Journal Paper Digests 2017 #29
- Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP
- Nitrogen balance in Australia and nitrogen use efficiency on Australian farms
- Deriving adaptive operating rules of hydropower reservoirs using time-varying parameters generated by the EnKF
- A geospatial decision support system for supporting quality viticulture at the landscape scale
- A generic ontological network for Agri-food experiment integration - Application to viticulture and winemaking
- Bayesian principal component regression model with spatial effects for forest inventory variables under small field sample size
Parallelization of interpolation, solar radiation and water flow simulation modules in GRASS GIS using OpenMP
Authors: Hofierka, J; Lacko, M; Zubal, S
Source: COMPUTERS & GEOSCIENCES, 107 20-27; OCT 2017
Abstract: In this paper, we describe the parallelization of three complex and computationally intensive modules of GRASS GIS using the OpenMP application programming interface for multi-core computers. These include the v.surf.rst module for spatial interpolation, the r.sun module for solar radiation modeling and the r.sim.water module for water flow simulation. We briefly describe the functionality of the modules and parallelization approaches used in the modules. Our approach includes the analysis of the module’s functionality, identification of source code segments suitable for parallelization and proper application of OpenMP parallelization code to create efficient threads processing the subtasks. We document the efficiency of the solutions using the airborne laser scanning data representing land surface in the test area and derived high-resolution digital terrain model grids. We discuss the performance speed-up and parallelization efficiency depending on the number of processor threads. The study showed a substantial increase in computation speeds on a standard multi-core computer while maintaining the accuracy of results in comparison to the output from original modules. The presented parallelization approach showed the simplicity and efficiency of the parallelization of open-source GRASS GIS modules using OpenMP, leading to an increased performance of this geospatial software on standard multi-core computers.
Nitrogen balance in Australia and nitrogen use efficiency on Australian farms
Authors: Angus, JF; Grace, PR
Source: SOIL RESEARCH, 55 (5-6):435-450; 2017
Abstract: The amount of reactive N in soils on the Australian continent appears to be increasing, mainly because of biological N-fixation by permanent pastures in the dryland farming zone. This gain is partly offset by N-mining by crops, which we estimate have removed between one-fifth and one-quarter of the original soil N. The vast areas of non-agricultural land and arid rangelands appear to be in neutral N balance and the relatively small area of intensive agriculture is in negative balance. There are regional N losses from the sugar and dairy industries to groundwater, estuaries and lagoons, including the Great Barrier Reef. Fertiliser N application is increasing, and is likely to increase further, to compensate for the soil-N mining and to meet increasing crop yield potential, but fertiliser-N represents a relatively small fraction of the Australian N balance. The dryland farming zone utilises the largest amounts of native and fertiliser N. The average fertiliser application to dryland cereals and oilseeds, 45kg N ha(-1), is low by international standards because of the small N-demand by dryland crops and because there are no subsidies on crops or fertiliser that promote overuse. The efficiency of N-use is relatively low, for example about 40% of fertiliser N is recovered in the aboveground parts of dryland wheat and the rest is retained in the soil, denitrified or otherwise lost. We suggest further research on fertiliser-application methods to increase crop recovery of fertiliser, as well as research to reduce the surplus N from permanent pasture.
Deriving adaptive operating rules of hydropower reservoirs using time-varying parameters generated by the EnKF
Authors: Feng, MY; Liu, P; Guo, SL; Shi, LS; Deng, C; Ming, B
Source: WATER RESOURCES RESEARCH, 53 (8):6885-6907; AUG 2017
Abstract: Operating rules have been used widely to decide reservoir operations because of their capacity for coping with uncertain inflow. However, stationary operating rules lack adaptability; thus, under changing environmental conditions, they cause inefficient reservoir operation. This paper derives adaptive operating rules based on time-varying parameters generated using the ensemble Kalman filter (EnKF). A deterministic optimization model is established to obtain optimal water releases, which are further taken as observations of the reservoir simulation model. The EnKF is formulated to update the operating rules sequentially, providing a series of time-varying parameters. To identify the index that dominates the variations of the operating rules, three hydrologic factors are selected: the reservoir inflow, ratio of future inflow to current available water, and available water. Finally, adaptive operating rules are derived by fitting the time-varying parameters with the identified dominant hydrologic factor. China’s Three Gorges Reservoir was selected as a case study. Results show that (1) the EnKF has the capability of capturing the variations of the operating rules, (2) reservoir inflow is the factor that dominates the variations of the operating rules, and (3) the derived adaptive operating rules are effective in improving hydropower benefits compared with stationary operating rules. The insightful findings of this study could be used to help adapt reservoir operations to mitigate the effects of changing environmental conditions.
A geospatial decision support system for supporting quality viticulture at the landscape scale
Authors: Terribile, F; Bonfante, A; D’Antonio, A; De Mascellis, R; De Michele, C; Langella, G; Manna, P; Mileti, FA; Vingiani, S; Basile, A
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 140 88-102; AUG 2017
Abstract: The world of viticulture connected to wine making has become a very important activity in many inland areas permitting both the generation of important income and the sustaining of agriculture systems.Recent progress in both crop modeling and Decision Support Systems (DSS) applied to viticulture promises important changes that combine both high quality production and environmental sustainability. However, most of this progress is only addressed at the farm level and does not challenge the viticulture landscape, which is a key issue when facing DOC, DOCG areas, wine growers’ cooperatives and consortiums and strategic viticulture planning.Thus, this paper aims to demonstrate that a new type of DSS, which is developed on a Geospatial Cyberinfrastructure (GCI) platform, may provide an important web-based operational tool for high quality viticulture as it connects farm and landscape levels better.The GCI platform supports acquisition, management, processing of both static and dynamic data (e.g. pedological, daily climatic, and vineyard distribution), data visualization, and on-the-fly computer applications in order to perform simulation modeling (e.g. grapevine water stress, evaluation of ecosystem services, etc.). These are all potentially accessible via the Web.This is possible thanks to the implementation of a set of modeling clusters that is strongly rooted in soil-plant-atmosphere and physically based simulation modeling.The DSS tool, applied to an area of 20,000 ha in Southern Italy, is designed to address viticulture planning and management by providing operational support for farmers, farmer associations and decision makers involved in the viticulture landscape.Output of the system includes viticulture planning and management scenario analysis, maps and evaluation of potential and current plant water stress.The tool will also be demonstrated through a short selection of practical case studies.
A generic ontological network for Agri-food experiment integration - Application to viticulture and winemaking
Authors: Muljarto, AR; Salmon, JM; Charnomordic, B; Buche, P; Tireau, A; Neveu, P
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 140 433-442; AUG 2017
Abstract: This paper presents an ontological approach of scientific experimental data integration across complementary sub-domains, i.e., agricultural production and food processing, with an application to viticulture and winemaking. The two main steps in this approach are (i) to integrate preexisting ontologies to create a so-called ontology network and (ii) to populate the ontology network with experimental data from various sources. The Agri-Food Experiment Ontology (AFEO), a new ontology network, was developed, based on two ontological resources, i.e., AEO (Ontology for Agricultural Experiments) and OFPE (Ontology for Food Processing Experiments). It contains 136 concepts which cover various viticulture practices, as well as winemaking products and operations. AFEO was used to guide the data integration of two different data sources, i.e., viticulture experimental data stored in a relational database, and winemaking experimental data stored in Microsoft Excel files. Two applications illustrate the approach. The first one is on wine traceability and the second one is related to the influence of irrigation practices and winemaking methods on GSH concentration in wine. These examples show that data integration guided by an ontology network can provide researchers with the information necessary to address extended research questions.
Bayesian principal component regression model with spatial effects for forest inventory variables under small field sample size
Authors: Junttila, V; Laine, M
Source: REMOTE SENSING OF ENVIRONMENT, 192 45-57; APR 2017
Abstract: Remote sensing observations are extensively used for analysis of environmental variables. These variables often exhibit spatial correlation, which has to be accounted for in the calibration models used in predictions, either by direct modelling of the dependencies or by allowing for spatially correlated stochastic effects. Another feature in many remote sensing instruments is that the derived predictor variables are highly correlated, which can lead to unnecessary model over-training and at worst, singularities in the estimates. Both of these affect the prediction accuracy, especially when the training set for model calibration is small. To overcome these modelling challenges, we present a general model calibration procedure for remotely sensed data and apply it to airborne laser scanning data for forest inventory. We use a linear regression model that accounts for multicollinearity in the predictors by principal components and Bayesian regularization. It has a spatial random effect component for the spatial correlations that are not explained by a simple linear model. An efficient Markov chain Monte Carlo sampling scheme is used to account for the uncertainty in all the model parameters. We tested the proposed model against several alternatives and it outperformed the other linear calibration models, especially when there were spatial effects, multicollinearity and the training set size was small.