crop

FAOSTAT provides time-series and cross sectional data relating to food and agriculture for some 200 countries.

The national version of FAOSTAT, CountrySTAT, is being developed and implemented in a number of target countries, primarily in sub-saharan Africa. It will offer a two-way data exchange facility between countries and FAO as well as a facility to store data at the national and sub-national levels.

This database contains current and historical official USDA data on production, supply and distribution of agricultural commodities for the United States and key producing and consuming countries.

Biomass is receiving increasing attention as scientists, policy makers, and growers search for clean, renewable energy alternatives. Compared with other renewable resources, biomass is very flexible it can be used as fuel for direct combustion, gasified, used in combined heat and power technologies, or biochemical conversions. Due to the wide range of feedstocks, biomass has a broad geographic distribution, in some cases offering a least-cost and near-term alternative. The objective of this research is to estimate the biomass resources available in the United States and map the results. To accomplish this objective, biomass feedstock data are analyzed both statistically and graphically using geographic information systems (GIS). A GIS is a computer-based information system used to create, manipulate, and analyze geographic information, allowing us to visualize relationships, patterns, or trends that are not possible to see with traditional charts, graphs, and spreadsheets. While other biomass resource assessments concentrate on the economic or theoretical availability, this study estimates the technical biomass resources available in the United States (page 59). The estimates are based on numerous assumptions, methodologies adopted from other studies, and factors that relate population to the amount of post-consumer residue generation. The main contribution of this research is that it adds a geographic perspective to biomass research by answering questions such as where the resources are and how much is available.

Agricultural activities have dramatically altered our planet?s land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite-derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite-derived products (Boston University?s MODIS-derived land cover product and the GLC2000 data set). Our data are presented at 5 min ( 10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2?17.1) million km2 of cropland (12% of the Earth?s ice-free land surface) and 28.0 (90% confidence range of 23.6?30.0) million km2 of pasture (22%) in the year 2000.

The U.S. Geological Survey (USGS) 2001 National Land Cover Database (NLCD) was compared to the U.S. Department of Agriculture (USDA) 2002 Census of Agriculture. Wecompared areal estimates for cropland at the state and county level for 14 States in the Upper Midwest region of the United States. Absolute differences between the NLCD and Census cropland areal estimates at the state level ranged from 1.3% (Minnesota) to 37.0% (Wisconsin). The majority of counties (74.5%) had differences of less than 100 km2. 7.2% of the counties had differences of more than 200 km2. Regions where the largest areal differences occurred were in southern Illinois, North Dakota, South Dakota, and Wisconsin, and generally occurred in areas with the lowest proportions of cropland (i.e., dominated by forest or grassland). Before using the 2001 NLCD for agricultural applications, such as mapping of specific crop types, users should be aware of the potential for misclassification errors, especially where the proportion of cropland to other land cover types is fairly low.

This paper describes a methodology to explore the (future) spatial distribution of biofuel crops in Europe. Two main types of biofuel crops are distinguished: biofuel crops used for the production of biodiesel or bioethanol, and second-generation biofuel crops. A multiscale, multi-model approach is used in which biofuel crops are allocated over the period 2000?2030. The area of biofuel crops at the national level is determined by a macroeconomic model. A spatially explicit land use model is used to allocate the biofuel crops within the countries. Four scenarios have been prepared based on storylines influencing the extent and spatial distribution of biofuel crop cultivation. The allocation algorithm consists of two steps. In the first step, processing plants are allocated based on location factors that are dependent on the type of biofuel crop processed and scenario conditions. In the second step, biofuel crops are allocated accounting for the transportation costs to the processing plants. Both types of biofuel crops are allocated separately based on different location factors. Despite differences between the scenarios, mostly the same areas are showing growth in biofuel crop cultivation in all scenarios. These areas stand out because they have a combination of well-developed infrastructural and industrial facilities and large areas of suitable arable land. The spatially explicit results allow an assessment of the potential consequences of large-scale biofuel crop cultivation for ecology and environment.

Ground-based data on crop production in the USA is provided through surveys conducted by the National Agricultural Statistics Service (NASS) and the Census of Agriculture (AgCensus). Statistics from these surveys are widely used in economic analyses, policy design, and for other purposes. However, missing data in the surveys presents limitations for research that requires comprehensive data for spatial analyses.We created comprehensive county-level databases for nine major crops of the USA for a 16-yr period, by filling the gaps in existing data reported by NASS and AgCensus. We used a combination of regression analyses with data reported by NASS and the AgCensus and linear mixed-effect models incorporating county-level environmental, management, and economic variables pertaining to different agroecozones. Predicted yield and crop area were very close to the data reported by NASS, within 10% relative error. The linear mixed-effect model approach gave the best results in filling 84% of the total gaps in yields and 83% of the gaps in crop areas of all the crops. Regression analyses with AgCensus data filled 16% of the gaps in yields and crop areas of the major crops reported by NASS.