In 2022, around 7.27 million tons of rice were produced in Japan, making rice the most commonly cultivated crop within the Japanese farming industry. The agricultural product with the second highest output was paddy field rice, followed by sugar beats.

In 2022, the output from crop cultivation in Japan amounted to nearly **** trillion Japanese yen, increasing slightly from around **** trillion yen in the previous year. Crops included were rice, wheat and barley, pulses, potatoes and sweet potatoes, vegetables, fruits and nuts, flowers, and industrial crops.

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 Japan were selected based on the following criterion: Location: Hokkaido Tokachi (JA Memuro, JA Otofuke, JA Tokachi Shimizu, JA Obihiro Taisho) --> initially focus on Memuro, Otofuke, Tokachi Shimizu, Obihiro Taisho // Added locations in GGP 2015 due to change of RF: Obhiro, Kamikawa, Abashiri
BF: no use of in furrow application (Amigo) - no use of Amistar

Contract farmers of snacks and other food companies --> screening question: 'Do you have quality contracts in place with snack and food companies for your potato production? Y/N --> if no, screen out

Increase of marketable yield --> screening question: 'Are you interested in growing branded potatoes (premium potatoes for processing industry)? Y/N --> if no, screen out

Potato growers for process use
Background info: No mention of Syngenta Background info: - Labor cost is very serious issue: In general, labor cost in Japan is very high. Growers try to reduce labor cost by mechanization. Percentage of labor cost in production cost. They would like to manage cost of labor - Quality and yield driven

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.

Forecast: Crops Gross Production in Japan 2023 - 2027 Discover more data with ReportLinker!

Japan JP: Production Index: 2004-2006: Crop data was reported at 81.790 2004-2006=100 in 2016. This records a decrease from the previous number of 89.780 2004-2006=100 for 2015. Japan JP: Production Index: 2004-2006: Crop data is updated yearly, averaging 123.000 2004-2006=100 from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 146.420 2004-2006=100 in 1968 and a record low of 81.790 2004-2006=100 in 2016. Japan JP: Production Index: 2004-2006: Crop data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Agricultural Production Index. Crop production index shows agricultural production for each year relative to the base period 2004-2006. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2004-2006.; ; Food and Agriculture Organization, electronic files and web site.; Weighted average;

A series of monitoring of insects visiting crop flowers was conducted on several crop species around Japan. The project, which ran from 2017 to 2021, was led by NARO and funded by the Japanese Ministry of Agriculture, Forestry and Fisheries (MAFF) to determine the abundance and diversity of wild insects that could potentially contribute to crop production across Japan. Flower-visiting wild insects were captured using plastic vials during the blooming season at multiple sites for one to several years. Domesticated pollinators, Apis mellifera and Osmia cornifrons were also captured in 2017. This data includes more than 6,500 individuals of flying insects from 18 families of Hymenoptera, 33 families of Diptera, 17 families of Coleoptera, Hemiptera, Lepidoptera, Psocodea, and Neuroptera captured in 20 fields of apple (Malus pumila Mill.), Japanese pear (Pyrus pyrifolia (Burm.f.) Nakai), and Oriental persimmon (Diospyros kaki Thunb.) as of August 2023.

In 2022, the crop production value of the Japanese agricultural industry amounted to approximately **** trillion Japanese yen, accounting for the majority of the total agricultural output value. Rice is the staple crop in Japan, making up the majority of grain crops in the country.

Forecast: Pesticides Use on Crops in Japan 2022 - 2026 Discover more data with ReportLinker!

We compiled 140 years of annual production, cultivated area, and yield records for six major crops (rice, wheat, barley, soybean, potato, and sweet potato) across all 47 prefectures from the national statistics of the Ministry of Agriculture, Forestry and Fisheries and its predecessors for the period 1883–2022. This comprehensive dataset enables analysis of long-term trends in crop productivity influenced by advances in genetics, breeding techniques, and changes in agricultural policy and practice. Furthermore, it supports investigations into the impacts of climate variability on agriculture over more than a century. The high temporal and spatial resolution of this dataset makes it a valuable resource for researchers aiming to understand the interplay between environmental change and food production in Japan.The data can be downloaded in one archive file (CropProduction_JP_1883-2022.zip), which contains four data sets: CropProduction_JP_1883-2022.csv : crop production between 1883 and 2022Japanese_Calendar.csv : The Japanese calendar systemPrefecture_Code.csv : The prefecture codes as a CSV spreadsheet CropProduction_JP_DataSources.xlsx. : Data sources as an Excel file.Since 1947, the Ministry of Agriculture, Forestry and Fisheries and its predecessors have conducted an annual crop statistical survey. The data sources are listed in the Excel file CropProduction_JP_DataSources.xlsx. The data from the oldest sources were compiled under the Japanese calendar system; in our dataset, the four Japanese eras are expressed using the first letter of their names (M, Meiji; T, Taisho; S, Showa; H, Heisei; and R, Reiwa). The years were converted to the Western calendar system (Japanese_Calendar.csv). Data were identified by original codes developed by us for this database corresponding to the 47 prefectures and 10 regions (Hokkaido, Tohoku, Hokuriku, Kanto-Tozan, Tokai, Kinki, Chugoku, Shikoku, Kyushu, and Okinawa) in Japan (Prefecture_Code.csv).

In fiscal year 2021, the shipment value of the preserved vegetables and fruits manufacturing industry in Japan decreased by *** percent to *** billion Japanese yen. Canned and preserved agricultural produce are a minor industry within the Japanese food manufacturing industry, overshadowed by the larger livestock product segment.

Japan: Crop production index (2004-2006 = 100): The latest value from 2022 is 94.6 index points, a decline from 95.8 index points in 2021. In comparison, the world average is 108.4 index points, based on data from 188 countries. Historically, the average for Japan from 1961 to 2022 is 121.1 index points. The minimum value, 94.6 index points, was reached in 2022 while the maximum of 153 index points was recorded in 1968.

Forecast: Sugar Crops Production in Japan 2023 - 2027 Discover more data with ReportLinker!

According to a survey that was conducted in Japan in February 2024, tomatoes and lettuce accounted for a share of ** percent each of cultivated produces of horticulture and plant factories in Japan. Flowers followed, with a share of ** percent.

Learn about the rising demand for primary fiber crops in Japan and how the market is expected to grow over the next decade, with a projected increase in market volume and value by 2035.

Japan ImPI: JPY Basis: BFAP: AP: Miscellaneous Edible Crops data was reported at 169.100 2020=100 in Mar 2025. This records a decrease from the previous number of 175.100 2020=100 for Feb 2025. Japan ImPI: JPY Basis: BFAP: AP: Miscellaneous Edible Crops data is updated monthly, averaging 192.500 2020=100 from Jan 2020 (Median) to Mar 2025, with 63 observations. The data reached an all-time high of 318.000 2020=100 in Jul 2022 and a record low of 94.700 2020=100 in Jun 2020. Japan ImPI: JPY Basis: BFAP: AP: Miscellaneous Edible Crops data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.I179: Import Price Index: 2020=100: JPY Basis: Beverages & Foods & Agriculture Products for Food.

Japan ImPI: JPY Basis: BFAP: AP: Feed & Forage Crops data was reported at 172.300 2020=100 in Mar 2025. This records a decrease from the previous number of 173.200 2020=100 for Feb 2025. Japan ImPI: JPY Basis: BFAP: AP: Feed & Forage Crops data is updated monthly, averaging 172.100 2020=100 from Jan 2020 (Median) to Mar 2025, with 63 observations. The data reached an all-time high of 238.600 2020=100 in Sep 2022 and a record low of 91.000 2020=100 in Jul 2020. Japan ImPI: JPY Basis: BFAP: AP: Feed & Forage Crops data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.I179: Import Price Index: 2020=100: JPY Basis: Beverages & Foods & Agriculture Products for Food.

Japan JP: Cereal Production data was reported at 9,034,597.000 Metric Ton in 2016. This records a decrease from the previous number of 11,202,384.000 Metric Ton for 2015. Japan JP: Cereal Production data is updated yearly, averaging 14,090,863.000 Metric Ton from Dec 1961 (Median) to 2016, with 56 observations. The data reached an all-time high of 21,023,600.000 Metric Ton in 1967 and a record low of 9,034,597.000 Metric Ton in 2016. Japan JP: Cereal Production data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Agricultural Production and Consumption. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

The Japan seed treatment market, valued at ¥324.30 million in 2025, is projected to experience steady growth, driven by increasing demand for high-yielding crops and a rising focus on sustainable agricultural practices. This growth is further fueled by the adoption of advanced seed treatment technologies, including biopesticides and novel formulations that enhance crop resilience against pests and diseases. The market is segmented by crop type (commercial crops, fruits and vegetables, grains and cereals, pulses and oilseeds, turf and ornamental crops) and by function (fungicide, insecticide, nematicide). The increasing prevalence of plant diseases and insect infestations, coupled with government initiatives promoting agricultural efficiency, are significant market drivers. While the market faces potential restraints like stringent regulatory approvals and the high cost of advanced seed treatment technologies, the overall outlook remains positive, driven by sustained investment in agricultural research and development. Major players like Adama Agricultural Solutions, BASF SE, Corteva Agriscience, Bayer Cropscience AG, Syngenta Co Ltd, UPL Ltd, Sumitomo Chemical, and Kureha Corporation are actively competing in this market, employing strategies focused on product innovation, strategic partnerships, and expansion into emerging crop segments. The competitive landscape is characterized by both established multinational corporations and regional players, resulting in a dynamic market environment. The market share is likely concentrated among the larger multinational companies, leveraging their extensive research and development capabilities and established distribution networks. However, smaller companies are innovating with specialized products and niche applications, carving out their market share. Future growth will likely be influenced by the increasing adoption of precision agriculture technologies, climate change mitigation strategies impacting crop production, and shifts in consumer preferences towards sustainably produced food. The forecast period (2025-2033) anticipates a consistent growth trajectory, aligning with the overall trend of increased investment in agricultural productivity and enhanced crop protection in Japan. Recent developments include: In January 2023, Bayer formed a new partnership with Oerth Bio to enhance crop protection technology and create more eco-friendly crop protection solutions.. Notable trends are: Rise in Cost of High Quality Seeds.

Japan Input Price Index: Mfg: CC: Crops for Feed and Forage data was reported at 111.100 1995=100 in Jun 2005. This records an increase from the previous number of 110.600 1995=100 for May 2005. Japan Input Price Index: Mfg: CC: Crops for Feed and Forage data is updated monthly, averaging 110.500 1995=100 from Jan 1995 (Median) to Jun 2005, with 126 observations. The data reached an all-time high of 149.600 1995=100 in Apr 1997 and a record low of 81.700 1995=100 in Dec 1999. Japan Input Price Index: Mfg: CC: Crops for Feed and Forage data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.I296: Input Price Index: Gross Weighted: 1995=100: Major Commodity.

Forecast: Arable Land and Permanent Crops in Japan 2022 - 2026 Discover more data with ReportLinker!