In 2024, the average value of U.S. farm real estate was 4,170 U.S. dollars per acre. Compared to one decade earlier, the value has increased by almost 40 percent. Generally, the value of U.S. farm real estate has had an upward trend since 1970. U.S. farms The number of farms in the United States has conversely been decreasing each year, reaching about two million farms as of 2022. That year, Texas had the most farms out of any other U.S. state by far, with about 246,000 farms. Missouri and Iowa had the second and third most farms, though neither state exceeded 100,000 farms. Agricultural trade Agricultural products encompass any products from agricultural origin that are meant for human consumption or animal feed. Agricultural products can include livestock products or crops. In 2022, the U.S. exported about 196.4 billion U.S. dollars’ worth of agricultural goods worldwide, increasing from the previous several years. Mexico is a key destination for U.S. agricultural products and imported just over 28 billion dollars’ worth in 2022, more than Europe and Eurasia combined.

Value of farmland and buildings per acre, for Canada and the provinces at July 1 (in dollars).

New Zealand's average farm sale prices showed significant regional variations in the three months to November 2024. The price of farm property in the country was the highest in the Nelson/Marlborough/Tasman region as of November 2024, with an average sale price of around ******* New Zealand dollars per hectare. In comparison, in the Auckland region, the average farm sales price came to just over ****** dollars per hectare. A farming nation The agriculture industry is a major economic pillar of the country. The contribution to the nation’s GDP is valued in the billions of New Zealand dollars. Horticulture, livestock, and dairying are all important segments, and the commodities produced within them are exported across the globe. While sheep livestock numbers have declined, they still make up a large share of the country’s livestock population. Horticultural farming While New Zealand exports various horticultural products, including wine grapes, potatoes, and apples, it is perhaps best known for its kiwi fruit. Accordingly, the land area dedicated to kiwi fruit farming has continued to increase over the years. New Zealand’s leading horticultural product export destinations include Asia, Europe, and Australia.

Data are compiled on transactions with land for which agricultural land is registered as the main purpose of real estate use in the Cadastre Information System. The transactions used in the statistical calculations are based on the following selection criteria: the land use objective of the rural group has been determined, the transaction has been recognised as typical in the analysis of market data, the area of agricultural land (UAA) in the transaction is greater/equal than 3 ha, the UAA area of the total area is greater/equal than 70 %, shrubs, swamps, water, land under roads, land under buildings, other land occupying less than 20 %, forest land area less than 20 %, the object is located in rural areas. Transactions corresponding to the free market (the price of their transactions has not been artificially reduced or increased, not reflecting the real market situation, e.g. related party transactions, impact of other land use, etc.) and selected criteria, were used to assess the overall price trends in the country.

In 2024, the average land price in Japan amounted to around ***** thousand Japanese yen per square meter. The average land price is based on land price surveys conducted by the Ministry of Land, Infrastructure, Transport, and Tourism and prefectural governments in January and July each year. Japan’s geography The Japanese archipelago consists of the five main islands of Honshu, Hokkaido, Kyushu, Shikoku, and Okinawa in addition to thousands of smaller islands. Together, they cover a surface area of around *** thousand square kilometers. Three-quarters of the country’s land area is covered by mountains. Forestland and farmland constitute about ** percent of its landmass, while developed land accounts for five percent. The population of *** million is concentrated in major cities like Tokyo, which is home to over **** million inhabitants. Urban-rural divide and land prices Owing to an overconcentration of economic activity in Tokyo and other major cities like Osaka and Nagoya, more than half of the population is located in ***** metropolitan areas. Tokyo and its surrounding prefectures that comprise the Tokyo metropolitan area attract many people from other parts of the country each year, often young individuals seeking jobs or starting university. In contrast, rural regions are confronted with depopulation and economic stagnation. Japan’s urban-rural divide is also reflected in land prices. Tokyo has by far the most expensive land prices. In terms of land price growth, the cities of Sapporo, Sendai, Hiroshima, and Fukuoka have outpaced the Greater Tokyo Area in the past decade.

Overview
This report presents the detailed financial performance estimates of grain farmers in 2014-15, 2015-16 and 2016-17, and discusses incomes, investment, farm debt, and costs of production in a historical context. The report draws on data from the ABARES annual Australian Agricultural and Grazing Industries Survey (AAGIS).

This report is a collation of chapters that have been previously published online.

Farm financial performance (published 18 May 2017) This chapter presents estimates of the incomes, profits, costs and rates of return for grain farms. Key Issues
Average farm cash income of Australian grain farms is projected to increase by around 27 per cent in 2016-17 to $290,000 per farm. Farm cash income in 2016-17 is projected to be the highest in over 20 years, an estimated 85 per cent higher than the average between 2000-01 and 2015-16 (in real terms*). The expected increase in incomes is a result of increased production of wheat, barley and oilseeds.

• Note: real dollar values are adjusted to remove the effect of inflation.

Farm debt and equity (published 12 July 2017) This chapter presents estimates of the debt, equity, and debt-servicing capacity for grain farms. Key Issues
Average farm debt of Australian grain farms is estimated to have increased by around 2 per cent to around $853,000 in 2015-16 (in 2016-17 dollars). Average grain farm debt is projected to increase a further 3 per cent in 2016-17. From 2000-01 to 2015-16 the average equity ratio of grain farms has fluctuated around 85 per cent. The average proportion of farm receipts needed to fund interest payments is projected to fall to just under 6 per cent in 2016-17.

Farm capital and investment (published 8 August 2017) This chapter presents estimates of farm capital and farm investment for grain farms. Key Issues
The total value of capital for Australian grain farms increased by 77 per cent in real terms from 2000-01 to 2015-16. On a per farm basis, total capital more than doubled to around $5.9 million per farm. The average value of land and fixed improvements per hectare of grain farms doubled from 2000-01 to 2015-16, with an average annual return on land appreciation of 5.2 per cent.

Physical characteristics (published 9 November 2017) This chapter presents estimates of physical characteristics for grain farms. Key Issues
From 2000–01 to 2015–16 the number of Australian farms sowing at least 40 hectares sown to grains, oilseeds or pulses fell by 27 per cent. The number of grain farms planting more than 1,200 hectares of grains increased with these larger farms accounting for an increased share of total output of grains, pulses and oilseeds. Total Australian grain production in 2015–16 was higher than in 2000–01 despite fewer grain farms and seasonal variations in production.

This data relates to the average annual cash income of broadacre and dairy farm properties which responded to the ABARE annual farm survey over a three year period from 1996-1997 to 1998-1999. Average farm cash income is calculated as difference between total cash receipts (total revenues received by the farm business during the financial year) and total cash costs (payments made by the farm business for materials and services, which includes debt repayments and permanent/casual labour costs). The data is reported at the Statistical Division (SD) level for Australia. This data relates to broadacre and dairy farms run by owner managers and has been collected by annual farm survey interview and is supplemented by information in the farm accounts.The data is presented at a scale of 25000000. Projection -P Albers Equal-Area Conic -D WGS84 -m 132 -o 0 -p -18 -p -36 -e 0 -n 0The following attributes are contained within the dataset; Sd code a a unique 3 digit code for Statistical Divisions (SD), Sd name a the name of the Statistical Division (SD),Cash_inc a the average annual farm cash income for the period 1996-1997 to 1998-1999RSE a the relative standard error of the average farm equity ratio for the period 1996-1997 to 1998-1999.Ag_land_ha a hectares of agricultural land use in the Statistical Division (SD).Note that metropolitan areas are assigned a value of -99999, whilst areas with no data are assigned a value of -88888.

See further metadata for more detail.

The mean annual TSS and TN load and concentration and area normalized (AN) load at the watershed outlet for the BASE20 and future scenarios. AN load includes the contribution of point sources. Load is expressed as metric tons for TSS and kg for TN. AN load is expressed as m tons/ ha/year for TSS and kg/ha/yr for TN. Concentration is expressed as mg/L for both. The ± represents the standard deviation in annual values.

The database documented in NDP-050/R1 is a revision to a database originally published by the Carbon Dioxide Information Analysis Center (CDIAC) in 1995. The data are annual estimates, from 1850 through 1990, of the net flux of carbon between terrestrial ecosystems and the atmosphere resulting from deliberate changes in land cover and land use, especially forest clearing for agriculture and the harvest of wood for wood products or energy. The data are provided on a year-by-year basis for nine regions (North America, South and Central America, Europe, North Africa and the Middle East, Tropical Africa, the Former Soviet Union, China, South and Southeast Asia, and the Pacific Developed Region) and the globe. Some data begin earlier than 1850 (e.g., for six regions, areas of different ecosystems are provided for the year 1700) or extend beyond 1990 (e.g., fuelwood harvest in South and Southeast Asia, by forest type, is provided through 1995).

Data on land-use change, wood harvest, and carbon in ecosystems were obtained from a number of sources, as detailed in Houghton (1999). First, annual rates of land-use change (expansion and contraction of agricultural area (ha/yr), including croplands, pastures, and shifting cultivation, and rates of wood harvest (m^3/yr) were used to determine the areal extent and the type of ecosystems affected by different land uses. Second, the per ha changes in carbon associated with these changes in land use formed the basis for response curves that were used in a model to calculate annual changes in carbon per ha that follow management or change in land use. The data are thus of two kinds, either related to rates of land-use change or to per ha changes in carbon storage following disturbance and management.

The global net flux during the period 1850 to 1990 was 124 Pg of carbon (1 petagram = 1015 grams). During this period, the greatest regional flux was from South and Southeast Asia (39 Pg of carbon), while the smallest regional flux was from North Africa and the Middle East (3 Pg of carbon). For the year 1990, the global total net flux was estimated to be 2.1 Pg of carbon. Houghton and Hackler (1999) consider at length the uncertainties associated with estimates of net carbon flux from land-use change.

This numeric data package contains a year-by-year regional data set of net flux estimates, a year-by-year data set comparing several estimates of global total net flux, and a documentation file (which includes SAS and Fortran codes to read the ASCII data files; SAS is a registered trademark of the SAS Institute, Inc., Cary, North Carolina 27511). The data files are provided in both flat ASCII and binary spreadsheet format. The data files and the documentation are available without charge on a variety of media and via the Internet from CDIAC.

This database will be useful for studies of the global carbon cycle, especially focusing on fluxes of carbon between terrestrial ecosystems and the atmosphere. The database will also be useful for studies of land-use change, agriculture, and forestry. The region- and ecosystem-specific parameters will be useful for estimating both the recovery of ecosystems following disturbance and the oxidation of carbon in wood products.

This data relates to the farm equity ratio of broadacre and dairy farm properties which responded to the ABARE annual farm survey over a three year period from 1996-1997 to 1998-1999. Median farm area is reported at the Statistical Division (SD) level for Australia. This data relates to farm business equity as a percentage of owned capital at 30 June for broadacre and dairy farms run by owner managers. Represented here as a three-year average the data shows the farm business equity-value of owned capital less farm business debt, as a percentage of owned capital. The data has been collected by annual farm survey interview and is supplemented by information in the farm accounts.The data is presented at a scale of 25000000. Projection -P Albers Equal-Area Conic -D WGS84 -m 132 -o 0 -p -18 -p -36 -e 0 -n 0The following attributes are contained within the dataset; Sd_code a a unique 3 digit code for Statistical Divisions (SD), Sd_name a the name of the Statistical Division (SD),Ratio a the average farm equity ratio for the period 1996-1997 to 1998-1999Less80er a proportion of properties with a farm equity ratio of less than 80% for the period 1996-1997 to 1998-1999RSE a the relative standard error of the average farm equity ratio for the period 1996-1997 to 1998-1999.Ag_land_ha a hectares of agricultural land use in the Statistical Division (SD).Note that metropolitan areas are assigned a value of -99999, whilst areas with no data are assigned a value of -88888. See further metadata for more detail.

In 2023, the total area of farmland in South Korea amounted to around 1.51 million hectares, a slight decrease from 1.53 million hectares in the previous year. The area of farmland in South Korea has been on a declining trend in the last decade. Overview of the agriculture industry While the gross domestic product from agriculture, forestry and fishing has remained stable in Korea, the agricultural sector overall has faced various issues in the last couple of years. Previously known as the most important crop of the country, the per capita consumption of rice has shown a rapid decrease, partly due to changing eating habits. The livestock and dairy sector, on the other hand, has increased greatly in the last five years. Another issue can be noticed in the share of farming household population among the total population, which also decreased in the last four years by around 0.5 percent. As people over 65 years make up over 50 percent of agricultural households, this trend of decreasing households will probably continue with no direct intervention. Agriculture and inflation South Korea’s inflation has remained relatively moderate in the past few decades. Still, the price of essential items, particularly food and clothing, has significantly increased and the price level index currently about 1.6 times above the OECD average. For example, agricultural products such as potatoes, pork, and apples are two to three times more expensive than in other developed countries. This high pricing is often attributed to several factors, including restricted imports of fresh fruits and vegetables, limited availability of agricultural land, and generally low productivity levels within the agricultural sector. Especially low-income households have been disproportionately affected by the rising cost of groceries and have spent over 30 percent of their income on food expenses.

This data relates to the average annual family income of broadacre and dairy farm properties which responded to the ABARE annual farm survey over a three year period from 1996 -1997 to 1998 -1999. Average annual family income is calculated as the family share of farm income plus any wages (that are included as farm costs for taxation assessment) paid to the owner manager, spouse and dependant children, plus all off-farm income of owner manager and spouse. The data is reported at the Statistical Division (SD) level for Australia. This data relates to broadacre and dairy farms run by owner managers and has been collected by annual farm survey interview and is supplemented by information in the farm accounts. The data is presented at a scale of 25000000. The following attributes are contained within the dataset; Sd code a a unique 3 digit code for Statistical Divisions (SD), Sd name a the name of the Statistical Division (SD), Faminc a the average annual farm family income for the period 1996-1997 to 1998-1999. RSE a the relative standard error of the average farm equity ratio for the period 1996-1997 to 1998-1999. Ag_land_ha a hectares of agricultural land use in the Statistical Division (SD). Note that metropolitan areas are assigned a value of -99999, whilst areas with no data are assigned a value of -88888. See further metadata for more detail.

Nitrous oxide (N2O) emissions response curves for crops grown outside temperate regions have been rare and have thus far arrived at conflicting conclusions. Most studies reporting N2O emissions from tropical cropping systems have examined only one or two nitrogen fertilizer application rate(s) which precludes the possibility of discovering nonlinear changes in emission factors (EF, % of added N converted to N2O-N) with increasing fertilizer-N rates. To examine the relationship between N rates and N2O fluxes in a tropical region, we compared farming practices with three or four N rates for their yield-scaled impacts from three crops in peninsular India. We measured N2O fluxes during nine seasons between 2012 and 2015, with N application rates ranging between 0 and 70, 0 and 90, and 0 and 480 kg-N ha-1 for foxtail-millet (Setaria italica L., locally called korra), groundnut (Arachis hypogaea L., also called peanut) and finger-millet (Eleusine coracana L., locally called ragi), respectively. In two cases, the highest N application rate greatly exceeded crop-N needs. Potential climate smart farming agricultural practices (with low/optimized N rates) led to a 50-150% reduction in N2O emissions intensity (per unit yield) along with a reduction of 0.2-0.75 tCO2e ha-1 season-1 as compared to high N conventional applications. We found a non-linear increase in N2O flux in response to increasing applied N for both N-fixing and non N-fixing crops and the extent of super-linearity for non N-fixing crops was much higher than what has been reported earlier. If a linear fit is imposed on our datasets, the emission factors (EFs) for finger-millet and groundnut were ~3.5% and ~1.8%, respectively. Our data shows that for low-N tropical cropping systems, even when they have low soil carbon content, increase in N use to levels just above crop needs to enhance productivity might lead to relatively small increase in N2O emissions as compared to the impact of equivalent changes in fertilizer-N use in systems fertilized far beyond crop N needs. Methods

The five study farms were in the Indian states of Karnataka and Andhra Pradesh. Emissions during cultivation of Groundnut (peanut), Foxtail- and Finger- millet were studied at two, one and two farms, respectively. The measurement of GHG emissions, yield and other agro-economic indicators was performed for a total of nine seasons at three regional laboratories established by a coalition of partners interested in promoting climate smart farming in agro-ecological regions 8.2 and 3.0 of the semi-arid peninsula of India. Soil characteristics and weather conditions Each of the five experimental sites was a farmer owned and managed small-holder plot and was located in peninsular India between 12.77-14.66 N (Latitude), 77.20-77.75 E (Longitude) and 350-790 m (elevation above sea level). The experimental sites had sandy-loam and loamy-sand texture (680-750 g kg-1 Sand, 120-170 g kg-1 Silt and 130-170 g kg-1 Clay) and soil organic matter concentration varying between 3.2 and 14.3 g kg-1 (i.e., between 1.9 and 8.3 g kg-1 soil C). Except in the case of foxtail millet (which was a newly cultivated site), the groundnut and finger millet plots were under continuous groundnut or finger-millet systems, respectively, for over a decade before establishment of our experiments. The soil characteristics of each site are given in S1 Table. The climate of all study locations was semi-arid with measured seasonal rainfall varying from 56-480 mm during the experimental period. The lowest and the highest temperatures observed at our sites varied from 10-21 and 33-40 °C, respectively (see S1 Table for details of each site). All experimental sites were between 0.1 and 0.42 ha in size and the experimental treatments were implemented by the farmer under supervision of a trained field and laboratory research team. There were three replicates for each treatment and each subplot received one treatment with stratified randomized block design. Nitrous oxide emissions were measured for both finger-millet and groundnut during four cropping seasons each, along with some fallow periods flanking these growing seasons between July 2012 and December 2015. Groundnut was sown between July 10-September 4 and harvested between November 3-December 25. Finger-millet was sown between August 3-August 25 and harvested between November 25-January 1. Due to severe drought and other complications, N2O emissions data from the foxtail-millet farm could be collected only for one season between October 12, 2014 and January 19, 2015. The data from two groundnut growing seasons (dry kharif and irrigated rabi in 2012) was published earlier (Kritee et al, 2015) and is presented here with new estimates of mineralized organic nitrogen which impacted the calculation of EFs. During the fallow periods, there were no inputs of water or fertilizer to the experimental sites, except to prepare for the upcoming cropping season. Treatments We compared N2O emissions from three or four broad categories of treatments: Very-high-N (VHN, conventional practices with N rates varying from 91 to 276 kg N ha-1), High-N (HN, conventional practices identified via our local farmer surveys with total N rate varying from 53 to 248 kg N ha-1; see S3 Table for farmer survey results), Low-N (LN, farm-specific potential climate-smart farming practices including completely organic practices for groundnut farms, total N varying from 17-78 kg N ha-1) and a zero N (control). We explored changes in N2O emissions with changing N fertilizer inputs under scenarios where water input was either below or above water requirements for groundnut (>280 mm) and finger-millet (>450 mm). The dry sites for groundnut had water input between 100-200 mm in the rainfed season (locally called kharif) whereas the wet site had a water input of 370 mm (irrigated winter season locally called rabi). The dry and wet rainfed sites for finger-millet had water inputs between 100-350 mm and ~480 mm, respectively. The Low-N treatment (Table 1 and S3-S4 Tables) represented farm-specific “alternate” practices that were investigated for their potential to deliver similar (or higher) yields and economic benefits to farmers as well as lower climate impacts. The potential climate-smart farming practices investigated for foxtail-millet and groundnut farms in agro-ecological region (AER) 3.0 involved completely organic (with no synthetic) inputs. Except in the case of finger-millet, the High-N treatment represents the conventional “business-as-usual” crop management practices as currently implemented by farmers with average to large land-holdings in this region. The conventional practices were identified via regional farmer surveys conducted during the study. The recommended inorganic N use for groundnut, finger- and foxtail- millet is 20-30, 50, and 30 kg N ha-1, respectively. Farmer surveys conducted during this study or by the Indian government indicated that farmers were using much higher fertilizer N application rates than the crop-specific recommendations by the state/district governments and/or academic institutions. Please see S3 Table for comparison of survey results with “High N” treatments. The Very-High-N treatments for finger-millet and groundnut included addition of nitrogen fertilizers much higher than the respective crop’s nitrogen needs. These treatments were included specifically to test the extent of super-linear response in N2O emissions when N inputs are very high. Overall, the N fertilization rates for groundnut, finger-millet and foxtail millet varied from 0 to 77, 0 to 470 and 0 to 49 kg N ha-1, respectively The rate and timing of all organic and inorganic fertilizer applications are provided in S2 Table and total N rate (including contribution from mineralized organic N) for each treatment is presented in Table 1.

In general, the soils in the two agro-ecological regions are not amenable to cultivation without ploughing. For groundnut and foxtail-millet, tillage was done once in each season about 25 days before sowing. For finger-millet, tillage was done 2-4 times between March and July soon after rainfall depending on soil hardness and manure (if any) was incorporated during the last 1-2 tillage events. Bullock cart ploughing tills soil to a depth of 12 cm and local tractors (used only when the soil is very hard) plough to the depth of up to 18 cm. There was no tillage done to control weeds and there was no use of herbicides and pesticides. During the rainfed south-west monsoon season (from July to December; locally called kharif), sowing was done manually at a seed rate 146 ± 27 kg ha-1 for groundnut (Kadiri 6 variety) at a 30 cm row spacing, 10 cm plant spacing and to a depth of 5 cm, 12 kg ha-1 for foxtail millet (local variety called Jadda Korra) at a 30 cm row spacing, 8-12 cm plant spacing and to a depth of 3-6 cm and 24.7 kg ha-1 for finger-millet (MR1 variety) at a 25 row spacing to a depth of 3-6 cm. Both millets are sown with a seed drill attached to a bullock and the plots are thinned/weeded 12-20 and 20-25 days after sowing of finger- and foxtail-millet, respectively. The seed rates used in a given crop and season were the same for all treatments. All of the aboveground biomass (as well as belowground biomass for groundnut) was harvested manually 110-130 days after sowing (see exact dates in S1 Table). N2O flux monitoring Manual closed chambers were used to collect air samples from each of the three replicate treatment plots and the air samples were analyzed by electron capture detector (ECD) in a gas chromatograph (Thermo Fisher Trace GC 600) to quantify N2O emissions rates based on methodology developed in our labs. Because most N2O emissions occur within 1-4 days following N addition and/or irrigation/rainfall, N2O flux measurements are more reliable when the sampling frequency is high and the sampling schedule captures spatio-temporal variability in

Overview This report presents the detailed financial performance estimates of grain farmers in 2014-15, 2015-16 and 2016-17, and discusses incomes, investment, farm debt, and costs of production in …Show full descriptionOverview This report presents the detailed financial performance estimates of grain farmers in 2014-15, 2015-16 and 2016-17, and discusses incomes, investment, farm debt, and costs of production in a historical context. The report draws on data from the ABARES annual Australian Agricultural and Grazing Industries Survey (AAGIS). This report is a collation of chapters that have been previously published online. Farm financial performance (published 18 May 2017) This chapter presents estimates of the incomes, profits, costs and rates of return for grain farms. Key Issues Average farm cash income of Australian grain farms is projected to increase by around 27 per cent in 2016-17 to $290,000 per farm. Farm cash income in 2016-17 is projected to be the highest in over 20 years, an estimated 85 per cent higher than the average between 2000-01 and 2015-16 (in real terms*). The expected increase in incomes is a result of increased production of wheat, barley and oilseeds. Note: real dollar values are adjusted to remove the effect of inflation. Farm debt and equity (published 12 July 2017) This chapter presents estimates of the debt, equity, and debt-servicing capacity for grain farms. Key Issues Average farm debt of Australian grain farms is estimated to have increased by around 2 per cent to around $853,000 in 2015-16 (in 2016-17 dollars). Average grain farm debt is projected to increase a further 3 per cent in 2016-17. From 2000-01 to 2015-16 the average equity ratio of grain farms has fluctuated around 85 per cent. The average proportion of farm receipts needed to fund interest payments is projected to fall to just under 6 per cent in 2016-17. Farm capital and investment (published 8 August 2017) This chapter presents estimates of farm capital and farm investment for grain farms. Key Issues The total value of capital for Australian grain farms increased by 77 per cent in real terms from 2000-01 to 2015-16. On a per farm basis, total capital more than doubled to around $5.9 million per farm. The average value of land and fixed improvements per hectare of grain farms doubled from 2000-01 to 2015-16, with an average annual return on land appreciation of 5.2 per cent. Physical characteristics (published 9 November 2017) This chapter presents estimates of physical characteristics for grain farms. Key Issues From 2000–01 to 2015–16 the number of Australian farms sowing at least 40 hectares sown to grains, oilseeds or pulses fell by 27 per cent. The number of grain farms planting more than 1,200 hectares of grains increased with these larger farms accounting for an increased share of total output of grains, pulses and oilseeds. Total Australian grain production in 2015–16 was higher than in 2000–01 despite fewer grain farms and seasonal variations in production.

a Classification one farms adopted spring calving and grazed > 274 days a year with limited concentrate feed supplements. Classification two, three and four farms adopted block or all year calving with increased use of concentrate feed supplementation as grazing days reduced. Classification five farms adopted all year round calving in a housed system with the greatest amount of concentrate use as a total mixed ration. (XLSX)

Abstract

The impact evaluation is not designed to measure the overall impact of the GHS activity. Instead, the impact evaluation will be able to provide evidence on the impact of the value chain training subactivity (alone) in an environment in which other value chain constraints are concurrently addressed.

The evaluation of the GHS value chain training subactivity will focus on measuring the extent, if any, to which the training activities improved the productivity and profitability of participants. In particular, the evaluation will address the following research questions: 1. What is the impact of GHS value chain training on adoption of new practices and production (yield) within the context of a value chain project? Do these impacts vary by value chain? Were some practices or combinations of practices adopted more than others, and why or why not? 2. Does distance from an GHS value chain training site affect participation in GHS value chain training? What other factors affect participation? 3. To what degree are new practices adopted by value chain participants who do not themselves participate in GHS value chain training activities? Can adoption by nonparticipants be attributed to program ripple effects, rather than broader trends? 4. How does the impact of value chain training on adoption of new practices and production vary with the characteristics of farm operators and farm households?

The impact evaluation of the GHS value chain training subactivity will use a random assignment evaluation design. Eighty potential training sites were randomly assigned to a treatment group (48 sites)--at which training activities will be conducted--or to a control group (32 sites)--at which training activities will not be conducted. Though random assignment will determine where GHS value chain training activities are held, it will not necessarily determine which farmers participate in training. Farmers living in communities that are near control sites will be free to attend trainings held in other communities and may travel to do so; likewise, not all farmers living near treatment sites will attend trainings. If all farmers in treatment sites attended training while all farmers in control sites did not, the impacts of training could be estimated by comparing the outcomes of treatment group farmers to the outcomes of control group farmers at follow-up. If instead some farmers living near treatment sites choose not to attend training while some farmers living near control sites do attend training--which is our expectation--the evaluation approach will have to account for this phenomenon.

The evaluator will be able to measure the impacts of the GHS value chain training subactivity as long as farmers living near treatment sites are more likely to attend GHS value chain training activities than farmers who live near control sites. The estimation approach will exploit the variation in the likelihood of attending GHS value chain training activities induced by random assignment. In particular, the impact of the GHS value chain training subactivity will be estimated using an instrumental variables (IV) framework, using distance from training as an instrument for participation in training. In this context, using an IV approach is not unlike a comparison of farmers in treatment and control sites, except that it adjusts for the fact that some control farmers will participate in GHS value chain training activities and some treatment farmers will not participate.

The IV approach is credible in this context because training sites were assigned randomly. Because training locations were assigned randomly, we can assume that farmers near treatment sites are the same, on average, as farmers living near control sites (before training activities take place). The IV approach isolates the component of participation that is driven by the instrument (here, distance). The IV estimates can be interpreted as the impact for a key group affected by the training subactivity--farmers who undertake training if it is offered nearby, but not if it is offered far away.

This evaluation design will enable the evaluator to measure the impacts of participating in GHS value chain training activities. Importantly, all value chain participants could benefit from the activities, whether or not they participate in training; furthermore, other activities in the value chain could amplify the benefits of training. Therefore, impacts measured through the evaluation will tell us the impacts of training in an environment in which other value chain barriers are addressed; they will not tell us the full impact of all of the activities or what the impact of training would be in the absence of other, related activities.

Geographic coverage

Data are collected from farmers in communities spread throughout rural Moldova, but only from communities that were considered for training (but may not have necessarily had training offered, such as for communities randomly assigned to the control group).

Analysis unit

Farms

Universe

The study population includes farm operators in approximately 88 communities--48 treatment communities, 32 control communities, and 8 A-list communities (high priority sites that were purposefully selected to receive training). To be included in the study, farmers must have cultivated targeted crops (which, for each community, were identified in advance by the implementer). Across these 88 communities, about 2100 farmers were interviewed in the 2013-2014 FOS.

Given this is a panel survey, the same households will be interviewed across multiple rounds.

Kind of data

Sample survey data [ssd]

Sampling procedure

1. Sample frame For the sample frame, the survey contractor developed a list of all farm operators cultivating crops in targeted value chains in the 80 study communities (treatment and control) and 8 A-list communities (high priority sites that were purposefully selected to receive training). This list included information about farm size and which of the targeted crops the farm operator cultivated., In three communities, the survey contractor did not identify any farmers cultivating targeted crops, so the final sample frame included 77 study communities and 8 A-list communities. Information on total farm size was used to draw separate samples for farms of different sizes.

2. Drawing the sample For small farms (less than 10 hectares), we drew a random sample of farm operators in targeted value chains in each community. To determine the number of farmers to select in each community and to select farmers, we implemented the following steps:

·We allocated the total small-farm sample across communities in proportion to their size (the number of small-farm operators in targeted value chains). For example, if one community had twice as many treatment small-farm operators as another, we allocated twice as many small-farm operators to that community. To ensure that very small communities were adequately represented and that very large communities do not drive the impact estimates, no community's sample could be below a minimum of 20 or above a maximum of 150 small farmers. Allocating the sample in this way ensured that the sample was balanced across communities but still close to self-weighting.

·We drew the sample in each community using implicit stratification by value chain. We used implicit stratification by value chain (sorting farmers in each community by value chain and selecting the sample so that it was evenly spread across this ordered list) to ensure that the randomly-selected sample provided proportional representation of the different value chains in each community.

For medium (between 10 and 100 hectares) and large (100 hectares or larger) farms, we determined that there were relatively few farms in the value chain training sample frame (174 medium farms and 77 large farms). We therefore attempted to interview all operators of these farms so that we would have precise estimates for these groups.

1. Use of replacements In some cases, the survey contractor was unable to conduct an interview with a selected farm operator. This occurred for various reasons, such as refusal to participate or ineligibility for the survey (if it was determined that the operator did not cultivate the targeted value chains). To account for this, we developed a list of replacement farmers in each community at the same time that we selected our initial sample. Because all medium and large farmers were selected for the sample, the replacement list included only small farmers. These procedures were designed to help ensure that we reached our target sample sizes for the analysis while maintaining the representativeness of the sample to the extent possible and keeping the replacement procedure reasonably straightforward.

Sampling deviation

The analysis sample does not include all respondents to the survey. The analysis sample excludes farmers from one stratum that had five treatment communities and three control communities. This stratum was excluded because it contained virtually no control farmers. As a result, the analysis sample includes 902 farmers in 41 treatment communities, 563 farmers in 28 control communities, and 200 farmers in 8 A-list communities.

Research instrument

The evaluation will draw on three key sources of data.

The first is longitudinal survey data from farm operators living near treatment and control sites that will enable us to track outcome changes over time. This survey, the Moldova Farm Operator Survey, included two questionnaires: one questionnaire for small and medium farms (< 100 Ha), and a separate questionnaire for large farms (>= 100 Ha). The

Farm income (USD ha-1) difference among different levels of nutrient users in crops relative to government-endorsed recommendations under diverse rice-based cropping patterns.

Individual environmental and financial impact of the seven mitigation methods selected in all cost−effective suites of methods to mitigate environmental phosphrus (P) loading from both a pasture−based and housed dairy farming system.

The area of arable land in Mexico amounted to 20.08 million hectares in 2020, the highest value reported in the since 2019 and an increase of approximately three million hectares in comparison to 1980. Mexico's area of arable land and permanent crops added up to 22.9 million hectares in 2021.

Structural and physical characteristics of two model farms generated to closely represent a pasture-based and a housed dairy farming system.