Trabajos previos

Six banana production systems were defined for the purpose of this analysis, using the ap-proach taken by specialists in Uganda (Tushemereirwe et al. 2001). This categorization in-volved the recognition of three classes of geographical area according to their intrinsic biophysical suitability to support banana production (high-, medium-, and low-potential pro-ductivity zones), in each of which production is subdivided into two categories according to the production orientation of growers (semicommercial and subsistence). Most producers maintain banana plantations primarily to meet food security and cultural needs, only intermit-tently entering the market as opportunities and needs arise. There is a much smaller but grow-ing share of farmers whose main focus is to produce for the banana market. Clearly, produc-tion and technology use decisions, and response to market opportunities and signals, are distinct for these two groups.

Table 10.1 summarizes the main characteristics of the six systems with respect to the production of cooking bananas (the endemic AAA-EA cultivar group matooke, as well as some exotic FHIA hybrids). Across all areas in Uganda, a total of some 5.5 million tons of cooking bananas are produced from about 450,000 ha of cropland, at a rough national average yield of around 12 tons per ha (UBOS 2002). Beer and sweet cultivars account for an

Table 10.1 Ugandan cooking banana production, by production system, 2000 Total

High productivityMedium productivityLow productivity TotalSemi- commercialSubsistenceTotalSemi- commercialSubsistenceTotalSemi- commercialSubsistence Production (million tons)5.5453.5492.4841.0650.9980.2000.7980.9980.1000.898 Production share (percent)100 64 4519 18 4 1418 216 Yield (tons/ha)12.3121.912517101895.29115 Area harvested (thousand ha)451162996310011891899180 Area share (percent)100 36 221422 22042 240 Sources: Compiled by the authors from the Uganda National Household Survey 1999/2000 (UBOS 2002) and Tushemereirwe et al. (2001). Notes: The Uganda National Household Survey data on banana production categorizes bananas by use: cooking, beer, and sweet (dessert). This table reports the allocation of the cooking banana production data into the six system categories.

ASSESSING THE IMPACT OF TECHNOLOGIES IN UGANDA 143

additional 538 thousand tons and 46 thou-sand tons, respectively, of the total national banana output (UBOS 2002). Although the high-productivity areas account for almost 65 percent of the total production, they ac-count for only about 36 percent of the area planted in bananas. Conversely, the low-productivity areas occupy some 42 percent of the area in bananas, but they provide only some 18 percent of total output. This pattern reflects the disparity in yield levels attained across production systems, ranging from only 5 tons per ha for the low-produc-tivity subsistence systems to around 25 tons per ha among semicommercial producers in high-productivity areas. Low-yield subsis-tence-oriented production represents 40 percent of the banana producing area, the largest area share among the six groups.

Medium- and low-yield semicommercial production each represent only 2 percent of the banana area. here><Table 10.1 near

Most of the high-productivity areas lie above 1,200 m.a.s.l., where the climate tends to suppress the virulence of common banana pests and diseases (Speijer et al.

1994; Karugaba and Kimaru 1999;

Tushe-mereirwe et al. 2001). Thus, from a geo-graphic perspective, high-productivity sys-tems are found almost exclusively in Western Region, Uganda, whereas the bulk of the low-productivity systems are found in Central Region (Tushemereirwe et al.

2001, 2003). Low-productivity systems make up some 78 percent, 66 percent, and 21 percent of the production areas in East, Central, and Western regions, respectively.

In Central Region, low productivity often reflects overexploitation of soil resources in the past (Gold et al. 1999; Bagamba et al.

2000). In many banana production areas the chronic underreplenishment of soil nu-trients appears to have exacerbated vulner-ability to pests and diseases (Gold et al.

1999).

Estimates of the losses attributable to the priority production constraints of this study are summarized by production sys-tem in Table 10.2. These include weevils (Rukazambuga, Gold, and Gowen 1994, 1998; Gold and Messiaen 2000; Gold et al.

2004), nematodes (Kashaija et al. 1994;

Speijer and Kajumba 1996), black Sigatoka (Tushemereirwe et al. 1996), bacteria wilt Table 10.2 Estimated yield losses caused by priority banana production constraints in each of the six

production systems

Constraint

Typical losses

High productivity Medium productivity Low productivity

Bananaweevil 50–70 Fourth 5 10 15 26 40 60

Nematodes 40–60 Fourth 10 15 14 28 23 51

Black Sigatoka 30–50 Third 6 15 18 28 30 50

Banana bacteria wilt 80–100 First 58 58 58 58 58 90

Low soil fertility 10–70 >Third 37 58 67 83 80 91

Sources: Compiled by the authors from unpublished sources (such as the National Agricultural Research Organization’s banana program surveys and reports) and from Rukazambuga, Gold, and Gowen (1998) for weevils, Speijer and Kajumba (1996) for nematodes, and Okaasai and Boa (2004) for bacteria wilt.

Notes: A two-stage process was used to obtain the data. Following an initial review and compilation of available published and unpublished sources, an expert consultation was undertaken to extrapolate available evidence and estimate losses within each production system category.

aYear of plantation cycle in which losses typically become significant.

(Kangire and Rutherford 2001; Okaasai and Boa 2004), and low soil fertility. Over-all, low soil fertility and banana bacteria wilt appear to constitute the greatest threat to banana productivity. Banana bacteria wilt has recently spread alarmingly through much of Central Region with devastating effects (Okaasai and Boa 2004). Just as striking is the assembled evidence that yields on the extensive low-productivity areas occupied by subsistence-oriented growers are reduced by 50 percent or more by a combination of biotic pressures (wee-vils, nematodes, and black Sigatoka); the same constraints reduce yields by an esti-mated 5–15 percent in high-productivity areas. This evidence reflects both differ-ences in biophysical conditions and greater use of improved cultural practices in the high-productivity areas (NARO, IITA, and NRI 1994; Tushemereirwe et al. 2003). here><Table 10.2 near

Average effects only are summarized in Table 10.2; the dynamic nature of losses and the interaction among constraints are key features of the process underlying low banana yields. With the likely exception of banana bacteria wilt, whose impacts are both short-term and extreme, the magnitude of losses increases over time frames mea-sured in years. Table 10.2 also notes the approximate time after which the cumula-tive effects of yield losses are generally considered to become economically signifi-cant. The pace and intensity at which yield losses increase depend on a range of fac-tors, but their interaction conspires to radi-cally alter the economiradi-cally viable life of any given mat and, hence, any given planta-tion. Assessing the economic impact of even a single constraint is complicated by the dynamic interplay of biophysical condi-tions, management practices, and the co-evolution of the banana plant and its con-straints. The situation is further complicated by the coexistence of multiple constraints, whose separate effects are difficult to dis-tinguish. To isolate the ameliorating influ-ence of improved management practices on individual constraints is similarly difficult.

Thus, although we have attempted to cap-ture the dynamic nacap-ture of yield losses and productivity-enhancing interventions, we have not sought to address the interaction of constraints.

Production structures, costs, and returns for different banana production systems are summarized in Table 10.3. In constructing partial farm budgets, data were compiled from a number of baseline studies con-ducted by the NARO banana research pro-gram between 1999 and 2002, including those by Bagamba et al. (2000) for Central Region (primarily low-productivity zones) and Ssenyonga et al. (1999) for Ntungamo and Mbarara districts (high-productivity zones). The data components needed to es-timate returns are reported: revenues (an-nual yield, farm-gate prices), costs (labor, other inputs), and fixed costs (plantation cycle). Though shown in summary form, revenues and costs were calculated over a 30-year time horizon. Plantation life cycles may be as short as 5 years in low-productiv-ity subsistence-oriented areas, and as long as 80 years for well-managed plantations in high-productivity areas. It is noteworthy that in the high-productivity zone, in par-ticular, not only do the more-productive areas utilized for commercial gain have higher yields and longer plantation life cy-cles, but they also have higher farm-gate prices. This correlation might be attributed to several factors, such as the capacity of growers to provide greater quantities on a more consistent basis, their greater bargain-ing power, or better bunch quality. Returns to production appear skewed in favor of the more-productive areas and the commer-cially focused farmers, as reflected in the unit cost of production. In high-productivity areas production costs range from Ush 11–16 per kg of bananas, whereas in all other areas they range from Ush 50–55 per kg. The unit production costs shown in Table 10.3 provide the basis for assessing the potential economic benefits of technol-ogy innovations. These innovations are dis-cussed next. here><Table 10.3 near

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