Search

Remote sensing has shown potential to provide accurate and real-time information on grassland forage quality, crucial for the management of livestock systems. However, there are still uncertainties that must be considered to make models reliable and practical. A source of discrepancy regards the measurement unit describing forage quality variables, namely either mass-based concentration (%) or mass per surface area content (kg ha−1). Furthermore, phenological patterns largely influence grassland reflectance and have a strong impact on model accuracy. Also, the generalisation of empirical models in heterogeneous grasslands can hinder their applicability. 1) Assess the suitability of retrieving forage quality parameters as concentration (%) versus content (kg ha−1). 2) Investigate the performance of multitemporal compared to phenophase-specific models. 3) Evaluate the generalisation ability of the models. Samples were collected from five farms to determine Dry Matter Yield (DMY) and both, concentration (%) and content (kg ha−1) of forage quality variables including crude protein (CP), neutral and acid detergent fibre (NDF, ADF), and enzyme digestibility of organic matter (EDOM). The relationship between forage quality variables and DMY were analysed by Pearson Correlations and Principal Component Analysis. Reflectance was recorded with a FieldSpec spectroradiometer. Partial Least Squares Regression (PLSR) was used to explore the relationship between forage variables and reflectance. The forage quality variables were strongly correlated to DMY when expressed as content (kg ha−1) r>0.83 but not when expressed as concentration (%). For the best predicted variables, CP and NDF, the results of the PLSR models indicated better performance in concentration-based estimation. CP% was the best predicted variable (R2 cv =0.8, NRMSE cv =9.8 %). Multitemporal models showed overall higher performance (CP%, R2 cv =0.81) than phenophase-specific models (CP%, R2 cv =0.60 green, R2 cv =0.70 green-senescent and R2 cv <0 senescent grasslands). The generalisation ability was low and varied among farms (R2 test 0–0.60). The use of concentration (%) is more accurate and representative of forage quality than content (kg ha−1), which seemed redundant with DMY and misleading from the true nutritive value for livestock. Multitemporal models performed better than phenophase-specific models due to their larger range of values. The ability to predict forage quality in senescent grasslands is low. The usefulness of the models is context-dependent, and their application requires knowledge of the limitations and status of the grasslands. Efforts must be directed toward improving the generalisation ability through the development of models calibrated with larger and more diverse datasets. • Forage quality expressed as concentration (%) is more representative of the nutritive value than as content (kg ha−1). • Forage quality as content (kg ha−1) is redundant with dry matter yield. • Multitemporal models have better performance than phenophase-specific models. • Generalisation ability of empirical models in Mediterranean grasslands is poor. • The usefulness of the models to predict forage quality is context-dependent. [ABSTRACT FROM AUTHOR]

Mountain pastures offer a multitude of ecosystem services (ES) such as fodder for ruminants, habitat for pollinators, climate change mitigation, aesthetic landscape for recreation, and biodiversity conservation. We aimed at analysing to which extend these ES are influenced by small-scale gradients of climate, site conditions and management – and to disentangle relationships among ES and the factors influencing them. Therefore, we quantified ES on six mountain summer farms in two contrasting regions in Switzerland: the Northern Alpine Foothills (lower elevation, higher precipitation, calcareous bedrock) and the Eastern Central Alps (higher elevation, lower precipitation, silicious bedrock). We measured six ES indicators (forage quantity, forage quality, carbon storage, colour abundance, resources for pollinators, plant diversity) and related them to explanatory factors of climate (temperature and precipitation), site conditions (soil fertility, soil acidity, terrain slope) and management (local grazing intensity, remoteness) in 66 study plots, i.e., 11 per farm. A holistic picture of the complex relationships among these factors was drawn by various statistical approaches: allometric line fitting, variance partitioning, and structural equations modelling. We found a huge heterogeneity of ES indicators and explanatory factors on each farm: the variability within farms was even higher than between regions. Variance partitioning and structural equations modelling demonstrated strongest influence of climate and site conditions and revealed trade-offs among ES indicators: High forage quantity and quality were associated with low plant diversity and grassland aesthetics, whereas diversity, aesthetics and pollinator resources were positively correlated with each other. ES indicators were explained by a range of climatic and topographic factors: High precipitation reduced plant diversity, whereas temperature increased forage quantity and quality; slope reduced soil fertility, forage quantity, forage quality and carbon storage; soil fertility in turn increased forage quantity; the farther away a pasture was from the main farm building, the lower was the forage quantity and the higher the plant diversity. Although allometric relations among local grazing intensity and ES indicators were strong, the direct influence of the management factors measured on ES was surprisingly small: Cattle preferred areas of high forage quantity and quality, and carbon storage was higher in these areas. On the other hand, places less visited by cattle offered more pollinator resources, and showed higher aesthetics and plant diversity. Trade-offs among ES prevent the realisation of all ES at the same place, but heterogeneity of mountain pastures allows to realise a broad bundle of contrasting ES on each individual summer farm. • Due to heterogeneity of mountain pastures, multiple ecosystem services are provided at small scale. • Cattle graze mountain pastures very unevenly. • Steep areas show lower forage quantity and quality, soil fertility and C storage. • The higher the biodiversity, the higher are aesthetic appreciation and pollinator resources, but the lower is productivity. • Forage quantity and quality of mountain pastures increase with temperature. [ABSTRACT FROM AUTHOR]