The number of people working in the agriculture sector increased in 2023 in comparison to the previous year. From over 10.83 million agricultural workers, this figure increased to 11.19 million in 2023. Most employers in this sector are engaged in agriculture and forestry.

Between 2016 and 2023, there were significantly more males employed in the agricultural industry in the Philippines in comparison to their female counterparts. In particular, there were 28.4 percent male workers compared to about 16 percent female workers according to preliminary data for 2023.

Philippines Production: Value: Agricultural Crops data was reported at 965,125.900 PHP mn in 2017. This records an increase from the previous number of 881,420.500 PHP mn for 2016. Philippines Production: Value: Agricultural Crops data is updated yearly, averaging 313,263.200 PHP mn from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 965,125.900 PHP mn in 2017 and a record low of 107,473.000 PHP mn in 1987. Philippines Production: Value: Agricultural Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B014: Production: Value: Agriculture (Annual).

Preliminary figures reported that the agriculture sector accounted for 23.2 percent of the total employment share in the Philippines in 2023, indicating a slight increase from the previous year. The employment share of agriculture was highest in 2016.

GDP from Agriculture in Philippines increased to 522710.71 PHP Million in the fourth quarter of 2024 from 399110.91 PHP Million in the third quarter of 2024. This dataset provides the latest reported value for - Philippines Gdp From Agriculture - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Philippines Production: Value: Agricultural Crops: Major Crops data was reported at 382,816.000 PHP mn in 2017. This records an increase from the previous number of 361,331.300 PHP mn for 2016. Philippines Production: Value: Agricultural Crops: Major Crops data is updated yearly, averaging 125,403.600 PHP mn from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 382,816.000 PHP mn in 2017 and a record low of 39,017.900 PHP mn in 1987. Philippines Production: Value: Agricultural Crops: Major Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B014: Production: Value: Agriculture (Annual).

Within the agriculture, forestry, and fishing industry in the Philippines, support activities to agriculture, forestry, and fishing production recorded the highest gross value added (GVA) growth rate for 2023 at 4.6 percent. In contrast, forestry and logging contracted by 21.9 percent that year.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Philippines were selected based on the following criterion: (a) smallholder rice growers Location: Luzon - Mindoro (Southern Luzon) mid-tier (sub-optimal CP/SE use): mid-tier growers use generic CP, cheaper CP, non hybrid (conventional) seeds
Smallholder farms with average to high levels of mechanization
Should be Integrated Pest Management advocates
less accessible to technology: poor farmers, don't have the money to buy quality seeds, fertilizers,... Don't use machinery yet
simple knowledge on agronomy and pests
influenced by fellow farmers and retailers
not strong financial status: don't have extra money on bank account and so need longer credit to pay (as a consequence: interest increases) may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab.

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Philippines PH: Aquaculture Production data was reported at 2,200,914.000 Metric Ton in 2016. This records a decrease from the previous number of 2,348,159.000 Metric Ton for 2015. Philippines PH: Aquaculture Production data is updated yearly, averaging 599,464.000 Metric Ton from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 2,608,120.000 Metric Ton in 2011 and a record low of 60,769.000 Metric Ton in 1960. Philippines PH: Aquaculture Production data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Agricultural Production and Consumption. Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption.; ; Food and Agriculture Organization.; Sum;

Map shows the reported estimated cost of damage to the agricultural sector.

In 2023, agricultural crops generated a gross value added (GVA) amounting to around 934.13 billion Philippine pesos. This sector contributes the highest share to the GVA of the Philippines' agriculture, forestry, and fishing (AFF) industry.

The Philippine agricultural harvester market soared to $130M in 2024, growing by 231% against the previous year. This figure reflects the total revenues of producers and importers (excluding logistics costs, retail marketing costs, and retailers' margins, which will be included in the final consumer price). In general, consumption posted a significant increase. Agricultural harvester consumption peaked at $244M in 2018; however, from 2019 to 2024, consumption stood at a somewhat lower figure.

Philippines Production: Volume: Agricultural Crops: Cereals data was reported at 27,191.200 Metric Ton th in 2017. This records an increase from the previous number of 24,846.000 Metric Ton th for 2016. Philippines Production: Volume: Agricultural Crops: Cereals data is updated yearly, averaging 17,590.000 Metric Ton th from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 27,191.200 Metric Ton th in 2017 and a record low of 12,378.000 Metric Ton th in 1998. Philippines Production: Volume: Agricultural Crops: Cereals 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).

In 2024, the Philippine agricultural appliance market increased by 174% to $3.5M, rising for the second year in a row after five years of decline. Overall, consumption, however, recorded a abrupt contraction. Over the period under review, the market reached the maximum level at $41M in 2017; however, from 2018 to 2024, consumption remained at a lower figure.

In 2023, edible fruits and nuts as well as peels of citrus fruit melons were the leading agricultural commodity group exported from the Philippines. This commodity group had an export value of around about two billion U.S. dollars. The second leading commodity group in terms of export value in that year were animal or vegetable fats and oils and their cleavage products, prepared edible fats, and animal or vegetable waxes, with an export value of about 1.3 billion U.S. dollars.

Imports of agricultural or horticultural watering appliances in the Philippines stood at X units in 2017, growing by X% against the previous year. In general, imports of agricultural or horticultural watering appliances continue to indicate a skyrocketing expansion. The most prominent rate of growth was recorded in 2017, an increase of X% against the previous year.

Philippines PH: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data was reported at 9.397 % in 2023. This records a decrease from the previous number of 9.552 % for 2022. Philippines PH: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data is updated yearly, averaging 19.134 % from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 27.630 % in 1974 and a record low of 8.820 % in 2019. Philippines PH: GDP: % of GDP: Gross Value Added: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Gross Domestic Product: Share of GDP. Agriculture, forestry, and fishing corresponds to ISIC divisions 1-3 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4. Note: For VAB countries, gross value added at factor cost is used as the denominator.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;Note: Data for OECD countries are based on ISIC, revision 4.

The Philippine agricultural and forestry tractor market skyrocketed to $142M in 2024, growing by 64% against the previous year. This figure reflects the total revenues of producers and importers (excluding logistics costs, retail marketing costs, and retailers' margins, which will be included in the final consumer price). In general, consumption continues to indicate significant growth.

Philippines Tractor Market size was valued at around USD 248.11 million in 2024 & is projected to reach USD 319.209 million by 2030 with a 4.49% CAGR.

Philippines Production: Value: Crops: Cassava data was reported at 11,653.000 PHP mn in 2024. This records an increase from the previous number of 9,330.000 PHP mn for 2023. Philippines Production: Value: Crops: Cassava data is updated yearly, averaging 4,716.000 PHP mn from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 11,653.000 PHP mn in 2024 and a record low of 1,484.000 PHP mn in 2004. Philippines Production: Value: Crops: Cassava data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B016: Production: Value: Agriculture: Current Price: Annual.