Abstract
With a growing human population facing multiple global change drivers (i.e. climate change and land management change), the future of food security is of major importance. Sustainable agriculture is therefore key to ensure food supply and food security under future climatic conditions. Forage provision (composed of forage quantity and forage quality) is an important ecosystem service of grasslands for dairy production. However, monitoring forage quality in semi-natural species-rich grasslands is rarely done due to the inherent complexity in determining forage quality, high variability within natural systems and financial and workload restrictions. Here, we i) demonstrate the ability of visible-near-infrared spectroscopy (vis-NIRS) to predict forage quality of bulk samples of species-rich montane pastures and ii) show its potential to reveal effects of two key global change drivers, climate change and land management, on forage quality. Spectral information and chemometrics allowed us to predict three (ash, fat and protein) out of four analyzed forage quality parameters with high accuracy. Land management intensity strongly influenced species-rich grasslands’ protein and fat content, whereas altered climatic conditions influenced ash and fat content. High management intensity increased protein content of high- and mid-elevation pastures by 22 % and 30 % and fat content by 19 % and 20 % respectively. Though forage quality was improved by intensive land management, extensive land management generally revealed sufficient forage quality for livestock. Vis-NIRS provides a rapid, cost-efficient and high-throughput technique to analyze forage quality, revealing effects of global change drivers on forage quality of grasslands. This approach will help to support stakeholders assure optimal nutrition feeding of livestock and achieve steps towards sustainable agriculture.
Highlights
Food demand is increasing with increasing human population (FAO, 2013; Godfray et al, 2010), land area suitable for agricultural production is limited (Azar, 2005; Stoll-Kleemann and O’Riordan, 2015; West et al, 2014) and intensification is unlikely to provide sustainable solutions (Allan et al, 2015; Foley et al, 2005; Gossner et al, 2016; Laliberté et al, 2010)
With combination with a variable selection procedure (CARS)-Partial-Least-Square regression (PLS) we identified robust and parsimonious models with high accuracy and predictive power in both internal as well as in external validation
Across all four modeled parameters, the best model was obtained for protein with the model evaluation of external validation (R2val = 0.83; RPDval = 2.4; RMSEval = 1.06) and of calibration (R2cal = 0.93; RPDcal = 3.76; RMSEcal = 0.47)
Summary
Food demand is increasing with increasing human population (FAO, 2013; Godfray et al, 2010), land area suitable for agricultural production is limited (Azar, 2005; Stoll-Kleemann and O’Riordan, 2015; West et al, 2014) and intensification is unlikely to provide sustainable solutions (Allan et al, 2015; Foley et al, 2005; Gossner et al, 2016; Laliberté et al, 2010). To meet future human food demands, agriculture must sustainably increase production from less land through efficient use of natural resources and with lowest impact on the environment (Hobbs et al, 2008). Forage provision determines carrying capacity and performance of livestock (Bailey et al, 1996; Schauer et al, 2005) and consists of two parts: quantity (yield or production) and quality (the nutritional value for livestock) (Beeri et al, 2007). Forage quality is important for maintaining a sufficient supply of energy and nutrients
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