Factors influencing the use of agricultural information by Vietnamese farmers

Abstract

This study examined the factors affecting the use of agricultural information by Vietnamese cereal farmers. A sample size of 245 cereal farmers was selected and surveyed. The participants were classified into small, medium and large cereal farmers. Descriptive statistics and inferential statistics were applied to analyse the data. The results show that large farmers used information from preferred traders, extension workers, input suppliers, mobile phones and the Internet; smaller farmers employed information from cooperatives, the farmers’ union and television. Large farmers had more access to information on soil preparation, pest/weed control, harvesting, and market and input prices, while small farmers accessed information on inorganic fertilisers. The regression analysis shows that the characteristics of gender, farming experience, participation in training programmes and community-based organisations, access to the Internet and television, information obtained from preferred traders, the Commune Peoples’ Committee, extension workers, cooperatives, the farmers’ union and input suppliers significantly affected farmers’ use of agricultural information (χ² = 140.784, p < .000).

Keywords

Cereal farmers factors agricultural information regression analysis Vietnam

Introduction

Agricultural information, including new and more efficient farming practices, the market price of inorganic fertilisers, pest and disease management strategies, temperature, rainfall and new crop varieties, is considered a key factor in agricultural production. It plays a significant role in improving the productivity, quality and effectiveness of agricultural production. Some research (Haile et al., 2019; Mokotjo and Kalusopa, 2010) suggests that farmers need appropriate, reliable and up-to-date information to make the right decisions and successfully manage farm-related issues leading to enhanced agricultural production. Prior studies (Abebe and Cherinet, 2018; Hoang, 2020a) show that farmers in developing countries have sourced a wide range of agricultural information from agricultural extension officers, preferred collectors, local markets, friends and other farmers to market their agricultural produce. Farmers in developing countries seek different types of agricultural information, including, but not limited to, improved production practices, new technologies, pest and disease management, and marketing strategies for agricultural produce (Ajani, 2014; Aonngernthayakorn and Pongquan, 2017). Recently, many farmers in developing nations, including Vietnam, have used information and communications technology (ICT) tools such as mobile phones, radio and television broadcasts to increase agricultural information for their production and marketing activities (Hoang, 2020d; Mittal and Mehar, 2016). According to Alavion and Allahyari (2012), ICT can assist farmers in accessing agricultural information such as marketing information more easily and in a more timely manner. Some researchers (e.g. Treinen and Elstraeten, 2018) suggest that the utilisation of ICT tools provides farmers with a good chance to enhance crop productivity and develop marketing networks through improved access to important information on agricultural services, government policies about agricultural development and markets, potential market choices and agribusiness.

However, many farmers in developing nations continue to face challenges in utilising important agricultural information (Phiri et al., 2018; Zhang et al., 2016). Several reasons – including the possession of small-scale farmland, financially limited access to new and reliable technology services, and a lack of knowledge and skills to use up-to-date and reliable information due to a low level of education – could account for this situation (Ghatak and Roy, 2007; Phiri et al., 2018; Tadesse, 2008). According to some researchers (Aonngernthayakorn and Pongquan, 2017; Koskei et al., 2013), a number of factors associated with the socio-economic characteristics of farms/households can affect farmers’ use of agricultural information. In order to impart important agricultural information to farmers and facilitate an improvement in their production and marketing, it is necessary to understand the factors that affect their use of agricultural information (Aonngernthayakorn and Pongquan, 2017; Magesa et al., 2020; Opara, 2008). However, the determinants of cereal farmers’ use of agricultural information in developing countries, including Vietnam, are not scientifically well documented. There is no known study that has looked at factors that shape cereal farmers’ use of agricultural information for production and marketing.

In recent years, the government of Vietnam has encouraged farmers to use up-to-date and reliable agricultural information with the aim of improving agricultural productivity and farmers’ income (Kaila and Tarp, 2019; VietNamNews, 2017). A number of agricultural activities have been implemented in Vietnam by extension workers to disseminate up-to-date and reliable agricultural information to farmers (Ministry of Agriculture and Rural Development, 2018). Vietnamese farmers are encouraged to use ICT tools such as mobile phones (Hoang, 2020d) to access agricultural information. However, the type and intensity of use of agricultural information by Vietnamese cereal farmers is not well documented. Little is formally known about how agricultural information is used by cereal farmers. The factors that impact cereal farmers’ use of agricultural information for production and marketing must be understood in order to determine how best to deliver agricultural information that meets their needs. The primary purposes of this study are to (1) describe the socio-economic profile of Vietnamese cereal farmers; (2) evaluate the level of use of agricultural information by the farmers; and (3) determine the factors that affect the farmers’ use of agricultural information in production and marketing.

Literature review

Research into farmers’ use of agricultural information such as marketing information, crop varieties and new farming practices has been undertaken in some developing nations (Fidelugwuowo, 2021; Hoang, 2020d; Koskei et al., 2013; Lwoga et al., 2011; Magesa et al., 2020; Mahindarathne and Min, 2019; Ndimbwa et al., 2020). The mainstream literature indicates that the use of agricultural information varies among farmers, and the extent of farmers’ use of agricultural information is likely to be connected with their socio-economic characteristics, including education level, household size, income, farming experience and access to ICTs (Aonngernthayakorn and Pongquan, 2017; Diekmann et al., 2009; Koskei et al., 2013; Olanrewaju and Farinde, 2014). However, little research has assessed the intensity of farmers’ use of agricultural information for production and marketing. In addition, there is no known research that has examined the use of agricultural information by cereal farmers in developing countries, including Vietnam. Moreover, the findings documented in the existing literature are not in agreement across the studies. Koskei et al. (2013) investigated the use of agricultural information by smallholder tea farmers in Kenya and found that the Kenyan smallholder tea farmers’ use of agricultural information was positively associated with their education level. However, in a study by Aonngernthayakorn and Pongquan (2017), who examined the use of agricultural information by rice farmers in Thailand, it was found that Thai rice farmers’ education level had no impact on their use of agricultural information. Koskei et al. (2013) also found that household size and off-farm income had positively significant relationships with Kenyan farmers’ use of agricultural information. In contrast, Aikins (2014) investigated the use of agricultural information by cocoa farmers in Ghana and found that Ghanaian cocoa farmers’ use of agricultural information was negatively associated with their household size. The author also found that the farmers’ use of agricultural information was not associated with their farming experience, which is not consistent with the findings of Aonngernthayakorn and Pongquan (2017) or Diekmann et al. (2009), who undertook a study in the USA. Ajani (2014) examined the use of ICT for agricultural transformation in sub-Saharan Africa and found that farmers’ use of agricultural information was positively associated with their access to the Internet. The results from a study by Hoang (2020d) undertaken in Vietnam suggest that there is a relationship between smallholders’ use of agricultural information and their use of mobile phones, radio networks/broadcasts and television. However, Hoang’s (2020d) research did not explore what factors had influenced smallholders’ use of agricultural information.

Taken together, it can be seen that the extent of farmers’ use of agricultural information is context-dependent. Although farmers’ use of agricultural information is influenced by their characteristics, the way these characteristics influence their use varies, depending on the contexts and farming systems in which the farmers operate. An investigation into the factors that influence Vietnamese cereal farmers’ use of agricultural information will provide useful insights into the factors that shape farmers’ use of agricultural information for production and marketing in developing countries. Such insights will contribute greatly to the literature examining the determinants of farmers’ use of agricultural information, as well as highlight aspects that need to be considered when developing policies to improve farmers’ use of agricultural information for production and marketing in developing nations.

Methodology

Study site and description

This research was conducted in the Tuy Phuoc district of Vietnam (see Figure 1; Vietnam Department of Survey, Mapping and Geographic Information (DSM), 2017). The Tuy Phuoc district covers an area of 216.77 km². Agricultural production is the main component in the district and made up about 83% of the gross output of the district in 2019 (Binh Dinh Statistical Office, 2020). More than 90% of the population of the Tuy Phuoc district reside in rural areas and participate in farming activities (Binh Dinh Statistical Office, 2020). According to the Tuy Phuoc District People’s Committee (2019), promoting the development of agricultural production is the focus of the development policy of the district. The agriculture in the Tuy Phuoc district comprises crops, livestock and fisheries, with crops being the most important activity for the majority of farmers. Cereals – including rice, maize and millet – are the key crops in the Tuy Phuoc district and hence central for agricultural development (Tuy Phuoc District People’s Committee, 2019). The cereal farmers mainly produce and market rice, maize, millet and potatoes. Generally, they often crop rice and maize in the spring season and then plant rice, millet and potatoes in the summer season. The district of Tuy Phuoc was chosen for this study because it is highly representative of cereal-related production and marketing systems in the central area of Vietnam. These production and marketing systems are portrayed to be a significant source of agricultural information used by farmers in Vietnam.

Figure 1.

The study region (Vietnam Department of Survey, Mapping and Geographic Information (DSM), 2017).

Sample, instruments, data collection and data analysis

In order to investigate the use of agricultural information by cereal farmers in the district of Tuy Phuoc, a cross-sectional survey design was applied (De Vaus, 2014). A multi-stage sampling strategy was used in this research. A stratified sampling method depicted in the relevant literature (Agresti and Finlay, 2009; De Vaus, 2014) was then employed to categorise small (≤ 3500 m²), medium (3600–6000 m²) and large farm (> 6000 m²) households. Finally, a simple random sampling technique was used to choose 245 farm households for the study, consisting of 80 small, 75 medium and 90 large households.

A four-section quantitative structured questionnaire was developed to gather data. The first section of the questionnaire comprised statements on sources of agricultural information used for cereal production and marketing. The second section consisted of statements on the intensity of agricultural information used by farmers, and this was measured by a 5-point Likert scale (1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often). The third section contained statements on the types of agricultural information used for cereal production and marketing. The statements on the types of agricultural information used for producing and marketing cereals were prepared based on the relevant literature (Aonngernthayakorn and Pongquan, 2017; Hoang, 2020c; Tuy Phuoc District People’s Committee, 2019) and listed beforehand. The final section gathered socio-economic information on the cereal farmers. The questionnaire was piloted with 15 cereal farmers and reviewed by a group of experts from a university for face and content validity. Five enumerators were employed to manage the questionnaires in the field, and the survey was implemented from August to November 2020. The data was analysed using SPSS version 20. Descriptive statistics and inferential statistics, including chi-square tests and analysis of variance (ANOVA), were used (Agresti and Finlay, 2009). The multinomial logistic regression analysis technique was employed to determine the factors that affected cereal farmers’ use of agricultural information. According to scholars (Deressa et al., 2009; Friedman et al., 2010), the multinomial logistic regression analysis technique should be used when the dependent variable of the logistic regression analysis has more than two ordinal or nominal categories. For this research, the multinomial logistic regression model employed was

Logit
 
(Pi)
 
=
 
Ln [\frac{Pi}{1 - Pi}] = β o + β 1 X 1 + β 2 X 2 + β 3 X 3 + \dots β i Xi + e

Ln [Pi/1− Pi] = logit for farm sizes; Pi = non-use of agricultural information; 1− Pi = use of agricultural information; $X i =$ independent variable; $β i =$ parameters to be estimated; and e = error term.

The independent variables were carefully selected from the relevant literature (Aikins, 2014; Ajani, 2014; Aonngernthayakorn and Pongquan, 2017; Diekmann et al., 2009; Hoang, 2020d; Koskei et al., 2013; Magesa et al., 2020; Minten et al., 2014) and based on the key characteristics of the cereal farmers in the study area. Table 1 describes the characteristics of the hypothesised variables in the farmers’ use of agricultural information.

Table 1.

Hypothesised variables in farmers’ use of agricultural information.

Variables	Explanation	Measurement
Dependent variables
Agricultural information users	Use of agricultural information	1 = small farmer 2 = medium farmer 3 = large farmer
Independent variables
AGE	Age of farmers	Numeric^a
EDULEV	Level of education of farmers	Numeric^b
FAREXP	Farming experience	Numeric^c
INCOME	Total annual income	Vietnamese dong
GENDER	Gender of farmers	1 = male, 0 = female
CREDITPA	Participation in credit programmes	1 = yes, 0 = no
TRAINPA	Participation in training programmes	1 = yes, 0 = no
CBOPA	Participation in community-based organisations	1 = yes, 0 = no
IUPT	Intensity of use of preferred traders	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUC	Intensity of use of cooperatives	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUCPC	Intensity of use of Commune People’s Committee	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUEW	Intensity of use of extension workers	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUTV	Intensity of use of television	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IURNB	Intensity of use of radio networks/broadcasts	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUMP	Intensity of use of mobile phones	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUI	Intensity of use of the Internet	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUFU	Intensity of use of farmers’ union	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often
IUIS	Intensity of use of input suppliers	1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often

^a1 = 18–24; 2 = 25–34; 3 = 35–44; 4 = 45–54; 5 =55–64; 6 = 65+.

^b1 = no schooling; 2 = primary school; 3 = junior high school; 4 = senior high school; 5 = technical training; 6 = college degree; 7 = university degree; 8 = postgraduate; 9 = other.

^c1 = 1–2; 2 = 3–5; 3 = 6–8; 4 = 9–11; 5 = 12–15; 6 = 16–20; 7 = 21–25; 8 = 26+.

Results

Socio-economic characteristics of cereal farmers

Table 2 describes the main characteristics of the cereal farmers participating in this research. The average age of small farmers (M = 3.40, SD = 1.19) was higher than that of large farmers (M = 3.38, SD = 1.10) and medium farmers (M = 3.29, SD = 1.18). However, the results of the ANOVA indicated that the farmers’ age was not statistically different among farm sizes. The level of education of large farmers (M = 3.40, SD = 0.96) was higher than that of small farmers (M = 3.28, SD = 1.24) and medium farmers (M = 3.28, SD = 1.05), but it was not statistically significant. Similarly, the average farming experience of large farmers (M = 5.06, SD = 1.12) was longer than that of medium farmers (M = 4.78, SD = 1.29) and small farmers (M = 4.76, SD = 1.41), and this difference was also not statistically significant. The average income of large farmers (M = 7.67, SD = 2.32) was higher than that of medium farmers (M = 6.98, SD = 2.17) and small farmers (M = 6.60, SD = 2.98). The results of the ANOVA showed that the annual income of the cereal farmers was statistically different among farm sizes.

Table 2.

Main characteristics of cereal farmers.

Socio-economic characteristics		Small farmers	Medium farmers	Large farmers	ANOVA/ χ² tests	p
Age		3.40 (1.19)	3.29 (1.18)	3.38 (1.10)	1.03	.395
Education level		3.28 (1.24)	3.28 (1.05)	3.40 (0.96)	1.25	.281
Farming experience		4.76 (1.41)	4.78 (1.29)	5.06 (1.12)	1.01	.425
Income		6.60 (2.98)	6.98 (2.17)	7.67 (2.32)	3.05***	.001
Gender	Male	30 (37.5)	41 (54.6)	57 (63.3)	11.58***	.003
Gender	Female	50 (62.5)	34 (45.3)	33 (36.6)	11.58***	.003
Participation in training programmes	Yes	32 (40.0)	37 (49.4)	61 (67.7)	13.72***	.001
Participation in training programmes	No	48 (60.0)	38 (50.6)	29 (32.3)	13.72***	.001
Participation in credit programmes	Yes	30 (37.5)	49 (65.4)	59 (65.6)	17.11***	.000
Participation in credit programmes	No	50 (62.5)	26 (34.6)	31 (34.4)	17.11***	.000
Participation in community-based organisations	Yes	33 (41.2)	43 (57.3)	58 (64.4)	9.49***	.009
Participation in community-based organisations	No	47 (58.8)	32 (42.7)	32 (35.6)	9.49***	.009

Note. The values in parentheses are standard deviations and percentages, and outside parentheses are means and numbers.

***p < .01.

More than half of the large (63.3%) and medium (54.6%) farmers and about one-third (37.5%) of the small farmers were male. In contrast, only about one-third (36.6%) of the large farmers, less than half (45.3%) of the medium farmers and more than half (62.5%) of the small farmers were female. Chi-square tests showed that the gender of the farmers was statistically different among the different categories. About two-thirds (67.7%) of the large farmers, half (49.4%) of the medium farmers and 40% of the small farmers participated in training programmes, whereas more than half of the small (60%) and medium (50.6%) farmers and one-third (32.3%) of the large farmers did not take part in these programmes. Chi-square tests indicated that farmers’ participation in training programmes was statistically different among farm sizes. More than half of the large (65.6%) and medium (65.4%) farmers and about one-third (37.5%) of the small farmers took part in credit programmes. On the other hand, more than half (62.5%) of the small farmers and about one-third of the medium (34.6%) and large (34.4%) farmers did not participate in these programmes. Chi-square tests revealed that farmers’ participation in credit programmes was statistically different among farm sizes. More than half of the large (64.4%) and medium (57.3%) farmers and about 41% of the small farmers participated in community-based organisations, while more than half (58.8%) of the small farmers, about 43% of the medium farmers and about one-third (35.6%) of the large farmers did not take part in these organisations. Chi-square tests indicated that farmers’ participation in community-based organisations was statistically different among farm sizes.

Intensity of using agricultural information from sources

The farmers in the study region used preferred traders, cooperatives, the Commune People’s Committee, extension workers, television, radio networks/broadcasts, mobile phones, the Internet, the farmers’ union and input suppliers as sources of agricultural information. The farmers were asked to rate the extent of use of agricultural information from the sources on a 5-point Likert scale (1 = rarely, 2 = sometimes, 3 = moderately, 4 = often, 5 = very often). Table 3 reports the intensity of the farmers’ use of agricultural information sources. For preferred traders, large farmers (M = 3.86, SD = 0.76) used agricultural information more frequently than medium farmers (M = 3.70, SD = 0.61) and small farmers (M = 3.47, SD = 0.77). In terms of cooperatives, medium farmers (M = 3.97, SD = 0.69) employed agricultural information more often than large farmers (M = 3.72, SD = 0.99) and small farmers (M = 3.38, SD = 1.10). The results of the ANOVA revealed that the intensity of the farmers’ use of agricultural information from the sources of preferred traders and cooperatives was statistically different among farm sizes. In reference to the Commune People’s Committee, small farmers (M = 2.32, SD = 1.04) utilised agricultural information more often than medium farmers (M = 2.36, SD = 0.824) and large farmers (M = 2.22, SD = 0.95).

Table 3.

Intensity of cereal farmers’ use of agricultural information sources.

Information sources	Small farmers M (SD)	Medium farmers M (SD)	Large farmers M (SD)	ANOVA	p
Preferred traders	3.47 (0.77)	3.70 (0.61)	3.86 (0.76)	4.41***	.002
Cooperatives	3.38 (1.10)	3.97 (0.69)	3.72 (0.99)	1.74 **	.038
Commune People’s Committee	2.32 (1.04)	2.30 (0.83)	2.22 (0.95)	0.60	.487
Extension workers	1.98 (0.80)	1.90 (0.82)	2.28 (1.12)	9.32***	.000
Television	3.28 (1.28)	3.94 (1.07)	3.78 (1.15)	3.80***	.005
Radio networks/broadcasts	3.87 (0.64)	4.01 (0.62)	4.01 (0.55)	1.58	.194
Mobile phones	3.96 (0.58)	3.98 (0.47)	4.18 (0.63)	3.98 **	.020
Internet	1.87 (0.90)	2.14 (1.03)	2.52 (1.32)	4.72***	.001
Farmers’ union	1.92 (0.90)	2.33 (1.03)	2.21 (1.01)	2.16 *	.074
Input suppliers	2.31 (0.92)	2.44 (1.04)	2.75 (1.27)	5.14***	.001

*p < .1. **p < .05. ***p < .01.

Large farmers (M = 2.28, SD = 1.12) used agricultural information from extension workers more often than small farmers (M = 1.98, SD = 0.80) and medium farmers (M = 1.90, SD = 0.82). However, medium farmers utilised agricultural information from television more frequently than small farmers and large farmers. The results of the ANOVA showed that the intensity of the farmers’ use of agricultural information from extension workers and television was statistically different among farm sizes. With regard to radio networks/broadcasts, small farmers (M = 3.85, SD = 0.64) used agricultural information less frequently than large farmers (M = 4.01, SD = 0.50) and medium farmers (M = 4.01, SD = 0.62). Regarding mobile phones, the Internet and input suppliers, large farmers (M = 4.18, SD = 0.63; M = 2.52, SD = 1.32; M = 2.75, SD = 1.27, respectively) employed agricultural information more frequently than medium (M = 3.98, SD = 0.47; M = 2.14, SD = 1.03; M = 2.44, SD = 1.04, respectively) and small farmers (M = 3.96, SD = 0.58; M = 1.87, SD = 0.90; M = 2.31, SD = 0.92, respectively). The results of the ANOVA showed that the intensity of the farmers’ use of agricultural information from mobile phones and the Internet was statistically different among farm sizes. In respect of the farmers’ union, medium farmers (M = 2.33, SD = 1.03) employed agricultural information more often than large farmers (M = 2.21, SD = 1.01) and small farmers (M = 1.91, SD = 0.90). The results of the ANOVA revealed that the intensity of the cereal farmers’ use of agricultural information from the farmers’ union was statistically different among farm sizes.

Types of information used by cereal farmers

Table 4 presents the types of agricultural information utilised by the cereal farmers participating in this research. It can be seen that all of the cereal farmers, despite farm sizes, were interested in using information on temperature (such as knowing the warmest and coldest months each year in order to adjust cereal planting dates), rainfall, seed quality, water distribution (pre-planting/planting stage), transportation and storage (harvesting stage). The chi-square tests showed that there was no statistical difference among farm sizes. However, there were less small farmers (27.7%, 27.1% and 25.4%, respectively) interested in using information on soil preparation (such as knowing methods of adding organic material to the soil before planting), herbicides/pesticides and pest/weed control than medium (36.5%, 38.8% and 34.8%, respectively) and large (35.8%, 34.1% and 39.9%, respectively) farmers. The chi-square tests indicated that the cereal farmers’ use of these types of information was statistically different among different farm sizes. In contrast, there were more large farmers (47.5%, 45.0% and 54.1%, respectively) interested in using information on organic fertilisers, water management (pre-planting/planting stage) and farm gate prices (marketing stage) than small (27.7%, 30.0% and 27.0%, respectively) and medium (24.8%, 25.0% and 18.9%, respectively) farmers. The chi-square tests revealed that the farmers’ use of these types of information was statistically different among different farm sizes.

Table 4.

Types of agricultural information utilised by cereal farmers.

Types of information	Small farmers (%)	Medium farmers (%)	Large farmers (%)	χ² tests	p
Pre-planting/planting stage
Temperature	72 (33.2)	65 (30.0)	80 (36.9)	0.439	.803
Amount of rainfall	70 (33.3)	62 (29.5)	78 (37.1)	0.884	.656
Seed quality	74 (32.3)	68 (29.7)	87 (38.0)	2.596	.273
Soil preparation	44 (27.7)	58 (36.5)	57 (35.8)	8.628**	.013
Water distribution	59 (30.4)	59 (30.4)	76 (39.2)	2.956	.222
Herbicides/pesticides	35 (27.1)	50 (38.8)	44 (34.1)	8.963**	.011
Pest and weed control	35 (25.4)	48 (34.8)	55 (39.9)	8.026**	.018
Organic fertilisers	28 (27.7)	25 (24.8)	48 (47.5)	8.653**	.013
Inorganic fertilisers	46 (38.0)	41 (33.9)	34 (28.1)	7.796**	.020
Water management	36 (30.0)	30 (25.0)	54 (45.0)	7.301**	.026
Harvesting stage
Harvesting	35 (25.4)	46 (33.3)	57 (41.3)	7.704**	.021
Transportation	31 (30.7)	28 (27.7)	42 (41.6)	1.771	.413
Storage	41 (35.7)	33 (28.7)	41 (35.7)	0.926	.629
Marketing stage
Present market prices	41 (25.3)	54 (33.3)	67 (41.4)	11.110***	.004
Future market prices	29 (23.6)	39 (31.7)	55 (44.7)	10.611***	.005
Farm gate prices	20 (27.0)	14 (18.9)	40 (54.1)	14.421***	.001
Input prices	38 (26.8)	42 (29.6)	62 (43.7)	8.122**	.017

**p < .05. ***p < .01.

There were more small farmers (38.0%) interested in using information on inorganic fertilisers (pre-planting/planting stage) than medium (33.9%) and large (28.1%) farmers. The chi-square tests revealed that the farmers’ use of information on inorganic fertilisers was statistically different among different farm sizes. In contrast, there were less small farmers (25.4%, 25.3%, 23.6% and 26.8%, respectively) interested in using information on harvesting (such as knowing harvesting dates for the spring season in order to identify planting dates for the summer season), present market prices, future market prices and input prices (marketing stage) than medium (33.3%, 33.3%, 31.7% and 29.6%, respectively) and large (41.3%, 41.4%, 44.7% and 43.7%, respectively) farmers. The chi-square tests showed that the farmers’ use of these types of information was statistically different among different farm sizes.

Factors affecting cereal farmers’ use of agricultural information

Table 5 describes the value of the multiple logistic regression model for cereal farmers’ use of agricultural information. Overall, the value of the multiple logistic regression model indicated that the prediction using this model was reasonable for decision-making. The chi-square statistics (140.784, p < .000) were highly significant, indicating that there was a statistically significant relationship between the sets of independent variables and cereal farmers’ use of agricultural information. For large farmers, there were 10 factors – gender, participation in community-based organisations, preferred traders, the Commune People’s Committee, television, the Internet (p ≤ .01), farming experience, participation in training programmes, extension workers (p ≤ .05) and input suppliers (p ≤ .1) – that were statistically associated with their use of agricultural information. Among these variables, participation in community-based organisations, preferred traders, participation in training programmes and gender played a key role in the farmers’ use of agricultural information.

Table 5.

Factors affecting cereal farmers’ use of agricultural information.

Independent variables	Small farmers			Medium farmers			Large farmers
Independent variables	β	p	Exp(B)	β	p	Exp(B)	β	p	Exp(B)
Constant	9.140***	.003		5.175	.100		−14.316***	.000
Age	0.332	.180	1.394	0.093	.672	1.098	−0.426	.101	0.653
Gender	0.529	.171	1.698	0.535	.155	1.708	−1.064***	.010	0.345
Education level	−0.101	.593	0.903	0.048	.789	1.049	0.053	.794	1.055
Farming experience	−0.303	.165	0.738	−0.245	.260	0.782	0.549**	.025	1.731
Income	0.068	.419	1.070	−0.109	.189	0.897	0.041	.631	1.042
Participation in training programmes	−0.010	.982	0.990	1.058**	.014	2.881	1.048**	.028	0.351
Participation in credit programmes	0.677	.100	1.968	−0.404	.310	0.667	−0.272	.525	0.762
Participation in community-based organisations	0.654	.111	1.923	0.634	.113	1.884	1.287***	.003	0.276
Preferred traders	−0.795***	.008	0.451	−0.457	.125	0.633	1.253***	.000	3.500
Cooperatives	−0.546**	.017	0.579	0.357	.113	1.430	0.189	.385	1.208
Commune People’s Committee	0.318	.155	1.375	0.366*	.077	1.442	−0.684***	.003	0.504
Extension workers	0.007	.977	1.007	−0.554***	.008	0.575	0.546**	.027	1.727
Television	−0.670***	.000	0.512	0.106	.545	1.112	0.564***	.002	1.758
Radio networks/broadcasts	−0.357	.250	0.700	−0.148	.639	0.863	0.505	.143	1.657
Mobile phones	0.096	.780	1.100	−0.687**	.037	0.503	0.592	.101	1.807
Internet	−0.450**	.027	0.638	−0.425***	.008	0.654	0.875***	.000	2.398
Farmers’ union	−0.409*	.057	0.665	0.283	.157	1.327	0.125	.573	1.134
Input suppliers	−0.185	.358	0.831	−0.216	.222	0.806	0.401*	.051	1.493

Note. Log likelihood ratio chi-square = 136.888; −2 log likelihood = 400.015; Sig. = .000.

*p ≤ .1. **p ≤ .05. ***p ≤ .01.

There were five factors – extension workers, the Internet (p ≤ .01), participation in training programmes, mobile phones (p ≤ .05) and the Commune People’s Committee (p ≤ .1) – that were statistically associated with the medium farmers’ use of agricultural information. Of these, the variables of participation in training programmes, mobile phones and extension workers played an important role in influencing their use of agricultural information. There were also five factors that were statistically associated with small farmers’ use of agricultural information: preferred traders, cooperatives, television (p ≤ .01), the Internet (p ≤ .05) and the farmers’ union (p ≤ .1). Of these, the variables of preferred traders, television and cooperatives played a major role in shaping the small farmers’ use of agricultural information.

Discussion

The present study found that participation in training programmes positively impacted the use of agricultural information among large and medium farmers. The exponential coefficients for the large and medium farmers were 0.351 and 2.881, respectively, which showed a 35.1% and 188.1% increase in use among large and medium farmers, respectively. This means that the more large and medium farmers participated in training programmes, the more they used agricultural information. It was also found that participation in community-based organisations positively affected the use of agricultural information among large farmers. The exponential coefficient for the large farmers was 0.276, which indicated a 27.6% increase in use. This suggests that the more large farmers participated in community-based organisations, the more they used agricultural information. A body of literature (Aonngernthayakorn and Pongquan, 2017; Durgun et al., 2021; Koskei et al., 2013; Lwoga et al., 2011; Magesa et al., 2020; Mtega, 2021; Mwalukasa, 2013) discusses farmers’ use of agricultural information, yet the findings from this research have not been reported in any previous studies.

The study found that the intensity of use through preferred traders positively influenced the use of agricultural information among large farmers, but negatively impacted the use of agricultural information among small farmers. The exponential coefficients for the large and small farmers were 3.500 and 0.451, respectively, which suggested a 250.0% increase in use among large farmers and a 45.1% decrease in use among small farmers. This implies that the higher the intensity of use through preferred traders, the more large farmers used agricultural information, and the less small farmers used agricultural information. In the existing literature (Aikins, 2014; Aonngernthayakorn and Pongquan, 2017; Durgun et al., 2021; Ndimbwa et al., 2020; Opara, 2008), nothing has been discussed about the importance of the intensity of using agricultural information from the source of preferred traders and its impact on farmers’ use of agricultural information. In developing countries such as Vietnam, preferred traders play an important role in the marketing of agricultural produce, as highlighted in the literature (Hoang, 2020b). As such, preferred traders are a key source of agricultural information for farmers, as evidenced in this study. The present study also found that the intensity of use through cooperatives negatively impacted the use of agricultural information among small farmers. The exponential coefficient for small farmers was 0.579, which showed a 57.9% decrease in use among small farmers. This suggests that the greater the intensity of use through cooperatives, the less small farmers used agricultural information.

The study found that the intensity of use through the Commune People’s Committee positively shaped the use of agricultural information among medium farmers, but negatively impacted the use of agricultural information among large farmers. The exponential coefficients for the medium and large farmers were 1.442 and 0.504, respectively, which showed a 44.2% increase in use among medium farmers and a 50.4% decrease in use among large farmers. This implies that the greater the intensity of use through the Commune People’s Committee, the more medium farmers used agricultural information, and the less large farmers used agricultural information. Although several researchers (Aonngernthayakorn and Pongquan, 2017; Diekmann et al., 2009; Magesa et al., 2020) have investigated farmers’ use of agricultural information, the findings from this study have not been reported in any previous research. The study also found that the intensity of use through extension workers positively impacted the use of agricultural information among large farmers, but negatively impacted the use of agricultural information among medium farmers. The exponential coefficients for the large and medium farmers were 1.727 and 0.575, respectively, which showed a 72.7% increase in use among large farmers and a 57.5% decrease in use among medium farmers. The results from the studies of Aonngernthayakorn and Pongquan (2017), Isaya et al. (2016), Lwoga et al. (2011) and Mwalukasa (2013) suggest that the higher the intensity of farmers’ participation in agricultural extension initiatives, the more they used agricultural information, which is partially supported by the results of this research.

This study found that the intensity of use through television positively impacted the use of agricultural information among large farmers, but negatively impacted the use of agricultural information among small farmers. The exponential coefficients for the large and small farmers were 1.758 and 0.512, respectively, which showed a 75.8% increase in use among large farmers and a 51.2% decrease in use among small farmers. This means that the higher the intensity of use through television, the more large farmers used agricultural information, and the less small farmers used agricultural information. The current study also found that the intensity of use through the Internet positively impacted the use of agricultural information among large farmers, but negatively impacted the use of agricultural information among medium and small farmers. The exponential coefficients for the large, medium and small farmers were 2.398, 0.654 and 0.638, respectively, which indicated a 139.8% increase in use among large farmers and a 65.4% and 63.8% decrease in use among medium and small farmers, respectively. This suggests that the greater the intensity of use through the Internet, the more large farmers used agricultural information, and the less small and medium farmers used agricultural information. The results from this study partially provide empirical support for the view of Ajani (2014) that sub-Saharan farmers’ access to the Internet had a positive impact on their use of agricultural information.

Unexpectedly, this study found that the intensity of use through mobile phones negatively impacted the use of agricultural information among medium farmers. The exponential coefficient for medium farmers was 0.503, which indicated a 50.3% decrease in use. This means that the higher the intensity of use through mobile phones, the less medium farmers used agricultural information. Similarly, it was found that the intensity of use through the farmers’ union negatively affected the use of agricultural information among small farmers. The exponential coefficient for small farmers was 0.665, which indicated a 66.5% decrease in use among small farmers. However, it was found that the intensity of use through input suppliers positively impacted the use of agricultural information among large farmers. The exponential coefficient for large farmers was 1.493, which indicated a 49.3% increase in use among large farmers – a finding that has not been reported in any previous studies.

This study found that the variable of gender was negatively and statistically associated with large farmers’ use of agricultural information. It had an exponential coefficient of 0.345, which indicated a 34.5% decrease in use among female large farmers. This suggests that female large farmers were in a better position to use agricultural information than male large farmers. It was also found that the farming experience of large farmers was positively and statistically associated with their use of agricultural information. It had an exponential coefficient of 1.731, which indicated a 73.1% increase in use among large farmers. This finding is consistent with the findings reported in the literature (Aonngernthayakorn and Pongquan, 2017; Diekmann et al., 2009; Koskei et al., 2013) that the length of farming experience positively affects farmers’ use of agricultural information.

Conclusions and recommendations

This study was carried out to (1) describe the socio-economic profile of cereal farmers; (2) evaluate the level of use of agricultural information by the farmers; and (3) determine the factors that affected cereal farmers’ use of agricultural information. Based on the results, the key conclusions are as follows: first, the socio-economic characteristics of cereal farmers vary, reflecting diverse cereal farming systems in the study region. Second, there were significant differences in the types of agricultural information used by the cereal farmers for production and marketing. Fewer small farmers used information on soil preparation, herbicides/pesticides and pest/weed control than medium and large farmers. Similarly, fewer small farmers utilised information on harvesting, present market prices, future market prices and input prices than medium and large farmers. In contrast, more small farmers employed information on inorganic fertilisers compared to medium and large farmers.

Third, there were significant differences in the intensity of use of agricultural information between the different farmer categories. Large farmers used information from preferred traders, extension workers and input suppliers more intensively than medium farmers and small farmers. Similarly, large farmers accessed information from mobile phones and the Internet more intensively than medium and small farmers. In contrast, medium farmers applied more information from cooperatives and the farmers’ union than large farmers and small farmers. Likewise, medium farmers accessed information from television more intensively than small farmers and large farmers.

Fourth, the determinants of the farmers’ use of agricultural information varied among the farmer categories. Overall, access to the Internet and television were common factors that affected farmers’ use of agricultural information. Other factors – gender, farming experience, participation in training programmes and community-based organisations, and information obtained from preferred traders, the Commune People’s Committee, extension workers and input suppliers – significantly shaped large farmers’ use of agricultural information. In contrast, small farmers were significantly more influenced by preferred traders, cooperatives and the farmers’ union.

The government of Vietnam should encourage use of the Internet and television for disseminating agricultural information to farmers. Extension programmes developed to help rural farmers access agricultural information should take into account farmers’ participation in existing community-based organisations. Developing and sustaining community-based organisations, including farmers’ unions, interested producer groups and agricultural cooperatives for farmers, and facilitating farmers’ use of agricultural information via these organisations may be a more appropriate method to deliver information to rural farmers.

The findings from this research should be disseminated to agricultural extension developers, agricultural educators and policymakers to identify the most appropriate strategies for sharing important agricultural information with Vietnamese cereal farmers. When selecting future extension strategies to distribute agricultural information to cereal farmers, the sources of agricultural information identified in this study should be considered.

It is acknowledged that this research has some limitations. This research has provided a significant understanding of the factors that affected cereal farmers’ use of agricultural information in an agricultural-based developing country. However, the data of this research was concentrated on cereal farmers. There is a need for more research in order to generalise these findings. Expanding this research to other types of farmers, such as livestock, fishery and fruit farmers, would be very interesting. Also, the research design employed in this study was cross-sectional. It only assessed farmers’ views at one point in time. Farmers’ views change over time as they acquire more practical knowledge, skills and experience. Therefore, more effort to assess the validity of the findings from this research is required.

Supplemental Material

Supplemental Material, sj-pdf-1-ifl-10.1177_03400352211066941 - Factors influencing the use of agricultural information by Vietnamese farmers

Supplemental Material, sj-pdf-1-ifl-10.1177_03400352211066941 for Factors influencing the use of agricultural information by Vietnamese farmers by Hung Gia Hoang, Duc Van Nguyen and Douglas Drysdale in IFLA Journal

Supplemental Material

Supplemental Material, sj-pdf-2-ifl-10.1177_03400352211066941 - Factors influencing the use of agricultural information by Vietnamese farmers

Supplemental Material, sj-pdf-2-ifl-10.1177_03400352211066941 for Factors influencing the use of agricultural information by Vietnamese farmers by Hung Gia Hoang, Duc Van Nguyen and Douglas Drysdale in IFLA Journal

Footnotes

Acknowledgment

The authors of this study also acknowledge the partial support of University of Agriculture and Forestry, Hue University under the Strategic Research Group Program, Grant No. NCM.DHNL.2021.05.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors would like to thank Hue University for its financial support in conducting this research.

ORCID iD

Hung Gia Hoang

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