Publications - Publikationer

Estimating Fuel Consumption of an Agricultural Robot by Applying Machine Learning Techniques during Seeding Operation

Vahdanjoo, M., Gislum, R., Grøn Sørensen, C. — Fri, 01 Mar 2024 00:57:51 +0100

The integration of agricultural robots in precision farming plays a pivotal role in tackling the pressing demands of minimizing energy usage, enhancing productivity, and maximizing crop yield to meet the needs of an expanding global population and depleting non-renewable resources. Evaluating the energy expenditure is vital when assessing agricultural machinery systems. Through the reduction of fuel consumption, operational costs can be curtailed while simultaneously minimizing the overall environmental footprint left by these machines. Accurately calculating fuel usage empowers farmers to make well-informed decisions about their farming operations, resulting in more sustainable and productive methods. In this study, the ASABE model was applied to predict the fuel consumption of the studied robot. Results show that the ASABE model can predict the fuel consumption of the robot with an average error equal to 27.5%. Moreover, different machine-learning techniques were applied to develop an effective and novel model for estimating the fuel consumption of an agricultural robot. The proposed GPR model (gaussian process regression) considers four operational features of the studied robot: total operational time, total traveled distance, automatic working distance, and automatic turning distance. The GPR model with four features, considering hyperparameter optimization, showed the best performance (R-squared validation = 0.93, R-squared test = 1.00) among other models. Furthermore, three different ML methods (gradient boosting, random forest, and XGBoost) were considered in this study and compared with the developed GPR model. The results show that the GPR model outperformed the mentioned models. Moreover, the one-way ANOVA test results revealed that the predicted values from the GPR model and observation do not have significantly different means. The results of the sensitivity analysis show that the traveled distance and the total time have a significant correlation with the fuel consumption of the studied robot.

Variations of seed size and seed number per spikelet and their effects on seed germination in hulled oats

Yuan, Z., Gislum, R., Jing, Z., et al. — Fri, 01 Mar 2024 00:57:51 +0100

Seed yield components and ear characteristics are critical parameters for seed yield improvement of cereal crops. In this study, 23 hulled oat (Avena sativa) cultivars were planted in four experimental sites during 2018–2020. We determined the ear parameters including the number of four spikelet types, the ear length, the seed weight per ear, and the size of the left and the right seed in the double-seed spikelet. In addition, five oat cultivars with the highest percentage of the triple-seed spikelet were selected to evaluate the germination performance of the left, the middle, and the right seed. The results showed that the double-seed spikelet was present in 73% of the ear, occupying the highest proportion than other spikelet types. The negative correlation showed the trade-off between total seed number per ear and average seed weight. According to the pathway analysis, the number of double-seed spikelet had the significantly highest direct effect on the seed weight per ear with the path coefficient of 0.93. In the double-seed spikelet, the length and the thickness both had significantly positive correlations to the seed weight of the left and the right seeds. The middle seed, which had the smallest seed size, had the lowest germination rate, germination index, vigour index, seedling weight, and length than the right and left seed among three seed positions in the triple-seed spikelet. We conclude that the double-seed spikelet number is a key ear characteristic when breeding high-yielding cultivars.

Kamera og kunstig intelligens til vurdering af sygdomstryk i sukkerroer

Mortensen, A. K., Hansen, A. L., Gislum, R. — Sun, 01 Jan 2023 00:57:51 +0100

Formålet med forsøget er at undersøge, om et høj kvalitets farvekamera sammen med kunstig intelligens kan anvendes til automatisk genkendelse af bladsygdomme i sukkerroer.

Operational, Economic, and Environmental Assessment of an Agricultural Robot in Seeding and Weeding Operations

Vahdanjoo, M., Gislum, R., Grøn Sørensen, C. — Wed, 01 Mar 2023 00:57:51 +0100

The development of robotic‐based agricultural machinery systems has significantly increased in recent years. Many autonomous systems have not yet been measured based on sustainability and economic performances, even though automation is regarded as an opportunity to increase safety, dependability, productivity, and efficiency. The operational aspect, economic viability, and environmental impact of replacing conventional machinery with robotized alternatives are the primary focus of this study. The robot considered in this research is designed for extensive field‐work, where PTO and external hydraulics are required. This robot is equipped with two 75 (hp)Kubota diesel engines with a total engine gross power of up to 144 (hp). Both robotic system and
conventional machinery were described, and different scenarios were used to examine various operational and environmental indicators, as well as individual cost elements, considering various field sizes and working widths of implements used in seeding and weeding operations. The findings demonstrate that the robotic system outperforms conventional machinery in terms of operational
efficiency by as much as 9%. However, the effective field capacity comparison reveals that the conventional system has a field capacity that is up to 3.6 times greater than that of the robotic system.
Additionally, the total cost per hour of the robotic system is up to 57% lower than that of the conventional system. The robotic system can save up to 63.3% of fuel during operation, resulting in the same percentage reduction in CO2 emissions as the conventional system, according to a comparison of fuel consumption.

Sensor spotter insekter – og aktiverer dyser

Topbjerg, H. B. — Tue, 01 Feb 2022 00:57:51 +0100

Udsætning af snyltehvepsekokoner i hvidkløver

Topbjerg, H. B., Boelt, B. — Fri, 01 Apr 2022 00:57:51 +0200

Kløverhovedgnaver har nedsat følsomhed i nogle områder

Topbjerg, H. B. — Fri, 01 Apr 2022 00:57:51 +0200

Alternative Management Strategy Towards Weevils in White Clover Seed Production

Wed, 01 Feb 2023 00:57:51 +0100

Hvordan kan vi bruge satellitbilleder i frøavlen?

Gislum, R. — Sat, 01 Oct 2022 00:57:51 +0200

Fond lukker efter mangeårig værdifuld støtte til frøavlen

Boelt, B., Gislum, R. — Fri, 01 Apr 2022 00:57:51 +0200

Er det nødvendigt at justere kvælstofmængden til frøafgrøden?

Gislum, R. — Tue, 01 Mar 2022 00:57:51 +0100

A chemometric method for the viability analysis of spinach seeds by near infrared spectroscopy with variable selection using successive projections algorithm

Lakshmanan, M. K., Boelt, B., Gislum, R. — Wed, 01 Feb 2023 00:57:51 +0100

This paper proposes a chemometric method for evaluating the viability of spinach seeds using near infrared (NIR) spectroscopy and successive projections algorithms (SPA). An essential step of the procedure is to apply the SPA to optimize the choice of variables for multivariate classification. Variable selection using SPA has been described as an optimization problem in which a cost function is minimized. Selecting the correct variables makes the chemometric models more complete, precise, accurate, and less complex. The NIR spectra were processed using the Savitzky-Golay and multiplicative scatter correction techniques. After that, the best wavelength subset was selected using SPA. Different classification techniques are then applied to the dimension-reduced data to determine the seeds’ viability. The results show that the proposed method is less complex compared to existing canonical variance methods (1.7% miscalculation error in the proposed way) and is also easier to implement.

Potential Impact of Learning Management Zones for Site-Specific N-Fertilization: A Case Study for Wheat Crops

Franco, C., Mejía, N., Pedersen, S. M., Gislum, R. — Mon, 13 Jun 2022 00:57:51 +0200

This paper proposes an automatic, machine learning methodology for precision agriculture, 1 aiming at learning management zones that allow a more efficient and sustainable use of fertilizer. 2 In particular, the methodology consists in clustering remote sensing data and estimating the impact 3 of decision making based on the extracted knowledge. A case study is developed on experimental 4 data coming from winter wheat (Triticum aestivum) crops receiving site-specific fertilization. A first 5 approximation to the data allows measuring the effects of the fertilization treatments on yield and 6 quality of the crops. After verifying the significance of such effects, clustering analysis is applied 7 on sensor readings on vegetation and soil electric conductivity in order to automatically learn the 8 best configuration of zones for differentiated treatment. The complete methodology for identifying 9 management zones from vegetation and soil sensing is validated for two experimental sites at 10 Denmark, estimating its potential impact for decision making on site-specific N-fertilization.

Scheimpflug lidar range profiling of bee activity patterns and spatial distributions

Rydhmer, K., Prangsma, J., Brydegaard, M., et al. — Sat, 01 Jan 2022 00:57:51 +0100

Background: Recent declines of honeybees and simplifications of wild bee communities, at least partly attributed to changes of agricultural landscapes, have worried both the public and the scientific community. To understand how wild and managed bees respond to landscape structure it is essential to investigate their spatial use of foraging habitats. However, such studies are challenging since the foraging behaviour of bees differs between species and can be highly dynamic. Consequently, the necessary data collection is laborious using conventional methods and there is a need for novel methods that allow for automated and continuous monitoring of bees. In this work, we deployed an entomological lidar in a homogenous white clover seed crop and profiled the activity of honeybees and other ambient insects in relation to a cluster of beehives. Results: In total, 566,609 insect observations were recorded by the lidar. The total measured range distribution was separated into three groups, out of which two were centered around the beehives and considered to be honeybees, while the remaining group was considered to be wild insects. The validity of this model in separating honeybees from wild insects was verified by the average wing modulation frequency spectra in the dominating range interval for each group. The temporal variation in measured activity of the assumed honeybee observations was well correlated with honeybee activity indirectly estimated using hive scales as well as directly observed using transect counts. Additional insight regarding the three-dimensional distribution of bees close to the hive was provided by alternating the beam between two heights, revealing a “funnel like” distribution around the beehives, widening with height. Conclusions: We demonstrate how lidar can record very high numbers of insects during a short time period. In this work, a spatial model, derived from the detection limit of the lidar and two Gaussian distributions of honeybees centered around their hives was sufficient to reproduce the observations of honeybees and background insects. This methodology can in the future provide valuable new information on how external factors influence pollination services and foraging habitat selection and range of both managed bees and wild pollinators.

Roboter im Ackerbau - Entwicklung aus der Nische

Gislum, R., Jørgensen, R., Mortensen, A. K., Mikkelsen, B. — Sat, 01 Jan 2022 00:57:51 +0100

Seed production of red clover (Trifolium pratense L.) under danish field conditions

Jing, S., Boelt, B. — Wed, 01 Dec 2021 00:57:51 +0100

High and stable seed yield is critical for red clover (Trifolium pratense L.) seed production and the commercial exploitation of the crop. A three-year experiment was conducted from 2013 to 2015 under Danish field conditions to explore the influence of precipitation during peak flowering on the seed yield of three red clover cultivars. We investigated the flowering duration and intensity based on a visual scale assessment, seed yield, and thousand seed weight in all three experimental years. In 2014 and 2015 we measured the seed yield components of floret number per flower head, seed number per flower head, and seed set. During the experimental period, high seed yields of more than 1000 kg ha⁻¹ were obtained for the diploid cultivars ‘Rajah’ and ‘Suez’. Although a relatively high seed yield of 500 kg ha⁻¹ was obtained in the tetraploid cultivar ‘Amos’, this was only around half of the seed yield and seed set of the diploid cultivars. Precipitation during peak flowering positively influenced the seed yield and thousand seed weight for the three cultivars. We conclude that observations of flowering phenology are required to determine the impact of environmental conditions on seed yield in red clover cultivars. Further, adequate water supply during peak flowering is important to obtain the high seed yield of red clover.

Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran

Azizi, K., Ayoubi, S., Nabiollahi, K., Garosi, Y., Gislum, R. — Tue, 01 Feb 2022 00:57:51 +0100

The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe, Cu, Mn) in western Iran, using environmental covariates and applying two machine learning methods comprised Random forest (RF), and Cubist. In this respect, a combination of different input environmental variables (remote sensing data, topographic attributes, thematic maps and soil properties) were used in modeling under four scenarios (I: remote sensing data (RS); II: RS + topographic attributes resulted from digital elevation model (DEM); III: RS + topographic attributes + thematic maps; IV: RS + topographic attributes + thematic maps +soil properties). The maps of Euclidean distance from mines and roads as well as the geology map have been used as thematic maps. A total of 346 soil samples were taken using stratified random sampling from the surface layers (0–20 cm depth) of the studied area and selected heavy metals (Ni, Fe, Cu, Mn), and soil properties were measured in the laboratory. RF and Cubist models were used to predict soil heavy metals in four scenarios. The results indicated that the best prediction accuracy was achieved for the fourth scenario (IV) when all input variables were combined to predict selected heavy metals. Moreover, two models showed different capability for various metals. According to our results, the random forest model had a high accuracy in predicting Ni (R² = 0.67) and Cu (R² = 0.60), In contrast, the Cubist model had a higher accuracy in predicting Mn (R² = 0.55). For predicting Fe, both models provided a similar accuracy (R² = 0.73). This study proved the high capability of machine learning methods to use easily available environmental data to predict studied heavy metals in the large scale that are essential for decision making in sustainable management in agricultural and environmental concerns.

Pollination and Plant Reproductive Success of Two Ploidy Levels in Red Clover (Trifolium pratense L.)

Jing, S., Kryger, P., Markussen, B., Boelt, B. — Sun, 01 Aug 2021 00:57:51 +0200

Plant reproduction in red clover requires cross-fertilization via insect pollination. However, the influences of visitation rate and timing on maximizing ovule utilization are yet to be determined. We aimed to study the influences of visitation rate, flowering stage, and self-incompatibility on reproductive success. We applied hand and honey bee pollination in the study of eight red clover cultivars with two ploidy levels released between 1964 and 2001. In hand pollination, increasing the visitation rates (from 10 to 80 pollinated florets per flower head) increased the seed number per flower head but reduced the seed number per pollinated floret. Different flowering stages (early, middle, and full flowering) did not influence the seed number per pollinated floret significantly. There was a marked difference in reproductive success depending on the ploidy level, with 0.52 seeds per pollinated floret in diploid and 0.16 in tetraploid cultivars. During the cultivar release history, seed number per pollinated floret seemed to decrease in diploid cultivars, whereas it increased in tetraploids. In honey bee pollination, diploid cultivars had more two-seeded florets than tetraploids. Different visitation rates and the stochastic nature of pollen transfer resulted in difficulties when the plant reproductive success between hand and bee pollination was compared. A maximum of 0.27 seeds per pollinated floret were produced in hand pollination compared to the 0.34 in honey bee pollination. In spite of this, hand pollination provided a valuable method for studying the pollination biology and reproduction of red clover. Future studies may employ hand pollination to unravel further aspects of the low reproductive success with the future perspective of improving seed number per pollinated floret in tetraploid red clover.

Assessment of grass lodging using texture and canopy height distribution features derived from UAV visual-band images

Tan, S., Mortensen, A. K., Ma, X., Boelt, B., Gislum, R. — Fri, 01 Oct 2021 00:57:51 +0200

Lodging is a major limiting factor for the yield, quality and harvesting efficiency of selected crops worldwide. This study presents an efficient, robust and non-destructive assessment of lodging severity for four different grasses for seed production, using images collected by an unoccupied aerial vehicle (UAV) in two field plot experiments across five growing seasons. Canopy texture and height related features were extracted from individual plot images and evaluated for estimating lodging severity. Histograms of oriented gradients (HOG) were used as texture features, and three canopy height distributions features (CHV1, CHV2 and CHV3) were proposed. Each canopy height distribution feature divides the plots into subplots and estimates the average height of each subplot. CHV1 concatenates average height of the subplots into its feature, while CHV2 concatenates the difference in average height between all subplots, and CHV3 concatenates the difference in average height between adjacent subplots. The plots were classified using support vector machines into three categories according to the lodging severity. The results showed that the HOG and height distribution features can be used for grading lodging severity in UAV images with high accuracy (71.9% and 79.1%, respectively). However, the HOG features showed a negative relationship to the ground sample distance (GSD), while the CHV1 had a constant accuracy across the GSDs. Combination of the two features did not significantly improve the classification accuracy. The present results have potential to generate lodging severity maps for application in precision farming and thereby to increase grass seed yield and harvest efficiency at farm scale. It should be noted that results and methods from the current study might not be transferred to other crops due to crop specific lodging characteristics and effect of yields.

Different pollination approaches to compare the seed set of diploid and tetraploid red clover Trifolium pratense L.

Jing, S., Kryger, P., Boelt, B. — Tue, 01 Jun 2021 00:57:51 +0200

In red clover seed production, low seed yield is limiting the commercial exploitation of tetraploid red clover. To explore if pollination is the limiting factor for the seed yield in tetraploid red clover, we investigated pollinator behaviour and plant reproductive success in diploid (2x) cultivar ‘Rajah' and tetraploid (4x) cultivar ‘Amos' using honey bee and hand pollination approaches. We measured the seed set at the flower head level with the increasing visitation rate of honey bees (one bee, two bees and open pollination) and pollen diversity (one and two pollen donors) of hand pollination. We found no difference in honey bee pollination behaviour (visitation rate) between the diploid and tetraploid red clover. Surprisingly, the seed set in two bees was significantly lower than that of one bee in ‘Rajah'. We suggest that honey bees prefer to visit already tripped florets or to visit fewer florets due to scent marks left by the previous honey bees. In open pollination, seed number per flower head in ‘Amos' (on average 11) was half of ‘Rajah' (on average 20). Seed set in ‘Amos' was lower than 50% of ‘Rajah' in honey bee (one bee treatment) and hand pollination (one and two pollen donors treatments). There were no differences in stigma receptivity whereas pollen viability in ‘Amos' was lower than ‘Rajah' after the peak pollen viability was reached (six days after the onset of flowering). We suggest that the fertility problems such as low pollen viability may be the reason for the low seed set in tetraploid red clover. This conclusion should be verified by investigating other fertility traits during the ovary, embryo and seed development with a broader range of diploid and tetraploid cultivars.

Data og teknologi i greenkeeperarbejdet

Mon, 10 May 2021 00:57:51 +0200

Greenkeepere i Danmark har stadigt flere muligheder for at bruge data og ny teknologi i arbejdet med at pleje golfbanen. Men hvordan og hvor meget skal det bruges. Og hvad kan det bruges til? Vi tager en diskussion med Réne Gislum, der er lektor ved Århus Universitet, Institut for Agroøkonomi, Thomas Pihl, der er chefgreenkeeper i Furesø Golfklub og Torben Kastrup Petersen, banechef i Dansk Golf Union.

The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production

Gislum, R., Thomopoulos, S., Gyldengren, J. G., Mortensen, A. K., Boelt, B. — Tue, 01 Jun 2021 00:57:51 +0200

Sufficient nitrogen (N) supply is decisive to achieve high grass seed yields while overfertilization will lead to negative environmental impact. From the literature, estimation of N rates taking into account the crop’s N status and its yield potential, seems promising for attaining high yields and averting adverse environmental impacts. This study aimed at an evaluation of remote sensing to predict final seed yield, N traits of the grass seed crop and the usability of nitrogen nutrition index (NNI) to measure additional N requirement. It included four years’ data and eight N application rates and strategies. Several reflectance measurements were made and used for the calculation of 18 vegetation indices. The predictions were made using partial least square regression and support vector machine. Three different yield responses to N fertilization were noted; one with linear response, one with optimum economic nitrogen (EON) at ~188 kg N ha−1, and one with EON at ~138 kg N ha−1. We conclude that although it is possible to make in-season predictions of NNI, it does not always portray the differences in yield potential; thus, it is challenging to utilize it to optimize N application.

Diseases in red fescue

Jørgensen, L. N., Madsen, H., Boelt, B. — Mon, 01 Mar 2021 00:57:51 +0100

Review of seed yield components and pollination conditions in red clover (Trifolium pratense L.) seed production

Jing, S., Kryger, P., Boelt, B. — Thu, 01 Apr 2021 00:57:51 +0200

Red clover (Trifolium pratense L.) is an important forage legume grown in many of the temperate regions around the world. In order to evaluate possible causes for the low seed yield in particular in tetraploid red clover, we surveyed 71 studies of red clover seed production published from 1948 to 2019, reviewing seed yield components, pollination and reproductive traits. Seed yield components are recorded to contribute to and improve our understanding of the complex processes leading to the red clover seed yield. However, the measurement approaches varied largely among the studies, depending on whether the seed yield components are directly measured or calculated. Red clover seed production is dependent on insect pollen transfer. However, much uncertainty of pollination and reproductive success remains. Pollination conditions were often neglected: one third of the papers did not mention pollination conditions. The information that connects pollination and seed set are lacking, very few studies measured traits in floral morphology, ovule fertilisation and seed maturation. We suggest that consistent and comprehensive evaluation of pollination and reproductive success are required to improve our understanding of reasons for the low seed yield in red clover and hence the possibilities for future improvement.

Zinc seed priming improves spinach germination at low temperature

Imran, M., Mahmood, A., Neumann, G., Boelt, B. — Mon, 01 Mar 2021 00:57:51 +0100

Low temperature during germination hinders germination speed and early seedling development. Zn seed priming is a useful and cost-effective tool to improve germination rate and resistance to low temperature stress during germination and early seedling development. Spinach was tested to improve germination and seedling development with Zn seed priming under low temperature stress conditions. Zn priming increased seed Zn concentration up to 48 times. The multispectral imaging technique with VideometerLab was used as a non-destructive method to differentiate unprimed, water-and Zn-primed spinach seeds successfully. Localization of Zn in the seeds was studied using the 1,5-diphenyl thiocarbazone (DTZ) dying technique. Active translocation of primed Zn in the roots of young seedlings was detected with laser confocal microscopy. Zn priming of spinach seeds at 6 mM Zn showed a significant increase in germination rate and total germination under low temperature at 8^◦C.

The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures:

Mortensen, A. K., Gislum, R., Jørgensen, J. R., Boelt, B. — Thu, 01 Apr 2021 00:57:51 +0200

The objective of seed testing is to provide high-quality seeds in terms of high varietal identity and purity, germination capacity, and seed health. Across the seed industry, it is widely acknowledged that quality assessment needs an upgrade and improvement by inclusion of faster and more cost-effective techniques. Consequently, there is a need to develop and apply new techniques alongside the classical testing methods, to increase efficiency, reduce analysis time, and meet the needs of stakeholders in seed testing. Multispectral imaging (MSI) and near-infrared spectroscopy (NIRS) are both quick and non-destructive methods that attract attention in seed research and in the seed industry. This review addresses the potential benefits and challenges of using MSI and NIRS for seed testing with a comprehensive focus on applications in physical and physiological seed quality as well as seed health.

Blomsterstriber for bier og biodiversitet

Nielsen, M. B., Dupont, Y. L., Boelt, B. — Mon, 01 Feb 2021 00:57:51 +0100

Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Bianchini, V. d. J. M., Mascarin, G. M., Silva, L. C. A. S., et al. — Fri, 01 Jan 2021 00:57:51 +0100

Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

Vurdering af blomsterstribers værdi for bier

Nielsen, M. B., Dupont, Y. L., Boelt, B. — Mon, 01 Feb 2021 00:57:51 +0100

Blomsterstriber er blevet populære langs vejene og i markkanter. De er ofte en fryd for øjet og giver variation i det dyrkede land for vildt og insekter. Men har bierne præferencer mellem blomsterstriberne og hvilke blandingerne skaber mest værdi for insekterne?

A simulation of variable rate nitrogen application in winter wheat with soil and sensor information - An economic feasibility study

Sun, 01 Aug 2021 00:57:51 +0200

Variable rate N management strategies are often based on information about soil texture or from canopy sensors, mounted on ground-based vehicles or satellites. However, disentangling the effect of each information type on N management strategy with experimental studies is often difficult, as
results are only valid for the specific experimental conditions as well as the weather conditions for
specific years. An alternative for this is to use deterministic crop growth models. This study examines
whether ‘static’ soil profile information or ‘dynamic’ canopy sensor type information provide the
best basis for decision making concerning N-application at the subfield level. The DAISY model was
used for simulating crop growth on six soil profiles found in a heterogeneous loamy sand field and a
five-year crop rotation. A range of management descriptions and simulations were made using
5x500 years of synthetic weather data with each crop in the rotation set at the first year of the five
parallel simulations. Simulated growth was used as a proxy for a ‘dynamic’ canopy sensor based
information system. The net revenue was then calculated for a range of price relations between
fertilizer (model input) and wheat yield (model output), including wheat price adjustments according
to protein content. Based on regressions and backward induction analysis, the N application that
maximizes the expected net revenue were estimated for four information cases; Case 1) Uniform
application, assuming no prior information, Case 2) application based only on soil type , Case 3)
application based on canopy sensor information only and Case 4) application based on combined soil
and canopy sensor information. Findings from this study indicate that decisions with soil information
alone provide an annual net revenue (without considering cost of collecting information and variable
rate technology) of variable rate application (VRA) between 3.88 and 13.30 € ha-1 across price and
soil variation. This net revenue is approximately doubled with applications based on only canopy
sensor information and again this is approximately doubled with applications based on both soil and
canopy sensor information. The results may guide developers to decide on what type of information
should be included in their decision support systems.

Skader det at vækstregulere, når det er tørt?

Topbjerg, H. B., Boelt, B. — Wed, 01 Apr 2020 00:57:51 +0200

Vækstregulering: AU Flakkebjerg har for hundegræs og strandsvingel undersøgt, hvad det betyder for effekten af en vækstregulering, at det sker under tørre vejrforhold.

Så meget af det tilførte N fjerner frøafgrøden

Gislum, R. — Thu, 01 Oct 2020 00:57:51 +0200

Seed germination and seedling growth parameters in nine tall fescue varieties under salinity stress

Shiade, S. R. G., Boelt, B. — Sat, 01 Aug 2020 00:57:51 +0200

To assess seed germination parameters and identifying tolerant varieties, seeds of nine tall fescue varieties (Festuca arundinacea Schreb.) were germinated under various salinity levels for 14 days. Tall fescue is considered ‘moderately tolerant’ to salinity stress, but our study revealed a remarkable diversity among the tested varieties. Armani, Essential, Fatcat, and Starlett were found to reach the same final germination (>90%), irrespective of NaCl concentration up to 15 ds m⁻¹ NaCl; Asterix and Meandre expressed lower germination under the highest salinity level (>75%); and final germination decreased in Eyecandy, Rhizing star, and Thomahawk gradually with increasing salinity (>55%). The main effect of increasing salinity was a delay in germination, and our study suggests that the recording of final germination, which is performed on day-14 in a standard germination test, should be postponed in order to understand the full effect of salinity on germination potential. Nonetheless, a delay in germination will affect turf quality negatively and hence there is good reason to test for salinity tolerance when choosing a variety for sowing on saline soil. Further, our findings indicate a future perspective for breeding for improved salinity tolerance in tall fescue by the identification of salinity-tolerant breeding lines or varieties.

Field scale agronomic and environmental consequences of overlapping Nfertilizer application by disc spreaders

Gyldengren, J. G., Greve, M. B., Skou-Nielsen, N., Olesen, J. E., Gislum, R. — Tue, 01 Sep 2020 00:57:51 +0200

Application of mineral nitrogen (N) fertilizer is not uniform at the field scale when applied with disc spreaders. Some of the distribution variation is due to potentially predictable mechanisms, but the variation is also influenced by stochastic events and multiple interactions, making each combination of field, spreader and application event a unique case. This may be one of the reasons why the agronomic, economic and environmental consequences of uneven fertilizer distribution at the field scale remains poorly understood and insufficiently quantified. Previous studies have addressed the problem statistically or empirically in regularly shaped experimental plots or a subsection of a field. While these efforts provide valuable contributions to our understanding, they neglect critical areas of the field with regards to fertilizer application: Wedges, interfaces between the in-field and the headland and in-field obstacles. In this modelling study, we present an attempt to describe the variation in fertilizer application at the field scale and assess the consequences on winter wheat grain yields, grain N yields and N losses to the environment. By combining GIS and agroecosystem modelling, we assess yield and environmental effects of two spreaders with different working widths (24 and 48 m) in four field polygons selected to represent a relevant span with regard to size and geometry for Danish conditions. The effects of the combinations were assessed for two soils, a coarse sandy soil and a sandy loam. Both accuracy (average N input rate relative to target) and precision (evenness of distribution) was found to decrease in small (4−6 ha) and geometrically irregular fields compared to large (40−50 ha) and regular fields. Increasing the working width from 24 to 48 m increased the variation in small fields, but not in large. Grain yields were negatively affected by distribution variation, while there was a poor correlation with average applied N rate. In contrast, grain N yields were insensitive to distribution variation, but showed strong correlation with average N input rate. N leaching was affected by both the amount and distribution of applied N. Across all field x spreader x soil scenarios, field scale grain yields decreased between 0–877 kg DM/ha and annual N leaching increased by up to 9 kg N/ha when fertilizer application was simulated with a spreader rather than even application. In general, the agronomic results from the large and regular field resembled the results from previous studies much better than results from the smaller and/or irregular fields. If not accounting for the impact of field size and shape distribution in the landscape, the effects of uneven N application, and thereby the potential gains by improving spreader performance, may therefore be underestimated.

Arbuscular mycorrhiza influences carbon-use efficiency and grain yield of wheat grown under pre- and post-anthesis salinity stress

Eroglu, C. G., Cabral, C., Ravnskov, S., Topbjerg, H. B., Wollenweber, B. — Tue, 01 Sep 2020 00:57:51 +0200

Soil salinity severely affects and constrains crop production worldwide. Salinity causes osmotic and ionic stress, inhibiting gas exchange and photosynthesis, ultimately impairing plant growth and development. Arbuscular mycorrhiza (AM) have been shown to maintain light and carbon use efficiency under stress, possibly providing a tool to improve salinity tolerance of the host plants. Thus, it was hypothesized that AM will contribute to improved growth and yield under stress conditions. Wheat plants (Triticum aestivum L.) were grown with (AMF+) or without (AMF−) arbuscular mycorrhizal fungi (AMF) inoculation. Plants were subjected to salinity stress (200 mm NaCl) either at pre- or post-anthesis or at both stages. Growth and yield components, leaf chlorophyll content as well as gas exchange parameters and AMF colonization were analysed. AM plants exhibited a higher rate of net photosynthesis and stomatal conductance and lower intrinsic water use efficiency. Furthermore, AM wheat plants subjected to salinity stress at both pre-anthesis and post-anthesis maintained higher grain yield than non-AM salinity-stressed plants. These results suggest that AMF inoculation mitigates the negative effects of salinity stress by influencing carbon use efficiency and maintaining higher grain yield under stress.

Effects of winter wheat N status on assimilate and N partitioning in the mechanistic agroecosystem model DAISY

Tue, 01 Dec 2020 00:57:51 +0100

Nitrogen (N) management in modern farming needs to balance the interests of yield quantity and quality with environmental impact of reactive N lost to the atmosphere and aquatic environments. Mechanistic agroecosystem models are useful tools to analyse the combined effects of management options and natural conditions, including soil fertility and climate, site‐specific optimal N application rates and environmental impact. An important component of the system description is the crop module, including the responses to N status and N stress. To improve the description of crop growth response to N status in the DAISY model system, we implemented an empirical model for N status effects in winter wheat on the partitioning of assimilated dry matter and N between leaves and stems, originally described by Ratjen and Kage (Journal of Agronomy and Crop Science , 2016, 202, 576s). To our knowledge, this mechanism has not been included in any of the most widespread and commonly used mechanistic agroecosystem models. We tested and compared the model performance with and without the new N status response against data from a two‐season winter wheat experiment in Denmark, where crop growth and partitioning of dry matter and N were measured. Implementation of the new N status response function improved model performance and ensured a more robust crop growth description, especially in scenarios with periods of low crop N status or N stress. The mechanism mimics an adaptation strategy, where the crop balances N stress and growth potential by dynamically adjusting the leaf‐stem ratio and thereby N demand. This behaviour opens a discussion regarding the empirical concept of a biomass driven critical N curve.

Fungicide application improves seed yield of perennial ryegrass upon infection with stem and crown rust fungi

Rodriguez-Algaba, J., Boelt, B., Matzen, N., Jørgensen, L. N. — Wed, 01 Jan 2020 00:57:51 +0100

Rust epidemics may have detrimental effects on seed yield in perennial ryegrass (Lolium perenne L.). Field trials comprising two cultivars of perennial ryegrass were established to evaluate the impact of stem rust (Puccinia graminis subsp. graminicola) and crown rust (Puccinia coronata f.sp. lolii) on seed yields. Two or three applications of a mixture of fungicides (epoxiconazole plus boscalid mixed with pyraclostrobin) provided effective control of both stem rust and crown rust and positive seed yield responses, although differences were observed between cultivars. For stem rust, two fungicide applications led to variable levels of disease control (from 86% to 100% for cv. Esquire and from 15% to 98% for cv. Calibra), whereas three applications resulted in 100% disease control in both cultivars. Two fungicide applications were sufficient for an appropriate control of crown rust on both cultivars (approx. 98–100% disease control). Generally, two fungicide applications resulted in an increase in seed yield for both cultivars. On average, an increment of 4% and 15% was observed in Esquire and Calibra equivalent to 86 kg/ha and 352 kg/ha, respectively. Three fungicide applications provided an increase of 2% and 17% in Esquire and Calibra equivalent to 45 kg/ha and 434 kg/ha, respectively. Significant differences were only observed in Calibra after two or three fungicide applications. Additionally, results from semi-open field trials testing for preventive and curative effects showed that tebuconazole, pyraclostrobin, picoxystrobin, azoxystrobin, and a mixture of epoxiconazole, pyraclostrobin, and boscalid fungicides resulted in high efficacy in reducing stem rust occurrence, the latter resulted in the largest preventive and curative effect. Experiences from field and semi-open field trials indicated that fungicide mixtures with contrasting modes of actions, e.g., triazoles, strobilurins, and SDHIs, and applied close to the time of inoculation provided an effective control of rust fungi and increased seed yield in perennial ryegrass.

Variation of seed oil content, oil yield, and fatty acids profile in Iranian Nigella sativa L. landraces

Kiani, M., Alahdadi, I., Soltani, E., Boelt, B., Benakashani, F. — Wed, 01 Jan 2020 00:57:51 +0100

Since there are several diverse landraces across Iran, screening the landraces with high production may lead to finding superior genotype(s) for the future conventional breeding programs. Thus, the aims of this study was to investigate the variation of oil content, yield, and profile of fatty acids in Iranian landraces of Nigella sativa L. Seed yield, oil content and oil yield significantly differed among landraces. The highest seed and oil yields were obtained from Semirom (1250 and 332 kg ha⁻¹). Twelve different fatty acids were detected. Linoleic acid (18:2) had the highest concentration in oil of N. sativa landraces with a range of 48–55 %. After linoleic acid, oleic (18:2) and palmitic (16:0) acids, with an average of 22 and 13 %, accounted for the highest percentage of seed oil compared with the other fatty acids. Saturated fatty acids (∑SFA) was significantly different among the landraces and included 19–22 % and 21–24 % of total fatty acids. Double bond index (DBI) was significantly different among landraces and ranged between 136.5–145.3 in 2018 and 127.5–134.5 in 2019. The Gardmiran landrace had the highest Iodine value (IV) (111.7, an average of two years) and Khaf landrace had the lowest IV (105.7, in average of two years) compared with the other landraces. Cluster analysis indicated that the landraces can be divided into three groups; (1) high yield (>1000 kg ha^-1 seeds and >250 kg ha⁻¹ oil), (2) medium yield (700–1000 kg ha⁻¹ seeds and 190–250 kg ha⁻¹ oil), and low yield (<700 kg ha⁻¹ seeds and <190 kg ha⁻¹ oil) landraces. Iranian landraces were similar in fatty acid compositions and conventional breeding methods may not be a good choice to improve a special fatty acid. However, a large variations existed in seed and oil yield among the landraces for the future conventional breeding programs.

En fælles front for vilde bier og honningbier

Dupont, Y. L., Bang Madsen, H., Rasmussen, C., et al. — Thu, 29 Aug 2019 00:57:51 +0200

En fælles front for vilde bier og honningbier

Dupont, Y. L., Bang Madsen, H., Rasmussen, C., et al. — Thu, 29 Aug 2019 00:57:51 +0200

En fælles front for vilde bier og honningbier

Dupont, Y. L., Bang Madsen, H., Rasmussen, C., et al. — Sun, 01 Sep 2019 00:57:51 +0200

Red bierne – mens tid er

Dupont, Y. L., Bang Madsen, H., Rasmussen, C., et al. — Tue, 01 Jan 2019 00:57:51 +0100

22 forskere: Danmark har brug for en national strategi for bestøvere

Dupont, Y. L., Bang Madsen, H., Rasmussen, C., et al. — Mon, 14 Oct 2019 00:57:51 +0200

En fælles front for alle bier

Dupont, Y. L., Bang Madsen, H., Rasmussen, C., et al. — Tue, 01 Jan 2019 00:57:51 +0100

Vi må redde bierne - mens tid er

Dupont, Y. L., Bang Madsen, H., Rasmussen, C., et al. — Sat, 24 Aug 2019 00:57:51 +0200

Expression of starch-binding factor CBM20 in barley plastids controls the number of starch granules and the level of CO2 fixation

Zhong, Y., Sagnelli, D., Topbjerg, H. B., et al. — Wed, 01 Jan 2020 00:57:51 +0100

The biosynthesis of starch granules in plant plastids is coordinated by the orchestrated action of transferases, hydrolases, and dikinases. These enzymes either contain starch-binding domain(s) themselves, or are dependent on direct interactions with co-factors containing starch-binding domains. As a means to competitively interfere with existing starch-protein interactions, we expressed the protein module Carbohydrate-Binding Motif 20 (CBM20), which has a very high affinity for starch, ectopically in barley plastids. This interference resulted in an increase in the number of starch granules in chloroplasts and in formation of compound starch granules in grain amyloplasts, which is unusual for barley. More importantly, we observed a photosystem-independent inhibition of CO2 fixation, with a subsequent reduced growth rate and lower accumulation of carbohydrates with effects throughout the metabolome, including lower accumulation of transient leaf starch. Our results demonstrate the importance of endogenous starch-protein interactions for controlling starch granule morphology and number, and plant growth, as substantiated by a metabolic link between starch-protein interactions and control of CO2 fixation in chloroplasts.

Changes in spatiotemporal protein and amino acid gradients in wheat caryopsis after N-topdressing

Zhong, Y., Vidkjær, N. H., Massange-Sanchez, J. A., et al. — Wed, 01 Jan 2020 00:57:51 +0100

Wheat grain nitrogen content displays large variations within different pearling fractions of grains because of radial gradients in the protein content. We identified how spatiotemporal mechanisms regulate this. The protein gradients emerged clearly at 19 days after anthesis, with the highest N content in aleurone and seed coat, followed by outer endosperm, whereas the lowest was in middle and inner endosperm. Laser microdissection, qRT-PCR and LC–MS were used to dissect tissue from aleurone, outer endosperm, middle endosperm, inner endosperm and transfer cells, measure gene expression and levels of free and protein-bound amino acids, respectively. The results showed that different FAA transportation pathways worked in parallel during grain filling stage while the grain protein gradient did not follow spatial expression of storage proteins. Additionally, two nitrogen (N) topdressing timings were conducted, either at the emergence of top third leaf (standard timing) or top first leaf (delayed timing), finding that delayed N topdressing enhanced both amino acids supply and protein synthesis capacity. The results provide insight into protein synthesis and amino acid transport pathways in endosperm and suggest targets for the enhancement of specialty pearled wheat with higher quality.

From emergence to flowering

Salimi, Z., Boelt, B. — Sun, 01 Dec 2019 00:57:51 +0100

Beta vulgaris comprise various variety groups that are cultivated for vegetative or reproductive production. As a biennial crop, beets have a photothermal induction requirement for the transition from the vegetative to the reproductive stage. Bolting and flowering are not desirable when beets are cultivated for vegetative production and are suppressed during breeding programs, though these structures are important in seed production. Therefore, the potential of seed hydro priming as an enhancement technique to partially induce vernalization in seeds was evaluated. Following hydro priming, seeds were sown in October 2018 and evaluated during three selected phenological stages. Treating seeds with hydro priming significantly improved the emergence rate in all four tested cultivars. Moreover, treatments significantly lowered the required growing degree days (GDD) for transition to the reproductive stage in all four tested cultivars. Regardless of the treatment effect on an individual developmental stage, the treatment efficiency should be evaluated on the whole production process, depending on the purpose of production. The focus of this study was mainly on improvement of seed production performance. The outcome showed the potential of priming to influence the reproductive stages of the plant life cycle rather than just the germination.

Optimization of Germination Inhibitors Elimination from Sugar Beet (Beta vulgaris L.) Seeds of Different Maturity Classes

Salimi, Z., Boelt, B. — Fri, 01 Nov 2019 00:57:51 +0100

Endogenous inhibitors have a crucial effect on the success of sugar beet (Beta vulgaris) seed germination. The strength of this effect changes during seed maturation, and washing away these inhibitors can facilitate germination. Investigation of various washing factors such as water-to-seed ratio, stirring time, and speed of stirring on a mixed-maturity lot of sugar beet seeds revealed the significant effect of the interaction of all factors. Seeds with different maturity classes, from the same lot, were washed using procedures determined from the first experiment. Statistical analysis of the germination data of the washed seeds showed a significant difference in mean germination time depending on maturity class. Washing seeds of different maturity classes with 40 mL of water per gram of seed improved the germination performance of maturity classes 2 and 5, compared with 20 mL water per gram of seed. The UV absorbance of inhibitors washed from seeds differed between maturity classes. The change in seed moisture content during the washing process is another factor altered by the extent of seed development, and this variation in water uptake may reflect the pericarp structure of seed in different maturity classes. The findings in this manuscript highlight the importance of optimizing the pre-treatment procedures in accordance with the individual seed lot. View Full-Text
Keywords: washing; phenol; pericarp; seed enhancement; spectrophotometry; multispectral imaging; moisture content; seed weight; maturity classification; dry fruit

Sådan kan frøavleren hjælpe bier og andre bestøvere

Dupont, Y. L., Boelt, B., Bang Madsen, H. — Tue, 01 Jan 2019 00:57:51 +0100