Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cultivation (SAC) to sustain optimal cell growth and minimize mutual shading. An aggregation-based sedimentation (ABS) strategy is then designed to achieve low-cost biomass harvesting and economical SAC. The ABS is achieved by engineering a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which increases cyanobacterial cell surface hydrophobicity and enables efficient cell aggregation and sedimentation. SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over a sustained period in photobioreactors. Scaling-up the SAC with an outdoor pond system achieves a biomass yield of 43.3 g/m2/day, bringing the minimum biomass selling price down to approximately $281 per ton.

Climate‐smart agriculture (CSA) is an integrated approach to sustainably meeting food, fiber, and feed production needs. The technical and socioeconomic feasibility of different CSA strategies depends on local conditions, and there is no one‐size‐fits‐all approach. Here, we review two key aspects of CSA with a focus on Texas: soil C sequestration and water management. Carbon sequestration potential is highly variable across Texas as it depends on local biophysical conditions and soil management practices in place, for example, tillage and cover crops. Grasslands also have an important role to play in C sequestration. Important co‐benefits of effective soil management for C sequestration, such as reduced CO2 emissions, enhanced soil structure, and increased microbial activity, can positively impact soil fertility and productivity. The economic and political realities of C sequestration will have a strong influence on the implementation of technically feasible strategies. The major challenge for water management is the sustainable allocation of increasingly scarce resources. Expanded irrigation is a short‐term solution, but in many cases, the existing water supply is insufficient to meet future demand. A drying Texas, and aquifer depletion, portends lower future supplies. The Panhandle, Llano Estacado, and Rio Grande regions have the greatest projected gaps between future supply and demand. Increasing water‐use efficiency and using drought‐tolerant crops are important management goals and precision agriculture with site‐specific management measures could help improve drought resiliency. Texas’ geographic diversity is reflected in the variety of agricultural commodities produced by the state, and CSA activities are likely to be equally diverse.

The Supplemental Nutrition Assistance Program (SNAP) has grown rapidly over the past 2 decades. A large literature relies on state‐level panel data on SNAP enrollment and implements traditional two‐way fixed effects estimators to identify the impact of economic conditions on SNAP enrollment. This empirical strategy implicitly assumes slope parameter homogeneity and ignores the possibility of cross‐sectional dependence in the regression error terms. The latter could feasibly arise in state‐level panel data if the time‐varying unobserved common shocks, such as national financial crises, have differential effects on SNAP participation across states in the United States. This study empirically evaluates the appropriateness of these two assumptions by adopting a more general common factor model, allowing for slope parameter heterogeneity and error term cross‐sectional dependence both separately and jointly. We find that although assuming a common slope parameter across states does not seem problematic for identification, allowing for the error term cross‐sectional dependence leads to a roughly 40% reduction in the estimated long‐run impact of the unemployment rate on SNAP enrollment. This finding has important implications for policymaking decisions—even small biases could lead to suboptimal policy responses considering the program's size. Our counterfactual simulations support our main results, implying the importance of carefully accounting for time‐varying unobserved heterogeneity when studying the cyclicality of SNAP enrollment using state‐level panel data.