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An object-oriented simulation model was developed to integrate detailed analyses of perennial ryegrass (Lolium perenne) plant part dimensions, and their mass, cell wall content (CWC) and cell wall digestibility (CWD). The model was validated against data from two field experiments, carried out in The Netherlands (52°N) and Norway (67°N). In the experiments, the effects of temperature × daylength interactions on development, growth and digestibility were determined. Three cultivars differing in heading date were used, and undisturbed growth and regrowth after early (only in The Netherlands) and late cutting were studied. The sensitivity of the model was tested for changes in parameters determining morphological development.A grass plant was modelled as an aggregation of shoot objects. A shoot is an aggregation of phytomers, plus an inflorescence in the reproductive development stage. A phytomer consists of a leaf blade, a leaf sheath, a stem internode and a root. Each phytomer has an axillary bud, which can develop into a new shoot.Morphological development, organic matter (OM) growth and composition were simulated well by the model. Only for regrowing crops in The Netherlands CWC was lower and CWD was higher in the simulations than observed in the field, whereas the differences increased when stem development was more advanced at the moment of cutting. This was attributed to residual stems in the stubble after cutting due to difficulties with machine cutting of lodged crops on the experimental plots. The model can be considered as sufficiently accurate to simulate OM yield and composition of grass cultivars differing in heading date and growing under strongly contrasting temperature and daylength conditions. When the leaf appearance rate, width or length of the leaf blade were increased in the sensitivity analysis, OM yield enhanced, but composition and digestibility of the reproductive grass crops were not systematically affected. [Copyright &y& Elsevier]

Examines the stomatal ammonia compensation point for ammonia of an extensively managed pasture of rye grass in the Netherlands. Role of the concentration of ammonium in the apoplast on the control of the gaseous ammonia concentration inside the leaves; Relationship between leaf nitrogen content and temperature.

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors. [ABSTRACT FROM AUTHOR]

Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model's prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha − 1 ). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha − 1 ). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 > 0.5), rendering them only suitable for qualitative grading. [ABSTRACT FROM AUTHOR]

Agriculture is of huge economic significance in The Netherlands where the provision of real-time, reliable information on crop development is essential to support the transition towards precision agriculture. Optical imagery can provide invaluable insights into crop growth and development but is severely hampered by cloud cover. This case study in the Flevopolder illustrates the potential value of Sentinel-1 for monitoring five key crops in The Netherlands, namely sugar beet, potato, maize, wheat and English rye grass. Time series of radar backscatter from the European Space Agency's Sentinel-1 Mission are analyzed and compared to ground measurements including phenological stage and height. Temporal variations in backscatter data reflect changes in water content and structure associated with phenological development. Emergence and closure dates are estimated from the backscatter time series and validated against a photo archive. Coherence data are compared to Normalized Difference Vegetation Index (NDVI) and ground data, illustrating that the sudden increase in coherence is a useful indicator of harvest. The results presented here demonstrate that Sentinel-1 data have significant potential value to monitor growth and development of key Dutch crops. Furthermore, the guaranteed availability of Sentinel-1 imagery in clouded conditions ensures the reliability of data to meet the monitoring needs of farmers, food producers and regulatory bodies. [ABSTRACT FROM AUTHOR]