Torfinn Torp
Senior Adviser (OAP Agreement)
Abstract
Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model.
Abstract
Weeds may reduce crop yields significantly if managed improperly. However, excessive herbicide use increases risk of unwanted effects on ecosystems, humans and herbicide resistance development. Weed harrowing is a traditional method to manage weeds mechanically in organic cereals but could also be used in conventional production. The weed control efficacy of weed harrowing can be adjusted by e.g. the angle of the tines. Due to its broadcast nature (both crop and weed plants are disturbed), weed harrowing may have relatively poor selectivity (i.e. small ratio between weed control and crop injury). To improve selectivity, a sensor-based model which takes into account the intra-field variation in weediness and “soil density” in the upper soil layer (draft force of tines), is proposed. The suggested model is a non-linear regression model with three parameters and was based on five field trials in spring barley in SE Norway. The model predicts the optimal weed harrowing intensity (in terms of the tine angle) from the estimated total weed cover and SD per sub-field management unit, as well as a pre-set biological weed threshold (defined as the acceptable total weed cover left untreated). Weed cover and SD were estimated with RGB images (analysed with custom-made machine vision) and an electronic load cell, respectively. With current parameter values, the model should be valid for precision weed harrowing in spring barley in SE Norway. The next step is to test the model, and if successful, adjust it to more cereal species. Weeds may reduce crop yields significantly if managed improperly. However, excessive herbicide use increases risk of unwanted effects on ecosystems, humans and herbicide resistance development. Weed harrowing is a traditional method to manage weeds mechanically in organic cereals but could also be used in conventional production. The weed control efficacy of weed harrowing can be adjusted by e.g. the angle of the tines. Due to its broadcast nature (both crop and weed plants are disturbed), weed harrowing may have relatively poor selectivity (i.e. small ratio between weed control and crop injury). To improve selectivity, a sensor-based model which takes into account the intra-field variation in weediness and “soil density” in the upper soil layer (draft force of tines), is proposed. The suggested model is a non-linear regression model with three parameters and was based on five field trials in spring barley in SE Norway. The model predicts the optimal weed harrowing intensity (in terms of the tine angle) from the estimated total weed cover and SD per sub-field management unit, as well as a pre-set biological weed threshold (defined as the acceptable total weed cover left untreated). Weed cover and SD were estimated with RGB images (analysed with custom-made machine vision) and an electronic load cell, respectively. With current parameter values, the model should be valid for precision weed harrowing in spring barley in SE Norway. The next step is to test the model, and if successful, adjust it to more cereal species.
Authors
Haftamu Gebretsadik Gebrehiwot Jens Bernt Aune Ole Martin Eklo Torfinn Torp Lars Olav BrandsæterAbstract
Field experiments were conducted in 2015 and 2016 to study the effect of tillage frequency, seed rate, and glyphosate on teff and weeds. The experiments were arranged in a split plot design with three replications consisting of tillage frequency (conventional, minimum, and zero tillage) as the main plot and the combination of seed rate (5, 15, and 25 kg ha−1) and glyphosate (with and without) as subplots. Results showed that zero tillage reduced teff biomass yield by 15% compared to minimum tillage and by 26% compared to conventional tillage. Zero tillage and minimum tillage also diminished grain yield by 21% and 13%, respectively, compared to conventional tillage. Lowering the seed rate to 5 kg ha−1 reduced biomass yield by 22% and 26% compared to 15 and 25 kg ha−1, respectively. It also reduced the grain yield by around 21% compared to 15 and 25 kg ha−1 seed rates. Conventional tillage significantly diminished weed density, dry weight, and cover by 19%, 29%, and 33%, respectively, compared to zero tillage. The highest seed rate significantly reduced total weed density, dry weight, and cover by 18%, 19%, and 15%, respectively, compared to the lowest seed rate. Glyphosate did not affect weed density but reduced weed dry weight by 14% and cover by 15%. Generally, sowing teff using minimum tillage combined with glyphosate application and seed rate of 15 kg ha−1 enhanced its productivity and minimized weed effects.