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Title: suggested method for improving the information base for establishing priorities in cassava research
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Permanent Link: http://ufdc.ufl.edu/UF00081512/00001
 Material Information
Title: suggested method for improving the information base for establishing priorities in cassava research
Physical Description: Book
Language: English
Creator: Pinstrup-Andersen, Per
Dian, Rafael O.
Publisher: CIAT
Publication Date: 1975
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Bibliographic ID: UF00081512
Volume ID: VID00001
Source Institution: University of Florida
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Resource Identifier: oclc - 191048018

Table of Contents
    Title Page
        Title Page
    A suggested method for improving the information base for establishing priorities in Cassava research
        Page 1
        Page 2
    A suggested methodology
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
    Illustration of empirical results
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
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Full Text

/1 ?a

Prelimi nary



Per Pinstrup-Andersen

Rafael 0. DTaz

Paper prepared for the cassava review meeting, CIAT,

Cali, Colombia, January 30-February 6, 1975.


In order to establish sound research priorities, information is needed

on expected benefits, costs and time requirements for each of the lines of
research considered. Ideally, the research manager- would have complete know-

ledge of research outcomes and their expected contribution to the achievement

of established goals as well as costs and time requirement for each line of

research. If this were the case, and assuming a well defined goal, single or

aggregate, available research resources could always be allocated in such a

way as to maximize the contribution to the achievement of this goal and no

subjective judgement would be needed in the decision-making process.

However, because of the very nature of research, decisions on research

priorities will always be subject to uncertainty. This inherent uncertainty

of research outcomes is frequently accompained by lack of information on issues

relevant to the decisions that can in fact be estimated with some certainty.

These issues include potential production and productivity gains from alterna-

tive research outcomes, and their impact on employment, nutrition, and farm


Portions of this paper are taken from: Per Pinstrup-Andersen, et. al. A Pro -
posed Model for Improving the Information Base for Research Resource Allocation.
Paper presented at Workshop on Methods Used to Allocate Resources in Applied
Agricultural Research in Latin America, Cali, Colombia, November 26-29, 1974.

1/ The term "research manager" is used to indicate the person or group of
persons making the decision on research priorities. Depending on the research
organization and the level in the research process at which priorities need
to be established, the "research manager" may be the individual scientist, a
team of scientists, a research director or any other person or group of persons
in the research system.


revenues. It is argued here that additional information on these and related

issues is likely to be highly useful to the research manager in establishing

research priorities.
An overall model for providing such information is suggested elsewhere-

This paper is limited to a presentation of a methodology for 1) describing

the production process, 2) identifying factors limiting production and pro -

ductivity, 3) estimating the relative importance of each of these factors, and

4) obtaining indications of the technology characteristics preferred by the


In addition to the information requirements discussed in this paper, other

information relevant to decision-making on research resource allocation include

expected future product demand, resource supply and consumer acceptance.

This paper is divided in two parts. First, a simple methodology for ob-

taining the above mentioned information from the farm sector is suggested and

second, the experience gained from the empirical testing of the methodology for

cassava in Colombia is discussed with illustrations of the kind of information

obtained. The paper is focused on the practical aspects of the data collection

and analysis.

2/ Pinstrup-Andersen, Per. Allocation of Resources in Applied Agricultural
Research in Latin America Preliminary Approach. Paper prepared for the
Regional Seminar on Socioeconomic Aspects of Agricultural Research, IICA,
Maracay, Venezuela, April 10-13, 1973.

Pinstrup-Andersen, Per. Toward a Workable Management Tool for Resource
Allocation in Applied Aricultural Research in Developing Countries. Re-
vised version of paper presented at the Ford Foundation Meeting for Pro-
gram Advisors in Agriculture, Ibadan, Nigeria, April 29-May 4, 1974.

Pinstrup-Andersen, Per and David Franklin. A Systems Approach to Agricul-
tural Research Resource Allocation in Developing Countries. Paper presen-
ted for Conference on Resource Allocation and Productivity in International
Agricultural Research, Airlie House, Virginia, January 26-29, 1975.


Priorities in applied agricultural research are frequently established

on the basis of very limited information about existing problems and their

relative economic importance in the production process. The communication

between the farm sector and the research institute is often deficient and

the demands at the farm level for problem solving research frequently are

not well known by the research manager. Farmers in most developing countries,

maybe with the exception of large commercial ones and members of efficient

producer associations, tend to have severe difficulties in communicating their

research needs to the research institutes because of institutional and social

barriers. Because of that situation, some research may be irrelevant to the

actual farm problems and research results may not be adopted.

Low rates of adoption of new technology are frequently explained as a

result of an ineffective extension service. Whereas the-ability of the exten-

sion service to assure adoption of new technology may indeed be deficient ,

one of the primary reasons for low adoption rates may well be that available

new technology does not meet the most urgent on-farm needs and farmer prefer-

ences. A continuous flow of information to the research manager on the poten-

tial gains in production, productivity and risk obtainable from such research

activities as 1) developing resistance to existing diseases and insects, 2)

changing cultural practices, 3) changing plant types, 4) chaDging plant res-

ponse to nutrients, etc., as well as information on the farmers' preferences

with respect to new technology, is likely to be useful to assure that new tech-

nology corresponds with the farmers' needs and preferences, hence accelerate

adoption and increase research pay-off.

Such an information flow may consist of a continuous feed back of in -

formation from the farmer through the extension service to the research ins-

titutions. Direct contact between researchers and farmers through meetings,

farm visits, etc., would be another effective vehicle for such information. To

complement these we are suggesting a third method. This method consists of a

combination of agro-economic surveys and agro-biological experiments. Each of

these will be discussed in the following.

Agro-economic surveys

The agro-economic survey attempts to transmit to the research manager

the farm level demand for applied agricultural research through he establish-

ment of a direct link between the farm and the research institute. In addi -

tion to serving the needs of research managers, the information generated by

the agro-economic surveys is expected to be useful for establishing or review-

ing public policy on such issues as agricultural extension, credit and prices.

Finally, the information may be useful to producer associations and individual

farmers (Fig. 1). However, the primary purpose of the surveys is to supply in-

formation for establishing research priorities.

The basic framework underlying the choice of data to be collected is

shown in Figure 2. Attempts are made to describe certain key aspects of the

structure, conduct and performance of the production process, the farmer objec-

tives, and the interaction among these factors. Emphasis is placed on iden -

tifying the principal factors limiting production and productivity and estimat-

ing the implications of removing these factors.

Process structure. The structure of the production process refers to the pro-

cess characteristics determined by factors external to the process itself.


The structure represents the constraints within which the process operates.

Some of the constraints may be modified or removed by the farmer while o -

thers are beyond his control. Figure 3 illustrates the structural factors

described by the agro-economic surveys. Given the purpose of the survey,

major emphasis is placed on agro-biological and ecological factors.

Most of the data related to the agro-biological factors are obtained

from direct observation in the farmers' fields. The occurrence and severity

of disease and insect damage, mineral deficiencies and weed occurrence are

noted. Furthermore, altitude, soil quality 'by means of soil tests), avail-

ability of water, plant type and general plant development are described. The

farmer's perception of the agro-blological problems are compared to the field

observations. In addition, data are obtained from the farmers on product and

input prices and their fluctuations; availability of commercial inputs, credit

and technical assistance; land tenure, farm size, capital and certain charac-

teristics of the farmer and his family.

Process conduct. The conduct describes the action resulting from the farmers

decisions with respect to the production process. The survey obtains data on

1) use of the land controlled by the farmers; 2) crops founds in the production

process studied; 3) planting, cultural and harvesting practices; 4) use of

inputs such as fertilizers and insecticides as well as credit and technical

assistance and 5) the utilization of the products produced by the process stu-

died (Fig.4). Emphasis is placed on analyzing the relationship between struc-

ture and objectives on the one hand and conduct on the other, to determine

the major production limiting factors.


Process performance. The performance measures the outcomes or results of the

production process in terms of established goals. The survey obtains data on

yields, production, costs, labor absorption, home consumption, yield varia -

tion (risk) and gross and net revenues (Fig.5).

Farmers' objectives. Attempts are made to describe the farmer's goals and the

relative importance of incomes, risk and availability of products for home con-

sumption in his objective function to help identify technology with high expec-

ted rate of adoption. This work includes the collection of data on reasons why

various types of new technology was or was not adopted and factors underlying

the choice of cropping systems.

Data gaatherin-mechanism. Primary data are obtained by a small specialized team

of agronomists and economists, from a panel of farms expected to be representa-

tive of the farms for which agro-biological research is intended. The field

team makes periodic visits (normally 3 4 visits) to each farm throughout a

complete crop cycle. About half of the time on the farm is spent in the field

collecting data on agro-biological issues (by direct observation), while the

other half is used to interview the farmer.

Before the farm visits are initiated the field team receives extensive

training in diagnosing farm level production problems. Training of the field

team is one of the most critical issues in assuring high quality data from the

agro-economic survey. Making a correct diagnosis in the field, e. g. distin -

guishing among the symptoms of certain diseases, insect damage, mineral defi -

ciencies, etc., in most cases requires considerable expertise. Hence, direct

participation of a highly qualified multidisciplinary research team in the

training and field execution phases is essential to the success of the survey.

The field teams working on the on-going CIAT agro-economic surveys have re -

ceived 3 4 months of such pre-survey training in direct contact with the

scientists from the relevant disciplines.

Agro-biological experiments

The agro-economic survey provides an estimate of the area affected by

each of the problems identified. Furthermore, it gives an indication of the

yield depressing effect. However, it is frequently difficult to estimate the

yield impact from survey data with a great deal of accuracy. Hence, contro-

lled experiments are carried out to help quantify the yield impact of the


Data analysis

The data obtained from the agro-economic survey and the related experi -

ments are analyzed for the general purpose of 1) describing the structure, con-

duct and performance of-the production process under study and 2) estimating

the impact of changing process structure and conduct on performance. In addi-

tion to aggregating the data for the purpose of presenting a description of

the process, attempts are made to estimate the economic loss caused by each

of the agro-biological and ecological factors such as diseases, insects, weeds,

soil deficiencies and adverse rain fall conditions and the implications of

changing these factors. Furthermore, estimation is made of 1) production costs

and labor absorption by production activity, 2) net returns to the process for

each of the principal cropping systems, 3) the contribution of each of the

principal resources to net returns and 4) the factors influencing the farmer

decision-making on adoption of new technology and choice of cropping system.


Projects are currently under way in Colombia to field test the above

methodology for maize, cassava and beans. While the information obtained from

these empirical studies is expected to be useful to Colombian national insti-

tutions and CIT, the primary purpose of the work is to develop and test a

simple methodology for use by national research agencies in Latin America and

elsewhere. The purpose of this section is to present a few preliminary re -

suits from the agro-economic analysis of cassava production in Colombia to

illustrate the kind of information obtained. Since the data collection is not

yet completed, little data analysis has been done.

The agro-economic analysis of the cassava production process in Colombia

is based on the collection of primary data from personal visits to approxima-

tely 300 cassava producers located in five regions of Colombia. Each farm is

visited three times during the growing season by a team-of two agronomists and

an agricultural economist previously trained in identifying agro-biological

problems in cassava and carrying out farm interviews. The growing season for

cassava in Colombia is around 12 months except in one zone (North Coast Region)

where it is 8 10 months. The three visits are distributed throughout the

growing season with the fist occurring less than four months after planting

and the last right after harvest.

As of October 1, 1974, all the sample farms have been visited once, and

80 and 53 per cent have been visited twice and three times, respectively. Hence,

except for those obtained during the first visit, the data reported below are

preliminary and serve primarily as illustration of the kind of information that

the agro-economic analysis can make available.


The location of the five zones included in the survey are shown in Fi-

gure 6. The selection of zones was based on their importance within total

national cassava production and their ability to represent the characteris-

tics of the various cassava producing regions of the country.

Table 1 shows the altitude, farm size and land use characteristics of

the sample farms. The altitude of the farms varies from over 1.000 meters

in zones I and II to almost sea level in zone V. The farms are relatively

large when measured in terms of total area. However, a large portion of the

land is idle or in-pasture, hence the cultivated area per farm is small. Al-

though a few of the farms had large cassava plantations, the average cassava

area per farm was estimated at about 5 ha. Meta (zone IV) shows the largest

cassava area per farm. The farms visited had, on the average, two lots with

cassava. The importance of other crops on the sample farms varied with lo -

cation. Coffee and plantain, in most cases intercropped, were the most impor-

tant crops in zones I, II and III. Furthermore, sugar cane, maize and banana

showed some importance in certain zones.

Table 2 shows the cropping systems most predominant among the sample

farms, and lot size and plant population for each of these systems. A total

of 14 different crop combinations were identified at the first visit. More

than half of the farmers grew cassava alone while about one-fourth grew cas-

sava intercropped with maize. About 60 per cent of the area was planted with

cassava alone. Although lot size varied greatly with cropping system, addi -

tional data analysis is needed to determine the possible relationship between

these two variabjls.


The plant population of cassava was found to be similar whether grown

alone or intercropped with one other crop. However, when grown with two or

more crops, the cassava plant population tends to diminish. A comparative

economic analysis of various cropping systems for cassava has been initiated

to provide more information on this issue including the factors determining

the farmer's choice of system.

The occurrence of insects, insect damage and diseases in cassava was

estimated on the basis of direct field observations. The final results from

the first visit and preliminary results from the second and third visits are

shown in Tables 3, 4, 5 and 6.

Thrips was the insect most frequently found, followed by gall midge and

white fly (Bemisia s.). (Table 3). It appears that the occurrence of these

insects and the visible damage they cause is less frequent in crops more than

eight months old. One explanation of this situation is that the crop in many

cases outgrows the visual damage caused by the initial attacks. However, data

are not yet available to determine whether in fact the attacks had any sign -

ficant impact on yields.

While the occurrence of most insects and their visible damage seems to

diminish as the plant grows older, the opposite seems to occur for some insects

including white fly and mites.

The occurrence of each of the major insects varies considerably among

zones (Table 4). For example, Fruit fly (in stems), was found on 76 per cent

of the farms in Zone II while it was of little importance od the other zones.

Leaf hopper was important only in Zone V. and White fly (Bemisia s_.) was found

on 70 per cent of the farms in Cauca, Magdalena and Atlantico (Zones I and V)

while it was much less important in the other three zones.


The visible damage caused by diseases in cassava was mos pronounced in

crops between 4 and 8 months of age. The diseases most frequently found were

White Leaf Spot, Phoma Leaf Spot, Brown Leaf Spot, Powder Mildew and Cercospora

Leaf Blight (Table 5). As in the case of insects, it appears that the cassava

plant in some cases is capable of outgrowing the disease symptoms. However, it

is interesting to note that the proportion of the lot affected.increases with

the age of the crop for most diseases. One possible conclusion might be that

while lighter attacks tend to be overcome by plant growth, the somewhat more

serious attacks continue to spread in the field. The relationship between

rainfall conditions and disease spread will be analyzed as more data are co-


As in the case of insects, the occurrence of cassava disease varies

greatly among zones. Phoma Leaf Spot, the disease most frequently found dur-

ing the second visit (in plantations 4 8 months old), was found on about 70

per cent of the farms in Cauca, Valle and Quindio (zones I and II) and only

30-40 per cent of the farms in the other three zones (Table 6). Superelongation,

while of little or no importance in four zones, was found on two-thirds of the

farms in Tolima (Zone III). Likewise, the occurrence of Cassava Bacterial Blight

and White Leaf Spot differed greatly among zones.

It should be noted that the data from second and third visits are not

complete, hence are subject to change as more data are collected. Therefore,

the above findings and conclusions are tentative and subject to revision when

the analysis is completed.

During the first visits, 92 weeds were identified. Table 7 shows the

ten weeds most important in terms of percentage of farms where they occurred.


Pteridium candatum was found on one-forth of the sample farms but the plant

density was relatively low. It was most frequently found in Zone III (79%

of all farms) while it was not found in Zone V.

Other agro-biological problems in cassava production assessed by the

-field team include scarcity or excess of water. It appears that excess of

water was a severe problem particularly in Valle and Quindio (zone II) while

water scarcity appeared to reduce yields in Magdalena and Atlantico (Zone V).

Once the data collection is completed, attempts will be made to estimate

the relative economic loss caused by each of the major insects, diseases,

weed and other agro-biological problems, in collaboration with the respective

biological scientist within the Cassava program. / Such estimates are ex-

pected to be useful to the cassava program in establishing and reviewing

priorities among and within disciplines.

The distribution of production costs and labor requirements among pro-

duction activities is another factor likely to provide guidelines for research

resource allocation. Table 8 shows the estimated labor requirements by pro-

duction~ activity and the percentage distribution of labor requirements and
available costs. Weeding was the most labor-consuming activity (and accoun-

ted for the highest percentage of variable costs), followed by harvesting/

3/ The data collection extends over a two-year period to cover two complete
growing seasons and most of the data analysis cannot be performed until
a complete data set is obtained. The data collection will be completed -
around June 1, 1975.

4/ Since the data collection within the agro-economic survey is not stffi-
ciently advanced to provide estimates of labor and cost distribution, the
data presented in Table 8 are taken from prior work (Rafael O.Diaz, Per
Pintrup-Andersen and Rub6n Dario Estrada. Costs and Use of Inputs in
Cassava Production in Colombia: A Brief Description. CIAT,EE-Noz.5,Sep-
tember, 1974).

packing, land preparation and planting.

The data reported in Table 8 suggest that high priority might be plh-

ced on research aimed at exploring whether and how the economic efficiency

in weeding, harvesting/planting and land preparation might be improved. Such

work might include the estimation of the impact of alternative degrees of

land preparation and weeding on yields and economic net return as well as

the impact of alternative methods applied in these activities and harvest-


The potential impact of the development and adoption of mechanical,

chemical and biological technology on labor use in cassava production was

estimated for various adoption rates. Extensive mechanization and/or her-

bicide use was estimated to have a significant negative impact on labor de-

mand while biological technology is expected to increase labor demand slight-

ly.- The impact of the various types of technology on costs would depend on

existing relative prices, hence may differ among localities.

Before data such as the above, are used to help establish research prio-

rities, the objectives of the society for which the research is intended must

be clearly specified. Social and private objectives may conflict, e.g. the

social objective of creating productive employment may conflict with private

objectives of maximizing profits. Chemical weed control, for example, may

increase net returns to the producer but reduce employment. The impact of

5/ The quantitative results of the analysis are reported in: Per Pinstrup-
Andersen and Rafael 0. Diaz. Present and Potential Labor Use in Cassava
Production in Colohbia. Paper presented at the third International Sym-.
posium on Tropical Root Crops, Ibadan, Nigeria, December 2-9, 1973.


new technology on-net returns depends, at least in part, on relative factor

prices, which in turn may be influenced by public policy. It is important

that possible conflicts between social and private objectives as well as

government's ability and desire to introduce corrective and facilitating

policy measures be fully understood before research priorities are establi-

shed in order to assure a large contribution of the research to the achieve-

ment of social and economic development goals.

In addition to the above, the agro-economic survey seeks information

on a number of other issues expected to be useful for the cassava program

in allocating its' research resources as discussed earlier in this paper.

This information will be reported.when the data collection an, analysis are


Training benefits

In addition to the expected utility of the information made available

by the agro-economic analyses, the work provides a valuable training oppor-

tunity for young agronomists and economists interested in production. The

extensive initial training along with the experience gained while carrying

out the surveys produce professionals extremely knowledgeable of farm level

production limitations and possible ways to remove these limitations in the

crop or crops for which the survey was .carried out. It is expected that

these professionals in their future activities will complement the empirical

survey results by providing a close linkage between research and farm level


Concluding remarks

No claims are made here that the agro-economic survey is a new inven-

tion. A very large number of farm surveys have been carried out in the past.


However, certain aspects of the work discussed above tend to distinguish it

from traditional farm surveys and hopefully make it more useful for establish-

ing priorities in applied agricultural research. These aspects are:

1. A considerable proportion of the data are obtained from direct

field observations made by agronomists previously trained for

this job;

2. Each farm is visited periodically during a complete growing


3. The work is multidisciplinary in nature and involves direct

participation by professionals from all the relevant disciplines.

4. The work is specifically focused on providing information nee-

ded to establish research priorities. Although the information

may be useful for other purposes, such utility is considered


The work described above is in its preliminary stages and no signifi-

cant contribution to research resource allocation can yet be identified. It

may be expected, however, that the direct participation of the CIAT agricul-

tural production scientists in project planning and training of field agrono-

mists as well as communication with these agronomists when they are back

at the research station and the distribution of preliminary project findings

may have been of some value to the scientists in planning their future re -

search. However, more time is needed to terminate the first round of data

collection and analysis before the real value of these efforts for research

resource allocation can be established.

The methodology and experience gained from the work will be made

available to interested national research agencies upon request. Further-

more, CIAT will consider requests for technical assistance for project of

this type. Currently, a collaborative project with INIAP, Ecuador for cas-

sava is being planned. The possibility of carrying out projects for cassava

in Brazil and Thailand are being discussed, and funds have been assured to

provide technical assistance for two similar projects for beans in Latin A -


Figure 1.

Figure 2. Basic model underlying choice of data to be

Figure 3. Factors determining the structure of the production

Figure 4. Factors expressing the conduct of the
production process.

Figure 5. Factors expressing the-performance of the
production process.

* Figure 6. Location of the five zones included in the agro-cconomic analysis
of cassava production in Colombia.


1 /
1 I I

/I /

I '
"-- -- /

__ ,' -r ,--..*'_ =,
'.* ._ %- ^ a

""% ..-- -,

Table 1. Altitude, farm size and land use on sample farms. Agro-economic analysis of cassava production in

Colombia (preliminary data)

Z 0 N E Averagne


Altitude of farm (meters) 1.254 1.187 886 396 33 761

Total farm size (ha) 7.2 37.5 16.5 61.3 18.0 25.9

Area in crops (ha) 3.5 18.3 4.7 10.9 8.4 9.9

Area in cassava (ha) 2.9 6.4 2.0 9.4 5.3 5.2

Area in pasture and unused land (ha) 3.7 19.2 11.8 50.1 9.6 16.0

Number of cassava lots/farm 2.16 1.91 2.16 1.98 1.59 1.96

Size of cassava lot observed (ha) 1.30 3.35 0.90 3.37 2.16 2.22

Crops other than cassava on farms:

Coffee (% of farms) 32.4 61.4 31.6 10.0 0.0 28.7

Plantain (% of farms) 18.9 54.5 5.3 10.0 4.5 22.0

Maize (% of farms) .2.7 11.4 15.8 15.0 4.5 8.5

Sugar cane (% of farms) 5.4 0.0 26.3 0.0 0.0 4.3

Banana (% of farms) 2.7 0.0 0.0 0.0 6.8 2.4

Other crops (% of farms) 0.0 9.1 5.3 10.0 18.2 9.1

Table 2. Cropping systems, lot sizes and plant population. Agro-economic analysis of cassava

production in Colombia ( preliminary data).

Cropping system

Cassava alone

Cassava Maize

Cassava Plantain

Cassava Beans

Cassava Maize Beans

Cassava Maize Plantain

Cassava Maize Sesame

Cassava with other crops

Percent of









Lot size









Percent of









Plant population (No. of Plants/ha)
Cassava 2nd.crop 3rd.crop


S9421 5578

12172 574

9455 2127

8988 5113 7813

7617 3583 833

7333 4133 4283


Table 3. Preliminary data on insect occurrence in cassava. Aro-economic analysis of cassava production in Colombia.

1/ 2/ 3/
First visit Second visit Third visit -

% of % of Inten- % of % of Inten-. % of % of Inten/
Insect farms lot sity f4/ farms lot sity_ farms lot sity -

Thrips 80.0 80.9 2 84.0 41.6 2 46.0 41.6 2
Gall midge 51.0 22.1 2 54.0 15.6 1 21.0 17.9 1
White fly (Bemisia sp.) 44.2 26.6 2 41.0 37.1 2 21.0 15.3 2
Shoot fly 16.7 25.0 3 16.0 15.9 2 1.0 10.0 1
Leaf cutter ants 14.4 35.2 4 12.0 14.2 2 10.0 25.3 1
Leaf hoppers 12.8 15.9 2 4.0 15.6 2 0.0 -
Fruit fly (in stems) 12.0 26.4 2 24.0 37.3 2 9.0 36.6 1
Horn worm 7.2 18.4 2 2.4 20.8 2 2.0 11.7 1
White fly 5.6 12.1 2 16.0 22.7 1 19.0 44.6 2
Chrysome lids 4.2 11.9 1 4.0 14.6 2 0.0 -
Tingids 3.6 22.7 2 8.0 18.8 2 4.0 15.7 1
Mites 2.0 3.5 2 25.0 41.0 2 27.0 60.0 3
Termites 1.0 36.7 2 0.0 2.0 27.5 1
Ants 0.6 10.0 2 2.0 14.0 1 0.0 -
Cutworms 0.6 45.0 1 0.0 0.0 -
Stemborers (hepidopterous) 0.3 15.0 1 0.4 30.0 2 0.0 -
Scale insects 0.0 0.0 1.0 35.0 2
Stemborers (colepterous) 0.0 -0.0 1.0 5.0 1

I/ 305 farms included
2/ 248 farms included
3/ 162 farms included
4/ Intensity of attack using scale of 1-4 with 1 being low and 4 high intensity

Table 4. Distribution of major insect occurrence among zones, second visit (% of farms).

Agro-economic analysis of cassava production in Colombia.


Insect I II III IV V

Thrips 61 89 100 100 83

Gall midge 25 46 68 68 85

White fly (Bemisia sp.) 70 10 .24 26 71

Shoot fly 8 32 5 38. 0

Leaf cutter ants 20 6 32 21 2

Leaf hoppers 2 2 0 0 15

Fruit fly (in stems) 7 76 3 6 8

Horn worm 0 2 0 0 10

White fly 46 5 16 0 4

Chrysome lids 5 6 0 0 6

Tingids 15 3 13 12 0

Mites 7 8 38 15 44

1/ Preliminary data from 248 farms

Table 5. Preliminary


Brown leaf spot

White leaf spot

Cassava ash disease

Cercospora leaf blight

Phoma leaf spot


Cassava bacterial'blight

Root rotting

Leaf sooty mold

Frog skin root disease

data on disease occurrence in cassava. Agro-economic analysis of cassava

production in

First visit -

% of % of
farms lot

34.4 22.3

28.2 33.1

19.0 39.7

15.1 16.8

15.0 19.9

5.9 22.5

4.6 26.7

1.3 42.5

0.3 10.0


sity -










production in

1/ 305 farms included
2/ 248 farms included
3/ 162 farms included
4/ Intensity of attack using a scale-of 1-4 with 1 being low and 4 high intensity.

Co0 ombia.


% of












% of










Intenr 7











% of












% of









-- --







Table 6. Distribution of major disease occurrence among Zones, second visit (% of farms).

A_~ro-economic analysis of cassava production in Colombia.

Z 0 N E

Disease I II III IV V

Brown leaf spot 28 32 79 68 83

White leaf spot 71' 95 28 9 54

Cassava ash disease 43 57 84 15 10

Cercospora leaf blight 39, 8 40 18 14

Phoma leaf spot 72 71 34 32 42

Superelongation 2 0 66 9 0

Cassava bacterial blight 2 0 11 24 37

Root rotting 2 3 0 0 0

_- Preliminary data from- 248 farms.
1/ Preliminary data from 248 farms.

Table 7. The ten most important weeds in cassava in terms of

proportion of sample farms where they occurred,(first visit).

Agro-economic analysis of cassava production in Colcnbia.


% of farms

Pteridium caudatum

Sida acuta

Commelina difusa

Bidenes pilosa

Melinis minutiflora

Portulaca oleracea

Cyperus ferax

Rychardia scabra

Cyperus rotundus

Drymaria cordata











Weed density











Table 8. D stribution of labor requirements and variable costs among

cassava production activities in Colombia.

Labor requirements

Land preparation




Fertilizers and apl.

Insecticides and apl.

Harvesting and packing



Mech. land
pnerrat ion
Man-days %
per ha.

9.1 10.4

0.3 0.3

46.8 53.4

0.5 0.6

0.3 0.3

30.7 35.0



Manual land
norenar t. on
Man-days %
per ha.

25.0 23.6

10.8 10.2

0.6 0.6

43.7 41.2

0.3 0.3

0.6 0.6

24.9 23.5



costs (?)










Source: Rafael O. Diaz, Per Pinstrup-Andersen y Rub6n Dario Estrada. Costs

and Use of Inputs in Cassava Production in Colombia: A Brief Des-

crijtion. CIAT, Series EE',No.5, September, 1974

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