Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables-large seasonal integral (LINTG), length of growing season, and end of season decreasing rate-performed best (MAE = 605 kg· DM/ha; R 2 = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999-2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg· DM/ha; R 2 = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg· DM/ha; R 2 = 0.77),indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas

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a b s t r a c tWe evaluated the cost effectiveness of a decision-support system (DSS) developed for assessing in real time the risk of progression of the main fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head blight) of winter wheat in the Grand-Duchy of Luxembourg (GDL). The study was conducted in replicated field experiments located in four agricultural locations (representative of the main agro-ecological regions of the country) over a 10-year period (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Three fungicide spray strategies were compared: a single DSS-based system and two commonly used spray practices in the GDL, a double-(2T) and a triple-spray (3T) treatment; there was also a non-treated control. In years with a high disease pressure, the DSS-based recommendation resulted in protection of the three upper leaves comparable to that achieved with the 2T and 3T treatments, with significant grain yield increases (P > 0.05) compared to the control (a 4 to 42% increase, depending on the site and year). Overall, the financial gain in treated plots compared with the control ranged from 3 to 16% at the study sites. Furthermore, in seasons when dry weather conditions precluded epidemic development, the DSS recommended no fungicide spray, reducing use of fungicide, and thus saving the cost of the product. The gain in yield for the 2T and 3T plots (compared with control) did not necessarily result in a financial gain during the duration of the experiment. This study demonstrates the potential advantages and profitability of using a DSS-based approach for disease management.

Food waste (FW) generally has high starch content and is rich in nutritional compounds, including lipids, proteins and acids. It is therefore potentially a renewable resource and its utilization for value-added product development is gaining interest. In this study, FW from a cafeteria was used as sole substrate for glucose production, and the fermentation conditions for optimum glucose yield were firstly optimized using response surface methodology. It was found that glucose yield was significantly affected by α-amylase loading, solid loading and temperature. The optimal conditions were found to be an α-amylase loading of 12.15 U/g FW, a solid loading of 22.4% and a culture temperature of 83.8°C for 90 min, which resulted in a maximum glucose yield of 217 mg/g. Secondly, in order to increase the final glucose concentration, an in situ produced fungal mash rich in glucoamylase was obtained from Aspergillus awamori which resulted in a glucose concentration of 99.1 g/L. When a fungal mash rich in cellulase obtained from Trichoderma reesei was combined with glucoamylase, a maximum of 140 g/L of glucose was obtained. This study showed that FW is a suitable substrate for saccharification with high conversion yield, indicating the potential utilization of food wastes for value-added chemicals production.

The effects of bias (over-and underestimates) in estimates of disease severity on hypothesis testing using different assessment methods was explored. Nearest percentage estimates (NPE), the Horsfall-Barratt (H-B) scale, and two linear category scales (10% increments, with and without additional grades at low severity) were compared using simulation modelling to assess effects of bias. Type I and type II error rates were used to compare two treatment differences. The power of the H-B scale and the 10% scale were least for correctly testing a hypothesis compared with the other methods, and the effects of rater bias on type II errors were greater over specific severity ranges. Apart from NPEs, the amended 10% category scale was most often superior to other methods at all severities tested for reducing the risk of type II errors. It should thus be a preferred method for raters who must use a category scale for disease assessments. Rater bias and assessment method had little effect on type I error rates. The power of the hypothesis test using unbiased estimates was most often greater compared with biased estimates, regardless of assessment method. An unanticipated observation was the greater impact of rater bias compared with assessment method on type II errors. Knowledge of the effects of rater bias and scale type on hypothesis testing can be used to improve accuracy and reliability of disease severity estimates, and can provide a logical framework for improving aids to estimate severity visually, including standard area diagrams and rater training software.