Factors influencing choice of grain storage technologies

CHAPTER 3 FACTORS INFLUENCING SMALLHOLDER FARMERS’ CHOICE OF STORAGE

3.3 Results and discussion

3.3.3 Factors influencing choice of grain storage technologies

Multicollinearity tests were done before the model was estimated (Appendix B). The results show no serious correlation problems. A total of thirteen independent variables were used in the MNL model. The MNL results (Table 3.4), show that the age of household head, marital status, total grain stored, per capita value of non-food crop quantity, per capita business and wages income, and extension access influenced the choice of insecticide storage technology relative to no insecticide storage technology among smallholder farmers in the study area.

Estimated coefficients for age, marital status, total grain stored and per capita value of non- food crop were positive and statistically significant for the use of insecticide technologies relative to no insecticide technologies. On the other hand, estimated coefficients for per capita business wages and income and extension access were negative and statistically significant in influencing the choice of insecticide technology relative to no insecticide technology.

53.96%

30.22%

15.83%

Insecticide

technology No

insecticide Other

technologies

The results show that education years, total grain stored, per capita business and wages income and access to extension services influenced the choice of other storage technologies relative to no insecticide technologies. The estimated coefficients of education years and total grain stored were positive and statistically significant while estimated coefficients of per capita business and wages income and extension access were negative and also statistically significant in influencing the choice of other storage technologies relative to no insecticide storage technologies.

Table 3.4: Factors influencing choice of storage technology used among smallholder farmers Variable Insecticide Technology Other Technologies

Sex 0.18152^ns 0.19446^ns

Age 0.01634* 0.01084^ns

Mar_status 0.63177* -0.08001^ns

Educyears 0.07452^ns 0.14743***

Ttstored 0.00029** 0.00023*

PCValuNONFOOD_Crop 0.00052** -0.00037^ns

PCbusiwages_income -0.00066** -0.00057*

PCLivestock_value -0.00020^ns -0.00036^ns

PCLandsize 0.02631^ns 0.00284^ns

Extension_acc -0.77778*** -0.72887**

PCVegetable_income -0.00038^ns -0.00042^ns

PCEquip_value 0.00017^ns 0.00038^ns

Own_cell 0.36822^ns 0.84218^ns

Constant -1.71162*** -2.89826***

Base outcome No insecticide

Number of observations 417

Wald chi2 (26) 55.92

Prob > chi2 0.0006

Pseudo R2 0.0720

Log pseudo-likelihood -381.66577

*** Significant at 1%, ** significant at 5%, *significant at 10%, ns signify not significant

The marginal effects (Table 3.5) implication of these results indicates that the probability of using insecticide technology relative to no insecticide increased by about 16.6% if a farmer was married. In this study area, the majority of smallholder farmers are married and often married couples combine resources and complement each other‟s efforts towards production and utilization of resources in technology acquisition. Married farmers can also share the related risk of adopting improved storage technologies thus are more flexible in exploring better storage technologies. This result conflicts with the findings of Abudulai et al. (2014), who reported that marital status had no influence on the choice of cowpea storage practices in Ghana.

In terms of storage, a kilogram increase in the total quantity of grain stored increased smallholder farmers‟ probability of using the insecticide technology relative to the no insecticide storage technology by about 0.005%. This means that farmers who store larger quantities of grain are more likely to use the insecticide storage technologies than those who store smaller quantities of grain. Preservation of stored grain becomes more important with the amount of grain to be stored. Abiodun et al. (2012) reported a similar result where the quantity of maize grain stored significantly influenced the choice of storage technologies among farmers.

More so, the probability of using the insecticide technology relative to the no insecticide technology increases by 0.015% with a US$1 increase in per capita value of non-food crop that smallholder farmers produced. Income from non-food crops improves the financial situation of smallholder farmers thus making them better able to choose appropriate storage technologies. Results from other studies on technology adoption indicate that the non-food crops income has a positive influence on technology adoption (Phiri et al., 2003; Keil et al., 2005).

On the other hand, the probability of using the insecticide technology relative to the no insecticide technology decreased by 0.011% if a farmer had income from business and wages. Business and salaried job activities are alternative sources of livelihood for smallholder farmers, which compete with maize production and thus are negatively correlated with storage. This result corroborates the findings of Kabwe et al. (2009) in Zambia where non-farm income had a statistically significant and negative relationship with technology adoption. Smallholder farmers‟ access to extension services decreased the probability of

using the insecticide technology relative to the no insecticide by 14%. Smallholder farmers in the study areas received extension training on the use of hermetic storage technologies and this could have negatively influenced the role of extension on farmers‟ preferences of insecticides relative to no insecticide technologies.

Conversely, the marginal effect implication of the education years‟ coefficient is that a one year increase in education years increases the probability of using the other technologies by about 1.3% relative to the no insecticides technologies among smallholder farmers. This finding met Apriori expectations. Education improves the capabilities of farmers to comprehend and acquire new knowledge and skills required in managing new storage technologies. Therefore the more educated a smallholder farmer is the more able to comprehend and acquire new skills he or she becomes. Similarly, Abiodun et al., (2012);

Maonga et al., (2013) and Achiyeng (2014) reported that education significantly influenced the use of improved maize storage technologies among farmers. However, Abudulai et al.

(2014) and Fakayode et al. (2014) did not observe any significant relationship between education level and use of cowpea storage technologies in Ghana and Nigeria respectively.

Table 3.5: Marginal effects of factors of choice of storage technology

Variable Insecticide Technology Other Technologies

Sex 0.03032^ns -0.00883^ns

Age 0.00307^ns -8.75e-06^ns

Mar_status 0.16135** -0.06675^ns

Educyears 0.00543^ns 0.01282**

TTstored 0.0000509** 4.92e-06^ns

PCValuNONFOOD_Crop 0.00016** -0.00009^ns

PCbusiwages_income -0.00011* -0.00002^ns

PCLivestock_value -0.00002^ns -0.00003^ns

PCLandsize 0.00622^ns -0.00193^ns

Extension_acc -0.13193** -0.02927^ns

PCEquip_value 7.95e-06^ns 0.00004^ns

Own_cell 0.03052^ns 0.06816^ns

PCVegetable_income -0.000058^ns -0.00002^ns

Base outcome No insecticide

Number of observations 417

Wald chi2 (26) 55.9

Prob > chi2 0.0008

Pseudo R2 0.0720

Log pseudo-likelihood -381.676773

***significant at 1%, ** significant at 5%, *significant at 10%, ns not significant

Dalam dokumen Economic analysis of determinants of grain storage practices and implications on storage losses and household food security in Makoni and Shamva Districts in Zimbabwe. (Halaman 64-68)