Agricultural land (% of land area) in Peru was reported at 19.05 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Peru - Agricultural land (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

The share of value added by the agriculture, forestry and fishing sector to the gross domestic product in Peru decreased by 0.7 percentage points (-10.54 percent) in 2023 in comparison to the previous year. Nevertheless, the last two years recorded a significantly higher share than the preceding years.The agriculture, forestry, and fishing sector includes crop cultivation, forestry, hunting, fishing, and livestock production. The value added refers to the net output of the sector, obtained by deducting the intermediate inputs (or intermediate consumption) from the gross production revenues.

In 2023, the employment in the agricultural sector as share of total employment in Peru amounted to 23.97 percent. Between 1991 and 2023, the figure dropped by 17.44 percentage points, though the decline followed an uneven course rather than a steady trajectory.

Agriculture, forestry, and fishing, value added (% of GDP) in Peru was reported at 6.0554 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Peru - Agriculture, value added (% of GDP) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Peru: Value added in the agricultural sector as percent of GDP: The latest value from 2024 is 6.06 percent, a decline from 7.24 percent in 2023. In comparison, the world average is 9.68 percent, based on data from 151 countries. Historically, the average for Peru from 1960 to 2024 is 10.69 percent. The minimum value, 6.06 percent, was reached in 2024 while the maximum of 27.94 percent was recorded in 1977.

This statistic shows the share of economic sectors in the gross domestic product (GDP) in Peru from 2013 to 2023. In 2023, the share of agriculture in Peru's gross domestic product was 7.19 percent, industry contributed approximately 33.86 percent and the services sector contributed about 51.33 percent.

Employment in agriculture (% of total employment) (modeled ILO estimate) in Peru was reported at 23.97 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Peru - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Abstract

The National Institute of Statistics and Informatics (INEI), the governing body of the National Statistical System, in a strategic alliance with the Ministry of Economy and Finance (MEF) and in coordination with the Ministry of Agriculture and Irrigation (MINAGRI), executed for the third consecutive time the National Agricultural Survey (ENA), in the 24 regions of the country.

The National Agricultural Survey 2016 is a statistical research that will have the fundamental purpose of generating updated information for the construction of indicators that will facilitate the follow-up and evaluation of the different budgetary programs, within the framework of a results-based budget, that the Ministry of Economy and Finance has been implementing in the public sector. In this way,it will contribute to the design and orientation of public policies for the improvement of the living conditions of agricultural producers.

The survey had the following objectives:

General objectives: - Estimate land uses, planted area, harvested area, production and yield of the main transitory and permanent crops, milk production and livestock inventory in regions of the national territory. - Generate information for the construction of indicators of the agricultural sector, within the framework of a results-based budget, that allow for the continuous evaluation of the evolution of said indicators and contribute to the design and orientation of public policies for the improvement of the living conditions of the population.

Specific objectives: - Identify and quantify land use (land uses). - Estimate the planted area of the transitory and permanent crops. - Estimate the harvested area, production and yield of the main crops, at the regional level. - Produce information that supports the estimation of the gross value of agricultural production. - Determine the national livestock inventory. - Identify the primary destinations of production, marketing channels and points of sale. - Determine the percentage of agricultural producers that carry out adequate agricultural and livestock practices. - Obtain information from agricultural producers who carry out an appropriate sowing orientation. - Determine the percentage of agricultural producers who have carried out soil analysis and received technical assistance to implement the results of said analysis in the last three years. - Determine the percentage of agricultural producers who were trained in water quality standards for irrigation in the last three years. - Determine the percentage of agricultural producers who have received technical assistance on the installation and management of pastures and apply it, in the last three years. - Determine the percentage of agricultural producers who have been trained in pasture installation and management in the last three years. - Obtain the percentage of agricultural producers that apply technical irrigation. - Estimate the agricultural area with technical irrigation. - Determine the percentage of agricultural producers informed on safety issues. - Determine the percentage increase in the annual average value of sales of small agricultural producers. - Determine the percentage increase in the average gross annual profit of the sales of small producers. - Determine the percentage of agricultural producers organized and managing their organizations business. - Percentage of small agricultural producers and organizations that access storage infrastructure and equipment for marketing. - Investigate and estimate other study variables.

Geographic coverage

National Coverage

Analysis unit

Agricultural holdings

Universe

The survey covers all the agricultural units of the country with less than 50 ha and the agricultural units that are agricultural or farming enterprises.

Kind of data

Sample survey data [ssd]

Sampling procedure

The basic sampling framework for the selection of the survey sample is constituted by the statistical information of the master framework of agricultural units, using information from the IV National Agricultural Census 2012 (IV CENAGRO 2012). The total sample of the National Agricultural Survey is 30,710 agricultural units, comprising of 29,218 agricultural units for medium and small producers; and 1,492 agricultural units for large agricultural producers (special stratum).

Sampling deviation

In the National Agricultural Survey 2016, out of a total of 2,260,973 agricultural units programmed, 2,244,679 are small and medium and 16,294 belong to the large units (enterprises, individuals, poultry farms, farms, stables, among others). The sample executed was 30,710 of which 29,218 are small and medium agricultural units and 1,492 belong to the large units.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The use of mobile technology to capture data online, ensured basic consistency of information and possible corrections in a timely manner. Also, the use of GPS for measuring the surface of the plots, helped to guarantee the correct location of the interviewers in the field and served as a mechanism of supervision and control.

The methodological documents of the survey were also validated; including questionnaires, manuals and auxiliary documents, in coordination with the technical areas of the Ministry of Agriculture and Irrigation (MINAGRI) and the Ministry of Economy and Finance (MEF). The collection instruments were further validated through pilot tests.

Quality control procedures (re-interview application, face-to-face supervision application, coverage monitoring, information quality monitoring, online consistency reports, mainly) were also applied in the field in order to ensure the quality of the information collected, especially of the main variables, such as: agricultural activity, number of plots, area, production, yields, among others.

Other tasks were also performed. They include:

1. Development of an integrated data entry system, which contained all the modules (segmentation, monitoring, data transfer, basic consistency checks, and reporting modules).
2. Analysis of the information: This task consists of evaluating, identifying and correcting errors and omissions in the variables of the database, as a result of processing, to obtain a consistent and reliable data for the generation of indicators. This task was carried out by the national supervisor and was monitored by a team installed in the data processing and methodology area at the headquarters.
3. Elaboration and analysis of quality indicators: Through the monitoring and data entry system, product indicators were generated, after the information had been consolidated.
4. Export of data to SPSS or STATA format: Finally, an export module was created which allowed the database to be exported to SPSS or STATA format.

Response rate

1. The non-response rate of small and medium agricultural units is 0.61%.

• The non-response rate of small and medium agricultural units in the coastal region is 0.63%
• The non-response rate of small and medium medium agricultural units in the sierra region is 0.50%
• The non-response rate of small and medium agricultural units in the jungle region is 0.91%

1. The non-response rate of large agricultural units is 0.58%.

Abstract

The National Institute of Statistics and Informatics (INEI), the governing body of the National Statistical System, in a strategic alliance with the Ministry of Economy and Finance (MEF) and in coordination with the Ministry of Agriculture and Irrigation (MINAGRI), has been executing the National Agricultural Survey (ENA), in the 24 regions of the country.

The fundamental purpose of the survey is to obtain statistical information that allows the characterizing of small, medium, and large agricultural units of the country. The survey is also used to generate updated information for the construction of indicators that facilitate the monitoring and evaluation of the different budgetary programs, within the framework of the budget for results that the MEF has been developing in the public sector. In this way, it contributes to the design and orientation of public policies for the improvement of the living conditions of this sector of the population, especially the small and medium-sized agricultural producers.

The survey had the following objectives:

General objectives: - To have statistical information that allows characterizing the small, medium and large agricultural units of the country. - To generate information for the construction of indicators of the agricultural sector, within the framework of a results-based budget, that allow for the continuous evaluation of the evolution of said indicators and contribute to the design and orientation of public policies for the improvement of the living conditions of the population, especially small and medium-sized agricultural producers.

Specific objectives: - Determine the percentage of agricultural producers who carry out adequate agricultural and livestock practices. - Obtain information from agricultural producers who carry out an appropriate sowing orientation. - Determine the percentage of agricultural producers who have carried out soil analyzes and received technical assistance to implement the results of said analysis in the last three years. - Percentage of agricultural producers who have received technical assistance on the installation and management of pastures and apply it, in the last three years. - Percentage of agricultural producers who have been trained in pasture installation and management in the last three years. - Obtain the percentage of agricultural producers that apply technical irrigation. - Estimate the agricultural area with technical irrigation. - Determine the percentage of agricultural producers informed on issues of agri-food safety. - Obtain a baseline to measure the percentage increase in gross profit from sales of small producers. - Determine the percentage of agricultural producers organized and managing their organizations business. - Obtain a baseline to measure the percentage increase in the sales value of small subsistence agricultural producers. - Obtain the percentage of organized small-scale agricultural producers that access storage infrastructure and equipment for commercialization.

Geographic coverage

National Coverage

Analysis unit

Agricultural holdings

Universe

The survey covers all the agricultural units of the country with less than 50 ha and the agricultural units that are agricultural or farming enterprises.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the selection of the survey sample is made up of statistical and cartographic information from the IV National Census of Agriculture 2012 (IV CENAGRO 2012). The total sample of the National Agricultural Survey is 30,755 agricultural units, comprising of 29,218 agricultural units for medium and small producers; and 1,537 agricultural units for large agricultural producers and enterprises.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

The non-response rate of the 2017 National Agricultural Survey of small and medium producers is 0.43%, while that of large producers is 1.69%.

Data appraisal

The tasks carried out in the information processing are described below.

1. DATA CAPTURE The data capture in the National Agricultural Survey 2017 was through the digital form captured in a tablet application which was validated by a team of consistency analysts to ensure that they do not present difficulties during the field operation. With this process, the survey official first filled out the digital form, then at the end of the interview he was instructed to export the database of the agricultural unit and send it to the central headquarters servers after reviewing the information completed, to which was directed to look for an internet booth, from where using the address, username and password, he proceeded to send said information through an integrated information system via the web. For the daily income it was not necessary that the questionnaire be complete, so the application allowed him to modify the information as many times as necessary, but only until the information is completed, at which time he should save the information by placing the agricultural unit in "Closed" status so that the system can proceed with its consistency. If once closed, it was necessary to modify the information, this process was only possible, through the computer team, who coordinated the cases with the consistency area and, according to their need, proceeded to correct them.

2. BASIC CONSISTENCY Coverage: The coverage process is carried out by the INEI Consistency staff, consisting of the crossing of information between the framework and what was actually found in the field. In the case of the National Agricultural Survey, we worked in two stages according to the natural region; First stage: Sierra; Second stage: Costa-Selva. The progress of coverage at the national level was monitored from the Lima headquarters through a series of reports, which guaranteed that the agricultural units are covered and consistent according to their natural region.

Structure: The structure process is carried out by INEI's OTIN staff. This process consisted of ensuring the integrity of the chapters that correspond to each agricultural unit according to their agricultural activity carried out by that agricultural producer in the reference period.

Basic consistency: Basic consistency is performed by the ENA and OTIN consistency staff together. The consistency analyst defines a set of flow rules, default values, etc. that apply to the database. The OTIN programmer implements and incorporates these rules into the basic consistency application. The process operators execute the basic consistency application and the consistency analyst verifies the obtained result.

1. CODING The coding process is automatic in the tablet application, however there are cases in which the interviewer was unable to determine the name and / or type of the crop, sub product and / or derivative, in these cases the coding is carried out by INEI's coding analysts and OTIN process operators using an interface to assign their corresponding codes in the database.

2. CONSISTENCY Consistency is performed by ENA and OTIN Consistency staff together. The consistency analyst defines a set of consistency rules that apply to the database. The OTIN developer implements and incorporates these rules into the consistency application. The process operators execute the consistency application and the consistency analyst verifies the obtained result. To facilitate the work of process operators, the process application for data processing was implemented, which consolidates in a single application all the processes involved in this task. Consistency was also a parallel action with the collection of information because, when entered into the survey database in a timely manner, it was immediately reviewed, consistent, verified and if errors or omissions were detected, they were gradually delivered to the operational headquarters for their timely recovery, correction and / or verification in the field and, if necessary, the information was returned to the headquarters.

3. RESULTS Generation of results: 23 data tables were generated according to each chapter of the form, each identified with a unique identifier (ID) in each table.

4. PRODUCTS From the database of the definitive results of the National Agricultural Survey, the microdata is generated as a product in the SPSS Database, which includes all the chapters and sections of the virtual form.

Abstract

The fundamental purpose of the 2018 National Agricultural Survey was to provide statistical information that enables the characterization of small, medium, and large agricultural holdings. The information from the survey also enables the construction of indicators to facilitate monitoring and evaluation of the various budgetary programs that are within the framework of the Budget for Results that the Ministry of Economics and Finance has been implementing in the public sector, and in this way, contribute to the design and orientation of public policies that aim to improve the living standards of agricultural producers.

The specific objectives of the 2018 National Agricultural Survey are to:

• Determine the percentage of agricultural producers who carry out adequate agricultural and livestock practices.
• Obtain information from agricultural producers who carry out an adequate planting orientation.
• Determine the percentage of agricultural producers who have carried out soil analysis and received technical assistance to implement the results of said analysis in the last three years.
• Determine the percentage of agricultural producers who have received technical assistance regarding the installation and management of pastures and have applied it in the last three years.
• Determine the percentage of agricultural producers who have been trained on the installation and management of pastures in the last three years.
• Obtain the percentage of agricultural producers who apply technical irrigation.
• Estimate the agricultural area with technical irrigation.
• Determine the percentage of agricultural producers informed on agri-food safety issues.
• Obtain a baseline against which to measure the percentage increase in gross profit from sales of small producers.
• Determine the percentage of agricultural producers organized and managing their organizations' business.
• Obtain a baseline against which to measure the percentage increase in the value of sales of small subsistence agricultural producers.
• Obtain the percentage of small organized agricultural producers who have access to storage infrastructure and equipment for marketing.

Geographic coverage

Agricultural Holdings

Analysis unit

Agricultural holdings

Universe

The survey covers all agricultural holdings within the country that are less than 50 ha and the agricultural holdings that are agricultural or farming enterprises, as well as enterprises and large producers (Special Stratum).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame from which the survey sample was selected is made up of the statistical information In the 2012 National Census of Agriculture.

The final sample of the 2018 National Agricultural Survey is made up of 30,806, agricultural holdings, out of which 29,218 are small and medium producers in 2,086 selected conglomerates.

The sample of the National Agricultural Survey aimed at small and medium agricultural holdings comprises two sampling types:

• Panel Sample: made up of agricultural holdings that were interviewed in 2016, 2017 and were again in 2018.
• Non-Panel Sample: made up of the agricultural holdings that were interviewed for the first time in 2018.

The sample of the National Agricultural Survey aimed at large agricultural holdings (enterprises and large producers) is classified into types of strata and sample.

Stratum type: - Business - Natural persons (large producers) - Poultry farm - Farms and stables (pig farm, guinea pig farm, dairy barn or cattle fattening center)

Sample type:

• Census: made up of agricultural holdings that have were interviewed in 2014, 2015, 2016 and 2017 and again in 2018.
• Sample: agricultural holdings that have been interviewed in 2014, 2015, 2016 or 2017, and other units will be interviewed for the first time in 2018.

Sampling deviation

100% of agricultural holdings in the planned sample were interviewed.

Agricultural holdings with no activity identified in the sample: It was observed that out of the 28,119 agricultural holdings with either complete or incomplete questionnaires, 1,021 correspond to agricultural holdings without any agricultural activity and 27,098 agricultural units had conducted some agricultural, livestock or agricultural activity in the last 12 months.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

OBJECTIVES

I. General objective

To establish strategies and procedures to ensure the quality and consistency of the data cleaning process of the information obtained from the ENA.

II. Specific objectives

• Develop procedures and work-flow for the different areas involved in the project.
• Develop systems that provide the necessary support for the proper performance of project activities.
• Guarantee the integrity and consistency of the information collected in the field operation of the project.

• Ensure the consistency of the structure of the data in digital form to ensure the correct digitization of agricultural holdings.

  STRATEGIES

• Implementation of a Monitoring and Data Entry System, containing modules of different processes, which provide support to the various project tasks.

• Use of WEB technology that enables access to the Monitoring System "Integrated System ENA 2018", from any place with an Internet connection.

• Application of a decentralized data entry and processing system in each of the operational headquarters.

TASKS TO BE CONDUCTED

I. Development of the Data Entry System ENA 2018

An Integrated System is developed to provide support to the project. This system contains the following modules:

• Tablet Coverage Module
• Segmentation Module
• Monitoring Module
• Data Transfer Module (Export)
• Basic Consistency Module
• Indicator Calculation Module
• Report Module

II. Information Analysis

This task consisted of evaluating, identifying, and fixing the errors and missing values in variables in the dataset. This task is under the mandate of the National Supervisor and is supervised by a team in charge of data processing and methodology in the headquarters

III. Development and analysis of the quality-check indicators

This task entails the development of methodological rules and procedures through which the Monitoring and Data Entry System generates the specified quality-check indicators, after checking the consistency of the data.

IV. Exporting the Database in a Stata or SPSS format

The microdata is generated in either a Stata or SPSS format.

Response rate

The rate of non-response of the small and medium agricultural holdings was 0.5% The non-response rate of small and medium agricultural holdings in the coastal region was 1.0% The non-response rate of small and medium agricultural holdings in the mountainous region was 0.4% The non-response rate of small and medium agricultural holdings in the jungle region was 0.6%

An agricultural holding is considered to have non-response if the final response of the interview is: rejection, absent and inactive, or as not having information relevant to the study. 0.2% of holdings had a rejection 0.3% were absent, and 3.5% did not have any agricultural activity.

Peru Agriculture & Fisheries: Share of Emissions Priced: Including Emissions from the Combustion of Biomass: Above EUR 60 per Tonne of CO2 data was reported at 0.000 % in 2021. Peru Agriculture & Fisheries: Share of Emissions Priced: Including Emissions from the Combustion of Biomass: Above EUR 60 per Tonne of CO2 data is updated yearly, averaging 0.000 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of 0.000 % in 2021 and a record low of 0.000 % in 2021. Peru Agriculture & Fisheries: Share of Emissions Priced: Including Emissions from the Combustion of Biomass: Above EUR 60 per Tonne of CO2 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Peru – Table PE.OECD.ESG: Environmental: Effective Carbon Rates: by Sector: Non OECD Member: Annual. The share of emissions priced above EUR Y per tonne of CO2 shows the share of emissions within a country or sector with a carbon price that exceed EUR Y in percent.

Agriculture, forestry, and fishing, value added (annual % growth) in Peru was reported at 5.9045 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Peru - Agriculture, value added (annual % growth) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

Peru PE: GDP: % of GDP: Gross Value Added: Agriculture data was reported at 7.005 % in 2016. This records a decrease from the previous number of 7.041 % for 2015. Peru PE: GDP: % of GDP: Gross Value Added: Agriculture data is updated yearly, averaging 8.321 % from Dec 1960 (Median) to 2016, with 46 observations. The data reached an all-time high of 27.943 % in 1977 and a record low of 6.603 % in 2006. Peru PE: GDP: % of GDP: Gross Value Added: Agriculture data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Peru – Table PE.World Bank: Gross Domestic Product: Share of GDP. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3 or 4.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

The statistic shows the distribution of employment in Peru by economic sector from 2013 to 2023. In 2023, 23.97 percent of the workforce in Peru were active in the agricultural sector, 16.36 percent in industry and 59.68 percent in the service sector.

Employment in agriculture, male (% of male employment) (modeled ILO estimate) in Peru was reported at 25.33 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Peru - Employees, agriculture, male (% of male employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

This statistic shows the share of economic sectors in the gross domestic product (GDP) in Peru from 2013 to 2023. In 2023, the share of agriculture in Peru's gross domestic product was 7.19 percent, industry contributed approximately 33.86 percent and the services sector contributed about 51.33 percent.

Peru Agriculture & Fisheries: Share of Emissions Priced: Including Emissions from the Combustion of Biomass: Above EUR 30 per Tonne of CO2 data was reported at 4.120 % in 2021. Peru Agriculture & Fisheries: Share of Emissions Priced: Including Emissions from the Combustion of Biomass: Above EUR 30 per Tonne of CO2 data is updated yearly, averaging 4.120 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of 4.120 % in 2021 and a record low of 4.120 % in 2021. Peru Agriculture & Fisheries: Share of Emissions Priced: Including Emissions from the Combustion of Biomass: Above EUR 30 per Tonne of CO2 data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Peru – Table PE.OECD.ESG: Environmental: Effective Carbon Rates: by Sector: Non OECD Member: Annual. The share of emissions priced above EUR Y per tonne of CO2 shows the share of emissions within a country or sector with a carbon price that exceed EUR Y in percent.

Pérou: Value added in the agricultural sector as percent of GDP: Pour cet indicateur, La Banque mondiale fournit des données pour la Pérou de 1960 à 2024. La valeur moyenne pour Pérou pendant cette période était de 10.69 pour cent avec un minimum de 6.06 pour cent en 2024 et un maximum de 27.94 pour cent en 1977.

In 2024, after three years of growth, there was significant decline in the Peruvian market for tyres for agriculture, forestry, construction, industry and other off the road vehicles, when its value decreased by -6.7% to $77M. In general, consumption, however, saw a perceptible setback. Over the period under review, the market hit record highs at $104M in 2012; however, from 2013 to 2024, consumption stood at a somewhat lower figure.

Peru Greenhouse Gases: Percentage of Greenhouse Gas Emission: Agriculture data was reported at 25.981 % in 2019. This records a decrease from the previous number of 26.284 % for 2018. Peru Greenhouse Gases: Percentage of Greenhouse Gas Emission: Agriculture data is updated yearly, averaging 30.990 % from Dec 2000 (Median) to 2019, with 20 observations. The data reached an all-time high of 36.659 % in 2000 and a record low of 25.981 % in 2019. Peru Greenhouse Gases: Percentage of Greenhouse Gas Emission: Agriculture data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Peru – Table PE.OECD.ESG: Environmental: Greenhouse Gas Emissions: Non OECD Member: Annual.