Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)
The mission of MCFNS is to publish peer-reviewed basic and applied research in Mathematical and Computational Forestry and Natural-Resource Sciences. This research can include analytical solutions, proofs, derivations, software developments, and simulations, in forest management, growth and yield modeling, and other natural resource related studies. Journal items are published collectively as part of an issue with its Table of Contents biannually, currently in April and October.
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The quality of outcomes from tabu search when enhanced to address contemporary forest harvest scheduling problems
Tabu search is a metaheuristic search process that is useful for locating high-quality solutions to complex combinatorial forest harvest scheduling problems. In this work, we illustrate the quality of outcomes produced by a tabu search process when it is applied to four forest management problems, and we statistically compare the effectiveness of tabu search when it uses a range of parameters and enhancements. This illustration of the variability in results is novel to the use of tabu search to address complex agriculture and forest management problems. The addition of enhancements, which allow n-opt moves through the solution space, reversion back to high-quality positions in the solution space, and randomly designated tabu states (tenures), all seem to improve the ability of tabu search to locate near-optimal solutions with regularity. In addition, we assess whether the second-best selection from the tabu neighborhood has any value in improving the outcomes from the search process. We found that the use of 2-opt exchange moves seems essential for developing forest harvest schedules of higher quality, and the use of search reversion can often help improve the quality of solutions. The selection of the second-best solution from the tabu search neighborhood, while of little value when used exclusively, may be valuable when used sparingly and in conjunction with other search process enhancements.
Operations Research and Decision Support Systems
Autorska prava (c) 2025 Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS)
Spatiotemporal Patterns of Temperature, Precipitation, Wind, and Sunshine Extremes in the Seomjin and Yeongsan River Basins (2020--2023)
Extreme weather poses increasing risks to agriculture in South Korea's Seomjin and Yeongsan River basins. Using observations from the Korea Meteorological Administration's Automated Synoptic Observing System (ASOS) and Automatic Weather Stations (AWS), I investigated spatiotemporal extremes in temperature, precipitation, wind, and sunshine during 2020-2023. Station records were summarized by year and location, and spatial fields were generated with GIS-based interpolation at 30 x 30 m to 270 x 270 m resolution; precipitation fields included PRISM-style topographic adjustments. Both basins exhibited substantial fluctuations in minimum temperatures over the four years. Coastal areas tended to have higher minimum temperatures, consistent with maritime moderation. While extreme daily minimum temperatures differed between even- and odd-numbered years, extreme daily maximum temperatures were relatively stable; nevertheless, basin-average maximum temperatures increased from 34.1 C (2020) to 34.6 C (2021) and 35.3 C (2023). Precipitation extremes varied markedly, with station maxima up to 361.3 mm day-1 (Sunchang, 2020) and gridded maxima of approximately 280 mm day-1 in the Seomjin basin. In the Seomjin basin, northeastern sectors recorded higher daily maximum precipitation than southwestern sectors, likely reflecting topographic controls. Maximum instantaneous wind speeds reached 47.4 m s-1 at Heuksan Island, and daily sunshine duration peaked at 13.6 h at selected coastal stations. We discuss topographic influences and agricultural implications. Because the 4-year period is short, the results are descriptive rather than inferential; we recommend adopting standardized extreme-indices and cross-validated interpolation to improve comparability and uncertainty characterization in future work.