Factors affecting farmers’ hiring decisions on agricultural mechanization services: A case study in Ethiopia

Figures & data

Figure 1. Map of the study area.

Table 1. Definition of variables and hypothesis

Variables	Description	Min	Max	Mean	SD	Percentage frequency with Dummy = 1	Percentage frequency with Dummy = 0	Assumption
Dependent variables
HIRETRAC (Hire = 1)	Farmers who hired tractor ploughing					39.10	60.90	N/A
HIRECOMB (Hire = 1)	Farmers who hired combine harvester					58.60	41.40	N/A
Independent variables
GENDER (male = 1, female = 2)	Gender of the household					81.20	18.80	-
EDU (Literate-1; Illiterte-2)	Level of education of the household head					66.20	33.80	+
AGE (years)	Age of the household head	25	70	42.9	9.71			-
FARMEXP (years)	Years of farming experience of the respondent	5	50	23.4	10.18			+
LABOURACT (number)	Number of people with age range of 15–60 years living in one household	0	6	2.59	1.18			-
LANDSIZE (ha)	Total cultivated land (ha)	1	5.25	1.96	1.04			+
RENTLAND (ha)	Size of additional land rented in by household	0	4	0.63	0.84			+
OXEN (number)	Number of oxen owned by household	0	8	2.95	1.88			-
OFFINCOME (1=yes; 0=no)	Income generated outside the farm					25.60	74.40	+
FARMGOAL seed producer (1=yes)	Seed producer, respondent					24.10	75.90	+
NEBRINFLU (1=yes; 2=no)	Hire mechanization service because of another farmer influence					21.80	78.20	+
INSTITINFLU (1=yes; 2=no)	Hire mechanization service because of extension worker influence					33.80	66.20	+
BROKERINFLU (1=yes; 2=no)	Hire mechanization service because of brokers influence					14.30	85.70	+
LABORSHORT (1=yes; 2=no)	Hire tractor because of labor shortage					24.10	75.90	+
FASTOPR (1=yes; 2=no)	Hire tractor because of time constraint and faster operation					10.50	89.50	+
LABORCOST (1=yes; 2=no)	Hire combine harvester because of high labor cost					39.80	60.20	+
UCERTAINITY (1=yes; 2=no)	Hire mechanization not to lose yield from unexpected weather					47.40	52.60	+

Table 2. Demographic characteristics of the respondents

Variable	Percent (n = 133)
Sex
Male	81.2
Female	18.8
Educational Status
Illiterate (cannot read and write)	33.8
Literate	66.2
Land size
Up to 1 ha	18.8
Above 1 ha	81.2
Land ownership
Owned land	89.5
Rented land	10.5
Oxen owned
None	12.8
1–2 oxen	32.3
3–4 oxen	37.6
More than 4 oxen	17.3
Farming experience
Up to 10 years	10.5
11 to 25 years	50.4
More than 25 years	39.1
Household size
Up to two members	9.8
Three to four members	30.8
More than four members	59.4
Economically active family members
Up to two members	50.4
Three to four members	42.1
More than four members	7.5
Hire mechanization services
Only tractor ploughing	18.8
Only combine harvesting	38.3
Both tractor and combiner harvester	20.3
No hire	22.6

Source: Field survey (2021).

Table 3. Perceptions and social factors

Variable	Percent (n=133)
Influence for making hiring decision
Farmers	22
Institutions	64
Brokers	14
Fee opinion for hiring tractor ploughing services
Fair	2.3
Costly	97.7
Fee opinion for hiring combine harvesting services
Fair	8.3
Costly	91.7

Source: Field survey (2021).

Figure 2. Cultural norms for operation days.

Figure 3. Farmers’ perceptions towards brokers.

Table 4. Factors affecting farmers’ tractor services hiring decision

Variables	Coefficient	S.E.	Wald	Sig.	Exp(B)
Gender of household head	2.587	1.73	2.240	0.135	13.288
Age of the household head	0.17	0.16	1.164	0.281	1.185
Education level of household head	−0.333	1.60	0.043	0.835	0.717
Years of farming experience	0.034	0.12	0.075	0.785	1.034
Economically active labour size	−3.76	1.28	8.583	0.003*	0.023
Total farm land size	1.527	0.80	3.668	0.055	4.603
Size of land rented-in	−1.575	1.01	2.459	0.117	0.207
Number of oxen	−1.509	0.62	6.021	0.014*	0.221
Off-farm income	5.091	2.08	5.967	0.015*	162.546
Goal of farming	3.734	1.80	4.288	0.038*	41.837
Shortage of labour	1.97	2.11	0.875	0.350	7.173
Time constraint and faster operation	2.842	1.78	2.561	0.110	17.143
Broker’s influence	1.714	2.19	0.613	0.434	5.551
Institutional influence	2.421	1.19	4.113	0.043*	11.262
Intercept	−2.436	4.59	0.282	0.596	0.087

Notes: *Significant at 5% level. −2 Log likelihood = 33.461, omnibus tests of model coefficients (chi-square, df, sig) = 144.541,14,0.000).

Hosmer and Lemeshow Test = 0.999 sig. 0.998 Nagelkerke R square = 0.898, percentage of correct predictions = 95.5%.

Table 5. Factors affecting farmers’ combine harvesting services hiring decision

Variables	Coefficient	S.E.	Wald	Sig.	Exp(B)
Gender of household head	−1.504	3.22	0.218	0.641	0.222
Age of the household head	−0.178	0.28	0.421	0.517	0.837
Education level of household head	−0.213	1.91	0.012	0.911	0.808
Years of farming experience	−0.169	0.27	0.394	0.530	0.845
Household size	1.879	1.07	3.063	0.080	6.545
Total farm land size	1.113	1.34	0.694	0.405	3.042
Rent in additional land	1.119	1.83	0.375	0.540	3.061
Total area planted with wheat	3.681	1.98	3.472	0.062	39.683
Off-farm income	0.754	4.26	0.031	0.860	2.125
Goal of farming	2.496	4.08	0.374	0.541	12.131
Harvesting labour cost	4.978	2.32	4.595	0.032*	145.231
Neighbour’s influence	0.925	1.84	0.254	0.615	2.523
Institutional influence	2.766	2.04	1.832	0.176	15.889
Uncertainty-weather	4.912	2.46	3.995	0.046*	135.957
Intercept	−9.404	7.42	1.606	0.205	0.000

Notes: *Significant at 5% level. −2 Log likelihood = 16.880, omnibus tests of Model coefficients (chi-square, df, sig) = 163.499,14,0.000).

Hosmer and Lemeshow Test = 0.687 sig. 1.000 Nagelkerke R square = 0.953, percentage of correct predictions = 97.7%.