Tropical deforestation is estimated to cause about one-quarter of anthropogenic carbon emissions, loss of biodiversity, and other environmental services. United Nations Framework Convention for Climate Change talks are now considering mechanisms for avoiding deforestation (AD), but the economic potential of AD has yet to be addressed. We use three economic models of global land use and management to analyze the potential contribution of AD activities to reduced greenhouse gas emissions. AD activities are found to be a competitive, low-cost abatement option. A program providing a 10% reduction in deforestation from 2005 to 2030 could provide 0.3–0.6 Gt (1 Gt = 1 × 10⁵g) CO₂·yr⁻¹in emission reductions and would require $0.4 billion to $1.7 billion·yr⁻¹for 30 years. A 50% reduction in deforestation from 2005 to 2030 could provide 1.5–2.7 Gt CO₂·yr⁻¹in emission reductions and would require $17.2 billion to $28.0 billion·yr⁻¹. Finally, some caveats to the analysis that could increase costs of AD programs are described.

Land‐based climate mitigation measures have gained significant attention and importance in public and private sector climate policies. Building on previous studies, we refine and update the mitigation potentials for 20 land‐based measures in >200 countries and five regions, comparing “bottom‐up” sectoral estimates with integrated assessment models (IAMs). We also assess implementation feasibility at the country level. Cost‐effective (available up to $100/tCO₂eq) land‐based mitigation is 8–13.8 GtCO₂eq yr⁻¹between 2020 and 2050, with the bottom end of this range representing the IAM median and the upper end representing the sectoral estimate. The cost‐effective sectoral estimate is about 40% of available technical potential and is in line with achieving a 1.5°C pathway in 2050. Compared to technical potentials, cost‐effective estimates represent a more realistic and actionable target for policy. The cost‐effective potential is approximately 50% from forests and other ecosystems, 35% from agriculture, and 15% from demand‐side measures. The potential varies sixfold across the five regions assessed (0.75–4.8 GtCO2eq yr⁻¹) and the top 15 countries account for about 60% of the global potential. Protection of forests and other ecosystems and demand‐side measures present particularly high mitigation efficiency, high provision of co‐benefits, and relatively lower costs. The feasibility assessment suggests that governance, economic investment, and socio‐cultural conditions influence the likelihood that land‐based mitigation potentials are realized. A substantial portion of potential (80%) is in developing countries and LDCs, where feasibility barriers are of greatest concern. Assisting countries to overcome barriers may result in significant quantities of near‐term, low‐cost mitigation while locally achieving important climate adaptation and development benefits. Opportunities among countries vary widely depending on types of land‐based measures available, their potential co‐benefits and risks, and their feasibility. Enhanced investments and country‐specific plans that accommodate this complexity are urgently needed to realize the large global potential from improved land stewardship.

Increasing atmospheric concentrations of carbon dioxide (CO2) affect global nutrition via effects on agricultural productivity and nutrient content of food crops. We combined these effects with economic projections to estimate net changes in nutrient availability between 2010 and 2050. In this modelling study, we used the International Model for Policy Analysis of Agricultural Commodities and Trade to project per capita availability of protein, iron, and zinc in 2050. We used estimated changes in productivity of individual agricultural commodities to model effects on production, trade, prices, and consumption under moderate and high greenhouse gas emission scenarios. Two independent sources of data, which used different methodologies to determine the effect of increased atmospheric CO2 on different key crops, were combined with the modelled food supply results to estimate future nutrient availability. Although technological change, market responses, and the effects of CO2 fertilisation on yield are projected to increase global availability of dietary protein, iron, and zinc, these increases are moderated by negative effects of climate change affecting productivity and carbon penalties on nutrient content. The carbon nutrient penalty results in decreases in the global availability of dietary protein of 4·1%, iron of 2·8%, and zinc of 2·5% as calculated using one dataset, and decreases in global availability of dietary protein of 2·9%, iron of 3·9%, and zinc of 3·4% using the other dataset. The combined effects of projected increases in atmospheric CO2 (ie, carbon nutrient penalty, CO2 fertilisation, and climate effects on productivity) will decrease growth in the global availability of nutrients by 19·5% for protein, 14·4% for iron, and 14·6% for zinc relative to expected technology and market gains by 2050. The many countries that currently have high levels of nutrient deficiency would continue to be disproportionately affected. This approach is an improvement in estimating future global food security by simultaneously projecting climate change effects on crop productivity and changes in nutrient content under increased concentrations of CO2, which accounts for a much larger effect on nutrient availability than CO2 fertilisation. Regardless of the scenario used to project future consumption patterns, the net effect of increasing concentrations of atmospheric CO2 will slow progress in decreasing global nutrient deficiencies. US Environmental Protection Agency, Consultative Group on International Agricultural Research (CIGAR) Research Program on Policies, Institutions and Markets (PIM), and the CGIAR Research Program on Climate Change and Food Security (CCAFS).

Accurate projections of seasonal agricultural output are essential for improving food security. However, the collection of agricultural information through seasonal agricultural surveys is often not timely enough to inform public and private stakeholders about crop status during the growing season. Acquiring timely and accurate crop estimates can be particularly challenging in countries with predominately smallholder farms because of the large number of small plots, intense intercropping, and high diversity of crop types. In this study, we used RGB images collected from unmanned aerial vehicles (UAVs) flown in Rwanda to develop a deep learning algorithm for identifying crop types, specifically bananas, maize, and legumes, which are key strategic food crops in Rwandan agriculture. The model leverages advances in deep convolutional neural networks and transfer learning, employing the VGG16 architecture and the publicly accessible ImageNet dataset for pretraining. The developed model performs with an overall test set F1 of 0.86, with individual classes ranging from 0.49 (legumes) to 0.96 (bananas). Our findings suggest that although certain staple crops such as bananas and maize can be classified at this scale with high accuracy, crops involved in intercropping (legumes) can be difficult to identify consistently. We discuss the potential use cases for the developed model and recommend directions for future research in this area.

Agricultural emissions account for 53% of 2010 global non-CO₂ emissions and are projected to increase substantially over the next 20 years, especially in Asia, Latin America and Africa. While agriculture is a substantial source of emissions, it is also generally considered to be a potential source of cost-effective non-CO₂ GHG abatement. Previous “bottom-up” analyses provided marginal abatement cost (MAC) curves for use in modeling these options within economy-wide and global mitigation analyses. In this paper, we utilize updated economic and biophysical data and models developed by the US Environmental Protection Agency (EPA) to investigate regional mitigation potential for major sources of agricultural GHG emissions. In addition, we explore mitigation potential available at costs at or below the estimated benefits of mitigation, as represented by the social cost of carbon. Key enhancements over previous regional assessments include incorporation of additional mitigation options, updated baseline emissions projections, greater spatial disaggregation, and development of MAC curves through 2030. For croplands and rice cultivation, biophysical, process-based models (DAYCENT and DNDC) are used to simulate yields and net GHG emissions under baseline and mitigation scenarios while the livestock sector is modeled by applying key mitigation options to baselines compiled by EPA. MAC curves are generated accounting for net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. MAC curves are developed at the regional level and reveal large potential for non-CO₂ GHG mitigation at low carbon prices, especially in Asia.