This report encapsulates the status of and outlook for agroforestry in the ASEAN countries and endeavours to help achieve the SDGs through more sustainable and productive agri-food systems. The status, trends and outlook in this report will support policy makers and practitioners to make appropriate decisions and advance local innovations with agroforestry, especially in the context of the United Nations Decades on Ecosystem Restoration and Family Farming. It will help address the much needed governance, institutional, finance and capacity constraints, especially for promoting smallholder-oriented agroforestry systems in rural areas. Also, the outlook will help enhance the agro-ecological dimensions in land use while yielding more sustainable and productive results combined with ecological and climate-smart outcomes, including those related to Nationally Determined Contributions (NDCs). Eventually, it will contribute to improving the food and nutritional security, livelihoods and wellbeing of marginalized people through sustainable delivery of various ecosystem services.
Tag: Adoption
Simulating Agroforestry Adoption in Rural Indonesia: The Potential of Trees on Farms for Livelihoods and Environment
In recent years, agroforestry has gained increasing attention as an option to simultaneously alleviate poverty, provide ecological benefits, and mitigate climate change. The present study simulates small-scale farmers’ agroforestry adoption decisions to investigate the consequences for livelihoods and the environment over time. To explore the interdependencies between agroforestry adoption, livelihoods, and the environment, an agent-based model adjusted to a case study area in rural Indonesia was implemented. Thereby, the model compares different scenarios, including a climate change scenario. The agroforestry system under investigation consists of an illipe (Shorea stenoptera) rubber (Hevea brasiliensis) mix, which are both locally valued tree species. The simulations reveal that farmers who adopt agroforestry diversify their livelihood portfolio while increasing income. Additionally, the model predicts environmental benefits: enhanced biodiversity and higher carbon sequestration in the landscape. The benefits of agroforestry for livelihoods and nature gain particular importance in the climate change scenario. The results therefore provide policy-makers and practitioners with insights into the dynamic economic and environmental advantages of promoting agroforestry.
What’s the Story on Agriculture? Using Narratives to Understand Farming Households’ Aspirations in Meru, Kenya
In the limited research on farming aspirations, little attention has been paid to the narratives which frame and shape them, and the ways in which the aspirations of those who farm intersect with the goals of extension services. Drawing on multimethod research conducted in Meru County, Kenya, we demonstrate how aspirations are not only situated within a consideration of personal circumstances, but are shaped in crucial ways by networks of relations and by the perceived possibilities afforded by material and cultural resources. We further highlight the accounts of state extension agents that link a lack of engagement with the desires and needs of those who farm to the failure of agricultural development initiatives. We argue that an engagement with aspirations opens up a route to understanding the obstacles and potentialities that matter to those who farm and, as such, might enable more responsive development initiatives centred on the perceptions and desires of those who farm.
Participatory versus traditional agricultural advisory models for training farmers in conservation agriculture: a comparative analysis from Kenya
Purpose: We investigated the variation between participatory and traditional agricultural advisory models in training farmers on Conservation Agriculture in Kenya in order to understand how these models influenced farmer uptake of practices, development of social networks and delivery cost of training. Design/methodology/approach: A pre–post-test was applied using panel data from before and after participants took part in Conservation Agriculture training. Data on the assessment criteria were collected, and key informant interviews, personal observation and training reports added context to the findings. Data analysis included logistic regression and social network analysis. Findings: All advisory models built social networks, enhancing information diffusion, but at different levels. Of the participatory models, Landcare, exhibited greater farmer uptake of Conservation Agriculture and more developed social networks, while the participatory Farmer Field School model performance was similar to the traditional advisory model. Both participatory models were more expensive to deliver than the traditional model. Practical implications: Agricultural advisory services should be delivered through a collaborative and pluralistic advisory system and include participatory needs identification and explicit social capital building strategies, with advisory agents’ capacity to deliver these strategies enhanced. Opportunities for strategic cost-saving measures should be sought. Theoretical implications: Findings from this study partly agree with the literature that participatory models enhance the adoption of complex agricultural practices compared with traditional models. Originality: Benefits of participatory models are outlined extensively in the literature; however, understanding the variable implementation of these models, and how they perform compared to traditional models, has not been sufficiently studied. © 2020 Wageningen University.
Factors affecting adoption of Agroforestry by women and youths in Mt. Elgon Region, Uganda
With the majority of the world’s poor living and working in developing countries, investment in agroforestry will only make sense if it addresses challenges of specific farming contexts. Even though agroforestry is beneficial to stakeholders in several ways, it has received mixed reactions from farming communities in the developing countries. Accordingly, adoption of “new” technologies has not been as fast as desired (Mwase2015). In Uganda agroforestry is spearheaded by the women and youth because they comprise a critical labor force on farm. In the Mt.Elgon region, interest in agroforestry technologies is low and yet prevailing soil and water management challenges require robust mechanism to addresst hem. Amongst them, is the integration of specific trees/shrubs on farms. A survey was undertaken in this region to assess the factors affecting adoption of agroforestry amongst women and youths. The objectives were to i) assess the socio- economic characteristics of farmers, ii) assess the benefits from agroforestry technologies and iii) identify challenges, incentives and strategies towards adoption of agroforestry among the women and youths.
Adoption of Climate-Smart Agriculture Technologies in Tanzania
Climate-smart agriculture (CSA) is agriculture that increases productivity, improves resilience, and mitigates climate change. Smallholder adoption of farming technology is necessary to speed the transition to CSA. Here, we assessed the determinants of adoption of five technologies that can help achieve some of the CSA outcomes in smallholder farms in Tanzania. They included crop and livestock diversity, irrigation, application of chemical fertilizers, and agroforestry. Using data collected from 821 farming households, a multivariate probit model was employed to evaluate the determinants of adoption, allowing for examination of synergies and trade-offs between the technologies. Application of chemical fertilizers was fairly well adopted (34% of farmers), while irrigation was least adopted (26%). Some technologies, including crop diversity and irrigation as well as application of chemical fertilizer and agro-forestry, complemented each other. Trade-offs were observed between livestock diversity and irrigation. Female control of farm resources, farm location, and household resources were major determinants of adoption. We, therefore, recommend strategies that seeks to enhance building household resource as pathway for improved adoption of new technologies.
Psychological Factors Influencing Farmers’ Intention to Adopt Agroforestry: A Structural Equation Modeling Approach
The biophysical characteristics of the farm and farmers’ socioeconomic factors have been used to explain adoption of technologies in Africa. However, agricultural technology adoption requires that we also understand the psychological factors that can encourage or discourage farmer adoption of technologies. The aim of this article is to assess the psychological drivers of farmers’ intentions to adopt agroforestry technologies on their farms. We obtained data from 400 smallholder farmers in the Mt. Elgon region of Uganda. The Theory of Planned Behavior was used as the main framework. Quantitative data were analyzed using structural equation modeling to assess the impact of a set of psychological factors on farmers’ intention to integrate trees in coffee. The intention of farmers to integrate trees in coffee plantations was mainly driven by their evaluation of the benefits of shaded coffee (attitude) followed by beliefs about their own capability (perceived behavioral control). However, social pressure (subjective norm) was insignificant, implying that smallholder farmers tend to deny the influence of other people’s behavior on their actions. Therefore, farmers’ positive evaluation of shading coffee and the perceived capability to overcome tree planting barriers reinforced their intention to integrate trees in coffee. This renders attitude and perceived behavioral control as reliable predictors of farmer tree planting behavior, especially in the context of developing countries.
Farmers’ perceptions of tree shelterbelts on agricultural land in rural Kyrgyzstan
The reestablishment of agroforestry systems in Central Asia, combining crop production with protective tree shelterbelts, provides significant potential to improve farming systems. This includes increasing crop yields, additional income from timber, as well as reducing soil degradation and wind erosion. Thus, adopting shelterbelts as a form of pro-environmental behavior provides a number of socio-economic benefits, although some trade-offs need to be considered as well. This paper investigates factors that shape the perception of-and attitude towards-the establishment of tree shelterbelts by farmers in two case study regions in Kyrgyzstan. Applying a conceptual framework distinguishing between extrinsic and intrinsic factors, research methods included semi-structured interviews with farmers and local policy makers. The results show spatial differences in the perception of shelterbelts, both on the regional and on the village scale. In general, shelterbelts were negatively perceived by more than half of the farmers. The main concern raised by farmers was the shading of crops by shelterbelts, resulting in a decrease of yield. In addition, small field sizes and potential conflicts with neighbors were key concerns. Furthermore, rules set by local self-government bodies were inconsistent with the legislative framework, posing significant restrictions to the adoption of sustainable land use practices. © 2020 by the authors.
Constructing A Climate-Smart readiness index for smallholder farmers: The case of prioritized bundles of climate information services and climate smart agriculture in Ghana
Issues around bundling of climate smart agriculture (CSA) and climate information services (CIS) have been kept relatively distinct whereas in reality, they are more impactful when integrated. Using the case of the Accelerating Impacts of CGIAR Climate Change Research in Africa (AICCRA) Project that emphasizes bundling as a critical component of research in development, six regions of Ghana were purposively selected based on the identified value chain crops for implementation. 120 respondents including practicing farmers and advisors as reference, rated contributions of 21 innovations to 25 climate smartness indicators. These include gender, youth and social inclusion (GSI), enabling environment (EE), ability to enhance soil, water, crop and animal health (One-Health Achievement) (OHA), end-user friendliness (EUF) and climate smart agriculture (CS) for prioritization, bundling and ultimately to construct a Climate Smart Readiness Index (CSRI). There was a high level of concordance between the ratings of farmers and advisors on the Climate Smartness; moderate concordance on OHA and a lower concordance on GSI. The CS and EUF had a significant and same agreement among farmers while EE had a substantial same agreement among advisors. These elements (CS, GSI, OHA, EUF, EE) formed an integral part of the CSRI construct confirmed by the Fornell-Larcker and the Heterotrait-Monotrait criteria. While OHA was the fundamental factor in determining CSRI for farmers, EE was considered more important by the advisors. CSRI informs policy makers and agricultural practitioners on appropriate bundling of CSA and CIS practices to generate evidence for farmer preparedness in the context of resilience, productivity, adaptation, and mitigation.
Three hurdles towards commercialisation: integrating subsistence chickpea producers in the market economy
Enhancing agricultural productivity through the adoption of improved technologies presents a credible pathway to economic development and poverty reduction especially through increased commercialisation of production. We used a triple hurdle (TH) model to estimate the production and commercialisation of smallholder farmers in Ethiopia. In doing so, we account for the adoption of improved Cicer arietinum (chickpea) varieties on commercialisation using a three-wave panel data set. We estimate a correlated random effect model with a control function and find the adoption of improved chickpea to have a significant positive effect on smallholder commercialisation. Our findings that are robust over different specifications and identification strategies also support the role of transaction cost in driving market participation (MP). Finally, we argue that the TH model is a better fit to the commonly used double hurdle model for MP, when not all households in a population produce a particular crop.