Farm input price index (FIPI). Quarterly data are available from from the first quarter of 2002. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2012=100).

Farm Inputs Market 2025: Projected to hit USD 404.16B by 2029 at 4.1% CAGR. Access in-depth analysis on trends, market dynamics, and competitive landscape for data-driven decisions.

This table contains 462 series, with data for years 1961 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Newfoundland and Labrador; Eastern Canada; Atlantic Region ...), Price index (33 items: Crop production; Grains; Wheat; Seed ...), Index year (2 items: 1981=100;1986=100 ...).

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.

Agricultural Inputs Market size is valued at USD 17798.76 Million in the year 2024 and it is expected to reach USD 35896.59 Million in 2031 at a CAGR of 10.11% from 2024 to 2031.

Agricultural Inputs Market Drivers

Rising Global Population and Food Demand:

The world’s population is projected to continue growing steadily, putting immense pressure on food production systems. This necessitates an increase in agricultural inputs like seeds, fertilizers, pesticides, and machinery to meet the rising demand for food.
Focus on Food Security and Self-Sufficiency:

Many governments are prioritizing food security and self-sufficiency by encouraging the use of agricultural inputs that can enhance crop yields and productivity. This includes subsidies for farmers to purchase essential inputs and investments in research and development of improved varieties.
Sustainable Agriculture Practices:

There’s a growing awareness of the environmental impact of conventional farming practices. This is driving the demand for sustainable agricultural inputs such as:
Biofertilizers and Biopesticides: These offer eco-friendly alternatives to chemical fertilizers and pesticides, reducing environmental pollution and promoting soil health.
Precision Farming Technologies: These technologies involve using sensors, data analytics, and automation to optimize input use, minimizing waste and environmental impact.
Technological Advancements in Agriculture:

Technological advancements are transforming the agricultural landscape, with new and improved agricultural inputs playing a crucial role:
High-Yielding Seed Varieties: Genetically modified (GM) seeds and other high-yielding varieties can significantly increase crop yields on the same amount of land.
Advanced Machinery: Modern agricultural machinery like precision planters, self-driving tractors, and automated irrigation systems improve efficiency, accuracy, and input use.
Other factors influencing market growth:

Climate Change and Extreme Weather Events: Climate change poses a significant threat to agricultural productivity. Farmers are increasingly relying on resilient crop varieties and improved irrigation systems to adapt to changing weather patterns.
Urbanization and Land Availability: The conversion of agricultural land for urban development is a growing concern. This emphasizes the need for increased efficiency and productivity in agricultural practices, potentially leading to a rise in demand for certain agricultural inputs.

This table contains 1318 series, with data for years 1992 - 1998 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (33 items: Newfoundland and Labrador; Prince Edward Island; New Brunswick; Nova Scotia ...), Farm input components (76 items: Gasoline; regular unleaded; Diesel fuel; Motor oil; Grease ...)

This table contains 189 series, with data for years 1998 - 2007 (not all combinations necessarily have data for all years), and was last released on 2011-03-01. This table contains data described by the following dimensions (Not all combinations are available): Geography (3 items: Eastern Canada; Canada; Western Canada ...), Price index (63 items: Farm inputs; total; Building and fencing; Machinery replacement; Machinery and motor vehicles ...).

This table contains 132 series, with data for years 1961 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Eastern Canada; Atlantic Region; Newfoundland and Labrador ...), Price index (6 items: Supplies and services; Telephone; Small tools; Electricity ...), Index year (2 items: 1981=100; 1986=100 ...).

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 Brazil were from Cerrado, Goias, Minas and Gerais and were selected based on the following criterion: - Small and medium growers: less or equal to 2000ha of soybean

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.

United States TFP: Non-Farm: Input: Labor Input data was reported at 116.751 2009=100 in 2017. This records an increase from the previous number of 114.615 2009=100 for 2016. United States TFP: Non-Farm: Input: Labor Input data is updated yearly, averaging 100.754 2009=100 from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 116.751 2009=100 in 2017 and a record low of 78.030 2009=100 in 1987. United States TFP: Non-Farm: Input: Labor Input data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G069: Total Factor Productivity.

This statistic shows the farm input price index for animal production in Canada from 2013 to 2019. In 2019, the input price index of animal production was measured at 114.5, whilst in 2017 it was measured at 110.5.

This table contains 78 series, with data for years 1961 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (12 items: Canada; Nova Scotia; Eastern Canada; Atlantic Region ...), Price index (4 items: Hired farm labour; Hourly rated; Daily rated; Monthly rated ...), Index year (2 items: 1981=100; 1986=100 ...).

PPI: Agricultural Input: Jiangsu: Farm Tool data was reported at 99.800 Prev Year=100 in 2020. This records a decrease from the previous number of 105.413 Prev Year=100 for 2019. PPI: Agricultural Input: Jiangsu: Farm Tool data is updated yearly, averaging 102.000 Prev Year=100 from Dec 1994 (Median) to 2020, with 27 observations. The data reached an all-time high of 116.800 Prev Year=100 in 1994 and a record low of 97.000 Prev Year=100 in 2003. PPI: Agricultural Input: Jiangsu: Farm Tool data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IC: Agricultural Price Index: Jiangsu.

Farm input total price index at the Canada level. The index is released on a quarterly basis. (2002=100) The index includes data for the current period, as well as those for the last four periods.

In March 2024, the agricultural input price index in Israel fell to around 116 points. The price of inputs in the country's agricultural sector fell by over 2 percent in October 2023. In the months following the Israel-Hamas war, which started on October 7, 2023, prices decreased by 3.6 percent.

Graph and download economic data for Intermediate Inputs by Industry: Agriculture, Forestry, Fishing, and Hunting (Chain-Type Price Index) (IIPIPAFH) from Q2 2005 to Q4 2024 about hunting, forestry, fishing, intermediate, chained, agriculture, private industries, private, industry, rate, price index, indexes, price, and USA.

This table contains 58 series, with data for years 1971 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (13 items: Canada; Atlantic Region; Prince Edward Island; Eastern Canada ...), Price index (3 items: Interest; Non-mortgage; Mortgage ...), Index year (2 items: 1981=100; 1986=100 ...).

This table contains 620 series, with data for years 1961 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Eastern Canada; Prince Edward Island; Atlantic Region ...), Price index (26 items: Machinery and motor vehicles; Machinery replacement; Power machinery; Tractors ...), Index year (2 items: 1981=100; 1986=100 ...).

This table contains 189 series, with data for years 1998 - 2007 (not all combinations necessarily have data for all years), and was last released on 2011-03-01. This table contains data described by the following dimensions (Not all combinations are available): Geography (3 items: Eastern Canada; Canada; Western Canada ...), Price index (63 items: Farm inputs; total; Building and fencing; Machinery replacement; Machinery and motor vehicles ...).

Purchase of agricultural inputs in Kenya increased in value to 69.3 billion Kenyan shillings (KSh), roughly 610 million U.S. dollars, in 2020. Material inputs, such as fertilizers, crop chemicals, and certified seeds, composed most of the total, at around 65.7 billion KSh (578 million U.S. dollars). Overall, the value of purchased material inputs expanded in the period under review, while that of service inputs only slightly fluctuated.