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World Soybean Production: Area Harvested, Yield, and Long-Term Projections

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Soybeans (Glycine max) serve as one of the most valuable crops in the world, not only as an oil seed crop and feed for livestock and aquaculture, but also as a good source of protein for the human diet and as a biofuel feedstock. The world soybean production increased by 4.6% annually from 1961 to 2007 and reached average annual production of 217.6 million tons in 2005-07. World production of soybeans is predicted to increase by 2.2% annually to 371.3 million tons by 2030 using an exponential smoothing model with a damped trend. Finally, three scenarios and their implications are presented for increasing supply as land availability declines. The scenarios highlight for agribusiness policy makers and managers the urgent need for significant investments in yield improving research.
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Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
1
World Soybean Production: Area Harvested, Yield, and Long-Term Projections
Tadayoshi Masuda* and Peter Goldsmith**
*Postdoctoral Research Associate, National Soybean Research Laboratory,
University of Illinois at Urbana-Champaign
1101 West Peabody Drive
Urbana, Illinois 61801, USA.
Phone: 1 (217) 244-2795
Fax: 1 (217) 244-1707
Email: tmasuda@illinois.edu
**Executive Director, National Soybean Research Laboratory, and
Associate Professor, Department of Agricultural and Consumer Economics,
University of Illinois at Urbana-Champaign
1101 West Peabody Drive
Urbana, Illinois 61801, USA.
Phone: 1 (217) 244-1706
Fax: 1 (217) 244-1701
Email: pgoldsmi@illinois.edu
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
2
World Soybean Production: Yield, Area Harvested, and Long-Term Projections
Abstract
Soybeans serve as one of the most valuable crops in the world, not only as an oil seed crop and
feed for livestock and aquaculture, but also as a good source of protein for the human diet and
recently as a biodiesel feedstock. The world soybean production quantity increased by 4.8%
annually from 1960s and reached 217.6 million tons in 2005-07. World production of soybeans
is predicted to increase by 2.1% annually to 359.7 million tons by 2030, using an exponential
smoothing model with a damped trend. Finally, various scenarios and their implications are
discussed for increasing supply.
Key words
Soybean, production, long-term projection, exponential smoothing with damped trend
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
3
Introduction
Soybeans are one of the most valuable crops in the world
1
not only as an oil seed crop and feed
for livestock and aquaculture, but also as a good source of protein for the human diet and
recently as a biodiesel feedstock. As the soybean demand increases, the supply is challenged, the
stocks reduce, and the market prices rise. In order to meet the demand, there are two
alternatives: increase planted hectares or increase yield (tons/ha)
2
. This paper examines the long
range forecasts of future production.
Objectives and Procedures
This paper has three objectives: 1) to examine the contribution of increased land use as a
component of overall production; 2) to analyze the contribution of yield to overall production
across large and small, but promising, producing countries; and 3) to estimate the long range
production quantities of soybeans at the country and international levels. The estimation results
are used for the discussion of land requirements and necessary yield improvements in the
projection period.
We use time series of soybean production quantity, yield, and area harvested of 7 top producing
countries (USA, Brazil, Argentina, China, India, Paraguay, and Canada)
3
and 6 continents
1
According to National Aeronautics and Space Administration (NASA), soybeans are chosen as one of the primary
crops to be grown on the lunar or planetary surface in their research projects. Other candidates include wheat, rice,
peanuts, dried beans, white potato, and sweet potatoes. Some fresh fruits and vegetables such as tomatoes, lettuce,
spinach, bell pepper, onions, carrots, radish, and strawberries are also considered. Regarding soybeans, some
processes like oil expression and tofu production will be tested on the lunar surface in 2024 or later.
2
Reducing losses also increases the available supply, but would have minor impact on the overall supply-demand
balance.
3
These 7 countries cover more than 95% of the world soybean production quantities in 2005-07.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
4
(Africa, Asia, Europe, North America & Caribbean, South America, and Oceania) from 1961 to
2007
4
. Ukraine and Russian Federation are combined and also picked up for the discussion of
yield and land use in the estimation period. With ‘Rest-Of’ terms
5
, the world total production
quantities are calculated in the model. In order to estimate production quantity through 2030,
exponential smoothing with a damped trend
6
is employed as a univariate time series model. The
estimation results by country and by continent are shown, and the long-term soybean supply
market is examined.
Increasing harvested land historically has been the most expedient manner to increase crop
output. World-wide soybean harvested acres increased over 60% while yield increased less than
30% since 1990. Going forward available farmland will be limited by decreasing quantities of
land not already in production, increased farmland loss for urbanization, heightened sensitivities
about agricultural uses of land, and weak property rights in regions such as Africa that constrains
the employment of modern agricultural methods (Goldsmith, 2008b). Soybean production
therefore will require research and development to increase yields in order to meet future
demand and compensate for declining stocks of available land. Combinations of growth rates of
area harvested and yield are simulated to meet the world soybean supply-demand. Based on the
simulations, we discuss some concerns such as arable land limitation and the environment,
technological progress and R&D investments, and intellectual property and trade policy issues.
4
Data are provided from FAOSTAT.
5
Rest of Asia, Rest of North America and Caribbean, Rest of South America, and Rest of Europe.
6
Developed by Gardner and McKenzie (1985).
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
5
Soybean Production: 1961-2007
7
Over the 2005-07 period on average 217.6 million tons of soybeans were annually produced in
the world (Table 2). By continent, South America produced 101.8 million tons (46.8% of the
world total), Northern America & Caribbean produced 83.9 million tons (38.6%) and Asia
produced 27.4 million tons (12.6%) (Table 3). By country, the top producer in the world was
USA., who produced 37.0% (80.6 million tons) out of the world total soybeans, and the second
largest producer was Brazil, who produced 53.9 million tons of soybeans (24.8%). Argentina
was the third producer and produced 41.4 million tons (19.0%). Including China (15.8 million
tons, 7.3%) and India (8.9 million tons, 4.1%), these top five countries produced more than 90
percent (92.2%) of the world total soybeans. Adding Paraguay (3.9 million tons, 1.8%) and
Canada (3.1 million tons, 1.4%), the top 7 countries shared more than 95 percent (95.4%) of total
world soybean production quantity.
In 1961-63, the world total soybean production quantity was 27.4 million tons. At that time,
68.5% out of the world total was produced in the Northern America & Caribbean and 28.3% was
in Asia. South America shared only 1.3%. By country, the USA was already the top soybean
grower and produced two third of the world total production quantity (67.8%, 18.6 million tons).
The second largest producer was China and it produced 6.6 million tons (24.1%). The former
USSR was the third producer (0.4 million tons, 1.5%). The fourth and fifth soybean growers
were Indonesia (0.39 million tons, 1.4%) and Japan (0.35 million tons, 1.3%) respectively, and
Brazil was the sixth producer (0.31 million tons, 1.1%) at that time.
7
As of December 2008, soybean production quantity, area harvested, and yield data are available during the period
by FAOSTAT. The covered period 1961-2007 is divided into 3 terms: 1961-63 to 1981-83, 1981-83 to 1995-97,
and 1995-97 to 2005-07. For example, the 3-year average of 1961, 1962 and 1963 is denoted by 1961-63.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
6
During the period of 1961-2007, the production quantity rose approximately 800% from 27.4
million tons to 217.6 million tons. However, most of the production was due to increased
harvested land as yield only doubled from 1.14 tons/ha (1961-63 world average) to 2.31 tons/ha
(2005-07) (Table 8). The world soybean area harvested approximately quadrupled from 24.0
million ha in 1961-63 to 94.1 million ha in 2005-07 (Table 6). Though every continent except
Oceania increased its area harvested during the period, major production areas shifted from
North America and Asia to South and North Americas in terms of shares of production quantity
and acreage harvested.
Disaggregation into Yield and Area Harvested
From soybean production quantity, P (metric tons), and its area harvested, A (ha), the yield, Y
(tons/ha), is calculated as P divided by A and replaced with Y as follows (Rosegrant et al, 2001
and 2002; FAPRI, 2008):
.
Taking each growth rate, production growth rate ( ) is disaggregated into yield growth rate ( )
and area harvested growth rate ( ) to obtain
.
For the world soybean production’s 44 years of growth, 1961-63 through 2005-07,
(annual average growth). = 3.2% and = 1.6%. From
1961-63 through 1981-83 world soybean production quantity increased from 27.4 million tons in
1961-63 to 86.7 million tons in 1981-83 by 5.93% annually on average (Figure 1 and Table 4).
Out of the annual average growth 5.93%, 3.80% came from area harvested growth and 2.05%
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
7
came from yield growth (Tables 7 and 9). During the term, the world soybean area harvested
increased from 24.0 million ha in 1961-63 to 50.6 million ha in 1981-83 (Table 6) and the world
average yield increased from 1.141 tons/ha to 1.712 tons/ha (Table 8). The 3.80% annual growth
of world area harvested was due to the rapid expansion in the USA (11.2 million ha to 26.7
million ha) and in Brazil (0.3 million ha to 8.3 million ha) and their contributions were 2.2 %
points and 1.6 % points respectively (Table 7). At the same time, however, China’s area
harvested declined from 9.7 to 8.0 million ha and pushed down the world annual growth rate of
soybean area harvested by 0.28 % points.
During the second term (1981-83 through 1995-97), the world soybean production quantity
increased by 2.94% annually on average and reached 133.9 million tons in 1995-97. Production
quantity growth slowed from 5.93% to 2.94% annually. The slowing of soybean production
resulted from decreased harvested growth that declined from 3.80% to 1.52%, lower yield
growth (2.05% to 1.39%). Though the world soybean area harvested increased to 63.5 million
ha in 1995-97, the USA’s area harvested decreased from 26.7 million ha in 1981-83 to 26.2
million ha in 1995-97 and pushed down the world area harvested annual growth by 0.07 %
points. While the USA harvested area was shrinking, India (0.7 to 5.4 million ha), Argentina
(2.1 to 6.1 million ha) and Brazil (8.2 to 11.2 million ha) all expanded, and contributed 0.73,
0.51, and 0.34 % points respectively to the world’s 1.52% annual growth of area harvested. The
world average yield increased to 2.11 tons/ha in 1995-97. The USA (2.51 tons/ha), Canada (2.63
tons/ha), and Paraguay (2.90 tons/ha) showed higher yields while China (1.73 tons/ha), India
(1.04 tons/ha), and Ukraine & Russian Federation (0.68 tons/ha) were below the world average
yield.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
8
During the third term (1995-97 through 2005-07), soybean production increased 4.98% per year
and world production reached 217.6 million tons (2005-07). The rapid annual average growth
was due to significant expansion of 4.0% per year of harvested acres (63.5 to 94.1 million ha).
Brazil (11.2 to 21.9 million ha) and Argentina (6.1 to 15.1 million ha) accounted for two thirds
of the growth annual increasing harvested acreage by 1.4 and 1.2 % points respectively (Table 7).
The world average annual growth rate of yield though slowed to 0.93% (Table 9). Yield in
Ukraine & Russia increased rapidly by 5.27% annually and reached 1.128 tons/ha in 2005-07
and Africa’s yield also rose by 3.26 % annually and reached 1.156 tons/ha. Now there is no area
where the soybean production yield is below 1.00 tons/ha from the viewpoint of average yield by
continent (Table 8). Argentina has the highest yield 2.75 tons/ha (2005-07). Average yield in
South America reached 2.52 tons/ha and is fast approaching average yields in the United States
(2.698 tons/ha) and Canada (2.661 tons/ha).
Method
The soybean production quantities by continent or major countries are estimated as univariate
time series (Equation 1). Out of Box-Jenkins or ARIMA type univariate time series model
employs exponential smoothing with a damped trend (See Gardner and McKenzie, 1985;
Hamilton, 1994; Mills, 1990). Introducing a damped trend into exponential smoothing makes
sense as growth rates in yield and expansion of harvested land begin to plateau. Both linear and
damped trends are estimated for comparison purposes.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
9
Following Gardner and McKenzie (1985) and Gardner (1985), the general damped-trend linear
exponential smoothing model is as follows
8
:
(1)
where is production at time t, is the mean or level of production at time t, is parameter
at t, t is the time trend or year, and is error term at t.
The smoothing equations are:
Level: , and
Trend:
where = smoothed level at t of the series, computed after is observed,
= smoothing parameter for the level of the series,
= trend modification or damping parameter,
= smoothed trend at the end of period t, and
= smoothing parameter for trend.
The error-correction form of the smoothing equations is:
, and
where is a one-period-ahead forecast error.
The forecast for k period(s) ahead from origin t is:
8
In the end, this paper estimated soybean production quantities first then examined three scenarios of yield and area
harvested growths. When using Box-Jenkins methodology to forecast a constructed variable, in our case
, it is not clear whether it is better to forecast A and Y separately to produce the forecast, or to forecast P
directly (Kennedy, 2003). There is no conclusive evidence at all as to the choice between the direct forecast of
aggregated variables (production quantity, P) and the indirect forecasts as the sum of forecasts of the components
(area harvested, A, and yield, Y), whereas indirect forecasts tend to outperform direct forecasts (Kang, 1986). From
the viewpoint of methodology analysis, comparisons between alternative approaches/models are called for.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
10
.
If , the trend is damped and the forecasts approach an asymptote given by the
horizontal linear line or plateau: . The equivalent process is ARIMA (1, 1, 2)
9
which can be written as:
,
where , and
.
If , the model is equivalent to the standard version of Holt (1960) model and the trend is
linear. The equivalent process is ARIMA (0, 2, 2):
where , and
.
Estimation Results
The world soybean production quantity is projected at 278.5 million metric tons in 2015 and
359.7 million metric tons in 2030 in the damped trend case (Table 2). The annual growth rates
are 2.78% from 2005-07 to 2015 (Term 4) and then 1.72% through 2030 (Term 5 in Table 4).
The estimated quantity level in 2030 is approximately 1.7 times greater than that in 2005-07. For
comparison or reference, in the case of linear trend, the quantity level was projected as 289.8
million tons in 2015 and 411.7 million tons in 2030. The annual growth rates are 3.23% in Term
4 and then 2.37% in Term 5. The differences of the 2030 estimated quantities between in the
9
In the general ARIMA (1, 1, 2), .
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
11
damped and linear trend cases are 52.0 million tons and the linear trend case is 1.14 times greater
than the damped trend case. As a moderate projection, we use the damped trend case for
following discussion and examination.
10
By continent, South America increases its production quantity over the projection period and
reaches 214.9 million tons in 2030 (Table 2), producing approximately 60% of world’s soybeans
(Figure 3 and Table 3). Argentine’s production quantity rises rapidly by 5.64% annually in Term
4 and 2.81% annually in Term 5, when it reaches 102.9 million tons in 2030 (Tables 2 and 4). At
that time, Argentina is projected to become the top soybean grower, producing 28.6% of the
world’s output (Table 3). Brazil keeps the position as the second largest soybean producer in the
world and produces 100.7 million metric tons (28.0%) of soybeans in 2030. On the other hand,
the United States becomes the third largest producer (96.6 million tons) and its share declines to
26.9%. Including Canada, the production share in the Northern America and Caribbean is
projected as 28.0% in 2030. Though China and India continue to increase their production
quantities by 18.3 and 14.7 million tons respectively in 2030, the Asia’s share gradually declines
to 11.3% in 2015 and 10.2% in 2030. These top 5 countries will still produce more than 90
percent of the world soybean supply. Ukraine and Russia show the highest annual average
growth rate (5.8% in Term 4 and 3.1% in Term 5) then the share roses above 1.0% in 2030.
Scenarios
Arable land for soybeans is limited and a yield plateau appears to exist at 3.00 tons per hectare
for many countries. Specht et al. (1999) discuss the biological limit to soybean yield
10
The annual average growth rates of world soybean production quantity for the period 2005-07 to 2030 are 2.69%
by the linear trend case and 2.12% by the damped trend case.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
12
improvement in the USA and the 4.00 tons per ha milestone could be achieved between in 2029
(earlier case) and in 2064 (later case). Recent research in Illinois, USA, has shown little yield
growth over since 2000 (Goldsmith, 2008a). On the 2005-07 year average, the USA soybean
yield was 2.70 tons/ha (16.9% greater than the world average yield). Land pressure and public
debates over land use will continue to limit the expansion of agricultural production.
Competition from other crops too may limit the rate of soybean expansion.
Three future scenarios are discussed set and the yield and area harvested are calculated.
• Scenario 1: Yield Annual Growth keeps the same rate (0.93% per year) of Term 3
(1995-97 to 2005-07).
• Scenario 2: Global Annual Yield Level reaches 3.000 tons/ha in 2030.
• Scenario 3: Annual Yield Growth slows to 0.85% annually.
Scenario 1: Yield Annual Growth keeps the same rate (0.93%) of Term 3 (1995-97 to 2005-07)
As a benchmark scenario, yield reaches 2.89 tons/ha in 2030 when the annual growth rate of the
world average yield remains 0.93% during the estimation period. If yield growth were to remain
constant at .93% per year, area harvested has to grow faster by 1.2% annually and total land
under cultivation for soybeans reaches 124.8 million ha to meet expected demand. Such land
expansion is 1.33 times greater than 94.1 million ha in 2005-07 (Figure 4 and Table 10).
Scenario 2: Yield Level reaches 3.00 tons/ha in 2030
Specht et al. (1999) state that the United States soybean yield could reach 4.00 tons/ha by 2029
as earlier case. Genetic and agronomic R&D investments in the leading soybean production
areas and the implementation of technology transfer policies to low-yield areas might easily raise
production in the western hemisphere to 3.00 tons per hectare by 2030. Currently (2005-07)
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
13
North America and the Caribbean average 2.70 metric tons per hectare and South America
averages 2.52 tons/ha. To reach the 3.00 tons/ha target, the average yield growth needs to
increase from its base level of .93% to 1.1%. Even under this positive yield growth scenario the
area harvested would still need to add over 25 million hectares and would reach 120 million
hectares in 2030.
Scenario 3: Annual yield growth slows to 0.85%
Weak intellectual property rights limit private incentives to invest in soybean research
(Goldsmith, 2006). As well increasing demand for liquid biofuels makes maize investment
increasingly attractive. Soybean yield growth may decline with reduced soybean research and
farmer investment in soybean production. Greater land expansion, though unlikely, would be
needed to meet demand. Declining availability of land, higher productivity from competing
crops, and greater sensitivity to maintain native biomes will limit the rate of soybean area
expansion. To meet production forecasts world soybean hectares would need to increase over 30
million hectares to 127 million, if yield growth fell to .85% per year. At that level, the world
average yield is 2.83 tons/ha, the lowest in these scenarios.
Concluding Remarks
This paper projected soybean production quantities by major counties and by continent, using an
exponential smoothing with a damped trend. The world soybean production quantity was
forecasted at 359.7 million metric tons in 2030. If 3.00 tons/ha in 2030 is set as the yield target,
the average yield needs to increase by 1.1% per year and the area harvested will expand to 120
million ha in 2030 (Scenario 2). On the other hand, if the yield growth slows from the recent
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
14
pace of 0.93% to 0.85 % per year (Scenario 3), more than 127 million ha of soybean will be
needed in 2030 to meet world demand.
Arable land on the globe is limited and the competition from other crops restricts the soybean
area expansion. The expansion of farmland will continue to be constrained as the international
community values environmental stewardship and biome preservation. Therefore, yield
improvement appears to be essential for the industry to meet growing demand. Raising yield
might take either or both of two directions: i) substantial R&D investments in genetics and
agronomics to (or beyond) the biological limit in advanced soybean producing areas, or ii)
technological transfers to low-yield areas. Soybean processing firms, livestock managers, and
policymakers, as well as producers, therefore need to relook at the important role of agricultural
research investment and associated intellectual property issues to assure adequate supply in the
future.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
15
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Goldsmith, P. 2008a. Executive Director’s Message. NSRL Bulletin. 15 (1): 7.
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L. A., White, P. J. and R. Galloway. Eds. Soybeans: Chemistry, Production, Processing,
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Hamilton, J. D. 1994. Time Series Analysis. Princeton University Press.
Holt, C. et al. 1960. Planning, Production, Innovation, and Work Force. Prentice-Hall.
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Economic Statistics. 4 (1): 81-86.
Kennedy, P. 2003. A Guide to Econometrics, 5
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Agricultural Commodities and Trade (IMPACT): Model Description. International Food
Policy Research Institute.
Rosegrant, M. W., Paisner M. S., Meijer, S. and J. Witcover. 2001. Global Food Projections To
2020: Emerging Trends and Alternative Futures. International Food Policy Research
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Specht, J. E., Hume, D. J. and S. V. Kumudini. 1999. Soybean Yield Potential – A Genetic and
Physiological Perspective. Crop Sci. 39: 1560-1570.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
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Figure 1. Annual Average Growth Rates for
World Soybean Production Quantity, Area Harvested, and Yield
Note. Term 1: 1961-63 to 1981-83, Term 2: 1981-83 to 1995-97, Term 3: 1995-97 to 2005-07.
Source: FAOSTAT.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
17
Figure 2. World Soybean Production Quantity
Note. Projections start from Year 2008.
Source: FAOSTAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
18
Figure 3. Shifts of Soybean Production Quantity Share by Continent
Note. Years 2015 and 2030 are projections.
Source: FAOSTAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
19
Figure 4. Scenarios for Soybean Area Harvested to 2030
Notes:
Scenatio 1: Yield Annual Growth 0.93%, 2.9 tons/ha in 2030.
Scenatio 2: Yield Annual Growth 1.09%, 3.0 tons/ha in 2030.
Scenatio 1: Yield Annual Growth 0.85%, 2.8 tons/ha in 2030.
Source: FAOSTAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
20
Table 1. Top 7 Countries of Soybeans Production in Years 1961-63, 1981-83, and 1995-97
Year 1961-63
Year 1981-83
Year 1995-97
country tons share
country tons share
country tons share
USA 18,569,669 67.8%
USA 52,855,168 61.0% USA 65,711,000 49.1%
China 6,596,519 24.1%
Brazil 14,141,920 16.3% Brazil 25,076,453 18.7%
USSR 421,333 1.5%
China 9,383,839 10.8% China 13,827,103 10.3%
Indonesia 391,100 1.4%
Argentina 3,973,333 4.6% Argentina
11,862,030 8.9%
Japan 349,600 1.3%
Paraguay 789,178 0.9% India 5,653,033 4.2%
Brazil 313,193 1.1%
Canada 729,933 0.8% Paraguay 2,425,635 1.8%
N Korea 175,000 0.6%
Mexico 681,263 0.8% Canada 2,400,233 1.8%
ROW 586,928 2.1%
ROW 4,149,960 4.8%
ROW 6,918,366 5.2%
World+ 27,403,342 100.0%
World+ 86,704,595 100.0%
World+ 133,873,854 100.0%
Africa + 81,222 0.3%
Africa + 376,481 0.4% Africa + 729,724 0.5%
N Am+ 18,779,373 68.5%
N Am+ 54,272,203 62.6% N Am+ 68,332,223 51.0%
S Am+ 354,849 1.3%
S Am+ 19,140,449 22.1% S Am+ 40,456,153 30.2%
Asia + 7,751,006 28.3%
Asia + 11,667,718 13.5% Asia + 22,466,785 16.8%
Europe + 436,539 1.6%
Europe + 1,179,701 1.4% Europe + 1,840,351 1.4%
Oceania+ 353 0.0%
Oceania+ 68,042 0.1%
Oceania+ 48,618 0.0%
Note. ‘ROW’ denotes Rest of World and ‘+’ total.
Source: FAOSTAT.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
21
Table 2. World Soybean Production (metric tons)
3-Year Average Linear Trend Case Damped Trend Case
continent/country 1961-63 1981-83 1995-97 2005-07 2015 2030 2015 2030
World + 27,403,342 86,704,595
133,873,854 217,597,616 289,755,239 411,698,024 278,472,217 359,693,531
Africa + 81,222 376,481
729,724 1,374,931 1,983,524 2,920,217 1,902,488 2,527,195
Asia + 7,751,006 11,667,718
22,466,785 27,357,042 32,551,478 41,288,560 31,426,774 36,776,406
-- China 6,596,519 9,383,839
13,827,103 15,816,867 17,320,768 20,014,183 16,817,443 18,259,570
-- India 6,000 485,767
5,653,033 8,854,500 12,021,573 17,122,342 11,454,609 14,692,857
-- Rest of Asia 1,148,488 1,798,112
2,986,649 2,685,675 3,209,137 4,152,036 3,154,722 3,823,979
N Am & C + 18,779,373 54,272,203
68,332,223 83,877,488 93,064,475 110,584,352 90,308,207 100,693,740
-- USA 18,569,669 52,855,168
65,711,000 80,581,667 89,311,947 105,714,724 86,796,166 96,565,209
-- Canada 165,465 729,933
2,400,233 3,135,500 3,592,208 4,709,308 3,428,514 4,082,223
-- Rest of N Am & C 44,239 687,102
220,989 160,320 160,320 160,320 83,527 46,307
South America + 354,849 19,140,449
40,456,153 101,789,883 158,393,170 251,587,026 151,173,016 214,943,163
-- Brazil 313,193 14,141,920
25,076,453 53,948,004 78,121,705 117,103,644 74,675,554 100,679,264
-- Argentina 10,366 3,973,333
11,862,030 41,422,367 70,978,772 120,613,154 67,869,814 102,924,789
-- Paraguay 4,422 789,178
2,425,635 3,896,000 4,894,840 6,448,612 4,440,854 5,056,276
-- Rest of S Am 26,868 236,018
1,092,035 2,523,512 4,397,853 7,421,616 4,186,795 6,282,833
Europe + 436,539 1,179,701
1,840,351 3,152,710 3,717,028 5,272,307 3,632,735 4,744,033
-- Ukraine + Russia n/a n/a
302,640 1,495,117 2,631,476 4,688,843 2,486,103 3,906,364
-- Rest of Europe n/a n/a
1,537,711 1,657,593 1,085,552 583,463 1,146,632 837,669
Oceania + 353 68,042
48,618 45,563 45,563 45,563 28,996 8,993
Source: FAOSTAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
22
Table 3. World Soybean Production: Quantity Share
3-Year Average Linear Trend Case Damped Trend Case
continent/country 1961-63 1981-83 1995-97 2005-07 2015 2030 2015 2030
World + 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%
Africa + 0.30% 0.43% 0.55% 0.63% 0.68% 0.71% 0.68% 0.70%
Asia + 28.28% 13.46% 16.78% 12.57% 11.23% 10.03% 11.29% 10.22%
-- China 24.07% 10.82% 10.33% 7.27% 5.98% 4.86% 6.04% 5.08%
-- India 0.02% 0.56% 4.22% 4.07% 4.15% 4.16% 4.11% 4.08%
-- Rest of Asia 4.19% 2.07% 2.23% 1.23% 1.11% 1.01% 1.13% 1.06%
N Am & C + 68.53% 62.59% 51.04% 38.55% 32.12% 26.86% 32.43% 27.99%
-- USA 67.76% 60.96% 49.08% 37.03% 30.82% 25.68% 31.17% 26.85%
-- Canada 0.60% 0.84% 1.79% 1.44% 1.24% 1.14% 1.23% 1.13%
-- Rest of N Am & C 0.16% 0.79% 0.17% 0.07% 0.06% 0.04% 0.03% 0.01%
South America + 1.29% 22.08% 30.22% 46.78% 54.66% 61.11% 54.29% 59.76%
-- Brazil 1.14% 16.31% 18.73% 24.79% 26.96% 28.44% 26.82% 27.99%
-- Argentina 0.04% 4.58% 8.86% 19.04% 24.50% 29.30% 24.37% 28.61%
-- Paraguay 0.02% 0.91% 1.81% 1.79% 1.69% 1.57% 1.59% 1.41%
-- Rest of S Am 0.10% 0.27% 0.82% 1.16% 1.52% 1.80% 1.50% 1.75%
Europe + 1.59% 1.36% 1.37% 1.45% 1.28% 1.28% 1.30% 1.32%
-- Ukraine + Russia n/a n/a 0.23% 0.69% 0.91% 1.14% 0.89% 1.09%
-- Rest of Europe n/a n/a 1.15% 0.76% 0.37% 0.14% 0.41% 0.23%
Oceania + 0.00% 0.08% 0.04% 0.02% 0.02% 0.01% 0.01% 0.00%
Source: FAOSTAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
23
Table 4. World Soybean Production: Quantity Annual Average Growth
Linear Trend Case Damped Trend Case
continent/country Term 1 Term 2 Term 3 Term 4 Term 5 Term 4 Term 5
World + 5.93% 2.94% 4.98% 3.23% 2.37% 2.78% 1.72%
Africa + 7.97% 4.51% 6.54% 4.16% 2.61% 3.67% 1.91%
Asia + 2.07% 4.46% 1.99% 1.95% 1.60% 1.55% 1.05%
-- China 1.78% 2.62% 1.35% 1.01% 0.97% 0.68% 0.55%
-- India 24.57% 17.78% 4.59% 3.46% 2.39% 2.90% 1.67%
-- Rest of Asia 2.27% 3.44% -1.06% 2.00% 1.73% 1.80% 1.29%
N Am & C + 5.45% 1.55% 2.07% 1.16% 1.16% 0.82% 0.73%
-- USA 5.37% 1.46% 2.06% 1.15% 1.13% 0.83% 0.71%
-- Canada 7.70% 8.26% 2.71% 1.52% 1.82% 1.00% 1.17%
-- Rest of N Am & C 14.70% -7.28% -3.16% 0.00% 0.00% -6.99% -3.86%
South America + 22.07% 5.12% 9.67% 5.04% 3.13% 4.49% 2.37%
-- Brazil 20.99% 3.89% 7.96% 4.20% 2.74% 3.68% 2.01%
-- Argentina 34.64% 7.56% 13.32% 6.17% 3.60% 5.64% 2.81%
-- Paraguay 29.59% 7.77% 4.85% 2.57% 1.85% 1.47% 0.87%
-- Rest of S Am 11.48% 10.75% 8.74% 6.37% 3.55% 5.79% 2.74%
Europe + 5.10% 3.01% 5.53% 1.85% 2.36% 1.59% 1.80%
-- Ukraine + Russia n/a n/a 17.32% 6.48% 3.93% 5.81% 3.06%
-- Rest of Europe n/a n/a 0.75% -4.59% -4.05% -4.01% -2.07%
Oceania + 30.09% -2.22% -0.65% 0.00% 0.00% -4.90% -7.51%
Note. Term 1: 1961-63 to 1981-83, Term 2: 1981-83 to 1995-97, Term 3: 1995-97 to 2005-07, Term 4: 2005-07 to 2015, Term 5: 2015-30.
Source: FAOSATAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
24
Table 5. World Soybean Production: Quantity Growth Contribution (% points)
Linear Trend Case Damped Trend Case
continent/country Term 1 Term 2 Term 3 Term 4 Term 5 Term 4 Term 5
World + 5.93% 2.94% 4.98% 3.23% 2.37% 2.78% 1.72%
Africa + 0.03% 0.02% 0.04% 0.03% 0.02% 0.02% 0.01%
Asia + 0.43% 0.68% 0.29% 0.23% 0.17% 0.19% 0.11%
-- China 0.31% 0.28% 0.12% 0.07% 0.05% 0.05% 0.03%
-- India 0.07% 0.43% 0.19% 0.14% 0.10% 0.12% 0.07%
-- Rest of Asia 0.07% 0.07% -0.02% 0.02% 0.02% 0.02% 0.01%
N Am & C + 3.57% 0.88% 0.93% 0.41% 0.34% 0.29% 0.22%
-- USA 3.46% 0.80% 0.89% 0.39% 0.32% 0.28% 0.21%
-- Canada 0.06% 0.11% 0.04% 0.02% 0.02% 0.01% 0.01%
-- Rest of N Am & C 0.07% -0.03% 0.00% 0.00% 0.00% 0.00% 0.00%
South America + 2.58% 1.34% 3.72% 2.55% 1.81% 2.27% 1.35%
-- Brazil 1.83% 0.68% 1.73% 1.09% 0.76% 0.95% 0.55%
-- Argentina 0.80% 0.51% 1.86% 1.34% 0.97% 1.22% 0.75%
-- Paraguay 0.14% 0.11% 0.09% 0.04% 0.03% 0.02% 0.01%
-- Rest of S Am 0.02% 0.06% 0.09% 0.09% 0.06% 0.08% 0.04%
Europe + 0.08% 0.04% 0.08% 0.03% 0.03% 0.02% 0.02%
-- Ukraine + Russia n/a n/a 0.08% 0.05% 0.04% 0.05% 0.03%
-- Rest of Europe n/a n/a 0.01% -0.03% -0.01% -0.02% -0.01%
Oceania + 0.01% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Note. Term 1: 1961-63 to 1981-83, Term 2: 1981-83 to 1995-97, Term 3: 1995-97 to 2005-07, Term 4: 2005-07 to 2015, Term 5: 2015-30.
Source: FAOSATAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
25
Table 6. World Soybean Production: Area Harvested (1)
continent/country 1961-63 1981-83 1995-97 2005-07
Hectare
World + 24,013,348 50,638,358 63,517,042 94,086,520
Africa + 209,020 454,158 870,044 1,189,550
Asia + 11,307,945 10,392,098 15,772,470 19,294,066
-- China 9,744,814 8,002,762 7,984,867 9,197,965
-- India 13,333 693,667 5,419,400 8,197,167
-- Rest of Asia 1,549,797 1,695,670 2,368,203 1,898,935
N Am & C + 11,337,396 27,442,440 27,216,784 31,126,633
-- USA 11,226,667 26,726,405 26,169,667 29,862,550
-- Canada 89,164 335,733 913,267 1,178,467
-- Rest of N Am & C 21,565 380,301 133,851 85,616
South America + 329,633 10,955,015 18,642,499 40,455,435
-- Brazil 298,118 8,280,519 11,151,000 21,877,955
-- Argentina 9,977 2,048,767 6,080,452 15,078,129
-- Paraguay 2,533 491,700 836,053 2,156,667
-- Rest of S Am 19,004 134,029 574,994 1,342,684
Europe + 828,808 1,351,751 988,382 1,999,307
-- Ukraine + Russia n/a n/a 448,370 1,325,323
-- Rest of Europe n/a n/a 540,012 673,983
Oceania + 546 42,895 26,863 21,528
Share World + 100.00% 100.00% 100.00% 100.00%
Africa + 0.87% 0.90% 1.37% 1.26%
Asia + 47.09% 20.52% 24.83% 20.51%
-- China 40.58% 15.80% 12.57% 9.78%
-- India 0.06% 1.37% 8.53% 8.71%
-- Rest of Asia 6.45% 3.35% 3.73% 2.02%
N Am & C + 47.21% 54.19% 42.85% 33.08%
-- USA 46.75% 52.78% 41.20% 31.74%
-- Canada 0.37% 0.66% 1.44% 1.25%
-- Rest of N Am & C 0.09% 0.75% 0.21% 0.09%
South America + 1.37% 21.63% 29.35% 43.00%
-- Brazil 1.24% 16.35% 17.56% 23.25%
-- Argentina 0.04% 4.05% 9.57% 16.03%
-- Paraguay 0.01% 0.97% 1.32% 2.29%
-- Rest of S Am 0.08% 0.26% 0.91% 1.43%
Europe + 3.45% 2.67% 1.56% 2.12%
-- Ukraine + Russia n/a n/a 0.71% 1.41%
-- Rest of Europe n/a n/a 0.85% 0.72%
Oceania + 0.00% 0.08% 0.04% 0.02%
Source: FAOSTAT.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
26
Table 7. World Soybean Production: Area Harvested (2)
continent/country Term 1 Term 2 Term 3
Annual Average Growth World + 3.80% 1.52% 4.01%
Africa + 3.96% 4.43% 3.18%
Asia + -0.42% 2.82% 2.04%
-- China -0.98% -0.01% 1.42%
-- India 21.85% 14.69% 4.22%
-- Rest of Asia 0.45% 2.25% -2.18%
N Am & C + 4.52% -0.06% 1.35%
-- USA 4.43% -0.14% 1.33%
-- Canada 6.85% 6.90% 2.58%
-- Rest of N Am & C 15.43% -6.72% -4.37%
South America + 19.15% 3.61% 8.06%
-- Brazil 18.08% 2.00% 6.97%
-- Argentina 30.50% 7.52% 9.51%
-- Paraguay 30.14% 3.60% 9.94%
-- Rest of S Am 10.26% 10.20% 8.85%
Europe + 2.48% -2.07% 7.30%
-- Ukraine + Russia n/a n/a 11.45%
-- Rest of Europe n/a n/a 2.24%
Oceania + 24.38% -3.07% -2.19%
Growth Contribution World + 3.80% 1.52% 4.01%
(% points) Africa + 0.03% 0.05% 0.04%
Asia + -0.14% 0.64% 0.46%
-- China -0.28% 0.00% 0.16%
-- India 0.16% 0.73% 0.36%
-- Rest of Asia 0.02% 0.08% -0.06%
N Am & C + 2.29% -0.03% 0.51%
-- USA 2.21% -0.07% 0.48%
-- Canada 0.04% 0.07% 0.03%
-- Rest of N Am & C 0.06% -0.03% -0.01%
South America + 2.20% 0.92% 2.91%
-- Brazil 1.59% 0.34% 1.42%
-- Argentina 0.62% 0.51% 1.22%
-- Paraguay 0.15% 0.04% 0.18%
-- Rest of S Am 0.02% 0.06% 0.10%
Europe + 0.08% -0.04% 0.13%
-- Ukraine + Russia n/a n/a 0.12%
-- Rest of Europe n/a n/a 0.02%
Oceania + 0.01% 0.00% 0.00%
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
27
Source: FAOSTAT.
Table 8. World Soybean Production: Yield (tons/ha)
3-Year Average
continent/country 1961-63 1981-83 1995-97 2005-07
World + 1.141 1.712 2.108 2.313
Africa + 0.389 0.829 0.839 1.156
Asia + 0.685 1.123 1.424 1.418
-- China 0.677 1.173 1.732 1.720
-- India 0.450 0.700 1.043 1.080
-- Rest of Asia 0.741 1.060 1.261 1.414
N Am & C + 1.656 1.978 2.511 2.695
-- USA 1.654 1.978 2.511 2.698
-- Canada 1.856 2.174 2.628 2.661
-- Rest of N Am & C 2.051 1.807 1.651 1.873
South America + 1.076 1.747 2.170 2.516
-- Brazil 1.051 1.708 2.249 2.466
-- Argentina 1.039 1.939 1.951 2.747
-- Paraguay 1.746 1.605 2.901 1.806
-- Rest of S Am 1.414 1.761 1.899 1.879
Europe + 0.527 0.873 1.862 1.577
-- Ukraine + Russia n/a n/a 0.675 1.128
-- Rest of Europe n/a n/a 2.848 2.459
Oceania + 0.647 1.586 1.810 2.116
Source: FAOSTAT.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
28
Table 9. World Soybean Production: Yield Annual Average Growth
continent/country Term 1 Term 2 Term 3
World + 2.05% 1.39%
0.93%
Africa + 3.86% 0.08%
3.26%
Asia + 2.50% 1.60%
-0.05%
-- China 2.79% 2.63%
-0.07%
-- India 2.24% 2.69%
0.35%
-- Rest of Asia 1.81% 1.16%
1.15%
N Am & C + 0.89% 1.60%
0.71%
-- USA 0.90% 1.60%
0.72%
-- Canada 0.79% 1.27%
0.12%
-- Rest of N Am & C -0.63% -0.60%
1.27%
South America + 2.45% 1.46%
1.49%
-- Brazil 2.46% 1.85%
0.93%
-- Argentina 3.17% 0.04%
3.48%
-- Paraguay -0.42% 4.03%
-4.63%
-- Rest of S Am 1.10% 0.51%
-0.10%
Europe + 2.56% 5.18%
-1.65%
-- Ukraine + Russia n/a n/a
5.27%
-- Rest of Europe n/a n/a
-1.45%
Oceania + 4.59% 0.88%
1.58%
Note. Term 1: 1961-63 to 1981-83, Term 2: 1981-83 to 1995-97, Term 3: 1995-97 to 2005-07.
Source: FAOSTAT.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
29
Table 10. Scenarios for World Soybean Area Harvested and Yield to 2030
World Total/Average Year 2005-07 Annual Growth Year 2030
a b b/a
Production Quantity (metric tons) 217,597,616 2.12% 359,693,531 1.65
Scenario 1 Area Harvested (ha) 94,086,520 1.18% 124,784,797 1.33
Yield (tons/ha) 2.313 0.93% 2.890 1.25
Scenario 2 Area Harvested (ha) 94,086,520 1.03% 120,213,463 1.28
Yield (tons/ha) 2.313 1.09% 3.000 1.30
Scenario 3 Area Harvested (ha) 94,086,520 1.27% 127,257,326 1.35
Yield (tons/ha) 2.313 0.85% 2.834 1.23
Notes:
Scenario 1: Yield Annual Growth keeps the same rate (0.93%) of Term 3 (1995-97 to 2005-07).
Scenario 2: Yield Level reaches 3.000 tons/ha in 2030.
Scenario 3: Yield Annual Growth slows to 0.85% annually.
Source: FAOSTAT and Author’s Estimation.
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
30
Appendix. Exponential Smoothing Estimation Procedure and Parameters
The estimation was preceded as follows:
i.) As a default, set (liner trend) and the level and trend parameters were chosen to
minimize the mean square error (MSE).
ii.) For damped trend, set and the level and trend parameters were chosen to minimize
the MSE.
Table 11. Parameters for Exponential Smoothing
Continent/Country Linear Trend ( = 1.00) Damped Trend ( = 0.98)
Level Trend Level Trend
Africa + 0.90 0.10
0.90 0.10
-- China 0.60 0.05
0.60 0.05
-- India 0.70 0.05
0.70 0.05
-- Rest of Asia 0.70 0.30
0.70 0.30
-- USA 0.40 0.05
0.40 0.05
-- Canada 0.70 0.05
0.70 0.05
-- Rest of N Am & C 0.40 0.05
0.40 0.05
-- Brazil 0.70 0.10
0.70 0.10
-- Argentina 0.50 0.20
0.50 0.25
-- Paraguay 0.40 0.10
0.40 0.15
-- Rest of S Am 0.70 0.15
0.70 0.15
-- Ukraine + Russia 0.90 0.30
0.90 0.30
-- Rest of Europe 0.90 0.05
0.90 0.05
Oceania + 0.50 0.05
0.50 0.05
iii.) Other continents and the world total were calculated for each trend model as follows:
Asia+ = China + India + Rest of Asia,
N Am & C+ = USA + Canada + Rest of N Am & C,
S Am+ = Brazil + Argentina + Paraguay + Rest of S Am,
Europe+ = Ukraine & Russia + Rest of Europe, and
World+ = (Africa+) + (Asia+) + (N Am & C+) + (S Am+) + (Europe+) (Oceania+).
Masuda, T. and P.D. Goldsmith. “World Soybean Production: Area Harvested, Yield, and Long-Term Projections.”
Under Review. The International Food and Agribusiness Management Review. December, 2008.
31
... Soybean (Glycine max L.) is an economically important crop with versatile end uses [1,2]; serving as an oil seed crop, feed for livestock, food for humans, and biofuel feedstock [3]. Germination enhances the availability of various nutrients such as vitamins, phytosterols, tocopherols, and isoflavones [4,5]. ...
... 1,1-diphenyl-2-picrylhydrazyl (DPPH), total polyphenol and flavonoid contents, and superoxide dismutase (SOD)-like activity of soybean sprouts treated with different concentrations of illite. Samples are defined inTable 1. 2 Gallic acid equivalent.3 Quercetin equivalent.4 ...
... Samples are defined inTable 1.2 Values are expressed as mean ± standard deviation of two replicates. Values followed by different letters in the same row are significantly different (p < 0.05, Tukey test).3 Non-detectable. ...
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Soybean sprouts, a nutritional food product, can contribute to food security because they can be grown within a week and do not require sophisticated technology. The yield and quality of soybean sprouts are influenced by various factors, including seed priming and growing conditions. The objective of this study was to investigate the effects of seed soaking in different concentrations of illite, a clay mineral, on the yield and quality of soybean sprouts. Soybean seeds soaked in five concentrations (0.5%, 1%, 3%, 5%, and 10%, w/v) of illite or tap water for 8 h were named IP-0.5, IP-1, IP3, IP-5, IP-10, and control, respectively. The highest sprout yield was found in IP-3, followed by IP-1, and IP-5, which had 11.1%, 8.8%, and 7.4% increments, respectively, compared to the control. The content of vitamin C, mineral element, isoflavone, total polyphenol, and total flavonoid was higher in many of the illite-treated soybean sprouts than in the control. The overall results indicated that pre-soaking soybean seeds in lower concentrations (0.5-3%, w/v) of illite could be helpful to enhance the yield and nutritional value of soybean sprouts in an easy and inexpensive way.
... Soybean provides more protein and lower levels of saturated fat than most other vegetable grains. In addition, it serves as human diet, livestock, aquaculture, and soybean also serves as a biofuel feedstock (Masuda et al., 2009). ...
... Some of the largest contributors of soybean production worldwide are Brazil, the United States, China, and Argentina. Since 1961, the soybean industry has shown increasing production for several decades (Masuda & Goldsmith, 2009). Almost 40% or 150.1 million tonnes of soybean products have been exported in 2017 at USD 58 billion (Organisation for Economic Co-operation and Development & Food and Agriculture Organization of the United Nations, 2019). ...
Article
Tilapia culture is one of the largest sectors of global aquaculture. Among the different species of tilapia, Nile tilapia (Oreochromis niloticus) is perhaps the top cultured species. The production of Nile tilapia has been continually increasing throughout the years resulting in genetic deterioration. Several tilapia strains with better growth performance and adaptive capability to survive in different culture conditions have been developed to alleviate the crisis. Increased demand for Nile tilapia implies higher farming cost. Plant-based proteins are utilized as partial or complete fishmeal replacements to reduce feed cost. However, these proteins can adversely affect and alter growth and feed performance, carcass composition and indices, and gut and hepatic health. This review discusses the use of seven plant-based proteins: namely, soybean, copra, pea, corn, palm kernel, microalgae, and seaweed as a Nile tilapia aquafeed. Different processing methods are employed to produce several types of plant-based proteins. Processed plant-protein types, when utilized as an aquafeed ingredient, vary in its effect on the performance, hemato-immunological parameters, and gut and hepatic health of Nile tilapia. Studies have shown that Nile tilapia can effectively maximize plant-based protein diets based on the preparation method, type of plant source, amino acid supplementation, and inclusion levels of the plant proteins. These readily available crops should be considered as primary protein sources for aquaculture. Hindrances to the use of plant-based proteins as a main dietary protein are limiting amino acids, presence of anti-nutritional factors, and the competition between its demand as human food and as animal feed.
... The future demand for soybean can be met by increasing farm productivity (Masuda and Goldsmith, 2009). Basically production and productivity can be boosted using two ways. ...
Article
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Abstract: This review investigates the technical, allocative and economic efficiency of soybean production and its determinants in Ethiopia. The review shows that there is huge variability in the technical, Allocative and economic efficiency level of farmers, and some of the farmers’ attained efficiency levels of greater than the average efficiency level. On the other hand, there are farmers operating at lower efficiency level in Ethiopia. Moreover, the result showed that demographic, institutional and socio-economic factors affected technical, allocative and economic efficiency of soybean production in Ethiopia. Based on the review the following recommendations are forwarded. Government should devote a great effort on a reduction in the interest rate, bureaucracies and collaterals of banks on loans which will facilitate credit accessibility to smallholder farmers, while strengthening and establishing both formal and informal type of framers education should be made and also should strengthening the existing agricultural extension system. Keywords:Soybean, Economic efficiency, Determinants, Ethiopia.
... It belongs to genus Glycine wild and family leguminosae, subfamily papilinoideae, and tribe phaseolae, tribe phaseolae is economically most important (Dupare et al., 2008;Ghani et al., 2016). It has been cultivated as cash crops, around 35 countries of the world (Masuda and Goldsmith, 2009). Globally soybean is developed in various part of the world such as USA, Brazil, Argentina and China (Hartman et al., 2011). ...
Research
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Soybean (Glycine max) is a valuable food crop which consists of various nutrients associated with health benefits. Large spectrums of phytochemicals having potential biological properties are associated with soybean. Soybean has 36% protein, 19 % oil, 35% carbohydrates and 5 % minerals. The USA, Brazil and Argentina are the world largest soybean producing countries. During current study soybean variety was evaluated for physicochemical composition to explore potential for utilization in bakery products. The soybean seed was subjected for soymilk production which was used as functional ingredients for the preparation of soy bread. The prepared products were analyzed for its physic-chemical analysis. The sensorial responses of products were checked by authentic 9 point hedonic scale for the boost of soymilk on industrial scale. The bread was arranged by three different concentrations of soybean which were 10%, 20% and 30%. Other constituent that were used in soy bread making were flour, sugar, oil, eggs, salt, baking powder, baking soda and water. The four treatments T0, T1, T2 and T3 were prepared with different soymilk concentration. Treatments were placed to sensory assessment and prepared products were stored for 0, 2, 4 and 6 days at room temperature. The sensory appraisal was observed by the panel of judges to use interior and exterior uniqueness of special soy Cite as: Fareed. N., Shahbaz, M., Farooq, U., Faried. H. N., Shafi, A., Murtaza, M. S., Hayat, K.,2020 Production and quality characterization of soymilk enriched bread. Agric. Sci. J. 2(1):39-50 bread samples. The internals uniqueness was consisted of color, taste, texture, appearance and mouth feel overall acceptability. Treatment T1 and T2 was found to be best among the other treatments and got the maximum score 8.15±0.03 and 7.98±0.03 at T1 and T2, respectively.
... Soybean (Glycine max (L.) Merr.), yields three times more than the protein per hectare of other cultivated cereal crops such as rice, wheat or maize (Masuda and Goldsmith 2009). It is one of the most efficient producers of protein (Khojely et al. 2018). ...
Article
This study simulates the leaf area index (LAI), above-ground dry matter (ADM) and seed yield of soybean grown in an alfisol soil and humid tropical climate of Nigeria, West Africa. It used the calibration datasets for 2011 and 2012 to validate the field experiment conducted at the Teaching and Research Farms, Obafemi Awolowo University, Ile-Ife, Nigeria from September 2015 to December 2015. The model was evaluated using root-mean-square-error (RMSE), mean bias, (MB) and percentage bias (PMB). Model sensitivity tests were also carried out to assess the potential impacts of higher temperatures on soybean growth and development. There were good agreements between model simulations of the crop parameters and the field measurements. The models effectively replicated the observations of LAI (MB = 0.339 kg ha-1 ; PMB = 26%; RMSE = 0.611 kg ha-1) and grain yields (MB = 3.28 kg ha-1 ; PMB = 0.17%; RMSE = 3.28 kg ha-1). Sensitivity tests revealed that additional warming up to 6 o C could reduce VPD (~ 2.0%) and LAI (~ 23.5%). However, soybean ADM and grain yield improved with increase in temperatures near the optimal threshold value during the growing period. Further increase in temperatures by ≥ 4 o C reduced the ADM by ~ 23.8% and the grain yield by ~ 1%. The findings suggested that future warmer climate could have significant negative impacts on the growth and development of soybeans in the study area. HIGHLIGHTS m There were fairly good agreements between model simulations and field measurements of leaf area index, biomass and seed yield of soybeans m The simulations replicated the essential hydro-meteorological features of humid tropical region of Nigeria adequately. m Grain yield and aerial biomass of soybean improved with increase in temperatures by 1 to 4 o C in the seasons. m Temperatures above the optimal threshold value, 30 o C reduced the aerial biomass and grain yield. m In the future, warmer climate could reduce productivity of soybeans in the humid southwest Nigeria.
... Soybean (Glycine max (L.) Merr.), yields three times more than the protein per hectare of other cultivated cereal crops such as rice, wheat or maize (Masuda and Goldsmith 2009). It is one of the most efficient producers of protein (Khojely et al. 2018). ...
Article
Full-text available
This study simulates the leaf area index (LAI), above-ground dry matter (ADM) and seed yield of soybean grown in an alfisol soil and humid tropical climate of Nigeria, West Africa. It used the calibration datasets for 2011 and 2012 to validate the field experiment conducted at the Teaching and Research Farms, Obafemi Awolowo University, Ile-Ife, Nigeria from September 2015 to December 2015. The model was evaluated using root-mean-square-error (RMSE), mean bias, (MB) and percentage bias (PMB). Model sensitivity tests were also carried out to assess the potential impacts of higher temperatures on soybean growth and development. There were good agreements between model simulations of the crop parameters and the field measurements. The models effectively replicated the observations of LAI (MB = 0.339 kg ha-1; PMB = 26%; RMSE = 0.611 kg ha-1) and grain yields (MB = 3.28 kg ha-1; PMB = 0.17%; RMSE = 3.28 kg ha-1). Sensitivity tests revealed that additional warming up to 6oC could reduce VPD (~ 2.0%) and LAI (~ 23.5%). However, soybean ADM and grain yield improved with increase in temperatures near the optimal threshold value during the growing period. Further increase in temperatures by ≥ 4oC reduced the ADM by ~ 23.8% and the grain yield by ~ 1%. The findings suggested that future warmer climate could have significant negative impacts on the growth and development of soybeans in the study area.
... Soybean (Glycine max (L.) Merr.), yields three times more than the protein per hectare of other cultivated cereal crops such as rice, wheat or maize (Masuda and Goldsmith 2009). It is one of the most efficient producers of protein (Khojely et al. 2018). ...
Article
This study simulates the leaf area index (LAI), above-ground dry matter (ADM) and seed yield of soybean grown in an alfisol soil and humid tropical climate of Nigeria, West Africa. It used the calibration datasets for 2011 and 2012 to validate the field experiment conducted at the Teaching and Research Farms, Obafemi Awolowo University, Ile-Ife, Nigeria from September 2015 to December 2015. The model was evaluated using root-mean-square-error (RMSE), mean bias, (MB) and percentage bias (PMB). Model sensitivity tests were also carried out to assess the potential impacts of higher temperatures on soybean growth and development. There were good agreements between model simulations of the crop parameters and the field measurements. The models effectively replicated the observations of LAI (MB = 0.339 kg ha-1; PMB = 26%; RMSE = 0.611 kg ha-1) and grain yields (MB = 3.28 kg ha-1; PMB = 0.17%; RMSE = 3.28 kg ha-1). Sensitivity tests revealed that additional warming up to 6oC could reduce VPD (~ 2.0%) and LAI (~ 23.5%). However, soybean ADM and grain yield improved with increase in temperatures near the optimal threshold value during the growing period. Further increase in temperatures by ≥ 4oC reduced the ADM by ~ 23.8% and the grain yield by ~ 1%. The findings suggested that future warmer climate could have significant negative impacts on the growth and development of soybeans in the study area.
Article
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Soybean has the highest protein and oil content and it is important crop for balance dieted nutrition of human beings and for soil fertility improvement. Productivity of soybean is very low due to low soil fertility and unbalanced plant population. Based on the national soil database, most of North West Ethiopian soils are deficient in macronutrients (N, P and S) and micronutrients (B, Zn) affecting growth, yield and quality of lowland oil and pulse crops. Thus far, emphasis has not been given on assessing influence of inorganic fertilizer and population density on the growth and yield of soybean. Therefore, a field experiment was conducted to determine the appropriate rate of blended NPS fertilizer by optimum plant population in Pawe District in the 2020 under irrigation condition. The treatments consisted of five blended NPS fertilizer rates (0, 50,100 150 and 200 kg ha-1) and four inter row spacing (40,50, 60 and 70 cm). The experiment was laid out in 5x4 factorial arrangements in randomized complete block design with three replications. Data on phenological and growth performance variables were collected and analyzed using SAS version 9.4 software. The analysis of variance revealed that application of blended NPS fertilizer by different inter row spacing significantly (P<0.05) influenced days to 50% flowering, days to 80% pod formation, days to 90% maturity, plant height and primary branch number. However, stand count and days to 50% emergency were not affected by the application of blended NPS fertilizer, inter row spacing and interaction. The highest days to 50% flowering (43.67), days to 80% pod formation (60.33), days to 90% maturity (93.33), plant height (107cm) and primary branch number (22.1) were recorded from application of 200 kg ha-1 blended NPS fertilizer by 70cm inter row spacing; while the lowest values were recorded from the control. The result of this study verified that phenological and growth variables were influenced by different blended NPS fertilizer and different inter row spacing. In conclusion, the above findings indicated that the growth and productivity of soybean (Pawe 1 variety) at study area can be improved by the application of blended NPS fertilizer and using different plant population. However, further study should be conducted at different season and locations. In addition, higher application rates of blended NPS fertilizer with below 40cm inter row spacing should considered for further study.
Article
This study was carried out to determine the effects of different irrigation levels and different tillage and sowing methods on the amount of irrigation water, evapotranspiration, water productivity (WP) and yield in the second crop soybean in Çukurova Region, Turkey. Three irrigation levels were applied (I 100 : completion to the field capacity of the available water of 60 cm soil depth weekly. I 70 : 70% of the water applied to I 100 , I 50 : 50% of the water applied to I 100 ), five tillage and sowing methods were used (T 1 : traditional soil tillage, T 2 : reduced soil tillage, T 3 : reduced soil tillage, T 4 : ridge tillage, T 5 : no-tillage). The research was carried out in a randomized block split-plot design with three replications. The result of, the highest yield was obtained in I 100 x T 1 with 4990 kg/ha, while the lowest yield was obtained in I 50 x T 3 with 3150 kg/ha in irrigation x tillage interactions. When the water consumption values of plants were analysed, the highest was obtained with 632 mm I 100 and the lowest with 399 mm I 50 . When WP values were analysed, the highest was obtained with 8.7 in I 50 and the lowest in 6.6 and I 100 . As a result, full irrigation and direct sowing methods (I 100 T 1 ) are recommended in soybean cultivation considering the highest water-yield relationship in the Mediterranean Region.
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Soybean (SB) production occupies close to 6% of the world's arable land. Soybean expansion is occurring much faster than with other major grains or oilseeds. Soybeans increasingly are being employed as the modern input of choice for buyers. They are mainly used as intermediate food, feed, and industrial inputs, not final consumer products, therefore remaining somewhat invisible in the economy. Only 2% of soybean protein is consumed directly by humans in the form of soy food products such as tofu, soy hamburger, or soy milk analogs. All but a very small percentage of the other 98% is processed into Soybean Meal (SBM) and fed to livestock, such as poultry and pigs. In this way, soybean demand is essentially a derived demand for meat. Soybean has risen to become a leading crop because the income elasticity of meat is high. This chapter provides an overview of soybean production, marketing, and utilization. The future of soybean production and utilization is bright because of the growing demand for protein. The United States continues to be the world's largest soybean producer with some of the world's lowest operating and logistics costs. New opportunities emerged with biodiesel that portend a significant new market for Soybean Oil (SBO).
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Soybean [Glycine max (L.) Merr.] yields in the USA have risen 22.6 kg ha-1 yr-1 from 1924 to 1997, but in the last quarter century (1972-1997) have risen 40% faster, 31.4 kg ha-1 yr-1. This upward trend in on-farm yield is fueled by rapid producer adoption of technologies emerging from agricultural research. Published estimates of the annual gain in yield attributable to genetic improvement averaged about 15 kg ha-1 yr-1 prior to the 1980s, but is now averaging about 30 kg ha-1 yr-1 in both the public and proprietary sectors. Periodic advances in agronomic technology, and a relentless rise in atmospheric CO2 (currently 1.5 μL L-1 yr-1), also contribute to the upward trend in on-farm yield. In Nebraska, irrigated yield averages 800 kg ha-1 more than rainfed yield, and is improving at a 40% faster annual rate (35.1 vs. 24.9 kg ha-1). About 36% of the annual variation in the irrigated-rainfed yield difference is attributable to annual variation in absolute rainfed yield. Inadequate water obviously limits absolute crop yield, but also seems to be an obstacle in terms of the rate of yield improvement. Several physiological traits changed during six decades of cultivar releases in Ontario that led to a genetic gain in yield of about 0.5% yr-1. Changes in some traits were obvious (improved lodging), but more subtle in others (greater N2-fixation, greater stress tolerance). In terms of photosynthate supplied to sinks across a wide range of environments, recent cultivars seem to be superior to obsolete ones. To sustain and enhance soybean yield improvement in the future, technological innovation must be continually injected into the agricultural enterprise.
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This paper is a critical review of exponential smoothing since the original work by Brown and Holt in the 1950s. Exponential smoothing is based on a pragmatic approach to forecasting which is shared in this review. The aim is to develop state-of-the-art guidelines for application of the exponential smoothing methodology. The first part of the paper discusses the class of relatively simple models which rely on the Holt-Winters procedure for seasonal adjustment of the data. Next, we review general exponential smoothing (GES), which uses Fourier functions of time to model seasonality. The research is reviewed according to the following questions. What are the useful properties of these models? What parameters should be used? How should the models be initialized? After the review of model-building, we turn to problems in the maintenance of forecasting systems based on exponential smoothing. Topics in the maintenance area include the use of quality control models to detect bias in the forecast errors, adaptive parameters to improve the response to structural changes in the time series, and two-stage forecasting, whereby we use a model of the errors or some other model of the data to improve our initial forecasts. Some of the major conclusions: the parameter ranges and starting values typically used in practice are arbitrary and may detract from accuracy. The empirical evidence favours Holt's model for trends over that of Brown. A linear trend should be damped at long horizons. The empirical evidence favours the Holt-Winters approach to seasonal data over GES. It is difficult to justify GES in standard form–the equivalent ARIMA model is simpler and more efficient. The cumulative sum of the errors appears to be the most practical forecast monitoring device. There is no evidence that adaptive parameters improve forecast accuracy. In fact, the reverse may be true.
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Most time series methods assume that any trend will continue unabated, regardless of the forecast lead time. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence. This paper develops an exponential smoothing model designed to damp erratic trends. The model is tested using the sample of 1,001 time series first analyzed by Makridakis et al. Compared to smoothing models based on a linear trend, the model improves forecast accuracy, particularly at long leadtimes. The model also compares favorably to sophisticated time series models noted for good long-range performance, such as those of Lewandowski and Parzen.
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The application of time series techniques in economics has become increasingly important, both for forecasting purposes and in the empirical analysis of time series in general. In this book, Terence Mills not only brings together recent research at the frontiers of the subject, but also analyses the areas of most importance to applied economics. It is an up-to-date text which extends the basic techniques of analysis to cover the development of methods that can be used to analyse a wide range of economic problems. The book analyses three basic areas of time series analysis: univariate models, multivariate models, and non-linear models. In each case the basic theory is outlined and then extended to cover recent developments. Particular emphasis is placed on applications of the theory to important areas of applied economics and on the computer software and programs needed to implement the techniques. This book clearly distinguishes itself from its competitors by emphasising the techniques of time series modelling rather than technical aspects such as estimation, and by the breadth of the models considered. It features many detailed real-world examples using a wide range of actual time series. It will be useful to econometricians and specialists in forecasting and finance and accessible to most practitioners in economics and the allied professions.
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A widespread forecasting method within supply chain models is the exponential smoothing method. The use of a particular forecasting method affects the costs of a supply chain. To improve the efficiency of the supply chain costs, this paper introduces the theory of wavelets. An application of this theory to the field of forecasting is wavelet denoising. Results obtained by the exponential smoothing method are compared to the results obtained by wavelet denoising. This comparison is supported by simulation experiments which include incorporation of forecasting algorithms within supply chain models. Different series of simulated data are used for testing these two methods and it is shown that wavelet denoising has an edge over the exponential smoothing method cost-wise.