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AN
ECONOMIC ASSESSMENT OF BIOLOGICAL NITROGEN FIXATION
IN A FARMING SYSTEM OF A THESIS SUBMITTED
TO THE FACULTY OF THE OF THE BY Maureen
Ruth Kilkenny IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER
OF SCIENCE in AGRICULTURAL
ECONOMICS March,
1984 |
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ABSTRACT
Biological
Nitrogen Fixation is investigated as a technical and economic substitute for
fertilizer nitrogen using a systems approach.
Recent agronomic
and farm management data from southeast
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Chapter One INTRODUCTION Nitrogen fertility is the single most limiting factor
in crop production worldwide (Subba Rao, 1978). In advanced agricultural economies, more than
twenty centuries of reliance on biological sources of nitrogen maintained by
crop rotation among grains and legumes, recently became redundant due to
inexpensive industrially produced nitrogen fertilizers. Dependence on fertilizers (manufactured from
natural gas) is now being questioned from several standpoints. Rising energy
costs jeopardize the cost-efficiency of fertilizer nitrogen in crop
production for advanced agriculture (C.A.S.T. no. 5, 1977). Second,
developing country agriculturalists are questioning whether the costly
fertilizer technology is the only alternative to raise grain cultivation
productivity (Sanders, 1980). Third, for some of these areas, acquiring the
large amounts of fertilizers indicated is not physically nor economically
feasible (Hughes and Pearson, 1974). Interest in local sources, especially
biologically fixed nitrogen (BNF) by legumes, is increasing (Ahmed, 1982).
New knowledge about the potential of BNF as a modern alternative to nitrogen
fertilizers has encouraged applied researchers on BNF to develop legumes for
greater agricultural productivity and intensity (Heichel,
1978b). Very little economic research has been done on BNF
per se. Legume based rotations were a common principle and farm management
topic since the heyday of soil fertility research in the 1920's. Heady (1948)
illustrated a product-product model using the BNF ability of legumes. Heady
and Jensen devoted a paper (1951) and a large part of their farm management
text (1954) to crop rotations and legume residual nitrogen. |
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2 In recent years, two trends of economic analyses of
BNF have appeared. Both fall short of describing the true economic
significance of BNF by legumes. Agronomists have used a sort of
partial-budget analysis to find a dollar value for fixed nitrogen. Partial
budgeting is criticized in the Appendix A. On the other hand, economists interested in
agricultural adjustments to energy constraints have prepared sophisticated
math programming models which include some specification of the BNF
alternative to nitrogen fertilizers. (Miranowski,
1979; Walker and Swanson, 1974; Heady, et. al., 1975, 1976 and 1977). These
specifications of the BNF alternative are considered by this author to be
flawed. At this point it is sufficient to suggest that the linear programming
models noted above were constructed without a clear formulation of the BNF
alternative. Therefore, the conclusions and implications about BNF are
probably not valid. Buttel, et. al (1980) have
reported-that a surprising number of studies concerning agricultural
adjustments to increases in energy and fertilizer nitrogen costs do not
include crop rotation-based exploitation of BNF as a substitute for nitrogen
fertilizer. For example, CAST publication no. 68 (1977) rejects the option of
grain legume rotations contending that grain acreage would necessarily be
diverted from production. And, there are other arguments. Doering (1977) argues
that the relative price of fertilizer will determine the adjustments in use,
and proposes that by the year 2000, despite higher prices, fertilizer use
will not decline because it will |
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3 remain cost-effective. He believes that legume nitrogen complements commercial
fertilizer use. Doering and Peart
(1977) deduce that biologically fixed nitrogen recovered through rotations
does now and will continue to provide a portion of the nitrogen required by
grains. But even if nitrogen fertilizer becomes much more expensive, a high
rate of fertilizer application will regain optimal. . OBJECTIVES Given the inadequacies of economic research on this topic, this thesis
is an attempt to refine a method to formulate the relationship between BNF
and the commercial fertilizer alternative in a specific farming system. The
results of this research and analysis are intended to support decision-makers dealing with issues requiring the
identification of cost-efficient farming systems despite availability or
price constraints on nitrogen fertilizer use. The overall objective of this research is to estimate the economic
status of BNF in a specific farming system. Specific objectives are to: 1. construct an
empirical model of the physical substitution between fertilizer and legume N 2. measure the
economic substitution between fertilizer Y and legume N at various levels of
fertilizer N price 3. initiate an
analysis of the economic effect of enhancement of BNF ability of alfalfa with
no direct use or sale value. PROCEDURE Objective one will be met in three steps. Biological nitrogen fixation
will be described and quantified. Crop rotation will be defined in terms of
integrated farming systems. Corn yield response to nitrogen |
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4 for different rotations will be estimated using
functional analysis of variance and To approach Objective Two, a decision rule for
nitrogen fertilizer use will be proposed. The decision rule is a variant of
profit maximization with a cash constraint. A farming system approach will
be applied to the development of a goal-oriented, time-disaggregated linear
programming computer model of an integrated crop and dairy farm. This model
is then verified according to the criterion of mimicking specific farmer
practice. Then the nitrogen price parameter will be ranged. The resulting
solutions will be analyzed by calculating the level of recovery of legume
nitrogen relative to the purchase and use of commercial nitrogen. For Objective Three, enhancement will be assumed to
consist of increasing the level of residual nitrogen available for crops in
successive years after alfalfa and soybeans. The livestock enterprise will
be excluded. SOURCE OF DATA To provide an empirical base and testing ground for
the study, a local area was chosen because of the availability of relevant
data. |
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5 The Southeast Minnesota Farm Management Association
members submit farm records which are summarized in their Annual Reports.
These reports have been maintained for over fifty years in the area.
Therefore, a sizeable body of detailed information exists about the
production activities, inputs, and associated costs and revenues on these
farms. ORGANIZATION The thesis is organized into six chapters, of which
this is the first. Chapter Two covers the physical and agronomic aspects of
biological nitrogen fixation and crop production. In Chapter Three, the corn
response to nitrogen function is estimated and the decision rule for
fertilizer application is formulated. The fourth chapter covers the conceptual
system model, the theoretical framework and incorporates a review of
previous analytical efforts. The model is described and verified. Chapter
Five provides the results of the price-ranging and enhancement analysis. The
exercise is summarized in Chapter Six and the implications are presented.
Appendix A is a critique of Partial Budgeting. The text is accompanied by an annotated
bibliography. The citation style follows a journal-article format, thus
footnotes have been replaced by the annotations. |
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Chapter 2 BIOLOGICAL NITROGEN FIXATION AND CROP ROTATION Nitrogen is 80% of our air and the key building block of both animal
and vegetable proteins. The supply of nitrogen in the earth's soil is
continually being used, depleted, lost and restored. The annual loss of
nitrogen from cultivated soils in the The natural replacement processes include 1) the return to the soil of
ammonia compounds by precipitation, 2) the incorporation of green manures,
and 3) the activities of nitrogen fixing bacteria. The third activity, known
as "biological nitrogen fixation" restores about 200 million tons
of nitrogen to the soil per year, worldwide (Burns and Hardy, 1975). The process of nitrogen fixation is a critical step in the recycling
process of nitrogen. This process is illustrated in Figure 2.1. The term
"fixation" refers to the splitting of the dinitrogen
(N2) molecule that characterizes gaseous nitrogen, and affixing
either hydrogen or oxygen atoms to form N03 – or NH4, known as nitrate and ammonium respectively.
Only in these forms can nitrogen be utilized by plants and animals to build
proteins, which are 18% nitrogen. Symbiotic biological nitrogen fixation is accomplished by bacteria that
infect the root hairs of legume plants (beans, clovers, alfalfa, |
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- 8 etc.). These bacteria are members of the genus Rhizobium, where "rhiza"
is derived from the Greek word for root and "bins" means
"life". These microbes colonize the root hairs in swellings called
"nodules". There they convert atmospheric dinitrogen
into ammonia. This ammonia is quickly circulated in the plant, building
proteins for growth, seed filling or photosynthesis activity. The rhizobia tap into the legume's own sugar supply to meet
their metabolic requirements. Due to the interdependence and mutually
beneficial characteristics of the association of the legume with rhizobium this system is symbiotic. Legumes thrive on much more fixed nitrogen than
other crops. They utilize more nitrogen than the typical un-augmented soil
can provide, if they host the rhizobia. Due to
symbiotic fixation legumes have evolved to be the most protein-rich crops
cultivated today. Compare a ton of soybeans containing 63 pounds of protein
nitrogen to a ton of corn grain which contains 16 pounds. One ton of alfalfa
hay contains 45 pounds of protein nitrogen (Appendix II). Soybean plants
generally derive 40 to 60% of their total nitrogen needs from the soil, while
alfalfa plants derive approximately 50% of their needs, and the remainder
for both species is provided through symbiosis. The majority of the total nitrogen content of
legumes is removed from the soil in harvest. How then can these crops
contribute to soil fertility? It was once believed that during fixation, ammonium
leaked into the - soil. This notion is contrary to the laws of nature. In
fact, it does not generally happen (Vance, 1981). As mentioned above, the
legume must |
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9 share its energy compounds (sugars) with the rhizobia. The legume plant will provide sugars to the rhizobia as long as it needs nitrogen above the quantity
of nitrogen available in the soil. Absorbing soil nitrogen requires much less
energy than supporting the actively-fixing rhizobia
colony. That's why legumes preferentially absorb nitrogen from the soil. If
there is enough sugar energy to go around, the legume will provide sugars to
the rhizobia to provide more nitrogen. When the
nitrogen fixed satisfies the legumes' needs, this process stops. Thus, the
nitrogen from legumes does not leak into the soil. But the decomposition of
legume roots does release organic nitrogen to be recycled by companion crops. Legume-derived nitrogen is released in the soil by
turning under the legume residue. The nitrogen-rich organic matter is
subsequently decomposed by other soil bacteria. Free-living bacteria and
fungi re-transform the nitrogen locked in decaying plant proteins into
nitrate (NO3) or ammonia (NH3). These organisms also
consume ammonia to proliferate. If the organic matter (leaves, roots, etc.)
being decomposed is high in protein nitrogen, a surplus of ammonia accrues in
the soil. Thus, decomposition of alfalfa roots and crowns results in a
greater addition of nitrogen to the soil than decomposition of wheat
straw--which is so low in nitrogen content that the decomposition of wheat
straw actually depletes the soil nitrogen (Giddens,
Arsjad, and Rogers, 1965). In the |
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10 without a legume crop. During the periods while a legume crop is
cultivated, the strain that is preferred by the legume will thrive. Specific
chemical surface characteristics of the rhizobia
strain aid in host-symbiont recognition. Some
strains of rhizobia are more competitive
in establishing the symbiotic infection and forming nodules. A strain which
establishes the legume roots and which fixes nitrogen without requiring too
much of the legume's sugar energy is considered effective and efficient. Environmental factors have considerable impact on the persistence,
competitiveness, effectiveness and efficiency of the symbiotic association
(Gibson, in Hardy and Gibson, 1977). Soil moisture (not too dry) and
temperature (not too hot) and soil pH (not acid) affect persistence. The
timing and intensity of daylight (photoperiodicity) also affects efficiency.
Toxicities such as excessive salinity or aluminum toxicity which occurs in
acid soils also constrains symbiotic fixation by harming the legume plant.
Good soil moisture and appropriate maintenance of soil nutrients, using lime
to correct acidity, promotes optimal conditions for both nitrogen fixation
and legume crop growth (Hera, 1979). In-summary, biological nitrogen fixation is a major part of the natural
nitrogen cycle. The symbiotic mode of biological nitrogen fixation
accomplished by rhizobium bacteria in the roots of
legumes can play a critical role in the provision of nitrogen in modern
agriculture. There are two ways to capture the biologically fixed nitrogen
from the symbiotic system of legumes and rhizobia
common in the |
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11 protein-rich food or feed. Or the legumes can be managed
as a green manure crop and the incorporation of the residues into the soil
will provide additional soil nitrogen to a subsequent crop. This is one way
to practice crop rotation. It is important to consider whether or not these
methods are mutually exclusive and to quantify value of each approach. The
following pages present some estimates of BNF of alfalfa and the potential of
alfalfa in rotation to supply nitrogen to corn. ENERGY AUDITS OF Over 40% of the energy consumed in In the In contrast with |
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12 He calculated the energy required for crop
production and expressed the values in an "energy audit." "This symbiotically fixed nitrogen might be
viewed as energy credit because it lessens the need to purchase the usual
quantity of manufactured fertilizer for a succeeding grain crop. Thus,
rotation incorporating alfalfa holds promise for increasing the energy
efficiency of grain production systems and may have a hitherto unappreciated
role in reducing the vulnerability of agriculture to increased energy
prices." In a related study, Heichel
(1978b) showed that a 36 percent reduction in fossil energy flux could be
achieved when corn is grown in an annual rotation with alfalfa, with only a
14 percent yield loss. An energy-balance approach was applied to compare
corn grown continuously with various rotations in an |
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13 rotation sequences and continuous corn. Crop rotation,
therefore, appears to be the most energy efficient approach to producing
crops in the Crop rotation is only one of many possible
adjustments of the farm enterprise to constrained energy availability. Other
possible adjustments include reduced tillage, in-field corn drying, use of
livestock manure, switch to lower yields at lower fertilizer application
rates, or increased continuous cropping of soybeans. Of these alternatives,
only crop rotation and continuous soybean cropping embody direct substitution
of biological nitrogen fixation for energy-intensive commercial nitrogen
fixation. The following pages describe crop rotation with legumes and provide
estimates of the nitrogen and non-nitrogen benefits to the farm associated
with grain-legume crop rotations. CROP ROTATION Crop rotation, as defined by Yates (1954), is
"a definite cycle (repetitive sequence of crops) grown in successive
years on the same land.," The separate crops in a rotation
(the crop grown each season) are called "courses". Rotation of crops
has been a common practice to maintain soil fertility since the dawn of
modern agriculture. Ancient Greeks rotated grain crops with fava beans, as described by Theophrastus in 300 B.C., and
according to Pliny, Romans turned lupins and
alfalfa under as green manure. During the Middle Ages, tilled land was
rotated among grains, legumes, and fallow. Crop rotation similar to modern
versions was practiced by farmers in |
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14 In the Despite the availability of commercially-produced
nitrogen after the opening of a Haber-Bosch plant
in the Lyon and Bizzell (1933)
showed that the more often alfalfa occurred in the rotation, the more
nitrogen was secured by subsequent crops. They documented a net benefit of
192 lbs. of nitrogen per acre after nine years of continual alfalfa cropping.
They also showed that the natural accretion of nitrogen in soils without
legume cultivation amounted to 25 lbs./acre/year. Haynes and Thatcher (1955)
reported their conclusions about rotations and fertility over time. With no
nitrogen fertilizer additions, crop rotations maintained the productivity of
soil, but could not improve soil quality beyond an upper limit. Continuous
unfertilized corn cropping seriously diminished soil productivity. Graphic
illustrations of these results are reproduced in Figure 2.2. |
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16 Experiments to quantify the importance of crop rotations originally
focused on identifying the types of crops, typically legumes, that would
provide the maximum crop yield benefit. Alfalfa was recognized as the most
effective rotation crop. It was assumed that yield benefits were due to both
nitrogen and "something else". As the use of nitrogen fertilizers
spread, more scientific attention was focused on specifying the nature and
magnitude of these effects known as the "nitrogen effect" and the
"rotation effect", respectively. The Nitrogen Effect As late as 1955, experiments commenced on the Morrow Plots to compare
nitrogen fertilizer with manure and/or crop rotation treatments for corn. The
two results found were (1) that
fertilizer treatments most quickly reinstated soil fertility but (2) that
fertilizer did not entirely substitute for rotations. The following
paragraphs present evidence of the extent to which fertilizer and nitrogen
recovered from rotations substitute for each other. Nitrogen contained in the residues of legumes is available to plants
over time, at the rate established by the microbial action decomposition
process. Also, as mineralization of these nitrates occurs, some is lost to denitrification, some leaches out of the soil profile,
and some is metabolized by the decomposition organisms themselves. Voss and Pesek (1962) estimated actual first-year legume nitrogen
availability from alfalfa at 123 to 200 lbs. per acre, and second-year
nitrogen at 54 to 83 lbs. per acre. Fribourg and
Bartholomew (1956) estimated legume nitrogen from a good stand of alfalfa at
100 lbs./acre. |
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17 These estimates resulted from two different types
of tests. Voss and Pesek chemically analyzed the
soil. Fribourg and Bartholomew compared yields of
fertilized corn at known nitrogen soil levels with rotation corn yields. The
range in available nitrogen estimates was attributed to variations in initial
soil fertility, cultivars, weather/soil moisture and other confounding effects,
the most important of which are the relative "efficiency" of
nitrogen from legumes or fertilizer, and the non-nitrogen "rotation
effects". The concept of efficiency refers to the rate at
which nitrogen is recovered by crops from various sources. If legume-derived
nitrogen was utilized by a crop at the same rate as fertilizer nitrogen,
pound for pound; it would be considered to be 100% efficient. Shrader and Johnson (1959), Sutherland, Shrader and Pesek (1961), Boawn, Nelson and Crawford (1960) and Voss and Pesek (1962) estimated the efficiency of legume nitrogen
at between 16 percent and 92 percent of fertilizer nitrogen. The conclusions
that use of commercial fertilizer was more efficient "pound for
pound" than reliance on BNF of alfalfa or other legumes only further confounded
the comparisons between crop rotations and fertilizer use. Mooers (1930), Schmid (1959) and Shrader,
Fuller and Cady (1966 established that corn yield data could be fit on one
common function relating yield to nitrogen from either manure, legume
residue, or fertilizer. Thus, the "common response curve" as
illustrated in Figure 2.3 became a tool to compare the productivity of
different forms of nitrogen inputs without having an exact measure of
efficiency. For example, the nitrogen from symbiosis in legume residue is
quantified in terms of "fertilizer nitrogen equivalents." A
fertilizer nitrogen equivalent is: |
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19 “the
quantity of nitrogen from whatever source such as soil, manure or legumes
which was needed to obtain the same yield as was obtained with a pound of
nitrogen supplied as ammonium nitrate.” Shrader,
Fuller, and Cady (1966). In figure 2.3, observe that the nitrogen equivalent of the crop rotation
CCOM1 is between 120 and 160 pounds of nitrogen. The Rotation Effect Experimental evidence of the non-nitrogen effect of rotations was
concurrently piling up. Corn grown in rotations with legumes yielded more
than continuously grown corn even at excessive levels of nitrogen fertilization
(Welch, 1976). The concept of fertilizer nitrogen equivalent therefore also
incorporates the non-nitrogen yield enhancing "rotation effect".
This rotation effect is a separate consequence of crop rotation alone. Up to a point, an equivalent yield of corn can be obtained using
fertilizers as with crop rotation. But if both rotated corn and continuous
corn are fertilized, the rotated corn can yield significantly more than
fertilized corn, no matter how much nitrogen fertilizer is applied. This is
the consequence of the "rotation effect". Baldock,
et. al. (1981), reported a rotation effect as 15 percent of the total
yield-enhancing effect of alfalfa in a CCCOA rotation. To date, the cause of
the rotation effect has not been satisfactorily explained. Here are some
propositions that have been positively tested: The "rotation"
effect is due to: 1) effects of plant growth regulators that stimulate growth (auxins and cytokinins) left in legume residue (Baldock,
et. al. 1981). 2) beneficial effect of deep root structure of legumes on soil tilth. (Barber, 1972). |
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20 3) avoiding autotoxicity:
continual cropping of the same species of crop depresses its own yield
(Hicks, 1981). Crop rotation interrupts continuous cropping. 4) relative buildup of phytotoxic
substances is reduced due to increased soil aeration under alfalfa (Barber,
1972). 5) reduced soil-borne disease infestation; (Curl,
1963); (Litsinger, 1976). 6) reduced concentration of denitrifying bacteria
that may interfere with efficiency of fertilizer nitrogen, ( The yield benefit associated with rotations is a
combination of the nitrogen effect and the rotation effect. This varies among
crops. Sundquist, Menz,
and Neumeyer (1982) propose that the yield response
of corn after soybeans is characterized almost entirely as a rotation effect.
In contrast, the main effect on corn in an alfalfa rotation is a nitrogen
effect. For the remainder of this thesis, the focus will be on the
corn/alfalfa rotation as an alternative to nitrogen fertilized continuous
corn. MANAGING ALFALFA FOR BNF The preceding discussion leads to the conclusion
that alfalfa provides the greatest crop rotation benefits of the |
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how symbiotic activity nitrogen carry-over are affected
by repeated harvesting; (3) how the timing of harvest, vis-à-vis the nitrogen
nutrition of alfalfa affects its winter hardiness; and (4) how symbiotic
activity varies over a typical four-five year perennial stand of alfalfa. Can
alfalfa be also managed as an annual and still provide comparable nitrogen
carry-over. benefits to corn as does perennial alfalfa? Alfalfa has been cultivated as a perennial for four
to six years and exploited as livestock feed, then plowed down and followed
by corn with consistent success for decades. Boawn,
et. al. (1963) reported that over half of the nitrogen taken up by fertilized
corn following harvested perennial alfalfa was from the decayed roots or from
the pool in the soil. But recent: research suggests that other management
alternatives with shorter time-horizons exist. Over the decades, yields of alfalfa have improved
as a result of proper liming and improved soil phosphorous and potassium
management. Recent studies reported by Higgs (1976) and others relate the
quantity of nitrogen that can be available after the alfalfa course to the yield
of alfalfa in terms of total dry matter production. A healthier stand of
alfalfa can also fix more nitrogen, so the relationship is positive: more
alfalfa implies larger quantities of organic nitrogen, to a maximum level.
Consequently, first and second-year yields of corn following alfalfa have
increased as alfalfa yields have increased. In addition, Vance and Heichel,
et. al. (1978) have found that although repeated harvesting impairs symbiotic
nitrogen fixation temporarily, this does not force alfalfa back to reliance
on soil nitrogen. |
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22 Results show that the quantity of soil nitrogen
remaining after an un-harvested alfalfa crop and a repeatedly harvested one
(in a year) are roughly the same. Thirdly, Heichel (1981) has suggested that through genetic selection,
nitrogen fixation of alfalfa could be some positive multiple of the current
level without a significant impairment of the production of hay. The largest
quantity of nitrogen from legume cultivation can be captured if a full re-growth
is plowed-down in fall, but the quantity contained in the roots and crowns
alone is quite substantial. (Jokela (1981)
conservatively estimates it at 90 lbs/acre.) Currently, research is underway
to identify the differences in nitrogen available from plowed-down crowns of
heights from 5 to 12 inches. In perennial
alfalfa management, the timing of the last harvest and the level of regrowth affects winter-hardiness. Some studies suggest
that the last cutting of alfalfa be done early in the fall to allow time for
re-growth and fixation before the dormancy period. Others suggest a very late
cutting, so that the cold weather will suppress growth and the nitrogen in
the roots will not be drawn into the vegetative parts. It is not clear which
technique eventually maximizes both yields and available nitrogen after the
perennial stand. But for an annual stand, Heichel,
Barnes and Vance (1981) recommend an early September, late summer final cut
and then immediate plow-down. That way both the seeding-year and second year
of alfalfa can contribute significant quantities of nitrogen. |
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23 ROTATIONS IN THE FARMING SYSTEM In addition to the yield-enhancing effects of crop
rotation with legumes, and the supply of livestock feed, crop rotation
provides a range of benefits to the farming system as a whole. Heady and
Jensen (1954) listed twelve ways in which rotations built stronger farms: 1.
prevent soil erosion 2.
maintain soil productivity 3.
control weeds, diseases and pests 4. help soil drainage 5.
spread labor and machinery and power (use) over the season 6. provide
livestock feed 7.
lessen risks and uncertainties 8. provide cash income 9.
adjust to rainfall limitations and (crop) moisture needs 10. use land in most
profitable crops 11.
allow soil-building crops frequently on each field 12.
select the crops best adapted to each soil. The following is an overview of current evidence
supporting the above postulates that have not been previously discussed.
These are points (1), (3), (4), (6), (7), (9). Points (5) and (10) concern
the allocation of farm resources over time to maximize farm earnings. These
issues will be investigated in detail later, in the modeling analysis. Erosion Control Evidence supporting the first assertion in the list
above is institutionalized in the formulation of the Universal Soil Loss
Equation. The USLE is documented in USDA Handbook 537. Soil
erosion can be calculated from knowledge of land slope, soil
characteristics, rainfall and crop management practices. Continuous alfalfa
cultivation is least prone to erosion and therefore serves as the reference
erosion index. Reduced tillage employed with crop rotation is an effective
means of |
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24 maintaining soil fertility concurrent with erosion
control, (Holt, 1979). Sixty percent of the commercial nitrogen fertilizers
applied on Cultivation of alfalfa can therefore reduce erosion
potential two ways: (1) by providing excellent ground cover against
rain-erosion, and (2) by serving as a green manure crop, even with no-till
techniques. "Monoculture
is a convenient cropping practice for growers, but it is also convenient for
insects." Gordon
Barnes, "Insect Control" (1980) Continuous cropping allows pathogenic organisms to
continue their life cycles uninterrupted. Many pathogens attack only a
limited range of crops, and cannot survive when the host plant is absent.
Inserting a |
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25 botanically unrelated crop into the cultivation sequence
can be an effective measure of control. The following discussion of the role
of alfalfa in crop rotation for pest reduction is summarized from the
excellent survey by Curl (1963). Rotating grain with alfalfa is a successful method
for controlling fungal diseases. The organic residue from alfalfa
decomposition produces fungi-toxic substances. Soil which is high in organic
matter favors predatory fungi that destroy nematodes. These are forms of
"biological control"--the promotion of antagonistic or predacious
interferences among soil organisms. Rotation also is beneficial for the
alfalfa because it interrupts the alfalfa root-rot infestation. Soil micro-flora have a variety of nutritional
requirements. Since living plant roots and crop residues qualitatively alter
the soil, this regulates the activity of microbes and plant pathogens in the
environment. Nutrient deficiencies affect a plant's susceptibility to disease.
Crops on land previously in alfalfa may have sufficient nitrogen, which helps
reduce the damage of a fungal attack. But alfalfa depletes soil potash (K)
and phosphorous (P). Subsequent crops could suffer deficiencies in these
nutrients and fall prey to a pathogenic attack. Litsinger and Moody
(1976) describe how crop rotation controls corn rootworm, the most severe
pest in the local corn belt. Ninety percent of the dollar value of pesticides
applied to corn is to control corn rootworm (Sundquist,
Menz, Neumeyer, 1982).
Seven percent of the total cash cost in corn production could be saved if
crop rotation is practiced, since applying corn rootworm pesticides would be
unnecessary. |
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26 An empirical study by Klepper,
et. al. (1977), in the corn belt area found that crop rotation was the most
popular technique of pest control on organic farms. Yield and profit pictures
from these farms were comparable to those of local conventional farms. In summary, crop rotation is a fundamental and
widespread practice of pest control for soil-borne diseases. Where chemicals
or disease-resistant varieties are employed, crop rotation is an important
supplementary measure. If pathogens develop immunities, or persistent strains
occur, crop rotation provides the best method to control these soil-borne diseases. Environmental Consequences Alfalfa's very deep tap root system facilitates
greater aeration of the soil and improves the soil tilth,
(Blake, 1980). These are important factors promoting drainage. Water-logged
soils promote the process of denitrification, a
reversal of the fixation processes. Good drainage precludes water-logging,
but it brings other problems. Nitrate is highly soluble and subject to
leaching out of the soil profile. Fertilizers applied as nitrates are largely
leached out of the soil during a rain. Ammonia is far less soluble, but the
action of soil microorganisms converts ammonia into nitrate (nitrification)
and then it too can be leached away. Leaching interferes with nitrogen uptake
by the intended crop and causes downstream nitrate pollution. Rates of
recovery by the crop and retention of nitrogen fertilizer in the soil, even
without leaching, are commonly around 60% of the quantity applied (Swanson,
et. al, 1978). Thus, particularly under rainy conditions, the use of |
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27 nitrogen fertilizers can be both inefficient and
environmentally destructive. Two studies which considered the value of crop
rotation with legumes toward reducing nitrate pollution are Walker and
Swanson (1974), and Olson, Heady, Chen and Meister (1977). In the first study
about reducing nitrate pollution potential to zero (called an "on farm
nitrogen balance") crop rotation was the optimal strategy modeled. The
second study did not account for legume nitrogen carry-over, but found that
under nitrogen fertilizer-use constraints rotation of corn and soybeans is
optimal. Seasonal Moisture and Yield Stability Legume nitrogen becomes available to a subsequent
crop as the microbial action on the legume residue releases it. This method
of nitrogen nutrient provision has been shown to be more reliable than use of
fertilizer nitrates under both excessively wet or drier-than-average
conditions. While heavy rain will leach fertilizer nitrates out of the soil
profile, the timed release of mineralizing legume nitrogen is merely
postponed. The organic nitrogen is not lost. On the other hand, during
seasons of lower than average precipitation, the corn rotated with alfalfa
displayed a positive yield effect; whereas, fertilized corn suffered a yield
loss according to studies by Barber (1972). Research by Higgs, et. al. (1976)
and "Consistent
response of rotations over years despite variation in seasonal suitability
for corn production indicates the significance of the use of rotation in a
management program aimed at high yields." (p. 24, Bolton, 1976). |
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28 Diversification against Risk Crop rotation reduces risk in two ways. First, the
relatively more consistent high yields of corn grown in rotation despite
weather variation is discussed above. Second, crop rotation implies
diversification during any one cropping season. Cropland is allocated to the
various courses of different crops with different management requirements.
Parcels of the farm will be in different stages of the rotation sequences at
any one time. Diversification not only provides insurance despite adverse
weather, but also against variation in crop revenues. The farm operator can
be assured of a harvest of the types of crops that survived the bad pests or
weather. Of if the price of one crop falls, his other crop revenues serve to
maintain at least a minimum income (Kim, 1981). Livestock Feed Jacobs and Stricker
(1976) argue that BNF is best exploited by growing legumes for livestock
feed. Alfalfa hay has long been prized as a feed for livestock, but its
popularity was constrained by the high fiber-to-nutrient proportion in mature
alfalfa. Although mature alfalfa is difficult for livestock to digest, young
pre-bud alfalfa is ideal. Improved understanding of both ruminant digestion
mechanisms and alfalfa management has stimulated interest in pre-bud alfalfa
as feed (Conrad, VanKeuren and Hibbs,
1978; and Rohweder and Baylor, 1980). As suggested earlier, management of alfalfa for
both high yields and the provision of nitrogen carry-over is possible
following guidelines of Martin (1979) and Heichel,
Barnes and Vance (1981). Pre-bud |
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29 alfalfa supplies high levels of protein and a good
proportion of fiber to the ruminant animal. It compares favorably with all
other feeds (Appendix III). With proper cutting management, it also supplies
significant nitrogen to the subsequent corn crop. NET BENEFITS OF CROP ROTATION The term "crop rotation" does not by
definition imply alternating cultivation of grains and legumes. Nevertheless,
it is obvious that crop rotation has become synonymous with the practice of
grain-legume rotation because of the superiority of legumes in rotations to
improve the quality of the soil and to provide fixed nitrogen. In section one of this chapter two methods of
exploiting BNF, not mutually exclusive, in a corn belt farming system were
presented. One is to capture the organic nitrogen of the legume crop residue
for a subsequent grain crop. The other is to directly utilize the nitrogen
protein rich legume crop as feed. The question of the economic value of BNF
for The
investigation of the economics of the crop rotation as a means of providing
nitrogen starts with the identification of the input and a description of
alternative sources. By describing BNF and crop rotation, some clues about
the implicit and explicit costs of symbiotic or legume nitrogen are exposed.
First, the soil must be prepared to support the microbial population. Then a
microbe population must be established if it does not already exist. Then a
legume must be cultivated. All of the expenses associated with these
activities are explicit |
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30 costs.
Coincidentally, the farm operator must forego the cultivation of the target
grain or cash crop if he chooses to cultivate the legume alfalfa. This is an
implicit cost. The operator must weigh these costs against the benefits of crop
rotation, harvest and use of legumes. The benefits of crop rotation have been
enumerated above. Many of these benefits are not derived in terms of saleable output, as in the case of improved soil tilth
or reduced erosion. Some benefits are elements of alternative sets of farm
inputs, such as the biological pest control due to crop rotation instead of
insecticides; and alfalfa hay as a high-quality livestock feed instead of
protein supplements. These outputs of crop rotation/legume cultivation are inputs for other
farm activities. To a certain extent, allocating farm resources to legume
cultivation activities will actually increase the productivity of corn
cultivation as it increases the fertility of the soil, etc. Thus there is a
level of crop rotation, given a target farm income, up to which the
cultivation of the two crops would not be competitive for farm resources, but
actually "complementary" (Heady, 1954). This proposition is the basis for the modeling and analytical effort of
this thesis. In order to proceed, some generalizations of the .local
specific agronomic and economic relationships fundamental to the question
must be formulated on the basis of the information contained in this chapter.
This is the substance of the following chapter. |
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Chapter Three NITROGEN IN CROP PRODUCTION Nitrogen and Productivity Nitrogen is the most limiting factor in corn
production since the adoption of high-yielding varieties of corn and the
technique of high plant density (Englestad and Terman, 1966). With the advent of corn hybrids, which by
1939 were cultivated on over half the acreage in the corn belt, (NFDC, 1970),
nutrients were more rapidly depleted from the soils than ever before.
Therefore, high soil fertility became a prerequisite for maximum yields. Figure 3.1 illustrates the importance of high
nitrogen fertility today. The two response curves compare the nitrogen
response of the native corn varieties with the nitrogen response of hybrids.
The location of the intercept of the hybrid corn nitrogen response curve
indicates that the hybridization alone accounts for at least one-third of the
total productivity increase. The accelerated slope of the curve displays
evidence of the greater response of hybrids to nitrogen. Therefore, while crop rotation was sufficient to
maintain adequate fertility for high yields of non-hybridized corn through
the 30's and fertilizers were used to complement rotations; after WWII the
roles of rotations and fertilizers reversed. Nitrogen fertilizer use had an unprecedented impact
on (1) crop productivity through the development of the hybrid corn (TVA,
1971); (2) land productivity by allowing more dense corn populations per acre |
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33 and enhanced yields (BLS, U.S. Dept. of Labor, 1952).
Coupled with the steady N-fertilizer price decline (Loomis, 1957; Tennessee
Valley Authority 1979; Fertilizer Institute 1980), these factors exerted
incredible influence supporting a switch from rotation to continuous cropping
relying on nitrogen fertilizers instead of BNF to maintain soil fertility. THE GENERALIZED MISTERLICH-SPILLMAN FUNCTION The response curves in Figure 3.1 are examples of
the functional form that could potentially be used to model corn response to
nitrogen. They are quadratic curves. Other functional forms include parabolic
(G. W. Cooke, 1982) and the "LRP", Linear
Response and Yield Plateau (Lanzer and Paris,
1981), and the Misterlich function. The Misterlich form is
commonly employed because it is an elegant formula that reflects the main
characteristics of the response of corn to nitrogen. Misterlich
interpreted the famous nineteenth century scientist von Leibig's
"Law of the Minimum" for asympotic
regression. The Law of the Minimum contains two assertions: (1) that crop
growth is proportional to the availability of the most. limiting essential
nutrient and (2) that nutrient substitution is extremely limited. Spillman reformulated
the expression for asymptotic regression as an exponential response curve.
The formula employed today (Baldock, et. al.,
1981), known as the Misterlich-Spillman functional
form is: y = y* - d(e-EN) |
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34 where y denotes estimated corn yield y* is the maximum yield attainable with added
nitrogen d is the
difference between the highest and lowest yields, * i.e.,
when N = 0, y* - d = y minimum. N is the level of added nitrogen e denotes natural.log E is a curvature parameter specific to nitrogen,
given the other factors. Figure 3.2 is an illustration of the Misterlich-Spillman function. The features of corn
response modeled so clearly are (1) the yield plateau is easily read from the
equation at y* , the asymptotic maximum (2) the minimum, unfertilized yield
is also easy to identify as the intercept, and (3) the curavature,
indicated by - E shows how intense the local response to nitrogen is. SITE-SPECIFICITY In fact, environmental conditions, soil type, and
climate determine whether the response increases at a steady high rate or a
low rate; (i.e., the curve is steep or gently-rounded) and also establish the
level of the maximum yield. Therefore, the appropriate functional form may
vary among agro-climatic regions. An example of the variations in response to
nitrogen of the same cultivar of corn among five locations in |
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37 rules as to how much nitrogen to apply are based on the
response curve (the production function). The recommendations will therefore
also vary among locales. DISCRETE VS. CONTINUOUS INPUTS Fertilizer nitrogen can be employed in continuously
divisible units.. The response in yield of a crop to additional
units of fertilizer can be plotted as a continuous curve. This implies a -different
type of analysis of response than for the discrete or lumpy input of nitrogen
available through crop rotation. Biologically fixed nitrogen is a lumpy input. It
can be recovered in a discrete quantity only if a legume crop was previously
cultivated. The lump sum quantity recoverable is a function of the type of
legume and management. This nitrogen is fixed in the soil under the legume
crop and cannot be varied or moved from site to site. The appropriate analytical approach to quantify
treatment effects of lumpy inputs is functional ANalysis
Of VAriance, whereas for the continuous input, OLS
regression can be used. Figures 3.4.a and 3.4.b illustrate the two approaches
(Dillon, 1966). The former approach does not generate a coefficient for the
independent variables that relates the magnitude of the effect on the
dependent variable. (Indeed, there is no logic in relating another "unit
of crop rotation" per acre to the yield of corn per acre.) But ANOVA provides evidence if significant
differences exist between factors and among levels of factors in an
experiment. The fact that legume nitrogen and fertilizer
nitrogen effects can be measured on the common N response curve (Ch. 2) only
meets part of the |
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39 challenge in the comparison exercise. The relative
productivity of fertilizer and legume-derived nitrogen with rotation effects
must still be determined from mixed data. Figure 3.4.c illustrates how both
OLS and ANOVA analytical techniques can be used. Analysis of variance is
employed on a data set to determine if a statistically significant difference
exists between rotations. If it does, OLS regression can be used on the
separate rotation data sets to generate distinct response curves. When
plotted in the same units, the nitrogen effect and the rotation effects can
be measured as the horizontal and vertical differences between the response
curves. CORN RESPONSE TO NITROGEN IN The following presentation summarizes the
estimation of two corn response to nitrogen functions from experiment station
data (Appendix VI). Six years, 1975 to 1980 inclusive, of rotation
experiments were conducted in Waseca county, which is within the Southeast
Minnesota Farm Management Association area. In the experiment, corn was
cultivated with six levels of added nitrogen (1) continuously, (2) after a
year of soybeans, (3) after wheat, and (4) after alfalfa established with
wheat. The two-step analytical approach introduced above
was employed. First, a 3 x 6 x 6 factorial design model was analyzed with a
multi-analysis of variance software package “IVAN”. The factors were
considered as Ri, Nj,
and Wk as follows: Ri for rotations,
i - 1, 2, 3. (The wheat-corn rotation was not of
interest.) |
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season's weather is the factor that influences yields.
This was modeled in proxy by the year. The model analyzed was: Y(IJK) - R(I) + N(J) + W(K) + RN(IJ) + RW(IK) +
NW(JK) + RNW(IJK). This model implies that a corn yield observation is due to
three main factors: the type of rotation, the level of added nitrogen, and
the weather that year. In addition to the main effects, synergistic effects
among those factors influence yield. The analysis highlighted (1) statistically
significant differences (probability less than .001) between continuous corn
and rotation corn (2) strong interactions between weather (years) and
rotations, (p < .05) and significant differences among years (p <
.001). In fact, weather in 1975 and 1976 was adverse for corn. There was a
drought one summer and early rains that delayed planting in both years.
Figure 3.5 illustrates the continuous corn yield data as a function of
nitrogen for each year. The |
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43 legume rotations mitigated the depressing effect on
yields of the weather, the spread was much less prominent among years for the
soy-corn and alfalfa/wheat-corn rotations. The graphs of corn yields over
nitrogen levels by years among rotations illustrate this (Figure 3.6). After testing various functional forms according to
the goodness-of- fit criterion of lowest standard error for interpolation,
the MisterlichSpillman functional form was
identified as the best model. Two functions were estimated from half of the
complete data set. These are the continuous corn and corn after soybean
response functions for "good weather years". The alfalfa (wheat
established)-corn response curve was close to indistinguishable from the
soy-corn response curve. This is presumed to be due to the wheat crop's
concurrently high nitrogen sink. The bad weather years of 1975, 76, and 1980
were dropped from the data set to reduce the variation due to weather in
order to focus on the nitrogen response. It is assumed that farmers expect
normal weather conditions and fertilize at the rate which will generate a
good yield. The generalized Misterlich-Spillman
form is, as above, y*i = yi - di(e -EiNij) where: yi
denotes predicted yield of corn in the ith
rotation. yi is the maximum
yield observation from the data for the ith
rotation di is the
difference between the observed minimum and maximum yields data for the ith rotation e denotes the natural log Ei is the
efficiency parameter for nitrogen in the ith
rotation |
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A response curve for corn following alfalfa that
was cultivated at least for two years (a "full stand" or "full
crop") is needed for the analytical activity in this thesis. From other
sources, notably the 1981 Soil Test Guide for |
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46 Organic nitrogen contained in the roots and crowns of alfalfa is
available over time at a rate illustrated in Figure 3.8; Heichel,
personal communication (1982). 60% of the total organic nitrogen is available
the following year, 30% more after two years, and the remainder available in
the third year. This means that if 90 lbs. is 60% of the total quantity of
legume nitrogen that could eventually be recovered by the corn grown in
successive years, then the total quantity is 150 lbs. The estimates of 45 and
15 lbs. in the subsequent years reflect 30% and 10% of 150 lbs. NL,
respectively. These nitrogen effect parameters imply leftward shifts of the
continuous corn response curve. But there is also the rotation effect. Again, the Waseca data was
consulted. The maximum yield observations for the alfalfa/wheat-corn rotation
was found to be 190 bu./acre. By incorporating this
information and the yield x N pair to correspond with a 90 lbs. nitrogen
effect which gives the same yield (154 bushels/acre) as corn at 130 lbs. N,
an interpolated response curve could-be estimated. The resulting equation
is: AC yield = y = 190 - 65 (e -.0148N) This equation implies a 19 bushel rotation effect. The magnitudes of
the nitrogen and rotation effects thus estimated are well within the range of
estimates for rotation corn in It is assumed that none of the rotation effect persists for the benefit
of second year corn, so that the estimated response curve is based on a 45
lb. nitrogen effect alone. Also, no rotation effect is assumed for third year
corn. Figure 3.9 presents the estimated and extrapolated response curves for
those five different courses of corn in |
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49 ECONOMIC DECISION RULES FOR NITROGEN APPLICATION The neoclassic theoretical economic model for
determining the quantity of an input (such as nitrogen fertilizer per acre)
in production expresses the optimal level of input use as a function of the
output price, the marginal productivity of the input in the production
function, and the input cost. Profit maximization implies that the marginal value
product is not exceeded by the marginal cost of an input. In the vernacular,
the last dollar spent on fertilizer returns a dollar's worth of product. This
relationship is derived as follows. Define profits as revenue minus costs due to
production. Let p denote an output sale price vector, f(x) denote the
production function using the x vector of n inputs, and w denote the input
(factor) cost vector: (1) π
= pf(x) - wx The optimization problem is to choose the level xi
for each ith input in the process f(x) such that
profits are largest possible. The limits of profitability are due to the
production function--the physical output possibilities from given inputs. Assuming that the profit function is strictly
concave, the maximum profit is determined analytically where the first
derivative of the profit function with respect to the choice variable x is
zero: (2) pf’(x) -
w = 0 This is the first-order condition for profit
maximization, and it is equivalently stated and interpreted as follows: (2.1) pf'(x) =
w |
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50 The Marginal Value Product (MVP) equals Marginal Factor
Cost (MFC). This relationship will obtain for every input: (2.2) P
∂ f(x)/∂xi = Wi for all i. Figure 3.10 illustrates the application of the
first order conditions for profit maximization to the corn/nitrogen problem
using the response function estimated earlier. From this data, the
economically optional rate of nitrogen to apply per acre is found to be 195
lbs. nitrogen, which is 237 lbs. of anhydrous ammonia. This "economically optimal rate" was not
the rate actually applied by the The modeling effort for this thesis will require a
fertilizer rate decision rule that will mimic the response of |
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52 Studies that attempt to explain the apparent
lower-than-optimal nitrogen application rates for corn comprise five general
topics. These are (1) the notion that fertilizer use technology has not yet
been fully adopted by farmers, (2) that the inherent riskiness
of yield depressing over-fertilization, or loss of the investment due to
unpredicted adverse weather, encourages farmers to use nitrogen conservatively;
(3) the initial level of soil fertility alters the value of the marginal
product of nitrogen and interferes with the farmer's subjective decisions
about rates; (4) that a high price elasticity of demand implies that some
type of optimal rate is being applied; and (5) that
"capital-rationing" is employed--i.e., a minimum rate of return per
dollar expenditure is subjectively established by the farmer, and the
expenditure on fertilizer is bounded by it, regardless of the low price of fertilizer
relative to its marginal value. Empirical evidence suggests that farmers are now
fully aware of the "optimal" fertilization strategies. Therefore,
it is not out of ignorance that the techniques are not fully adopted, and
technical progress lag theories do not sufficiently explain the current
underutilization. Three studies on the question of a conservative
response by farmers .to subjective ideas about weather-related
risk were reviewed. Dry weather could disadvantage lush fertilized corn to
the point that cobs don't mature; and wet spring weather could render all the
applied fertilizer useless since rain leaches the nitrates down and way from
the corn roots where it was needed. Experiments conducted over a seven-year
period designed to estimate the magnitude of year-to-year variations in
economically optimal estimates for fertilizer nitrogen rates concluded |
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54 high initial levels of soil fertility, resulting from
improved grain-legume rotation management. The study by Swanson, Taylor and Welch (1973) introduced
the theory that fertilizer demand is relatively price-insensitive because
fertilizer is employed at levels where the revenue exceeds the cost. They
propose that this differential is a cushion between the cost and the return
to nitrogen fertilizer, so that farmers are not compelled to reduce
fertilizer application rates even through the price increases relative to
corn prices. Theory suggests that at optimal factor employment levels, a
change in the factor price would result in a larger change in factor
employment than would result if factor employment was sub-optimal initially.
Therefore, the low price elasticity would imply that the neoclassically
optimal level of fertilizer is indeed' being applied. A high price
elasticity would imply that it's not. But this does not explain why the
strategy is chosen. AN ALGORITHM FOR FERTILIZER RATE The most plausible explanation for the deviation
between neoclassically-prescribed economically
optimal fertilizer application rates and actual farmer practice in |
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57 available cash. If the price of corn is net-out as a
scale factor, this relationship can be expressed as a constant function of
the nitrogen input price: ∂f(N) = .20 =.87
(8) ∂N .23 This relationship results from the behavior of a
farmer who expects a return of over 2.2 dollars per each dollar allocated to
purchase each input, assuming the price of output doesn't change. A critical assumption is required in order to
employ this fixed .877 MPP/MFC ratio condition as an algorithm for fertilizer
application rate. This rationing formula must be assumed to hold at all
prices of nitrogen fertilizer, all other things constant. Then the formula is
employed simply by interpreting the level of nitrogen use implied to equate ∂F(
N* ) = (price
of nitrogen)(.877). ∂N An example of how the nitrogen fertilizer rate is
obtained from the production function, cost and price information, and the
assumption of cash-rationing is shown in Figure 3.12. Consider a nitrogen
price of .23$/lb. 100% N. The level of nitrogen required to obtain F'(N) =
(.23)(.877) is 130/lbs. for corn grown continuously. This is read from the
graph through the MPP functions for the continuous corn rotation to obtain
the indicated levels of fertilizer application that maintains the
relationship. For corn after soybeans, that level is 116 lbs. For corn in an
alfalfa rotation it is 104 lbs. of 100% N. In order to employ this algorithm the following
generalized formulas were used in addition to (3) the production function and
(6) the marginal |
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59 productivity formula, to find MPP* and N* , the optimal
marginal productivity and level of application of 100% nitrogen according to
the fixed returns/cost ratio of 2.22: MPP* = [(2.22)(PN)] (1/P c) (9) N*
= [ln(MPP*/(E*d)] (1/E) (10)
where Pc, PN are the current nominal prices of corn per bushel and
per lb. of 100% nitrogen. E, d
are the curvature and difference parameters for each rotation corn response to nitrogen function. The algorithm compares favorably with historical
data on nitrogen fertilizer use. Assume that farmers base their decision on
fertilizer rate given only previous years' corn prices plus past and current
nitrogen fertilizer prices. Assume also that farmers' expectation of corn
prices 7 to 12 months later is a simple average of previous and expected
higher future prices. Over the thirteen years 1970 through 1982, taking the
average price P received for corn as an expected price, the formulas (9) and
(10) provide prescriptions for the rate of 100% N application as a function
of the revenue/cost ratio, the nitrogen price, and the response parameters.
These are the estimates illustrated in Figure 3.11; page 55. The dashed line
in Fig. 3.11 indicates the suggested trend for the years 1974 through 1982.
Prior to 1974, both nitrogen and corn prices were significantly different
from post-1974 prices. Thus the prescribed rate is a poor mimic of actual
practice for those unusual years. The prescription for the nine years after
1974 is quite similar in both level and direction of response to PN
changes to the actual data. In contrast |
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67 the neoclassically prescribed
rate peaks and toughs in an inverse relationship to the actual data. The fertilizer rate decision algorithm will be used
to model the demand response to fertilizer price fluctuations. The algorithm
incorporates the notions of cash-rationing, the constraints due to the
agronomic possibilities for corn production in the specific area, and the
current parametric input prices. In the context of the farming system model
to be discussed in the next chapter, it will provide a straightforward
formula to construct the appropriate corn production functions at different
nitrogen prices, given a ceteris paribus output price environment. |
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Chapter Four MODELING There are four parts to this chapter on modeling.
The first part applies the rudiments of the system-analytical approach to
describe the THE SYSTEMS APPROACH The procedure followed in the construction of the
model is illustrated in Diagram 4.1. Defining, analyzing, constructing and
validating the model requires more than simply carrying out a sequential set
of steps. At each phase there is continual rechecking, feedback and
reformulation. For example, this research started with the question,
"What is BNF worth in |
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63 This more narrow specification of the research
question does not imply a less wholistic analytical
approach than the broader question would have. BNF is a factor in three
subsets of a Boundaries An essential concept in systems analysis is that of
the boundary, which delimits the system being analyzed. Systems are hierarchical
(each system is a subsystem of a larger system). The analyst limits the analysis
to a manageable study by stipulating the boundary. Within the boundary, values of variables are
determined endogeneously. Outside of the boundary
they are parametric. The upper boundary for this study includes the farm and
excludes the market, such that the quantity of farm output sold has no effect
on market prices, but market prices determine farm output. The lower bound is
set at the crop-soil interface. Therefore, fertilizer demand and crop yields
are a function of soil and other environmental parameters, but soil microbial
activity is determined exogenously. |
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64 These concepts are illustrated in Figure 4.2. The
hatched area encloses the crop, livestock, and internally produced input
transfer subsystems which are directly relevant to the model. The system
environment contains the driving variables, such as prices. Driving variables
can be defined in this example as those parameters faced by the farmer that
influence his decision-making but are not affected by the outcome of those
decisions. Other aspects of the system environment as defined
for this problem include the fixed equipment and capital of the farm, the
land and it's soil characteristics, weather, and the genetic load of the
relevant cultivars. The entire crop subsystem is constrained by a fixed
quantity of farmland, and a specified machinery complement. A relatively
short-run period of time is assumed wherein no land or capital equipment
purchases are allowed to occur. The number and types of tractors, plows,
etc.; and the capacity of the shelter and milking equipment is set at the
average level documented in the Southeast Minnesota Farm Record accounts,
1981-82. Maintaining a ceteris paribus fixed factor complement should
help avoid misinterpretation of confounding adjustments. If the model were to
be designed to compare an investment in BNF and crop rotation to, for
example, corn drying technology or on-farm gasohol production as farm adjustments
to energy constraints, then setting the boundary to include equipment or
other capital structures would be required. Since the model is not postulated
to identify optimal farm adjustments to parametric shocks, avoiding such
provisions is acceptable. |
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66 The soil must be defined in terms of initial fertility, organic matter,
tilth, and type (clay or silt, etc.) because these
factors define further fertility requirements and management considerations
such as how soon after rains can the soil be worked, and how far and how
rapidly nitrates leach out of the profile. These characteristics were
researched from a soil test guide for The nitrogen-fixing capacity of the alfalfa (or soybean) rhizobia subsystem is one subset of the genetic load
environment which is defined and fixed exogeneously.
Another is the yield response of corn to the management and environmental
variables facing the farm. Interactions
and Driving Forces Designating the associated management responses to these fixed characteristics
in the system environment as invariant features simplifies the model of the
farming system. If an activity neither affects nor is affected by the level
of exploitation of BNF, then it can be parameterized without restricting the
validity of the modeling implications. Also, the interdependencies among activities must be characterized. The first step in this modeling process is to identify the driving
variables and the interdependencies between activities on the farm which
directly affect a farmer's decision to exploit BNF from rotations and/or use
nitrogen fertilizer. At the heart of the modeling problem lies the
relationship between biologically-fixed nitrogen and commercially applied
nitrogen. The driving variables which determine the level of activity of |
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67 this subsector are (1) the
relative price of commercial nitrogen and the derived-demand for nitrogen as
an input to the corn production activity, and (2) the supply of legume
nitrogen as a joint product of soybean or alfalfa production. Production of legume nitrogen as a joint product of
alfalfa production is driven mainly by the derived demand from the livestock
sector for alfalfa as feed. The livestock system is in turn driven by the revenue
from milk. Feed can be either produced on farm or purchased, so the market
price of feed is also related to the derived-demand for alfalfa. The types
and quantities of feed must be endogeneous to the
model to mimic the flexibility of the decision-maker, given his farm
resources to produce feed and the market alternatives. The supply of legume nitrogen as a joint product of
soybean production is driven by the relative price of soybeans to corn which
are the two marketable crops. The farmer maximizes his profits by allocating
acreage to the more profitable crop, given his equipment and time constraints. The various crops substitute for each other on the
market and in - the feed ration, so all of the crop and feed prices, and all
of the costs associated with each alternative will affect the
decision-maker's choices. How much fertilizer to purchase depends on which
crop rotation is followed, and on the acres devoted to corn in the rotation.
For these reasons, all of those subsystems are inside the system boundary and
are interconnected as illustrated in Figure 4.2. |
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68 LINEAR
PROGRAMMING Of the available simulation modeling techniques, linear programming is
best suited to this problem for at least four reasons. First, the L.P. format
allows a large number of variables to be considered simultaneously. This
permits analysis of the interrelationships among technical alternatives in a
whole-farm context. Second, the method relies on response analysis and
direct-cost functions, insuring that both the agronomic and economic
functions relevant to the question are incorporated. Third, the price
environment is modeled parametrically. Fourth, by using a dynamic LP formula
it is possible to approximate the timeliness of crop production activities
and the time-related value of flow resources such as labor. The next section
contains a discussion of several previous applications of mathematical
programming to problems involving a tradeoff comparison between
legume-derived nitrogen and nitrogen fertilizers. REVIEW OF RESEARCH All four of the studies reviewed here included the question of the
substitutability of BNF for commercial fertilizer as only one of a set of
questions on farm adjustments to energy price increases and/or to controls on
nitrate pollution. Unfortunately very little information was provided in the
published papers which documented the models about how the substitutability
was quantified. The following reiterates each specification of the
relationship between Nf and NL
and a discussion of the results and shortcomings. |
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69 Farm Level Models Miranowski (1979)
developed a farm-level model to evaluate energy use reducing alternatives
for typical grain/livestock farms in the Substitutes for commercial fertilizers are legume-N
and manure. Legume-N is supplied by alfalfa and by soybeans (levels
unspecified) in rotation with corn. Two levels of fertility are modeled,
reflecting two levels of corn yields. This implies that a very narrow range
of adjustment of fertility/yield is possible in response to a fertilizer
price parameter increase. In the base scenario, the model farmer grows corn
continuously on more than three quarters of the cropland and a CCOM rotation
on the rest. This base plan differs drastically from perceived practice in
the corn belt as. documented in Sundquist,
Neumeyer and Menz (1982)
pages VII-7, who report that only nine percent of corn belt corn acreage is
actually in corn grown continuously, while 48 percent is typically in a
corn-soybean rotation.. Although this varies among regions of the corn belt,
nowhere do farmers on the average cultivate more than 50 percent of their
acreage in corn in any sequence. Miranowski's base
plan does not change even at a doubling of energy (and therefore fertilizer)
prices. Miranowski concludes that
such results, "indicate insensitivity to moderate increases in energy prices ...
because direct energy costs
account for less than 8% of total agricultural production costs." |
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70 An alternative explanation of such results is that the model as
constructed without a range of fertility/yield alternative and without
appropriate disaggregation of time is both a poor
simulation model and insensitive to input price parameters. Without an
appropriate disaggregation of the time periods,
the crop mix chosen may inaccurately reflect the existing time and weather
related constraints on flow resources, and thus specialization towards
continuous corn production appeared optimal (Baker and McCarl,
1982). Only at a five-fold increase in energy and fertilizer price levels did
the crop pattern alter towards CCS and COMM rotations. This thesis avoids two major weaknesses of the Miranowski
study. First, this analysis will provide a description of a variety of corn-nitrogen
fertility levels. This means that more than two fertility levels will be used
for the nitrogen-fertilizer price-ranging analysis. Second, the model developed
for this thesis will be time-disaggregated to a higher degree, to the extent
that a reference or base farm plan reflects actual crop diversification of
the target area of The second farm-level model was developed by Walker and Swanson (1974)
to assess the impact on dollar returns less direct costs to the farm, and the
adjustments of crop activity on a cash grain farm, of (1) a fertilizer quota,
and (2) restrictions on the nitrate pollution potential. The model is
structured to generate an on-farm nitrogen balance account. In this way the
numerous sources and sinks of nitrogen for farm use are modeled. This is
represented in Figure 4.3 which is a reproduction from their paper. |
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72 Although the model's nitrogen balance is calculated
exclusive of a livestock sector, the authors suggested that an analysis which
includes livestock is a relevant extension of the work. In contrast, this
thesis is based on the premise that the substitutability of legume-derived
nitrogen for commercial fertilizer alone is insufficiently profitable to be
adopted. Direct use of the legume is a necessary feature for crop
rotation to be efficient under relevant circumstances. In When restrictions on runoff of fertilizer and/or
restrictions on the quantity of commercial fertilizer use are imposed, a
rapid drop in farm profits is observed as acreage is shifted toward corn
rotations with an alfalfa crop for which there is no other use than as green
manure. As with Miranowski's
model, the base plan is continuous corn, and this plan is quite stable. Out
of the possible 50 constrained scenarios of fertilizer quota cum nitrate
balance, half of the farm plans are continuous corn; 6 are corn-soybean
rotations, and the remainder are mainly |
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73 soybeans. There is no mention of time-disaggregation. Thus, the criticism of the
specialization tendency of time undisaggregated
models is certainly germane in this case as well. The shortcomings of the Walker and Swanson approach
fall mainly into two categories. One, the model may be improved by disaggregation of the time-related constraints such that
the base plan appears more typical. This would surely have consequences under
all scenarios implying different results. Two, the production function for
corn and the substitutability of organic and commercial nitrogen needs
clarification and possible reformulation. Both of these issues are dealt with
in this thesis. A general shortcoming of farm-level models is the
assumption of parametric prices vis-a-vis
aggregation. If the farm level model results are being used to assess
aggregate farm responses to changing conditions and to formulate policy, the fact
that crop prices have not been modeled endogeneously
allows an upward bias on prices, for example, favoring soybean production. In
actuality, if all farms responded similarly and flooded the market with
soybeans, the soybean price would fall too low to justify the level of
production indicated. General Equilibrium Econometric Simulation Olson, Heady, Chen and Meister (1977) produced a
national model, using quadratic programming, that incorporated the market
supply price response. Prices as well as quantites
of agricultural outputs and inputs are determined endogeneously.
They proposed to assess the optimal |
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74 response to constraints on nitrogen fertilizer. But they
did not formulate any substitution alternatives for commercial nitrogen
fertilizer. That report epitomizes the approach many analyses take which
ignore the potential of legume-grain rotations. It also suggests the seriousness
of endogenized crop prices when assessing a farm
choice of alternatives when one input price fluctuates. The implication for
this thesis is that if large crop output fluctuations results from the analysis,
then quadratic programming should be employed to reflect market feedback. National Farm Sector Model The final model considered is by Nicol and Heady (1976), a national farm sector model with
endogenized market prices. It has been elaborated
upon and employed to analyze many various farm sector problems. The problems
concerning fertilizer use include a specification of substitutability
between legume nitrogen and commercial fertilizer nitrogen. Unfortunately,
the documentation of this substitution relationship contains contradictory
explanations. There are two formulations: (1) the contribution of legume N
increases the yield of grain above the trend level fertilization rate yield.
(page 106 of Nicol and Heady). This means that for
example, under normal circumstances farmers would not be able to benefit from
rotations because they would be applying excessive fertilizer and getting
only a rotation-effect additional yield increment. The second formulation is
presented three pages later. (2) Legume nitrogen substitutes directly for
commercial fertilizer, reducing the quantity of applied fertilizer required
to obtain the reference yield. |
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75 This approach seems to capture the economic potential of
rotations, but this formulation of substitutability has some flaws; to be
explained as follows. The supply of legume nitrogen carried-over is
assumed to be a function of the legume crop yield, with respect to time. Nicol and Heady developed the following equations in
consultation with W. Shrader to express
carried-over nitrogen as a function of yield: N1=50.0 *Y-5.0Y2+ .2 Y3 N2 = 8.5 * Y - (81.5) .8Y where N1 and N2 denote legume nitrogen available in the
first and second years after a legume crop and Y denotes the legume harvested
in units of tons of dry matter. As an illustration, applying these algorithms to
yield data of a good stand of alfalfa at 85 percent D.M. gives an N1 estimate
of 137 lbs./acre and an estimate of N2 at about 50 lbs./acre. This implies a
total net contribution of 190 lbs. N/acre from alfalfa, which is entirely
within the previously estimated ranges discussed earlier in this thesis. It
is not, however, clear how these algorithms can be applied to soybeans. It
seems that the nitrogen contribution of soybeans will be overstated. Apart from the lack of clarity concerning which
method is actually employed and how the nitrogen contribution from soybeans
is calculated, there is another, more fundamental question. This question is
whether or not it is correct to express the available legume nitrogen as a
function of yield when the legume is not harvested or removed from the
field. Results of agronomic experiments by Professors Heichel
and Vance (19781981) suggest that this is not the case. For example, the
no-harvest, |
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76 green
manuring zero "yield" approach to an
alfalfa rotation provides a similar nitrogen benefit as that of a normal
harvest and an early fall plowdown approach. Implications These four papers represent the existing analyses wherein BNF is
assessed as farm input. None of the attempts have clearly explained how the
substitution relationship is modeled, and each of the models have additional
shortcomings such as lack of time disaggregation, unvalidated base plans or lack of general equilibrium
(market price) effects also leading to overspecialization. Each paper that incorporates the BNF benefit to the farming system does
so by tying the nitrogen remaining in the soil after a legume to a crop
rotation sequence in order for that nitrogen to have economic value. If the
legume nitrogen was not recovered by another fertilizable crop then it would
not have a value since the nitrogen credit would be redundant. The model
developed for this thesis will also tie the recovery of legume nitrogen to a
crop rotation sequence. The other problem with the farm level models is that only a few
discrete corn fertility alternatives are available. To assess the nature of
the substitutability between fertilizer nitrogen and legume nitrogen it is
possible to range the fertilizer price and observe the adjustments in
quantities of each input demanded. But without a more complete specification
of the production function (corn response to nitrogen) the models above
remained insensitive to gradual price changes. |
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77 THEORETICAL
FRAMEWORK In this section the theoretical underpinnings of
the analytical approach to linearly programming the BNF/fertilizer nitrogen
substitutability problem are presented. First, the concepts of profit maximization
for math programming, substitution between inputs and substitution between
production techniques are explained. Then, a list of the economic, agronomic
and technical criteria relevant to the problem is compiled. The section also
includes discussions of the limitations of the L.P. technique and the steps
taken for this thesis to overcome them. Profit Maximization for Math Programming It has been demonstrated by numerous studies that
American farmers generally behave as profit-maximizing economic agents. Cases
in which they do not are usually explained by the effects of imperfect
knowledge, uncertainty, asset fixity, and the length of biologically based
production processes. As entrepreneurs, farmers must make the usual short
run managerial decisions of how much to produce, and to a certain extent,
what to produce, although because of the biological nature of agricultural
production, what to produce usually is an intermediate or long run decision. For this study the corn-response to nitrogen function
developed for Chapter 3 will serve as a single variable input production
function as illustrated in Figure 3.9. In the short run the profit-maximizing
farmer chooses the production process defined by the combinations and levels
of inputs, the sum of which can be illustrated by the corn production function.
He surveys his fixed equipment and considers the costs of variable |
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78 inputs
needed to produce crops on his endowment of land. Then he chooses inputs not
simply to maximize output, as described below. The usual decision rule is to maximize returns above variable costs.
Because farmers typically do not think about paying themselves or family
workers for their labor, management, and equity capital, the concept of
"gross marginal" is both useful and logical term to use to describe
how farmers interpret profit maximization. The gross margin is defined as the
gross revenue minus variable cost. It is the dollar return over costs
resulting from the sales of production due to short-run decisions. This
includes both profits and a return to fixed resources, and will be the
measure to be maximized as a proxy for profit in this analysis. The unconstrained neoclassical determination of the level of input use
which maximizes profit is not suitable for the problem in which resource
constraints exist. The constrained optimization algorithm of linear
programming is well-suited to this type of problem. The algorithm applied to
a linear programming problem consists of simultaneous comparison of a fixed
number of production functions subject to the resource constraints. The
analytical question of "how much" is necessarily reformulated into,
"What use, if any, is to be made of a resource given the choice of
production processes and the input supply constraints? Derived
Demand for Inputs The level of use of or the demand for a factor of production is
"derived" from the demand for the final product. Derived demand for
a factor can be easily quantified if all other factors used by the firm are |
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79 held fixed. In perfectly competitive markets and
assuming no operational constraints, the factor should be employed where VMP
= MC. The derived demand curve for factor N would simply be the marginal
physical productivity curve converted into monetary units by multiplication
with the (corn) product price, i.e., the VMP curve of Figure 4.4. A reduction in the cost of an input would allow an
increase in the quantity demanded, which would in turn increase the
productivity of all other factors. For example, increased application of
fertilizer results in a higher yield of corn, implying a greater return per
labor hour at harvest time. Since the employment of these other inputs can be
profitably increased as a result, the productivity of the orginal input again increases, i.e., the MP curve shifts
right. Due to these associated shifts in average and
marginal productivity at different levels of employment of a factor, the
actual derived-demand curve for that factor consists of points from different
MP curves. At different prices for the input, the quantity demanded of other
inputs will be different. This derived demand curve will also be more elastic
than the VMP-based derived-demand curve because of the increased demand for
complementary factors at low prices of the
input. In the linear programming formulation of this
problem in which a derived-demand curve for legume nitrogen is sought, these
theoretical results are approached in a somewhat different manner. To discuss
this it is necessary to digress for an explanation of how production activities
are mathematically expressed for linear programs. A corn production activity
will be posited as an example. |
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|
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81 Linearity and Additivity The linear programming model rests on the
assumption that the relationships between variables can be expressed linearly,
i.e., as a fixed proportions production function. A change in the level of
use of an input results in a proportional change in output. This implies
constant returns to scale. The Misterlich form of
the corn-response-to-nitrogen function is non-linear. Output increases at a
decreasing-rate when increments of nitrogen are added (decreasing marginal
productivity). But it can be modeled linearly in the following way. For farm problems the widespread approach to
modeling crop production is to express the relationship between inputs
required to produce a stipulated yield per acre. Therefore, one
point of the Misterlich function, i.e., one pair
of (yield, N level) variables, is chosen as the distinguishing feature of the
corn production activity, and the other variable input requirements are
expressed per acre. All of the inputs are therefore required in constant
proportions to each other in order to produce a specific yield per acre. This
formulation is known as a Leontief or
fixed-coefficient production function. Implied by this is a zero elasticity
of substitution between inputs. For example, having imposed the condition
that a certain level of nitrogen will result in a specific corn yield, there
is no possibility in the fixed-coefficient production function for a
substitution of more labor time for less fertilizer while the same yield is
obtained. Obviously, one point from the Misterlich
equation does not describe the full relationship of corn to nitrogen. But it
could be approximated by the formulation of, for example, three corn
production activities as |
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82 suggested in Heady and Substitution A very important implication of this fixed
coefficient formulation for the BNF problem is that inputs are strongly
complementary. Instead of increasing the level of employment of a single
input as its price is decreased, the entire activity in which the input
accounts for a larger proportion of the marginal cost should be increased.
This contrasts with the traditional analytical results in the following way.
Neoclassical analysis provides a quantitative estimate for the substitution
between inputs in a production activity. In linear programming it is activities
which are substituted. A comparison is illustrated in Figure 4.6. If the
costs of production for an activity are reduced because of an input price
decrease, then the cost-minimizing choice of activities is reflected as an
increase in the level of the less costly activity, and a decreased level of
the relatively more costly activity. Although the two approaches fundamentally obtain
the same analytical results of a shift among total employment of inputs as
relative costs |
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85 change, the linear programming formulation is only an
approximation of continuous production function analysis. For these reasons
the term "derived-demand function" will not be applied to the
results the price ranging analysis for this thesis in order to avoid
confusion due to misuse of terminology specific to only one of the two
distinct analytical techniques. The input-cost induced shifts among production
activities are the consequences of composite expansion and substitution
effects. The decrease in marginal cost associated with one activity results
in an expansion in the use of all inputs required for that activity. Meanwhile,
resources are transferred from the more costly activities to the less costly
activity when the activities are substituted for each other. Therefore, the
relationship between the cost of an input and the level of employment of the
input analyzed with linear programming should be negative regardless of which
effect is more powerful. The relationship between inputs that perform
similar roles in the production process as the price of one changes is more
difficult to stipulate a priori. Fertilizer nitrogen and nitrogen
carried-over from a legume crop both provide the same input into corn
production. First of all, if the corn production activity is differentiated
among types of rotations, and corn in a legume rotation receives no added N,
then as fertilizer becomes more costly, a transition to rotations would be
observed. As long as it would be profitable to engage in any corn production,
the two techniques would substitute for 'each other. More legume nitrogen
would be employed. On the other hand, if the corn activities are
distinguished by rotations and the level of added nitrogen, then |
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86 both rotation-derived nitrogen and the fertilizer
nitrogen would be required as complementary inputs in fixed proportions.
Therefore, as the cost of fertilizer increased and the total farm employment
of it decreased due to a concommittant decrease in
the level of those corn production activities, less legume nitrogen would be
exploited as well. Thus, whether or not legume N-based technologies
appear to substitute or complement the use of nitrogen fertilizer depends on
(1) the formulation of the model alternatives and (2) the relative weight of
the expansion and substitution effects. Due to the strict complementary
relationship among inputs implied by fixed coefficient linear production
functions, the bias against the expansion effect is stronger if potential
substitutability among production activities that do not require any nitrogen
are well specified. Synopsis At this point it may be helpful to recap this
chapter by compiling the list of economic, agronomic and technical criteria
important for the model. While describing the farm system seven points were
raised. (1) The question about the economic value of
biological nitrogen fixation is best stated as a question concerning the
substitutability between the two inputs of commercial or legume-derived
nitrogen. Therefore, the model must focus on the activities that require or
drive the demand for either input. (2) The model must be formulated appropriately
flexible in terms of commercial nitrogen purchase activities and legume
nitrogen substitution |
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87 such that simulation of farm response to commercial
nitrogen price increase has validity. (3) The problem requires a system approach due to
the interdependencies among farm activities involving either type of
nitrogen. Foremost of these system interdependencies is the one between the
crop and livestock sectors. (4) Due to the demand for legumes as protein-rich
feed, and in order to reflect in the feed ration the adjustments in crop
acreage, this fourth criterion of a flexible least-cost feed ration must be
met. This is directly related to the next criteria: (5) The supply of legume nitrogen is driven by the
derived demand for alfalfa as feed for livestock, or for soybeans. Therefore,
given the far level assumption: (6) Prices for inputs and produce should be
parametric. (7) The farm problem should be formulated with time
disaggregated so that it adequately reflects the timeliness of crop
production activity and how this affects the demand for flow resources. From the discussion of the theoretical basis for
the formulation of the LP model an additional three points were added. (8) Profit maximization, in the form of maximizing
gross margins is an appropriate objective function for the (9) The simple profit maximizing rule-of-thumb for
input employment, i.e., where VMPi = MCi should not be assumed for this problem where the
existence of constraints on the availability of farm resources exist. For
example, there are limits on the quantity of land, number of machines, |
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88 facilities
and laborers, and cash. Most of these constraints are easily modeled using
the L.P. format where the rows of the matrix sum the input requirements
across the activity columns and charge the total against the stipulated
available quantity. Expenditures on purchased inputs that are not restricted
in supply are assumed to be controlled according to a cash-rationing
principle. Specifically for this model an algorithm is developed in Chapter 3
that stipulates the rate of N fertilizer applied as a function of nitrogen
fertilizer price, given a benchmark expense/return ratio. This algorithm must
be incorporated in the model for the N price analytical phase. (10) The tenth challenge is to specify the corn production activities
and crop alternatives with an appropriate range of technical alternatives
such that the substitution effects and the complementary expansion effects
can be observed. The "appropriate range" must reflect the range of
practices observed in THE
MODEL The following pages describe the model in it's final form. Five
subtopics are presented. First, the main distinguishing features of |
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89 the model are discussed. Those features are (1) time disaggregation, (2) crop rotation constraints, (3)
variable input specifications, and (4) the least cost-minimum nutrient feed
ration specifications. This subtopic is accompanied by a schematic tableau
illustrating the full model. Second, the method of accounting for the costs
of revenues is discussed. The third and largest subtopic contains the details
of the constraint and special restrictions set. Frequent reference is made
to tables in the appendix containing actual model data. Fourth, details of the activities set are provided
and illustrated by exemplary schematic crop activity tableaus. The fifth and final subtopic presents the actual
benchmark model. This comprises the verification step. The model's base plan
is compared to farm record data averages. The Data Base and Software The linear programming model is constructed from
production data collected from three main sources. The main data source is
the Southeastern Minnesota Farm Management Association Annual Reports (1980-81).
The annual reports contain data on the farm resource base and levels of
activities typical for the region. The farmer (with vacation time help from
a son, or equivalent) manages a 60-cow dairy herd (140 head in total,
including replacements) and cultivates 400 acres of well drained silt-loam
cropland. Facing the current (1980-82) market prices for corn, soybeans, oats
and alfalfa hay, the acreage can be devoted to any combination of these crops
in rotations or in continuous cropping. |
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90 The alfalfa hay and corn silage markets are severely
limited to reflect actual conditions in The records data does not disaggregate fertilizer
use so that a typical practice must be extrapolated from complementary
sources. The next most useful sources of data were the machinery and crop
budgets prepared in the Department of Agricultural and Applied Economics by
Professor Fred Benson and staff. These budgets are drawn up by region and
incorporate perceived practice of farmers, agricultural engineering
standards, and other information. The records data reports that successful farm
managers are harvesting an average 140 bu/acre of
corn. This information is reiterated in the crop budgets where a nitrogen
application of 130 pounds of 100% N is recommended. Given the corn response
to nitrogen function estimated in Chapter 3 which is based on good weather
years (excluding 1980 and 1981), the three sources are considered to be
reasonably concurrent regarding the nitrogen-corn relationship. Additional information concerning timing of
cropping activities and special management techniques to capitalize on
rotation benefits were gathered from a wide variety of agronomy publications.
All of the data are presented in crop budgets, appendices VII.1 through
VII.6. The data were formatted to correspond with a matrix
generator to employ the generalized computer model ROMP-FS1 (Apland 1983). The L.P. data are transmitted to an APEX-1
(Control Data Corporation) linear programming solver, from which a
Fortran-readable solution file is |
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91 created. Apland also prepared
a report writer which organizes row and column solution values into an easily
readable report on the crop, livestock, labor and variable input activities
in the final bases. The matrix generator and report writer programs
were edited for use in this thesis to accommodate the dimensions and the crop
rotation restrictions encountered. These programs were named BNFMG and BNFRW,
respectively. But these programs are otherwise identical-to those
available for ROMP-FS1. The One distinguishing feature of the model is that it
is formulated with discrete production time periods within an annual
production cycle. All production activities are generated with tillage,
planting, cultivating, harvest or other operations occurring in 21 specified
periods. By disaggregating time, two conceptual problems are solved. One,
this captures the timeliness of crop production activities. Two is that flow
variables such as labor and machinery time are disaggregated into units
available each period. Crop production activity is regulated by
time-related factors such as weather. Weather influences production in two
ways. First, the spring rainfall determines the number of days available for
field work. Land prep machinery cannot work in too wet soil. Thus,
particularly in spring, the model's specification of available field days in
each period, Appendices VIII.1, 2, and 3; reflect the weather trends. These
estimates were-interpolated from two main sources: Boisvert
and Jensen |
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92 (1973)
and dissertation research (unpublished) of Judy Ohannesian,
Department of Agricultural and Applied Economics, The second weather-related influence on the crop activities is the set
of maturation and crop drying time factors. Each crop can be planted and
harvested in several production periods. The typical corn variety in The second major feature of this model is the way crop rotations are
imposed. At this point it is most helpful to observe the schematic tableau,
Figure 4.7. There are several equations which stipulate that a rotation crop cannot
be grown in excess of the acreage of the crop it follows. For example, the
corn rotation with alfalfa credits corn with a nitrogen benefit and a reduction in pesticide expenses. The rotation is actually a
three year rotation which involves at least two rotation constraints. First
there is an alfalfa establishment constraint. Alfalfa can be established by
seeding it under an oat cover-crop. In this case, two constraints are imposed
which maintain equal acreage in oats and established alfalfa, since they are
two distinct outputs. The other method of establishing alfalfa is by
herbicide clearing and direct-seeding the alfalfa. In either case, the next
year of alfalfa must be |
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94 constrained at a level less than or equal to the total
established acreage. Subsequently, the sum of corn crops following
alfalfa--either first year corn for grain as for silage, cannot exceed the
available full crop alfalfa acreage. These constraints are specified
completely in Appendix IX. The rotation constraints guarantee that the
benefits credited to crops following legumes are always accompanied by the
requirement that a previous investment has been made by cultivating that
legume. In this way the nitrogen available for a subsequent crop after
alfalfa or soybeans is effectively tied to the soil. Meanwhile, the options
to grow corn continuously and to grow alfalfa without rotating are still
available. Other formulations for the recovery of legume
nitrogen were considered, such as employing transfer rows into which both
commercial fertilizer nitrogen and legume nitrogen would be pooled. This
approach was rejected because (1) it does not effectively tie the legume
nitrogen to the soil, and (2) another benefit from rotation--the yield
increment simply due to the change in crop (explained in Chapter 2)--is-also
left unaccounted for. Under the current approach, rotation corn is credited
with a yield increase and a cost decrease relative to non-rotated corn. This
difference could not be captured if transfer rows for nitrogen were employed,
even if rotation constraints were included and corn crops were
distinguishable. Transfer rows might also result in legume nitrogen being
credited to continuous corn while no rotation corn is being cultivated under
that specification. |
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95 This discussion has introduced the topic of the
third distinguishing feature of the model, the specification of variable
inputs. Inputs whose use incurs costs that change with changes in activity
level are considered variable inputs. For farm problems labor, machinery hours, seed,
fertilizers, pesticides, full, etc.; are variable inputs. The labor and
equipment inputs are modeled in terms of hours of availability which reflect
day length, season, and weather. The seed, pesticide and fuel type inputs are
stipulated at recommended and/or typical practice levels of use in each fixed
coefficient crop production function. The costs are summed and entered in two
categories: (1) variable costs for the entire land preparation sequence of
tillage operations; and (2) variable costs for all of the plant/post-planting
operations. There is also a variable cost charge if crop drying must be
undertaken. These costs are summed across final basis activities at their
respective levels and charged against the objective function. Notably, the nitrogen fertilizer variable input has
been isolated and modeled as a distinct variable input with its own variable
cost transfer to the objective function. As discussed in the system modeling
section of this chapter, other factors could be set at specified levels to
maintain the simplicity and directness of the model if their level of use was
not linked with the choice of nitrogen source. To facilitate accounting, a nitrogen input has been
distinguished into four categories. "Starter Nitrogen" refers to
the solid nitrogen applied during the last land prep operations. Since in
ROMP-FS1 (BNFMG) variable inputs are associated with land prep systems,
crops, and special |
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96 activities, this 10 pounds of starter nitrogen is
applied to all corn crops employing the same land prep system, including the
rotation corn. Then anhydrous ammonia, expressed in pounds of 100 percent
nitrogen, is associated with each crop that is fertilized, at levels
appropriate for each rotation. The third category is the anyhdrous
required for corn crops in the second corn year of a legume rotation.
Likewise, the last category is anhydrous required for the third corn year in
an alfalfa-corn rotation. All of the added nitrogen levels are determined
according to the response analysis Figure 4.8. The second and third year corn
activities N level are an extrapolation based on hypothetical estimates of
legume nitrogen availability over time. It is assumed that 60 percent of the
total nitrogen locked in legume residue is-available in the following year.
Note that along the horizontal yield level of 154 bushels, the "nitrogen
effect" can be seen at 90 lbs. N between corn rotated with alfalfa and
continuous corn. If 90 lbs. N is 60 percent of the total N, then the total
residual N is 150 lbs., of which 30 percent is available the second year (45
lbs.) and the remaining 10 percent in the third year (15 lbs.). In addition to the nitrogen effect, the rotation
effect is seen as the vertical yield difference between the response curves
after the maximum yield plateau has been achieved with maximum N
fertilization. This yield difference is 19 bushels between alfalfa rotated
with corn and continuous corn. The assumption is that this effect is
non-existent for second year and third year corn. Otherwise, the second and
third years of rotation corn require the same variable inputs as continuous
corn, specifically, pesticide inputs; and the yields are targeted as the same
as continuous corn. |
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98 The main reason that the nitrogen input is
disaggregated separately from the other variable crop inputs; in addition to
its role in differentiating among rotation crops, is to facilitate the
nitrogen price ranging analysis. With this separate accounting row, only the
unit charge against the objective function (the price parameter) must be
adjusted by hand, instead of recalculating the per crop variable costs each
time. Therefore, to calculate nitrogen use as a function of price it was
sufficient to employ the nitrogen rate algorithm based on cash rationing at
each price level, and indicate the associated yield from the response
function. These data points are listed in a schedule in Appendix XI. The fourth distinguishing feature of the model is
the way in which the livestock feed requirements are modeled. As stated
earlier, the dairy activity constitutes a derived demand for both corn and
alfalfa hay as feeds. Despite the existence of a variety of milk production
functions based on fixed coefficient feed rations, a least-cost type nutrient
content matrix is employed instead, described as follows. Corn, silage, oats and hay harvested from the farm
can be transferred to the dairy activities. The dairy activity is modeled in
terms of seven nutrient, mineral,
vitamin, and fiber constraint rows and a feed activity column for each type
of the above four crops. This is illustrated as the central block in the exemplary
tableau 4.7 page 93. The coefficients in the matrix represent the units of
the nutrient per unit of 100 percent dry matter for each feed. One column
activity represents meeting the minimum (or maximum) requirement "per
cow equivalent". |
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99 A cow equivalent is a unit of feed per year to
support one milking cow for 316 producing days (15,000 #/year) and 49
maintenance days, plus portions of the rations for replacement in the herd; springers (full ration), yearlings (half-ration) and
heifer calves (quarter ration). Earlier it was explained that the herd size
is bounded from above by the capacity of the milking facility. The facility
is assumed to have a capacity to handle 60 head. A typical herd consists of
60 cow-equivalents or less. That is, 60 producing cows, plus 20 springers, 30 yearlings and 30 heifer calves. This
calculated to approximately 105 yearly complete rations which is normalized
to a per producing cow equivalent ration by division by 60. Therefore, the
solution herd can be from 1 to 60 cow-equivalents in size, and a
proportional replacement herd ration will also be accounted for along with
the producing cow ration. The feed ration is modeled in this way to be
completely flexible in terms of size of herd and composition of the ration within
the constraints imposed by facility size and nutritional necessity. Most
importantly, the ration can reflect the variations in acreage between corn
for grain or silage, oats, and hay by adjusting the corn/hay mix in the
ration within the nutritional and fiber limits. Corn, hay, and 44 percent
protein concentrate can also be purchased in limited amounts to reflect
maximum feed market activity documented in the farm record data books. All of
the model data for the livestock sector is presented in Appendices X.1, 2,
and 3. |
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100 The
Constraints and Special Restrictions In the following section the constraint and special restrictions set
will be fully described. The constraint set defines the productive potential
of the farm. The previous discussions of time disaggregation,
crop rotation constraints, and the dairy ration requirements covered the most
influential aspects of the constraint set. Additional constraints include the
quantity of tillable acres, the quantity of workers available, the sequencing
of operations within activities, and the market sale and purchase activities. The most important accounting rows in the L.P. matrix are the charges
to the objective function (maximization of the gross margin). The
coefficients are the crop prices, the fertilizer price, the variable costs of
land preparation and crop production activity per acre, the feed costs, and
the dairy products gross margin. These items are summed and charged or
credited to the total gross margin. This model is not formulated to include many
income and expense categories faced by the typical The land constraint expresses two things. The quantity of tillable
acres totals 400. These acres are considered homogeneous with respect to crop
except in one case. In order to harvest both the oat cover crop and the
establishment year alfalfa crop, a provision of 400 acres of a |
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101 second
type of land was made. The two crops are grown in equal proportions as
constrained in the rotation constraint matrix. The same land appears to be
counted twice but it is really only used to produce two distinct crops. The
double-counting has been avoided by providing the "ghost" land
type. The labor, tractor-time, land preparation, machinery time, planter
time, and harvester time constraints are modeled similarly. Appendix entry
VIII.1 lists production periods within which the number of good field days
are distributed; Appendix VIII.2 the hours per day by period; and by type are
listed in Appendix VIII.3. The available labor time appears in the Right Hand
Side, and the coefficients are the hours per acre. The exemplary crop tableau
Figure 4.9 highlights the labor flow resource allocation scheme. Machinery
data is handled in an analogous manner. The sequencing constraints are an integral part of the modeling of the
flow resources. In the tableau these appear as rows of 1's and (-1)'s in
triangular fields. A (-1) appears in a time period column if the operation is
performed. This will be multiplied by the level of that activity in that
period. The following operation can then be performed up to that level in the
time period column indicated by a (+1) coefficient. These must sum to 0 as
required in the RHS. Some operations can overlap in time, such as the harvest
and fall land prep operations. But the operations, once completed in one time
period do not have to be redone in the next to maintain the required
equality. These constraints maintain the sequencing of each crop-related
operation. There is no charge to the objective function. |
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103 There is also a section of crop drying time, use,
and cost constraints. The use of the crop dryer is constrained to equal the
level of drying activity, which cannot exceed available dryer time. The
variable cost for drying is summed over harvested crop drying activity and
cannot exceed the cost transferred to the objective function. There are two
drying alternatives: one farm (in the field) or on farm using a dryer. The
crops can also be sold directly or stored, given the maximum storage
capacity; or they can be transferred for use in the dairy system. These
alternatives are accompanied by a number of constraints that act as control
rows among the storage, use, or sale activities. There is a volume restriction on the alfalfa hay
sale activity to model the imperfect alfalfa market faced by farmers in The Activity Set The ROMP-FS1 farming system model employs a more
sophisticated specification of activities than was explained in the previous
"theoretical underpinnings" section. At that time, farm activities
were described in gross terms such as corn production and milk production,
etc. To model these activities in a linear program, fixed coefficient,
production functions are used. These equations become columns in a matrix
where the rows are the designations for the resources used or |
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104 transferred. The coefficients in these columns represent
the rate at which the resource must be employed to generate the stipulated
output, given the rate at which all other inputs are employed. For the ROMP model, activities as described above
are actually the sums of sequences of time-specific activities (Figure 4.10).
And, tableau 4.9, page 102, is one illustration of this. Notice that the
columns are specific operations differentiated by the timing option. The sum
of all of these options and operations comprise the continuous corn crop
production model. The rows in the tableau designate the quantity of labor
available per production time period. There are analogous rows for machinery
and all other time disaggregated resources, for each crop and special
activity. Five rows at the top of the matrix in 4.9 are also
noteworthy. The uppermost is the cost row. The coefficients are the variable
costs per land prep, and plant/harvest activities. In another part of the
full tableau, this would contain the sale prices of crops, etc. The second
row is the land constraint. One acre is debited from the total 400 tillable
for each acre of time-specific corn production alternatives. The other three
rows transfer corn output to storage or livestock. These are examples of the
types of transfer rows accounting for crop use. In the corn output row, the
coefficients are the yields achieved for the given management, specifically,
the planting-harvest dates. The activity set for the model therefore consists
of land prep, plant/post-plant/harvest, land use, labor use, variable input
use, crop drying, storage, sale or on farm use, and "special
activities": meeting nutritional requirements for the dairy herd and
producing milk. |
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106 In terms of gross crop production activity designation the model posits
fifteen cropping activities. These are five corn rotations, five silage
rotations, two types of alfalfa establishment, full production alfalfa and
soybeans. The oat crop grown to establish alfalfa is distinguished (as
discussed earlier) by requiring a dummy land type. The five corn crops and the analogous silage crops are differentiated
on three counts. Major distinctions exist between the levels of added
nitrogen and the yields, reflecting the nitrogen and rotation effects of
alternating corn with legumes. There is a continuous corn activity which also
requires an expenditure and application of pesticides. Corn grown after
alfalfa or soybeans does not require that. There are also second and third
years of corn grown in corn-alfalfa rotations. The data on added N and yield
can be found in Appendix XI. All other data is most easily found in the crop
budgets, Appendices VII.1 through VII.6. For a detailed example of the structure of the time disaggregated,
sequenced activities involved in the crop production activities, Table 4.11
is presented. This table contains all of the data on equipment, field rates,
and operation timing required to prepare the linear
program. Table 4.11 can be used in-.conjunction with the exemplary
tableau 4.9. |
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108 than one, implying that more than a unit of feed must be
harvested in order that one unit be available as feed. These coefficients are
1.15 for silage and 1.2 for hay. In summary, the model in its final form has 650
constraint rows and 880 activity columns comprised of over 5,200
coefficients. The matrix density is .875. Solving required under 20 seconds
of central processing on a Cyber 730 with an APEX routine. Degeneracies The degeneracies in the
model resulted from the number of crops with similar operation requirements,
the dummy land category for oats, the congruence during certain periods of
the available labor, and the overlapping of the oat harvest with post-plant
operation on the established alfalfa. The existence of these degeneracies is not considered harmful since they do not
affect the sensitivity of the model on the nitrogen choice question. Verification To verify that the linear programming farm model
could be used to predict and define farm demand for nitrogen and legume
nitrogen, the final basis of the model under a current price scenario was
compared to common practice in |
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109 The typical record-keeper's farm consists of 160 acres corn (rotation
unspecified), 120 acres of soybeans, 80 acres of oats/hay and 40 acres of
silage. The model farm without any acreage constraints on silage consisted of
over 150 acres of corn and alfalfa in a three-year rotation, 240 acres in a
soybean-corn rotation, and the rest in silage after soybeans. This is
acceptably similar to be considered a mimic farm plan. The final basis did
not include silage at even an approximate level. The discrepancy in silage
acreage can be explained as follows. There are two attractions to harvest
corn as silage that are not embodied in our model. One, farmers often harvest
enough corn silage to fill the silo as an insurance against the risk of
losing other feed crops. Silage is almost fool-proof to harvest and store.
Two, the mechanized silage feeding equipment makes it easier to feed the
dairy herd silage than hay. If dairy labor is not constrained and if no off
farm work opportunities exist, the labor time has no imputed value. This
appeared to be the case in almost all periods. Therefore, while profit
maximization may dictate more hay and oats than silage, a concern about risk
and a preference for leisure may mean more silage. These differences between
the model and common practice are not considered serious for the question.
The unconstrained plan was accepted. A close to perfect mimic farm plan could be obtained with a single
minimum restriction on silage acreage. Alfalfa as modeled provided the large
nitrogen and rotation benefits, so this is seen as the major reason why the
freely optimizing model chose more alfalfa over silage. This factor should
not be obscured by constraining silage in the model. |
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Chapter Five ANALYSIS The analytical procedure of ranging the nitrogen
price and calculating the resulting level of nitrogen demanded from both
commercial and organic sources will be the main subject of this chapter. This
chapter opens with a restatement of the analytical procedure. This will be
followed by an explanation of how nitrogen use accounting was conducted.
Then, the results of the price-ranging analysis will be discussed in terms of
the questions formulated in the previous chapters. The activity levels and
adjustments will be interpreted to provide implications about the
substitutability between legume and fertilizer nitrogen. The analysis consists of two parts. The
price-ranging analysis is used to describe the substitutability of the two
sources of nitrogen assuming the current capacity for nitrogen fixation and
potential for carry-over. The second part of the analysis posits enhanced
nitrogen fixation performance by both alfalfa and soybeans, and the demand
for direct feed use of alfalfa is deleted. This tests the economic viability
of the green-manuring of alfalfa as a very high
organic nitrogen supplying technique. This part of the analysis is
speculative. HYPOTHETICAL RESULTS Figure 5.1 illustrates the hypothetical results of
both the price- . ranging and the BNF enhancement analysis. The darkest
downward sloping curve labeled Farm Nitrogen Demand represents the sum of all
demands for nitrogen in farm production. This includes nitrogen for crop
growth and |
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112 nitrogen
in the feed proteins necessary to maintain a producing dairy herd. This curve
is postulated to have a lower bound beneath which farm activity could not occur
at all. This reflects the essential role nitrogen plays. The actual curve
must be estimated in the analysis. The supply of nitrogen is indicated by two different classes of
functions. The supply of commercial, industrially produced nitrogen is
represented by a horizontal line, the vertical height is set by the market
price. The small farm assumption of infinite supply elasticity is crucial at
this point. The consequence of infinite elasticity is that individual farm
demand for commercial nitrogen (fertilizers and/or protein concentrate or
feeds) cannot affect the market price(s). This assumption simplifies the
analysis as it is not necessary to estimate supply as a function of price,
only the actual demand as a function of price. The calculated demand at each
price parameter will provide the coordinates of the normative demand curve
for total nitrogen. The legume nitrogen supply function is represented by the dash-dot
curve. This curve is a-shaped because it is hypothesized that generating
legume nitrogen is initially subject to economies of scale. Up to a point,
the implicit unit costs of legume nitrogen decrease as more acres of alfalfa
are cultivated. The economy of scale may also reflect the associated benefits
to the farming system of legume cultivation such as the rotation effect on
corn yield. This supply locus eventually trends upward because of the further
hypothesis that diseconomies of scale are encountered. Legume cultivation at
that point implies greater |
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113 and more costly reallocation of farm resources to the
less profitable crop. Finally, a maximum of legume nitrogen supply is
hypothesized, the vertical portion of the curve. No matter how intensively
legumes are cultivated, there is a physiological limit to the quantity of
nitrogen that can be fixed and provided on a given size farm. This physiological limit is what basic scientists
are trying to expand. If legumes could provide more nitrogen at each level of
cropping intensity and cost, then the legume nitrogen supply curve would
shift rightward. Since the total nitrogen supply will be demanded from the
least cost source, this may result in a greater proportion of total farm
nitrogen being derived from non-commercial sources without implying a drop in
the farm's earnings. The theoretical solution for the model presented in
Figure 5.1 is indicated by the intersections of three curves: the two supply
curves for nitrogen (fertilizer N and legume N) and the whole farm nitrogen
demand curves. The fertilizer N supply curve, intersects the legume N supply
curve PF1 at point A, and the whole farm nitrogen demand curve at point B.
The solution is to supply O-NL of legume N, and Nf-NL of fertilizer N, with total
fertilizer demand equal supply at ON L +
ONf, or O-Nf.
If the price of fertilizer nitrogen rises to PF2, then whole farm nitrogen
demand would be O-S, and met totally from organic sources. PRICE RANGING ANALYSIS The first analytical objective is to map the total
use of nitrogen as a function of fertilizer price. The nitrogen fertilizer
price |
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114 parameter was ranged from two levels below the
current price to five above it. These price intervals are in percentage
terms: -100%, -50%, the current price,+ 50%, + 100, 200 and 300%, and above
400%, respectively: free, $.115/lb., $.23/lb., $.345/lb., $.46/lb., $.69/lb.,
$.92/lb and "prohibitively costly". The cost of 44 percent nitrogen
protein concentrate dairy ration supplement was also adjusted for each of the
eight runs on the basis of the cost of the nitrogen component in the
supplement. At each nitrogen price, the rate of nitrogen
fertilizer was stipulated according to the algorithm for fertilizer use
developed in Chapter 3. The level of fertilizer for corn is a function of the
nitrogen price and the type of rotation. The data for the model was adjusted
accordingly for each price scenario. The appropriate fertilizer rates per
acre and the resulting yields (determined according to the response of corn
to nitrogen function, also Chapter 3) at each price of N are listed in
Appendix XI. The resulting solutions from running the model were
analyzed to determine the degree of complementarity
between legume N and fertilizer N. The quantity of legume nitrogen recovered
by corn was calculated from the acreage results of each solution. The
quantity of legume nitrogen sold (embodied in soybeans or hay) and/or fed was
also calculated from the sale and use of crop results of each solution. These
recovery, sale, and feed nitrogen totals were summed to obtain an estimate of
total nitrogen use at each price of fertilizer N. Table 5.1 is one of the
tally sheets illustrating the calculations. |
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116 Notes
to Table 5.1: _a/ Acres harvested (maximum of
400) on which it was profitable to produce corn in the rotation indicated. _b/ The "reference-N"
level is the quantity of nitrogen which would otherwise be required on
continuous corn to achieve the same yield. Since continuous corn never
reaches some yield levels attainable in rotation, maximum fertilizer rate is
used as a reference. (This caused some difficulty due to a "rotation
effect" (Chapter 3) and some perturbation of the NL
estimate). _c/ "Applied" nitrogen
is the quantity of fertilizer nitrogen added, determined according to the fertilizer rate algorithm designed in
Chapter 3. _d/ "N " is the
difference between the reference or required nitrogen level and the actual
applied level. The assumption is
that this differential is supplied through the decomposition of the
legume residue. _e/ "E NL" is simply the level of NL
per acre multiplied by the number of acres under that particular rotation. |
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117 The summary of results from each price scenario is
presented in Table 5.2, and graphically illustrated in Figure 5.2. The values
of interest are indicated by numbers in parentheses. Total nitrogen contributed
to the farm by legumes either recovered, sold or fed is item (1). Purchased
fertilizer is item (2) and the sum of (1) and (2) is total farm use of
nitrogen, item (3). These three values are plotted over nitrogen fertilizer
price in Figure 5.2. RESULTS There are two major features of the curves in
Figure 5.2 which warrant discussion. First, there is a part of the price
domain within which the demand for legume nitrogen appears to be a decreasing
function of commercial N price. Second, there is an apparent minimum level of
nitrogen, net of soil nitrogen, derived entirely via legumes. Commercial
nitrogen displays a classic decreasing function of its own price with a
regular downward slope. This discussion will focus on the legume nitrogen
locus. The range of prices from 0 to .23 is denoted range A. The range from
.23 to is denoted range B. Starting from left to right, as the cost of
commercial nitrogen increases, the exploitation of legume nitrogen at first
increases from 23,200 to a maximum of 67,240 lbs. on the whole farm, then
slowly the level drops to an asymptotic minimum around 63,000 lbs./ farm. In range A, legume nitrogen is being
"substituted" for commercial fertilizer. Even when nitrogen is
free, some legume rotations are desirable for two reasons. The first is the
non-nitrogen rotation |
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120 effect (Ch. 3). Fertilizer is available
"free", so the maximum yield of continuous corn can be obtained
with the highest fertilizer rate at no cost. An even greater yield of corn can
be obtained from corn grown in a legume rotation. Thus, acreage is devoted to
corn in a rotation as well as continuously. Second, alfalfa is demanded for
feed. (There is no minimum requirement for alfalfa as feed in the
specification of the model.) These results imply that a significant,
non-nitrogen benefit to the farming system arises from legume-based
crop rotation. It is characterized as a non-nitrogen benefit because a
nitrogen contribution from crop rotation and/or feeding alfalfa to livestock
could be met equivalently with free commercial fertilizer and/or free
nitrogen protein concentrate feed supplement. Crop rotations and hay/corn
rations are a part of the profit-maximizing solutions even when fertilizer
and the nitrogen in feed supplements are free. As the fertilizer price increases from 0 to
$.23/lb. more legume nitrogen is demanded. (Figure 5.2) Within range A
prices, most of the nitrogen use adjustments occur in the cropping subsystem.
Legume nitrogen substitutes for commercial fertilizer. Figure 5.3 shows in
more detail how commercial fertilizer reliance declines in favor of increased
reliance on recovery of legume N, as fertilizers become more costly. Through range B prices legume nitrogen
and fertilizer nitrogen appear to be complementary inputs. As the price of
fertilizer exceeds the current price, use of both decreases. Over the B range
there is a 96% reduction in fertilizer use and a correspondingly large
reduction |
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122 in the quantity of legume N being recovered in rotations
with corn. The smooth curve in Figure 5.3 illustrating legume
N recovered in rotations obscures the two abrupt changes in the types of
rotation. The adjustments in crop acreages on the farm as the price of
fertilizer varied are illustrated in Figure 5.4. Not surprisingly, continuous corn production is
favored over rotations when fertilizer nitrogen is free. Soy-corn rotations
become cost-effective at the next price interval of .115$/lb.; completely
replacing the continuous corn acreage. When the nitrogen price reaches
.69$/lb., the acreage in soy-corn rotations declines rapidly to be replaced
by continuous soybean cultivation. Up to that point, about one-half of the
farm is under a soy-corn rotation and the other half in a three year alfalfa
establishment--alfalfa production--corn rotation. Corn rotated with alfalfa
requires much less added nitrogen than either soy-rotated corn or continuous
corn. Crops are cultivated at levels where the gross
margins are most favorable. When the acreage devoted to the soy-corn rotation
drops out in favor of continuous soybean cultivation it is because the
returns to fixed factors from rotation corn is less than the return from pure
soybeans. The alfalfa-corn rotation acreage is virtually constant over the
entire price N range. When the acreage in the two legume rotations
varies, the proportion of legume nitrogen recovered by corn from each source
varies. This is illustrated by Figure 5.5. At zero nitrogen prices corn is
grown continuously, and in an alfalfa rotation. Thus, 100% of the legume N |
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124 |
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125 recovered for corn derives from alfalfa. This level of
nitrogen from alfalfa stays fairly constant, while more corn is grown in
soybean rotations. Even though the soybean rotation provides lower levels of
carried over nitrogen, it is economically quite attractive since the
soybeans are also profitable to sell. At the highest fertilizer prices, only
the alfalfa rotation corn is economically attractive, and alfalfa again provides
100 percent of the recovered legume nitrogen for corn. While some inference can be found about the
relative gross margins among the three types of rotation corn, other
information is needed to complete the picture. The confounding but necessary
features of the farming systems model is the integration of the crop and
livestock enterprises. Corn as well as alfalfa are fed to the herd. The
variations in the composition of the feed ration may reflect the variations
in profitability among the types of corn. On
the other hand, that may be irrelevant. Unfortunately, the linear programming
model does not provide conclusive evidence about relative gross margins
through analysis of the feed ration adjustments because of the potential degeneracies among types of feeds as suppliers of the
nutrient requirements for the herd. The adjustments in the feed ration are illustrated
in Figure 5.6. This documents how the ration changed to reflect changes in
the types of crops while maintaining the prescribed diets for the dairy herd.
At all relevant nitrogen price level scenarios, a ration of 50-50 corn and
hay is economically and nutritionally superior. |
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127 ANALYSIS OF EHNANCED BNF The second analytical objective was to estimate
what level of BNF would be necessary so that legume nitrogen is a cost
effective supply of nitrogen for a cash crop farm (excluding a market for
hay, excluding an on-farm use of hay as feed). In this section the results of
the simulation modeling of the role of alfalfa and soybeans with enhanced
BNF capacity are presented. The first step was calculating an enhanced level of
nitrogen fixation capacity of both alfalfa and soybeans. This was done in
terms of nitrogen carried-over on the basis of the following assumptions. It
is proposed that legumes could be developed that support enough nitrogen fixation
to (1) supply all of the nitrogen required to obtain the current high perennial
and/or annual legume yield, and (2) to supply a net addition of N to the soil
equivalent to current levels plus the current nitrogen sink of legumes. The
estimates of the nitrogen sink of the legumes soy and alfalfa are derived
from the estimated proportion of soil nitrogen in total plant nitrogen. These
estimates are 60 percent for soybeans and 40 percent for alfalfa. The enhanced BNF levels are calculated as follows.
For soybeans, the quantity of. nitrogen carried-over in the basic
scenario is estimated at 68 lbs/acre. Of that 68 lbs., forty percent (27
lbs.) is assumed to derive from symbiosis. Doubling the symbiotic fixation
rate would imply a net nitrogen carry-over of 95 lbs/acre. The calculations for alfalfa are more complicated
because alfalfa's nitrogen credit is recovered over a three year period. The
total nitrogen |
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128 credit is 165 lbs. sixty percent is assumed to be
derived from symbiotic fixation (99 lbs.). In order for that total nitrogen
carry-over to be met by symbiosis alone, an increase of the capacity of
alfalfa to support a level of fixation up to 83 percent of the requirements
is necessary. The level of nitrogen carried-over would then be a total of 231
lbs. over 3 years. The 95 lb. N/acre nitrogen benefit from soybeans
and the 139 lbs/ acre (year one), 69 lbs/acre (in year two) and 23 lbs/acre
(in year three) benefits for corn in a five year OA-A-C-C-C rotation were
entered in the model. Yield of corn and the appropriate fertilizer recommendations
were also entered. The dairy enterprise was excluded from the model.
Therefore, there was no on-farm demand for any crop, corn or alfalfa hay.
Also, the opportunity to sell hay was entirely deleted. If an alfalfa-corn
rotation entered the final solution, it would be due entirely to the
cost-effectiveness of corn rotations with green manure management of alfalfa. This did not obtain. In the optimal solution the
whole farm was under a two year corn-soybean rotation. Nitrogen from alfalfa
(under these circumstances: no direct use or sale) was shown to be too costly,
even if the fixation productivity of alfalfa was increased 23 percent. This analysis is far from complete. Different
levels of alfalfa fixation capacity could be postulated. At some point, even
green manure alfalfa-corn rotations may become profitable. Also, the level of
fixation by soybeans could be varied downward to identify the precise level |
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129 of fixation at which soy-corn rotations gain a profitability
edge over continuous corn. There are many confounding factors in the analysis
of that level of fixation by soybeans. The relative sale values of corn and
soybeans are enough to alter the profit picture. Such tasks are beyond the
scope of this thesis. This simple investigation did provide some
insights. One., even though the additional BNF capacity for soybeans was less
(in absolute terms) than one-third the additional capacity of enhanced
alfalfa, soybean-corn rotations proved most profitable. Two, the results
showed that a two year alfalfa-corn rotation--where the first year of corn
recovers 100 percent of the quantity of N that would otherwise have been
added as fertilizer from the legume residue--is not an economically
competitive rotation when there is no direct use or sale value for the
alfalfa. |
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Chapter Six SUMMARY AND CONCLUSIONS The objective of this research was to identify the
economic role of BNF in a farming system. The system was chosen from
southeast Two functions, (1) corn yield response to nitrogen and
(2) nitrogen fertilizer use as a function of price; were generalized from
agronomic and farm management data of southeast |
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131 In addition to these functions, a
least-cost/minimum nutrient requirement feed ration matrix was included in
the model. These three features defined the roles of nitrogen in farm
production and the economic decision rules concerning the level of use in a
flexible way. The relationship between commercial nitrogen and legume
nitrogen was explored using the farm model. The prices of nitrogen fertilizer
and (nitrogen) protein concentrate feed supplement were ranged from zero
(free) to three hundred percent of a current price (1981-82) in seven steps,
and then to a prohibitively expensive level. In addition, the levels of
nitrogen carried-over from alfalfa and soybeans were doubled in the model,
and a solution was obtained at current nitrogen prices. The results of the analysis can be summarized as
follows. Land use shifted from continuous corn to corn-soybean rotation on
two-thirds of the crop land, with a fairly constant level of a three year
oats-alfalfa corn rotation providing the feed for the dairy herd, over the
first seven steps in the price range. At prohibitively expensive nitrogen
prices, continuous soybeans displaced the soy-corn rotation, but the OA-A-C
remained optimal on one third of the available acreage. The dairy herd was
maintained at the upper bound determined by facility size across all nitrogen
prices. All feed was produced on the farm, except for the solution under free
protein supplement price, wherein a small amount of supplement was used.
Otherwise, a 50-50 grade 1 hay-shelled corn ration with oats prevailed. |
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132 CONCLUSIONS Fertilizer nitrogen and legume nitrogen are both
economic complements and substitutes. From zero to the current price of
nitrogen they appear to be substitutes. While fertilizer use declined with
increases in its own price, more and more legume nitrogen was recovered by
crop rotations. Beyond the current .price, they display complementarity. Fertilizer use continued to decline with
a concomitant decline in the level of legume nitrogen recovered in rotations.
At the prohibitively expensive nitrogen fertilizer price, the recovery of
soybean residual nitrogen also dropped to zero. Nitrogen recovered by corn in
the OA-A-C rotation completely substituted for fertilizer; but the nitrogen
recovered after soybeans was only enough to complement fertilizer use, and
was not optimal at zero or the highest N prices. When the model was run with the BNF of soybeans and
alfalfa enhanced, while excluding both dairy derived-demand and markets for
hay, no alfalfa hay was produced at all. Therefore, no legume N was recovered
from alfalfa. The farm went entirely to a soy-corn rotation. There is no a-priori explanation for the economic
relationship changing from substitution to complementarity
at the current price, other than the following speculations. On one hand, the
results may express the true relationship and it is only the length of the
price range intervals that obscure the exact break price,
while approximating it near the current price. This can be tested by
increasing the number of price intervals. On the other hand, the results may
arise from Type Two error: a bias
toward base scenario/current price in |
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133 the model. The possible source of this bias is an
unsolved puzzle. A conclusion from the enhancement analysis is that
alfalfa-corn rotations where alfalfa is only a green-manure crop are not
economically competitive with soy-corn rotations. This holds at current
prices of nitrogen even when alfalfa is modeled to provide 100 percent of the
corn's nitrogen in the first year of the corn sequence. The residual nitrogen
alone is not valuable enough to warrant displacement of cash crops.
Specifically, the alfalfa in a rotation must also provide sale or use value. These results imply that there exist at least four
types of economic benefits to a farming system from legume rotation. In order
of importance according to the profit maximizing criterion they are (1)
legume direct sale value (soybeans), (2) alfalfa hay value as a feed, (3) the
nitrogen benefit to rotation corn, and (4) the net revenue increase due to
lower costs and higher yields of rotation corn. It is difficult to establish
the order between benefits (3) and (4). Both of these last benefits relate to
BNF directly. The order between them is less relevant than the conclusion
that the BNF benefits in such a farming system are secondary to the direct
crop use and sale benefits of legumes. This conclusion echoes the postulates
discussed in CAST Special Publication no. 5 (1977). IMPLICATIONS This study suggests to BNF researchers that forage
yield and use value are essential for the economic performance of a
nitrogen-fixing crop also valued as a provider of residual nitrogen in a
rotation. |
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134 Alfalfa as a green manure crop in cash crop grain
farming systems probably has important prospects in a world of extremely high
nitrogen prices. As its nitrogen contribution is enhanced, convergence
between high cost nitrogen and high value legume rotations approaches sooner.
This appears to be some years in the future. With regard to economic research, this study is
only an introduction. There are three levels of analysis implied for
production problems: farm level, regional level, and national level. This
study has demonstrated the appropriateness of goal-oriented linear
programming for technical substitution problems. The present model can also
be used for more detailed analyses at the farm level, such as testing the
price ranging analysis at smaller intervals, or simultaneously enhancing BNF
and ranging the nitrogen price to find the point where alfalfa as a green
manure nitrogen source for corn is profitable. It could be reformulated for
different geographical regions, and the analytical results from other regions
could be summarized and generalized for a more complete picture of the nature
of the relationship between BNF and fertilizer use. The basic format could
also be used in developing countries to model potential cropping pattern
changes toward greater exploitation of legumes in traditional agricultural
systems where fertilizer is scarce. This effort would require greater levels
of interaction between the modeler and the farmers to develop the data base,
but the payoff may be very high. The present model could also be improved by adding
some kind of erosion cost function. Continuous row cropping of corn and
soybeans |
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135 have
serious soil erosion consequences. Alfalfa cultivation and no-till green manuring techniques are soil-conserving. Results from
analysis would probably support land-use policies encouraging integrated
livestock and crop production farms based on alfalfa-grain rotations.
Unfortunately, a soil-conserving rotation for non-livestock farms is not
currently an economic potentiality. On the regional level, some study has already been done to assess corn
belt farming adjustments to energy cost increases. In the final analysis,
ignoring the aggregation problem, this study's results corresponded to the
results of the other works cited in Chapter Four, by Miranowski
(1979) and Walker and Swanson (1974), in terms of adjustments from
fertilizer-based continuous corn cropping to soy-corn rotations. In all
cases, the increase in fertilizer prices, ceteris paribus, caused
decreases in farm incomes. Implications for further research are to assess
the general equilibrium impact on farm output mix, prices, farm earnings, and
fertilizer demand--with the more detailed and complete formulation of the
crop rotation alternatives in this study. Implications for research policy are based on the notion that cost decreasing
innovations which shift a supply curve out or halt an inward shift benefit
consumers and at least stabilize producer surplus. This justifies public
support for the basic research in BNF, and for forage improvement towards
mitigating the upward push on grain prices and downward push on supply in the
long run, and the squeeze on farm incomes in the short run. |
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APPENDIX A PARTIAL BUDGETS/PARTIAL ANSWERS Several different attempts to estimate the economic
value of BNF by legumes as a substitute for fertilizer have been made. These
include (1) the response-function approach using marginal analysis, (2)
activity analysis and (3) partial budgeting. To conduct a partial budget
analysis, data on expenses and revenues must be gathered. Both of these
measures are difficult to quantify. In this section partial budgeting will be
considered and critiqued. Some investigators have employed the "gross
benefit," and the "net benefit" calculation approaches to
quantify these parameters for BNF. "Gross benefit" is a method of
calculating the value of a substitute input, in this case,
symbiotic/legume-derived nitrogen, by multiplying the total quantity of the substitute by the market
price of the commercial input. The gross benefit assessment of the value of
BNF is often calculated in this manner by scientists in the field, for
example, P.J. Dart (1979)(Table A-1). The implicit assumption basic to this method, that
symbiotically fixed nitrogen and fertilizer nitrogen are perfect
substitutes, is not sound. Also, an input has no economic value if it is not
used, or if its substitute is free. No interpretation of the results of the
gross benefit approach avoids these conceptual problems.
Symbiotically-derived nitrogen is important for legume production, as the
major portion of the total legume-derived organic nitrogen added to the soil,
and also as part of the constituents in the protein-rich food |
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3 or feed harvested from legumes. The value of
symbiotically-generated nitrogen derives from the value of the product in
which it is embodied. Thus, the value of symbiotic nitrogen in legume
production is a function of the value of the legume. Table A-1 lists quantities of symbiotically fixed
nitrogen by various legumes. In order to accept the statement that the
maximum quantity of fixed nitrogen by alfalfa (lucerne)
is worth 138 dollars per hectare, one must also assume either that $138 per
hectare would have been spent on nitrogen fertilizer applied to the alfalfa,
or that the alfalfa would be sold at a price reflecting a similar $.30/kg of
embodied nitrogen. Alfalfa which is sold for its nitrogen content alone would
have to earn $276 above costs per hectare. Since alfalfa fixes about half of
its nitrogen, this figure is double the value listed on Table A.1 for the
nitrogen. That sums to over $646 dollars gross return per hectare of alfalfa.
It is hard to imagine any farmer either applying $138 worth of nitrogen
fertilizer to his alfalfa, or being able to earn $646 from each hectare of
alfalfa. This is one way that the gross benefit calculation leads one astray. The second misleading implication from the gross
benefit evaluation refers to fixed nitrogen as a fertilizer. Many assumptions
underlie that evaluation. First, legume-derived organic nitrogen is assumed
equivalent to fertilizer nitrogen pound for pound. This is plausible, as
shown by |
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4 Schrader, Fuller and Cady (1966) who estimated a common
nitrogen response function. If no harvest removal of any of the legume
occurred, the nitrogen absorbed by the legume from the soil and the nitrogen
fixed could be returned to the soil, available for a subsequent crop. This
may amount to a net accretion of 460 kg N per hectare as indicated by Dart in
Table A.1. But to capture the value of that nitrogen, a crop
which would otherwise be fertilized must be cultivated following the
alfalfa. That legume nitrogen is worthless if it simply sits in the soil.
Also the fixed nitrogen cannot be extracted, stored, nor transported about to
other plots as if it were fertilizer. And again, its value is not $.30/kg.
Even assuming the maximum fixation, the costs of cultivating alfalfa
($370/ha) implies a cost of $.80 per kg. N. In other words, the fixed
nitrogen actually costs $.50/kg more than commercial nitrogen fertilizer. Fixed nitrogen is not a "free good". When
the costs of cultivating the legume plus the loss of income due to foregone
earnings from not cultivating a cash crop are taken into account, it is clear
that legume nitrogen is actually more expensive than commercial fertilizer. This does not imply that legume cultivation is not
economically profitable. It must be accurately assessed. The above provides a
clear example of the shortcomings of analytical approaches that isolate the
fixed-nitrogen aspect of legume cultivation from the integrated role legumes
perform in the farming system. Intuitively, fixed nitrogen is an added bonus
brought to a farming system by legumes through innoculation.
This is true. Legumes are cultivated and harvested for sale or |
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5 feed, and the residue can be reincorporated into the
soil. A grain crop rotated with legumes can earn higher profits since
fertilizer costs may be reduced by the quantity of legume-derived nitrogen it
recovers. Clearly, the value of nitrogen-fixing legumes must be investigated
in this integrated framework. Costs and revenues from all the activities must
be summed and compared. In another form of "gross benefit"
calculation by J.-Burton (Table A.2) some of the oversights
mentioned above are avoided. By assuming that all of the nitrogen fixation is
due to inocculation, |
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7 significant
enough to warrant consideration in the net $/ha "gain" calculation?
And, as before, we still don't know what is done with the legume nitrogen. If
alfalfa at 45 lbs. N per ton is sold at market prices of $80.00 per ton for
the nitrogen alone, its value is $4.22/kg N, fifteen times The general conclusion from this criticism of the "gross
benefit" calculation is that the method of exploiting the legume N is as
important a clue to its value as is the method of producing it. Cultivating
legumes incurs two types of costs, the direct cost of labor and materials
and the indirect cost of foregone production of any more profitable crops.
Therefore, symbiotically fixed legume N is not a free substitute for
commercial nitrogen. The exploitation of the legume crop and the associated
fixed nitrogen is accomplished in a number of ways. For each type of legume
there are many possibilities. Forage legumes enrich the grass swards upon
which livestock graze. The high-protein content of this grazing material due
to BNF fattens the livestock faster and saves time and earns money (Jacobs
and Stricker, 1976). Bean and pea legumes are
harvested and sold for feed or food, and if the residue is plowed under, the
remaining legume-nitrogen could be exploited by a subsequent grain crop in a
crop rotation program. These are the types of costs and benefits the gross
benefit approach overlooks. Partial budgeting is a method which incorporates these costs and
benefits and is commonly practiced by economists in the field for quick
assessment of new techniques. An introduction to the partial-budgeting
approach, known as "net-benefit" approach by Perrin, et. al. (1976)
at |
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8 CIMMYT, and which is used widely in the international
community, is summarized here by way of example. The net benefit approach
requires data on all relevant costs of inputs, prices of outputs, yields,
labor data, variability factors, grossly defined costs of capital and opportunity
costs (foregone earnings for other activities) be acquired. Then the debits
and credits are summed and compared among techniques. The technique which
shows the largest net benefits is the technique of choice. The following exercise compares a four-year
rotation of alfalfa and corn with conti nuous corn. Prices and costs are assumed constant during
the four year period. No means is available of calculating the value of labor
among the periods where labor is more scarce than is periods of labor
abundance. In both input and output markets, no price/cost adjustments are
made for scarcity or abundance. It is also assumed that all the alfalfa can
be sold in a convenient market at a constant price. Variations in yields and market prices are
summarized in nine "net benefit scenarios." All combinations of
high/average/low prices and high/average/low yields are in the matrix of
Table A-3. The necessary data on yields and p ices are
listed following the matrix. The crop budgets in appendix entries VII through
VII.6 were developed from Benson's machinery data (1982) and the Farm Management Association Annual Reports
(1981-82). The cash costs from the relevant budgets are summed to generate
the variable cost for each crop rotation. For the CCCC rotation, costs over
four years total to $528/acre. For the OA-A-CC rotation, the total cost is
4498/ acre. Corn drying costs are entirely excluded. These costs are |
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10 exclusively related to timing of planting and harvest,
and are another aspect of time-related costs that partial budgeting cannot
account for. The results are presented in Table A.4. In four of the five scenarios, the rotation plan
appears clearly more profitable than the continuous cropping. In the
high-yields, low prices scenario, the continuous corn technique is slightly
more profitable. This is due to the extreme range in the alfalfa prices from
high $120/ton to a low of $40/ton. Conversely, the stability of yields in
rotation with alfalfa is the main reason for the higher profitability of
rotation corn even under the low price, low yield situation. This exercise could lead on to wonder why, if
rotating corn with alfalfa could be so profitable, is it not clearly the
technique of choice? The exercise also gives the evidence as to why partial
budgeting or "net benefit" approach is not a sufficient technique
to assess the value of crop rotation with legumes. The problems of (1) lack
of time dynamics, (2) inability to account for variations in costs/prices due
to scarcity or abundance, and (3) the constraints on computation complexity
(to be discussed) are serious drawbacks. This partial budgeting approach also assumed a
market for alfalfa which does not actually exist. An alternative to an
alfalfa market is to integrate the activity of crop production with a
livestock operation, (as is common in |
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12 Another serious oversight of partial budgeting is
that the flow of labor services are considered unbounded. Alfalfa harvest
requires labor during the summer when other activities do not require labor.
But the planting operations occur during the spring when all cropping
activities compete for the available labor. To adequately assess the farm
problem, an algorithm is needed that will evaluate labor values among each
cropping activity during periods of excess demand and allocate labor where it
would be most profitable. Summary The gross-benefit approach to evaluating the dollar
benefit of biological nitrogen fixation over-simplifies the cost/benefit
picture of legume cultivation so much that very relevant factors are obscured
and ignored. The partial budgeting "net benefit" technique is based
on unrealistic assumptions in the attempt to make the complicated integrated
farm system problem setting tractable. Neither approach captures the full
range of benefits to a farming system which result from integrating
nitrogen-fixing legumes with other crop and livestock activities. Neither
approach provides an estimate of the cost of fixed nitrogen. It is thus demonstrated that a more.sophisticated
analytical approach is required to assess the value of BNF for a farmer. This
approach must be able to consider many integrated production activities
simultaneously. Costs and revenues must be summed for a clear net benefit
estimate. The approach must account for use of legumes as a food or feed,
and/or accurately reflect market conditions for legumes. |
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13 The opportunity costs (shadow prices) must also be
considered-. And in order to evaluate the required flow resources
(e.g., labor), some type of time-disaggregated distinctions for each such
resource should be incorporated. One such analytical approach is
goal-oriented math programming. An example of math programming is presented in
this M.S. thesis. |
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APPENDIX |
VIII.1 Time |
Disaggregations--Production Periods |
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Period |
First day |
Last day |
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Number |
of year |
of year |
Period |
|
1 |
74 |
87 |
March 15 - March 23 |
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2 |
88 |
101 |
March 29 - April 11 |
|
3 |
102 |
115 |
April 12 - April 25 |
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4 |
116 |
129 |
April 26 - May 02 |
|
5 |
130 |
136 |
May 03 - May 09 |
|
6 |
137 |
143 |
May 10 - May 16 |
|
7 |
144 |
150 |
May 17 - May 23 |
|
8 |
151 |
158 |
May 24 - May 30 |
|
9 |
159 |
172 |
May 31 - June
13 |
|
10 |
173 |
186 |
June 14 - June 27 |
|
11 |
187 |
200 |
June 28 - July 11 |
|
12 |
201 |
214 |
July 12 - July 25 |
|
13 |
215 |
228 |
July 25 - August 08 |
|
14 |
229 |
242 |
August 09 - August 22 |
|
15 |
243 |
256 |
August 23 - September 05 |
|
16 |
257 |
270 |
September 06 - September 19 |
|
17 |
271 |
284 |
September 20 - October 3 |
|
18 |
285 |
298 |
October 04 - October 17 |
|
19 |
299 |
312 |
October 18 -October 31 |
|
20 |
313 |
326 |
November 01 - November 14 |
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21 |
327 |
342 |
November 15 - November 30 |
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Hours available on good field days |
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APPENDIX VIII.3
Time Available on Good Field Days, Flow Resources |
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APPENDIX VIII.4 Planting/Harvest
Timing |
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APPENDIX VIII.5 YIELD AND MOISTURE MATRICES, CORN AND SOYBEANS Corn
grain Yield coefficient matrix by planting and harvest dates: |
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#9 soy acreage must exceed silage or corn in soy rotations S-corn + S-silage
<
soybean acreage RHS +1 +1 -1 ≤ 0 #10
corn rotated with either directly-seeded establishment year alfalfa or full
production year alfalfa,cannot exceed the alfalfa
acreage A-corn + A-silage _< D-S
alfalfa + full alfalfa +1 +1 -1 -1 ≤
0 #11,12
Oats for alfalfa establishment must be equal to alfalfa oat-est. OEA = oats +1 -1
≤ 0 -1 +1 ≤
0 #13 Full crop alfalfa cannot
exceed establishment year alfalfa FA < OEA + DAlf +1 -1
-1 ≤ 0 #14
Second year rotated corn cannot exceed
first-year rotated corn ACC + ASS
≤ AS + AC +1 +1
-1 -1 ≤
0 Continued |
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APPENDIX X.2 Dairy
Gross Margin 1981 records
data: $+2078 gross return per
producing cow $-209 direct
costs net of feed per cow $+340 revenue
per head in replacement herd for 1982 subtract 4% off GM due to
government milk penalty of .51¢ a cwt down from $13.42/cwt: |
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APPENDIX XII. Marginal
Physical Product of Nitrogen for Corn The Marginal
Physical Product of Nitrogen for Corn by Rotation Misterlich - Spillman response function |
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APPENDIX XIV. Total Farm Nitrogen for Corn
Production by Source |
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ANNOTATED BIBLIOGRAPHY Ackoff, R.L. "Systems, Organizations, and
Interdisciplinary Research", General Systems Yearbook, Vol.
5 (1960) Society for General Systems Research, pp. 1-8. see Emery, F.E. for annotation Ahmed,
Saleem. "Projected Nitrogen Needs in the Year
2000 and Alternative Supply Sources." Draft of working paper, Allos, H. F., and W. V. Bartholomew. "Replacement of
Symbiotic Fixation by Available Nitrogen." Soil Science, 1959,
Vol. 87, pp. 61-66, No. 2, February. The early comparative study using 15N tracers to determine
the effect of inorganic nitrogen on N-fixation. Two postulates which still
hold were shown: 1) fertilizer nitrogen resulted in increased growth of the
legumes and 2) this stimulated growth and concommittently
the need for fixation, but high levels of fertilizer nitrogen replaced the
fixation process. Anderson, J., J. Dillon, and B. Hardacker. Agricultural
Decision Analysis. Apland, J. ROMP-FS1 Documentation.
Staff Paper P83-17, August 1983, Department of Agricultural and Applied Economic, A guide to use and applications
of a time disaggregated research oriented mathematical programming model
ROMP-FS1. Explains design, structure and includes data format sheets, plus
exemplary problem. Terse, yet highly recommended for modelers. Baker, T. G., and B. A. McCarl. "Representing Farm Resource Availability
Over Time in Linear Programs: A Case Study." North Central Journal of
Ag. Economics, Vol. 4, No. 1, January 1982, p. 60. The study explores the consequences of different degrees of time
aggregation in L.P.'s on responsiveness to parameter changes in the context
of risk. Higher aggregation - more responsive over-exaggerated importance of
risk. Time aggregation requires average crop data so obscures and/or
eliminate time-related reasons for crop selection and/or diversification,
i.e., more crop specialization. Baldock, Jon 0., R. L. Higgs, W. H.
Paulson, J. A. Jakobs, and W. D. Shrader. "Legume and Mineral N Effects on Crop
Yields in Several Crop Sequences in the p. 885-890. What is the unidentified "rotation" affect (or
"legume") affect? Over ten years of rotations conducted at |
|
Banta, G. R. "Information Required to Design and Test for Economic
Criteria," in Report of the Cropping Systems Working Group, 4th
Cropping Systems Meeting, IRRI, Los Banos,
Philippines, 1976. Short
and limited in scope; a simple-prescription of the gross categories of data
useful in quantifying some applied economic ' questions about new cropping
technologies. Barber, S. A. "Relation of Weather to the Influence of Hay Crops
on Subsequent Corn Yields on a Chalmers Silt Loam," Agronomy Journal,
1972, Vol. 64, pp. 8-10. Reiterates and adds
documentation to positive rotation effect. Relates effect of previous alfalfa
crop on corn yield to the weather during corn growth. Continuous corn
displayed phytotoxicity under residues, aggravated
by higher temperatures, low aeration and infiltration. With lower than
average precipitation alfalfa has a more positive rotation effect on corn. Barker, R., H. E. Kaufman, and R. W. Herdt.
"Production Constraints and Priorities for Research." IRRI
Agricultural Economics Department Paper No. 75-8, 1975. Highlights
IRRI's rice program, but useful in developing an
approach to explain the "yield gap", partitioned among technical,
cultural and environmental factors. Barnes, Gordon. "Insect Control." Part one in series
"Crop Rotation vs. Monoculture" in Crops and Soils, Vol.
32, No. 4, p. 15, 1980. The first in a series of six
articles for laymen on the question of rotations. The articles claim
inspiration from recent responses to rising energy costs, new environmental
laws, product scarcities, and other problems, to take a new look at
rotations. Bauer, F. C. "Nitrogen Problems in the A
period piece about nitrogen nutrient maintenance in the corn belt. Historical
facts cited: corn and wheat received the largest amounts of Nf in the early forties. Yet 72% of the Baum, E. L., E. 0. Heady, J. T. Pesek, and C.
G. Hildreth. Economic and Technical Analysis of
Fertilizer Innovations and Resource Use. Seminar
papers for TVA sponsored symposium dedicated to greater fertilizer use
efficiency are the content of this primer book. Two vanguard papers by C. Hildreth raise the relevance of soil test analysis and
use of LPs, respectively. |
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Backer, G.S. Economic Theory.
Alfred A. Knopf, Inc., 1971. Intended to serve as a first
year graduate text (it has been superceded by more rigorous and less wordy
efforts), this book contains a detailed discussion of derived demand for
factors of production (in Chapter 8) and substitution among factors. The
prose is not esoteric and the maths are basic,
therefore it’s recommended to non-economists. Benson, F. J. "A Comparison
of Corn Storage Costs..." 1982, photocopy. Benson, F. J., and S. Waldorf. "1982
Custom Rate Estimates for Compares various hay systems on
basis of costs and losses. Identifies feasible systems for different herd
sizes, specifically; approving of conventional hay baling for herds
requiring 250 tons of hay per year or less. Benson, F. J., and S. Waldorf. " Blake,
George R. "Crop Rotation vs. Monoculture: Soil Physical
Properties," Crops and Soils, Vol. 32, No. 6, p. 10, March
1980. (Part three in series, see Barges, G.) Boawn, L. C., J. L. Nelson, and C. L. Crawford.
"Residual Nitrogen from NH NO3 Fertilizer and from Alfalfa Plowed Under," Agronomy
Journal, 1993, Vol. 55, pp. 231-325. Residual legume nitrogen is
herein documented to be equivalent to 70-90 lbs./acre of Nf commercial for corn. Boisvert, R. N., and H. R. Jensen. "A Method for Farm Planning Under Uncertain Weather
Conditions with Application to Corn-Soybean Farming in Includes a method for
specification of available field-work days. Documents positive rotation
effect over years despite weather variation. "Consistent response of
rotations over years despite variation in seasonal suitability for corn
production, indicates the significance of use of rotation in a management
program aimed at high yields." |
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Fieldwork
days are a function of soil moisture content. Some variables used: rainfall,
temperature, soil characteristics, crop type, wind, and relative humidity. Boulding, K. E., and W. A. Spivey. Linear Programming
and the Theory of the Firm. The Macmillan Co., A collection of seminar papers covering the mathematical aspects of
linear programming and the relationships between theory of the firm and this
tool of operations research. Particularly useful is Chapter 4 by Wu and Kwang concerning a comparison of neoclassical theory and
math programming. This is a -thinking man's introduction to linear
programming firm-level problems. Bowbrick, P. "The Role of the Economist in Planning
Applied Biological Research," Agricultural Administration, Vol.
3, No. 1, January 1976, pp. 11-15. ( The author proposes that the agricultural economist involves himself
with 1) research priorities, 2) multidisciplinary research, and 3)
theoretical research and dissemination of results. Anecdotal. Brill, W. J. "Biological Nitrogen Fixation," Scientific
American, March 1977, Vol. 236, No. 3, p. 68. Summarizes basic research on mechanisms of BNF. Good background. Broeshart, H. "Quantitative Measurement of Fertilizer
Uptake by Crops," Contrasts three methods to
assess fertilizer uptake. "Crop yield" is not well justified due to
experimental difficulty in obtaining yield curves. The direct methods
"difference" and "isotope" are compared, the isotope
method preferred because it avoids the incorrect specification of- control
due to the defined "priming"
effect. Brown, W. G., and G. H. Arscott. "A
Method for Dealing with Time in Determining Optimum Factor Inputs," Journal
of Farm Economics, 1958, Vol. 40, No. 1-3, p. 666. Discusses the application of general rule-of-thumb: the ratios among
outputs to be used as inputs in the next period should be equal to the ratio
required in that next period. Brown, W. G., T. L. Jackson, and R. G. Peterson. "A Method for
Incorporating Soil Test Measurement into Fertilizer Response
Functions," Agronomy Journal, Vol. 54, 1 62, pp. 152-154. Once the rate of nutrient availability is established and with
knowledge of the yield x nutrient yield curve, the optimal fertilizer
level can be determined. |
|
Bureau of Agricultural Economics, "Fertilizer Materials: Price per
Ton Paid by Farmers, Buttel, F. H., W. Lockerets, M.
Strange, and E. C. Terhune. "Energy and Small
Farms: A Review of Existing Literature and Suggestions Concerning Further
Research." Paper II, National Rural Center, _ Small Farms Project, 1980, Summarizes
and reviews empirical studies of 1) effects on production and resource use,
2) conservation recommendations, 3) economic viability of low-input
("organic") farming, and 4) explains recent trends in fertilizer
use relative to other factors. Documents
that no studies consider possibilities of new production functions-- for
example, improved BNF. Chowdhury, A., Earl Heady, and S. Bhide. "Optimum Crop Production and Resource
Use Under Alternative Energy Prices and Agricultural Exports. A Separable
Chance-Constrained Programming Analysis." CARD Report 103, A macro-economic quadratic
programming model is used to simulate partial equilibrium adjustments in
major export grain industries resulting from changing energy situation. Does
not look at crop rotations or legumes. Christensen, D. A., R. J. Schatzer, E. 0.
Heady, and B. C. English. "The Effects of Increased Energy Prices on An easy to read study report
which finds in the fertilizer analysis little change in use of Nf if prices increase. CIMMYT,
Planning Technologies Appropriate to Farmers; Concepts and Procedures.
CIMMYT, 1980. A very readable handbook for
directing research efforts within F.S.R. framework. Not
restricted to any area. F.S.R. begins and ends with farmer as client
and farmer as expert. Conrad, H. R., R. W. Van Keuren, J.W. Hibbs (OARDC, Provides empirical results of
tests with pre-bud alfalfa in dairy rations. Shows excellent performance
relative to grains and substitutes. Cooke, G.W. Fertilizing for Maximum Yield (3rd edition). Granada
Publishing Ltd., A text that covers the subject
from A to Z without becoming a promotional brochure for commercial
fertilizers. Each topic includes a brief historical overview documenting the path
of innovations. Includes a comprehensive review of various agronomic
functional forms to model crop response to nitrogen. |
|
Council
for Ag. Science and Technology. Stout, Bill A. (Chairman). Energy Use in
Agriculture Now
and for the Future.
Report No. 68,
1977 August.
C.A.S.T.: Discussion section considers the
substitution of legume nitrogen for fertilizer nitrogen. Rejects crop
rotation on premise of the competitive relation between crops outweighing the
complementarity. Proposes intercropping for temperate
grain farming without solid empirical justification and naively recommends
genetic engineering and nif gene transfer to
non-legumes. Council for Ag. Science and Technology. Energy Conservation in Agriculture. C.A.S.T. Special Publication No. 5, October 1977, A collection of short papers
mostly addressing policy alternatives. Paper by R. Hoeft
discusses energy for crop production, states fertilizer use a major portion (33%) and explicitly recommends
corn-soy and grain/legume rotations. Reid 6 White's contribution concerning
livestock proposes (1) increase legumes and top grade forages in feeds and
extensive livestock production, (2) develop soy substitutes for beef, etc.,
(3) develop the potential of blue-green algaes,
which all depend on BNF which he
does not specifically note. In the discussion section: the issue of
non-existing markets for legumes (ex. soybeans) and a discussion of
alternatives to commercial nitrogen production. Cralle, Harry T., and Gary H. Heichel.
"Nitrogen Fixation and Vegetative Regrowth of
Alfalfa and Birdsfoot Trefoil After Successive
Harvests or Floral Debudding." Plant
Physiology (1981)
67, 898-905. Crawford,
Eric W. "Farming Systems Research and Agricultural Economics," in Farming
Systems Research Group, ( State of the art definition of
terms and methods of R.F.S. (research on farming systems) and F.S.R. (farming
systems research): resources function, household goals, cost/returns.
Discusses use of L.P.s and the role of agricultural
economists. Curl,
E. A. "Control of Plant Diseases by Crop Rotation." Botannical Review, Vol. 29 (1963), pp. 413-479. Dart, P. J. "Biological Nitrogen Fixation." Development Digest, Vol.
XIII, No. 4, October 1979, pp. 18-28. Well-integrated overview about
BNF, not limited to discussion
of tropical systems. Contains misleading calculation of the "economic
value" of N-fixed. Dent,
J. B., and M. J. Blackie. Systems
Simulation in Agriculture, 1979, Applied Science Publications: The first text concerned with
methods of system research of agricultural systems where biological, social,
economic components interact. Designed primarily for agricultural researchers
without model-building experience; covers conception, construction,
implementation, validation and use of computer-based ag-system
models. |
|
Dillon,
J. L. The Analysis of Response in Crop and Livestock Production. Pergammon Press: A "principles"
handbook for analyzing crop-fertilizer and livestock-feed responses. Concise
chapters include examples of each topic, summarizes with the common
shortcomings in agricultural response research. Dillon, J. L. "Economic
Considerations in the design and analysis of agricultural experiments."
(Australian) Review of Marketing and Agricultural Economics,
1966, Vol. 34, pp. 64-75. Contrasts dichotomous
experiments (requiring functional analysis of variance) with
"levels" research, (requiring OLS regress on estimation) and
concentrates on the "how much" experiment design and analysis. Mode
of analysis should be considered before experiments designed. Discuss
complete and fractional, factorial, central composite and rotatable
designs. Doering, Otto C. III "Agriculture and Energy Use in the
Year 2000." AJAE, Vol. 59, No. 5, December 1977, p. 1067. Identifying the relationships
characterizing energy use in Agriculture: relative price changes among
energy, substituting inputs and final goods, shortages -most likely. Projects
anhydrous may climb to $275/ton reflecting costs (recall demand price of
$400/ton 1974). Nevertheless, chemical fertilizer use would probably NOT
decline. Doering, 0. C., III, and R. M. Peart.
"Evaluating Alternative Energy Technologies in Agriculture."
NSF/RA-77Ul24. Purdue University Ag. Experiment Station, Doll, J. P. "A Comparison of Annual vs. Average Optima for
Fertilizer Experiments," American Journal of Agricultural Economics.
Vol. 54, No. 2, 1972, p. 226. On the question of estimating
response functions, Doll argues against the wide range of levels experiments
ala Heady and Pesek by showing the insensitivity of
average profit over years of experiments to variations in fertilizer levels.
The price of nitrogen is as important as the level applied, in determining
profitability. Dovring, Folks, and D. R. McDowell. "Energy Used for
Fertilizers." Illinois Ag. Economics Staff Paper 80-E-102, February
1980. Estimates BTUs of energy
consumed for fertilizer use and production in the Duncan,
Marvin, and Kerry Webb. "Energy and American Agriculture." Kansas
City Federal Reserve Bank, 1980. A
macroeconomic study highlighting elasticity of substitution among input
categories of labor and mechanical versus chemical energy. Crop
rotation/legume nitrogen not explicitly considered. |
|
Dvoskin, D., and E. 0. Heady. " Models nationwide agricultural
adjustments to energy constraints. This is an interesting study that (along
with others) concludes crop rotation will assume greater importance as energy
is constrained. Nitrogen fertilizer can be supplied by manure and from legumes
in the rotation qualified by tillage and crop management. Edwards,
A review of the historical
documents of Thomas Jefferson c. 1780-1820 that contains transcripts of his
writings on crop rotations with alfalfa and clover to maintain soil
fertility, along the lines of the European techniques. He lists crop rotation
research at the top of agronomy priorities. Eidman, Acknowledgement that linear
programming models are useful, but they should be developed with (1)
sufficient detail in specifying alternatives, (2) deal with adjustments over
time, and (9) consider embodied energy when evaluating
alternative technologies. A call is made for investigating new technologies x energy price
scenarios. Eidman, A discussion of simulation modeling--contains general introduction to
simulation that classifies management techniques as 1) budgeting, 2)
functional analysis, 3) activity analysis, 4) simulation, 5) management gaming, compares them and details a specific firm simulator. Eidman, Discusses economic concepts important for developing enterprise
budgets: Fixed vs. Variable Costs as a function of timing, concepts of
diminishing marginal returns, input substitution. 'Computing procedures and
examples are included. Highly recommended also for non-economists. Emery, F.E., editor. Systems
Thinking. Penguin Books, Inc. 1969. An introductory anthology concerning the philosophical and mechanical
fundamentals of system thinking. Contains chapters by class of system (open, physical, behavioral, goal oriented, etc.). Papers are
reprinted from the major proponents in the field. Esoteric vocabulary is
introduced. |
|
Engelstad, 0. P., and G. L. Terman. "Fertilizer Nitrogen: Its Role in Determining
Crop Yield Levels." Agronomy Journal, Vol. 58, No. 5,
1966, p. 536. The relative immobility of K and P in soils, especially in humid
regions is why applied fertilizer N generally sets the yield level of
non-legume crops. The response curves for N are far greater in magnitude than
curves for P or K. Also, in contrast to P and K, crop N response is highly
dependent on seasonal moisture and other yield limiting factors. Evans, H. J., and Lynn Barber. "Biological
Nitrogen Fixation for Food and Fiber Production. What are some immediately
feasible possibilities." Science, Overview article on major aspects of BNF applications to future world
needs. Concludes that research on nodulating
legumes and algae systems has greatest probability of producing
"economic" (not derived) benefits to society in the short run. Evans, Harold J., Editor. Enhancing
Biological Nitrogen Fixation. Proceedings of Workshop: Energy
Related General Research, N.S.F., June 1974. Published in June 1975. Ewald, Ursula. Recent Developments of the World Fertilizer
Market: A Statistical Analysis. Institut
fur Weltwirtschaft, Universitat
Kiel. Fertilizer Institute. Fertilizer
Reference Manual. Fertilizer Institute, Contains world production and consumption data, Food and Agriculture Organization. "Current World Fertilzer Situation and Outlook, 1980/81-1985/86."
FAO/UNIDO/World Bank Working Group on Fertilizer, 7th Session, Forecasts supply and demand of
fertilizers up to 1986. Data shows LDC nitrogen fertilizer consumption
growing fast. Relates nitrogenous fertilizer supply to surplus ammonia (not
natural gas) availability, i.e., fertilizer production is not assumed a
number of priority and supply will be in deficit soon. (This is debatable.) Francis,
C. A., and J. H. Sanders. "Economic Analysis of Bean and Maize Systems:
Monoculture versus Associated Cropping." Field Crops Research,
1(1978), p. 319-335 (Elserier-Amsterdam). Twenty trials of bean-maize analyzed-associated cropping compared with
monoculture in yields, returns, average risk, evaluated over price ratios
from 1:1 to 8:1. Methodology is very straightforward and can be used by
agronomists in the field. Frank, Gary G. USDA E.R.S. A Guide to Energy Savings for the Dairy
Farmer. USDA, FEA, June 1977. Freeman,
M. L. "1982 Crop Production: Cash Costs...S.E. Minnesota."
photocopy (corn, soybeans, alfalfa) |
|
Fribourg, H. A., and W. V. Bartholomew. "Availability of
Nitrogen from Crop Residues During the First and Second Seasons after
Application." Soil Science Amer. Proceedings 20:505-508, 1956. Compares green manure crops,
ranking alfalfa above soy or clover straws. Oat hulls actually depressed
subsequent corn yields. Calculated availability rate: year after full crop
alfalfa 43% of alfalfa nitrogen available from two tons of alfalfa tops/acre;
yield: 108 bu. corn. Fried,
Maurice, and L. A. Dean. "A Concept Concerning the Measurement of
Available Soil Nutrients." USDA, 1951. From the concept that a plant
will absorbs nutrients from different "sources" in direct
proportion to the amounts available. A
mathematical expression for determining the amounts available is derived (A= B (1-Y)) Y Fried, M., and H. B. Broeshart. "An I
dependent Measurement of the Amount of of Nitrogen
Fixed by a Legume Crop." Plant and Soil, Vol. 43, 1975. Nsy
is calculated by A values x % Nf utilization by legume crop. Frissel, M. J. and J. A. van Veen. "Simulation of Nitrogen Behavior of
Soil-Plant Systems: Models for Nitrogen in Soil and Uptake by Plants."
Papers from workshop. Wageningen Publications: Fuller,
Earl I., Dale Nordquist, and Tony L. Groble. "User's Guide for FACILITY: An Investment
Cost and Labor Requirement Generator for Livestock System." Can be used to develop estimates
of costs and labor requirements for various dairy facility systems. Ghodake, R. D., and J. B. Hardaker. "Whole Farm Modeling for the
Assessment of Dryland Technology." ICRISAT,
Economic Report No. 29, December 1981. FSR for
technology assessment considers the social structure, the institutions,
extension, markets, farm resources, current technologies, skills, attitudes (eg: towards risk) and objectives, in the farm context.
An assessment consists of projected farm income, output, and the associated riskiness of the new technology. It identifies limiting
factors and key constraints. Technology and/or policy revisions can then be
suggested that alleviate those constraints. Ghodake, R. D. "The
Potential of Mathematical Programming for the Analysis of Yield Gaps in
Semi-Arid Tropical Agriculture." ICRISAT, Economics Report No. 24,
September 1981. Allocative (or price) efficiency and
technical efficiency are two components of overall economic efficiency.
Technical efficiency - Management (controllable), physical and social
(uncontrollable). Price (allocative): suboptimal
input combinations. |
|
Gibson,
A. H. "The Influence of the Environment and Managerial Practices on the
Legume-Rhizobium Symbiosis." Chapter II in
Section IV of A Treatise on Dinitrogen Fixation. by R.W.F. Hardy and A. Gibson,
Editors, Wiley S Sons, 1977. The most comprehensive
discussion on the subject encountered by this author, to include: influences
of temperature, light, moisture, oxygen and carbon dioxide, pathogens, defolication, nutrition, sowing, mulching, lime, starter
nitrogen, transplantation, photosynthesis, timing, irrigation and more. Giddens, Joel,
S. Arsjad, and T. H. Rogers. "Effect of
Nitrogen and Green Manures on Corn Yield and Properties of a Cecil
Soil," Agronomy Journal, Vol. 57, No. 5, 1965, p. 466. Will a rye crop turned under as
green manure supply the fertilizer N it absorbed to a subsequent crop? This experiment
evidenced that organic nitrogen cannot be built up to the same level that
inorganic fertilizer directly applied can. Green manuring
this non-legume conserved soil N but crop recovery was only 60% as high as
recovery from fertilizer applied. Green,
J. T., et. al. "Inoculation of Forage Legumes." Greenberg, Edward, Christopher T. Hill, and David J. Newbar. Regulation,
Market Prices and Process Innovation: The Case of the Ammonia Industry. Westview
Press: Guar,
Y. D., A. N. San, and N. S. Subba Rao. "Improved Legume-Rhizobium
Synthesis by Inoculating Preceding Cereal Crop with Rhizobium."
Plant and Soil. Vol. 54, No.
2, 1980, p. 313-316. Gustafson,
C. R. "Optimum Production Adjustments of a The bulk of the L.P. model used
to identify farm adjustments to various fuel price scenarios. The author
discusses other adjustments with some unsubstantiated summaries to conclude
timing and tillage alterations are the major focus of production adjustments.
Does contrast continuous with rotated corn and beans. Halsey,
A useful summary of the U.S.L.E.
and guide to quick computation. Unfortunately, rainfall erosion index data
and computations cannot be made without the original U.S.L.E. USDA Ag.
Handbook 537. Hardin, Lowell S., and Glenn L. Johnson. "Economics of Forage Evaluation." Journal of Farm
Economics, Vol. 37, p. 1457. Correct economic evaluation of
forages require (1) pricing as an input in livestock feed, (2) with feed
ration balancing between farm production and market purchasing, (3) pricing
as output, and (4) comparative budgeting relative to other alternative crops. |
|
Hargett, Norman L. 1974
Fertilizer Summary Data. N.F.D.C., T.V.A., Bulletin Y-86, January 1975. Haynes, J. L. and L. E. Thatcher. "Crop Rotations and Soil
Nitrogen," Soil Science Society of American Proceedings, 1955,
Vol. 19, p. 324-327. To test the notion that legume
rotations are "soil building," 39 years of rotation experiments
were examined to show long-term cummulative trends
in soil fertility. Results: rotations maintained a high level of fertility
but did not cause continual amelioration. Continuous corn caused long run
downward trend in soil productivity. Heady, E.O., and An early discussion of the variety of farm problems and L.P. formats
that can be encountered. It is proposed that exogeneous
(by hand) calculation is an alternative to L.P. calculation of optimal
fertilizer with only a few discrete fertilizer rate alternative processes. Heady, E. 0., and J. Dillon. Agricultural Production Functions. Heady, E. 0., and H. R. Jensen.
"The Economics of Crop Rotations and Land Use." Agricultural
Experiment Station Research Bulletin 383, August 1951. An elaboration of the neo-classical approach of enterprise complementarity applied to determination of optimum
rotations and livestock ration. Reasons for complementarity
between forage legumes and grain include: (1) the accretion of nitrogen, (2)
pest reduction, (3) tilth, and (4) erosion control. Heady, E. 0. "The Economics of Rotations with Farm and Production
Policy Applications," Journal of Farm Economics, Vol. 30,
1948, p. 645. Employing the basic product-product production relations with
assumption of diminishing marginal complementarity
between forage legumes and grain, Heady details the iso-revenue=
isocost determination of optimal rotation. Heady, E. 0., and H. Jensen. Farm Management Economics. Prentice-Hall:
A primer on farm management economics of historical interest,. i.e.,
how crop rotation was analyzed in terms of the many benefits of rotation and
the complementarity among legume and grain crops.
Livestock feed requirements are also considered. Heady, E. 0., J. T. Pesek, and V. Y. Rao. "Fertilizer Production Functions from
Experimental Data with Associated Supply and Demand Relationships," Ag.
and Home Ec. Experiment Station, Production function regression
analysis used to derive single nutrient response curves. Includes static
analyses of corn supply and fertilizer demands, relating various price
scenarios to "optimum" fertilizer use. Results should be
interpreted with extreme caution. |
|
Heady,
E. 0., J. A. Schuittker, N. L. Jacobson, and S.
Bloom. "Milk Production Functions. Hay/Grain Substitution Rates and
Economic Optima in Dairy Cow Rations," Agricultural Experiment Station, Strong production economics
presentation of the substitutability of hay and grain, grossly defined in
dairy cow rations. This study concerns wide range of hay in ration, but does
not define hay or grain according to
quality or nutrient content. Heady,
E. 0., N. L. Jacobson, J. P. Madden, and A. E. Freeman. "Milk Production
Functions in Relation to Feed Inputs, Cow Characteristics and Environmental
Conditions," Ag. and Home Ec. Experiment
Station, Presents regression models for economic
optima in ration specification, and milk isoclines and isoquants,
exogenously determined prices, and cow characteristics. The data consists of
experimental points on the production surface. Does not include a wide range
of hay feeding or quality levels. Heichel, G. H. "Breeding Alfalfa for Improved Nitrogen
Fixation: A Physiological Perspective." Photocopy of submitted article,
October 1981. Of high value in the thick of
research, but soon to be superceded. This paper explains results of two
cycles of bidirectional selection vis a vis enhancing nitrogen fixation considering other
characteristics, management, dormancy of alfalfa; in cropping systems.
Outlook: positive! Heichel, G. H. "Energy Analysis of Alfalfa
Production." USDA-Minnesota Agricultural Experiment Station Scientific
Journal Series Paper No. 1U, 176 (1978a). Alfalfa and forages account for 73% of feed fed to ruminants and
alfalfa provides nitrogen in crop rotations. The crude oil equivalents of
energy required for seed processing, distribution, and cropping and
harvesting are audited. Heichel, G. H., D. K. Barnes, and C. P. Vance. "Nitrogen
Fixation of Alfalfa in the Seeding Year," Crop Science, Vol.
20, March 19811 p. 330. During
the seeding year of alfalfa, 43% of their nitrogen needs were supplied by symbiosis.
This paper also contains a good description of the N-method. Fixation performance over four harvests shows
best fixation harvest during intervals 2 and 3. Heichel,
G. H., and C. P. Vance. "Nitrate-N and Rhizobium
Strain Roles in Alfalfa Seedling Modulation
and Growth," Crop Science, Vol. 19, July-August 1979, p.
512-518. Heichel, G. H. "Stabilizing Agricultural Energy Needs:
Role of Forages, Rotations and Nitrogen Fixation," Journal of Soil
and Water Conservation, Nov.-Dec. 1978b, Vol. 33, No. 6, p.
279-282. Compares
energy requirements for major crops. Estimates the savings in terms of fossil
energy flux if crop rotations are better exploited. |
|
Heichel, G. H., D. K. Barnes, and C. P. Vance. "Nitrogen
Fixation by Forage Legumes and Benefits to the Cropping System," in
Proceedings, Sixth Annual Symposium, Minnesota Forage and Grassland Council,
St. Paul, Minnesota, 1981. Reports comparisons of yield and
nitrogen fixation patterns for alfalfa, trefoil and red clover. Alfalfa
harvest management and plowing effects benefits to
system. Late summer incorporation into soil may add significant amounts of
nitrogen to soil. Hera, C. "Effects of Management Practice of Dinitrogen Fixation in Temperate Regions,"
IAEA-AG-92/12. Nice background article by
member of Romanian cereal and industrial crops research institute about how
fixation works and how temperate agricultural management can effect fixation
by non-symbiotic organisms and also legume symbiotic systems. Hicks,
D. R., S. D. Evans, J. H. Ford, W.-E. Lueschen,
W. W. Nelson, C. J. Overdahl, R. H. Peterson, G.
W. Randall, and D. D. Warnes. "Corn Management
Studies 1973-75." University of Minnesota Ag. Exp. Station, Misc. Report 149, 1977. Higgs,
R. L., W. H. Paulson, J. W. Pendelton, A. F.
Peterson, J. A. Jackobs, W. D. Schrader. "Crop
Rotations and Nitrogen. Crop Sequence Comparison on Soils of the Driftless Area of S.W. Eight years crop rotation
studies show: - first year corn after legumes
needs no Nf to equal 150Nf corn yield. - legume N available for corn depends on
amount of legume in the field. - hay yields are not affected by rotation or
fertilizer. -
the year x rotation positive effect on corn was significant to 1%
significance level. Hildreth, C. G. Chapter 16 "Possible Models for
Agronomic-Economic Research", and Chapter 21 "Some Problems and
Possibilities of Farm Programming". in Baum, et al, Economic and
Technical Analysis of Fertilizer Innovations and Resource Use, Holt,
R. F. "Crop Residue, Soil Erosion, and Plant Nutrient Relationships." Journal of Soil
and Water Conservation, Vol. 34, No. 2, March-April 1979, p. 97. Compares plant nutrients
associated with crop residues by state with commercial fertilizers. In
Hughes,
H. and Scott Pearson. "Principle Issues Facing the World Fertilizer
Economy." Ag. Development Council, RTN, Seminar Report, 1974. Concise and readable. Explicitly
identifies BNF and inoculation as a principal analytical question regarding
world fertilizer supply. |
|
Hutjens, M. F. "Alfalfa in the Ration: Pros and
Cons." in 8th Annual Alfalfa Symposium, Alfalfa: Energy, Protein, and
Nitrogen. March 1978. Pros:
nutrient content, protein solubility: is high for immature, feed intake: is
highest for immature hay, milk production: excellent, buffering capacity. Cons:
dry matter losses, carmelization, chop length; dry cow
rations. A reference source on fertilizer
production technology and economics for developing countries. Part one
contains brief historical overview on Jacobs, J. E., and J. A. Stricker.
"Economic Comparisons of Legume Nitrogen and Fertilizer Nitrogen in
Pastures," in Stelly, M., Editor, Biological
Nitrogen Fixation in Forage-Livestock Systems. A.S.A., No.
28, 1 Missouri Beef Cow-Forage systems
experiment economic analysis compares legume-grass forages with no legume +
nitrogen fixation forage. Legume-grass is shown superior. Includes an
interesting six-point guideline for designing agronomic research for economic
analysis. Jensen,
D., and J. T. Pesek. "Generalization of Yield
Equations in Two or More Variables," Ag. Journal, No. 51,
1959, p. 255. Jokela, W. E., W. E. Fenster, C. J. Overdahl, C. A. Simkins, and J.
Grave. "Guide to Computer Programmed Soil Test Recommendations for Field
Crops in Provides fertilizer (N, P, K)
and lime recommendations based on soil test analyses for maximum
"economic" yields. Nitrogen .fertilizer recommendations are
explicitly a function of the crop rotation course for almost all crops. Kim, Y. J. "Multiple-Crop
Diversification: A Macro-Economic View."ASPAC, Food and Argues extent/how crop
diversification provides advantages of reduced risk, increased whole-farm
efficiency, and improved farm income. |
|
Klepper, R., W. Lockeretz, B.
Commoner, M. Gertler, S. Fast, D. O'Leary, and R. Blobaum. "Economic Performance and Energy
Intensiveness on Organic and Conventional Farms in the Paired comparison study of
existing livestock and grain farms of Kliebenstein, J. B., and J. P. Chaves. "Adjustments of Ties prices of fuel, propane,
chemicals, and fertilizers to fossil fuel energy price changes equally. As
with other studies, no-till plus high chemicals is preferred to a point,
then conventional tillage and low chemical technology is preferable.
Model is not sensitive to legume nitrogen since rotation nitrogen does not
appear. Koopmans, T. Activity
Analysis of Production and Allocation. Wiley, 1951. Lanzer, Edgar A., and Q. Paris. "A New Analytical
Framework for the Fertilization Problem," American Journal of Agricutlural Economics, Vol. 63, No. 1, 1981,
p. 93. Fertility carry-over is like
money in the bank, and therefore it must be considered part of fertilizer
economics. The two main thrusts of this paper are (1) explaining the linear
response and plateau function "LRP" which is based on von Liebig's Law of the Minimum and Misterlich's
relative yield theory, and (2) showing how this model can
improve on the P and K recommendations from the quadratic model. Larson,
W. E. "Crop Residues: Energy Production or Erosion Control?", Journal
of Soil and Water Conservation, March-April 1979, Vol. 34(2). 2.2 metric tons hectare
(approximately 1 ton/acre of residue) on the soil surface reduces
water-erosion soil loss by 65%. Residue removal means loss of plant nutrients
and increased loss by erosion. Linn,
J. G., and R. D. Appleman. "Feed Inventory for
Inventory for Dairy Cattle." Feed inventories can be used to
balance rations on nutritional bases for a dairy herd and therefore plan
other farm adjustments. Costs and economic optima are not considered. Linn, J. G., D. E. Otterby, R. D. Appleman, and M. F. Hutjens.
"Feeding the Dairy Herd." State-of-the-art overview of dairy herd nutrition. Details and summarizes
requirements, functions, and rations for all parts of herd. |
|
Litsinger, James A., and K. Moody. "Integrated Pest
Management in Multiple Cropping Systems," Chapter 15 in Multi le Cro in , 1976, Papeadick, et.
al., (Editors), ASA Special Publication Discusses pest control
implications of most cropping management systems. Crop rotation vs. perennial
crop pest management is compared on the general level. Rotation is effective
in lowering soil-borne diseases and interrupting pest buildup. Biocontrol of insects is more successful with perennial
cropping. Lofgren, John. "Insect
Control of Forage Crops." Loomis, Ralph A. "Effect of
Weight-Period Selection on Measurement of Agricultural Inputs," Agricultural
Economics Research, Vol. IX, No. 4, October 1957, USDA, Ag. Mkt.
Service, Ag. Research Service. Lyon,
T. L., and J. A. Bizzell. "Nitrogen
Accumulation in Soil as Influenced by the Cropping System." Journal
of the American Society of Agronomy, Vol. 25, 1933, p. 266-272. An early modern account of the
accretion of soil nitrogen due to alfalfa cropping. Without legumes the
nitrogen accretion appeared to be about 25 lbs./acre/year. Lyon
and Bizzell. "A Comparison of Several Legumes
with Respect to Nitrogen Production." Journal of the American Society
of Agronomy, Vol. 26, 1934, p. 651-666. Great
"why alfalfa BNF?", similar to previous Malzer, G. L. "Nitrate Nitrogen Soil Test for Corn."
Mimeo, Fall 1982. Algorithm for calculating
available soil nitrogen as function of position in soil profile, moisture,
organic matter (mineralization) and non-nitrogen effects on yields. Markham,
J. W. The Fertilizer Industry: Study of An Imperfect Market. Study by Markowitz. "Portfolio Selection, Efficient Diversification
of Investments." Cowles Foundation Monograph No. 16, 1959. |
|
Marten,
John F. "Can Farmers Afford Thirty Cent N?" Fertilizer Solutions,
Vol. 19, No. 2, March-April 1975, p. 30, N.S.F.A. Building on the principle that
fertilizer should continue to be applied until the rate of added returns no
longer exceeds the cost of additional units, considering the diminishing
affect of N on corn after 200 lbs./acre, this ag.
economist from Purdue concludes that under most conditions and wide range of
N prices, added returns exceed additional costs; no reduction of use is
implied if N prices increased. Also, it is demonstrated that under rationing
constraints it is more profitable to equally allocate N (due to the high rate
of response under lower application levels) than to concentrate application
on a few plots and leave the rest unfertilized. Martin,
Neal P. "Growing, Harvesting and Storing Alfalfa in S.E. Shows management methods of top Martin,
N. P. "Managing Alfalfa for Higher Yields." Photocopy: Paper
presented at 4th Annual Minnesota Forage S Grassland Council Symposium, Neal Martin's recipe for 6-9
ton/acre top producers: 1) use productive soil, 2) choose a persistent
disease-resistant variety, 3) buy certified (pre-inoculated)seed, 4) lime the
soil - if required, 5) establish 15-20 plants/sq. ft. (thick), recommends
direct-seeding, EARLY, 6) top dress with K20 and P205,
and 7) cut "close" but "not frequent" 30-35 days. Martin, N. P., F. Benson, J. Linn, and R. Arthaud.
"Profitable Preservation and Feeding of Quality Silage." Concise articles summarizing
recent U of M research on silage and forage crop silage
"economical" management, storage, and use. McVickar, Malcolm, W. P. Martin, Proceedings of symposium: from
production to consumption of nitrogen fertilizers. Chapter 4 by J. R.
Douglas, Jr., and S. A. Cogswell on trends takes
input-substitution perspective. Chapter 16 on profitability by B. B. Tucker
and G. B. Crowe reviews agrononometric work on
fertilizer response and partial budgeting for inter-comparisons among types
of fertilizer forms. Best for historical data. Mehring, A. L., J. R. Adams, and K. D. Jacobs. "Statistics on Fertilizers and Liming
Materials in the The most comprehensive and
consistent source of data on state and national consumption, prices,
production, and use trends of nitrogenous fertilizers as well as organic
substitutes. Excellent historical reference. |
|
Menegay, M. R. "Farm Management Research on Cropping
Systems." ASPAC Food and Comparison
and recommendation of methods to measure multiple-cropping outputs.
Describes the crop intensity index and its applications to define homogeneous
recommendation domains. Useful papers include D. G.
Johnson's "Alfalfa Yield and Quality as Affected by Forage Harvesting
Systems," where conventional hay
baling earns a low rank; and "Potential for Alfalfa Production in Miranowski, John A. "Effects
of Energy Price Rises, Energy Constraints, and Energy Minimization of Crop
and Livestock Production Activities." North Central Journal of
Agricultural Economics, Vol. One #1, January 1979. An L.P. model focusing on 3 main
energy-reducing alternatives: (1) methane-generated electricity; (2) crop
residue and/or livestock excreta as livestock feed; (3) manure and legume
nitrogen for fertilizer is used to assess 1.25x to l0x energy price changes.
Although the modeled farm is insensitive up to 5x, increased rotation occurs
as nitrogen price reaches 5x. Moncrief, John. "Corn-Soybean Rotations Sensible for
Conservation Tillage." NEWS for County Agents, Mooers, C. A. "The Effects of Various Legumes on the Yield
of Corn." This article is a classic, but
the "data" is quite out of date about rotations among corn and
various legumes turned under for green manure ... experiments from 1908
showed that sweet clover enriched soil nitrogen levels enough to provide a
21-bushel/acre advantage over continuous corn in a 5-year rotation. Other
experiments showed corn rotated with alfalfa gave over 50 bu.
more corn over five years than from continuous corn. Average corn yields: 41 bu./acre/year. Mudahar, Mohinder S. "Needed
Information and Economic Analysis for Fertilizer Policy Formation."
A.D.C. No. 24, 1980. Addresses basis for policy
formulation concerning commercial fertilizers. Suggests framework for
analysis of alternatives. Mukhopadhysy, Sudhin. "Demand Functions for Fertilzers:
A Methodological Survey." IFDC, August 1975, Mimeo. A thought-provoking study
developing a framework to explain fertilizer consumption and adjustments
under changing production,
distribution, and pricing. MundlakI Y. "Transcendental Multiproduct Production Functions." International
Economic Review, Vol. 5, No.
3, 1964, p. 273-284. |
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Munson,
R. D. (Potash/Phosphate Institute, The best succinct assessment of
alfalfa and legume nitrogen available. Documents from the most respectable
rotation studies to support 4-point list: (1) nitrogen fixation, (2) high
phosphate and potassium sink, (3) moisture depletion, and (4) growth substances
(triacontanol). This directory lists 1425 recent
and current fertilizer projects concerned with agronomic, economic, and
marketing research in the Identifying the natural gas
feedstock and fuel high energy requirements to produce nitrogen fertilizers,
this paper concludes that despite the inseparable link between oil price and
gas price increases, no process substitution could occur for ten years. Also,
despite nitrogen fertilizer price increases, demand was stable and profits
went up. Abstracts of all fertilizer
related scientific journal research including topics on nitrogen fixation,
crop rotations, sources of fertility for corn, energy conservation, and
economics. Also includes publications from foreign countries. (This should
inspire all those on nitrogen fixation to choose key words that will insure
the abstract is included here.) |
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.Taylor, T.R. "The Theory
of the Firm: A Comparison of Marginal Analysis and Linear Programming". The
Southern Economic Journal, Vol. 32, Jan. 1966, No. 3, p. 263. Discusses rigorously the
differences between marginal analysis and linear programming approaches to
theory of the firm. The principle difference: how the production function is
handled, lack of analogy for input substitution analytical results in L.P.
analysis. Nicol, K. J., and E. 0. Heady. "A Model for Regional Agricultural Analysis of Land and
Water Use, Agricultural Structure, and the Environment: A Documentation." CARD, on Norman, D. W. "The Farming Systems Approach: Relevance for the
Small Farmer." MSU Rural Development Paper No. 5, Department of
Agricultural Economics, Excellent overview on farming systems research. Concise, readable; from
historical to current applications by one of the well-known practitioners of
FSR/X. Oelke, E. A., and R. L. Thompson. "Seeding Dates for
Small Grains and Flax." Fact Sheet No. 26, 1973. Olson,
R. 0. "The Economics of Forage Production and Utilization." Journal
of Farm Economics, Vol. 37, 1955, p. 1440. Discusses analytical models
directing research regarding optimum production of forage legumes on
grain-livestock farms. Structure should allow for increasing marginal rates
of transformation between forages and grain in rotations, diminishing
marginal rates of substitution between legume and other feeds, and to
determine optimal feeding and production of forages simultaneously. Olson, K. D., E. 0. Ready, C. C.
Chen, and A. D. Meister. "Estimated Impacts of Two Environmental Alternatives in Agriculture: A Quadratic
Programming Analysis." CARD Report 72, The two alternatives are restrictions on insecticides and constraints
on use of nitrogen fertilizers to control nitrite pollution and illustrate
effect on agricultural product prices of constrained natural gas availability.
Legume nitrogen is not modeled as a substitute per-se. Soybean production
does become the major crop under nitrogen limits, and its price drops as
surpluses are grown. Overdahl, C. J., W. E. Fenster,
and C. A. Simkins. "Fertilizing Corn."
Soils Fact Sheet No. 24, revised 1976. |
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Overdahl, C. J., and G. E. Ham. "Fertilizing
Soybeans." Overdahl,
C. J., C. A. Simkins, and W. E. Jokela.
"Nitrogen for Papendick, R. I., and L. F. Elliot. "Tillage and Cropping
Systems for Soil Erosion Control and Efficient Nutrient Utilization." Agronomy
Abstracts, 1981, p. 166. Recommends to avoid moldboard
plowing to reduce erosion, and have legumes in your rotation. Papendick, R. I., Sanchez and Triplett, (Editors) Multiple
Cropping. 1977 American Society of Agronomy, 1977, Paul,
Duane, and Richard Kilmer, "The Manufacturing and Marketing of Nitrogen
Fertilizers in the A documented general overview
about the current situation from actual original data obtained to define the
characteristics of firms in the industry. From information about current
trends, a few projections are made: despite more energy efficient production,
costs are rising. Certainty of supply of intrastate gas (no curtailments)
influences location of plants. Perrin, Richard IC.. "The Value of Information and the Value of
Theoretical Models in Crop Response Research," American Journal of
Agricultural Economics, Vol. 58 (1976), No. 1, p. 54. In the context of economics of
information which states that: information should be sought to the point
where the additional value earned due to having the information is just met
by the marginal cost of acquiring the information; the questions concerning
the choice of functional forms and soil test measurements are addressed.
Expected payoff provides the estimate for the value of information. The LRP model
(see Lanzer) is chosen to model crop responses to
fertilizer. Perrin,
R. K., D. L. Winkleman, E. R. Moscardi,
and J. R. Anderson. From Agronomic Data to Farmer Recommendations: An
Economic Training Manual. CIMMYT, Information Bulletin No. 27, 1976. Designed for agronomists, yet
particularly useful for economists engaged in agronomic research design.
Defines new terminology for target groups: "recommendation domain."
Assuming "net benefit" approach mirrors farmers' decision rule,
explains the partial budgeting approach and net benefit curves. Discusses
risk and sensitivity analysis, and where to get "correct" prices
and costs data. Peterson,
Arthur G. "Price Administration, Priorities, and Conservation of Supplies
Affecting |
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Robinson, R. G. "Pulse or Grain Legume Crops for Rohweder,
D. A., and J. E. Baylor. "New Forage Analyses Offer New Horizons for Hay
Grading-Marketing, Evaluating Forages." Forage and Grassland Progress,
Vol. XX, Winter 1980. Describes the "Two-fiber
Technique" and, proposes revamped grading of grasses and hay. Finds that
early cut alfalfa (pre-bloom) at 40-50% leaves by weight has approximately
40% higher feed value than other headed grasses or full-bloom alfalfa. Sanders, John K. "Definition
of the Relevant Constraints for Research Resource Allocation in Crop Breeding
Programs." CIAT, Mimeo, November 1980, A rare and useful pragmatic
approach to the definition of the agricultural economist's mandate in a crop
program. Has guidelines for choice between vertical and horizontal strategies
of plant pathogen resistance. Agro-economic estimates of problem magnitude
are done by multiple regressions using production function data. Schienbein, Allen (USDA ERS) "A Guide to Energy Savings for
the Field Crops Producer." USDA FEA, June 1977. Contains
section on fertilizer use that accounts for use and waste. -Recommends
fertilizing on soil test basis and use of manure, but overlooks relevance of
crop rotations. Includes BTU accounting algorithm. Schmid, A. R., A. C. Caldwell, and R. A. Briggs. "Effects
of Various Meadow Crops, Soybeans and Grains on the Crops Which Follow."
Agronomy Journal, . Vol.
51, p. 160-162. Scott, J. T., Jr. "The Economics of Corn Conditioning and Storage
Alternatives for Farmers." aerr #98,
Department of Agricultural Economics, Ag. Experiment Station, Shrader, W. D., W. A. Fuller, and F. B. Cady. "Estimation
of a Common Nitrogen Response Function for Corn (zea
mays) in different crop
Rotations," Agronomy Journal, Vol. 58, 1966, p. 397-404. Supports
the hypothesis that legume nitrogen can be expressed as a fertilizer nitrogen
equivalent (defined). Also found equivalents were the same regardless of the
age of the meadow stand for the first year of corn, but longer stands had
higher nitrogen equivalents for second year corn. Shrader, W. D., and R. D. Voss. "Crop Rotation vs.
Monoculture: Soil Fertility," Crops and Soils, Vol. 32,
No. 8, p. 15, June-July 1980, (part four of series, see Barnes, G.) |
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Smith,
D. "Influence of Fall Cutting in the Seeding Year on the Dry Matter and
Nitrogen Yields of Legumes," Agronomy Journal, Vol. 58,
1956, p. 236. Root-rot confounds the results
of experiments this paper is based upon - superceeded
by research of Heichel, Shaeffer,
et. Snedecor, George W. Statistical Methods Applied to
Experiments in Agriculture and Biology. Stengel, Dr. Paul J. (IFDC) "Market Trends and Agronomic
Suitability of Key Fertilizers Commonly Sold in World Trade." Paper
mimeo from seminar, "Optimizing Agricultural Production Under Limited
Availability of Fertilizers."
Sponsored by Fertilizer Assoc. of Good descriptive reference on
popular fertilizers and their usefulness and characteristics. Stelly, Mathias (Editor in Chief). Biological Nitrogen
Fixation in Forage-Livestock Systems, American Society of
Agronomy, Special Publication No. 28, 1976. Stelly, M. (Editor in Chief). Multiple Cropping. American
Society of Agronomy, Special Publication No. 27, 1976 Stickler,
F. C., W. D. Shrader, and Rather
unclear presentation on "efficiency" of legume nitrogen, in summary
that it ranges 16% to 92% as "effective" as inorganic nitrogen.
(Consideration of rates of mineralization information would have been an
improvement.) Year one: 150 lb/A Nf
equiv. Year two: 78 lb/A Nf equiv. Stinchfield, Joseph: "Impacts of Energy Uncertainties
on the Food System in the Prescribes method of herbicide
establishment of alfalfa when other grasses are also cultivated. Subba, Rao. "Nitrogen Deficiency As A World-Wide Problem." Chapter 1 in Global Impacts of Applied
Microbiology, W. R. Stanton and E. J. Da Silva,
Editors, A provocative essay of current
wisdom relating BNF to meeting world food and energy needs. Summary discusses
potentials, points to reducing rhizobial hydrogenase to increase Nsy
efficiency. |
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Subba Rao, N. S. Recent Advances
in Biological Nitrogen Fixation. Sundquist, B., Ken M. Manz, and
C. F. Neumeyer. "A
Technology Assessment of Commercial Corn Production in the Sutherland,
W. N., W. D. Shrader, and J. T. Pesek.
"Efficiency of Legume Residue Nitrogen and Inorganic Nitrogen in Corn
Production," Agronomy Journal, Vol. 53, 1961, p. 339-342. Estimates
nitrogen production by meadow crop between 123-200 pounds per acre in first
corn year, 54-83 pounds per acre for second year corn.. Excessively wet
conditions disadvantageous to rotation corn. Does not fin3 evidence of a
non-nitrogen "rotation effect." Organic matter for rotated plots at
intermediate level between check plots and highly fertilized plots. Swan,
J. B., and J. A. True. "Management Considerations in Primary Tillage for
Corn and Soybeans." A good discussion on choice of
tillage operations. Highlights corn-soy rotation with chisel vs. moldboard
plows recommends less costly practice which gives equivalent yields. Swan, J. B., W. W. Nelson, and R. R. Allmaras.
"Soil Management by Fall Tillage for Corn." Swanson,
Earl R. Cropping Systems: Economic Considerations. Swanson,
Earl R., C.R. Taylor, and P.J. Van Blokland. Economic
Effects of Controls on Nitrogen Fertilizer. Agricultural
Experiment Station Bulletin No. 57, College of Agriculture, Simulation of effects of (six)
different methods of policy corresponding to controls on use of nitrogen
fertilizer: (1) Education, (2) Per-acre use limit, (3)
Excise tax, (4) Market for rights, (5) Restricted nitrate
concentration in the watershed, (6) Restrictions on nitrogen balance at farm
level. Used L.P. for modeling; see M.E. Walker 1974 reference for
further discussion of (6). |
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Swanson,
Earl R., C.R. Taylor, and L.F. Welch. "Economically Optimal Levels of
Nitrogen Fertilizer for Corn: An Analysis Based on Experimental Data." Swanson, Earl R., and E.S. Tyner. "Influence of Moisture Regime on
Optimum Nitrogen level and Plant Population for Corn: A Game Theoretic
Analysis." Agronomy Journal, Vol. 57, 1965, p. 361-364. Taylor,
C. R., K. K. Frohberg, and W. D. Seitz.
"Potential Erosion and Fertilizer Controls in the The welfare effects of erosion
controls and nitrogen controls are investigated using an L. P. model. Looking
at the impact of N-restrictions: soybeans replace corn acreage. Due to the
model's specification that the N-restriction applies to legume N also,
rotations do not appear technologically superior. This study incorporates
restrictions that seem too severe and this reflects poorly on the validity of
the conclusions. Taylor,
C. R., and K. K. Frohberg. "The Welfare
Effects of Erosion Controls, Banning Pesticides and Limiting Fertilizer
Applications in the The L.P. developed for this
model does not tie the nitrogen available from legumes to the land on which
the legumes were cropped. The fertilizer restriction also amounts to a
restriction on all soil fertility, organic or inorganic. The propriety of
these restrictions is dubious. Tang,
Paul S. "Validation of Computer Models of Plant Disease Epidemics:a Review of Philosophy and Methodology." Journal
of Plant Diseases and Protection, Vol. 88, No. 1, pg. 49-63, 1981. A compact, comprehensive
overview of modeling/simulation building as it is related to and improved by
the process of validation. It is liberally referenced - serves as an
abstract on systems research and model validation. Collection of
seminar reports sponsored by T.V.A.- Market research and analysis chapter by
G.C. Sweeney Jr. covers concept of "shifting yield curves." J.
Mahan's article "Fertilizer Use Reports" is well documented, but
the sources cited can lead one on a wild goose chase. "Role of
Technology is expanding Fertilizer Markets," by W.E. O'Brien covers
advent of granulation, bulk blending and scale increase effects of industry
and costs. See also Gleason article on fertilizer aid to L.D.C's. |
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Abstracts of allagronometric
studies on nutrition of corn, from around the world over two decades. Major overview that supercedes
all others in detail, clarity, and accessibility. Identifies current
situation as in oversupply due to overbuilding of world ammonia capacity. Surprising lack of emphasis on
alternatives to fertilizer support policies. Only one buried acknowledgement
re: BNF in terms of improving fertilizer use efficiency. A good example of
the lack of encouragement towards national BNF programs. Triplett,
G. B., Jr., F. Haghiri, and D. M. Van Doren, Jr. "Plowing Effect on Corn Yield Response to
N Following Alfalfa," Agronomy Journal, Vol. 71, No. 5,
1979, p. 801. The study was initiated to
compare the additional N required for continuous corn versus rotated corn
with alfalfa under no-till. Results: tillage was not necessary to release the
organic nitrogen. The legumes under either tillage method were able to supply
all of the corn's nitrogen needs. USDA. "Crops and Markets." Vol. 34, 1957,
Economics and Statistics. USDA. Crop Production. 1963 January, 1973 January, 1981 January
- All Annual Summaries. Statistical Reporting Service. For historical data on acreage,
yields and indices of production of various field crops. USDA. 1980 Fertilizer
Situation, December 1979. Vance,
C. P. "Nitrogen Fixation in Alfalfa: An Overview." Paper presented
at 8th Annual Alfalfa Symposium, Beautiful all-inclusive overview
relevant for laymen. Discusses the mechanisms involved in BNF of alfalfa/rhizobia system and effecting factors. Contains
interesting accounting of "natural gas equivalents" for Nsy of legumes. Vance,
C. P., G. H. Heichel, D. K. Barnes, J. W. Bryan,
and L. E. Johnson. "Nitrogen Fixation, Nodule Development, and Vegetative
Regrowth of Alfalfa Following Harvest," Plant
Physiology, (1979) 64, 1-8. |
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Vance, C.P., and L. E. B. Johnson. "Modulation:
A Plant Disease Perspective." Plant Disease, Vol. 65, No.
2, 1981, p. 118. Voss,
R. D., and W. D. Shrader. "Crop Rotations:
Effects on Yields and Response to Nitrogen." "The
response to fertilizer nitrogen by corn following a forage legume is
primarily a function of the legume yield." Summarizes 25 years of crop
rotation studies: good legumes provide 100-160 lbs/a nitrogen to corn. One
year full crop is as good as two, years of meadow. The more
continuous years of corn, the lower the corn yield. A greater frequency of
high yields of corn are obtained in rotation. Voss, R., and J. T. Pesek. "Estimation
of Effect Coefficients Relating Soil Test Values and Units of Added
Fertilizer," Agronomy Journal, Vol. 54, 1962b, p. 339-341. To resolve the dissimilarity of
the coefficients in yield functions o£ soil nitrogen and of fertilizer
nitrogen, regression estimates were evaluated. Soil nitrifiable
nitrogen provides .7665 of the estimated effect coefficients of fertilizer
nitrogen. Waggoner,
P. E., and W. A. Norvell. "Fitting the Law of
the Minimum to Fertilizer Applications and crop Yields," Agronomy
Journal, Vol. 71, March-April 1979, p. 352. Application
of Liebig's Law of the Minimum to identify
nutrients in corn, clover, and alfalfa production as opposed to regression
analysis a'la' Heady. Walker,
M. E., and E. R. Swanson. "Economic Effects of a Total Farm Nitroge Balance Approach to Reduction of Potential
Nitrate Pollution." Very interesting paper -- linear
programming assessment of a subset of oir-farm
adjustments to controls on nitrate pollution of waters. Includes good
nitrogen balance chart on page 22. Rotations are highlighted, and under both
nitrogen constraints a corn--soy rotation is optimal. The model farm is a
cash grain farm and, therefore, legumes are merely a fallow crop. No price
variation scenarios are considered either. Conclusions include: when
commercial nitrogen fertilizer is controlled at low levels concurrently with
a constraint to maintain higher levels of soil nitrogen balance: more soy,
small grain, and alfalfa production occurs. Walker, N. Soil Microbiology,
1975, Halsted Press. A very low combining rate (less
than 0.6%) of DNA from soybeans and from R. japonicum
is such that any transfer of genetic material would be quite unstable. |
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Walters, H. J. "Disease Control." (Part two of series, see Barnes, G.) Crops and Soils,
Vol. 32, No. 7, p. 1980. Welch, L. F. "Nitrogen Use and Behavior in Crop Production," Excellent, readable to layman overview of nitrogen cycle; use of
manure; use of commercial nitrogen fertilizer; (w/ref to Illinois) nitrogen
content and Nsy content in plants;
corn-nitrogen price ratios. "Rotation effects management practices
effecting nitrogen economy." Welch, L. F. "The Morrow Plots - Hundred Years of Research." Annales Agronues,
1976, Vol. 27, 5-6 p. 881-890. Over
100 years of crop rotations and experiments that reflect the innovations and
management practice changes in highly fertile prairie soil
could be depleted with cropping; and depletion could be postponed by rotating
grains with legumes. Welsch, D., and Lorin Westman, 1980: Pfeifer, H. B. 1980, 1981 Annual Report
of the S.E. Welsch, D.
"Economic Analysis of Biological Nitrogen Fixation". pg 675 in
Graham, P.H., and S.C. Harris, editors, Biological Nitrogen Fixation for
Tropical Agriculture, CIAT, Cali,
Columbia, 1981. Whittlesey, N. K., and W. R. Butcher. "Energy Research
Opportunities for Agricultural Economists," American Journal of Agricultural
Economics,
Vol. 56, No. 5, December
1974, p. 89 7. Yates, F. "The Analysis of Experiments Containing Different Crop
Rotations," BIOMETRICS, September 1954, p. 324. Deals exclusively of analysis of crop rotation experiments. Leads with
a very useful glossary of rotation terminology. |
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