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.

Agricultural land (% of land area) in Philippines was reported at 42.67 % in 2023, 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 October of 2025.

As of 2023, about 4.82 million hectares of land were dedicated to cultivating palay in the Philippines. The total land area used for growing palay in the country fluctuated within the given period of time, with 2023 recording the highest values.  How much does it cost to produce palay in the Philippines? The Philippines ranks high alongside countries such as China and India when it comes to rice consumption globally. Rice is a main staple for Filipinos, making this crop among the most important agricultural products produced by farmers in the country. On average, palay production costs in the Philippines amounted to about 54 Philippine pesos per hectare in 2022, with Cagayan Valley recording the highest production costs nationwide. Meanwhile, the cost of palay production per kilogram amounted to an average of 15 Philippine pesos in the same year. The cost of producing palay is attributed to factors such as the cost of planting materials, labor and transport costs, irrigation fees, as well as rental fees for land used.  Average wage rate on palay farms in the Philippines In 2019, the average wage rate on palay farms in the Philippines was highest in CALABARZON, amounting to around 357 Philippine pesos per day. The lowest average was recorded in the BARMM region with 213 Philippine pesos. Although no recent reports have been published regarding this, the poverty incidence of farmers in the country has gradually declined since 2015.

Agricultural land (sq. km) in Philippines was reported at 127226 sq. Km in 2023, 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 October of 2025.

Under political pressure to hasten the Philippine Comprehensive Agrarian Reform Program's land acquisition and distribution process, the Department of Agrarian Reform (DAR) redistributed land in bundles by awarding groups of farmers collective titles. While issued under a strong political rationale, these awarded lands are handicapped in terms of their economic development: they cannot be used as collateral to access credit, legally sold or leased to other farmers, and largely do not provide the tenure security that comes with individual titling. Given the current rate of parcelization, it will take the DAR about 20 years to subdivide the remaining lands under collective title. With such a large outstanding balance, the DAR would like to know where parcelization has the largest impact on agricultural investment and welfare so it can re-focus its strategy to prioritize these lands.

In 2023, about **** million hectares of land used for coconut cultivation in the Philippines. The production volume of coconut has been fluctuating over the past decade. Coconut is one of the country's major exports.

Abstract

Basic and current data on agriculture are usually generated through national censuses and surveys. However, data from these sources are too aggregated. Thus, the available data series are deemed inadequate to meet the needs of planners and policy makers of local government units, particularly the cities/municipalities and barangays. What they require are more disaggregated information in analyzing the agricultural situation in their localities.

In response to the felt need for comprehensive, timely and reliable data at the municipal and barangay levels of disaggregation, the Bureau of Agricultural Statistics (BAS) has conceptualized an activity entitled “Barangay Agricultural Profiling Survey (BAPS)”. The BAPS is an improved version of the Barangay Screening Survey (BSS), which was undertaken by the Bureau from 1997 to 1999. The BAPS has been designed to collect information on the basic structure of agriculture and fishery at the barangay level. The output of this activity would be effective inputs to the government in the identification, design and implementation of appropriate development programs and in identification of target beneficiaries. In particular, a very important application of the output of this inquiry would be in the identification of areas suitable for the production and marketing of priority commodities in a province.

The general objective of the BAPS is to provide the policymakers and other data users with comprehensive agriculture profiles at the sub-national levels. Its specific objectives are: to establish the database on the basic characteristics of agriculture; to assist in the identification of areas suitable for the production and marketing of priority commodities in the province; to provide a common set of updated basic data for use in agricultural development planning at the municipal and barangay levels in support to government programs, particularly those of the Department of Agriculture; and to provide the basis for the updating/construction of new sampling frames for agricultural and fishery surveys.

The data items selected by BAPS include the following: basic barangay characterististics, information on agricultural crops, irrigation, livestock and poultry, fishery, agricultural practices, marketing, farm machineries/implements, and other information such as associations providing support to farmers, projects implemented in the barangay, major and other sources of livelihood and women's participation in agriculture/fishery related activities.

Geographic coverage

Cordillera Administrative Region (CAR) All barangays in CAR

Analysis unit

Barangays

Universe

The survey covered all barangays with areas devoted to agricultural activities such as farming, raising of animals and fishing. Barangays without any farming or fishing activities but with agro-fishery facilities/establishments were likewise enumerated.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable.

Mode of data collection

Focus Group [foc] / Face-to-Face, Key Informants

Research instrument

Structured questionnaire was used in the conduct of BAPS. The questionnaire was administered in each barangay to collect various data on basic barangay characteristics including general geographic terrain, land area, land use, population, vulnerability of the barangay to natural calamities, number of farming, non-farming and fishing households. Data on agricultural crops, irrigation, livestock and poultry, fishery, agricultural practices, marketing,farm machineries / other Implements, infrastructure and services, and other information such as associations providing support to farmers, projects implemented in the barangay, major and other sources of livelihood and women's participation in agriculture/fishery related activities were also collected.

The questionnaire was developed in English language.

Cleaning operations

Field Data Processing

Data processing was done at the Provincial Operations Center (POC) in two stages: manual editing and electronic data processing.

      Manual editing involved item-by-item checks on the consistency and completeness of the data and data ranges. This was the initial stage where data were judged as acceptable or not based on the situation in the barangay. The supervisors did the manual editing of returns, although the data collector also saw to it that the information collected were complete and acceptable before the questionnaires were submitted to him/her. A set of manual editing procedure facilitated the completion of this activity. The manual editing and coding guidelines document was provided as an external resource. 

Electronic data processing was done at the Regional Operations Center (ROC) and POC with the Regional Processing Officer (RPO) and Provincial Processing Officer (PPO) on top of the activity. This involved data coding, encoding, electronic data cleaning and generation of summary statistics. Before the data were submitted for electronic processing, the supervisor saw to it that these data have passed the consistency requirements set in the editing guidelines. Errors that were overlooked in the manual editing were captured in electronic data cleaning. The electronic data processing document was provided as an external resource.

Two types of outputs were generated after the electronic processing stage: the raw data and the summary statistics. Both were used as inputs in the data validation stage of the survey. For this reason, it was recommended that the raw data generated should be in Excel format so that the staff involved can access, examine and correct them as necessary.

      The data processing software used was Integrated Micro-Computer Processing System (IMPS) which was developed by the United States Census Bureau.

Sampling error estimates

Not applicable

Data appraisal

Data Validation

Multi-level data validation was intended to ensure that the results of this survey are acceptable, reliable and usable. Technical working groups (TWGs) at the municipal and provincial levels were created. The objectives were to thoroughly scrutinize the data and to allow the members of the group to evaluate the consistency of data. Inputs in validation included the raw data, survey questionnaires and summary statistics. Validation materials were prepared by the POC staff who were also responsible in facilitating and documenting the validation process.

The municipal TWG were composed of the BAS POC staff covering the municipality, the MAO, MPDO, Municipal Agriculture and Fishery Council (MAFC) Officer, Farm Cluster Leaders and the President of the Association of the Barangay Chairmen (ABC). The Municipal TWG was responsible in reviewing the results of the survey at the municipal level with barangay disaggregation. They ensured that the changes made during the municipal data validation are reflected in the raw data and in the questionnaires. This further highlighted the importance of the raw data being readily converted to Excel format right after table generation at the POC.

The PASO, the Provincial Agriculturist (PA), Provincial Agriculture and Fishery Council (PAFC) Officer, Provincial Planning and Development Officer (PPDO) to be supported by staff of the Research and Evaluation Division, and the President of the Provincial League of the MPDOs composed the Provincial TWG. The TWG at the provincial level was responsible in examining the results of the survey using the consolidated municipal results. They sent feedback to all municipalities on the analysis of data generated. As in the case of the municipal data validation, it was also important that all changes made in the data as a result of the validation process must be reflected at once to the municipal results and eventually to the raw data and the questionnaires.

On-site validation at each level was conducted by the TWG, especially if there were still unsettled issues and disagreements in the data after the review process.

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

Abstract

Information on onion farming will help provide directions to agricultural entrpreneurs and investors. It also serves the statistical requirements of onion growers and policy makers for planning and decision making regarding onion production and marketing.

The survey aimed to generate updated data on levels and structure of production costs and returns. Specifically, it was conducted to detemine the indicators of profitability such as gross and net returns, returns above cash costs, net profit - cost ratio, etc.; usage of materials and labor inputs; and other related socio-economic variables including information on new production technologies.

Geographic coverage

The survey covered the top three (3) onion producing provinces namely: Ilocos Norte, Pangasinan and Nueva Ecija.

Analysis unit

Onion farmers and onion farms with harvests during the reference period as the units of analysis.

Universe

The survey covered all onion farms with harvests during the last completed cropping in 2006 as the reference period.

Kind of data

Sample survey data [ssd]

Sampling procedure

The domain of the study was the province, with the last completed production cycle in 2006 as reference period. The lists of onion producing barangays by province prepared by the concerned BAS Provincial Operations Centers (POCs) served as the sampling frame for this study. The lists contained data on area devoted to onion production and number of onion farmers as of 2006. These data were obtained from the Municipal Agriculturist Offices, Agricultural Technicians, barangay officials and other key informants in the barangays and updated results of the Barangay Screening Survey (BSS).

A two-stage sampling design was employed with the barangay as the primary sampling unit and the onion farmer as the secondary and ultimate sampling unit. The barangays were drawn using systematic sampling from an ordered list of barangays with at least five onion farmers. Systematic sampling was used so that both large and small barangays in the province in terms of onion production would be represented in the sample. On the other hand, sample operators were identified using snowball approach during data collection. During the search for sample operators, a set of screening questions was applied to see to it that the samples satisfy the following criteria: 1) must be engaged in onion production, and 2) must have harvested onion in 2006

The total sample size was 100 onion growers per province and this was equally allocated to 20 sample barangays. The survey resulted in the following distribution of sample farmers by province.

Ilocos Norte Multiplier and Shallot 95 Red Creole 5 Yellow Granex Total Sample 100 Pangasinan
Multiplier and Shallot
Red Creole 97 Yellow Granex 3 Total Sample 100 Nueva Ecija Multiplier and Shallot 7 Red Creole 89 Yellow Granex 4 Total Sample 100

TOTAL Multiplier and Shallot 102
Red Creole 191 Yellow Granex 7 Total Sample 300

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was a structured questionnaire written in English. It was designed in tabular form and some in question type format. The data items/variables in the questionnaire were based on the previous questionnaires with some modifications and additions.

The questionnaire was pre-tested and reviewed before its implementation.

The questionnaire consisted of 12 pages covering 13 blocks as follows:

A. GEOGRAPHIC INFORMATION includes the location of the farm such as the name of the region, province, city/municipality and barangay.

B. SAMPLE IDENTIFICATION such as the name, age, sex, highest educational attainment, main occupation and farming experience of the sample farmer/operator in onion production and the name of the respondent.

C. FARM CHARACTERISTICS such as total farm area, number of parcels operated by the farmer, area planted and harvested to onion and other crops, number of croppings per year, variety of onion planted, tenurial status, month of planting and harvesting onion.

D. FARM INVESTMENTS such as inventory of farm investments used, year and cost of acquisition, repairs and improvement cost and estimated life and usage in the focus onion farm.

E. MATERIAL INPUTS contain the quantity and cost of planting materials, fertilizers, mulching materials, insecticides, herbicides/weedicides, fungicides and other chemicals.

F. LABOR INPUTS such as labor utilization (in terms of mandays) and labor cost by type of farming activity, by source of labor and by sex and food cost incurred.

G. OTHER PRODUCTION COSTS cover cash and non-cash payments for land tax, land lease/rental, rental value of owned land, rentals of machine, animals and tools and equipment, fuel and oil, transport costs of inputs, irrigation fee, electricity, interest payment on crop loans, storage cost and other production costs.

H. PRODUCTION AND DISPOSITION such as volume of onion production and its disposition in terms of sold, harvesters' share, threshers' share, other laborers' share, landowners' share, lease rental, for home consumption, given away, used for seeds, wastage and other purposes.

I. BUYER INFORMATION includes the major buyer of onion and the percentage of onion sold to each buyer and the perceived right price of onion.

J. PROBLEMS ENCOUNTERED such as problems affecting production and marketing of onion.

K. ACCESS TO CREDIT such as the amount and source of crop loan, interest rate per annum and percentage of loan used in onion production.

L. OTHER INFORMATION such as membership in onion-related association and benefits derived, access to extension services, future plans of onion farmers and their recommendations to improve onion production

M. INTERVIEW/SURVEY PARTICULARS contain the name and signature of data collector, field supervisor/editor and PASO and date accomplished.

The questionnaire is provided as External Resources

Cleaning operations

Manual editing was initially done at the Provincial Operations Center during and after data collection using the CRS editing guidelines prepared by the Central Office. The edited questionnaires were again checked at the Central Office. Coding and encoding were likewise done at the Central Office.

The document on Editing Guidelines is provided in the Technical Documents.

Response rate

Response rate of 100 percent

Data appraisal

Series of reviews were done to assess the quality of the data in terms of reliability and acceptability. A comparison with the results of past surveys on input usage, labor utilization, production cost and return structure of onion was made.

Abstract

The profitability of garlic is one of the primary concerns among planners and policy makers in setting-up goals and strategies as they prepare the sector for global competition. Likewise, this is the concern of agribusiness players who are interested to venture in garlic farming. Thus, the need to generate updated information on the costs and returns of producing garlic which can guide concerned stakeholders in their decision making.

Information on costs and returns in garlic production is a critical input for the improvement of the supply/volume chain and enhancing the food security situation in the country.

The survey aimed to generate updated data on levels and structure of production costs and returns. Specifically, it was conducted to determine the indicators of profitability such as gross and net returns, returns above cash costs, net profit - cost ratio, etc.; average use of materials and labor inputs; and other related socio-economic variables including information on new production technologies.

Geographic coverage

The survey covered the top 3 garlic producing provinces namely: Ilocos Norte, Ilocos Sur and Nueva Ecija.

Analysis unit

Garlic farmers and garlic farms with harvests during the reference period.

Universe

The survey covered all garlic farms with harvest during the last completed cropping in 2006 as the reference period.

Kind of data

Sample survey data [ssd]

Sampling procedure

The domain of the study was the province, with the last completed production cycle in 2006 as reference period. The lists of garlic producing barangays by province prepared by the concerned BAS Provincial Operations Centers (POCs) served as the sampling frame for this study. The lists contained data on the area devoted to garlic production and number of garlic farmers by barangay as of 2006. These data were obtained from the Municipal Agriculturist Offices, Agricultural Technicians, barangay officials and other key informants in the barangay and updated results of the Barangay Screening Survey (BSS).

A two-stage sampling design was employed with the barangays as the primary sampling unit, and the garlic farmers as the secondary and ultimate sampling unit. The barangays were drawn using systematic sampling from an ordered list of barangays with at least five (5) garlic farmers. Systematic sampling was used in the selection of sample barangays so that both large and small barangays in terms of area would be represented in the sample. For Nueva Ecija, all garlic producing barangays were taken as samples. Sample farmers were identified using snowball approach during data collection. During the search for sample operators, a set of screening questions was applied to see to it that the samples satisfied the following criteria: 1) must be engaged in garlic farming, and 2) must have harvested garlic in 2006.

The sample size was 100 garlic farmers each in Ilocos Norte and Ilocos Sur, equally allocated to 20 sample barangays. In Nueva Ecija, the sample size was 80 garlic farmers.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was a structured questionnaire written in English. It was designed in tabular form and some in question type format. The data items/variables in the questionnaire were based on the previous questionnaires with some modifications and additions.

The questionnaire was pre-tested and reviewed before its implementation.

The questionnaire consisted of 10 pages covering 13 blocks as follows:

A. GEOGRAPHIC INFORMATION includes the location of the farm such as the name of the region, province, city/municipality and barangay.

B. SAMPLE IDENTIFICATION such as the name, age, sex, highest educational attainment, main occupation and farming experience of the sample farmer/operator in garlic production and the name of the respondent.

C. FARM CHARACTERISTICS such as total farm area, number of parcels operated by the farmer, area planted and harvested to garlic and other crops, number of croppings per year, variety of garlic planted, tenurial status, month of planting and harvesting garlic.

D. FARM INVESTMENTS such as inventory of farm investments used, year and cost of acquisition, repairs and improvement cost and estimated life and usage in the focus garlic farm.

E. MATERIAL INPUTS contain the usage and cost of planting materials, fertilizers, mulching materials, insecticides, herbicides/weedicides, fungicides and other chemicals.

F. LABOR INPUTS such as labor utilization (in terms of mandays) and labor cost by type of farming activity, by source of labor and by sex and food cost incurred.

G. OTHER PRODUCTION COSTS cover cash and non-cash payments for land tax, land lease/rental, rental value of owned land, rentals of machine, animals and tools, fuel and oil, transport costs of inputs, irrigation fee, electricity, interest payment on crop loans, storage cost and other production costs.

H. PRODUCTION AND DISPOSITION such as volume of garlic production and its disposition in terms of sold, harvesters' share, threshers' share, other laborers' share, landowners' share, lease/rental, for home consumption, given away, used for seeds, wastage and other purposes.

I. BUYER INFORMATION includes the major buyer of garlic and the percentage of garlic sold to each buyer and the perceived right price of garlic.

J. PROBLEMS ENCOUNTERED such as problems affecting production and marketing of garlic.

K. ACCESS TO CREDIT such as the amount and source of crop loan, interest rate per annum and percentage of loan used in garlic production.

L. OTHER INFORMATION such as membership in garlic-related association and benefits derived, access to extension services, future plans of garlic farmers and their recommendations to improve garlic production.

M. INTERVIEW/SURVEY PARTICULARS contain the name and signature of data collector, field supervisor/editor and PASO and date accomplished.

The questionnaire is provided as External Resources

Cleaning operations

Manual editing was initially done at the Provincial Operations Center during and after data collection using the CRS editing guidelines prepared by the Central Office. The edited questionnaires were again checked at the Central Office. Coding and encoding were likewise done at the Central Office.

The document on Editing Guidelines is provided in the Technical Documents.

Response rate

Response rate of 100 percent

Sampling error estimates

Not applicable.

Data appraisal

Series of reviews were done to assess the quality of the data in terms of reliability and acceptability. A comparison with the results of past surveys on input usage, labor utilization, production cost and return structure of garlic was made.

The Philippines Hybrid Rice Seed Market size was valued at USD 84.68 Million in 2023 and is projected to reach USD 116.95 Million by 2032, exhibiting a CAGR of 4.72 % during the forecasts periods. The growth is driven by the benefits of hybrid seeds, including their higher yield potential, resistance to pests and diseases, and adaptability to changing climatic conditions. Government initiatives, such as the National Rice Program and the Rice Competitiveness Enhancement Fund, have also played a key role in promoting the adoption of hybrid seeds. Additionally, the rising food security concerns in the country, coupled with technological advancements in seed breeding, are contributing to the growth of the hybrid rice seed market. The major applications for hybrid seeds include commercial farming, seed production, and research and development. Key players in the market include Longping Tropical Rice Development Inc., Bayer AG, SL Agritech Corporation, and Bioseed Research Philippines Inc. Recent developments include: July 2022: Department of Agriculture Philippines launched an information system that will hasten rice seed-related transactions and processes. This system is developed by Philippine Rice Research Institute (PhilRice) and the Bureau of Plant Industry (BPI) which includes production planning, field data collection, documentation, and geotagging; monitoring, inventory, and distribution of seed reserve; seed source tracing; application and approval of accreditation and seed certifications; and report generation, among other modules and apps., July 2022: The Philippine Rice Research Institute (PhilRice) collaborated with the Department of Agriculture-Regional Field Office and distributed Golden Rice (GR) seeds to farmers in areas of Urdaneta City and Manaoag, Pangasinan., February 2022: According to the Department of Agriculture, the mass production of Golden Rice seeds has started in the current year in the country, especially in the vitamin A-deficient provinces, to utilize and promote biotechnology in the Philippines. Philippine Rice Research Institute leads this program.. Key drivers for this market are: Adoption of Organic and Eco-friendly Farming Practices, Declining Area of Arable Land and Rising Food Security Concerns. Potential restraints include: High Demand for Conventional and Synthetic Products, Lack of Awareness and Other Factors Limiting the Adoption of Agricultural Inoculants. Notable trends are: Increasing Consumption of Rice as a Staple Food.

Snapshot img

Crayfish Market Size 2024-2028

The crayfish market size is projected to increase by USD 2.26 billion at a CAGR of 6.15% between 2023 and 2028. The market is experiencing significant growth, driven by various factors. The increasing demand for aquaculture-based fish varieties, particularly in regions with a high preference for seafood, is a primary catalyst. Aquaculture methods for crayfish production have become more affordable, making it an attractive option for farmers and investors. Additionally, the health benefits associated with crayfish consumption, such as high protein content and rich nutritional value, have contributed to its popularity. This trend is expected to continue, as the market expands both domestically and internationally. EndFragment

Market Overview

The market shows an Accelerate CAGR during the forecast period.

To get additional information about the market report, Request Free Sample

Market Dynamics

The market presents a vibrant landscape, where freshwater crustaceans like the Red Swamp Crayfish and Virile crayfish thrive in lakes, ponds, swamps, and marshes across the Midwest. These crayfish, ranging in colors from green to dark brown and sandy hues, are prized in freshwater aquariums and as pets. Aquaculture plays a pivotal role, supporting crayfish farmers and sellers in meeting demands for proteins rich in vitamins and minerals. As veganism grows, crayfish offer a sustainable protein source, enhancing their export potential. Varieties such as the Ringed crayfish and Rusty crayfish highlight the diversity and opportunity within the market. With increasing awareness and consumption, the crayfish market is poised for continued growth, driven by both domestic and international markets.

Key Market Driver

Affordable aquaculture methods are notably driving market growth. The most common method of farming is in cultivated forage ponds or in double-crop rotation systems, especially in rice fields. Crayfish cultivation in rice fields is expected to boost the growth of the market as many farmers are turning to farming in their farmlands to earn an extra income.

Good quality water sources are another important factor in farming. Any land chosen for cultivation must have a clay content of above 20%. Also, other important factors needed to carry out farming effectively include the appropriate equipment, harvest bait, labor, electricity, and fuel. Accordingly, all the necessary equipment and supplies are locally sourced and do not require huge investments for new people entering farming. This is one of the major reasons why more agriculture farmers are opting to enter the market.

Accordingly, more farmers are attracted to the crayfish industry because the farming method is affordable and does not require high levels of investment. Such factors are expected to fuel the growth of the market during the market growth analysis period.

Significant Market Trends and Analysis

The growth of organized retailing in the market is an emerging trend. The growth of organized retailing has ensured the effectiveness of the supply chain and logistics in the crustacean (including crayfish) industry. Large retail chains such as Walmart, Inc. (Walmart), Costco Wholesale Corp. (Costco), and Tesco PLC (Tesco), among others, sell different varieties of processed and packaged fish in both online and offline stores. These retail giants are driving high sales volumes of processed crustaceans. Moreover, these large retailers can secure high-profit margins by reducing their external production costs.

Different processed varieties are increasingly being sold in large retail chains. For instance, Walmart provides both in-store purchases and online sales of different products and processed varieties such as tail meat, whole seasoned, frozen cooked, and balls through its own brands as well as other private labels. Additionally, restaurants and quick-service restaurants (QSR) in China and the US are increasingly using the product in their cuisine. Accordingly, the varieties are reaching end-consumers through large retailers, online retailers, and food chains, among others. Such factors are expected to fuel the growth of the market during the market research and growth period.

Major Market Challenge

Lack of marketing and capital is the major challenge in the market. The market structure of the market in various countries, such as China, the US, and Australia, is fragmented. Also, the market faces various marketing challenges due to the lack of regional and international efforts. Various vendors that operate in the market in focus are mid-to small-sized. These vendors mostly form small cooperative groups in their localities and, therefore, are able only to undertake limited marketing and promotional activities. The lack of marketing and promotional activities leads to low consumer awareness of locally produced products. Hence, the low focus on marketing and promotio

Abstract

The profitability of producing milkfish is one of the primary concerns among planners and policy makers in setting up goals and strategies for the development of fisheries. Likewise, this is the concern of agribusiness players who are interested to venture in milkfish farming.

The survey aimed to generate updated data on the levels and structure of costs and returns of milkfish production. Specifically, it was conducted to determine the production cost structures; indicators of profitability such as gross and net returns, returns above cash costs, net profit - cost ratio, etc.; usage of materials and labor inputs; and other related socio-economic variables.

Geographic coverage

The survey covered the top four (4) milkfish producing provinces namely: Pangasinan, Bulacan, Capiz and Iloilo.

Analysis unit

Milkfish pond operators and milkfish ponds with harvests during the reference period

Universe

The survey covered all milkfish ponds with harvests during the last completed production cycle in 2006 as the reference period.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered fishpond operators in the top four (4) milkfish producing provinces in the country namely: Pangasinan, Bulacan, Iloilo and Capiz. The domain of the study was the province, with the last completed production cycle in 2006 as the reference period.

The lists of milkfish producing barangays by province prepared by the concerned BAS Provincial Operations Centers (POCs) were used as the sampling frame for this study. The lists contained data on the area devoted to milkfish production and number of milkfish pond operators by barangay as of 2006. These data were obtained from BAS-BFAR lists of aquafarms, updated Barangay Screening Survey (BSS) data, existing POC lists and the local government units.

A two-stage sampling design was employed with the barangay as the primary sampling unit and the fishpond operator as the secondary and ultimate sampling unit. The barangays were drawn using systematic sampling from an ordered list of barangays with at least five (5) milkfish pond operators. Systematic sampling was used so that both large and small farm operators in the province in terms of milkfish production would be represented in the sample. On the other hand, sample operators were identified using snowball approach during data collection. During the search for sample operators, a set of screening questions was applied to see to it that the samples satisfy the following criteria:

1. must be engaged in milkfish culture in fishpond, and
2. must have harvested milkfish in 2006

The total sample size was 100 fishpond operators per province, equally allocated to 20 sample barangays. Following was the distribution of sample fishpond operators by province.

Pangasinan (100) : All monoculture Bulacan (100) : 69 monoculture and 31 polyculture
Capiz (99) : 94 monoculture and 5 polyculture Iloilo (100) : 95 monoculture and 5 polyculture

All four provinces (399) : 358 monoculture and 41 polyculture

In Capiz, one sample did not satisfy the second survey criterion, i.e. there was no reported production.

The data attached in the Data Set include only monoculture.

Mode of data collection

Face-to-face [f2f]

Research instrument

A structured questionnaire written in English was used. It was designed in tabular form and other parts were in question type format. The data items/variables in the questionnaire were based on the previous (2001) questionnaires with some modifications and additions.

The questionnaire was pre-tested and reviewed before its implementation.

The questionnaire consisted of 9 pages covering 13 blocks as follows:

A. GEOGRAPHIC INFORMATION includes the location of the farm such as the name of the region, province, city/municipality and barangay.

B. SAMPLE IDENTIFICATION such as the name, age, sex, highest educational attainment, main occupation and number of years engaged in milkfish production, name of the respondent and relationship of respondent to owner/operator.

C. AQUAFARM CHARACTERISTICS include the name of aquafarm, physical area of aquafarm, number of ponds and its size, tenurial status, aquafarm environment and culture method adopted.

D. FARM INVESTMENTS cover data on inventory of farm investments used, year and cost of acquisition, repairs and improvement cost, estimated life and percent of use in the focus pond.

E. MATERIAL INPUTS contain data on the quantity and cost of stocking materials, fertilizers, lime, pesticides, disease prevention and pollution control and other chemicals.

F. LABOR INPUTS cover data on labor utilization (in terms of mandays) and labor cost by type of farming activity, by source of labor and by sex and food cost incurred.

G. OTHER PRODUCTION COSTS include data on cash and non-cash payments for land tax, salaries and wages, lease/rental, rental value of owned land, rentals of machine and tools, fuel and oil, transport costs of inputs, license/permits, electricity, and interest payment on loans.

H. PRODUCTION AND DISPOSITION contain data on volume of milkfish production and its disposition in terms of sold, harvesters' share, caretakers' share, other laborers' share, landowners' share, lease/rental, for home consumption, given away, and other dispositions.

I. BUYER INFORMATION contain data on the major buyer of milkfish.

J. PROBLEMS ENCOUNTERED include problems affecting production and marketing of milkfish.

K. ACCESS TO CREDIT covers data on the amount and source of loan, and interest rate per annum.

L. OTHER INFORMATION include daata on the membership in fishery related association, access to extension services, future plans of fishpond operators and their recommendations to improve milkfish production

M. INTERVIEW/SURVEY PARTICULARS contain the name and signature of data collector, field supervisor/editor and PASO and date accomplished.

Cleaning operations

Manual editing was initially done at the Provincial Operations Center during and after data collection using the CRS editing guidelines prepared by the Central Office. The edited questionnaires were again checked at the Central Office. Coding and encoding were likewise done at the Central Office.

Response rate

Response rate of 99.8 percent

Data appraisal

A series of reviews was done to assess the quality of the data in terms of reliability and acceptability. A comparison with the results of past surveys on input usage, labor utilization, production cost and return structure of milkfish was made.

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 *** million metric tons, higher than the produced quantity of *** million metric tons in the previous year. Corn farming Over the past six years, about *** 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 Philippines. 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 is exported, especially maize seeds and frozen sweet corn.

Abstract

The survey aimed to generate updated data on levels and structure of production costs and returns. It was conducted to detemine the indicators of profitability such as gross and net returns, returns above cash costs, net profit - cost ratio, etc.; usage of materials and labor inputs; and other related socio-economic variables including information on new production technologies.

Geographic coverage

The survey covered six camote producing provinces: Camarines Sur, Negros Occidental, Quezon, Agusan del Norte, Bohol and Agusan del Sur.

Universe

The survey covered farmers who harvested camote within the reference period and knowledgeable on the details of camote farming particularly on investments, material inputs, labor expenses incurred and disposition of produce. The reference period was the production for the last completed harvest within May 2013 to April 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

The domain of the survey was the province. A two-stage sampling design was employed with the barangay as the primary sampling unit and the sample farmer as the secondary sampling unit. The top producing barangays were selected from an ordered list of barangays. The sample farmers were identified in each sample barangay using snowball approach during data collection.

The total number of sample barangays per province was fifteen or less. If the number of major producing barangays that contributed to 80 percent based on area planted were more than 15, 15 barangays were selected. Those provinces with less than 15 barangays that produced sweet potato were completely enumerated. This approach ensured representation of the barangays in the province in terms of area planted to sweet potato. The total number of sample farmers per province was set at 75 and equally allocated to the sample barangays. The list of sample barangays per province and corresponding number of samples were provided to the Provincial Operations Center (POC) of the former Bureau of Agricultural Statistics (BAS) prior to the survey.

During data collection, the names and addresses of sweet potato farmers residing in the barangay were obtained from the office of the barangay chairman or any other key informants in the barangay. It served as the data collector's starting point in searching for potential sample farmers. The target numbers of sweet potato farmers in the sample barangays were obtained using snowball sampling. A set of screening questions was applied to confirm if those listed actually harvested sweet potato during the reference period and satisfied the other criteria to qualify for enumeration.

Whether the interviewed farmer was qualified for the survey or not, he/she was asked to identify other sweet potato farmers in the barangay to be added in the initial list. The search continued, and the farmer who met the criteria specified in the screening questions was qualified as sample for the survey and was interviewed using the questionnaire for the 2014 Survey on Costs and Returns of Sweet Potato (Camote) Production. If the interview was successfully carried out (meaning, all the needed information had been supplied), the household number, full name and residential address of the sample farmer were written in the List of Sample Farmers. The enumerator selected again any farmer in the initial list as the next potential sample for the survey. The process continued until the required number of samples in the barangay was obtained.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was a structured questionnaire written in English. It was designed in tabular form and some in question type format. The data items/variables in the questionnaire were based on the previous questionnaires with some modifications and additions.

The questionnaire was pre-tested and reviewed before its implementation.

The questionnaire consisted of 12 pages covering 13 blocks as follows:

A. GEOGRAPHIC INFORMATION includes the location of the farm such as the name of the region, province, city/municipality and barangay.

B. SAMPLE IDENTIFICATION such as the name, age, sex,level of education completed, main occupation, number of years engaged in camote farming (as operator), name of respondent and its classification, contact number

C. BASIC CHARACTERISTICS OF THE FARM such as total physical area, number of parcels operated by the farmer, area planted and harvested to camote, cropping pattern, number of croppings per year, variety of camote planted, tenurial status, month of planting and harvesting camote, main use of camote and source of planting materials,

D. FARM INVESTMENTS such as inventory of farm investments used, year and cost of acquisition, repairs and improvement cost and estimated life and usage in the focus camote farm.

E. MATERIAL INPUTS contain the quantity, cost and mode of acquisition of planting materials, fertilizers, soil ameliorants and pesticides.

F. LABOR INPUTS such as labor utilization (in terms of mandays) and labor cost by type of farming activity and by source and type of labor and food cost incurred.

G. OTHER PRODUCTION COSTS cover cash and non-cash payments for land tax, land lease/rental, rental value of owned land, rentals of machine, animals and tools and equipment, fuel and oil, transport costs of inputs, electricity and water, interest payment on crop loans, storage cost and other production costs.

H. PRODUCTION AND DISPOSITION such as volume of the produce and its disposition in the form of camote roots and planting materials terms of sold, harvesters' share, threshers' share, other laborers' share, landowners' share, lease rental, for home consumption and home-based processing, given away, used for seeds and feeds, wastage and other purposes.

I. PRODUCTION-RELATED INFORMATION such as problems affecting camote production and comparison of production during the reference period with the same period of last year and the reasons for such changes.

J. MARKETING RELATED INFOMATION includes the major buyer of camote and problems related to marketing of the produce.

K. ACCESS TO CREDIT such as the amount and source of crop loan and interest rate per annum

L. FARMER'S PARTICIPATION IN CAMOTE PROGRAMS/PROJECTS such as awareness in government program/intervention on camote and benefits gained

M. OTHER INFORMATION such as the effect of climate change on farming practices and the practice of natural faming method and membership and name of camote farmers' organization and benefits derived

N. PLANS AND RECOMMENDATIONS includes plans and recommendations to improve camote production

O. INTERVIEW PARTICULARS contain the name and signature of contractual data collector, field supervisor/editor and PSO and date accomplished.

Cleaning operations

Editing and coding of survey returns were done at the provincial offices upon submission of the accomplished questionnaires by the CDCs. These activities were undertaken to ensure the quality of data that were collected.

Response rate

100 percent response rate

Sampling error estimates

Not applicable.

Data appraisal

Series of reviews were done to assess the quality of the data in terms of reliability and acceptability. A comparison with the results of past surveys on input usage, labor utilization, production cost and return structure of camote was made.

フィリピンの農地面積の統計データです。最新の2021年の数値「126,830(k㎡)」を含む1961～2021年までの推移表や他国との比較情報を無料で公開しています。csv形式でのダウンロードも可能でEXCELでも開けますので、研究や分析レポートにお役立て下さい。

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.

The average net returns per hectare of palay in the Philippines reached 36,211 Philippine pesos in 2024. Nationwide, Central Luzon registered the highest net returns at 58,932 Philippine pesos. In contrast, CALABARZON had the lowest net returns in that year.

The average cost of producing palay in the Philippines was about 59,695 Philippine pesos per hectare in 2024. Nationwide, Cagayan Valley recorded the highest production cost at 78,245 Philippine pesos. In contrast, BARMM recorded the lowest palay production cost per hectare in that year.

Palay production in the Philippines had an average cost of about 13.38 Philippine pesos per kilogram in 2024. Nationwide, Central Visayas recorded the highest average production cost at 19.73 Philippine pesos per kilogram.