In the realm of global agriculture

This dataset was made by augmenting optimum soil and environmental characteristics for crop growth

pH

Real time Crop recommendation system for agriculture.

Analysis of ‘Crop Recommendation Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/siddharthss/crop-recommendation-dataset on 30 September 2021.

--- Dataset description provided by original source is as follows ---

THE INFORMATION IN THE DATASET IS PROVIDED TO THE BEST OF KNOWLEDGE OF ICAR. THE BELOW DATA CAN BE USED PUBLICALLY UNDER ALL PUBLIC AND PRIVATE UNDERTAKINGS

Context Precision agriculture is in trend nowadays. It helps the farmers to get informed decision about the farming strategy. Here, we present you a dataset which would allow the users to build a predictive model to recommend the most suitable crops to grow in a particular farm based on various parameters.**

Source This dataset was build by augmenting datasets of rainfall, climate and fertilizer data available for India. Gathered over the period by ICFA, India.

Data fields N - ratio of Nitrogen content in soil P - ratio of Phosphorous content in soil K - ratio of Potassium content in soil temperature - temperature in degree Celsius humidity - relative humidity in % ph - ph value of the soil rainfall - rainfall in mm

COPYRIGHT: Indian Chamber of Food and Agriculture https://www.icfa.org.in/ https://www.google.com/url?sa=i&url=https%3A%2F%2F10times.com%2Fcompany%2Findian-council-of-food-and-agriculture&psig=AOvVaw0S9UpuXsVmmje0SgSjybK5&ust=1622035121203000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCNie_uv15PACFQAAAAAdAAAAABAn" alt="">

--- Original source retains full ownership of the source dataset ---

the ideal cycle is 14-18).

FARM FUTURO is a cutting-edge Machine Learning (ML) project designed to address the challenges faced by farmers in making informed crop decisions. This innovative system utilizes advanced ML algorithms to analyze user-provided inputs, such as geographical location (states), crop preferences (cereals, pulses, fruits, cash crops), and environmental factors (rainfall, temperature,humidity, soil pH). FARM FUTURO aims to empower farmers with precise and timely recommendations, guiding them towards optimal crop selection based on their specific conditions and seasonal variations. This project represents a pivotal advancement in precision agriculture, fostering sustainable farming practices and maximizing agricultural productivity. The heart of FARM FUTURO lies in its sophisticated ML algorithms that analyze historical agricultural data, climatic patterns, and soil conditions. By employing advanced data analytics techniques, the system identifies patterns and correlations that traditional farming methods may overlook. This enables FARM FUTURO to offer precise and timely crop recommendations that are not only suited to the farmer's preferences but also optimized for the specific environmental conditions of the region. Key features of FARM FUTURO include its ability to predict the most suitable crops for cultivation based on real-time and historical data, taking into account the variations in temperature, rainfall, and soil pH throughout the year. The system also considers seasonal factors, ensuring that farmers receive recommendations tailored to the specific planting and harvesting windows for each crop. FARM FUTURO represents a significant step forward in the realm of precision agriculture, harnessing the power of machine learning to empower farmers with actionable insights for informed decision-making. As agriculture faces increasing challenges posed by climate change and resource constraints, FARM FUTURO stands as a beacon of innovation, offering a scalable solution to enhance agricultural productivity, profitability, and sustainability.

The dataset comprises 2,200 entries with 7 key attributes, covering both soil and climate factors. Soil attributes include pH, nitrogen, phosphorus, and potassium, while climate factors include temperature, rainfall, and humidity. It features data for 22 crops, such as watermelon, coconut, lentil, rice, cotton, grapes, muskmelon, black gram, coffee, banana, apple, orange, moth beans, chickpea, kidney beans, mango, pigeon peas, papaya, jute, pomegranate, mung bean, and maize, with around 100 records per crop.

This dataset contains agricultural data for 1,000,000 samples aimed at predicting crop yield (in tons per hectare) based on various factors. The dataset can be used for regression tasks in machine learning, especially for predicting crop productivity.

• Region: The geographical region where the crop is grown (North, East, South, West).
• Soil_Type: The type of soil in which the crop is planted (Clay, Sandy, Loam, Silt, Peaty, Chalky).
• Crop: The type of crop grown (Wheat, Rice, Maize, Barley, Soybean, Cotton).
• Rainfall_mm: The amount of rainfall received in millimeters during the crop growth period.
• Temperature_Celsius: The average temperature during the crop growth period, measured in degrees Celsius.
• Fertilizer_Used: Indicates whether fertilizer was applied (True = Yes, False = No).
• Irrigation_Used: Indicates whether irrigation was used during the crop growth period (True = Yes, False = No).
• Weather_Condition: The predominant weather condition during the growing season (Sunny, Rainy, Cloudy).
• Days_to_Harvest: The number of days taken for the crop to be harvested after planting.
• Yield_tons_per_hectare: The total crop yield produced, measured in tons per hectare.

Most farmers and extension officers in Tanzania use blanket fertilizer recommendations, which can be ineffective in sustaining crops productions. The most recent fertilizer recommendation report (Marandu et al., 2014) do not have guidelines for semiarid zones. Besides, developing agronomic rates (Mkoma, 2015), our work in 2016 seasons addressed the aspects of use efficiency and costs of fertilizer by refining the recommended rates into micro-dose rate. Fertilizer micro dosing involves application of small doses of fertilizer 5-6 kg P/ha (2-4g/hill as NPK) at sowing or shortly after germination to improve uptake or use efficiency and crops yields. The technique also holds high potential to reduce inputs costs because the amount of fertilizer is reduced substantially compared to recommended rate. Fertilizer micro-dosing trials were established in Mlali, Molet and Njoro villages during the 2016 growing seasons using the randomized complete block design (RCBD) with the three replications. Treatments include N (0, 15, 30 and 60 kg/ha) and P (0, 7.5, 15, and 30kg P/ha) in a factorial combination and the 16 treatment combinations were allocated randomly to each block. The test crop was maize, variety Staha. Maize was planted at 90 cm x 60 cm in Mlali and Molet villages and at a spacing of 75 cm x 60 cm in Njoro village. The plot size in both sites was 5 m x 6 m. This study includes data generated from this study trial. About the project Project title: Intensification of Maize-Legume Based Systems in the Semi-Arid Areas of Tanzania to Increase Farm Productivity and Improve Farming Natural Resource Base Project abstract The aim of the Africa RISING project in Kongwa and Kiteto Districts, Tanzania is to provide a scientific basis for sustainably intensifying agricultural production in semi-arid areas of central Tanzania. The project activities are falls under 4 thematic areas that address three critical elements of sustainable intensification (SI), i.e. genetic, ecological and socio-economic intensification technologies. The scope of activities being implemented include: packaging of new legume and cereal varieties with over 120% yield advantage, packaging and validation of integrated productivity enhancing technologies for cereals, legumes, legume trees and soil health technologies, food safety primarily to reduce aflatoxin contamination and integration of livestock into the cropping systems. The innovation platform is used to set R4D priority in the action sites. The project team is comprised of national partners (e.g. ARI-Hombolo, District Agricultural Officers, SUA and UDOM) and CG Partners (CIMMYT and ICRAF) under the leadership of ICRISAT. Project website: http://africa-rising.net/where-we-work/west-africa/ Project start date: 2012-05-01 Project end date : 2016-09-30

Cost analysis for prototype implementation for crop and fertilizer recommendation system.

This dataset is a dataset from farmers in Thailand. Designed to predict the suitability of areas for growing rice, cassava, sugarcane, corn, oil palm according to various characteristics. of various agricultural areas The dataset includes important factors affecting crop cultivation such as soil pH, temperature, humidity, rainfall, soil type, and land slope. and drainage in the area

The dataset consists of the following features: *ph (Soil pH): The pH level of the soil in the agricultural area. *max_temperature_avg: Average maximum temperature of the area *humidity_avg: The average humidity percentage in the air. *precip_avg(Annual Precipitation): The amount of averrage precipitation in millimeters per year. *soil_type: The type of soil in the area (e.g., clay soil, loamy soil, sandy soil). *area_slope: The slope of the land in the agricultural area. *drainage: Drainage conditions in the area (e.g., yes or no). *target: The target variable indicating the recommended crop for the specific conditions.

The Crops Suitability Tool combines soil types, aspect (slope orientation), and percentage of slope to determine the best and least suitable sites in which to grow crops in Loudoun County. It includes different types of Agricultural Soils (Prime Farmland, Secondary Cropland, Grassland Agriculture, Orchard Land, Woodland Use and Wildlife) and its grade of suitability for grapes, tree fruits, hops, vegetables, flowers, herbs, small fruits, field crops, pasture and hay.A spatial model uses existing geographic data to predict an outcome. In this application, we combined soil types, aspect (slope orientation), and percentage of slope to determine the best and least suitable site in which to grow crops in Loudoun County, Virginia. It includes different types of Agricultural Soils (Prime Farmland, Secondary Cropland, Grassland Agriculture, Orchard Land, Woodland Use and Wildlife) and its grade of suitability for grapes, tree fruits, hops, vegetables (ethnic crops), flowers, herbs, and small fruits, field crops, pasture, and hay.This tool does not account for the incidence and prevalence of any type of pests (weed, insects, and diseases -nematodes, fungi, bacteria, or viruses) or weather conditions that can affect crops. The accuracy of the predicted outcomes is not 100% (for example: 17B soils in a concave position are not suitable for growing perennial crops or high cash valued crops); therefore, it is highly recommended to contact VCE Loudoun Commercial Horticulturist Beth Sastre to get a soil map report of the property and/or to have a site evaluation for further recommendation.We encourage farmers, beginner farmers, people interested in farming, and realtors to use this tool to make guided decisions before starting a crop for the first time or buying land. If you see major discrepancies while using this tool, please report them.

This dataset includes soil health, crop biomass, and crop yield data for a 13-year corn stover harvest trial in central Iowa. Following the release in 2005 of the Billion Ton Study assessment of biofuel sources, several soil health assessments associated with harvesting corn stover were initiated across ARS locations to help provide industry guidelines for sustainable stover harvest. This dataset is from a trial conducted by the National Laboratory for Agriculture and Environment from 2007-2021 at the Iowa State University Ag Engineering and Agronomy farm. Management factors evaluated in the trial included the following. Stover harvest rate at three levels: No, moderate (3.5 ± 1.1 Mg ha-1 yr-1), or high (5.0 ± 1.7 Mg ha-1 yr-1) stover harvest rates. No-till versus chisel-plow tillage. Originally, the 3 stover harvest rates were evaluated in a complete factorial design with tillage system. However, the no-till, no-harvest system performed poorly in continuous corn and was discontinued in 2012 due to lack of producer interest. Cropping sequence. In addition to evaluating continuous corn for all stover harvest rates and tillage systems, a corn-alfalfa rotation, and a corn-soybean-wheat rotation with winter cover crops were evaluated in a subset of the tillage and stover harvest rate treatments. One-time additions of biochar in 2013 at rates of either 9 Mg/ha or 30 Mg/ha were evaluated in a continuous corn cropping system. The dataset includes: 1) Crop biomass and yields for all crop phases in every year. 2) Soil organic carbon, total carbon, total nitrogen, and pH to 120 cm depth in 2012, 2016, and 2017. Soil cores from 2005 (pre-study) were also sampled to 90 cm depth. 3) Soil chemistry sampled to 15 cm depth every 1-2 years from 2007 to 2017. 4) Soil strength and compaction was assessed to 60 cm depth in April 2021. These data have been presented in several manuscripts, including Phillips et al. (in review), O'Brien et al. (2020), and Obrycki et al. (2018). Resources in this dataset:Resource Title: R Script for Phillips et al. 2022. File Name: Field 70-71 Analysis Script_AgDataCommons.RResource Description: This R script includes analysis and figures for Phillips et al. "Thirteen-year Stover Harvest and Tillage Effects on Soil Compaction in Iowa". It focuses primarily on the soil compaction and strength data found in "Field 70-71 ConeIndex_BulkDensityDepths_2021". It also includes analysis of corn yields from "Field 70-71 CornYield_2008-2021" and weather conditions from "PRISM_MayTemps" and "Rainfall_AEA".Resource Software Recommended: R version 4.1.3 or higher,url: https://cran.r-project.org/bin/windows/base/ Resource Title: Field 70-71 ConeIndex_BulkDensityDepths_2021. File Name: Field 70-71 ConeIndex_BulkDensityDepths_2021.csvResource Description: This dataset provides an assessment of soil strength (penetration resistance) and soil compaction (bulk density) to 60 cm depth, in continuous corn plots. Penetration resistance was measured in most-trafficked and least-trafficked areas of the plots to assess compaction from increased traffic associated with stover harvest. This spreadsheet also has associated data, including soil water, carbon, and organic matter content. Data were collected in April 2021 and are described in Phillips et al. (in review, 2022).Resource Title: Field 70-71 CornYield_2008-2021. File Name: Field 70-71 CornYield_2008-2021_ForR.csvResource Description: This dataset provides corn stover biomass and grain yields from 2008-2021. Note that this dataset is just for corn, which were presented in Phillips et al., 2022. Yields for all crop phases, including soybeans, wheat, alfalfa, and winter cover crops, are in the file "Field 70-71 Crop Yield File 2008-2020".Resource Title: PRISM_MayTemps. File Name: PRISM_MayTemps.csvResource Description: Average May temperatures during the study period, obtained from interpolation of regional weather stations using the PRISM climate model (https://prism.oregonstate.edu/). These data were used to evaluate how spring temperatures may have impacted corn establishment.Resource Title: Rainfall_AEA. File Name: Rainfall_AEA.csvResource Description: Daily rainfall for the study location, 2008-2021. Data were obtained from the Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/rainfall/). Title: Field 70-71 Plot Status 2007-2021. File Name: Field 70-71 Plot Status 2007-2021.xlsxResource Description: This file contains descriptions of experimental treatments and diagrams of plot layouts as they were modified through several phases of the trial. Also includes an image of plot locations relative to NRCS soil survey map units.Resource Title: Field 70-71 Deep Soil Cores 2012-2017. File Name: Field 70-71 Deep Soil Cores 2012-2017.xlsxResource Description: Soil carbon, nitrogen, organic matter, and pH to 120 cm depth in 2012, 2016, and 2017.Resource Title: Field 70-71 Baseline Deep Soil Cores 2005. File Name: Field 70-71 Baseline Deep Soil Cores 2005.csvResource Description: Baseline soil carbon, nitrogen, and pH data from an earlier trial in 2005, prior to stover trial establishment.Resource Title: Field 70-71 Crop Yield File 2008-2020. File Name: Field 70-71 Crop Yield File 2008-2020.xlsxResource Description: Yields for all crops in all cropping sequences, 2008-2020. Some of the crop sequences have not been summarized in publications.Resource Title: Field 70-71 Surface Soil Test Data 2007-2021. File Name: Field 70-71 Surface Soil Test Data 2007-2021.xlsxResource Description: Soil chemistry data, 0-15 cm, collect near-annually from 2007 to 2021. Most analyses were performed by Harris Laboratories (now AgSource) in Lincoln, Nebraska, USA. Resource Title: Iowa Stover Harvest Trial Data Dictionary. File Name: Field 70-71 Data Dictionary.xlsxResource Description: Data dictionary for all data files.

There is an increasing food demand with growing population and limited land for agriculture. Conventional agriculture with nitrogen (N) fertilizer applications, however, is a key source of ammonia (NH3) emissions that cause severe haze pollution and impair human health. Organic and conservation agricultural (OCA) practices are thereby recommended to address these dual challenges; however, whether OCA provides cobenefits for both air quality and crop productivity is controversial. Here, we perform a meta-analysis and machine learning algorithm with data from China, a global hotspot for agricultural NH3 emissions, to quantify the effects of OCA on NH3 emissions, crop yields and nitrogen use efficiency (NUE). We find that the effects of OCA depend on soil and climate conditions, and the 40–60% substitution of synthetic fertilizers with livestock manure achieves the maximum cobenefits of enhanced crop production and reduced NH3 emissions. Model forecasts further suggest that the appropriate application of livestock manure, straw return, and no-till could increase grain production up to 59.7 million metric tons (100% of straw return) and reduce maximum US$2.7 billion (60% substitution with livestock manure) in damage costs to human health from NH3 emissions by 2030. Our findings provide data-driven pathways and options for achieving multiple sustainable development goals and improving food systems and air quality in China.

An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions

The CropNet dataset is an open, large-scale, and deep learning-ready dataset, specifically targeting climate change-aware crop yield predictions for the contiguous United States (U.S.) continent at the county level. It is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, aligned in both the spatial and temporal domains, for over… See the full description on the dataset page: https://huggingface.co/datasets/CropNet/CropNet.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Peru were selected based on the following criterion: (a) smallholder potato growers Location: Huara, Barranca, Cañete (Canta Gallo),Huanuco Med Tech Adoption: -productivity 20T/Ha -CP usage -traditional growers: minimum tillage, use a mix of generic and CP quality products

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab.

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

B. Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

ABSTRACT Sulfur (S) fertilization recommendations for grain crops in Brazil were formerly established from studies on crops with a low yield potential grown on soils under conventional tillage (CT). However, the subsequent adoption of no-tillage (NT) altered S dynamics in the soil, making it necessary to carefully evaluate the applicability of these S fertilizer recommendations. In addition, the emergence of modern high-yield-potential genotypes, the successive application of concentrated low-sulfur fertilizers, and reduction in S atmospheric deposition have raised the likelihood of positive responses of crops to S fertilization. Available literature reports contrasting crop responses to S fertilization in Brazilian soils, ranging from substantial gains to slight yield losses depending on the particular crop, soil, and climate. The primary aim of this study was to examine available data for crop grain responses to S application in NT soils in order to ascertain whether existing recommendations established for Brazilian CT soils also hold for NT soils. A systematic review of data from 35 scientific publications spanning 58 crop harvests revealed a positive yield response to S fertilization in 31 % of the crop harvests, with an average yield increase of 16 %. Crops on soils with available SO42–-S contents above the critical level (viz., 7.5 mg dm-3) exhibited no positive response to S fertilization in any crop harvest (n = 18). Dry edible bean and corn were the most responsive crops, and canola and wheat, the least. For the trials with positive crop responses, a fertilizer rate of 26 kg ha-1 S sufficed to obtain at least 95 % of the maximum possible yield. In general, the S fertilization recommendations previously established for CT soil proved effective with grain crops on NT soils as a result of the critical levels of soil available SO42--S and the fact that the recommended S rates are similar to those found in this study considering trials conducted under NT conditions only. However, existing recommendations could be improved by using additional criteria for soils with available SO42--S contents below the critical level since a positive response was observed in 22 % (n = 18) and 92 % (n = 12) of the crop harvests under a subtropical and a tropical climate, respectively. Our results suggest that S fertilization must be prioritized in NT soils with available SO42--S contents below 7.5 mg dm-3 in the 0.00-0.20 m layer, especially in tropical climate zones. In addition, regional fertilizer recommendation guidelines should consider crop type and yield expectation in order to facilitate more sustainable S management and increased crop yields in Brazil.

Accurate crop yield forecasting is vital for ensuring food security and making informed decisions. With the increasing population and global warming, addressing food security has become a priority, so accurate yield forecasting is very important. Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model’s leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. The proposed methodology consists of different stages, like Data Collection, Preprocessing, Feature Extraction with Support Vector Machine (SVM), correlation with Normalized Google Distance (NGD), feature ranking with rising star. This study combines Bidirectional Gated Recurrent Unit (Bi-GRU) and Time Series CNN to predict crop yield and then recommendation for further improvement. The proposed model showed very good results in all datasets and showed significant improvement compared to baseline models. The ECP-IEM achieved an accuracy 96.34%, precision 94.56% and recall 95.23% on different datasets. Moreover, the proposed model was also evaluated based on MAE, MSE, and RMSE, which produced values of 0.191, 0.0674, and 0.238, respectively. This will help in improving production of crops by giving an early look about the yield of crops which will than help the farmer in improving the crops yield.