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Beef cattle have high market demand in Vietnam and the Dak Lak local government encourages the development of beef value chains. Household surveys were carried out in Cu Jut and Ea Kar districts and farming systems and production specialization were found to differ in each district. Ea Kar farmers were more specialized in livestock production while Cu Jut farmers were more focused on cash crop production. The FarmDESIGN bio-economic model allowed us to study two representative farms, one from Ea Kar and one from Cu Jut district. The Ea Kar farm had a more integrated livestock production system, providing manure to the fields that produced feed for the livestock. Both farms had high farm-level nitrogen balances due to high feed and fertilizer imports. The soil organic matter (SOM) balance in Cu Jut was negative (-48 kg/ha) because of its manure management strategy. On both farms, the residues were removed from the fields, providing no input to SOM and were fed to livestock (Ea Kar) or burnt (CuJut). Livestock intensification scenarios that were implemented for the Ea Kar case study farm showed two possible pathways – forage-based and grain-based cattle fattening. Both strategies could lead to higher operating profits (+35% for forage-based cattle fattening and +59% for grain-based cattle fattening) and lower labor demands if they were skillfully implemented for the latter scenario. However, grain-based fattening negatively affected SOM balance, in contrast to forage-based fattening. The optimization of the current Ea Kar farm with FarmDESIGN indicated that there are options to change the farm setup in order to increase profitability and reduce family labor demands. However there are some trade-offs to consider. If reducing environmental impact is a priority, there are alternative farm configurations that will produce lower greenhouse gas emissions while increasing SOM and increasing overall farm profitability. These should be assessed along with the farmers' interests and priorities. Quantitative farm modeling of complex mixed farming systems can assess potential impact and support decision-making, targeting, prioritization and program design for sustainable intensification of livestock systems.

Rwanda's agricultural sector is facing severe challenges of increasing environmental degradation, resulting in declining productivity. The problem is likely to be further aggravated by the growing population pressure. A viable pathway is climate smart agriculture, aiming at the triple win of improving food security and climate change adaptation, while contributing to mitigation if possible. The Government of Rwanda has initiated ambitious policies and programs aiming at low emission agricultural development. Crop focused policies include the Crop Intensification Program (CIP) which facilitates access to inorganic fertilizer and improved seeds. In the livestock subsector, zero-grazing and improved livestock feeding are encouraged, and the Girinka program provides poor farm households with a crossbred dairy cow. In this study, we aimed at assessing the potential impact of these policy programs on food availability and greenhouse gas (GHG) emissions of 884 households across different agro-ecologies and farming systems in Rwanda. Household level calculations were used to assess the contribution of current crops, livestock and off-farm activities to food availability and GHG emissions. Across all sites, 46% of households were below the 2500 kcal MAE− 1 yr− 1 line, with lower food availability in the Southern and Eastern Rwanda. Consumed and sold food crops were the mainstay of food availability, contributing between 81.2% (low FA class) to 53.1% (high FA class). Livestock and off-farm income were the most important pathways to higher FA. Baseline GHG emissions were low, ranging between 395 and 1506 kg CO2e hh− 1 yr− 1 per site, and livestock related emissions from enteric fermentation (47.6–48.9%) and manure (26.7–31.8%) were the largest contributors to total GHG emissions across sites and FA classes. GHG emissions increased with FA, with 50% of the total GHG being emitted by 22% of the households with the highest FA scores. Scenario assessment of the three policy options showed strong differences in potential impacts: Girinka only reached one third of the household population, but acted highly pro-poor by decreasing the households below the 2500 kcal MAE− 1 yr− 1 line from 46% to 35%. However, Girinka also increased GHG by 1174 kg CO2e hh− 1 yr− 1, and can therefore not be considered climate-smart. Improved livestock feeding was the least equitable strategy, decreasing food insufficient households by only 3%. However, it increased median FA by 755 kcal MAE− 1 yr− 1 at a small GHG increase (50 kg CO2e hh− 1 yr− 1). Therefore, it is a promising option to reach the CSA triple win. Crop and soil improvement resulted in the smallest increase in median FA (FA by 755 kcal MAE− 1 yr− 1), and decreasing the proportion of households below 2500 kcal MAE− 1 yr− 1 by 6%. This came only at minimal increase in GHG emissions (23 kg CO2e hh− 1 yr− 1). All policy programs had different potential impacts and trade-offs on different sections of the farm household population. Quick calculations like the ones presented in this study can assist in policy dialogue and stakeholder engagement to better select and prioritize policies and development programs, despite the complexity of its impacts and trade-offs.