Ulf Böttcher

Comparing different approaches to calculate the effects of heterogeneous root distribution on nutrient uptake: a case study on subsoil nitrate uptake by a barley root system

Simulation models of nutrient uptake of root systems starting with one-dimensional single root approaches up to complex three-dimensional models are increasingly used for examining the interacting of root distribution and nutrient uptake. However, their accuracy was seldom systematically tested. The objective of the study is to compare one-dimensional and two-dimensional modelling approaches and to test their applicability for simulation of nutrient uptake of heterogeneously distributed root systems giving particular attention to the impact of spatial resolution. Therefore, a field experiment was carried out with spring barley (Hordeum vulgare L. cv. Barke) in order to obtain data of in situ root distribution patterns as model input. Results indicate that a comparable coarse spatial resolution can be used with sufficient modelling results when a steady state approximation is applied to the sink cells of the two-dimensional model. Furthermore, the accuracy of the model was clearly improved compared to a simple zero sink approach assuming both near zero concentrations within the sink cell and a linear gradient between the sink cell and its adjacent neighbours. However, for modelling nitrate uptake of a heterogeneous root system a minimum number of grid cells is still necessary. The tested single root approach provided a computational efficient opportunity to simulate nitrate uptake of an irregular distributed root system. Nevertheless, two-dimensional models are better suited for a number of applications (e.g. surveys made on the impact of soil heterogeneity on plant nutrient uptake). Different settings for the suggested modelling techniques are discussed.

A phenological model of winter oilseed rape according to the BBCH scale

Abstract. Implementation of the BBCH coding system for winter oilseed rape (OSR) phenology simulation can allow detailed description of crop ontogeny necessary for crop management and crop growth modelling. We developed such a BBCH model using an existing approach (Habekotté 1997). The new model describes winter OSR development by a combination of differential and conversion equations based on the structure of the BRASNAP-PH model (Habekotté 1997). Six phenological phases were reproduced daily according to the BBCH codes (00–89): emergence (00–09), leaf development (10–19), stem elongation (30–39), inflorescence emergence (50–59), flowering (60–69) and pod development-maturation period (70–89). The model takes into account temperature (including vernalisation) and photoperiod as the main environmental forces affecting crop phenology. The macro stages of leaf development and shooting were reproduced considering the rates of leaf appearance and internode extension. Model calibration and validation were performed using an extensive database of phenological observations collected from several experimental sites across France (n = 144), Germany (n = 839) and Italy (n = 577). The stability of the parameterisation was checked by a cross-calibration procedure. Applied to the independent datasets used for validation and cross-validation, the model was able to predict the whole-crop cycle with a root mean square error (RMSE) of 2.8 and 3.2 BBCH stages, respectively. Particularly accurate predictions of winter OSR development were obtained with the Italian datasets (RMSE: 2.1 and 2.3 BBCH stages for validation and cross-validation, respectively). Considering the phenological phases separately, emergence, leaf development, flowering and the pod development–maturation period were simulated with RMSE of 1.0, 2.4, 2.9 and 3.2 BBCH stages, respectively (validation datasets). Slightly higher uncertainty emerged in the prediction of stem elongation and inflorescence emergence phases (RMSE: 3.5 and 4.1 BBCH stages, validation datasets). The model reproduced winter OSR development with a sufficient degree of accuracy for a wide range of years, locations, sowing dates and genotypes, resulting in an efficient and widely applicable prediction tool with relevant practical purposes in the crop management scheduling.

Specific leaf area development of autumn-sown sugar beet ( Beta vulgaris L. ) on different sowing dates in northern Germany

In most regions, sugar beet is normally sown as a spring crop. If sown in autumn the crop remains on the field over winter and may achieve fast re-growth in spring from assimilates stored within the beet, allowing earlier leaf growth and light interception in spring. The specific leaf area (SLA) (ratio between leaf surface and leaf mass) is mainly affected by leaf area expansion and consequently affects productivity in early growth stages. The aim of the present study was (i) to examine the SLA dynamics of autumn-sown sugar beet before and after winter and (ii) to develop an empiric approach describing SLA changes during the growth period. A field trial in northern Germany with three different sowing times (mid-April, mid-June and mid-August) and varying plant densities (148 000, 246 000 and 370 000 plants/ha) was carried out in 2009/10 to 2011/12. The average SLA of the canopy was the highest (>25 m 2 /kg) directly after emergence, then decreased until autumn ( 2 /kg) and increased again up to 20 m 2 /kg during re-growth of winter sugar beet in spring. A stepwise multiple regression analysis revealed mean photosynthetically active radiation over 10 days before measurement (PAR mean ), leaf area index (LAI), mean temperature over 10 days before measurement ( T mean ) and temperature sum since sowing ( T sum ) as the main influences on SLA dynamics. The strongest correlation to SLA was shown by T mean ( r = 0·69) and the weakest by T sum ( r = −0·28). A multiple linear regression model was fitted to the dataset with T mean , PAR mean and log ( T sum ) achieving an adjusted R 2 of 0·64. This empirical equation is suitable for use in a crop growth model for winter sugar beet.