87398 Global export shipment records of Household Cleaning Products with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Tire & Wheel Cleaning Brush market has emerged as an essential segment within the broader automotive care industry, catering to both professional detailers and everyday car owners. These specialized cleaning tools are designed to tackle the toughest grime and brake dust that can accumulate on tires and wheels, e

The Tire & Wheel Cleaning Tools market is a crucial segment of the automotive care industry, catering to both professional service providers and enthusiastic car owners. This market encompasses a wide range of products designed to efficiently clean and maintain tires and wheels, ensuring optimal vehicle appearance a

Graph and download economic data for Producer Price Index by Industry: Polish and Other Sanitation Good Manufacturing: Specialty Cleaning and Sanitation Products (Including Household Bleaches) (DISCONTINUED) (PCU3256123256125) from Jun 1983 to May 2017 about cleaning, households, goods, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

662640 Global import shipment records of Cleaning Products with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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 Philippines were selected based on the following criterion: (a) smallholder rice growers Location: Luzon - Mindoro (Southern Luzon) mid-tier (sub-optimal CP/SE use): mid-tier growers use generic CP, cheaper CP, non hybrid (conventional) seeds
Smallholder farms with average to high levels of mechanization
Should be Integrated Pest Management advocates
less accessible to technology: poor farmers, don't have the money to buy quality seeds, fertilizers,... Don't use machinery yet
simple knowledge on agronomy and pests
influenced by fellow farmers and retailers
not strong financial status: don't have extra money on bank account and so need longer credit to pay (as a consequence: interest increases) may need longer credit

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.

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.

A collection of datasets and python scripts for extraction and analysis of isograms (and some palindromes and tautonyms) from corpus-based word-lists, specifically Google Ngram and the British National Corpus (BNC).Below follows a brief description, first, of the included datasets and, second, of the included scripts.1. DatasetsThe data from English Google Ngrams and the BNC is available in two formats: as a plain text CSV file and as a SQLite3 database.1.1 CSV formatThe CSV files for each dataset actually come in two parts: one labelled ".csv" and one ".totals". The ".csv" contains the actual extracted data, and the ".totals" file contains some basic summary statistics about the ".csv" dataset with the same name.The CSV files contain one row per data point, with the colums separated by a single tab stop. There are no labels at the top of the files. Each line has the following columns, in this order (the labels below are what I use in the database, which has an identical structure, see section below):

Label Data type Description

isogramy int The order of isogramy, e.g. "2" is a second order isogram

length int The length of the word in letters

word text The actual word/isogram in ASCII

source_pos text The Part of Speech tag from the original corpus

count int Token count (total number of occurences)

vol_count int Volume count (number of different sources which contain the word)

count_per_million int Token count per million words

vol_count_as_percent int Volume count as percentage of the total number of volumes

is_palindrome bool Whether the word is a palindrome (1) or not (0)

is_tautonym bool Whether the word is a tautonym (1) or not (0)

The ".totals" files have a slightly different format, with one row per data point, where the first column is the label and the second column is the associated value. The ".totals" files contain the following data:

Label

Data type

Description

!total_1grams

int

The total number of words in the corpus

!total_volumes

int

The total number of volumes (individual sources) in the corpus

!total_isograms

int

The total number of isograms found in the corpus (before compacting)

!total_palindromes

int

How many of the isograms found are palindromes

!total_tautonyms

int

How many of the isograms found are tautonyms

The CSV files are mainly useful for further automated data processing. For working with the data set directly (e.g. to do statistics or cross-check entries), I would recommend using the database format described below.1.2 SQLite database formatOn the other hand, the SQLite database combines the data from all four of the plain text files, and adds various useful combinations of the two datasets, namely:• Compacted versions of each dataset, where identical headwords are combined into a single entry.• A combined compacted dataset, combining and compacting the data from both Ngrams and the BNC.• An intersected dataset, which contains only those words which are found in both the Ngrams and the BNC dataset.The intersected dataset is by far the least noisy, but is missing some real isograms, too.The columns/layout of each of the tables in the database is identical to that described for the CSV/.totals files above.To get an idea of the various ways the database can be queried for various bits of data see the R script described below, which computes statistics based on the SQLite database.2. ScriptsThere are three scripts: one for tiding Ngram and BNC word lists and extracting isograms, one to create a neat SQLite database from the output, and one to compute some basic statistics from the data. The first script can be run using Python 3, the second script can be run using SQLite 3 from the command line, and the third script can be run in R/RStudio (R version 3).2.1 Source dataThe scripts were written to work with word lists from Google Ngram and the BNC, which can be obtained from http://storage.googleapis.com/books/ngrams/books/datasetsv2.html and [https://www.kilgarriff.co.uk/bnc-readme.html], (download all.al.gz).For Ngram the script expects the path to the directory containing the various files, for BNC the direct path to the *.gz file.2.2 Data preparationBefore processing proper, the word lists need to be tidied to exclude superfluous material and some of the most obvious noise. This will also bring them into a uniform format.Tidying and reformatting can be done by running one of the following commands:python isograms.py --ngrams --indir=INDIR --outfile=OUTFILEpython isograms.py --bnc --indir=INFILE --outfile=OUTFILEReplace INDIR/INFILE with the input directory or filename and OUTFILE with the filename for the tidied and reformatted output.2.3 Isogram ExtractionAfter preparing the data as above, isograms can be extracted from by running the following command on the reformatted and tidied files:python isograms.py --batch --infile=INFILE --outfile=OUTFILEHere INFILE should refer the the output from the previosu data cleaning process. Please note that the script will actually write two output files, one named OUTFILE with a word list of all the isograms and their associated frequency data, and one named "OUTFILE.totals" with very basic summary statistics.2.4 Creating a SQLite3 databaseThe output data from the above step can be easily collated into a SQLite3 database which allows for easy querying of the data directly for specific properties. The database can be created by following these steps:1. Make sure the files with the Ngrams and BNC data are named “ngrams-isograms.csv” and “bnc-isograms.csv” respectively. (The script assumes you have both of them, if you only want to load one, just create an empty file for the other one).2. Copy the “create-database.sql” script into the same directory as the two data files.3. On the command line, go to the directory where the files and the SQL script are. 4. Type: sqlite3 isograms.db 5. This will create a database called “isograms.db”.See the section 1 for a basic descript of the output data and how to work with the database.2.5 Statistical processingThe repository includes an R script (R version 3) named “statistics.r” that computes a number of statistics about the distribution of isograms by length, frequency, contextual diversity, etc. This can be used as a starting point for running your own stats. It uses RSQLite to access the SQLite database version of the data described above.

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).

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.

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.

The Global Eco-Friendly Cleaning Products Market Size Was Worth USD 31 Billion in 2023 and Is Expected To Reach USD 71 Billion by 2032, CAGR of 11%.

The Global Natural Cleaning Products Market Size Was Worth USD 6,347.58 Million in 2023 and Is Expected To Reach USD 15,489.52 Million by 2032, CAGR of 11.80%.

47707 Global export shipment records of Household cleaning products with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Colombia Consumer Price Index (CPI): Products for Cleaning & Preservation of the Vehicle data was reported at 99.960 Dec2018=100 in Jan 2019. Colombia Consumer Price Index (CPI): Products for Cleaning & Preservation of the Vehicle data is updated monthly, averaging 99.960 Dec2018=100 from Jan 2019 (Median) to Jan 2019, with 1 observations. Colombia Consumer Price Index (CPI): Products for Cleaning & Preservation of the Vehicle data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.I015: Consumer Price Index: COICOP: Dec2018=100: by Sub Class of Good and Services.

File Analysis Software Market size was valued at USD 12.04 Billion in 2023 and is projected to reach USD 20.49 Billion by 2030, growing at a CAGR of 11% during the forecast period 2024-2030.

Global File Analysis Software Market Drivers

The market drivers for the File Analysis Software Market can be influenced by various factors. These may include:

Data Growth: Organisations are having difficulty efficiently managing, organising, and analysing their files due to the exponential growth of digital data. File analysis software offers insights into file usage, content, and permissions, which aids in managing this enormous volume of data.

Regulatory Compliance: Organisations must securely and efficiently manage their data in order to comply with regulations like the GDPR, CCPA, HIPAA, etc. Software for file analysis assists in locating sensitive material, guaranteeing compliance, and reducing the risks connected to non-compliance and data breaches.

Data security concerns are a top priority for organisations due to the rise in cyber threats and data breaches. Software for file analysis is essential for locating security holes, unapproved access, and other possible threats in the file system.

Data Governance Initiatives: In order to guarantee the availability, quality, and integrity of their data, organisations are progressively implementing data governance techniques. Software for file analysis offers insights into data ownership, consumption trends, and lifecycle management, which aids in the implementation of data governance policies.

Cloud Adoption: The increasing use of hybrid environments and cloud services calls for efficient file management and analysis across several platforms. Software for file analysis gives users access to and control over files kept on private servers, cloud computing platforms, and third-party services.

Cost Optimisation: By identifying redundant, outdated, and trivial (ROT) material, organisations hope to minimise their storage expenses. Software for file analysis aids in the identification of such material, makes data cleanup easier, and maximises storage capacity.

Digital Transformation: Tools that can extract actionable insights from data are necessary when organisations embark on digital transformation programmes. Advanced analytics and machine learning techniques are employed by file analysis software to offer significant insights into user behaviour, file usage patterns, and data classification.

Collaboration and Remote Work: As more people work remotely and use collaboration technologies, more digital files are created and shared within the company. In remote work situations, file analysis software ensures efficiency and data security by managing and protecting these files.

Colombia Consumer Price Index (CPI): Weights: FHI: GSO: NDH: Cleaning & Maintenance Products data was reported at 1.350 % in 2019. Colombia Consumer Price Index (CPI): Weights: FHI: GSO: NDH: Cleaning & Maintenance Products data is updated yearly, averaging 1.350 % from Dec 2019 (Median) to 2019, with 1 observations. Colombia Consumer Price Index (CPI): Weights: FHI: GSO: NDH: Cleaning & Maintenance Products data remains active status in CEIC and is reported by National Statistics Administrative Department. The data is categorized under Global Database’s Colombia – Table CO.I016: Consumer Price Index: COICOP: Dec2018=100: by Sub Class of Good and Services: Weights.

The PC cleaner software market is experiencing steady growth, projected to reach a market size of $511.4 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 5.3%. This growth is fueled by several factors. The increasing prevalence of malware and unwanted software, coupled with the growing user base of personal computers, creates a consistent demand for effective PC cleaning solutions. Furthermore, the rise in sophisticated cyber threats necessitates robust security and optimization tools, driving adoption of both on-premises and cloud-based PC cleaner software across individual users, enterprises, and government sectors. The market's segmentation reflects this diverse user base; while on-premises solutions maintain a significant share, cloud-based options are rapidly gaining traction due to their accessibility, ease of use, and scalability. The enterprise and government segments are key growth drivers, as they require comprehensive solutions for managing large numbers of devices and ensuring data security. Competition in the market is intense, with established players like Norton and Avast alongside numerous smaller, specialized providers. This competitive landscape fosters innovation and drives the development of advanced features, such as real-time protection, performance optimization, and privacy enhancement tools. The market is expected to continue its growth trajectory throughout the forecast period (2025-2033), driven by ongoing technological advancements and the evolving digital landscape. The geographical distribution of the PC cleaner software market is spread across various regions, with North America and Europe currently holding the largest market shares. However, growth potential is significant in emerging markets within Asia-Pacific and the Middle East & Africa, driven by rising internet penetration and increasing PC usage. While factors such as evolving operating system capabilities (inbuilt cleaning utilities) and user awareness of best practices in digital hygiene pose some restraints, the overall market outlook remains positive, with continued growth driven by the persistent need for robust security and system optimization. The market will likely see further consolidation, with larger companies acquiring smaller players to expand their product portfolios and market reach. Focus on developing AI-powered features and proactive threat detection is expected to be a key differentiator in the competitive landscape.

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Information Stewardship Application Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024-2031.

Global Information Stewardship Application Market Drivers

The market drivers for the Information Stewardship Application Market can be influenced by various factors. These may include:

Growing Data Volume: The demand for information stewardship apps is being driven by the exponential expansion in data generation across many industries, which calls for effective data management and governance solutions.
Regulatory Compliance: Organizations are being pushed to implement information stewardship apps in order to assure compliance and avoid heavy penalties due to stringent rules and compliance requirements linked to data privacy and security, such as GDPR and CCPA.
Data Quality Management: Information stewardship systems that assist in cleaning, validating, and controlling data quality are becoming more and more popular as decision-making processes depend on good data quality and accuracy.
Risk management: As a result of organizations’ growing recognition of the value of data governance in reducing the risks associated with data breaches and misuse, information stewardship solutions are being used at a higher rate.
Initiatives for Digital Transformation: As businesses go through digital transformation, there is an increasing focus on using data as a strategic asset, which drives the requirement for strong data governance frameworks that are made possible by information stewardship tools.
Cloud Adoption: Information stewardship apps are growing in demand as a result of the movement of data to cloud platforms, which necessitates improved data governance and stewardship to guarantee data integrity and security.
Industry-Specific Requirements: Because of the sensitive nature of their data, some industries, like healthcare, banking, and retail, have particular requirements for data governance. As a result, the usage of customized information stewardship solutions has expanded.
Integration with Business Intelligence Tools: By improving data visibility and accessibility, information stewardship apps can be integrated with business intelligence and analytics tools to drive market growth.
Rise in Data-Driven Decision Making: As organizations increasingly rely on data to influence their decisions, the need for accurate and dependable data is becoming more pressing, which is driving up demand for information stewardship software.
Technological Developments: Information stewardship applications are becoming more capable and efficient because to ongoing developments in fields like artificial intelligence, machine learning, and big data analytics.

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 Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- may need longer credit

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.

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.

Touchless Vehicle Wash Systems Market size was valued at USD 4.4 Billion in 2023 and is projected to reach USD 7.3 Billion by 2030, growing at a CAGR of 7.2% during the forecast period 2024-2030.

Global Touchless Vehicle Wash Systems Market Drivers

Technological Advancements: High-pressure water jets, chemical application systems, and sensors for accurate vehicle recognition and alignment are just a few examples of the cutting-edge technology that have been included into touchless car wash systems over the years. These developments reduce the possibility of surface damage to the car while guaranteeing effective and complete cleaning. Innovations like IoT integration and AI-driven control systems also make it possible to remotely monitor and optimize wash processes, which improves both customer happiness and operational efficiency.

Enhanced Cleaning Performance: Touchless wash systems present a convincing answer to the rising expectations of consumers for better cleaning outcomes. These devices remove filth, grime, and pollutants from car surfaces without requiring direct physical touch by using strong water jets and specific cleaning solutions. Improved cleaning efficiency keeps the car looking great for longer by lowering the possibility of swirl marks and scratches in addition to guaranteeing a flawless surface.

Environmental Concerns: As people’s awareness of the environment grows, the automotive industry is moving toward more environmentally friendly cleaning products. In comparison to conventional techniques, touchless wash systems use water more effectively, lowering total water consumption and limiting chemical runoff into the environment. Furthermore, the sustainability profile of touchless wash systems is further improved by developments in recycling technologies and biodegradable cleaning agents, which comply with environmental regulations and consumer expectations for eco-friendly practices.

Convenience and Time Efficiency: For customers, convenience and time efficiency are critical in the fast-paced world of today. With less human intervention needed and quick cleaning cycles, touchless car wash systems are a practical substitute for brush-based or manual systems. Automated procedures and features that integrate with mobile apps for scheduling and payment improve the car wash process so that clients can keep their cars clean without having to give up important time.

Maintenance of Vehicle Finish: Owners place a high value on keeping their cars looking good, which is why touchless wash systems with their gentle yet efficient cleaning method are so popular. These methods reduce the possibility of scratches, swirls, and paint damage by removing direct contact with brushes or abrasive materials. This increases the longevity and resale value of automobiles. This is a feature that appeals to auto enthusiasts and luxury automobile buyers who value having a spotless outside appearance.

Safety and Hygiene: Touchless wash systems provide a safe and hygienic way to clean cars in the face of public health concerns. These methods lessen the possibility of cross-contamination and pathogen transmission by preventing physical contact between cleaning tools and the vehicle’s surface. This particular issue has particular relevance when discussing shared or rented automobiles, since upholding cleanliness and sanitation standards is crucial to fostering consumer satisfaction and confidence.

9803 Global export shipment records of Cleaning Products with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.