630 Landwirtschaft und verwandte Bereiche
Refine
Year of publication
Document Type
- Book (87)
- Article (52)
- Part of Periodical (11)
- Doctoral Thesis (7)
- Working Paper (5)
- Part of a Book (3)
- Contribution to a Periodical (3)
- Periodical (2)
- Preprint (2)
- Report (2)
Has Fulltext
- yes (176)
Is part of the Bibliography
- no (176)
Keywords
- Imkerei (22)
- Biene (7)
- Landwirtschaft (7)
- Quelle (4)
- Bienenkorb (3)
- Welternährung (3)
- agriculture (3)
- Bauen (2)
- Bewässerung (2)
- Boden (2)
Institute
- Extern (22)
- SFB 268 (19)
- Biowissenschaften (11)
- Geowissenschaften (5)
- Präsidium (5)
- Institut für sozial-ökologische Forschung (ISOE) (4)
- Geographie (3)
- Biodiversität und Klima Forschungszentrum (BiK-F) (2)
- Frobenius Institut (2)
- Gesellschaftswissenschaften (2)
- Senckenbergische Naturforschende Gesellschaft (2)
- Biochemie und Chemie (1)
- Geowissenschaften / Geographie (1)
- Geschichtswissenschaften (1)
- Wirtschaftswissenschaften (1)
Summary:
1) Three types of forest, evergreen seasonal forest, heath forest and Melaleuca swamp forest, were distinguished and studied in the vicinity of Cheko in southwestern Cambodia, where moist tropical climate with a pronounced dry season in three winter months prevails.
2) These three forest types respectively occupied deep latosol derived from sandstone, very sandy soil around the swamp forest, and deep deposit of silica sand with underground hardpan in shallow valleys.
3) Total plant biomass was estimated by the allometric method based on some 140 sample trees (DBH24.5 cm) which were felled in four sample plots (two 50 mX50 m plots in the evergreen seasonal forest, and each one 20 m x 50 m plot in the other two types). Biomass of ground vegetation was estimated separately by similar technique and clipping.
4) The biomass of evergreen seasonal forest was estimated as follows. Stem 215 ton/ha, branch 99 ton/ha, root 61 ton/ha, leaf 7.3 ton/ha, leaf area index 7.4 ha/ha, density of trees over 4.5 cm DBH 1,280/ha, relative basal area of Whole stand 3.19 o/oo.
5) The biomass of heath forest was as follows. Stem 111 ton/ha, branch 35 ton/ha·, root 19 ton/ha, leaf 7.7 ton/ha, leaf area index 7.1 ha/ha, tree density 2,570/ha, relative basal area 2.3 o/oo.
6) The biomass of M elaleuca swamp forest was as follows. Stem 7.4 ton/ha, branch 3.9 ton/ha, root 2.6 ton/ha, leaf 0.79 ton/ha, leaf area index 0.37, undergrowth of sedge 2.57 ton/ha, tree density 200/ha, relative basal area of trees 0.35 o/oo.
7) It was found that the biomass of small trees (4.5 cm>DBH>1 cm) and ground vegetation (4.5 cm <= DBH) was so unevenly distributed over the forest floor that a few hundred square meters of sample area would be needed for estimating them at a moderate level of statistical reliability.
8) The estimated biomass of the evergreen seasonal forest was compared with the data hitherto obtained in moist tropical forests of Cote d'Ivoire and Thailand. The forest of Cheko was found to have the biomass equivalent to other rain forests, but to be characterized by a specific DBH-tree height curve, a rather small leaf area index and a high value of leaf area/leaf weight ratio.
The estimation model PhytoCalc allows a non-destructive quantification of dry weight and nutrient pools of understorey plants in forests by using the relationship between species biomass, cover and mean shoot length. The model has been validated with independent samples in several German forest types and can be a useful tool in forest monitoring. However, in open areas within forests (e.g. clearcuts), the current model version underestimates biomass and produces unreliable nutrient pool estimations. Thus, tissue density, as approximated by leaf dry matter content (LDMC), is systematically higher under high light compared to low light conditions. We demonstrate that the ratio of LDMC under clearcut conditions to LDMC under forest conditions can be used to adjust the PhytoCalc model to clearcut conditions. We investigated the LDMC ratio of five exemplary species commonly occurring on clearcuts. Integrating the square of the ratio as a correction factor improved estimates of biomass to more than 70% fit between observations and predictions. Results also suggest this ratio can be used to correct nutrient concentrations modelled in PhytoCalc, which tend to be overestimated in clearcuts. As morphological groups of plant species exhibit significantly different ratios, we advise using group-specific correction factors for clearcut adjustments in the future.
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.
Agriculture of crops provides more than 85% of the energy in human diet, while also securing income of more than 2.6 billion people. To investigate past, present and future changes in the domain of food security, water resources and water use, nutrient cycles, and land management it is required to know the agricultural land use, in particular which crop grows where and when. The current global land use or land cover data sets are based on remote sensing and agricultural census statistics. In general, these only contain one or very few classes of agricultural land use. When crop-specific areas are given, no distinction of irrigated and rainfed areas is made, whereas it is necessary to distinguish rainfed and irrigated crops, because crop productivity and water use differ significantly between them.
To support global-scale assessments that are sensitive to agricultural land use, the global data set of Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) was developed by the author. With a spatial resolution of 5 arc-minutes (approximately 9.2 km at the equator), MIRCA2000 provides for the first time, spatially explicit irrigated and rainfed crop areas separately for each of the 26 crop classes for each month of the year, and includes multi-cropping. The data set covers all major food crops as well as cotton, while the remaining crops are grouped into three categories (perennial, annual and fodder grasses). Also for the first time, crop calendars on national or sub-national level were consistently linked to annual values of harvested area at the 5 arc-minutes grid cell level, such that monthly growing areas could be computed that are representative for the time period 1998 to 2002.
The downscaling algorithm maximizes the consistency to the grid-based input data of cropland extent [Ramankutty et al., 2008], crop-specific total annual harvested area [Monfreda et al., 2008], and area equipped for irrigation [Siebert et al., 2007]. In addition to the methodology, this dissertation describes differences to other datasets and standard scaling methods, as well as some applications. For quality assessment independent datasets and newly developed quality parameters are used, and scale effects are discussed.
Supplementary Appendices document crop calendars for irrigated and rainfed crops for each of the 402 spatial units (Appendix I), data sources of harvested area and of cropping periods for irrigated crops, country by country (Appendix K), as well as data quality parameters (Appendix L, including spreadsheet files).