Finding Optimal Conditions for Single Response with rsm package

Join us on an hour session on using R to find the best settings when environmental outcomes depend on 2+ factors. Example: What fertilizer rate + irrigation level gives maximum maize yield with minimum runoff? What pH + temperature maximizes bioremediation of oil-polluted soil?

Why `rsm` package? Field trials are expensive and time-consuming. RSM designs 13–20 smart experiments, fits a response surface with `rsm()`, and pinpoints optimal conditions on a contour map. No more testing one-factor-at-a-time during rainy season.

Environmental focus: We’ll use Kwara data: optimize NPK dose + watering schedule for max cassava yield, or optimize incubation temp + time for maximum degradation of waste oil by Pseudomonas.

Who to Attend? Kwara-Environmental-Statistics-R members doing field work, pollution studies, climate-agriculture research, and other related researches.

Takeaway: Design your next field trial with `ccd()`, model it with `rsm()`, and report optimal conditions using `contour()` plots. Leave with `env_rsm_template.R` + practice dataset.

Replace guesswork with designed experiments. One season, one answer.