Mogens Humlekrog Greve | Aarhus University (original) (raw)

Papers by Mogens Humlekrog Greve

Geografisk Tidsskrift, 2000

Soil pH influences a wide range of functionalities in soil system controlling ions mobility, solu... more Soil pH influences a wide range of functionalities in soil system controlling ions mobility, solubility and also microbial activities at extreme pH. So, a better land and crop-nutrient management plan needs detailed information on soil pH distribution especially in Denmark where 61% of total area is cultivated. Our research purpose is to investigate and visualize pH variability of Danish soils to 1m depth from the surface. Total 1950 profiles with pH data (1soil:5CaCl2) gathered from different sources (nation-wide 7km grid and other sources) were analyzed. Equal area splines were fitted to harmonize the pH depth function, and averaged for 0-5, 5-10, 10-20, 20-30, 30-50, 50-70 and 70-100cm depths and later on aggregated to 0-30 and 30-100cm to know the top and subsoil pH status. Rule-based regression method was applied to build prediction models on 75% training profiles and validated on the remaining profiles. The predictors used were elevation, slope, aspect, TWI, overland flow dist...

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

ABSTRACT Despite using multiple input datasets for effective crop classification, it is important... more ABSTRACT Despite using multiple input datasets for effective crop classification, it is important to select an appropriate method that efficiently integrates these multiple datasets to produce accurate classification results. In this paper, we present an endorsement theory-based crop classification approach that considers the qualitative information, in terms of prediction probabilities, from different input datasets and integrates them efficiently to produce final classification results. Three different input datasets are used in this study: 1) spectral; 2) texture; and 3) indices from multitemporal (spring, early summer) WorldView-2 multispectral imagery. A multilayer perceptron classifier is trained with the multitemporal datasets separately using a backpropagation learning algorithm, and prediction probabilities are produced for each pixel as evidence against each crop class. An integration rule based on endorsement theory is applied to these multiple evidence by considering their individual contribution, and the most probable class of a pixel is identified. Integration of the three multidate datasets by the proposed method is found to produce higher overall classification accuracy (91.2%) when compared to conventional winner-takes-all approach (89%). In order to determine which individual dataset is more useful for crop discrimination, the dataset's performance is compared using evidence and contributions produced in the proposed integration method for four selected crops, for both single- and multidate. The results of this analyses showed that seasonal textures information outperformed both spectral and indices. To verify this finding, results of individual dataset classification are examined. The highest overall classification accuracy of 88.8% is achieved by the use of multidate texture, where multidate spectral and indices resulted in 86.3% and 84.4%, respectively.

Remote Sensing

Peatlands constitute extremely valuable areas because of their ability to store large amounts of ... more Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range ...

Journal of Contaminant Hydrology, 2016

Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soi... more Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soil structure, and macropore networks. Attempts have been made in previous studies to use infiltration experiments to identify the degree of preferential flow, but these attempts have often been based on small datasets or data collected from literature with differing initial and boundary conditions. This study examined the relationship between tracer breakthrough characteristics, soil hydraulic properties, and basic soil properties. From six agricultural fields in Denmark, 193 intact surface soil columns 20cm in height and 20cm in diameter were collected. The soils exhibited a wide range in texture, with clay and organic carbon (OC) contents ranging from 0.03 to 0.41 and 0.01 to 0.08kgkg(-1), respectively. All experiments were carried out under the same initial and boundary conditions using tritium as a conservative tracer. The breakthrough characteristics ranged from being near normally distributed to gradually skewed to the right along with an increase in the content of the mineral fines (particles ≤50μm). The results showed that the mineral fines content was strongly correlated to functional soil structure and the derived tracer breakthrough curves (BTCs), whereas the OC content appeared less important for the shape of the BTC. Organic carbon was believed to support the stability of the soil structure rather than the actual formation of macropores causing preferential flow. The arrival times of 5% and up to 50% of the tracer mass were found to be strongly correlated with volumetric fines content. Predicted tracer concentration breakthrough points as a function of time up to 50% of applied tracer mass could be well fitted to an analytical solution to the classical advection-dispersion equation. Both cumulative tracer mass and concentration as a function of time were well predicted from the simple inputs of bulk density, clay and silt contents, and applied tracer mass. The new concept seems promising as a platform towards more accurate proxy functions for dissolved contaminant transport in intact soil.

Geografisk Tidsskrift, 2007

Geografisk Tidsskrift, Danish Journal of Geography 107(2) 1 Denmark is characterized by an intens... more Geografisk Tidsskrift, Danish Journal of Geography 107(2) 1 Denmark is characterized by an intensive agricultural sy-stem with a large animal production. ... Geografisk Tidsskrift Danish Journal of Geography 107(2):1-12, 2007 Generating a Danish raster-based topsoil property ...

Soil Science Society of America Journal

Agronomy

It is vital for farmers to know if their land is suitable for the crops that they plan to grow. A... more 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 am...

Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping i... more Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping intra-field variability. At the same time, sensor data, such as electromagnetic induction measurements and aerial imagery, can be highly useful for mapping soil properties that correlate with electrical conductivity or soil color. However, maps based on these data nearly always require calibration with local samples, as multiple factors can affect the sensor measurements. In this study, we present a method, which combines coarse-resolution, large extent soil maps with sensor data in order to improve predictions of soil properties. We test this method for predicting clay and soil organic matter contents at five agricultural fields located in Denmark. We test the method for one field at a time, using soil samples from the four other fields to predict soil properties. Results show that the method generally improves predictions over the predictions from the coarse-resolution maps, especially ...

<p&amp... more <p>Phosphorus (P) is one of the most important plant nutrients, and farmers regularly apply P as mineral fertilizer and with animal manures. Typically, reactions with amorphous aluminum and iron oxides or carbonates retain P in the soil. However, if P additions exceed the soil’s ability to bind them, P may leach from soil to surface waters, where it causes eutrophication. The phosphorus sorption capacity (PSC) is thus an inherent soil property that, when related to bound P, can describe the P saturation of the soil. Detailed knowledge of the spatial distribution of the PSC is therefore important information for assessing the risk of P leaching from agricultural land.</p><p>In weakly acidic soils predominant in Denmark, the PSC depends mainly on the oxalate-extractable contents of aluminum and iron. In this study, we aimed to map PSC in four depth intervals (0 – 25; 25 – 50; 50 – 75; 75 – 100 cm) for Denmark using measurements of oxalate-extractable aluminum and iron from 1,623 locations.</p><p>We mapped both elements using quantile regression forests. Predictions of oxalate-extractable aluminum had a weighted RMSE of 13.9 mmol kg<sup>-1</sup>. For oxalate-extractable iron, weighted RMSE was 33.5 mmol kg<sup>-1</sup>.</p><p>We included depth as a covariate and therefore trained one model for each element. For each element in each depth interval, we predicted the mean prediction value as well as 100 quantiles ranging from 0.5% to 99.5% in 1% intervals. The maps had a 30.4 m resolution. We then calculated PSC by convoluting the prediction quantiles of the two elements, using every combination of quantiles, in order to obtain the prediction uncertainty for PSC.</p><p>Oxalate-extractable aluminum was roughly normal distributed, while oxalate-extractable iron had a large positive skew. The age and origin of the parent material had a large effect on oxalate-extractable aluminum, and soil-forming processes such as weathering and podzolization had clear effects on the distribution in depth. Meanwhile, organic matter, texture and wetland processes were the main factors affecting oxalate-extractable iron, so much so that they obscured any trends with depth.</p><p>The weighted RMSE of the predicted PSC was 19.1 mmol kg<sup>-1</sup>. PSC was highest in wetland areas and lowest in young upland deposits, such as aeolian deposits and the loamy Weichselian moraines of eastern Denmark. The sandy glaciofluvial plains and Saalian moraines of western Denmark had intermediate PSC. In most cases, PSC was highest in the top soil, but in the sandy soils of western Denmark, PSC was highest in the depth interval 25…

Agronomy

Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling ... more Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling yield, the difference between potential and actual yield consistently changes because of advances in technology. Considering historical yield potential would help determine spatiotemporal trends in agricultural development. Comparing current and historical yields in Denmark is possible because yield potential has been documented throughout history. However, the current national winter wheat yield map solely uses soil properties within the model. The aim of this study was to generate a new Danish winter wheat yield map and compare the results to historical yield potential. Utilizing random forest with soil, climate, and topography variables, a winter wheat yield map was generated from 876 field trials carried out from 1992 to 2018. The random forest model performed better than the model based only on soil. The updated national yield map was then compared to yield potential maps from 1688 ...

Geografisk Tidsskrift, 2000

Soil pH influences a wide range of functionalities in soil system controlling ions mobility, solu... more Soil pH influences a wide range of functionalities in soil system controlling ions mobility, solubility and also microbial activities at extreme pH. So, a better land and crop-nutrient management plan needs detailed information on soil pH distribution especially in Denmark where 61% of total area is cultivated. Our research purpose is to investigate and visualize pH variability of Danish soils to 1m depth from the surface. Total 1950 profiles with pH data (1soil:5CaCl2) gathered from different sources (nation-wide 7km grid and other sources) were analyzed. Equal area splines were fitted to harmonize the pH depth function, and averaged for 0-5, 5-10, 10-20, 20-30, 30-50, 50-70 and 70-100cm depths and later on aggregated to 0-30 and 30-100cm to know the top and subsoil pH status. Rule-based regression method was applied to build prediction models on 75% training profiles and validated on the remaining profiles. The predictors used were elevation, slope, aspect, TWI, overland flow dist...

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014

ABSTRACT Despite using multiple input datasets for effective crop classification, it is important... more ABSTRACT Despite using multiple input datasets for effective crop classification, it is important to select an appropriate method that efficiently integrates these multiple datasets to produce accurate classification results. In this paper, we present an endorsement theory-based crop classification approach that considers the qualitative information, in terms of prediction probabilities, from different input datasets and integrates them efficiently to produce final classification results. Three different input datasets are used in this study: 1) spectral; 2) texture; and 3) indices from multitemporal (spring, early summer) WorldView-2 multispectral imagery. A multilayer perceptron classifier is trained with the multitemporal datasets separately using a backpropagation learning algorithm, and prediction probabilities are produced for each pixel as evidence against each crop class. An integration rule based on endorsement theory is applied to these multiple evidence by considering their individual contribution, and the most probable class of a pixel is identified. Integration of the three multidate datasets by the proposed method is found to produce higher overall classification accuracy (91.2%) when compared to conventional winner-takes-all approach (89%). In order to determine which individual dataset is more useful for crop discrimination, the dataset's performance is compared using evidence and contributions produced in the proposed integration method for four selected crops, for both single- and multidate. The results of this analyses showed that seasonal textures information outperformed both spectral and indices. To verify this finding, results of individual dataset classification are examined. The highest overall classification accuracy of 88.8% is achieved by the use of multidate texture, where multidate spectral and indices resulted in 86.3% and 84.4%, respectively.

Remote Sensing

Peatlands constitute extremely valuable areas because of their ability to store large amounts of ... more Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (ECa) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range ...

Journal of Contaminant Hydrology, 2016

Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soi... more Solute transport through the soil matrix is non-uniform and greatly affected by soil texture, soil structure, and macropore networks. Attempts have been made in previous studies to use infiltration experiments to identify the degree of preferential flow, but these attempts have often been based on small datasets or data collected from literature with differing initial and boundary conditions. This study examined the relationship between tracer breakthrough characteristics, soil hydraulic properties, and basic soil properties. From six agricultural fields in Denmark, 193 intact surface soil columns 20cm in height and 20cm in diameter were collected. The soils exhibited a wide range in texture, with clay and organic carbon (OC) contents ranging from 0.03 to 0.41 and 0.01 to 0.08kgkg(-1), respectively. All experiments were carried out under the same initial and boundary conditions using tritium as a conservative tracer. The breakthrough characteristics ranged from being near normally distributed to gradually skewed to the right along with an increase in the content of the mineral fines (particles ≤50μm). The results showed that the mineral fines content was strongly correlated to functional soil structure and the derived tracer breakthrough curves (BTCs), whereas the OC content appeared less important for the shape of the BTC. Organic carbon was believed to support the stability of the soil structure rather than the actual formation of macropores causing preferential flow. The arrival times of 5% and up to 50% of the tracer mass were found to be strongly correlated with volumetric fines content. Predicted tracer concentration breakthrough points as a function of time up to 50% of applied tracer mass could be well fitted to an analytical solution to the classical advection-dispersion equation. Both cumulative tracer mass and concentration as a function of time were well predicted from the simple inputs of bulk density, clay and silt contents, and applied tracer mass. The new concept seems promising as a platform towards more accurate proxy functions for dissolved contaminant transport in intact soil.

Geografisk Tidsskrift, 2007

Geografisk Tidsskrift, Danish Journal of Geography 107(2) 1 Denmark is characterized by an intens... more Geografisk Tidsskrift, Danish Journal of Geography 107(2) 1 Denmark is characterized by an intensive agricultural sy-stem with a large animal production. ... Geografisk Tidsskrift Danish Journal of Geography 107(2):1-12, 2007 Generating a Danish raster-based topsoil property ...

Soil Science Society of America Journal

Agronomy

It is vital for farmers to know if their land is suitable for the crops that they plan to grow. A... more 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 am...

Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping i... more Coarse-resolution soil maps at regional to national extents are often inappropriate for mapping intra-field variability. At the same time, sensor data, such as electromagnetic induction measurements and aerial imagery, can be highly useful for mapping soil properties that correlate with electrical conductivity or soil color. However, maps based on these data nearly always require calibration with local samples, as multiple factors can affect the sensor measurements. In this study, we present a method, which combines coarse-resolution, large extent soil maps with sensor data in order to improve predictions of soil properties. We test this method for predicting clay and soil organic matter contents at five agricultural fields located in Denmark. We test the method for one field at a time, using soil samples from the four other fields to predict soil properties. Results show that the method generally improves predictions over the predictions from the coarse-resolution maps, especially ...

<p&amp... more <p>Phosphorus (P) is one of the most important plant nutrients, and farmers regularly apply P as mineral fertilizer and with animal manures. Typically, reactions with amorphous aluminum and iron oxides or carbonates retain P in the soil. However, if P additions exceed the soil’s ability to bind them, P may leach from soil to surface waters, where it causes eutrophication. The phosphorus sorption capacity (PSC) is thus an inherent soil property that, when related to bound P, can describe the P saturation of the soil. Detailed knowledge of the spatial distribution of the PSC is therefore important information for assessing the risk of P leaching from agricultural land.</p><p>In weakly acidic soils predominant in Denmark, the PSC depends mainly on the oxalate-extractable contents of aluminum and iron. In this study, we aimed to map PSC in four depth intervals (0 – 25; 25 – 50; 50 – 75; 75 – 100 cm) for Denmark using measurements of oxalate-extractable aluminum and iron from 1,623 locations.</p><p>We mapped both elements using quantile regression forests. Predictions of oxalate-extractable aluminum had a weighted RMSE of 13.9 mmol kg<sup>-1</sup>. For oxalate-extractable iron, weighted RMSE was 33.5 mmol kg<sup>-1</sup>.</p><p>We included depth as a covariate and therefore trained one model for each element. For each element in each depth interval, we predicted the mean prediction value as well as 100 quantiles ranging from 0.5% to 99.5% in 1% intervals. The maps had a 30.4 m resolution. We then calculated PSC by convoluting the prediction quantiles of the two elements, using every combination of quantiles, in order to obtain the prediction uncertainty for PSC.</p><p>Oxalate-extractable aluminum was roughly normal distributed, while oxalate-extractable iron had a large positive skew. The age and origin of the parent material had a large effect on oxalate-extractable aluminum, and soil-forming processes such as weathering and podzolization had clear effects on the distribution in depth. Meanwhile, organic matter, texture and wetland processes were the main factors affecting oxalate-extractable iron, so much so that they obscured any trends with depth.</p><p>The weighted RMSE of the predicted PSC was 19.1 mmol kg<sup>-1</sup>. PSC was highest in wetland areas and lowest in young upland deposits, such as aeolian deposits and the loamy Weichselian moraines of eastern Denmark. The sandy glaciofluvial plains and Saalian moraines of western Denmark had intermediate PSC. In most cases, PSC was highest in the top soil, but in the sandy soils of western Denmark, PSC was highest in the depth interval 25…

Agronomy

Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling ... more Predicting wheat yield is crucial due to the importance of wheat across the world. When modeling yield, the difference between potential and actual yield consistently changes because of advances in technology. Considering historical yield potential would help determine spatiotemporal trends in agricultural development. Comparing current and historical yields in Denmark is possible because yield potential has been documented throughout history. However, the current national winter wheat yield map solely uses soil properties within the model. The aim of this study was to generate a new Danish winter wheat yield map and compare the results to historical yield potential. Utilizing random forest with soil, climate, and topography variables, a winter wheat yield map was generated from 876 field trials carried out from 1992 to 2018. The random forest model performed better than the model based only on soil. The updated national yield map was then compared to yield potential maps from 1688 ...