Light is more important than nutrient ratios of fertilization for cymodocea nodosa seedling development

Restoration of seagrass beds through seedlings is an alternative to the transplantation of adult plants that reduces the impact over donor areas and increases the genetic variability of restored meadows. To improve the use of Cymodocea nodosa seedlings, obtained from seeds germinated in vitro, in restoration programs, we investigated the ammonium and phosphate uptake rates of seedlings, and the synergistic effects of light levels (20 and 200 mu mol quanta m(-2) s(-1)) and different nitrogen to phosphorus molar ratios (40 mu M N:10 mu M P, 25 mu M N:25 mu M P, and 10 mu N:40 mu M P) on the photosynthetic activity and growth of seedlings. The nutrient content of seedlings was also compared to the seed nutrient reserves to assess the relative importance of external nutrient uptake for seedling development. Eighty two percent of the seeds germinated after 48 days at a mean rate of 1.5 seeds per day. All seedlings under all treatments survived and grew during the 4 weeks of the experiment. Seedlings of C. nodosa acquired ammonium and phosphate from the incubation media while still attached to the seed, at rates of about twice of adult plants. The relevance of external nutrient uptake was further highlighted by the observation that seedlings' tissues were richer in nitrogen and phosphorus than non-germinated seeds. The uptake of ammonium followed saturation kinetics with a half saturation constant of 32 mu M whereas the uptake of phosphate increased linearly with nutrient concentration within the range tested (5 - 100 mu M). Light was more important than the nutrient ratio of fertilization for the successful development of the young seedlings. The seedlings' photosynthetic and growth rates were about 20% higher in the high light treatment, whereas different nitrogen to phosphorus ratios did not significantly affect growth. The photosynthetic responses of the seedlings to changes in the light level and their capacity to use external nutrient sources showed that seedlings of C. nodosa have the ability to rapidly acclimate to the surrounding light and nutrient environment while still attached to the seeds. C. nodosa seedlings experiencing fertilization under low light levels showed slightly enhanced growth if nourished with a balanced formulation, whereas a slight increase in growth was also observed with unbalanced formulations under a higher light level. Our results highlight the importance of high light availability at the seedling restoration sites.Department of the Environment, Heritage and Climate Change of Gibraltar; FCT, the Portuguese Foundation for Science and Technology [SFRH/BPD/91629/2012]; FCT [UID/Multi/04326/2013

Transition from a maternal to external nitrogen source in maize seedlings

Maximizing NO3− uptake during seedling development is important as it has a major influence on plant growth and yield. However, little is known about the processes leading to, and involved in, the initiation of root NO3− uptake capacity in developing seedlings. This study examines the physiological processes involved in root NO3− uptake and metabolism, to gain an understanding of how the NO3− uptake system responds to meet demand as maize seedlings transition from seed N use to external N capture. The concentrations of seed‐derived free amino acids within root and shoot tissues are initially high, but decrease rapidly until stabilizing eight days after imbibition (DAI). Similarly, shoot N% decreases, but does not stabilize until 12–13 DAI. Following the decrease in free amino acid concentrations, root NO3− uptake capacity increases until shoot N% stabilizes. The increase in root NO3− uptake capacity corresponds with a rapid rise in transcript levels of putative NO3− transporters, ZmNRT2.1 and ZmNRT2.2 . The processes underlying the increase in root NO3− uptake capacity to meet N demand provide an insight into the processes controlling N uptake

Automated method to determine two critical growth stages of wheat: heading and flowering

Recording growth stage information is an important aspect of precision agriculture, crop breeding and phenotyping. In practice, crop growth stage is still primarily monitored by-eye, which is not only laborious and time-consuming, but also subjective and error-prone. The application of computer vision on digital images offers a high-throughput and non-invasive alternative to manual observations and its use in agriculture and high-throughput phenotyping is increasing. This paper presents an automated method to detect wheat heading and flowering stages, which uses the application of computer vision on digital images. The bag-of-visual-word technique is used to identify the growth stage during heading and flowering within digital images. Scale invariant feature transformation feature extraction technique is used for lower level feature extraction; subsequently, local linear constraint coding and spatial pyramid matching are developed in the mid-level representation stage. At the end, support vector machine classification is used to train and test the data samples. The method outperformed existing algorithms, having yielded 95.24, 97.79, 99.59% at early, medium and late stages of heading, respectively and 85.45% accuracy for flowering detection. The results also illustrate that the proposed method is robust enough to handle complex environmental changes (illumination, occlusion). Although the proposed method is applied only on identifying growth stage in wheat, there is potential for application to other crops and categorization concepts, such as disease classification

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility

The responses of maize roots to nitrogen supply

Substantial quantities of costly nitrogen (N) fertilisers are applied to cereal crops each year to maximise yields, but only approximately half of the N is captured by cereals, providing scope to increase root N uptake. However, our understanding of how the nitrate (NO₃⁻) uptake system is regulated and how it could be improved is limited. Furthermore, the changes to root morphology in response to NO₃⁻ supply are not well understood, in this case due to the difficulties associated with phenotyping roots in soil. To investigate how the NO₃⁻ uptake system is up-regulated, maize (Zea mays var. B73 and Mo17) was grown hydroponically with low or sufficient NO₃⁻ supply, and a range of physiological parameters associated with NO₃⁻ uptake were measured across the transition from seed N use, to external N capture. This transition provides an ideal system to clarify how the NO₃⁻ uptake system up-regulates as this is when plants first rely on increasing root N capture to meet demand. Across both lines and treatments, concentrations of shoot N and free amino acids in roots and shoots rapidly decrease as seed N reserves exhaust. Once free amino acid concentrations decrease to a critical level, root NO₃⁻ uptake capacity rapidly increased, corresponding with a rise in transcript levels of putative NO₃⁻ transporter genes ZmNRT2.1 and ZmNRT2.2. As NO₃⁻ uptake capacity reached maximum levels, shoot N concentrations stabilised. Despite shoot N concentrations stabilising, B73 was unable to maintain net N uptake and shoot growth in low NO₃⁻, relative to sufficient NO₃⁻. Conversely, Mo17 maintained shoot growth and net N uptake, and increased root mass in low NO₃⁻ relative to sufficient NO₃⁻. The effects of NO₃⁻ limitation on growth were reflected by an increased root:shoot, which emerged just prior to shoot N concentrations stabilising. In order to understand how root morphology may reflect the NO₃⁻ treatments differences observed in growth and net N uptake, morphological root traits were quantified across seedling development. Analysis showed that although B73 achieved greater absorption area per unit root mass than Mo17, its morphology was unresponsive to NO₃⁻ supply. Conversely, Mo17 responded to NO₃⁻ limitation by increasing lateral and axial root length before increasing root mass or volume. Subsequently, 11 putative quantitative trait loci (QTL) associated with morphological root traits corresponding with shoot growth or N uptake were detected across low and sufficient NO₃⁻, with one major QTL for lateral root length and surface area being identified in low NO₃⁻ on chromosome 5. These results provide insight into the processes involved in up-regulating root NO₃⁻ uptake capacity and how root morphology can adapt to NO₃⁻ supply. These findings identify potential control points in the regulation of NO₃⁻ uptake capacity and root morphology, which may be investigated further via global transcriptional analysis or fine-mapping of identified QTL respectively. Ultimately, this work may lead to identification of candidate regulatory genes that could be either manipulated to generate new lines with enhanced N uptake efficiencies, or allow the identification of germplasm with this trait.Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Agriculture, Food and Wine, 2014

Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping

Abstract Background Accurately segmenting vegetation from the background within digital images is both a fundamental and a challenging task in phenotyping. The performance of traditional methods is satisfactory in homogeneous environments, however, performance decreases when applied to images acquired in dynamic field environments. Results In this paper, a multi-feature learning method is proposed to quantify vegetation growth in outdoor field conditions. The introduced technique is compared with the state-of the-art and other learning methods on digital images. All methods are compared and evaluated with different environmental conditions and the following criteria: (1) comparison with ground-truth images, (2) variation along a day with changes in ambient illumination, (3) comparison with manual measurements and (4) an estimation of performance along the full life cycle of a wheat canopy. Conclusion The method described is capable of coping with the environmental challenges faced in field conditions, with high levels of adaptiveness and without the need for adjusting a threshold for each digital image. The proposed method is also an ideal candidate to process a time series of phenotypic information throughout the crop growth acquired in the field. Moreover, the introduced method has an advantage that it is not limited to growth measurements only but can be applied on other applications such as identifying weeds, diseases, stress, etc

Small amounts of ammonium (NH(+)(4)) can increase growth of maize (Zea mays)

Nitrate (NOequation image) and ammonium (NHequation image) are the predominant forms of nitrogen (N) available to plants in agricultural soils. Nitrate concentrations are generally ten times higher than those of NHequation image and this ratio is consistent across a wide range of soil types. The possible contribution of these small concentrations of NHequation image to the overall N budget of crop plants is often overlooked. In this study the importance of this for the growth and nitrogen budget of maize (Zea mays L.) was investigated, using agriculturally relevant concentrations of NHequation image. Maize inbred line B73 was grown hydroponically for 30 d at low (0.5 mM) and sufficient (2.5 mM) levels of NOequation image. Ammonium was added at 0.05 mM and 0.25 mM to both levels of NOequation image. At low NOequation image levels, addition of NHequation image was found to improve the growth of maize plants. This increased plant growth was accompanied by an increase in total N uptake, as well as total phosphorus, sulphur and other micronutrients in the shoot. Ammonium influx was higher than NOequation image influx for all the plants and decreased as the total N in the nutrient medium increased. This study shows that agriculturally relevant proportions of NHequation image supplied in addition to NOequation image can increase growth of maize.Jessey George, Luke Holtham, Kasra Sabermanesh, Sigrid Heuer, Mark Tester, Darren Plett and Trevor Garnet