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Land Rented: 10 acres
Rent: 13 pence (13 sheaves) per acre
|Year||1336||Production per Acre||40|
|Wages||Rent (not serfs)||130|
|Thomas Wood (mason)||160||Food Consumption||320|
|Assets Total||560||Debits Total|
|Debits Total||490||Assets Total|
|Sheaves per Acre||4 acres||5 acres||6 acres||7 acres||8 acres||9 acres||10 acres||11 acres||12 acres|
|Rent (13 sheaves per acre)||Food Consumption (80 sheaves per person)|
A girl named Mary Richards, who was thought remarkably handsome when she left the workhouse, and, who was not quite ten years of age, attended a drawing frame, below which, and about a foot from the floor, was a horizontal shaft, by which the frames above were turned. It happened one evening, when her apron was caught by the shaft. In an instant the poor girl was drawn by an irresistible force and dashed on the floor. She uttered the most heart-rending shrieks! Blincoe ran towards her, an agonized and helpless beholder of a scene of horror. He saw her whirled round and round with the shaft - he heard the bones of her arms, legs, thighs, etc. successively snap asunder, crushed, seemingly, to atoms, as the machinery whirled her round, and drew tighter and tighter her body within the works, her blood was scattered over the frame and streamed upon the floor, her head appeared dashed to pieces - at last, her mangled body was jammed in so fast, between the shafts and the floor, that the water being low and the wheels off the gear, it stopped the main shaft. When she was extricated, every bone was found broken - her head dreadfully crushed. She was carried off quite lifeless.
As per Section 10(1), any income derived from the agricultural land shall not be included in Total Taxable Income.
Now, the question arises,
As per Section 2(1A) of the Income Tax Act, agricultural income can be defined as follows:
(a) Any rent or revenue derived from land which is situated in India and is used for agricultural purposes.
(b) Any income derived from such land by agriculture operations including processing of agricultural produce so as to render it fit for the market or sale of such produce.
(c) Any income attributable to a farm house subject to satisfaction of certain conditions specified in this regard in section 2(1A). Also, any income derived from saplings or seedlings grown in a nursery shall be deemed to be agricultural income.
Finding it Difficult to File ITR Yourself?
Hire our Smartest eCA's to File Your Income Tax Return and Get Maximum Refunds.
The first academic publication about ecological footprints was by William Rees in 1992.  The ecological footprint concept and calculation method was developed as the PhD dissertation of Mathis Wackernagel, under Rees' supervision at the University of British Columbia in Vancouver, Canada, from 1990–1994.  Originally, Wackernagel and Rees called the concept "appropriated carrying capacity".  To make the idea more accessible, Rees came up with the term "ecological footprint", inspired by a computer technician who praised his new computer's "small footprint on the desk".  In 1996, Wackernagel and Rees published the book Our Ecological Footprint: Reducing Human Impact on the Earth. 
The simplest way to define ecological footprint is the amount of the environment necessary to produce the goods and services necessary to support a particular lifestyle. 
The model is a means of comparing consumption and lifestyles, and checking this against biocapacity. The tool can inform policy by examining to what extent a nation uses more (or less) than is available within its territory, or to what extent the nation's lifestyle would be replicable worldwide. The footprint can also be a useful tool to educate people about overconsumption, with the aim of altering personal behavior. Ecological footprints may be used to argue that many current lifestyles are not sustainable. Country-by-country comparisons show the inequalities of resource use on this planet.
The GHG footprint or the more narrow carbon footprint are a component of the ecological footprint. Often, when only the carbon footprint is reported, it is expressed in weight of CO
2 (or CO2e representing GHG warming potential (GGWP)), but it can also be expressed in land areas like ecological footprints. Both can be applied to products, people or whole societies. 
The focus of ecological footprint accounting is renewable resources. The total amount of such resources which the planet produces according to this model has been dubbed biocapacity. Ecological footprints can be calculated at any scale: for an activity, a person, a community, a city, a town, a region, a nation, or humanity as a whole. Footprint values are categorized for carbon, food, housing, goods and services. This approach can be applied to an activity such as the manufacturing of a product or driving of a car. This resource accounting is similar to life-cycle analysis wherein the consumption of energy, biomass (food, fiber), building material, water and other resources are converted into a normalized measure of land area called global hectares (gha). [ citation needed ]
Since 2003, Global Footprint Network has calculated the ecological footprint from UN data sources for the world as a whole and for over 200 nations (known as the National Footprint Accounts). The total footprint number of Earths needed to sustain the world's population at that level of consumption are also calculated. Every year the calculations are updated to the latest year with complete UN statistics. The time series are also recalculated with every update since UN statistics sometimes correct historical data sets. Results are available on an open data platform.  Lin et al. (2018) finds that the trends for countries and the world have stayed consistent despite data updates.  Also, a recent study by the Swiss Ministry of Environment independently recalculated the Swiss trends and reproduced them within 1–4% for the time period that they studied (1996–2015).  Since 2006, a first set of ecological footprint standards exist that detail both communication and calculation procedures. The latest version are the updated standards from 2009. 
The ecological footprint accounting method at the national level is described on the website of Global Footprint Network  or in greater detail in academic papers, including Borucke et al. 
The National Accounts Review Committee has published a research agenda on how to improve the accounts. 
For 2014 Global Footprint Network estimated humanity's ecological footprint as 1.7 planet Earths. According to their calculations this means that humanity's demands were 1.7 times more than what the planet's ecosystems renewed.  
In 2007, the average biologically productive area per person worldwide was approximately 1.8 global hectares (gha) per capita. The U.S. footprint per capita was 9.0 gha, and that of Switzerland was 5.6 gha, while China's was 1.8 gha.   The WWF claims that the human footprint has exceeded the biocapacity (the available supply of natural resources) of the planet by 20%.  Wackernagel and Rees originally estimated that the available biological capacity for the 6 billion people on Earth at that time was about 1.3 hectares per person, which is smaller than the 1.8 global hectares published for 2006, because the initial studies neither used global hectares nor included bioproductive marine areas. 
According to the 2018 edition of the National footprint accounts, humanity's total ecological footprint has exhibited an increasing trend since 1961, growing an average of 2.1% per year (SD= 1.9).  Humanity's ecological footprint was 7.0 billion gha in 1961 and increased to 20.6 billion gha in 2014.  The world-average ecological footprint in 2014 was 2.8 global hectares per person.  The carbon footprint is the fastest growing part of the ecological footprint and accounts currently for about 60% of humanity's total ecological footprint. 
The Earth's biocapacity has not increased at the same rate as the ecological footprint. The increase of biocapacity averaged at only 0.5% per year (SD = 0.7).  Because of agricultural intensification, biocapacity was at 9.6 billion gha in 1961 and grew to 12.2 billion gha in 2016. 
According to Wackernagel and his organisation, the Earth has been in "overshoot", where humanity is using more resources and generating waste at a pace that the ecosystem can't renew, since the 1970s.  In 2018, Earth Overshoot Day, the date where humanity has used more from nature than the planet can renew in the entire year, was estimated to be August 1.  In 2020, because of reduction in resource demand due to COVID-19 lockdowns, Earth Overshoot Day was delayed to August 22.  Now more than 85% of humanity lives in countries that run an ecological deficit. 
According to Rees, "the average world citizen has an eco-footprint of about 2.7 global average hectares while there are only 2.1 global hectare of bioproductive land and water per capita on earth. This means that humanity has already overshot global biocapacity by 30% and now lives unsustainabily by depleting stocks of 'natural capital'." 
The world-average ecological footprint in 2013 was 2.8 global hectares per person.  The average per country ranges from over 10 to under 1 global hectares per person. There is also a high variation within countries, based on individual lifestyle and economic possibilities. 
The Western Australian government State of the Environment Report included an Ecological Footprint measure for the average Western Australian seven times the average footprint per person on the planet in 2007, a total of about 15 hectares. 
Studies in the United Kingdom Edit
The UK's average ecological footprint is 5.45 global hectares per capita (gha) with variations between regions ranging from 4.80 gha (Wales) to 5.56 gha (East England). 
BedZED, a 96-home mixed-income housing development in South London, was designed by Bill Dunster Architects and sustainability consultants BioRegional for the Peabody Trust. Despite being populated by relatively average people, BedZED was found to have a footprint of 3.20 gha (not including visitors), due to on-site renewable energy production, energy-efficient architecture, and an extensive green lifestyles program that included London's first carsharing club. [ citation needed ] Findhorn Ecovillage, a rural intentional community in Moray, Scotland, had a total footprint of 2.56 gha, including both the many guests and visitors who travel to the community. However, the residents alone had a footprint of 2.71 gha, a little over half the UK national average and one of the lowest ecological footprints of any community measured so far in the industrialized world.   Keveral Farm, an organic farming community in Cornwall, was found to have a footprint of 2.4 gha, though with substantial differences in footprints among community members. 
In a 2012 study of consumers acting 'green' vs. 'brown' (where green people are "expected to have significantly lower ecological impact than 'brown' consumers"), the conclusion was "the research found no significant difference between the carbon footprints of green and brown consumers".   A 2013 study concluded the same.  
Early criticism was published by van den Bergh and Verbruggen in 1999,  which was updated in 2014.  Their colleague Fiala published similar criticism in 2008. 
A comprehensive review commissioned by the Directorate-General for the Environment (European Commission) was published in June 2008. The European Commission's review found the concept unique and useful for assessing progress on the EU’s Resource Strategy. They also recommended further improvements in data quality, methodologies and assumptions. 
Blomqvist et al.  published a critical paper in 2013. It lead to a reply from Rees and Wackernagel (2013),  and a rejoinder by Blomqvist et al. (2013). 
An additional strand of critique is from Giampietro and Saltelli (2014),  with a reply from Goldfinger et al., 2014,  and a rejoinder by Giampietro and Saltelli (2014).  A joint paper authored by the critical researchers (Giampietro and Saltelli) and proponents (various Global Footprint Network researchers) summarised the terms of the controversy in a paper published by the journal Ecological indicators.  Additional comments were offered by van den Bergh and Grazi (2015). 
A number of national government agencies have performed collaborative or independent research to test the reliability of the ecological footprint accounting method and its results.  They have largely confirmed the accounts' results those who reproduced the assessment generating near-identical results. Such reviews include those of Switzerland,   Germany,  France,  Ireland,  the United Arab Emirates  and the European Commission.  
Global Footprint Network has summarized methodological limitations and criticism in a comprehensive report available on its website. 
Some researchers have misinterpreted ecological footprint accounting as a social theory or a policy guideline, while in reality it is merely a metric that adds up human demands that compete for the planet's regenerative capacity. Examples of such confusions include Grazi et al. (2007) who performed a systematic comparison of the ecological footprint method with spatial welfare analysis that includes environmental externalities, agglomeration effects and trade advantages. Not recognizing that the ecological footprint is merely a metric, they conclude that the footprint method does not lead to maximum social welfare.  Similarly, Newman (2006) has argued that the ecological footprint concept may have an anti-urban bias, as it does not consider the opportunities created by urban growth.  He argues that calculating the ecological footprint for densely populated areas, such as a city or small country with a comparatively large population — e.g. New York and Singapore respectively — may lead to the perception of these populations as "parasitic". But in reality, ecological footprints just document the resource dependence of cities - like a fuel gauge documents a car's fuel availability. Newman questions the metric because these communities have little intrinsic biocapacity, and instead must rely upon large hinterlands. Critics argue that this is a dubious characterization since farmers in developed nations may easily consume more resources than urban inhabitants, due to transportation requirements and the unavailability of economies of scale. Furthermore, such moral conclusions seem to be an argument for autarky. This is similar to blaming a scale for the user's dietary choices. Some even take this train of thought a step further, claiming that the footprint denies the benefits of trade. Therefore such critics argue that the footprint can only be applied globally.  Others have made the opposite argument showing that national assessments do provide helpful insights. 
Since this metric tracks biocapacity, the replacement of original ecosystems with high-productivity agricultural monocultures can lead to attributing a higher biocapacity to such regions. For example, replacing ancient woodlands or tropical forests with monoculture forests or plantations may therefore decrease the ecological footprint. Similarly if organic farming yields were lower than those of conventional methods, this could result in the former being "penalized" with a larger ecological footprint.  Complementary biodiversity indicators attempt to address this. The WWF's Living Planet Report combines the footprint calculations with the Living Planet Index of biodiversity.  A modified ecological footprint that takes biodiversity into account has been created for use in Australia. 
Ministry of Agriculture, Food and Rural Affairs
The Universal Soil Loss Equation (USLE) predicts the long-term average annual rate of erosion on a field slope based on rainfall pattern, soil type, topography, crop system and management practices. USLE only predicts the amount of soil loss that results from sheet or rill erosion on a single slope and does not account for additional soil losses that might occur from gully, wind or tillage erosion. This erosion model was created for use in selected cropping and management systems, but is also applicable to non-agricultural conditions such as construction sites. The USLE can be used to compare soil losses from a particular field with a specific crop and management system to "tolerable soil loss" rates. Alternative management and crop systems may also be evaluated to determine the adequacy of conservation measures in farm planning.
Five major factors are used to calculate the soil loss for a given site. Each factor is the numerical estimate of a specific condition that affects the severity of soil erosion at a particular location. The erosion values reflected by these factors can vary considerably due to varying weather conditions. Therefore, the values obtained from the USLE more accurately represent long-term averages.
Universal Soil Loss Equation (USLE)
A represents the potential long-term average annual soil loss in tonnes per hectare (tons per acre) per year. This is the amount, which is compared to the "tolerable soil loss" limits.
R is the rainfall and runoff factor by geographic location as given in Table 1. The greater the intensity and duration of the rain storm, the higher the erosion potential. Select the R factor from Table 1 based on the upper tier municipality designation and corresponding weather station where the calculation is to be made.
K is the soil erodibility factor (Table 2). It is the average soil loss in tonnes/hectare (tons/acre) for a particular soil in cultivated, continuous fallow with an arbitrarily selected slope length of 22.13 m (72.6 ft) and slope steepness of 9%. K is a measure of the susceptibility of soil particles to detachment and transport by rainfall and runoff. Texture is the principal factor affecting K, but structure, organic matter and permeability also contribute.
LS is the slope length-gradient factor. The LS factor represents a ratio of soil loss under given conditions to that at a site with the "standard" slope steepness of 9% and slope length of 22.13 m (72.6 ft). The steeper and longer the slope, the higher the risk for erosion. Use either Table 3A or the "Equation for Calculating LS" included in this Factsheet to obtain LS.
C is the crop/vegetation and management factor. It is used to determine the relative effectiveness of soil and crop management systems in terms of preventing soil loss. The C factor is a ratio comparing the soil loss from land under a specific crop and management system to the corresponding loss from continuously fallow and tilled land. The C Factor can be determined by selecting the crop type and tillage method (Table 4A and Table 4B, respectively) that corresponds to the field and then multiplying these factors together.
The C factor resulting from this calculation is a generalized C factor value for a specific crop that does not account for crop rotations or climate and annual rainfall distribution for the different agricultural regions of the country. This generalized C factor, however, provides relative numbers for the different cropping and tillage systems, thereby helping you weigh the merits of each system.
P is the support practice factor. It reflects the effects of practices that will reduce the amount and rate of the water runoff and thus reduce the amount of erosion. The P factor represents the ratio of soil loss by a support practice to that of straight-row farming up and down the slope. The most commonly used supporting cropland practices are cross-slope cultivation, contour farming and strip cropping (Table 5).
Procedure for Using the USLE
|Weather Station||Upper Tier Municipality Designation||R Factor|
|Brantford||County of Brant||90|
|Essex||County of Essex||110|
|Fergus||Counties of Dufferin and Wellington||120|
|Hamilton||City of Hamilton Regional Municipality of Halton||100|
|Kingston||City of Prince Edward County Counties of Frontenac and Lennox & Addington||90|
|Kitchener||Regional Municipality of Waterloo||110|
|London||Counties of Lambton, Middlesex, and Oxford||100|
|Mount Forest||Counties of Bruce, Grey, Haliburton, and Simcoe District of Muskoka||90|
|Niagara||Regional Municipality of Niagara||90|
|Northern Ontario||Districts of Algoma, Cochrane, Kenora, Manitoulin Island, Parry Sound, Rainy River, Sudbury, Thunder Bay, and Timiskaming||90|
|Ottawa||City of Ottawa Counties of Lanark and Renfrew United Counties of Leeds and Grenville, Prescott and Russell, and Stormont, Dundas and Glengarry District of Nipissing||90|
|Prospect Hill||Counties of Huron and Perth||120|
|Ridgetown||Municipality of Chatham-Kent||110|
|Simcoe||Counties of Haldimand and Norfolk||120|
|St. Thomas||County of Elgin||90|
|Toronto||City of Toronto, Regional Municipalities of Peel and York||90|
|Tweed||City of Kawartha Lakes Counties of Hastings, Northumberland, and Peterborough Regional Municipality of Durham||90|
|Textural Class||K Factor |
|Average OMC*||Less than 2% OMC||More than 2% OMC|
|Clay||0.49 (0.22)||0.54 (0.24)||0.47 (0.21)|
|Clay loam||0.67 (0.30)||0.74 (0.33)||0.63 (0.28)|
|Coarse sandy loam||0.16 (0.07)||&ndash||0.16 (0.07)|
|Fine sand||0.18 (0.08)||0.20 (0.09)||0.13 (0.06)|
|Fine sandy loam||0.40 (0.18)||0.49 (0.22)||0.38 (0.17)|
|Heavy clay||0.38 (0.17)||0.43 (0.19)||0.34 (0.15)|
|Loam||0.67 (0.30)||0.76 (0.34)||0.58 (0.26)|
|Loamy fine sand||0.25 (0.11)||0.34 (0.15)||0.20 (0.09)|
|Loamy sand||0.09 (0.04)||0.11 (0.05)||0.09 (0.04)|
|Loamy very fine sand||0.87 (0.39)||0.99 (0.44)||0.56 (0.25)|
|Sand||0.04 (0.02)||0.07 (0.03)||0.02 (0.01)|
|Sandy clay loam||0.45 (0.20)||&ndash||0.45 (0.20)|
|Sandy loam||0.29 (0.13)||0.31 (0.14)||0.27 (0.12)|
|Silt loam||0.85 (0.38)||0.92 (0.41)||0.83 (0.37)|
|Silty clay||0.58 (0.26)||0.61 (0.27)||0.58 (0.26)|
|Silty clay loam||0.72 (0.32)||0.79 (0.35)||0.67 (0.30)|
|Very fine sand||0.96 (0.43)||1.03 (0.46)||0.83 (0.37)|
|Very fine sandy loam||0.79 (0.35)||0.92 (0.41)||0.74 (0.33)|
Soil Loss Tolerance Rates
A tolerable soil loss is the maximum annual amount of soil, which can be removed before the long-term natural soil productivity is adversely affected.
The impact of erosion on a given soil type, and hence the tolerance level, varies, depending on the type and depth of soil. Generally, soils with deep, uniform, stone-free topsoil materials and/or not previously eroded have been assumed to have a higher tolerance limit than soils that are shallow or previously eroded.
Soil loss tolerance rates are included in Table 6.
The suggested tolerance level for most soils in Ontario is 6.7 tonnes/hectare/year (3 tons/acre/year) or less.
Management Strategies to Reduce Soil Losses
Having obtained an estimate of the potential annual soil loss for a field, you may want to consider ways to reduce this loss to a tolerable level. Table 7 outlines management strategies to help you reduce soil erosion.
|Slope Length: m (ft)||Slope (%)||LS Factor|
Equation for Calculation of LS (if Not Using Table 3A)
LS = [0.065 + 0.0456 (slope) + 0.006541 (slope) 2 ](slope length ÷ constant) NN
Crop Production Research
USDA focuses on enhancing economical crop production through its Crop Production Program by producing sound, research-driven knowledge to be shared and leveraged by its users.
Crop Production Program
CSGC applies systems theory to the solution of complex agricultural problems and to the development of computer-aided farm decision support systems.
Crop Systems & Global Change (CSGC)
USDA provides details on a wide range of information on crops including grains, oilseeds, fruits, vegetables, cotton, and tobacco.
Main U.S. Crop Information
USDA assists farmers by providing information on the planting and harvesting dates for major field crops by state.
USDA supports several strategic science-based regulatory programs designed to protect crops and natural resources.
USDA provides unbiased price and sales information to assist in the orderly marketing and distribution of farm commodities.
Crop Market Research
USDA provides numerous reports related to crop production information such as acreage, areas harvested, and yield.
Field Crop Reports
USDA analyzes data to create comprehensive forecast reports for U.S. agricultural commodities.
Commodity Outlook Reports
In accordance with the 1990 Farm Bill, all private applicators are required by law to keep records of their use of federally restricted use pesticides for a period of two years. The Pesticide Recordkeeping Branch (PRB) ensures that the Federal pesticide recordkeeping regulations are being followed through compliance and educational outreach activities.
Pesticide Recordkeeping Program
USDA provides information such as crop bulletins, historical data, planting and harvesting dates, and plant hardiness zone maps to assist producers in planning and managing the production of their crops.
Crop Weather Reports
USDA creates a weekly report during the growing season listing planting, fruiting, and harvesting progress and overall conditions in the major planting states.
State Crop Progress and Condition
USDA highlights some of the more memorable changes that took place in U.S. agriculture over the past century.
Trends in Agriculture
The Agricultural Statistics Book is published each year to meet the diverse need for a reliable reference book on agricultural production, supplies, consumption, facilities, costs, and return.
Agriculture Statistics Book
USDA highlights agricultural statistics and data reports, charts, and maps annually.
A Historical Perspective on Illinois Farmland Sales
Farmland prices have soared in recent years, leading to increased interest in farmland markets and in factors affecting farmland prices. Sales records from the Illinois Department of Revenue (IDOR) allow for detailed comparisons of parcel-level information through time and for the construction of summary price trends and other descriptive statistics. In this post, we utilize detailed sales records from 1979 through 2011 for all farmland sold in Illinois, filtered for arms-length transfers of unimproved farmland parcels between 10 and 1,280 acres in size and $100 and $20,000 in per-acre sales price. In total, 97,599 sales records representing an average of 195,633 acres per year remain in this sample. On average, 2,958 parcels are represented in each year’s average. Figure 1 summarizes Illinois farmland price information through time with series for the acre-weighted average price, the median price, and the 25th and 75th percentiles of prices in each year. In contrast to the average price, which can be influenced by a few very high priced transfers that may be unrepresentative of actual farmland values, the median price is the value at which 50% of a year’s prices are above and below. The interquartile range provides a region containing the central 50% of all sales, thereby conveying the dispersion of prices. Although the interquartile range for farmland prices grows somewhat from 1979 to 2011, the increase is roughly proportional to increases in median and acre-weighted average farmland prices. In other words, there is more room for variation in farmland prices as the overall price level of farmland has increased.
To better understand the variation in sale prices within a single year, a sample of farmland sales data from 2011 is shown in Figure 2. Sales in the highest 25% of the sample in price per acre are shaded in green and sales in the lowest 25% are shaded in blue. The edges of the red-shaded data represent the quartile breakpoints. Figure 2 indicates that there is considerable variation in per-acre prices for Illinois farmland, reflecting differences among farmland quality, farmland buyers, and farmland sellers. A particularly large price range exists for small parcels, which are often purchased for nonagricultural purposes. Nonetheless, the red-shaded cluster in Figure 2 (the interquartile range) provides a meaningful measure of farmland prices that is useful for historical comparisons.
A few well-known features are apparent in Figure 1. Similarly to most other agricultural production regions, Illinois farmland prices dipped considerably during the farm crisis of the 1980s. After peaking in 1981, Illinois farmland prices declined at an annually compounded rate of 8.7 percent through 1987. After bottoming out in 1987, Illinois farmland prices increased slowly but steadily for the next decade and a half. Illinois farmland then entered a period of rapid price appreciation that has continued until today. From 2003 to 2011, Illinois farmland prices more than doubled, reflecting 9.2 percent annually compounded growth. According to USDA estimates of farm real estate values, comparably strong and long-lived growth had not occurred since the farmland boom of thirty years prior. Farmland price growth has been exceptionally robust during the past few years. In 2011, Illinois farmland prices increased 21.4 percent, the largest single-year jump in over three decades. IDOR data for 2012 are not yet fully available for certain counties and months, but preliminary estimates reveal large (approximately 15.0%) growth in Illinois farmland prices. This indication of continued strong farmland price growth agrees with both USDA farmland value data and Illinois Society of Professional Farm Managers and Rural Appraisers (ISPFMRA) data that show comparable gains in 2012.
A more detailed description of Illinois farmland values is provided in Figures 3 and 4. Figures 3 and 4 report average farmland prices summarized by ISPFMRA regions (shown in Figure 5). Figure 3 displays farmland prices for nine of Illinois’ ten regions while Figure 4 represents the region containing Chicago. Price differences by region reflect differences in general productivity, and while price levels change through time, relative values remain reasonably stable. Additional premiums are present in regions near large population centers such as St. Louis.
As Figure 4 shows, in both magnitude and time path, farmland prices in Region 1 are markedly different from those of other regions. Very high prices exist for farmland in Region 1 due to proximity to Chicago, which creates transitional opportunities that are unavailable in other areas. Farmland sale prices in urban-influenced areas are higher whether nonagricultural conversion is imminent or merely anticipated. Compared to other regions of Illinois, farmland prices in Region 1 have experienced relatively steady growth since 1979. It is noteworthy that the 1980s farm crisis, which depressed farmland prices in other regions, had relatively little impact on farmland prices near Chicago. However, farmland price growth in Region 1 slowed in 2005 and turned slightly negative in 2007. This downturn is attributable to diminished nonagricultural demand for farmland. Between 2005 and 2008, transitional farmland transactions (defined according to a parcel’s “current” and “intended” use) declined from 18.0 percent to 5.6 percent of the sales in Region 1. As a result, farmland prices in Region 1 fell after 2007 despite a strong farm economy that caused record farmland prices in other areas.
Sharp increases in farmland prices lead to questions regarding the sustainability of current farmland prices. The most basic estimate of farmland prices is provided by capitalizing income or rental values. Figure 6 displays actual Illinois farmland prices and imputed farmland prices calculated by dividing USDA cash rent estimates by 10-year constant maturity treasury interest rates. As shown, Illinois farmland prices tend to mirror capitalized cash rents fairly closely. When differences occur, they can be the result of changes in income expectations, effective capitalization rates, or market factors related to changing uses of the parcels. Plausible explanations of differences between actual and imputed farmland prices have included slow adjustment periods for cash rents, changing nonagricultural returns to farmland, or differences in true capital costs. Based on imputed farmland prices, Illinois farmland was overvalued by 58 percent in 1981, but corrected in the following years. Unlike the 1980s, the current market does not appear to be overvaluing farmland. In fact, farmland prices still have some room to “catch up” to high farm incomes and low capitalization rates. Perhaps more sensibly, the current disparity between imputed and actual farmland prices may reflect a sense that current low interest rates are not sustainable in the long-term. Overall, although Illinois farmland prices have increased to record heights at a rapid pace, they are explained well by fundamental factors.
It is apparent that the recent run-up in Illinois farmland prices has been of truly historic proportions. Although the current pairing of farm incomes and interest rates has driven farmland prices to record levels in almost all regions of Illinois, skepticism over the sustainability of these factors may be holding current farmland prices below levels predicted by capitalized rents. For better or worse, farmland prices promise to be a topic of much discussion in the foreseeable future.
Hanson is a Master’s Degree Candidate in the Dept. of Ag and Consumer Economics, and Sherrick is Marjorie and Jerry Fruin Professor of Farmland Economics and Director of the TIAA Center for Farmland Research at the University of Illinois. The views expressed herein are solely the authors’ opinions and do not necessarily reflect those of entities with whom professionally affiliated.
Disclaimer: We request all readers, electronic media and others follow our citation guidelines when re-posting articles from farmdoc daily. Guidelines are available here. The farmdoc daily website falls under University of Illinois copyright and intellectual property rights. For a detailed statement, please see the University of Illinois Copyright Information and Policies here.
Yield farming is a new way of making money with cryptocurrency that has become a major phenomenon this year.
From its sudden explosion in the summer of 2020, yield farming — one of the main investment methods associated with the decentralized finance (DeFi) movement — has built a large community and generated dizzying amounts of value in a matter of months.
What is yield farming? Explained simply for beginners, it’s a way to maximize the potential profitability of your cryptocurrency by putting it to work as a financial tool.
DeFi allows anyone to engage in all sorts of financial activities — which previously required trusted intermediaries, ID verification and a lot of fees — anonymously and for free.
One example revolves around loans. One person puts up cryptocurrency for another to borrow, and the platform this occurs on rewards them for doing so.
With DeFi, platforms have begun not only rewarding via interest on loans and other traditional methods, but also by giving both lenders and borrowers in-house governance tokens.
The combination of these rewards, coupled with the fact that the price of these in-house tokens is free-floating, allows for the potential profitability of lending and even borrowing to be considerable.
The practise of putting cryptocurrency to work in this way, often in multiple capacities at once, is what is called yield farming. There are already practically infinite permutations of yield farming — for example, you can put up cryptocurrency as a loan and then borrow from yourself, maximizing returns and token allocation.
The ecosystem is fleshed out with automated trading markets — computers orchestrating “pools” of tokens to ensure that there is liquidity for any given trade that token holders wish to make. Uniswap is one of the best known of these “automated liquidity protocols.”
Curve is an example of a decentralized exchange which concentrates on stablecoins such as Tether (USDT), and has its own token which borrowers and lenders can receive as a reward for participation — providing liquidity.
What Are the Costs of Yield Farming?
How much can you expect to pay for yield farming? The costs of yield farming are notoriously difficult to calculate given the complexity of the DeFi model. The yield farming model contains inherent risk which varies depending on the tokens used.
In the loan example, cost considerations consist of the original cryptocurrency put up by a lender, the interest and the value of the in-house governance token reward.
Given that all three are free-floating, the profit (or loss) potential for participants is significant. Using stablecoins reduces this, but if the goal is maximizing gains from governance tokens, risk remains extremely high.
There are also secondary considerations, such as the Ether gas price, which has spiked recently, resulting in inflated transaction fees for ERC-20 token transfers.
What’s the best way of knowing how to yield farm with as little risk as possible? Dedicated tools exist to work out the likely cost, for example, predictions exchanges, which monitor changes in non-stablecoin token prices.
Can I Lose Money Yield Farming?
The answer to this — as with any high-risk cryptocurrency trading strategy — is simple: yes. With an attentive strategy and suitable background knowledge, it is possible to keep the risk of loss to a minimum, but not remove it altogether.
A useful comparison is that of the initial coin offering (ICO) craze from 2017, which notoriously punished opportunist investors who put capital into projects without in-depth knowledge of their validity as investments.
Despite the obvious advantages of the other, more available power sources, progressive farmers in a number of countries were determined to exploit the possibilities of electricity on their farms. To get electricity, farmers formed cooperatives that either bought bulk power from existing facilities or built their own generating stations.
It is believed that the first such cooperatives were formed in Japan in 1900, followed by similar organizations in Germany in 1901. Multiplying at a considerable rate, these farmer cooperatives not only initiated rural electrification as such but provided the basis for its future development.
From these small beginnings the progress of rural electrification, though necessarily slow, steadily gained impetus until, in the 1920s, public opinion eventually compelled governments to consider the development of rural electrification on a national basis. Today in the more developed countries virtually all rural premises—domestic, commercial, industrial, and farms—have an adequate supply of electricity.
Early applications of electricity were of necessity restricted to power and some lighting, although the full value of lighting was not completely realized for years. Electric motors were used to drive barn machinery, chaffcutters and root cutters, cattle cake and grain crushers, and water pumps. Electricity’s ease of operation and low maintenance showed savings in time and labour. It was not long before the electric motor began to replace the mobile steam engine on threshing, winnowing, and other crop-processing equipment outside the barn.
In the fields, a number of electrically driven, rope-haulage plowing installations, some of them quite large, came into use in several European countries. These systems, however, did not stand the test of time or competition from the mobile internal-combustion-driven tractor.
Applications of electricity in agriculture did not increase greatly until the 1920s, when economic pressures and the increasing drift of labour from the land brought about a change in the whole structure of agriculture. This change, based on new techniques of intensive crop production resulting from the development of a wide range of mechanical, electrical, and electromechanical equipment, was the start of the evolution of agriculture from a labour-intensive industry to the present capital-intensive industry, and in this electricity played a major part.
Southdown sheep with turnips © Before this time, farmers did not know formally of the existence of nitrogen, but we can interpret many of their actions in terms of the conservation of existing stocks of nitrogen, and the addition of new nitrogen to the soil. Existing stocks were exploited, for example, by ploughing up permanent pasture to grow cereals. Available nitrogen was conserved by feeding bullocks in stalls, collecting their manure (which is rich in nitrogen), and placing it where it was needed. Also, most importantly, new nitrogen was added to the soil using legumes - a class of plants that have bacteria attached to their roots, which convert atmospheric nitrogen into nitrates in the soil that can be used by whatever plants are grown there in the following few years.
An essentially organic agriculture was gradually replaced by a farming system that depended on energy-intensive inputs.
Legumes had been sown since the Middle Ages in the form of peas, beans and vetches, but from the mid-17th century farmers began to grow clover, both white and red, for the same purpose, and by the 19th century had dramatically increased the quantity of nitrogen in the soil available for cereal crops. In Norfolk, for example, between 1700 and 1850, the doubling of the area of legumes and a switch to clover tripled the rate of symbiotic nitrogen fixation.
This new system of farming was remarkable because it was sustainable the output of food was increased dramatically, without endangering the long-term viability of English agriculture. But just as a sustainable agriculture had been achieved, the development of chemical fertilisers and other external inputs undermined this sustainability. An essentially organic agriculture was gradually replaced by a farming system that depended on energy-intensive inputs dependent on the exploitation of fossil fuels.
Farming Calculations - History
COMET-Farm estimates the ‘carbon footprint’ for all or part of your farm/ranch operation and allows you to evaluate different options, which you select, for reducing GHG emissions and sequestering more carbon. General guidance is provided about potential changes to your management practices that are likely to sequester carbon and reduce greenhouse gas emissions.
Because the tool uses detailed spatially-explicit data on climate and soil conditions for your location and allows you to enter detailed information for your field and livestock operations, it is able to produce an accurate estimate tailored to your specific situation. No prior training is needed to run the tool and embedded ‘Help’ functions are provided to assist you in running the tool.
You only need information on your field and livestock management practices.
For example, the Field Module asks you for your crop or pasture management practices starting from at least 2000, but more historical data if available, including cropping sequence and approximate planting and harvest date type of grazing system (for pasture or range areas), type of tillage system rate, timing, type and application method for fertilizer and manure applications irrigation method and application rate, and residue management. Instead of requiring users to manually input data for all applicable years, you will be given an option to complete the management for one year, and copy that information to other years, allowing users to make minor changes to their management for all subsequent years. This effectively saves a substantial amount of time for the user.
For the Livestock Module you need information on your herd size and composition (i.e., species, sex and age ratios) and type of manure management system. More advanced methods to estimate livestock-related emissions are available if you have information on feed characteristics and feed supplements.
For the Energy Module, most of what is needed for the calculations is taken from information you’ve already provided for field and livestock management practices - some additional information on capital equipment and if you have any on-farm renewable energy production can also be entered into the tool.
You may use COMET-Farm in one of two ways – as a registered or unregistered user. Regardless of the method you choose, the USDA will not use, share, or view your information. You are the only person who is able to see your information.
Data stored for later use
Data purged immediately after use
Although registering allows you to conveniently store and later retrieve your management information, we recognize that not all users feel comfortable with this. You may continue without registering and the information you enter during this session will be purged and not stored permanently. If you change your mind at any time during your session, simply click on the Register link at the top right of the application and we will store any information previously entered.
The system uses your information on management practices together with spatially-explicit information on climate and soil conditions from USDA databases (which are provided automatically in the tool) to run a series of models for each potential source of greenhouse gas emissions.
For the Field Module, estimates are made using the DayCent dynamic model, which is the same model used in the official U.S. National Greenhouse Gas Inventory.
Emissions in the Livestock Module are estimated using statistical models based on USDA and university research result and are similar to models used in the U.S. National Inventory.
Estimates in the Energy Module are based on the models used in the USDA/NRCS Energy Tool along with supplemental peer-reviewed research results.