How to generate DSSAT soil profile data from SoilGrids?
Best insight from top research papers
To generate DSSAT soil profile data from SoilGrids, a 10-km resolution global soil profile dataset compatible with DSSAT was developed using SoilGrids1km . SoilGrids utilizes machine learning methods and global environmental covariates to produce maps of soil properties at a medium spatial resolution of 250 meters . The dataset includes soil physical and chemical properties extracted from SoilGrids1km, with additional soil parameters estimated from HarvestChoice HC27 generic soil profiles. Pedo-transfer functions were employed to derive soil hydraulic properties, ensuring compatibility with DSSAT requirements. This approach enables the direct extraction of spatial data from grids, which is then converted into a format suitable for DSSAT model operation using Python scripts . The SoilGrids1km system provides a valuable resource for generating global soil information for crop simulation models like DSSAT, enhancing the accuracy and efficiency of soil profile data utilization .
Answers from top 5 papers
More filters
| Papers (5) | Insight |
|---|---|
| Not addressed in the paper. | |
| Spatial data from SoilGrids can be used to generate DSSAT soil profile data by dividing locations into grids, extracting data, and converting it into the required format using Python scripts. | |
09 Nov 2020 41 Citations | Not addressed in the paper. |
14 Jun 2021 371 Citations | Not addressed in the paper. |
37 Citations | Soil physical and chemical properties for DSSAT were extracted from SoilGrids1km, while soil hydraulic properties were derived using pedo-transfer functions. Other parameters were estimated from HarvestChoice HC27 generic soil profiles. |
Related Questions
How to generate DSSAT soil.SOL using SoilGrids?5 answersTo generate DSSAT soil.SOL using SoilGrids, one can utilize the spatial data division approach outlined in a study by Sachin et al.. This method involves dividing locations into grids and extracting data from each grid, which is then converted into the required crop model format using Python scripts. The converted data can be run through DSSAT on an individual basis, producing outputs that can be re-entered back into their respective grids as spatial data. Additionally, the use of state-of-the-art machine learning methods in SoilGridsand the high-resolution global predictions provided by SoilGridscan enhance the accuracy and efficiency of generating DSSAT soil.SOL files. Moreover, the geo-spatial parallelization framework described in a study by de Sousa et al.can aid in handling the expanding pool of input data and computational demands required for predictive models.
How can soil health be determined?5 answersSoil health can be determined through a combination of physical, chemical, and biological indicators. These indicators include factors like pH, organic carbon, total nitrogen, water-stable aggregate stability, and microbial activity. Assessing soil health involves evaluating its capacity to support crop growth, nutrient cycling, water retention, and carbon sequestration. Methods such as establishing a minimum data set (MDS) through principal component analysis (PCA), cluster analysis, and expert opinion can help identify key indicators for soil health assessment. Additionally, soil spectroscopy using algorithms like Partial Least Squares (PLS) and Bayesian Regularization for Feed-Forward Neural Networks (BRNN) can predict key soil properties like soil organic carbon, pH, and magnesium efficiently. Overall, a holistic approach considering various soil properties and their interconnections is crucial for determining soil health.
What are the different methods of soil preparation?5 answersSoil preparation methods vary depending on the specific needs and goals of the farmer. One method involves the use of Artificial Intelligence to optimize soil levelling, land scraping, and banding, with the aim of maximizing resource utilization and crop yield. Another method involves the preparation of a soil conditioner using potassium-containing rock powder and alkali liquid, followed by hydrothermal and silicon precipitating reactions, resulting in a cation-containing solid conditioner. Additionally, a soil preparation for preventing and treating metabolic diseases can be prepared by sterilizing soil and mixing it with probiotics or auxiliary materials, such as capsules or powders. Another method involves the preparation of a soil amendment through three stages of substrate fermentation, resulting in improved soil nutrient utilization and quality, as well as growth promotion and insect resistance. Lastly, a soil conditioner can be prepared by fermenting straw, gasified fine slag, humic acid, and residue raw materials, resulting in a product rich in calcium, phosphorus, and potassium, which can repair and condition soil and improve soil properties.
How does GLDAS-2.0 compare to other models for evaluating soil moisture?5 answersGLDAS-2.0, the Global Land Data Assimilation System, has been compared to other models for evaluating soil moisture. Results show that GLDAS performed well in arid climates in Australia and the United States, with a root mean square error (RMSE) of 0.100 m3/m3 and an unbiased RMSE (ubRMSE) of 0.060 m3/m3 on average. However, GLDAS systematically overestimated soil moisture values, with a bias of 0.067 m3/m3. In another study, GLDAS_CLSM, a component of GLDAS, was found to perform best among all root-zone soil moisture (RZSM) products, with the highest correlation coefficient (R) and lowest ubRMSE. All RZSM products tend to overestimate in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The intercomparison of RZSM products showed that MERRA-2 and SMAP L4 RZSM are the most correlated with each other.
What is SoilGrids and explain it in 3 paragraph?3 answersSoilGrids is a global soil information system that produces maps of soil properties for the entire globe at medium spatial resolution. It uses machine learning methods and incorporates state-of-the-art practices to generate models based on soil observations and global environmental covariates. The aim of SoilGrids is to provide quality-assessed global maps of soil properties with cross-validation, hyper-parameters selection, and quantification of spatially explicit uncertainty. The system has been evaluated for various soil properties at different depths and has shown metrics in line with previous studies. It has also highlighted the need for more soil observations, especially in high latitude regions. SoilGrids provides high-resolution maps of soil properties over large areas and is part of the GlobalSoilMap project. It utilizes available covariates, remote sensing products, and point soil data to generate fine grids of soil properties. The system aims to continuously improve by incorporating new models, estimating uncertainties, and merging different model strategies and products. It also emphasizes the importance of rescuing and utilizing point soil data for better predictions.
What is the best way for Digital mapping of soil classes?5 answersThe best way for digital mapping of soil classes is to use machine learning models such as ensemble gradient boosting, cost-sensitive learning, and one-class classification (OCC) combined with multi-class classification. These approaches have been shown to effectively deal with the imbalanced number of observations in soil mapping, ensuring the retention of minority classes in the final map. Specifically, extreme gradient boosting (XGB), cost-sensitive decision tree (CSDT), and one-class support vector machine combined with multi-class classification (OCCM) have been found to be successful in mapping soil classes with imbalanced frequencies of observations. These methods have been shown to significantly increase overall and individual class accuracy for mapping soil classes compared to previous approaches.