Sclerotinia stem rot (SSR) is an increasing threat to winter oilseed rape (OSR) in Germany and other European countries due to the growing area of OSR cultivation. A forecasting model was developed to provide decision support for the fungicide spray against SSR at flowering. Four weather variables-air temperature, relative humidity, rainfall, and sunshine duration-were used to calculate the microclimate in the plant canopy. From data reinvestigated in a climate chamber study, 7 to 11 degrees C and 80 to 86% relative humidity (RH) were established as minimum conditions for stem infection with ascospores and expressed as an index to discriminate infection hours (Inh). Disease incidence (DI) significantly correlated with Inh occurring post-growth stage (GS) 58 (late bud stage) (r(2) = 0.42, P /= Inh(i). Historical field data (1994 to 2004) were used to assess the impact of agronomic factors on SSR incidence. A 2-year crop rotation enhanced disease risk and, therefore, lowered the infection threshold in the model by a factor of 0.8, whereas in 4-year rotations, the threshold was elevated by a factor 1.3. Number of plants per square meter, nitrogen fertilization, and soil management did not have significant effects on DI. In an evaluation of SkleroPro with 76 historical (1994 to 2004) and 32 actual field experiments conducted in 2005, the percentage of economically correct decisions was 70 and 81%, respectively. Compared with the common practice of routine sprays, this corresponded to savings in fungicides of 39 and 81% and to increases in net return for the grower of 23 and 45 euro/ha, respectively. This study demonstrates that, particularly in areas with abundant inoculum, the level of SSR in OSR can be predicted from conditions of stem infection during late bud or flowering with sufficient accuracy, and does not require simulation of apothecial development and ascospore dispersal. SkleroPro is the first crop-loss-related forecasting model for a Sclerotinia disease, with the potential of being widely used in agricultural practice, accessible through the Internet. Its concept, components, and implementation may be useful in developing forecasting systems for Sclerotinia diseases in other crops or climates.

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.

The accuracy of plant growth models depends strongly on a precise calculation of radiation uptake. Numerous approaches exist to estimate light absorption in spatially heterogeneous canopies, but these either have restrictions with respect to canopy structure or involve complex and inflexible calculations. The objective of this study was to develop a simulation tool to assess radiation penetration into canopies that should (i) give details on light absorption in heterogeneous canopy architectures and (ii) comprise simple and easily adaptable routines. In the model, the complete canopy volume is subdivided into cubic units that are either empty or filled with leaf area. Leaf area can be distributed in an arbitrarily chosen geometric solid positioned anywhere in the model domain. Transmission through the cubes is calculated by following the path of solar rays from the top of the canopy to ground level. Daily canopy absorption is calculated separately for direct and diffuse radiation, taking reflection and scattering of the direct beam into account. Using only a few readily obtainable parameters, a close agreement between simulated and measured canopy transmission of a cauliflower (Brassica oleracea var. botrytis L.) crop was found (r2=0.97) Comparing different canopy structures ranging from single‐plant canopies to a closed canopy gave detailed information on the absorption characteristics and the distribution of light absorption in individual plants. Results for closed canopies and row crops were consistent with those of earlier models. It is thus useful as a reference model to identify possible simplifications in the quantification of light interception by heterogeneous crops.

Simple models were developed to quantitatively describe (a) dry matter production and (b) the effects of competition on dry matter partitioning of Chenopodium album L. Data on total biomass and its allocation to roots, stems and leaves were obtained from field experiments with C. album planted at two densities in pure and mixed stands with either cauliflower (Brassica oleracea L. convar. botrytis var. botrytis) or faba beans (Vicia faba L.). After germination, C. album produced biomass rapidly; weeds planted at low density accumulated 20–30% less dry matter than plants growing at a 2.5‐fold higher density per m2. A close correlation between the transmission of photosynthetically active radiation (PAR) and leaf area index was found. Biomass production was linearly related to cumulative PAR intercepted, but a seasonal variation of the radiation‐use efficiency could not entirely be explained. The root:shoot ratio was constant, whereas the level of competitive stress changed the distribution pattern between stems and leaves. With increasing competition in the cauliflower experiments, C. album allocated relatively more biomass to stems than to leaves; this was less evident in mixtures with faba beans. In field vegetable production with an abundant water and nutrient supply, the growth processes of C. album may be described quantitatively using simple functional relationships.

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