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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]

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]

• High-frequency evapotranspiration (ET) phenotyping is key to drought-stress analyses. • Optimum ET sampling frequency identified by downscaling 15-min interval ET profiles. • Time series forecasting of ET was performed using ensemble machine-learning models. • ET forecasting and genotype classification performance was compared at each scale. • 60-min interval of ET was found optimum with minimum redundancy and information loss. Efficient selection of drought-tolerant crops requires identification and high-throughput phenotyping (HTP) of the complex functional (especially canopy-conductance) traits that elicit plant responses to continually fluctuating environmental conditions. However, phenotyping of such dynamic physiology-based traits has been immensely challenging especially due to the limited availability of adequate methods that can provide continuous measurements of plant-water relations. Therefore, gravimetric phenotyping of plants is being increasingly used to allow one-to-one monitoring of plant-water relations and generate continuous evapotranspiration (ET) profiles. The gravimetric sensors or load cells can provide ET estimates at very high frequencies, e.g. 15-min interval, as chosen by the user. There is however, no study on understanding the optimum frequency or the sampling time at which ET needs to be monitored, such that data-redundancy, noise and processing overhead could be reduced. Hence, this paper makes a novel attempt in identifying the optimum sampling time for phenotyping ET from load cells time series. The proposed procedure includes an ensemble Machine-Learning (ML) approach for optimizing the sampling time through time series forecasting of ET profiles and classification of genotypes using the forecasted ET values. High-frequency load cells data from the LeasyScan, HTP platform, ICRISAT were used to derive the ET profiles at frequencies or scales varying from 15-min to 180-min, followed by ET forecasting and classification at each frequency. For both forecasting and classification, an ensemble of three ML algorithms i.e. Support Vector Machines (SVM), Artificial Neural Network (ANN) and Random Forests (RF) were leveraged. Consequently, the performance metrics (of both the operations) obtained from the ensemble were used to compute the entropy-based optimum sampling time. The results reveal that 60-min interval HTP data could be credibly used for both, forecasting ET as well as correctly classifying the genotypes. [ABSTRACT FROM AUTHOR]