CIMMYT. Sustainable Maize and Wheat Systems for the Poor. An Agroclimatological Overview of Wheat Production Regions of Bolivia - PDF

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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
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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 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) 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 An Agroclimatological Overview of Wheat Production Regions of Bolivia. NRG-GIS Paper 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: 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: Contents Page iii Tables iii Figures iv Summary vi Resumen en Español 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. iii 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. iv 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 colletions 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. v Resumen En la investigación del manejo de los sistemas de cultivo y de los recursos naturales existe con frecuencia la suposición de que los resultados son específicos a un sitio. Esta suposición es a veces incorrecta y simplemente manifiesta la frustración o falta de conocimientos de los investigadores a nivel local, abrumados por las complejidades de extrapolar la tecnología. Tradicionalmente, las comparaciones entre las regiones agroecológicas se han basado en la definición de áreas extensas, como los mega-ambientes empleados por el CIMMYT. Aunque debido a su extensión estos mega-ambientes son útiles al establecer las prioridades de investigación mundiales de los programas fitotécnicos, a veces no revelan el nivel de variación ecológica necesario para predecir la respuesta de los cultivos a prácticas agronómicas específicas ni permiten evaluar la diversidad genética y la sustentabilidad. Los análisis de similitud climática utilizando los sistemas de información geográfica (GIS) permiten efectuar comparaciones más cuantitativas entre regiones y sitios, lo cual hace más valiosa la investigación del manejo de los sistemas de cultivo y de los recursos naturales. En Bolivia, la investigación de los sistemas de producción de trigo está restringida por la irregular topografía, los complejos ambientes y los limitados recursos financieros del país. Comprender la forma en que los ambientes de producción en Bolivia se comparan con los de sus países vecinos, de América Central, México, e incluso Africa, permite aprovechar mejor las experiencias de la investigación en regiones similares. Este informe describe el uso de la Herramienta de Caracterización Espacial (Spatial Characterization Tool; SCT), creada en la Universidad Texas A& M, en el análisis de la similitud climática de los sitios de investigación en las principales zonas productoras de trigo en Bolivia los valles altos y las llanuras de tierras bajas con la de otras regiones. La SCT emplea superficies interpoladas de datos mensuales de precipitación, evapotranspiración y temperaturas máxima y mínima. En ambas regiones, las comparaciones iniciales clasificaron un ambiente como similar al de otro sitio cuando su precipitación (P) y evapotranspiración potencial (PET) estaban dentro de un rango de + 20% de similitud y sus temperaturas máxima y mínima dentro de un rango de + 10%, durante los cinco meses más favorables del año (los mayores valores de la proporción P/PET). En cuanto a los ambientes de tierras altas, se encontraron áreas similares sólo en regiones dispersas de Bolivia y Perú. De hecho, la comparación de los ambientes en Bolivia con los de los sitios altos de Perú y Colombia, más un sitio de tierras bajas y frescas en Chile, confirmaron la suposición de que los ambientes similares de tierras altas son numerosos pero dispersos geográficamente. Además, la compleja topografía andina, combinada con las grandes celdas reticuladas (9 km x 9 km) de la SCT, dificultó la caracterización climática. Se solucionó este problema mediante otro método que caracterizó los ambientes de tierras altas con base en los patrones pluviométricos durante el ciclo favorable de cultivo. Respecto a los sitios de tierras bajas, los resultados combinados de los análisis del ciclo favorable, más los de los trimestres más fríos o más secos del año (cuando se cultiva el trigo en las tierras bajas de Bolivia) identificaron los ambientes de algunas áreas adyacentes de Bolivia, dos regiones en Brasil, más extensiones pequeñas en Venezuela, como similares a los de los sitios en Bolivia. vi El análisis de similitud de sitios parece ser un método valioso para entender las relaciones entre los ambientes de producción, que podría tener muchas aplicaciones en la agricultura. Al igual que en los ejemplos antes mencionados, este método se puede utilizar para identificar las probables zonas de adaptación de las nuevas variedades o también para la recolección de germoplasma, las regiones donde es más probable encontrar materiales, con base en la similitud con sitios de recolección que ya hayan sido caracterizados. El método podría adaptarse fácilmente a estudios de distribución de enfermedades, plagas y malezas por ejemplo, se podrían crear mapas de las probables zonas de dispersión de organismos de introducción reciente o que se hayan vuelto problemáticos debido a cambios en las prácticas de cultivo. Sin embargo, los científicos deben estar conscientes de las posibles limitaciones de los análisis de similitud. La más problemática de éstas es la calidad de los datos. Las superficies climáticas interpoladas forzosamente tienen limitaciones, debido a la escala espacial. Como se ha visto en el caso de las tierras altas de Bolivia, los análisis de una región donde la elevación varía 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 metereológicas en el caso de superficies climáticas. Finalmente, el clima es sólo uno de los factores determinantes del comportamiento agronómico; los análisis de similitud podrían extenderse fácilmente para abarcar, por ejemplo, las características edafológicas. Sin embargo, la falta de buenos datos edafológicos y de otros factores no climáticos que afectan el comportamiento de los sistemas de cultivo, limita el alcance y la utilidad de los estudios de similitud, a pesar de que éstos superan al sistema de los mega-ambientes en las investigaciones agronómica y de la sustentabilidad. vii 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) Estimated growth rate of population, (%/year) Per capita income, 1994 (US $) 770 2,110 1,280 1,670 2,970 1,580 4,660 8,110 3,520 Average wheat area harvested, (000 ha) , , Average wheat yield, (t/ha) Average wheat production, (000 t) 119* , ,874 1,326 Nitrogen applied per hectare of wheat harvested, (kg N/ha) 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 underl
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