• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Table of Contents
 Introduction
 Section 1: Field price of...
 Section 2: Gross field benefit...
 Section 3: Adjusting for "lost...
 Section 4: Including the value...
 Section 5: Net benefits
 Section 6: Field price of bulky...
 Section 7: Dominance analysis and...
 Section 8: The marginal rate of...
 Section 9: Cost of investment...
 Section 10: Recommendations and...
 Section 11: Partial budgets and...
 Section 12: Economic analysis of...
 Section 13: Minimum returns...
 Section 14: Sensitivity analys...
 Section 15: Combining statistical...
 Section 16:Partial budgets for...
 Answers to exercises






Group Title: Economics prograpm working paper ; 1982 i.e. 82/2
Title: Exercises in the economic analysis of agronomic data
CITATION THUMBNAILS PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00081825/00001
 Material Information
Title: Exercises in the economic analysis of agronomic data
Series Title: Economics prograpm working paper
Physical Description: 66 p. : ill. ; 28 cm.
Language: English
Creator: Harrington, Larry
Perrin, Richard K
Publisher: International Maize and Wheat Improvement Center
Place of Publication: México D.F. México
Publication Date: 1982
 Subjects
Subject: Agriculture -- Economic aspects   ( lcsh )
Genre: non-fiction   ( marcgt )
 Notes
Statement of Responsibility: Larry Harrington.
General Note: "A workbook to accompany Perrin et al., 1976. "From Agronomic Data to Farmer Recommendations"."
Funding: CIMMVT economics program working paper ;
 Record Information
Bibliographic ID: UF00081825
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 25923762

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Title Page
    Table of Contents
        Table of Contents
    Introduction
        Page 1
        Page 2
    Section 1: Field price of the product
        Page 3
        Page 4
    Section 2: Gross field benefits
        Page 5
        Page 6
        Page 7
    Section 3: Adjusting for "lost sites"
        Page 8
        Page 9
    Section 4: Including the value of byproducts
        Page 10
        Page 11
    Section 5: Net benefits
        Page 12
        Page 14
    Section 6: Field price of bulky purchased inputs
        Page 15
        Page 16
    Section 7: Dominance analysis and the net benefit curve
        Page 17
        Page 18
    Section 8: The marginal rate of return
        Page 19
        Page 20
    Section 9: Cost of investment capital
        Page 21
        Page 22
        Page 23
    Section 10: Recommendations and the marginal rate of return
        Page 24
        Page 25
        Page 25a
    Section 11: Partial budgets and fixed costs
        Page 26
        Page 27
        Page 28
    Section 12: Economic analysis of verification trials
        Page 29
        Page 30
        Page 31
        Page 32
    Section 13: Minimum returns analysis
        Page 33
        Page 34
    Section 14: Sensitivity analysis
        Page 35
        Page 36
        Page 37
    Section 15: Combining statistical and economic analysis: 2 factorial experiments
        Page 38
        Page 39
        Page 40
        Page 41
        Page 42
        Page 43
    Section 16:Partial budgets for planning experiments
        Page 44
        Page 45
        Page 46
    Answers to exercises
        Page 47
        Page 48
        Page 49
        Page 50
        Page 51
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
        Page 63
        Page 64
        Page 65
        Page 66
Full Text


















EXERCISES IN THE ECONOMIC ANALYSIS OF AGRONOMIC
DATA*

Larry Harrington**

1982 Working Paper



































S CENTRO INTERNATIONAL DE MEJORAMIENTO DE MAIZ Y TRIGO
INTERNATIONAL MAlZE AND ''WHEAT IVPO:; .. E.T CENTER
r Londres 40, Apdo. Postal 6-641, MIxico 6, D.F. Mdxxico





















EXERCISES IN THE ECONOMIC ANALYSIS OF AGRONOMIC
DATA*

Larry Harrington**

1982 Working Paper























A workbook to accompany Perrin et al, 1976. "From Agronomic Data
to Farmer Recommendations".

** Economist at CIMMYT, Mexico. The views expressed are not necessarily
those of CIMMYT.









TABLE OF CONTENTS


Page
Introduction. . . . . . . 1
Section 1. Field Price of the Product. . . . 3
Section 2. Gross Field Benefits. . . . . 5
Section 3. Adjusting for "Lost Sites". . . . 8
Section 4. Including the Value of By Products. . ... 10
Section 5. Net Benefits. . . ... . .. 12
Section 6. Field Price of Bulky Purchased Inputs. ; . .. 15
Section 7. Dominance Analysis and the Net Benefit Curve. .... .17
Section 8. The Marginal Rate of Return. . ... ... .. 19
Section 9. Cost of Investment Capital .. ...... 21
Section 10. Recommendations and the Marginal Rate of Return .. 24
Section 11. Partial Budgets and Fixed Costs. . . .26
Section 12. Economic Analysis of Verification Trials ...... 29
Section 13. Minimum Returns Analysis . . .. . 33
Section 14. Sensitivity Analysis. . . .... ..35
Section 15. Combining Statistical and Economic Analysis:
2 Factorial Experiments . . . .38
Section 16. Partial Budgets for Planning Experiments ... . 44
Answers to Exercises. ........ . . * 47










Introduction


The following set of exercises were initially developed for the
benefit of in-service production agronomy trainees at CIMMYT headquarters
in Mexico. They augment and complement materials found in the CIMMYT
Economics Manual (Perrin et al, 1976) for instructing these trainees in
the use of partial budgets.We believe this set of complementary exercises
will help them to confidently conduct their own economic analyses.


The exercises present the elements of partial budgets in a step-by-
step manner. Before each exercise, an explanation of the underlying
concepts is given. They begin with elementary concepts and end with
analytical complications that researchers must face in practice. Consequently,
the exercises are best used in the sequence in which they are presented.


The concept of .the recommendation domain pervades all of the exercises
and is therefore best explained in this introduction. A recommendation
domain is a group of farmers with similar practices and circumstances,
for whom a unique recommendation will be roughly appropriate. Delineation
of domains in a target area is nothing more than the stratification of
farmers into a few roughly homogeneous groups. The use of domains stems
from the practical recognition that (1) it is not feasible to make
individual recommendations for each farmer, and (2) a global, general
recommendation for a whole study area will probable not be appropriate
for many of the farmers in the area.


The concept of the recommendation domain is made operational in
economic analysis by means of analysis of pooled data. Given that a
single recommendation is to be formulated for a given domain, the results
of all experiments planted in the domain, and for the domain, should be
included in the analysis. Pooling should be undertaken across years as
well as across sites, within one domain. The specific techniques for
analysis of pooled data are presented in the exercises.








This set of exercises uses, for the most part,
on maize. The concepts and procedures can easily b(
on other crops.


Thanks are due to many people for their help in
exercises. I would like to express special gratitud
CIMMYT economist; Federico Kocher, A.F.E. Palmer and
CIMMYT agronomists; and the in-service production agr
whom it has been such a pleasure to work.









SECTION 1) FIELD PRICE OF THE PRODUCT


A key concept in the CIMMYT Economics Manual is that of the field
price of the product, eg. maize or wheat. It is defined as "the value
to the farmer of an additional unit of production in the field, prior to
harvest ....." (Perrin et al, 1976, p 7).


The field price of the product is calculated by subtracting from
the sales price of the product (where the farmer sells it, when he sells
it and in the form in which he sells it) those costs which are roughly
proportional to yield. These frequently include such costs as harvest
ing, shelling, transport from field to point of sale, and farmers'
storage costs. (When the farmer does not sell, an opportunity field
price should be used, equal to the money price incurred to acquire an
additional unit of the product for. consumption.)


The "field price" concept is used for three purposes: 1) To
insure that the costs mentioned above are included in the analysis.
(These costs are frequently overlooked by researchers but must nonetheless
be faced by farmers.) 2) To simplify the succeeding steps in partial
budgeting. (Once these costs are handled via the field price, they do
not have to be individually estimated for each treatment.) 3) To exclude
harvest and post-harvest costs from marginal analysis, because the
farmer's capital invested in these activities will be recuperated almost
immediately.









Exercise No. 1 Field Price of Maize


Calculate the field price of maize for the following cases:


a) The farmer sells his maize in his house to a tra~er for $5.50/kg.
He also has the following costs: harvesting = $0.40/kg; shelling =
$0.60/kg; transport from field to house = $0.20/kg.


Field price of maize = $ /kg.





b) The farmer sells his maize in his house to an intermediary for
L15/quintal, abbreviated "qq" (1 qq = 100 Ibs. = 45 kg.). He
must also pay the following costs: maize harvest = L 1.20/qq; shelling
= L 1.40/qq; transportation from field to house = L 2.50/"carga"
(1 carga = 4 qq = 400 Ibs. = 180 kg.).


Field price of maize = L /kg.









SECTION 2) GROSS FIELD BENEFITS
Gross field benefits are defined as "Net yield times field price
for all products from the crop. In general, this may include money
benefits or opportunity benefits, or both." (Perrin, et al, p 7). Gross
field benefits are estimated for each treatment to be evaluated.


"Field price" was defined in section (1). "Net yield" is defined
as "The measured yield per hectare in the field, minus harvest losses
and storage losses where appropriate." (Perrin, et al, p 6).


The concept of "net yield" stems from the recognition that farmers
often do not receive the same yield as researchers, even.when they apply
the "same" treatment. This has several causes:


1) Management: Researchers can often be more precise and
timely than farmers in applying a given treatment, e.g. plant spacing,
timing of planting, fertilization, and weed control, etc.


2) Harvest date: Researchers often harvest fields at "physio-
logical maturity" whereas farmers tend to let their crop dry in the
field. Even when the yields of both researchers and farmers are adjusted
to a constant moisture (eg. 14%), the researchers' yield is higher --
because of fewer yield losses to insects, birds, rats, ear rots, or
shattering.


3) Form of harvest: At times, mechanized harvest by farmers
leads to heavy field loss if the crop has lodged or if the rows were
planted unevenly. In these cases, a careful manual harvest by researchers
will lead to yield levels that farmers cannot obtain.


4) Storage losses: If the farmer stores his harvest for home
consumption or for later sale, and thereby incurs insect or rat damage,
his effective production is less than that predicted by researchers on
the basis of experimental data. (Note: storage losses should not be









counted if they were already included in the "storage cost" used to
calculate field price.)


5) Plot Size: Even when researchers are careful to use
harvesting techniques that reduce border effects, yields estimated from
small plots tend to be higher than yields taken from an entire field.










Exercise No. 2 Gross Field Benefits


Calculate gross field benefits for the following nitrogen by density
(N x D) treatments. It is estimated that researchers obtain higher
yields than farmers (for the same levels of N and density) due to
management (10%) and harvest date (10%). Farmers sell their maize immediately
after harvest.


The farmer receives $6.00/kg for his shelled maize. Transport cost
from the field to place of sale = $0.40/kg., shelling cost = $0.30/Kg.,
and harvest cost = $1.10/kg.


VARIABLE


Average yield (kg/ha)
Adjusted Yield
(Kg/ha)
Gross Field Benefit
($/ha)


NO-
D25
1360


T
NO
D50
1040


R E
NO
D75
940


A T M
N50 N50
D25 D50
1070 1180


E N
N50
075
1200


T
N100
D25
1180


NIOO
D50
860


N100
D75
910


1/ NO = 0 Kg/ha N, etc.
D25 = 25,000 plants/ha, etc.









SECTION 3) ADJUSTING FOR "LOST SITES"


Researchers at times discard experimental data (or do not even
harvest the experiment) when yields are extraordinarily low due to such
natural factor,, as drought, frost or flooding. Insofar as these natural
factors must also be faced by farmers, these cases of "Zero Response"
should be accounted for. If the data on these lost sites are available,
yields per treatment from "lost sites" should be included when averaging
to obtain average treatment means for yield, for the recommendation
domain under study (see Introduction). If the data from lost sites are
not available, suitable uniform low yields per treatment should be
estimated and used. Often an estimate of "zero yield" for all treatments
is most accurate. It should be noted that any minor errors in estimating
the uniformly low yield for treatments in a "lost site" are much less
serious than the errors introduced by ignoring the problem.









Exercise No. 3 Adjusting for Lost Sites


Calculate gross field benefits for the following weed control treat-
ments. It is considered that researchers obtain higher yields than
farmers (for the same weed control practice) due to'an earlier harvest
date (10%) and a more painstaking manual harvest (5%). The farmer
receives B1.80/kg for his shelled maize. Harvest cost = BO.30/kg; this
includes shelling. The government pays transport cost from the field to
location of sale. Farmers sell their maize immediately after harvest.


The results of three experiments are available, all of which were planted
for the same recommendation domain. A fourth experiment for the same
domain was abandoned due to drought it was not harvested. No response
to improved weed control is expected under drought conditions, so a
uniform yield of 500 kg/ha was estimated for all treatments.



Variable T R E AT M E N T
Manual Gesaprim Prowl 2,4-D
Control


Yield-Experiment 1 (kg/ha)
Yield-Experiment 2 (kg/ha)
Yield-Experiment 3 (kg/ha)
Yield-Experiment 4 (Kg/ha)


2500
2000
2700


2800 3100 2600
2500 2600 2200
3500 3700 2900
(Not harvested drought)


Average yield (kg/ha)
Adjusted yield (kg/ha)
Gross Field Benefits (B/ha)









Field price of maize = B / kg..








SECTION 4) INCLUDING THE VALUE OF BYPRODUCTS


Frequently, maize or wheat grain is not the only product with '
economic value that comes from maize or wheat fields. Leaves, tassels,
stover and straw may all have value to the farmer. (When a market for
these byproducts exists, it is usually easy to calculate a sales price,
from which a field price may be estimated by subtracting costs that are
proportional to yield. (See section 1). Gross field benefits for
byproducts should be added to gross field benefits for grain in order to
obtain total gross field benefits.











Exercise No 4 Value of Byproducts


Calculate total gross field benefits for the following experiment on the
response of wheat to levels of N. Farmers sell their wheat immediately
after harvest for $4.00/kg. Harvesting and threshing cost $0.30/kg.,
and transport to place of sale costs $0.2u/kg. Wheat straw is baled and
sold as animal feed for $5.50 per 18 Kg. bale. The purchaser of the
straw, not the farmer, pays transport cost. The farmer does pay, however,
the cost of baling of $0.60 per bale.


It is estimated that researchers obtain higher wheat and straw
yields than farmers even with the same N levels, due to management
precision (10%) and earlier harvest that leads to fewer losses to shattering
(5% for wheat only). No experimental sites were lost.



Variable T R E A T M E N T
1/
NO- N50 N100 N150


Grain yield (Kg/ha) 1500 2100 2400. 2500
Straw yield (Kg/ha) 1800 2520 2880 3000


Adjusted grain yield (Kg/ha)
Adjusted straw yield (Kg/ha)


Gross field benefits-wheat ($/ha)
Gross field benefits-straw ($/ha)


Total gross field benefits ($/ha)



Field price of wheat = $
Field price of straw = $



S NO 0 Kg/ha N, etc.







SECTION 5) NET BENEFITS


Net benefits are defined by Perrin et al as "total gross field
benefits minus total variable costs" (p.9). "Gross field benefits" were
discussed in section (2). "Total variable costs" are defined as "the
sum of field costs for all inputs which are affected by the choice...
Variable costs can consist of either money costs or opportunity costs or
both" (p. 9). That is, costs can reflect either a cash payment by the
farmer (monetary cost) or the value of a farmer owned resource (oppor-
tunity cost).


Net benefits should not be confused with "profits". Recall that
only costs that vary over treatments need be included in the net benefit
calculation, i.e. Costs that do not vary need not be taken into account.
It should be noted, however, that the inclusion of costs that do not
vary over treatments will not make the economic analysis incorrect. In
fact, the rate of return to investment capital (the measure of profitability
used here) will not change at all if non-varying costs are included.


There is one cost that should not be included in "costs that vary
over treatments". This is the "cost of investment capital", of which
interest is usually a major element. This is because rates of return to
capital are compared with this "cost of capital" when a recommendation
is formulated (see sections (9) and (10)).


In the exercise that follows, data is given from three insect
control experiments. All experiments were planted with the same recom-
mendation domain in mind. So the calculation of an average yield for
each treatment is the first step in the analysis which leads sequentially
to the calculation of net yields, gross field benefits, total costs that
vary, and net benefits.










Cont'n. Exercise No. 5


Here are the data needed to complete the calculation:


Sales price of maize
Harvesting cost
Shelling cost
Transport field to
sales point
Wage in Market
Price of Birlane
Price of Furadan
Application cost:
Birlane
Furadan
Yield adjustment
1 qq


L 14.50/qq
L 1.50/qq
L 1.00/qq

L 1.60/qq
L 6.00/day
L 1.70/kg
L 4.30/kg


1 man-day/application
0.5 man-day/application
20%
45 kg










SECTION 6) FIELD PRICE OF BULKY PURCHASED INPUTS


The calculation of total costs that vary and net benefits is at
times complicated by transport charges for bulky purchased inputs, eg.
fertilizer and seed. This can have a large impact on treatment costs
where transport costs are high. For example, consider the following
calculation of the field price of N:


$ 5.00/kg price of urea in store


3.50/kg
$ 8.50/kg


transport to field
price of urea in the field


$ 8.50
.46


$ 18.48 =


price of N in the field,
in the form of urea (46% N)


To find the cost of N for a given N dose, one only has to multiply this
field price by the dose (eg. $ 18.48/kg x 100 kg/ha = $ 1848/ha).









Exercise No. 6 Field Price of Fer.tilizer


Calculate the field price of N and P, and the cost of the N P dose, for
each treatment in the following N P experiment.


TREATMENTS



NOPO1/ NOP40 N50PO NSOP40 N100PO N100P40
N Cost ($/ha)
P Cost ($/ha)
Fertilizer Cost
($/ha)


1/ Numbers refer to kg/ha of N and P element
Data:
ammonium nitrate (33.5% N) $
triple super phosphate (46% P) $
transport of fertilizer $


4.80/kg
7.50/kg
3.00/kg


N field price
P field price


= $ /kg
= $ /kg









SECTION 7) DOMINANCE ANALYSIS AND THE NET BENEFIT CURVE


The calculation of net benefits for each treatment is only an
intermediate step in the economic analysis of agronomic data. That is,
the treatment with the highest net benefit does not always make the best
recommendation. Such factors as capital scarcity and risk aversion have
yet to be included in the analysis.


Net benefits and "costs that vary" are used to calculate "marginal
rates of return to investment capital" as one moves from a less expensive
to a more expensive treatment (sections (8) and (10)). This "marginal
analysis", however, can be made more efficient by an intermediate step
-- "dominance analysis" -- in which clearly unprofitable treatments are
discarded. (See Perrin et al, p 18).


A "dominated" treatment has lower net benefits and higher costs
that vary, than some other treatment in the experiment. Dominated
treatments need not be considered further in the analysis.


Dominance analysis can be seen graphically in the "net benefit
curve". The net benefit curve "shows the relation between the variable
costs ... and the net benefits ..." (see Perrin et al, p 16, for an
example). To construct a net benefit curve, each treatment is plotted
on a graph, the vertical axis representing net benefits and the horizontal
axis representing costs that vary. The net benefit curve is formed by
connecting these points with a solid line having a positive or upward
slope.


That is, beginning with the point that corresponds to the least
expensive treatment, a line is drawn to a point that represents the next
most expensive treatment -- but only an upward sloping line is allowed.
Undominated treatments will be on the net benefit curve but dominated
treatments will be below the net benefit curve.










EXERCISE NO.7 DOMINANCE ANALYSIS AND THE NET BENEFIT CURVE


Based on five N'by density experiments, all from one recommendation
domain, the following data were obtained. Perform a dominance analysis
and draw-the net benefit curve.


NET BENEFIT
($/HA)
3670
4963
5870
3984
4877
4717
3174
4758
4075


TOTAL COST THAT
($/HA)
670
830
990
1373
1533
1693
2074
2234
2444


TREATMENT


VARIES


s









SECTION 8) THE MARGINAL RATE OF RETURN


After dominated treatments have been discarded, marginal analysis
can begin. The purpose of marginal analysis "is to.reveal just how the
net benefits from an investment increase as the amount invested increases."
(Perrin et al, p 17).


Marginal analysis is based on the "marginal rate of return", which
is defined as the increment in net benefits divided by the increment in
costs that vary, as one moves from one treatment to the next more expen-
sive treatment. This is usually expressed as a percentage.

increment NB
MRR = x 100
increment TCV


The marginal rate of return can be fruitfully interpreted as the
percent return on investment capital, after that capital has been re-
paid. For example, if a farmer receives a MRR of 50% on an investment
of $100, then that $ 100 investment has not only been recovered but a
further return of $ 50 has also been earned.


It should be stressed that the MRR does not measure the returns
corresponding to a single treatment, but rather to the returns that
correspond to a change from a less expensive to a more expensive treatment.
It follows from this that the slope of the net benefit curve is a measure
of the MRR: the flatter the net benefit curve (small increment in met
benefits compared to the increment in costs that vary), the lower the
MRR.


This section only deals with the calculation of the MRR. Tht use
of the MRR in the formulation of farmer recommendations must be left to
a later section (10) because the topic of the cost of investment capital
must first be addressed.









EXERCISE NO. 8 MARGINAL.RATE OF RETURN


Based on the following data you should obtain, for recommendation domain
one, marginal rates of return and the net benefit curve.



RECOMMENDATION EXPERIMENT T R E A T M E N T1
DOMAIN NO. NO N50 N100 N150


1
1
2



2
1
1
2
2


1
2
3
4
5
6
7
8


1000
900
1900
1300
2000
1100
1400
1700


1850
1860
2400
2200
2600
2100
2050
2200


2200
2100
2500
2400
2600
2400
2600
2100


2250
2400
2600
2500
2700
2500
2600
2200


(abandoned drought)


Data:
Yield adjustment
Maize sales price
Shelling cost
Harvest cost
Transport cost (maize)
Wage
Urea (46% N)
Transport (urea)
Fertilizer application:
2 man-days/ha


Numbers refer to kg/ha N


15%
$ 6.50/kg
$ 0.50/kg
$ 1.00/kg
$ 0.75/kg
$ 150/day
$ 4.00/kg
$ 0.30/kg









SECTION 9) COST OF INVESTMENT CAPITAL


Consider a farmer who invests $100 in fertilizer. If the increased
value of production (due to fertilizer use) were exactly $100, the
farmer would undoubtedly be sorry he bought the fertilizer. In order to
willingly invest, he would require that both the $ 100 be repaid and
that a "minimum rate of return" be earned. If his minimum required rate
of return were 50%, he-would have to expect a return of $150 ($ 100 +
50%) before investing. Any investment expected to earn a rate of
return lower than this minimum would be rejected; likewise, any investment
expected to earn a rate of return higher than this minimum would be
accepted (risk aside for the moment). The problem lies in estimating
this "minimum required rate of return".


In a few areas, the minimum rate of return required to induce
investment can be estimated directly. In one area, for example, a
common rule of thumb for farmers was "2 to 1"; i.e. an expected return
of $ 2 was required by farmers for each $ 1 invested. This is equi-
valent to a minimum rate of return of 100% ($ 1 + 100% = $ 2).


Usually, however, no such rule of thumb exists and the minimum rate
of return must be inferred from an estimate of the cost of borrowed
capital. (This is usually easier to estimate than the opportunity cost
of the farmer's own capital.)

Suppose, for example, that a farmer borrows $ 1000 for 8 months, at
an 18% annual interest rate, and that he pays a $ 30 service charge and
$ 70 in personal expenses in order to obtain the loan. His cost of
capital is estimated as follows:


$ 1000 x .18 = $ 180 annual interest
$ 180 x 8 = $ 120 interest for loan period
12
$ 120 + $ 30 + $ 70 = 220 loan costs
$ 220/$1000 = 22% cost of borrowed capital for 8 months








The minimum rate of return that is requested to induce
will usually be above this "cost of borrowed capital". Per
suggest adding 20 percentage points ("risk premium") onto the
borrowed capital to estimate the minimum required rate of retL
further suggest that a 40% minimum rate of return "rule of thur
roughly appropriate for many areas.


In Perrin et al, "cost of capital" and "minimum rate of retu
used interchangeably. In the following exercises, references wil
be made to "cost of capital".









Exercise No. 9 Cost of Capital


a) A farmer borrows $3000 for eight months, at an annual interest
rate of 20%. Besides interest, he must pay a service charge of $60
and he has $140 in personal expenses related to obtaining the
loan. He also has to pay a crop insurance premium of $90. What
is his cost of borrowed capital? What is his cost of capital
(minimum rate of return) when a 20% "risk premium" is added?






b) A farmer borrows $2000 from the village money-lender. He does not
have to pay any service charge, insurance premium or personal
expenses. But the money-lender charges him 10% per month interest.
What is his cost -of borrowed capital if the loan runs for seven
months? What is his cost of capital (minimum rate of return)
including a 20% "risk premium"?









SECTION 10) RECOMMENDATIONS AND THE MARGINAL RATE OF RETURN


In the previous exercises, emphasis was placed on calculation and
estimation of field price, gross field benefits, net benefits, cost of
capital, etc. Now these calculations and estimations must be interpreted
in order to formulate a farmer recommendation.


Researchers have-used several incorrect criteria for the formulation
of recommendations: .highest yield, highest net benefit, or highest mar-
ginal rate of return. All of the above are likely to give incorrect and
misleading results. The correct way to interpret partial budget calcu-
lations is a bit more complicated, involving a series of comparisons
between marginal rates of return and the cost of capital.


Consider a net benefit curve, in which undominated treatments are
joined. Beginning with the least expensive treatment (lowest TCV), cal-
culate the marginal rate of return that is earned when moving to the
next treatment on the net benefit curve. If this marginal rate or re-
turn is greater than the cost of capital, the change (or investment) is
accepted as profitable (risk aside). Each succeeding change is evaluated
in the same way. In summary, researchers are asked to consider each
increment in cost separately; they should keep increasing costs until
the marginal rate of return approaches (but does not fall below) the
cost of capital. (See Perrin et al, Chapter 4, for further information.)








Exercise No. 10 Recommendations and the MRR


;3sed on the following data, conduct dominance analysis and marginal
analysis (MRR). What should be recommended if the cost of capital
is 30%7 If the cost of capital is 60%? Draw the net benefit curve.


a) N x P Experiment


NET BENEFIT
($/HA)
500
480
610
520
650
580
420
350


COSTS THAT VARY
($/HA)
0
91
99
178
186
265
273
352


b) Insect Control


Experiment


TREATMENT


NE


Without control
Birlane 1 X
Birlane 2 X
Birlane + Furadan


r BENEFIT
($/HA)
450
475
480
460


COSTS THAT VARY
($/HA)
0
30
45
42


TREATMENT


NO
NO
N50
N50
N100
NlOO
N150
N150


PO
P40
PO
P40
PO
P40
PO
P40










Cont'n. Exercise No. 10


c) Verification Trial


TREATMENT


1) Farmer practice
2) (1) + new variety
3) (1) + chemical weed
control
4) (2) + chemical weed
control
5) (3) + fertilizer
6) (4) + fertilizer


NET BENEFIT
($/HA)
350
320
380


COSTS THAT VARY
($/HA)
50
58
35










SECTION 11) PARTIAL BUDGETS AND FIXED COSTS


In section (5) it was asserted that the results of economic analy-
sis using partial budgets would be identical whether or not "fixed
costs" (costs that do not vary due to treatments) were included in the
analysis. Many researchers find this difficult to believe -- that so
many costs can be safely ignored in economic analysis.


The following exercise demonstrates that marginal rates of return
to investment capital do not change when fixed costs are excluded from
economic analysis using partial budgets.









Exercise No. 11 Partial Budgets and Fixed Costs


To demonstrate the value of partial budgets, perform dominance analysis
and marginal analysis on the following two data sets. Data set 1 includes
only those costs that vary due to treatment changes. Data set 2 also
includes some fixed costs. Yields and gross benefits are identical for
both data sets.


DATA SET 1 N x P EXPERIMENT


VARIABLE T R E AT M E N T
NO PO NOP40 NSOPO N50P40
Yields (kg/ha) 2000 2100 2500 2600
Adjusted yield/ (kg/ha)
Gross Benefits2/ (S/ha)
Cost of N3 (S/ha) 0 0 350 350
Cost of P3 ($/ha) 0 300 0 300
Application cost (S/ha) 0 150 150 150
Total TVC (S/ha)
Net Benefits ($/ha)


20% adjustment

Field price of maize = $ 3.50/kg

Transport cost already included









Cont'd. Exercise No. 11


DATA SET 2


- N x P EXPERIMENT


VARIABLE T R E AT M E N T
NO PO NOP40 N50PO N50P40
Yields (kg/ha) 2000 2100 2500 2600
Adjusted Yield'/ (kg/ha)
2/
Gross Benefits/ ($/ha)
Tillage Cost (S/ha) 1200 1200 1200 1200
Planting Cost (S/ha) 400 400 400 400
Cost of Seed (S/ha) 75 75 75 75
Weeding Cost ($/ha) 1600 1600 1600 1600
Cost of N3 (S/ha) 0 0 350 350
Cost of P3 (S/ha) 0 300 0 300
Application Cost (S/ha) 0 150 150 150
Total TVC ($/ha)
Net Benefits (S/ha)


20% adjustment

Field Price of Maize = $ 3.50/kg

Transport cost already included









SECTION 12) ECONOMIC ANALYSIS OF VERIFICATION TRIALS


As improved production practices are developed through on-farm
research, a need arises to measure the consistency with which those
improved practices prove to be economically superior to the current
farmer practice. This measurement is performed via "verification
trials" in which the farmer practice is compared with the improved
practice in many locations, within a recommendation domain. The eco-
nomic analysis of verification trials is crucial, profitability and risk
being the major criteria for comparison. Put bluntly, if economic
analysis of verifications is not performed, it is probably not worth
while to plant them.


Verification trials present special problems for economic analysis.
It is usual to find many factors changing simultaneously, as one moves
from one treatment to another. Specification of "costs that vary" must
be conducted very carefully to insure that all costs that vary are
included.


As with other experiments, economic analysis of verification trials
is best performed on the average yields (over many experiments) for each
treatment, within a given recommendation domain.









Exercise No. 12 Verifications


Perform an economic analysis of the following set of verification
for recommendation domain two. Include marginal analysis and the
benefit curv3. What is the proper recommendation for RD 2?


trials
net


RECOMMENDATION EXPERIMENT TREATMENT YIELDS (KG/HA)
DOMAIN NUMBER 1 2 3 4 5 6
1 1 1200 1150 1500 1510 2000 2000
2 2 900 910 1100 1000 1500, 1400
2 3 700 500 900 700 1100 1100
1 4 1500 1550 2100 2150 2460 2600
2 5 1500 1700 2100 2300 2700 2800
2 6 1400 1350 1800 1900 2550 2600


TREATMENTS:


1) Criollo Seed
Density = (12 kg/ha seed)
No fertilizer
No insecticide
Conventional tillage and weed control


2) Same as (1) but with improved seed


3) Criollo Seed
Density = (12 kg/ha seed)
No fertilizer
No insecticide
Zero tillage with chemical weed control









Cont'd. Exercise No. 12


4) Same as (3) but with improved seed


5) Criollo Seed
Density = (20 kg/ha seed)
50 kg/ha N
Birlane applied once
Zero tillage with chemical weed control


6) Same as (5) but with improved seed


DATA:
- Yield adjustment
- Farm Gate Price of Maize
- Harvesting Cost
- Shelling Cost
- Transport Cost (field to location
of maize sale)
Transport Cost (store to field)
Criollo Seed
Improved Seed
Increased Planting Cost (due to
density increase)
Increased Harvesting Cost (.due
density increase)
Conventional Tillage Cost
Conventional Weed Control Cost
Zero Tillage Uses:
2.5 It/ha Gramoxone at
3.0 kg/ha Gesaprim 50 at
- Sprayer Rental
- Herbicide application takes


S 20%
S $ 6.50/kg
= $ 1.50/kg
= $ 0.30/kg


= $
= $
= $
- $


0.60/kg
0.40/kg
7.00/kg
25.00/kg


= 1 man-day/ha


S 0
= $
= $


= $
= $
= S$
4


1400/ha
800/ha


300/ t
240/kg
50/ha
man-days










Cont'd. Exercise No. 12


- Hauling water for herbicide
application takes
S Wage
- Birlane treatment uses 12 kg/ha
Birlane at
- Urea (at store) costs
S N application takes
- Cost of capital
S Birlane application takes


2 man-days
$ 150/day

= $ 32/kg
S $ 4.20/kg
2 man-days/ha
S 55%
1 man-day/ha








SECTION 13) MINIMUM RETURNS ANALYSIS


Farmers normally wish to earn more income -- but will often insist
that this increased income be accompanied by a reasonably low level of
risk. Perrin et al note that "farmers want to avoid the possibility of
occasional high losses as they seek higher average net benefits" (p 20).
These "occasional high losses" can be attributed to yield variability
and price variability.


"Minimum returns analysis" is used to look at the effects of yield
variability on net benefits, especially the effects of "disaster". This
analysis merely entails the examination of the net benefits for each
treatment for the worst cases.


Consider a set of ten identical experiments conducted in one
recommendation domain. Marginal analysis leads to the selection of one
of the treatments as a farmer recommendation. However, researchers
should compare the net benefits earned with this treatment in the two or
three worst cases (roughly 20% of the total number of experiments) with
the net benefits earned by alternative treatments in these worst cases.
If the recommended treatment demonstrates "worst-case" net benefits that
are much lower than those of some reasonable alternative, researchers
may wish to re-consider their recommendation.


For minimum returns analysis to be valid, all experiments of a
given kind that are planted in a given domain (except those lost to
researcher mismanagement) should be included in the analysis. Specifically,
those experiments that are due to natural causes (flooding, drought,
etc.) that farmers must face must be included in minimum returns analysis.
Otherwise, the riskiness of selected treatments will be under-estimated.


Minimum returns analysis is especially important for experiments
with high cost treatments in areas of substantial yield variability.








Exercise No. 13 Minimum Returns Analysis


Conduct a minimuii returns analysis and a marginal analysis on the
following data. If cost of capital = 40%, what is the recommendation if
we do not consider risk? Might this recommendation be re-considered due
to yield Variability? Why?


Net Benefits by Site, Nitrogen Experiments in RD= 1
SITE TREATMENT
NO N50 N100 N150
- - ($/HA) - - -

1 2000 3000 1200 1000
2 5000 7500 10000 10500
3 3000 6500 8000 8100
4 4000 5000 2000 3000
5 4500 7000 9000 10000
6 2500 4000 1000 500
7 5000 8000 11640 13700
8 6000 7000 9000 9000
Average net benefits 4000 6000 6480 6600
TCV 0 1000 2000 3000








SECTION 14) SENSITIVITY ANALYSIS


As noted in the previous section, farmers face two primary sources
of risk: yield variability and price variability. The effects of yield
variability are examined through minimum returns analysis. The effects
of price variability are examined through "sensitivity analysis".


At times, researchers have difficulty estimating some input or
product prices. In these cases, the researcher can examine the sta-
bility of his recommendation by conducting the economic analysis twice:
once using a high (but likely) price and once using a low (but likely)
price. Similarly, researchers can study the effect of input subsidies
or recommendations by constructing budgets with and without the subsidy.


A stable recommendation (one that does not change given likely
price variability) can be extended with much more confidence than an
unstable one. If a recommendation is not stable, farmers must be given
more information on needed adjustments in technology as prices change.








Exercise No. 14 Sensitivity Analysis


Perform, with the following data, marginal analysis of the tillage and
weed control experiments for recommendation domain No. 1. First, use
the subsidized price, then use the non-subsidized price for herbicides.
Which is the correct recommendation for the farmers at present, given
subsidies? Which would be the effect on recommendations if the herbi-
cide subsidy were removed (ignore the possible effect on the maize
price)?



RECOMMENDATION EXPERIMENT YIELDS BY TREATMENTS (KG/HA)
DOMAIN NUMBER FARMERS ZERO ZERO
PRACTICE TILLAGE 1 TILLAGE 2


1 1 2000 1900 2400
1 2 1800 2100 2200
2 3 1200 1400 1500.
2 4 1000 1300 1700
1 5 2200 2300 2600
DATA:


Cost of capital
Yield adjustment
Maize field price
Farmer practice cost
Machete chopping, zero till 1 and 2
Herbicide application
Hauling water for herbicide applic.
Wage


40%
20%
$ 5.00/kg
$ 2000/ha
4 man-days/ha
2 man-days/ha
2 man-days/ha
$ 120/day









Cont'n Exercise No. 14


Gramoxone (subsidized price)
Gramoxone (non-subsidized price)
Gesaprim 50 (subsidized price)
Gesaprim 50 (non-subsidized price)
Sprayer rental
Farmer practice


=


Zero tillage.1



Zero tillage 2


$ 250/1t
$ 360/1t
$ 200/kg
$ 340/kq
$ 50/ha
Land preparation with animal
traction, one weeding with hoe
Machete chopping followed by
1.0 It/ha of Gramoxone and
2.0 kg/ha of Gesaprim 50
Machete chopping followed by
2.5 It/ha of Gramoxone and
3.0 kg/ha of Gesaprim 50








SECTION 15) COMBINING STATISTICAL AND ECONOMIC ANALYSIS: 2 FACTORIAL EX-
PERIMENTS



The 24 experiment has become increasingly popular in on-farm
agronomic experimentation, in part due to the efforts of CIMMYT's maize
training program. This experiment is used to examine main effects and
interactions for four different factors, each of which is set at two
levels. If the two levels for each Factor are respectively set at the
farmer's level and at a high, non-limiting level, the experiment is
useful in identifying those factors that limit crbp yield. If the levels
are respectively set at the farmer's level and at a higher level that
appears to be possible for target farmers, the experiment can also serve
as a basis for formulating recommendations for farmers.


However, the very characteristic that makes this experiment useful
-- the simultaneous testing of multiple factors -- creates complications
in the economic analysis of results. The major complication is that not
all treatments in a given experiment are necessarily included in the
partial budget used in economic analysis. Sometimes data from individual
treatments are used in analysis; at other times averages for main
effects are used, depending on the results of statistical analysis.


In the partial budgeting, increased "costs that vary" are compared
with increased "net benefits" to calculate a "marginal rate of return".
Clearly, the analysis assumes that net benefits and gross benefits are
calculated on the basis of yield changes that really exist, and that are
really due to treatment effects, i.e., not due to random variation.
If yield changes do not exist (or are not due to treatment effects),
then the procedures for partial budgets do not entirely apply. In the
absence of yield changes (and hence, in the absence of change in gross
benefits) preference is normally given to the least-cost treatment.-



This follows classical statistics in guarding against accepting a
difference that does not exist. At times, however, it may be less
costly to do the reverse: guard against rejecting a difference that
does exist.








Whether or not yield changes really exist Is determined by statis-
tical analysis. Perrin et al, (p.4) however note two cautions.


"Most statistical tests are geared to the 0.05 or 0.01
levels of significance. But farmers may be willing to accept evidence
that is much less persuasive than this. For instance if variety A
yields 3 tons in an experiment, while variety B yields 4 tons, farmers
may be quite happy to choose variety B even though this difference is
statistically significant at, say only the 0.10 level.


Furthermore, it is quite possible that two treatment
means are not significantly different at any of five trial sites, but
the treatment means are different at the 0.01 level of significance when
the data are pooled. Because of these considerations, we suggest that
both statistical and economic analysis be conducted. If only one expe-
riment is available, little can be said of the desirability of the
treatment for farmers in the area, unless the results are overwhelming.
When several experiments are available (from different sites or year or
both), a statistical analysis of the pooled data should be conducted.
The analysis of variance should include treatments, sites, and site-by-
treatment interaction as sources of variation".


The above two points refer to ways in which the search for "sig-
nificance" may be facilitated. Nonetheless, research programs fre-
quently find themselves forced to analyze one or few experiments, to
focus future experimental work and/or make preliminary farmer recom-
mendations. Such is the case when research is begun in a new study
area. In these cases, "significance" may be elusive for some factors.
Even in those cases where researchers have access to several cycles of
data, not even pooled analysis will lead to "significant" differences
between treatment means if none exist in the universe under study.


Researchers must be ready, then, to deai with situations in which










some factors, demonstrate "significant" differences between treatment
means while other factors do not. This possibility creates special
4
complications in such multiple-factor experiments as 2 factorials.


In the procedures and examples used by Perrin et al, experimental
treatments are analyzed one by one. In the case of the 2 factorials,
each of the sixteen treatments included in a given experiment would be
analyzed: net benefits calculated, dominated treatments excluded, etc.
This treatment by treatment analysis of 2 factorials is complex due to
the large number of treatments included in the budgets, and can be mis-
leading due to the relative difficulty of combining statistical and
economic results.


An alternative to a treatment by treatment approach to economic
analysis is to pool data, using yield averages for main effects.
Further disaggreagation would only be needed in the presence of sig-
nificant interactions. Thus, instead of a single budget with 16 treat-
ments, there may be several budgets, each with two or possibly four
treatments. The exact form of the budgets, however, depends on the
results of statistical analysis.


15.1) Case I No Significance
At times, statistical analys-is indicates that there is no

significant difference in yields for either main factors or interactions.
As noted, the required level of significance is up to the researcher and
may range from the .01 level (large cost increase with a marginal rate
of return just above the cost of capital) to the .20 level (small cost
change with an excellent MRR). In this case, there is no need to use
partial budget analysis because yields (and therefore gross benefits)
are the same for all treatments. A comparison of costs is all that is
needed to select a recommendation: the least-cost treatment. This may be
performed on a factor by factor basis.









15.2) Case II Some Main Effects Significant No Significant
Interactions
Normally, some of the factors in a 2 experiment will
demonstrate significant yield differences between the selected levels.
This is especially the case when the selected factors are serious li-
mitations to increased production by representative farmers, when the
two levels for each factor are set "far apart", and when the experiment
is reasonably precise.


When some main effects are significant -- but there are
no significant interactions -- it is possible to conduct economic ana-
lysis by means of separate budgets for each significant factor. (Fac-
tors without significant yield differences between the two chosen levels
are treated as in section 15.1 -- the least-cost level is chosen for
each such factor.)


15.3) Case III Some Main Effects and Some Interactions
Significant
When some main effects and some interactions are signi-
ficant, the factor-by-factor approach discussed in Section 15.2 is no
longer valid. Nonetheless, it is not necessary to return to the long,
complicated treatment-by-treatment approach. A middle ground does
exist, in which budgets are constructed for significant main factors and
factors with which a significant interaction exists. (In the same
experiment if a main factor is not significant and does not interact
with other factors, choose the least-cost treatment. If a main factor
is significant but does not interact with other factors, construct a
budget with two treatments).


The 24 factorial experiment has become more popular in on-farm
agronomic research, but the economic analysis of these experiments is
somewhat complicated. The purpose of th.i section was to describe a
method of economic analysis that focuses on factors, not on individual
treatments, and that uses the result of statistical analysis to help
plan economic analysis.










Exercise No. 15 Combining Economic and Statistical Analysis 24 Factorials


Using the following data analyze the 24 experiment in the simplest way
using the statistical analysis to plan the economic analysis. For sim-
plicity, use the .05 level of significance (F > 4.60).

STATISTICAL ANALYSIS


SOURCE 0


0 N
= OP
= 0 Boron
S0 Zinc


F VARIATION1-
A
B
C
D
AB
AC
AD
BC
BD
CD
ABC
ABD
ACD
BCD


OBSERVED F2/
135.27
0.44
1.61
0.29
4.84
1.04
2.30
0.29
0.02
2.43
2.18
1.40
0.11
0.33


= 100 kg/ha N
= 80 kg/ha P
= 1 kg/ha Boron
= 2 kg/ha Zinc


Tabular F for 0.05 significance level 4.60


-------







Cont'd. Exercise No. 15


DATA:


Cost of capital
Yield adjustment
Maize Field Price
Urea Price
TSP
Hauling of fertilizer
Fertilizer application
Wage
1 qq


YIELDS

A 8 C D
0 0 0 0
1 0 0 0
0 1 0 0
1 1 0 0
0 0 1 0
1 0 1 0
0 1 1 0
1 1 1 0
0 0 0 1
1 0 0 1
0 1 0 1
1 1 0 1
0 0 1 1
1 0 1 1
0 1 1 1
01 1
1000
0100
1100
0010
1010
0110
1110
0001
1001
0101
1101
0011
1011
0111
1 11i


=


50%
20%
$ 10.00/qq
$ 34.00/qq
$ 39.00/qq
$ 3.00/qq
1 man-day/ha
$ 6.00/day
45 kg


2.03
3.66
2.48
3.68
1.98
3.30
1.52
3.69
2.42
3.20
2.13
3.61
2.41
3.28
2.05
3.77








SECTION 16) PARTIAL BUDGETS FOR PLANNING EXPERIMENTS


The previous sections have focused on the use of partial budgets
for the economic analysis of experimental data. These budgeting con-
cepts can also be used, however, in the planning of experiments.


Researchers should use several criteria in the selection of ex-
perimental treatments, when the purpose of the research is the formula-
tion of near-term recommendations useful to farmers. Specifically, high
priority should be given to experimented treatments that researchers
expect to be profitable, not too risky, and that mesh well with the
current farming system (e.g. cropping calendar, labor supply, cash flow,
consumption needs, etc.).


An estimate of the likely profitability of a treatment may be ob-
tained by calculating the minimum yield increase needed to pay the in-
crement in cost that is incurred. Agronomists can then assess (through
intuition or judgement) the likelihood of obtaining this minimum re-
quired response.


Consider, for example, a case where weeds are limiting maize pro-
duction. Farmers currently control weeds through horse cultivation but
researchers are considering chemical weed control as an alternative.
The increment in costs that vary (when changing from horse cultivation
to chemical weed control) is $500/ha. Is this change likely to be prof-
itable?


The minimum yield increase needed to pay the increment in costs may
by found as follows:

SY A.TCV x (1 + C)
P


where A Y = minimum required yield increase, per ha
ATCV = increment in costs that vary, per ha
C = cost of capital (%)








P = field price of product


with a cost of capital of 40% and a maize field price of $ 2.00/kg, the
minimum required yield increase for our example is:


S 500 x 1.4
A Y 2 350 kg/ha


Agronomists consider a 600 kg/ha yield increase to be likely (averaging
over good and bad crop cycles), so chemical weed control emerges as a
priority practice'for on-farm testing, at least from the viewpoint of
expected profitability. (Note that it still be screened, however,
for riskiness and -consistency with the farming system).









Exercise No. 16 Partial Budgets for Planning Experiments


Researchers in one recommendation domain conclude that N deficiency is a
major limiting factor in the maize crop. They feel that 150 kg/ha N
would overcome this deficiency, and would lead to a yield increase of
one ton/ha. Is this level likely to be profitable for local farmers?
(If not, researchers might wish to set N treatment levels a bit lower).


DATA:
Fertilizer application
Price of urea (in the store)
Transport of urea (store to field)
Cost of capital
Maize sales price
Shelling cost
Harvesting cost
Transport (for maize, from
field to place of sale)


$ 100/ha
$ 7.00/kg
$ 3.00/kg
60%
$ 3.00/kg
$ 0.20/kg
$ 0.70

$ 0.30/kg









ANSWERS TO EXERCISES


Exercise No. 1 Field Price of Maize


a) Sales price = $ 5.50/kg
Harvest cost = $ 0.40/kg
Shelling cost = $ 0.60/kg
Transport cost = $ 0.20/kg


Field price = $ 4.30/kg



b) Sales price = L 15.00/qq
Harvest cost = L 1.20/qq
Shelling cost = L 1.40/qq
Transport cost = L 0.63/qq


Field price = L 11.77/qq
or L 0.26/kg








- Gross Field Benefits


VARIABLE TREATMENT
NO NO NO N50 N5 N5 N50 N100 N100 N100
D25 D50 075 D25 D50 D75 D25 D50 D75
Average Yield
(kg/ha) 1360 1040 940 1070 1180 1200 1180 860 910

1/
Adjusted Yield
(kg/ha) 1088 832 752 856 944 960 944 688 728


Gross Field Bene-
fit-/ ($/ha) 4570 3494 3158 3595 3965 4032 3965 2890 3058


/ Yield adjustment = 20%

2/ Field price of maize = $ 4.2/kg


Exercise No. 2








Exercise No. 3 Adjusting for Lost Sites


TREATMENT
Variable Manual Gesaprim Prow 2,40
Control

Yield 1 (Kg/ha) 2500 2800 3100 2600
Yield 2 (Kg/ha) 2000 2500 2600 2200
Yield 3 (Kg/ha) 2700 3500 3700 2900
Yield 4 (abandoned) (Kg/ha) 500 500 500 500
Average yield (Kg/ha) 1925 2325 2475 2050
Adjusted yield/ (Kg/ha) 1636 1976 2104 1742
Gross Field Benefits (B/ha) 2454 2964 3156 2614


Field price of maize = B 1.50/Kg.


/ Yield adjustment = 15%








E;cercise No. 4 Value of By-Products


Variable T R E AT M E N T
NO N50 N100 N150
Grain yield (Kg/ha) 1500 3100 2400 .2500
Straw yield (Kg/ha) 1800 2520 2880 3000

Adjusted grain yield!/ (Kg/ha) 1275 1785 2040 2125
Adjusted straw yield- (Kg/ha) 1620 2268 2592 2700

Gross field benefit-wheat ($/ha) 4463 6248 7140 7438
Gross field benefit-straw ($/ha) 437 61.2 700 729

Total gross field benefit ($/ha) 4900 6860 7840 8167


15% adjustment

S10% adjustment


Field price of wheat = $3.50/kg.

Field price of straw = $0.27/kg.












Exercise No. 5 Net Benefits


VARIABLE NO
CONTROL
Average yield
(kg/ha) 2717


Adjusted yield!
(kg/ha) 2174

2/
Gross Benefits-
(L/ha) 502


Insecticide cost
(L/ha) 0


Application Cost
(L/ha) 0


TCV (L/ha) 0


Net Benefits (L/ha) 498


S Yield adjustment = 20%


S Field Price of Maize =


T R E A
BIRLANE
IX

2635



2108



485



13.6



6.0


19.6


465


L 0.23/kg


T


BIRLANE
FURADAN

3233



2586



595



30.8


T M E N
BIRLANE
2X

2917



2334



537



27.2



12.0


39.2


498


9.0


39.8


555










Exercise No. 6 Field Price of Fertilizer


4.8 + 3.0
N field price = .335 =
.335


$ 23.3/kg


P field price = 7.5 3.0 $ 22.8/kg
.46 =$ .8k


VARIABLE


N cost (S/ha)


P cost ($/ha)


Fertilizer cost ($/ha)


TREAT
NO N50
P40 PO


0 1165


912 0


912 1165


M ENT
N50
P40


1165 :


912


2077


~J100
PO


2330


0


2330


N100
P40


2330


912


3242











TREATMENT NET BENEFIT TCV
($/HA) ($/HA)
NO DO 3670 670
NO D1 4963 830
NO D2 5870 990
N1 DO 3984 1373 D
Ni D1 4877 1533 D
N1 D2 4717 1693 D
N2 DO 3174 2074 D
N2 D1 4758 2234 D
N2 D2 4075 2444 D


All treatments marked
treatment (NO D2).


6000

5500

5000

4500

4000


3500


"D" are dominated, in this example by a single


(o-))


(1-1)


(0-1)


.(1-2)


* (2-41)


.(2-2)


* (1-0)


(0-0)


(2-0)
3000


600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600
Variable Cost, S/ha.


Exercise no.


7 Dominance Analysis and the Net


Benefit Curve








Exercise No. 8 nargin.al Rate ol Ret urn


T R E A T M E N T S


VARIABLE NO
Average yield-RD1 (kg/ha) 1140


Adjusted yield/ (kg/ha) 969


Gross benefits2/ ($/ha) 4118


Cost of N/ ($/ha) 0


Application cost (S/ha) 0


TCV ($/ha) 0


Net Benefits ($/ha) 4118

1/ Yield adjustment = 15%
2/ Maize field price = $ 4.25/kg

3/ Field price of N = $ 7.35/kg


MARGINAL ANALYSIS:

N150 is dominated
6500 4118
MRR NO + N50 = 680 o x 100

7218 6500
-MRR N50 W NOO 1235 768 x 100
1235 -X


N50 N100 N150


2012


1710


7268


468


300


768


6500


2340


1989


8453


935


300


1235


7218


2450


2083


8850


1403


300


1703


7148


= 310%

= 154%



















*(150)


7000


CE 6500 / (50)

6000

S5500
z

5000


4500

4000 (0)


0 200 400 600 800 1000 1200 1400 1600 1800


Costs that vary, $/ha.


(100)








Exercise No. 9- Cost of Capital


a) $ 3000 borrowed
x .20 annual interest rate
$ 600 annual interest charge



$ 600 x- $ 400 interest charge 8 month loan
12

60 service charge
+ 140 personal expenses
90 crop insurance
$ 690 total expenses re loan


$ 690 = 23% cost of borrowed capital

$ 3000 20% "risk premium"
43% cost of capital (minimum required rate of return)


b) $ 2000 borrowed
x .10 monthly interest rate
$ 200 monthly interest charge
x 7 months
$ 1400 interest charge 7 month loan


$ 1400 = 70% cost of borrowed capital
$ 2000
+ 20% "risk premium"
90% cost of capital (minimum required rate of return)










b) Insect >'


Domi nat ce


MRR


330
3 3 06


TREATMENT :---


No Control -

Birlane iX -


,4 '" .\ I. f the '"


If the :s:

capital =


S."* Birlanf"' /


1 .1 I'II


I1t)


. Iirlanie.- ""1''e









b) Insect Control Experiment


Dominated treatment:


Birlane + Furadan


TREATMENT CHANGE INCREMENT INCREMENT MRR
'NET BENEFIT TCV

No Control Birlane IX 25 30 83%
Birlane 1X Birlane 2X 5 15 33%


If the cost of capital = 30%, recommend Birlane 2X. If the cost of
capital = 60%, recommend Birlane IX

Birlane 2 X


445 Birlane 1X

470


S 465

460 Birlane+Furadan


without control


20 30 40

Costs that Vary, $/ha.










c) Verification Trial


Dominated treatments: 1, 2, 4, 6


TREATMENT CHANGE INCREMENT INCREMENT MRR
NET BENEFIT TCV


3 5 70 100 70%



If the cost of capital = 30% or 60%, recommend treatment (5).


460

440 .

420

400

380

360

340


320

S..


(5) (6)


(3) (4
(4)


* (1)


(2)


30 40 50 60 70 80 90 100 110 120 130 140

Costs that Vary, S/ha.










Exercise No. 11 Partial Budgets and Fixed Costs


TREATMENT
VARIABLE NOPO NOP40 N50PO N50P40
Data Set 1
Yield (kg/ha) 2000 2100 2500 2600
Gross Benefits ($/ha) 5600 5880 7000 7280
TCV ($/HA) 0 450 500 800
Net Benefits ($/HA) 5600 5430 6500 6480



Data Set 2
Yield (kg/ha) 2000 2100 2500 2600
Gross Benefits (S/ha) 5600 5880 7000 7280
TCV ($/ha) 3275 3725 3775 4075
Net Benefits (S/Ha) 2325 2155 3225 3205


For both data sets:


NOP40 and N50P40 are dominated


TREATMENT CHANGE INCREMENT INCREMENT MRR
NET BENEFITS TCV


NOPO N50PO 900 500 180%








Exercise No. 12 Verification


T R E A T M E N T
TREATMENT_
VARIABLE 1 2 3 4 5
Average yield-RD2 (kg/ha) 1125 1115 1475 1475 1963 1975
Adjusted yield (kg/ha) 900 892 1180 1180 1570 1580
Gross benefits ($/ha) 3690 3657 4838 4838 6439 6478
Local seed ($/ha) 84 0 84 0 140 0
Improved seed (S/ha) 0 300 0 300 0 500
Increased planting (S/ha) 0 0 0 0 150 150
Conventional tillage and
weed control (S/ha) 2200 2200 0 0 0 0
Gramoxone (S/ha) 0 0 750 750 750 750
Gesaprim 50 (S/ha) 0 0 720 720 720 720
Sprayer rental (S/ha) 0 0 50 50 50 50
Herbicide application and
hauling water (S/ha) 0 0 900 900 900 900
Insecticides (S/ha) 0 0 0 0 384 384
Insecticide application (S/ha) 0 0 0 0 150 150
N (S/ha) 0 0 0 0 500 500
N Application (S/ha) 0 0 0 0 300 300
TCV (S/ha) 2284 2500 2504 2720 4044 4044
Net benefit (S/ha) 1406 1157 2334 2118 2395 2434


Dominated Treatments:


Marginal analysis:


2, 4, and 6


TREATMENT CHANGE INCREMENT IN INCREMENT MRR
NET BENEFITS IN TCV
1 3 928 220 422x
3 5 61 1540 4%

If cost of capital = 55%, treatment 3 should be recommended. (The only
profitable change is from conventional to chemical tillage and weed controi .





































2400
(3) (5)
2200

2000 ( 64)
(6)

1800

1600

1400 (1)
z
1200
S (2)
1000


2200 2400 2600 2800 3000 3200 3400 3600 3800 4000

Variable Cost, $/ha.








Exercise No. 13 Minimum Returns Analysis


TREATMENT
VARIABLE NO N50 N100 N150
TCV 0 1000 2000 3000
Net benefits (average) 4000 6000 6480 6600
Net benefits (average
of two worst cases) 2250 3500 1100 500


Dominated Treatment:. None


Marginal Analysis (risk not considered)


TREATMENT CHANGE INCREMENT IN INCREMENT MRR
NET BENEFITS IN TCV
NO N50 2000 1000 200%
N50 -W NOO 480 1000 48%
N100 N150 120 1000 12%


If the cost of capital is 40% and risk is not considered, N100 should
be recommended.


Minimum Returns Analysis
Despite the fact that N100 is just profitable (on the average),
net benefits for the worst cases are quite low, even in comparison to
NO. Researchers might wish to consider N50 as a possible recommendation
if target farmers are small and risk-averse. Net benefits for N50
are higher even in the worst cases than NO net benefits.







Exercise No. 14 Sensitivity Analysis


TREATMENT
FARMER ZERO TILL ZERO TILL- ZERO TILL ZERO TILL
PRACTICE 1 + SUB- 1 SUB- 2 + SUB- 2 SUB-
VARIABLE SIDY SIDY SIDY SIDY
Average yield-RD 1
(kg/ha) 2000 2100 2100 2400 2400
Adjusted yield (kg/ha) 1600 1680 1680 1920 1920
Gross Benefits (S/ha) 8000 8400 8400 9600 9600
Farmer practice (S/ha) 2000 0 0 0 0
Machete chopping (S/ha) 0 480 480 480 480
Herbicide application
(S/ha) 0 240 240 240 240
Hauling water (S/ha) 0 240 240 240 240
Sprayer (S/ha) 0 50 50 50 50
Gramoxone ($/ha) 0 250 360 625 900
Gesaprim (S/ha) O 400 680 600 1020
TCV (S/ha) 2000 1660 2050 2235 2930
Net Benefits (S/ha) 6000 6740 6350 7365 6670


Marginal Analysis:
With the Subsidy on Herbicides
Dominated treatments: Farmer practice


TREATMENT CHANGE INCREMENT INCREMENT MRR
N B TCV

Zero till 1 -Zero till 2 625 575 109%



Without the Subsidy on Herbicide:


TREATMENT CHANGE INCREMENT INCREMENT MRR
NB TCV

Farmer practice -Z T 1 350 50 700%
Z T 1 Z T 2 320 880 36%

If the subsidy on herbicides were to be dropped, zero tillage would remain
profitable, but farmers should reduce their herbicide dose. ;:erbicide dose
is sensitive to the herbicide subsidy.









Exercise No. 15 combining Economic and Statistical Analysis 2 Factorial


Factor A: (Significant, and with a significant interaction with factor B).



TREATMENT
VARIABLE AO BO Al BO AO BI Al BI
Average yield- (kg/ha) 2210 3360 2045 3688
Adjusted yield (kg/ha) 1768 2688 1636 2950
Gross benefits ($/ha) 389 591 360 649
N cost (S/ha) 0 178 0 178
P cost (S/ha) 0 0 162 162
Application ($/ha) 0 6 6 6
TCV (S/ha) 0 184 168 246
Net Benefits ($/ha) 389 407 192 303

The average yield for each noted combination (AO BO etc.) is found
by averaging the four of sixteen individual treatment yield contain-
ing that combination.


Dominated treatments: AO B1, Al BI
Marginal Analysis: MRR for AO BO -Al BO = 10%
Recommendation: AO BO


Factor B: (Interacts with factor A, included with factor A)


Factor C: (Not significant, no significant interaction, so recommend the
least cost level, CO)


Factor D: (Not significant, no significant interaction, so recommend the
least cost level: DO)


Recommendation: AO BO CO DO










Exercise No. 16 Partial Budgets for Planning Experiments

$ 7.00 + $ 3.00
Field price of N = 4 = $ 21.74/kg


TCV Increment


= $ 21.74/kg
x 150 kg/ha
$ 3261/ha
+ 100/ha
$ 3361


N field price
N dose
N cost/ha
N application/ha
Increment TCV


Field price of maize = $ 1.80/kg


AY = minimum yield increase = 3361 x 1. = 2988 kg/ha

required to pay costs


Almost a three ton yield increase is needed to pay treatment costs, but
the treatment is only expected to give a one ton increase. This treatment
should be re-considered.




University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs