Dissertation
The digital age has changed the way farmers obtain information related to crop
production. The growth of science and technology has shaped the way farmers obtain
information and knowledge related to crop productivity undertakings. The advent of
Information and Communication Technologies (ICTs) has drawn farmers’ attention to
curb information asymmetry existing in crop production sub sector. However, the
information related to crop productivity status existing among farmers is still open for
discussion. This study analysed the influence of ICTs on crop productivity among young
farmers. The study intended to: determine the extent of ICT usage among young
farmers who engage in crop productivity, and examine factors influencing adoption
and intensity use of ICTs for crop productivity. Furthermore, the study aimed to
analyse on key players/actors influencing young farmers to benefit from agricultural
innovation systems (AIS) related to crop productivity dispensed by other actors, and
assess the impact of ICT usage on crop productivity among young farmers.
This study employed mixed research techniques (qualitative and quantitative) and cross-
sectional research design with multistage sampling. Using a simple random sampling
technique, a total of 400 respondents were selected out of 10 790 young farmers from
Misungwi and Kilosa districts and 11 key informants participated in the household survey
and in-depth interviews respectively. Regarding data analysis, descriptive statistics was
used to describe demographic characteristics of the respondents. Descriptive statistics
involved describing the relationship between independent variables (age, sex, education,
marital status, farm size, crop diversification, and young farmers’ interaction with various
actors such as NGOs, extension agents, and researchers) and dependent variable ICTs
adoption. Logit and Tobit Regression Models were used to determine the factors
influencing ICTs adoption and intensity of ICTs adoption among young farmers,
respectively. Logit regression model provided categorization of data into discrete classes
by studying the relationship between independent variables and dependent variable
(ICT adoption). Since dependent variable is binary (probability to adopt ICTs or not),
Logit model was relevant to show the relationship between these variables. Tobit
regression model was used to measure the intensity of using the ICTs once adopted.
Intensity of adoption refers to the frequency of using ICTs in crop productivity by the
same farmer. ICTs as enablers of effective functioning of agricultural innovation system
(AIS) was analysed using Social Network Analysis (SNA). The SNA mapped
actors/organisations/players and their relationship with young farmers using ICTs in
improving crop productivity. Propensity Score Matching (PSM) and Inverse Probability
Weighted Regression Adjustment (IPWRA) were used to analyse the impact of ICTs on
crop productivity. Content analysis was used to analyse qualitative data.
The results showed that, out of the 400 respondents, 60% had adopted ICTs to enhance
crop productivity. The feature phone was the most used device (49.75%) followed by
smart phones (17.25 %), while only 2.5% of the respondents had access to computers.
The factors such as respondents’ level of education, crop diversification, access to credit,
and farm location had some influence on the adoption of ICTs and its intensity of
adoption in crop productivity. Social Network Analysis revealed that young farmers have
limited interaction with important actors/players for crop productivity such as agricultural
training and research institutes, agricultural seed agencies, and financial institutions. The
PSM and IPWRA showed that adopters of ICTs had higher productivity compared to non-
adopters at 1% level of significance. The findings consolidate the need for promoting
ICTs penetration and/or adopting for development that is more inclusive and crucial in
enhancing crop productivity among young farmers in Tanzania. The Government of
Tanzania (GoT) through Tanzania Communication and Regulatory Authority (TCRA)
should formulate and implement policies that enable universal access mechanisms of
ICTs via low pricing and sharing schemes and increase the infrastructure needed for its
penetration especially to the rural communities. Besides, public policies and public-
private partnership programmes could work together to strengthen interaction by
engaging all actors in the crop production sub-sector to develop an innovation system
platform for sharing various technologies and relevant information tha will enhance
agricultural productivity. Moreover, it is recommended that financial institutions should
work more closely with young farmers so as to expand their capital base in crop
productivity endeavours.The digital age has changed the way farmers obtain information related to crop
production. The growth of science and technology has shaped the way farmers obtain
information and knowledge related to crop productivity undertakings. The advent of
Information and Communication Technologies (ICTs) has drawn farmers’ attention to
curb information asymmetry existing in crop production sub sector. However, the
information related to crop productivity status existing among farmers is still open for
discussion. This study analysed the influence of ICTs on crop productivity among young
farmers. The study intended to: determine the extent of ICT usage among young
farmers who engage in crop productivity, and examine factors influencing adoption
and intensity use of ICTs for crop productivity. Furthermore, the study aimed to
analyse on key players/actors influencing young farmers to benefit from agricultural
innovation systems (AIS) related to crop productivity dispensed by other actors, and
assess the impact of ICT usage on crop productivity among young farmers.
This study employed mixed research techniques (qualitative and quantitative) and cross-
sectional research design with multistage sampling. Using a simple random sampling
technique, a total of 400 respondents were selected out of 10 790 young farmers from
Misungwi and Kilosa districts and 11 key informants participated in the household survey
and in-depth interviews respectively. Regarding data analysis, descriptive statistics was
used to describe demographic characteristics of the respondents. Descriptive statistics
involved describing the relationship between independent variables (age, sex, education,
marital status, farm size, crop diversification, and young farmers’ interaction with various
actors such as NGOs, extension agents, and researchers) and dependent variable ICTs
adoption. Logit and Tobit Regression Models were used to determine the factors
influencing ICTs adoption and intensity of ICTs adoption among young farmers,
respectively. Logit regression model provided categorization of data into discrete classes
by studying the relationship between independent variables and dependent variable
(ICT adoption). Since dependent variable is binary (probability to adopt ICTs or not),
Logit model was relevant to show the relationship between these variables. Tobit
regression model was used to measure the intensity of using the ICTs once adopted.
Intensity of adoption refers to the frequency of using ICTs in crop productivity by the
same farmer. ICTs as enablers of effective functioning of agricultural innovation system
(AIS) was analysed using Social Network Analysis (SNA). The SNA mapped
actors/organisations/players and their relationship with young farmers using ICTs in
improving crop productivity. Propensity Score Matching (PSM) and Inverse Probability
Weighted Regression Adjustment (IPWRA) were used to analyse the impact of ICTs on
crop productivity. Content analysis was used to analyse qualitative data.
The results showed that, out of the 400 respondents, 60% had adopted ICTs to enhance
crop productivity. The feature phone was the most used device (49.75%) followed by
smart phones (17.25 %), while only 2.5% of the respondents had access to computers.
The factors such as respondents’ level of education, crop diversification, access to credit,
and farm location had some influence on the adoption of ICTs and its intensity of
adoption in crop productivity. Social Network Analysis revealed that young farmers have
limited interaction with important actors/players for crop productivity such as agricultural
training and research institutes, agricultural seed agencies, and financial institutions. The
PSM and IPWRA showed that adopters of ICTs had higher productivity compared to non-
adopters at 1% level of significance. The findings consolidate the need for promoting
ICTs penetration and/or adopting for development that is more inclusive and crucial in
enhancing crop productivity among young farmers in Tanzania. The Government of
Tanzania (GoT) through Tanzania Communication and Regulatory Authority (TCRA)
should formulate and implement policies that enable universal access mechanisms of
ICTs via low pricing and sharing schemes and increase the infrastructure needed for its
penetration especially to the rural communities. Besides, public policies and public-
private partnership programmes could work together to strengthen interaction by
engaging all actors in the crop production sub-sector to develop an innovation system
platform for sharing various technologies and relevant information tha will enhance
agricultural productivity. Moreover, it is recommended that financial institutions should
work more closely with young farmers so as to expand their capital base in crop
productivity endeavours.
International Institute of Tropical Agriculture