6 Perspectives

Pesticides Research No. 116 2008 of fungicide application in winter wheat

6.1 Application of sensor-based algorithms
6.2 Research issues

Sensor-based graduation of fungicide application in crops can be seen as an integral part of precision agriculture, where the combination of data collection, data communication, biological models and decision support systems enable more precise determination of need for crop management inputs in a spatial context (Srinivasan, 2006). This development was initially driven by the availability of georeferenced technologies, in particular the GPS system, and by new sensor systems to monitor crops and soils. However, more recently there has been an increasing recognition that there is a substantial need to improve the decision support models and algorithms, which converts the sensor readings to decisions and variable application rates (Stafford, 2006).

Precision agriculture has in Denmark mostly been used for patch spraying of weeds and for varying N fertiliser rates. However, experiments and modelling studies have shown that the economic benefit for spatially varying N fertiliser rates in Denmark is very small (Berntsen et al., 2006). There may be some environmental benefits of varying N fertiliser rates, but this still needs to be better quantified. The results presented here for sensor-based graduation of fungicide in winter wheat seems to resemble the situation for spatially varying N fertiliser rates. There is very little economic gain, whereas there is probably an environmental benefit, in this case in the form of reduced fungicide deposition on the soil surface.

The reasons for the small economic gains from sensor-based graduation of either fertilisers or fungicides in Denmark is probably related to the fact, that there has already been a large focus on reducing and targeting N fertiliser rates and fungicides to the need of the crop at field scale. A high economic gain from spatially varying fertiliser or fungicide inputs is only obtained if there is a strong non-linear response between input rate and grain yield within the field, or if there is a general over-application in the reference situation. Neither of this is the general picture in Danish agriculture. However, there may certainly be situations (particular locations or climatic conditions), where such conditions may arise, and the use of this technology should therefore not be discarded.

One of the main reasons for investing in technologies for spatially varying fertiliser or pesticide application rates is the ever-increasing farm and field sizes in Danish agriculture. This trend is caused by the economy of scale with increasing profitability caused by reductions in operational costs per area. This results in larger intra-field spatial variation in soils and crops, giving rise to spatial variation in need for inputs, including fungicides. The use of automated detection of need for fungicide application may increase the willingness by farmers to accept reduced dosages and thereby contribute to the political targets for reduced pesticide use in Danish agriculture.

6.1 Application of sensor-based algorithms

The results have shown that it is possible to derive algorithms, which based on commercially available sensor measurements can be used for varying fungicide application to winter wheat, where septoria is the main disease problem. For practical applications to be developed, these algorithms need to be implemented in commercial systems such as the N-sensor system developed by Yara, which is primarily used for adjusting N fertiliser rates, but which has also been applied for varying fungicide dosages.

However, before the algorithms can be effectively integrated in commercial systems, some remaining issues need to be dealt with, and the algorithms as well as the total system would require some additional test. These issues can be summarised in the following points:

The models should account for variety differences in susceptibility to septoria. This may be difficult to obtain for the empirical model, where it probably will be the coefficient of the dose response in eqn (13) that needs changing. For the causal model, it will also be the coefficient of the dose response in eqn (12) that needs to be changed. However, available data from fungicide experiments in winter wheat may be used for estimating these parameters.
The causal model needs information on the septoria disease pressure at GS39. It may be difficult for farmers to visually assess septoria at this stage. Therefore it is suggested that such information is made available on the web (e.g. PlanteInfo). This information could be based on recordings in dedicated fields across the country, e.g. from the existing Danish network for disease monitoring. However, the applicability of such information needs to be tested.
There is a need to explore whether additional available field maps, e.g. of crop yield distribution, can be used for improving the precision on the algorithms.
There is a need to test and document the algorithms in field experiments over several (2-3) years to ensure that algorithms function properly and to estimate the economic and environmental benefits of applying the system.

Given that steps are taken to resolve these needs for further model refinement and implementation, it should be realistic to implement sensor-based graduation of fungicide application to winter wheat within 3-4 years.

6.2 Research issues

We verified one of the initial hypotheses in the project that the crop conduciveness to septoria is related to leaf N concentrations. However, it is not currently possible to measure leaf N concentration using tractor-mounted sensors. In theory the leaf N concentration should be related to the ratio of RVI to LAI. However, it is very difficult to measure LAI values of above 2.5 to 3 using sensors that measure from above the crop canopy. This is due to a saturation response of the sensors at higher LAI. There seems to be some scope for estimating LAI in dense canopies by only measuring gap fraction profile in the upper part of the canopy. However, such approximate methods are likely to be influenced by variety and management effects affecting leaf angle distribution and canopy height. Such methods therefore need to be developed and tested for a range of conditions. Another problem with the estimation of leaf N concentration from indirect measurements is the influence of weeds and other factors affecting either RVI or LAI measurements. The sensitivity of the leaf N concentration measurements to such factors needs to be studied and ways of dealing with this should be researched.

The results indicated that it was more difficult to obtain good fungicide control of septoria in crops with high leaf N concentrations (Table 16) and/or at high N fertiliser rates (Figs. 15 to 18). The dose-response functions evidently varied between the experiments and in some cases also between N fertiliser treatments, and a further analysis of this may point towards possibilities of better predicting the dose-response under practical conditions. This is needed for better evaluating situations, where fungicide use may be avoided or of particular benefit. This may be possible since there seems to be a relationship to the crop N nutrition.

There was a rather poor relationship between yield gain from fungicide application and the level of septoria control, despite the fact that septoria was the main yield reducing disease. A better relationships was obtained by Olesen et al. (2003b), who however used a different indicator for septoria disease based on several observations during the growing season, whereas the index used in this study is based on observations at two times only (GS65 and GS75). However, this is possibly not the only cause for the lack of response of grain yield to septoria, and other reasons should be investigated, including the effects of other diseases and uncertainties in disease assessments.

The results have shown that crop measurements of leaf N concentration relate to crop susceptibility to septoria disease. However, there are also considerable variety differences in susceptibility to septoria. Only two different varieties were used in this project. However, the results indicate that there may be variety differences in leaf N concentration and possibly in other crop physiological traits that may relate to crop susceptibility to disease. If such differences can be revealed using sensor based measurements, this also opens opportunities for breeding for more resistant varieties.