Global patterns of cropland use intensity

This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. Global cropland extent was 16 million km2 around the year 2000 of which 4.4 million km2 (28%) was fallow, resulting in an average cropping intensity of 0.82 for total cropland extent and of 1.13 when excluding fallow land. The lowest cropping intensities related to total cropland extent were found for Southern Africa (0.45), Central America (0.49) and Middle Africa (0.54), while highest cropping intensities were computed for Eastern Asia (1.04) and Southern Asia (1.0). In remote or arid regions where shifting cultivation is practiced, fallow periods last 3–10 years or even longer. In contrast, crops are harvested two or more times per year in highly populated, often irrigated tropical or subtropical lowlands where multi-cropping systems are common. This indicates that intensification of agricultural land use is a strategy that may be able to significantly improve global food security. There exist large uncertainties regarding extent of cropland, harvested crop area and therefore cropping intensity at larger scales. Satellite imagery and remote sensing techniques provide opportunities for decreasing these uncertainties and to improve the MIRCA2000 inventory

Challenges and opportunities in mapping land use intensity globally

Future increases in land-based production will need to focus more on sustainably intensifying existing production systems. Unfortunately, our understanding of the global patterns of land use intensity is weak, partly because land use intensity is a complex, multidimensional term, and partly because we lack appropriate datasets to assess land use intensity across broad geographic extents. Here, we review the state of the art regarding approaches for mapping land use intensity and provide a comprehensive overview of available global-scale datasets on land use intensity. We also outline major challenges and opportunities for mappinglanduseintensityfor cropland, grazing, and forestry systems, and identify key issues for future research.Peer Reviewe

Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

Time tracking of different cropping patterns using Landsat images under different agricultural systems during 1990-2050 in Cold China

Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990-1995, 1995-2000, 2000-2005, 2005-2010, and 2010-2015) and predicted future patterns for the period of 2020-2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km(2) vs. 2840.29 km(2)) during 1990-2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development

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,

Management Effects on Greenhouse Gas Dynamics in Fen Ditches

Globally, large areas of peatland have been drained through the digging of ditches, generally to increase agricultural production. By lowering the water table it is often assumed that drainage reduces landscape-scale emissions of methane (CH4) into the atmosphere to negligible levels. However, drainage ditches themselves are known to be sources of CH4 and other greenhouse gases (GHGs), but emissions data are scarce, particularly for carbon dioxide (CO2) and nitrous oxide (N2O), and show high spatial and temporal variability. Here, we report dissolved GHGs and diffusive fluxes of CH4 and CO2 from ditches at three UK lowland fens under different management; semi-natural fen, cropland, and cropland restored to low-intensity grassland. Ditches at all three fens emitted GHGs to the atmosphere, but both fluxes and dissolved GHGs showed extensive variation both seasonally and within-site. CH4 fluxes were particularly large, with medians peaking at all three sites in August at 120-230 mg m-2 d-1. Significant between site differences were detected between the cropland and the other two sites for CO2 flux and all three dissolved GHGs, suggested that intensive agriculture has major effects on ditch biogeochemistry. Multiple regression models using environmental and water chemistry data were able to explain 29-59% of observed variation in dissolved GHGs. Annual CH4 fluxes from the ditches were 37.8, 18.3 and 27.2 g CH4 m-2 yr-1 for the semi-natural, grassland and cropland, and annual CO2 fluxes were similar (1100 to 1440 g CO2 m-2 yr-1) among sites. We suggest that fen ditches are important contributors to landscape-scale GHG emissions, particularly for CH4. Ditch emissions should be included in GHG budgets of human modified fens, particularly where drainage has removed the original terrestrial CH4 source, e.g. agricultural peatlands

Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian Monsoon Belt

We present a conceptual synthesis of the impact that agricultural activity in India can have on land-atmosphere interactions through irrigation. We illustrate a “bottom up” approach to evaluate the effects of land use change on both physical processes and human vulnerability. We compared vapor fluxes (estimated evaporation and transpiration) from a pre-agricultural and a contemporary land cover and found that mean annual vapor fluxes have increased by 17% (340 km3) with a 7% increase (117 km3) in the wet season and a 55% increase (223 km3) in the dry season. Two thirds of this increase was attributed to irrigation, with groundwater-based irrigation contributing 14% and 35% of the vapor fluxes in the wet and dry seasons, respectively. The area averaged change in latent heat flux across India was estimated to be 9 Wm−2. The largest increases occurred where both cropland and irrigated lands were the predominant contemporary land uses

Insights on the role of forest cover and on the changes in forest cover on thirty-five endangered mammal species distributions

The changes in forest cover can determine the survival of terrestrial endangered mammal species in the wild. This study assessed the impacts of forest cover changes on endangered mammal species distribution at global scale aiming to understand how the changes in forest cover may have impacted the distributions of 35 endangered small and large-body terrestrial mammals. There were used forest data obtained from time-series analyses of Landsat images between 2000 and 2014, species occurrence records collected by observations between 2000 and 2015 of Global Biodiversity Information Facility and species range data of International Union for Nature Conservation (IUCN) of the year 2015, to test the ‘natural and resource conditions’ hypothesis. Hypothesis on ‘natural and resource conditions’ produced models with high prediction accuracy of above 70 percent for 88 percent of 35 species models. The changes in forest cover explained species occurrences in 10 percent of all species models. In average, 59 percent of species occurrence records overlapped with species range data. The 51 percent of all species had no occurrence records between 2000 and 2015. Species and forest data collection as well as transnational cooperation for conservation of species roaming in the wild in upland forested areas and in cross-border areas may be critical for endangered mammal species conservation

Fields of Fuel: Market and Environmental Implications of Switching to Grass for U.S. Transport

Analyzes how increased commercial switchgrass production for biofuel affects land use, commodity prices, and the environment. Suggests policy actions on research, carbon impact estimates, mitigation payments, and air, soil, and water quality protection