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The contribution of four variables, namely Chlorophyll-a (Chl-a), Sea Surface Temperature (SST), diffuse attenuation coefficient (Kd_490 or Kd), and Photosynthetically Active Radiation (PAR), in predicting the catches of major pelagic fish species (Indian mackerel, horse mackerel, Bombay duck, oil sardine, and other sardines) was evaluated using Canonical Correlation Analysis (CCA). The outcome of the analysis was compared with those obtained by using the following models and methods: the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), connection weight methods, and the explanatory methods of Artificial Neural Networks (ANNs). Both the sets of results were in agreement. Neither the GAM nor the ANNs method showed any clear advantage over each other, although the GAM performed better than the GLM. [ABSTRACT FROM AUTHOR]

Quarterly landings or catches of total fishes and the major pelagic fish species, were forecasted using the methods and models viz. autoregressive integrated moving average (ARIMA), non-linear autoregressive (NAR) artificial neural network (ANN), autoregressive integrated moving average with exogenous inputs (ARIMAX), non-linear autoregressive with external (exogenous) inputs (NARX) artificial neural network. The models were also developed by considering only two important variables (differ for total fish and selected fish species) obtained from the ANN model. These simplified models proved nearly as good in their predictions. Simulated sea surface temperature (SST) for the A2 climate change scenario was used as an input for the NARX model to estimate the catches of Bombay duck over a short term (2020 - 2025) and a long term (2030 - 2050) with the last two years' (2012 - 2013) average catch of training data as a benchmark. The catches increased on average by 41 % in the short term but decreased by 17.72 % in the long term. [ABSTRACT FROM AUTHOR]

Women constitute half of world population and makeup a significant percentage of the labor work force. In this regard, the United Nations (UN) has chartered gender-specific laws to protect the rights and interests of working women. In the last few decades Indian society has witnessed transition of women from their perceived conventional role in full-time household work to a larger participation in workforce. Globally, the Human Rights framework promotes the basic rights for both men and women. In 1948, the Universal Declaration of Human Rights proclaimed equal entitlements to all human beings on issues of education, housing and property, etc. However, women continue to face multiple challenges at workplace like sexual harassment, fear of job loss due to pregnancy, late night shift and so on which can be intimidating and have negative impact on their mental health. There is a bidirectional relationship between human rights and mental health. Violation of human rights has a negative effect on the individual's mental health; inversely following human rights can positively influence the mental health. The laws are made to protect rights of working women, but it cannot be denied that the discrimination is structurally embedded in our society. At the level of society, structural changes, change in attitude towards women is required. There is a high demand to raise the awareness on women's rights at workplace and women must advocate their rights. The first part of this paper discloses the mental health of Indian women at workplace. The second part consists of human rights of Indian working women, few relevant case laws wherein Indian women were given justice and the general association of these with the mental health of working women worldwide. At the end, suggestions are made to increase the awareness and strict implementation of the laws for the mental well-being of working women. [ABSTRACT FROM AUTHOR]

Realization of agricultural crop condition through field survey is quite expensive, time consuming and sometimes not practical for remote locations. Optical remote sensing techniques can provide information on real condition of the crops by observing spectral reflectance at different crop growth phases and is less expensive and less time consuming. Hyperspectral remote sensing provides a unique opportunity for non-destructive, timely and accurate estimation of crop biophysical and biochemical properties. In this study, a field experiment was conducted to identify the water and nitrogen stress indicative spectral bands using ground-based hyperspectral data and to assess the predictive capability of selective bands on yield of maize under water and nitrogen stress environment. The experiment comprised of three irrigation scheduling treatments based on IW/CPE ration of 0.6, 0.8 and 1.2 and three nitrogen level treatments, i.e., 100, 200 and 300 kg of N ha−1, respectively, with three replications in a split plot design. The spectral reflectance was measured before irrigation at tasseling and dough stage of the maize crop using portable field spectroradiometer. The results of stepwise multiple linear regression indicated the highest predicting capability of spectral bands 540 nm, 780 nm and 860 nm for leaf nitrogen and 700 nm, 740 nm and 860 nm for leaf water content. The derived biophysical parameters based on spectral reflectance viz. relative leaf water content (%), leaf area index and leaf nitrogen contentment (%) at tasseling stage of maize crop accounted for 80%, 61% and 66% variation in grain yield, respectively. [ABSTRACT FROM AUTHOR]

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP. [ABSTRACT FROM AUTHOR]

This work is undertaken considering the significance of functional phenotyping (primarily measured from continuous profiles of plant-water relations) for crop selection purposes. High-Throughput Plant Phenotyping (HTPP) platforms which largely employ state-of-the-art sensor technologies for acquisition of vast amount of field data, often fail to efficiently translate sensor information into knowledge due to the major challenges of data handling and processing. Hence, it is imperative to concurrently find a way for dissociating noise from useful data. Additionally, another important aspect is understanding how frequent should be the data collection, so that information is maximized. This paper presents a novel approach for identifying the optimal frequency for phenotyping evapotranspiration (ET) by assimilating results from both time series forecast as well as classification models. Thus, at the optimal frequency, plant-water relations can not only be desirably predicted but genotypes can also be classified based on the characteristics of their ET profiles. Consequently, this will aid better crop selection, besides minimizing noise, redundancy, cost and effort in HTPP data collection. High frequency (15 min) ET time series data of 48 chickpea varieties (with considerable genotypic diversity) collected at the LeasyScan HTPP platform, ICRISAT is used for this study. Time series forecast and classification is performed by varying frequency up to 180 min. Multiple performance measures of time series forecast and classification are combined, followed by implementation of entropy theory for sampling frequency optimization. The results demonstrate that ET time series with a frequency of 60 min per day potentially yield the optimum information. [ABSTRACT FROM AUTHOR]

In-situ data of chlorophyll-a concentrations (Chl-a) and sea surface temperature (SST) of the Gujarat region for the period, 2002-2009 were obtained from Indian National Centre for Ocean Information Services (INCOIS), Hyderabad. Out of nearly 100 sampling points, 22 and 67 points qualified for comparison with the satellite measurements of Chl-a and SST, respectively. Chl-a concentrations were estimated from the MODIS satellite data (4 km resolution) with the existing global ocean color algorithms, namely, OC2V4, OC4V4, and OC3M. The SST was calculated with the help of bands 31 and 32 using MODIS-Aqua sensor long wave SST algorithm and European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation SST retrieval model (split window method). The satellite images were processed using global Sea WiFS Data Analysis System (SeaDAS) software v.7.3.1. Chl-a retrieved from OC3M algorithm had high coefficient of determination (R2=0.74) and less root mean square error (RMSE=1.24) as compared to OC2V4 and OC4V4 (R2=0.541 & 0.542 and RMSE=1.94 and 1.84, respectively) with in-situ data. The SST retrieved from MODIS-Aqua sensor long wave SST algorithm had a high coefficient of correlation as compared to ECMWF assimilation model (0.798 & 0.32 respectively) with in-situ data and RMSE were 0.80 and 2.65, respectively. SST and Chl-a showed an inverse correlation, with a coefficient of correlation (R) =0.530. Daily retrieval of Chl-a and SST value had very high degree of correlation with remote sensed eight days composite and monthly composite value (0.958 & 0.876, respectively). Retrieval of the value of diffuse attenuation coefficient at 490 nm wavelength (Kd or Kd_490), photosynthetically active radiation (PAR) and vertical attenuation coefficient of PAR (Kd(PAR)) were done and found that Kd and Kd(PAR) had very high degree of positive correlation (R=0.994). In addition, it was found that PAR had a positive correlation with SST(R=0.512) and negative correlation with Chl-a (R=-0.446). The range of this parameter values supports the case-I water and fish assemblage area. [ABSTRACT FROM AUTHOR]

Core Ideas: Soil–water–disease interactions were analyzed using field and numerical studies.ERT was able to image the soil moisture front separated by active roots.HYDRUS overpredicted RWU from diseased trees, requiring a compensation mechanism.Parameters defining the root distribution were optimized considering plant health. Variably saturated flow models that simulate soil–water–plant interactions within the rhizosphere largely ignore the effect of plant health. This makes it difficult for them to effectively simulate root water uptake (RWU) and implement alternate management practices for diseased trees. This research was aimed at understanding the hydrological and plant controls on RWU simulations dominated by the health of the tree using experimental and numerical frameworks. Two research plots, one around a healthy mature and the other around a diseased mature (Phytophthora spp. affected) orange tree (Citrus reticulate Blanco), in the Vidarbha region of India were considered for our analysis. Three‐dimensional electrical resistivity tomography (ERT) performed at the two locations revealed that the soil moisture profiles following irrigation are different between the two plots. A two‐dimensional axisymmetric form of Richards' equation was then solved using HYDRUS (2D/3D) by incorporating a root distribution function. A global sensitivity analysis was performed to identify the root distribution parameters that influence soil moisture simulations. These parameters were then optimized using a genetic algorithm for healthy and diseased conditions. We observed that the diseased orange tree had consumed less water, leaving high soil moisture in the rhizosphere, a condition favorable for the further growth of disease‐causing fungi. Hence, managing irrigation water in accordance with RWU patterns is essential for diseased trees. To understand the error propagation in RWU estimation, we simulated RWU from a diseased tree with the optimal parameters derived for a healthy case. Results show that the error in estimating RWU from the model cells progressively increases with radial distance and soil depth. [ABSTRACT FROM AUTHOR]

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters. [ABSTRACT FROM AUTHOR]

The right to work means that people have a human right to work or engage in productive employment and may not be prevented from doing so. The right to work is enshrined in the Universal Declaration of Human Rights (UDHR) and recognized in international human rights law through its inclusion in the International Covenant on Economic, Social and Cultural Rights, where the right to work emphasizes economic, social and cultural development. The preamble to the constitution secures social, economic and political justice to all citizens of India. The same resolve is more elaborately repeated in DPSP which, among others, specifically require the state to minimize the inequalities in income and to eliminate inequalities in status, facilities and opportunities as well as to direct its policy towards ensuring that "the ownership and control of the material resources of the community are so distributed as best to sub-serve the common good" and that the operation of the economic system does not result in the concentration of wealth and means of production to the common detriment. At one time, FRs were having primacy over DPSP. But in recent times, some DPSP have transformed into and become an integral part of the named FRs under part III. Therefore, the sanction of the state or the judiciary is needed for the enforcement of DPSP. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare two different rice simulation models-standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])-with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994-1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making. [ABSTRACT FROM AUTHOR]

The Indian education system has played a key role in developing the skilled workforce required to capitalize on the wave of offshore outsourcing. India has a long history of organized education that goes back thousands of years in history. Prior to the British reign, India had a well-developed regional system. Much of this was uprooted and replaced with western structure and content. While much was lost in the transition, India emerged with a world-class set of universities including the famous Indian Institutes of Technology (IITs). Though producing a limited number of graduates, IIT is considered one of the top technical universities in the world and, by some metrics, the most difficult to get into. [ABSTRACT FROM AUTHOR]

In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem. [ABSTRACT FROM AUTHOR]

Background: Restricting transpiration under high vapor pressure deficit (VPD) is a promising water-saving trait for drought adaptation. However, it is often measured under controlled conditions and at very low throughput, unsuitable for breeding. A few high-throughput phenotyping (HTP) studies exist, and have considered only maximum transpiration rate in analyzing genotypic differences in this trait. Further, no study has precisely identified the VPD breakpoints where genotypes restrict transpiration under natural conditions. Therefore, outdoors HTP data (15 min frequency) of a chickpea population were used to automate the generation of smooth transpiration profiles, extract informative features of the transpiration response to VPD for optimal genotypic discretization, identify VPD breakpoints, and compare genotypes. Results: Fifteen biologically relevant features were extracted from the transpiration rate profiles derived from load cells data. Genotypes were clustered (C1, C2, C3) and 6 most important features (with heritability > 0.5) were selected using unsupervised Random Forest. All the wild relatives were found in C1, while C2 and C3 mostly comprised high TE and low TE lines, respectively. Assessment of the distinct p-value groups within each selected feature revealed highest genotypic variation for the feature representing transpiration response to high VPD condition. Sensitivity analysis on a multi-output neural network model (with R of 0.931, 0.944, 0.953 for C1, C2, C3, respectively) found C1 with the highest water saving ability, that restricted transpiration at relatively low VPD levels, 56% (i.e. 3.52 kPa) or 62% (i.e. 3.90 kPa), depending whether the influence of other environmental variables was minimum or maximum. Also, VPD appeared to have the most striking influence on the transpiration response independently of other environment variable, whereas light, temperature, and relative humidity alone had little/no effect. Conclusion: Through this study, we present a novel approach to identifying genotypes with drought-tolerance potential, which overcomes the challenges in HTP of the water-saving trait. The six selected features served as proxy phenotypes for reliable genotypic discretization. The wild chickpeas were found to limit water-loss faster than the water-profligate cultivated ones. Such an analytic approach can be directly used for prescriptive breeding applications, applied to other traits, and help expedite maximized information extraction from HTP data. [ABSTRACT FROM AUTHOR]

Variably saturated flow models that simulate soil-water-plant interactions within the rhizosphere largely ignore the effect of plant health. This makes it difficult for them to effectively simulate root water uptake (RWU) and implement alternate management practices for diseased trees. This research was aimed at understanding the hydrological and plant controls on RWU simulations dominated by the health of the tree using experimental and numerical frameworks. Two research plots, one around a healthy mature and the other around a diseased mature (Phytophthora spp. affected) orange tree (Citrus reticulate Blanco), in the Vidarbha region of India were considered for our analysis. Three-dimensional electrical resistivity tomography (ERT) performed at the two locations revealed that the soil moisture profiles following irrigation are different between the two plots. A two-dimensional axisymmetric form of Richards' equation was then solved using HYDRUS (2D/3D) by incorporating a root distribution function. A global sensitivity analysis was performed to identify the root distribution parameters that influence soil moisture simulations. These parameters were then optimized using a genetic algorithm for healthy and diseased conditions. We observed that the diseased orange tree had consumed less water, leaving high soil moisture in the rhizosphere, a condition favorable for the further growth of disease-causing fungi. Hence, managing irrigation water in accordance with RWU patterns is essential for diseased trees. T o understand the error propagation in RWU estimation, we simulated RWU from a diseased tree with the optimal parameters derived for a healthy case. Results show that the error in estimating RWU from the model cells progressively increases with radial distance and soil depth. [ABSTRACT FROM AUTHOR]