Abstract: Gender differentials in the adoption of improved maize production technologies were analysed using the logit regression model. Two dependent variables that constitute the main components of the improved maize production technology were used. These were: improved seed varieties and fertiliser. The results indicated that the adoption of improved maize seed and fertiliser is biased by gender, where female-headed households adopt the technologies less. The number of cattle, extension services and years of education had a positive influence on the adoption of improved maize seed for male-headed households, while the use of organic fertiliser, household size, district (Mbozi), and radio ownership had a positive influence on the adoption of inorganic fertiliser for male-headed households. The number of cattle, years of education, extension services, and area under maize did not affect the adoption of improved maize seed or fertiliser for female-headed households, mainly due to significantly less access of female heads to these resources or services. Therefore, policy should address gender disparities in access to extension services, formal education and cattle ownership that exist because of socio-cultural and institutional factors limiting the adoption of technologies by female-headed households.
Maize is the single most important food crop in Tanzania. Over eighty per cent of the population depends on it and it is grown on more than forty per cent of the total cultivated area. Per capita consumption is around 113 kg per year and maize contributes about sixty one per cent of the total calories in the diets of Tanzanians (Mayagilla, 1988). Maize is produced in all twenty regions of the mainland; there is surplus production in the southern highlands zone, i.e. Iringa, Mbeya, Ruvuma and Rukwa, all of which account for about forty six per cent of the national production (Moshi and Nnko, 1989). Small-scale farmers produce about eighty per cent of the maize production, while the remaining twenty per cent is produced by public and private large-scale farms.
Research efforts to develop improved maize technologies in the southern highland zone started in the 1970-1 cropping season at Uyole. Over the years many maize technologies have been developed, and efforts have been made to transfer them to the small-scale farmers in the zone. However, the extent to which male and female farmers have adopted these maize technologies is not known. This paper, which is a part of a larger study on farmers' adoption of recommended maize technologies in Mbeya region of the southern highlands of Tanzania (Bisanda and Mwangi, 1996), focuses on inter-household analysis of gender differentials in adoption of improved maize technologies.
This type of analysis is not only helpful in predicating how different households will adopt improved maize technologies but provides gender-disaggregated data that are useful in designing more effective, efficient and equitable policies (Feldstein and Jiggings, 1994).
Mbeya region and the southern highlands in general receive unimodal rains, which are fairly uniformly distributed and exceed the minimum requirements for maize production. Soils and temperatures are also adequate for a good crop of maize (Marandu, et al. 1989). Maize is grown in four distinct agro-ecological zones (Croon et al., 1984). First, the central Mbeya zone which rises up to 1200 meters above sea level (masl) and receives an average of 700 mm of annual rainfall. Here maize is the chief crop and it is grown during the wet season. Second, the Ilembo plateau and Poroto mountains with an altitude of between 1900 and 2400 masl and annual rainfall of between 2500 mm and 3500 mm. Maize, beans and potatoes are the major crops grown; maize is planted during the dry season to utilise residual moisture. Third, the Tukuyu volcano and Rungwe valleys with an altitude of between 900 and 1800 masl and annual rainfall of between 1900 and 2400 mm. Here, again, maize is planted during the dry season to take advantage of residual moisture. Fourth, Mbozi, Isuto and Ndola zone whose altitude ranges between 900 and 1800 masl with between 1100 and 1200 mm of annual rainfall. Maize is planted during the wet season.
Since the 1970-1 cropping season, many promising maize technologies have been developed for the southern highlands including the study area (Table 1).
Table 1. A Summary of Recommended Practices for Maize Production in Southern Highlands and Expected Yields
Management practices |
Characteristics |
Estimated yields (kg/ha) |
Zero management |
Extremely low management |
300 |
One timely weeding |
2-3 weeks after planting |
700 |
Timely planting |
At optimum time (2 weeks after rains) |
1200 |
Fertility improvement |
20 kg P + 40-50 kg N/ha (basal application) |
21000a |
Optimum plant population |
75 x 60 cm, 2 plants per hill |
2700 |
Improved seed b |
Hybrid/composite |
3800 |
Further fertility improvement |
50 kg N/ha + second weeding |
6000 |
Pest control |
Control of stalk borers |
7200 |
a Assumes a low soil fertility
b Recommended varieties for the study area included H614, H6302, UCA, Kilima, TMV-1 and TMV-2.
Source: Lyimo and Temu (1992).
Based on information from existing literature, a reconnaissance survey was conducted to obtain first-hand information on the study area and to select sample villages, farmers, wards, divisions and districts. In total 100 farmers, out of whom thirty-seven were female heads of households, were selected from ten villages (ten farmers from each village) in Mbeya, Mbozi and Rungwe districts (Table 2). While male household heads were selected randomly, female heads were selected purposively because such households are not very common in the study area. This is despite the observation that in Africa, women are the de facto or de jure heads of 25 to 35 per cent of rural and urban households (Dean and Gladwin, 1991). Female-headed households were those which were run and represented by a widow, divorced or single woman without the mediation of a husband, father or male relative in the routine day to day activities of that household. Male-headed households were those where a husband was present and was the final decision-maker in the important issues pertaining to the household. The situation does not only vary from country to country but from region to region within a country. Based on the importance of maize production in each district and agro-ecological zonation, forty farmers were selected each from Mbeya and Mbozi while the remaining twenty came from Rungwe. Although, it seems that most female respondents lived in rural Mbeya, the chi-square test (Table 2) showed a non-significant result (_2=3.15, p=0.2). Thus, gender and districts are not associated. A formal survey of the 100 farmers was then undertaken using a structured questionnaire. Multiple visits were made to each sample farmer after each farm operation viz. planting, weeding and harvesting, to capture farmer practices.
Table 2. Sample Size by Gender of Household Head and District
DISTRICTS | ||||
Gender |
Rural Mbeya |
Rungwe |
Mbozi |
Total |
Male |
24 |
10 |
29 |
63 |
Female |
16 |
10 |
11 |
37 |
Total |
40 |
20 |
40 |
100 |
Feeder et al. (1985) noted that the most commonly used qualitative models to study the adoption behaviour in developing countries are the logit and the probit models. These models specify a functional relationship between the probability of adoption and various explanatory variables. Usually a choice has to be made between probit and logit models, but Amemiya (1981) states that the statistical similarities between the logit and probit models makes such a choice difficult. Choice of any model is therefore not dominant and may be evaluated a posteriori on statistical grounds, although even here, in practice, there will not usually be strong reasons for choosing one model over the other.
In this paper, the logit model is used to analyse the differences in adoption of improved maize seed and fertiliser between male-and female-headed households. Following Pindyck and Rubinfeld (1981), the model is written as:
In (Pi/(1-Pi)) = Zi = _o + _ _i Xi + e
The logit model is based on the cumulative logistic probability function. The dependent variable Zi is the logarithm of odds that a particular choice will be made. It is an index reflecting the combined effects of Xi factors that promote or prevent adoption. The importance of each factor is influenced by the coefficients of the adoption equation (_i). The appeal of the logit model is that it transforms the problem of predicting probabilities within a (0,1)-range interval to the problem of predicting odds of events occurring within the range of a real line. The logistic model is estimated using the maximum likelihood estimator (MLE) (Kmenta, 1986).
The estimated model is specified as follows:
In (P/(1-P)) = _o + _1X1 + ... + _11X11 + e
Where:
X1 = Area under cash crops (CASHAREA) 1, acres;
X2 = Extension visit (EXTEN), dummy variable;
X3 = Number of cattle (CATTLE);
X4 = Use of organic fertiliser (ORGFERT), dummy variable;
X5 = Household head age (AGE), years;
X6 = Household size (HHSIZE), number;
X7 = District Rural Mbeya (MBEYA), dummy variable;
X8 = District Mbozi (MBOZI), dummy variable;
X9 = Level of education (EDUCYRS), years;
X10 = Area under maize (MZAREA), acres;
X11 = Ownership of a radio (RADIO), dummy variable;
e = Disturbance term.
The dependent variable is the natural log of the probability to adopt improved maize seed or inorganic fertiliser (P) divided by the probability not to adopt improved seed or inorganic fertiliser (1-P). These dependent variable take the values 1 for adopting and 0 for not adopting.
The formation of the model was influenced by a number of working hypotheses. Farmers who have larger areas under cash crops1 were hypothesised to adopt more the improved maize technologies, because they will have more cash available to purchase them. Access to information in the form of an extension visit is necessary for a farmer to be aware of the new technology as well as how to use it. An extension visit is therefore hypothesised to positively influence the decision of farmers to adopt new technologies. Cattle is a proxy for wealth, and wealthier farmers have the means to purchase improved maize technologies. Therefore, it is hypothesised to be positively related to the adoption of improved maize technologies. In the survey districts farmers use organic fertiliser from their cattle. Farmers who use organic fertiliser will be aware of the positive effect of fertilisers on crops, and are therefore likely to adopt fertilisers. Farmers' age can increase as well as decrease the probability of adoption. Older farmers may have more experience and/or education that allows them to adopt improved technologies, while younger farmers might be less risk-averse and therefore more willing to adopt improved technologies. The size of the household is an indication of the availability of labour in the household. This is hypothesised to increase the probability of adopting improved technologies that require more labour. Districts can influence the probability of adopting improved technologies either positively or negatively depending on their accessibility by extension or other development agents. Exposure to education will increase farmer's ability of obtain, process, and use information relevant to the adoption of improved maize technologies. Hence, this exposure will increase farmer's probability of adopting improved maize technologies. The area under maize is a proxy for wealth, hence, it is expected to be positively associated with the adoption of improved maize technologies. Radio communication can be a channel through which extension messages reaches farmers. This gives radio owners and advantage of access to technical information. Ownership of a radio is therefore hypothesised to increase the probability that a farmer adopts improved maize technologies.
Ninety per cent of male-headed households had on average about three adults while female-headed households had on average two adults. Adults were defined as members in the household with an age above eighteen years. Household sizes tended to vary more in female-headed households than in the male ones. Mean household size for male-headed households was significantly different from that of female-headed households (Table 3). Mean age for male-headed households was not significantly different from that of female-headed households. Almost all members of male-headed households (ninety-six per cent) had completed elementary formal education (below seven years) while eighty two per cent of members of female-headed households had no formal education. The mean number of years of education for male heads was significantly greater than that of female heads. Land ownership is communal and farmers have individual user rights. Mean farm size (6.46 acres) and cultivated area (5.80 acres) for male-headed households were significantly higher than mean farm size (3.74 acres) and cultivated area (3.19 acres) for female-headed households.
Eighty-two per cent of all respondents used hand hoes and only eight per cent used oxen for cultivation. Ten per cent used both hand hoes and oxen. The chi-square test (_2 = 7.1 p<0.05) indicates a strong association between gender and farm power. Most female-headed households used hand hoes (none used oxen) while sixteen per cent of male-headed households used oxen or both oxen and hand hoes. About half of the surveyed households hired labour mainly for land preparation. Another twenty-one per cent hired labour for weeding and twenty five per cent for harvesting. More male-headed households used hired labour for land preparation and harvesting compared to female-headed households (_2 = 2.6, p<0.1 and _2 = 4.1, p<0.05, respectively).
Table 3. Socio-economic Characteristics of the Household Heads by Gender
Male-headed household (N=63) |
Female-headed household (N=37) |
||
Mean (SD |
Mean (SD) |
t-statistic | |
No. of adult members working on farm |
3.3 (1.5) |
2.4 (1.7) |
2.9* |
Age (years) |
42.8 (15.1) |
46.9 (12.6) |
1.38 (NS) |
Education (years) |
4.8 (3.2) |
0.9 (2.2) |
7.25 |
Farm size (acres) |
6.46 (4.2) |
3.74 (2.3) |
4.17 |
Cultivated area (acres) |
5.80 (3.8) |
3.19 (1.7) |
4.67 |
No. of cattle owned |
2.14 (2.5) |
0.86 (1.6) |
3.17 |
Note: NS = Not significant; *= significant at p<0.01; SD = Standard deviation
Source: Bisanda and Mwangi, 1996.
Forty-three per cent of sample farmers kept livestock. About seventy-two per cent of female heads did not keep cattle compared to seventy one per cent of male heads. Independent t-test between sex and cattle ownership, indicates a significant association between sex and cattle ownership, cattle keeping being more associated with male-headed households (Table 3).
Sixty-two and fifty-seven per cent of male- and female-headed households, respectively, planted maize at the recommended time. Almost all farmers except two from each gender adopted row planting. There was a significant association between spacing between hills and the gender of the household head (_2=7.5, p<0.1). More male farmers tended to use spacing of 30 x 90 cm. None of the sampled farmers followed the exact recommendations for spacing between and within hills. The seeding rate commonly used was 1-2 seeds per hill.
Eighty-four and sixty two per cent of male-and female-headed households had adopted improved maize varieties, respectively. There was a strong association between gender and growing of improved maize varieties (_2=6.2, p<0.01). Male-headed households had adopted improved maize varieties more often compared to female-headed households. The recommended weeding was widely adopted, with all farmers weeding their plots at least once. Sixty-five per cent of male-headed and fifty-seven per cent of female-headed households weeded twice. About 87 and 95 per cent of male-and female-households, respectively, used hand hoes for weeding. Herbicides were not widely used because of their high cost and lack of knowledge about them.
Farmers in the study area understand fertiliser to mean inorganic fertiliser. During the survey year 65 and 46 per cent of male-and female-headed households used fertilisers of different types, respectively. The use of inorganic fertiliser was associated with gender of the household head (_2=3.5, p<0.1). Several reasons were given as to why some farmers do not use fertilisers. These included lack of money to purchase fertiliser, fertile soils, which do not need fertiliser and non-availability of fertilisers. Fifty-five and fifty per cent of male- and female-headed households, respectively, were not using fertiliser because it was not available. Twenty-three per cent of male-headed households, were not using fertiliser because their land was fertile, while thirty-five per cent of female-headed households lacked money to purchase fertiliser. Seventy-eight per cent of female-headed households and seventy per cent of male-headed households had their crop attacked by stalk borers. Fifty-seven per cent of female-headed households had their crop attacked by stalk borers. Fifty-seven per cent of female-headed households and forty per cent of male-headed households had at least one of their maize plots affected by maize streak virus (MSV). Eighty-six per cent of male-headed and eighty-nine per cent of female-headed households took no measures to control either the pest or the disease.
In general, farmers adopted the recommended package of improved maize production technologies in a step-wise manner. Four major factors contributed to this step-wise adoption: cost of technologies, environmental stress, lack of availability and/or timely availability of technologies, and lack of information on new technologies (Bisanda and Mwangi, 1996).
The analysis of association between maize technologies and gender shows that significant associations exist in the case of improved maize seed and fertiliser. Male-headed households had adopted improved maize seed at significantly higher levels than female-headed households (_2=6.2, 0<0.01). Kumar (1994:2) found similar results for the adoption of hybrid maize in Zambia. The adoption of inorganic fertiliser was also significantly higher for male-headed households than for female-headed households (_2=3.5, p<0.1). The model is used to estimate the factors that influence adoption for each of the technologies by male- and female headed households. The results of the estimates are given in Table 4 and 5. The log-likelihood ratio rest was used as a "goodness-of-fit" test of the model. The null hypothesis is that the independent variables are not significantly different from zero in determining the expected value of the decision to adopt improved maize seed and fertiliser. Using a chi-square statistic, the null hypothesis was rejected, implying a significant difference from zero for the independent variables (Kmenta, 1986).
Table 4 shows the parameter estimates of a logistic model of factors influencing the decision of male- and female-headed households to adopt improved maize seed in Mbeya region. The adoption of improved maize seed for male-headed households was significantly influenced by the number of cattle, extension visit, and years of education, while the adoption of improved maize seed was only positively and significantly influenced by radio ownership. The district variable was omitted from the model in the logistic analysis of improved maize seed because of multicollinearity with the extension variable.
Cattle ownership significantly and positively influenced the adoption of improved maize seed for male-headed households. Cattle can be a source of income that can be used to buy improved maize seed. There was no significant influence for female-headed households. This can be explained by the Spearman correlation test that showed a negative significant correlation between cattle and gender (_=-0.3, p<0.01). Female-headed households owned fewer cattle compared to male-headed households. Chipande (1987:318) found that male-headed households had farm incomes twelve times the size of female-headed households, which negatively affected the adoption of maize technologies by female-headed households.
In the study area, male-headed households received more visits by extension agents than female-headed households (Bisanda and Mwangi, 1996). Studies elsewhere in Africa have shown that male-headed households have more extension contact/training compared to female-headed households (Gittinger, 1990; Gladwin 1996, 1997; Quisumbing et al., 1995; Saito, 1994). However, radio-ownership had a positive and significant impact on the adoption of improved maize seed by female-headed households. Thus, female-headed households used a different strategy to acquire information about improved maize seed.
The level of education significantly influenced the adoption of improved maize seed for male-headed households, but not for male-headed households. It was shown in Table 3 that male-headed households had five years of education compared to about one year for female-headed households. Also, there is a strong significant and negative correlation between education and gender (_=-0.56, p<0.01). Female-heads of households are significantly less educated than male-heads of households which might make it difficult for them to understand how to use improved maize seed or to apply fertiliser to maize. Various studies have shown that improvements in agriculture and the likelihood of adoption of improved technologies are strongly linked to access to education (Moock, 1976:835; Saito, 1994:39-40).
Although female-headed households have significantly less area under maize compared to male-headed households (_=-0.31, p<0.01), there were no significant results found in relation to the adoption of improved maize seed or fertiliser. Cultivated land is often a good proxy for wealth. More land enables farmers to increase production, which provides more income that can be used to buy fertiliser. Due et al. (1987:214) calculated that contact farmer with larger acreage and production sold eighteen per cent of their production, while female-headed households sold only two per cent of their production.
Table 5 shows the parameter estimates of a logistic model of factors influencing the decision of male- and female-headed households to adopt inorganic fertiliser in Mbeya region. The adoption of improved maize seed for male-headed households was significantly influenced by the use of organic fertiliser, household size, radio ownership, and district.
Table 4. Parameter Estimates of a Logistic Model of Factors Influencing the Decision of Male- and Female-headed Households to Adopt Improved Maize Seed in Mbeya Region.
Male-headed households |
Female-headed households | |
Explanatory variable |
Parameter estimate _ (Wald-statistic) |
Parameter estimate _ (Wald-statistic) |
Intercept |
-3.7305 (1.26) |
2.3341 (1.1) |
Area under cash crops (acres) |
0.826 (1.23) |
1.3554 (1.74) |
Number of cattle |
0.6306 (2.94)*** |
0.0981 (0.08) |
Extension visit |
2.2431 (4.02)** |
1.0556 (1.13) |
Use of organic fertiliser |
1.3705 (0.71) |
-0.9208 (0.69) |
Household head age (yrs.) |
-0.0553 (0.97) |
-0.0578 (1.94) |
Household size (no.) |
0.5331 (0.96) |
-06112 (1.57) |
Level of education (yrs.) |
0.4546 (4.36)** |
-01921 (0.51) |
Area under maize (acres) |
0.4179 (0.94) |
0.5219 (1.08) |
Radio ownership |
0.2332 (0.06) |
2.2878 (1.94)*** |
Model Chi-square |
24.5* |
15.2*** |
Overall cases correctly predicated |
89% |
81% |
Sample Size (N) |
63 |
37 |
Note:*** = significant at p<0.1; ** =significant at p<0.05; * = significance at p<0.01.
The adoption of inorganic fertiliser was positively influenced by the use of organic fertiliser for male-headed households. Those farmers who used organic fertiliser were probably more aware of the importance of fertilisers for increased crop production. More male-headed households used organic fertiliser than female-headed households, which might have led to more male-headed households adopting inorganic fertiliser.
Household size had a significantly negative influence on the adoption of fertiliser for male-headed households, where larger households seem to use less fertiliser than smaller households at the ten per cent level. This means that although larger households have more labour than smaller households, fertiliser use is not labour-intensive nor does it have peak labour demand.
The district where farmers live, positively influenced the adoption of fertiliser for male-headed households. More farmers in Mbozi district adopted fertilisers, followed by farmers in rural Mbeya then by those in Rungwe districts. The extension services of the Ministry of Agriculture (MOA) and non-governmental organisations (NGOs) are concentrated in Mbozi and Rural Mbeya districts. There was no significant influence for female-headed households, because, as shown before, female-headed households benefit less from these extension services. Also, most large fertiliser stockists are found in these two districts and farmers plant during the wet season when fertiliser is abundant. In Rungwe District, unlike the other two districts, extension services emphasise dairy cattle production over maize production. Farmers in Rungwe plant their maize during the dry season. This is a period when most stockists have exhausted their fertiliser stock. Most fertiliser imports and sales start in November when the rest of the southern highlands start planting maize. Thus, even if farmers in Rungwe District wanted to purchase fertiliser, they would not be able to because it is unavailable during the dry season when they plant maize.
Table 5. Parameter Estimates of a Logistic Model of Factors Influencing the Decision of Male- and Female-headed Households to Adopt Inorganic Fertiliser in Mbeya Region.
Male-headed households |
Female-headed households | |
Explanatory variable |
Parameter estimate _ (Wald-statistic) |
Parameter estimate _ (Wald-statistic) |
Intercept |
-3.5151 (1.58) |
-0.6343 (0.01) |
Area under cash crops (acres) |
-0.029 (0.01) |
3.5982 (0.13) |
Number of cattle |
-0.094 (0.18) |
-9.9117 (0.13) |
Extension visit |
0.9121 (1.22) |
4.7478 (1.90) |
Use of organic fertiliser |
3.3727 (4.59)** |
73.373 (0.09) |
Household head age (yrs.) |
0.0263 (0.36) |
-0.2177 (1.63) |
Household size (no.) |
-0.6331 (3.04)*** |
3.2867 (1.68) |
District Rural Mbeya |
1.5992 (1.80) |
4.0722 (1.53) |
District Mbozi |
4.3979 (9.36)* |
3.3083 (1.12) |
Level of education (yrs.) |
0.1999 (2.03) |
-3.1231 (0.03) |
Area under maize (acres) |
-0.0557 (0.05) |
-0.5764 (0.20) |
Radio ownership |
1.6362 (3.12)*** |
-1.8767 (0.71) |
Model Chi-Square |
31.3* |
32.3* |
Overall cases correctly predicated |
78% |
89% |
Sample Size (N) |
63 |
37 |
Note:*** = significant at p<0.1; ** = significant at p<0.05; * = significance at p<0.01.
The predicted probabilities of adoption of technologies for changes in the explanatory variables that significantly affected adoption were calculated using the regression coefficients and the model. The probabilities were calculated keeping the continuous variables constant at their mean levels and the dummy variables zero. The predicted probabilities show us the effect of a change of the significant variables. The change in probabilities as a result of a change in the significant factors for improved maize seed and fertiliser for male-headed households are shown in Table 6.
The probability of adopting improved maize seed was seventy-four per cent for a male-headed household with one cow and no extension visit. For male-headed households the probability of adoption increased to ninety-six per cent if a farmer received an extension visit.
The adoption increased if the number of cattle that a farmer owned increased. The probability of adopting improved maize seed was sixty-two per cent for a male-headed household with two years of education and no extension visit. For male-headed households the probability of adoption increased to ninety-four per cent if a farmer received an extension visit. The adoption increased if the number of years of education increased. The probability of adopting improved maize seed was forty-six per cent for a female-headed household, and the probability of adoption increased ot ninety per cent if the female household owned a radio.
The probability of adopting fertiliser was about five per cent for a male-headed household with two adult members in the family and not living in Mbozi. For male-headed households the probability of adoption increased to seventy-nine per cent if a farmer lived in Mbozi. The adoption decreased if the number of adult members in the family increased. The probability of adopting fertiliser was two per cent for a male-headed household without a radio and not living in Mbozi. For male-headed households the probability of adoption increased to thirty-nine per cent if a farmer owned a radio and to ninety-eight per cent if he also lived in Mbozi. The probability of adopting fertiliser was two per cent for a male-headed household without a radio and not living in Mbozi. For male-headed households the probability of adoption increased to ten per cent if a farmer used organic fertiliser and to ninety per cent if he also lived in Mbozi.
Table 6. Impact of Significant Factors on the Predicted Probabilities of the use of Improved Maize Seed and Fertiliser by Male-headed Households
Changes in probabilities (%) | ||||
Improved maize seed |
Inorganic fertiliser | |||
Male-headed households |
Male-headed households | |||
Factor |
Extension |
Mbozi | ||
No |
Yes |
No |
Yes | |
Number of cattle 1 2 3 |
74 84 91 |
96 98 99 |
||
Years of education 2 4 6 |
62 80 91 |
94 97 99 |
||
Household size 2 3 4 |
4.5 2.5 1.5 |
79 68 54 | ||
Radio ownership No Yes |
2 39 |
64 98 | ||
Organic Fertiliser No Yes |
2 10 |
64 90 | ||
The results of this paper have shown that the adoption of improved maize seed and fertiliser by female-headed households is quite low. This was mainly because female household heads were less educated, owned fewer cattle and had less access to extension services and information on the improved technology. There is need for policies to address this imbalance between female- and male-headed households. The policies should target female-headed households for provision of extension services and information and special education programs as well as giving more education opportunities for children from such households. Thus, improvement and strengthening of extension services in the Mbeya region is crucial to realising higher levels of adoption, especially for female-headed households.
Both female- and male-headed households in Rungwe District were lower adopters of improved seed and fertiliser compared to farmers in Mbozi and Rural Mbeya districts. The recommended maize technology might not be suitable for Rungwe District where maize is planted during the dry season and where extension services emphasise dairy cattle production. There is need for research to review the recommended maize production technology and for extension to emphasise crop/livestock interactions if higher levels of adoption are to be realised for both female-and male-headed households.
The results of this study are in line with other studies in Africa, which have shown that male-headed households rather than female-headed households had access to yield-increasing inputs (land, labour, capital or credit, fertiliser or manure, improved seed, extension training/contact) and access to education and markets (Dean and Gladwin, 1991; Gittinger, 1990; Gladwin, 1996 and 1997; Quisumbing et al., 1995; Saito, 1994). Others, have observed that gender disparities in access to productive resources in agriculture exist and persist because of legal, social, and institutional factors that create barriers for women (Quisumbing, et al., 1995).
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1 The cash crops included pyrethrum, coffee, and tea.