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

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 3.67 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.

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.

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.

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 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 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 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.

The Philippines reported about 21.65 million metric tons of sugarcane production in 2023, indicating an approximately eight percent decline from the previous year. Sugarcane production peaked in 2017 at 29.29 million metric tons. Western Visayas as the leading sugarcane producer  Region VI, or Western Visayas, continued to be the leading sugarcane producer in the Philippines, producing about 13.2 million metric tons of sugarcane or about 56 percent of the total production in 2023. In particular, the province of Negros Occidental accounts for most of the sugarcane supply, earning the title “Sugar Bowl of the Philippines”. The region also allocated about 208,000 hectares of land for planting and harvesting sugarcane used for raw sugar production – the highest nationwide.   Retail price of refined sugar price continues to soar, despite falling wholesale prices  As of March 2024, the wholesale price of refined sugar in Metro Manila reached 3,565 Philippine pesos for every 50-kilogram bag. This was also a significant decline from the peak price of around 4,800 Philippine pesos per 50-kg bag and about 20 percent from its first spike in January 2023. Despite the decrease, refined sugar still retails at around 94.51 Philippine pesos per kilogram in March 2024, up from just 55 Philippine pesos in the last quarter of 2022.

The average cost of producing palay in the Philippines was about 55,814 Philippine pesos per hectare in 2023. Nationwide, Cagayan Valley recorded the highest production cost at 72,255 Philippine pesos. In contrast, BARMM recorded the lowest palay production cost per hectare in that year.