B.A.M. Bouman

LINGRA, a sink/source model to simulate grassland productivity in Europe

A simulation model for the prediction of the productivity of Lolium perenne L. grasslands is described and validated. Simulated key processes are light utilization, leaf formation, leaf elongation, tillering, and carbon partitioning (storage, shoot, root). Source- and sink-limited growth are simulated independently. Sink-limited growth is characterized by temperature-dependent leaf expansion and tiller development, whereas source-limited growth is determined by photosynthetic light-use-efficiency of the canopy and the remobilization of stored carbohydrates in the stubble. At each integration step, commonly 1 day, the available amount of carbon from the source is compared with the carbon required by the sink. The actual growth is determined by the minimum value of either the sink or the source. If the source is in excess of the sink, the surplus is allocated to storage carbohydrates in the stubble. This storage carbon is available for remobilization at times that the sink requires more carbohydrates than are available from photosynthesis. In contrast to previous grassland models, LINGRA describes regrowth after defoliation in a mechanistic way, balanced by temperature-driven remobilization of stored carbohydrates. In order to validate LINGRA, an extensive set of experimental data was used, derived from measurements at 35 sites in Europe. The average error between the observed and predicted yields was 14% at the level of irrigated, and 19% at the level of non-irrigated, treatments for the whole of Europe.

An agroecological modeling approach to explain ERS SAR radar backscatter of agricultural crops

Abstract An agroecological approach is presented to simulate time series of ERS SAR radar backscatter of agricultural crops from integrated crop growth (SUCROS), water balance (SAHEL), and radar backscatter (Cloud) modeling. SUCROS is used to calculate the amount of water in canopies and SAHEL the amount of water in the top soil, which serve both as driving variables for the Cloud model. The methodology was tested on three years of ERS SAR observations, 1992–1994, over an agricultural area in Flevoland, The Netherlands. Area-average model input data and radar backscatter values per crop type were used. Observed temporal radar backscatter curves of sugar beet, potato, and winter wheat were reproduced well with 1–2 dB accuracy in all three years using the same set of SUCROS, SAHEL, and Cloud model parameters per crop type each year. Main reasons for success were the use of “area-average” instead of “field-average” data, the flatness of the area, the relative homogeneity in agroecological conditions, and the near-potential growing conditions.