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Data driven precision agriculture aspects, particularly the dynamic disease management, require dynamic crop-weather-environment data at micro level. An experiment was conducted during four consecutive seasons (2009 Kharif, 2009-10 Rabi, 2010 Kharif and 2010-11 Rabi) in a semi-arid tropic region of India to understand the crop-weather-environment-disease relations using wireless sensory and field-level surveillance data on the groundnut crop for Leaf Spot (LS) disease, which is economically important yet more prone in the semi-arid tropic. Tailor-made various data mining techniques (Naïve Bayes classification with Gaussian distribution, rapid association rule mining and multivariate regression mining) were developed and applied to turn the data into useful information/knowledge/relations/trends and correlation to understand crop-weather-environment-disease continuum. These dynamics obtained from the data mining techniques and trained through proposed multivariate regression (MVR) mathematical models were validated with ground level surveillance data as well as ARI model (derived from ongoing long term weather-based experiment with diversely pooled data experimented from 10 seasons in semi-arid and arid zones). It was found that LS disease infection is strongly influenced by minimum temperature (18-20 °C), prolonged duration of leaf wetness (7-10 h), high humidity (75% or more) and age of the crop. These findings have been used for development of prediction models (One week and cumulative predictions), which can assist the user community to take respective ameliorative measures and it has been found that cumulative prediction model has performed better than ARI model with respect to ground level observations in all 16 diverse dates of sowing experiments spanning for two model-years. [ABSTRACT FROM AUTHOR]

Highlights: [•] Prior watershed prioritization techniques are cumbersome for data scarcity regions. [•] Past efforts for watershed planning treated various morphometric variables equally. [•] Removal of individual biasness of morphometric parameters leads in precise ranking. [•] Novel Weighted Sum Analysis technique was proposed for prioritization of watershed. [•] Geospatial tools were coupled with statistical method to demonstrate the technique. [Copyright &y& Elsevier]

Conservation of available natural resources through demarcation of potential zones at micro level are primary necessitate for sustainable development, particularly in the fragile semi-arid tropics. Delineation of potential zones for implementation of conservation measures above the entire watershed at similar occurrence is inaccessible as well as uneconomical; consequently it is a prerequisite to apply viable technique for prioritization of sub-watersheds (SWDs). Keeping this in view, the present research attempted to study various morphological characteristics and to implement Geographical Information System (GIS) and Multi Criteria Decision Making (MCDM) through Fuzzy Analytical Hierarchy Process (FAHP) techniques for identification of critical sub-watersheds situated in transaction zone between mountainous and water scarcity region of Western Part of India. The morphometric characterization was obtained through the measurement of three distinct linear, areal and relief aspects over the eight sub-watersheds. The morphometric characterization showed imperative role in distinguishing the topographical and hydrological behavior of the watershed. Each hydrological unit was ranked with respect to the value and weightages obtained by deriving the relationships between the morphometric parameters obtained through classification of the SWDs by associating the robustness of fuzzy logic and the Analytical Hierarchy Processes (AHP). Based on FAHP approach, sub-watersheds were evaluated as vulnerability assessment zones and alienated into five prioritization levels: very less, less, medium, high and very high classes. The evaluated results illustrated that 60.85% of sub-watersheds (five sub-watersheds) were in the medium to high susceptible zones, which depicted potential areas for necessity of establishment of conservation interventions for the sustainable watershed management planning. The FAHP based technique is a viable approach in illustrating the dilemma particularly over data hungry and complex conventional soil and water risk assessment methods and will be useful to various stakeholders (rural extension community, agriculturists and water resources managers) for better decision making with an obliging rule based system for implementing various assessment measures. [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]

Abstract: In the context of rural India, planning is mainly prescriptive (through mandated schemes) and top–down (by extension community from the line departments). Realizing the above mode of planning and the increasing demand for Geographical-Information & Communication Technology (Geo-ICT) applications in the rural systems in India, a prototype distributed collaboration tool, called GramyaVikas (rural development), has been developed to assist the rural extension community in their own decision-making processes in a more interactive, integrated and coordinated manner. GramyaVikas, evolved out of the needs assessment of the user community, is a secure and cost-effective system developed for defined users with open source (a) content management system and (b) Geographical Information Systems. This web-based tool will help the users to share and retrieve data/information; communicate for taking mutual decisions; make useful queries on spatial and nonspatial database to identify candidate villages/entities; and generate various views or scenarios for different rural development schemes. Presently, this Geo-ICT tool is being developed in an Intranet environment. The resulting system is intended to assist the remote users in analyzing rural-informatics for rural development planning decisions online, with customized GIS tools to suit the requirements of a few line departments for decision-making. [Copyright &y& Elsevier]

To meet rising global food demands, existing agricultural management strategies will need to be transformed to mitigate the negative impacts of climate change on crop yields. Climate change which includes elevated CO 2 , temperature increase, and change in precipitation variability give rise to uncertainties for predicting crop yields. We used a multilayer canopy-root-soil model (MLCan) to (i) explore the adverse impacts of climate change on corn yields, and (ii) investigate three irrigation scheduling methods to improve the yield. To estimate crop yields, we implemented crop growth processes in MLCan. This model was applied to an experimental farm located in Urbana, Illinois, USA and was validated using aboveground carbon and leaf area index measurements. A weather generator was used to develop forcings corresponding to future climate scenarios. Climate change scenarios were considered with ambient and elevated CO 2 concentration, 1oC to 3oC temperature increases, and precipitation changes. The 2oC and 3oC temperature change reduces the crop yields up to ~38%. The simulation results showed that a 30% decrease in precipitation could reduce the mean yields of up to ~10%. The three irrigation scheduling methods were applied on dry years as adaptation strategies, which were decided based on water balance and two plant attributes of canopy temperature-based crop water stress index and leaf water potential. The water balance approach was designed to reflect an existing irrigation scheduling method, which was found to be not efficient and required more irrigation to improve crop yields under future climate conditions. We found that the leaf water potential method was more effective and efficient to improve crop yields under climate change among the three irrigation methods considered in this study. • Studied the role elevated CO 2 , temperature change, and precipitation change and variability on corn yields. • Three alternative irrigation scheduling methods were compared under climate change. • Leaf water potential-based irrigation scheduling method is most efficient to mitigate the impacts of climate change. [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]

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]

• Disease stress functions to simulate the reduction in root water uptake from unhealthy trees were proposed. • HYDRUS (2D/3D) code was modified to include a disease stress factor and rigorously tested in the presence of the water stress. • Optimal root distribution and disease sensing parameters were derived for citrus crop simulation. Macroscopic modeling approaches based on the solution of the Richards equation with root water uptake (RWU) as a sink term can help in understanding soil-water-plant interactions within the rhizosphere. However, these models currently cannot capture the differences in RWU attributed to variations in plant health. Errors in simulating RWU from unhealthy plants are significant when disease-causing fungus inhibits water uptake rather than other usually considered plant stresses. We developed RWU reduction functions to simulate plant transpiration under combined water and disease stress conditions using linear and non-linear response models. The developed functions were implemented in the numerical model HYDRUS (2D/3D) to simulate water uptake from a root system in a radially symmetrical flow domain. Field experiments were conducted in the Vidarbha region of central India for one crop cycle on four citrus trees with varying disease intensities (healthy to severely diseased). The proposed model was rigorously tested by comparing its results with measured soil water contents and plant transpiration fluxes under various water and disease limiting conditions. Error in simulating RWU fluxes from unhealthy trees by ignoring the disease stress factor was found to be significant (15% for slightly diseased to 26% for the severely diseased tree). Parameters of the spatial root distribution and the disease stress response functions were optimized for each scenario using a genetic algorithm approach. Our results indicate that calibration targets to validate uptake reduction functions should be chosen cautiously based on the dominant stress experienced by the plant root system. [ABSTRACT FROM AUTHOR]