The labor productivity of the agriculture, forestry, and fishing industry in the Philippines reached 161,247 Philippine pesos in 2023, indicating a decline from the previous year. The labor productivity of this sector peaked in 2019.

Philippines Production: Volume: Agricultural Crops: Other Crops data was reported at 8,268.700 Metric Ton th in 2017. This records an increase from the previous number of 8,063.800 Metric Ton th for 2016. Philippines Production: Volume: Agricultural Crops: Other Crops data is updated yearly, averaging 8,063.800 Metric Ton th from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 12,719.200 Metric Ton th in 1996 and a record low of 6,138.300 Metric Ton th in 2000. Philippines Production: Volume: Agricultural Crops: Other Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B015: Production: Volume: Agriculture (Annual).

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).

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.

Within the agriculture, forestry, and fisheries industry in the Philippines, the crops sector accounted for the highest production value in 2023, amounting to about *** trillion Philippine pesos. In comparison, the poultry sector had a production value of ***** billion Philippine pesos.

Philippines Production: Value: Agricultural Crops: Other Crops data was reported at 138,449.500 PHP mn in 2017. This records an increase from the previous number of 130,164.000 PHP mn for 2016. Philippines Production: Value: Agricultural Crops: Other Crops data is updated yearly, averaging 58,635.100 PHP mn from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 138,449.500 PHP mn in 2017 and a record low of 30,156.000 PHP mn in 1987. Philippines Production: Value: Agricultural Crops: Other Crops data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B014: Production: Value: Agriculture (Annual).

In 2020, the production value in the agriculture sector in the Philippines reached approximately **** trillion Philippine pesos. The production value in this sector contracted by *** percent during this year.

The production value of the agriculture industry in the Philippines increased by *** percent in 2023 in comparison to the previous year. The industry recorded its highest contraction in 2021, amounting to **** percent.

Philippines Production: Volume: Agricultural Crops: Major Crops data was reported at 56,060.100 Metric Ton th in 2017. This records an increase from the previous number of 48,733.800 Metric Ton th for 2016. Philippines Production: Volume: Agricultural Crops: Major Crops data is updated yearly, averaging 45,076.200 Metric Ton th from Dec 1987 (Median) to 2017, with 31 observations. The data reached an all-time high of 56,060.100 Metric Ton th in 2017 and a record low of 32,660.300 Metric Ton th in 1987. Philippines Production: Volume: 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.B015: Production: Volume: Agriculture (Annual).

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.

Agriculture value added per worker of Philippines increased by 1.16% from 3,235 US dollars in 2022 to 3,272 US dollars in 2023. Since the 2.52% fall in 2021, agriculture value added per worker fell by 2.99% in 2023. Agriculture value added per worker is a measure of agricultural productivity. Value added in agriculture measures the output of the agricultural sector (ISIC divisions 1-5) less the value of intermediate inputs. Agriculture comprises value added from forestry, hunting, and fishing as well as cultivation of crops and livestock production. Data are in constant 2005 U.S. dollars.

Baseline survey profiling socio-economic and agronomic information of 2050 small holder coconut and cacao farmers in 4 provinces of Mindanao region of Philippines This aims to build the resilience of coconut and cacao smallholder farmers in the Philippines through improved crop productivity and diversification, increased access and usage of financial services, improved access to higher paying markets and value chains, and access to valuable early warning systems related to pest-control and weather events. Grameen Foundation’s FarmerLink project combines satellite and farm data collected by mobile-equipped field agents to help coconut farmers increase productivity, deal with crop pests and diseases and increase the sustainability of their farms3.The project duration was from 1 January 2016 until 30 June 2017 (18 months). This aims to build the resilience of coconut and cacao smallholder farmers in the Philippines through improved crop productivity and diversification, increased access and usage of financial services, improved access to higher paying markets and value chains, and access to valuable early warning systems related to pest-control and weather events. Grameen Foundation’s FarmerLink project combines satellite and farm data collected by mobile-equipped field agents to help coconut farmers increase productivity, deal with crop pests and diseases and increase the sustainability of their farms3.The project duration was from 1 January 2016 until 30 June 2017 (18 months). This aims to build the resilience of coconut and cacao smallholder farmers in the Philippines through improved crop productivity and diversification, increased access and usage of financial services, improved access to higher paying markets and value chains, and access to valuable early warning systems related to pest-control and weather events. Grameen Foundation’s FarmerLink project combines satellite and farm data collected by mobile-equipped field agents to help coconut farmers increase productivity, deal with crop pests and diseases and increase the sustainability of their farms3.The project duration was from 1 January 2016 until 30 June 2017 (18 months).

Forecast: Total Value of Agricultural Production at Farm Gate in Philippines 2024 - 2028 Discover more data with ReportLinker!

With the agriculture, forestry, and fishing industry in the Philippines, the fisheries sector recorded the highest growth rate of the production value in 2023, which amounted to *** percent. In comparison, the production value of the crops sector only increased by *** percent.

Abstract

Smallholder rice farming is central to poverty reduction, food security, and rural development in the Philippines. One key issue is that around 41 percent of the country's irrigable land is not irrigated. Moreover, many irrigation systems are suggested to be poorly managed with unequal water distribution.

The Irrigated Rice Production Enhancement Project (IRPEP) was implemented in three regions (VI, VII and X) of the Philippines, between 2010-2015. It was designed to improve rice productivity and smallholder livelihoods by strengthening canal irrigation infrastructure of Communal Irrigation Systems (CIS), improving the capacity of the Irrigators' Associations (IAs) that manage the CIS, and offering complementary marketing support, Farmer Field Schools, and emergency seed buffer stocks.

The data collected are used to test the effectiveness of the 5-year Irrigated Rice Production Enhancement Project to improve the livelihoods of smallholder rice farmers in the Philippines.

For more information, please, click on the following link https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-irrigated-rice-production-enhancement-project.

Geographic coverage

Rural coverage. Sample covers six provinces of the Philippines across three regions (Region VI, VIII, X).

Analysis unit

Households

Universe

Smallholder farmer households

Kind of data

Sample survey data [ssd]

Sampling procedure

The analysis is based on quantitative data from 2,104 households and 113 IAs covering beneficiary and non-beneficiary groups, along with qualitative data from project and IA staff. The IRPEP's impact is estimated by comparing beneficiary and nonbeneficiary households and IAs using statistical matching techniques to ensure a clean and unbiased comparison. This process resulted in a household dataset used for analysis that covers 1,015 treatment and 664 control households, and an IA dataset used to assess impact on IA level indicators from 58 treatment and 55 control IAs.

To identify a well-matched set of treatment and control CISs and households, the sample selection for the impact assessment sought to mirror IRPEP's beneficiary selection process by initially conducting the identification at the CIS level. At the start of the process there were a number of non- beneficiary CIS in the project provinces, allowing for control CIS to be selected from within the same provinces. Using these IRPEP and non-IRPEP CIS, a two-stage process was used to select the final set of treatment and control CIS. This involved both data analysis and the knowledge of local staff.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household and IA questionnaires collected a wide range of information, which was then used to create the impact indicators and other variables to be used in the data analysis. The household questionnaire included detailed questions on agricultural production and marketing collected by season, parcel and crop for the previous 12 months, as well as socio-demographic characteristics, other income generating activities, asset ownership, experience of shocks, access to credit, and receipt of external support from various sources. The IA questionnaire gathered information on their structure and facilities, irrigation water coverage, gender differentiated membership, and income and expenditures over the past 12 months, including irrigation fee collection and operation and maintenance spending.

Note: some variables have missing labels. Please, refer to the questionnaire for more details.

This aims to build the resilience of coconut and cacao smallholder farmers in the Philippines through improved crop productivity and diversification, increased access and usage of financial services, improved access to higher paying markets and value chains, and access to valuable early warning systems related to pest-control and weather events. Grameen Foundation’s FarmerLink project combines satellite and farm data collected by mobile-equipped field agents to help coconut farmers increase productivity, deal with crop pests and diseases and increase the sustainability of their farms3.The project duration was from 1 January 2016 until 30 June 2017 (18 months). Farmers are registered under the FarmerLink program, entitled to receive both SMS and IVR messages from time to time on coconut and cacao good agricultural practices, market price information, pest and disease information, and weather forecasts. Collects info about the farmers. This aims to build the resilience of coconut and cacao smallholder farmers in the Philippines through improved crop productivity and diversification, increased access and usage of financial services, improved access to higher paying markets and value chains, and access to valuable early warning systems related to pest-control and weather events. Grameen Foundation’s FarmerLink project combines satellite and farm data collected by mobile-equipped field agents to help coconut farmers increase productivity, deal with crop pests and diseases and increase the sustainability of their farms3.The project duration was from 1 January 2016 until 30 June 2017 (18 months).

Philippines Production: Volume: Year to Date: Other Crops: Calamansi data was reported at 86.810 Metric Ton th in Sep 2018. This records an increase from the previous number of 33.240 Metric Ton th for Jun 2018. Philippines Production: Volume: Year to Date: Other Crops: Calamansi data is updated quarterly, averaging 45.930 Metric Ton th from Mar 1998 (Median) to Sep 2018, with 83 observations. The data reached an all-time high of 201.620 Metric Ton th in Dec 2007 and a record low of 13.810 Metric Ton th in Mar 1998. Philippines Production: Volume: Year to Date: Other Crops: Calamansi data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.B013: Production: Volume: Agriculture: ytd.

Within the agriculture, forestry, and fishing industry in the Philippines, forestry and logging recorded the highest gross value added (GVA) growth rate between 2023 and 2024 at nearly ** percent. In contrast, sugarcane production registered the highest contraction in that period.

This aims to build the resilience of coconut and cacao smallholder farmers in the Philippines through improved crop productivity and diversification, increased access and usage of financial services, improved access to higher paying markets and value chains, and access to valuable early warning systems related to pest-control and weather events. Grameen Foundation’s FarmerLink project combines satellite and farm data collected by mobile-equipped field agents to help coconut farmers increase productivity, deal with crop pests and diseases and increase the sustainability of their farms3.The project duration was from 1 January 2016 until 30 June 2017 (18 months).

This aims to build the resilience of coconut and cacao smallholder farmers in the Philippines through improved crop productivity and diversification, increased access and usage of financial services, improved access to higher paying markets and value chains, and access to valuable early warning systems related to pest-control and weather events. Grameen Foundation’s FarmerLink project combines satellite and farm data collected by mobile-equipped field agents to help coconut farmers increase productivity, deal with crop pests and diseases and increase the sustainability of their farms3.The project duration was from 1 January 2016 until 30 June 2017 (18 months).