Generating plausible crop distribution and performance maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach:

"Agricultural production statistics reported at country or sub-national geopolitical scales are used in a wide range of economic analyses, and spatially explicit (geo-referenced) production data are increasingly needed to support improved approaches to the planning and implementation of agricultural development. However, it is extremely challenging to compile and maintain collections of sub-national crop production data, particularly for poorer regions of the world. Large gaps exist in our knowledge of the current geographic distribution and spatial patterns of crop performance, and these gaps are unlikely to be filled in the near future. Regardless, the spatial scale of many sub-national statistical reporting units remains too coarse to capture the patterns of spatial heterogeneity in crop production and performance that are likely to be important from a policy and investment planning perspective. To fill these spatial data gaps, we have developed and applied a meso-scale model for the spatial disaggregation of crop production. Using a cross-entropy approach, our model makes plausible pixel-scale assessment of the spatial distribution of crop production within geopolitical units (e.g. countries or sub-national provinces and districts). The pixel-scale allocations are performed through the compilation and judicious fusion of relevant spatially explicit data, including production statistics, land use data, satellite imagery, biophysical crop “suitability” assessments, population density, and distance to urban centers, as wells as any prior knowledge about the spatial distribution of individual crops. The development, application and validation of a prior version of the model using data from Brazil strongly suggested that our spatial allocation approach shows considerable promise. This paper describes efforts to generate crop distribution maps for Sub-Saharan Africa for the year 2000 using this approach. Apart from the empirical challenge of applying the approach across many countries, the application includes three significant model improvements, namely (1) the ability to cope with production data sources that provided different degrees of spatial disaggregation for different crops within a single country; (2) the inclusion of a digital map of irrigation intensity as a new input layer; and (3) increased disaggregation of rainfed production systems. Using the modified spatial allocation model, we generated 5-minute (approximately 10-km) resolution grid maps for 20 major crops across Sub-Saharan Africa, namely barley, dry beans, cassava, cocoa, coffee, cotton, cowpeas, groundnuts, maize, millet, oil palm, plantain, potato, rice, sorghum, soybeans, sugar cane, sweet potato, wheat, and yam. The approach provides plausible results but also highlights the need for much more reliable input data for the region, especially with regard to sub-national production statistics and satellite-based estimates of cropland extent and intensity." from Author's AbstractCross entropy, Spatial allocation, Agricultural production, crop suitability, Geographic information systems, Satellite image,

Generating Global Crop Distribution Maps: From Census to Grid

In order to evaluate food security, technology potential and the environmental impacts of production in a strategic and regional context, it is critical to have reliable information on the spatial distribution and coincidence of people, agricultural production, and environmental services. This paper proposes a spatial allocation model for generating highly disaggregated, crop-specific production data by a triangulation of any and all relevant background and partial information. This includes national or sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rainfed production systems, cropping intensity, and crop prices. This information is compiled and integrated to generate "prior" estimates of the spatial distribution of individual crops. Priors are then submitted to an optimization model that uses cross-entropy principles and area and production accounting constraints to simultaneously allocate crops into the individual "pixels" of a GIS database. The result for each pixel (notionally of any size, but typically from 25 to 100 square km) is the area and production of each crop produced, split by the shares grown under irrigated, high-input rainfed, low-input rainfed conditions (each with distinct yield levels). Tested in Latin America and sub-Saharan Africa, the spatial allocation model is applied here to generate a global distribution of crop area and production for 20 major crops (wheat, rice, maize, barley, millet, sorghum, potato, sweet potato, cassava and yams, plantain and banana, soyb ean, dry beans, other pulse, sugar cane, sugar beets, coffee, cotton, other fibres, groundnuts, and other oil crops). The detailed spatial datasets represent a truly unique and extremely rich platform for exploring the social, economic and environmental consequences of agricultural production in a strategic policy context.Global, cross entropy, satellite image, spatial allocation, agricultural production, crop suitability, Crop Production/Industries, C6, Q15, Q24,

Assessing the spatial distribution of crop production using a generalized cross-entropy approach:

While agricultural production statistics are reported on a geopolitical – often national - basis we often need to know the status of production or productivity within specific sub-regions, watersheds, or agro-ecological zones. Such re-aggregations are typically made using expert judgments or simple area-weighting rules. We describe a new, entropy-based approach to making spatially disaggregated assessments of the distribution of crop production. Using this approach tabular crop production statistics are blended judiciously with an array of other secondary data to assess the production of specific crops within individual ‘pixels' – typically 25 to 100 square kilometers in size. The information utilized includes crop production statistics, farming system characteristics, satellite-derived land cover data, biophysical crop suitability assessments, and population density. An application is presented in which Brazilian state level production statistics are used to generate pixel level crop production data for eight crops. To validate the spatial allocation we aggregated the pixel estimates to obtain synthetic estimates of municipio level production in Brazil, and compared those estimates with actual municipio statistics. The approach produced extremely promising results. We then examined the robustness of these results compared to short-cut approaches to spatializing crop production statistics and showed that, while computationally intensive, the cross-entropy method does provide more reliable estimates of crop production patterns.Entropy, Cross entropy, Remote sensing, Spatial allocation, Crop distribution,

Generating global crop distribution maps: from census to grid

In order to evaluate food security, technology potential and the environmental impacts of production in a strategic and regional context, it is critical to have reliable information on the spatial distribution and coincidence of people, agricultural production, and environmental services. This paper proposes a spatial allocation model for generating highly disaggregated, crop-specific production data by a triangulation of any and all relevant background and partial information. This includes national or sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rainfed production systems, cropping intensity, and crop prices. This information is compiled and integrated to generate "prior" estimates of the spatial distribution of individual crops. Priors are then submitted to an optimization model that uses cross-entropy principles and area and production accounting constraints to simultaneously allocate crops into the individual pixels of a GIS database. The result for each pixel (notionally of any size, but typically from 25 to 100 square km) is the area and production of each crop produced, split by the shares grown under irrigated, high-input rainfed, low-input rainfed conditions (each with distinct yield levels). Tested in Latin America and sub-Saharan Africa, the spatial allocation model is applied here to generate a global distribution of crop area and production for 20 major crops (wheat, rice, maize, barley, millet, sorghum, potato, sweet potato, cassava and yams, plantain and banana, soybean, dry beans, other pulse, sugar cane, sugar beets, coffee, cotton, other fibres, groundnuts, and other oil crops). The detailed spatial datasets represent a truly unique and extremely rich platform for exploring the social, economic and environmental consequences of agricultural production in a strategic policy context.Research Methods/ Statistical Methods,

GENERATING PLAUSIBLE CROP DISTRIBUTION MAPS FOR SUB-SAHARA AFRICA USING SPATIAL ALLOCATION MODEL

Spatial data, which are data that include the coordinates (either by latitude/longitude or by other addressing methods) on the surface of the earth, are essential for agricultural development. As fundamental parameters for agriculture policy research agricultural production statistics by geopolitical units such as country or sub-national entities have been used in many econometric analyses. However, collecting sub-national data is quite difficult in particular for developing countries. Even with great effort and only on regional scales, enormous data gaps exist and are unlikely to be filled. On the other hand, the spatial scale of even the subnational unit is relatively large for detailed spatial analysis. To fill these spatial data gaps we proposed a spatial allocation model. Using a classic cross-entropy approach, our spatial allocation model makes plausible allocations of crop production in geopolitical units (country, or state) into individual pixels, through judicious interpretation of all accessible evidence such as production statistics, farming systems, satellite image, crop biophysical suitability, crop price, local market access and prior knowledge. The prior application of the model to Brazil shows that the spatial allocation has relative good or acceptable agreement with actual statistic data. The current paper attempts to generate crop distribution maps for Sub-Sahara Africa for the year 2000 using the spatial allocation model. We modified the original model in the following three aspects: (1) Handle partial subnational statistics; (2) Include the irrigation map as another layer of information in the model; (3) Add subsistence portion of crops in addition to the existing three input and management levels (irrigated, high-input rainfed and low-input rainfed). With the modified spatial allocation model we obtain 5 by 5 minutes resolution maps for the following 20 major crops in Sub-Sahara Africa: Barley, Beans, Cassava, Cocoa, Coffee, Cotton, Cow Peas, Groundnuts, Maize, Millet, Oil Palm, Plantain, Potato, Rice, Sorghum, Soybeans, Sugar Cane, Sweet Potato, Wheat, Yam. This approach demonstrates that remote sensing technology such as satellite imagery could be quite useful in improved understanding of the spatial variation of land cover, agricultural production, and natural resources.Sub-Sahara Africa, cross entropy, satellite image, spatial allocation, agricultural production, crop suitability, Crop Production/Industries, C60, Q15, Q24,

Agroecological aspects of evaluating agricultural research and development:

In this paper we describe how biophysical data can be used, in conjunction with agroecological concepts and multimarket economic models, to systematically evaluate the effects of agricultural R&D in ways that inform research priority setting and resource allocation decisions. Agroecological zones can be devised to help estimate the varying, site-specific responses to new agricultural technologies and to evaluate the potential for research to spill over from one agroecological zone to another. The application of agroecological zonation procedures in an international agricultural research context is given special attention.Agricultural research., Technological innovations., Agricultural economics and policies.,

Spatial Patterns of Crop Yields in Latin America and the Caribbean

Because of the apparent slowdown in the growth of crop yield potential, the increasing share of farmers already using modern crop varieties, and the accelerating flow of knowledge on agricultural technology, one would expect to find gradual convergence inLatin america, crop yield, convergence, spillover, weather variability

ECONOMIC IMPACTS OF INTERNATIONAL AGRICULTURAL RESEARCH: CASE OF US-EGYPT-IRRI COLLABORATIVE PROJECT ON THE GENERATION OF NEW RICE TECHNOLOGIES

Agricultural research managers and scientists are under increasing pressure to demonstrate the efficient and socially-effective use of funds spent on agricultural R&D. These pressures stem from heightened expectations of transparency and accountability in the use of public funds, as well as from the growing demand for evidence of impact on target social groups and environmental services. Finally, advances in agricultural biotechnology research and the ensuing dialogue about the desirability of using biotechnology tools for increasing food production in developing countries have highlighted the need to assess the impacts of international agricultural research in the US, the developing countries, and the international agricultural research centers (IARCs). The US-Egypt ATUT project, funding involves collaborative research among plant breeders, molecular geneticists, and other agricultural scientists in the US, Egypt and IRRI. ATUT rice research accelerated the utilization of three methods for improving the speed and reliability of the screening and evaluation process for identifying salt resistant varieties: shuttle breeding, anther culture and marker-assisted selection. ATUT initiated the application of Marker Assisted Selection (MAS) technology for screening Egyptian rice germplasm. Other ATUT rice technologies in the pipeline have various levels of AATUTness in their research and development. Some of the varieties to be released starting 2003 such as short duration HYVs, will have benefitted less directly from ATUT funding and scientific collaboration. Others- such as hybrid rice varieties will have been very significantly shaped by ATUT. The DREAM model under IFPRI's Global and Regional Program on Agricultural Science and Technology Policy, is used to assess the potential economic benefit of technology outputs for rice, under a range of likely adoption, market and trade scenarios. The simulation model, based on economic surplus theory, uses data and parameters from interviews with scientists, policy makers on the impact and adoption of technology. For this study, ex-ante simulations of the most likely range of outcomes with and without the innovations from ATUT investments. Analyzing the impact of technical change (a simulation over a specified number of years) has provided year-by year estimates of changes in: prices, quantities produced, consumed and traded, levels of adoption, economic benefits to consumers, economic benefits to adopters or losses (non-adopters) to producers. For US and IRRI benefits: Enhanced germplasm pool, stock of knowledge and facilities, and better informed scientists. US scientists in California and Arkansas benefit More integrated into the international rice research community. Gross benefits are estimated for governorates, by producers and consumers, by saline and normal soils, for 1997 to 2017 (end of GoE's current strategic horizon) discounted to 1997 US$. Producers in normal soils derive higher benefits than those in saline soils, some governorates reap more of the producer benefits than others; rural consumers benefit more than urban consumers. Consumer benefits are also estimated for importers of Egyptian rice such as Turkey, Sudan and aggregated Arabian countries. Cost of rice R&D and technology transfer will be measured to derive the IRR and B/C ratios.Research and Development/Tech Change/Emerging Technologies,

The relationship between music participation of students in the public high school and in the community in the city of Worcester, Massachusetts.

Thesis (Ed.M.)--Boston Universit

Impact of global warming on Chinese wheat productivity:

"Climate change continues to have major impact on crop productivity all over the world. While many researchers have evaluated the possible impact of global warming on crop yields using mainly indirect crop simulation models, there are relatively few direct assessments on the impact of observed climate change on past crop yield and growth. We use a 1979-2000 Chinese crop-specific panel dataset to investigate the climate impact on Chinese wheat yield growth. We find that a 1 percent increase in wheat growing season temperature reduces wheat yields by about 0.3 percent. This negative impact is less severe than those reported in other regions. Rising temperature over the past two decades accounts for a 2.4 percent decline in wheat yields in China while the majority of the wheat yield growth, 75 percent, comes from increased use of physical inputs. We emphasize the necessity of including such major influencing factors as physical inputs into the crop yield-climate function in order to have an accurate estimation of climate impact on crop yields." Authors' AbstractGlobal warming, Climate, Wheat production,