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Laser weeding may contribute to less dependency on herbicides and soil tillage. Several research and commercial projects are underway to develop robots equipped with lasers to control weeds. Artificial intelligence can be used to locate and identify weed plants, and mirrors can be used to direct a laser beam towards the target to kill it with heat. Unlike chemical and mechanical weed control, laser weeding only exposes a tiny part of the field for treatment. Laser weeding leaves behind only ashes from the burned plants and does not disturb the soil. Therefore, it is an eco-friendly method to control weed seedlings. However, perennial weeds regrow from the belowground parts after the laser destroys the aerial shoots. Depletion of the belowground parts for resources might be possible if the laser continuously kills new shoots, but it may require many laser treatments. We studied how laser could be used to destroy the widespread and aggressive perennial weed Elymus repens after the rhizomes were cut into fragments. Plants were killed with even small dosages of laser energy and stopped regrowing. Generally, the highest efficacy was achieved when the plants from small rhizomes were treated at the 3-leaf stage.

Background – In the successful cultivation of crops for improved quality and high yield, the health status of the originating seed is of utmost importance as this can affect the performance and quality of the ensuring plant after germination. Seed sanitation and use of biological control agents (BCAs) have become a major method of reducing pathogen and insect infestation and improving crop yield. This applies to most crops including Quinoa whose cultivation in the more humid and colder regions of the world have resulted in the manifestation of new plant pathogens and diseases. Methods – A total of 4 growth experiments were conducted using the Puno and Vikinga quinoa variety with 3 replicates of 30 seeds each, comprising of 9 different treatments (1 control with just water, 2 with pathogens alone, 2 with BCAs alone. 4 with 1 pathogen plus 1 BCA) to evaluate the effect of pathogens on quinoa seedlings. The impact of beneficial fungi that show antagonistic activity towards pathogenic fungi was also investigated to determine their potential as biocontrol agents. Physical measurements and dry weight analyses on the harvested seedings showed increased biomass accumulation in seedlings with pathogen plus BCA as against those with the pathogen alone. In addition, their seed germination rates were also higher. Results – Statistical analysis showed significant difference between plant biomass accumulation (dry weight) and treatments. Supporting our hypothesis that BCAs do improve the growth of quinoa seedlings inoculated with certain fungal pathogens. Conclusions – The obtained results underlined the importance and role of biopriming in the prevention and control of certain seed-borne pathogens. It’s further study and adoption will have a significant positive impact in overall plant yield, biodiversity and sustainable agriculture.

With challenges posed by chemical and mechanical weed control, there are now several research and commercial projects underway to develop autonomous vehicles equipped with lasers to control weeds in field crops. Recognition systems based on artificial intelligence have been developed to locate and identify small weed seedlings, and mirrors can direct a laser beam towards the target to kill the weed with heat. Unlike chemical and mechanical weed control, laser weeding only exposes a small area of the field for the treatment. Laser weeding leaves no chemicals in the field after the treatment or does not move the soil which may harm crop roots and non-target organisms. Yet, it is well- known that laser beams can harm living organisms; the effect on the environment and fauna should be studied before laser weeding becomes a common practice. This project aimed to study the effect of laser on some living non-target organisms. We investigated the effect of laser treatment on the mortality of two species of earthworms (Enchytraeus albidus and Enchytraeus crypticus), larvae, pupas, and beetles of yellow mealworm beetles (Tenebrio molitor) and the two-spotted lady beetle (Adalia bipunctata) for increasing dosages of laser energy. In all earthworms experiments except one, the mortality rates of the worms living in the uppermost soil layer of clay, sandy, and organic soil exposed to laser heating were not significantly different from the controls even with laser dosages up to 236 J mm -2 . Laser doses sufficient to kill plants were lethal to the insects, and lower doses that did not kill plants, killed or harmed the insects across all life stages tested. The larger beetles survived higher doses than smaller. Laser weeding is a relatively new technology and not yet widely practiced or commercialized. Therefore, we do not discuss and compare the costs of the different weeding method

Yield maps give farmers information about growth conditions and can be a tool for site-specific crop management. Combine harvesters may provide farmers with detailed yield maps if there is a constant flow of a certain amount of biomass through the yield sensor. This is unachievable for grass seeds because the weight of the intake is generally too small to record the variation. Therefore, there is a need to find another way to make grass seed yield maps. We studied seed yield variation in two red fescue (Festuca rubra) fields with variation in management and soil fertility, respectively. We estimated five vegetation indices (VI) based on RGB images taken from a drone to describe yield variation, and trained prediction models based on relatively few harvested plots. Only results from the VI showing the strongest correlation between the index and the yield are presented (Normalized Excess Green Index (ExG) and Normalized Green/Red Difference Index (NGRDI)). The study indicates that it is possible to predict the yield variation in a grass field based on relatively few harvested plots, provided the plots represent contrasting yield levels. The prediction errors in yield (RMSE) ranged from 171 kg ha−1 to 231 kg ha−1, with no clear influence of the size of the training data set. Using random selection of plots instead of selecting plots representing contrasting yield levels resulted in slightly better predictions when evaluated on an average of ten random selections. However, using random selection of plots came with a risk of poor predictions due to the occasional lack of correlation between yield and VI. The exact timing of unmanned aerial vehicles (UAVs) image capture showed to be unimportant in the weeks before harvest.

Yield maps give farmers information about growth conditions and can be a tool for site-specific crop management. Combine harvesters may provide farmers with detailed yield maps if there is a constant flow of a certain amount of biomass through the yield sensor. This is unachievable for grass seeds because the weight of the intake is generally too small to record the variation. Therefore, there is a need to find another way to make grass seed yield maps. We studied seed yield variation in two red fescue (Festuca rubra) fields with variation in management and soil fertility, respectively. We estimated five vegetation indices (VI) based on RGB images taken from a drone to describe yield variation, and trained prediction models based on relatively few harvested plots. Only results from the VI showing the strongest correlation between the index and the yield are presented (Normalized Excess Green Index (ExG) and Normalized Green/Red Difference Index (NGRDI)). The study indicates that it is possible to predict the yield variation in a grass field based on relatively few harvested plots, provided the plots represent contrasting yield levels. The prediction errors in yield (RMSE) ranged from 171 kg ha−1 to 231 kg ha−1, with no clear influence of the size of the training data set. Using random selection of plots instead of selecting plots representing contrasting yield levels resulted in slightly better predictions when evaluated on an average of ten random selections. However, using random selection of plots came with a risk of poor predictions due to the occasional lack of correlation between yield and VI. The exact timing of unmanned aerial vehicles (UAVs) image capture showed to be unimportant in the weeks before harvest.

The rising prices of peat moss and coco-peat caused by the gradual depletion of natural resources have derived researchers to explore new soilless alternatives which are inexpensive, productive, and sustainable. Conversion of sawdust waste into productive compost can be an applicable solution. The research objective was to test the composted mixtures of Bombax ceiba L. sawdust as soilless substrates to raise sugarcane seedlings. Over two years of experiments, a range of B. ceiba sawdust treatments were used. The germination, seedlings height, leave numbers plant−1, chlorophyll content index, leaf area, the diameter of the stem, and shoot and root (fresh and dry) weights were higher in seedlings grown in all B. ceiba sawdust compost mixtures than in soil. In conclusion, B. ceiba 60 %, mixed with banana peels 20 %, eggshells 20 % (v/v), and urea at a rate of 158 kg N ha−1 can be a productive and inexpensive soilless alternative to raise sugarcane seedlings., The rising prices of peat moss and coco-peat caused by the gradual depletion of natural resources have derived researchers to explore new soilless alternatives which are inexpensive, productive, and sustainable. Conversion of sawdust waste into productive compost can be an applicable solution. The research objective was to test the composted mixtures of Bombax ceiba L. sawdust as soilless substrates to raise sugarcane seedlings. Over two years of experiments, a range of B. ceiba sawdust treatments were used. The germination, seedlings height, leave numbers plant−1, chlorophyll content index, leaf area, the diameter of the stem, and shoot and root (fresh and dry) weights were higher in seedlings grown in all B. ceiba sawdust compost mixtures than in soil. In conclusion, B. ceiba 60 %, mixed with banana peels 20 %, eggshells 20 % (v/v), and urea at a rate of 158 kg N ha−1 can be a productive and inexpensive soilless alternative to raise sugarcane seedlings.

Quinoa (Chenopodium quinoa Willd.) is a native American crop mainly grown in the Andes of Bolivia and Peru. During the last decades, the cultivation of quinoa has expanded to more than 125 countries. Since then, several diseases of quinoa have been characterized. A leaf disease was observed on quinoa plants growing in an experimental plot in Eastern Denmark in 2018. The symptoms produced by the associated fungi consisted of small yellow blotches on the upper surface of leaves with a pale chlorotic halo surrounding the lesion. These studies used a combination of morphology, molecular diagnostics, and pathogenicity test to identify two different Alternaria species belonging to Alternaria section Infectoriae and alternata as the causal agent of observed disease symptoms. To the best of our knowledge, this is the first report of Alternaria spp. as foliar pathogens of quinoa. Our findings indicate the need for additional studies to determine potential risks to quinoa production.