Development of hierarchical terron workflow based on gridded data – A case study in Denmark

Abstract

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Can We Use Machine Learning for Agricultural Land Suitability Assessment?

“…They also included plant-available water, pH, and bulk density [66] in the same three intervals, the phosphorus sorption capacity in four 25-cm depth intervals [71], the soil drainage class [68], and the geology at 1 m depth [72]. The climatic variables included eight bioclimatic variables from the WordClim 2 dataset [73], the number of degree days above 5 • C calculated from the same dataset, four agroclimatic variables from [74], and potential incoming solar radiation calculated based on a DEM. The topographical variables included a digital elevation model (DEM) [75], and derived variables including the slope gradient, the sine and cosine of the surface aspect, the topographical wetness index, the SAGA GIS wetness index, the relative slope position, the valley depth, and a map of landscape elements [76].…”

“…In the extreme cases, climatic and socioeconomic covariates were several times more important than the topographic and soil-related covariates. The number of growing days and annual precipitation from [74] and precipitation in the wettest month from the WorldClim 2 dataset [73] were the three most important covariates. Furthermore, elevation was the most important terrain-related covariate with a mean importance of 13 (rank 12).…”

“…The ten most important covariates in the Maxent model included eight climatic covariates and two soil-related covariates (Table 2). The most important climatic covariates were solar radiation from [74], the risk of frost, and the mean annual precipitation. The two soil-related covariates were the post-glacial marine landscape type and silt contents in the depth interval 60-100 cm.…”

Exploring social-ecological impacts on trade-offs and synergies among ecosystem services

“…To identify ecosystem service bundles, k-means method was applied, which is widely used in the identification of ecosystem service clusters (Zhao et al, 2018;Roell et al, 2020). It can cluster continuous variables, so that the sum of inter-group deviation is maximized with the minimized intra-group deviation of the identified clusters.…”

An integrated package to evaluate climatic suitability for agriculture

“…The detection of impacts essentially depends on two component parts: 1) a long, reliable, continuous and representative data series at a temporal scale allowing for impacts detection; and 2) a suited and tailored statistical approach for data processing to separate the regular behaviour of data from unusual mo-In light of this, the analysis of temperature variations based on agroclimatic variables appears as a useful tool to detect and prevent climatic risks for agriculture, crop management and economic welfare. Several previous works dedicated e orts to identify climatic limits for specific crops (Trnka et al, 2014a;Rötter et al, 2012;Bois et al, 2016) and use climatic gridded data for classification purposes (Roell et al, 2020), amongst others, but none of them were particularly focused on the impacts of the extreme events, except a few ones working with extreme temperatures probabilities (e.g. Klein et al, 2017;Perondi et al, 2019).…”