Production gradients in smallholder banana (cv. Giant Cavendish) farms in Central Kenya
Research highlights
▶ Bunch yields were higher nearer homesteads, and higher than previously reported. ▶ There was a gradient of Soil K and Tissue K levels, declining from the homesteads. ▶ The Compositional Nutrient Diagnosis Indices showed K as most deficient nutrient. ▶ Resource-poor farmers’ soils were higher in exchangeable K and Mg, pH, and total N. ▶ Soil quality problems were the biggest yield loss factors, not pests and diseases.
Introduction
Banana is an important cash crop for urban markets in sub-Saharan Africa. With the steady rise in urban populations, its importance in the urban diets as well as farmer incomes is steadily increasing (Nguthi, 2007, Qaim, 1999). In Kenya, bananas are grown for home-consumption, but the majority of the bananas (75%) are sold (Qaim, 1999). Consequently, the year-round production of banana provides food security and income for many rural households. Bananas, nonetheless, receive a comparatively low priority in terms of labor and input allocation within diversified farming systems (Qaim, 1999).
Though bananas are an important cash crop, actual yields (14 t ha−1 yr−1 – FAO, 2007) in the Kenyan smallholder systems seem low, compared to commercial production systems in Latin America that normally aim for 50 t ha−1 yields. However, reported yield levels vary substantially from 11.3 t ha−1 (Nguthi, 2007), to 14 t ha−1 (Qaim, 1999), up to 30 t ha−1 yr−1 (Vanlauwe et al., 2005). AHBI (2008) reports a yield average of 5.58 t ha−1 yr−1 for non tissue culture bananas and 12.59 t ha−1 yr−1 for tissue culture banana. The large variation in reported yields may be due to the difficulties encountered when trying to quantify yields (van Asten et al., 2004). These difficulties are, amongst others related to (i) the large spatial (within a field) and temporal (i.e., within a year) variability of bunch weights, and (ii) the difficulties to accurately estimate plant densities of different crop generations, especially in mixed cropping systems (Hauser and van Asten, 2008).
Besides difficulties with quantifying yields, it seems that the importance and spread of yield constraints has neither been properly quantified. Vanlauwe et al. (2005) conducted a rapid farm characterization survey on 50 farms in Maragua district, which is the key banana supplying area for the Nairobi market. The observed disease pressure (e.g., Fusarium wilt, Sigatoka leaf fungal diseases) was generally low. The Fusarium wilt (Fusarium oxysporum f.p.cubense – also known as Panama disease) resistant Cavendish varieties (AAA) had become the dominant variety after Gross Michel was largely wiped out by this fungal disease in the past decade. Farmers reported that initial Cavendish yields were good, but yields declined in the years after its introduction. Farmers’ observations (i.e., snapping and toppling of the pseudostems) suggested that nematode and weevil (Cosmopolitus sordidus) damage were important constraints. However, the actual damage levels recorded during the survey were low (Vanlauwe et al., 2005) for weevil-induced corm damage (2.6%) and nematode-induced root necrosis (4.4%). The lack of severe pest and disease pressure suggests that low yields are primarily caused by abiotic stresses such as drought and poor soil fertility. Bananas require about 1300 mm of evenly distributed rainfall per year (Purseglove, 1985). The average rainfall in the banana growing area of Maragua is around 1560 mm yr−1 (Ovuka and Lindqvist, 2000), but with dry seasons from July to September and December to March.
Studies on crop yield constraints conducted in the East African Highlands reveal that soil fertility is often a major constraint for smallholder cropping systems (e.g., Smaling et al., 1997, Drechsel et al., 2001). This is particularly visible in distinct soil fertility gradients that are often observed in smallholder systems, such as for maize (Tittonell, 2007, Vanlauwe et al., 2006), and East African Highland Bananas systems (van Asten et al., 2004). Fields close to the homestead, sometimes referred to as kitchen gardens, often receive higher nutrient inputs through the application of ash, kitchen waste, crop residues, and from livestock located close to the homesteads, particularly at night (e.g., Bekunda and Woomer, 1996, Wortmann and Kaizzi, 1998). The resulting man-made soil fertility gradients have been found to be related to farm typology, since nutrient inputs require investments in labor and capital, which resource-poor farmers often lack (Vanlauwe et al., 2006).
The objectives of this study are to quantify the yield levels and primary yield constraints in Maragua district, the major supplier of bananas to the increasing urban population of Nairobi. Subsequently, we want to quantify to what extend spatial production gradients occur in such commercial smallholder systems, and to what extend these gradients correspond to those observed in staple food systems elsewhere in the region. Lastly, we would like to explore how soil fertility gradients relate to gradients in soil fertility management, and whether this is a function of farmer resource availability (i.e., capital, land, livestock).
In order to achieve its objectives, this study focused on the detailed monitoring of some 150 plants at 10 different farms from three different wealth classes. Within each farm, banana mats located near, at mid-distance, and far away from the homestead were selected. In order to avoid temporal bias in the collection of plant performance and environmental data, we monitored plant performance and productivity for individual mats over a 14-month period, so that at least one bunch per mat was harvested.
Section snippets
Site description
This study was carried out in Maragua District in Central Kenya (00̊47′S, 037̊07′E). This is the leading banana producing area in Central Kenya. Over 90% of the bananas cultivated are dessert cultivars (Vanlauwe et al., 2005). The altitude range of the study area is 1300–1470 m above sea level. Annual rainfall averages 1560 mm, but totalled 932 mm and 1247 mm in 2005 and 2006 respectively (MoA, 2006). The study area lies in the upper midland zone 4 (UM4), an area of high agricultural activities (
Yield levels
Bunches were heavier near the homesteads (15.4 kg) compared to mid-distance (12.2 kg) and far (11.6 kg), P < 0.05 (Table 1). Fresh bunch yields per hectare were significantly (P < 0.05) higher near the homestead (29.8 t ha−1 yr−1), than at mid-distance (26.8 t ha−1 yr−1), or far away (20.2 t ha−1 yr−1) (Table 1). The average harvest cycle duration per mat (i.e., the time between two subsequent harvests from the same mat) was 13 months. Harvest indices followed a similar trend as yields, with values decreasing
Yield levels
Recorded fresh bunch yields (20.2–29.8 t ha−1 yr−1) seem much larger than those earlier reported (11–14 t ha−1 yr−1) for the region (see FAO, 2007, Nguthi, 2007, Qaim, 1999). The estimates given by Nguthi (2007) and FAO (2007) may have not factored in the spatial variations within farms that results to heavier bunches near homesteads as compared to far. Moreover, the area sampled in this study is not entirely overlapping with the areas that the previous authors reported on. However, similar large
Conclusion
Yield levels in the Maragua area (20–30 t ha−1 yr−1) appear to be double of what is generally being reported for Kenya. Soil quality problems appear to be the single biggest yield loss factor. CND analysis revealed that K deficiency was dominant. Pest and diseases were not a problem, which is in contrast with past research and development efforts that focused on combating pests and disease, e.g., through the introduction of TC planting material (Mbaka et al., 2008, Waele et al., 1998, Sági et al.,
Acknowledgements
We wish to thank Dr. Turoop Losenge, Mr. Wilson Nguluyi, Dr. Tittonell Pablo, for their support in data collection, sample and data analyses.
We also thank the banana farmers of Maragua, Central Kenya for their cooperation and participation in the study, the staff of JKUAT Horticulture Department (Plant Nutrition Laboratory) and staff of TSBF – CIAT for laboratory assistance. Special thanks to the TSBF – CIAT – JKUAT Banana Research Team, and IITA Uganda team for their contribution, and The
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