Abstract

The main purpose of the Survey of Agricultural Holdings is to produce official indicators in line with agricultural sector. The survey allows the compilation of statistics on crops and animal husbandry, of which information annual and permanent crops, sown area, average yield of annual crops and etc. Statistical tables are accessible through the following link: https://www.geostat.ge/en/modules/categories/196/agriculture.

One round of the survey (reference year) includes 5 inquiries: The Inception interview is carried out using the inception questionnaire during the period of January-February of the reference year. During this interview the sampled holdings are identified and situation existing at the holding as of first January is recorded. I, II and III quarter interviews are conducted by means of quarterly questionnaire at the beginning of the following month of the corresponding quarter of the reference year. Based on these surveys, the information about agricultural activities during the corresponding quarter is collected. The final interview is conducted by means of final questionnaire in January of the following year of the reference year. During this interview, the information about agricultural activities at the holding during IV quarter of the reference year and the summary information about agricultural activities at the holding during the whole reference year (from 1 January to 31 December of the previous year) are collected. During all five interviews, the same agricultural holdings (about 12 000) are interviewed which are selected by a two-stage stratified cluster random sampling procedure out of about 642 000 agricultural holdings operated in Georgia. On the first stage, clusters (settlements) are selected. On the second stage, holdings are selected within the selected clusters.

The survey completely covers the territory of Georgia, excluding the occupied territories of Autonomous Republic of Abkhazia and Tskhinvali region. Each year a new sample is selected based on a rotational design (on a 3-year basis). In particular, every year approximately 4000 holdings out of the 12000 sampled holdings are replaced by new holdings. Sampled holdings participate in the survey for 3 years. Large agricultural holdings are sampled every year with complete coverage. The statistical unit of the survey is the agricultural holding (family holdings and agricultural enterprises) – which is defined as an economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size. Agricultural activities are conducted under the supervision of a holder (in case of households - a member of household, in case of agricultural enterprises - director or authorized person), who is responsible for making decisions and takes all economic risks and expenses related to agricultural activities.

More than 270 interviewers participated in the survey fieldwork. For the Data collection, computer-assisted personal interviewing method (CAPI) was used in the family holdings. In case of agricultural enterprises, the authorized persons of the enterprises (respondent) fill the electronic (online) questionnaires by themselves (CAWI). Coordination of the interviewers and the primary control of the collected data during the field is carried out by coordinators. Their working area covers several municipalities. The function of the coordinators also includes consultation for agricultural enterprises on methodological and technical issues related to the survey.

Geographic coverage

Entire country (Georgia), excluding occupied regions (Abkhazia and Tskhinvali region)

Analysis unit

Agricultural holding – economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size in which agricultural activities are conducted under the supervision of a holder, who is responsible for making decisions and takes all economic risks and expenses related to agricultural activities.

Universe

Survey sampling frame includes about 642,000 agriculture holdings (households and agricultural enterprises) operated in country. The Agricultural Census 2014 is the main source of the sample frame. Sampling frame is updated on a permanent basis in according to the results of survey of agricultural holdings, business register and different administrative sources.

Kind of data

Sample survey data [ssd]

Sampling procedure

• Main Source of the sample frame since 2016 - Agricultural Census 2014; • Sample frame contained 642,000 holding - sample size 12,000 (1.9%); • Sample Design: two-stage stratified cluster random sampling; - First stage - selection of cluster (Settlement); - Second stage - Selection of holdings within the selected clusters; • Each year a new sample is selected based on a rotational design; - Every year 1/3 of holdings (4,000) selected a year before are replaced (Sampled holdings participate in the survey during 3 years); • Extremely large agricultural holdings are sampled every year with complete coverage; • Additional Sources for updating sample frame: Sample Survey of Agricultural Holdings, Statistical Business Register, Administrative data existing in MEPA (large agricultural holdings); Sampling error of main indicators do not exceed 5% for a country level and 10% for a regional level.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Detailed information on structure, and sections of questionnaires used in the survey of agricultural holdings are available in following link: https://www.geostat.ge/en/modules/categories/564/questionnaires-Agricultural-Statistics

Cleaning operations

After the field work, cleaning and harmonization of all inquiries are established at the Geostat head office - logical and arithmetical inconsistencies, as well as non-typical and suspicious data are detected, checked and corrected. Verification of the data is performed by contacting the respondents by phone. If verification with respondent is impossible, different imputation methods are used. Finally, indicators are calculated using weighted data. The obtained results are compared with corresponding results of the previous periods. In case of significant differences, the possible causes are identified and analyzed.

Response rate

In the 2022 fourth quarter, 1,349 holdings were not surveyed, due to the fact that some holdings refused to be interviewed or were not found during the fieldwork despite its existence. This is about 10.7% of the total sampled holdings of 12,589 holdings involved in the sample 2022 fourth quarter.

Lebanon has traditionally been a major potato producer with 451,860 tons produced in 2014. Generally, potatoes make up 30% of the total Lebanese agricultural exports where approximately 60% of the potato production is exported to the Arab region, the UK and Brazil. The purpose of this study is to promote precision agriculture techniques in Lebanon that will help local farmers in the central Bekaa Valley with land management decisions. The European Space Agency’s satellite missions Sentinel-2A, launched June 23rd 2015, and the Sentinel-2B, recently launched on March 7th 2017, are multispectral high resolution imaging systems that provide global coverage every 5 days. The Sentinel program is a land monitoring program that includes an aim to improve agricultural practices. The imagery is 13 band data in the visible, near infrared and short wave infrared parts of the electromagnetic spectrum and ranges from 10-20 m including three 60 m bands pixel resolution. Sentinel is freely available data that has the potential to empower farmers with information to respond quickly to maximize crop health. Due to the political and security conflicts in the region, utilizing satellite imagery for Lebanon is more reasonable and realistic than operating Unmanned Aircraft Systems (UAS) for high resolution remote sensing. During the 2017 growing season, local farmers provided detailed information in designated fields on their farming practices, crop health, and pest threats. In parallel, Sentinel-2 imagery was processed to study crop health using the following vegetation indices: Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index and Modified Soil Adjusted Vegetation Index 2. Most Lebanese farmers inherit their land from their parents over generations, and as a result most still use traditional farming techniques for irrigation, where decisions are based on prior generations’ practices. However, with the changes in climate conditions within the region, these practices are no longer as efficient as they used to be. Normalized Difference Water Indices are calculated from satellite bands in the near-infrared and short-wave infrared to provide a better understanding about the water stress status of crops within the field. Preliminary results demonstrate that Sentinel-2 data can provide detailed and timely data for farmers to effectively manage fields. Despite the fact that most Lebanese farmers rely on traditional farming methods, providing them with crop health information on their mobile phones and allowing them to test its efficiency has the potential to be a catalyst to help them improve their farming practices.

Abstract

The health and wealth of a nation and its potential to develop and grow depend on its ability to feed its people. To help ensure that food will remain available to those who need it, there is nothing more important to give priority to than agriculture. Accurate and timely statistics about the basic produce and supplies of agriculture are essential to assess the agricultural situation. To help policy maker's deal with the fundamental challenge they are faced within the agricultural sector of the economy and develop measures and policies to maintain food security, there should be a continuous provision of statistics. The collection of reliable, comprehensive and timely data on agriculture is thus required for the above purposes. In this perspective, the Central Statistical Agency (CSA) has endeavored to generate agricultural data for policy makers and other users. The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food security, etc. The AgSS is composed of four components: Crop production forecast survey, Main (“Meher”) season survey, Livestock survey, and survey of the “Belg” season crop area and production.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2000/2001 (1993 E.C) Meher season agricultural sample survey covered the rural part of the country except three zones in Afar regional state and six zones in Somalie regional state that are predominantly nomadic. A two-stage stratified sample design was used to select the sample. Each zones/special wereda was adopted as stratum for which major findings of the survey are reported except the four regions; namely, Gambella, Harari, Addis Ababa and Dire Dawa which were considered as strata/reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs were determined for each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size systematic sampling; size being total number of agricultural households in the EAs as obtained from the 1994 population and housing census. From each sample EA, 40 agricultural households were systematically selected for the annual agricultural sample survey from a fresh list of households prepared at the beginning of the field work of the annual agricultural survey. Of the forty agricultural households, the first twenty-five were used for obtaining information on area under crops, Meher and Beleg season production of crops, land use, agricultural practices, crop damage, and quantity of agricultural households sampled in each of the selected EAs, data on crop cutting were collected for only the fifteen households (11th - 25th households selected). A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households.

Note: Distribution of the number of sampling units sampled and covered by strata is given in Appendix I of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000-2001 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. Lists of forms in the questionnaires: - AgSS Form 93/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 93/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 93/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 93/4A: Used to collect results of area measurement. - AgSS Form 93/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting.

Note: The questionnaires are presented in the Appendix IV of the 2000-2001 Agricultural Sample Survey Volume I report which is provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of the collected survey data an editing, coding and verification instruction manual was prepared and printed. Then 23 editors-coders and 22 verifiers were trained for two days in the editing, coding and verification operation using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100% basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires was completed in about 30 days.

Data Entry, Cleaning and Tabulation: Before starting data entry, professional staff of Agricultural Statistics Department prepared edit specifications to use on personal computers utilizing the Integrated Microcomputer Processing System (IMPS) software for data consistency checking purposes. The data on the coded questionnaires were then entered into personal computers using IMPS software. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 31 data encoders and it took 28 days to complete the job. Finally, tabulation was done on personal computers to produce results as indicated in the tabulation plan.

Response rate

A total of 1,430 EAs were selected for the survey. However, 8 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1422 (99.44%) EAs. Within respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 35,750 agricultural households. The response rate was found to be 99.14%.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 2000-2001 annual Agricultural Sample Survey report which is provided as external resource.

From 2000 onwards, the total area of land in U.S. farms has decreased annually, aside from a small increase in 2012. Over the time period displayed, the total farmland area has decreased by over 66 million acres, reaching a total of 876.5 million acres as of 2024. Farming in the U.S. Not only has the land for farming been decreasing in the U.S., but so has the total number of farms. From 2000 to 2021, the number of farms in the U.S. decreased from about 2.17 million farms in 2000 to just under 1.9 million in 2023. Texas has more than double the number of farms compared to other U.S. states, with 231,000 farms in 2023. U.S. agricultural exports The U.S. is known for agriculture production and is the leading exporter of agricultural products worldwide. The total U.S. agricultural exports were valued at over 178 billion U.S. dollars in 2023. Over 4.8 billion dollars’ worth of agricultural exports came from fresh or processed vegetables in 2022.

Abstract

The objective of the GAPS is to strengthen the Multi-Round Annual Crop and Livestock Surveys (MRACLS) that the ministry implements through SRID. The MRACLS is the national agricultural survey on the basis of which SRID releases information on agricultural production and yields of important crops. The ultimate goal of GAPS is to provide more accurate and timely agricultural production estimates at the district, regional, and national levels. The survey is also to offer an opportunity for SRID to experiment with a number of potential improvements with a view to developing the required skills and competencies before scaling up, over time, to all the districts in the country.

As part of the terms of implementing GAPS, MoFA agreed to assign four Agriculture Extension Agents (AEAs) per district for data collection. The Agents were relieved from all extension duties. To distinguish these field data collection officers from other extension agents, they were referred to as District Agricultural Statistical Assistants (DASAs). One officer per district was designated as a District Management Information System (MIS) officer and was given additional responsibility as field supervisor and referred to as District Agricultural Statistical Officer (DASO). A total of 100 DASAs and DASOs were successfully trained and deployed to their districts for GAPS implementation and given the task of collecting and processing datafrom the field.

Geographic coverage

National Level Regions Districts

Analysis unit

Household

Universe

Agricultural household and holder

Kind of data

Census/enumeration data [cen]

Sampling procedure

The GAPS employed a three stage multi-sampling design in response to the Government of Ghana's requirement for reliable agricultural statistics at the national, regional and district levels.

· First Stage Sampling- Selection of 2 Districts from each of the 10 Regions. A total of 20 districts, 2 from each of the 10 regions were randomly selected with probability proportional to size, using districts' population in year 2000 as a measure of size.1. Eleven Metropolitan and Municipal Assemblies (Kumasi, Sunyani, Cape Coast, New Juaben, Accra, Tema, Tamale, Bolgatanga, Wa, Ho and Shama Ahanta East) were excluded from the study, given their urban predominance.

· Second Stage Sampling - Selection of 40 Enumeration Areas (EAs) from each of the 20 Districts. A total of 800 EAs was selected; 40 EAs were randomly selected with probability proportional to size in each district, using the list of EAs compiled by the 2010 Census as a sample frame, and projected total population as a measure of size.2 In the Kassena-Nankana East district, 53 of the 187 EAs compiled by the 2010 census were excluded from the study because of the land disputes prevalent in the area earlier in 2011.

· Third Stage Sampling - Selection of 5 holders At the third stage, five holders were randomly chosen in each EA, using as a sample frame; the full list of all holders, compiled from the Household and Holders Listing questionnaire. This provides a total sample of 4000 holders, consisting of 200 holders per district.

Sampling deviation

Not reported

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires used in the minor season survey include the followings:-

(a) The Household and Holding Inquiry - Pre-Harvest questionnaire, also known as the form 2a. This was used to make enquiries on the general characteristics of households and holdings for pre-harvest farming activities during the minor season. Information sought included changes in the household composition, detailed information on livestock, poultry and other animals owned by the selected holders, detailed information on tree crops grown by the selected holders, information on aquaculture practices, inputs, outputs and assets.

(b) The Household and Holding Inquiry - Post-Harvest questionnaire, also known as form 2b. This was used to make enquiries on field practices, inputs and outputs. The following information were sought: inventory of fields, inputs and expenses, Remaining major season production and marketing of crops, minor season crop production and marketing, holding information, shocks and adaptation to shocks, other income generating activities and household health status.

(c) The Household and Holding Inquiry - Pre-harvest field measurements questionnaire known as the form 3. This questionnaire was used to gather data on the nature and characteristics of crop fields and area measurements for individual crop fields for all selected holdings.

(d) Crop Yield Measurement questionnaire also known as the form 4. This was used to seek for data on the yields of food crops such as the cereals, root and tubers, plantain, legumes and nuts, and vegetables.

Cleaning operations

The set of questionnaires used in the minor season survey include:-

(a) The Household and Holding Inquiry – Pre-Harvest questionnaire, also known as the form 2a. This was used to make enquiries on the general characteristics of households and holdings for pre-harvest farming activities during the minor season. Information sought included changes in the household composition, detailed information on livestock, poultry and other animals owned by the selected holders, detailed information on tree crops grown by the selected holders, information on aquaculture practices, inputs, outputs and assets.

(b) The Household and Holding Inquiry – Post-Harvest questionnaire, also known as form 2b. This was used to make enquiries on field practices, inputs and outputs. The following information were sought: inventory of fields, inputs and expenses, Remaining major season production and marketing of crops, minor season crop production and marketing, holding information, shocks and adaptation to shocks, other income generating activities and household health status.

(c) The Household and Holding Inquiry – Pre-harvest field measurements questionnaire known as the form 3. This questionnaire was used to gather data on the nature and characteristics of crop fields and area measurements for individual crop fields for all selected holdings.

(d) Crop Yield Measurement questionnaire also known as the form 4. This was used to seek for data on the yields of food crops such as the cereals, root and tubers, plantain, legumes and nuts, and vegetables.

Response rate

The repond rate reported was 70%

Sampling error estimates

No estimates of sampling error given

Data appraisal

District information and communication infrastructure was upgraded in the 20 districts to improve data collection and management. Each office was provided with a computer, printer, voltage stabilizers, an internet modem, 5 GPS units, and other field equipment. Motorbikes were also provided to the DASAs to enhance mobility.

Similarly, the SRID head office was also upgraded with ICT equipment to facilitate work.To oversee the implementation of the pilot survey a cross-sectoral steering committee was established.

At the end of each phase of implementation, a team was put together to assess the institutional and financial feasibility of scaling up GAPS, and both assessment reports are available at SRID.

1. Increasing landscape heterogeneity by restoring semi-natural elements to reverse farmland biodiversity declines is not always economically feasible or acceptable to farmers due to competition for land. We hypothesized that increasing the heterogeneity of the crop mosaic itself, hereafter referred to as crop heterogeneity, can have beneficial effects on within-field plant diversity.

2. Using a unique multi-country dataset from a cross-continent collaborative project covering 1451 agricultural fields within 432 landscapes in Europe and Canada, we assessed the relative effects of compositional and configurational crop heterogeneity on within-field plant diversity components. We also examined how these relationships were modulated by the position within the field.

3. We found strong positive effects of configurational crop heterogeneity on within-field plant alpha and gamma diversity in field interiors. These effects were as high as the effect of semi-natural cover. In field borders, effects of crop heterogeneity were limited to alpha diversity. We suggest that a heterogeneous crop mosaic may overcome the high negative impact of management practices on plant diversity in field interiors, whereas in field borders, where plant diversity is already high, landscape effects are more limited.

4. Synthesis and applications. Our study shows that increasing configurational crop heterogeneity is beneficial to within-field plant diversity. It opens up a new effective and complementary way to promote farmland biodiversity without taking land out of agricultural production. We therefore recommend adopting manipulation of crop heterogeneity as a specific, effective management option in future policy measures, perhaps adding to agri-environment schemes, to contribute to the conservation of farmland plant diversity.

Methods Region and landscape selection

The study was conducted in eight agricultural regions comprising seven regions in Europe and one region in eastern Canada (near Ottawa). The European regions followed a south-to-north gradient, with four regions in France (near Arles, Niort, Rennes, Toulouse), one in England (centred on Ely, Cambridgeshire), one in Germany (near Goettingen) and one in Spain (near Lleida; Fig. 1). Within these agricultural regions, we selected a total of 432 1 km × 1 km landscapes, with 60 to 90 % of crop cover in each. These landscapes represented, by design, uncorrelated gradients of compositional crop heterogeneity, assessed by the Shannon diversity index of the crop cover types, and of configurational crop heterogeneity, assessed by the total length of crop field borders (see Pasher et al., 2003 and Sirami et al., 2019 for details). The landscape selection process used the most recent remotely sensed data or land cover map available for each agricultural region (see Table S1 in Supporting Information).

While land cover maps were adequate for landscape selection, their coarse spatial resolution did not allow for the accurate delineation of narrow strips of non-crop covers between fields. Thus, all landscapes were digitized from aerial photos to create detailed maps delineating all fields managed for agricultural production (including crops, and temporary and permanent grasslands), linear semi-natural boundaries between crop fields and non-crop patches. Non-crop cover types included woodland, open land, wetland and built-up areas. Linear semi-natural boundaries included hedgerows, grassy strips and watery boundaries such as ditches. These maps were visually validated by field crews within each agricultural region before the sampling of the vegetation in a given landscape.

Based on these more accurate and recent maps, several landscape variables were calculated. Compositional crop heterogeneity was assessed using the Shannon diversity index of agricultural cover types (Crop_SHDI). Configurational crop heterogeneity was measured as the total field border length (Crop_TBL). Crop_TBL was the sum of perimeters of all fields within the 1 x 1 km landscape minus the length of perimeters artificially created by intersection with the limits of the 1 km × 1 km landscape. The percentage of semi-natural cover types (Seminat_Cover) was calculated as the sum of the proportions of woodland, open land and wetland in the landscape. The length of semi-natural boundaries (Seminat_boundary) was calculated as half of the sum of the perimeter of woody, grassy and watery boundaries in the landscape.

Sampling site (field) selection

Within each landscape, we selected three to four sampling sites. Sampling sites were fields managed for agricultural production including crops, temporary and permanent grasslands. Fields were selected such that at least one contained the dominant crop type in the region, the other fields being representative of crops present within the focal landscape. Fields were at least 200 m apart, at least 50 m away from the border of the 1 km × 1 km landscape and at least 50 m away from large non-crop cover type patches such as woodland. We selected fields bordered by a similar boundary types within each region, i.e. only grassy strips or hedgerows, wherever possible. In total, 1451 agricultural fields were sampled.

Vegetation sampling

Within each sampling site, we surveyed within-field plant species along two parallel, 1 m wide and 50 m long transects, one located on the field border, the other within the field interior resulting in 2788 transects surveyed. Transects were about 25 m distant from each other. We sampled five plots (4 m × 1 m) along each transect, i.e. 20 m² per transect. Note that in Ottawa, transects were 2 m wide and the field border transect encompassed part of the boundary vegetation. We verified that this slight difference in sampling protocol did not affect our conclusions. Percentage cover of all vascular plant species was recorded. We conducted these plant surveys over two years between 2011 and 2014, each sampling site being sampled only within a single year. Surveys were conducted once before crop harvesting, except in Ely, Goettingen and Ottawa where surveys were conducted twice. In those regions, we pooled within-field plant data from the two visits per year and retained the total number of plant species for each sampled plot. Plant nomenclature followed TaxRef (Gargominy et al., 2014).

Data processing

Following Whittaker (1972), we used the multiplicative diversity partitioning method to assess plant species diversity components where β = γ/α. Gamma diversity (γ) was the total number of species across all plots sampled in a given transect and alpha (α) diversity was the number of within-field plant species present in each plot averaged across the five plots per transect. This measure of beta diversity (β) describes variation in plant species composition in the whole transect by comparison with an average plot.

Escherichia coli survival in soils containing either composted poultry litter (CPL), heat-treated poultry pellets (HTPP), poultry litter (PL) or unamended (chemical fertilizer). Test plots were either covered with plastic mulch (M) or not mulched (NoM). The study was conducted in 2018 and 2019 during cucumber growing seasons at the University of Delaware research farm and each study lasted 120 days. Data from the current study were collected to examine the survival of non-pathogenic Escherichia coli and transfer to cucumbers grown in same field in two separate years. Soil moisture, total nitrogen, nitrate, total carbon, soluble carbon, soluble solids, rainfall, soil temperature and air temperature, along with the number of days needed for E. coli to decline by 4 log CFU/gdw, were included in random forest models used to a) predict 4-log declines of E. coli inoculated to soils and b) transfer of E. coli to cucumbers from soils with different biological soil amendments. The data included here are specifically for other investigators to use to make different forms or versions of three different statistical models used in the submitted manuscript. Data for three models are included: 1) Dpi4log, the number of days needed for E. coli levels in various combinations of year, amendment and mulch, were calculated by applying sigmoidal (single, double, triple, or quadruple) model to E. coli data collected over time. 2) A random forest model using soil and weather data was used to determine which factors listed above best predicted dpi4log values. This model accounted for 98% of the observed variance. 3) A random forest model using soil and weather data, along with dpi4log, was used to predict transfer of E. coli to soils from cucumbers (log MPN/cucumber). This model accounted for approximately 63% of the variance in the study. Resources in this dataset:Resource Title: Graph of E. coli levels over 120 days in soils under various conditions. File Name: Graphs of Fitted Sigmoidal Regression Models onto Observed gEclog vs DPI.pdfResource Description: Graph of E. coli levels in 24 different combinations of year, amendment, and mulch status over 120 days Resource Title: Comparison of actual model-generated log CFU/gdw data . File Name: Observed and Sigmoidal Model Predicted gEcLog values - Daily Increment.csvResource Description: Comparison of sigmoidal model-generated log CFU/gdw vs observed data Resource Title: Soil temperature, Air temperature and Cumulative Rainfall observed in 2018 and 2019. File Name: Soil air temp cumulative rainfall 2018 2019.xlsxResource Description: These are the climate data used to inform and predict E. coli survival in soils containing biological and chemical fertilizerResource Title: Data set used in Random Forest model to predict transfer of E. coli from soils to cucumber fruits . File Name: UD ARS Cucumber Study Consolidated Data Version 2 Single Transference Column Original Data Scale.csvResource Description: This data set includes the sigmoidal model-estimated values of dpi4log (the number of days needed to achieve 4 log decline in E. coli levels) in this model Resource Title: Dataset used in Random Forest model to identify variables and factors which predict dpi4log values of E. coli in soils containing biological soil amendment of animal origin. File Name: Formatted Soil Data for Random Forest Analysis.xlsxResource Description: Dataset used in the Random Forest model to identify variables and factors which predict dpi4log values - the number of days needed to observe a 4 log reduction, estimated by sigmoidal modeling of collected E. coli data - of E. coli in soils containing biological soil amendment of animal origin

Long term experiment on comparison of different crop production systems established in 1994. Data compares 2 different farming systems: organic and conventional. Data is available from 1995/1996 season up to 2022/2023 season.
The experiment is conducted without replication (8ha in total - 1ha per rotational field). All crops of the rotation within one system are grown at the same time (all crops from the rotation present in the field each year). Size of single rotational field is 1ha.
Crop rotation (general design - there are some differences between years)
Organic Farming System: Potato xx ; spring wheat + undersown red clover ; red clover 1st year ; red clover 2nd year ; winter wheat
Conventional Farming System: Oil seed rape; winter wheat ; spring wheat

This dataset is part of the database compiled as an outcome of Work Area 1 in project OrganicYieldsUP https://zenodo.org/communities/oyup_2428/records?q=&l=list&p=1&s=10&sort=newest. Variable definitions can be found here: https://doi.org/10.5281/zenodo.15276083

Analysis of ‘Alameda County Land Use Survey 2006’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8ede3163-532e-495b-a5cb-6efcaf291897 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Abstract

The issue of food security has continual national importance in Ethiopia. To achieve social and political stability, the government has to be able to create and maintain food security by issuing an appropriate agricultural policy. Agricultural statistics is just one element that enters into this policy process to formulae, monitor, assess and evaluate the policy. The collection of reliable, comprehensive and timely data on agriculture is thus essential for the above purpose. In this regard, the Central Statistical Agency (CSA) has exerted effort to provide policy makers and users with reliable and timely agricultural data. The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agricultural that is considered essential for development planning, socio-economic policy formulation, food security, etc. The AgSS is composed of four components: Crop production forecast survey, Main (“Mehe”) season survey. Livestock survey and survey of the “Belg” season crop area and production.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops. - To estimate the total land used for various purposes by type of land use and the number of agricultural households, holders, members of agricultural households, average household size, average land holding per household and others

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1999-2000 (1992 E.C) Meher seasons annual Agricultural Sample Survey covered the rural part of the country except two zones in Afar region and six zones in Somali region that are predominantly nomadic. A two-stage stratified sample design was used to select the samples. Each zones/special wereda was adopted as stratum for which major findings of the survey are reported except the four regions; namely, Gambella, Harari, Addis Ababa and Dire Dawa which were considered as strata/reporting levels. The primary sampling units (PSUs) were enumeration area (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs were determined for each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size; size being total number of households in the EAs as obtained from the 1994 Population and Housing Census. From each sample EA, 40 agricultural households were systematically selected for the annual agricultural sample survey from a fresh list of households prepared at the beginning of the field work of the annual agricultural survey. Of the forty agricultural households, the first twenty five were used for obtaining information on area under crops. Meher and Belg season production of crops, land use, agricultural practices, crop damage, and quantity of agricultural inputs used. It is important to note that of the total forty agricultural households sampled in each of the selected EAs, data on crop cutting were collected for only the fifteen households (11th - 25th household selected).

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1999-2000 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 92/0: Used to list all agricultural households and holders in the sample enumeration areas. - AgSS Form 92/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 92/2: Used to collect information about crop condition. - AgSS Form 92/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 92/4A: Used to collect results of area measurement. - AgSS Form 92/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting. - AgSS Form 92/6: Used to collect information about cattle by sex, age and purpose

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of the collected survey data an editing, coding and verification instruction manual was prepared and printed. Then 35 editors-coders and 20 verifiers were trained for two days in the editing, coding and verification operation using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 100% basis before the questionnaires were passed over to the data entry unit. The editing, coding and verification exercise of all questionnaires was completed in about 40 days.

Data Entry Cleaning and Tabulation: Before starting data entry, professional staff of Agricultural Statistics Department prepared edit specifications for use on personal computers utilizing the Integrated Microcomputer Processing System (IMPS) software for data consistency checking purposes. The data on the coded questionnaires were then entered into personal computers using IMPS software. The data were then checked and cleaned using the edit specifications prepared earlier for this purpose. The data entry operation involved about 35 data encoders and it took 30 days to complete the job. Finally, tabulation was done on personal computers to produce results as indicated in the tabulation plan.

Response rate

A total of 1,450 EAs (2.9% of the total EAs in the rural areas of the county) were selected for the survey. However 5 EAs were closed for various reasons beyond the control of the Authority and the survey succeeded in covering 1445 (99.7 %) EAs. With respect to ultimate sampling units, for the Meher season agricultural sample survey, it was planned to cover 36,250 agricultural households. The response rate was found to be 98.5%.

Sampling error estimates

Estimation procedures of totals, ratios and sampling errors are given in Appendix I of 1999-2000 annual Agricultural Sample Survey, Volume I report which is provided in this documentation.

Estimated areas, production, yield, average farm price and total farm value of principal field crops.

Abstract

The Agricultural Census aims to provide data on the structure of the agricultural sector as the basis for the efficient utilization of agricultural resources and the projection of related indicators to develop and make optimal use of agricultural resources. In addition, data provide a benchmark for setting estimates for subsequent years and to build a sampling frame as the basis for future agriculture-related surveys on different holdings in the Palestinian Territory. These could include periodic surveys of agricultural holdings, livestock, gardening, and farm management to provide basic and detailed data on the characteristics of the agricultural sector to meet the needs of ministries for planning and monitoring. Such data also contribute to regional planning, best distribution of resources, and meeting the needs of the private sector

Geographic coverage

The Agricultural Census is a comprehensive enumeration that should cover a specific geographical area accurately. The census covered all of the Palestinian Territory, including rural and urban areas and refugee camps

Analysis unit

The agricultural holding

Universe

The frame of the Agricultural Census 2010 includes a complete record of holdings by households and collaborative institutions. The frame was prepared by listing all holders through visiting every household, using maps to reach all addresses

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable

Sampling deviation

Not applicable

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were designed to collect data covered by the census. The first questionnaire was designed to list households and agricultural holdings, while the second was related to the enumeration of the agricultural holdings. Items and variables were as follows:

1. Household and agricultural holdings questionnaire: This included data of households and agricultural holdings, in addition to identification data, building name or owner, type of building, current use of the building, the total number of housing units in the building, current use of housing unit, the name of the householder, number of household members (males, females), and number of holdings of the household.
2. Agricultural holdings enumeration questionnaire: The enumeration questionnaire of the agricultural holdings included the following:

Part One: Identification data: Identification data included the enumeration area number, building number, housing unit number in the building, in addition to identification data on the holder and the respondent.

Part Two: Holders and holding data: Holders and holding data included data on the holder, such as the legal status, age, sex, main occupation, holder's relation to the householder, number of holder's household members, educational level, specialization, and data about the holding, including the holding type, the holding management method and main purpose of production.

Part Three: Land use: This included the unit's address, total area, uncultivated area (buildings used for holding's purposes, building not used for holding's purposes, permanent meadows and pastures, other). Cultivated areas include areas of permanent and temporary crops, forests, land that is temporarily fallow, nurseries, sources of irrigation, and utility rights.

Part Four: Crops /field crops, vegetables, horticultural trees: This included the following:

• Field crops: Questions related to the cultivation of field crops during the agricultural year: crop name, agricultural session, crop status, rainfed area, irrigated area, method of irrigation, and harvested area. • Vegetable crops: Questions related to the cultivation of vegetable crops during the agricultural year: crop name, agricultural session, crop status, open air area, method of irrigation, protected area, type of protection, irrigation method, and harvested area. • Horticultural trees: Questions related to the cultivation of horticultural trees during the agricultural year: crop name, method of farming, crop status, number and area of bearing trees and method of irrigation, area and number of nonbearing trees and method of irrigation, area and number of protected bearing trees and method of irrigation, area and number of protected nonbearing trees and method of irrigation.

Part Five: Farm animals: This included the following:

• Raising farm animals (sheep, goats and cows): type and species, address, type of rearing, the number according to sex and age group, the main purpose of raising the animals. • Poultry farming: type, address, number of barns, area of barns, maximum production capacity, actual number on the enumeration day of the first of October 2010, average number of barn cycles per year, total number of poultry raised during 2009/2010. • Domestic poultry breeding: type, number, beekeeping, and other livestock.

Part Six: Agricultural labor force: It included data on the agricultural labor force in the agricultural holding: employment status, sex, age, number and temporary employment. Part Seven: Agricultural machinery and equipment: It included questions on the use of agricultural machinery and equipment during the agricultural year.

Part Eight: Agricultural practices during the agricultural year: This section included questions related to the use of agricultural practices; the availability of brooders or fish breeding; benefits from land reclamation projects; the construction of agricultural roads or any other agricultural projects.

Cleaning operations

Data processing included all activities that followed the field work, such as office editing of questionnaires, coding, data entry and computer editing. This process started on 15 December 2010 according to the plan, which included training the editors and coders and hiring 100 personnel in addition to the supervisory team.

Special data processing programs were developed and tested to capture the census data. The computer was used to enter the data of the households and holdings listing and enumeration questionnaires.

Data editing, coding, entry, checking and cleaning were finalized on 16 June 2011 in the West Bank and on 31 August 2011 in the Gaza Strip.

The technical team followed up the data processing, testing its accuracy and quality and comparing it with the preliminary results and other data resources, in addition to preparing the tables and the report of the final results of the census in the Palestinian Territory.

Response rate

100%.

Sampling error estimates

There are two types of error: statistical errors and non-statistical errors. Statistical errors occur in survey samples and not in censuses. These errors can easily be measured and the error rate estimated since it is an error in sampling. Non-statistical errors occur at any stage of the implementation of a survey or census. Therefore, a data quality system had to be established when conducting the Agricultural Census 2010 to achieve the highest level of data coverage and accuracy for the statistics produced in order for them to be utilized for planning, decision making, and research purposes. The impact of errors on data quality was minimized due to the high level of competency and professional performance of the well-trained field work team, and also due to the existence of a quality control program to prevent or minimize errors as much as possible, find these errors when they occurred, and take the relevant procedures to correct them. A strict quality control system was established at all stages of the census, from the preparatory stage to the data processing and dissemination stage, to ensure that highly accurate data would be obtained. Quality control in the preparatory stage is crucial as it is succeeded by all census stages. Therefore, adequate time and appropriate procedures were taken into consideration at each stage to ensure high quality and authentic census data

Abstract

Agriculture is the major contributor to the Ethiopian economy. A majority of the Ethiopian populations are engaged in agriculture to earn their livelihood and most of the nation's exports are made up of agriculture produces. The collection of reliable, comprehensive and timely data on agriculture is, thus, essential for policy formulation, decision making and other uses. In this regard the Central Statistical Agency(CSA) has exerted effort to provide users and policy makers with reliable and timely agriculture data.

The general objectives of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is considered essential for development planning, socio-economic policy formulation, food assistance, etc.

The specific objectives of the Main (“Meher”) season area and production survey are: - To estimate the total cultivated land area, production and yield per hectare of major crops (temporary). - To estimate the total farm inputs applied area and quantity of inputs applied by type for major temporary and permanent crops.

Geographic coverage

The survey covered all sedentary rural agricultural population in all regions of the country except urban and nomadic areas which were not included in the survey.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 1998-1999 (1991 E.C) Main ("Mehere") season agricultural survey covered the rural part of the country except two zones in Afar region and six zones in Somalie region that are predominantly nomadic. A two-stage stratified sample design was used to select the sample EAs and the agricultural households. Each zone/ special wereda in the sampled population of Tigray, Afar, Amhara, Oromiya, Somalie, Benishangul_Gumuz, SNNP regions was adopted as stratum for which major finings of the survey are reported. But each of the four regions, namely; Gambela, Harari, Addis Ababa and Dire Dawa were considered as reporting levels. The primary sampling units (PSUs) were enumeration areas (EAs) and agricultural households were the secondary sampling units. The survey questionnaires were administered to all agricultural holders within the sample households. A fixed number of sample EAs was determined fro each stratum/reporting level based on precision of major estimates and cost considerations. Within each stratum EAs were selected using probability proportional to size; size being total number of households in the EAs as obtained form the 1994 population and housing census. From each sample EA, 25 agricultural households were systematically selected for the 'Meher" season survey from a fresh list of households prepared at the beginning of the fieldwork of the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1998-1999 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaire: - AgSS Form 91/0: Used to list all agricultural households and holders in the sample enumeration areas. - AgSS Form 91/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 91/2: Used to collect information about crop condition. - AgSS Form 91/3A: Used to list fields and agricultural practices only pure stand temporary and permanent crops, list of fields and agricultural practices for mixed crops, other land use, quantity of improved and local seeds by type of crop and type and quantity of crop protection chemicals. - AgSS Form 91/3B: Used to collect information about quantity of production of crops. - AgSS Form 91/4A: Used to collect information about results of area measurement and field area measurement. - AgSS Form 91/4B: Used to collect information about results of area measurement and field area measurement. - AgSS Form 91/5: Used to list fields for selecting fields for crop cuttings and collect information about details of crop cutting. - AgSS Form 91/6: Used to collect information about cattle by sex, age and purpose.

Note: The questionnaires are provided as external resource.

Cleaning operations

Editing, Coding and Verification: In order to insure the quality of collected survey data an editing, coding and verification instruction manual was prepared and fifty editors/coders and ten verifiers were trained for two days to edit, code and verify the data using the aforementioned manual as a reference and teaching aid. The filled-in questionnaires were edited, coded and later verified by supervisors on a 100% basis before the questionnaires were sent to the data processing unit for data entry. The editing, coding and verification of all questionnaires was completed in fourty days.

Data Entry, Cleaning and Tabulation: Before starting data entry professional staffs of Agricultural Statistics Department of Central Statistical Authority prepared edit specification that used to developed data entry and cleaning computer programs by data processing staffs using Integrated Microcomputer Processing System (IMPS). The edited and coded questionnaires were captured into computers and later cleaned using cleaning program that was developed for this purpose earlier. Fifty data encoders were involved in this process and it took thirty-five days to complete the job. Finally, using tabulations format provided by the subject matter specialist computer program was developed and survey results were produced accordingly.

Response rate

A total of 1,450 EAs (2.9 % of total EAs in the rural areas of the country) were selected for the survey. However, 22 EAs were not covered by the survey due to various reasons that are beyond the control of the Agency. Thus, the survey succeeded in covering 1428 (98.48%) EAs. With respect to ultimate sampling units, it was planned to cover a total of 36,250 agricultural households for area measurement and 21,750 agricultural households for crop cutting (see Appendix III in the report which is provided as external resource). The response rate was found to be 98.94 % for area measurement and 95.50 % for crop cutting.

Sampling error estimates

Estimation procedures of parameters of interest (total and ratio) and their sampling error is presented in Appendix II of the 1998-1999 annual Agricultural Sample Survey, Volume I report which is provided in this documentation.

The increased use of pesticides and tillage intensification is known to negatively affect biodiversity. Changes in these agricultural practices such as herbicide and tillage reduction have variable effects among taxa, especially at the top of the trophic network including insectivorous bats. Very few studies compared the effects of agricultural practices on such taxa, and overall, only as a comparison of conventional versus organic farming without accurately accounting for underlying practices, especially in conventional where many alternatives exist. Divergent results founded in these previous studies could be driven by this lack of clarification about some unconsidered practices inside both conventional and organic systems. We simultaneously compared, over whole nights, bat activity on contiguous wheat fields of one organic and three conventional farming systems located in an intensive agricultural landscape. The studied organic fields (OT) used tillage (i.e., inversion of soil) without chemical inputs. In studied conventional fields, differences consisted of the following: tillage using few herbicides (T), conservation tillage (i.e., no inversion of soil) using few herbicides (CT), and conservation tillage using more herbicide (CTH), to control weeds. Using 64 recording sites (OT = 12; T = 21; CT = 13; CTH = 18), we sampled several sites per system placed inside the fields each night. We showed that bat activity was always higher in OT than in T systems for two (Pipistrellus kuhlii and Pipistrellus pipistrellus) of three species and for one (Pipistrellus spp.) of two genera, as well as greater species richness. The same results were found for the CT versus T system comparison. CTH system showed higher activity than T for only one genus (Pipistrellus spp.). We did not detect any differences between OT and CT systems, and CT showed higher activity than CTH system for only one species (Pipistrellus kuhlii). Activity in OT of Pipistrellus spp. was overall 3.6 and 9.3 times higher than CTH and T systems, respectively, and 6.9 times higher in CT than T systems. Our results highlight an important benefit of organic farming and contrasted effects in conventional farming. That there were no differences detected between the organic and one conventional system is a major result. This demonstrates that even if organic farming is presently difficult to implement and requires a change of economic context for farmers, considerable and easy improvements in conventional farming are attainable, while maintaining yields and approaching the ecological benefits of organic methods.

Farmers’ use of fungicides and insecticides constitutes a major threat to biodiversity that is also endangering agriculture itself. Landscapes could be designed to take advantage of the dependencies of pests, pathogens, and their natural enemies on elements of the landscape. Yet the complexity of the interactions makes it difficult to establish general rules. In our study, we sought to characterize the impact of the landscape on pest and pathogen prevalence, taking into account both crop and semi-natural areas. We drew on a nine-year national survey of 30 major pests and pathogens of arable crops, distributed throughout the latitudes of metropolitan France. We performed binomial LASSO generalized linear regressions on the pest and pathogen prevalence as a function of the landscape composition in a total of 39,880 field × year × pest observation series. We observed a strong disequilibrium between the number of pests or pathogens favored (15) and disadvantaged (2) by the area of their host crop in the landscape during the previous growing season. The impact of the host crop area during the ongoing growing season was different on pests than on fungal pathogens: the density of most pathogens increased (11 of 17, and no decreases) while the density of a small majority of pests decreased (7 of 13, and 4 increases). We also found that woodlands, scrublands, hedgerows, and grasslands did not have a consistent effect on the studied spectrum of pests and pathogens. Although overall the estimated effect of the landscape is small compared to the effect of the climate, a territorial coordination that generally favors crop diversity but excludes a crop at risk in a given year might prove useful in reducing pesticide use.

Methods Relevant extracts from the methods of the paper:

Pests and pathogens data

Since 2008, the French epidemiological services record and centralize observational data of crop pests and pathogens from arable field monitoring. In this study, we made use of two epidemiological information subsystems: Vigicultures ® (Sine et al. 2010) and VIGIBET (ITB – Sugar Beet Research Institute), that covered 17 of the 22 former French administrative regions including approximately two-thirds of its territory over the 2009-2017 period. From these two databases, we extracted information for 30 pests or pathogens on six crops (winter wheat, winter barley, corn, oilseed rape, sugar beet, potato).

We eliminated from the data the observations for which the reported crop didn’t match the crop indicated in the RPG data, considered here as the gold standard as they are tax data and have been successfully used to train automated detection of crops based on satellite imagery (Inglada et al. 2017). Depending on the crop, this could affect 5 to 30% of the field x year combinations in the database. Many of these observations also had little to no observations of pests or pathogens. We understand them as monitoring points entered by mistake and never really monitored.

Data were collected each year during the cropping season from weekly monitoring of georeferenced fields by technicians from various organizations and trained farmers (Table SI.1.). A different set of fields was monitored each year, freely chosen by the technicians performing the surveillance. It was requested that the fields be far enough apart to reflect the diversity of the territory for which the technicians are responsible, but practical access considerations are also taken into account. Possible issues with repeated measurements and auto-correlation in the data are discussed in Supplementary Materials SI.7.

All fields were conventional farming fields. The head of the observation network informed us that some observations were performed in non-treated spots but we could not account for the crop protection practices because the information was often missing in the database.

In each field, several observation types assessing the state of crop epidemics were retrieved through standardized protocols for each monitored pest and pathogen (e.g. damage severity scale on the plant for pathogens, relative or absolute organism abundance observed on the plant or in traps, amount of plants with symptoms, etc.). Not all the observation types were reported in equal numbers.

Here we kept for each organism considered only the observation type with the highest number of field x year observed to maximize the spatiotemporal extent of each pest or pathogen information. We also expected these widely used observation types to be relevant to describe the risk linked to the organisms as they are originally used to motivate pesticide applications. In total, data for 13 pests of winter wheat, corn, and oilseed rape, and 17 pathogens of winter wheat, winter barley, oilseed rape, sugar beet, and potatoes were analyzed. Detailed information on the pests and pathogens studied, observation periods, and observation types we used can be found in tables SI.1 and SI.2.

Landscape composition data

The delimitation of all French agricultural fields subsidized within the framework of the European Common Agricultural Policy is provided through the "Registre Parcellaire Graphique" (RPG). For annual crops, it is reputed to be nearly exhaustive. The geometry of the fields is described by farmers based on the aerial photographs of the BD Ortho®, a departmental orthophotography of 50 cm resolution provided by the French National Institute for Geographic and Forestry Information (IGN) (ASP et IGN 2019; Font 2018). From 2006 to 2014, fields were described by islets, a group of contiguous fields, but 80% of them had only one type of crop. In each islet, the detailed areas were given by crop types (28 crop types for 329 crops registered). Here we used six of them: winter wheat, oilseed rape, winter barley, corn (including both silage and grain corn), other industrial crops (mainly and considered here to be beet), and flowering vegetables (mainly and considered here to be potatoes). From 2015 to 2017, the description of crops in the RPG was available by species (not crop type) and by field (not islet) and we used this more precise information.

The semi-natural components considered were woods, grasslands, scrublands, and hedgerows. The RPG provided us with grassland delineations for the year of the observation (temporary and perennial grasslands are not distinguished here). The BD TOPO® (vegetation layer version 2.2 2017), a vector map with a resolution of 1 m (IGN 2016) drawn from the BD ORTHO® by the French National Institute for Geographic and Forestry Information, provided us with the geometry of the other components: woods, hedgerows, and scrublands, considered to be stable over the studied years. From this database, we grouped as “woodlands” the broadleaved, coniferous, and mixed woodlands, with closed or open canopy.

Variables preparation and control variables

Pest and pathogen abundance measurements were not normally distributed, often rounded informally, and sometimes distributed into categories. Also, the number of observations of a given pest or pathogen varied by field and year. As a result, we simplified the data into two counts per field and year: the count of observations above and under the median of the observations for all fields and years (SI.2.). For half of the organisms, only presence-absence data were available (SI.2) we then used the counts of observations with or without the pest or pathogen among the observations of the year in a given field. In both cases (with/without or above/under median), the two counts have by construction, a binomial distribution and describe the risk of being above a threshold (presence or median), hereafter referred to as the risk.

We quantified the landscape composition by measuring the area (m2) of semi-natural components and of the pest or pathogen host crop around each observation in buffers with radii of 200 m, 1 km, 5 km, and 10 km. As the abundance of a crop in the landscape could be correlated with its recurrence in the rotation at the field level, the field level rotation effect could be attributed by the regression models to landscape variables. To avoid such confusions we explicitly considered two crop rotation variables: the time elapsed in the observed field since 1) the host crop or 2) grassland, were cultivated. As only 2 years of RPG data were available before the first observations of pests and pathogens, we simplified these variables to three values: 1, 2, and 3 years or more. We discarded the points when the host crop or the grassland was not alone in the islet the last time it appeared.

To account for the potential effect of annual weather and the heterogeneity of crop management in different sub regions, we added two variables to the pool of variables: first, a categorical variable by year and region based on a supra-regional zonation of agroclimatic conditions (SI.3, Figure SI.1b) aggregating French départements (Lorgeou et al. 2012) and second, a sub-regional zonation of homogeneous farming systems (SI.3, Figure SI.1a), as defined by the French technical institute for cereals Arvalis, Institut du Végétal, (Arvalis 2011).

This statistic depicts organic food sales in the United States from 2005 to 2023. In 2023, organic food sales in the United States amounted to about **** billion U.S. dollars. Organic foodOrganic foods are foods that are manufactured using organic farming standards. Those agricultural standards are regulated by the department of government responsible for them and contain regulations for the cultivation of food and animal welfare. In particular, the use of chemical fertilizers, synthetic pesticides, growth hormones and antibiotics is not allowed or only partly permitted. In addition, fewer food additives are permitted for the processing of organic foods. In the United States, organic foods are certified by the National Organic Program. Products which have fulfilled the criteria are labeled with the term ‘organic’ on the food packaging. Organic food sales have experienced tremendous growth in the last decade. For this reason, not only specialized retailers sell organic foods but also traditional supermarkets and discounters. Widely known specialized organic retailers in the United States include Whole Foods Market and Sprouts Farmers Market. Whole Foods Market is the largest specialized organic retailer in the United States with headquarters in Austin, Texas. It was founded in 1980 and operates more than**** stores around the world. In general, organic foods are usually offered at a higher price level than their conventional counterparts. Among Millennials, the by now largest generation in the U.S., a sizeable share of almost ** percent count organic food as one of their leading food priorities. Whether or not organic foods are healthier and more nutritious compared to conventionally farmed produce is a much debated subject. Up to now, no evidence of significant differences between the two farming methods where taste or health are concerned has been established.

Abstract

The Fiji National Agricultural Census 2009 is the fourth agricultural census. After a lapse of 18 years, National Agricultural Census 2009 was carried out in Fiji beginning in October 2009; data collection was interrupted by two cyclones and not completed until March of 2010.

The Agriculture Census is a national obligation conducted by the country to provide benchmark data for planning and policy decisions in sustainable agricultural and rural development; and to strengthen and improve the ongoing Fiji Agriculture Statistics System (FASS) to generate key agricultural data on a regular basis using the results of the 2009 NAC as the benchmark and the dissemination of this statistical information in the form of regular reports.

The 2009 National Agriculture Census (NAC) is the first census programme to be conducted in the country using Multiple Sample Frame (MSF) as the main methodology. Given the experiences of the previous census programmes in terms of funding and availability of resources, the 2009 agriculture census programme provides a platform for more diversification and improvement programmes within the agriculture sector thus ensuring compatible foreign exchange earnings as well as uplifting the living standards of rural populace.

Geographic coverage

National

Analysis unit

• Agricultural Holding and Holders

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey design used the multiple sampling frame methodology. This methodology combines the advantages of an area frame (complete coverage) and a list frame (rare commodities and large and special farms). In the 2009 NAC, it was expected to provide reliable results at district level for most tables, although results for smaller districts might not be possible. In addition, a small island strategy (SIS) was used where complete enumeration of villages occurred within some districts.

The underlying basis for an area frame sample is to select small areas (in this case, one square kilometer - 100 hectares) that represent the entire area of interest. To improve the efficiency of the sample, the entire country was stratified (or characterized) by the intensity of agriculture. The stratification split the country into areas of high intensity agriculture, medium intensity agriculture, low intensity agriculture, forest areas, peri-urban areas and urban areas/non agricultural areas. The overall sample size was limited by the resources available; it was determined to use a ten percent sample of "agricultural land" as determined during the stratification process.

Initially the Fiji Bureau of Statistics (FIBOS) enumeration areas (EAs) for the 2007 Population and Housing Census were used for stratum identification. Subsequently it was determined that re- stratification of whole EAs and subdivision of other EAs would be more efficient. In many of the FIBOS EAs, farms were present only in small pockets; the uniformity of agriculture in the EA, one of the strengths of the stratification, did not exist. These EAs were, first, reviewed for the presence of natural pine forest and natural reserves. After these areas were removed, the remainder of the EA was divided into one square kilometer grids before the sampling process occurred. After the grids were selected, the Land Use Section of the DOA prepared maps using detectable boundaries "around the grid". It was not possible for segments to retain the gridlines as boundaries because they seldom were along recognizable boundaries; however, it was possible to approximate 100 hectares in that general area.

A farm can consist of land areas that are separated by physical boundaries or by land use patterns; these are called tracts. The method of data collection was to account for each tract inside the segment, but, also to collect information about areas outside the segment for farms with tracts both inside and outside. If a segment boundary splits an existing tract, it is divided into one tract inside the segment and one tract outside the segment. The percentage of the farmland inside the segment is used as a weighting factor for the farm in the expansions.

One of the limitations of area frame samples is the accurate expansion of rare or concentrated (non- uniform) variables - such as poultry houses or large dairy or beef farms. The list frame sample, developed from the knowledge and experience of DOA Animal Health and Production Division and Extension Division staff, was expanded as data collection occurred and there was better awareness of large and specialized farms. Data were collected from all of these farms. It should be noted that shortly before the beginning of data collection, a severe outbreak of brucellosis occurred and some culling took place.

Three levels of data presentation were identified for tabulation of the data of the National Agriculture Census 2009 (NAC 2009). The first is tables and expansions at district level; the second is tables and expansions at provincial and national level; the third is tables and (estimates) for special variables.

The census data were collected at farm level, at tract level, at crop level and at animal/poultry level. Information about households and their demographics were also collected. One priority area has been the role of gender in agriculture in Fiji. A special section of the census questionnaire was targeted at identifying these roles and highlighting any special differences. These data also have been broken out by age group.

Accurate land stratification for the 2009 NAC was essential; it was necessary to estimate the percentage of agriculture land use. Initially the stratification was made for each of the Fiji Islands Bureau of Statistics (FIBOS) enumeration areas (EAs).

The census estimates were requested at national, divisional, provincial and also tikina levels. The 15 provinces including Rotuma Island were the main focus of the tabulation. Consequently, the entire country was divided into strata according to the intensity of land use for agriculture. They were further subdivided into sub-strata according to specific land use. This sub-stratification technique guaranteed the sample allocation for priority and special crops. Another stratum was created for special farms including large commercial and freehold farms.

A total of 1,602 existing EAs from the 2007 population census were overlaid on the ASF topographic maps scale 1:50,000 in preparation for stratification activities according to land use. Each EA was classified into one of the strata keeping the same geographical identification codes as those used in the population census. The percentage of area under crops, pastures, forest, etc. (land use) of each EA was estimated by field observation to check that each EA was classified in the right stratum and sub-stratum.

The sampling procedures are more fully described in "National Agricultural Census 2009 - Final Report" pp.7-13.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires, NAC 1 and NAC 3, were used to record information about the segments from the sample. The NAC 1 itemized all tracts inside the segment and all associated farm tracts outside the segments. The NAC 3 documented the nonfarm tracts inside the segment. Enumerators were required to fill out these questionnaires; during the interview process the main questionnaire (NAC 2) was used. Neither the NAC 1 nor NAC 3 was necessary for List Frame farms.

The questionnaire was designed and tested by the staff of the Agricultural Statistics Unit and training manuals were prepared for supervisors and enumerators. A Pilot Census was carried out in several locations to evaluate the content and layout of the questionnaires and the completeness of the census documents. The questionnaire and training materials were updated as the result of the Pilot Census.

Cleaning operations

After a prioritized order of data collection from the provinces, the questionnaires were received at the Agricultural Statistics Unit in batches. Unique questionnaire numbers were assigned by the data processing administrator and recorded in a management system designed to prevent duplicate numbers and to coordinate the collection and processing of the three types of questionnaires. The questionnaire numbers consisted of province, district and a sequence number starting with an initial value assigned previously to each of the segments.

The editing and coding process for a total of 9,341 NAC 2 questionnaires containing farm data started in mid November 2009. Four persons managed the archives of census materials (questionnaires, cartography and photo-enlargements, etc.). Eleven coders were contracted and trained using the Field Team Manual and the Coding, Editing and Data Processing Manual. One table head checked the manual editing and coding. Data entry activities were conducted by ten data entry operators beginning in early December.

Consistency checks were also carried out in the ACCESS databases. Queries were designed to identify data entry and coding errors. Data were entered into 15 provincial databases (including Rotuma Island) which were combined into four divisional databases. The LSF database was kept separate, but combined in SPSS for tabulation and analysis.

Abstract

Food security has become a burring issue in Ethiopia since it is an absolute prerequisite for political and social stability. It received national prominence in the aftermath of the recurring drought and famine and obviously became an immediate domestic policy concern. The gap between the dire need for food supply is compounded by rapidly increasing population, depletion of natural resources and the existing traditional way of farming. It even requires sacrifice to provide adequate supply of food in such a situation where natural and human factors have negatively impacted in the agricultural production and resulted in recurrent droughts and sometimes in catastrophe. Pressed by these problems and other economic factors, the Ethiopian government has centered its agricultural policy on ensuring food security by allocating more resources to increase agricultural production so as to ward off food shortage and ensure continuous adequate supply of food. To monitor and evaluate the performance of the policy and the trends in the charging patterns in agricultural, statistical information on agriculture is required as an input since agriculture is a primary activity connected with food availability. The Central Statistical Agency (CSA) has been generating statistical information used as inputs in the formulation of agricultural policies by collecting processing and summarizing reliable, comprehensive and timely data on the country's agriculture. As part of this mission the 2003-2004 (1996 E.C) Annual Agricultural Sample Survey was conducted to furnish data on cropland area and production of crops within the private peasant holdings for Main (“Meher”) season of the quoted year.

The general objective of CSA's annual Agricultural Sample Survey (AgSS) is to collect basic quantitative information on the country's agriculture that is essential for planning, policy formulation, food security, etc. The survey is composed of four components: Crop production forecast survey. Main (“Meher”) season survey, Livestock survey and “Belg” season survey.

The specific objectives of Main (“Meher”) season survey are: - To estimate the total cultivated area, production and yield of crops. - To estimate the total volume of inputs used, inputs applied area and number of holders using inputs. - To estimate the total cultivated area and other forms of land use.

Geographic coverage

The 2003-2004 annual Agricultural Sample Survey covered the entire rural parts of the country except all zones of Gambella region, and the non-sedentary population of three zones of Afar and six zones of Somali regions.

Note: The crop cutting exercise part of the survey from November 2003 up to January 2004 was not done in Gambela regional state, therefore no production estimates for the region was computed for Meher (main) season.

Analysis unit

Agricultural household/ Holder/ Crop

Universe

Agricultural households

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The list containing EAs of all regions and their respective agricultural households obtained from the 2001/02 Ethiopian Agricultural Sample Enumeration (EASE) was used as the sampling frame in order to select the primary sampling units (EAs). Consequently, all sample EAs were selected from this frame based on the design proposed for the survey. Sample Design A stratified two-stage cluster sample design was used to select the sample. Enumeration Areas (EAs) were taken to be the primary sampling units (PSUs) and the secondary sampling units (SSUs) were agricultural households. Sample enumeration areas from each stratum were sub-samples of the 2001/02 (1994 E.C) Ethiopian Agricultural Sample Enumeration. They were selected using probability proportional to size systematic sampling; size being number of agricultural households obtained from the 1994 Population & Housing Census and adjusted for the sub-sampling effect. Within each sample EA a fresh list of households was prepared and 25 agricultural households from each sample EA were systematically selected at the second stage. The survey questionnaire was finally administered to the 25 agricultural households selected at the second stage. Information on area under crops and Meher season production of crops was obtained from the 25 households that were ultimately selected. It is important to note, however, that data on crop cutting were obtained only from fifteen sampled households (the 11th - 25th households selected).

The sample size for the 2003-04 agricultural sample survey was determined by taking into account both the required level of precision for the most important estimates within each domain and the amount of resources allocated to the survey. In order to reduce non- sampling errors, manageability of the survey in terms of quality and operational capability was also considered. Except Harari, Addis Ababa and Dire Dawa, where each region as a whole was taken to be the domain of estimation; each zone of a region / special wereda was adopted as a stratum for which major findings of the survey are reported.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2003-2004 annual Agricultural Sample Survey used structured questionnaires to collect agricultural information from selected sample households. List of forms in the questionnaires: - AgSS Form 96/0: Used to list all households and agricultural holders in the sample enumeration areas. - AgSS Form 96/1: Used to list selected households and agricultural holders in the sample enumeration areas. - AgSS Form 96/3A: Used to list fields under temporary crops and farm management practice. - AgSS Form 96/3B: Used to list fields under permanent crops and farm management practice. - AgSS Form 96/3C: Used to list fields under mixed crops and farm management practice. - AgSS Form 96/3D: Used to collect information about other land use type and area and other agricultural related questions. - AgSS Form 96/5: Used to list temporary crop fields for selecting crop fields for crop cutting. - AgSS Form 96/6: Used to collect information about temporary crop cutting results.

Cleaning operations

Editing, Coding and Verification: Statistical data editing plays an important role in ensuring the quality of the collected survey data. It minimizes the effects of errors introduced while collecting data in the field , hence the need for data editing, and verification. An editing, coding and verification instruction manual was perpared and reproduced. Then 65 editors-coders and verifiers were trained for two days in editing , coding and.verification using the aforementioned manual as a reference and teaching aid. The completed questionnaires were edited, coded and later verified on a 1OO % basis before the questioners were passed over to the data entry unit. The editlng, coding and verification exercise of all questionnaires took 40 days.

Data Entry, Cleaning and Tabulation: Before data entry, the Natural resource and Agricultural Statistics Department prepared edit specification for the survey for use on personal computers for data consistency checking purposes . The data on the edited and coded questionnaires were then entered into personal computers. The data were then checked and cleaned using the edit specification prepared earlier for this purpose. The data entry operation involved about 64 data encoders and it took 50 days to finsh the job. Finally, tabulation was done on personal computers to produce statistical tables as per the tabulation plan.

Response rate

A total of 2,072 enumeration areas were initially selected to be covered by the survey, however, due to various reasons 16 EA's were not covered and the survey was successfully carried out in 2,056 (99.23 %) EAs. As regards the ultimate sampling unit, it was planned to conduct the survey on 51,800 agricultural households and 51,300 (99.03 %) households were actually covered by the Meher season Agricultural Sample Survey.

Sampling error estimates

Estimation procedure of totals, ratios, sampling error and the measurement of precision of estimates (CV) are given in Appendix I and II of 2003-2004 Agricultural Sample Survey, Volume I report.

Data appraisal

As it was explained in the response rate under sampling section, the non response rate was minimal. There is no testing for bias made in this survey.

Farming impacts on animal-mediated seed dispersal through mechanisms operating at least at two spatial scales: first, at the landscape scale, through habitat loss and land conversion to agriculture/livestock grazing, and second, by local intensification (farm scale) of the agricultural practices. Nonetheless, these two scales of farming impact on the seed dispersal function have been rarely integrated. In particular, studies evaluating the effect of agriculture in the seed dispersal function of frugivorous birds in Mediterranean ecosystems are lacking. We evaluate the role of the landscape transformation, from fruit-rich woodland habitats to olive grove landscapes, together with the local intensive practices of soil management on the persistence of the seed dispersal function for Mediterranean fleshy-fruited plants in olive landscapes of south Spain. We used bird censuses, mist-nets and seed traps to characterize avian frugivore assemblages, frugivory, and seed deposition in seminatural woodland habitat (SNWH) patches and olive fields of 40 olives farms of 20 localities distributed across the whole range of olive cultivation in Andalusia (southern Spain). We found that despite of a still remarkable dispersal function in olive grove landscapes, avian frugivore abundance and diversity, frugivory, and seed arrival decreased in olive fields compared to SNWH patches. Likewise, SNWH cover loss and/or olive growing expansion decreased avian frugivory and seed arrival. Interestingly, habitat effects in the olive farms often depended on the landscape context. In particular, less diverse fruit-eating bird assemblages pooled in SNWH patches as olive grove cover increased or SNWH decreased in the landscape, while remained relatively invariant in the olive fields. Finally, compared to conventional intensive agriculture, low-intensity management increased frugivory and seed deposition. We conclude that olive fields are less permeable to frugivores than expected by its agroforest-like nature, and that presence of SNWH patches is crucial for the maintenance of frugivory and seed dispersal in agricultural landscapes. Results evidence that woodland habitat loss by olive expansion and the intensive practices seriously threaten the dispersal service in olive-dominated landscapes. Maintenance, restoration and promotion of woodland patches should be prioritized for the conservation of the seed dispersal service and for enhancing the functional connectivity in human-shaped olive landscapes.