Hasil untuk "Land use"

S. Seibold, M. Gossner, N. Simons et al.

M. Tsiafouli, Élisa Thébault, S. Sgardelis et al.

T. Tscharntke, Y. Clough, T. Wanger et al.

D. Tilman, Christian Balzer, Jason D. Hill et al.

Junxiang Li, Conghe Song, Lu Cao et al.

Erle C. Ellis, K. K. Goldewijk, S. Siebert et al.

C. Monfreda, N. Ramankutty, J. Foley

H. Haberl, K. Erb, F. Krausmann et al.

A. Bondeau, Pascalle C. Smith, S. Zaehle et al.

In order to better assess the role of agriculture within the global climate‐vegetation system, we present a model of the managed planetary land surface, Lund–Potsdam–Jena managed Land (LPJmL), which simulates biophysical and biogeochemical processes as well as productivity and yield of the most important crops worldwide, using a concept of crop functional types (CFTs). Based on the LPJ‐Dynamic Global Vegetation Model, LPJmL simulates the transient changes in carbon and water cycles due to land use, the specific phenology and seasonal CO2 fluxes of agricultural‐dominated areas, and the production of crops and grazing land. It uses 13 CFTs (11 arable crops and two managed grass types), with specific parameterizations of phenology connected to leaf area development. Carbon is allocated daily towards four carbon pools, one being the yield‐bearing storage organs. Management (irrigation, treatment of residues, intercropping) can be considered in order to capture their effect on productivity, on soil organic carbon and on carbon extracted from the ecosystem. For transient simulations for the 20th century, a global historical land use data set was developed, providing the annual cover fraction of the 13 CFTs, rain‐fed and/or irrigated, within 0.5° grid cells for the period 1901–2000, using published data on land use, crop distributions and irrigated areas. Several key results are compared with observations. The simulated spatial distribution of sowing dates for temperate cereals is comparable with the reported crop calendars. The simulated seasonal canopy development agrees better with satellite observations when actual cropland distribution is taken into account. Simulated yields for temperate cereals and maize compare well with FAO statistics. Monthly carbon fluxes measured at three agricultural sites also compare well with simulations. Global simulations indicate a ∼24% (respectively ∼10%) reduction in global vegetation (respectively soil) carbon due to agriculture, and 6–9 Pg C of yearly harvested biomass in the 1990s. In contrast to simulations of the potential natural vegetation showing the land biosphere to be an increasing carbon sink during the 20th century, LPJmL simulates a net carbon source until the 1970s (due to land use), and a small sink (mostly due to changing climate and CO2) after 1970. This is comparable with earlier LPJ simulations using a more simple land use scheme, and within the uncertainty range of estimates in the 1980s and 1990s. The fluxes attributed to land use change compare well with Houghton's estimates on the land use related fluxes until the 1970s, but then they begin to diverge, probably due to the different rates of deforestation considered. The simulated impacts of agriculture on the global water cycle for the 1990s are∼5% (respectively∼20%) reduction in transpiration (respectively interception), and∼44% increase in evaporation. Global runoff, which includes a simple irrigation scheme, is practically not affected.

J. Omernik

B. Saelens, J. Sallis, L. Frank

C. Arnold, C. Gibbons

D. Schröter, W. Cramer, R. Leemans et al.

G. Galster, Royce Hanson, M. Ratcliffe et al.

P. Fisher, Charles Arnot, R. Wadsworth et al.

R. Lunetta, J. Knight, J. Ediriwickrema et al.

J. García‐Ruiz

R. Pontius, Laura C. Schneider

E. Uuemaa, Ü. Mander, R. Marja

Lingang Xiao, Yan Ma, Xingdong Gao et al.

Chlorophyll content measured by a Soil and Plant Analyzer Development (SPAD) meter is a key indicator of nitrogen status and photosynthetic capacity in greenhouse-grown tomatoes. However, hyperspectral data collected under greenhouse conditions are strongly affected by leaf posture, illumination variability, high-dimensional redundancy, and multicollinearity, which make small-sample modeling unstable To address these challenges, this study proposes an advanced and lightweight inversion framework integrating multiscale spectral enhancement, deep latent compression, ensemble modeling, and output calibration. A total of 240 leaf spectra (450–950 nm) were processed using Savitzky-Golay (SG) smoothing, fractional-order differentiation (FOD), and Morlet-L7 continuous wavelet transform (CWT) to enhance chlorophyll-sensitive structural features. A convolutional autoencoder (CAE) was used to extract 64-dimensional latent representations, which were fused with red-edge parameters, vegetation indices, and wavelet statistics to form a multi-source feature set. Support vector regression (SVR), gradient boosting regression tree (GBRT), kernel ridge regression (KRR), partial least squares regression (PLSR), and a lightweight Lightformer model were trained, and their out-of-fold (OOF) predictions were integrated through Ridge Stacking, followed by linear calibration. The proposed “Stacking + LinearCal” framework achieved R² = 0.782, RMSE = 1.451, and RPD = 2.156 on the independent test set (n = 72), outperforming all single models. SHAP analysis showed that CAE features, red-edge slope, red-edge inflection point (REIP), and near-infrared tail statistics within 940–950 nm contributed most to prediction. The framework demonstrates high accuracy, stability and interpretability, providing a practical basis for nutrient monitoring in greenhouse tomato production.