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GeoPEARL_NL is used as a higher tier instrument in the leaching assessment of plant protection products in the Netherlands. Because the soil organic matter contents in arable soils in the current version were too high, a new soil organic matter content map for the Netherlands was needed. The new 3D organic matter content map was created in two step...
Citations
... F I G U R E 1 Time evolution of global and KNN inefficienciesSPATIAL-DYNAMIC INEFFICIENCY ANALYSIS of the Netherlands) or differences in soil conditions as dairy farms can be found on sandy, peat, clay and loss soils (van denBerg et al., 2017). However, inefficiencies with respect to variable inputs exhibit lower values when a global technology is considered. ...
This paper accounts for spatial effects by benchmarking farms against their k-nearest neighbours (KNN) and measuring their inefficiency in a non-parametric dynamic by-production setting. The optimal number of neighbours k against which farms are compared corresponds to the value of k that maximises the Moran I test for spatial autocorrelation of the good and the bad output of the farms' two sub-technologies. The inefficiency scores for farms' good output, variable inputs, investments and bad outputs are then computed and compared with those calculated based on a global technology, which benchmarks all farms together. The application focuses on an unbalanced panel of specialised Dutch dairy farms over the period 2009–2016 that contains information on their exact geographical locations. The results suggest that the inefficiency scores exhibit statistically significant differences between the KNN and the global model. Specifically, the inefficiencies are generally deflated when a KNN technology is considered, suggesting that ignoring spatial effects can overestimate inefficiency.
... Soil map of the Netherlands (Van den Berg et al., 2017). Purple areas are peat soils, green areas are clay, and beige areas are sand. ...
In The Netherlands, peatlands have been drained on a wide scale, causing the decomposition of peat with subsidence and greenhouse gas emissions as a result. The Dutch government aims to limit greenhouse gas emissions from peatlands by 1,0 Mt CO2-eq in 2030 and has committed itself to a 95% reduction in 2050, which will translate to a reduction of 3,9 Mt CO2-eq compared to 1990. Provinces are currently developing strategies to achieve the 2030 goal and a nationwide research program has been set up to monitor and model emissions better. Based on these ongoing strategies and programs, this study wants to inform about the necessary intermediate steps and ultimate objective needed to reach the reduction goals of 1 Mt CO2-eq reduction in 2030 and a reduction of 95%, 3,9 Mt, in 2050 for the Dutch peatlands by designing possible scenarios and pathways.
Where scenarios only describe management strategies in detail, pathways showcase the effect of these scenarios on greenhouse gas emissions and radiative forcing in the form of trajectories. In order to develop pathways for this study, the current literature around emission factors was summarized, existing provinces’ strategies were analysed for trends and weaknesses, new scenarios were developed, and the scenarios’ effects on greenhouse gas emissions and radiative forcing were calculated.
Based on this study, several recommendations can be made. Firstly, it is recommended that CH4 emissions are incorporated in National Inventory Reporting and the Soil Organic Matter Emission Registration System model. Secondly, it is recommended to stop neglecting peaty soils in Dutch reduction goals since peaty soils emit more greenhouse gasses than peatlands in 2050 when no actions are taken. Thirdly, conventional farming practices, defined as land uses with “very deep drainage”, “deep drainage”, “moderate drainage”, and “submerged drainage”, will be impossible to maintain if a 95% reduction goal in 2050 wants to be reached for the Dutch peatlands. Lastly, measures that work in reaching the 2030 goal proved to be insufficient in reaching the 2050 goal. Therefore, it is recommended that the government set up a process to define a realistic goal for 2050. In this way, all parties can already take this 2050 goal into account when developing their strategy for the coming decade.
... Therefore, the WG was pleased to welcome the involvement of new very large countries, India (Dharumarajan et al., 2019), China (e.g. Liang et al., 2019;Liu et al., 2019) and of smaller ones (e.g., The Netherlands, Van den Berg et al., 2017). This strong involvement of new countries indicates that new country-based products will be delivered and that the GlobalSoilMap WG will enlarge its participants and its geographical coverage. ...
In this concluding paper, we summarize the main advances coming forward from the joint conference of the International Union of Soil Sciences (IUSS) Working Groups (WG) “Digital Soil mapping” (DSM) and “GlobalSoilMap”. We outline the increased availability of data and covariates. Large efforts to rescue legacy data and to put them in a harmonized format are ongoing in many parts of the world. New countries are joining the GlobalSoilMap initiative. During the same time, significant progress have been made in the countries which were among the first to develop GlobalSoilMap products. We stress the recent trends in tools used for predictive mapping of soil properties. Some solutions were proposed to solve issues about data privacy. We give examples on how to move from DSM soil digital soil mapping assessment. Aligning our research with ongoing activities within the Global Soil Partnership of the FAO has been proven successful. A need was expressed to work on the uncertainty of indicators of prediction performances and to re-evaluate validation strategies. It is necessary to develop more intuitive metrics for uncertainty assessment for interpreting and evaluating soil maps. The main progresses, remaining issues and challenges and the way forward are summarized and we propose ambitious working plans and road-maps for the two WGs and stress their complementarities.
... The reaction of the traditional soil mapper may then be: "not only does DSM ignore my knowledge, but it steals my data". Secondly, legacy soil maps, even at rather coarse scales, are often a source of valuable soil information that is used with soil profile point data, used in digital soil mapping (e.g., Kempen et al., 2010;Marchant et al., 2010;Lilburne et al., 2012;Collard et al., 2014;Van den Berg et al., 2017) or they may be used as learning areas to predict soil classes or soil properties over larger areas (e.g., Lagacherie et al., 1995;Voltz et al., 1997;Grinand et al., 2008). Indeed, soil maps from legacy soil survey data across the world are being rescued, compiled, and repurposed to serve as input for a number of DSM projects in many countries (Arrouays et al., 2017b). ...
Since the turn of the millennium, digital soil mapping (DSM) has revolutionized the production of fine resolution gridded soil data with associated uncertainty. However, the link to conventional soil maps has not been sufficiently explained nor are the approaches complementary and synergistic. Further training on the digital soil mapping approaches, and associated strengths and weaknesses is required. The user community requires training in, and experience with, the new digital soil map products, especially about the use of uncertainties for risk modelling and policy development. Standards are required for public and private sector digital soil map products to prevent the production of poor-quality information which will become misleading and counter-productive. Machine-learning methods are to be used with caution with respect to their interpretability and parsimony. The use of DSM products for improved pedological understanding and soil survey interpretations requires urgent investigation.
... Some countries have invested in defining this; the Netherlands have, for example, indicated that the SPG is that the annual average concentration in groundwater at 10 m beneath an average treated field should not exceed 0.1 μg L −1 in 90% of years. 9 For most countries the debate about SPG is ongoing and the authors intend that this paper will contribute to it. ...
... To be sure of this requires an element of calibration and examples exist of this. 9 The scenarios demonstrated in this paper suggest that the system is quite well calibrated already, but a systematic calibration should be conducted before Tier 3b could be considered a standardized system. A significant advantage of the Tier 3b approach is that local issues are picked up that can be missed with national level statistics. ...
BACKGROUND
This study compares standard regulatory methodology (fixed scenarios and models) to spatial modelling at a 1 km landscape resolution for the evaluation of predicted environmental concentrations of pesticides in groundwater. The use of spatial modelling in the decision‐making processes is discussed and three options for the sub‐national evaluation and restriction of substances based on spatial environmental fate modelling are examined. Wheat and sugar beet are tested with two modified FOCUS substances (A and D) in the PEARL and GeoPEARL models. The 80th percentile value in time and space, aggregated to three different sub‐national divisions of interest to a regulator, is used as a regulatory relevant output.
RESULTS
Means and medians of predicted environmental concentrations at the national level are not useful summary statistics in the age of extensive and freely available geospatial data. A better statistic to use is the P80 (or other desired threshold/percentile combination) in time and space of predicted environmental concentration, combined with flexible and adaptable sub‐divisions of the country based on the desired protective target.
CONCLUSION
Tier 3b modelling is shown to provide an increase in localism and regulatory nuance over Tier 1 scenarios when combined with soil and aquifer type sub‐national units. © 2019 Society of Chemical Industry
Soil Organic Carbon (SOC) influences many soil properties including nutrient and water holding capacity, nutrient cycling and stability, improved water infiltration, and aeration. It also is an essential parameter in the assessment of soil quality, especially for agricultural production. However, SOC mapping is a complicated process that is costly and time-consuming due to the physical challenges of the natural conditions that are being surveyed. The best model for SOC mapping is still in debate among many researchers. Recently, the development of machine learning and Geographical Information Systems (GIS) has provided the potential for more accurate spatial prediction of SOC content.
This research was conducted in a relatively small-scale capacity in the Central Vietnam region. The aim of this study is to compare the accuracy of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Random Forest (RF) methods for SOC interpolation, with a dataset of 47 soil samples for an area of 145 hectares. Three environmental variables including elevation, slope, and the Normalized Difference Vegetation Index
(NDVI) were used for the RF model. In the RF model, the values of the number of variables randomly sampled as candidates at each split, (mtry),
and the number of bootstraps replicates, (ntree), were determined in terms of 1 and 1,000 respectively The results of our research site showed that using IDW is the most accurate method for SOC mapping, followed by the methods of RF and OK respectively. Concerning SOC mapping based-on auxiliary variables, in areas where there is human activity, the selection of auxiliary variables should be carefully considered because the variation in the SOC may not only be due to environmental variables but also by farming technologies.
