Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton

The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Towards this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across three years. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate to high broad-sense heritabilities as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for the more dynamic HTPP canopy traits, canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy and agronomic traits such as lint yield displayed a time-dependent relationship. We also found that the position of some QTL controlling HTPP canopy traits were shared with agronomic and physiological traits. This work demonstrates the novel use of a field-based, HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars

Evaluating maize genotype performance under low nitrogen conditions using RGB UAV phenotyping techniques

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.This research and APC was funded by Bill & Melinda Gates Foundation and USAID Stress Tolerant Maize for Africa program, grant number [OPP1134248], and the MAIZE CGIAR research program. The CGIAR Research Program MAIZE receives W1&W2 support from the Governments of Australia, Belgium, Canada, China, France, India, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Sweden, Switzerland, U.K., U.S., and the World Bank

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Opportunities and limitations of crop phenotyping in southern european countries

ReviewThe Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of “phytotron research” to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainabilityinfo:eu-repo/semantics/publishedVersio

Magnetic properties of the frustrated AFM spinel ZnCr_2O_4 and the spin-glass Zn_{1-x}Cd_xCr_2O_4 (x=0.05,0.10)

The $T$-dependence (2- 400 K) of the electron paramagnetic resonance (EPR), magnetic susceptibility, $\chi (T)$, and specific heat, $C_{v}(T)$, of the $normal$ antiferromagnetic (AFM) spinel ZnCr$_{2}$O$_{4}$ and the spin-glass (SG) Zn$_{1-x}$Cd$_{x}$Cr$_{2}$O$_{4}$ ($x=0.05,0.10$) is reported. These systems behave as a strongly frustrated AFM and SG with $$ $\approx T_{G}\approx 12$ K and -400 K $\gtrsim \Theta_{CW}\gtrsim -500$ K. At high-$T$ the EPR intensity follows the $\chi (T)$ and the $g$-value is $T$-independent. The linewidth broadens as the temperature is lowered, suggesting the existence of short range AFM correlations in the paramagnetic phase. For ZnCr$_{2}$O$_{4}$ the EPR intensity and $\chi (T)$ decreases below 90 K and 50 K, respectively. These results are discussed in terms of nearest-neighbor Cr$^{3+}$ (S $=3/2$%) spin-coupled pairs with an exchange coupling of $| J/k| \approx$ 50 K. The appearance of small resonance modes for $T\lesssim 17$ K, the observation of a sharp drop in $\chi (T)$ and a strong peak in $C_{v}(T)$ at $T_{N}=12$ K confirms, as previously reported, the existence of long range AFM correlations in the low-$T$ phase. A comparison with recent neutron diffraction experiments that found a near dispersionless excitation at 4.5 meV for $T\lesssim T_{N}$ and a continuous gapless spectrum for $T\gtrsim T_{N}$, is also given.Comment: 17 pages, 8 figures, 1 Table. Submitted to Physical Review

Magnetic and Orbital States and Their Phase Transition of the Perovskite-Type Ti Oxides: Strong Coupling Approach

The properties and mechanism of the magnetic phase transition of the perovskite-type Ti oxides, which is driven by the Ti-O-Ti bond angle distortion, are studied theoretically by using the effective spin and pseudospin Hamiltonian with strong Coulomb repulsion. It is shown that the A-type antiferromagnetic (AFM(A)) to ferromagnetic (FM) phase transition occurs as the Ti-O-Ti bond angle is decreased. Through this phase transition, the orbital state changes only little whereas the spin-exchange coupling along the c-axis is expected to change from positive to negative nearly continuously and approaches zero at the phase boundary. The resultant strong two-dimensionality in the spin coupling causes rapid suppression of the critical temperature, as observed experimentally. It may induce large quantum fluctuations in this region.Comment: 13 pages, 15 figure

G-type antiferromagnetism and orbital ordering due to the crystal field from the rare-earth ions induced by the GdFeO_3-type distortion in RTiO_3 with R=La, Pr, Nd and Sm

The origin of the antiferromagnetic order and puzzling properties of LaTiO_3 as well as the magnetic phase diagram of the perovskite titanates are studied theoretically. We show that in LaTiO_3, the t_{2g} degeneracy is eventually lifted by the La cations in the GdFeO_3-type structure, which generates a crystal field with nearly trigonal symmetry. This allows the description of the low-energy structure of LaTiO_3 by a single-band Hubbard model as a good starting point. The lowest-orbital occupation in this crystal field stabilizes the AFM(G) state, and well explains the spin-wave spectrum of LaTiO_3 obtained by the neutron scattering experiment. The orbital-spin structures for RTiO_3 with R=Pr, Nd and Sm are also accounted for by the same mechanism. We point out that through generating the R crystal field, the GdFeO_3-type distortion has a universal relevance in determining the orbital-spin structure of the perovskite compounds in competition with the Jahn-Teller mechanism, which has been overlooked in the literature. Since the GdFeO_3-type distortion is a universal phenomenon as is seen in a large number of perovskite compounds, this mechanism may also play important roles in other compounds of this type.Comment: 20 pages, 15 figure

Atmospheric muon background in the ANTARES detector

An evaluation of the background due to atmospheric muons in the ANTARES high energy neutrino telescope is presented. Two different codes for atmospheric shower simulation have been used. Results from comparisons between these codes at sea level and detector level are presented. The first results on the capability of ANTARES to reject this class of background are given.Comment: 4 pages, 4 figures, To appear in Proceedings of the 29th International Cosmic Ray Conference (ICRC 2005), Pune, India, 3 - 10 Aug 200

REFORMING THE CAP: AN AGENDA FOR REGIONAL GROWTH?

This paper aims at analysing the recent CAP reform from the perspective of the current general and strategic objectives of the EU as defined by the Lisbon Strategy. A critical appraisal of the CAP impact in terms of regional growth is carried out. Firstly from a strictly conceptual and methodological point of view, then by analysing more in detail how CAP reform (of both Pillar I and II) might have actually affected the role of the CAP in promoting (or hindering) regional growth and, therefore, convergence. Empirical evidence provided by the different available methodologies has progressively emerged in the very last years. Though a conclusive answer on the impact of the reform can not be drawn, it still emerges that the role of CAP design and implementation in affecting regional growth and convergence is usually underestimated and often neglected in the discussions about the future of the CAP. At the same time, however, this role is not univocal and strongly case-specific, as it substantially differs across regions according to their socio-economic structure and how reforms are jointly implemented.Common Agricultural Policy, Regional Growth and Convergence, Lisbon Strategy, Agricultural and Food Policy, Community/Rural/Urban Development, Q180, R110, O410,