Impact of agricultural land use in Central Asia: a review

International audienceAbstractAgriculture is major sector in the economy of Central Asia. The sustainable use of agricultural land is therefore essential to economic growth, human well-being, social equity, and ecosystem services. However, salinization, erosion, and desertification cause severe land degradation which, in turn, degrade human health and ecosystem services. Here, we review the impact of agricultural land use in the five countries of Central Asia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, during 2008–2013 in 362 articles. We use the Land Use Functions framework to analyze the type and relative shares of environmental, economic, and social topics related to agricultural land use. Our major findings are (1) research on land use in Central Asia received high levels of international attention and the trend in the number of publications exceeded the global average. (2) The impacts of land use on abiotic environmental resources were the most explored. (3) Little research is available about how agricultural land use affects biotic resources. (4) Relationships between land degradation, e.g., salinization and dust storms, and human health were the least explored. (5) The literature is dominated by indirect methods of data analysis, such as remote sensing and mathematical modeling, and in situ data collection makes up only a small proportion

Integrated Water Resources Management Karlsruhe 2010 : IWRM, International Conference, 24 - 25 November 2010 conference proceedings

In dieser Arbeit werden dual-orthogonal, linear polarisierte Antennen für die UWB-Technik konzipiert. Das Prinzip zur Realisierung der Strahler wird vorgestellt, theoretisch und simulativ untersucht, sowie messtechnisch verifiziert. Danach werden Konzepte zur Miniaturisierung der Strahler dargelegt, die anschließend zum Aufbau von Antennengruppen verwendet werden. Die Vorteile der entwickelten Antennen werden praktisch anhand des bildgebenden Radars und des Monopuls-Radars gezeigt

The economics of desertification, land degradation, and drought: Toward an integrated global assessment

cost of inaction, desertification, drought, Economics, Land degradation, prevention of land degradation,

The Economics of Desertification, Land Degradation, and Drought; Toward an Integrated Global Assessment

Land degradation has not been comprehensively addressed at the global level or in developing countries. A suitable economic framework that could guide investments and institutional action is lacking. This study aims to overcome this deficiency and to provide a framework for a global assessment based on a consideration of the costs of action versus inaction regarding desertification, land degradation, and drought (DLDD). Most of the studies on the costs of land degradation (mainly limited to soil erosion) give cost estimates of less than 1 percent up to about 10 percent of the agricultural gross domestic product (GDP) for various countries worldwide. But the indirect costs of DLDD on the economy (national income), as well as their socioeconomic consequences (particularly poverty impacts), must be accounted for, too. Despite the numerous challenges, a global assessment of the costs of action and inaction against DLDD is possible, urgent, and necessary. This study provides a framework for such a global assessment and provides insights from some related country studies.Agricultural Finance, Crop Production/Industries, Environmental Economics and Policy, Land Economics/Use, Resource /Energy Economics and Policy,

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

Innovation Issues in Water, Agriculture and Food

In a worldwide context of ever-growing competition for water and land, climate change, droughts and man-made water scarcity, and less-participatory water governance, agriculture faces the great challenge of producing enough food for a continually increasing population. In this line, this book provides a broad overview of innovation issues in the complex water–agriculture–food nexus, thus also relative to their interconnections and dependences. Issues refer to different spatial scales, from the field or the farm to the irrigation system or the river basin. Multidisciplinary approaches are used when analyzing the relationships between water, agriculture, and food security. The covered issues are quite diverse and include: innovation in crop evapotranspiration, crop coefficients and modeling; updates in research relative to crop water use and saving; irrigation scheduling and systems design; simulation models to support water and agricultural decisions; issues to cope with water scarcity and climate change; advances in water resource quality and sustainable uses; new tools for mapping and use of remote sensing information; and fostering a participative and inclusive governance of water for food security and population welfare. This book brings together a variety of contributions by leading international experts, professionals, and scholars in those diverse fields. It represents a major synthesis and state-of-the-art on various subjects, thus providing a valuable and updated resource for all researchers, professionals, policymakers, and post-graduate students interested in the complex world of the water–agriculture–food nexus

Earth Resources: A continuing bibliography with indexes (Issue 37)

This bibliography lists 512 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1983. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis