A contextual ICT model to explain adoption of mobile applications in developing countries: A case study of Tunisia.

In Tunisia, agriculture is the main source of livelihood for more than 75% of small and subsistence farmers with minimal use of technology. The use of information and communication technology (ICT),such as mobile applications, represents a pertinent opportunity for these smallholders to access agricultural innovation and market information and improve their farming technologies and farm management. Thus, ICT can act as a replacement to foster access to innovation for this category of farmers. Unfortunately, the underuse of mobile applications has contributed to low and slow adoption of agricultural innovation and consequently the benefits of this technology have not been attained. The purpose of this study is to identify the factors affecting the adoption of Short Message Service (SMS) through a contextual ICT model for livestock, olive crop, and beekeeping. Data were collected from 200 small-scale beekeepers, 225 olive growers, and 140 livestock breeders selected in Jendouba, Kairouan, and Zaghouan in Tunisia. The objective of this paper is to examine the factors that influence mobile applications using the partial least squares structural equation modelling technique, for livestock, olive crop, and beekeeping agricultural activities. The results showed that the final ICT-induced structural models were highly predictive of the use of SMS and its increased adoption. Factors affecting the use of SMS differed according to the farming system. The major perceived factor affecting the use of SMS was 'observability' for livestock farmers, 'compatibility' for olive growers, and 'information quality' for beekeepers. Understanding these factors by taking into account the specificity of the agricultural activity leads to a better understanding of the adoption of ICT tools by smallholder farmers in Tunisia.

Generic algorithm for multicriteria ranking of crop technological options based on the "Technique for Order of Preference by Similarity to Ideal Solution" using ShinyApps.

Many agricultural research and development programs aiming at enhancing tradeoffs related to different adoption, management and policy decisions face a methodological problem in which multi-criteria ranking is used to reach acceptable compromises between different objectives (e.g. those of farms, research managers, donors or policy makers). A typical situation is where many farm management options will result in different conflicting economic, social and environmental impacts. Ranking these options and the choice of those to promote is challenging. The literature provides a set of methodological solutions that need background data organization and simulation through coding using different computing software. Here, we provide a generic solution and friendly interface, made on Shiny (an R-package) based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We apply this method for ranking different crop technological products of grain legumes and dry cereals based on their respective impacts on poverty, child malnutrition and economic benefits in more than 40 countries in eight different geographic zones across South Asia and Sub-Saharan Africa. • The developed algorithms and interface can help rank different options based on the weights (preferences) of their respective outcome indicators. • The interface allows for changing the weights (preferences) and automatically generates new ranking tables and graphs accordingly, which can serve for scenario simulations, which saves time compared to manually performing these calculations.

Data on how tree planting and management practices influence tree seedling survival in Kenya and Ethiopia.

Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful.