suggested method for improving the information base for establishing priorities in cassava research

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suggested method for improving the information base for establishing priorities in cassava research
Pinstrup-Andersen, Per
Dian, Rafael O.
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Cali, Colombia
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Full Text


Prel mli narv



Per Pinstrup-Andersen

Rafael 0. DTa*

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 I/
research considered. Ideally, the research manager- would have complete knowledge 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 alternative 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 _odel for Imoroving 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 farmer.

In addition to the information requirements dicussed 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 obtaining 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 Asricultural Research, IICA,
Maracay, Venezuela, April 10-13, 1973.

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

Pinstrup-Andersen, Per and David Franklin. A Systems Ap roach to Agricultural Research Resource Allocation in Developing Countries. Paper presented 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-extension 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 preferences. A continuous flow of information to the research manager on the potential 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) chhging plant response 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 technology corresponds with the farmers' needs and preferences, hence accelarate 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 institutions. 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 establishment 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 reviewing public policy on such issues as agricultural extension, credit and pridesFinally, the information may be useful to producer associations and individual farmers (Fig. 1). However, the primary purpose of the surveys is to supply information 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 objectives, and the interaction among these factors. Emphasis is placed on iden tifying the principal factors limiting production and productivity and estimating the implications of removing these factors. Process structure. The structure of the production process refers to the process 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), availability of water, plant type and general plant development are described. The farmer's perception of the agro-biological 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 characteristics 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 studied (Fig.4). Emphasis Is placed on analyzing the relationship between structure 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' obiectives. Attempts are made to describe the farmer's goals and the relative importance of incomes, risk and availability of products for home consuiption 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.

Da_&ah n ,ehanism. Primary data are obtained by a small specialized team

of agronomists and economists, from a panel of farms expected to be representative 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, controlled experiments are carried out to help quantify the yield impact of the problems.

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, conduct and performance of the-production process under study and 2) estimating the impact of changing process structure and conduct on performance. In addition 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 institutions and CIAT, 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 sults 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 approximately 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 occuring 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 Figure 6. The selection of zones was based on their importance within total national cassava production and their ability to represent the characteristics 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. Although 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 important 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 cassava 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 variabjQs.


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 sp.). (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 (Bemis ia 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 collected.

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

greatly among zones. Phoma Leaf Spot, the disease most frequently found during 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 expected 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 production activities is another factor likely to provide guidelines for reseach resource allocation. Table 8 shows the estimated labor requirements by productio, activity and the percentage distribution of labor requirements and 4/
available costs. Weeding was the most labor-consuming activity (and accounted 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 sdfficiently advanced to provide estimates of labor and cost distribution, the
data presented in Table 8 are taken from prior work (Rafael O.Draz, Per
Pintrup-Andersen and Rub6n Darlo Estrada. Costs and Use of Inputs in
Cassava Production in Colombia: A Brief Description. CIAT,EE-Noz.5,September, 1974).


packing, land preparation and planting.

The data reported in Table 8 suggest that high priority might be pl 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 harvesting/packing.

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 nnd/or herbicide use was estimated to have a significant negative impact on labor demand while biological technology is expected to increase labor demand slight5/Th
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 priorities, 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 PinstrupAndersen 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 established in order to assure a large contribution of the research to the achievement 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 completed.

Trai ning benefits

In addition to the expected utility of the Information made available by the agro-economic analyses, the work provides a valuable training opportunity 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 problems.

Conclupin z remarks

No claims are made here that the agro-economic survey is a new invention. 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 establishing 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 needed 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 significant contribution to research resource allocation can yet be identified. It may be expected, however, that the direct participation of the CIAT agricultural production dentists in project planning and training of field agronomists 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. Furthermore, 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 merica.


ure 1. Illustration of the expected
utility of the-agro-econom'ic/



Figure 2. Basic model underlying choice of data to be collected.


Figure 3. Factors determining the structure of the production process.


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


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


Figure 6. Location of th five zons included in the agro-econoric analysis of csava nroduction in Colombia.

I____-} -, II I t .*
/, I

.' ",
"1 5f '

-- = /

___ __ -- ?=

/ '- S

5 -. .


Table 1. Altitude, farm size and land use on same farms. Agro-economic analysis of cassava production In Colombia (preliminary data)

Z 0 N E Avern ,a


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 i0.9 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.A 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 (7. 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 (7 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 fartms) 2.7 0.0 0.0 0.0 6.8 2.4

Other crops (7% 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

60.0 24.5

4.1 3.4 2.2 1.3 1.0 2.3

Lot size

2.5 1.4 3.6 2.7 06 2.0 0.6 1.7

Percent of

69.3 15.8 6.8 4.2 0.6 1.2 0.3 1.8

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


9421 5578

12172 574

9455 2127

8988 5113 7813

761.7 3583 833

7333 4133 4283



Table 3. Preliminary data on Insect occurrence in cassava. Agro-economic analysis of cassava production in Colombia.

11 2/ 3/
First visit Second visit 2/ Third visit

% of 7. of Inten- % of % of Inten-o % of % of IntenInsect farms lot sity 4-/ farms lot S ty farms lot sit v

Thrips 80.0 80.9 2 84.0 Z:1.6 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

1-/ 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 16 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

I/ Preliminary data from 248 farms


Table 5. Preliminary


Brown leaf spot White leaf spot Cassava ash disease Cercospora leaf blight Phoma leaf spot Superelongation 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

. I/
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

0.0 -

Inten4s ity 4

2 2 2 2 2 3 2 3 I

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.

Co omb ia.


% of

53.6 58.5

43.1 23.0 54.4 11.7 12.9

1.2 2.4 0.0

visit% of

32.9 40.8 42.3 25.6 32.7 44.5 38.3 15.0 4.3

4/ Inten*7

2 2 2 1

2 4

3 3



% of
f arms

34.6 35.8 20.4 6.8 43.2 1.2 9.3 0.0 1.9 3.7


% of

36.0 53.7

56.8 39.5 35.8

47.5 44.7


Inten-,/ sitv







Table 6. Distribution of maior disease occurrence am 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

-----------------------------------I/ 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 cossava production in Colombia.

1 eed

% of farms

Pteridium caudatum Sida acuta Commelina difusa Bidenes pilosa Melinis minutiflora Portulaca oleracea Cyperus ferax Rychardia scabra Cyperus rotundus Drymaria cordata

24 18 17

16 14 12 10 10 10


Weed density

78.000 90.000 136.000




148.000 84.000




Table 8. Dstributfon of labor requirements and variable costs among

cassava production activities in Colombia.

Labor requirements

Land preparation Planting Re-planting Weeding Fertilizers and apl. Insecticides and apl. Harvesting and packing Seed


Mech. land
Qyrparat ion
Man-days %

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 nrena t L, o n 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



Va~iab1 e costs

23 8 1


1 1

24 6


Source: Rafael 0. Diaz, Per Pinstru?-Andersen y Rub6n Dario Estrada. Costs

and Use of Inputs in Cassava Production in Colombia: A Brief Descrjption. CIAT, Series EE',No.5, Septeiaber, 1974