Unlocking the potential: challenges and factors influencing the use of ICTs by smallholder maize farmers in Zimbabwe

Figures & data

Figure 1. Conceptual Framework on the use of mobiles and computers in agriculture. (Source: Authors’ conceptualization).

Figure 2. Map for Marondera, Zimbabwe. (Source: Dzwairo et al., 2006).

Table 1. Definitions and descriptions of variables.

Variable	Description	Measurement	Expected results
Age	The age of the farmer.	Years	Positive/ negative
Maritalstatus	Marital status of the farmer.	0 = single, 1 = married, 2 = divorced, 3 = widow	Positive/negative
Household size	Household size	Number of people living together in one dwelling	Positive
Farming experience	Number of years in farming	Years	Positive
Gender	Male or female.	1 = male, 0 = female	Positive/negative
Education level	Number of years in formal education.	Years in formal education.	Positive
Farm size	Total hectares of land owned by a farmer.	Hectares	positive
Non-farm activities	Having non-farm activity.	1 = yes, 0 = no	positive
Access to credit	Having received credit	1 = yes, 0 = no	positive
Extension visit	Having frequent extension visits	1 = yes, 0 = no	positive
Remittances	Having received remittance	1 = yes, 0 = no	positive

Table 2. Mobile and computer use for agriculture.

Mobile use for agriculture	Freq	Percentage
n = 155
Yes	73	47.10
No	82	52.90
Computer use for agriculture
Yes	34	21.94
No	121	78.06

Table 3. Comparison between mobile ownership and mobile use for agricultural purposes.

Mobile ownership	Mobile use for agriculture		Total
	Yes	No
Yes	73	64	137
No	0	18	18
Total	73	82	155
Pearson chi2(1) = 18.1298; Pr = 0.000

Table 4. Comparison between computer ownership and computer use for agricultural purposes.

Computer ownership	Computer use for agriculture		Total
	Yes	No
Yes	34	32	66
No	0	89	89
Total	34	121	155
Pearson chi2(1) = 58.7315; Pr = 0.000

Table 5. Chi-square tests of association regarding mobile use in agriculture.

Variables		Frequency for mobile users in agriculture	Frequency for non-mobile users in agriculture	Chi2 value	p-value
Gender	Male	34	54	5.8486	.016
	Female	39	28
Marital status	Unmarried	11	9	6.0439	.094
	Married	42	61
	Divorced	9	11
	Widowed	3	9
Received Remittances	Yes	41	31	5.2334	.022
	No	32	51
Received credit	Yes	25	42	4.5334	.033
	No	48	40

Table 6. Chi-square tests of associations regarding the use of computers in agriculture.

Variables		Frequency for computer users in agriculture	Frequency for non-computer users in agriculture	Chi2 value	p-value
Gender	Male	15	73	2.8429	.092
	Female	19	48
Having Non-Farm Activity	Yes	12	63	2.9896	.084
	No	22	58
Received Credit	Yes	19	48	2.8429	.092
	No	15	73

Table 7. Challenges associated with the use of ICTs in agriculture.

Challenge	Strongly agree	Agree	Neutral	Disagree	Strongly disagree	Score	Rank
Electricity shortage	119	36	0	0	0	274	1
Network challenge	109	19	8	19	0	218	2
Scarce transport to access the internet	32	63	42	18	0	109	3
Insufficient repairing facilities	36	62	11	46	0	88	4
Do not afford ICT gadgets	44	30	20	61	0	57	5
Inadequate knowledge	30	43	35	47	0	56	6
Lack of time	21	38	32	64	0	16	7
Literacy rate	16	23	40	76	0	−21	8

Table 8. Average marginal effects showing factors affecting mobile use in agriculture.

Variable	dy/dx	Std. Err	z	P > z	95% Confidence Interval
Age	−0.0014	.0042498	−0.32	0.749	−.0096873	.0069715
Gender
Male	−0.1256	.0846332	−1.48	0.138	−.2914584	.0402975
Marital status
Married	0.0452	.1191137	0.38	0.704	−.1882489	.2786683
Divorced	0.1068	.1464042	0.73	0.466	−.1801274	.3937666
Widow	0.4548	.1553009	2.93	0.003***	.1503847	.7591531
Household size	−0.0073	.0192211	−0.38	0.703	−.0449926	.0303527
Farming Experience	−0.0119	.006632	−1.80	0.072*	−.0249398	.0010572
Farm size	0.0127	.035164	0.36	0.718	−.0562036	.0816365
Non-farm activity
Yes	−0.0237	.0824088	−0.29	0.774	−.1852182	.1378186
Remittance
Yes	0.2428	.0781253	3.11	0.002***	.0896914	.3959368
Access to credit
Yes	−0.1546	.0803807	−1.92	0.054*	−.3121948	.0028919
Extension Visit
Yes	0.2429	.0926518	2.62	0.009***	.061385	.4245734
Number of obs = 155, LR chi2(12) = 29.06, Prob > chi2 = 0.0039, Pseudo R2 = 0.1356

*** 1% LOS, ** 5% LOS and * 10% LOS.

Note. dy/dx for factor levels is the discrete change from the base level.

Table 9. Average marginal effects showing the factors affecting computer use in agriculture.

Variable	dy/dx	Std. Err	z	P > z	95% Confidence Interval
Age	−0.0069	.0028953	−2.38	0.017**	−.0125783	−.0012287
Gender
Male	−0.1894	.0761186	−2.49	0.013**	−.3386523	−.0402729
Marital status
Married	0.1007	.0817424	1.23	0.218	−.0595118	.2609127
Divorced	0.0907	.1183511	0.77	0.443	−.1412032	.3227247
Widow	−0.0283	.1396849	−0.20	0.839	−.3021674	.2453872
Education Level
Primary	−0.2548	.2011318	−1.27	0.205	−.6490696	.1393524
Secondary	−0.2204	.1924958	−1.15	0.252	−.59772	.1568495
Tertiary	0.0011	.2118894	0.01	0.996	−.4141075	.4164836
Non-farm activity
Yes	−0.0296	.0697953	−0.43	0.671	−.1664693	.1071231
Access to credit
Yes	0.1178	.0702885	1.68	0.094*	−.0199102	.2556155
Extension Visit
Yes	0.1368	.0761379	1.80	0.072*	−.0123776	.2860774
Number of obs = 155, LR chi2(11) = 20.96, Prob > chi2 = 0.0031, Pseudo R2 = 0.1525

*** 1% LOS, ** 5% LOS and * 10% LOS.

Note. dy/dx for factor levels is the discrete change from the base level.

Dzwairo, B.,Hoko, Z.,Love, D., &Guzha, E. (2006). Assessment of the impacts of pit latrines on groundwater quality in rural areas: A case study from Marondera district, Zimbabwe. Physics and Chemistry of the Earth, Parts A/B/C, 31(15-16), 779–788. 10.1016/j.pce.2006.08.031

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Data availability statement

The data and materials supporting the results and analysis presented in this paper is readily available upon reasonable request.