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Food system innovations will be instrumental to achieving multiple Sustainable Development Goals (SDGs). However, major innovation breakthroughs can trigger profound and disruptive changes, leading to simultaneous and interlinked reconfigurations of multiple parts of the global food system. The emergence of new technologies or social solutions, therefore, have very different impact profiles, with favourable consequences for some SDGs and unintended adverse side-effects for others. Stand-alone innovations seldom achieve positive outcomes over multiple sustainability dimensions. Instead, they should be embedded as part of systemic changes that facilitate the implementation of the SDGs. Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging. Trade-offs with undesirable consequences are manageable through the development of well planned transition pathways, careful monitoring of key indicators, and through the implementation of transparent science targets at the local level., The Lancet Planetary Health, 5 (1), ISSN:2542-5196

Mapping the within-field variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands. Satellite imagery and the delineation of management zones based on remote sensing plays a key role. However, satellite imagery is dependent on a cloud-free view, which is especially challenging in temperate regions such as Northern Europe. This disadvantage can be overcome with unmanned aerial vehicles (UAV), which provide an alternative to satellites. An investigation was conducted to establish whether UAV imagery can generate similar crop heterogeneity maps to satellites (Sentinel 2) and the extent to which crop heterogeneity and management zones can be reproduced by repeated data collection within short time intervals. Three winter wheat fields were monitored during the growing season. Two vegetation indices (NDVI and MSAVI2) based on red and near-infrared (NIR) reflectance were calculated to delineate fields into five management zones based on NDVI raster maps using quintiles. The Pearson correlation coefficient, the Nash–Sutcliffe agreement coefficient and the smallest real difference coefficient (SRD), also called the reproducibility coefficient were used to evaluate the reproducibility. NDVI and MSAVI2 gave similar results, but NDVI was a slightly better descriptor of crop heterogeneity after canopy closure and NDVI was used for the remainder of the study. The results showed that substitution of satellite data with UAV data resulted in an average reclassification of 10 m by 10 m management zones corresponding to 58% of the total field area. Reclassification means that management pixels were classified differently according to origin of images. Repeated satellite and UAV imagery resulted in 39% and 47% reclassification, respectively. The results showed that the reproduction of remote sensing data with different sensor systems added more measurement error to measurements than was the case with repeated measurements using the same sensor systems. In this study, SRD averaged 2.5 management zones, which means that differences up to 2.5 management zones were within the measurement error. This paper discusses the practical aspects of these findings and clarifies that the reclassification of management zones is depending on the heterogeneity of the studied fields. Therefore, the achieved results may not be generalized but the presented methodology can be used in future studies.

The developments of information and automation technologies have opened a new era for weed management to fit physical and chemical control treatments to the spatial and temporal heterogeneity of weed distributions in agricultural fields. This review describes the technologies of site-specific weed management (SSWM) systems, evaluates their ecological and economic benefits and gives a perspective for the implementation in practical farming. Sensor technologies including 3D cameras, multispectral imaging and Artificial Intelligence (AI) for weed classification and computer-based decision algorithms are described in combination with precise spraying and hoeing operations. Those treatments are targeted for patches of weeds or individual weed plants. Cameras can also guide inter-row hoes precisely in the centre between two crop rows at much higher driving speed. Camera-guided hoeing increased selectivity and weed control efficacy compared with manual steered hoeing. Robots combine those guiding systems with in-row hoeing or spot spraying systems that can selectively control individual weeds within crop rows. Results with patch spraying show at least 50% saving of herbicides in various crops without causing additional costs for weed control in the following years. A challenge with these technologies is the interoperability of sensing and controllers. Most of the current SSWM technologies use their own IT protocols that do not allow connecting different sensors and implements. Plug & play standards for linking detection, decision making and weeding would improve the adoption of new SSWM technologies and reduce operational costs. An important impact of SSWM is the potential contribution to the EU-Green Deal targets to reduce pesticide use and increase biodiversity. However, further on-farm research is needed for integrating those technologies into agricultural practice.

The foliar microbiome can extend the host plant phenotype by expanding its genomic and metabolic capabilities. Despite increasing recognition of the importance of the foliar microbiome for plant fitness, stress physiology, and yield, the diversity, function, and contribution of foliar microbiomes to plant phenotypic traits remain largely elusive. The recent adoption of high-throughput technologies is helping to unravel the diversityand spatiotemporal dynamics of foliar microbiomes, but we have yet to resolve their functional importance for plant growth, development, and ecology. Here, we focus on the processes that govern the assembly of the foliar microbiome and the potential mechanisms involved in extended plant phenotypes. We highlight knowledge gaps and provide suggestions for new research directions that can propel the field forward. These efforts will be instrumental in maximizing the functional potential of the foliar microbiome for sustainable crop production.

The adoption of site-specific weed management (SSWM) technologies by farmers is not aligned with the scientific achievements in this field. While scientists have demonstrated significant success in real-time weed identification, phenotyping and accurate weed mapping by using various sensors and platforms, the integration by farmers of SSWM and weed phenotyping tools into weed management protocols is limited. This gap was therefore a central topic of discussion at the most recent workshop of the SSWM Working Group arranged by the European Weed Research Society (EWRS). This insight paper aims to summarise the presentations and discussions of some of the workshop panels and to highlight different aspects of weed identification and spray application that were thought to hinder SSWM adoption. It also aims to share views and thoughts regarding steps that can be taken to facilitate future implementation of SSWM.

On/off patch spraying based on weed maps is used in site-specific weed management. Two prerequisites for realising patch spraying are accurate weed detection and targeting of the herbicides on weed patches. There is plenty in the literature about weed detection, but little attention has been paid to the spatial accuracy of herbicide application. This study was conducted in order to assess the extent to which patch-sprayed herbicides are targeted precisely according to pre-loaded prescription maps and to evaluate a new spatial assessment method. The new method consisted of spraying with red Ponceau 4R dye, unmanned aerial vehicle (UAV) imagery and spatial image analysis based on a geographic information system (GIS). The sprayed dye was clearly visible in aereal images and the locations of the sprayed areas were compared with the locations given by the prescription maps. Four different commercial sprayers with boom section width in the range of 0.5 to 3 m and driving speed in the range of 2.5 to 8 km h−1 were used in ten experiments. All the experiments were carried out in autumn in stubble fields. The results showed that the new method was fast and reliable. The incorrectly sprayed area outside targeted areas on prescription maps averaged 81% for three different sprayers with 3 m boom sections, and 5% for a sprayer with 0.5 m boom sections (individual spray nozzle control). The target areas not sprayed within the planned weed patch areas averaged 6% of the pre-defined patch area for sprayers with 3 m boom sections, and 14% for the sprayer with 0.5 m boom sections. This study revealed that the sprayer with 0.5 m boom sections had a controller that was not quick enough at opening and closing spray nozzles at normal driving speeds. Log files from the sprayer console overestimated the sprayed area by 24% and were less accurate than the spatial analysis of the sprayed areas.

In agricultural research involving unmanned aerial vehicle (UAV) imagery, the quality of measurements is often benchmarked against ground measurements. This study adopted a supplementary approach by introducing repeatability and reproducibility analyses. Repeatability was defined as the variation associated with the measurement itself, and reproducibility was defined as the impact of different ways of measuring or different measuring conditions. In this study, different ways of measuring were represented by different cameras, and different light conditions and flight altitudes represented different measuring conditions. The aim was to investigate the repeatability and reproducibility of measurements based on consumer-grade colour cameras mounted on UAVs without calibration of incoming light. Three field-plot experiments with different genotypes of winter cereals were used to investigate how repeatability and reproducibility affected comparisons of genotypes based on the excess green vegetation index (ExG). Four different statistical methods were used to analyse data. The results showed little variation between repeated measurements based on UAV imagery, and that the variation was smaller than ground measurements of crop coverage (CC) based on sampling. Reproducibility was mainly influenced by the cameras. When incoming light was stable during image capture, light conditions and flight altitude had little or no impact on reproducibility. Shifting light conditions during image capture reduced it. Different cameras resulted in different ExG levels for the genotypes, whereas flight altitude and light conditions were found to be of minor importance for genotype comparisons. The extent to which the camera posed a problem was discussed and potential solutions proposed.

Food production per unit land area needs to be increased, thus cropping systems need to use nutrients, water and solar radiation at as close to maximal efficiencies as possible. We deconstruct these efficiencies into their components to define a theoretical crop ideosystem, in which all resource use efficiencies are maximised. This defines an upper biological limit to food production. We then quantify the difference between maximum use efficiencies and those observed in three agronomic systems (maize, cocksfoot, sugarcane) and identify how, in actual farm systems, efficiencies can be raised to raise food production. We find that crop nutrient use efficiency can be limited by low water availability; thus adding nutrients would not raise production but adding water would. The converse situation of water use efficiency being affected by nutrition is not as evident. Ideosystem thinking can be used to define small- and large-scale agronomic systems that optimize water and nutrient use to maximise food production. Highlights - Novel ideosystem method of analysing processes of food production, focussing on resource use efficiencies. - Interactions between resource use efficiencies are asymmetrical. - The ideosystem concept portrays how far a production system approaches maximum efficiency.

There is an urgent need among plant breeders for a deeper understanding of the links between wheat genotypes and their ability to utilize light for biomass production and their efficiency at converting the biomass into grain yield. This field trail was conducted to investigate the variations in radiation use efficiency (RUE) and harvest index (HI) of four spring wheat cultivars grown on two soil types with two nitrogen (N) fertilization levels. Grain yield (GY) was significantly higher with 200 kg N ha&minus, 1 than 100 kg N ha&minus, 1 and on clay soil than on sandy soil, and a similar trend was observed for shoot dry matter (DM) at maturity. RUE and HI was neither affected by cultivar nor N-fertilization, but was affected by soil type, with a significantly higher RUE and HI on clay than on sandy soil. The differences of water holding capacity between the two soil types was suggested to be a major factor influencing RUE and HI as exemplified by the principal component analysis. Thus, to achieve a high RUE and/or HI, sustaining a good soil water status during the critical growth stages of wheat crops is essential, especially on sandy soils with a low water holding capacity.

Technologies and systemic innovation are critical for the transformation of the food system. This Perspective identifies promising technologies, assesses their readiness and proposes eight action points to accelerate innovation.Future technologies and systemic innovation are critical for the profound transformation the food system needs. These innovations range from food production, land use and emissions, all the way to improved diets and waste management. Here, we identify these technologies, assess their readiness and propose eight action points that could accelerate the transition towards a more sustainable food system. We argue that the speed of innovation could be significantly increased with the appropriate incentives, regulations and social licence. These, in turn, require constructive stakeholder dialogue and clear transition pathways.

Necrotic and chlorotic symptoms induced during Pyrenophora teres infection in barley leaves indicate a compatible interaction that allows the hemi-biotrophic fungus Pyrenophora teres to colonise the host. However, it is unexplored how this fungus affects the physiological responses of resistant and susceptible cultivars during infection. To assess the degree of resistance in four different cultivars, we quantified visible symptoms and fungal DNA and performed expression analyses of genes involved in plant defence and ROS scavenging. To obtain insight into the interaction between fungus and host, we determined the activity of 19 key enzymes of carbohydrate and antioxidant metabolism. The pathogen impact was also phenotyped non-invasively by sensor-based multireflectance and –fluorescence imaging. Symptoms, regulation of stress-related genes and pathogen DNA content distinguished the cultivar Guld as being resistant. Severity of net blotch symptoms was also strongly correlated with the dynamics of enzyme activities already within the first day of infection. In contrast to the resistant cultivar, the three susceptible cultivars showed a higher reflectance over seven spectral bands and higher fluorescence intensities at specific excitation wavelengths. The combination of semi high-throughput physiological and molecular analyses with non-invasive phenotyping enabled the identification of bio-signatures that discriminates the resistant from susceptible cultivars.

Spectral correction of colour (RGB) cameras mounted on unmanned aerial vehicles (UAV) is considered important to produce accurate data for quantitative studies in agricultural field research and breeding nurseries. This study investigated the extent to which a simplified spectral correction improves the precision and accuracy of early vigour assessments in winter wheat and winter barley genotypes based on the excess green vegetation Index (nExG) from a pairwise comparison of colour-corrected orthomosaics produced with two different RGB cameras. Two methods of spectral correction were compared, the empirical line method (ELM) and the spectral correction performed in a commercial photogrammetry software package (Pix4D Mapper). Both methods improved the accuracy, with the ELM spectral correction performing better than the Pix4D Mapper spectral correction. Despite high precision and improved accuracy after spectral correction, there were still statistically significant camera effects on vigour assessments because of the minute differences in nExG between genotypes. However, camera effects were evaluated as marginal and with little practical and agronomical relevance under field conditions and in breeding nurseries, as the minute differences between genotypes is overall difficult to reproduce in outdoor field experiments.

Recent technological advances in UAV (unmanned aerial vehicle) technology offer new opportunities for assessing agricultural plot experiments using UAV imagery. Vegetation indices (VIs) based on aerial images derived from consumer-grade cameras are a simple and cheap alternative compared to VIs derived from proximal (on-ground) sensors. The objective of this study was to assess whether VIs derived from consumer-grade cameras mounted on UAVs are reliable and whether there are any shortcomings in image acquisition and analysis that need to be addressed before their general application. This objective was investigated using a rotary-wing and a fixed-wing UAV, true colour (RGB) and colour-infrared (CIR) cameras, four different VIs (ExG, NGRDI, NDVI and ENDVI), altitudes in the range of 30–100 m, different ambient lighting conditions and two different software packages for stitching images together. Results were compared with ground-based recordings by consumer-grade cameras and multispectral sensors. Field experiments in cereals were used to evaluate the assessments. The study showed that VIs based on UAV imagery have the same ability to quantify crop responses to experimental treatments as ground-based recordings with cameras and advanced sensors. However, there are shortcomings that need to be taken into consideration: (1) angular variation in reflectance (bidirectional reflectance), (2) stitching and (3) ambient light fluctuations. Bidirectional reflectance was so extensive that it could lead to misleading conclusions in sunny conditions and this effect could be amplified further by stitching. A procedure for avoiding impacts from bidirectional reflectance is demonstrated when plots were cropped from individual images and a procedure is suggested for stitching images. Camera, VIs and image acquisition altitude were of minor importance, but fluctuating ambient lighting conditions is an issue that should be addressed in future studies.

Plants are affected by complex genome×environment×management interactions which determine phenotypic plasticity as a result of the variability of genetic components. Whereas great advances have been made in the cost-efficient and high-throughput analyses of genetic information and non-invasive phenotyping, the large-scale analyses of the underlying physiological mechanisms lag behind. The external phenotype is determined by the sum of the complex interactions of metabolic pathways and intracellular regulatory networks that is reflected in an internal, physiological, and biochemical phenotype. These various scales of dynamic physiological responses need to be considered, and genotyping and external phenotyping should be linked to the physiology at the cellular and tissue level. A high-dimensional physiological phenotyping across scales is needed that integrates the precise characterization of the internal phenotype into high-throughput phenotyping of whole plants and canopies. By this means, complex traits can be broken down into individual components of physiological traits. Since the higher resolution of physiological phenotyping by 'wet chemistry' is inherently limited in throughput, high-throughput non-invasive phenotyping needs to be validated and verified across scales to be used as proxy for the underlying processes. Armed with this interdisciplinary and multidimensional phenomics approach, plant physiology, non-invasive phenotyping, and functional genomics will complement each other, ultimately enabling the in silico assessment of responses under defined environments with advanced crop models. This will allow generation of robust physiological predictors also for complex traits to bridge the knowledge gap between genotype and phenotype for applications in breeding, precision farming, and basic research.

Biogas production from animal slurry can provide substantial contributions to reach renewable energy targets, yet due to the low methane potential of slurry, biogas plants depend on the addition of co-substrates to make operations profitable. The environmental performance of three underexploited co-substrates, straw, organic household waste and the solid fraction of separated slurry, were assessed against slurry management without biogas production, using LCA methodology. The analysis showed straw, which would have been left on arable fields, to be an environmentally superior co-substrate. Due to its low nutrient content and high methane potential, straw yields the lowest impacts for eutrophication and the highest climate change and fossil depletion savings. Co-substrates diverted from incineration to biogas production had fewer environmental benefits, due to the loss of energy production, which is then produced from conventional fossil fuels. The scenarios can often provide benefits for one impact category while causing impacts in another.

Summary Recent development of site-specific weed management strategies suggests patch application of herbicides to avoid their excessive use in crops. The estimation of infestation of weeds and control thresholds are important components for taking spray decisions. If weed pressure is below a certain level in some parts of the field and if late germinating weeds do not affect yield, it may not be necessary the spray such places from an economic point of view. Consequently, it makes sense to develop weed control thresholds for patch spraying, based on weed cover early in the growing season. In Danish maize field experiments conducted from 2010 to 2012, we estimated competitive ability parameters and control thresholds of naturally established weed populations in the context of decision-making for patch spraying. The most frequent weed was Chenopodium album, accompanied by Capsella bursa-pastoris, Cirsium arvense, Lamium amplexicaule, Tripleurospermum inodorum, Poa annua, Polygonum aviculare, Polygonum persicaria, Stellaria media and Veronica persica. Relative leaf cover of weeds was estimated using an image analysis method. The relation between relative weed leaf cover and yield loss was analysed by nonlinear regression models. The competitive ability parameters and economic thresholds were estimated from the regression models. The competitive ability of weed mixtures was influenced by the increasing proportion of large size weeds in the mixtures. There was no significant effect of weeds which survived or established after the first herbicide application, indicating that early image analysis was robust for use under these conditions.

Summary Small unmanned aerial systems (UAS) with cameras have not been adopted in weed research, but offer low-cost sensing with high flexibility in terms of spatial resolution. A small rotary-wing UAS was tested as part of a search for an inexpensive, user-friendly and reliable aircraft for practical applications in UAS imagery weed research. In two experiments with post-emergence weed harrowing in barley, the crop resistance parameter, which reflects the crop response to harrowing, was unaffected by image capture altitude in the range from 1 to 50 m. This corresponded to image spatial resolution in the range from 0.3 to 17.1 mm per pixel. This finding is important because spatial resolution is inversely related to sensing capacity. We captured 20 plots comprising a total of about 0.2 ha in one image at 50 m altitude without losing information about the cultivation impacts on vegetation compared with ground truth data. UAS imagery also gave excellent results in logarithmic sprayer experiments in oilseed rape, where we captured 37 m long plots in each image from an altitude of 35 m. Furthermore, perennial weeds could be mapped from UAS images. These first experiences with a small rotary-wing UAS show that it is relatively easy to integrate as a tool in weed research and offers great potential for site-specific weed management.

Global livestock production is increasing rapidly, leading to larger amounts of manure and environmental impacts. Technologies that can be applied to treat manure in order to decrease certain environmental impacts include separation and acidification. In this study, a life cycle assessment was used to investigate the environmental effects of slurry acidification and separation, and whether there were synergetic environmental benefits to combining these technologies. Furthermore, an analysis was undertaken into the effect of implementing regulations restricting the P application rate to soils on the environmental impacts of the technologies. The impact categories analysed were climate change, terrestrial, marine and freshwater eutrophication, fossil resource depletion and toxicity potential. In-house slurry acidification appeared to be the most beneficial scenario under both N and P regulations. Slurry separation led to a lower freshwater eutrophication potential than the other scenarios in which N regulations alone were in force, while these environmental benefits disappeared after implementation of stricter P regulations. With N regulations alone, there was a synergetic positive effect of combining in-house acidification and separation on marine eutrophication potential compared to these technologies individually. The model was sensitive to the chosen ammonia emission coefficients and to the choice of inclusion of indirect nitrous oxide emissions, since scenarios changed ranking for certain impact categories.

Animal welfare is an issue of great importance in modern food production systems. Because animal behavior provides reliable information about animal health and welfare, recent research has aimed at designing monitoring systems capable of measuring behavioral parameters and transforming them into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high communication reliability, low energy consumption and low packet loss rate (14.8%) due to the deployment of modern communication protocols (e.g. multi-hop communication and handshaking protocol). The measured behavioral parameters were transformed into the corresponding behavioral modes using a multilayer perceptron (MLP)-based artificial neural network (ANN). The best performance of the ANN in terms of the mean squared error (MSE) and the convergence speed was achieved when it was initialized and trained using the Nguyen-Widrow and Levenberg-Marquardt back-propagation algorithms, respectively. The success rate of behavior classification into five classes (i.e. grazing, lying down, walking, standing and others) was 76.2% (@s"m"e"a"n=1.06) on average. The results of this study showed an important improvement regarding the performance of the designed MANET and behavior classification compared to the results of other similar studies.

This article discusses the structure of weed reproduction incorporating the application of a mathematical model. This mathematical methodology enables the construction, testing and application of distribution models for the analysis of the structure of weed reproduction and weed ecology. The mathematical model was applied, at the individual level, to the weed species, Bromus sterilis. The application of this method, to the weed under competition, resulted in an analysis of the overall reproduction structure of the weed which follows approximately Gaussian distribution patterns and an analysis of the shoots in the weed plant which follow approximately Sigmoid distribution patterns. It was also discovered that the application of the mathematical distribution models, when applied under specific conditions could, effectively estimate the seed production and total number of shoots in a weed plant. On the average, a weed plant has 3 shoots, with each shoot measuring 90cm in height and being composed of 21 spikelets. Besides the estimations of the total shoots and seed production within the experimental field, one may also apply these mathematical distribution models to estimate the germination rate of the species within the experimental field in following years.

Summary Information on temporal and spatial variation in weed seedling populations within agricultural fields is very important for weed population assessment and management. Most of all, it allows a potential reduction in herbicide use, when post-emergence herbicides are only applied to field sections with weed infestation levels higher than the economic weed threshold; a review of such work is provided. This paper presents a system for site-specific weed control in sugarbeet (Beta vulgaris L.), maize (Zea mays L.), winter wheat (Triticum aestivum L.) and winter barley (Hordeum vulgare L.), including online weed detection using digital image analysis, computer-based decision making and global positioning systems (GPS)-controlled patch spraying. In a 4-year study, herbicide use with this map-based approach was reduced in winter cereals by 60% for herbicides against broad-leaved weeds and 90% for grass weed herbicides. In sugarbeet and maize, average savings for grass weed herbicides were 78% in maize and 36% in sugarbeet. For herbicides against broad-leaved weeds, 11% were saved in maize and 41% in sugarbeet.

Using cellular automata (CA) model, in this article, a mechanistic approach has been conducted to help to insight into spread process of plant population such as annual weed population. A controllable CA model with 25 neighbourhood cells has been built, in which seed dispersal, as a key process of annual weed population dynamics, has been described by Gaussian distribution. The hypothesis that initial configurations affect annual weed population dynamics and control strategies has been tested by simulating. And the results support strongly the hypothesis. Patch, pattern and configuration are important concepts in weed patch management, which have been mathematically defined in this article. The aggregation effects have been found out while simulating, which is subject to patch size, patch density and distribution function of seed dispersal. Perhaps it could partly explain the reason why weeds often distribute in patch. Accordingly, true-patch and generalised patch have been distinguished from each other, which would be practically applicable to weed patch management. Based on this approach to the particular weed-crop system, a new hypothesis has been proposed: in a real controllable weed-crop system, the mean law might hold. If so, algorithm of patch control for long-term weed management could be simplified to control a true-patch only according to its mean density, it is unnecessary to control it according to the density in its each cell. (C) 2003 Elsevier B.V. All rights reserved.

Summary It has been established that weeds are spatially aggregated with a spatially varying composition of weed species within agricultural fields. Site-specific spraying therefore requires a decision method that includes the spatial variation of the weed composition and density. A computerized decision method that estimates an economic optimal herbicide dose according to site-specific weed composition and density is presented in this paper. The method was termed a ‘decision algorithm for patch spraying’ (DAPS) and was evaluated in a 5-year experiment, in Denmark. DAPS consists of a competition model, a herbicide dose–response model and an algorithm that estimates the economically optimal doses. The experiment was designed to compare herbicide treatments with DAPS recommendations and the Danish decision support system PC-Plant Protection. The results did not show any significant grain yield difference between DAPS and PC-Plant Protection; however, the recommended herbicide doses were significantly lower when using DAPS than PC-Plant Protection in all years. The main difference between the two decision models is that DAPS integrates crop–weed competition and estimates the net return as a continuous function of herbicide dose. The hypothesis tested is that the benefit of using lower herbicide doses recommended by DAPS would disappear after a few years because weed density will increase and thus require higher doses. However, the results of weed counting every year did not confirm this hypothesis.

Summary A sugarbeet field experiment was conducted in 1999 and 2000 to measure beet yield where Sinapis arvensis or Lolium perenne were growing in the crop row at 2, 4 or 8 cm from the beet plants. The weeds were removed by cutting once in the growing season in either late May, mid-June or early July. The number of neighbouring beet plants to every target beet plant was recorded. Projected leaf cover of a subset of the data with non-cut weeds was analysed by using image analysis to investigate whether this could be used to predict beet yield loss early in the growing season. Increasing the distance between beet and weed from 2 to 8 cm increased the beet yield significantly by an average of 20%, regardless of weed species. The dry weight of non-cut and re-growing weeds at harvest time decreased when cutting was postponed to the period between mid-June and early July. The number of neighbours described a sigmoidal yield decline of the single beet plants. Results from image analysis showed that approximately 33 g of beet yield was lost in October/November for each per cent relative projected leaf cover of the weeds in May, despite variation in growing conditions. The results are discussed in relation to potentials for robotic in-row weed control.

Phenotyping in field experiments is challenging due to interactions between plants and effects from biotic and abiotic factors which increase complexity in plant development. In such environments, visual or destructive measurements are considered the limiting factor and novel approaches are necessary. Remote multispectral imaging is a powerful method that has shown significant potential to estimate crop physiology. However, precise measurements of phenotypic differences between crop varieties in field experiments require exclusion of the disturbances caused by wind and varying sunlight. A mobile and closed multispectral imaging system was developed to study canopies in field experiments. This system shuts out wind and sunlight to ensure the highest possible precision and accuracy. Multispectral images were acquired in an experiment with four different wheat varieties, two different nitrogen levels, replicated on two different soil types at four different dates from 15 May (BBCH 13) to 18 June (BBCH 41 to 57). The images were analyzed and derived vegetation coverage and Normalized Difference Vegetation index (NDVI) were used to assess varietal differences. The results showed potentials for differentiating between the varieties using both vegetation coverage and NDVI, especially at the early growth stages. The perspectives of high-precision and high-throughput imaging for field phenotyping are discussed including the potentials of measuring varietal differences via spectral imaging in comparison to other simpler technologies such as spectral reflectance and RGB imaging.

BackgroundOvaSpec is a new, fully automated, vision-based instrument for assessing the quantity (concentration) and quality (embryonation percentage) of Trichuris suis parasite eggs in liquid suspension. The eggs constitute the active pharmaceutical ingredient in a medicinal drug for the treatment of immune-mediated diseases such as Crohn?s disease, ulcerative colitis, and multiple sclerosis. MethodsThis paper describes the development of an automated microscopy technology, including methodological challenges and design decisions of relevance for the future development of comparable vision-based instruments. Morphological properties are used to distinguish eggs from impurities and two features of the egg contents under brightfield and darkfield illumination are used in a statistical classification to distinguish eggs with undifferentiated contents (non-embryonated eggs) from eggs with fully developed larvae inside (embryonated eggs). ResultsFor assessment of the instrument?s performance, six egg suspensions of varying quality were used to generate a dataset of unseen images. Subsequently, annotation of the detected eggs and impurities revealed a high agreement with the manual, image-based assessments for both concentration and embryonation percentage (both error rates

Non-destructive assessment of herbicide effects may be able to support integrated weed management. To test whether effects of herbicides on canopy variables could be detected by sensors, two crops were used as models and treated with herbicides at BBCH 20 using a loga rithmic sprayer. Twelve days after spraying at BBCH 25 and 42 days after sowing, nine sensor systems scanned a spring barley and an oilseed rape field experiment sown at different densities and sprayed with increasing field rates of glyphosate and tribenuron-methyl. The objec tive was to compare ED50s for crops and weeds derived by the different sensors in relation to crop density and herbicides. Although sensors were not directly developed to detect herbicide symptoms, they all detected changes in canopy colours or height and crop density. Generally ED50s showed the same pattern in response to crop density within herbicide, but there were marked differences between barley and oilseed rape. We suggest that the results of comparing the various sensor outputs could become a stepping stone to future stan dardisation for the benefit of the research and develop ment of sensors that will detect herbicide effect on crops and weeds, particularly at the most vulnerable stages of development of the canopy., We wish to thank EWRS and the University of Copenhagen for partly financial support.

Summary Stems of Chenopodium album. and Sinapis arvensis. and leaves of Lolium perenne. were cut with a CO2 laser or with a pair of scissors. Treatments were carried out on greenhouse-grown pot plants at three diAerent growth stages and at two heights. Plant dry matter was measured 2 to 5 weeks after treatment. The relationship between dry weight and laser energy was analysed using a nonlinear dose‐response regression model. The regression parameters diAered significantly between the weed species. At all growth stages and heights S. arvensis was more diAcult to cut with a CO2 laser than C. album. When stems were cut below the meristems, 0.9 and 2.3 J mm )1 of CO2 laser energy dose was suAcient to reduce by 90% the biomass of C. album and S. arvensis respectively. Regrowth appeared when dicotyledonous plant stems were cut above meristems, indicating that it is important to cut close to the soil surface to obtain a significant eAect. When cutting L. perenne plants with 2-true leaves at a height of 2 cm from the soil surface with a laser, the biomass decreased significantly compared with plants cut by scissors, indicating a delay in regrowth. This delay was not observed for the dicotyledonous plants nor for the other growth stages of L. perenne.

Importance of the paper: This paper is important because it offers examples of how simulation models can be used to develop understanding and predict how crops respond to environmental factors. It also presents and utilizes a new concept, the Kaya-Porter identity, as a means of deconstructing biological use efficiencies into component parts as a stimulus to new thinking, models and experiments in crop physiology.

Animal slurry management is associated with a range of impacts on fossil resource use and the environment. The impacts are greatest when large amounts of nutrient-rich slurry from livestock production cannot be adequately utilised on adjacent land. To facilitate nutrient redistribution, a range of different technologies are available. This study comprised a life cycle assessment of the environmental impacts from handling 1000 kg of pig slurry ex-animal. Application of untreated pig slurry onto adjacent land was compared with using four different treatment technologies to enable nutrient redistribution before land application: (a) separation by mechanical screw press, (b) screw press separation with composting of the solid fraction, (c) separation by decanter centrifuge, and (d) decanter centrifuge separation with ammonia stripping of the liquid fraction. Emissions were determined based on a combination of values derived from the literature and simulations with the Farm-N model for Danish agricultural and climatic conditions. The environmental impact categories assessed were climate change, freshwater eutrophication, marine eutrophication, terrestrial acidification, natural resource use, and soil carbon, nitrogen and phosphorus storage. In all separation scenarios, the liquid fraction was applied to land on the pig-producing (donor) farm and the solid fraction transported to a recipient farm and utilised for crop production. Separation, especially by centrifuge, was found to result in a lower environmental impact potential than application of untreated slurry to adjacent land. Composting and ammonia stripping either slightly increased or slightly decreased the environmental impact potential, depending on the impact category considered. The relative ranking of scenarios did not change after a sensitivity analysis in which coefficients for field emissions of nitrous oxide, ammonia and phosphorus were varied within the range cited in the literature. Therefore, the best technology to implement in a given situation depends on the environmental problem in question, local policy, cost and practicality.

This paper is part review and part opinion piece; it has three parts of increasing novelty and speculation in approach. The first presents an overview of how some of the major crop simulation models approach the issue of simulating the responses of crops to changing climatic and weather variables, mainly atmospheric CO2 concentration and increased and/or varying temperatures. It illustrates an important principle in models of a single cause having alternative effects and vice versa. The second part suggests some features, mostly missing in current crop models, that need to be included in the future, focussing on extreme events such as high temperature or extreme drought. The final opinion part is speculative but novel. It describes an approach to deconstruct resource use efficiencies into their constituent identities or elements based on the Kaya-Porter identity, each of which can be examined for responses to climate and climatic change. We give no promise that the final part is 'correct', but we hope it can be a stimulation to thought, hypothesis and experiment, and perhaps a new modelling approach.

A method for calculating the fraction of intercepted photosynthetically active radiation (fPAR) from measurements of infrared and red radiation reflected from the canopy is described and used to estimate the accumulated intercepted PAR (AIPAR) of 12 spring barley cultivars. The quotient between measured dry matter production (HDM) and AIPAR was the same for all cultivars but changed when the temperatures were below 12 °C. A simple approach was used to obtain a constant quotient irrespective of the temperature. Using this approach, dry matter production (CDM) of the cultivars was estimated. CDM was less variable than the measured HDM. Hence, CDM gave an accurate description of the growth patterns of the cultivars. An expo-linear equation, which combines the relative growth rate (RGR) and the growth rate (GR), showed significant cultivar differences for these parameters. The correlation between RGR and GR was negative, which indicates that cultivars having a low RGR just after emergence compensate later on by having a higher GR.

Udgivelsesdato: June Site-specific weed control technologies are defined as machinery or equipment embedded with technologies that detect weeds growing in a crop and, taking into account predefined factors such as economics, takes action to maximise the chances of successfully controlling them. In the article, we describe the basic parts of site specific weed control technologies, comprising of weed sensing systems, weed management models and precision weed control implements. A review of state-of-the-art technologies shows that several weed sensing systems and precision implements have been developed over the last two decades, though barriers prevent their breakthrough. Most important among these is the lack of a truly robust weed recognition method, owing to mutual shading among plants and limitations in the capacity of highly accurate spraying and weeding apparatuses. Another barrier is the lack of knowledge about the economic and environmental potential for increasing the resolution of weed control. The integration of site-specific information on weed distribution, weed species composition and density, and the effect on crop yield, is decisive for successful site-specific weed management. Keywords: Weed patches, weed management, weed sensing, weed recognition, patch spraying, precision weeding.

An in-depth study of ad hoc wireless sensor networks applied to monitoring the behavior of a herd of sheep in the field was carried out. Herd motion data such as head movements were monitored by IRIS motes and MTS310 sensor boards equipped with a 2-axis accelerometer in a multi hop wireless sensor networks. Acceleration measurements were transmitted through a wireless sensor network based on ZigBee (2.4 GHz) communication protocol. After data filtering, the measurements of the head movements were used to classify the animal behavior into two classes as grazing and non-grazing. Taking the advantages of artificial neural networks compared to the other classification methods into account, a standard two-layer feed-forward network was preferred. The neural network was constructed and trained based on the measurements of the head movements of some of the animals in the herd and was used to predict the behavior of other animals in the herd. The classification results showed the possibility of determining a general decision rule which can classify the behavior of each individual in a herd of animals. The results have been confirmed by manual registration.

Spatial model of annual weed seed dispersal, in this article, was theoretically derived. According to the requirements of building the spatial model, we designed and done an indoor experiment of weed seed dispersal by wind. In the experiment, the seeds of Bromus sterilis were released at 100 cm height under different wind velocity conditions. Based on the experimental data, the spatial models of seed dispersal of the weed species were built, which were divided into three types according to the coefficient β 0. The results showed that dispersal of annual weed seed in any direction obeyed an approximate Gaussian distribution; under the experimental conditions, spatial distribution type of weed seed dispersal changed with variation of wind velocity. Well-known Howard et al.'s model ( Howard et al., 1991 ) of Bromus sterilis seed dispersal is an especial example of the model built in this article. The result of model analysis indicated that the distribution type described by Howard's model was similar to that of seed dispersal of the weed species at the height of 100 cm under the condition of lower wind velocity (about 2.18 m/s). Using CA simulation analysis we found that mean control agent applying to a cell with weed should have a decrease with an increase of wind velocity to prevent weed with the initial configuration from spreading, which implicated less herbicide needs spraying in every cell with weed on average when wind velocity increases.

A laser beam directed towards weeds can be an efficient weed control method as an alternative to herbicides. Lasers may deliver high-density energy to selected plant material, raising the temperature of the water in the plant cells and thereby stop or delay the growth. A commercial use of lasers for weed control, however, require a systematic investigation of the relationship between energy density and the biological effect on different weed species, growth stages, etc. This paper investigates the effect of laser treatment directed towards the apical meristems of selected weed species at the cotyledon stage. Experiments were carried out under controlled conditions, using pot-grown weeds. Two lasers and two spot sizes were tested and different energy doses were applied by varying the exposure time. The biological efficacy was examined on three different weed species: Stellaria media (common chickweed), Tripleurospermum inodorum (scentless mayweed) and Brassica napus (oilseed rape). The experiment showed that laser treatment of the apical meristems caused significant growth reduction and in some cases had lethal effects on the weed species. The biological efficacy of the laser control method was related to wavelength, exposure time, spot size and laser power. The efficacy also varied between the weed species. The results indicate that the efficacy of laser treatments can be improved by a more precise pointing of the laser beam towards the apical meristems and optimisation of the energy density (exposure time and spot size of the laser beam). The experiment also showed a significant difference between two wavelengths. In order to improve the performance and to validate the efficacy on a broader spectrum of weed species, further research and development is needed.

This article discusses the structure of weed reproduction incorporating the application of a mathematical model. This mathematical methodology enables the construction, testing and application of distribution models for the analysis of the structure of weed reproduction and weed ecology. The mathematical model was applied, at the individual level, to the weed species, Bromus sterilis. The application of this method, to the weed under competition, resulted in an analysis of the overall reproduction structure of the weed which follows approximately Gaussian distribution patterns and an analysis of the shoots in the weed plant which follow approximately Sigmoid distribution patterns. It was also discovered that the application of the mathematical distribution models, when applied under specific conditions could, effectively estimate the seed production and total number of shoots in a weed plant. On the average, a weed plant has 3 shoots, with each shoot measuring 90cm in height and being composed of 21 spikelets. Besides the estimations of the total shoots and seed production within the experimental field, one may also apply these mathematical distribution models to estimate the germination rate of the species within the experimental field in following years.

Cereal crop species and varieties differ in competitive ability against weeds mainly as influenced by differences in canopy architecture. The FASSET crop model was used to separate the effects of a number of crop traits on the suppressive ability of winter wheat varieties and the ability to tolerate weeds. The model simulated the competition between different varieties of winter wheat and a sown grass mixture for light, water and nitrogen. Crop physiological parameters of eight varieties and one variety mixture were estimated from measurements in a 3-year field experiment. The parameters estimated were thermal time from emergence to flag leaf appearance, thermal time from flag leaf appearance to anthesis, thermal time from anthesis to yellow ripeness, height development rate and final plant height, specific leaf area, leaf area per N uptake, vertical displacement of leaf area, and extinction coefficient for light. Variety differences were found in most of the parameters. The varieties were evaluated in terms of their abilities for suppression and tolerance. A suppression index (SI) was defined as the ratio of grass biomass at start of grain filling in a specific variety divided by the average biomass over all cultivars. A tolerance index (TI) was defined as the slope of a linear regression between crop yield and weed biomass at anthesis. Significant variety differences were observed for SI, but not for TI. The simulation model was able to capture about 75% of the variation in observed SI and about 47% of the variation in TI. The suppression and tolerance indices were highly correlated both for the observed and simulated data. A model sensitivity analysis documented that several crop traits were affecting both SI and TI leading to this correlation. The crop traits that affected the suppression index most in the simulations were early crop development, rapid height growth and specific leaf area. The response of the tolerance index to variation in crop characters was smaller and also related to factors such as the extinction coefficient and leaf area per unit N uptake. The suppressive ability of the winter wheat varieties were found in the simulations to have a smaller inter-annual variability than the ability to tolerate a given weed pressure. The results show that a simulation model may be used in selecting for competitive genotypes by measuring a few key variety traits. This may be done either by direct use of the model or indirectly through the development of simpler indices based on the simulation model. It is further proposed that a simulation model may be an effective tool in including crop competitiveness in the official variety approval since the measurements of the variety dependent input parameters defining competitiveness may be less costly than performing variety trials under a range of weed densities.