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).

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.

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.

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 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.

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: 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).

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.

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.

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.

Abstract

The Integrated Farm Household Survey (IFHS) supported the agricultural Research and Development Program in terms of benchmark data on the characteristics of farms and farmers. The IFHS results provided inputs for the development and/or improvement of the performance indicators system in agriculture. Further, the survey results could quantify the impact of agricultural policies of the government.

The survey gathered household level data on the following; Household Information, Farm Particulars, Inventory of Farm Investments, Household Income, Household Expenditures and Credit Information.

Specifically, the following data are generated: 1. Level, structure and/or sources of farm household income; 2. Characteristics of farms/farm enterprises and the farm households; 3. Access of farm households to agricultural support services; 4. Farm management such as input use and cultivation practices; 5. Expenditure patterns of the farm households; 6. Farm and households investments; and 7. Other socio-economic data.

Geographic coverage

National Coverage.

Analysis unit

Households

Universe

The survey covered farm households with farming/fishing operations.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IFHS utilized different sampling frames at the barangay and household levels. At the barangay level, the list of agricultural barangays covered in the 1999 Barangay Screening Survey (BSS) served as the sampling frame while at the household level, the listing of households generated from the 2000 Census of Population and Housing (CPH) of the National Statistics Office (NSO) was used as basis for drawing the samples. The 2000 CPH listing was utilized as sampling frame for the IFHS despite the limitation that households were not classified into farming and non-farming categories for two major considerations. First, the 2000 CPH provided the most updated lists of households by barangay. Second, budgetary constraints precluded the conduct of household screening in the selected sample barangays for the survey.

The domain of the survey was the province. A two-stage stratified sampling design was adopted with the barangay as primary sampling unit and the farming household as secondary sampling unit. The number of farming households was used as the stratification variable. Primary and secondary sampling units were both drawn using simple random sampling.

In getting the number of barangays as representative of the domain (province) level, the total number of agricultural barangays in the province reported in the 1999 Barangay Screening Survey (BSS) was used in proportionately allocating the target sample size of around 600 barangays to the Integrated Farm Household Survey (IFHS) provinces. Due to budgetary consideration, the total number of barangays included for small and large agricultural sampling of households with at least one member engaged in agricultural activity. provinces was set at six (6) and nine (9) barangays, respectively, depending on the computed total sample size for the province, that is,

        n' = 6 if n < 6, and
        n' = 9 otherwise.

Ten (10) sample households were allocated for each sample barangay. This procedure resulted in total sample size of 592 barangays and 5,920 households for the entire country.

A general feature of the design was the division of the primary sampling units into strata of approximately equal sizes relative to the number of farming households reported in the 1999 BSS. The division of the barangays within the province and the drawing of sample was done as follows:

The barangays were arrayed in descending order based on the total number of farming households. These barangays were then divided into three (3) strata such that the cumulative total number of farming households of all the barangays in any one stratum was approximately of the same magnitude as the rest of the individual strata. Thus, Stratum 1 barangays constitute all "large barangays", Stratum 2 barangays constitute all "medium barangays", and Stratum 3 barangays constitute all "small barangays"; with respect to total number of farming households.

Equal sample sizes were allocated and drawn from the three strata, resulting in two (2) and three (3) sample barangays, respectively, per stratum depending on the sample size for the province. Selection of sample barangays wss done at the BAS Central Office using simple random sampling. The generated lists of sample barangays were then submitted to NSO for the drawing of sample households and for the photocopying of corresponding barangay maps.

Drawing of sample households was made at the NSO field offices using simple random sampling of households with at least one member engaged in agricultural activity. The generated lists of samples were sent back to BAS Central Office for control and distribution to concerned Provincial Operations Centers (POCs).

Sampling deviation

As in any survey, there were cases wherein samples need to be substituted or replaced. Following were the guidelines in replacing sample barangays and/or households:

Sample Barangays - Only two general reasons were considered valid for substituting barangays: 1. Transportation costs were way above the allocated budget for operations; or 2. Unfavorable peace and order situation in the area.

The list of replacement barangays served as the only source of substitute barangays. It was emphasized that a replacement barangay should be taken only from the list of replacement barangays in the same stratum.

Sample Households - Only the reasons enumerated below are considered valid for replacing households. 1.Household was not a qualified IFHS sample: a. For regions except NCR: Candidate household was not a farming household; b. For NCR: Candidate household was not into agricultural activities, or into agricultural activities but produce was not intended to generate income for the household; c. Conditions (a) and (b) were satisfied but there was no agricultural operation during the reference period (July 2002 to June 2003); 2. Household was a qualified IFHS sample but any of the following situations arose during visit: a. No qualified respondent was available for interview during the entire survey period; b. Qualified respondent refused to be interviewed; c. Interview was terminated;

It was emphasized that reasons for substituting sample households should be validated first by the field supervisor before replacement is allowed. Replacement households should be taken only from the list of replacements for the barangay.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Consistencies of data items within and across record types were first verified and checked according to the Data Processing Guidelines of the study. First stage of the editing was done manualy. A second stage consistency check was a component of the Computerized Processing System.

Initial editing of data was done by the Contractual Data Collectors (CDCs) on every filled up questionaire. These questionnaires were turned over to their supervisors for checking. Editing/Checking for consistencies of data items in particular record types and accross record types were done.

Second stage of editing was done at the Central Office. The Data Processing System (DPS) was equipped with a customized editing program to filter out-of-range data items to generate an errorlist. The errorlist is a compilation of errors on specific data item that did not pass the specification. The errorlist list was checked based on the information in the questionnaire. The correction was reflected to the data file using the the CENTRY module of the Integrated Micro-computer Processing System (IMPS).

Response rate

From 5920 sample households, 5448 sample units were successfuly interviewed for a response rate of 92.03%.

Abstract

The 2002 Census of Agriculture (CA 2002) is a large-scale government operation geared towards the collection and compilation of statistics in the agriculture sector of the country. The collected data will constitute the bases from which policymakers and planners will formulate plans for the country's development.

The following were the objectives of CA 2002:

1. To determine the structure and characteristics of agricultural holdings;
2. To determine the number and distribution of households and enterprises engaged in agriculture and to gather information on the operation of these households and enterprises;
3. To provide the basis for sampling frame for other statistical undertakings; and
4. To provide basic data for use in national as well as sub-national development planning.

Specifically, it aims to: 1. Obtain comprehensive data on farm characterisitcs such as size, location, tenure status, irrigation system, crops planted, livestock/poultry raised, etc.; 2. Determine the type and number of equipment, machineries and facilities used in the operation of agricultural activities whether owned or rented; and 3. Provide benchmarks for the various statistical series which are designed to measure progress in agriculture.

Major findings include the following: 1. Central Visayas accounted for the highest number of farms but Bicol Region had the biggest farm area. 2. Almost all farms in the country were operated individually. 3. Most farms were owned by the agricultural operators. 4. More than half of the farms in the country were under temporary crops. 5. Palay remained as the major temporary crop in the country. 6. Coconut also remained as the dominant permanent crop. 7. Individual system irrigation was the most common in the country. 8. Number of hogs reared and tended increased by 1.1 milliion heads. 9. Raising of chicken was the prevalent poultry raising activity. 10. Ornamental and flower gardening (excluding orchid) was also common in the country. 11. Male operators dominated the agriculture sector. 12. Almost 80 percent of the household members engaged in agricultural activity were working in own agricultural holding. 13. Plow was the most common farm equipment in the country.

Geographic coverage

National Coverage

Analysis unit

Households

Universe

The census covered all households, agricultural operators, and agricultural establishments.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The CA 2002 adopted a one-stage stratified systematic sampling design where selection of sample barangays was done by city/muncipality (by district for the National Capital Region or NCR) and by stratum. However, for the provinces of Laguna, Isabela, Bukidnon, and Batanes, a full sample-census was adopted.

Except for the cities/municipalities of the full-sample barangays, all cities/municipalities (6 districts for NCR) were treated as domains and the barangays as the ultimate sampling units. The six districts of NCR are as follows: NCR I - Manila; NCR II - Quezon City; NCR III - San Juan, Cities of Mandaluyong, Marikina and Pasig; NCR IV - Malabon, Navotas, Cities of Kalookan and Valenzuela; NCR V - Pateros, Taguig and Makati City; and NCR VI - Cities of Pasay, Las Piñas, Muntinlupa, Parañaque

The sampling frame was based on the list of barangays taken from the results of the 2000 Census of Population and Housing (Census 2000) as of June 2002.

In each domain, all barangays were grouped into three strata, as follows: Stratum 1 - Barangays with the largest Total Farm Area (TFA) in the municipality based on the 1991 Census of Agriculture and fisheries (CAF) Stratum 2 - All other sample barangays of the 1991 CAF Stratum 3 - All other barangays in the sampling frame

The 1991 sample barangays in each domain were ranked by descending values of TFA. The barangays with the largest TFA in 1991, referred to as the certainty barangays, were included in Stratum 1. In cases where the certainty barangay was split into two or more barangays as a result of the creation of a new barangay (as of June 2002 master list of barangays), the new barangay was also treated as a certainty barangay. Sample barangays of the 1991 CAF not included in Stratum 1 were assigned in Stratum 2. Barangays with no TFA because they were not samples during the 1991 CAF were arranged in ascending order of the total number of households based on Census 2000. These barangays were assigned in Stratum 3.

All barangays in Stratum 1 were automatically taken as samples. Sample barangays in Strata 2 and 3 were systematically selected using a 25-percent sampling rate, except for NCR. The sampling rates for NCR were 50 percent and 10 percent for Stratum 2 and Stratum 3, respectively. In each sampled barangay, all households were covered.

All agricultural establishments identified in the 2002 List of Establishments, whether or not located in the sample barangays of CA 2002, and new agricultural establishments in the sample barangays during the enumeration of CA 2002, were enumerated.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

The accomplished census forms undergone several stages of data editing. These stages include the following:

1. Field editing which consisted of checking of consistency, correctness and completness of entries while in the field.
2. Manual Processing of accomplished questionnaires at the Provincial Offices where the following were done: a. Verification of geographic identification and completeness of forms b. Checking for legibility of entries c. Coding
3. Machine Processing which includes machine validation, consistency checking and completeness checking of entries.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CA estimates, the estimation of the magnitude of sampling error in the census data was undertaken by the NSO for the 2002 CA. The standard error (SE) and coefficient of variation (C.V.) were used as measures of sampling error.

Forecast: Total Value of Agricultural Production at Farm Gate in Philippines 2024 - 2028 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.

Sugarcane was the leading crop produced in the Philippines, with a total volume of production at 21.65 million metric tons in 2023. Palay, coconut, and banana were also among the crops with the highest production volume in that year.

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.

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.

The total land area used for agricultural crop cultivation in the Philippines was around ***** million hectares in 2023. The land area used for agricultural crop cultivation in the country was mainly used for cultivating palay, corn, and coconut.