Crop hybrid improvement is an efficient and environmental-friendly option to adapt to climate change and increase grain production. However, the adaptability of existing hybrids to a changing climate has not been systematically investigated. Therefore, little is known about the appropriate timing of hybrid adaptation. Here, using a novel hybrid model which coupled CERES-Maize with machine learning, we critically investigated the impacts of climate change on maize productivity with an ensemble of hybrid-specific estimations in China. We determined when and where current hybrids would become unviable and hybrid adaptation need be implemented, as well as which hybrid traits would be desirable. Climate change would have mostly negative impacts on maize productivity, and the magnitudes of yield reductions would highly depend on the growth cycle of the hybrids. Hybrid replacement could partially, but not completely, offset the yield loss caused by projected climate change. Without adaptation, approximately 53% of the cultivation areas would require hybrid renewal before 2050 under the RCP 4.5 and RCP 8.5 emission scenarios. The medium-maturing hybrids with a long grain-filling duration and a high light use efficiency would be promising, although the ideotypic traits could be different for a specific environment. The findings highlight the necessity and urgency of breeding climate resilient hybrids, providing policy-makers and crop breeders with the early signals of when, where and what hybrids will be required, which stimulate proactive investment to facilitate breeding. The proposed crop modelling approach is scalable, largely data-driven and can be used to tackle the longstanding problem of predicting hybrids’ future performance to accelerate development of new crop hybrids.

Strategic drone-delivery planning couples field locations with drone assignments and customer allocations under field capacity, cumulative payload, and round-trip energy constraints. We formulate this problem as a mixed-integer linear program and derive a Lagrangian primal heuristic with capacity-aware recovery and facility-exchange search. We show that the Lagrangian dual equals the linear-programming relaxation of the original model. For divisible demand, integer drone counts yield an exact reformulation that removes interchangeable drone labels; type-specific allocation flows extend the result to heterogeneous fleets. Experiments on 45 synthetic instances assess feasible solution quality against independent bounds. A same-machine comparison shows that the count formulation solves all 15 instances with 30 customers to optimality, while the Lagrangian heuristic has a mean objective excess of 0.72%. The count formulation also supplies incumbents and bounds for all 60- and 100-customer instances. Resource experiments distinguish changes in optimal planning cost from variation in heuristic solutions: payload tightening leaves the optimum unchanged over the tested ample fleet range, whereas a restrictive battery budget increases cost in a designated random-layout instance. The computational results favor the compact formulation for the tested divisible-demand model and identify the conditions under which the Lagrangian search improves feasible plans.

Online shopping and drone delivery has gained great popularity since COVID-19 pandemic. Given its potential for widespread deployment in cities to assist last-mile delivery, comprehensively evaluating its long-term effects on socioeconomic profiles and CO2 emissions is imperative. This paper proposes a novel urban economics model that quantifies these impacts within transportation networks. The model considers interactions between households, retailers, housing property developers, and the transportation system. Distinct from prior linear-city models, our framework explicitly incorporates network-wide congestion effects from commuters, shoppers, and delivery trucks. Numerical experiments conducted on the Sioux Falls network demonstrate that the introduction of online shopping reduces traffic congestion by 3% and annual CO2 emissions by 10%. Drone delivery captures a comparable market share to truck delivery and offers similar consumer delivery costs, and it proves more advantageous in reducing environmental impacts. This model is readily adaptable to other urban networks to assess the long-term implications of online shopping and drone delivery adoption.