Baseline Study of Striga Control using IR Maize in Western Kenya

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Alene and Hassan (2003) for maize production in western Ethiopia obtained a positive andsignificant influence of education on technical efficiency.The relationship between age and technical inefficiency was non-linear. That is, youngfarmers tend to become more efficient as they gain experience, but after a certain age, theirinefficiency begins to increase with age. However, the non-linear effect of age on technicalinefficiency is not significant. Farm size had a similar and even significant effect on farmerinefficiency. That is, as farm size increases from smaller landholdings, technical inefficiencydeclines, but after a certain land size, inefficiency begins to increase with farm size. It canthus be concluded that the relationship between farm size and technical efficiency exhibits aninverted U shape. A lot of empirical work has been carried out to test the inverse farm sizeefficiencyhypothesis, but such a non-linear relationship has not been explored. Rather, alinear relationship has been assumed and results have been mixed. For instance while Squiresand Tabor (1991), and Llewelyn and Williams (1996), found no significant associationbetween farm size and technical efficiency in the Java area of Indonesia, Daryanto et al(2002), found a negative and significant relationship between farm size and technicalefficiency. In Indian agriculture, while Huang and Bagi (1984), Ray (1985), and Kalirajan(1991), obtained no significant association between farm size and technical efficiency, Coelliand Battese (1996), obtained a positive and significant influence of farm size on technicalefficiency.6.3. Technical efficiency estimatesTables 6.3–6.5 present the summary statistics of technical efficiency predictions by province,district and gender. The results show that the sampled farmers achieved an average technicalefficiency of 62%, indicating substantial inefficiencies of maize production in westernKenya. There are only slight gender and location differentials in technical efficiency. Maleheaded households have an average technical efficiency of 63%, whereas female headedhouseholds achieved 60% efficiency. This variation is more apparent in Nyanza (64% vs.58%) than in Western (64% vs. 62%). While the sample maize producers in Nyanza achieved61% efficiency, those in Western achieved an average technical efficiency of 63%,confirming the higher maize productivity of farmers in Western. Technical efficiencyvariations across districts are more apparent than variations across provinces. Averageefficiency estimates range from 55% for Kisumu in Nyanza to 66% for Bungoma in Western.The sampled maize farmers in Bungoma and Busia are relatively more efficient than those inthe other districts in western Kenya.Table 6.3. Technical efficiency distributions by province and gender (% households)Technical efficiency ProvinceGender(%) All Nyanza Western Male headed Female headed≤25 5 7 3 4 925–50 19 20 18 19 1750–75 51 47 55 50 5375–100 25 26 24 27 21Mean 62 61 63 63 60Minimum 4 4 4 6 4Maximum 92 92 90 90 92Table 6.4. Technical efficiency distributions by province and gender (% households)Technical efficiency NyanzaWestern(%) Male headed Female headed Male headed Female headed≤25 5 13 3 425–50 21 56 18 1844
50–75 45 53 54 5475–100 29 18 25 24Mean 62 58 64 62Minimum 4 6 4 9Maximum 92 85 89 90Table 6.5. Technical efficiency distributions by district (% households)Technical efficiency NyanzaWestern(%) Bondo Kisumu Nyando Siaya Busia Teso Vihiga Bungoma≤25 5 12 4 7 4 2 3 325–50 21 28 17 15 12 28 22 1150–75 45 39 55 50 54 44 61 5875–90 29 21 24 28 30 26 14 28Mean 62 55 62 63 65 60 61 66Minimum 7 4 11 17 9 16 10 4Maximum 89 92 88 88 85 89 88 90The finding that maize farmers in western Kenya exhibit high levels of inefficiency isconsistent with the fact that there is a lag between farmers’ attempts to adjust their productiondecisions to keep pace with changes in the production and economic environment andachieving the ultimate efficient use of their resources. Deviations of farmers' practices fromtechnical recommendations coupled with production as well as system constraints always leadto technical inefficiencies among farmers. Knowledge of the extent of such inefficiencies andthe underlying farm-level as well as system-level constraints will help guide policy makers toincrease agricultural production through interventions that counteract major farmingconstraints. The results in this study point to the existence of a considerable potential forincreasing maize production in western Kenya. Current levels of maize production are lowdue to under-utilisation of this potential. The sampled maize producers could increaseproduction by an average 38% if they were fully technically efficient. In other words, if theproduction practices adopted by the best practice farmers in the sample were equally used,much of the maize production potential could have been exploited, all translating to moremaize output. In this study, improved technologies such as hybrid maize and innovative localpractices such as integrated Striga control have been shown to increase frontier maize output.This means that greater adoption of these practices and other alternative practices couldincrease maize production. AATF’s technological intervention for Striga control using IRmaize thus holds a lot of promise towards unlocking the maize production potential andenhancing maize productivity in western Kenya.45
Page 5 and 6: Acronyms and AbbreviationsAATF Afri
Page 7: control technologies. Promoting bot
Page 10 and 11: Figure 1.1. GIS maps showing sample
Page 12 and 13: ln(maizeoutput ) = β + β ln(Land
Page 15 and 16: Chapter 2Socioeconomic Characterist
Page 17 and 18: Figure 2.2. Major forms of financia
Page 19 and 20: Another interesting parameter on hu
Page 21 and 22: Chapter 3Crop Production and Produc
Page 23 and 24: Table 3.3. Seed use in NyanzaLong r
Page 25 and 26: Table 3.5. Crop productivity (kg/ha
Page 27 and 28: Chapter 4Striga and Striga Control
Page 29 and 30: N 402 400Uprooting (%) 82.3 86.0Bur
Page 31 and 32: of seed (Table 4.9). For the sustai
Page 33 and 34: Table 5.2. Household income in both
Page 35 and 36: Table 5.3. Logit model estimates of
Page 37 and 38: household members were used for thi
Page 39 and 40: nutritional status of children are
Page 41 and 42: Chapter 6Empirical Analysis of Maiz
Page 43: Land × Labour β 12-0.011 -0.385La
Page 47 and 48: Table 7.1. Types of development pro
Page 49 and 50: Chapter 8Conclusions and Recommenda
Page 51 and 52: promise for unlocking the maize pro
Page 53: Ray SC. 1985. Measurement and test

Baseline Study of Striga Control using IR Maize in Western Kenya

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