Los determinantes de las ganancias

6.2.1. Geography of the study area

Goima Ward is situated in the northern part of semi-arid central Tanzania, 40 km south-east of Kondoa town. The area is characterized by an undulating landscape of plains and scattered small hills and has sandy soils. Goima and Mirambu villages lie on the foot of the Burunge Hills, at an altitude between 1,300 and 1,400 m.a.s.l. The upper slopes of the Hills consist of bare rocks and forests.

Goima has a uni-modal rainfall pattern; rain can be expected from November through May. Inter-seasonal and intra-seasonal rainfall variability is very high. The mean seasonal rainfall measured at the meteorological station of Kondoa town is 650 mm³. Evapotranspiration in this area is about 1,700 mm year^-1 (Östberg, 1995: 31). Rivers are ephemeral and have high peak discharges during and shortly after rainfall events, but the water flows mostly disappear within a day after a rainfall event.

Increasing parts of the Burunge Hills are being cleared, both legally and illegally, as a result of population growth. Goima farmers traditionally practiced rotational bush fallow in which long periods of fallow were common. The use of fallow has diminished in the past few decades due to increased population pressure.

6.2.2. Farmers’ classification of soils and their agricultural practices

Farmers classify soil types mainly on the basis of colour, but also distinguish between texture and fertility (cf. Schechambo et al., 1999). The three major soil types distinguished are Black, Red and White. Since these colours do not correspond to scientific soil classifications, we will denote these soil types as Dark, Red and Pale, respectively. Soils can also be a mixture of two colours. Soil textures distinguished are light and heavy. Soil fertility is referred to as good or bad. The Dark soil was considered to be heavy, the Red soil could be both heavy and light and the Pale soil was considered to be light. The fertility of heavy soil was considered as naturally good and that of light soil as naturally bad.

Crop residues are most commonly left on the field for off season grazing. Before the onset of the rains, farmers clean their land by scraping together plant residues in lines in the direction of the wind which then are burnt. It is also common to plough under the residues.

Most farmers till their land with hand hoe. Some farmers can afford to use ox-plough and even fewer farmers are able to hire a tractor. Many farmers fear that mechanized cultivation for consecutive years will damage their land due to its deeper tillage compared to other techniques. Deep tillage is perceived to exhaust the soil as it brings infertile subsoil to the surface.

Most common soil and water conservation (SWC) techniques used are diversion ditches at the border of the field and ridge cultivation. Only a small number of farmers apply

3 Data for 1970-2006

manure on their fields, partially because of transport costs, lack of livestock and thus manure, but also because of the fear that the manure will burn their crop during a bad year.

Farmers do not apply fresh manure, but two or three-year old manure. Old manure has already “cooled down” and is less strong than the fresh manure. Other farmers felt that their land was productive enough, or used short fallow periods to maintain soil fertility.

6.2.3. Methodology

Field selection, experimental design and crop management

With help of farmers different zones were identified with predominant presence of one of the three soil types distinguished by farmers. Thereafter, meetings and field visits with farmers were held to find suitable fields. Plots were selected with similar relief and similar period of continuous cultivation to limit diversity of environmental factors as much as possible. Farmers were provided with the same improved maize variety, Kilima, with a growth period of 4.5-5 months. It is preferred by farmers because of its good yield potential, drought resistance and resistance to storage pests. Its yield potential is 4.5-6.25 tons ha^-1 (Kaliba et al., 1998).

On each soil type, a field with four sample plots of five by five meters each was demarcated. The experimental design was a 2² factorial with manure and water conservation as factors, each at two levels (with or without). Water conservation comprised the formation of ridges. Ridge construction and manure application as well as sowing and weeding were done by the farmers themselves and according to their usual practice.

Ridges were made with hand hoe by scraping together surface soil, ashes and plant residues. Seeds were put in small holes. Manure was applied in the seeding holes below the seeds, at rates of 0.5 tons ha^-1 on the Dark and Pale soils and of 0.6 tons ha^-1 on the Red soil.

At the end of the season, farmers were asked to predict their grain yield. Due to the delayed start of the rainy season by a month, and thus of the harvest period, the author could not be present at that time, and the actual yield has not been measured.

Meteorological data

Since spatial and temporal rainfall variability is high, we measured the daily rainfall near to the plots in Goima and Mirambu villages during the 2005/06 cropping season.

Rainfall was measured with two tipping bucket rain gauges with a WatchDog data logger that registered rainfall amounts every ten minutes. The loggers were read out regularly, the data were compared and amounts were checked on the basis of observations by researcher, field assistant and farmers. On a few occasions one or the other rain gauge showed failures.

For these occasions the other rain gauge served as a back-up. Data from the two rain gauges were averaged, and this data set will further on be referred to as Goima Rain Gauge (GRG).

Soil moisture

Volumetric ring samples (100 cm³) were taken at 5 and 27.5 cm depth in each sample plot to determine its field capacity (FC), permanent wilting point (PWP), maximum

available moisture (MAM) and pF curve. FC was determined using undisturbed samples and PWP was determined using disturbed samples on 1.6 MPa pressure plates. Total porosity was determined based on moisture content of saturated soil samples. The texture of each sample was determined using a laser particle size analyzer. Soil profiles were studied using auger samples up to 1.20 meters.

Two different methods were used to measure soil moisture during the experimental period of 24 weeks. One method consisted of weekly measurements on all twelve sample plots with a mobile ThetaProbe soil moisture sensor, at 15-21 cm (15 cm) and 45-51 (45 cm) cm in the first five weeks and after the start of the rainy season, at 30-36 cm (30 cm) as well.

The second method comprised continuous soil moisture measurements with six E+ soil MCT (moisture, conductivity, temperature) sensors, two per soil type. They were buried in the sample plots without manure. The sensors were installed in a horizontal position at 30 cm depth. Data loggers recorded soil moisture content at a 30 minutes interval. Due to problems with both sensors in the Dark soil and one sensor in the Pale soil, no complete data set is available for the whole period.

Data were used to characterize the three soil types in terms of infiltration and water holding capacity and to study soil moisture fluctuations over time in different soil layers.

Data were tested using an analysis of variance (ANOVA) in which the main effects of soils, ridges and manure were compared to the sum of the interaction terms with soils.

A water balance model (Stroosnijder, personal communication) was used to estimate available moisture (AM) in the 60 cm root zone, soil evaporation (E), plant transpiration (T) of Kilima Maize and water loss (L) during the 2005/06 growing season. Sources of input and calculation procedures are summarized in Table 6.1. Deep percolation below the root zone at 60 cm and runoff are both comprised in the term loss (L). Daily rainfall in Goima, soil moisture data, plant development and a number of soil physical properties were used as input data. Due to the great variability in soil moisture data between the sample plots, it was decided to use work with the aggregated figures for each soil type.

Soil, crop and manure nutrients

Mixed soil samples (from 0-15 cm and 15-45 cm soil layers) and four leaf samples (the leave opposite the cob) were taken at each sample plot 60 days after sowing. A manure sample was taken of the manure applied on each soil type. Soil and manure samples were air-dried, and crop samples were cleaned and air-dried before laboratory analyses.

The soil and manure samples were analyzed for nitrogen (N), phosphorus (P; P-Olsen), exchangeable potassium (K), pH (H2O), and soil organic carbon content (org. C). Leaves were analyzed for N, P and K. The results were compared with values found in literature (Beaufils, 1973; Jones and Eck, 1977).

For interpretation of the chemical soil data, the system QUEFTS (QUantitative Evaluation of the Fertility of Tropical Soils) was used (Janssen et al., 1990). In this system, soil fertility is interpreted as: “the capacity of a soil to provide plants with nutrients”. Yields

Table 6.1. Type and source of input data and brief description of calculation procedures for the daily water balance model used to estimate evaporation (E), transpiration (T) and loss (L) of soil moisture for Dark, Red and Pale soils of Goima, Tanzania (2006 season).

Input Code Calculation procedure

Days after planting DAP Planting of Kilima maize on the Pale soil at 17.01.2006 (DAP =0) with 40 000 hills per ha, mature at 12.06.2006 (DAP = 147).

Rainfall (mm d^-1) P Measured in field (average two gauges) Potential

evapotranspira-tion (mm d^-1)

ET-pot CLIMWAT database (FAO, 2003) for Kondoa, 40 km from Goima.

Weekly measured available moisture in 60 cm root zone (mm)

AM-m [200*(measured moisture content 15 cm depth (MC-15) + MC-30 + MC 45)] – [200*(Moisture content at permanent wilting point at 15 cm depth (PWP-15) + PWP-30 + PWP-45)]

Leaf area index (-) LAI Estimation based on pictures taken during crop season Daily dry matter

accumulation (kg ha^-1 d^-1)

∆DM Calculation using estimates of total dry matter (∑DM in t ha^-1), derived from LAI values and data for Kilima maize (Vesterager et al, 2007).

Daily plant transpiration

DSLR Calculated using daily rainfall Actual soil evaporation

(mm d^-1)

E-act On day with P > Epot: Eact = Epot On day with P < Epot: Eact = P On dry day: Eact = minimum of Epot or

3.3 * [SQRT(DSLR) – SQRT(DSLR - 1)] (Stroosnijder, 1982) Daily calculated AM in 60

cm root zone (mm) for each day after planting

AM-c AM-c = AM (DAP-1) + P (DAP) – T (DAP) –E (DAP)

Weekly rainfall (mm) ∑P Sum of daily rainfall over a week Weekly transpiration (mm) ∑T Sum of daily transpiration over a week Weekly evaporation (mm) ∑E Sum of daily evaporation over a week

Weekly loss (mm) ∑L ∑L = AM (DAP-1) + P (DAP) – T (DAP) –E (DAP) – AM-m

are calculated as a function of some selected soil parameters and the quantities and properties of applied fertilizers. Other limiting factors such as pests and water deficiency are not taken into account.

The values of soil parameters in the original QUEFTS version had to be within certain boundaries. Our samples were within the boundaries set by QUEFTS. An exception was that pH (H₂O) in some samples of the Dark and Pale soils were above the upper boundary of 7.

Therefore we used the modified version of QUEFTS (Smaling and Janssen, 1993), which has been calibrated for pH(H₂O) between 4.8 and 8. Another reason was that in Kenya this modified version gave a better fit to measured data than the original version.

The required soil data for the modified version of QUEFTS are organic carbon, organic nitrogen, Total P, exchangeable K, pH(H₂O), clay percentage and average temperature. All data were available except Total P. Using data of sandy soils in the study by Smaling and Janssen, we could relate Total P to organic C: Total P (mg kg^-1) = 26 * Organic C (g kg ^-1). The factor 26 is very close to the value of 25 which was used in the original version of QUEFTS (Janssen et al., 1990).

QUEFTS has been calibrated on the basis of soil data from 0-20 cm. Because our soil samples had been taken from 0-15 cm and 15-45 cm, representing the topsoil and subsoil layers that farmers distinguish, the parameter value (PV) of the 0-20 cm soil layer was calculated with Eq. 1:

PV(0-20cm) = 0.75 * PV (0-15 cm) + 0.25 * PV (15-45 cm) (1)

Input of nutrients to the soil increases the quantity of available nutrients, but part of the added nutrients is not available as it is lost by leaching, microbial immobilisation, chemical immobilisation (fixation), or volatilization. Therefore the recovery by the crop of added nutrients is always less than 1. Default values for the maximum recovery fraction (MRF) are set at 0.5 for N and K, and at 0.1 for P (Smaling and Janssen, 1993).

Since the availability of one nutrient influences the uptake of other nutrients, the available quantities of N, P and K must be well-balanced to get optimum nutrient use efficiency. When a situation of balanced nutrition exists, the uptake of 1 kg of N increases maize grain yield by 50 kg. The same yield effect can be reached when 0.143 kg of P or 0.769 kg of K is taken up (Janssen and De Willigen, 2006). These quantities of N, P and K are indicated by kPNE, standing for (kilo) plant nutrient equivalent (Janssen, 2008). Plant nutrition is in balance when available N, P and K quantities, expressed in PNE, are equal.

The analysis of variance (ANOVA) for nutrient related data implied a comparison of the main affects of soils, ridges and manure to the sum of the interaction terms with soils.

6.3. Results and discussion