Here’s a detailed description for updating and improving your crop recommendation system based on soil data:

Description of a Crop Recommendation System with Soil Data

A crop recommendation system helps farmers select the best crops to grow based on the specific properties of their soil. This system uses soil characteristics and environmental factors to determine the crops that are most likely to thrive. Recommendations are provided to improve crop yield, optimize resource use, and ensure sustainable farming practices.

Core Components for Recommendations

The system should consider the following soil parameters and external factors to make accurate recommendations:

1. Soil Nutrients:

   • Nitrogen (N): Promotes leafy growth; ideal for crops like spinach, lettuce, and wheat.
   • Phosphorus (P): Essential for root development; crucial for legumes, peas, and root vegetables like carrots.
   • Potassium (K): Enhances disease resistance and fruit quality; important for fruiting plants like tomatoes, bananas, and potatoes.

2. Soil pH:

   • Indicates soil acidity or alkalinity.
   • Neutral pH (6.5-7.5) supports most crops like rice, wheat, and maize.
   • Acidic soil (<6.5) favors crops like tea and coffee.
   • Alkaline soil (>7.5) supports crops like barley and asparagus.

3. Organic Matter:

   • High organic content improves water retention and nutrient availability.
   • Crops like vegetables and fruits benefit from rich organic matter.

4. Moisture Level:

   • Determines irrigation needs and crop suitability.
   • High moisture crops: Paddy, sugarcane.
   • Low moisture crops: Millet, sunflower.

5. Temperature:

   • Warm crops: Maize, rice, and cotton.
   • Cool crops: Wheat, barley, and cabbage.

6. Rainfall:

   • Rain-fed crops (e.g., rice) thrive in high rainfall areas.
   • Drought-resistant crops (e.g., millets) perform well in low-rainfall zones.

7. Geographical Factors:

   • Altitude, latitude, and local climate conditions.
   • Example: Coffee grows well in high altitudes, while coconut thrives in coastal regions.

How to Update Recommendations

1. Dynamic Soil Profiles:

   • Use real-time soil testing data to determine nutrient levels, pH, and moisture.
   • Example: If the nitrogen level is low, recommend nitrogen-fixing crops like legumes.

2. Crop Rotation Insights:

   • Suggest crop rotations to maintain soil health.
   • Example: After a nitrogen-depleting crop like wheat, recommend a nitrogen-fixing crop like lentils.

3. Fertilizer Suggestions:

   • Provide recommendations for fertilizers based on deficiencies.
   • Example: If phosphorus is low, suggest adding rock phosphate.

4. Weather and Climate Integration:

   • Include real-time weather data like rainfall forecasts and temperature trends.
   • Example: Recommend drought-tolerant crops during dry seasons.

5. Regional Crop Suitability:

   • Use regional data to match crops with local soil and climate.
   • Example: Recommend paddy in water-rich regions like Punjab, and millet in arid regions like Rajasthan.

Sample Output for Crop Recommendations

Based on soil and environmental data: - Soil Parameters: - pH: 6.8 (neutral) - Nitrogen: Medium - Phosphorus: Low - Potassium: High - Moisture: Moderate - Recommendations: - Primary Crops: Wheat, Maize, Barley. - Secondary Crops (Improving Soil Health): Lentils, Chickpeas (for nitrogen fixation). - Fertilizer Recommendation: Use phosphorus-rich fertilizers (e.g., DAP).

How to Present Recommendations

• Use a dashboard or mobile app for farmers.
• Show clear visualizations of soil test results and matched crops.
• Include:
  • Top recommended crops.
  • Fertilizer and irrigation tips. -``

The National Soil Database has produced a national database of soil geochemistry including point and spatial distribution maps of major nutrients, major elements, essential trace elements, trace elements of special interest and minor elements. In addition, this study has generated a National Soil Archive, comprising bulk soil samples and a nucleic acids archive each of which represent a valuable resource for future soils research in Ireland. The geographical coherence of the geochemical results was considered to be predominantly underpinned by underlying parent material and glacial geology. Other factors such as soil type, land use, anthropogenic effects and climatic effects were also evident. The coherence between elements, as displayed by multivariate analyses, was evident in this study. Examples included strong relationships between Co, Fe, As, Mn and Cu. This study applied large-scale microbiological analysis of soils for the first time in Ireland and in doing so also investigated microbial community structure in a range of soil types in order to determine the relationship between soil microbiology and chemistry. The results of the microbiological analyses were consistent with geochemical analyses and demonstrated that bacterial community populations appeared to be predominantly determined by soil parent material and soil type. .hidden { display: none }

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Context

https://storage.googleapis.com/kagglesdsdata/datasets/1262694/2104731/GridMaps250m_Info.png?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20210410%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20210410T121915Z&X-Goog-Expires=172799&X-Goog-SignedHeaders=host&X-Goog-Signature=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" alt="IMG">

Content

Maps of clay, silt and sand contents (g kg-1) were predicted at 0-20 cm, 20-60 cm and 60-100 cm depths intervals by random forest regression in Google Earth Engine. Gridded soil information covers a part of the Midwest Brazil, from 12° S to 20° S and from 45° W to 54° W, and is available with 250m resolution. The maps were cross-validated and had Coefficient of Determination ranging from 0.64 to 0.85 at all depth intervals.

Acknowledgements

Poppiel, Raúl Roberto; Lacerda, Marilusa Pinto Coelho; Safanelli, José Lucas; Rizzo, Rodnei; Pereira de Oliveira Junior, Manuel; Novais, Jean Jesus; Dematte, Jose Alexandre (2020), “250 m-gridded soil texture at multiple depths of Midwest Brazil”, Mendeley Data, V4, doi: 10.17632/52cfcm3xr7.4

Photo by Clay Banks on Unsplash

The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change. The Harmonized World Soil Database (HWSD) was established in 2008 by the International Institute for Applied Systems Analysis (IIASA) and FAO, and in partnership with International Soil Reference and Information Centre (ISRIC), the European Soil Bureau Network (ESBN) and the Institute for Soil Sciences Chinese Academy of Sciences (CAS). The data entry and harmonization within a Geographic Information System (GIS) was carried out at IIASA, with verification of the database undertaken by all partners. HWSD was then updated in 2013 (HWSD v1.2) and in 2023 (HWSD v2.0). This updated version (HWSD v2.0) is built on the previous versions of HWSD with several improvements on (i) the data source that now includes several national soil databases, (ii) an enhanced number of soil attributes available for seven soil depth layers, instead of two in HWSD v1.2, and (iii) a common soil reference for all soil units (FAO1990 and the World Reference Base for Soil Resources). This contributes to a further harmonization of the database. The GIS raster image file is linked to the soil attribute database. The HWSD v2.0 soil attribute database provides information on the soil unit composition for each of the near 30 000 soil association mapping units. The HWSD v2.0 Viewer, provided with the database, creates this link automatically and provides direct access to the soil attribute data and the soil association information. Note: - A tutorial for accessing HWSD ver. 2.0 using R (prepared by David Rossiter, June 2023) has been added as an 'associated resource' (NOTE: Needs the SQLite version of HWSD v2 as provided below). - Soil property estimates in HWSDv2 were derived from Batjes (2016), Geoderma (https://doi.org/10.1016/j.geoderma.2016.01.034).

Version 3.1 of the ISRIC-WISE database (WISE3) was compiled from a wide range of soil profile data collected by many soil professionals worldwide. All profiles have been harmonized with respect to the original Legend (1974) and Revised Legend (1988) of FAO-Unesco. Thereby, the primary soil data ─ and any secondary data derived from them ─ can be linked using GIS to the spatial units of the digitized Soil Map of the World as well as more recent digital Soil and Terrain (SOTER) databases through the soil legend code.

WISE3 holds selected attribute data for some 10,250 soil profiles, with some 47,800 horizons, from 149 countries. Individual profiles have been sampled, described, and analyzed according to methods and standards in use in the originating countries. There is no uniform set of properties for which all profiles have analytical data, generally because only selected measurements were planned during the original surveys. Methods used for laboratory determinations of specific soil properties vary between laboratories and over time; sometimes, results for the same property cannot be compared directly. WISE3 will inevitably include gaps, being a compilation of legacy soil data derived from traditional soil survey, which can be of a taxonomic, geographic, and soil analytical nature. As a result, the amount of data available for modelling is sometimes much less than expected. Adroit use of the data, however, will permit a wide range of agricultural and environmental applications at a global and continental scale (1:500 000 and broader).

Preferred citation: Batjes NH 2009. Harmonized soil profile data for applications at global and continental scales: updates to the WISE database. Soil Use and Management 5:124–127, http://dx.doi.org/10.1111/j.1475-2743.2009.00202.x

This hosted feature layer has been published in RI State Plane Feet NAD 83.This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the Rhode Island Soil Survey Program in partnership with the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped.

For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.Information for SOILS data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five-year mapping program completed in 1980 for the purpose of forestland taxation. It was funded by the Washington State Department of Revenue. The Department of Natural Resources, Soil Conservation Service (now known as the Natural Resources Conservation Service or NRCS), USDA Forest Service and Washington State University conducted soil mapping cooperatively following national soil survey standards. Private lands having the potential of supporting commercial forests were surveyed along with interspersed small areas of State lands, Indian tribal lands, and federal lands. Because this was a cooperative soil survey project, agricultural and non-commercial forestlands were included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soil map unit delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were later added and are now available through associated lookup tables. SCS (NRCS) soils data on agricultural lands also have been subsequently added to this data layer. The SOILS data layer includes approximately 1,100 townships with wholly or partially digitized soils data. State and private lands which have the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soils survey project, agricultural and non-commercial forestlands were also included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were added at a later time and are now available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. This layer includes approximately 1, 100 townships with wholly or partially digitized soils data (2,101 townships would provide complete coverage of the state of Washington).-

The soils_sv resolves one to many relationships and as such is one of those special "DNR" spatial views ( ie. is implemented similar to a feature class). Column names may not match between SOILS_SV and the originating datasets. Use limitations

This Spatial View is available to Washingotn DNR users and those with access to the Washington State Uplands IMS site.

The following cautions only apply to one-to-many and many-to-many spatial views! Use these in the metadata only if the SV is one-to-many or many-to-many.

CAUTIONS: Area and Length Calculations: Use care when summarizing or totaling area or length calculations from spatial views with one-to-many or many-to-many relationships. One-to-many or many-to-many relationships between tabular and spatial data create multiple features in the same geometry. In other words, if there are two or more records in the table that correspond to the same feature (a single polygon, line or point), the spatial view will contain an identical copy of that feature's geometry for every corresponding record in the table. Area and length calculations should be performed carefully, to ensure they are not being exaggerated by including copies of the same feature's geometry.

Symbolizing Spatial Features:
Use care when symbolizing data in one-to-many or many-to-many spatial views. If there are multiple attributes tied to the same feature, symbolizing with a solid fill may mask other important features within the spatial view. This can be most commonly seen when symbolizing features based on a field with multiple table records.

Labeling Spatial Features: Spatial views with one-to-many or many-to-many relationships may present duplicate labels for those features with multiple table records. This is because there are multiple features in the same geometry, and each one receives a label.Soils Metadata

Processing of soil data from datasets published in the Brazilian Soil Data Repository (FEBR, https://www.pedometria.org/febr/; SoilData, https://soildata.mapbiomas.org/) until the end of 2019. The data undergoes cleaning, standardization and, when possible, harmonization. The resulting dataset is made available in a single TXT file for reuse, respecting the original data use licenses.

A global data set of soil types is available at 1-degree latitude by 1-degree longitude resolution. There are 26 soil units based on Zobler?s assessment of FAO Soil Units (Zobler, 1986). The data set was compiled as part of an effort to improve modeling of the hydrologic cycle portion of global climate models. A more extensive version of these data, including 106 soil units as well as soil texture and slope, is available from NCAR, Scientific Computing Division, Data Support Section; the more extensive data set is entitled "Staub and Rosenweig's GISS Soil & Sfc Slope, 1-Deg" [http://www.dss.ucar.edu/datasets/ds770.0/]. A help file prepared by Matthews and Fung (1987) (soil1x1.help) is provided as a companion file. Image of 26 soil types available at 1-degree by 1-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information

Sixty one soils (soil types) represent the range of soils found across South Australia’s agricultural lands. Mapping shows the most common soil within each map unit, while more detailed proportion data are supplied for calculating respective areas of each soil type (spatial data statistics).

Hydric soils are defined as those soils that are sufficiently wet in the upper part to develop anaerobic conditions during the growing season. The Hydric Soils section presents the most current information about hydric soils. The lists of hydric soils were created by using National Soil Information System (NASIS) database selection criteria that were developed by the National Technical Committee for Hydric Soils. These criteria are selected soil properties that are documented in Soil Taxonomy (Soil Survey Staff, 1999) and were designed primarily to generate a list of potentially hydric soils from the National Soil Information System (NASIS) database. It updates information that was previously published in Hydric Soils of the United States and coordinates it with information that has been published in the Federal Register. It also includes the most recent set of field indicators of hydric soils. The database selection criteria are selected soil properties that are documented in Soil Taxonomy and were designed primarily to generate a list of potentially hydric soils from soil survey databases. Only criteria 1, 3, and 4 can be used in the field to determine hydric soils; however, proof of anaerobic conditions must also be obtained for criteria 1, 3, and 4 either through data or best professional judgment (from Tech Note 1). The primary purpose of these selection criteria is to generate a list of soil map unit components that are likely to meet the hydric soil definition. Caution must be used when comparing the list of hydric components to soil survey maps. Many of the soils on the list have ranges in water table depths that allow the soil component to range from hydric to nonhydric depending on the location of the soil within the landscape as described in the map unit. Lists of hydric soils along with soil survey maps are good off-site ancillary tools to assist in wetland determinations, but they are not a substitute for observations made during on-site investigations. The list of field indicators of hydric soils — The field indicators are morphological properties known to be associated with soils that meet the definition of a hydric soil. Presence of one or more field indicators suggests that the processes associated with hydric soil formation have taken place on the site being observed. The field indicators are essential for hydric soil identification because once formed, they persist in the soil during both wet and dry seasonal periods. The Hydric Soil Technical Notes — Contain National Technical Committee for Hydric Soils (NTCHS) updates, insights, standards, and clarifications. Users can query the database by State or by Soil Survey Area. Resources in this dataset:Resource Title: Website Pointer to Hydric Soils . File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/use/hydric/ Includes description of Criteria, Query by State or Soil Survey Area, national Technical Committee for Hydric Soils. Technical Notes, and Related Links. Report Metadata:

• Area_Symbol: A symbol that uniquely identifies a single occurrence of a particular type of area (e.g. Dane Co., Wisconsin is WI025).
• Area_Name: The name given to the specified geographic area.
• mukey: A non-connotative string of characters used to uniquely identify a record in the Mapunit table.
• Mapunit_SYM: The symbol used to uniquely identify the soil mapunit in the soil survey.
• Mapunit_Name: Correlated name of the mapunit (recommended name or field name for surveys in progress).
• Comp_Name_phase: Component name - Name assigned to a component based on its range of properties. Local Phase - Phase criterion to be used at a local level, in conjunction with "component name" to help identify a soil component.
• muacres: The number of acres of a particular mapunit.
• Comp_RV_Pct: The percentage of the component of the mapunit.
• majcompflag: Indicates whether or not a component is a major component in the mapunit.
• Comp_Acres: The number of acres of a particular component in a mapunit. ((muacres*comppct_r)/100)
• Comp_Landform: A word or group of words used to name a feature on the earth's surface, expressed in the plural form. Column Physical
• Hydric_Rating: A yes/no field that indicates whether or not a map unit component is classified as a "hydric soil". If rated as hydric, the specific criteria met are listed in the Component Hydric Criteria table.
• Hydric_criteria: Criterion code for the soil characteristic(s) and/or feature(s) that cause the map unit component to be classified as a "hydric soil." These codes are the paragraph numbers in the hydric soil criteria publication.

Criteria:

1. All Histels except Folistels and Histosols except Folists; or
2. Map unit components in Aquic suborders, great groups, or subgroups, Albolls suborder, Historthels great group, Histoturbels great group, or Andic, Cumulic, Pachic, or Vitrandic subgroups that: a. Based on the range of characteristics for the soil series, will at least in part meet one or more Field Indicators of Hydric Soils in the United States, or b. Show evidence that the soil meets the definition of a hydric soil;
3. Map unit components that are frequently ponded for long duration or very long duration during the growing season that: a. Based on the range of characteristics for the soil series, will at least in part meet one or more Field Indicators of Hydric Soils in the United States, or b. Show evidence that the soil meets the definition of a hydric soil; or
4. Map unit components that are frequently flooded for long duration or very long duration during the growing season that: a. Based on the range of characteristics for the soil series, will at least in part meet one or more Field Indicators of Hydric Soils in the United States, or b. Show evidence that the soils meet the definition of a hydric soil.

Soil information, from the global to the local scale, has often been the one missing biophysical information layer, the absence of which has added to the uncertainties of predicting potentials and constraints for food and fiber production. The lack of reliable and harmonized soil data has considerably hampered land degradation assessments, environmental impact studies and adapted sustainable land management interventions.

Recognizing the urgent need for improved soil information worldwide, particularly in the context of the Climate Change Convention and the Kyoto Protocol for soil carbon measurements and the immediate requirement for the FAO/IIASA Global Agro-ecological Assessment study (GAEZ v3.0), the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) took the initiativeof combining the recently collected vast volumes of regional and national updates of soil information with the information already contained within the 1:5,000,000 scale FAOUNESCO Digital Soil Map of the World, into a new comprehensive Harmonized World Soil Database (HWSD).

This database was achieved in partnership with: • ISRIC-World Soil Information together with FAO, which were responsible for the development of regional soil and terrain databases and the WISE soil profile database; • the European Soil Bureau Network, which had recently completed a major update of soil information for Europe and northern Eurasia, and • the Institute of Soil Science, Chinese Academy of Sciences which provided the recent 1:1,000,000 scale Soil Map of China.

A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. The computer code used to convert the original 1-degree data to 0.5-degree is provided as a companion file. A JPG image of the data is provided in this document. The Zobler data (1-degree resolution) as distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] contains two columns, one column for continent and one column for soil type. The Soil Map of the World consists of 9 maps that represent parts of the world. The texture data that Webb et al.(1993) provided allowed for the fact that a soil type in one part of the world may have different properties than the same soil in a different part of the world. This continent-specific information is retained in this 0.5-degree resolution data set, as well as the soil type information which is the second column. A code was written (one2half.c) to take the file CONTIZOB.LER distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] and simply divide the 1-degree cells into quarters. This code also reads in a land/water file (land.wave) that specifies the cells that are land at 0.5 degrees. The code checks for consistency between the newly quartered map and the land/water map to which the quartered map is to be registered. If there is a discrepancy between the two, an attempt was made to make the two consistent using the following logic. If the cell is supposed to be water, it is forced to be water. If it is supposed to be land but was resolved to water at 1 degree, the code looks at the surrounding 8 cells and picks the most frequent soil type and assigns it to the cell. If there are no surrounding land cells then it is kept as water in the hopes that on the next pass one or more of the surrounding cells might be converted from water to a soil type. The whole map is iterated 5 times. The remaining cells that should be land but couldn't be determined from surrounding cells (mostly islands that are resolved at 0.5 degree but not at 1 degree) are printed out with coordinate information. A temporary map is output with -9 indicating where data is required. This is repeated for the continent code in CONTIZOB.LER as well. A separate map of the temporary continent codes is produced with -9 indicating required data. A nearly identical code (one2half.c) does the same for the continent codes. The printout allows one to consult the printed versions of the soil map and look up the soil type with the largest coverage in the 0.5-degree cell. The program manfix.c then will go through the temporary map and prompt for input to correct both the soil codes and the continent codes for the map. This can be done manually or by preparing a file of changes (new_fix.dat) and redirecting stdin. A new complete version of the map is outputted. This is in the form of the original CONTIZOB.LER file (contizob.half) but four times larger. Original documentation and computer codes prepared by Post et al. (1996) are provided as companion files with this data set. Image of 106 global soil types available at 0.5-degree by 0.5-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information.

This dataset is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information.

This dataset consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Note: This metadata record was created by MnGeo to serve as a generic record for all SSURGO data sets within Minnesota. See the individual county metadata records created by NRCS for county-specific information; these records are included in the data set download files.

Dataset

This dataset was created by Siwar Nasri

Contents

Sowing Success: How Machine Learning Helps Farmers Select the Best Crops Farmer in a field

Measuring essential soil metrics such as nitrogen, phosphorous, potassium levels, and pH value is an important aspect of assessing soil condition. However, it can be an expensive and time-consuming process, which can cause farmers to prioritize which metrics to measure based on their budget constraints.

Farmers have various options when it comes to deciding which crop to plant each season. Their primary objective is to maximize the yield of their crops, taking into account different factors. One crucial factor that affects crop growth is the condition of the soil in the field, which can be assessed by measuring basic elements such as nitrogen and potassium levels. Each crop has an ideal soil condition that ensures optimal growth and maximum yield.

A farmer reached out to you as a machine learning expert for assistance in selecting the best crop for his field. They've provided you with a dataset called soil_measures.csv, which contains:

"N": Nitrogen content ratio in the soil "P": Phosphorous content ratio in the soil "K": Potassium content ratio in the soil "pH" value of the soil "crop": categorical values that contain various crops (target variable). Each row in this dataset represents various measures of the soil in a particular field. Based on these measurements, the crop specified in the "crop" column is the optimal choice for that field.

In this project, you will build multi-class classification models to predict the type of "crop" and identify the single most importance feature for predictive performance.https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14076524%2F398c0df123c37fd028bc9619f6ac878d%2Ffarmer_in_a_field.jpg?generation=1723464449510660&alt=media" alt="">

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

The National Cooperative Soil Survey - Soil Characterization Database (NCSS-SCD) contains laboratory data for more than 65,000 locations (i.e. xy coordinates) throughout the United States and its Territories, and about 2,100 locations from other countries. It is a compilation of data from the Kellogg Soil Survey Laboratory (KSSL) and several cooperating laboratories. The data steward and distributor is the National Soil Survey Center (NSSC). Information contained within the database includes physical, chemical, biological, mineralogical, morphological, and mid infrared reflectance (MIR) soil measurements, as well a collection of calculated values. The intended use of the data is to support interpretations related to soil use and management. Data Usage Access to the data is provided via the following user interfaces: 1. Interactive Web Map 2. Lab Data Mart (LDM) for querying data and generating reports 3. Soil Data Access (SDA) web services for querying data 5. Direct download of the entire database in several formats Data at each location includes measurements at multiple depths (e.g. soil horizons). However, not all analyses have been conducted for each location and depth. Typically, a suite of measurements was collected based upon assumed or known conditions regarding the soil being analyzed. For example, soils of arid environments are routinely analyzed for salts and carbonates as part of the standard analysis suite. Standard morphological soil descriptions are available for about 60,000 of these locations. Mid-infrared (MIR) spectroscopy is available for about 7,000 locations. Soil fertility measurements, such as those made by Agricultural Experiment Stations, were not made. Most of the data were obtained over the last 40 years, with about 4,000 locations before 1960, 25,000 from 1960-1990, 27,000 from 1990-2010, and 13,000 from 2010 to 2021. Generally, the number of measurements recorded per location has increased over time. Typically, the data were collected to represent a soil series or map unit component concept. They may also have been sampled to determine the range of variation within a given landscape. Although strict quality-control measures are applied, the NSSC does not warrant that the data are error free. Also, in some cases the measurements are not within the applicability range of the laboratory methods. For example, dispersion of clay is incomplete in some soils by the standard method used for determining particle-size distribution. Soils producing incomplete dispersion include those that are derived from volcanic materials or that have a high content of iron oxides, gypsum, carbonates, or other cementing materials. Also note that determination of clay minerals by x-ray diffraction is relative. Measurements of very high or very low quantities by any method are not very precise. Other measurements have other limitations in some kinds of soils. Such data are retained in the database for research purposes. Also, some of the data for were obtained from cooperating laboratories within the NCSS. The accuracy of the location coordinates has not been quantified but can be inferred from the precision of their decimal degrees and the presence of a map datum. Some older records may correspond to a county centroid. When the map datum is missing it can be assumed that data prior to 1990 was recorded using NAD27 and with WGS84 after 1995. For detailed information about methods used in the KSSL and other laboratories refer to "Soil Survey Investigation Report No. 42". For information on the application of laboratory data, refer to "Soil Survey Investigation Report No. 45". If you are unfamiliar with any terms or methods feel free to consult your NRCS State Soil Scientist. Terms of Use This dataset is not designed for use as a primary regulatory tool in permitting or citing decisions but may be used as a reference source. This is public information and may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Natural Resources Conservation Service or the National Cooperative Soil Survey any authority for the decisions that they make. The Natural Resources Conservation Service will not perform any evaluations of these data for purposes related solely to State or local regulatory programs.

This map depicts soils data from the USDA NRCS SSURGO dataset. The soil type is indicated in the MUSYM field. The data was downloaded from the NRCS website.The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/