Temporal Growth Patterns Research Articles

Deep learning-based prediction of plant height and crown area of vegetable crops using LiDAR point cloud

Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

New estimation of critical insolation–CO2 relationship for triggering glacial inception

Abstract. It has been previously proposed that glacial inception represents a bifurcation transition between interglacial and glacial states and is governed by the nonlinear dynamics of the climate–cryosphere system. To trigger glacial inception, the orbital forcing (defined as the maximum of summer insolation at 65° N and determined by Earth’s orbital parameters) must be lower than a critical level, which depends on the atmospheric CO2 concentration. While paleoclimatic data do not provide a strong constraint on the dependence between CO2 and critical insolation, its accurate estimation is of fundamental importance for predicting future glaciations and the effect that anthropogenic CO2 emissions might have on them. In this study, we use the novel Earth system model of intermediate complexity CLIMBER-X with interactive ice sheets to produce a new estimation of the critical insolation–CO2 relationship for triggering glacial inception. We perform a series of experiments in which different combinations of orbital forcing and atmospheric CO2 concentration are maintained constant in time. We analyze for which combinations of orbital forcing and CO2 glacial inception occurs and trace the critical relationship between them, separating conditions under which glacial inception is possible from those where glacial inception is not materialized. We also provide a theoretical foundation for the proposed critical insolation–CO2 relation. We find that the use of the maximum summer insolation at 65° N as a single metric for orbital forcing is adequate for tracing the glacial inception bifurcation. Moreover, we find that the temporal and spatial patterns of ice sheet growth during glacial inception are not always the same but depend on the critical insolation and CO2 level. The experiments evidence the fact that during glacial inception, ice sheets grow mostly in North America, and only under low CO2 conditions are ice sheets also formed over Scandinavia. The latter is associated with a weak Atlantic Meridional Overturning Circulation (AMOC) for low CO2. We find that the strength of AMOC also affects the rate of ice sheet growth during glacial inception.

Dynamics of lentil (Lens culinaris) production and trade: Global scenario and Indian interdependence

Increasing pulse production is one of the national priorities for food and nutritional security of India. In this context, incremental changes in pulse production can play a pivotal role. During 2003–2022, for example, the area under lentil (Lens culinaris L.) registered a decline (2.08%), however; productivity improvement (41.26%) led to the enhancement in its production (by 37%) in the country. This study portrays the temporal trend and patterns of lentil production growth during the period 2003–2022. A substantial growth in imports of lentil from 63.97 thousand tonnes in Triennium estimate (TE) 2003 to 814.20 thousand tonnes in TE 2022 was registered in India. Moreover, the share of lentil imports to total lentil production increased from 6.94% in TE 2002 to 63.24% in TE 2022. Lentil imports exhibited a high annual growth rate (15.83%) and high instability during the overall study period (2003–2022), which was higher than the annual growth rate of imports of overall pulses during the same period. Approximately 0.65 million tonnes of lentils were imported in the year 2022, to meet domestic consumption demands. Canada and Australia accounted for 61% and 36% of the total lentil imports to India in year 2022, while Bangladesh (49.61%) followed by UAE (21.74%) and Nepal (18.33%) were the major export destinations of the total lentil export (2022). The gap between production and consumption, coupled with the changing trade regulations and consumer preferences, contributed to the observed instability in lentil trade in India over the past two decades. Concerted efforts in intensifying the technology transfer, capacity building and convergence of resources among the stakeholders can impact the productivity of lentils, thereby developing strategies for optimizing its import and export dynamics.

Imaging Findings Post-Stereotactic Radiosurgery for Vestibular Schwannoma: A Primer for the Radiologist.

Noninvasive tumor control of vestibular schwannomas through stereotactic radiosurgery allows high rates of long-term tumor control and has been used primarily for small- and medium-sized vestibular schwannomas. The posttreatment imaging appearance of the tumor, temporal patterns of growth and treatment response, as well as extratumoral complications can often be both subtle or confusing and should be appropriately recognized. Herein, the authors present an imaging-based review of expected changes as well as associated complications related to radiosurgery for vestibular schwannomas.

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations.

Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green-Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant-environment interactions and have implications for crop improvement strategies.

Spatial and temporal patterns of scyphozoan jellyfish abundance and growth in Icelandic coastal waters – a climate change perspective

ABSTRACT Scyphozoan jellyfish in Icelandic waters have received limited attention, despite apparent recent increases in blooms and changes in the regional oceanographic setting. The objectives of this study were to describe the species composition, abundance, phenology, and growth of scyphomedusae around Iceland and explore changes by comparing our findings with reports from the 1930s. Specimens were collected with a Bongo net in five regions around Iceland in the spring, summer, and autumn of 2008. Three jellyfish species were collected in the study, Aurelia aurita, followed by Cyanea capillata, and C. lamarckii. In the western and northwestern parts of Iceland, A. aurita abundance was highest in spring and summer, whereas in the North and East, abundances were highest in autumn. Cyanea capillata was not encountered in the Southwest, but in the west, Northwest, and North. There, numbers were highest in spring, whereas in the East, abundance was highest in autumn. Clockwise coastal current dispersal and regional interconnectivity are indicated. Weight-specific growth rates varied between 7.0 and 8.5% d−1 for A. aurita and 11.1 and 15.7% d−1 for C. capillata and are comparable to rates from often food-limited ecosystems at temperate latitudes. Presented evidence suggests that ephyrae and small medusae appear earlier in the season and in previously (in the 1930s) not occupied regions, while some regions (Southwest for C. capillata) were not populated anymore.

Complex network-based analysis of inter-basin water transfer networks

Driven by water resources scarcity, inter-basin water transfer (IBWT) projects have emerged and evolved, thereby shaping the current water supply framework. As IBWT projects increase, they gradually acquire network characteristics, giving rise to IBWT networks. However, the impact of the development of IBWT networks on water-supply patterns remains unclear? Analyzing the spatial and temporal growth patterns of IBWT networks through the lens of complex network theory can help answer this question. This study established a framework for analyzing IBWT networks using complex network analysis (CNA). Within this framework, we analyzed the spatial and temporal development characteristics of the IBWT network by quantifying its global properties across three dimensions: efficiency, elasticity, and coordination. Six centrality indicators were employed to measure the significance of individual nodes. Finally, the Infomap method was employed for network community detection. The results demonstrated that the CNA-based framework effectively captured the comprehensive development of the IBWT network, which underwent six stages from inception to high-speed, near-term, and long-term Stages. IBWT network efficiency, elasticity, and coordination all showed growth, which was most pronounced in 2013–2022, where the scaled global efficiency, network efficiency, average node connectivity, and average betweenness centrality metrics all grew by a factor of greater than 4. Importantly, the total percentage of centrality greater than 0.2 in the Huang-Huai-Hai-Chang Basin was 63.0%. The 125 initial IBWT projects in the IBWT network contained 58 communities, while the subsequent construction of 57 projects added only 12 communities. Future new IBWT network projects will tend to joined existing communities.

Forecasting carrot yield with optimal timing of Sentinel 2 image acquisition

Accurate, non-destructive forecasting of carrot yield is difficult due to its subterranean growing habit. Furthermore, the timing of forecasting usually occurs when the crop is mature, limiting the opportunity to implement alternative management decisions to improve yield (during the growing season). This study aims to improve the accuracy of carrot yield forecasting by exploring time series and multivariate approaches. Using Sentinel-2 satellite imagery in three Australian vegetable regions, we established a time series of carrot phenological stages (PhS) from ‘days after sowing’ (DAS) to enhance prediction timing. Numerous vegetation indices (VIs) were analyzed to derive temporal growth patterns. Correlations with yield at different PhS were established. Although the average root yield (t ha−1) did not significantly differ across the regions, the temporal VI signatures, indicating different regional crop growth trends, did vary as well as the PhS at when the maximum correlation with yield occurred (PhSR2max)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PhS_{{R2_{max} }} )$$\\end{document} with two of the regions producing a delayed PhSR2max\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PhS_{{R2_{max} }}$$\\end{document} (i.e. 90–130 DAS). The best multivariate model was identified at 70 DAS, extending the forecasting window before harvest between 20 to 60 days. The performance of this model was validated with new crops producing an average error of 16.9 t ha−1 (27% of total yield). These results demonstrate the potential of the model at such early stage under varying growing conditions offering growers and stakeholders the chance to optimize farming practices, make informed decisions on selling, harvesting, and labor planning, and adopt precision agriculture methods.

Vegetation responses to ecological water delivery and prolonged droughts in an artificial desert oasis, Northwestern China

Irrational water usage in the middle reaches of inland rivers is detrimental to downstream oases, and the Qingtu Oasis is a typical downstream oasis that is degraded by water resource scarcity. The Qingtu Lake ecological water diversion project (EWDP) has restored the oasis, but the response pattern of different vegetation communities to environmental water and the dominant drivers of vegetation dynamics are not clear. Here, based on ground and satellite observations, this study investigated the spatial and temporal patterns of vegetation distribution and growth in the Qingtu Oasis from 2010 to 2022, and the reasons for the interannual variability in vegetation growth were also explained. The results showed that temporal variations in the distribution area of Phragmites australis (PA) followed a logistic curve after the EWDP, which increased rapidly from 2010 to 2019 and stabilized afterward. Meanwhile, the distribution area of Nitraria tangutorum (NT) presented a linear increase during the study period, with a 121.2% increase. Due to water inundation, the central region was occupied by PA communities, while NT communities moved to the edge. The spatial connectivity of PA patches increased significantly in contrast to the overall fragmentation tendency of NT patches in 2010–2022. The oasis tended to be homogenized and aggregated according to the morphological spatial pattern analysis, along with the landscape diversity and contagion indices. Enhanced vegetation growth, as indicated by the normalized difference vegetation index (NDVI), was observed in 76.1% of the vegetated lands, and the NDVI increasing rate was high near the canal. Using the random forest model, this study found that changes in groundwater depth were the most important driver of NDVI variability. The following influencing factors were atmospheric CO2 concentration, water inundation area and water delivery amount. In the absence of large increases in water delivery amount, oasis vegetation growth was susceptible to prolonged droughts. This study uncovers a nonlinear response of different vegetation communities to climate change and the EWDP in the downstream region of arid endorheic basins and identifies meteorological droughts as a key factor that should be fully considered in water delivery strategies. These findings lay a solid foundation for managing oasis restoration and water resource allocation in arid inland river basins.

CATAIN: An underwater camera system for studying settlement in fouling communities at high temporal resolution

AbstractAt present, studies on settlement in marine fouling communities are limited to easily accessible habitats that can be monitored on a daily basis. There are large knowledge gaps regarding settlement patterns on sub‐daily temporal scales and in remote areas. In order to fill these knowledge gaps, we have designed an underwater camera system named CATAIN—CAmera To Analyze INvertebrates—and have compared our approach to standard methods. The key innovation of CATAIN is that the acrylic endcap of the housing serves as the fouling panel itself, with organisms being photographed through the transparent endcap from the underside. This set‐up circumvents the common problem of biofouling on camera housings. Using this system, researchers can record high‐resolution images of sessile benthic invertebrates and macroalgae at time intervals ranging from just a few minutes to several days and for months to a year at a time. CATAIN enables studies on temporal patterns in settlement, postsettlement mortality, growth, and other ecological parameters with no human intervention. This system expands sampling possibilities in remote environments where it is impractical for a researcher to be present every day.

The carbon sink of secondary and degraded humid tropical forests.

The globally important carbon sink of intact, old-growth tropical humid forests is declining because of climate change, deforestation and degradation from fire and logging1-3. Recovering tropical secondary and degraded forests now cover about 10% of the tropical forest area4, but how much carbon they accumulate remains uncertain. Here we quantify the aboveground carbon (AGC) sink of recovering forests across three main continuous tropical humid regions: the Amazon, Borneo and Central Africa5,6. On the basis of satellite data products4,7, our analysis encompasses the heterogeneous spatial and temporal patterns of growth in degraded and secondary forests, influenced by key environmental and anthropogenic drivers. In the first 20 years of recovery, regrowth rates in Borneo were up to 45% and 58% higher than in Central Africa and the Amazon, respectively. This is due to variables such as temperature, water deficit and disturbance regimes. We find that regrowing degraded and secondary forests accumulated 107 Tg C year-1 (90-130 Tg C year-1) between 1984 and 2018, counterbalancing 26% (21-34%) of carbon emissions from humid tropical forest loss during the same period. Protecting old-growth forests is therefore a priority. Furthermore, we estimate that conserving recovering degraded and secondary forests can have a feasible future carbon sink potential of 53 Tg C year-1 (44-62 Tg C year-1) across the main tropical regions studied.

Geoenvironment factors guided coastal urban growth prospect (UGP) delineation using heuristic and machine learning models

Unplanned urban growth along the coastal cities may disturb the balance between the ecosystem and urbanisation; thus, a development strategy guided by geoenvironmental factors is necessary. We have attempted to delineate suitable Urban Growth Prospect (UGP) zones within the coastal region of the Vishakhapatnam district of India, based on geoenvironmental factors. We conduct an initial assessment of the temporal urban growth pattern in last 15 years using Landsat data and published landuse maps, demonstrating that the urban growth has percolated to unstable geomorphic units, making it vulnerable to flooding. Geoenvironmental factors (geomorphology, lithology, structure, land use, soil, slope and groundwater) based multi-thematic data integration approach is adopted to delineate UGP zones using heuristic and machine learning techniques like Analytical Hierarchy Process (AHP), Artificial Neural Network (ANN), Random Forest (RF), Logistic Regression (LR). We used present-day field observations to train the predictive models like ANN, LR & RF and test the outcomes with accuracy assessment ratios (detection percentage, miss factor etc.). The ANN-derived result presents the best accuracy with a detection percentage of 85% and predicts a 55% spatial scope of urban expansion, which was further refined using the mask of the strategic and ecologically sensitive location with appropriate buffers. The final map represents the UGP zones within the city and surroundings, encompassing stable landform units. The study provides insight into the usefulness of the heuristic and machine learning models in predictive analysis, using geoenvironmental factors for urban expansion in fast-growing coastal cities.

Comparative Transcriptome Analysis of Tolerant and Sensitive Genotypes of Common Bean (Phaseolus vulgaris L.) in Response to Terminal Drought Stress

We conducted a genome-wide transcriptomic analysis of three drought tolerant and sensitive genotypes of common bean to examine their transcriptional responses to terminal drought stress. We then conducted pairwise comparisons between the root and leaf transcriptomes from the resulting tissue based on combined transcriptomic data from the tolerant and sensitive genotypes. Our transcriptomic data revealed that 491 (6.4%) DEGs (differentially expressed genes) were upregulated in tolerant genotypes, whereas they were downregulated in sensitive genotypes; likewise, 396 (5.1%) DEGs upregulated in sensitive genotypes were downregulated in tolerant genotypes. Several transcription factors, heat shock proteins, and chaperones were identified in the study. Several DEGs in drought DB (data Base) overlapped between genotypes. The GO (gene ontology) terms for biological processes showed upregulation of DEGs in tolerant genotypes for sulfate and drug transmembrane transport when compared to sensitive genotypes. A GO term for cellular components enriched with upregulated DEGs for the apoplast in tolerant genotypes. These results substantiated the temporal pattern of root growth (elongation and initiation of root growth), and ABA-mediated drought response in tolerant genotypes. KEGG (kyoto encyclopedia of genes and genomes) analysis revealed an upregulation of MAPK (mitogen activated protein kinase) signaling pathways and plant hormone signaling pathways in tolerant genotypes. As a result of this study, it will be possible to uncover the molecular mechanisms of drought tolerance in response to terminal drought stress in the field. Further, genome-wide transcriptomic analysis of both tolerant and sensitive genotypes will assist us in identifying potential genes that may contribute to improving drought tolerance in the common bean.

The Interplay of the Tree and Stand-Level Processes Mediate Drought-Induced Forest Dieback: Evidence from Complementary Remote Sensing and Tree-Ring Approaches

Drought-induced forest dieback can lead to a tipping point in community dominance, but the coupled response at the tree and stand-level response has not been properly addressed. New spatially and temporally integrated monitoring approaches that target different biological organization levels are needed. Here, we compared the temporal responses of dendrochronological and spectral indices from 1984 to 2020 at both tree and stand levels, respectively, of a drought-prone Mediterranean Pinus pinea forest currently suffering strong dieback. We test the influence of climate on temporal patterns of tree radial growth, greenness and wetness spectral indices; and we address the influence of major drought episodes on resilience metrics. Tree-ring data and spectral indices followed different spatio-temporal patterns over the study period (1984–2020). Combined information from tree growth and spectral trajectories suggests that a reduction in tree density during the mid-1990s could have promoted tree growth and reduced dieback risk. Additionally, over the last decade, extreme and recurrent droughts have resulted in crown defoliation greater than 40% in most plots since 2019. We found that tree growth and the greenness spectral index were positively related to annual precipitation, while the wetness index was positively related to mean annual temperature. The response to drought, however, was stronger for tree growth than for spectral indices. Our study demonstrates the value of long-term retrospective multiscale analyses including tree and stand-level scales to disentangle mechanisms triggering and driving forest dieback.

Three decades of global trends in urban microwave backscatter, building volume and city GDP

Most mapped urban information is essentially two-dimensional, e.g., areal extent by urban cover type. There is a gap in knowledge about patterns and changes of urban built-up volumes and development intensity globally. Here we use a new dataset of global microwave backscatter for 1993–2020 to explore this third dimension of urban growth across 477 large cities. Our results show that urban backscatter correlates with an independent estimate of building volume across 8000+ urban grid cells (0.05° lat/lon) in large cities in China, Europe, and the U.S.A. Regional rates of urban backscatter increase vary substantially across the three decades, with the highest rates in the 2010s across Asia, particularly in China and Southeast Asia. Microwave backscatter, a proxy for urban building volume, is correlated with Gross Domestic Product at the scale of urban grid cells across the globe, and at the city level across most regions. This time-series of scatterometer data provides an important tool for understanding both the spatial and temporal patterns of urban volumetric growth over the past several decades.

Changes in the water environment and its major driving factors in Poyang Lake from 2016 to 2019, China.

The hydrological exchange process between Poyang Lake (PYL), the largest freshwater lake in China, and the Yangtze River leads to drastic changes in water area (WA) and water level (WL), as well as apparent fluctuations in lake nutrients, algal organisms, and trophic level index. This study investigated the current status of the PYL water environment and the influence of hydrological changes on the nutrient status of the floodplain of the lake. Based on monthly measured data from six hydrological stations from 2016 to 2019, it was hypothesized that WA and WL were the key regulators of the spatial and temporal distribution patterns of lake water quality and algal growth, including water temperature, water clarity (Secchi depth [SD]), and nutrient levels. The results revealed that (1) the spatial and temporal distribution characteristics of major nutrients in PYL were influenced by dynamic changes in hydrological characteristics (SD, total nitrogen [TN], and total phosphorus [TP]); (2) the eutrophication level in PYL has been in a steady state in recent years, while the central area has been more prone to the risk of eutrophication (e.g., the peak eutrophication index during Period 1 [January to April] in the water near the Duchang station reached 70); and (3) there were significant correlations among environmental variables, nutrients, and algal organisms, with different spatial and temporal distribution characteristics (p < 0.05), while the changes in WA and WL considerably influenced the water environment in the PYL.

Maize prolificacy under contrasting plant densities and N supplies: I. Plant growth, biomass allocation and development of apical and sub-apical ears from floral induction to silking

In limited-production regions for maize (Zea mays, L.) crop, late sowing date and low plant densities are progressively adopted. At low plant densities, the expression of prolificacy (i.e., more than one fertile ear per plant) can stabilize maize yield throughout environments. However, the limited adoption of N fertilization can reduce kernel set on the apical (E1) and sub-apical ear (E2) and consequently crop grain yield at low plant densities. Five Argentinian maize hybrids with different degrees of prolificacy (1.37–1.92 ears pl-1 at 4 pl m-2 irrigated and without N limitations) were cultivated under contrasting plant densities (4 and 8 pl m-2) and N fertilization rates (70 and 270 kg N ha-1) to analyze (i) plant biomass, biomass allocation in E1 and E2 (BAE1 and BAE2), and floret differentiation of both ears from floral induction to silking stage, (ii) the anthesis-silking intervals of E1 and E2 (ASIE1 and ASIE2) and the silking interval between E1 and E2 (ESI), and (iii) silk extrusion of both ears. From the eight-ligulated leaf stage onwards, low N availability and high density decreased plant biomass and BAE2 without modifying BAE1. Additionally, N stress reduced floret differentiation of E2 in the less prolific hybrids (DK-747 and DK-7210). On the contrary, E1 and E2 of the most prolific hybrids (DK-3F22, DK-4F37 and DK-664) showed similar temporal patterns of growth and floret differentiation. These hybrids presented the highest proportion of exposed silks in both ears coinciding with their highest BAE1 and BAE2, which possibly modulated silk elongation. Finally, under the most stressful environments, the lower BAE2 than BAE1 at silking increased ASI of both ears, ESI and reduced the proportion of plants with silks of E2 exposed to pollen. The latter response (i.e., low proportion of plants with silks extrusion in E2) was more pronounced in less prolific hybrids. Therefore, floret differentiation and BA in E2 of the most prolific hybrids would be more stable in the face of changes in nutritional offer, a prevalent scenario of limited-production regions.

Maize prolificacy under contrasting plant densities and N supplies: II. Growth per plant, biomass partitioning to apical and sub-apical ears during the critical period and kernel setting

Maize ( Zea mays L.) prolificacy (i.e., more than one fertile ear per plant) is a source of reproductive plasticity that contributes to increase or sustain crop grain yield when plant density is reduced in drought-prone environments. The rare use of N fertilization, however, can limit prolificacy. In a companion paper we studied floret differentiation and biomass allocation in apical (E1) and sub-apical ear (E2) and flowering dynamics of five maize hybrids with contrasting prolificacy under different plant densities and N supplies. Briefly, after apex induction, E1 and E2 of the most prolific hybrids (DK-3F22, DK-4F37 and DK-664) had similar temporal patterns of growth and floret differentiation. On the contrary, E2 in the less prolific hybrids (DK-747 and DK-7210) had less biomass allocation and floret differentiation than E1, which were reduced under low soil N supply, resulting in a fewer proportion of plants with silk extrusion of E2. In this work, we explore the relationships between (i) kernel number per plant vs the growth rates of plants around flowering, (ii) growth rate of ears vs plant growth rate around flowering (i.e. biomass partitioning to E1 and E2), (iii) kernel number of E1 and E2 vs the growth rates of ears around flowering (i.e. reproductive efficiency of E1 and E2), and (iv) kernel number and prolificacy vs flowering synchrony (i.e., ASI: anthesis-silking interval and ESI: ear silking interval between E1 and E2). For all hybrids, N supply modulated plant growth rate and biomass partitioning to both ears around flowering, kernel number per unit of plant growth rate around flowering and reproductive efficiency of E2, with no impact on the reproductive efficiency of E1. Reductions of kernel number per plant were related to failures of early emerged silks (of E1 under low N supply and of E2 under both N supplies) to set kernels. Failures in kernel setting of E2 were also related to a reduced biomass partitioning to E2 around flowering. Higher ASI E1 values described crops with lower kernel numbers of E1, while higher ESI values depicted both a lower proportion of plants with more than one fertile ear (i.e., prolificacy) and lower kernel numbers of E2. Results complement evidence from the companion paper of the role of N supply on plant biomass production and biomass partitioning to E2 from early floral development stages to the period around flowering, and on the reproductive efficiency of E2. Consequently, N supply must be ensured from early vegetative stages of low-density maize crops, so as not to compromise prolificacy and E2 kernel setting. • Prolificacy was highly sensitive to N supply even at low plant density. • High N supply increased biomass partitioning to ears around flowering. • Only reproductive efficiency of sub-apical ear (E2) was improved by N supply. • Ear silking interval modulate prolificacy expression and kernel setting of E2.

Growth Characteristics of Female Radiation/Clinical Oncologists in South Asia: Assessment of Gender Neutrality and Leadership Position

AimTo evaluate the temporal growth pattern of female radiation/clinical oncologists (FRCOs) and, if applicable, predict the gender neutrality in different countries of South Asia. Materials and methodsSouth Asia is composed of Afghanistan, Bhutan, Maldives, Bangladesh, India, Nepal, Pakistan and Sri Lanka. The growth pattern of FRCOs in the latter five countries having radiation oncology facilities was evaluated from respective national registration data. Based on the average annual differential growth rate, together with the already existing female and male radiation/clinical oncologists (MRCOs), the cumulative numbers of FRCOs and MRCOs were forecasted for the next 10 years. The data regarding FRCOs in a leadership position were also calculated from different sources. ResultsThe total number of radiation/clinical oncologists in the region was 4074, of which 91.8% were in India, because of its vast population. The overall number of FRCOs and MRCOs stood at 1370 and 2704, with a 1:2 female:male ratio. The average incremental annual growth of FRCOs in India was the highest (12.7 persons/year) and Nepal was the lowest (0.4 persons/year), with no data from Pakistan. If the current growth rate is sustained, Indian gender neutrality will be achieved by 2027–2030. In other countries, gender neutrality is unlikely to be achieved in the near future. With regards to leadership positions, 56–77 radiation oncology departments in India, one each in Bangladesh and Sri Lanka are headed by FRCOs, whereas Nepal and Pakistan have none. ConclusionsWith the current growth rate of FRCOs and MRCOs, India will achieve gender parity within a decade; however, the rest of the countries will not achieve this in the near future. Analysis of radiation/clinical oncologists' registration data with their respective national bodies revealed an encouraging growth in the number of FRCOs as against their male counterparts in the last 5 years, compared with previous decades, especially in Bangladesh, Sri Lanka and India. Sri Lanka show high gender neutrality and adopted a multi-tasking and holistic approach of clinical oncology practices as also seen in Scandinavian countries. Such practice may be helpful to improve gender equality in radiation/clinical oncology practice for the other countries in the South Asian region.

The First Network of Tree-Ring Chronologies for Co-Occurring Nothofagus nervosa and Nothofagus obliqua along a Precipitation Gradient in Patagonia, Argentina

We present the first network of tree-ring chronologies for co-occurring Nothofagus nervosa and Nothofagus obliqua in Argentina. Using standard dendrochronological techniques, we developed seven tree-ring width chronologies for each species, encompassing their E-W distribution along a precipitation gradient. Chronology statistics indicated that N. nervosa and N. obliqua are strong candidates for research in dendrochronology. The chronologies were used to study and compare the temporal and spatial growth patterns of each species. Correlation and principal components analysis of the tree-ring chronologies serve as evidence of a strong common signal, indicating shared regional growth patterns across the gradient with some exceptions between sites and species. These results indicate the high potential of N. nervosa and N. obliqua for their use in dendroclimatology.