Abstract
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.
Highlights
Farmers face many risks in the form of adverse weather, pests, diseases, and changes in crop prices, laws, and regulations [1,2,3]
The ECOCROP database [33] lists crop requirements for a long list of environmental properties, but we focused on temperature, precipitation, soil pH, texture, and drainage, as experience has shown that these are some of the most important properties for crop yields in Denmark, e.g., [88]
We suggest that suitability maps based on ECOCROP and other mechanistic models display ecological suitability, whereas suitability maps based on land systems models or Machine learning (ML) models trained on land use data display socioeconomic suitability
Summary
Farmers face many risks in the form of adverse weather, pests, diseases, and changes in crop prices, laws, and regulations [1,2,3]. A first step in managing and minimizing many of these risks is often to select appropriate crops for the cultivated areas. Knowing if the land is suitable for a specific crop can decide the success or failure of agricultural strategies. As farmers are subject to climate change and a globalized economy, where frameworks for agriculture change at unprecedented speed, it is vital for them to be able to adapt to new trends [4,5,6]. Increasing the availability of land suitability information for agricultural crops would be a valuable aid for farmers to devise new agricultural strategies. At the same time, growing computational capabilities and the increasing availability of geographic data have made it quicker and easier to conduct land suitability assessments
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