Abstract
For years, biochar has gained increasing interest in the scientific community due to its significant potential to sequester carbon from the atmosphere and improve the physicochemical properties of soil which in turn increases crop yield. Multiple investigations on crop productivity using biochar-induced soil were done before but gave variable results. Different biochar properties, production methods, and application conditions have led to varied responses when applied to different soils, ranging from positive to neutral or even negative crop yield effects, necessitating the need to identify the most suitable combination of parameters to achieve the most favorable outcome. This study developed a model to maximize the beneficial effects of biochar in agricultural settings with the aid of rough set-based machine learning (RSML). Four if-then rules were accepted correlating the feedstock type, application rate, pyrolysis temperature, and soil type to the % change in crop yield. The coverage of Rules 1, 2, 3, and 4 in the training set are 19 %, 14 %, 11 %, and 6 % with an accuracy of 100 %. They also cover 13 %, 21 %, 14 %, and 4 % of the validation set at 100 % accuracy. The findings indicate that these condition attributes can have a notable impact on crop yield in biochar-induced soil. This study can also guide the agricultural sector in choosing the appropriate biochar parameters to improve soil quality and maximize crop productivity.
