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 Front Cover
 Copyright
 Table of Contents
 List of Tables
 Figures
 Summary
 Resumen
 Introduction
 Wheat production environments in...
 Materials and methods
 Results and discussion
 Conclusion
 Acknowledgement
 Reference
 Maps






Group Title: Paper - Natural Resources Group - 98-01
Title: An agroclimataological overview of wheat production regions of Bolivia
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 Material Information
Title: An agroclimataological overview of wheat production regions of Bolivia
Series Title: Paper - Natural Resources Group - 98-01
Physical Description: Book
Language: English
Creator: Hodson, Dave
Corbett, John D.
Wall, Patrick C.
White, Jeffrey W.
Publisher: International Maize and Wheat Improvement Center (CIMMYT)
Publication Date: 1998
 Subjects
Subject: South America   ( lcsh )
Farming   ( lcsh )
Spatial Coverage: South America -- Bolivia
South America
 Record Information
Bibliographic ID: UF00077524
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: issn - 1405-7484

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Table of Contents
    Front Cover
        Front cover
    Copyright
        Page i
    Table of Contents
        Page ii
    List of Tables
        Page iii
    Figures
        Page iii
    Summary
        Page iv
        Page v
    Resumen
        Page vi
        Page vii
    Introduction
        Page 1
    Wheat production environments in Bolivia and nearby countries
        Page 2
    Materials and methods
        Page 3
        Page 4
    Results and discussion
        Page 5
        Page 6
    Conclusion
        Page 7
    Acknowledgement
        Page 7
    Reference
        Page 8
    Maps
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
Full Text





$I
CIMMYT
Sustainable
Maize and Wheat
Systems for the Poor


An Agroclimatological

Overview of Wheat Production

Regions of Bolivia


Dave Hodson, John D. Corbett,
Patrick C. Wall, and Jeffrey W. White
















Natural Resources Group

Geographic Information Systems
Series 98-01


I










CIMMYT is an internationally funded, nonprofit scientific research and training organization.
Headquartered in Mexico, the Center works with agricultural research institutions worldwide to improve
the productivity and sustainability of maize and wheat systems for poor farmers in developing countries. It
is one of 16 similar centers supported by the Consultative Group on International Agricultural Research
(CGIAR). The CGIAR comprises over 50 partner countries, international and regional organizations, and
private foundations. It is co-sponsored by the Food and Agriculture Organization (FAO) of the United
Nations, the International Bank for Reconstruction and Development (World Bank), the United Nations
Development Programme (UNDP), and the United Nations Environment Programme (UNEP).

Financial support for CIMMYT's research agenda currently comes from many sources, including
governments and agencies of Australia, Austria, Bangladesh, Belgium, Bolivia, Brazil, Canada, China,
Colombia, Denmark, France, Germany, India, Iran, Italy, Japan, the Republic of Korea, Mexico, the
Netherlands, Norway, Pakistan, the Philippines, Portugal, South Africa, Spain, Sweden, Switzerland,
Thailand, the United Kingdom, Uruguay, and the USA, along with (among others) Cornell University, the
European Union, the Ford Foundation, the Grains Research and Development Corporation, the Inter-
American Development Bank, the International Development Research Centre, the International Fund for
Agricultural Development, the Kellogg Foundation, the Leverhulme Trust, the Nippon Foundation, the
OPEC Fund for International Development, the Rockefeller Foundation, the Sasakawa Africa Association,
Stanford University, the Tropical Agriculture Research Center (Japan), UNDP, the University of Wisconsin,
and the World Bank.

International Maize and Wheat Improvement Center (CIMMYT) 1998. Responsibility for this publication
rests solely with CIMMYT. The designations employed in the presentation of material in this publication do
not imply the expressions of any opinion whatsoever on the part of CIMMYT or contributory organizations
concerning the legal status of any country, territory, city, or area, or of its authorities, or concerning the
delimitation of its frontiers or boundaries.

Printed in Mexico.

Correct citation: Hodson, D., J.D. Corbett, P.C. Wall, and J.W. White. 1998. An Agroclimatological Overview of
Wheat Production Regions of Bolivia. NRG-GIS Paper 98-01. Mexico, D.F.: CIMMYT.

Abstract: This report describes use of the Spatial Characterization Tool (SCT) developed by Texas A&M
University to analyze the similarity of the climates of research sites in the major wheat production areas in
Bolivia the highland intermountain valleys and the lowland plains to those of other regions. For
highland environments, zones of similarity were only found in scattered regions of Bolivia and Peru, and the
complex topography of the Andean region and the relatively large SCT grid cells (9 km x 9 km) hampered
climate characterization. For lowland sites, combined results of analyses of the favorable season plus the
coolest or driest quarters of the year (when wheat is actually grown in lowland Bolivia) identified the
environments of adjacent areas of Bolivia, two regions in Brazil, plus small regions in Venezuela phis areas
in Mexico, Central America, and Africa as similar to those of the target sites in Bolivia. Site similarity
analysis appears to be a valuable method for understanding relations among crop production environments,
allowing prediction of crop responses to agronomic practices, assessments of genetic diversity or
sustainability, and other types of studies. Current applications, however, are limited by a lack of quality
data.

ISSN: 1405-7484
AGROVOC descriptors: Bolivia; Agroclimatic zones; Climatic zones; Wheats; Varieties; Plant production;
Production factors; Environments; Environmental factors; Farming systems; Cropping patterns; Cropping
systems; Rain; Plant response; Plant breeding; Genotype environment interaction; Soil chemicophysical
properties; Peru; Colombia; Chile; Brazil; Paraguay; Argentina; Venezuela; Mexico; Central America; South
America; Africa; Highlands; Lowland; Andean region; Research projects; Diffusion of research; Technology
transfer; Innovation adoption
Additional Keywords: Agroecological zones; Climatic similarity; Tarata; Tarabuco; Paraiso; GIS; CIMMYT
AGRIS category codes: F01 Crop Husbandry
P40 Meteorology and Climatology
U40 Surveying Methods
Dewey decimal classification: 633.11











Contents

Page

iii Tables
iii Figures
iv Summary
vi Resumen en Espafiol
1 Introduction
2 Wheat Production Environments in Bolivia and Nearby Countries
3 Materials and Methods
5 Results and Discussion
7 Conclusions
7 Acknowledgments
8 References










Tables


Page

1 Table 1. Comparison of selected economic and wheat production parameters for
Bolivia and other countries in South America. (from Aquino et al. 1996)
4 Table 2. Base sites used for comparisons.
5 Table 3. Comaparison of elevations and climatic conditions for Cochabamba, Oruro,
and Sucre, Bolivia, from the SCT and from FAO



Figures


4 figure 1. Base sites used for the comparisons.
9 figure 2. Zones that are climatically similar to Bolivian highland sites for the 5-month
optimal crop growth period.
10 figure 3. Zones that are climatically similar to other sites in South America for the 5-
month optimal crop growth period.
11 figure 4. Non-Andean zones that are climatically similar to Tarata, Bolivia, for the 5-
month optimal crop growth period.
12 figure 5. Zones that are climatically similar to Cochabamba, Bolivia, for the 5-month
optimal crop growth period.
13 figure 6. Total precipitation zones within the elevation rang of 2,000-3,700 m for the 5-
month optimal crop growth period.
14 figure 7. Zones that are climatically similar to lowland, wheat regions of Bolivia for the
5- month optimal crop growth period.
15 figure 8. Zones that are climatically similar to Paraiso, Bolivia, for the 5-month optimal
crop growth period.
16 figure 9. Zones that are climatically similar to Paraiso, Bolivia, for a) the coolest quarter
of the year, and b) the dry season.
17 figure 10. Zones that are climatically similar to Paraiso, Bolivia, for both the coolest
quarter of the year and the 5-month optimal crop growth period.










Tables


Page

1 Table 1. Comparison of selected economic and wheat production parameters for
Bolivia and other countries in South America. (from Aquino et al. 1996)
4 Table 2. Base sites used for comparisons.
5 Table 3. Comaparison of elevations and climatic conditions for Cochabamba, Oruro,
and Sucre, Bolivia, from the SCT and from FAO



Figures


4 figure 1. Base sites used for the comparisons.
9 figure 2. Zones that are climatically similar to Bolivian highland sites for the 5-month
optimal crop growth period.
10 figure 3. Zones that are climatically similar to other sites in South America for the 5-
month optimal crop growth period.
11 figure 4. Non-Andean zones that are climatically similar to Tarata, Bolivia, for the 5-
month optimal crop growth period.
12 figure 5. Zones that are climatically similar to Cochabamba, Bolivia, for the 5-month
optimal crop growth period.
13 figure 6. Total precipitation zones within the elevation rang of 2,000-3,700 m for the 5-
month optimal crop growth period.
14 figure 7. Zones that are climatically similar to lowland, wheat regions of Bolivia for the
5- month optimal crop growth period.
15 figure 8. Zones that are climatically similar to Paraiso, Bolivia, for the 5-month optimal
crop growth period.
16 figure 9. Zones that are climatically similar to Paraiso, Bolivia, for a) the coolest quarter
of the year, and b) the dry season.
17 figure 10. Zones that are climatically similar to Paraiso, Bolivia, for both the coolest
quarter of the year and the 5-month optimal crop growth period.










Summary


Within cropping system/natural resource management research, there is often the assumption
that results are site-specific. This assumption may sometimes be incorrect, and simply reflect the
frustration or limited knowledge of site-level researchers overwhelmed by the complexities of
extrapolating technology. Traditionally, comparisons among agroecological regions have been
based on definitions of broad zones, such as the "mega-environments" used by CIMMYT. Whereas
their broadness makes them useful in setting global research priorities for crop breeding
programs, it also means that they may not capture the level of ecological variation needed to
predict the responses of crops to specific agronomic practices or allow assessments of genetic
diversity or sustainability. Climate similarity analyses using geographic information systems (GIS)
can permit more quantitative comparisons among regions and sites, adding value to research on
cropping systems and natural resource management.

In Bolivia, research on wheat production systems is constrained by the country's broken
topography, complex environments, and limited financial resources. An understanding of how
production environments in Bolivia compare to those in nearby countries or in Central America,
Mexico, or even Africa might generate major efficiencies in the form of shared research
experiences, where regions are similar. This report describes use of the Spatial Characterization
Tool (SCT; developed by Texas A&M University) to analyze the similarity of the climates of
research sites in the major wheat production areas in Bolivia the highland Andean valleys and
the lowland plains to those of other regions. The SCT uses interpolated surfaces of monthly
data for precipitation evapotranspiration, and maximum and minimum temperature. For both
regions, initial comparisons classified an environment as similar to that of another site if their
precipitation (P) and potential evapotranspiration (PET) fell within a + 20% range of similarity
and their maximum and minimum temperatures within a + 10% range, for the most favorable
(largest values of P/PET ratio) five months of the year.

For highland environments, zones of similarity were found only in scattered regions of Bolivia
and Peru. In fact, comparison of Bolivian environments with those at highland sites of Peru and
Colombia, plus one cool lowland site in Chile, confirmed the impression that similar highland
environments may be numerous but are geographically scattered. In addition, the complex
Andean topography coupled with the relatively large grid cells (9 km x 9 km) of the SCT
hampered climate characterization. An alternative approach, which characterized the highland
environments in terms of rainfall patterns during the favorable season, provided a workable
solution to this problem. For lowland sites, combined results of analyses of the favorable season
plus the coolest quarter and the dry season (when wheat is actually grown in lowland Bolivia)
identified the environments of adjacent areas of Bolivia, two regions in Brazil, small regions in
Venezuela, plus areas in Mexico, Central America, and Africa as similar to those of the target sites
in Bolivia.










Site similarity analysis appears to be a valuable method for understanding relations among crop
production environments. Many agricultural applications can be envisaged. Similar to the
examples from agronomy that are discussed above, the approach can be used to identify probable
areas of adaptation for new cultivars or in germplasm collection efforts, identifying regions where
materials are most likely to be found, based on similarity to known collections sites. The approach
could also be adapted easily for studies of disease, pest, and weed distributions for instance, to
map potential areas of spread for organisms that have been introduced recently or that have
become problematic due to changes in cropping practices.

In applying similarity analyses, however, scientists should be aware of possible limitations. The
most problematic of these is data quality. Interpolated climate surfaces necessarily have
limitations due to spatial scale. As seen in the case of the Bolivian highlands, analyses for a region
where elevation varies dramatically over relatively short distances must be handled with caution.
Similarly, where the density of source data (meteorological stations in the case of climate surfaces)
is low, interpolations necessarily become less reliable. Finally, climate is only one determinant of
agronomic performance; similarity analysis could easily be extended to, say, soil characteristics.
Here again, though, the lack of good data on soils and other non-climate factors affecting cropping
system performance limits the scope and usefulness of similarity studies, despite their
representing an improvement over the mega-environment approach for agronomic and
sustainability research.











Resumen


En la investigaci6n del manejo de los sistemas de cultivo y de los recursos naturales existe con
frecuencia la suposici6n de que los resultados son especificos a un sitio. Esta suposici6n es a veces
incorrect y simplemente manifiesta la frustraci6n o falta de conocimientos de los investigadores a
nivel local, abrumados por las complejidades de extrapolar la tecnologia. Tradicionalmente, las
comparaciones entire las regions agroecol6gicas se han basado en la definici6n de areas extensas,
como los "mega-ambientes" empleados por el CIMMYT. Aunque debido a su extension estos
mega-ambientes son tiles al establecer las prioridades de investigaci6n mundiales de los
programs fitot6cnicos, a veces no revelan el nivel de variaci6n ecol6gica necesario para predecir
la respuesta de los cultivos a practices agron6micas especificas ni permiten evaluar la diversidad
gen6tica y la sustentabilidad. Los analisis de similitud climatica utilizando los sistemas de
informaci6n geografica (GIS) permiten efectuar comparaciones mas cuantitativas entire regions y
sitios, lo cual hace mas valiosa la investigaci6n del manejo de los sistemas de cultivo y de los
recursos naturales.

En Bolivia, la investigaci6n de los sistemas de producci6n de trigo esta restringida por la irregular
topografia, los complejos ambientes y los limitados recursos financieros del pais. Comprender la
forma en que los ambientes de producci6n en Bolivia se comparan con los de sus paises vecinos,
de Am6rica Central, M6xico, e incluso Africa, permit aprovechar mejor las experiencias de la
investigaci6n en regions similares. Este informed describe el uso de la Herramienta de
Caracterizaci6n Espacial (Spatial Characterization Tool; SCT), creada en la Universidad Texas
A&M, en el analisis de la similitud climatica de los sitios de investigaci6n en las principles zonas
productoras de trigo en Bolivia -los valles altos y las llanuras de tierras bajas- con la de otras
regions. La SCT emplea superficies interpoladas de datos mensuales de precipitaci6n,
evapotranspiraci6n y temperatures maxima y minima. En ambas regions, las comparaciones
iniciales clasificaron un ambiente como similar al de otro sitio cuando su precipitaci6n (P) y
evapotranspiraci6n potential (PET) estaban dentro de un rango de + 20% de similitud y sus
temperatures maxima y minima dentro de un rango de + 10%, durante los cinco meses mas
favorables del afio (los mayores valores de la proporci6n P/PET).

En cuanto a los ambientes de tierras altas, se encontraron areas similares s6lo en regions
dispersas de Bolivia y Per6. De hecho, la comparaci6n de los ambientes en Bolivia con los de los
sitios altos de Per6 y Colombia, mas un sitio de tierras bajas y frescas en Chile, confirmaron la
suposici6n de que los ambientes similares de tierras altas son numerosos pero dispersos
geograficamente. Ademas, la compleja topografia andina, combinada con las grandes celdas
reticuladas (9 km x 9 km) de la SCT, dificult6 la caracterizaci6n climatica. Se solucion6 este
problema mediante otro m6todo que caracteriz6 los ambientes de tierras altas con base en los
patrons pluviom6tricos durante el ciclo favorable de cultivo. Respecto a los sitios de tierras bajas,
los resultados combinados de los analisis del ciclo favorable, mas los de los trimestres mas frios o
mas secos del afio (cuando se cultiva el trigo en las tierras bajas de Bolivia) identificaron los
ambientes de algunas areas adyacentes de Bolivia, dos regions en Brasil, mas extensions
pequefias en Venezuela, como similares a los de los sitios en Bolivia.











El analisis de similitud de sitios parece ser un m6todo valioso para entender las relaciones entire
los ambientes de producci6n, que podria tener muchas aplicaciones en la agriculture. Al igual que
en los ejemplos antes mencionados, este m6todo se puede utilizar para identificar las probables
zonas de adaptaci6n de las nuevas variedades o tambien para la recolecci6n de germoplasma, las
regions donde es mAs probable encontrar materials, con base en la similitud con sitios de
recolecci6n que ya hayan sido caracterizados. El m6todo podria adaptarse fAcilmente a studios de
distribuci6n de enfermedades, plagas y malezas -por ejemplo, se podrian crear mapas de las
probables zonas de dispersi6n de organismos de introducci6n reciente o que se hayan vuelto
problemAticos debido a cambios en las prActicas de cultivo.

Sin embargo, los cientificos deben estar conscientes de las posibles limitaciones de los analisis de
similitud. La mAs problemAtica de 6stas es la calidad de los datos. Las superficies climaticas
interpoladas forzosamente tienen limitaciones, debido a la escala espacial. Como se ha visto en el
caso de las tierras altas de Bolivia, los analisis de una region donde la elevaci6n varia en forma
impresionante en distancias muy cortas deben manejarse con cautela. De igual forma, las
interpolaciones se vuelven menos confiables donde es baja la densidad de datos fuente,
procedentes de las estaciones metereol6gicas en el caso de superficies climAticas. Finalmente, el
clima es s61o uno de los factors determinantes del comportamiento agron6mico; los analisis de
similitud podrian extenderse fAcilmente para abarcar, por ejemplo, las caracteristicas edafol6gicas.
Sin embargo, la falta de buenos datos edafol6gicos y de otros factors no climAticos que afectan el
comportamiento de los sistemas de cultivo, limita el alcance y la utilidad de los studios de
similitud, a pesar de que 6stos superan al sistema de los mega-ambientes en las investigaciones
agron6mica y de la sustentabilidad.











An Agroclimatological Overview of Wheat
Production Regions of Bolivia


Introduction

Wheat research in Bolivia faces multiple
challenges. Among these are the difficulties of
using limited research resources to address
problems in diverse production environments.
The latter constraint reflects both the overall
economic situation of Bolivia and, more
specifically, the intermediate importance of
wheat production in the national economy
(Table 1). In this context, researchers in Bolivia
can benefit from experiences in other wheat
regions with similar production conditions, and
may also want to participate in regional projects
where partners from other countries will seek
similar information about how Bolivian wheat
environments relate to their own.

Within cropping system/natural resource
management research, there is often the
assumption that results are uniquely site
specific. This assumption may sometimes be
incorrect, and simply reflect premature
acquiescence of site-level researchers
overwhelmed by the complexities of


extrapolating technology. Lacking suitable tools
for identifying areas similar to their own home
site in well defined ways, they may conclude
that there are no such areas one way of
dealing with an awkward, seemingly intractable
problem.

The analysis described here is part of a broader
effort to add value to cropping system and
natural resource management research by
fostering more effective learning, extrapolation
and synthesis. These three aims may be
described in the following manner:

* Learning Drawing on results from other
(similar) research sites that may be of
relevance for scientists at a home site.
* Extrapolation Defining and identifying
additional, similar areas for potential
application of technologies developed at the
home site.
* Synthesis Pooling the experience gained at
several (similar) home sites to better
understand the conditions that govern the
performance of specific technologies.


Table 1. Comparison of selected economic and wheat production parameters for Bolivia and
other countries in South America (from Aquino et al. 1996).

Bolivia Ecuador Brazil Uruguay Chile
Peru Colombia Paraguay Argentina
Estimated population, 1995 (million) 7.4 23.8 11.5 35.1 161.8 5.0 3.2 34.6 14.3
Estimated growth rate of population,
1993-2000 (%/year) 2.4 1.9 2.0 1.4 1.6 2.5 0.6 1.2 1.5
Per capital income, 1994 (US $) 770 2,110 1,280 1,670 2,970 1,580 4,660 8,110 3,520
Average wheat area harvested,
1993-1995 (000 ha) 124 95 33 48 1,278 202 201 4,812 382
Average wheat yield, 1993-1995 (t/ha) 1.0 1.3 0.7 2.0 1.5 2.2 2.0 2.1 3.5
Average wheat production,
1993-1995 (000 t) 119* 120 22 94 1,922 442 400 9,874 1,326
Nitrogen applied per hectare of wheat
harvested, 1993-1994 (kg N/ha) 1 39 50 27 15 16 40 10 62










Although the full characterization of a
production environment should cover soils,
diseases, pests, weeds, and socioeconomic
conditions, climate is usually the primary
determinant of crop adaptation. The first order
characteristic of climate, in ecological terms,
was the underlying reason why the Spatial
Characterization Tool (SCT) technology has
been focused around interpolated climate
surfaces. This paper compares climate
conditions of wheat producing regions in
Bolivia with those of other countries in Latin
America and Africa. The method is based on a
comparison of climatic conditions at specific,
representative sites with climate over a region.
This permits greater flexibility than traditional
methods for defining agroecological zones at
the continental scale. The Bolivian sites used in
this study were also being used in research to
develop improved agronomic practices for
wheat production in moisture stressed
environments of Bolivia.


Wheat Production Environments in
Bolivia and Nearby Countries

Wheat is produced in Bolivia in two
contrasting regions: the Andean highlands and
the eastern lowlands. The highlands present
broken topography and wheat is grown in and
around numerous valleys and small plateaus
characterized by very diverse climates. The
eastern lowlands are almost flat, with relatively
uniform climatic parameters but with a rainfall
gradient from 800 to 1,600 mm/yr in the
agricultural area.

In the Andean highlands, wheat is grown
predominantly in small-scale, subsistence
farming systems. Production is rainfed,
depending on the summer rains from October
to March. In favorable (higher precipitation)
areas, potato is the preferred crop, receiving


most inputs and being planted prior to wheat or
other crops. A legume crop generally follows
potatoes, and after that wheat or other cereals,
which receive little if any inputs. In the lower
rainfall areas and areas with severely degraded
soils, wheat and barley are usually the only
crops produced. These marginal areas account
for some 60% of wheat and barley production in
the highlands. According to production
surveys, most highland farmers have livestock,
and crop residues are an important source of
fodder during the dry season (Wall et al. 1997a,
b).

Moisture stress is the major limitation to small-
grain cereal productivity in the inter-Andean
valleys (Wall et al. 1997a, b). Rainfall in the
wheat producing areas ranges from
approximately 300 to 600 mm per year.
However, moisture stress is a function not only
of low and/or poorly distributed rainfall, but
also of run-off brought about by excessive
cultivation of sloping lands. This results in
another major problem: erosion. Other
constraints coupled with moisture stress are
declining soil chemical fertility and poor plant
stands.

The Tarata site in the High Valley of
Cochabamba is on a gently sloping valley
bottom in a low rainfall area on degraded
alluvial soils. Under rainfed conditions, the
main crops are wheat or (with irrigation) maize.
Average farm size in the valley is approximately
three hectares. The headquarters of the Bolivian
Institute of Agricultural Technology (IBTA) is
located close to Tarata.

Tarabuco and Yamparaez are located in
adjacent valleys some 60 km apart in gently
rolling areas of the Department of Chuquisaca.
Tarabuco is slightly wetter and has more fertile
soils than Yamparaez. In the former area, potato
is the major crop, followed by barley, while on










the more degraded and less fertile soils of
Yamparaez, wheat is the major crop. As in the
Tarata area, farm size is about three hectares,
usually divided among several dispersed fields.

The lowland wheat production region
corresponds to non-traditional areas that were
originally covered with forest but are being
developed as Bolivia's major agricultural
frontier. These regions grow warm season crops
such as soybean during the summer rainy
season, and wheat is planted as temperatures
drop going into the drier winter season, typically
late April to early May. The largest lowland
wheat areas are in the Department of Santa Cruz,
where wheat production has increased ten-fold
over the past decade in conjunction with an
expansion in soybean production, free-market
economic policies and a concerted research and
extension program. Again the major limitation to
wheat productivity is water; scarce winter
rainfall (150-250 mm during the crop season) is
exacerbated by the structural degradation of
fragile alluvial soils as a result of disc-tillage,
leading to low water infiltration rates and
increased run-off and soil compaction, the latter
hampering root exploration and further reducing
available water.

The two sites in the eastern lowlands are the
locations of a multi-institution research and
extension effort called the Sustainable
Agriculture Program, based on two large-scale
mechanised commercial farms, Paraiso and San
Rafael. These lie some 80 km apart northeast of
the city of Santa Cruz. Both sites are flat with
alluvial soils. The Paraiso site has been
cultivated for over 30 years, whereas the forest
was cleared at San Rafael some 10 years ago. The
main crops in the region are summer soybeans
alternated with winter wheat, sunflower or
sorghum in a system with two crops per year.


Materials and Methods


The primary data source for the climate
analyses were the climate surfaces of Latin
America developed by Corbett (1994) using
trivariate thin plate smoothing splines
(Hutchinson 1995). With this technique,
monthly mean data for precipitation and
temperature are interpolated from point data
corresponding to long-term records of
meteorological stations. This variant of spline
techniques allows use of data from a digital
elevation model (DEM, essentially a
topographic map converted to grid-based
format) to improve estimation of variation in
climate with elevation. The DEM used for
Latin America was ETOP05 (EROS Data
Center, U.S. Geological Survey). It uses a 5
arc-minute grid size, which is roughly
equivalent to a 9 km x 9 km grid size near the
equator. In addition to the basic climate
variables, the set of surfaces includes data for
potential evapotranspiration (PET), ratios of
precipitation to PET (P/PET) both on a
monthly basis and for a favorable season
defined as the five consecutive months with
the greatest P/PET ratios. Climate surfaces
for Africa were from Corbett and O'Brien
(1997) and are based on a 2.5 arc-minute
(roughly 5 km x 5 km) DEM. Climate data for
long-term monthly means at specific sites
were obtained from the FAO climate database
for Latin America (FAO 1985).

Climatic and site similarity zones were
defined using the Spatial Characterization
Tool (Corbett and O'Brien 1997). This tool
works within the ArcInfo GIS software
(Environmental Systems Research Institute,
Inc., Redlands, CA). Climatic zones are
defined through conventional map overlay












and selection procedures. The
similarity zones are created by
specifying the latitude and longitude
of a reference site, and then selecting
criteria for similarity. This includes
climate variables plus the range of
variation another location may show
for a given variable and still be
considered "similar" to the reference
site. For this study, the similarity
zones were based on the favorable
five-month growing period and
considered ranges of P, PET and mean
maximum and minimum
temperatures, unless specified
otherwise. All maps are presented in a
geographic reference system
(unprojected, as latitude and
longitude). A list of the locations
considered in the study is given in
Table 2. The same locations are shown
in Figure 1.


*13
t 12 3
M,42.


y77r


11


1 89
3 ,4 ,
6 )


/ ^' 1
2
3
K 4
5
6
7
8
9
10
11
12
13
14


Cochabamba, Bolivia
Oruro, Bolivia
Sucre, Bolivia
Tarabuco, Bolivia
Tarata, Bolivia
Yamparaez, Bolivia
Canada Larga, Bolivia
Paraiso, Bolivia
San Rafael, Bolivia
Abancay, Peru
Huancayo, Peru
Ipiales, Colombia
Tunja, Colombia
Cauquennes, Chile


Figure 1. Base sites used for comparisons.


Table 2. Base sites used for comparisons.

Location Latitude Longitude Comments

Cochabamba, Bolivia -17.38 -66.17 Inter-Andean valley. City surrounded by irrigated area
and mountains.
Oruro, Bolivia -17.97 -67.12 High plateau. City surrounded by hills.
Sucre, Bolivia -19.05 -65.27 Inter-Andean valley.
Tarabuco, Bolivia -19.18 -64.90 Large, favorable inter-Andean valley. Area of potato
production and site of IBTA research center.
Tarata, Bolivia -17.62 -66.00 Inter-Andean valley. Some irrigation. Wheat in marginal
areas. Main IBTA wheat research center.
Yamparaez, Bolivia -19.20 -65.15 Large inter-Andean valley. Mostly cereal production.
IBTA research area.
Canada Larga, Bolivia -17.45 -62.22 Lowland Santa Cruz region. CIAT (Bolivian Inst.)
experiment station.
Paraiso, Bolivia -17.43 -62.77 Lowland Santa Cruz region. Wheat production area.
Close to Okinawa 2 colonization area.
San Rafael, Bolivia -17.23 -62.27 Lowland Santa Cruz region. Wheat production area.
Recent expansion on east bank of Rio Grande.
Abancay, Peru -13.73 -72.77 Highland, inter-Andean valley of southern Peru.
Huancayo, Peru -12.15 -75.07 Favorable, highland inter-Andean valley of Peru.
Ipiales, Colombia 0.80 -77.75 High rainfall, highland region of southern Colombia,
similar to northern Ecuador.
Tunja, Colombia 5.53 -73.32 Intermediate rainfall highland region of central Colombia.
Cauquennes, Chile -35.90 -72.23 Lowland, winter rainfall region of west-central Chile.










Results and Discussion


Highland regions
Figure 2 shows similarity zones for Tarata,
Tarabuco, and Yamparaez. For all locations, the
most striking result is that only small regions
elsewhere in the Andes are identified as having
similar environments, even allowing for a
20% range in variation of P and PET. This was
confirmed by similarity maps for other Andean
highland sites such as Tunja, Ipiales, and
Huancayo, which again showed relatively
small regions of similarity (fig. 3a-c). Similarly,
Cauquennes, which represents a cool, lowland
(elevation 120 m) rainfed wheat environment of
Chile, showed similarities only to a narrow
zone completely within Chile (Fig.3d). The
same pattern of limited similarity also held for
comparisons of Tarata with regions in Central
America and Mexico
(Fig. 4a) and Africa (Fig. 4b). Interestingly, the
Tarata environment appeared to be similar to
those of regions near CIMMYT's headquarters
at El Batan, Mexico, and of areas in the
highlands of Ethiopia and Lesotho. Ethiopia
grows around 900,000ha of wheat each year
(Aquino et al. 1996). In the early 1970s, some
100,000 ha of wheat was sown annually in
Lesotho, but in recent years this has dropped to
10,000-15,000 ha (FAO 1997).


The SCT-derived elevation for a given latitude
and longitude differed from the elevation
reported from other sources (Table 3). The SCT
generally overestimated elevation, which
reflects the fact that the agricultural areas were
located in valleys surrounded by mountains:
the SCT uses data from a digital elevation
model that provides an average elevation over
a 9 km square region. Since climate varies
considerably with elevation, the difference is
also a source of considerable bias. This error
was apparent in comparisons of climate data
from the SCT with meteorological station data
from FAO (Table 3), where SCT temperatures
tend to be cooler than the reported
temperatures. To estimate how this error might
affect the climate similarity maps, pairs of
maps were created for sites where
meteorological station data were also available
(e.g., Fig. 5). The maps based directly on the
SCT were created by entering the latitude and
longitude of the location and then using the
SCT directly to produce climate similarity
zones based on the data of that grid cell. The
maps based on climate data were produced by
taking the station data (FAO 1985) and using
these to calculate ranges ( 20% or 10%) for
precipitation, evapotranspiration, and
maximum/minimum temperature for the five
consecutive wettest months. These ranges were


Table 3. Comparison of elevations and climatic conditions for Cochabamba, Oruro and
Sucre, Bolivia, from the SCT and from FAO (1985). Climate data are for the five-month
favorable growing season based on P/PET.

Cochabamba Oruro Sucre

Data source: SCT FAO SCT FAO SCT FAO

Elevation (m) 3,025 2,548 4,039 3,708 2,972 2,750
Total precipitation (mm): 540 430 374 277 524 512
PET (mm) 585 586 554 544 601 601
Mean maximum temperature (C): 21 24.7 18 18.9 19 22.7
Mean minimum temperature (C): 8 11.7 3 2.9 8 10.6










then entered into the computer, and the
appropriate zones were determined with the
SCT's zonal analysis tool. Comparing the two
maps for Cochabamba (Fig. 5), the zones
clearly differed. The overall conclusion was
still valid ie., limited zones of similarity exist
for highland environments but the locations
and areas of these similarity zones cannot be
accurately predicted using the SCT
methodology.

A 30 arc-second DEM (approximately 1 km x 1
km), GTOPO30, is now available (EROS Data
Center, U.S. Geological Survey), suggesting the
possibility of improving the climate surfaces by
enhancing the resolution of the elevation data
used to generate the surfaces. Although this
would help define the similarity zones with
greater precision, we should not be overly
optimistic about obtaining improvements via
reduced grid sizes: the accuracy of the
interpolated surfaces is also limited by the
number and quality of the meteorological
stations available, and mountainous regions
require much higher densities of data than
relatively flat regions.

Given their complex topology and the low
resolution DEM used, we took additional
measures to improve the characterization of
highland environments. Variation in rainfall in
highland environments (regions over 2,000m)
was examined for South America and for
Central America and Mexico (Fig. 6) by
splitting the highland environments into three
favorable-season rainfall zones. The driest and
intermediate zones (300-500 mm, and 500-700
mm) correspond most closely to the areas
where most wheat production occurs
(including the marginal areas) and where
technologies to address moisture stress would
find most application. All highland study sites


considered in this paper fell within these zones.
For South America, these two zones would
include most of the Ecuadorian, Peruvian, and
Bolivian high Andes, and the northern Andes of
Argentina. In Central America and Mexico, a
large part of the eastern slopes of the northern
Sierra Madre in Mexico would also form part of
these climatological zones.

Lowland regions
Figure 7 shows zones of climatic similarity for
three sites in the Santa Cruz wheat growing
region. In all cases, this region showed climates
similar to large regions in Brazil, Argentina and
Paraguay, as well as a smaller area in northern
Venezuela and Colombia. Using Paraiso as an
example, large areas in Central America and
Africa also showed similarities (Fig. 8).
However, these results must be interpreted with
caution. The favorable five-month growing
season was defined as the period with the
largest values of P/PET. For this region, the
period coincides with the hot, wet season when
soybean and other warm-season crops are
grown. For wheat, which is grown in the coolest
and driest months of the year, some regions
identified as similar might be completely
inappropriate.

This consideration led us to modify the SCT to
permit definition of growing seasons as the dry
season* or coolest quarter of the year. Results of
similarity analyses using these two criteria for
Paraiso in South America (Fig. 9) produced
maps that were quite different from the map
based on the favorable growing season. Of the
two new maps, the coolest quarter season is
probably more representative of the wheat
growing season, since it corresponds to June
through August, whereas the dry season map is
for July to September.










If the need to understand similarities among
sites were restricted solely to the wheat
growing season (e.g., for a disease with no
interactions with off-season climate), then
Figure 9a would be sufficient. However, for
studies of crop rotations or any wheat
production problem that interacts with the off-
season environment, a more appropriate
similarity map would combine the cool season
and favorable season criteria (Fig. 10). For
South America, this intersection zone
constitutes a much smaller area that is
basically restricted to parts of Bolivia, two
regions in Brazil, and small areas in Venezuela.


Conclusions

For lowland wheat regions of Bolivia, climate
similarity analyses allowed identification of
other regions where wheat production
conditions should be similar enough that
technologies can be transferred either to or
from Bolivia with a low investment in adaptive
research. However, the regions were smaller
than might have been expected.

In highland areas, problems stemmed from the
topology of the Andean region combined with
the low resolution DEM incorporated into the
SCT. This resulted in little confidence in the
precise location and extent of the generated site
similarity zones, other than the likelihood of
these zones not covering extensive areas. An
alternative approach, which divided the
highland areas into different rainfall zones
during the favorable cropping season,
provided a potentially more realistic
characterization of highland environments. The
nature of these zones were more in line with
traditional agroecological regions, yet quite
refined due to their climatic component. Given
the special problems associated with highland


regions, these zones were considered to
represent the most feasible potential areas for
technology transfer/exchange.

All zonations produced in the present study
could be further refined with the addition of
data on soils or land use. The SCT can produce
similarity analyses using these data as surface
layers, but available data were not considered
to be of a suitable scale for inclusion in this
analysis. Moreover, using these data would
further reduce the regions considered similar.

The present study has demonstrated the
potential of applying GIS applications -
particularly the SCT- to help identify
comparable production environments and
facilitate the efficient exchange of knowledge
among research in such regions. The
complexity of the Andean region presented
numerous problems and highlighted potential
deficiencies associated with current systems
and datasets when trying to work in
mountainous areas. More flexibility may be
obtained by coupling tools such as crop
simulation models with GIS, to enable users to
compare crop or system performance across
regions.


Acknowledgments

The authors would like to express their
gratitude to Environmental Systems Research
Institute, Inc., for providing the ArcInfo and
ArcView software and to the Integrated
Information Management Laboratory of Texas
A&M University System for providing the
Spatial Characterization Tool. We also thank
Mike Listman for editorial assistance and
Marcelo Ortiz for the layout of this publication.










If the need to understand similarities among
sites were restricted solely to the wheat
growing season (e.g., for a disease with no
interactions with off-season climate), then
Figure 9a would be sufficient. However, for
studies of crop rotations or any wheat
production problem that interacts with the off-
season environment, a more appropriate
similarity map would combine the cool season
and favorable season criteria (Fig. 10). For
South America, this intersection zone
constitutes a much smaller area that is
basically restricted to parts of Bolivia, two
regions in Brazil, and small areas in Venezuela.


Conclusions

For lowland wheat regions of Bolivia, climate
similarity analyses allowed identification of
other regions where wheat production
conditions should be similar enough that
technologies can be transferred either to or
from Bolivia with a low investment in adaptive
research. However, the regions were smaller
than might have been expected.

In highland areas, problems stemmed from the
topology of the Andean region combined with
the low resolution DEM incorporated into the
SCT. This resulted in little confidence in the
precise location and extent of the generated site
similarity zones, other than the likelihood of
these zones not covering extensive areas. An
alternative approach, which divided the
highland areas into different rainfall zones
during the favorable cropping season,
provided a potentially more realistic
characterization of highland environments. The
nature of these zones were more in line with
traditional agroecological regions, yet quite
refined due to their climatic component. Given
the special problems associated with highland


regions, these zones were considered to
represent the most feasible potential areas for
technology transfer/exchange.

All zonations produced in the present study
could be further refined with the addition of
data on soils or land use. The SCT can produce
similarity analyses using these data as surface
layers, but available data were not considered
to be of a suitable scale for inclusion in this
analysis. Moreover, using these data would
further reduce the regions considered similar.

The present study has demonstrated the
potential of applying GIS applications -
particularly the SCT- to help identify
comparable production environments and
facilitate the efficient exchange of knowledge
among research in such regions. The
complexity of the Andean region presented
numerous problems and highlighted potential
deficiencies associated with current systems
and datasets when trying to work in
mountainous areas. More flexibility may be
obtained by coupling tools such as crop
simulation models with GIS, to enable users to
compare crop or system performance across
regions.


Acknowledgments

The authors would like to express their
gratitude to Environmental Systems Research
Institute, Inc., for providing the ArcInfo and
ArcView software and to the Integrated
Information Management Laboratory of Texas
A&M University System for providing the
Spatial Characterization Tool. We also thank
Mike Listman for editorial assistance and
Marcelo Ortiz for the layout of this publication.











References


Aquino, P., V. Hernandez, and R. M. Rejesus. 1996.
Selected wheat statistics. p. 39-62. In CIMMYT
1995/1996 World Wheat Facts and Trends:
Understanding Global trends in the Use of
Wheat Diversity and International Flows of
Wheat Genetic Resources. Mexico, D.F.:
CIMMYT.
Corbett, J.D. 1994. Climate surfaces for Latin America
v1.1. Five arc-minute resolution. Climate
coefficients created using ANUSPLIN, ICRAF,
Nairobi, Kenya.
Corbett, J.D., and R.F. O'Brien. 1997. The Spatial
Characterization Tool Africa v 1.0. Texas
Agricultural Experiment Station, Texas A&M
University System, Blackland Research Center
Report No. 97-03, CD-ROM Publication.


FAO. 1985. Agroclimatological data for Latin America
and the Caribbean. FAO, Rome.
FAO. 1997. Agrostat PC Data File. Food and Agriculture
Organization of the United Nations, Rome.
Hutchinson, M.F. 1995. Interpolating mean rainfall
using thin plate smoothing splines. Int. Journal of
GIS 106: 211-232.
Wall, P.C., I. Ortiz-Monasterio, and J. Velasco L.
1997a. Resultados de un sondeo de
productores de trigo en el Departamento de
Cochabamba, Bolivia, Abr. 1994. Santa Cruz,
Bolivia: CIMMYT/IBTA.
Wall, P.C., I. Ortiz-Monasterio, J. Velasco L., and L.
Zegada G. 1997b. Resultados de un sondeo de
productores de trigo en el norte del
Departamento de Chuquisaca, Bolivia; Abr.
1994. Santa Cruz, Bolivia: CIMMYT/IBTA.












I







Tarata

















a. Tarata, Bolivia.









Figure 2. Zones that are climatically similar
to Bolivian highland sites for the 5-month
optimal crop growth period (+ 20% similarity
for precipitation and evapotranspiration;
+ 10% similarity for maximum and minimum
temperature).


b. Tarabuco, Bolivia.


c. Yamparaez, Bolivia.




































b. Ipiales, Colombia.


Cauquennes


c. Huancayo, Peru.


d. Cauquennes, Chile.


Figure 3. Zones that are climatically similar to other sites in South America for the 5-month
optimal crop growth period ( 20% similarity for precipitation and evapotranspiration; (+10%
similarity for maximum and minimum temperature.


a. Tunja, Colombia.



























a. In Mexico and Central America.


b. In Africa.


Figure 4. Non-Andean zones that are climatically similar to Tarata, Bolivia, for
the 5-month optimal crop growth period (+ 20% similarity for precipitation and
evapotranspiration; + 10% similarity for maximum and minimum temperature).










































a. Based on Spatial
Characterization Tool (SCT) values.


Cochabamba




Figure 5. Zones that are climatically
similar to Cochabamba, Bolivia, for
the 5-month optimal crop growth
period (+ 20% similarity for
precipitation and evapotranspiration;
+ 10% similarity for maximum and
minimum temperature).



b. Based on FAO
meteorological station values.
























Paraiso


Caiada
Larga


a. Paraiso, Bolivia.


Figure 7. Zones that are climatically
similar to lowland wheat regions of Bolivia
for the 5-month optimal crop growth
period (+ 20% similarity for precipitation
and evapotranspiration; + 10% similarity
for maximum and minimum temperature).


b. Canada Larga, Bolivia.


San
Rafael


c. San Rafael, Bolivia.



























a. In Mexico and Central America.


b. In Africa.


Figure 8. Zones that are climatically similar to Paraiso, Bolivia, for the 5-month
optimal crop growth period (+ 20% similarity for precipitation and
evapotranspiration; + 10% similarity for maximum and minimum temperature).


























Paraiso


a. For the coolest quarter of the year.







Figure 9. Zones that are
climatically similar to Paraiso,
Bolivia (+ 20% similarity for
precipitation and
evapotranspiration; + 10%
similarity for maximum and
minimum temperature).


b. For the dry season.




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