Can We Use Machine Learning for Agricultural Land Suitability Assessment?

It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment.

Automated summary

This study explored the suitability of specialty crops in Denmark using machine learning and compared it to the mechanistic model ECOCROP, finding little agreement between the two methods. The research suggests that machine learning predicts socioeconomic suitability while the term land suitability may lead to misinterpretation, highlighting the importance of distinguishing between socioeconomic and ecological suitability in agricultural land assessment.