Field Experimental Data Research Articles

Integrated environmental and economic assessments of producing energy crops with cover crops for simultaneous use as biofuel feedstocks and animal fodder

Energy crops grown on marginal lands offer an alternative supply of renewable resources while avoiding competition with food crops and supporting feed crops. To sustainably grow energy crops, the economic and environmental impacts of various crop management practices such as the adoption of cover crops, fertilization rates, harvesting methods and its end use applications for animal feed or biofuels should be investigated. In this study, we investigated the life cycle analysis (LCA) and economic evaluation of growing two energy crops (energy cane, and napier grass) on the marginal lands with three fertilizer treatments (0, 100, and 200 kg N ha⁻¹), and with a cover crop (clover, Trifolium incarnatum L) in the southeastern United States (US). Energy crop was harvested once a year in the late fall for biofuel applications, while the Napier grass was harvested twice a year: first harvested early in spring for animal feed and later harvested in the fall for biofuel application. Experimental field data such as biomass yield, crop management practices, farm inputs, and carbon stored in the soil, were determined to assess the global warming potential and the delivered cost of each energy crop. Napier grass had lower global warming (GW) impacts and biomass delivery costs, 34–153 kg CO₂ eq. per oven-dry metric ton (ODMT) and $51–$57 perODMT, respectively than that of energy cane. However, both the energy crops provided carbon sequestrations (−17 and −232 kg CO₂ eq. ODMT⁻¹) and thus net GW impact varied between 51 and (−14) kg CO₂ eq. ODMT⁻¹ based on the treatments. When the napier grass was harvested for both biofuel and fodder applications, the overall GW impacts and the delivered costs were reduced. Therefore, energy crops can be grown in marginal lands for increased carbon sequestration while reducing the GW impacts of energy crops for biofuel production. The integrated environmental and economic analyzes further demonstrated that the energy crop delivered costs and GW impacts could be further reduced, if the energy crops can be utilized for both biofuel and feed applications.

Invasive species impacts on sub-Antarctic Collembola support the Antarctic climate-diversity-invasion hypothesis

The Antarctic climate-diversity-invasion hypothesis (ACDI) predicts that in Antarctic soil systems, climate change should lead to increases in the abundance and diversity of indigenous assemblages. Where biological invasions have occurred, however, invasive alien species should have negative effects on indigenous faunal assemblages. To assess these predictions, we provide the first systematic ecological survey of the Collembola assemblages of pristine, sub-Antarctic Heard Island (53.1°S, 73.5°E) and compare the results to similarly conducted surveys of three other sub-Antarctic islands (Marion, Prince Edward, and Macquarie), characterised by assemblages including invasive Collembola. In particular, we examine differences in densities of three indigenous species (Cryptopygus antarcticus, Mucrosomia caeca, Tullbergia bisetosa) shared between the invaded islands and Heard Island. On average, density of these species was four or more-fold significantly lower on the invaded islands than on uninvaded Heard Island. Yet mean assemblage densities of springtails, accounting for variation among vegetation communities, did not differ substantially or significantly among the islands, suggesting that compensatory dynamics may be a feature of these systems. The invasion impact prediction of the ACDI is therefore supported. On Heard Island, indigenous assemblage variation is strongly related to vegetation community and less so to elevation, in keeping with investigations of Collembola assemblage variation elsewhere across the Antarctic. These findings, in the context of field experimental and physiological data on Collembola from the region, suggest that the climate-diversity predictions of the ACDI will play out in different ways across the Antarctic, depending on whether precipitation increases or decreases as climates change.

Cultivation of leguminous crops of chickpeas and ways to increase the yield and quality of grain in the arid steppe of the Urals

The article discusses the data of field experiments on the cultivation of chickpea with the use of mineral fertilizers and biological products. Chickpea, for the dry steppe of the Ural region of Kazakhstan, is a good drought-resistant crop to ensure stable grain yields with a high protein content. Experiments in 2012-2014 showed that the yield of the crop was in the range from 0.78 t / ha in the control variant to 1.16 t / ha, where the strain H-27 was applied according to the background of the applied fertilizers N20P20. From the use of only one strains of the genus Rhizobium, the increase in yield, on average for the options, amounted to 0.17 t / ha (21.7%), according to the background of mineral fertilizers from 0.22 to 0.38 t / ha. In 2015-2017 the yield from the use of mineral fertilizers and microbial preparations increased by 0.74-1.12 t / ha, or by 22-51%. The increase from nitrogen fertilization is 8-12%, from microbial preparations - 6-12%. With the combined use of nitrogen fertilizer and Rhizoagrin or Flavobacterin preparations, the yield increased by almost 50%, as well as with the combined use of microbial preparations. An increase in the protein content of grain is associated with the use of nitrogen fertilization and the use of biological products for inoculating seeds, since these factors improve the nitrogen nutrition of plants.

Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.

Spatial structure of natural boxwood and the invasive box tree moth can promote coexistence

In the absence of top-down and bottom-up controls, herbivores eventually drive themselves to extinction by exhausting their host plants. Poorly mobile herbivores may experiment only local disappearance, because they can recolonize intact plant patches elsewhere, leaving time to previously over-exploited patches to regrow. However most herbivores such as winged insects are highly mobile, which may prevent the formation of spatial heterogeneity. We test if long-distance dispersal can preclude coexistence using the invasion of box tree moth (Cydalima perspectalis) in Europe as a model system. We build a lattice model and estimate the parameters with a combination of field measurements, experimental data and literature sources. Space corresponds either to a realistic boxwood landscape in the Alps, or to theoretical landscapes of various sizes. We find that both species persist under a large range of realistic parameter values, despite a severe reduction in boxwood biomass, with an alternation of outbreaks and near-to-extinction moth densities. Large landscapes are necessary for coexistence, allowing the formation of spatial structure. Slow plant regrowth combined with long-distance dispersal could drive moths to extinction, because of resources depletion at the global scale even without a complete synchronization of the local dynamics. The spatial dynamics leads to formation of small plant patches evenly distributed in the landscape, because of a combination of local plant dispersal and global indirect competition between plants through their positive effect on moth population size. Coexistence is favoured by such heterogeneous landscapes, because empty patches increase moth mortality during dispersal: the system thus creates its own stability conditions.

Practical prediction method on thaw deformation of soft clay subject to artificial ground freezing based on elaborate centrifuge modeling experiments

Artificial ground freezing (AGF) is a method of paramount importance for underground construction in soft soil area. Compared to the freezing process, the mechanism governing thaw response is much more complicated and harder to specifically control. The prediction of thaw deformation after AGF is of great significance in large-scale perturbation construction underpass existing structures. Currently most field experiments have focused on freezing, but are prohibited to thaw process due to high cost and long-duration, resulting in a lack of well-documented field experimental data to validate empirical or theoretical models from practical points. In this paper, a semi-analytical and semi-empirical practical thaw deformation prediction method was presented, combined with the practical thaw deformation model and a specific designed centrifuge modeling experiment. After the deduction and confirmation of similarity criterion of heat transfer by freezing pipe and heat conduction in surrounding soils from the perspective of temperature field and moisture field, simulation of thaw process under specifically back-deduced geometry and time scaling ratios was evaluated to be reliable through the method of modeling of model in centrifuge. Subsequently, multilayered temperatures and deformations were measured to calculate the actual thawing coefficient, thaw compressibility coefficient and height of thaw deformation domain, serving for practical thaw deformation model computation. Further, the practical thaw deformation model was verified by comparing the calculated thaw settlement with measured value. The relative error of calculation result range between 4.39 ∼ 7.23%, much higher than conventionl empirical model, which proves the applicability of this practical thaw deformation prediction method for AGF. This paper provides valuable practical method combined centrifuge modeling for predicting thaw deformation induced by AGF construction under large scale, extreme conditions in coastal cities, instead of long-term field experiments.

Verification and validation of numerical models for the materials of the lumbar spine

Degenerative diseases of the spine significantly reduce the quality of human life. The spine consists of vertebral bodies and intervertebral discs. The most degraded are intervertebral discs. To study the factors affecting the damageability of the discs, we propose assessing the stress and strain fields by numerical simulation. The vertebral body consists of a shell (cortical bone tissue) and internal contents (cancellous bone tissue). The intervertebral disc is a complex structural element of the spine, consisting of the nucleus pulposus, annulus fibrosus, and cartilaginous plates. To develop numerical models for vertebrae and intervertebral disc, first, it is necessary to verify and validate the material models. This paper for the first time presents new numerical models based on the movable cellular automation method for the materials of the constituent elements of the lumbar spine, their validation, and verification. The models are validated using tensile, compression, and indentation experiments. A good qualitative and quantitative agreement was found with the data of field experiments from the literature.

ONTOLOGICAL APPROACH APPLICATION TO THE DESIGN OF A GEOSPATIAL EXPERIMENTAL DATABASE FOR INFORMATION SUPPORT OF RESEARCH IN PRECISION AGRICULTURE

Thanks to the development of information technologies and computing resources, it became possible to obtain and process big data, including geospatial data. Most research in the field of precision farming is interdisciplinary in nature, with experimental field data used by disparate scientific groups. In this connection, it became necessary to develop a unified web-based system for storing, organizing, and exchanging experimental information between researchers. The first step in achieving this goal was to create a geospatial database. Since the system being developed in the future may require extensions, modifications, adjustments, integration into other projects, it seems appropriate to use the ontology to form the database structure. The most popular tools were used as the main tools: the ontology language OWL (Ontology Web Language), the Protege 5.5 development environment. The main initial information obtained in the course of experimental studies carried out at the biopolygon: weather data, agrochemical indicators (sampling of soil and plants with georeferencing), agrophysical parameters (humidity, electrical conductivity), remote sensing data. Based on the results of the analysis of the current state of research in the field of storage and systematization of experimental information in crop production, as well as a survey of ARI employees, a prototype of the database structure was formed based on the ontological approach. Nine parent classes were defined as the foundation: Field, Crop rotation - experience, Agrotechnology, Yield, Meteo, Ground samples, Orthophoto, Calendar, and Dictionary - units of measurement.

QES-Fire: a dynamically coupled fast-response wildfire model

A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model’s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire’s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data.

Adaptive spraying decision system for plant protection unmanned aerial vehicle based on reinforcement learning

To solve the problem of lacking scientific guidance in aerial pesticide application, this study introduced an adaptive spraying decision system (ASDS) for the unmanned aerial vehicle (UAV) spraying to guide the operators of plant protection UAVs to set reasonable spraying parameters under complicated environment. The minimum applied volume rate, proper spraying velocity, spraying height, and initial droplet size were recommended by the ASDS. The key factor of the decision system is the decision model of reinforcement learning based on the actor-critic neural network. In specific, the field experimental data were used to train the critic and actor networks, which made the model adaptive to optimize the output of spraying parameters. Compared with the conventional spraying parameters, the spraying parameters recommended by the ASDS had a positive impact on wheat parcels. The decision results of the ASDS showed that the spraying volume rate was lower in the blocks with a small leaf area index. In addition, the spraying volume rate for the whole parcel was reduced by 14%. After UAV spraying, the uniformity of the droplet deposition in the ASDS parcel was better than that in the conventional parcel. Moreover, the penetrability of the droplets and the control efficacy for the brown wheat mite Petrobia latens (Muller) were similar in the two parcels. The ASDS can recommend the optimal spraying parameters to minimize pesticide application. Keywords: unmanned aerial vehicle, spraying parameters, decision, reinforcement learning, droplet deposition DOI: 10.25165/j.ijabe.20221504.6929 Citation: Hao Z Y, Li X Z, Meng C, Yang W, Li M Z. Adaptive spraying decision system for plant protection unmanned aerial vehicle based on reinforcement learning. Int J Agric & Biol Eng, 2022; 15(4): 16–26.

MODIFICATION AND PERFORMANCE EVALUATION OF A SMALL-SCALE RAINFALL SIMULATOR

Rainfall simulator is an essential tool to simulate natural rainfall accurately and precisely. A reliable, accurate and portable small scale rainfall simulator is required for runoff, infiltration, sediment generation and erosion studies. And this has been used extensively to gather runoff, infiltration and erosion data in both laboratory and field experiments. This study was conducted to determine rainfall intensity, rainfall drop sizes and erosivity. An existing rainfall simulator was modified to be easily assembled, transported and maintained as well as to create a variety of rainfall regimes. Performance evaluation of the modified rainfall simulator was carried out with 10 trials to determine the intensity of rainfall, drop sizes and erosivity. Correlations were drawn out between the data of the simulated rainfall and that of the natural rainfall data. The results show that rainfall amount, intensity and kinetic energy are the main variables that influence rainfall erosivity index at 99% confidence level. The erosivity index of both simulated rainfall and natural rainfall are 36395.40JM-2mmhr-1 and 34792.51JM-2mmhr-1, respectively. The results of regression analysis of the simulated and natural rainfall show the influence of intensity and amount of rainfall on erosivity index. The linear regression models of simulated and natural rainfall show strong influence to varying degrees of (R2-values) which are 0.949, 0.190, 0.949 and 0.955, respectively. It was concluded that the modified rainfall simulator is suitable to simulate and reproduce natural rainfall characteristics such as rain drop size, intensity, kinetic energy and erosivity. The modified rainfall simulator is a portable type which can be easily assembled, maintained, transported and it can also be used in both laboratory and field experiments for irrigation, infiltration, runoff, sediment and erosion control studies. The estimated cost of modification was ₦44,520.00.

Local Similarity Theory of Convective Turbulent Layer Using “Spectral” Prandtl Mixing Length and Second Moment of Vertical Velocity

Abstract Approximations of the turbulent moments of the atmospheric convective boundary layer are constructed based on a variant of the local similarity theory. As the basic parameters of this theory, the second moment of vertical velocity and the “spectral” Prandtl mixing length are used. This specific choice of the basic parameters allows us to consider the coefficient of turbulent transfer and the dissipation of kinetic energy of the Prandtl turbulence theory as the forms of the local similarity. Therefore, the obtained approximations of the turbulent moments should be considered as natural complementation to the semiempirical turbulence theory. Moreover, within the atmospheric surface layer, the approximations of the new local similarity theory are identical to the relations of the Monin–Obukhov similarity theory (MOST). Therefore, the proposed approximations should be considered as a direct generalization of the MOST under free-convection conditions. The new approximations are compared with the relations of the known local similarity theories. The advantages and limitations of the new theory are discussed. The comparison of the approximations of the new local similarity theory with the field and laboratory experimental data indicates the high effectiveness of the proposed approach.

Reconstruction of Missing Ground-Penetrating Radar Traces Using Simplified U-Net

Ground-penetrating radar is an effective tool for the exploration of subsurface and obscured objects by exploiting the electromagnetic wave characteristics. However, some problems in the measurement process produce missing traces. The resulting incomplete data create difficulties in visualization, interpretation, and analysis. This article proposes a lightweight deep learning method to reconstruct the missing traces. The model is a simplification of a well-known convolutional neural network model, U-Net, which performs well in this context. We use a multi-and-wide kernel size, shallow convolutional layers, a reduced encoding–decoding step, and a minimized feature map size to minimize the model while maintaining high performance. In this study, we focus on the case of inspection of reinforced concrete, where the examined structure consists of a series of rebar and/or void cracks. The numerical and experimental field data results indicate that the method can reconstruct the missing traces with high accuracy. The proposed method also shows comparable performance with U-Net, although the model size is more than 45 times smaller, and its computation speed is more than three times faster. Thus, the proposed method is promising for use in a wide range of applications and devices.

Mutual interference and its effects on searching efficiency between predator–prey interaction with bifurcation analysis and chaos control

In this paper, we study the dynamic of the predator–prey model based on mutual interference and its effects on searching efficiency. The parametric conditions, existence, and stability for trivial and boundary equilibrium points are studied. Also, it has shown that by applying the center manifold theorem and bifurcation theory, system undergoes Neimark–Sacker bifurcation across the neighborhood of a positive fixed point. Moreover, due to the bifurcation and chaos which objectively exist in a system, three chaos control strategies are designed and used. Moreover, to validate our theoretical and analytical discussions, numerical simulations are applied to show complex and chaotic behavior. Finally, theoretical discussions are validated with experimental field data.

CRITICAL REVIEW OF MODERN NUMERICAL MODELLING OF SNOW ACCUMULATION ON ROOFS WITH ARBITRARY GEOMETRY

The calculation of snow loads on roofs of buildings and structures with arbitrary geometry is a complex problem, solving which requires simulating snow accumulation with acceptable engineering accuracy. Experiments in wind tunnels, although widely used in recent years, do not allow to reproduce the real full-scale effects of all snow transport subprocesses, since it is impossible to satisfy all the similarity conditions. This situation, coupled with the continuous improvement of mathematical models, numerical methods, computer technologies and related software, makes the development and future implementation of numerical modelling in real construction practice and regulatory documents inevitable. This paper reviews currently existing mathematical models and numerical methods used to calculate the forms of snow deposits. And, although the lack of significant progress in the field of modelling snow accumulation still remains one of the major problems in CFD, use of existing models, supported by field observations and experimental data, allows to reproduce reasonably accurate snow distributions. The importance of the “symbiosis” between classical experimental methods and modern numerical models is specifically emphasized in the paper, as well as the fact that only the joint use of approaches can comprehensively describe modelling of snow accumulation and snow transport and provide better solutions to a wider range of problems.

The impact of formal and informal organizational norms on susceptibility to phishing: Combining survey and field experiment data

Phishing is one of the most common forms of social engineering that exploits human vulnerabilities and causes immense personal and organizational costs. This study advances the research on the factors of susceptibility to phishing in three regards. First, it addressed the role of organizational norms in susceptibility to phishing. Second, it aimed for high external and ecological validity by combining survey and phishing experiments data on large samples of organizations and their employees. Third, it employed a two-level design that considered explanatory variables at the individual and organizational levels. The study chiefly explored how formal, descriptive, injunctive, and personal norms influence employee interactions with phishing emails. To this end, an explanatory model was tested on 83,269 employees in 510 organizations using a multilevel modeling approach. Clicking on links in simulated phishing emails and entering personal information in simulated fraudulent websites were deemed as two types of susceptibility to phishing. Formal norms and collectively shared injunctive norms were found to exert the strongest effects on susceptibility to phishing; in contrast, personal norms exert a weak influence, and descriptive norms can result in a boomerang effect. These results have significant theoretical and practical implications for both researchers and managers seeking organizational-level mechanisms to reduce the threat of phishing emails.

Medicaid prevalence and opioid use disorder treatment access disparities.

To examine how variation in the size of the local Medicaid population moderates Medicaid-to-private treatment access differentials for women with opioid use disorder (OUD). County-level information on total Medicaid enrollment combined with randomized field experiment data from 10 diverse states that used a simulated patient (audit) methodology to examine buprenorphine providers' appointment granting behavior. We used multiple regression modeling approaches to capture the moderating influence of Medicaid prevalence on differences in the likelihood of receiving an insurance-covered appointment between Medicaid and privately insured female patients. Completed calls to buprenorphine treatment providers. We find a 0.37 percentage point (p value <0.01) narrowing of the Medicaid-to-private access gap with each one percentage point increase in the local insured population on Medicaid. There is effectively no difference in the likelihood of being granted an insurance-covered appointment across the two payer groups in the top tercile of Medicaid penetration. When Medicaid is a common source of insurance within the local population, buprenorphine providers are much less likely to discriminate between Medicaid and privately insured prospective patients. Efforts to enhance equitable access across patient groups are perhaps best targeted where Medicaid prevalence is lower.

Determining the width of the ship’s course using a mathematical model to ensure the safety of the ship’s navigation

Determination of the ship’s course width necessary for the ships safe operation is an urgent task due to the increase in the modern ships’ dimensions. The existing methods for assessing the fairway are calculated with a full re-positioning of the propulsion-rudder complex, according to the maximum drift angle. The vessel movement is considered to be steady, that is, the speed, the drift angles do not depend on time. The relevance of this study is associated with the assessment of determining the width of the fairway at any time interval. This is due to the fact that when passing the river sections, the vessels perform maneuvering with the rudder gear shifted for short periods of time and not at the maximum shift angle. Determination of the parameters of the ship’s movement over time when the navigator manipulates the ship’s controls (control of the rudder device, changing the parameters of the main engines) can be determined by the mathematical model of the ship’s movement. This article discusses the issues of creating a model that adequately describe the processes of vessel movement, including in the conditions of vessel movement along a limited ship’s course. The adequacy of the model was verified using the data of field and model experiments. According to the compiled mathematical model, the calculations were made for various projects of dry cargo ships.

Investigation of sand production prediction shortcomings in terms of numerical uncertainties and experimental simplifications

Sand production is one of the main research topics in the petroleum industry. This problematic phenomenon is related to the mechanical and hydrodynamic behavior and reservoir specifications. Sand production often leads to equipment damage and significant losses. This is usually studied by experimental and numerical methods. This study first gives a brief overview of the methods used to predict sand production in terms of experimental tests, numerical simulation, and field data. The main objective of this research is to highlight the shortcomings of these methods. In addition, experimental hollow cylinder test data and widely used continues numerical simulation are used to investigate these shortcomings. This study is performed by scrutinizing the most important and basic parameters in numerical modeling including two constitutive models (Mohr-Coulomb elastic-perfectly plastic and Mohr-Coulomb cohesion softening/friction hardening failure criteria) and various element sizes and shapes. Since most studies use continuum approaches, it was decided to use a finite difference program. Further, to reduce the undesirable influencing factors the fracture energy regularization method was implemented to diminish mesh dependency related to energy dissipation. In addition, a mesh size sensitivity analysis was performed to show the effects of size, shape and pattern of mesh on the results and cumulative probability distribution versus absolute relative error diagrams were applied to compare the accuracy of the models. After all, the best model of prediction was selected to simulate a real sand production in an oil well with 50° inclination and 6 SPF in North Sea Reservoir. Review of experimental papers shows that these tests usually have several hypotheses for simplification. Rock strength, stress state, fluid flow properties, test duration, and sample dimensions are the most important parameters in sand production tests. Researchers usually focus on only one or two of these parameters, which may lead to many errors in contrast to reality. In addition, numerical methods have some deficiencies such as mesh size problems, lack of critical-state-based constitutive models, sanding criteria, and lack of sufficient research for the calibration procedure in the DEM model. A small change in both the numerical simulation input data and the experimental procedure leads to different results. Indeed, the experimental test of sand production with multiple hypotheses can qualitatively simulate the steps and shape of well or perforation failure not the exact sand production rate. Also, numerical simulation results are very sensitive due to uncertainties and errors of the model and input data. It has been shown that the results are highly dependent on every simple parameter such as the shape of the elements. The differences between experimental results and numerical modeling in only one perforation can be 2 to 10 times. Despite all errors and uncertainties in both the laboratory and modeling studies, the simulation of a real sand production in North Sea Reservoir with same sized element was relatively acceptable. This level of accuracy of the model can be very helpful in subsequent decision making and sand control management.

Predicting Climate Change Impact on Water Productivity of Irrigated Rice in Malaysia Using FAO-AquaCrop Model

Water productivity (WP) is a key indicator of agricultural water management, since it affects the quantity of water used for crop yield in various management scenarios. This study evaluated the WP of irrigated rice due to a changing climate in the Northwest Selangor Rice Irrigation Scheme (NSRIS) by using field experimental data and the FAO-AquaCrop Model. Pertinent soil, water, climate, and crop data were acquired by executing a field investigation during the off-season (dry season, January–April) and main season (wet season, July–October) in 2017. The AquaCrop 6.0 model was calibrated and validated using the measured data. A Climate-smart Decision Support System (CSDSS) with an ensemble of 10 Global Climate Models (GCMs) was used to downscale climate variables under RCP4.5, RCP6.0, and RCP8.5 emission scenarios during baseline (1976 to 2005) and future (2020 to 2099) periods. The AquaCrop model fairly predicted rice yields under field conditions with root-mean-square error (RMSE), mean absolute error (MAE), prediction error (PE) and index of agreement (d) between the observed and estimated yields of 0.173, 0.157, −0.31 to 5.4 and 0.78, respectively for the off-season; and 0.167, 0.127, −5.6 to 2.3 and 0.73, respectively for the main season. It predicted a 10% decrease in actual crop evapotranspiration (ETc) in both crop seasons in the future. The WP of rice based on total water input (WPIrr+RF), applied irrigation (WPIrr), and actual crop evapotranspiration (WPETc) will likely increase by 14–24%, 14–19%, and 17–29%, respectively under the three RCP emission scenarios in the off-season. The likely increase in WP for the corresponding base is 13–22%, 15–24%, and 14–25% in the main season. Various agronomic management options linked to WP will most likely become important in making crucial decisions to cope with the risk of impacts on climate change.