A linear regression model to demonstrate balancing productivity and sustainability for small-scale farmers: A case study in Malawi

Adverse climate change effects, specifically droughts, floods, and dry spells, negatively affect agricultural production. The application of several machine learning methods assists with crop production prediction while factoring in these environmental variables. Machine learning is a crucial tool to ensure crop yield estimation, good agricultural planning practices, and effective decision-making, enabling better application of proposed interventions. Ecological intelligence signifies a paradigm shift toward balancing the competing goals of sustainability and productivity. This study aimed to demonstrate efficient agricultural productivity that addresses SDGs 12 (Responsible consumption and production), SDG 13 (Climate action), and SDG 15 (Life on land). The study was carried out in Lilongwe and Dowa districts, Malawi, and compared single and dual crop yields of farmers cultivating the same crop on similar hectarage, and their respective crop value, profitability, and sustainability. The study population comprised 62 (29.7%) male and 140 (70.3%) female farmers. A linear regression model analysis showed the importance and value of both crop yield and ecosystem resilience. The 80:20 train-test ratio split was used to produce good and effective output. Results showed that the dual crop yields of maize and beans were more profitable in comparison to both monocrop beans and maize plots. Male farmers had higher profits and yields than female farmers. These results show that sustainable practices can be incorporated into farming systems and could ensure both profitability and sustainability. However, future research will be done using intensive multiple-cropping and environmentally friendly methods that focus on consistent yields over an extended period.