops within the study area: tea, coffee, maize, beans, Napier grass, sorghum, cowpeas, cassava, potatoes, sweet potatoes and arrow roots. No data were available for Napier grass, so the study used a yield estimate of 35 tonnes/ha (Van den Bosch et al., 1998b), and for the harvested area a value of 3 percent of the total cultivated area was taken (Abdullahi et al., 1986). The fallow area was based on the non-cultivated area for the Manyatta and Nembure division, which is 24 km². Of this area, 75 percent was estimated as fallow, the remaining area as buildings and forest. The resulting fallow area was 1 800 ha, which is 8 percent of the total cultivated area. The estimated production of fallow land was 10 tonnes/ha, of which 5 percent was removed for fodder and firewood.

TABLE 20
Mineral fertilizer application rates, Embu District

TABLE 21
Estimated erosion per crop, Embu District

* Recommended fertilizer rates based on Ministry of Agriculture (1997 - 2001).

Mali

For Mali, the nutrient-balance calculation considered the CMDT Koutiala Region in Mali-Sud. The CMDT has undertaken many farm surveys in this area, which have also generated a lot of data relating to nutrient management. Therefore, a lot of high-quality data for important flows, such as mineral fertilizers, organic fertilizers and crop-residue removal, were available for this study area. However, spatial data were practically non-existent, only one handdrawn morphological map of the region was usable. The cultivated areas lie on the lower slopes with three different soil types, depending on the parent material (Sissoko, 1999). The study used an average of the soil properties of these soils (Table 22). The main crops in the study area are: cotton, maize, sorghum, millet, rice, groundnut and cowpea. These crops account for 97 percent of the total cultivated area. Research in two representative villages provided the basis for the estimate of the fallow area (Kanté, 2001). In these villages, 48 percent of the cultivated area was under fallow. The resulting estimate for the whole of Koutiala Region is a fallow area of 227 200 ha. Annex 9 lists all the primary data.

TABLE 22
Soil properties for the lower slopes in Koutiala Region

Source: Bitchibaly et al. (1995).

TABLE 23
Crop residue use for Koutiala Region

* Total removal calculated as the sum of fodder, grazing and other.
Source: Camara (1996).

TABLE 24
Estimated erosion rates for Koutiala Region