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In this study, aqueous extracts of Calliandra haematocephala Hassk. leaves and inforescences were tested on seeds of quinoa (Chenopodium album L.) and rice (Oryza sativa L.), and on some of the most noxious-associated weeds, Chenopodium album L. and Holcus lanatus L. in quinoa, and Echinochloa crus-galli (L.) P. Beauv., Echinochloa colona L., Eclipta prostrata L. and Rottboellia cochinchinensis (Lou.) W.D. Clayton in rice. The objectives were to identify extract concentrations at which 50 and 90% of germination (GR[50,90] ) and radicle elongation (RR[50,90] ) were inhibited, to fractionate inforescence extracts for facilitating identifying the chemical group causing allelopathic efects, and to evaluate the fraction showing the stronger weed suppression efects and the least crop damage. Increasing extract concentration rates (0, 6.25, 12.5, 25, 50 and 100% crude extract) were applied to seeds of target crops and weeds. Flower extracts at rates < 0.30 produced GR[50] and RR[50] on H. lanatus, and GR[90] and RR[90] in C. album, while quinoa seeds were not afected. Rice and its target weeds were minimally afected by fower extracts, whereas radicle elongation of all species was signifcantly reduced. A concentration rate > 0.52 caused the RR[50] on rice and all its target weeds. Fractions were quantitatively and qualitatively analysed to detect phytochemical groups, using specifc chemical reagents and thin-layer chromatography (TLC). The fraction F3 from aqueous fower extract showed a high content of favonoids, assumed as the potential allelochemical substance. Total favonoid content in F3 was quantifed as 2.7 mg of quercetin per g F3, i.e., 12.8 mg of quercetin per g of inforescence material. Additionally, feld equivalent extract rates obtained from the harvested fresh inforescence biomass could be determined. These rates ranged between 90 and 143 mL l −1 of F3 aqueous fraction, while for ethanol F3 were 131 mL l −1. Our results are encouraging for fnding sustainable and ecologically friendly alternatives for weed management in crops of high nutritional value, contributing also to counteract the growing problem of herbicide resistance.

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Precision farming technologies were implemented into a commercial harrow to increase selectivity of weed harrowing in spring cereals. Digital cameras were mounted before and after the harrow measuring crop cover. Crop soil cover (CSC) was computed out of these two images. Eight field experiments were carried out in spring cereals. Mode of harrowing intensity was changed in four experiments by speed, number of passes and tine angle. Each mode was varied in five intensities. In four experiments, only intensity of harrowing was changed. Weed control efficacy (WCE) and CSC were measured immediately after harrowing. Crop recovery was assessed 14 days after harrowing. Modes of intensity were not significantly different. However, intensity had significant effects on WCE and CSC. Cereals recovered from 10% CSC, and selectivity was in the constant range at 10% CSC. Therefore, 10% CSC was the threshold for the decision algorithm. If the actual CSC was below 10% CSC, intensity was increased. If the actual CSC was higher than 10%, intensity was decreased. Image analysis, decision support system and automatic control of harrowing intensity by hydraulic adjustment of tine angle were installed on a controller mounted on the harrow. The new system was tested in an additional field study. Threshold values for CSC were set at 10%, 30% and 60%. Automatic tine angle adjustment precisely realised the three different CSC values with variations of 1.5% to 3%. This development contributes to selective weed control and supports farmers during harrowing.

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A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six different management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R2 = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R2 = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness

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Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop x treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of relative yield anomalies being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability.

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Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass and volume, using digital grass models. Two fields were sampled, one timothy-dominant and the other ryegrass-dominant. Both sensing systems allowed estimation of biomass, volume and plant height, which were compared with ground truth, also taking into consideration basic economical aspects. To obtain ground-truth data for validation, 10 plots of 1 m2 were manually and destructively sampled on each field. The studied systems differed in data resolution, thus in estimation capability. There was a reasonably good agreement between the UAV-based, the RGB-D-based estimates and the manual height measurements on both fields. RGB-D-based estimation correlated well with ground truth of plant height (R 2 > 0.80) for both fields, and with dry biomass (R 2 = 0.88), only for the timothy field. RGB-D-based estimation of plant volume for ryegrass showed a high agreement (R 2 = 0.87). The UAV-based system showed a weaker estimation capability for plant height and dry biomass (R 2 < 0.6). UAV-systems are more affordable, easier to operate and can cover a larger surface. On-ground techniques with RGB-D cameras can produce highly detailed models, but with more variable results than UAV-based models. On-ground RGB-D data can be effectively analysed with open source software, which is a cost reduction advantage, compared with aerial image analysis. Since the resolution for agricultural operations does not need fine identification the end-details of the grass plants, the use of aerial platforms could result a better option in grasslands.

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This study aimed at identifying optimal sward conditions for successful establishment of red clover (Trifolium pratense L.) through sod-seeding two typical Norwegian grassland systems dominated by timothy (Phleum pratense L.) and perennial ryegrass (Lolium perenne L.), respectively. A total of four sod-seeding trials were implemented, two in late summer (SUM) and two in spring (SPR), one for each sward type and time point for reseeding. The sward coverage status was the basis for threshold definition, and image analysis techniques were used for objective coverage estimation of living plants, dead material and bare soil. Plots with different coverage levels (0–100% of the soil covered by vegetation) were created by spraying a broad-spectrum herbicide (glyphosate) in a spot-wise pattern, mimicking common types of patchiness caused by stressful weather events, e.g., frost or mechanical damage from wheels or hoofs. Seed germination and emergence started similarly in all coverage ranges. However, as time progressed clover seedlings started to die at a coverage dependent rate, and at the final harvest red clover dry matter (RCDM) was the lowest on plots with the highest pre-seeding coverage level. Dose-response curves explained these relationships and allowed estimating the effective-coverage ( ECov80 ), being the initial sward coverage at which 80% of all established red clover plants contributed significantly to the total biomass. Above 2500 kg ha−1 RCDM were produced on timothy ( ECov80 : 15–50%) in SUM, while less than 1000 kg ha−1 RCDM were produced on ryegrass ( ECov80:±10% ), indicating better conditions for clover establishment in timothy compared with ryegrass. In SPR, an ECov80 : 10–15% allowed a good red clover estabishment in ryegrass at cut 3, while RCDM was important and significant in timothy even between ECov80 20 and 60%, at cut 2 and cut 3, respectively. These thresholds for sod-seeding mark the challenges to introduce red clover in dense swards and could be applicable for grassland renovation with other desirable legume and grasses species. Our findings represent particular soil and climatic characteristics of the study site, thus should be taken with caution. Due to the lack of experimentally and sytematically determined thresholds for reseeding, future studies could benefit from our experimental approach, as a base for more complex, multi-site and multi-seasonal investigations, and farmers could use these thresholds for decision making on successful grassland renovation, to avoid wasting seed resources and yield loses.

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For highly productive regions such as Germany, the increase of wheat grain yields observed throughout the 20th century is largely attributed to the progress in crop breeding and agronomic management. However, several studies indicate a strong variability of the genetic contribution across locations that further varies with experimental design and variety selection. It is therefore still unclear to which extent management conditions have promoted the realization of the breeding progress in Germany over the last 100+ years. We established a side-by-side cultivation experiment over two seasons(2014/2015 and 2015/2016)including 16 winter wheat varieties released in Germany between 1895 and 2007. The varieties were grown using 24 different long-term fertilization treatments established since 1904 (Dikopshof, Germany). Averaged over all cultivars and treatments mean yields of 6.88 t ha−1 and 5.15 t ha−1were estimated in 2015 and 2016, respectively. A linear mixed effects analysis was performed to study the treatment-specific relation between grain yields and year of variety release. Results indicate a linear increase in grain yields ranging from 0.025 to 0.032 t ha−1 yr−1 (0.304 to 0.387% yr−1 )in plots that were treated with combined synthetic-organic fertilizers without signs of a leveling-off. Yields from low or unfertilized plots do not show a significant progress in yield. Responsiveness of mean yields to fertilizer management increases with year of release and indicates small yield penalties under very low nutrient supply. Results highlight the need to consider the importance of long-term soil fertilization management for the realization of genetic gains and the value of long-term fertilization experiments to study interactions between genetic potential and management.

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Impacts of nutrient supply and different cultivars (genotypes) on actual yield levels have been studied before, but the long-term response of yield trends is hardly known. We present the effects of 24 different fertilizer treatments on long-term yield trends (1953–2009) of winter wheat, winter rye, sugar beet and potato, with improved cultivars changing gradually over time. Data was obtained from the crop rotation within the long-term fertilization experiment at Dikopshof, Germany. Yield trends were derived as the slope regression estimates between adjusted yield means and polynomials of the first year of cultivation of each tested cultivar, when tested for more than two years. A linear trend fitted best all data and crops. Yields in highly fertilized treatments increased linearly, exceeding 0.08 t ha−1 a−1 for both, winter wheat and winter rye, and ≥0.30 and ≥0.20 t ha−1 a−1 for sugar beet and potato fresh matter yields. Yield trends of winter cereals and sugar beet increased over time at N rates ≥40 kg ha−1 a−1, being 0.04–0.10 t ha−1 a−1 for cereals and 0.26–0.34 t ha−1 a−1 for sugar beet, although N rates >80 kg ha−1 a−1 produced a stronger effect. Nitrogen was the most influential nutrient for realisation of the genetic yield potential. Additional supply of P and K had an effect on yield trends for rye and sugar beet, when N fertilization was also sufficient; high K rates benefited potato yield trends. We highlight the importance of adequate nutrient supply for maintaining yield progress to actually achieve the crop genetic yield potentials. The explicit consideration of the interaction between crop fertilization and genetic progress on a long-term basis is critical for understanding past and projecting future yield trends. Long-term fertilization experiments provide a suitable data source for such studies.

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New technologies, such as Differential Global Positioning Systems (DGPS) and Geographic Information Systems (GIS), may be useful in order to create models to predict the spatio-temporal behaviour of weeds. The aim of this study was to generate a geometric model able to predict the patch expansion of S. halepense, a problematic perennial weed in maize crops in Central Spain. From previous infestation maps, the model describes new possible spreading areas for the upcoming growing season, and therefore, herbicide treatments can be planned on time. Two different experiments were implemented, in which initial patch density and size were examined. Patches of different size (1, 10 and 100 m2) and density (4, 20 and 100 shoots m−2), were established. These patches were visually identified, their perimeter defined and their density characterized, during three growing seasons (from 2008 to 2010 campaigns). According to this information different descriptors were built: (1) area and density of each patch; (2) the relative growth in width and length, according to space and time and compared with previous years; and (3) the increased density ratio, calculated in relation of patch size and distance to previous patch in the new infestation areas of expansion. All these descriptors were added to the model in order to predict the patch expansion in the last studied season (i. e., 2010) using previous maps (i. e., season 2008 and 2009). The model uses geometrical assimilation to predict, and two expansion assumptions were considered: (a) a conservative approach based on triangular geometry; and (b) a rectangular geometry which maximizes the simulated infested area. The results were compared with the ground truth map created in 2010. Each method showed weaknesses and strengths. The triangular approach minimized the infested area, mainly in the small patches, and therefore it could predict the expansion of previously established patches, but not the emergence of new ones. In contrast, the rectangular approach simulated the position of new foci, maximizing the infested area. Therefore, although a substantial reduction of herbicides is possible using both models, a final decision must be taken individually for each field.

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Image analysis is essential through a wide range of scientific areas and most of them have one task in common, i.e. object detection. Thus automated detection algorithms had generated a lot of interest. This proposal identifies objects with similar features on a frame. The inputs are the image where to look at, and a single appearance of the object we are looking for. The object is searched by a sliding window of various sizes. A positive detection is given by a cascaded classifier that compares input patches from sliding window to the object model. The cascaded classifier has three stages: variance comparison, layers of pixel comparisons and patch correlation. Object model is a collection of templates which are generated from scales and rotations of the first appearance. This algorithm is capable to handle change in scale, in plane rotation, illumination, partial occlusion and background clutter. The proposed framework was tested on high cluttered background aerial image, for identifying palm oil trees. Promising results were achieved, suggesting this is a powerful tool for remote sensing image analysis and has potential applications for a wide range of sciences which require image analysis.