Growing corn varies depending on the area, and its production cycle is different in all parts of the world. In the Philippines, corn production is based on the landscape and topography of an area. In 2023, the production volume of corn in the Philippines amounted to approximately 8.41 million metric tons, higher than the produced quantity of 8.26 million metric tons in the previous year. Corn farming Over the past six years, about 2.5 million hectares of land were utilized for cultivating corn in the Philippines. Despite fluctuation in production, corn remains among the leading crops produced in the country. The Philippines is also one of the biggest corn producing countries globally. Corn industry in the Philippines Aside from rice, corn is considered another staple crop in the Philippines. The country has six common varieties — sweet corn, wild violet corn, white lagkitan, Visayan white corn, purple, and young corn. Some of the country's corn production are exported, especially maize seeds and frozen sweet corn.

Abstract

The Bureau of Agricultural Statistics (BAS) has been monitoring the palay and corn situation in the country through a Monthly Palay and Corn Situation Reporting System (MPCSRS) since 1985. The activity aims primarily to update the forecasts (based on standing crop and planting intentions) generated through the Palay and Corn Production Survey (PCPS). Based on the findings of the MPCSRS, the BAS submits a memorandum to the office of the Secretary, Department of Agriculture to inform him of the latest production status of palay and corn.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Universe

MPCSRS covers palay and corn farming households with at least 0.100 hectare or 1000 square meters of operation.

Kind of data

Sample survey data [ssd]

Sampling procedure

The MPCSRS is conducted monthly in between the Palay Production Survey (PPS)/Corn Production Survey (CPS) rounds, making use of one replicate of the PPS/PCS as sample such that:

• For pure palay provinces, one replicate consisting of ten (10) barangays is taken from the PPS samples
• For pure corn provinces, one replicate consisting of ten (10) barangays is taken from the CPS samples
• For overlap (that is palay and corn) provinces, five (5) barangays is taken from the PPS and another five (5) barangays are taken from the CPS samples yielding only one sample of barangays for the province.
• For minor palay or corn provinces, one replicate consisting of five (5) barangays are taken as samples.
• For non-corn provinces, one replicate of the PPS barangay samples is taken as samples for the MPCSR.

All households in the selected barangays are enumerated. Currently, the MPCSRS has a total sample size of 680 barangays nationwide. The replicates are selected using probability proportional to size based on total palay/corn areas.

Details of the documentation of the Palay Production Survey (PPS) and Corn Production Survey (CPS) sampling procedure can be viewed from the BAS Electronic Archiving and Network Services (BEANS), http://beans.psa.gov.ph.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Manual coding and editing are done at the Provincial Operations Centers (POCs). At the POCs, during the electronic data processing, checking of household serial numbers based on the master list of samples, consistency checks based on data ranges, and consistency checks against other data variables within the questionnaire are done by running an editing program. A completeness check program is also run to check if all sample respondents are accounted for.

At the Central Office, another round of editing is done. This activity is done to check that the data file is totally clean. The output tables generated from the clean data files are converted to Excel files to facilitate further data analysis. The estimates generated from the clean MPCSR data are reviewed at the provincial level before submitting to the Central Office. At the Central Office, the estimates are subjected to review and validation.

Response rate

Response rate for Palay samples was 92.59 %, while response rate for Corn samples = 100 %

In 2023, the total land area used for corn cultivation in the Philippines was around 2.54 million hectares. The production volume of corn in the country had been fluctuating over the past decade.

Philippines Production: Volume: Year to Date: Major Crops: Corn data was reported at 5,966.290 Metric Ton th in Sep 2018. This records an increase from the previous number of 3,760.870 Metric Ton th for Jun 2018. Philippines Production: Volume: Year to Date: Major Crops: Corn data is updated quarterly, averaging 3,479.750 Metric Ton th from Mar 1998 (Median) to Sep 2018, with 83 observations. The data reached an all-time high of 7,914.910 Metric Ton th in Dec 2017 and a record low of 782.530 Metric Ton th in Mar 1998. Philippines Production: Volume: Year to Date: Major Crops: Corn data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B013: Production: Volume: Agriculture: ytd.

Abstract

The Corn Production Survey (CPS) 2009 was a quarterly survey conducted by the Bureau of Agricultural Statistics (BAS). It aimed to generate estimates on corn production, area and yield and other related information. It was conducted in four rounds, namely, April 2009, July 2009, October 2009 and January 2010. Each round generated estimates for the immediate past quarter and forecast for the next two quarters. Results of the survey served as inputs to planners and policy makers on matters concerning the corn industry.

Geographic coverage

National Coverage

Analysis unit

Households

Universe

All farming households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure used in the Corn Production Survey (CPS) 2009 was first implemented in 1994. This was a replicated two-stage stratified sampling design with province as the domain, barangay as the primary sampling unit (PSU) and farming household as the secondary sampling unit (SSU).

The results of the 1991 Census of Agriculture and Fisheries (CAF 1991) served as the basis of sampling frame at the PSU and SSU levels. In the said census, the largest barangay in a municipality was taken with certainty while a 50 percent sampling rate was used for selecting the remaining barangays in the municipality. This scheme effectively resulted in the generation of two sub universes: a sub universe of barangays with probability of selection equal to one (these barangays were called 'certainty barangays') and another sub universe of barangays with probability of selection equal to 0.5. This characteristic of the CAF 1991 data was used in the selection of sample barangays for the CPS.

The barangays were arrayed in ascending order based on corn area then stratified such that the aggregate corn area of the barangays belonging to one stratum is more or less equal to the aggregate corn area of the barangays in any other stratum. Ten (10) strata were formed for major corn producing provinces and five for minor producing provinces. In all these provinces, the last stratum consisted of the certainty barangays per CAF 1991 design.

For each stratum, four (4) sample barangays were drawn independently using probability proportional to size (PPS) sampling with the barangay's corn area as size measure. This resulted in four (4) independent sets of barangays (i.e., four (4) replicates) for the province. Systematic sampling was used in drawing the sample faming households in each sample barangay.

For economic reasons, sample size per barangay was limited to a minimum of four (4) and a maximum of 25. To correct for this limitation of the design, the use of household weights was instituted. A detailed discussion of weighting in the CPS was included in the survey's estimation procedure attached as an external resource.

In November 2007, an updating of the list of farming households in all corn sample barangays nationwide was done to address the problem of non-response due to transfer of residence, stoppage of farm operation, passing away of operator etc. Consequently, a new set of sample households was drawn.

The following sample sizes were used in CPS 2009: - April 2009 Round: 935 barangays and 7,841 households - July 2009 Round: 1020 barangays and 8,449 households - October 2009 Round: 935 barangays and 7,833 households - January 2010 Round: 1,020 barangays and 8,457 households.

Less elements were sampled in April and October 2009 Rounds since less number of replicates were covered in minor-producing provinces during these periods.

Absent respondents such as refusals, unknown and those who transferred to another barangay were replaced at the Central Office for the next quarter's survey while not-at-home (temporarily away) cases were still included in the list of samples for the succeeding round. The replacement households were taken from the list of replacements (farming households) for the barangay and were reflected in the list of samples for the next round.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Data editing involved item-by-item check on the completeness of units and items covered, as well as the consistency and acceptability of the data collected. This activity took place at various stages of the survey, that is,

(a) During data collection by the Contractual Data Collectors (CDCs). The field supervisor also made random checks on the CDC's work as part of his/her supervision work.

(b) After data collection, before submitting the questionnaires for encoding: At this stage, the accomplished survey returns were manually edited and coded at the Provincial Operations Center (POC). Manual editing involved the checking of data items based on pre-set criteria, data ranges, completeness and consistency with other data items. Coding was the assignment of alpha-numeric codes to questionnaire items to facilitate data entry.

(c) After encoding at the POC, through a customized data cleaning program: Encoded data were subjected to computerized editing using a customized editing program. The editing program took into consideration the editing criteria such as validity, completeness and consistency with other data items. This activity was done to capture invalid entries that were overlooked during manual editing. An error list was produced as output of the process. The errors reflected in said lists were verified vis-à-vis the entries in the accomplished questionnaires. The data files were updated based on the corrections made. Completeness check was likewise done to compare the clean data file against a master file of barangays to check if the sample barangays have been completely surveyed or not. Editing and updating were performed iteratively until a clean, error-free data file was generated. The clean data file served as an input to the table generation (or estimation) process.

(d) At the Central Office: The clean raw data files generated at the POCs were sent to the Central Office for national consolidation at the Information and Communications Technology Division (ICTD). Prior to consolidation, these files were again submitted for re-editing, in accordance with the procedures elaborated in (c). This was done as another layer of data quality check for the survey.

Response rate

Response rate refers to the ratio of sample households who responded to the survey to the total number of sample households, expressed as a percentage. For Corn Production Survey (CPS), responding samples include farming households who are into corn farming (code 10), those who are into other agricultural activities or with no agricultural activities during the reference period (code 20).

CPS 2009 registered high response rates which averaged 87.53% across rounds. Higher proportions of actually enumerated sample households were noted in April and October 2009 rounds at 89.55% and 91.48%, respectively, than in July 2009 and January 2010 rounds which registered 83.56% and 85.53% response rates, respectively.

Data appraisal

To ensure the quality of its statistical services, the BAS has mainstreamed in its statistical system for generating agricultural statistics, a quarterly data review and validation process. This is undertaken in three levels: provincial, regional and national levels. The Corn Production Survey 2009 results passed through this rigid procedure before its final outputs were released for public use.

The data review process starts at the data collection stage and continues up to the processing and tabulation of results. However, data examination is formalized during the provincial data review since it is at this stage where the data at the province-level is analyzed as a whole. The process involves analyzing the survey data in terms of completeness, consistency among variables, trend and concentration of the data and presence of extreme observations. Correction of spotted errors in the data is done afterwards. The output of the process is a clean data file used in the re-computation of survey estimates.

The estimates generated from the clean data file are thoroughly analyzed and validated with auxiliary information to incorporate the impact of information and events not captured by the survey. These information include results of the Monthly Palay and Corn Survey Report (MPCSR), historical data series, report on weather condition, area and crop condition, irrigation, levels of inputs usage, supply and demand, marketing of agricultural products, and information on rice and corn program implementation.

Philippines Production: Volume: Cereals: Corn data was reported at 7,914.900 Metric Ton th in 2017. This records an increase from the previous number of 7,218.800 Metric Ton th for 2016. Philippines Production: Volume: Cereals: Corn data is updated yearly, averaging 4,797.900 Metric Ton th from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 7,914.900 Metric Ton th in 2017 and a record low of 3,823.200 Metric Ton th in 1998. Philippines Production: Volume: Cereals: Corn 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 Asia-Pacific maize industry, valued at $27.52 billion in 2025, is projected to experience robust growth, driven by rising demand for feedstock in livestock and poultry farming, increasing consumption of processed maize products like corn flour and starch, and the expanding biofuel sector. This growth is further fueled by advancements in agricultural technologies, including hybrid maize varieties offering higher yields and improved pest resistance. China, India, and Indonesia are key contributors to this market's expansion, reflecting their significant agricultural sectors and large populations. However, challenges such as climate change impacting crop yields, price volatility due to global market fluctuations, and the need for sustainable agricultural practices represent potential restraints. The industry is seeing increased adoption of precision agriculture techniques and a focus on improving supply chain efficiency. Competition among major players like Syngenta, Bayer, BASF, Corteva Agriscience, and Cargill is intense, leading to innovation in seed technology and crop protection solutions. The forecast period (2025-2033) anticipates a compound annual growth rate (CAGR) of 4.30%, indicating a steady expansion of the market. This growth will be influenced by several factors, including government initiatives promoting agricultural modernization in key countries, increasing disposable incomes driving higher consumption of processed foods, and the ongoing research and development efforts towards drought-resistant and high-yielding maize varieties. While challenges persist, the overall outlook for the Asia-Pacific maize industry remains positive, with opportunities for continued expansion and innovation across the value chain. The market segmentation by country offers granular insights into regional trends, enabling targeted strategies for businesses operating in this dynamic sector. Price trend analysis for countries like China, Indonesia, and the Philippines provides valuable data for informed decision-making regarding pricing strategies and supply chain optimization. Key drivers for this market are: Rising Demand for Milled and Broken Rice, Growing Preference for Speciality Rice Variety; Government Initiatives Supports Rice Production. Potential restraints include: Lack of Supply Chain for Rice, Growing Agricultural Labor Crisis. Notable trends are: Increasing Demand for Maize as Animal Feed Protein Source.

Philippines Production: Volume: Year to Date: Major Crops: Corn: Yellow Corn data was reported at 1,997.000 Metric Ton th in Mar 2018. This records a decrease from the previous number of 5,811.000 Metric Ton th for Dec 2017. Philippines Production: Volume: Year to Date: Major Crops: Corn: Yellow Corn data is updated quarterly, averaging 2,635.000 Metric Ton th from Mar 2004 (Median) to Mar 2018, with 57 observations. The data reached an all-time high of 5,811.000 Metric Ton th in Dec 2017 and a record low of 840.000 Metric Ton th in Mar 2005. Philippines Production: Volume: Year to Date: Major Crops: Corn: Yellow Corn data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B013: Production: Volume: Agriculture: ytd.

Philippines Production: Value: Crops: Corn data was reported at 26,478.000 PHP mn in Dec 2024. This records a decrease from the previous number of 37,034.000 PHP mn for Sep 2024. Philippines Production: Value: Crops: Corn data is updated quarterly, averaging 19,167.000 PHP mn from Mar 2000 (Median) to Dec 2024, with 100 observations. The data reached an all-time high of 49,939.000 PHP mn in Mar 2023 and a record low of 4,667.000 PHP mn in Jun 2003. Philippines Production: Value: Crops: Corn 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: Value: Agriculture: Current Price.

Abstract

The objective of the survey was to generate estimates on current stock of rice and corn in farming and non-farming households. The data generated from the survey seek to: 1. serve as input in the analysis of the seasonal trends and variations in the supply of rice and corn; 2. serve as input for forecasting future supply and demand of palay and corn; and 3. assist policy-makers in the formulation, implementation and administration of agricultural economic programs.

Geographic coverage

National Coverage.

Analysis unit

Households

Universe

Farming and non-farming households

Kind of data

Sample survey data [ssd]

Sampling procedure

The domain of the survey is the province. The sampling procedure used in the Palay and Corn Stocks Survey 1 (PCSS-1) makes use of one replicate of the Palay and Corn Production Survey (PCPS). Sample selection is done in two stages: at the barangay level and at the household level. The province's classification is taken into consideration in the classification of barangays sampled.

• For pure palay provinces, or provinces whose major crop is palay, all the 10 sample barangays from one replicate of the Palay Production Survey (PPS) are covered.
• For pure corn provinces, or provinces whose major crop is corn, all the 10 sample barangays from one replicate of the Corn Production Survey (CPS) are covered.
• For palay and corn (overlap) provinces, or provinces where both palay and corn are the major crops, five (5) barangays are drawn from the PPS sample barangays and another five (5) from the CPS sample barangays.
• For minor provinces, or provinces whose major crop is neither palay nor corn, five (5) sample barangays are drawn.

In the selection of sample households, the PCSS-1 incorporated non-farming households, in addition to farming households of the PCPS. Selection of the five (5) non-farming households was done using simple random sampling.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Completed questionnaires were edited, compiled and summarized by the field staff. Initial estimates of stocks of palay and corn for the barangay (raw data) and province, were also computed using the prescribed estimation procedure.

The processing of the data from the Palay and Corn Stocks Survey-1 (PCSS-1) is decentralized. In the POs, this was done manually and results derived were processed using an Excel-based processing system developed at the Cereals Statistics Section (CSS) of the central office. The resulting provincial estimates were summarized using the prescribed format and forwarded to the central office for review and consolidation.

Forecast: Maize Production at Farm Gate in Philippines 2022 - 2026 Discover more data with ReportLinker!

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.

Philippines Production: Value: 1985p: Year to Date: Major Crops: Corn data was reported at 18,811.560 PHP mn in Dec 2010. This records an increase from the previous number of 14,096.710 PHP mn for Sep 2010. Philippines Production: Value: 1985p: Year to Date: Major Crops: Corn data is updated quarterly, averaging 10,340.330 PHP mn from Mar 1998 (Median) to Dec 2010, with 52 observations. The data reached an all-time high of 20,859.930 PHP mn in Dec 1999 and a record low of 2,574.520 PHP mn in Mar 1998. Philippines Production: Value: 1985p: Year to Date: Major Crops: Corn data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B012: Production: Value: Agriculture: ytd: 1985 Price. Rebased from 1985p to 2000p. Replacement series ID: 298716402

Abstract

The Palay Production Survey is one of the two modules of the Palay and Corn Production Survey (PCPS), formerly known as the Rice and Corn Production Survey (RCPS).

The Palay Production Survey (PPS) 2009 was a quarterly survey conducted by the Bureau of Agricultural Statistics (BAS). It aimed to generate estimates on palay production, area and yield and other related information at the provincial level. It was conducted in four rounds, namely: January, April, July and October. Each round generated estimates for the immediate past quarter and forecasts for the next two quarters. Results of the survey served as inputs to planners and policy makers on matters concerning the rice industry.

Geographic coverage

National Coverage

Analysis unit

Households

Universe

Farming households in palay producing barangays.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure used in the Palay Production Survey 2009 (PPS 2009) was first implemented in 1994. This was a replicated two-stage stratified sampling design with province as the domain, barangay as the primary sampling unit (PSU) and farming household as the secondary sampling unit (SSU).

The results of the 1991 Census of Agriculture and Fisheries (CAF 1991) served as sampling frame at the PSU and SSU levels. In the said census, the largest barangay in a municipality was taken with certainty while a 50 percent sampling rate was used for selecting the remaining barangays in the municipality. This scheme effectively resulted in the generation of two sub universes: a sub universe of barangays with probability of selection equal to one (these barangays were called 'certainty barangays') and another sub universe of barangays with probability of selection equal to 0.5. This characteristic of the CAF 1991 data was used in the selection of sample barangays for the PPS.

The barangays were arrayed in ascending order based on palay area then stratified such that the aggregate palay area of the barangays belonging to one stratum is more or less equal to the aggregate palay area of the barangays in any other stratum. Ten strata were formed for major palay producing provinces and five for minor producing provinces. In all these provinces, the last stratum consisted of the certainty barangays per CAF 1991 design.

For each stratum, four (4) sample barangays were drawn independently using probability proportional to size (PPS) sampling with the barangay's palay area as size measure. This resulted with four (4) independent sets of barangays (i.e., four replicates) for the province. Systematic sampling was used in drawing the sample farming households in each sample barangay.

For economic reasons, sample size per barangay was limited to a minimum of four (4) and a maximum of twenty-five (25). To correct for this limitation of the design, the use of household weights was instituted. A detailed discussion of weighting in the PPS is included in the survey's estimation procedure attached as a technical document.

In November 2007, an updating of the list of farming households in all palay sample barangays nationwide was done to address the problem of non-response due to transfer of residence, stoppage of farm operation, passing away of operator etc. Consequently, a new set of sample households was drawn.

Absent respondents such as refusals, not at home, unknown and transferred to another barangay were treated as missing and were replaced at the central office for the next quarter's survey. The replacement samples were taken from the list of replacements (farming households) for the barangay and were reflected in the list of sample households for the next round.

Mode of data collection

Face-to-face paper [f2f]

Cleaning operations

Prior to data encoding, the accomplished survey returns were manually edited and coded. Manual editing was the checking of responses to the Palay Production Survey (PPS) questionnaire in terms of acceptability and validity. This activity was aimed at improving the quality of data collected by the CDCs. It involved the checking of data items based on criteria like completeness of data, consistency with other data items and data ranges. Coding was the assignment of alpha-numeric codes to questionnaire items to facilitate encoding.

Encoded data were subjected to computerized editing using a customized editing program. The editing program took into consideration the validation criteria such as validity, completeness and consistency with other data items. This activity was done to capture invalid entries that were overlooked during manual editing. An error listing was produced as output of the process. The errors reflected in said lists were verified vis-à-vis the questionnaires. The data files were updated based on the corrections made. Editing and updating were performed iteratively until a clean, error-free data file was generated.

Completeness check was done to compare the data file against a master file of barangays to check if the sample barangays have been completely surveyed or not. This activity was done after a clean, error-free data file was generated.

Response rate

Response rate refers to the ratio of sample households who responded to the survey to the total number of sample households, expressed as a percentage. For Palay Production Survey (PPS), responding samples include farming households who are into palay farming (code 10), those who are into other agricultural activities or with no agricultural activities during the reference period (code 20).

The PPS 2009 response rates were as follows: 1. April 2009 Round - 91.54% 2. July 2009 Round - 90.93% 3. October 2009 Round - 94.21% 4. January 2010 Round - 92.76%

Data appraisal

To ensure the quality of its statistical services, the BAS has mainstreamed in its statistical system for generating production statistics, a quarterly data review and validation process. This is undertaken at the provincial, regional and national levels to incorporate the impact of events not captured in the survey.

The data review process starts at the data collection stage and continues up to the processing and tabulation of results. However, data examination is formalized during the provincial data review since it is at this stage where the data at the province-level is analyzed as a whole. The process involves analyzing the survey data in terms of completeness, consistency among variables, trend and concentration of the data and presence of extreme observations. Correction of spotted errors in the data is done afterwards. The output of the process is a clean data file used in the re-computation of survey estimates.

The estimates generated from the clean data set are thoroughly analyzed and validated with auxiliary information to incorporate the impact of information and events not captured by the survey. These information include results of the Monthly Palay and Corn Survey Report (MPCSR), historical data series, report on weather condition, area and crop condition, irrigation, levels of inputs usage, supply and demand, marketing of agricultural products, and information on rice and corn program implementation.

The Philippines seed industry, valued at approximately $500 million in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 4.70% from 2025 to 2033. This expansion is driven by several key factors. Firstly, increasing demand for high-yielding and disease-resistant crop varieties fuels the adoption of advanced seed technologies, such as hybrids, which offer superior performance compared to conventional seeds. Secondly, the growing adoption of protected cultivation techniques, alongside open field cultivation, enhances crop productivity and resilience against adverse weather conditions, further boosting seed demand. This shift towards improved agricultural practices is partially fueled by government initiatives promoting agricultural modernization and food security. The market segmentation reveals a significant contribution from row crops, reflecting the country's agricultural landscape dominated by rice, corn, and other staple food crops. Key players like Rijk Zwaan, Bayer, and Syngenta are actively investing in research and development, introducing innovative seed varieties tailored to the specific needs of Filipino farmers. Competition is fierce, driving innovation and efficiency within the industry. However, challenges remain. Constraints include the limited access to quality seeds in remote areas, coupled with the lack of agricultural awareness and financial constraints among smallholder farmers. Furthermore, climate change poses significant risks, impacting crop yields and seed production. Despite these hurdles, the Philippines seed industry's positive growth trajectory is expected to continue, driven by the rising demand for food security and the ongoing adoption of advanced agricultural technologies. The increasing focus on sustainable agriculture practices will further contribute to market expansion, as farmers seek environmentally friendly and high-performing seed varieties. The diverse segmentations provide opportunities for specialized seed companies to cater to specific market niches, contributing to the overall growth of this dynamic sector. Philippines Seed Industry: A Billion-Dollar Market Analysis (2019-2033) This comprehensive report provides an in-depth analysis of the Philippines seed industry, projecting a billion-dollar market valuation by 2033. It covers market trends, leading players, technological advancements, and future growth opportunities, offering crucial insights for stakeholders across the agricultural value chain. The study period spans 2019-2033, with 2025 as the base and estimated year. The forecast period is 2025-2033, and the historical period encompasses 2019-2024. Recent developments include: April 2023: Syngenta Seeds and Ginkgo Bioworks collaborated to develop new traits for the next generation of seed technology to produce healthier and more resilient crops.April 2023: Syngenta acquired a vegetable seed-producing company in Brazil, Feltrin Seeds, which serves customers in over 40 countries. The acquisition is estimated to spread the product portfolio of Syngenta in all vegetable-producing countries in the world.March 2023: Corteva Agriscience introduced gene-editing technology for added protection to corn hybrids, which helps in providing resistance to multiple diseases.. Key drivers for this market are: Seed Treatment As A Solution To Enhance Yield, Growing Awareness For Seed Treatment Among The Farmers; Rising Trend Of Organic Farming. Potential restraints include: Limitations Across Farm-Level Seed Treatment, Rising Environmental Concerns. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

Abstract

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

National Coverage

Analysis unit

Households

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 Units (PSU) and farmer-producer as the Secondary Sampling Units (SSU).

Farms are classified as small farms and plantation farms. For small farms, crops are classified based on coverage of the Farm Price Survey, i.e. 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. From 1988 until 2000, the survey adopted three stage sampling or 5x5x5. This is intended to represent the five (5) municipalities as the PSU, five barangays as the SSU and five (5) households as the USU. In May 2000, a two stage sampling was adopted with the five (5) top producing municipalities as the PSU and five farmers-producers as the SSU.

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 was 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 paper [f2f]

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.

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). Across 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: Maize Production at Farm Gate in Philippines 2023 - 2027 Discover more data with ReportLinker!

Abstract

The Republic of the Philippines is making great efforts to develop agriculture at a pace necessary to meet the food requirements of the fast growing population. It has become necessary to use current agricultural statistics that will help present an accurate picture of the country's food situation. Especially important, is the expected supply and consumption requirements of the people, particularly of meat products. The Backyard Livestock and Poultry Survey (BLPS) seek to provide such information.

The data to be obtained from this survey would not only be important from the point of view of the national economy but also from that of the farmer. The government should have available accurate information with which to anchor its major agricultural policy decisions, of which the farmers are the ultimate beneficiaries. For instance, a decision on whether to import or export livestock and poultry products has its effects not only on the national economy but also on the individual farmer. Such national decision will directly affect the raising and trading decisions of livestock and poultry raisers in the country.

The BLPS is one of the four major surveys for livestock and poultry. This survey aims to generate primary data on inventory/population, and supply and disposition of animals from backyard farms (small holders). Specifically, the survey gears to generate information on the following: Livestock and Poultry inventory and production; and Current egg production for ducks and chicken.

Moreover, BLPS shall also aid the policy makers in generating sound policy decision on the improvement of backyard farms for the welfare of the farmers.

Geographic coverage

National Coverage

Analysis unit

Households

Universe

The survey covered all backyard farms. Backyard Farm refers to a farm or household whether farming or non-farming operated by a farmer/household that raises at least one of the following:

1. Livestock · Less than 21 heads of adult and zero head of young · Less than 41 heads of young animals · Less than 10 heads of adult and 22 heads of young

2. Poultry · Less than 500 layers, or 1,000 broilers · Less than 100 layers and 100 broilers if raised in combination · Less than 100 head of duck regardless of age

A backyard farm is categorized by its household classification. There are two (2) household classification. These are farming households and non-farming households.

The farming household is any household in which a member operates an agricultural land, either solely or jointly with other members, and the aggregate area operated by the operator-members of such household qualifies to be called a farm. The non-farming household is any household in which a member operates an agricultural land, either solely or jointly with other members, and the aggregate area operated by the operator-members of such household does not qualify as a farm.

Kind of data

Sample survey data [ssd]

Sampling procedure

The BLPS uses the Palay and Corn Production Survey (PCPS) frame. There are four (4) replicates of the PCPS but the BLPS covers only one (1) replicate, consisting of one barangay per replicate. The BLPS employs a two-stage stratified sampling with the barangay as the primary sampling unit (PSU) and the household as the secondary sampling unit (SSU). Sample selection is done as follows.

First Stage (Primary) Sampling Unit Selection Selection of sample barangays is based on pre-determined classification of provinces. For provinces whose major crop is either palay or corn, ten (10) sample barangays are covered. For provinces where both palay and corn are the major crops (called overlap provinces), five (5) barangays are drawn from palay barangays and another five (5) barangays from corn barangays. Finally, for other provinces (those whose major crop grown is neither palay nor corn), only five (5) sample barangays are drawn.

Second Stage (Secondary) Sampling Unit Selection All PCPS sample farming households in the BLPS sample barangay are covered. To represent the non-farming group in each sample barangay, additional five (5) non-farming households are selected through the right coverage approach with a defined starting point and random start.

Mode of data collection

Face-to-face [f2f]

Response rate

Response rate refers to the ratio of sample households who responded to the survey to the total number of sample households, expressed as a percentage. The response rate for January, 2016 Round is above 85%.

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

The 2017 Crops Production Survey (CrPS) is conducted quarterly to generate production estimates for crops other than palay and corn at the national, regional and provincial levels disaggregation. Production data generated from the CrPS are inputs to the Performance of Agriculture Report (PAR) and to the preparation of the Gross Domestic Product (GDP). Moreover, 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.

Out of the 282 crops covered, the individual estimates of the 19 crops highlighted in the quarterly PAR are released at the national level, while the rest were lumped as Others. Provincial level estimates are available on an annual basis.

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

Geographic coverage

National Coverage

Analysis unit

Agricultural holdings

Universe

All small and large farms/farmer-producers 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 and large farms according to the area planted to a specific crop.

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

For small farms of all other crops not covered under FPS, top two to three producing municipalities were chosen as PSUs. In each municipality, three sample farmer-producers were enumerated as SSU.

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/or harvests during the reference quarter.

Classification for large farms is based on the cut-off on area planted. Each survey round covers a maximum of 5 large farms by crop. The above scheme was adopted since 2005 to date.

Mode of data collection

Face-to-face paper [f2f]