IPMwise is a 4th generation online DSS framework for integrated weed management (IWM) developed by IPM Consult. It has presently been customized and commercialized for conditions in Denmark but the framework can be customized and disseminated to many countries.

The main target groups are farmers, farm advisors, schools for young farmers, agricultural universities, agrochemical companies and local distributers of herbicide products.

IPM Consult Aps and Datalogisk A/S, Denmark collaborates in tems of sale and marketing of IPMwise in Denmark. Datalogisk has >25 years experience in design and sale of IT-based farm management systems. Mainly for bigger farms in Denmark but increasingly in other countries.

Plans for future development include development of deep learnning in the field of automatic analysis of digital pictures of weeds in fields, with the ambition to design realtime systems for weed detection, decision making and patch-spraying with herbicides.

In case these plans are successful, a new paradigm for IWM will be in force.

You can explore IPMwise, our commercial solution for Denmark. To get a trial login for IPMwise, please use contact details at the bottom of this page


The results from field validation trials show that the methods and algorithms we use for IPMwise are generally very robust and have a big potential. Also, as the results represent varying geography, climates, weed flora, control measures etc. it indicates that the methods used possess generic qualities in terms of:

• biological models (interactions between crops, weeds, conditions, control measures)

• IT framework

which makes IPMwise a potent candidate for scaling up and out as referred to in present calls for proposals from the EU programme Horizon 2020, i.e.:

• scaling up to locally more complete assortments of crops, weed species, control measures, etc.

• scaling out to additional countries, weed floras, zones of climate, IWM strategies, etc.

Economic benefits

In the IPMwise concept, economic potentials may occur on 2 levels:

• for end-users

• for designers of new strategies for IWM

Benefits for end-users

Potentials for end-users, farmers, advisors etc. arise from options for optimizing input of control measures to conditions on farm and field levels, without jeopardizing requirements for agronomical robustness.

Results from more than 50 years of applied research on application of herbicides show that 20-40% reductions can be achieved by picking ‘lowhanging fruits’ in various countries and crops.

These potentials arise from a systematic exploitation of the following conditions:

• weeds are not homogeneously distributed in time and space

• different weed species cause different types and levels of losses in different crops. For example, some inferior weed species occurring only in low densities may be completely ignored in some crops, while more noxious species may need effective control even when occurring in small densities

• different weed species have different susceptibility to different control measures. For example, some herbicides provide sufficient efficacy on some weed species by use of down to 5% of registered dose rates, while other weeds may require >100% of a registered dose rate.

Benefits for herbicide-designers

Considering development of new herbicides, the mathematical principles, which are integrated in IPMwise for optimization of tank-mixing 2-4 or 2-x way tank-mixtures on a field level, may also be used to predict the expected efficacy of new co-formulations of herbicides and to optimize strategies for multiple herbicide applications.

Consequently, in processes of developing new co-formulated herbicide products, the expected efficacy on various combinations of weed species and conditions, may be simulated use efficacy data on single MOA products as a basis.

When IPM Consult looks into the ‘crystal ball’ of IWM, we see:

• no new herbicides ‘mode-of-action’ (MOA) in the ‘product pipeline’ for the next 5-10 years

• increasing activities from national EPAs to restrict and/or ban still more MOA/products

• more intensive use of remaining MOA leading to increasing problems with herbicide resistance

Consequently, a total reliance on herbicides will soon be seriously challenged. In ‘major crops’ for example in winter oilseed rape and also, of course, in several ‘minor crops’.

So, innovation is required, both in the farming industry and in the weed control industry, and IPMwise is well prepared to support such development.

Economic potential on different levels

The economic potentials of the methods used in DSS listed above, depend on access to efficacy data, where measures for control have been tested in various dose rates / intensities.

In Denmark, where the methods were originally developed, efficacy testing of new herbicides on a weed species level, included systematic test of reduced dosages, for example ½ and ¼ of registered dose rates. In addition, numerous efficacy tests were made in semi-field conditions to quantify influence of additional conditions, e.g. classes of weed growth stages , temperatures / relative humidity and water stress.

However, you consider the possible number of combinations of: countries, crops, weed species and classes of additional conditions of importance, a huge matrix is required, and it is not possible to fill in all combinations based on real data. Even when exploiting options for extrapolation and filling in some gaps by estimates from experts, still many blank spots will exist.

Consequently, IPMwise may be perceived as a dynamic frame and a point of reference to interpret, integrate and optimize many non-perfect sources of documentation. For example to quantify needs for control, expected efficacy of control measures, full-season and longer term strategies, legal restrictions, etc. and match these with actual conditions on farm and field levels.

For example, in ‘minor use’ crops, the only data available may be just product label texts, however, such information may still be valuable to match options for control with conditions on farm and field levels. In major crops, where more real data are likely to be available, more sufisticated and more potent recommendations may be made.