Matches in SemOpenAlex for { <https://semopenalex.org/work/W3152124088> ?p ?o ?g. }
- W3152124088 endingPage "703" @default.
- W3152124088 startingPage "703" @default.
- W3152124088 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." @default.
- W3152124088 created "2021-04-13" @default.
- W3152124088 creator A5011620756 @default.
- W3152124088 creator A5022046816 @default.
- W3152124088 creator A5042263519 @default.
- W3152124088 creator A5076644461 @default.
- W3152124088 creator A5079317921 @default.
- W3152124088 date "2021-04-07" @default.
- W3152124088 modified "2023-10-15" @default.
- W3152124088 title "Can We Use Machine Learning for Agricultural Land Suitability Assessment?" @default.
- W3152124088 cites W1058055990 @default.
- W3152124088 cites W1488884546 @default.
- W3152124088 cites W1568201516 @default.
- W3152124088 cites W1596960762 @default.
- W3152124088 cites W1882059913 @default.
- W3152124088 cites W1966811787 @default.
- W3152124088 cites W1973745582 @default.
- W3152124088 cites W1977028660 @default.
- W3152124088 cites W1980193681 @default.
- W3152124088 cites W2011751105 @default.
- W3152124088 cites W2014208327 @default.
- W3152124088 cites W2015116932 @default.
- W3152124088 cites W2015613907 @default.
- W3152124088 cites W2019566784 @default.
- W3152124088 cites W2019894796 @default.
- W3152124088 cites W2029686625 @default.
- W3152124088 cites W2033686454 @default.
- W3152124088 cites W2038546254 @default.
- W3152124088 cites W2044894889 @default.
- W3152124088 cites W2045491940 @default.
- W3152124088 cites W2049842380 @default.
- W3152124088 cites W2054325787 @default.
- W3152124088 cites W2068008351 @default.
- W3152124088 cites W2069543434 @default.
- W3152124088 cites W2073358285 @default.
- W3152124088 cites W2078741229 @default.
- W3152124088 cites W2080331507 @default.
- W3152124088 cites W2084028661 @default.
- W3152124088 cites W2091720785 @default.
- W3152124088 cites W2094120089 @default.
- W3152124088 cites W2108805342 @default.
- W3152124088 cites W2144093935 @default.
- W3152124088 cites W2147638649 @default.
- W3152124088 cites W2149276985 @default.
- W3152124088 cites W2155202598 @default.
- W3152124088 cites W2166590093 @default.
- W3152124088 cites W2255544629 @default.
- W3152124088 cites W2527277647 @default.
- W3152124088 cites W2604154178 @default.
- W3152124088 cites W2614464134 @default.
- W3152124088 cites W2766470662 @default.
- W3152124088 cites W2769311935 @default.
- W3152124088 cites W2773188111 @default.
- W3152124088 cites W2793182436 @default.
- W3152124088 cites W2793927960 @default.
- W3152124088 cites W2793997912 @default.
- W3152124088 cites W2795895615 @default.
- W3152124088 cites W2800180237 @default.
- W3152124088 cites W2802622685 @default.
- W3152124088 cites W2807945028 @default.
- W3152124088 cites W2815885864 @default.
- W3152124088 cites W2885739077 @default.
- W3152124088 cites W2914042770 @default.
- W3152124088 cites W2918825222 @default.
- W3152124088 cites W2944767846 @default.
- W3152124088 cites W2951372522 @default.
- W3152124088 cites W2952317421 @default.
- W3152124088 cites W2954490652 @default.
- W3152124088 cites W2969282511 @default.
- W3152124088 cites W2984096509 @default.
- W3152124088 cites W2985571275 @default.
- W3152124088 cites W2987708268 @default.
- W3152124088 cites W2990868965 @default.
- W3152124088 cites W2995238593 @default.
- W3152124088 cites W3008948638 @default.
- W3152124088 cites W3010840107 @default.
- W3152124088 cites W3025411477 @default.
- W3152124088 cites W3045100496 @default.
- W3152124088 cites W3093172781 @default.
- W3152124088 cites W3108268288 @default.
- W3152124088 cites W3122521071 @default.
- W3152124088 cites W3128323903 @default.
- W3152124088 cites W3198350258 @default.
- W3152124088 cites W96375871 @default.
- W3152124088 doi "https://doi.org/10.3390/agronomy11040703" @default.
- W3152124088 hasPublicationYear "2021" @default.
- W3152124088 type Work @default.
- W3152124088 sameAs 3152124088 @default.
- W3152124088 citedByCount "14" @default.
- W3152124088 countsByYear W31521240882021 @default.
- W3152124088 countsByYear W31521240882022 @default.
- W3152124088 countsByYear W31521240882023 @default.
- W3152124088 crossrefType "journal-article" @default.
- W3152124088 hasAuthorship W3152124088A5011620756 @default.
- W3152124088 hasAuthorship W3152124088A5022046816 @default.
- W3152124088 hasAuthorship W3152124088A5042263519 @default.
- W3152124088 hasAuthorship W3152124088A5076644461 @default.
- W3152124088 hasAuthorship W3152124088A5079317921 @default.