REDD+ and climate smart agriculture in landscapes: A case study in Vietnam using companion modelling

The Progress of the Development of a Climate-smart Agriculture in Europe: Is there Cohesion in the European Union?

The development of climate-smart agriculture (CSA) is crucial in ensuring the creation of a low-carbon society and mitigation of climate change. These tasks require concerted actions from multiple stakeholders since the very concept of CSA is rather complex and requires multi-dimensional consideration. This study defines and applies various indicators to evaluate the development of CSA in the European Union (EU). To do this, three different multi-criteria decision-making methods, namely Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR; multi-criteria optimization and compromise solution), were employed for the construction of a composite indicator. A combination of both objective (entropy) and subjective (Analytic Hierarchy Process) weighting techniques was utilized to derive the weights of the indicators. The leaders in the EU in terms of CSA are Austria, Denmark and the Netherlands, whereas the countries with the lowest levels of CSA development are Cyprus, Greece and Portugal. This study also revealed divergence in the development of these practices in the EU-24 for the period 2004-2019. Thus, a more inclusive approach is needed to ensure the spread of climate-smart ideas in European agriculture sectors.

Factors Affecting Climate-Smart Agriculture Practice Adaptation of Farming Households in Coastal Central Vietnam: The Case of Ninh Thuan Province

Agriculture is a sector with a long tradition and an important contribution to economic growth in Vietnam, providing food security and generating revenue for the country's exports. However, agricultural activities in Vietnam are significantly affected by climate change. In Vietnam, the central coastal area in general and Ninh Thuan province in particular are the most vulnerable areas to natural disasters. In that context, climate-smart agriculture (CSA) is a good strategy to help local famers coping with climate change while securing food security. This study has two main objectives: to assess farmers' awareness on disasters and to analyze factors affecting the decision to apply CSA practices of farming households in Ninh Thuan. The study conducted focus group discussion, in-depth interview and survey of 436 households in 3 districts of Ninh Thuan to collect data. Research results showed that local farmers have a high awareness of natural disasters trends and impacts on agricultural livelihoods. Facing with disasters in the context of climate change, farming households have applied some models of CSA such as minimal tillage, intercropping, crop rotation, crop transformation, and the use of plant varieties. Binary logit regression analysis indicated that age, education level, household income, membership of local associations, training attendance, and support policies were variables significantly influencing the farmers' decision to apply CSA. Challenges for CSA adoption include lack of capital, information, technical support and land ownership institutions in Vietnam. The study also made recommendations to promote CSA in Vietnam, including reforming the legal system, strengthening agricultural extension services, and providing technical and capital support to farmers.

Factors influencing climate change adaptation strategies in North-Western Ghana: evidence of farmers in the Black Volta Basin in Upper West region

The erratic rainfall patterns and decline in crop production are due to the threat of climate change. The responses of farm households to climate change play a very essential role in addressing the risk of climate change. This study investigated factors influencing adaptation strategies to climate change in the Black Volta Basin of Ghana. Survey questions were administered to 450 households sampled from eight districts using a multistage sampling technique. Out of the six adaptation strategies, four major identified adaptation strategies were subsequently used as the dependent variable in the multivariate (MV) probit and binary logit models. The results revealed that factors such as gender, age, household size, farmer-based-organizations membership, farm income, years of education, districts of location of respondents, farm size and climate change awareness—statistically and significantly—influenced households’ adaptation to the changing climate. However, the positive correlation matrix from the MV probit model showed complementarities among all the adaptation strategies adopted by household heads. Priority should be given to improving household heads’ adaptive capacity. Also, governmental and non-governmental organizations should invest in climate-resilient programmes. Finally, programmes aimed at sensitizing household heads on climate change adaptation strategies such as changing planting time, changing farming methods, and planting early maturing and drought-resistant varieties through pieces of trainings and workshops may be duly supported by the Government; particularly, for household heads in the Black Volta Basin.

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.

Review of Artificial Intelligence Applied in Decision-Making Processes in Agricultural Public Policy

The objective of this article is to review how Artificial Intelligence (AI) tools have helped the process of formulating agricultural public policies in the world. For this, a search process was carried out in the main scientific repositories finding different publications. The findings have shown that, first, the most commonly used AI tools are agent-based models, cellular automata, and genetic algorithms. Secondly, they have been utilized to determine land and water use, and agricultural production. In the end, the large usefulness that AI tools have in the process of formulating agricultural public policies is concluded.