A formative evaluation of Valle Grande Rural Institute in Cañete, Peru


Material Information

A formative evaluation of Valle Grande Rural Institute in Cañete, Peru
Physical Description:
6 leaves : ; 28 cm.
Cabrera, Victor
Baker, Matt
Hildebrand, Peter E
State University System of Florida
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Subjects / Keywords:
Agriculture -- Peru -- Cañete   ( lcsh )
Agricultural systems   ( lcsh )
bibliography   ( marcgt )
non-fiction   ( marcgt )
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Includes bibliographical references (leaf 6).
Statement of Responsibility:
Victor Cabrera, Matt Baker, Peter E. Hildebrand.

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University of Florida
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All applicable rights reserved by the source institution and holding location.
Resource Identifier:
oclc - 618229813
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Full Text

A~ Formative Evaluationr of Vane Grande Rtralt Insrtitute in
Callete, Peru

Victor Cabrera, M.S.
Former Graduate Student
Agricultural Education and Communication
University of Florida
Professor, Valle Grande Rural Institute, Cafiete, Peru

Matt Baker, Associate Professor
Agricultural Education and Communication
University of Florida
P.O. Box 110540, Gainesville, FI 32611-0504
Phone: (352) 392-0502, Fax: (352) 392-9585,

Peter E. Hildebrand, Professor
Food and Resource Economics
University of Florida

The purpose of this formative evaluation was to assess the appropriateness of
recommended fertilization practices for cotton production, and to determine the economic
feasibility of recommending grape and asparagus production to limited resource farmers in
Peru's Car~ete Valley. This evaluation was conducted in cooperation with Valle Grande
Rural Institute, a non-governmental extension organization that has worked with limited
resource farmers in Caftete for over 30 years. Cotton production records of over 600
farmers were used to develop the cotton production functions. Linear programming with
data from numerous qualitative and quantitative sources was used to determine the
appropriateness of recommending grape and asparagus production. The production
function analyses revealed that extensionists should consult farmers on an individual basis,
as opposed to the current practice of recommending fertilizer rates based upon geographic
region within the Cafiete Valley. In no case should grape production be recommend to
limited resource farmers, and asparagus production should be recommended to this same
client group with caution.
Introduction & Theoretical Framework

Formative program evaluations provide program performance feedback relative to
program process and/or program outcomes (Rossi, Freeman, & Lipsey, 1999; Worthen,
Sanders, & Fitzpatrick, 1997). Formative evaluations of agricultural extension programs in
developing countries are essential. Two major factors contribute to the need for formative
evaluations. First, much of the on-station research, which results in approved practices,
has limited generalizability beyond the agricultural experiment stations (Hildebrand &

Russell, 1996). Secondly, often practices are a result of research or indigenous knowledge
conducted exclusively on-farm, and may suffer credibility which limits broader adoption
(Baker, Koyama & Hildebrand, 1999; Baker, Araujo & Hildebrand, 1998).

Small, limited resource farming communities are highly elaborate systems. A
comprehensive analysis of a livelihood system includes land, labor, and capital
requirements for sustaining the household. Household composition, gender-related
responsibilities, off-farm or non-farm activities, land ownership, credit availability, marketing
information, and production seasons and cycles all directly or indirectly impact crop and
animal agro-systems, which impact households (Rocheleau, 1987; McDowell & Hildebrand,
1986; Cabrera, 1999; Sullivan, 1999).

Background Information

The Caiiete Valley is located on the central coast of Peru. It consists of 22,600 ha of
agricultural land, and its elevation varies from 0 to 700 meters. The life of this desert-like
valley is the Cadete River, which flows continuously throughout the year. The temperature
varies from 12* C in the winter to 32" C in the summer. There are 152,379 valley residents,
with an average annual income of US$1,420 per household. There are seven individuals
per household.

Valle Grande Rural Institute (VGRI) is a non-governmental organization (NGO) that
has been in existence for more than 30 years, promoting rural improvement through
extension and education programs designed for low income farmers. The VGRI has a
target population of 4,800 small farmers with 12 ha or less.

Purpose and Objectives

The overall purpose of this study was to appraise the quality of selected
recommended agricultural practices of VGRI. The specific objectives of the study were to:
(1) assess the validity of VGRI recommended fertilization practices for cotton production;
and (2) determine the capability of limited resource farmers to adopt grape and asparagus
enterprises that had been recommended by VGRI in previous years.

Methods and Data Sources

Production functions were utilized to assess the approved practices for cotton
fertilization. Small farmers who borrowed money through the VGRI between 1992 and
1998 (N= 1,860) served as the population. A purposeful sample (n= 622) consisting of
farmers with complete records was used to develop the production functions. The
dependent variable was cotton yield per ha in quintals (100 Ib.). The independent variables
in the regression models were nitrogen in kilograms (N), phosphorus in kilograms (P),
potassium in kilograms (K), annual environmental index (average production per ha for the
specific year in quintals EI), and the following interactions (El x N, El x P, El x K).

The El is the result of calculating the average of all available production data for
each year. In 1996, Hildebrand and Russell indicated that an environment includes both
biophysical and socioeconomic factors. Broadly speaking, environments can be classified
by farm type, nature of the farm household, climate, soils, farmer management, and others
(i.e. agro-ecological zone or by commonly reoccurring pests). Production functions were
calculated for seven unique agro-ecological zones within the Caiiete Valley. As seen in
Figure 1, the annual environmental conditions are responsible for drastic changes in the
yield variable of the cotton crop. For analysis and recommendation purposes the
production years were divided into good (more than 60 qqlha), fair (between 46-59 qqlha),
and poor (45 qqlha or less).

75 Good Year
a 65 64.99 64.3
W 55 ii Fair Year 5-- 55.03
S454 -~ 65
40 Poor Year
92/93 93/94 94/95 95/96 96/97 97/98
PRoduction Year

Figure 1. Annual environmental index for cotton yield in Caiete.

Linear programming was used to determine the capability of the targets to adopt the
recommended alternative crops of grapes and asparagus. Data from numerous sources
including a sondeo, survey, and selected secondary data were used in the development of
the linear programming (LP) model. First, six multidisciplinary professionals conducted a
sondeo (May 11 to 15, 1998) consisting of a sample of 22 farmers in the area. A sondeo is
an open-ended, non-structured interview technique (Hildebrand, 1976). Second, one of the
researchers conducted a survey (May 18 to July 17, 1998) consisting of structured
questions developed based upon knowledge of the Caiete Valley, and the sondeo results.
A questionnaire consisting of 70 items was developed. The instrument contained three
sections. The first section had three subsections: (1) household information, (2)
agricultural factors, and (3) economic information. The second section consisted of seven
open-ended needs assessment questions. The final section included 13 open-ended
questions regarding farm problems and concerns.

The population for the survey consisted of limited resource farmers in the Caiiete
Valley (N=4,800). A random sample of 60 farmers was selected for participation in the
survey. Secondary data were also used to complete the LP model from records maintained

by the VGRI, from records maintained by the city government, and from records of Peru's
Ministry of Agriculture. The data were analyzed using Microsoft@ Access 97 SR-1,
Microsoft@ Excel 97 SR-1, and Microsoft@ Visual Basic.

Based upon the data gathered, the assumptions identified in Table 1 of the
livelihood systems of limited resource farmers in the Car"ete Valley were made by the
researchers. The linear programming model was designed to maximize discretionary cash
at the end of the six-year model, after first satisfying all basic family needs

Table 1

Assumptions of the Linear Programminq Model

1 There are two production seasons in Caiiete. The matrix was divided into these two seasons: (1) August
15- il 14, and 2)Arl15 Auu14.
2 Land is a limited resource in Caf~ete. Land use is intensive.
3 Renting land out to others and renting land from others were common practices of the limited resource
farmers in Cariete.
4 Labor is a limited resource, and labor available is related to household copsiin
5 Households can employ people in labor-intensive seasons, and it is common in households with
available labor to work for others to supeethousehold income.
a Water is not a limited resource in the August through April production season, but it is in the subsequent
7 Management is an aggregate index computed by summing the total years of education of every member
in each household.
8 Credit is an available resource for cotton and maize in the August through April production season and
for maize in the subsequent season. Interest rates range from 8-10% from development agencies and
the banking industry. Credit is available for grape and asparagus production. However, cash credits for
inputs from retailers are available at a rate of interest up to 100%.
9 Each household has some cash at the beginning of each season, used for household expenses,
livestock, or production inpus
10 The household and livestock consume maize and sweet potatoes produced on the farm. The family
reursa certain amount of livestock prdcdon the farm.
11 Cash is transferred from one prdcinseason to another, and cosqetyone yerto another.
12 The cash at the end of the yercould be a neaie value, indicating a nonsustainable stem.


The analysis of the cotton production functions demonstrated enormous variability
among geographic zones in relation to yield and its response to fertilizers and
environmental factors (Table 2). For example, the addition of N significantly contributed to
production in only three of the seven agro-ecological zones. It should be noted that the
regression coefficient for N was negative in two of these three zones. However in all
zones the approved cotton production practice recommended by VGRI was to add from 110

- 250 kg/ha of N. Similar results were found for the regression coefficient for P (significant
in three of the zones, and positive in only one of the zones).

Table 2

Summary of Cotton Production Function Coefficients Based Upon Geographic Reqion

Gorhic Zone Itret R Na 90 KC EI ElxNe Ext ElxKg
Cerro Aere 88.79 .51 C -4.01 -0.34 -6.16 0.071 C
La Quebrada 77.69 .51 Ch -0.19 Ch h 0.014 C
Palo Isla -81.50 .84 -1.72 Ch 3.58 C 0.012 Ch b
Santa Barbara 119.45 .30 Chh -1.66 C~ .006 0.020
San Benito 44.57 .36 -0.87 Ch 1.58 Ch 0.016 -.025 Cn
San Francisco -63.01 .77 0.46 4.90 -5.57 C~ .088 0.103
Quilmana 52.06 .54 C~ .84 Ch n 0.010
Na Nitrogen in kg/ha; P" Phosphorus in kg/ha; KCPotassium in kgh;EldEvrnetldx EIxNe the
Environmental Index and Nitrogen in kg/ha Interaction Variable; ElxP te Environmental Index and
Phosphorus in kg/ha Interaction Variable; ElxKB the Environmental Index and Potassium in kg/ha Interaction
Variable; CNh Regression Coefficient Not Statistically Significant at alpha of .05

A six-year linear programming model was developed to examine the viability of VGRI
clients in adopting either a grape or an asparagus enterprise. Asparagus and grapes are
two introduced crops being encouraged by development agencies. They are perceived as
complex, but profitable. In an effort to encourage the adoption of these perennial crops, the
development agencies are providing the financing necessary to establish the crops.

The model maximized the sum of the end of the year cash for all six years after
meeting all household (family) consumption needs. VGRI collaborates with other
development agencies in financing the establishment of both crops. In the case of
asparagus, there is a requirement that a small farmer plant at least one hectare due to
harvesting and marketing concerns. The LP revealed that no household was financially
capable of adopting a grape production enterprise. However, 25 of the 60 would be able to
adopt one-hectare of asparagus.

In an attempt to explain the adoption curve for the production of asparagus, the
researchers examined overall household system dynamics. Without losing system
diversity, there were some naturally occurring household groupings (Table 3). Those 25
households were characterized as having fewer children living at home and consequently,
more available adult labor. These households were also characterized as having larger
farms and more fertile farms (located in the lower to middle valley range). Finally, these
households were the more highly educated.

Table 3

The Relationship between the Adoption of Asparaqus Production and Household

Ha of Composition Compos~ition Composition Composition Management
Asaaus One' Two2 Three3 Four4 Land (h) /Education
(13.33%) 0.50 0.79 1.71 1.64 4.35 20.69
Less than
1ha (3%) 0.19 0.67 2.24 2.14 4.11 31.90
1 ha or
(41.67%) 0.08 0.56 2.56 2.60 5.45 38.19
Solution for
.84 ha of
Cro 0.21 0.65 2.25 2.22 0.18 31.48
SNumber of males and females less than five years of age
2 Number of males and females between five and fourteen years of age
3 Number of males between fourteen and sixty-five years of age
4 Number of females between fourteen and sixty-five years of age
Educational Importance

In terms of cotton production, the results of this study revealed the need for VGRI
extensionists to make fertilization recommendations on an individual household basis,
being particularly cognizant of agro-ecological zones. The production functions
demonstrated that, contrary to common belief, higher yields are not necessarily reached
with higher amounts of fertilizers. Actual recommended fertilizer rates are too high,
probably being based upon trails conducted on the very best soils in good years. This
finding also has significant implications for environmental pollution associated with
overfertilization practices and subsequent leaching from the soil into the water system.

The production functions can also be used as decision-making tools based upon
rather predictable weather patterns in the area. During the El Nino and La Nina years, a
poor year (due to extreme weather conditions) might become a good year for some
geographic regions of Cariete (i.e. Cerro Alegre and San Francisco) if recommended
fertilizations were adequately adjusted. Not only might production be increased, but also
due to the deleterious effect of the weather on production in other growing regions, the
farmers could get the added benefit of higher cotton prices.

As per the linear programming results, small farmers should not be a targeted
audience for grape production. In addition, only approximately 40% of the target clientele
would be able to add an asparagus enterprise. Perhaps the biggest advantage to
developing the linear programming model is that it is now readily available to use as a
consulting tool at the individual household level. It can be used by extensionists to predict
differing household livelihood system responses based upon various scenarios.


Baker, M., Araujo, A., & Hildebrand, P.E. (1998). Program planning and evaluation in
farming systems research and extension: A study of the Brazilian Amazon community of
Grupo Novo Ideal. Paper presented at the 14th Annual Association for International
Agricultural and Extension Education Conference, Tucson, Arizona.

Baker, M., Koyama, A., & Hildebrand, P.E. (1999). Korean Natural Farming
Association: A comparison of selected performance factors with national data. Journal of
International Aqricultural and Extension Education, 6 (1), 79-85.

Cabrera, V. (1999). Farm problems, solutions, and extension programs for small
farmers in, Cahete. Lima, Peru. Unpublished master's thesis, University of Florida,
Gainesville, Florida. Available Internet: http://nersp. nerdc. ufl.edul-vecy/Thesis/thesis. pdf

Hildebrand, P.E. (1976). The sondeo: A team rapid survey approach. In P.E.
Hildebrand (Ed.), Perspectives on farming systems research and extension. (pp. 93-102).
Boulder, CO: Lynne Rienner Publishers.

Hildebrand, P.E., & Russell, J.T. (1996). Adaptability analysis. Ames, IA: iowa State
University Press.

McDowell, R.E., & Hildebrand, P.E. (1986). Characteristics of selected systems. In
P. E. Hildebrand (Ed.), Perspectives on farming systems research and extension. (pp. 39-
51). Boulder, CO: Lynne Rienner Publishers.

Rocheleau, D.E. (1987). Gender, resource management and the rural landscape:
Implications for agroforestry and farming systems research. In S.V. Poats, M. Schmink, &
A. Spring (Eds.), Gender issues in farminqI systems research and extension. (pp. 149-169).
Boulder, CO: Westview Press.

Rossi, P.H., Freeman, H.W., & Lipsey, M.W. (1999). Evaluation: A systematic
approach (6th ed.). Thousand Oaks, CA: Sage Publications, Inc.

Sullivan, A. (1999). Decoding diversity: Strategies to mitigate household stress.
Symposium conducted at the North American Chapter of International Farming Systems
Association. Guelph, Ontario, Canada.

Worthen, B.R., Sanders, J.R., & Fitzpatrick, J.L. (1997). Program evaluation:
Alternative approaches and practical guidelines (2nd ed.). New York, NY: Longman.