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.

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.

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).

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.

Agriculture, forestry, and fishing, value added (% of GDP) in Philippines was reported at 9.3968 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Agriculture, value added (% of GDP) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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.

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.

Agricultural land (% of land area) in Philippines was reported at 42.54 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Agricultural land (% of land area) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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).

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.

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).

Agricultural land (sq. km) in Philippines was reported at 126830 sq. Km in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Agricultural land (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Agricultural land area of Philippines rose by 0.19% from 126,590 sq. km in 2020 to 126,830 sq. km in 2021. Since the 1.32% upward trend in 2011, agricultural land area went up by 3.45% in 2021. Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.

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.

The Philippines: Value added in the agricultural sector as percent of GDP: The latest value from 2023 is 9.4 percent, a decline from 9.55 percent in 2022. In comparison, the world average is 9.91 percent, based on data from 166 countries. Historically, the average for the Philippines from 1960 to 2023 is 18.54 percent. The minimum value, 8.82 percent, was reached in 2019 while the maximum of 27.63 percent was recorded in 1974.

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.

Abstract

Smallholder rice farming is central to poverty reduction, food security, and rural development in the Philippines. One key issue is that around 41 percent of the country's irrigable land is not irrigated. Moreover, many irrigation systems are suggested to be poorly managed with unequal water distribution.

The Irrigated Rice Production Enhancement Project (IRPEP) was implemented in three regions (VI, VII and X) of the Philippines, between 2010-2015. It was designed to improve rice productivity and smallholder livelihoods by strengthening canal irrigation infrastructure of Communal Irrigation Systems (CIS), improving the capacity of the Irrigators' Associations (IAs) that manage the CIS, and offering complementary marketing support, Farmer Field Schools, and emergency seed buffer stocks.

The data collected are used to test the effectiveness of the 5-year Irrigated Rice Production Enhancement Project to improve the livelihoods of smallholder rice farmers in the Philippines.

For more information, please, click on the following link https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-irrigated-rice-production-enhancement-project.

Geographic coverage

Rural coverage. Sample covers six provinces of the Philippines across three regions (Region VI, VIII, X).

Analysis unit

Households

Universe

Smallholder farmer households

Kind of data

Sample survey data [ssd]

Sampling procedure

The analysis is based on quantitative data from 2,104 households and 113 IAs covering beneficiary and non-beneficiary groups, along with qualitative data from project and IA staff. The IRPEP's impact is estimated by comparing beneficiary and nonbeneficiary households and IAs using statistical matching techniques to ensure a clean and unbiased comparison. This process resulted in a household dataset used for analysis that covers 1,015 treatment and 664 control households, and an IA dataset used to assess impact on IA level indicators from 58 treatment and 55 control IAs.

To identify a well-matched set of treatment and control CISs and households, the sample selection for the impact assessment sought to mirror IRPEP's beneficiary selection process by initially conducting the identification at the CIS level. At the start of the process there were a number of non- beneficiary CIS in the project provinces, allowing for control CIS to be selected from within the same provinces. Using these IRPEP and non-IRPEP CIS, a two-stage process was used to select the final set of treatment and control CIS. This involved both data analysis and the knowledge of local staff.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household and IA questionnaires collected a wide range of information, which was then used to create the impact indicators and other variables to be used in the data analysis. The household questionnaire included detailed questions on agricultural production and marketing collected by season, parcel and crop for the previous 12 months, as well as socio-demographic characteristics, other income generating activities, asset ownership, experience of shocks, access to credit, and receipt of external support from various sources. The IA questionnaire gathered information on their structure and facilities, irrigation water coverage, gender differentiated membership, and income and expenditures over the past 12 months, including irrigation fee collection and operation and maintenance spending.

Note: some variables have missing labels. Please, refer to the questionnaire for more details.

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

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.

Preliminary figures reported that the agriculture sector accounted for **** percent of the total employment share in the Philippines in 2023, indicating a slight increase from the previous year. The employment share of agriculture was highest in 2016.