Linking farmers' perceptions and management decision toward sustainable agroecological transition: evidence from rural Tunisia.

Global food systems face sustainability challenges like undernourishment, inequity, resource degradation, and pollution. Food production and consumption drive environmental change with greenhouse gas emissions, biodiversity loss, and land-system shifts. The climate change crisis has intensified concerns about the ecological impact of these systems. Sustainable food networks, such as community-supported agriculture, are promoting sustainable production and consumption through short supply chains. International bodies like the Food and Agriculture Organization (FAO) and the Consultative Group for International Agricultural Research (CGIAR) are also spearheading initiatives for more equitable and sustainable food systems. In Tunisia, where dryland areas predominate, the ongoing implementation of the Agroecology Initiative provides the context for this study, which explores the drivers and barriers of agroecological transformation in this challenging environment. The research focuses on stakeholder engagement, with a gender perspective to explore farmer perceptions. The study, conducted in the northwest of Tunisia in 2022-2023, involved focus groups, workshops, surveys, and questionnaires with various stakeholders. Findings highlight farmer organizations' potential in promoting sustainable farming, with clear goals, diversified systems, and collaborations. However, challenges such as input scarcity, water shortage, low income, and marketing must be addressed. Results also indicate that over 90% of farmers who received assistance with agroecological practices reported a change in their ideas and practices. Fifty seven percent of the workshops participants identified the olive oil value chain as having the greatest potential for agroecological transformation, but it faces constraints such as climate, lack of policy incentives, training, funding, and difficulty in adopting technical innovations. Women's inclusion in agriculture, environmental, social, and economic challenges were also highlighted. Despite these obstacles, key drivers for agroecological transition were identified. These include the compatibility of many agroecological practices with existing farmer capabilities, their cultural and economic benefits, and the positive outcomes for environmental sustainability and health. The study advocates for a socio-technical systems analysis to address the root causes hindering Tunisia's agroecological transformation. A participatory approach is crucial to understanding priorities and developing a sustainable and resilient food system. Furthermore, the research underscores the importance of considering diverse farmer perspectives and tailoring strategies to support this critical transition effectively.

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.

Pathways to wheat self-sufficiency in Africa.

A growing urban population and dietary changes increased wheat import bills in Africa to 9% per year. Though wheat production in the continent has been increasing over the past decades, to varying degrees depending on regions, this has not been commensurate with the rapidly increasing demand for wheat. Analyses of wheat yield gaps show that there is ample opportunity to increase wheat production in Africa through improved genetics and agronomic practices. Doing so would reduce import dependency and increase wheat self-sufficiency at national level in many African countries. In view of the uncertainties revealed by the global COVID-19 pandemic, extreme weather events, and world security issues, national policies in Africa should re-consider the value of self-sufficiency in production of staple food crops, specifically wheat. This is particularly so for areas where water-limited wheat yield gaps can be narrowed through intensification on existing cropland and judicious expansion of rainfed and irrigated wheat areas. Increasing the production of other sources of calories (and proteins) should also be considered to reduce dependency on wheat imports.

Plot-level impacts of improved lentil varieties in Bangladesh.

The advent of improved lentil varieties (ILVs) in the mid-1990s solved the disease problem which almost halted lentil production in Bangladesh. Levels of adoption of ILVs have been documented in the literature, but little is known about their impacts. Applying an instrumental variables regression to data collected from a sample of 1,694 lentil plots and DNA fingerprinting for varietal identification, this study provides estimates of the plot-level impacts of adoption of ILVs in Bangladesh. Model results show that adoption of ILVs is associated with 14.3% (181.14 kg/ha) higher yields and 17.23% (US$169.44/ha) higher gross margins. Since 45% of lentil area is under ILVs, they generated over 8.77 tones (6%) more supply of lentils from domestic sources, saving the country US$8.22 million in imports in 2015 alone. By investing in the generation and scaling of ILVs, Bangladesh and other South Asian countries with similar agro-ecologies can increase production and decrease dependency on lentil imports.

Projecting wheat demand in China and India for 2030 and 2050: Implications for food security.

Introduction: The combined populations of China and India were 2.78 billion in 2020, representing 36% of the world population (7.75 billion). Wheat is the second most important staple grain in both China and India. In 2019, the aggregate wheat consumption in China was 96.4 million ton and in India it was 82.5 million ton, together it was more than 35% of the world's wheat that year. In China, in 2050, the projected population will be 1294-1515 million, and in India, it is projected to be 14.89-1793 million, under the low and high-fertility rate assumptions. A question arises as to, what will be aggregate demand for wheat in China and India in 2030 and 2050? Methods: Applying the Vector Error Correction model estimation process in the time series econometric estimation setting, this study projected the per capita and annual aggregate wheat consumptions of China and India during 2019-2050. In the process, this study relies on agricultural data sourced from the Food and Agriculture Organization of the United States (FAO) database (FAOSTAT), as well as the World Bank's World Development Indicators (WDI) data catalog. The presence of unit root in the data series are tested by applying the augmented Dickey-Fuller test; Philips-Perron unit root test; Kwiatkowski-Phillips-Schmidt-Shin test, and Zivot-Andrews Unit Root test allowing for a single break in intercept and/or trend. The test statistics suggest that a natural log transformation and with the first difference of the variables provides stationarity of the data series for both China and India. The Zivot-Andrews Unit Root test, however, suggested that there is a structural break in urban population share and GDP per capita. To tackle the issue, we have included a year dummy and two multiplicative dummies in our model. Furthermore, the Johansen cointegration test suggests that at least one variable in both data series were cointegrated. These tests enable us to apply Vector Error Correction (VEC) model estimation procedure. In estimation the model, the appropriate number of lags of the variables is confirmed by applying the "varsoc" command in Stata 17 software interface. The estimated yearly per capita wheat consumption in 2030 and 2050 from the VEC model, are multiplied by the projected population in 2030 and 2050 to calculate the projected aggregate wheat demand in China and India in 2030 and 2050. After projecting the yearly per capita wheat consumption (KG), we multiply with the projected population to get the expected consumption demand. Results: This study found that the yearly per capita wheat consumption of China will increase from 65.8 kg in 2019 to 76 kg in 2030, and 95 kg in 2050. In India, the yearly per capita wheat consumption will increase to 74 kg in 2030 and 94 kg in 2050 from 60.4 kg in 2019. Considering the projected population growth rates under low-fertility assumptions, aggregate wheat consumption of China will increase by more than 13% in 2030 and by 28% in 2050. Under the high-fertility rate assumption, however the aggregate wheat consumption of China will increase by 18% in 2030 and nearly 50% in 2050. In the case of India, under both low and high-fertility rate assumptions, aggregate wheat demand in India will increase by 32-38% in 2030 and by 70-104% in 2050 compared to 2019 level of consumption. Discussions: Our results underline the importance of wheat in both countries, which are the world's top wheat producers and consumers, and suggest the importance of research and development investments to maintain sufficient national wheat grain production levels to meet China and India's domestic demand. This is critical both to ensure the food security of this large segment of the world populace, which also includes 23% of the total population of the world who live on less than US $1.90/day, as well as to avoid potential grain market destabilization and price hikes that arise in the event of large import demands.

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.

Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context.

Several circulation models are forecasting climate changes in the Mediterranean region. Accordingly, it is expected that water scarcity in the region will be higher with drastic shifts of hydrological and erosive watershed responses. In Tunisia, wheat yields have been variable over the years and are lower than the potential yields. In response, the adoption of conservation agriculture (CA), introduced into Tunisia in 1999 to help adaptation to climate change, has resulted in a substantial reduction in agricultural productivity. CA areas increased from 52 ha in 1999 to 14,000 ha in 2015. Using a modelling approach, the present paper evaluates the potential of CA to adapt wheat-based-systems to climate change in Tunisia. The Agricultural Production Systems Simulator (APSIM) model was used to predict the effect of tillage (conventional tillage [CT] vs. zero-tillage [ZT] and soil residue retention [ZT-RR]) on wheat productivity and soil fertility. Two contrasting locations in Tunisia were studied; one semi-arid (Kef) and one sub-humid (Bizerte). Results showed that the sustainable production of durum wheat under climate change conditions in Tunisia is possible through the adoption of CA practices (ZT and ZT-RR) in both sub-humid and semi-arid areas. In fact, mulching (residue retention) is more effective than CT (under semi-arid and sub-humid conditions) in enhancing wheat yield (15%), water use efficiency (18% and 13%) and soil organic carbon accumulation (0.13 t ha-1 year-1 and 0.18 t ha-1 year-1). It is also more effective for soil resilience - preventing water erosion (1.7 t ha-1 year-1 and 4.6 t ha-1 year-1 of soil loss). The present study allowed identification of 260,000 ha as priority areas for CA adoption; this represent one-third of the total cereal area in Tunisia. Appropriate evaluation of the benefits of CA on sustainable agricultural intensification would provide more arguments for effectively supporting CA adoption in Tunisia.

Estimation of the economic loss due to irrigation water use inefficiency in Tunisia.

The main objective of this study is to estimate the total economic loss due to inefficient use of irrigation water in Tunisia. Several approaches have been used for this purpose. The optimal level of water application for different crops is calculated using the actual crop evapotranspiration which is based on FAO-56 method. The residual imputation and yield comparison methods have been used to estimate the economic value of irrigation water for different irrigated crops in different bioclimatic areas. For the empirical analysis, primary data were obtained from a series of surveys that covered 78% of the total irrigated areas and were collected within the framework of the "Virtual Water and Food Security in Tunisia project" (2013-2015). Secondary data about land distribution of crops in Tunisia were taken from the Ministry of Agriculture (2016). Around 724 farms were randomly sampled considering their bioclimatic area, farm type, and production system. The survey included the main 20 crops produced in Tunisia. Results show that most of farmers are either under or over utilizing irrigation water. The value of total direct economic losses, at the country level, of both types of water inefficiencies, was estimated to around 470 million Tunisian Dinars. Therefore, an improvement of water use efficiency at field level through dissemination of information/knowledge on irrigation scheduling and crop water requirements by extension services to farmers is needed to reduce this huge economic loss, reach higher sustainability in water use and improve food security.

Irrigation pricing policies and its impact on agricultural inputs demand in Tunisia: a DEA-based methodology.

This paper estimates farmers' individual irrigation water demand functions employing the information hidden in individual farmers' technical efficiency. This information is extracted through the development of a new deductive methodology based on inverse Data Envelopment Analysis (DEA) models. The empirical results for Tunisia show that farmers who are more technically efficient have less elastic irrigation water demand functions; these farmers would adjust demand only to a limited extent and they can afford the water price. In contrast, water pricing significantly affects those that are less efficient. These farmers shift towards a different cropping pattern using significantly less water and more land when the price of water increases. Thus, higher water prices would threaten this category's livelihood if their efficiency is not improved. However, if the technical efficiency of these farmers were to improve, then it would be more difficult to reach water saving objectives since their demand will also become highly inelastic. The findings have important implications in view of the objectives of Tunisia water policy which include:full cost recovery, continuity of the irrigation activity, and water saving at the national level.