In 2024, farmers in China produced around ***** million metric tons of rice. At the same time, the production volume of wheat amounted to approximately *** million metric tons in China. The production volume of rice, wheat, and corn in China increased continuously until 2015, remained flat between 2015 and 2020, but started growing again thereafter.

The statistic shows the total acreage of agricultural land in China in selected years from 1980 to 2024. In 2024, the acreage of agricultural land in China amounted to approximately *** million hectares.

The North China Plain is an important food production area in China, with a large area of cropland and a complex planting structure. Accurately identifying the distribution of typical crops in this area and tracking the dynamic changes of planting structure are fundamental for detecting crop growth, evaluating crop irrigation water consumption and optimizing agricultural water resources allocation. This study used Fourier transform to obtatin the amplitudes and phases of the 0-5 harmonics of the MOD13Q1 NDVI data. Based on the field sample points and maximum likelihood supervised classification, the planting area of 6 typical crops (winter wheat-summer maize; winter wheat-rice; other double cropping systems; spring maize; cotton; other single cropping systems) in the North China Plain from 2001 to 2018 was identified. The identification results accuracy were evaluated through confusion matrix, comparison with the winter wheat planting area in the county-level statistical yearbook, and comparison with the proportion of winter wheat extracted by Landsat images, all of which showed good performance and high accuracy. The data can be applied to related research and analysis on crop production, irrigation water consumption estimation, and groundwater protection in the North China Plain.

The China Agricultural Biologicals Market is segmented by Function (Crop Nutrition, Crop Protection) and by Crop Type (Cash Crops, Horticultural Crops, Row Crops). The report offers market size in both market value in USD and market volume in metric ton. Further, the report includes market split by form and various crop types.

China Production Price Index: AP: Farming Product: Sugar-Bearing Crop data was reported at 100.200 Prev Year=100 in Mar 2025. This records a decrease from the previous number of 101.500 Prev Year=100 for Dec 2024. China Production Price Index: AP: Farming Product: Sugar-Bearing Crop data is updated quarterly, averaging 101.700 Prev Year=100 from Mar 2002 (Median) to Mar 2025, with 83 observations. The data reached an all-time high of 135.050 Prev Year=100 in Mar 2006 and a record low of 70.900 Prev Year=100 in Sep 2002. China Production Price Index: AP: Farming Product: Sugar-Bearing Crop data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IC: Agricultural Production Price Index: Quarterly.

China Production Price Index: AP: Farming Product: Oil-Bearing Crop data was reported at 96.200 Prev Year=100 in Mar 2025. This records an increase from the previous number of 96.100 Prev Year=100 for Dec 2024. China Production Price Index: AP: Farming Product: Oil-Bearing Crop data is updated quarterly, averaging 103.910 Prev Year=100 from Mar 2002 (Median) to Mar 2025, with 93 observations. The data reached an all-time high of 140.080 Prev Year=100 in Jun 2008 and a record low of 75.820 Prev Year=100 in Jun 2009. China Production Price Index: AP: Farming Product: Oil-Bearing Crop data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IC: Agricultural Production Price Index: Quarterly.

In 2023, farmers in China grew rice on an acreage of around ********** hectares. At the same time, the agricultural land used for wheat production amounted to around ************ hectares in China.

The Agricultural Management Dataset contains two variables which help describe management regimes within agricultural lands of China. The first variable, Irrigation Index, reports the fraction of cropland in a county which is under irrigation, excluding rice paddies. The Second variable, Nitrogen Fertilizer, reports the tonnes of nitrogen fertilizer applied in the county per year.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

a. Data content (data file/table name, including observation index content)

The dataset of crop planting area in Northeast China (1840-1949) includes elements such as the planting area and cultivated area of the main crops in Northeast China from 1840-1949.

b. Construction purpose

Mainly used for research on agricultural production and dynamic changes in agricultural cultivation processes.

c. Service recipients

Students and researchers engaged in related research, as well as management and teaching personnel.

d. Time range of data

1840-1949

e. The spatial range of data

Northeast region

We have created a dataset that conducts a comprehensive analysis of carbon harvested from crop production in China over a 40-year period from 1981 to 2020. This dataset has a spatial resolution of 10x10 km, categorizes arable land into five types, and shows that cereal crops account for the largest proportion of total carbon harvested, at 75.9%. Over the past 40 years, the average carbon harvested in agricultural ecosystems was 0.31 Gt C yr-1. This dataset is expected to aid in estimating the carbon budget of arable ecosystems and of China as a whole. The data are available through open data repositories, providing valuable insights for research and policy-making aimed at sustainable agriculture and mitigating climate change.

China Agricultural Biologicals Market size was valued at USD 4.82 Billion in 2024 and is projected to reach USD 10.88 Billion by 2032, growing at a CAGR of 10.72% from 2026 to 2032.

China Agricultural Biologicals Market Drivers

Emphasis on Sustainable Agriculture: The Chinese government is actively promoting sustainable agricultural practices, including the use of biologicals, to reduce reliance on chemical pesticides and fertilizers.

National Action Plans: National action plans and regulations are being implemented to encourage the adoption of biologicals and improve food safety.

Investment in R&D: The government is investing in research and development of agricultural biologicals to enhance their efficacy and availability.

Food Safety Concerns: Growing consumer awareness of food safety issues and pesticide residues is driving demand for organic and residue-free produce.

Premium Products: Consumers are willing to pay a premium for food products that are perceived as safe and healthy, creating a market for biological-based agriculture.

The China Corn Market size was valued at USD 6.17 Million in 2023 and is projected to reach USD 6.52 Million by 2032, exhibiting a CAGR of 3.10 % during the forecast periods. China's corn market is a major global player, characterized by extensive production, government support, and a focus on diverse applications such as food, feed, and biofuels. Advanced farming technologies and various corn types drive growth. The market impacts rural economies, food security, and trade, offering advantages like enhanced food security and rural development. Recent developments include: June 2022: The Chinese National Crop Variety Approval Committee released two sets of standards to clear the cultivation of genetically modified (GM) crops in China. For the commercial production of GM maize in China, the government has two steps in these regulations: a "safety certificate" and a "variety approval" before crops can be commercially cultivated in the provinces., July 2021: Chinese farmers sharply increased corn planting to cash in on demand-fuelled record prices, a trend that cooled the country's rampant import appetite. This expansion, mainly at the expense of soybeans and other crops, including sorghum and edible beans, boosted China's maize output in 2021-22 by at least 6 percent., June 2021: China started sustainable production of maize that could 'boost yields and cut greenhouse gas emissions and fertilizer use' in the country by 2035.. Notable trends are: Increasing Demand for Corn as Animal-based Protein Sources.

Forecast: Output of Grain Crops Harvested in Autumn in China 2022 - 2026 Discover more data with ReportLinker!

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.

Screened China BF were from Licheng Town ( Liyang City, Jiangsu province) + Dinggou Town (Jiangdu District, Yangzhou city, Jiangsu province) + Shuikou Town (Tianchang city, Anhui Province) and were selected based on the following criterion: - Rice rotation with wheat growers (professional)
- Professional farmer with rice being main income source
- Mechanical planting
- Co-op operation: Co-op operation means a local professional farmer who leases small fragmented pieces of lands from his neighbors (consolidation) to make it bigger and commercial farming scale
- Receive tech supports from CP suppliers or dealers
- Hire labor
- Suggest mechanical, Co-op type of farmers as benchmark farms. Compare SYT vs generic products. Rice-wheat rotation.

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.

Agricultural greenhouses, serving as facilities to protect crops and enhance farmland production efficiency, play a crucial role in the modernization process of agricultural production. With continuous innovation and promotion of greenhouse technology, the area covered by greenhouses in China has been steadily increasing over the past 20 years, far surpassing that of other countries. In this study, we utilized Landsat-7 imagery on the Google Earth Engine platform and employed a Random Forest classifier to extract the distribution of agricultural greenhouses across China mainland for the years 2010, 2016, and 2022. The classification results underwent further visual inspection and refinement through morphological checks, hole filling, and manual contour adjustments. The resulting dataset achieved an overall accuracy exceeding 97% for each of the years 2010, 2016, and 2022. The dataset, China GH, encompasses both the distribution of greenhouse sample points across mainland China and the precise geographic coordinates of agricultural greenhouses. It is stored in Shapefile format, TIFF format, and uses the WGS-84 coordinate system. The dataset includes: • Vector data of agricultural greenhouse sample points in mainland China.( Shapefile ) • Raster data of agricultural greenhouse distribution in mainland China. ( TIFF ) • Relevant thematic maps. ( TIFF ) All raster files are in unsigned 8-bit integer format and use 255 as no-data value (pixels ignored by prediction), following an specific naming convention: 1. Project name: China Greenhouse (China GH) 2. Procedure combination: Random Forest ( RF ) 3. Spatial resolution: 30m 4. Begin of time reference, date of first Landsat composite used by the modeling (20100101) 5. End of time reference: date of last Landsat composite used by the modeling (20221231) 6. Spatial extent: china mainland 7. Coordinate system: World Geodetic System 1984 8. Version: v1 Users can employ our China GH dataset in the following aspects :(1) Assist in agricultural layout and land use planning. (2) Assist agricultural producers to master the layout and scale of greenhouses to achieve the optimization of crop planting and management. (3) Research on the layout of agricultural greenhouses and crop growth in combination with other agriculture-related data. (3) Analyze the supply and demand of agricultural products in facilities, and guide the market pricing and sales strategy of agricultural products.

The Crops Dataset contains nineteen variables which represent different crops sown in China. For each crop (variable) the number of hectares of that crop sown are given. The following crops are represented: Cereal Grains, Corn, Cotton, Double Season Rice, Green Manure, Potatoes, Rapeseed, Rice and Rapeseed, Single Season Rice, Spring Wheat, Sorghum, Soybeans, Sugarbeets, Sugarcane, Tobacco, Vegetables, Winter Wheat, Winter Wheat and Corn, Winter Wheat and Rice.

See the references for the sources of these data.

China County Data collection contains seven datasets which were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties

The Wanfunao site was a large Chu settlement in Zhou Dynasty. It was located on an alluvial plain along the Yangtze River in the Yichang section. The region around the site comprised mountains, hills, and plains, which was a compatible environment for the cultivation of various crops. Previous studies have suggested that the middle and lower reaches of the Yangtze River are one of the most productive regions for rice cultivation. Besides rice, however, seven dryland crops have been found at the Wanfunao site: foxtail millet, broomcorn millet, wheat, barley, oat, buckwheat, and adzuki bean. Among them, foxtail millet and rice are most ubiquitous. The crop assemblage has revealed that the northern dryland crops, including those were newly adapted cereals such as foxtail millet, wheat, and barley, gradually dispersed southward and became a part of the diet along with rice. This can be attributed to southern Chinese inhabitants’ reclamation of the hilly environment for agriculture. Although communities in southern China had cultivated rice on the plains for thousands of years, newly introduced dryland crops from north China adapted to mountainous environments better. The development of multi-cropping systems in southern China likely involved changes in agricultural ontology associated with the adaptation of northern crops in southern environments newly encountered. Additionally, the assemblage of foxtail millet grain/rice spikelet base in the site may have been used for livestock feeding. A wide range of landforms, compatible farming, and surplus agricultural products for husbandry may have been a part of the economic foundation that facilitated the rise of Chu.

The China Seed Market is segmented by Breeding Technology (Hybrids, Open Pollinated Varieties & Hybrid Derivatives), by Cultivation Mechanism (Open Field, Protected Cultivation) and by Crop Type (Row Crops, Vegetables). The market volume and value are presented in metric ton and USD respectively. The key data points include the market size of seeds by breeding technology, cultivation mechanism and crop.

The dataset includes 21 files of the planting area of rice, maize and wheat in China from 2009 to 2015 in TIF format, one file for each crop per year. The pixels with values of 2 and 4 in the rice file represent the planting grids of one-season rice and early-late rice, respectively. The pixels with values of 3 and 6 in the maize file represent the planting grids of summer maize and spring maize respectively. The pixels with values of 5 and 7 in the wheat file represent the planting grids of winter wheat and spring wheat, respectively. The spatial resolution is 1km. The "Asia North Albers Equal Area Conic" coordinate system was used for the projection.

The agricultural and geographic datasets included on the China County Data collection were compiled in the early 1990s for use as inputs to the DNDC (Denitrification-Decomposition) model at UNH. DNDC is a computer simulation model for predicting carbon (C) and nitrogen (N) biogeochemistry in agricultural ecosystems. The datasets were compiled from multiple Chinese sources and all are at the county scale for 1990. The datasets which comprise this collection are listed below. Each dataset has a Child DIF designated by a numerical suffix, based on the list number below, added on to the entry id.

1) Agricultural Management 2) Crops 3) N-Deposition 4) Geography and Population 5) Land Use 6) Livestock 7) Soil Properties