CHAPTER 4 FACTORS DETERMINING SMALLHOLDER FARMERS’ WILLINGNESS TO PAY FOR A
4.4. Results and discussions
4.4.2. Logit results of WTP for a one-tonne metal silo
70
Table 4.3: Differences of continuous explanatory variables between willing and non-willing groups
Variable (mean) Willing Not willing Total p-value
Hhsize 6.0 5.8 5.9 ns
Age 47 54 51 ***
Educyears 8 6 7 ***
land size 3.8 2.8 3.3 ***
EQUIPValue 2479 1730 2079 ns
Nonfoodcrop_quantity 842 259 530 ***
perc_loss 10 7 8 **
ValueANIM_PRODsales 575 252 402 **
Vegincome 327 202 260 **
Value_livestock 2011 1573 1777 ns
***, **, *, ns, signify statistical significance at 1%, 5%, and 10% probability levels and not significant, respectively
71
Table 4.4: Logit parameter estimates of factors influencing WTP for a metal silo
Variable Coefficient Marginal Effect
Gender -0.00194 0.00048ns
Educyears 0.01535 0.00382 ns
Hhsize -0.05668 -0.01409 ns
marital_status 1.28292 0.29871***
Age -0.02366 -0.00588**
Vegincome 0.00077 0.00019*
Informalactivity 0.64751 0.16044*
Salariedactivity 0.38648 0.09628ns
Nonfoodcrop_quantity 0.00060 0.00015***
Value_livestock 0.00007 0.00002 ns
ValueANIM_PRODsales 0.00018 0.00005ns
EQUIPValue -0.00007 -0.00002*
Landsize 0.08543 0.02124 ns
perc_loss 0.02497 0.00621*
Constant -0.90568
N 249
Log Likelihood -137.769
Wald Chi-Square value 47.61***
Source: Model Output. ***, **, *, ns signify significant at 1%, 5%, 10% level and not significant, respectively.
Marital status of the household head showed a positive and significant effect on the probability of WTP for the metal silo (p<0.01). The marginal effect value indicated that the probability of WTP for metal silo storage technology for farmers who are married increases by 30%, holding all other factors constant. The result is consistent with the finding of Umar et al. (2014) on the determinants of adoption of improved maize varieties in Nigeria. Married household heads share the risks of investing in new technologies by combining their resources.
As expected, age had a negative and statistically significant influence on the probability of WTP (p<0.05). This result is in line with a study done by Gang and Ping (2012) on WTP for
72
information. The younger the smallholder farmer, the stronger the WTP for storage technology. This implies that younger farmers may have longer planning horizons and hence more likely to invest in metal silo storage technology. The marginal effects results indicated that a one year increase in the age of a respondent will reduce the probability of willingness of the farmer to pay for the metal silo by 0.6%, holding all other factors constant.
Vegetable income had a positive and statistically significant effect on probability to be WTP for a metal silo storage technology (p<0.10). The marginal effects results show that a dollar increase in vegetable income increases WTP for metal silo storage by 0.02%, all other factors being constant. This shows that vegetable income enables farmers to adopt a new storage innovation. Oladele (2008) found similar results in a study of willingness to pay for extension services in Nigeria. Income was a significant determinant of farmers WTP for extension services.
Participation of smallholder farmers in informal activities had a positive and statistically significant effect on the probability of WTP for a metal silo (p<0.1). Informal activities boost farmers‟ income, therefore, making them more willing to invest in new storage technologies.
The result supports findings from Tolera et al. (2014), Abu et al. (2011) and Oladele (2008).
Furthermore, the marginal effect shows that the probability of being WTP for the metal silo storage technology for farmers who participated in informal activities increased by 16%, ceteris paribus.
The quantity of non-food crops produced by respondents was statistically significant and positively influenced WTP for a metal silo storage technology (p<0.01). The result from the logit model shows that the probability of WTP for metal silo increased by 0.01% for a 1kg increase in the quantity of non-food crops produced, ceteris paribus. Non-food crops are cash crops, hence they enhance financial capacity of farmers to demand storage technologies.
Farmers who produce non-food crops can use the income they get from selling these crops to invest in new storage innovations. This result is in conformity with findings from other studies (IFPRI, 2011; Kong et al., 2014) although they used income from agricultural crops rather than quantity of crops grown. A tonne increase of cash crops produced results in a 15%
increase in the probability of WTP for a metal silo.
73
The value of household equipment had a negative but statistically significant effect on WTP for metal silo storage (p<0.10). This implies that households with a higher value of equipment were less likely to adopt the metal silo for grain storage than their counterparts.
This result was not expected and it could mean the equipment households own favoured the production of other crops instead of maize grain hence households were less likely WTP for the storage technology. The marginal effects result indicated that the probability of WTP for metal silo decreased by a factor of 0.002% as the value of equipment owned increases by a
$1000, ceteris paribus.
Another variable that influenced WTP for metal silo is the percentage physical grain storage loss. The amount of physical grain storage loss (%) in the study area directly and significantly influenced WTP for metal silo storage technology (p<0.1). The probability of WTP for metal silo increases as the percentage grain storage loss increases because farmers would tend to invest in technologies that reduce or curb storage losses. This probability increased by 0.006 for a one percent grain storage loss. Storage losses reduce the amount of grain available for consumption and also for sale. This result thus supports Bokusheva et al. (2012), who reported that household self-sufficiency in maize is an important factor for explaining farmers‟ demand for metal silos.