Between 2016 and 2023, there were significantly more males employed in the agricultural industry in the Philippines in comparison to their female counterparts. In particular, there were over ************* male workers compared to about **** million female workers according to preliminary data for 2023.

The number of people working in the agriculture sector increased in 2023 in comparison to the previous year. From over **** million agricultural workers, this figure increased to **** million in 2023. Most employers in this sector are engaged in agriculture and forestry.

Philippines Production: Volume: Agricultural Crops: Other Crops data was reported at 8,268.700 Metric Ton th in 2017. This records an increase from the previous number of 8,063.800 Metric Ton th for 2016. Philippines Production: Volume: Agricultural Crops: Other Crops data is updated yearly, averaging 8,063.800 Metric Ton th from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 12,719.200 Metric Ton th in 1996 and a record low of 6,138.300 Metric Ton th in 2000. Philippines Production: Volume: Agricultural Crops: Other Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B015: Production: Volume: Agriculture (Annual).

Philippines Production: Volume: Agricultural Crops data was reported at 91,520.000 Metric Ton th in 2017. This records an increase from the previous number of 81,643.600 Metric Ton th for 2016. Philippines Production: Volume: Agricultural Crops data is updated yearly, averaging 69,128.500 Metric Ton th from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 91,520.000 Metric Ton th in 2017 and a record low of 56,685.300 Metric Ton th in 1987. Philippines Production: Volume: Agricultural Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B015: Production: Volume: Agriculture (Annual).

The Philippines agricultural machinery market was valued at USD 693.90 Million in 2024. The industry is expected to grow at a CAGR of 7.90% during the forecast period of 2025-2034, to attain a valuation of USD 1484.26 Million by 2034.

The Philippines agricultural machinery market plays a vital role in the country's efforts to modernise its agricultural sector, which remains a significant contributor to employment and rural development. As the country seeks to boost productivity and reduce reliance on manual labor, demand for modern machinery such as tractors, harvesters, and irrigation systems is steadily increasing. Government initiatives like the Philippine Mechanization Program, along with support from agencies such as the Department of Agriculture (DA), have spurred mechanisation adoption, particularly among smallholder farmers. In February 2024, the Board of Investments (BOI) in Philippines declared a surge in agricultural investments ranging from PHP 1 billion to PHP 15 billion after the issuance of Fiscal Incentives Review Board (FIRB). These investments are aimed towards adopting new technologies and strengthening food security.

The Philippines agricultural machinery market expansion is further bolstered with the growing dominance of technology for transforming the agricultural space in the country. Advanced solutions, including automated machinery, precision farming, and vertical farming systems are revolutionising the farming practices. Agricultural mechanisation through machinery for tasks, such as ploughing, irrigation, and harvesting not only boosts production, but also reduces labour. The growing support to implement agri tech solutions to respond to various issues in the agriculture domain will also drive the industry growth. In April 2025, Grow Asia launched the 2025 Grow Asia Innovation Challenge in the Philippines to advance the adoption of more climate-resilient agri tech agricultural solutions across Southeast Asia.

The report covers Philippines Agricultural Equipment Market Sector, Leading Players in Philippines Agricultural Equipment Market, Major Players in Philippines Agricultural Equipment Market.

In Philippines Vertical Farming Market, offering valuable insights, key market trends, competitive landscape, and future outlook to support strategic decision-making and business growth.

Philippines Employment: Agriculture data was reported at 10,244.000 Person th in Jan 2025. This records an increase from the previous number of 10,187.000 Person th for Oct 2024. Philippines Employment: Agriculture data is updated quarterly, averaging 10,560.000 Person th from Jan 2012 (Median) to Jan 2025, with 53 observations. The data reached an all-time high of 12,467.000 Person th in Apr 2012 and a record low of 8,761.000 Person th in Apr 2020. Philippines Employment: Agriculture data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G026: Labour Force Survey: Employment: by Industry, Occupation and Class: Quarterly.

Within the agriculture, forestry, and fishing industry in the Philippines, forestry and logging recorded the highest gross value added (GVA) growth rate between 2023 and 2024 at nearly ** percent. In contrast, sugarcane production registered the highest contraction in that period.

Philippines Production: Value: Agricultural Crops: Other Crops data was reported at 138,449.500 PHP mn in 2017. This records an increase from the previous number of 130,164.000 PHP mn for 2016. Philippines Production: Value: Agricultural Crops: Other Crops data is updated yearly, averaging 58,635.100 PHP mn from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 138,449.500 PHP mn in 2017 and a record low of 30,156.000 PHP mn in 1987. Philippines Production: Value: Agricultural Crops: Other Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B014: Production: Value: Agriculture (Annual).

GDP from Agriculture in Philippines decreased to 456664.44 PHP Million in the first quarter of 2025 from 523784.28 PHP Million in the fourth quarter of 2024. This dataset provides the latest reported value for - Philippines Gdp From Agriculture - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Forecast: Total Value of Agricultural Production at Farm Gate in Philippines 2024 - 2028 Discover more data with ReportLinker!

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.

Agriculture, forestry, and fishing, value added (annual % growth) in Philippines was reported at 1.187 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Agriculture, value added (annual % growth) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Philippines Production: Volume: Agricultural Crops: Major Crops data was reported at 56,060.100 Metric Ton th in 2017. This records an increase from the previous number of 48,733.800 Metric Ton th for 2016. Philippines Production: Volume: Agricultural Crops: Major Crops data is updated yearly, averaging 45,076.200 Metric Ton th from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 56,060.100 Metric Ton th in 2017 and a record low of 32,660.300 Metric Ton th in 1987. Philippines Production: Volume: Agricultural Crops: Major Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B015: Production: Volume: Agriculture (Annual).

Philippines PH: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data was reported at 9.397 % in 2023. This records a decrease from the previous number of 9.552 % for 2022. Philippines PH: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data is updated yearly, averaging 19.134 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 27.630 % in 1974 and a record low of 8.820 % in 2019. Philippines PH: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Gross Domestic Product: Share of GDP. Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 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 4. Note: For VAB countries, gross value added at factor cost is used as the denominator.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;Note: Data for OECD countries are based on ISIC, revision 4.

This dataset provides information on 14 in Ilocos Norte, Philippines as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Abstract

The CrPS is conducted quarterly to generate production estimates for crops other than cereals at the national, regional and provincial levels.

Of the 230 crops covered, the individual estimates of the 19 crops highlighted in the Quarterly Report on the Production in Agriculture are released at the national level while the rest were lumped as Other Crops. Provincial level estimates are available on an annual basis.

The survey aims to support the data needs of planners, policy and decision makers and other stakeholders in the agricultural sector, and to provide periodic updates on crop related developments.

The survey adopts two-stage sampling with the municipality as the primary sampling unit and the households as the secondary sampling unit.

Geographic coverage

Provinces covered for Crops Production Survey are the following: CAR: Abra Apayao Benguet Ifugao Kalinga Mt. Province ILOCOS REGION:
Ilocos Norte Ilocos Sur La Union Pangasinan CAGAYAN VALLEY: Cagayan Isabela Nueva Vizcaya Quirino CENTRAL LUZON: Aurora Bataan Bulacan Nueva Ecija Pampanga Tarlac Zambales CALABARZON: Batangas Cavite Laguna Quezon Rizal MIMAROPA: Marinduque Mindoro Occidental Mindoro Oriental Palawan Romblon BICOL: Albay Camarines Norte Camarines Sur Catanduanes Masbate Sorsogon WESTERN VISAYAS: Aklan Antique Capiz Guimaras Iloilo Negros Occidental CENTRAL VISAYAS: Bohol Cebu Negros Oriental Siquijor EASTERN VISAYAS: Biliran Eastern Samar Leyte Northern Samar Southern Leyte Samar ZAMBOANGA PENINSULA: Zamboanga City Zamboanga del Norte Zamboanga del Sur Zamboanga Sibugay NORTHERN MINDANAO: Bukidnon Camiguin Lanao del Norte Misamis Occidental Misamis Oriental DAVAO REGION: Compostela Valley Davao City Davao Norte Davao Oriental Davao del Sur SOCCSKSARGEN: North Cotabato Saranggani South Cotabato Sultan Kudarat CARAGA: Agusan del Norte Agusan del Sur Surigao del Norte Surigao del Sur ARMM: Basilan Lanao Del Sur Maguindanao Sulu Tawi-Tawi

Analysis unit

An agricultural production-related survey with a household-level questionnaire which would have provincial unit of analysis.

Universe

The survey covers all small farm producers and plantation farms of all agricultural crops, other than palay and corn, nationwide.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey employs two-stage sampling design with municipality as the primary sampling unit (psu) and farmer-producer as the secondary sampling unit (ssu).

Farms are classified as small farms and plantation farms. For small farms, crops are classified based on coverage of the Farm Price Survey, e.i. Farm Price Survey and non-Farm Price Survey. For crops under Farm Price Survey, the top five producing municipalities based on the volume of production were chosen as psu. In each municipality, five sample farmer-producers as ssu were enumerated.

For small farms of all other crops not covered under Farm Price Survey, top two to three producing municipalities were chosen as psus. In each municipality, three sample farmer-producers as ssu were enumerated.

This scheme is applied to each of the crops being covered every survey round. It is possible for a farmer-producer to be a respondent for several crops, which he plants and harvests during the reference quarter.

Classification for plantation farms is based on the cut-off on area planted. Each survey round covers a maximum of 5 plantations by crop.

The above scheme was adopted since 2005 to date.

The sampling design for CrPS has undergone several changes. In 1988 until 2000, the survey adopted three stage sampling or 5x5x5. This is intended to represent the five (5) municipalities as the primary sampling unit, five barangays as the secondary sampling unit and five (5) households as the ultimate sampling unit. In May 2000, a two stage sampling was adopted with the five (5) top producing municipalities as the primary sampling unit and five farmers-producers as the secondary sampling unit.

For coconut, the sampling procedure was in collaboration with the PCA which was developed in 1996. The Bureau was responsible for the survey methodology and data processing while the PCA was responsible for the data collection.

A three-stage sampling is being employed. The domain of the survey is the municipality, classified as coastal flat, coastal upland, inland flat, and inland upland. The barangays, also classified according to the classification used for the municipalities, serve as the first stage. The second stage is the two coconut farmers from each sample barangay drawn using simple random sampling. The third stage is the 10 sample coconut trees lying along the longest diagonal line bisecting the parcel. The sampling design cut across the small and plantation farms and remain the same until the frame is updated or the sampling design is changed.

The survey was piloted in Davao Region provinces which started on the fourth quarter of 1996. This was replicated in the Western Visayas provinces in the first quarter of the following year. The provinces in the rest of the regions conducted this survey beginning in June 1997. The PASOs and the Provincial Coconut Development Managers jointly validate the results. The PASOs forward the result to the region for further joint review by the RASOs and the Regional Managers.

Mode of data collection

Face-to-face [f2f]

Research instrument

The title of the questionnaire is Crops Production Survey, and is in the English language. This captures production, area, and bearing trees for the current quarter and last year same period. A remaks column is also provided for the explanation on the changes this year versus last year.

The questionnaire also serves as summary worksheet for the small farms and plantation farms and provinvial summary.

The instrument is a one-page questionnaire which could accommodate as many as five crops. The number of sheets may vary depending on the number of crops covered in the province.

Cleaning operations

Editing is done in four stages during the data review. The initial stage is at the collection point while with the respondent. This starts with the completeness and correctness of the entries in the answer grid. The yield per unit area, or kilograms per bearing tree and planting density were computed and verified with the respondents when these are out of range. The range varies by crop and reference period. The farmer-respondents are asked on the climatic condition a quarter ago up to the prevailing quarter and explanations on the change in the level against the same period a year ago. During the Provincial Data Review, Regional Data Review and National Data Review, data editing is done after encoding and data transfer from one form or system to another during the generation of estimates.

Sampling error estimates

Not estimated.

Data appraisal

The estimates are subjected to three levels of data review and validation. These are the Provincial Data Review (PDR), Regional Data Review (RDR) and National Data Review (NDR).

Accross all data validation levels, a set of parameters is being used as guideposts and the available data from other agencies.

The existing indicators also accounts for the situation in the province. At the RDR, the data is assessed to reflect the situation of the region and the levels in comparison between and among the provinces in the region. At the NDR, the data are validated in comparison to national level data and the data between and among the regions.

To some extent and for valid reasons, this involves adjustment of the levels of the data generated.

Forecast: Fish Farming Production in Philippines 2022 - 2026 Discover more data with ReportLinker!

Employment in agriculture (% of total employment) (modeled ILO estimate) in Philippines was reported at 22.36 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Employment in agriculture (% of total employment) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.