Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Gerard B.M. Heuvelink"'
Publikováno v:
Ecological Informatics, Vol 84, Iss , Pp 102892- (2024)
In digital soil mapping, machine learning is gradually replacing traditional statistical models because of their greater flexibility and better prediction performance. However, unlike traditional models, a notable drawback of machine learning models
Externí odkaz:
https://doaj.org/article/d889c8d817d14721835199949e887665
Autor:
Niels H. Batjes, Eric Ceschia, Gerard B.M. Heuvelink, Julien Demenois, Guerric le Maire, Rémi Cardinael, Cristina Arias-Navarro, Fenny van Egmond
Publikováno v:
Carbon Management, Vol 15, Iss 1 (2024)
Soils are the largest terrestrial reservoir of organic carbon, yet they are easily degraded. Consistent and accurate monitoring of changes in soil organic carbon stocks and net greenhouse gas emissions, reporting, and their verification is key to fac
Externí odkaz:
https://doaj.org/article/f5bf9733ace14951aedea5426e4cbb0d
Publikováno v:
Geoderma, Vol 450, Iss , Pp 117052- (2024)
Soil information is critical for a wide range of land resource and environmental decisions. These decisions will be compromised when the soil information quality is unsatisfactory. Thus, users of soil information need to understand and consider the u
Externí odkaz:
https://doaj.org/article/0c8938738efb466eb1eb79e99edbf782
Publikováno v:
Heliyon, Vol 10, Iss 17, Pp e37065- (2024)
Maize (Zea mays) is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequen
Externí odkaz:
https://doaj.org/article/7679f02104614693960b266977d355dd
Autor:
Maarten van Doorn, Anatol Helfenstein, Gerard H. Ros, Gerard B.M. Heuvelink, Debby A.M.D. van Rotterdam-Los, Sven E. Verweij, Wim de Vries
Publikováno v:
Geoderma, Vol 443, Iss , Pp 116838- (2024)
Amorphous iron- and aluminium-(hydr)oxides are key soil properties in controlling the dynamics of phosphorus availability and carbon storage. These oxides affect the potential of soils to retain phosphorus and carbon, thus affecting ecosystem service
Externí odkaz:
https://doaj.org/article/4aa10cf360304c9fb4026d159896bda7
Publikováno v:
Geoderma, Vol 442, Iss , Pp 116762- (2024)
Soil properties that are considered difficult to measure are frequently determined through pedotransfer functions (PTFs). Calibration and validation datasets, containing measurements of the target soil property as well as widely available basic soil
Externí odkaz:
https://doaj.org/article/1306ccc3f1de4be99be33d77f82bdf1c
Publikováno v:
Geoderma, Vol 441, Iss , Pp 116740- (2024)
Spatial soil information is essential for informed decision-making in a wide range of fields. Digital soil mapping (DSM) using machine learning algorithms has become a popular approach for generating soil maps. DSM capitalises on the relation between
Externí odkaz:
https://doaj.org/article/c4c38f83676e477f93182469b2c0a6e6
Autor:
Maria Eliza Turek, Laura Poggio, Niels H. Batjes, Robson André Armindo, Quirijn de Jong van Lier, Luis de Sousa, Gerard B.M. Heuvelink
Publikováno v:
International Soil and Water Conservation Research, Vol 11, Iss 2, Pp 225-239 (2023)
Present global maps of soil water retention (SWR) are mostly derived from pedotransfer functions (PTFs) applied to maps of other basic soil properties. As an alternative, ‘point-based’ mapping of soil water content can improve global soil data av
Externí odkaz:
https://doaj.org/article/17f54989f43642ef88b554413345603c
Autor:
Ying-xia LIU, Gerard B.M. HEUVELINK, Zhanguo BAI, Ping HE, Rong JIANG, Shao-hui HUANG, Xin-peng XU
Publikováno v:
Journal of Integrative Agriculture, Vol 21, Iss 12, Pp 3637-3657 (2022)
Understanding the spatial-temporal dynamics of crop nitrogen (N) use efficiency (NUE) and the relationship with explanatory environmental variables can support land-use management and policymaking. Nevertheless, the application of statistical models
Externí odkaz:
https://doaj.org/article/9439ce7b2f14476d88c6d15fc5928816
Publikováno v:
PeerJ, Vol 8, p e9558 (2020)
River discharges are often predicted based on a calibrated rainfall-runoff model. The major sources of uncertainty, namely input, parameter and model structural uncertainty must all be taken into account to obtain realistic estimates of the accuracy
Externí odkaz:
https://doaj.org/article/f67f5f58695942bc9fb70ecd91b0db72