Get statistical data on the annual averages of the Farm Input Price Index for Ontario.

This index measures the change in selected farm input costs over time. These costs are related to items like:

• buildings
• machinery and motor vehicles
• depreciation on machinery and motor vehicles
• machinery fuel
• machine repairs
• general business costs
• crop production
• commercial seed and plant
• fertilizer
• animal production
• livestock purchases
• commercial feed

NUOnet Vision: Efficient use of nutrients to optimize production and product quality of food for animals and humans, fuel and fiber in a sustainable manner that contributes to ecosystem services. This record contains the DET and Data Dictionary for NUOnet - the data files may be found at https://usdaars.maps.arcgis.com/apps/MapSeries/index.html?appid=e90392a99d5c427487c6c37cf6d47844 Best nutrient management practices are critical for maintaining profitable economic returns, sustaining higher yields, lowering environmental impacts, optimizing nutritional quality, and providing ecosystem services. Best management practices that improve nutrient use efficiencies can reduce nutrient losses from agricultural systems. However, we need to improve our understanding of biological, physical and chemical influences on nutrient processes. For instance, crop use efficiency of nitrogen (N), the primary macronutrient regulating yield and protein content, can be reduced by processes such as denitrification (N2O and N2 emission), leaching (NH4-N, NO3-N, and organic-N), ammonia (NH3-N,) volatilization, surface runoff and erosion, disease, and non-crop competition. Similarly, we need to obtain more information about biological and physical cycles of nutrients, especially phosphorus (P), including factors that influence nutrient availability from fertilizers, crop residues, cover crops, manures, and other byproducts. We need a better understanding of relationships between soil biological communities and ecosystems, including plant roots and root exudates, and availability and uptake of macro- and micro-nutrients. In addition, we need information regarding how these practices impact yields, organoleptic qualities, and the macro- and micro-nutritional composition of plants. This information will improve our ability to develop best nutrient management practices. Optimal soil nutrient levels are critical for maximizing economic returns, increasing sustainable yields, lowering environmental impacts, sustaining ecosystem services and optimizing nutritional and organoleptic qualities of human and animal foods. Efficient management practices are crucial for increasing economic returns for land managers in a sustainable manner while producing high quality of food for animals and humans with reduced off-site transfer of nutrients from agricultural areas in watersheds. Optimizing N and P inputs requires more information about nutrient inputs from fertilizers, manures, composts, agricultural byproducts, cover crops, and other nutrient sources in addition to nutrient cycling within soils. This requires data from long-term nutrient management studies across a wide range of soils, crops, and environmental conditions. Land management needs are to connect nutrient management practices for crops with nutrient use efficiency; crop quality; crop chemical composition and nutritional value, quality and acceptability for animal and human health. Development of databases that enable the scientific exploration of connections among data generated from diverse research efforts such as nutrient management, fate and ecosystem service outcomes, nutritional composition of crops, and animal and human health, is needed. Nitrogen is a key nutrient that enhances agricultural yield and protein content, but multiple N loss pathways, as previously mentioned, reduce crop N use efficiency (NUE). Implementing proper management practices is needed to reduce N losses from agricultural systems. ARS has multidisciplinary scientific teams with expertise in soils, ecological engineering, hydrology, livestock management and nutrition, horticulture, crop breeding, human and animal nutrition, post-harvest management and processing, and other areas, and intentional collaboration among these teams offers opportunities to rapidly improve NUE and crop quality and reduce off-site N losses. Similarly, increased P use efficiencies are needed to enhance and ensure sustainable agricultural production and to reduce environmental degradation of water sources. Manure is a valuable source of P and it can be used as a soil amendment to reduce crop production costs. However, there is a need to improve our understanding of the biological and physical cycles of soil P, as well as to obtain more information about P supplies from fertilizer, crop residues, cover crops, manure, and byproducts, and livestock nutrition impacts on manure properties. There is also a need for a better understanding of soil biological communities and ecosystems, including plant roots and root exudates and how their interactions with crops and community ecology affect yield and the uptake of macro- and micro-nutrients and the ultimate nutritional composition and organoleptic qualities of the crop. Studies documenting the responses of crop-associated biological communities to management practices and genetic technologies implemented across multiple environments (e.g., soil types and chemistries, hydrologic regimes, climates) will improve our understanding of gaps in macro- and micro-nutrient management strategies. A goal of the USDA-ARS is to increase agricultural production and quality while reducing environmental impacts. The Nutrient Uptake and Outcomes (NUOnet) database will be able to help establish baselines on nutrient use efficiencies; processes contributing to nutrient losses; and processes contributing to optimal crop yield, nutritional and organoleptic quality. This national database could be used to calculate many different environmental indicators from a comprehensive understanding of nutrient stocks and flows. Increasing our understanding of stocks and flows could help in the identification of knowledge gaps as well as areas where increased efficiencies can be achieved at a national level. NUOnet could also be used to develop tools to derive cost-benefit curves associated with nutrient management improvement scenarios and assess local, regional and national impacts of off-site nutrient loss. Understanding how agricultural production impacts human health is a challenge, and the database could be used to link crop management strategies to crop chemical composition to human consumption patterns and ultimately to human health outcomes. A national database will also be very important for development and evaluation of new technologies such as real-time sensing or other proximal and remote sensing technologies that enable assessment of nutrient use efficiencies, particularly at the grower level. The database could also be used to develop analyses that will contribute to the recommendation of policies for resource allocations that will most effectively fulfill the goals of the Grand Challenge. Such a national database with contributions from peers across different national programs could also enhance collaborations between ARS, universities, and extension specialists, as well as with producers, industry, and other partners. See the NUOnet Home Page for more information about this database and strategic goals. Resources in this dataset:Resource Title: GRACEnet-NUOnet Data Dictionary. File Name: GRACEnet-NUOnet_DD.csvResource Title: NUOnet Data Entry Template. File Name: DET_NATRES_NUO.zipResource Description: A multi-tab worksheet for data entry. Users can customize fields to be mandatory, set minimum and maximum values, and run a validation on fields as specified by the user.

https://gpsr.ars.usda.gov/html/NUOnet_DET/DET_NATRES_NUO.xlsm

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 Bangladesh BF were from Jessore, Rajshahi, Rangpur, Bogra, Comilla and Mymensingh and were selected based on the following criterion: - Rice growers
- Partly smallholder
- Professional farmer with rice being main income source
- Manual planting and harvesting. But land preparation and threshing are mechanized.
- Receive tech supports from SYT FFs, CP suppliers or dealers
- Hire labor
- Leading local farmer
- Using SYT products (read remark in next column)
- Loyal to SYT (only for RF - read remark in next column)
- Rice to rice 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.

B. 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.

This dataset contains interpolated and aggregated soil and climate data of the region of North Rhine-Westphalia (Germany). The data is provided for grids of 1, 10, 25, 50 and 100 km resolutions. These data grids represent spatial aggregations of the climate of approximately 1 km resolution and soil data of approximately 300 m resolution raster. The purpose of this data is the use as input for crop models. It thus contains the key relevant soil and climate variables for running crop models. Additionally, the data is specifically designed to analyze effects of scale and resolution in crop models, e.g. data aggregation effects. It has been used for several studies on spatial scales with regard to different scaling approaches, crops, crop models, model output variables, production situations and crop management among others.

This table contains 462 series, with data for years 1961 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Newfoundland and Labrador; Eastern Canada; Atlantic Region ...), Price index (33 items: Crop production; Grains; Wheat; Seed ...), Index year (2 items: 1981=100;1986=100 ...).

Crop Input Retail Market Outlook

As per our latest research, the global crop input retail market size reached USD 234.7 billion in 2024, supported by a robust CAGR of 5.2% during the review period. The market is expected to advance to USD 366.1 billion by 2033 as per CAGR calculations. This strong growth trajectory is primarily driven by rising global food demand, rapid technological advancements in agricultural inputs, and the increasing adoption of sustainable farming practices worldwide.

The growth of the crop input retail market is fundamentally underpinned by the ever-increasing global population, which has intensified the demand for food production. As arable land becomes increasingly scarce, farmers are compelled to maximize yields through the use of advanced inputs such as high-yielding seeds, innovative fertilizers, and crop protection chemicals. Moreover, the shift towards sustainable agriculture and the need to minimize environmental impact have led to a surge in demand for biologicals and precision farming solutions. These factors collectively are pushing retailers to diversify their portfolios and offer a broader range of crop input products, thus fueling the market's expansion.

Technological innovation plays a pivotal role in shaping the crop input retail landscape. The advent of digital platforms, data-driven agriculture, and precision farming tools has enabled retailers to offer tailored solutions to farmers, enhancing both productivity and efficiency. Companies are increasingly leveraging artificial intelligence, satellite imagery, and IoT-based devices to provide real-time recommendations and optimize input usage. This digital transformation is fostering a more connected and informed agricultural community, which in turn is driving sales through both traditional and online retail channels. The integration of technology not only streamlines supply chains but also empowers smallholder and commercial farmers to make data-backed decisions, further boosting market growth.

Another significant growth factor is the evolving regulatory landscape and governmental initiatives aimed at promoting sustainable agriculture. Many countries are introducing subsidies, tax breaks, and financial incentives to encourage the adoption of eco-friendly crop inputs such as organic fertilizers and biologicals. These policy measures are prompting retailers to expand their offerings and invest in research and development. Additionally, increasing awareness among farmers regarding the long-term benefits of sustainable inputs is gradually shifting purchasing patterns from conventional to environmentally friendly products. This transition is expected to sustain the market’s momentum over the forecast period.

Regionally, Asia Pacific continues to dominate the crop input retail market, accounting for the largest share owing to its vast agricultural base and rapidly modernizing farming sector. North America and Europe are also significant contributors, driven by advanced technological adoption and stringent regulatory frameworks promoting sustainable inputs. Meanwhile, Latin America and the Middle East & Africa are witnessing accelerated growth, supported by rising investments in agricultural infrastructure and expanding distribution networks. Each region exhibits unique market dynamics, shaped by local crop patterns, regulatory environments, and consumer preferences, which collectively influence the overall market trajectory.

Product Type Analysis

The crop input retail market is segmented by product type into seeds, fertilizers, crop protection chemicals, biologicals, and others. Among these, seeds represent a critical component, as they form the foundation of agricultural productivity. The demand for hybrid and genetically modified seeds is surging, particularly in regions facing climatic challenges and pest infestations. Retailers are increasingly offering a diverse portfolio of seeds tailored to various agro-climatic conditions, ensuring

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 Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- 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.

Ireland Agricultural Input: Value: IC: Crop Protection Products data was reported at 69.390 EUR mn in 2017. This records an increase from the previous number of 68.840 EUR mn for 2016. Ireland Agricultural Input: Value: IC: Crop Protection Products data is updated yearly, averaging 60.350 EUR mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 74.820 EUR mn in 2015 and a record low of 46.400 EUR mn in 2006. Ireland Agricultural Input: Value: IC: Crop Protection Products data remains active status in CEIC and is reported by Central Statistics Office of Ireland. The data is categorized under Global Database’s Ireland – Table IE.B016: Agricultural Input: Value.

Cropland Index The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better CroplandsCalifornia Important Farmland data – statistical data used for analyzing impacts on California’s agricultural resources from the Farmland Mapping and Monitoring Program. Agricultural land is rated according to soil quality and irrigation status. The maps are updated every two years (on even numbered years) with the use of a computer mapping system, aerial imagery, public review, and field reconnaissance. Cropland Index Mask - This is a constructed data set used to define the model domain. Its footprint is defined by combining the extent of the California Important Farmland data (2018) classifications listed above and the area defined by California Statewide Crop Mapping for the state of California.Prime Farmland – farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.Farmland of Statewide Importance – farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Unique Farmland – farmland of lesser quality soils used for the production of the state’s leading agricultural crops. This land is usually irrigated but may include Non irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date. Gridded Soil Survey Geographic Database (gSSURGO) – a database containing information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. California Revised Storie Index - is a soil rating based on soil properties that govern a soil’s potential for cultivated agriculture in California. The Revised Storie Index assesses the productivity of a soil from the following four characteristics: Factor A, degree of soil profile development; factor B, texture of the surface layer; factor C, slope; and factor X, manageable features, including drainage, microrelief, fertility, acidity, erosion, and salt content. A score ranging from 0 to 100 percent is determined for each factor, and the scores are then multiplied together to derive an index rating.Electrical Conductivity - is the electrolytic conductivity of an extract from saturated soil paste, expressed as Deci siemens per meter at 25 degrees C. Electrical conductivity is a measure of the concentration of water-soluble salts in soils. It is used to indicate saline soils. High concentrations of neutral salts, such as sodium chloride and sodium sulfate, may interfere with the adsorption of water by plants because the osmotic pressure in the soil solution is nearly as high as or higher than that in the plant cells. Sodium Adsorption Ratio - is a measure of the amount of sodium (Na) relative to calcium (Ca) and magnesium (Mg) in the water extract from saturated soil paste. It is the ratio of the Na concentration divided by the square root of one-half of the Ca + Mg concentration. Soils that have SAR values of 13 or more may be characterized by an increased dispersion of organic matter and clay particles, reduced saturated hydraulic conductivity (Ksat) and aeration, and a general degradation of soil structure.

Spatial data on soils, land use, and topography, combined with knowledge of conservation effectiveness can be used to identify alternatives to reduce nutrient discharge from small watersheds. This database was developed to be used in conjunction with the Agricultural Conservation Planning Framework Toolkit. Data comprise soil survey information and land use. Soil characterization data were extracted from the Natural Resources Conservation Service (NRCS) Web Soil Survey (Soil Survey Staff, 2013). Land use coverages were developed to represent agricultural fields and the types and rotations of agricultural crops and other land cover types. Land use boundaries were produced by editing a publicly available USDA field boundaries dataset (pre-2008), with all ownership and county-level attributes removed. To ensure these field polygons were consistent with recent land use, the 2009 Cropland Data Layer (USDA-NASS, 2013) was examined for all fields larger than 16 ha. For those fields with multiple cover types, 2009 National Agricultural Imagery Program (NAIP) aerial photography was used as a basis to manually edit field boundaries. A field was considered to have multiple cover types and was edited if the dominant cover occupied <75% of the field, as indicated by the 2009 Cropland Data Layer. Updated field boundaries were then overlaid with data from USDA-National Agricultural Statistics Service (2013) Cropland Data Layer for 2000 – 2014, and each field was classified to represent crop rotations and land cover using the most recent six-year (2009-2014) sequence of land cover. Six-year land-cover strings (e.g., corn-corn-soybean-corn-soybean-corn) generated for each field were classified to represent major crop rotations, which were dominantly comprised of corn (Zea mays L.) and soybean (Glycine max (L.) Merr) annual row crops. The database does not include high-resolution digital elevation models (DEMs) derived from LiDAR (light detection and ranging) survey data, although these are needed by the Agricultural Conservation Planning Framework Toolkit and must be obtained independently. Database is scheduled to become available on October 1, 2015. Resources in this dataset:Resource Title: Land Use and Soils data, viewing and downloading page. File Name: Web Page, url: https://www.nrrig.mwa.ars.usda.gov/st40_huc/dwnldACPF.html Recent land use, field boundary, and soil survey information for individual HUC12 watersheds in Iowa, Illinois, and southern Minnesota. With this land use viewer web page, users may navigate to individual HUC12 watersheds, view land-use maps, and download land use and soils data that can be directly used as input data for the ACPF toolbox. Before developing information on conservation priorities and opportunities using the ACPF toolbox, users will need to obtain elevation data for their watershed, which is usually available from your state government.

Farm input price index (FIPI). Quarterly data are available from from the first quarter of 2002. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2012=100).

The global onboard crop input systems market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the rising demand for efficient and sustainable farming practices. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching an estimated value of $4.2 billion by 2033. This growth is fueled by several key factors, including the increasing need to optimize resource utilization (water, fertilizers, seeds), enhance crop yields, and minimize environmental impact. Technological advancements in sensor technology, GPS navigation, and automation are further contributing to the market expansion. The integration of onboard systems with data analytics platforms allows farmers to make informed decisions regarding input application, leading to improved efficiency and profitability. The diverse application segments, including seeding, fertilizer application, and harvesting, each contribute significantly to the overall market size, with the fertilizer application segment expected to show particularly strong growth due to the rising demand for precision fertilizer placement to maximize nutrient uptake and reduce environmental pollution. Significant regional variations exist, with North America and Europe currently holding the largest market shares due to advanced agricultural practices and higher adoption rates of precision technologies. However, the Asia-Pacific region is anticipated to witness significant growth in the coming years, driven by rising agricultural output, increasing investments in agricultural technology, and government initiatives promoting sustainable farming methods. While challenges such as high initial investment costs and the need for skilled labor might restrain market growth to some extent, the overall long-term outlook for the onboard crop input systems market remains highly positive, with continued innovation and technological advancements expected to drive further expansion. This report provides a detailed analysis of the global Onboard Crop Input Systems market, projecting a market valuation exceeding $2.5 billion by 2028. It delves into key trends, regional performance, competitive landscapes, and future growth drivers, offering invaluable insights for stakeholders across the precision agriculture sector. The report incorporates data from leading players like John Deere, Trimble, and Amazonen-Werke, analyzing market share, innovation strategies, and future projections. Keywords: Precision Agriculture, Smart Farming, Automated Farming, Crop Management, Fertilizer Application, Seeding Systems, Harvest Optimization, Telemetry, Control Systems, Agricultural Technology.

Ireland Agricultural Input Volume Index: IC: Crop Protection Products data was reported at 69.200 2000=100 in 2010. This records an increase from the previous number of 67.300 2000=100 for 2009. Ireland Agricultural Input Volume Index: IC: Crop Protection Products data is updated yearly, averaging 91.300 2000=100 from Dec 1990 (Median) to 2010, with 21 observations. The data reached an all-time high of 106.700 2000=100 in 2002 and a record low of 67.300 2000=100 in 2009. Ireland Agricultural Input Volume Index: IC: Crop Protection Products data remains active status in CEIC and is reported by Central Statistics Office of Ireland. The data is categorized under Global Database’s Ireland – Table IE.B015: Agricultural Output and Input Volume Index: 2000=100.

This table contains 462 series, with data for years 1961 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Newfoundland and Labrador; Eastern Canada; Atlantic Region ...), Price index (33 items: Crop production; Grains; Wheat; Seed ...), Index year (2 items: 1981=100;1986=100 ...).

This dataset provides detailed field-level crop yield estimates, including field location, crop type, season, weather conditions, and input usage such as fertilizer and irrigation. It enables robust yield prediction, agronomic benchmarking, and optimization of farming practices by supporting both estimated and actual yields. The comprehensive structure is ideal for agronomy research, precision agriculture, and machine learning applications in crop management.

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).

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.

One of the obstacles in applying advanced crop simulation models such as DSSAT at a grid-based platform is the lack of gridded soil input data at various resolutions. Recently, there has been many efforts in scientific communities to develop spatially continuous soil database across the globe. The most representative example is the SoilGrids 1km released by ISRIC in 2014. In addition recent AfSIS project put a lot of efforts to develop more accurate soil database in Africa at high spatial resolution. Taking advantage of those two available high resolution soil databases (SoilGrids 1km and ISRIC-AfSIS at 1km resolution), this project aims to develop a set of DSSAT compatible soil profiles on 5 arc-minute grid (which is HarvestChoice’s standard grid). Six soil properties (bulk density, organic carbon, percentage of clay and silt, soil pH and cation exchange capacity) available from the original SoilGrids 1km or ISRIC-AfSIS were directly used as DSSAT inputs. We applied a pedo-transfer function to derive some soil hydraulic properties (saturated hydraulic conductivity, soil water content at field capacity, wilting point and saturation) which are critical to simulate crop growth. For other required variables, HarvestChoice’s HC27 database are used as a reference. Final outputs are provided in *.SOL file format (DSSAT soil database) for each country at 5-min resolution. In addition, uncertainty maps for organic carbon and soil water content at wilting points at the top 15 cm soil layers were generated to provide brief idea about accuracy of the final products. The generated soil properties were evaluated by visualizing their global maps and by comparing them with IIASA-IFPRI cropland map and AfSIS-GYGA’s available water content maps.

This dataset comprises spatial and temporal data related to our analysis on blue and green water consumption (WC) of global crop production in high spatial resolution (5 arc-minutes – approximately 10 km at the equator) for the years 2020, 2010 and 2000.

Modelling water consumption of SPAM data

We use SPAM (Spatial Production Allocation Model) data, released by the International Food Policy research Institute (IFPRI). We use SPAM2020 data for the year 2020 (46 crops), SPAM2010 data for the year 2010 (42 crops) and SPAM2000 data for the year 2000 (20 crops).

We develop a Python-based global gridded crop green and blue WC assessment tool, entitled CropGBWater. Operating on a daily time scale, CropGBWater dynamically simulates rootzone water balance and related fluxes. We provide this model open access as Data_S10

SPAM2020 crop data are modelled for the years 2018-2022, SPAM2010 crop data for the years 2008-2012 and SPAM2000 crop data for the years 1998-2002. We compute WCbl (blue WC) and WCgn (green WC), with components WCgn,irr (green WC of irrigated area) and WCgn,rf (green WC of rainfed area)

File description:

The data-set consists of the following files:

• Data_S4: Data_S4_Y2020_WC_m3_gridded.zip
  Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn,rf values in m3 in csv format, year 2020. Individual crop GIS-Rasters for annual m3 amounts are provided as Data_S17
• Data_S5: Data_S5_YR2020_WC_mm_gridded_csv
  Folder with 46 individual crop grid files (5arc min resolution, with x & y coordinates), monthly and annual WCbl, WCgn,irr and WCgn, rf in mm as well as SPAM harvested area values in csv format, year 2020. Individual crop GIS-Rasters for annual mm amounts are provided as Data_S18
• Data_S6: Data_S6_YR2020_WC_gridded_individual-crops-m3_annual.xlsx
  One grid file (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in m3, differentiating between individual crops, year 2020.
• Data_S7: Data_S7_YR2020_WC_gridded_sum-of-crops-m3_monthly-annual.csv
  One grid file (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in m3, for the sum of all crops, year 2020
• Data_S8: Data_S8_YR2000_WC_mm_m3_gridded.zip
  Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2000
• Data_S9: Data_S9_YR2010_WC_mm_m3_gridded.zip
  Grid (5arc min resolution, with x & y coordinates) with annual WCbl, WCgn,irr and WCgn, rf values in mm and m3, as well as SPAM harvested area amounts, for each crop, year 2010
• Data_S10: Data_S10_CropGBWater_v02_1c-clean.ipynb Python-based global gridded crop green and blue WC assessment tool, entitled CropGBWater

• Data_S11: Data_S11_YR2020_WC-mm_Rice_RiceAtlas.xls
  Grid (5arc min resolution, with x & y coordinates) with monthly and annual WCbl, WCgn,irr and WCgn, rf values in mm, for rice, year 2020, RiceAtlas calendar
• Data_S12: Data_S12_INPUT_YR2020_cropcalendars.zip
  Modelling INPUT data for year 2020: Crop calendars
• Data_S13: Data_S13_INPUT_YR2020_ET0.zip
  Modelling INPUT data for year 2020: daily ET0
• Data_S14: Data_S14_INPUT_YR2020_Precip.zip
  Modelling INPUT data for year 2020: daily Precipitation
• Data_S15: Data_S15_INPUT_YR2020_Soil.zip
  Modelling INPUT data for year 2020: Soil
• Data_S16: Data_S16_INPUT_YR2020_Climate-SPAM-grid_&_Scripts.zip
  Modelling INPUT for year 2020: Climate-SPAM-grid and different processing scripts
• Data_S17: Data_S17_Y2020_WC_m3_GisRasters
  GIS-Rasters of individual crops for year 2020 (as well as the sum of all crops), values in m3 per year, differentiation between WCbl, WCgn,irr and WCgn
• Data_S18: Data_S18_Y2020_WC_mm_GisRasters
  GIS-Rasters of individual crops for year 2020, values in mm per year, differentiation between WCbl, WCgn,irr and WCgn

Please only use the latest version of this zenodo repository

Publication:

For all details, please refer to the open access paper:

Chukalla, A.D., Mekonnen, M.M., Gunathilake, D., Wolkeba, F.T., Gunasekara, B., Vanham, D. (2025) Global spatially explicit crop water consumption shows an overall increase of 9% for 46 agricultural crops from 2010 to 2020, Nature Food, Volume 6, https://doi.org/10.1038/s43016-025-01231-x

Funding:

This research, led by IWMI, a CGIAR centre, was carried out under the CGIAR Initiative on Foresight (www.cgiar.org/initiative/foresight/) as well as the CGIAR “Policy innovations” Science Program (www.cgiar.org/cgiar-research-porfolio-2025-2030/policy-innovations). The authors would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund (www.cgiar.org/funders).

The data was collected from 165 smallholder farmer households in Zhombe North Rural District, Zimbabwe using the 2017/ 2018 farming data in a cross-sectional survey design. Data were collected from respondents and were used to test the following research hypotheses; (i) Household’s socio-economic characteristics differ between commercialized and non-commercialized smallholder farmers; (ii) Household’s socio-economic characteristics significantly affect agricultural commercialization in smallholder farmers; (iii) Agricultural commercialization has an impact on household food security in smallholder farmers; (iv) Household’s socio-economic characteristics significantly affect household food security in smallholder farmers. The captured data included the location of the village of the household, ward, age of the household head, gender, marital status, household head education level, household size, access to sanitary services, off-farm activities, household income, land size, use of credit in farming, agriculture training, area on which the crops were grown, the yield of the crops, the amount of the crops sold, the amount of income obtained from crop sales, etc. The crop output market participation share (COMPS) and the crop input market participation share (CIMPS) were computed using crop production, input use, and crop sales data. The inputs captured in this section included fertilizer, seed, and labor. Information on the household consumption frequencies of food in the eight food groups was captured and used to compute the food consumption scores then aggregated to form the modified food consumption score (MFCS). Food consumption scores for three different seasons of the year were collected. The three seasons have different implications on household food availability and access characteristics. Notable findings were that only 32% of the households had a COPMS value above 0.4 and 68% had COMPS value below 0.4 indicating very low levels of agricultural commercialization. The mean modified food consumption score for commercialized households was significantly higher than the value for non-commercialised households.

This dataset offers detailed records of farm yield monitoring, capturing crop types, planting and harvest dates, input usage, and key environmental factors at the field level. It enables agricultural businesses to analyze productivity, optimize input application, and make data-driven decisions for improved crop management and planning. The dataset is ideal for yield forecasting, resource optimization, and precision agriculture analytics.